Author: "Roy, P." - Searchworks@Jio Institute Digital Library Search Results

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142,571 results on '"Roy, P."'

151. Reliability of in-band and broadband spectral index measurement: systematic study of the effect of signal to noise for uGMRT data

Author: Rashid, Md, Roy, Nirupam, Pandian, J. D., Dutta, Prasun, Dokara, R., Vig, S., and Menten, K. M.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Low radio frequency spectral index measurements are a powerful tool to distinguish between different emission mechanisms and, in turn, to understand the nature of the sources. Besides the standard method of estimating the ``broadband" spectral index of sources from observations in two different frequency ``bands", if the observations were made with large instantaneous bandwidth, the ``in-band" spectral index can be determined, either using images of emission at multiple frequency ranges within a band or using the novel Multi Term-Multi Frequency Synthesis (MT-MFS) imaging algorithm. Here, using simulated upgraded Giant Metrewave Radio Telescope (uGMRT) data, we have systematically studied the reliability of various methods of spectral index estimation for sources with a wide range of signal-to-noise ratio (SNR). It is found that, for synthetic uGMRT point source data, the MT-MFS imaging algorithm produces in-band spectral indices for SNR~$\lesssim100$ that have errors $\gtrsim 0.2$, making them unreliable. However, at a similar SNR, the sub-band splitting method produces errors $\lesssim 0.2$, which are more accurate and unbiased in-band spectral indices. The broadband spectral indices produce errors $\lesssim 0.2$ even for SNR $\gtrsim 15$, and hence, they are most reliable if there are no higher-order variations in the spectral index. These results may be used to improve the uGMRT observation and data analysis strategies depending on the brightness of the target source., Comment: 10 pages, 5 figures, accepted by ApJ on 24th May 2024
Published: 2024

152. Environmental Effects on the Stellar Mass Function in a z~3.3 Overdensity of Galaxies in the COSMOS Field

Author: Forrest, Ben, Lemaux, Brian C., Shah, Ekta A., Staab, Priti, Gal, Roy R., Lubin, Lori M., Cooper, M. C., Cucciati, Olga, Hung, Denise, McConachie, Ian, Muzzin, Adam, Wilson, Gillian, Bardelli, Sandro, Cassarà, Letizia P., Chang, Wenjun, Giddings, Finn, Golden-Marx, Emmet, Hathi, Nimish, Stawinski, Stephanie M. Urbano, and Zucca, Elena
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We present an analysis of the number density of galaxies as a function of stellar mass (i.e., the stellar mass function, SMF) in the COSMOS field at z~3.3, making a comparison between the SMF in overdense environments and the SMF in the coeval field. In particular, this region contains the Elent\'ari proto-supercluster, a system of 6 extended overdensities spanning ~70 cMpc on a side. A clear difference is seen in the high-mass slope of these SMFs, with overdense regions showing an increase in the ratio of high-mass galaxies to low-mass galaxies relative to the field, indicating a more rapid build-up of stellar mass in overdense environments. This result qualitatively agrees with analyses of clusters at z~1, though the differences between protocluster and field SMFs at z~3.3 are smaller. While this is consistent with overdensities enhancing the evolution of their member galaxies, potentially through increased merger rates, whether this enhancement begins in protocluster environments or even earlier in group environments is still unclear. Though the measured fractions of quiescent galaxies between the field and overdense environments do not vary significantly, implying that this stellar mass enhancement is ongoing and any starbursts triggered by merger activity have not yet quenched, we note that spectroscopic observations are biased towards star-forming populations, particularly for low-mass galaxies. If mergers are indeed responsible, high resolution imaging of Elent\'ari and similar structures at these early epochs should then reveal increased merger rates relative to the field. Larger samples of well-characterized overdensities are necessary to draw broader conclusions in these areas., Comment: 15 pages, 8 figures, plus references and appendices. Submitted to ApJ, posted after responding to referee comments. Further comments welcome
Published: 2024

153. Evaluating radiation impact on transmon qubits in above and underground facilities

Author: De Dominicis, Francesco, Roy, Tanay, Mariani, Ambra, Bal, Mustafa, Casali, Nicola, Colantoni, Ivan, Crisa, Francesco, Cruciani, Angelo, Ferroni, Fernando, Helis, Dounia L, Pagnanini, Lorenzo, Pettinacci, Valerio, Pilipenko, Roman M, Pirro, Stefano, Puiu, Andrei, Romanenko, Alexander, Zanten, David v, Zhu, Shaojiang, Grassellino, Anna, and Cardani, Laura
Subjects: Quantum Physics
Abstract: Superconducting qubits can be sensitive to abrupt energy deposits caused by cosmic rays and ambient radioactivity. Previous studies have focused on understanding possible correlated effects over time and distance due to cosmic rays. In this study, for the first time, we directly compare the response of a transmon qubit measured initially at the Fermilab SQMS above-ground facilities and then at the deep underground Gran Sasso Laboratory (INFN-LNGS, Italy). We observe same average qubit lifetime T$_1$ of roughly 80 microseconds at above and underground facilities. We then apply a fast decay detection protocol and investigate the time structure, sensitivity and relative rates of triggered events due to radiation versus intrinsic noise, comparing above and underground performance of several high-coherence qubits. Using gamma sources of variable activity we calibrate the response of the qubit to different levels of radiation in an environment with minimal background radiation. Results indicate that qubits respond to a strong gamma source and it is possible to detect particle impacts. However, when comparing above and underground results, we do not observe a difference in radiation induced-like events for these sapphire and niobium-based transmon qubits. We conclude that the majority of these events are not radiation related and to be attributed to other noise sources which by far dominate single qubit errors in modern transmon qubits., Comment: 9+5 pages, 10+6 figures
Published: 2024

154. Revisiting the decoupling limit of the Georgi-Machacek model with a scalar singlet

Author: Bélanger, Geneviève, Dutta, Juhi, Godbole, Rohini M., Kraml, Sabine, Mitra, Manimala, Padhan, Rojalin, and Roy, Abhishek
Subjects: High Energy Physics - Phenomenology
Abstract: We study the connection between collider and dark matter phenomenology in the singlet extension of the Georgi-Machacek model. In this framework, the singlet scalar serves as a suitable thermal dark matter (DM) candidate. Our focus lies on the region $v_{\chi}<1$ GeV, where $v_{\chi}$ is the common vacuum expectation value of the neutral components of the scalar triplets of the model. Setting bounds on the model parameters from theoretical, electroweak precision and LHC experimental constraints, we find that the BSM Higgs sector is highly constrained. Allowed values for the masses of the custodial fiveplets, triplets and singlet are restricted to the range $140~ {\rm GeV }< M_{H_5} < 350~ {\rm GeV }$, $150~ {\rm GeV }< M_{H_3} < 270 ~{\rm GeV }$ and $145~ {\rm GeV }< M_{H} < 300~ {\rm GeV }$. The extended scalar sector provides new channels for DM annihilation into BSM scalars that allow to satisfy the observed relic density constraint while being consistent with direct DM detection limits. The allowed region of the parameter space of the model can be explored in the upcoming DM detection experiments, both direct and indirect. In particular, the possible high values of BR$(H^0_5\to\gamma\gamma)$ can lead to an indirect DM signal within the reach of CTA. The same feature also provides the possibility of exploring the model at the High-Luminosity run of the LHC. In a simple cut-based analysis, we find that a signal of about $4\sigma$ significance can be achieved in final states with at least two photons for one of our benchmark points., Comment: 44 pages, 20 figures, and 7 tables
Published: 2024

155. VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections

Author: Miles, Roy, Reddy, Pradyumna, Elezi, Ismail, and Deng, Jiankang
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: Large language models (LLMs) have recently emerged as powerful tools for tackling many language-processing tasks. Despite their success, training and fine-tuning these models is still far too computationally and memory intensive. In this paper, we identify and characterise the important components needed for effective model convergence using gradient descent. In doing so we find that the intermediate activations used to implement backpropagation can be excessively compressed without incurring any degradation in performance. This result leads us to a cheap and memory-efficient algorithm for both fine-tuning and pre-training LLMs. The proposed algorithm simply divides the tokens up into smaller sub-tokens before projecting them onto a fixed 1-dimensional subspace during the forward pass. These features are then coarsely reconstructed during the backward pass to implement the update rules. We confirm the effectiveness of our algorithm as being complimentary to many state-of-the-art PEFT methods on the VTAB-1k fine-tuning benchmark. Furthermore, we outperform QLoRA for fine-tuning LLaMA and show competitive performance against other memory-efficient pre-training methods on the large-scale C4 dataset.
Published: 2024

156. The communication power of a noisy qubit

Author: Roy, Saptarshi, Guha, Tamal, Saha, Sutapa, and Chiribella, Giulio
Subjects: Quantum Physics
Abstract: A fundamental property of quantum mechanics is that a single qubit can carry at most 1 bit of classical information. For an important class of quantum communication channels, known as entanglement-breaking, this limitation remains valid even if the sender and receiver share entangled particles before the start of the communication: for every entanglement-breaking channel, the rate at which classical messages can be reliably communicated cannot exceed 1 bit per transmitted qubit even with the assistance of quantum entanglement. But does this mean that, for the purpose of communicating classical messages, a noisy entanglement-breaking qubit channel can be replaced by a noisy bit channel? Here we answer the question in the negative. We introduce a game where a player (the sender) assists another player (the receiver) in finding a prize hidden into one of four possible boxes, while avoiding a bomb hidden in one of the three remaining boxes. In this game, the bomb cannot be avoided with certainty if the players communicate through a noisy bit channel. In contrast, the players can deterministically avoid the bomb and find the prize with a guaranteed 1/3 probability if they communicate through an entanglement-breaking qubit channel known as the universal NOT channel. We show that the features of the quantum strategy can be simulated with a noiseless bit channel, but this simulation requires the transmission to be assisted by shared randomness: without shared randomness, even the noiseless transmission of a three-level classical system cannot match the transmission of a single noisy qubit., Comment: 5+6 pages, 1 figure, comments are welcome
Published: 2024

