Author: "Fischer, A." - Searchworks@Jio Institute Digital Library Search Results

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628,833 results on '"Fischer, A."'

1. Lateral Mn5Ge3 spin-valve in contact with a high-mobility Ge two-dimensional hole gas

Author: Weißhaupt, David, Sürgers, Christoph, Bloos, Dominik, Funk, Hannes Simon, Oehme, Michael, Fischer, Gerda, Schubert, Markus Andreas, Wenger, Christian, van Slageren, Joris, Fischer, Inga Anita, and Schulze, Jörg
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract: Ge two-dimensional hole gases in strained modulation-doped quantum-wells represent a promising material platform for future spintronic applications due to their excellent spin transport properties and the theoretical possibility of efficient spin manipulation. Due to the continuous development of epitaxial growth recipes extreme high hole mobilities and low effective masses can be achieved, promising an efficient spin transport. Furthermore, the Ge two-dimensional hole gas (2DHG) can be integrated in the well-established industrial complementary metal-oxide-semiconductor (CMOS) devices technology. However, efficient electrical spin injection into a Ge 2DHG - a prerequisite for the realization of spintronic devices - has not yet been demonstrated. In this work, we report the fabrication and low-temperature magnetoresistance measurements of a laterally structured Mn5Ge3/Ge 2DHG/ Mn5Ge3 device. The ferromagnetic Mn5Ge3 contacts are grown directly into the Ge quantum well by means of an interdiffusion process with a spacing of approximately 130 nm. We observe a magnetoresistance signal for temperatures below 13 K possibly arising from successful spin injection. The results represent a step forward toward the realization of CMOS compatible spintronic devices based on a 2DHG., Comment: 6 figures
Published: 2024

2. Providing Faculty-to-Faculty Support: Moving the Needle Forward in Service-Learning from Limited Exposure to Implementing a Campuswide Program

Author: Rhonda K. Lewis, Chelsea Redger-Marquardt, and Kara Fischer
Abstract: The benefits of service-learning have been well documented in the literature in terms of student outcomes (i.e., increasing retention rates). The purpose of this article was to gather the experiences of faculty who participated in the Service-Learning Faculty Scholars program, a faculty development program designed to infuse service-learning into their courses and across campus at a midsized university in the Midwest. Faculty participated in a faculty cohort model. Listening sessions were held to gather faculty input, and a total of seven faculty participated. Participants were asked a series of open-ended questions. After a thematic analysis of the data, several themes emerged: service-learning competency/development, challenges, cohort effect, scholar experience, program-level support/resources and training, student experiences, community partner relationships, and faculty reflections on course design. Limitations and future research are discussed.
Published: 2024

3. FUSELOC: Fusing Global and Local Descriptors to Disambiguate 2D-3D Matching in Visual Localization

Author: Nguyen, Son Tung, Fontan, Alejandro, Milford, Michael, and Fischer, Tobias
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract: Hierarchical methods represent state-of-the-art visual localization, optimizing search efficiency by using global descriptors to focus on relevant map regions. However, this state-of-the-art performance comes at the cost of substantial memory requirements, as all database images must be stored for feature matching. In contrast, direct 2D-3D matching algorithms require significantly less memory but suffer from lower accuracy due to the larger and more ambiguous search space. We address this ambiguity by fusing local and global descriptors using a weighted average operator within a 2D-3D search framework. This fusion rearranges the local descriptor space such that geographically nearby local descriptors are closer in the feature space according to the global descriptors. Therefore, the number of irrelevant competing descriptors decreases, specifically if they are geographically distant, thereby increasing the likelihood of correctly matching a query descriptor. We consistently improve the accuracy over local-only systems and achieve performance close to hierarchical methods while halving memory requirements. Extensive experiments using various state-of-the-art local and global descriptors across four different datasets demonstrate the effectiveness of our approach. For the first time, our approach enables direct matching algorithms to benefit from global descriptors while maintaining memory efficiency. The code for this paper will be published at \href{https://github.com/sontung/descriptor-disambiguation}{github.com/sontung/descriptor-disambiguation}.
Published: 2024

4. LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification

Author: Dorent, Reuben, Khajavi, Roya, Idris, Tagwa, Ziegler, Erik, Somarouthu, Bhanusupriya, Jacene, Heather, LaCasce, Ann, Deissler, Jonathan, Ehrhardt, Jan, Engelson, Sofija, Fischer, Stefan M., Gu, Yun, Handels, Heinz, Kasai, Satoshi, Kondo, Satoshi, Maier-Hein, Klaus, Schnabel, Julia A., Wang, Guotai, Wang, Litingyu, Wald, Tassilo, Yang, Guang-Zhong, Zhang, Hanxiao, Zhang, Minghui, Pieper, Steve, Harris, Gordon, Kikinis, Ron, and Kapur, Tina
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets. However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans. Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution. Despite the variety of weakly-supervised techniques proposed, most have been validated only on private datasets or small publicly available datasets. To address this limitation, the Mediastinal Lymph Node Quantification (LNQ) challenge was organized in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to advance weakly-supervised segmentation methods by providing a new, partially annotated dataset and a robust evaluation framework. A total of 16 teams from 5 countries submitted predictions to the validation leaderboard, and 6 teams from 3 countries participated in the evaluation phase. The results highlighted both the potential and the current limitations of weakly-supervised approaches. On one hand, weakly-supervised approaches obtained relatively good performance with a median Dice score of $61.0\%$. On the other hand, top-ranked teams, with a median Dice score exceeding $70\%$, boosted their performance by leveraging smaller but fully annotated datasets to combine weak supervision and full supervision. This highlights both the promise of weakly-supervised methods and the ongoing need for high-quality, fully annotated data to achieve higher segmentation performance., Comment: Submitted to MELBA
Published: 2024

5. The Expressive Power of Uniform Population Protocols with Logarithmic Space

Author: Czerner, Philipp, Fischer, Vincent, and Guttenberg, Roland
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: Population protocols are a model of computation in which indistinguishable mobile agents interact in pairs to decide a property of their initial configuration. Originally introduced by Angluin et. al. in 2004 with a constant number of states, research nowadays focuses on protocols where the space usage depends on the number of agents. The expressive power of population protocols has so far however only been determined for protocols using $o(\log n)$ states, which compute only semilinear predicates, and for $\Omega(n)$ states. This leaves a significant gap, particularly concerning protocols with $\Theta(\log n)$ or $\Theta(\operatorname{polylog} n)$ states, which are the most common constructions in the literature. In this paper we close the gap and prove that for any $\varepsilon>0$ and $f\in\Omega(\log n)\cap\mathcal{O}(n^{1-\varepsilon})$, both uniform and non-uniform population protocols with $\Theta(f(n))$ states can decide exactly $\mathsf{NSPACE}(f(n) \log n)$.
Published: 2024

6. Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis

Author: Maag, Kira, Resner, Roman, and Fischer, Asja
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Cryptography and Security
Abstract: Deep neural networks have demonstrated remarkable effectiveness across a wide range of tasks such as semantic segmentation. Nevertheless, these networks are vulnerable to adversarial attacks that add imperceptible perturbations to the input image, leading to false predictions. This vulnerability is particularly dangerous in safety-critical applications like automated driving. While adversarial examples and defense strategies are well-researched in the context of image classification, there is comparatively less research focused on semantic segmentation. Recently, we have proposed an uncertainty-based method for detecting adversarial attacks on neural networks for semantic segmentation. We observed that uncertainty, as measured by the entropy of the output distribution, behaves differently on clean versus adversely perturbed images, and we utilize this property to differentiate between the two. In this extended version of our work, we conduct a detailed analysis of uncertainty-based detection of adversarial attacks including a diverse set of adversarial attacks and various state-of-the-art neural networks. Our numerical experiments show the effectiveness of the proposed uncertainty-based detection method, which is lightweight and operates as a post-processing step, i.e., no model modifications or knowledge of the adversarial example generation process are required.
Published: 2024