157. Cross-Modal Safety Alignment: Is textual unlearning all you need?

Author: Chakraborty, Trishna, Shayegani, Erfan, Cai, Zikui, Abu-Ghazaleh, Nael, Asif, M. Salman, Dong, Yue, Roy-Chowdhury, Amit K., and Song, Chengyu
Subjects: Computer Science - Computation and Language, Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract: Recent studies reveal that integrating new modalities into Large Language Models (LLMs), such as Vision-Language Models (VLMs), creates a new attack surface that bypasses existing safety training techniques like Supervised Fine-tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF). While further SFT and RLHF-based safety training can be conducted in multi-modal settings, collecting multi-modal training datasets poses a significant challenge. Inspired by the structural design of recent multi-modal models, where, regardless of the combination of input modalities, all inputs are ultimately fused into the language space, we aim to explore whether unlearning solely in the textual domain can be effective for cross-modality safety alignment. Our evaluation across six datasets empirically demonstrates the transferability -- textual unlearning in VLMs significantly reduces the Attack Success Rate (ASR) to less than 8\% and in some cases, even as low as nearly 2\% for both text-based and vision-text-based attacks, alongside preserving the utility. Moreover, our experiments show that unlearning with a multi-modal dataset offers no potential benefits but incurs significantly increased computational demands, possibly up to 6 times higher.
Published: 2024

158. NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Author: Lee, Chankyu, Roy, Rajarshi, Xu, Mengyao, Raiman, Jonathan, Shoeybi, Mohammad, Catanzaro, Bryan, and Ping, Wei
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract: Decoder-only large language model (LLM)-based embedding models are beginning to outperform BERT or T5-based embedding models in general-purpose text embedding tasks, including dense vector-based retrieval. In this work, we introduce the NV-Embed model with a variety of architectural designs and training procedures to significantly enhance the performance of LLM as a versatile embedding model, while maintaining its simplicity and reproducibility. For model architecture, we propose a latent attention layer to obtain pooled embeddings, which consistently improves retrieval and downstream task accuracy compared to mean pooling or using the last token embedding from LLMs. To enhance representation learning, we remove the causal attention mask of LLMs during contrastive training. For model training, we introduce a two-stage contrastive instruction-tuning method. It first applies contrastive training with instructions on retrieval datasets, utilizing in-batch negatives and curated hard negative examples. At stage-2, it blends various non-retrieval datasets into instruction tuning, which not only enhances non-retrieval task accuracy but also improves retrieval performance. Combining these techniques, our NV-Embed model, using only publicly available data, has achieved a record-high score of 69.32, ranking No. 1 on the Massive Text Embedding Benchmark (MTEB) (as of May 24, 2024), with 56 tasks, encompassing retrieval, reranking, classification, clustering, and semantic textual similarity tasks. Notably, our model also attains the highest score of 59.36 on 15 retrieval tasks in the MTEB benchmark (also known as BEIR). We will open-source the model at: https://huggingface.co/nvidia/NV-Embed-v1.
Published: 2024

159. Comprehensive analysis of local and nonlocal amplitudes in the $B^0\rightarrow K^{*0}\mu^+\mu^-$ decay

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataide, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Baldini, W., Bao, H., Leite, J. Baptista de Souza, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bassi, G., Batsukh, B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bondar, N., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butkevich, A., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Godinez, J. Castro, Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Barajas, C. A. Chavez, Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collins, P., Colombo, T., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Epple, E., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Fischer, K. M., Fitzgerald, D. S., Fitzpatrick, C., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Fuehring, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, Y., Garau, M., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Gomes, A., Fernandez, S. Gomez, Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Haimberger, J., Hajheidari, M., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Hartmann, M., He, J., Hecker, M., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Hill, D., Hollitt, S. E., Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O. K., Kubat, J., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Leverington, B., Li, A., Li, H., Li, K., Li, L., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Castro, J. Lomba, Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Ma, R., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. M., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Santos, D. Martinez, Vidal, F. Martinez, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mödden, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Nogarolli, P., Nogga, P., Nolte, N. S., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perro, A., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Pomery, G. J., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Lamas, M. Romero, Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Ruf, T., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Gras, C. Sanchez, Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A. S., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sun, Y., Lima, D. S. Sundfeld, Sutcliffe, W., Swallow, P. N., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Vesterinen, M., Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vouters, G., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, D. J., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. R. J., Williams, R., Wilson, F. F., Wislicki, W., Witek, M., Witola, L., Wong, C. P., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, Y., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, D., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yu, C. Y., Yu, J., Yuan, X., Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: A comprehensive study of the local and nonlocal amplitudes contributing to the decay $B^0\rightarrow K^{*0}(\to K^+\pi^-) \mu^+\mu^-$ is performed by analysing the phase-space distribution of the decay products. The analysis is based on \proton\proton collision data corresponding to an integrated luminosity of 8.4fb$^{-1}$ collected by the LHCb experiment. This measurement employs for the first time a model of both one-particle and two-particle nonlocal amplitudes, and utilises the complete dimuon mass spectrum without any veto regions around the narrow charmonium resonances. In this way it is possible to explicitly isolate the local and nonlocal contributions and capture the interference between them. The results show that interference with nonlocal contributions, although larger than predicted, only has a minor impact on the Wilson Coefficients determined from the fit to the data. For the local contributions, the Wilson Coefficient $C_9$, responsible for vector dimuon currents, exhibits a $2.1\sigma$ deviation from the Standard Model expectation. The Wilson Coefficients $C_{10}$, $C_{9}'$ and $C_{10}'$ are all in better agreement than $C_{9}$ with the Standard Model and the global significance is at the level of $1.5\sigma$. The model used also accounts for nonlocal contributions from $B^{0}\to K^{*0}\left[\tau^+\tau^-\to \mu^+\mu^-\right]$ rescattering, resulting in the first direct measurement of the $b s\tau\tau$ vector effective-coupling $C_{9\tau}$., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-011.html (LHCb public pages)
Published: 2024

160. Large-scale time-multiplexed nanophotonic parametric oscillators

Author: Gray, Robert M., Sekine, Ryoto, Ledezma, Luis, Li, Gordon H. Y., Zhou, Selina, Roy, Arkadev, Parto, Midya, and Marandi, Alireza
Subjects: Physics - Optics
Abstract: Arrays of nonlinear resonators offer a fertile ground for a wide range of complex phenomena and opportunities for advanced photonic sensing and computing. Recently, significant attention has focused on studying coupled resonators in special-purpose configurations either on chips or in table-top experiments. However, a path to realizing a large-scale programmable network of nonlinear photonic resonators remains elusive because of the challenges associated with simultaneously achieving strong nonlinearity, independent operation of the resonators, and programmability of the couplings. In this work, we break these barriers by realizing large-scale, time-multiplexed optical parametric oscillators (OPOs) on a single lithium niobate nanophotonic chip. We show independent operation of 70 identical OPOs in an ultrafast nanophotonic circuit. The OPOs exhibit an ultra-low threshold of a few picojoules, substantially surpassing the strength of nonlinearity of other platforms. Using our ultrafast nanophotonic circuit, a network of N OPOs with programmable all-to-all couplings requires only a few additional components. The time-multiplexed nanophotonic OPOs can enable myriad applications, including ultrafast classical and quantum information processing., Comment: 19 pages, 5 figures
Published: 2024

161. Stochastic Omega-Regular Verification and Control with Supermartingales

Author: Abate, Alessandro, Giacobbe, Mirco, and Roy, Diptarko
Subjects: Computer Science - Logic in Computer Science, Electrical Engineering and Systems Science - Systems and Control
Abstract: We present for the first time a supermartingale certificate for $\omega$-regular specifications. We leverage the Robbins & Siegmund convergence theorem to characterize supermartingale certificates for the almost-sure acceptance of Streett conditions on general stochastic processes, which we call Streett supermartingales. This enables effective verification and control of discrete-time stochastic dynamical models with infinite state space under $\omega$-regular and linear temporal logic specifications. Our result generalises reachability, safety, reach-avoid, persistence and recurrence specifications; our contribution applies to discrete-time stochastic dynamical models and probabilistic programs with discrete and continuous state spaces and distributions, and carries over to deterministic models and programs. We provide a synthesis algorithm for control policies and Streett supermartingales as proof certificates for $\omega$-regular objectives, which is sound and complete for supermartingales and control policies with polynomial templates and any stochastic dynamical model whose post-expectation is expressible as a polynomial. We additionally provide an optimisation of our algorithm that reduces the problem to satisfiability modulo theories, under the assumption that templates and post-expectation are in piecewise linear form. We have built a prototype and have demonstrated the efficacy of our approach on several exemplar $\omega$-regular verification and control synthesis problems., Comment: The conference version of this manuscript appeared at CAV'24
Published: 2024

162. Gradients of Functions of Large Matrices

Author: Krämer, Nicholas, Moreno-Muñoz, Pablo, Roy, Hrittik, and Hauberg, Søren
Subjects: Computer Science - Machine Learning, Mathematics - Numerical Analysis, Statistics - Machine Learning
Abstract: Tuning scientific and probabilistic machine learning models -- for example, partial differential equations, Gaussian processes, or Bayesian neural networks -- often relies on evaluating functions of matrices whose size grows with the data set or the number of parameters. While the state-of-the-art for evaluating these quantities is almost always based on Lanczos and Arnoldi iterations, the present work is the first to explain how to differentiate these workhorses of numerical linear algebra efficiently. To get there, we derive previously unknown adjoint systems for Lanczos and Arnoldi iterations, implement them in JAX, and show that the resulting code can compete with Diffrax when it comes to differentiating PDEs, GPyTorch for selecting Gaussian process models and beats standard factorisation methods for calibrating Bayesian neural networks. All this is achieved without any problem-specific code optimisation. Find the code at https://github.com/pnkraemer/experiments-lanczos-adjoints and install the library with pip install matfree.
Published: 2024

163. On a Generalized Moment Integral containing Riemann's Zeta Function: Analysis and Experiment

Author: Milgram, Michael and Hughes, Roy
Subjects: Mathematics - Number Theory, Mathematics - Classical Analysis and ODEs, 40G05, 40A10, 30B40, 11M06, 44A20
Abstract: Here, we study both analytically and numerically, an integral $Z(\sigma,r)$ related to the mean value of a generalized moment of Riemann's zeta function. Analytically, we predict finite, but discontinuous values and verify the prediction numerically, employing a modified form of Ces\`aro summation. Further, it is proven and verified numerically that for certain values of $\sigma$, the derivative function $Z^{\prime}(\sigma,n)$ equates to one generalized tine of the Dirac comb function without recourse to the use of limits, test functions or distributions. A surprising outcome of the numerical study arises from the observation that the proper integral form of the derivative function is quasi-periodic, which in turn suggests a periodicity of the integrand. This possibility is also explored and it is found experimentally that zeta function values offset (shifted) over certain segments of the imaginary complex number line are moderately auto-correlated., Comment: 33 pages, 19 Figures
Published: 2024

164. Few-shot Tuning of Foundation Models for Class-incremental Learning

Author: Roy, Shuvendu, Dolatabadi, Elham, Afkanpour, Arash, and Etemad, Ali
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: For the first time, we explore few-shot tuning of vision foundation models for class-incremental learning. Unlike existing few-shot class incremental learning (FSCIL) methods, which train an encoder on a base session to ensure forward compatibility for future continual learning, foundation models are generally trained on large unlabelled data without such considerations. This renders prior methods from traditional FSCIL incompatible for FSCIL with the foundation model. To this end, we propose Consistency-guided Asynchronous Contrastive Tuning (CoACT), a new approach to continually tune foundation models for new classes in few-shot settings. CoACT comprises three components: (i) asynchronous contrastive tuning, which learns new classes by including LoRA modules in the pre-trained encoder, while enforcing consistency between two asynchronous encoders; (ii) controlled fine-tuning, which facilitates effective tuning of a subset of the foundation model; and (iii) consistency-guided incremental tuning, which enforces additional regularization during later sessions to reduce forgetting of the learned classes. We perform an extensive study on 16 diverse datasets and demonstrate the effectiveness of CoACT, outperforming the best baseline method by 2.47% on average and with up to 12.52% on individual datasets. Additionally, CoACT shows reduced forgetting and robustness in low-shot experiments. As an added bonus, CoACT shows up to 13.5% improvement in standard FSCIL over the current SOTA on benchmark evaluations. We make our code publicly available at https://github.com/ShuvenduRoy/CoACT-FSCIL.
Published: 2024

165. Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination

Author: Golan, Shelly, Ganz, Roy, and Elad, Michael
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: The recently introduced Consistency models pose an efficient alternative to diffusion algorithms, enabling rapid and good quality image synthesis. These methods overcome the slowness of diffusion models by directly mapping noise to data, while maintaining a (relatively) simpler training. Consistency models enable a fast one- or few-step generation, but they typically fall somewhat short in sample quality when compared to their diffusion origins. In this work we propose a novel and highly effective technique for post-processing Consistency-based generated images, enhancing their perceptual quality. Our approach utilizes a joint classifier-discriminator model, in which both portions are trained adversarially. While the classifier aims to grade an image based on its assignment to a designated class, the discriminator portion of the very same network leverages the softmax values to assess the proximity of the input image to the targeted data manifold, thereby serving as an Energy-based Model. By employing example-specific projected gradient iterations under the guidance of this joint machine, we refine synthesized images and achieve an improved FID scores on the ImageNet 64x64 dataset for both Consistency-Training and Consistency-Distillation techniques.
Published: 2024

166. AT2020ohl: its nature and probable implications

Author: Roy, Rupak, Mandal, Samir, Sahu, D. K., Anupama, G. C., Nandi, Sumana, and Kumar, Brijesh
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: ASASSN-20hx, a.k.a AT2020ohl, is an ambiguous nuclear transient (ANT), which was discovered in the nearby galaxy NGC6297 by the All-Sky Automated Survey for Supernovae (ASAS-SN). We have investigated the evolution of AT2020ohl using a multi-wavelength dataset to explain the geometry of the system and the energy radiated by it between X-ray and radio wavelengths. Our X-ray, UV/optical, and radio observations of the object jointly clarify the association of AT2020ohl with the nuclear activity of NGC6297. We detected radio counterpart of AT2020ohl 111 days and 313 days after the discovery in Jansky Very Large Array X-band with flux densities 47$\pm$14 $\mu$Jy and 34$\pm$3 $\mu$Jy, respectively. Using multi-wavelength data analysis, we nullify the possibility of associating any stellar disruption process with this event. We found some evidence showing that the host galaxy is a merger remnant, so the possibility of a binary SMBH system can not be ruled out. The central SMBH has a mass of $\sim1.2\times10^7$ M$_\odot$. We propose the accretion disk activity as the origin of AT2020ohl $-$ it is either due to disk accretion event onto the central SMBH or due to the sudden accretion activity in a preexisting accretion disk of the system during the interaction of two SMBHs which became gravitationally bound during a merger process. However, we also admit that with the existing dataset, it is impossible to say definitively, among these two probabilities, which one is the origin of this nuclear transient., Comment: 17 pages, 9 figures. Published in MNRAS ( see https://doi.org/10.1093/mnras/stae395 )
Published: 2024
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167. Rationalizability, Iterated Dominance, and the Theorems of Radon and Carath\'eodory

Author: Long, Roy
Subjects: Computer Science - Computer Science and Game Theory, Economics - Theoretical Economics
Abstract: The game theoretic concepts of rationalizability and iterated dominance are closely related and provide characterizations of each other. Indeed, the equivalence between them implies that in a two player finite game, the remaining set of actions available to players after iterated elimination of strictly dominated strategies coincides with the rationalizable actions. I prove a dimensionality result following from these ideas. I show that for two player games, the number of actions available to the opposing player provides a (tight) upper bound on how a player's pure strategies may be strictly dominated by mixed strategies. I provide two different frameworks and interpretations of dominance to prove this result, and in doing so relate it to Radon's Theorem and Carath\'eodory's Theorem from convex geometry. These approaches may be seen as following from point-line duality. A new proof of the classical equivalence between these solution concepts is also given., Comment: Published in Stanford Economic Review
Published: 2024

168. On the path ideals of chordal graphs

Author: Das, Kanoy Kumar, Roy, Amit, and Saha, Kamalesh
Subjects: Mathematics - Combinatorics, Mathematics - Commutative Algebra, 05E40, 13F55, 05C05
Abstract: In this article, we give combinatorial formulas for the regularity and the projective dimension of $3$-path ideals of chordal graphs, extending the well-known formulas for the edge ideals of chordal graphs given in terms of the induced matching number and the big height, respectively. As a consequence, we get that the $3$-path ideal of a chordal graph is Cohen-Macaulay if and only if it is unmixed. Additionally, we show that the Alexander dual of the $3$-path ideal of a tree is vertex splittable, thereby resolving the $t=3$ case of a recent conjecture in [Internat. J. Algebra Comput., 33(3):481--498, 2023]. Also, we give examples of chordal graphs where the duals of their $t$-path ideals are not vertex splittable for $t\ge 3$. Furthermore, we extend the formula of the regularity of $3$-path ideals of chordal graphs to all $t$-path ideals of caterpillar graphs. We then provide some families of graphs to show that these formulas for the regularity and the projective dimension cannot be extended to higher $t$-path ideals of chordal graphs (even in the case of trees)., Comment: 22 pages. Comments are welcome
Published: 2024

169. Synthetic high angular momentum spin dynamics in a microwave oscillator

Author: Roy, Saswata, Senanian, Alen, Wang, Christopher S., Wetherbee, Owen C., Zhang, Luojia, Cole, B., Larson, C. P., Yelton, E., Arora, Kartikeya, McMahon, Peter L., Plourde, B. L. T., Royer, Baptiste, and Fatemi, Valla
Subjects: Quantum Physics
Abstract: Spins and oscillators are foundational to much of physics and applied sciences. For quantum information, a spin 1/2 exemplifies the most basic unit, a qubit. High angular momentum spins and harmonic oscillators provide multi-level manifolds (e.g., qudits) which have the potential for hardware-efficient protected encodings of quantum information and simulation of many-body quantum systems. In this work, we demonstrate a new quantum control protocol that conceptually merges these disparate hardware platforms. Namely, we show how to modify a harmonic oscillator on-demand to implement a continuous range of generators associated to resonant driving of a harmonic qudit, and then specifically design a harmonic multi-level spin degree of freedom. The synthetic spin is verified by demonstration of spin coherent (SU(2)) rotations and comparison to other manifolds like simply-truncated oscillators. Our scheme allows universal control of the qudit, and, for the first time, we use linear, harmonic operations to accomplish four logical gates on a harmonic qudit encoding. Our results show how motion on a closed Hilbert space can be useful for quantum information processing and opens the door to superconducting circuit simulations of higher angular momentum quantum magnetism.
Published: 2024

170. Less is more: Summarizing Patch Tokens for efficient Multi-Label Class-Incremental Learning

Author: De Min, Thomas, Mancini, Massimiliano, Lathuilière, Stéphane, Roy, Subhankar, and Ricci, Elisa
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: Prompt tuning has emerged as an effective rehearsal-free technique for class-incremental learning (CIL) that learns a tiny set of task-specific parameters (or prompts) to instruct a pre-trained transformer to learn on a sequence of tasks. Albeit effective, prompt tuning methods do not lend well in the multi-label class incremental learning (MLCIL) scenario (where an image contains multiple foreground classes) due to the ambiguity in selecting the correct prompt(s) corresponding to different foreground objects belonging to multiple tasks. To circumvent this issue we propose to eliminate the prompt selection mechanism by maintaining task-specific pathways, which allow us to learn representations that do not interact with the ones from the other tasks. Since independent pathways in truly incremental scenarios will result in an explosion of computation due to the quadratically complex multi-head self-attention (MSA) operation in prompt tuning, we propose to reduce the original patch token embeddings into summarized tokens. Prompt tuning is then applied to these fewer summarized tokens to compute the final representation. Our proposed method Multi-Label class incremental learning via summarising pAtch tokeN Embeddings (MULTI-LANE) enables learning disentangled task-specific representations in MLCIL while ensuring fast inference. We conduct experiments in common benchmarks and demonstrate that our MULTI-LANE achieves a new state-of-the-art in MLCIL. Additionally, we show that MULTI-LANE is also competitive in the CIL setting. Source code available at https://github.com/tdemin16/multi-lane, Comment: Published at 3rd Conference on Lifelong Learning Agents (CoLLAs), 2024
Published: 2024