7. New spectral imaging biomarkers for sepsis and mortality in intensive care

Author: Seidlitz, Silvia, Hölzl, Katharina, von Garrel, Ayca, Sellner, Jan, Katzenschlager, Stephan, Hölle, Tobias, Fischer, Dania, von der Forst, Maik, Schmitt, Felix C. F., Weigand, Markus A., Maier-Hein, Lena, and Dietrich, Maximilian
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: With sepsis remaining a leading cause of mortality, early identification of septic patients and those at high risk of death is a challenge of high socioeconomic importance. The driving hypothesis of this study was that hyperspectral imaging (HSI) could provide novel biomarkers for sepsis diagnosis and treatment management due to its potential to monitor microcirculatory alterations. We conducted a comprehensive study involving HSI data of the palm and fingers from more than 480 patients on the day of their intensive care unit (ICU) admission. The findings demonstrate that HSI measurements can predict sepsis with an area under the receiver operating characteristic curve (AUROC) of 0.80 (95 % confidence interval (CI) [0.76; 0.84]) and mortality with an AUROC of 0.72 (95 % CI [0.65; 0.79]). The predictive performance improves substantially when additional clinical data is incorporated, leading to an AUROC of up to 0.94 (95 % CI [0.92; 0.96]) for sepsis and 0.84 (95 % CI [0.78; 0.89]) for mortality. We conclude that HSI presents novel imaging biomarkers for the rapid, non-invasive prediction of sepsis and mortality, suggesting its potential as an important modality for guiding diagnosis and treatment., Comment: Markus A. Weigand, Lena Maier-Hein and Maximilian Dietrich contributed equally
Published: 2024

8. Demystifying Reinforcement Learning in Production Scheduling via Explainable AI

Author: Fischer, Daniel, Hüsener, Hannah M., Grumbach, Felix, Vollenkemper, Lukas, Müller, Arthur, and Reusch, Pascal
Subjects: Computer Science - Artificial Intelligence
Abstract: Deep Reinforcement Learning (DRL) is a frequently employed technique to solve scheduling problems. Although DRL agents ace at delivering viable results in short computing times, their reasoning remains opaque. We conduct a case study where we systematically apply two explainable AI (xAI) frameworks, namely SHAP (DeepSHAP) and Captum (Input x Gradient), to describe the reasoning behind scheduling decisions of a specialized DRL agent in a flow production. We find that methods in the xAI literature lack falsifiability and consistent terminology, do not adequately consider domain-knowledge, the target audience or real-world scenarios, and typically provide simple input-output explanations rather than causal interpretations. To resolve this issue, we introduce a hypotheses-based workflow. This approach enables us to inspect whether explanations align with domain knowledge and match the reward hypotheses of the agent. We furthermore tackle the challenge of communicating these insights to third parties by tailoring hypotheses to the target audience, which can serve as interpretations of the agent's behavior after verification. Our proposed workflow emphasizes the repeated verification of explanations and may be applicable to various DRL-based scheduling use cases.
Published: 2024

9. Study of MRI-compatible Notched Plastic Ultrasonic Stator with FEM Simulation and Holography Validation

Author: Zhao, Zhanyue, Tang, Haimi, Carvalho, Paulo, Furlong, Cosme, and Fischer, Gregory S.
Subjects: Computer Science - Robotics
Abstract: Intra-operative image guidance using magnetic resonance imaging (MRI) can significantly enhance the precision of surgical procedures, such as deep brain tumor ablation. However, the powerful magnetic fields and limited space within an MRI scanner require the use of robotic devices to aid surgeons. Piezoelectric motors are commonly utilized to drive these robots, with piezoelectric ultrasonic motors being particularly notable. These motors consist of a piezoelectric ring stator that is bonded to a rotor through frictional coupling. When the stator is excited at specific frequencies, it generates distinctive mode shapes with surface waves that exhibit both in-plane and out-of-plane displacement, leading to the rotation of the rotor. In this study, we continue our previous work and refine the motor design and performance, we combine finite element modeling (FEM) with stroboscopic and time-averaged digital holography to validate a further plastic-based ultrasonic motor with better rotary performance., Comment: 4 pages, 9 figures, 1 table
Published: 2024

10. Match predictions in soccer: Machine learning vs. Poisson approaches

Author: Fischer, Mirko and Heuer, Andreas
Subjects: Statistics - Applications
Abstract: Predicting the results of soccer matches is of great interest. This is not only due to the popularity of the sport and the joy of private "betting rounds", but also due to the large sports betting market. Where previously expert knowledge and intuition were used, today there are models that analyze large amounts of data and make predictions based on them. In addition to Poisson models, approaches that belong to the machine learning (ML) category are increasingly being used. These include, for example, neural network or random forest models, which are compared in this article with each other as well as with Poisson models with regard to single-match prediction. In each case, the match results of a season are used as the data basis. The analysis is carried out for 5 European top leagues. A statistical analysis shows that the performance levels of the teams do not change systematically during a season. In order to characterize performance levels as accurately as possible, all match results, except from the match to be predicted, can be used as features with equal weighting. It can be seen that both, the exact choice of features and the choice of model, have only a minor influence on the prediction quality. Possible improvements in match prediction are discussed., Comment: 10 pages, 1 figure
Published: 2024

11. A complete characterization of pairs of binary phylogenetic trees with identical $A_k$-alignments

Author: Wilde, Mirko and Fischer, Mareike
Subjects: Quantitative Biology - Populations and Evolution, Mathematics - Combinatorics
Abstract: Phylogenetic trees play a key role in the reconstruction of evolutionary relationships. Typically, they are derived from aligned sequence data (like DNA, RNA, or proteins) by using optimization criteria like, e.g., maximum parsimony (MP). It is believed that the latter is able to reconstruct the \enquote{true} tree, i.e., the tree that generated the data, whenever the number of substitutions required to explain the data with that tree is relatively small compared to the size of the tree (measured in the number $n$ of leaves of the tree, which represent the species under investigation). However, reconstructing the correct tree from any alignment first and foremost requires the given alignment to perform differently on the \enquote{correct} tree than on others. A special type of alignments, namely so-called $A_k$-alignments, has gained considerable interest in recent literature. These alignments consist of all binary characters (\enquote{sites}) which require precisely $k$ substitutions on a given tree. It has been found that whenever $k$ is small enough (in comparison to $n$), $A_k$-alignments uniquely characterize the trees that generated them. However, recent literature has left a significant gap between $k\leq 2k+2$ -- namely the cases in which no such characterization is possible -- and $k\geq 4k$ -- namely the cases in which this characterization works. It is the main aim of the present manuscript to close this gap, i.e., to present a full characterization of all pairs of trees that share the same $A_k$-alignment. In particular, we show that indeed every binary phylogenetic tree with $n$ leaves is uniquely defined by its $A_k$-alignments if $n\geq 2k+3$. By closing said gap, we also ensure that our result is optimal.
Published: 2024

12. Landmark-based Vehicle Self-Localization Using Automotive Polarimetric Radars

Author: Weishaupt, Fabio, Tilly, Julius F., Appenrodt, Nils, Fischer, Pascal, Dickmann, Jürgen, and Heberling, Dirk
Subjects: Computer Science - Robotics, Electrical Engineering and Systems Science - Signal Processing
Abstract: Automotive self-localization is an essential task for any automated driving function. This means that the vehicle has to reliably know its position and orientation with an accuracy of a few centimeters and degrees, respectively. This paper presents a radar-based approach to self-localization, which exploits fully polarimetric scattering information for robust landmark detection. The proposed method requires no input from sensors other than radar during localization for a given map. By association of landmark observations with map landmarks, the vehicle's position is inferred. Abstract point- and line-shaped landmarks allow for compact map sizes and, in combination with the factor graph formulation used, for an efficient implementation. Evaluation of extensive real-world experiments in diverse environments shows a promising overall localization performance of $0.12 \text{m}$ RMS absolute trajectory and $0.43 {}^\circ$ RMS heading error by leveraging the polarimetric information. A comparison of the performance of different levels of polarimetric information proves the advantage in challenging scenarios., Comment: Accepted in IEEE Transactions on Intelligent Transportation Systems (T-ITS); 17 pages, 14 figures, 6 tables
Published: 2024
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13. A New Spin on Material Properties: Local Magnetic Structure in Functional and Quantum Materials

Author: Frandsen, Benjamin A. and Fischer, Henry E.
Subjects: Condensed Matter - Materials Science
Abstract: The past few decades have made clear that the properties and performances of emerging functional and quantum materials can depend strongly on their local atomic and/or magnetic structure, particularly when details of the local structure deviate from the long-range structure averaged over space and time. Traditional methods of structural refinement (e.g. Rietveld) are typically sensitive only to the average structure, creating a need for more advanced structural probes suitable for extracting information about structural correlations on short length- and time-scales. In this Perspective, we describe the importance of local magnetic structure in several classes of emerging materials and present the magnetic pair distribution function (mPDF) technique as a powerful tool for studying short-range magnetism from neutron total-scattering data. We then provide a selection of examples of mPDF analysis applied to magnetic materials of recent technological and fundamental interest, including the antiferromagnetic semiconductor MnTe, geometrically frustrated magnets, and iron-oxide magnetic nanoparticles. The rapid development of mPDF analysis since its formalization a decade ago puts this technique in a strong position for making continued impact in the study of local magnetism in emerging materials.
Published: 2024