171. Ultra-cold atoms quantum tunneling through single and double optical barriers

Author: Eid, Roy, Hammond, Alfred, Lavoine, Lucas, and Bourdel, Thomas
Subjects: Physics - Atomic Physics, Quantum Physics
Abstract: We realize textbook experiments on Bose-Einstein condensate tunnelling through thin repulsive potential barriers. In particular, we demonstrate atom tunnelling though a single optical barrier in the quantum scattering regime where the De Broglie wavelength of the atoms is larger than the barrier width. Such a beam splitter can be used for atom interferometry and we study the case of two barriers creating an atomic Fabry-P{\'e}rot cavity. Technically, the velocity of the atoms is reduced thanks to the use of a 39K Bose-Einstein condensate with no interactions. The potential barriers are created optically and their width is tunable thanks to the use of a digital micro-mirror device. In addition, our scattering experiments enable in-situ characterization of the optical aberrations of the barrier optical system.
Published: 2024

172. Algebraic Independence of Special Points on Shimura Varieties

Author: Fu, Yu and Zhao, Roy
Subjects: Mathematics - Number Theory, Mathematics - Algebraic Geometry
Abstract: Given a correspondence $V$ between a connected Shimura variety $S$, a commutative connected algebraic group $G$, and $n \in \mathbb{N}$, we prove that the $V$-images of any $n$ special points on $S$ outside a proper Zariski closed subset are algebraically independent. Our result unifies previous unlikely intersection results on multiplicative independence and linear independence. We prove multiplicative independence of differences of singular moduli, generalizing previous results by Pila-Tsimerman, and Aslanlyan-Eterovi\'c-Fowler. We also give an application to abelian varieties by proving that the special points of $S$ whose $V$-images lie in a finite-rank subgroup of $T$ are contained in a finite union of proper special subvarieties of $S$, only dependent on the rank of the subgroup. In this way, our result is a generalization of the works of Pila-Tsimerman and Buium-Poonen., Comment: 17 pages, comments welcome. We changed the notation for the commutative connected algebraic group and corrected typos
Published: 2024

173. FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits

Author: Chakraborty, Sunrit, Roy, Saptarshi, and Basu, Debabrota
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Statistics Theory
Abstract: High dimensional sparse linear bandits serve as an efficient model for sequential decision-making problems (e.g. personalized medicine), where high dimensional features (e.g. genomic data) on the users are available, but only a small subset of them are relevant. Motivated by data privacy concerns in these applications, we study the joint differentially private high dimensional sparse linear bandits, where both rewards and contexts are considered as private data. First, to quantify the cost of privacy, we derive a lower bound on the regret achievable in this setting. To further address the problem, we design a computationally efficient bandit algorithm, \textbf{F}orgetfu\textbf{L} \textbf{I}terative \textbf{P}rivate \textbf{HA}rd \textbf{T}hresholding (FLIPHAT). Along with doubling of episodes and episodic forgetting, FLIPHAT deploys a variant of Noisy Iterative Hard Thresholding (N-IHT) algorithm as a sparse linear regression oracle to ensure both privacy and regret-optimality. We show that FLIPHAT achieves optimal regret up to logarithmic factors. We analyze the regret by providing a novel refined analysis of the estimation error of N-IHT, which is of parallel interest., Comment: 28 pages, 1 figure
Published: 2024

174. SADDLe: Sharpness-Aware Decentralized Deep Learning with Heterogeneous Data

Author: Choudhary, Sakshi, Aketi, Sai Aparna, and Roy, Kaushik
Subjects: Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Multiagent Systems
Abstract: Decentralized training enables learning with distributed datasets generated at different locations without relying on a central server. In realistic scenarios, the data distribution across these sparsely connected learning agents can be significantly heterogeneous, leading to local model over-fitting and poor global model generalization. Another challenge is the high communication cost of training models in such a peer-to-peer fashion without any central coordination. In this paper, we jointly tackle these two-fold practical challenges by proposing SADDLe, a set of sharpness-aware decentralized deep learning algorithms. SADDLe leverages Sharpness-Aware Minimization (SAM) to seek a flatter loss landscape during training, resulting in better model generalization as well as enhanced robustness to communication compression. We present two versions of our approach and conduct extensive experiments to show that SADDLe leads to 1-20% improvement in test accuracy compared to other existing techniques. Additionally, our proposed approach is robust to communication compression, with an average drop of only 1% in the presence of up to 4x compression.
Published: 2024

175. Exogenous Consideration and Extended Random Utility

Author: Allen, Roy
Subjects: Economics - Econometrics
Abstract: In a consideration set model, an individual maximizes utility among the considered alternatives. I relate a consideration set additive random utility model to classic discrete choice and the extended additive random utility model, in which utility can be $-\infty$ for infeasible alternatives. When observable utility shifters are bounded, all three models are observationally equivalent. Moreover, they have the same counterfactual bounds and welfare formulas for changes in utility shifters like price. For attention interventions, welfare cannot change in the full consideration model but is completely unbounded in the limited consideration model. The identified set for consideration set probabilities has a minimal width for any bounded support of shifters, but with unbounded support it is a point: identification "towards" infinity does not resemble identification "at" infinity., Comment: 44 pages
Published: 2024

176. Nonparametric quantile regression for spatio-temporal processes

Author: Deb, Soudeep, Neves, Claudia, and Roy, Subhrajyoty
Subjects: Statistics - Methodology, Mathematics - Statistics Theory
Abstract: In this paper, we develop a new and effective approach to nonparametric quantile regression that accommodates ultrahigh-dimensional data arising from spatio-temporal processes. This approach proves advantageous in staving off computational challenges that constitute known hindrances to existing nonparametric quantile regression methods when the number of predictors is much larger than the available sample size. We investigate conditions under which estimation is feasible and of good overall quality and obtain sharp approximations that we employ to devising statistical inference methodology. These include simultaneous confidence intervals and tests of hypotheses, whose asymptotics is borne by a non-trivial functional central limit theorem tailored to martingale differences. Additionally, we provide finite-sample results through various simulations which, accompanied by an illustrative application to real-worldesque data (on electricity demand), offer guarantees on the performance of the proposed methodology., Comment: 33 pages, 2 figures and accompanying supplementary documentation
Published: 2024

177. Search for the lepton-flavor violating decay $B^0_s\to\phi\mu^\pm\tau^\mp$

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Baldini, W., Bao, H., Leite, J. Baptista de Souza, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Baryshnikov, F., Basels, J. M., Bassi, G., Batsukh, B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bondar, N., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bowcock, T. J. V., Boyer, A., Bozzi, C., Bradley, M. J., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butkevich, A., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Godinez, J. Castro, Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Barajas, C. A. Chavez, Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collins, P., Colombo, T., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Epple, E., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Fischer, K. M., Fitzgerald, D. S., Fitzpatrick, C., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Fuehring, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, Y., Garau, M., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Gomes, A., Fernandez, S. Gomez, Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Haimberger, J., Hajheidari, M., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Hartmann, M., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Hill, D., Hollitt, S. E., Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O. K., Kubat, J., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Leverington, B., Li, A., Li, H., Li, K., Li, L., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Castro, J. Lomba, Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Ma, R., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. M., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Santos, D. Martinez, Vidal, F. Martinez, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mödden, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Nogarolli, P., Nogga, P., Nolte, N. S., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perro, A., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Pugatch, V., Punzi, G., Qasim, S., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Lamas, M. Romero, Vidal, A. Romero, Romolini, G., Ronchetti, F., Rotondo, M., Roy, S. R., Rudolph, M. S., Ruf, T., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Gras, C. Sanchez, Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shields, E. B., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sun, Y., Lima, D. S. Sundfeld, Sutcliffe, W., Swallow, P. N., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Valassi, A., Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Vesterinen, M., Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vouters, G., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, D. J., Whitehead, M., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. R. J., Williams, R., Wilson, F. F., Wislicki, W., Witek, M., Witola, L., Wong, C. P., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, Y., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, D., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yu, C. Y., Yu, J., Yuan, X., Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: A search for the lepton-flavor violating decays $B^0_s\to\phi\mu^\pm\tau^\mp$ is presented, using a sample of proton-proton collisions at center-of-mass energies of 7, 8, and 13 TeV, collected with the LHCb detector and corresponding to a total integrated luminosity of $9\,\text{fb}^{-1}$. The $\tau$ leptons are selected using decays with three charged pions. No significant excess is observed, and an upper limit on the branching fraction is determined to be ${\cal B}( B^0_s\to\phi\mu^\pm\tau^\mp) < 1.0\times 10^{-5}$ at 90% confidence level., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-006.html (LHCb public pages)
Published: 2024

178. Tutorly: Turning Programming Videos Into Apprenticeship Learning Environments with LLMs

Author: Li, Wengxi, Pea, Roy, Haber, Nick, and Subramonyam, Hari
Subjects: Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract: Online programming videos, including tutorials and streamcasts, are widely popular and contain a wealth of expert knowledge. However, effectively utilizing these resources to achieve targeted learning goals can be challenging. Unlike direct tutoring, video content lacks tailored guidance based on individual learning paces, personalized feedback, and interactive engagement necessary for support and monitoring. Our work transforms programming videos into one-on-one tutoring experiences using the cognitive apprenticeship framework. Tutorly, developed as a JupyterLab Plugin, allows learners to (1) set personalized learning goals, (2) engage in learning-by-doing through a conversational LLM-based mentor agent, (3) receive guidance and feedback based on a student model that steers the mentor moves. In a within-subject study with 16 participants learning exploratory data analysis from a streamcast, Tutorly significantly improved their performance from 61.9% to 76.6% based on a post-test questionnaire. Tutorly demonstrates the potential for enhancing programming video learning experiences with LLM and learner modeling.
Published: 2024