14. Observation of muonic Dalitz decays of $\chi_{b}$ mesons and precise spectroscopy of hidden-beauty 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. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. 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., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., 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., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., 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., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., 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., Colonna, M. C., 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., Dekkers, S., 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., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, 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, T. Q., 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., Ghizzo, S. G., 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., Guerry, L., 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., Hallett, G. H., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Hartmann, M., Hashmi, S., 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., Idzik, M., 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., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., 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., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. 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Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., 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., 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., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., 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., Piccolo, L., 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., Provenzano, D., 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, Roensch, J., Rogachev, A., 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., 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., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., 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. 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J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. V. Vidrier, 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, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, Y., Wu, Z., 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., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., 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: The decays of the $\chi_{b1}(1P)$, $\chi_{b2}(1P)$, $\chi_{b1}(2P)$ and $\chi_{b2}(2P)$~mesons into the~$\Upsilon(1S)\mu^+\mu^-$ final state are observed with a high significance using proton-proton collision data collected with the LHCb detector and corresponding to an integrated luminosity of 9fb$^{-1}$. The newly observed decays together with the $\Upsilon(2S)\rightarrow \Upsilon(1S)\pi^+\pi^-$ and $\Upsilon(3S)\rightarrow \Upsilon(2S)\pi^+\pi^-$ decay modes are used for precision measurements of the mass and mass splittings for the hidden-beauty states., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-025.html
Published: 2024

15. Local Analogs of Primordial Galaxies: In Search of Intermediate Mass Black Holes with JWST NIRSpec

Author: Doan, Sara, Satyapal, Shobita, Matzko, William, Abel, Nicholas P., Böker, Torsten, Bohn, Thomas, Canalizo, Gabriela, Cann, Jenna M., Fischer, Jacqueline, LaMassa, Stephanie, Madden, Suzanne C., McKaig, Jeffrey D., Schaerer, D., Secrest, Nathan J., Seth, Anil, Blecha, Laura, Molina, Mallory, and Rothberg, Barry
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Local low metallicity galaxies with signatures of possible accretion activity are ideal laboratories in which to search for the lowest mass black holes and study their impact on the host galaxy. Here we present the first JWST NIRSpec IFS observations of SDSS J120122.30+021108.3, a nearby ($z=0.00354$) extremely metal poor dwarf galaxy with no optical signatures of accretion activity but identified by WISE to have extremely red mid-infrared colors consistent with AGNs. We identify over one hundred lines between $\sim$ 1.7-5.2 microns, an unresolved nuclear continuum source with an extremely steep spectral slope consistent with hot dust from an AGN ($F_\nu \approx\nu^{-1.5}$), and a plethora of H I, He I, and H$_2$ lines, with no lines from heavier elements, CO or ice absorption features, or PAHs.Our observations reveal that the red WISE source arises exclusively from a bright central unresolved source ($<$ 3pc) suggestive of an AGN, yet there are no He II lines or coronal lines identified in the spectrum, and, importantly, there is no evidence that the radiation field is harder in the nuclear source compared with surrounding regions. These observations can be explained with a young ($<$ 5 Myr) nuclear star cluster with stellar mass $\sim3\times 10^4$ M$_\odot$ and a deeply embedded AGN with bolometric luminosity $\sim$ $2\times10^{41}$ ergs $^{-1}$. The implied black hole mass is $\sim$ 1450 M$_\odot$, based on the Eddington limit, roughly consistent with that expected based on extrapolations of black hole galaxy scaling relations derived for more massive black holes. Longer wavelength observations are crucial to confirm this scenario., Comment: submitted to ApJ; comments welcome
Published: 2024

16. Non-maximizing policies that fulfill multi-criterion aspirations in expectation

Author: Dima, Simon, Fischer, Simon, Heitzig, Jobst, and Oliver, Joss
Subjects: Computer Science - Artificial Intelligence, Economics - Theoretical Economics, Mathematics - Optimization and Control, 68T20, 90C40, 91B06, I.2.8, F.2.2
Abstract: In dynamic programming and reinforcement learning, the policy for the sequential decision making of an agent in a stochastic environment is usually determined by expressing the goal as a scalar reward function and seeking a policy that maximizes the expected total reward. However, many goals that humans care about naturally concern multiple aspects of the world, and it may not be obvious how to condense those into a single reward function. Furthermore, maximization suffers from specification gaming, where the obtained policy achieves a high expected total reward in an unintended way, often taking extreme or nonsensical actions. Here we consider finite acyclic Markov Decision Processes with multiple distinct evaluation metrics, which do not necessarily represent quantities that the user wants to be maximized. We assume the task of the agent is to ensure that the vector of expected totals of the evaluation metrics falls into some given convex set, called the aspiration set. Our algorithm guarantees that this task is fulfilled by using simplices to approximate feasibility sets and propagate aspirations forward while ensuring they remain feasible. It has complexity linear in the number of possible state-action-successor triples and polynomial in the number of evaluation metrics. Moreover, the explicitly non-maximizing nature of the chosen policy and goals yields additional degrees of freedom, which can be used to apply heuristic safety criteria to the choice of actions. We discuss several such safety criteria that aim to steer the agent towards more conservative behavior., Comment: 16 pages main text + 4 pages supplement. Accepted for Algorithmic Decision Theory 2024
Published: 2024

17. Fact Finder -- Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs

Author: Steinigen, Daniel, Teucher, Roman, Ruland, Timm Heine, Rudat, Max, Flores-Herr, Nicolas, Fischer, Peter, Milosevic, Nikola, Schymura, Christopher, and Ziletti, Angelo
Subjects: Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract: Recent advancements in Large Language Models (LLMs) have showcased their proficiency in answering natural language queries. However, their effectiveness is hindered by limited domain-specific knowledge, raising concerns about the reliability of their responses. We introduce a hybrid system that augments LLMs with domain-specific knowledge graphs (KGs), thereby aiming to enhance factual correctness using a KG-based retrieval approach. We focus on a medical KG to demonstrate our methodology, which includes (1) pre-processing, (2) Cypher query generation, (3) Cypher query processing, (4) KG retrieval, and (5) LLM-enhanced response generation. We evaluate our system on a curated dataset of 69 samples, achieving a precision of 78\% in retrieving correct KG nodes. Our findings indicate that the hybrid system surpasses a standalone LLM in accuracy and completeness, as verified by an LLM-as-a-Judge evaluation method. This positions the system as a promising tool for applications that demand factual correctness and completeness, such as target identification -- a critical process in pinpointing biological entities for disease treatment or crop enhancement. Moreover, its intuitive search interface and ability to provide accurate responses within seconds make it well-suited for time-sensitive, precision-focused research contexts. We publish the source code together with the dataset and the prompt templates used., Comment: 10 pages, 7 figures
Published: 2024

18. Aircrew rostering workload patterns and associated fatigue and sleepiness scores in short/medium haul flights under RBAC 117 rules in Brazil

Author: Rodrigues, Tulio E., Furlan, Eduardo, Helene, André F., Helene, Otaviano, Pessini, Eduardo, Simões, Alexandre, Pontes, Maurício, and Fischer, Frida M.
Subjects: Physics - Physics and Society
Abstract: The relationships between workload and fatigue or sleepiness are investigated through the analysis of rosters and responses to questionnaires from Brazilian aircrews, taken from Fadig\^ometro database. The approach includes temporal markers - coinciding with Samn-Perelli (SP) and Karolinska Sleepiness Scale (KSS) responses - where SAFTE-FAST model outcomes are calculated. The model results follow the increase of fatigue and sleepiness perceptions during the dawn (0h00 to 05h59), but underestimate the self-rated scores during the evening (18h00 to 23h59). On the other hand, the KSS scores fit the relative risk of pilot errors, representing a reasonable proxy for risk assessment. Linear relationships obtained between workload metrics, computed within 168-hours prior to the responses, and self-rated SP and KSS scores provide a consistent method to estimate accumulated fatigue and sleepiness. Considering 7149 rosters of 2023, the duty time ($DT$), the number of flight sectors ($N_{CREW}$) and the sum of flight sectors with sit periods longer than one hour ($N_{CREW}+N_{SIT}$) are associated with 70.1%/60.6% of the highest predicted scores of SP/KSS. Applying the mitigations $DT\leq44h$, $N_{CREW}\leq15$ and $N_{CREW}+N_{SIT}\leq19$ for every 168-hour interval yields a significant decrease in the higher values of SP/KSS with minimal impact on aircrew productivity., Comment: 16 pages and 5 figures
Published: 2024