179. Study of $b$-hadron decays to $\Lambda_c^+ h^- h^{\prime -}$ final states

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataide, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Baldini, W., Bao, H., Leite, J. Baptista de Souza, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bassi, G., Batsukh, B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bondar, N., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butkevich, A., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Godinez, J. Castro, Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Barajas, C. A. Chavez, Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collins, P., Colombo, T., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Epple, E., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Fischer, K. M., Fitzgerald, D. S., Fitzpatrick, C., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Fuehring, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, Y., Garau, M., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Gomes, A., Fernandez, S. Gomez, Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Haimberger, J., Hajheidari, M., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Hartmann, M., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Hill, D., Hollitt, S. E., Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O. K., Kubat, J., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Leverington, B., Li, A., Li, H., Li, K., Li, L., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Castro, J. Lomba, Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Ma, R., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. M., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Santos, D. Martinez, Vidal, F. Martinez, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mödden, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Nogarolli, P., Nogga, P., Nolte, N. S., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perro, A., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Lamas, M. Romero, Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Ruf, T., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Gras, C. Sanchez, Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A. S., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sun, Y., Lima, D. S. Sundfeld, Sutcliffe, W., Swallow, P. N., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Vesterinen, M., Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vouters, G., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, D. J., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. R. J., Williams, R., Wilson, F. F., Wislicki, W., Witek, M., Witola, L., Wong, C. P., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, Y., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, D., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yu, C. Y., Yu, J., Yuan, X., Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: Decays of $\Xi_b^-$ and $\Omega_b^-$ baryons to $\Lambda_c^+ h^- h^{\prime -}$ final states, with $h^- h^{\prime -}$ being $\pi^-\pi^-$, $K^-\pi^-$ and $K^-K^-$ meson pairs, are searched for using data collected with the LHCb detector. The data sample studied corresponds to an integrated luminosity of $8.7\,\mathrm{fb}^{-1}$ of $pp$ collisions collected at centre-of-mass energies $\sqrt{s} = 7$, $8$ and $13\,\mathrm{Te\kern -0.1em V}$. The products of the relative branching fractions and fragmentation fractions for each signal mode, relative to the $B^- \to \Lambda_c^+ \overline{p} \pi^-$ mode, are measured, with $\Xi_{b}^- \to\Lambda_{c}^+ K^- \pi^-$, $\Xi_{b}^- \to\Lambda_{c}^+ K^- K^-$ and $\Omega_{b}^- \to\Lambda_{c}^+ K^- K^-$ decays being observed at over $5\,\sigma$ significance. The $\Xi_{b}^- \to\Lambda_{c}^+ K^- \pi^-$ mode is also used to measure the $\Xi_{b}^-$ production asymmetry, which is found to be consistent with zero. In addition, the $B^- \to \Lambda_{c}^+ \overline{p} K^-$ decay is observed for the first time, and its branching fraction is measured relative to that of the $B^- \to \Lambda_{c}^+ \overline{p} \pi^-$ mode., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-013.html
Published: 2024

180. First joint oscillation analysis of Super-Kamiokande atmospheric and T2K accelerator neutrino data

Author: Super-Kamiokande, collaborations, T2K, Abe, S., Abe, K., Akhlaq, N., Akutsu, R., Alarakia-Charles, H., Ali, A., Hakim, Y. I. Alj, Monsalve, S. Alonso, Amanai, S., Andreopoulos, C., Anthony, L. H. V., Antonova, M., Aoki, S., Apte, K. A., Arai, T., Arihara, T., Arimoto, S., Asada, Y., Asaka, R., Ashida, Y., Atkin, E. T., Babu, N., Barbi, M., Barker, G. J., Barr, G., Barrow, D., Bates, P., Batkiewicz-Kwasniak, M., Beauchêne, A., Berardi, V., Berns, L., Bhadra, S., Bhuiyan, N., Bian, J., Blanchet, A., Blondel, A., Bodur, B., Bolognesi, S., Bordoni, S., Boyd, S. B., Bravar, A., Bronner, C., Bubak, A., Avanzini, M. Buizza, Burton, G. T., Caballero, J. A., Calabria, N. F., Cao, S., Carabadjac, D., Carter, A. J., Cartwright, S. L., Casado, M. P., Catanesi, M. G., Cervera, A., Chakrani, J., Chalumeau, A., Chen, S., Cherdack, D., Choi, K., Chong, P. S., Chvirova, A., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cormier, F., Cudd, A., Dalmazzone, C., Daret, T., Dasgupta, P., Davis, C., Davydov, Yu. I., De Roeck, A., De Rosa, G., Dealtry, T., Delogu, C. C., Densham, C., Dergacheva, A., Dharmapal, R., Di Lodovico, F., Lopez, G. Diaz, Dolan, S., Douqa, D., Doyle, T. A., Drapier, O., Duffy, K. E., Dumarchez, J., Dunne, P., Dygnarowicz, K., D'ago, D., Edwards, R., Eguchi, A., Elias, J., Emery-Schrenk, S., Erofeev, G., Ershova, A., Eurin, G., Fannon, J. E. P., Fedorova, D., Fedotov, S., Feltre, M., Feng, J., Feng, L., Ferlewicz, D., Fernandez, P., Finch, A. J., Aguirre, G. A. Fiorentini, Fiorillo, G., Fitton, M. D., Patiño, J. M. Franco, Friend, M., Fujii, Y., Fujisawa, C., Fujita, S., Fukuda, Y., Furui, Y., Gao, J., Gaur, R., Giampaolo, A., Giannessi, L., Giganti, C., Glagolev, V., Goldsack, A., Gonin, M., Rosa, J. González, Goodman, E. A. G., Gorin, A., Gorshanov, K., Gousy-Leblanc, V., Grassi, M., Griskevich, N. J., Guigue, M., Hadley, D., Haigh, J. T., Han, S., Harada, M., Harris, D. A., Hartz, M., Hasegawa, T., Hassani, S., Hastings, N. C., Hayato, Y., Heitkamp, I., Henaff, D., Hill, J., Hino, Y., Hiraide, K., Hogan, M., Holeczek, J., Holin, A., Holvey, T., Van, N. T. Hong, Honjo, T., Horiuchi, S., Hosokawa, K., Hu, Z., Hu, J., Iacob, F., Ichikawa, A. K., Ieki, K., Ikeda, M., Iovine, N., Ishida, T., Ishino, H., Ishitsuka, M., Ishizuka, T., Ito, H., Itow, Y., Izmaylov, A., Izumiyama, S., Jakkapu, M., Jamieson, B., Jang, M. C., Jang, J. S., Jenkins, S. J., Jesús-Valls, C., Ji, J. Y., Jia, M., Jiang, J., Jonsson, P., Joshi, S., Jung, C. K., Jung, S., Kabirnezhad, M., Kaboth, A. C., Kajita, T., Kakuno, H., Kameda, J., Kanemura, Y., Kaneshima, R., Karpova, S., Kasetti, S. P., Kashiwagi, Y., Kasturi, V. S., Kataoka, Y., Katori, T., Kawamura, Y., Kawaue, M., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., Kim, S. B., King, S., Kiseeva, V., Kisiel, J., Kneale, L., Kobayashi, H., Kobayashi, T., Kobayashi, M., Koch, L., Kodama, S., Kolupanova, M., Konaka, A., Kormos, L. L., Koshio, Y., Koto, T., Kowalik, K., Kudenko, Y., Kudo, Y., Kuribayashi, S., Kurjata, R., Kurochka, V., Kutter, T., Kuze, M., Kwon, E., La Commara, M., Labarga, L., Lachat, M., Lachner, K., Lagoda, J., Lakshmi, S. M., LamersJames, M., Langella, A., Laporte, J. -F., Last, D., Latham, N., Laveder, M., Lavitola, L., Lawe, M., Learned, J. G., Lee, Y., Lee, S. H., Silverio, D. Leon, Levorato, S., Lewis, S., Li, X., Li, W., Lin, C., Litchfield, R. P., Liu, S. L., Liu, Y. M., Long, K. R., Longhin, A., Moreno, A. Lopez, Lu, X., Ludovici, L., Lux, T., Machado, L. N., Maekawa, Y., Magaletti, L., Mahn, K., Mahtani, K. K., Malek, M., Mandal, M., Manly, S., Marino, A. D., Martens, K., Marti, Ll., Martin, D. G. R., Martin, J. F., Martin, D., Martini, M., Maruyama, T., Matsubara, T., Matsumoto, R., Mattiazzi, M., Matveev, V., Mauger, C., Mavrokoridis, K., Mazzucato, E., McCauley, N., McElwee, J. M., McFarland, K. S., McGrew, C., McKean, J., Mefodiev, A., Megias, G. D., Mehta, P., Mellet, L., Menjo, H., Metelko, C., Mezzetto, M., Migenda, J., Mijakowski, P., Miki, S., Miller, E., Minamino, A., Mine, S., Mineev, O., Mirabito, J., Miura, M., Bueno, L. Molina, Moon, D. H., Mori, M., Moriyama, S., Morrison, P., Muñoz, A., Mueller, Th. A., Munford, D., Munteanu, L., Nagai, Y., Nagai, K., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakajima, Y., Nakamura, A., Nakamura, K., Nakamura, K. D., Nakamura, T., Nakanishi, F., Nakano, Y., Nakaya, T., Nakayama, S., Nakayoshi, K., Naseby, C. E. R., Ngoc, T. V., Nguyen, V. Q., Nguyen, D. T., Nicholson, M., Niewczas, K., Ninomiya, K., Nishijima, K., Nishimori, S., Nishimura, Y., Noguchi, Y., Nosek, T., Nova, F., Novella, P., Nugent, J. C., Odagawa, T., Okazaki, R., Okazawa, H., Okinaga, W., Okumura, K., Okusawa, T., Ommura, Y., Onda, N., Ospina, N., Osu, L., Oyama, Y., O'Flaherty, M., O'Keeffe, H. M., O'Sullivan, L., Périssé, L., Paganini, P., Palladino, V., Paolone, V., Pari, M., Park, R. G., Parlone, J., Pasternak, J., Payne, D., Penn, G. C., de Perio, P., Pershey, D., Pfaff, M., Pickering, L., Pintaudi, G., Pistillo, C., Pointon, B. W., Popov, B., Yrey, A. Portocarrero, Porwit, K., Posiadala-Zezula, M., Prabhu, Y. S., Prasad, H., Pronost, G., Prouse, N. W., Pupilli, F., Quilain, B., Quyen, P. T., Raaf, J. L., Radermacher, T., Radicioni, E., Radics, B., Ramirez, M. A., Ramsden, R. M., Ratoff, P. N., Reh, M., Riccio, C., Richards, B., Rogly, R., Rondio, E., Roth, S., Roy, N., Rubbia, A., Russo, L., Rychter, A., Saenz, W., Sakai, S., Sakashita, K., Samani, S., Santos, A. D., Sato, Y., Sato, K., Schefke, T., Schloesser, C. M., Scholberg, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Seo, J. W., Sgalaberna, D., Shaikhiev, A., Shi, W., Shiba, H., Shibayama, R., Shigeta, N., Shima, S., Shimamura, R., Shimizu, K., Shinoki, M., Shiozawa, M., Shiraishi, Y., Shvartsman, A., Skrobova, N., Skwarczynski, K., Smy, M. B., Smyczek, D., Sobczyk, J. T., Sobel, H. W., Soler, F. J. P., Sonoda, Y., Speers, A. J., Spina, R., Stroke, Y., Suslov, I. A., Suvorov, S., Suzuki, S., Suzuki, A., Suzuki, S. Y., Suzuki, Y., Sánchez, F., Tada, T., Tada, M., Tairafune, S., Takagi, Y., Takeda, A., Takemoto, Y., Takeuchi, Y., Takhistov, V., Takifuji, K., Tanaka, H., Tanaka, H. K., Tanigawa, H., Taniuchi, N., Tano, T., Tarrant, A., Tashiro, T., Teklu, A., Terada, K., Tereshchenko, V. V., Thamm, N., Thiesse, M. D., Thompson, L. F., Toki, W., Tomiya, T., Touramanis, C., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M. R., Vargas, D., Varghese, M., Vasseur, G., Villa, E., Vinning, W. G. S., Virginet, U., Vladisavljevic, T., Wachala, T., Wakabayashi, D., Wallace, H. T., Walsh, J. G., Walter, C. W., Wan, L., Wang, X., Wang, Y., Wark, D., Wascko, M. O., Watanabe, E., Weber, A., Wendell, R. A., Wester, T., Wilking, M. J., Wilkinson, C., Wilson, S. T., Wilson, J. R., Wood, K., Wret, C., Wu, Y., Xia, J., Xie, Z., Xu, B. D., Xu, Y. -H., Yamamoto, K., Yamamoto, T., Yamauchi, K., Yanagisawa, C., Yang, G., Yang, B. S., Yang, J. Y., Yankelevich, A., Yano, T., Yasutome, K., Yershov, N., Yevarouskaya, U., Yokoyama, M., Yoo, J., Yoshida, T., Yoshida, S., Yoshimoto, Y., Yoshimura, N., Yoshioka, Y., Yu, M., Yu, I., Zaki, R., Zaldivar, B., Zalewska, A., Zalipska, J., Zaremba, K., Zarnecki, G., Zhang, J., Zhang, A. Q., Zhang, B., Zhao, X. Y., Zhong, H., Zhu, T., Ziembicki, M., Zimmerman, E. D., Zito, M., and Zsoldos, S.
Subjects: High Energy Physics - Experiment
Abstract: The Super-Kamiokande and T2K collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of $19.7(16.3) \times 10^{20}$ protons on target in (anti)neutrino mode, the analysis finds a 1.9$\sigma$ exclusion of CP-conservation (defined as $J_{CP}=0$) and a preference for the normal mass ordering., Comment: 10 pages, 3 figures
Published: 2024