19. Chiral kagome superconductivity modulations with residual Fermi arcs in KV3Sb5 and CsV3Sb5

Author: Deng, Hanbin, Qin, Hailang, Liu, Guowei, Yang, Tianyu, Fu, Ruiqing, Zhang, Zhongyi, Wu, Xianxin, Wang, Zhiwei, Shi, Youguo, Liu, Jinjin, Liu, Hongxiong, Yan, Xiao-Yu, Song, Wei, Xu, Xitong, Zhao, Yuanyuan, Yi, Mingsheng, Xu, Gang, Hohmann, Hendrik, Holbæk, Sofie Castro, Dürrnage, Matteo, Zhou, Sen, Chang, Guoqing, Yao, Yugui, Wang, Qianghua, Guguchia, Zurab, Neupert, Titus, Thomale, Ronny, Fischer, Mark H., and Yin, Jia-Xin
Subjects: Condensed Matter - Superconductivity, Condensed Matter - Materials Science
Abstract: Superconductivity involving finite momentum pairing can lead to spatial gap and pair density modulations, as well as Bogoliubov Fermi states within the superconducting gap. However, the experimental realization of their intertwined relations has been challenging. Here, we detect chiral kagome superconductivity modulations with residual Fermi arcs in KV3Sb5 and CsV3Sb5 by normal and Josephson scanning tunneling microscopy down to 30mK with resolved electronic energy difference at microelectronvolt level. We observe a U-shaped superconducting gap with flat residual in-gap states. This gap exhibits chiral 2 by 2 spatial modulations with magnetic field tunable chirality, which align with the chiral 2 by 2 pair density modulations observed through Josephson tunneling. These findings demonstrate a chiral pair density wave (PDW) that breaks time-reversal symmetry. Quasiparticle interference imaging of the in-gap zero-energy states reveals segmented arcs, with high-temperature data linking them to parts of the reconstructed V d-orbital states within the charge order. The detected residual Fermi arcs can be explained by the partial suppression of these d-orbital states through an interorbital 2 by 2 PDW and thus serve as candidate Bogoliubov Fermi states. Additionally, we differentiate the observed PDW order from impurity-induced gap modulations. Our observations not only uncover a chiral PDW order with orbital-selectivity, but also illuminate the fundamental space-momentum correspondence inherent in finite momentum paired superconductivity., Comment: To appear in Nature (2024)
Published: 2024

20. First Measurement of Solar $^8$B Neutrinos via Coherent Elastic Neutrino-Nucleus Scattering with XENONnT

Author: Aprile, E., Aalbers, J., Abe, K., Maouloud, S. Ahmed, Althueser, L., Andrieu, B., Angelino, E., Martin, D. Antón, Arneodo, F., Baudis, L., Bazyk, M., Bellagamba, L., Biondi, R., Bismark, A., Boese, K., Brown, A., Bruno, G., Budnik, R., Cai, C., Capelli, C., Cardoso, J. M. R., Chávez, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-García, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Eitel, K., Elykov, A., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Galloway, M., Gao, F., Ghosh, S., Giacomobono, R., Glade-Beucke, R., Grandi, L., Grigat, J., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Higuera, A., Hils, C., Hoetzsch, L., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., Joerg, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Kobayashi, M., Koke, D., Kopec, A., Kuger, F., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, S., Liang, S., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Liu, M., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Melchiorre, A., Merz, J., Messina, M., Michael, A., Miuchi, K., Molinario, A., Moriyama, S., Morå, K., Mosbacher, Y., Murra, M., Müller, J., Ni, K., Oberlack, U., Paetsch, B., Pan, Y., Pellegrini, Q., Peres, R., Peters, C., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qin, J., García, D. Ramírez, Rajado, M., Singh, R., Sanchez, L., Santos, J. M. F. dos, Sarnoff, I., Sartorelli, G., Schreiner, J., Schulte, P., Eißing, H. Schulze, Schumann, M., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Shi, S., Shi, J., Silva, M., Simgen, H., Takeda, A., Tan, P. -L., Thers, D., Toschi, F., Trinchero, G., Tunnell, C. D., Tönnies, F., Valerius, K., Vecchi, S., Vetter, S., Solar, F. I. Villazon, Volta, G., Weinheimer, C., Weiss, M., Wenz, D., Wittweg, C., Wu, V. H. S., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yuan, L., Zavattini, G., and Zhong, M.
Subjects: Nuclear Experiment, Astrophysics - Solar and Stellar Astrophysics, High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: We present the first measurement of nuclear recoils from solar $^8$B neutrinos via coherent elastic neutrino-nucleus scattering with the XENONnT dark matter experiment. The central detector of XENONnT is a low-background, two-phase time projection chamber with a 5.9\,t sensitive liquid xenon target. A blind analysis with an exposure of 3.51\,t$\times$y resulted in 37 observed events above 0.5\,keV, with ($26.4^{+1.4}_{-1.3}$) events expected from backgrounds. The background-only hypothesis is rejected with a statistical significance of 2.73\,$\sigma$. The measured $^8$B solar neutrino flux of $(4.7_{-2.3}^{+3.6})\times 10^6\,\mathrm{cm}^{-2}\mathrm{s}^{-1}$ is consistent with results from dedicated solar neutrino experiments. The measured neutrino flux-weighted CE$\nu$NS cross-section on Xe of $(1.1^{+0.8}_{-0.5})\times10^{-39}\,\mathrm{cm}^2$ is consistent with the Standard Model prediction. This is the first direct measurement of nuclear recoils from solar neutrinos with a dark matter detector.
Published: 2024

21. Improved Bounds for High-Dimensional Equivalence and Product Testing using Subcube Queries

Author: Adar, Tomer, Fischer, Eldar, and Levi, Amit
Subjects: Computer Science - Data Structures and Algorithms
Abstract: We study property testing in the subcube conditional model introduced by Bhattacharyya and Chakraborty (2017). We obtain the first equivalence test for $n$-dimensional distributions that is quasi-linear in $n$, improving the previously known $\tilde{O}(n^2/\varepsilon^2)$ query complexity bound to $\tilde{O}(n/\varepsilon^2)$. We extend this result to general finite alphabets with logarithmic cost in the alphabet size. By exploiting the specific structure of the queries that we use (which are more restrictive than general subcube queries), we obtain a cubic improvement over the best known test for distributions over $\{1,\ldots,N\}$ under the interval querying model of Canonne, Ron and Servedio (2015), attaining a query complexity of $\tilde{O}((\log N)/\varepsilon^2)$, which for fixed $\varepsilon$ almost matches the known lower bound of $\Omega((\log N)/\log\log N)$. We also derive a product test for $n$-dimensional distributions with $\tilde{O}(n / \varepsilon^2)$ queries, and provide an $\Omega(\sqrt{n} / \varepsilon^2)$ lower bound for this property.
Published: 2024

22. Hyperons during proto-neutron star deleptonization and the emission of dark flavored particles

Author: Fischer, Tobias, Camalich, Jorge Martin, Kochankovski, Hristijan, and Tolos, Laura
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology, Nuclear Theory
Abstract: Complementary to high-energy experimental efforts, indirect astrophysical searches of particles beyond the standard model have long been pursued. The present article follows the latter approach and considers, for the first time, the self-consistent treatment of the energy losses from dark flavored particles produced in the decay of hyperons during a core-collapse supernova (CCSN). To this end, general relativistic supernova simulations in spherical symmetry are performed, featuring six-species Boltzmann neutrino transport, and covering the long-term evolution of the nascent remnant proto-neutron star (PNS) deleptonization for several tens of seconds. A well-calibrated hyperon equation of state (EOS) is therefore implemented into the supernova simulations and tested against the corresponding nucleonic model. It is found that supernova observables, such as the neutrino signal, are robustly insensitive to the appearance of hyperons for the simulation times considered in the present study. The presence of hyperons enables an additional channel for the appearance of dark sector particles, which is considered at the level of the $\Lambda$ hyperon decay. Assuming massless particles that escape the PNS after being produced, these channels expedite the deleptonizing PNS and the cooling behaviour. This, in turn, shortens the neutrino emission timescale. The present study confirms the previously estimated upper limits on the corresponding branching ratios for low and high mass PNS, by effectively reducing the neutrino emission timescale by a factor of two. This is consistent with the classical argument deduced from the neutrino detection associated with SN1987A., Comment: 24 pages, 7 figures, submitted to JCAP
Published: 2024

23. An online generalization of the e-BH procedure

Author: Fischer, Lasse and Ramdas, Aaditya
Subjects: Statistics - Methodology
Abstract: In online multiple testing the hypotheses arrive one-by-one over time and at each time it must be decided on the current hypothesis solely based on the data and hypotheses observed so far. In this paper we relax this setup by allowing initially accepted hypotheses to be rejected due to information gathered at later steps. We propose online e-BH, an online ARC (online with acceptance-to-rejection changes) version of the e-BH procedure. Online e-BH is the first nontrivial online procedure which provably controls the FDR at data-adaptive stopping times and under arbitrary dependence between the test statistics. Online e-BH uniformly improves e-LOND, the existing method for e-value based online FDR control. In addition, we introduce new boosting techniques for online e-BH to increase the power in case of locally dependent e-values. Furthermore, based on the same proof technique as used for online e-BH, we show that all existing online procedures with valid FDR control under arbitrary dependence also control the FDR at data-adaptive stopping times., Comment: 17 pages, 3 figures
Published: 2024

24. Quantum and thermal pressures from light scalar fields

Author: Fischer, Hauke, Käding, Christian, and Pitschmann, Mario
Subjects: High Energy Physics - Phenomenology, Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Theory, Quantum Physics
Abstract: Light scalar fields play a variety of roles in modern physics, especially in cosmology and modified theories of gravity. For this reason, there is a zoo of experiments actively trying to find evidence for many scalar field models that have been proposed in theoretical considerations. Among those are setups in which the pressures expected to be induced by light scalar fields between two parallel plates are studied, for example, Casimir force experiments. While it is known that classical and quantum pressures caused by light scalar fields could have significant impacts on such experiments, in this article, we show that this can also be the case for thermal pressure. More specifically, we derive expressions for the quantum and thermal pressures induced by exchanges of light scalar field fluctuations between two thin parallel plates. As particular examples, we then look at screened scalar fields. For chameleon, symmetron and environment-dependent dilaton models, we find large regions in their parameter spaces that allow for thermal pressures to equal or exceed the quantum pressures. By comparing with earlier constraints from quantum pressure calculations, we conclude that thermal pressures induced by chameleons are actually of experimental significance., Comment: 18 pages, 4 figures
Published: 2024

25. Design and demonstration of an operating system for executing applications on quantum network nodes

Author: Donne, Carlo Delle, Iuliano, Mariagrazia, van der Vecht, Bart, Ferreira, Guilherme Maciel, Jirovská, Hana, van der Steenhoven, Thom, Dahlberg, Axel, Skrzypczyk, Matt, Fioretto, Dario, Teller, Markus, Filippov, Pavel, Montblanch, Alejandro Rodríguez-Pardo, Fischer, Julius, van Ommen, Benjamin, Demetriou, Nicolas, Leichtle, Dominik, Music, Luka, Ollivier, Harold, Raa, Ingmar te, Kozlowski, Wojciech, Taminiau, Tim, Pawełczak, Przemysław, Northup, Tracy, Hanson, Ronald, and Wehner, Stephanie
Subjects: Quantum Physics, Computer Science - Networking and Internet Architecture, Computer Science - Operating Systems
Abstract: The goal of future quantum networks is to enable new internet applications that are impossible to achieve using solely classical communication. Up to now, demonstrations of quantum network applications and functionalities on quantum processors have been performed in ad-hoc software that was specific to the experimental setup, programmed to perform one single task (the application experiment) directly into low-level control devices using expertise in experimental physics. Here, we report on the design and implementation of the first architecture capable of executing quantum network applications on quantum processors in platform-independent high-level software. We demonstrate the architecture's capability to execute applications in high-level software, by implementing it as a quantum network operating system -- QNodeOS -- and executing test programs including a delegated computation from a client to a server on two quantum network nodes based on nitrogen-vacancy (NV) centers in diamond. We show how our architecture allows us to maximize the use of quantum network hardware, by multitasking different applications on a quantum network for the first time. Our architecture can be used to execute programs on any quantum processor platform corresponding to our system model, which we illustrate by demonstrating an additional driver for QNodeOS for a trapped-ion quantum network node based on a single $^{40}\text{Ca}^+$ atom. Our architecture lays the groundwork for computer science research in the domain of quantum network programming, and paves the way for the development of software that can bring quantum network technology to society., Comment: 12 pages, 5 figures, supplementary materials (48 pages, 24 figures, 11 tables)
Published: 2024

26. Probing Penrose-type singularities in sonic black holes

Author: Datta, Satadal and Fischer, Uwe R.
Subjects: General Relativity and Quantum Cosmology, Condensed Matter - Quantum Gases
Abstract: Addressing the general question whether Penrose singularities physically exist inside black holes, we investigate the problem in the context of an analogue system, a flowing laboratory liquid, for which the governing equations are at least in principle known to all relevant scales, and in all regions of the effective spacetime. We suggest to probe the physical phenomena taking place close to the singularity in the interior of a $2+1$D analogue black hole arising from a polytropic, inviscid, irrotational, and axisymmetric steady flow, and propose to this end an experimental setup in a Bose-Einstein condensate. Our study provides concrete evidence, for a well understood dynamical system, that the Einstein equations are not necessary for a singularity to form, demonstrating that Penrose-type spacetime singularities can potentially also exist in non-Einsteinian theories of gravity. Finally, we demonstrate how the singularity is physically avoided in our proposed laboratory setup., Comment: 7+7 pages, 3+1 figures; version as submitted to journal
Published: 2024

27. Measurement of $D^0-\overline{D}^0$ mixing and search for $CP$ violation with $D^0\rightarrow K^+\pi^-$ decays

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., 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., Sundfeld, D., 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., Wu, Z., 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 time-dependent ratio of the $D^0\rightarrow K^+\pi^-$ to $\overline{D}^0\rightarrow K^+\pi^-$ decay rates is reported. The analysis uses a sample of proton-proton collisions corresponding to an integrated luminosity of 6 fb$^-1$ recorded by the LHCb experiment from 2015 through 2018 at a center-of-mass energy of 13 TeV. The $D^0$ meson is required to originate from a $D^{*+}\rightarrow D^0\pi^+$ decay, such that its flavor at production is inferred from the charge of the accompanying pion. The measurement is performed simultaneously for the $K^+\pi^-$ and $K^-\pi^+$ final states, allowing both mixing and $CP$-violation parameters to be determined. The value of the ratio of the decay rates at production is determined to be $R_{K\pi} = (343.1 \pm 2.0) \times 10^{-5}$. The mixing parameters are measured to be $c_{K\pi} = (51.4 \pm 3.5) \times 10^{-4}$ and $c_{K\pi}^{\prime} = (13 \pm 4) \times 10^{-6}$, where $\sqrt{R_{K\pi}}c_{K\pi}$ is the linear coefficient of the expansion of the ratio as a function of decay time in units of the $D^0$ lifetime, and $c_{K\pi}^{\prime}$ is the quadratic coefficient, both averaged between the $K^+\pi^-$ and $K^-\pi^+$ final states. The precision is improved relative to the previous best measurement by approximately 60%. No evidence for $CP$ violation is found., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lhcbproject.web.cern.ch/Publications/LHCbProjectPublic/LHCb-PAPER-2024-008.html
Published: 2024

28. Compilation of Commit Changes within Java Source Code Repositories

Author: Schott, Stefan, Fischer, Wolfram, Ponta, Serena Elisa, Klauke, Jonas, and Bodden, Eric
Subjects: Computer Science - Software Engineering, Computer Science - Programming Languages
Abstract: Java applications include third-party dependencies as bytecode. To keep these applications secure, researchers have proposed tools to re-identify dependencies that contain known vulnerabilities. Yet, to allow such re-identification, one must obtain, for each vulnerability patch, the bytecode fixing the respective vulnerability at first. Such patches for dependencies are curated in databases in the form of fix-commits. But fixcommits are in source code, and automatically compiling whole Java projects to bytecode is notoriously hard, particularly for non-current versions of the code. In this paper, we thus propose JESS, an approach that largely avoids this problem by compiling solely the relevant code that was modified within a given commit. JESS reduces the code, retaining only those parts that the committed change references. To avoid name-resolution errors, JESS automatically infers stubs for references to entities that are unavailable to the compiler. A challenge is here that, to facilitate the above mentioned reidentification, JESS must seek to produce bytecode that is almost identical to the bytecode which one would obtain by a successful compilation of the full project. An evaluation on 347 GitHub projects shows that JESS is able to compile, in isolation, 72% of methods and constructors, of which 89% have bytecode equal to the original one. Furthermore, on the Project KB database of fix-commits, in which only 8% of files modified within the commits can be compiled with the provided build scripts, JESS is able to compile 73% of all files that these commits modify., Comment: To be published in: ICSME 2024 Proceedings
Published: 2024