181. Bond percolation games on the $2$-dimensional square lattice, and ergodicity of associated probabilistic cellular automata

Author: Bhasin, Dhruv, Karmakar, Sayar, Podder, Moumanti, and Roy, Souvik
Subjects: Mathematics - Probability
Abstract: We consider bond percolation games on the $2$-dimensional square lattice in which each edge (that is either between the sites $(x,y)$ and $(x+1,y)$ or between the sites $(x,y)$ and $(x,y+1)$, for all $(x,y) \in \mathbb{Z}^{2}$) has been assigned, independently, a label that reads "trap" with probability $p$, "target" with probability $q$, and "open" with probability $1-p-q$. Once a realization of this labeling is generated, it is revealed in its entirety to the players before the game starts. The game involves a single token, initially placed at the origin, and two players who take turns to make moves. A move involves relocating the token from where it is currently located, say the site $(x,y)$, to any one of $(x+1,y)$ and $(x,y+1)$. A player wins if she is able to move the token along an edge labeled a target, or if she is able to force her opponent to move the token along an edge labeled a trap. The game is said to result in a draw if it continues indefinitely (i.e.\ with the token always being moved along open edges). We ask the question: for what values of $p$ and $q$ is the probability of draw equal to $0$? By establishing a close connection between the event of draw and the ergodicity of a suitably defined probabilistic cellualar automaton, we are able to show that the probability of draw is $0$ when $p > 0.157175$ and $q=0$, and when $p=q \geqslant 0.10883$., Comment: Main body of the paper: 21 pages. Appendix: from page 21 till page 43. No. of figures: 4
Published: 2024

182. Strategy-Proof Auctions through Conformal Prediction

Author: Lotan, Roy Maor, Talgam-Cohen, Inbal, and Romano, Yaniv
Subjects: Computer Science - Computer Science and Game Theory, Computer Science - Machine Learning
Abstract: Auctions are key for maximizing sellers' revenue and ensuring truthful bidding among buyers. Recently, an approach known as differentiable economics based on deep learning shows promise in learning optimal auction mechanisms for multiple items and participants. However, this approach has no guarantee of strategy-proofness at test time. Strategy-proofness is crucial as it ensures that buyers are incentivized to bid their true valuations, leading to optimal and fair auction outcomes without the risk of manipulation. Building on conformal prediction, we introduce a novel approach to achieve strategy-proofness with rigorous statistical guarantees. The key novelties of our method are: (i) the formulation of a regret prediction model, used to quantify at test time violations of strategy-proofness; and (ii) an auction acceptance rule that leverages the predicted regret to ensure that for a new auction, the data-driven mechanism meets the strategy-proofness requirement with high probability (e.g., 99\%). Numerical experiments demonstrate the necessity for rigorous guarantees, the validity of our theoretical results, and the applicability of our proposed method.
Published: 2024

183. MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections

Author: Liu, Jiayue, Tang, Xiao, Cheng, Freeman, Yang, Roy, Li, Zhihao, Liu, Jianzhuang, Huang, Yi, Lin, Jiaqi, Liu, Shiyong, Wu, Xiaofei, Xu, Songcen, and Yuan, Chun
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: 3D Gaussian Splatting showcases notable advancements in photo-realistic and real-time novel view synthesis. However, it faces challenges in modeling mirror reflections, which exhibit substantial appearance variations from different viewpoints. To tackle this problem, we present MirrorGaussian, the first method for mirror scene reconstruction with real-time rendering based on 3D Gaussian Splatting. The key insight is grounded on the mirror symmetry between the real-world space and the virtual mirror space. We introduce an intuitive dual-rendering strategy that enables differentiable rasterization of both the real-world 3D Gaussians and the mirrored counterpart obtained by reflecting the former about the mirror plane. All 3D Gaussians are jointly optimized with the mirror plane in an end-to-end framework. MirrorGaussian achieves high-quality and real-time rendering in scenes with mirrors, empowering scene editing like adding new mirrors and objects. Comprehensive experiments on multiple datasets demonstrate that our approach significantly outperforms existing methods, achieving state-of-the-art results. Project page: https://mirror-gaussian.github.io/.
Published: 2024

184. Multi-dimension Transformer with Attention-based Filtering for Medical Image Segmentation

Author: Wang, Wentao, Xiao, Xi, Liu, Mingjie, Tian, Qing, Huang, Xuanyao, Lan, Qizhen, Roy, Swalpa Kumar, and Wang, Tianyang
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: The accurate segmentation of medical images is crucial for diagnosing and treating diseases. Recent studies demonstrate that vision transformer-based methods have significantly improved performance in medical image segmentation, primarily due to their superior ability to establish global relationships among features and adaptability to various inputs. However, these methods struggle with the low signal-to-noise ratio inherent to medical images. Additionally, the effective utilization of channel and spatial information, which are essential for medical image segmentation, is limited by the representation capacity of self-attention. To address these challenges, we propose a multi-dimension transformer with attention-based filtering (MDT-AF), which redesigns the patch embedding and self-attention mechanism for medical image segmentation. MDT-AF incorporates an attention-based feature filtering mechanism into the patch embedding blocks and employs a coarse-to-fine process to mitigate the impact of low signal-to-noise ratio. To better capture complex structures in medical images, MDT-AF extends the self-attention mechanism to incorporate spatial and channel dimensions, enriching feature representation. Moreover, we introduce an interaction mechanism to improve the feature aggregation between spatial and channel dimensions. Experimental results on three public medical image segmentation benchmarks show that MDT-AF achieves state-of-the-art (SOTA) performance.
Published: 2024