29. Engineering two-dimensional materials from single-layer NbS$_2$

Author: Knispel, Timo, Mohrenstecher, Daniela, Speckmann, Carsten, Safeer, Affan, van Efferen, Camiel, Boix, Virgínia, Grüneis, Alexander, Jolie, Wouter, Preobrajenski, Alexei, Knudsen, Jan, Atodiresei, Nicolae, Michely, Thomas, and Fischer, Jeison
Subjects: Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: Starting from a single layer of NbS$_2$ grown on graphene by molecular beam epitaxy, the single unit cell thick 2D materials Nb$_{5/3}$S$_3$-2D and Nb$_2$S$_3$-2D are created using two different pathways. Either annealing under sulfur-deficient conditions at progressively higher temperatures or deposition of increasing amounts of Nb at elevated temperature result in phase-pure Nb$_{5/3}$S$_3$-2D followed by Nb$_2$S$_3$-2D. The materials are characterized by scanning tunneling microscopy, scanning tunneling spectroscopy and X-ray photoemission spectroscopy. The experimental assessment combined with systematic density functional theory calculations reveals their structure. The 2D materials are covalently bound without any van der Waals gap. Their stacking sequence and structure are at variance with expectations based on corresponding bulk materials highlighting the importance of surface and interface effects in structure formation.
Published: 2024

30. Determining the Extents, Geometries, and Kinematics of Narrow-Line Region Outflows in Nearby Seyfert Galaxies

Author: Polack, Garrett E., Revalski, Mitchell, Crenshaw, D. Michael, Fischer, Travis C., Schmitt, Henrique R., Kraemer, Steven B., Meena, Beena, and Rafelski, Marc
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Outflowing gas from supermassive black holes in the centers of active galaxies has been postulated as a major contributor to galactic evolution. To explore the interaction between narrow-line region (NLR) outflows and their host galaxies, we use Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) spectra and Wide Field Camera 3 (WFC3) images of 15 nearby (z < 0.02) active galactic nuclei (AGN) to determine the extents and geometries of their NLRs. We combine new HST WFC3 continuum and [O III] $\lambda$5007A images of 11 AGN with 4 archival AGN to match existing spectra from HST STIS. For the 6 AGN with suitable long-slit coverage of their NLRs, we use isophotal fitting of ground-based images, continuum-subtracted [O III] images, and the STIS spectra, to resolve, measure, and de-project the gas kinematics to the plane of the host galaxy disk and distinguish NLR outflows from galaxy rotation and/or kinematically disturbed gas. We find an average [O III] extent of $\sim$680pc with a correlation between gas extent and [O III] luminosity of R$_\mathrm{[O III]}$ $\propto$ L$_{\text{[O III]}}^{0.39}$. The measured extents depend strongly on the depth of the [O III] images, highlighting the importance of adopting uniform thresholds when analyzing scaling relationships. The outflows reach from 39-88% of the full NLR extents, and we find that all 6 of the AGN with STIS coverage of their entire NLRs show strong kinematic evidence for outflows, despite previous uncertainty for these AGN. This suggests that NLR outflows are ubiquitous in moderate luminosity AGN and that standard criteria for kinematic modeling are essential for identifying outflows., Comment: Accepted for publication in ApJ on July 9, 2024. The paper has 21 pages, 10 figures, and 6 tables
Published: 2024

31. Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?

Author: Kiechle, Johannes, Lang, Daniel M., Fischer, Stefan M., Felsner, Lina, Peeken, Jan C., and Schnabel, Julia A.
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Recent studies have underscored the capabilities of natural imaging foundation models to serve as powerful feature extractors, even in a zero-shot setting for medical imaging data. Most commonly, a shallow multi-layer perceptron (MLP) is appended to the feature extractor to facilitate end-to-end learning and downstream prediction tasks such as classification, thus representing the de facto standard. However, as graph neural networks (GNNs) have become a practicable choice for various tasks in medical research in the recent past, we direct attention to the question of how effective GNNs are compared to MLP prediction heads for the task of 3D medical image classification, proposing them as a potential alternative. In our experiments, we devise a subject-level graph for each volumetric dataset instance. Therein latent representations of all slices in the volume, encoded through a DINOv2 pretrained vision transformer (ViT), constitute the nodes and their respective node features. We use public datasets to compare the classification heads numerically and evaluate various graph construction and graph convolution methods in our experiments. Our findings show enhancements of the GNN in classification performance and substantial improvements in runtime compared to an MLP prediction head. Additional robustness evaluations further validate the promising performance of the GNN, promoting them as a suitable alternative to traditional MLP classification heads. Our code is publicly available at: https://github.com/compai-lab/2024-miccai-grail-kiechle, Comment: Accepted at MICCAI 2024 - GRAIL Workshop
Published: 2024

32. A new problem qualification based on approximate KKT conditions for Lipschitzian optimization with application to bilevel programming

Author: Käming, Isabella, Fischer, Andreas, and Zemkoho, Alain B.
Subjects: Mathematics - Optimization and Control, 90C30, 49J52, 90C46, 65K05
Abstract: When dealing with general Lipschitzian optimization problems, there are many problem classes where standard constraint qualifications fail at local minimizers. In contrast to a constraint qualification, a problem qualification does not only rely on the constraints but also on the objective function to guarantee that a local minimizer is a Karush-Kuhn-Tucker (KKT) point. For example, calmness in the sense of Clarke is a problem qualification. In this article, we introduce the Subset Mangasarian-Fromovitz Condition (subMFC). This new problem qualification is based on a nonsmooth version of the approximate KKT conditions, which hold at every local minimizer without further assumptions. A comparison with existing constraint qualifications and problem qualifications for the given problem class reveals that subMFC is strictly weaker than quasinormality and can hold even if the local error bound condition, the cone-continuity property, Guignard's constraint qualification and calmness in the sense of Clarke are violated. Further, we emphasize the power of the new problem qualification within the context of bilevel optimization. More precisely, under mild assumptions on the problem data, we suggest a version of subMFC that is tailored to the lower-level value function reformulation. It turns out that this new condition can be satisfied even if the widely used partial calmness condition does not hold.
Published: 2024

33. Online closed testing with e-values

Author: Fischer, Lasse and Ramdas, Aaditya
Subjects: Statistics - Methodology
Abstract: In contemporary research, data scientists often test an infinite sequence of hypotheses $H_1,H_2,\ldots $ one by one, and are required to make real-time decisions without knowing the future hypotheses or data. In this paper, we consider such an online multiple testing problem with the goal of providing simultaneous lower bounds for the number of true discoveries in data-adaptively chosen rejection sets. Using the (online) closure principle, we show that for this task it is necessary to use an anytime-valid test for each intersection hypothesis. Motivated by this result, we construct a new online closed testing procedure and a corresponding short-cut with a true discovery guarantee based on multiplying sequential e-values. This general but simple procedure gives uniform improvements over existing methods but also allows to construct entirely new and powerful procedures. In addition, we introduce new ideas for hedging and boosting of sequential e-values that provably increase power. Finally, we also propose the first online true discovery procedure for arbitrarily dependent e-values., Comment: 26 pages, 4 figures
Published: 2024

34. Retinomorphic Machine Vision in a Network Laser

Author: Ng, Wai Kit, Dranczewski, Jakub, Fischer, Anna, Raziman, T V, Saxena, Dhruv, Farchy, Tobias, Stenning, Kilian, Peters, Jonathan, Schmid, Heinz, Branford, Will R, Barahona, Mauricio, Moselund, Kirsten, Sapienza, Riccardo, and Gartside, Jack C.
Subjects: Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science, Physics - Optics
Abstract: With the growing prevalence of AI, demand increases for efficient machine learning hardware. Physical systems are sought which combine image feature detection with the essential nonlinearity for tasks such as image classification. Existing physical hardware typically detects features linearly, then employs digital processing for nonlinear activation. Biological vision systems excel at nonlinear image processing. The retina detects features in ganglion cells via lateral inhibition, where cells nonlinearly compete for neuronal firing while supressing neighbouring cells. We present a bio-inspired 'retinomorphic' machine vision platform using an on-chip semiconductor network laser. The system detects multiple features in parallel via spatially-overlapping lasing modes, with integrated nonlinearity provided by antagonistic gain competition between modes - a photonic analogue of retinal inhibition. Parallel feature-detection enhances efficiency relative to feature-detection schemes which operate sequentially or via multiple device copies, with Si-compatible processing and a compact micron-scale footprint relative to existing mm-scale systems. We report 98.05% accuracy on MNIST-digits and 87.85% on Fashion-MNIST, with strong performance on short training datasets.
Published: 2024

35. A generalization of conjugation of integer partitions

Author: Albion, Seamus, Eisenkölbl, Theresia, Fischer, Ilse, Gangl, Moritz, Höngesberg, Hans, Krattenthaler, Christian, and Rubey, Martin
Subjects: Mathematics - Combinatorics, Primary 05A19, Secondary 05A15 05A17 05A30
Abstract: We exhibit, for any positive integer parameter $s$, an involution on the set of integer partitions of $n$. These involutions show the joint symmetry of the distributions of the following two statistics. The first counts the number of parts of a partition divisible by $s$, whereas the second counts the number of cells in the Ferrers diagram of a partition whose leg length is zero and whose arm length has remainder $s-1$ when dividing by $s$. In particular, for $s=1$ this involution is just conjugation. Additionally, we provide explicit expressions for the bivariate generating functions. Our primary motivation to construct these involutions is that we know only of two other "natural" bijections on integer partitions of a given size, one of which is the Glaisher-Franklin bijection sending the set of parts divisible by $s$, each divided by $s$, to the set of parts occurring at least $s$ times., Comment: AmS-LaTeX, 20 pages
Published: 2024

36. Probing the spin polarization of an Anderson impurity

Author: Bagchi, Mahasweta, Tounsi, Tfyeche Y., Safeer, Affan, van Efferen, Camiel, Rosch, Achim, Michely, Thomas, Jolie, Wouter, Costi, Theo A., and Fischer, Jeison
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Strongly Correlated Electrons
Abstract: We report spin-polarized scanning tunneling microscopy measurements of an Anderson impurity system in MoS$_{2}$ mirror twin boundaries, where both the quantum confined impurity state and the Kondo resonance resulting from the interaction with the substrate are accessible. Using a spin-polarized tip, we observe magnetic field induced changes in the peak heights of the Anderson impurity states as well as in the magnetic field-split Kondo resonance. Quantitative comparison with numerical renormalization group calculations provides evidence of the notable spin polarization of the spin-resolved impurity spectral function under the influence of a magnetic field. Moreover, we extract the field and temperature dependence of the impurity magnetization from the differential conductance measurements and demonstrate that this exhibits the universality and asymptotic freedom of the $S=1/2$ Kondo effect. This work shows that mirror twin boundaries can be used as a testing ground for theoretical predictions on quantum impurity models.
Published: 2024

37. Observation of exotic $J/\psi\phi$ resonances in diffractive processes in proton-proton collisions

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., Leite, J. Baptista de Souza, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Baryshnikov, F., Basels, J. M., Bassi, G., Batsukh, B., Battig, A., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Beiter, A., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertella, C., 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., Bursche, A., Butkevich, A., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Calabrese, R., Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, 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., Cerasoli, J., 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, D., 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., Dolmatov, A., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Downes, A. G., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. 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Subjects: High Energy Physics - Experiment
Abstract: The first study of $J/\psi\phi$ production in diffractive processes in proton-proton collisions is presented. The study is based on an LHCb dataset recorded at centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 5 fb$^{-1}$. The data disfavour a nonresonant $J/\psi\phi$ production but are consistent with a resonant model including several resonant states observed previously only in $B^+ \to J/\psi\phi K^+$ decays. The $\chi_{c0}(4500)$ state is observed with a significance over $5\sigma$ and the $\chi_{c1}(4274)$ is confirmed with a significance of more than $4\sigma$., Comment: All figures and tables, along with any supplementary material and additional information, are available at: https://lhcbproject.web.cern.ch/Publications/LHCbProjectPublic/LHCb-PAPER-2023-043.html (LHCb public pages)
Published: 2024

38. Study of charmonium production via the decay to $p\bar{p}$ at $\sqrt{s} = 13 TeV$

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. 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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: Charmonium production cross-section in proton-proton collisions is measured at the centre-of-mass energy $\sqrt{s}=13\,TeV$ using decays to $p\bar{p}$ final state. The study is performed using a data sample corresponding to an integrated luminosity of $2.2\,{fb}^{-1}$ collected in 2018 with the $LHCb$ detector. The production cross-section of the $\eta_c$ meson is measured in a rapidity range of $2.0 < y < 4.0$ and in a transverse momentum range of $5.0 < p_{T} < 20.0\,{GeV/\it{c}}$, which is extended compared with previous $LHCb$ analyses. The differential cross-section is measured in bins of $p_{T}$ and, for the first time, of $y$. Upper limits, at 90% and 95% confidence levels, on the $\eta_c(2S)$ and $h_c(1P)$ prompt production cross-sections are determined for the first time., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-004.html (LHCb public pages)
Published: 2024

39. Conformal Performance Range Prediction for Segmentation Output Quality Control

Author: Wundram, Anna M., Fischer, Paul, Muehlebach, Michael, Koch, Lisa M., and Baumgartner, Christian F.
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Recent works have introduced methods to estimate segmentation performance without ground truth, relying solely on neural network softmax outputs. These techniques hold potential for intuitive output quality control. However, such performance estimates rely on calibrated softmax outputs, which is often not the case in modern neural networks. Moreover, the estimates do not take into account inherent uncertainty in segmentation tasks. These limitations may render precise performance predictions unattainable, restricting the practical applicability of performance estimation methods. To address these challenges, we develop a novel approach for predicting performance ranges with statistical guarantees of containing the ground truth with a user specified probability. Our method leverages sampling-based segmentation uncertainty estimation to derive heuristic performance ranges, and applies split conformal prediction to transform these estimates into rigorous prediction ranges that meet the desired guarantees. We demonstrate our approach on the FIVES retinal vessel segmentation dataset and compare five commonly used sampling-based uncertainty estimation techniques. Our results show that it is possible to achieve the desired coverage with small prediction ranges, highlighting the potential of performance range prediction as a valuable tool for output quality control., Comment: Accepted as an oral presentation at MICCAI UNSURE 2024
Published: 2024

40. Amplitude analysis of $B^+ \to \psi(2S) K^+ \pi^+ \pi^-$ decays

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., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. B., Belin, S., Bellee, V., Belous, K., Belov, 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., Bovill, M., 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., Caillet, A. C., 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., Chiotopoulos, X., 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., Colonna, M. C., 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. 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M., Martel, L., 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., 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., Osthues, D., 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, Roensch, J., 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., 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. 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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., Benet, D. Vico, 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, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Wislicki, W., Witek, M., Witola, L., Wong, C. P., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, Y., Wu, Z., 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., Yuan, Y, 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: The first full amplitude analysis of $B^+ \to \psi(2S) K^+ \pi^+ \pi^-$ decays is performed using proton-proton collision data corresponding to an integrated luminosity of $9\,\text{fb}^{-1}$ recorded with the LHCb detector. The rich $K^+ \pi^+ \pi^-$ spectrum is studied and the branching fractions of the resonant substructure associated with the prominent $K_1(1270)^+$ contribution are measured. The data cannot be described by conventional strange and charmonium resonances only. An amplitude model with 53 components is developed comprising 11 hidden-charm exotic hadrons. New production mechanisms for charged charmonium-like states are observed. Significant resonant activity with spin-parity $J^P = 1^+$ in the $\psi(2S) \pi^+$ system is confirmed and a multi-pole structure is demonstrated. The spectral decomposition of the $\psi(2S) \pi^+ \pi^-$ invariant-mass structure, dominated by $X^0 \to \psi(2S) \rho(770)^0$ decays, broadly resembles the $J/\psi \phi$ spectrum observed in $B^+ \to J/\psi \phi K^+$ decays. Exotic $\psi(2S) K^+ \pi^-$ resonances are observed for the first time., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-014.html (LHCb public pages)
Published: 2024