185. Crossing The Gap Using Variational Quantum Eigensolver: A Comparative Study

Author: Chen, I-Chi, Innan, Nouhaila, Roy, Suman Kumar, and Saroni, Jason
Subjects: Quantum Physics
Abstract: Within the evolving domain of quantum computational chemistry, the Variational Quantum Eigensolver (VQE) has been developed to explore not only the ground state but also the excited states of molecules. In this study, we compare the performance of Variational Quantum Deflation (VQD) and Subspace-Search Variational Quantum Eigensolver (SSVQE) methods in determining the low-lying excited states of $LiH$. Our investigation reveals that while VQD exhibits a slight advantage in accuracy, SSVQE stands out for its efficiency, allowing the determination of all low-lying excited states through a single parameter optimization procedure. We further evaluate the effectiveness of optimizers, including Gradient Descent (GD), Quantum Natural Gradient (QNG), and Adam optimizer, in obtaining $LiH$'s first excited state, with the Adam optimizer demonstrating superior efficiency in requiring the fewest iterations. Moreover, we propose a novel approach combining Folded Spectrum VQE (FS-VQE) with either VQD or SSVQE, enabling the exploration of highly excited states. We test the new approaches for finding all three $H_4$'s excited states. Folded Spectrum SSVQE (FS-SSVQE) can find all three highly excited states near $-1.0$ Ha with only one optimizing procedure, but the procedure converges slowly. In contrast, although Folded spectrum VQD (FS-VQD) gets highly excited states with individual optimizing procedures, the optimizing procedure converges faster., Comment: 10 pages, 8 figures
Published: 2024

186. Transverse polarization measurement of $\Lambda$ hyperons in $p$Ne collisions at $\sqrt{s_{NN}}$ = 68.4 GeV with the $\mbox{LHCb}$ detector

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataide, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Baldini, W., Bao, H., Leite, J. Baptista de Souza, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bassi, G., Batsukh, B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bondar, N., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butkevich, A., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Godinez, J. Castro, Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Barajas, C. A. Chavez, Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collins, P., Colombo, T., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Epple, E., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Fischer, K. M., Fitzgerald, D. S., Fitzpatrick, C., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Fuehring, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, Y., Garau, M., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Gomes, A., Fernandez, S. Gomez, Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Haimberger, J., Hajheidari, M., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Hartmann, M., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Hill, D., Hollitt, S. E., Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O. K., Kubat, J., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Leverington, B., Li, A., Li, H., Li, K., Li, L., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Castro, J. Lomba, Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Ma, R., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. M., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Santos, D. Martinez, Vidal, F. Martinez, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mödden, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Nogarolli, P., Nogga, P., Nolte, N. S., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perro, A., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Lamas, M. Romero, Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Ruf, T., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Gras, C. Sanchez, Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A. S., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sun, Y., Lima, D. S. Sundfeld, Sutcliffe, W., Swallow, P. N., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Vesterinen, M., Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vouters, G., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, D. J., Whitehead, M., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. R. J., Williams, R., Wilson, F. F., Wislicki, W., Witek, M., Witola, L., Wong, C. P., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, Y., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, D., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yu, C. Y., Yu, J., Yuan, X., Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: A measurement of the transverse polarization of the $\Lambda$ and $\bar{\Lambda}$ hyperons in $p$Ne fixed-target collisions at $\sqrt{s_{NN}}$ = 68.4 GeV is presented using data collected by the LHCb detector. The polarization is studied using the decay $\Lambda \rightarrow p \pi^-$ together with its charge conjugated process, the integrated values measured are $$ P_{\Lambda} = 0.029 \pm 0.019 \, (\rm{stat}) \pm 0.012 \, (\rm{syst}) \, , $$ $$ P_{\bar{\Lambda}} = 0.003 \pm 0.023 \, (\rm{stat}) \pm 0.014 \,(\rm{syst}) \,. $$ Furthermore, the results are shown as a function of the Feynman~$x$~variable, transverse momentum, pseudorapidity and rapidity of the hyperons, and are compared with previous measurements., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3120 (LHCb public pages)
Published: 2024

187. New Uncertainty Principle for a particle on a Torus Knot

Author: Chowdhury, Madhushri Roy and Ghosh, Subir
Subjects: Quantum Physics, High Energy Physics - Theory, Mathematical Physics
Abstract: The present work deals with quantum Uncertainty Relations (UR) subjected to the Standard Deviations (SD) of the relevant dynamical variables for a particle constrained to move on a torus knot. It is important to note that these variables have to obey the two distinct periodicities of the knotted paths embedded on the torus. We compute generalized forms of the SDs and the subsequent URs (following the Kennard-Robertson formalism). These quantities explicitly involve the torus parameters and the knot parameters where restrictions on the latter have to be taken into account. These induce restrictions on the possible form of wave functions that are used to calculate the SDs and URs and in our simple example, two distinct SDs and URs are possible. In a certain limit (thin torus limit), our results will reduce to the results for a particle moving in a circle. An interesting fact emerges that in the case of the SDs and URs, the local geometry of the knots plays the decisive role and not their topological properties.
Published: 2024

188. Different driving protocols and multiple Majorana modes in a Rashba coupled superconducting nanowire

Author: Roy, Koustav and Basu, Saurabh
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: We perform systematic analyses of single and multi-frequency driving protocols on a Rashba nanowire with superconducting correlations induced by proximity effects. The results for the single-mode drive reveal interesting frequency dependencies of the Majorana modes, in the sense that the parameters corresponding to the trivial and topological limits of the undriven (static) case host Majorana zero modes, respectively at low and high frequencies. Further, emergence of long-range interactions are noted that give rise to multiple gap-closing scenarios, which imply occurrence of multiple Majorana modes. On the other hand, the multi-frequency driving protocol, sub-grouped into commensurate and incommensurate ratios of the frequencies, demonstrates intriguing consequences. The commensurate case yields dynamical control over the stability of the edge modes. Moreover, complex driving protocols harm the Majoranas by pushing them into the bulk. Finally, the incommensurate case yields independent Majorana modes occurring at low-symmetry points in the Brillouin zone. While the single and the commensurate multi-frequency driving protocols admit the usage of symmetric time frames for the computation of the topological invariants, the incommensurate case relies on the framework of many-mode Floquet theory, where the topological properties are ascertained via calculating the Berry phase. We present band structure and phase diagrams to substantiate all our results. The robustness and concurrent existence of these unique Majorana modes, even amidst a very dense energy spectrum along with a lack of global time-periodicity, hold promise for applications in quantum computing and the development of Floquet time crystals.
Published: 2024

189. Harnessing moir\'e ferroelectricity to modulate semiconductor monolayer light emission

Author: Kim, Dong Seob, Xiao, Chengxin, Dominguez, Roy C., Liu, Zhida, Abudayyeh, Hamza, Lee, Kyoungpyo, Mayorga-Luna, Rigo, Kim, Hyunsue, Watanabe, Kenji, Taniguchi, Takashi, Shih, Chih-Kang, Miyahara, Yoichi, Yao, Wang, and Li, Xiaoqin
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract: Ferroelectricity has been recently discovered in stacked or twisted van der Waals (vdW) moir\'e systems. The versatility of producing a large array of size-tunable domains and integration with diverse functional materials make them an enticing platform for developing multifunctional devices. Here, we show that ferroelectric polar domains formed in a twisted hexagonal boron nitride (t-hBN) substrate can modulate light emission from an adjacent semiconductor monolayer. The abrupt change in electrostatic potential across the domains produces an in-plane electric field (E-field) and leads to a remarkably large exciton Stark shift in the adjacent MoSe$_2$ monolayer, previously only observable in \textit{p-n} junctions created by the advanced e-beam lithography tools. Both the spectrum and spatial pattern of the light emission from the monolayer are periodically modulated by the remote moir\'e potential imposed by the t-hBN substrate. We further observe a characteristic hysteresis behavior in the light emission as an electric gate erases and restores the domains. Our findings chart an exciting pathway for integrating nanometer-scale moir\'e ferroelectric domains with various optically active functional layers, paving the way for advanced nanophotonic applications.
Published: 2024

190. CMOS-Compatible, AlScN-Based Integrated Electro-Optic Modulator

Author: Yoshioka, Valerie, Jin, Jicheng, Zhou, Haiqi, Tang, Zichen, Olsson III, Roy H., and Zhen, Bo
Subjects: Physics - Optics, Physics - Applied Physics
Abstract: Commercial production of integrated photonic devices is limited by scalability of desirable material platforms. We explore a relatively new photonic material, AlScN, for its use in electro-optic modulation. Its CMOS-compatibility could facilitate large-scale production of integrated photonic modulators, and it exhibits an enhanced second-order optical nonlinearity compared to intrinsic AlN, indicating the possibility for efficient modulation. Here, we measure the electro-optic effect in AlScN-based modulators, demonstrating $V_{\pi}L$ around 750 V$\cdot$cm. Since the electro-optic response is smaller than expected, we discuss potential causes for the reduced response and future outlook for AlScN-based photonics., Comment: 9 pages, 3 figures
Published: 2024

191. Out-of-plane Parallel Current in the Diffusion Regions: The Interaction Between Diffusion Region Systems and their Impact on the Outer EDR

Author: Beedle, Jason M. H., Gershman, Daniel J., Uritsky, Vadim M., Shuster, Jason R., Phan, Tai D., Giles, Barbara L., Genestreti, Kevin J., and Torbert, Roy B.
Subjects: Physics - Space Physics
Abstract: Dayside magnetic reconnection allows for the transfer of the solar wind's energy into Earth's magnetosphere. This process takes place in electron diffusion regions (EDRs) embedded in ion diffusion regions (IDRs), which form in the magnetopause boundary's current sheet. A significant out-of-plane parallel current contribution in the diffusion regions was reported in Beedle et al. 2023. In order to understand the underlying structure of this parallel current, we compared EDR statistical results with a 2.5D Particle-In Cell (PIC) simulation. From this comparison, we identified out-of-plane parallel current signatures as defining features of the outer EDR and IDR. This significant out-of-plane parallel current indicates the interaction of the IDR and EDR systems, and provides implications for not only understanding energy dissipation in the diffusion regions, but also determining the location of the outer EDR.
Published: 2024

192. The Convergence Problem Of Gradient Expansion In The Relaxation Time Approximation

Author: Gangadharan, Reghukrishnan and Roy, Victor
Subjects: Nuclear Theory, High Energy Physics - Phenomenology, High Energy Physics - Theory, Mathematical Physics
Abstract: We obtain a formal integral solution to the 3+1 D Boltzmann Equation in relaxation time approximation. The gradient series obtained from this integral solution contains exponentially decaying non-hydrodynamic terms. It is shown that this gradient expansion can have a finite radius of convergence under certain assumptions of analyticity. We then argue that, in the relaxation time model, proximity to local thermal equilibrium is not necessary for the system to be described by hydrodynamic equations., Comment: 12 pages, 0 figures
Published: 2024

193. Interacting dark sector: a dynamical system perspective

Author: Kritpetch, Chonticha, Roy, Nandan, and Banerjee, Narayan
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We investigate the interaction between the dark sectors from a dynamical systems perspective. A general setup for interacting dark energy models that incorporates both quintessence and phantom fields through a switch parameter, allowing an interaction in the dark sectors has been considered. In the first part of our analysis, we have not assumed any specific form of the interaction and in the second part, we invoked examples in general framework of the interaction. The potentials of the scalar field are classified into two broad classes of potentials: exponential and non-exponential. We identify the potential late-time attractors of the system which have a complete dark energy domination. From our analysis, it is evident there could be an interaction between the dark sector. The interaction, if any, weakens over time. We find for the quintessence field the transfer of energy from dark matter to dark energy can flip the direction and on the contrary, for the phantom field, it is only from dark matter to dark energy., Comment: 19 pages, 8 figures
Published: 2024

194. LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting

Author: Verdenius, Stijn, Zerio, Andrea, and Wang, Roy L. M.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning, 62, 68, I.2.6
Abstract: We introduce LatentTimePFN (LaT-PFN), a foundational Time Series model with a strong embedding space that enables zero-shot forecasting. To achieve this, we perform in-context learning in latent space utilizing a novel integration of the Prior-data Fitted Networks (PFN) and Joint Embedding Predictive Architecture (JEPA) frameworks. We leverage the JEPA framework to create a prediction-optimized latent representation of the underlying stochastic process that generates time series and combines it with contextual learning, using a PFN. Furthermore, we improve on preceding works by utilizing related time series as a context and introducing a normalized abstract time axis. This reduces training time and increases the versatility of the model by allowing any time granularity and forecast horizon. We show that this results in superior zero-shot predictions compared to established baselines. We also demonstrate our latent space produces informative embeddings of both individual time steps and fixed-length summaries of entire series. Finally, we observe the emergence of multi-step patch embeddings without explicit training, suggesting the model actively learns discrete tokens that encode local structures in the data, analogous to vision transformers., Comment: 9 pages plus references and appendix, 2 tables, 11 figures, added seeds, corrections
Published: 2024

195. Boundary Hardy inequality on functions of bounded variation

Author: Adimurthi, Roy, Prosenjit, and Sahu, Vivek
Subjects: Mathematics - Analysis of PDEs, 46E35, 26D15, 39B62
Abstract: Classical boundary Hardy inequality, that goes back to 1988, states that if $1 < p < \infty, \ ~\Omega$ is bounded Lipschitz domain, then for all $u \in C^{\infty}_{c}(\Omega)$, $$\int_{\Omega} \frac{|u(x)|^{p}}{\delta^{p}_{\Omega}(x)} dx \leq C\int_{\Omega} |\nabla u(x) |^{p}dx,$$ where $\delta_\Omega(x)$ is the distance function from $\Omega^c$. In this article, we address the long standing open question on the case $p=1$ by establishing appropriate boundary Hardy inequalities in the space of functions of bounded variation. We first establish appropriate inequalities on fractional Sobolev spaces $W^{s,1}(\Omega)$ and then Brezis, Bourgain and Mironescu's result on limiting behavior of fractional Sobolev spaces as $s\rightarrow 1^{-}$ plays an important role in the proof. Moreover, we also derive an infinite series Hardy inequality for the case $p=1$., Comment: 25 pages
Published: 2024

196. The Alfv\'en Transition Zone observed by the Parker Solar Probe in Young Solar Wind -- Global Properties and Model Comparisons

Author: Chhiber, Rohit, Pecora, Francesco, Usmanov, Arcadi V, Matthaeus, William H, Goldstein, Melvyn L, Roy, Sohom, Wang, Jiaming, Thepthong, Panisara, and Ruffolo, David
Subjects: Astrophysics - Solar and Stellar Astrophysics, Physics - Space Physics
Abstract: The transition from subAlfv\'enic to superAlfv\'enic flow in the solar atmosphere is examined by means of Parker Solar Probe (PSP) measurements during solar encounters 8 to 14. Around 220 subAlfv\'enic periods with a duration $\ge$ 10 minutes are identified. The distribution of their durations, heliocentric distances, and Alfv\'en Mach number are analyzed and compared with a global magnetohydrodynamic model of the solar corona and wind, which includes turbulence effects. The results are consistent with a patchy and fragmented morphology, and suggestive of a turbulent Alfv\'en zone within which the transition from subAlfv\'enic to superAlfv\'enic flow occurs over an extended range of helioradii. These results inform and establish context for detailed analyses of subAlfv\'enic coronal plasma that are expected to emerge from PSP's final mission phase, as well as for NASA's planned PUNCH mission.
Published: 2024

197. Cosmic rays for imaging cultural heritage objects

Author: Giammanco, Andrea, Moussawi, Marwa Al, Boone, Matthieu, De Kock, Tim, De Roy, Judy, Huysmans, Sam, Kumar, Vishal, Lagrange, Maxime, and Tytgat, Michael
Subjects: Physics - Physics and Society, Physics - Applied Physics
Abstract: In cultural heritage conservation, it is increasingly common to rely on non-destructive imaging methods based on the absorption or scattering of photons ($X$ or $\gamma$ rays) or neutrons. However, physical and practical issues limit these techniques: their penetration depth may be insufficient for large and dense objects, they require transporting the objects of interest to dedicated laboratories, artificial radiation is hazardous and may induce activation in the material under study. Muons are elementary particles abundantly and freely produced in cosmic-ray interactions in the atmosphere. Their absorption and scattering in matter are characteristically dependent on the density and elemental composition of the material that they traverse, which offers the possibility of exploiting them for sub-surface remote imaging. This novel technique, nicknamed "muography", has been applied in use cases ranging from geophysics to archaeology to nuclear safety, but it has been so far under-explored for a vast category of cultural heritage objects that are relatively large (from decimeters to human size) and dense (stone, metals). The development of portable muon detectors makes muography particularly competitive in cases where the items to be analysed are not transportable, or set up in a confined environment. This document reviews the relevant literature, presents some exemplary use cases, and critically assesses the strengths and weaknesses of muography in this context., Comment: Submitted to a special issue of iScience about "Cosmic-Ray Muography and Applications"
Published: 2024

198. Neutrino Mixing from a Fresh Perspective

Author: Chakraborty, Pralay, Dey, Manash, Karmakar, Biswajit, and Roy, Subhankar
Subjects: High Energy Physics - Phenomenology
Abstract: We propose a new mixing scheme for neutrinos which eventually can be linked to a neutrino mass matrix texture bearing a suitable correlation ($m_{22} + 2\,m_{13} = 0$) among its elements. The texture predicts three mass eigenvalues in the light of normal ordering, in addition to the Majorana phases. The texture is realized from $A_4 \times Z_{10} \times Z_2$ group in the framework of general type-I+II seesaw mechanism.
Published: 2024

199. Quantum Vision Transformers for Quark-Gluon Classification

Author: Cara, Marçal Comajoan, Dahale, Gopal Ramesh, Dong, Zhongtian, Forestano, Roy T., Gleyzer, Sergei, Justice, Daniel, Kong, Kyoungchul, Magorsch, Tom, Matchev, Konstantin T., Matcheva, Katia, and Unlu, Eyup B.
Subjects: Quantum Physics, Computer Science - Machine Learning, High Energy Physics - Phenomenology, 68Q12 (Primary) 81P68, 68T07 (Secondary)
Abstract: We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical challenge of computational efficiency and resource constraints in analyzing data from the upcoming High Luminosity Large Hadron Collider, presenting the architecture as a potential solution. In particular, we evaluate our method by applying the model to multi-detector jet images from CMS Open Data. The goal is to distinguish quark-initiated from gluon-initiated jets. We successfully train the quantum model and evaluate it via numerical simulations. Using this approach, we achieve classification performance almost on par with the one obtained with the completely classical architecture, considering a similar number of parameters., Comment: 14 pages, 8 figures. Published in MDPI Axioms 2024, 13(5), 323
Published: 2024
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200. Low-frequency, wideband study of an active repeater, FRB 20240114A, with the GMRT

Author: Panda, Ujjwal, Roy, Jayanta, Bhattacharyya, Siddhartha, Dudeja, Chahat, and Kudale, Sanjay
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We report the detection of a total of 135 bursts from a recently discovered active, repeating fast radio burst, FRB 20240114A with the GMRT over a frequency range of 300$-$750 MHz. The bursts were detected with intrinsic widths ranging from 0.308 to 39.364 ms, a median scattering timescale of 2.059 ms at 400 MHz and 1.372 ms at 650 MHz. The fluences of the detected bursts range from 36.81 mJy ms to 7.47 Jy ms. Both the energy and waiting time distributions of the bursts can be fitted with broken power laws, indicating the presence of two distinct populations of bursts. The energy distributions were modeled via broken power law with $\alpha_{1} = -0.62 \pm 0.01$ and $\alpha_{2} = -1.98 \pm 0.11$, while the waiting time distribution was modeled via a broken power law with $\alpha_{1} = -0.71 \pm 0.01$ and $\alpha_{2} = -2.09 \pm 0.09$. Both the energy and waiting time distributions of FRB 20240114A are comparable to high-energy bursts from magnetars, and giant radio pulses from pulsars, indicating that such objects could be likely progenitors., Comment: Submitted for publication in ApJ
Published: 2024

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