41. Nitrogen Abundance Distribution in the inner Milky Way

Author: Pineda, Jorge L., Horiuchi, Shinji, Anderson, L. D., Luisi, Matteo, Langer, William D., Goldsmith, Paul F., Kuiper, Thomas B. H., Fischer, Christian, Gong, Yan, Brunthaler, Andreas, Rugel, Michael, and Menten, Karl M.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We combine a new Galactic plane survey of Hydrogen Radio Recombination Lines (RRLs) with far-infrared (FIR) surveys of ionized Nitrogen, N+, to determine Nitrogen abundance across Galactic radius. RRLs were observed with NASA DSS-43 70m antenna and the Green Bank Telescope in 108 lines-of-sight spanning -135 degrees < l < 60 degrees, at b=0 degrees. These positions were also observed in [N II] 122 um and 205 um lines with the Herschel Space Observatory. Combining RRL and [N II] 122 um and 205 um observations in 41 of 108 samples with high signal-to-noise ratio, we studied ionized Nitrogen abundance distribution across Galactocentric distances of 0-8 kpc. Combined with existing Solar neighborhood and Outer galaxy N/H abundance determinations, we studied this quantity's distribution within the Milky Way's inner 17 kpc for the first time. We found a Nitrogen abundance gradient extending from Galactocentric radii of 4-17 kpc in the Galactic plane, while within 0-4 kpc, the N/H distribution remained flat. The gradient observed at large Galactocentric distances supports inside-out galaxy growth with the additional steepening resulting from variable star formation efficiency and/or radial flows in the Galactic disk, while the inner 4 kpc flattening, coinciding with the Galactic bar's onset, may be linked to radial flows induced by the bar potential. Using SOFIA/FIFI-LS and Herschel/PACS, we observed the [N III] 57 um line to trace doubly ionized gas contribution in a sub-sample of sightlines. We found negligible N++ contributions along these sightlines, suggesting mostly singly ionized Nitrogen originating from low ionization H II region outskirts., Comment: Accepted for publication at the Astrophysical Journal. 25 pages, 13 figures
Published: 2024
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42. Search for the rare $\Lambda_c^+ \to p \mu^+ \mu^-$ 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., 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. 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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. 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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. 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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 nonresonant $\Lambda_c^+ \to p \mu^+ \mu^-$ decay is performed using proton-proton collision data recorded at a centre-of-mass energy of 13 TeV by the LHCb experiment, corresponding to an integrated luminosity of 5.4 fb$^{-1}$. No evidence for the decay is found in the dimuon invariant-mass regions where the expected contributions of resonances is subdominant. The upper limit on the branching fraction of the $\Lambda_c^+ \to p \mu^+ \mu^-$ decay is determined to be $2.9~(3.2) \times 10^{-8}$ at 90% (95%) confidence level. The branching fractions in the dimuon invariant-mass regions dominated by the $\eta$, $\rho$ and $\omega$ resonances are also determined., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-005.html (LHCb public pages)
Published: 2024

43. PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration

Author: Siegert, Leonard, Fischer, Paul, Heinrich, Mattias P., and Baumgartner, Christian F.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Deformable image registration is fundamental to many medical imaging applications. Registration is an inherently ambiguous task often admitting many viable solutions. While neural network-based registration techniques enable fast and accurate registration, the majority of existing approaches are not able to estimate uncertainty. Here, we present PULPo, a method for probabilistic deformable registration capable of uncertainty quantification. PULPo probabilistically models the distribution of deformation fields on different hierarchical levels combining them using Laplacian pyramids. This allows our method to model global as well as local aspects of the deformation field. We evaluate our method on two widely used neuroimaging datasets and find that it achieves high registration performance as well as substantially better calibrated uncertainty quantification compared to the current state-of-the-art., Comment: Accepted as full paper to MICCAI 2024
Published: 2024

44. Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent

Author: Metz, Yannick, Ackermann, Dennis, Keim, Daniel A., and Fischer, Maximilian T.
Subjects: Computer Science - Human-Computer Interaction, H.5.2
Abstract: Efficient public transport systems are crucial for sustainable urban development as cities face increasing mobility demands. Yet, many public transport networks struggle to meet diverse user needs due to historical development, urban constraints, and financial limitations. Traditionally, planning of transport network structure is often based on limited surveys, expert opinions, or partial usage statistics. This provides an incomplete basis for decision-making. We introduce an data-driven approach to public transport planning and optimization, calculating detailed accessibility measures at the individual housing level. Our visual analytics workflow combines population-group-based simulations with dynamic infrastructure analysis, utilizing a scenario-based model to simulate daily travel patterns of varied demographic groups, including schoolchildren, students, workers, and pensioners. These population groups, each with unique mobility requirements and routines, interact with the transport system under different scenarios traveling to and from Points of Interest (POI), assessed through travel time calculations. Results are visualized through heatmaps, density maps, and network overlays, as well as detailed statistics. Our system allows us to analyze both the underlying data and simulation results on multiple levels of granularity, delivering both broad insights and granular details. Case studies with the city of Konstanz, Germany reveal key areas where public transport does not meet specific needs, confirmed through a formative user study. Due to the high cost of changing legacy networks, our analysis facilitates the identification of strategic enhancements, such as optimized schedules or rerouting, and few targeted stop relocations, highlighting consequential variations in accessibility to pinpointing critical service gaps., Comment: 9 pages, 8 figures
Published: 2024

45. The Missing Link: Allocation Performance in Causal Machine Learning

Author: Fischer-Abaigar, Unai, Kern, Christoph, and Kreuter, Frauke
Subjects: Computer Science - Machine Learning, Computer Science - Computers and Society, Statistics - Methodology
Abstract: Automated decision-making (ADM) systems are being deployed across a diverse range of critical problem areas such as social welfare and healthcare. Recent work highlights the importance of causal ML models in ADM systems, but implementing them in complex social environments poses significant challenges. Research on how these challenges impact the performance in specific downstream decision-making tasks is limited. Addressing this gap, we make use of a comprehensive real-world dataset of jobseekers to illustrate how the performance of a single CATE model can vary significantly across different decision-making scenarios and highlight the differential influence of challenges such as distribution shifts on predictions and allocations.
Published: 2024

46. Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews

Author: Joos, Lucas, Keim, Daniel A., and Fischer, Maximilian T.
Subjects: Computer Science - Machine Learning, Computer Science - Digital Libraries, Computer Science - Human-Computer Interaction, H.5.2
Abstract: In academic research, systematic literature reviews are foundational and highly relevant, yet tedious to create due to the high volume of publications and labor-intensive processes involved. Systematic selection of relevant papers through conventional means like keyword-based filtering techniques can sometimes be inadequate, plagued by semantic ambiguities and inconsistent terminology, which can lead to sub-optimal outcomes. To mitigate the required extensive manual filtering, we explore and evaluate the potential of using Large Language Models (LLMs) to enhance the efficiency, speed, and precision of literature review filtering, reducing the amount of manual screening required. By using models as classification agents acting on a structured database only, we prevent common problems inherent in LLMs, such as hallucinations. We evaluate the real-world performance of such a setup during the construction of a recent literature survey paper with initially more than 8.3k potentially relevant articles under consideration and compare this with human performance on the same dataset. Our findings indicate that employing advanced LLMs like GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Flash, or Llama3 with simple prompting can significantly reduce the time required for literature filtering - from usually weeks of manual research to only a few minutes. Simultaneously, we crucially show that false negatives can indeed be controlled through a consensus scheme, achieving recalls >98.8% at or even beyond the typical human error threshold, thereby also providing for more accurate and relevant articles selected. Our research not only demonstrates a substantial improvement in the methodology of literature reviews but also sets the stage for further integration and extensive future applications of responsible AI in academic research practices., Comment: 5 pages, 5 figures, 1 table
Published: 2024

47. Analytical expressions for the two basic modes of surface displacement and overall deformation of a free-standing or elastically embedded sphere

Author: Fischer, Lukas and Menzel, Andreas M.
Subjects: Condensed Matter - Soft Condensed Matter, Mathematical Physics
Abstract: Calculating by analytical theory the deformation of finite-sized elastic bodies in response to internally applied forces is a challenge. Here, we derive explicit analytical expressions for the amplitudes of modes of surface deformation of a homogeneous, isotropic, linearly elastic sphere. The modes under consideration correspond to overall changes in volume and to relative uniaxial elongation or contraction. Both situations of an elastic sphere embedded under no-slip conditions in a linearly elastic, homogeneous, isotropic, infinitely extended background medium and of a free-standing elastic sphere are considered. For example, our analytical expressions are important as a basis for computational material optimization in the context of spherical soft actuators., Comment: 29 pages, 0 figures
Published: 2024

48. iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning

Author: Fischer, Tom, Liu, Yaoyao, Jesslen, Artur, Ahmed, Noor, Kaushik, Prakhar, Wang, Angtian, Yuille, Alan, Kortylewski, Adam, and Ilg, Eddy
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have recently addressed handling continual data streams. However, extending these methods to manage out-of-distribution (OOD) scenarios has not effectively been investigated. On the other hand, it has recently been shown that non-continual neural mesh models exhibit strong performance in generalizing to such OOD scenarios. To leverage this decisive property in a continual learning setting, we propose incremental neural mesh models that can be extended with new meshes over time. In addition, we present a latent space initialization strategy that enables us to allocate feature space for future unseen classes in advance and a positional regularization term that forces the features of the different classes to consistently stay in respective latent space regions. We demonstrate the effectiveness of our method through extensive experiments on the Pascal3D and ObjectNet3D datasets and show that our approach outperforms the baselines for classification by $2-6\%$ in the in-domain and by $6-50\%$ in the OOD setting. Our work also presents the first incremental learning approach for pose estimation. Our code and model can be found at https://github.com/Fischer-Tom/iNeMo., Comment: ECCV-24
Published: 2024

49. Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy

Author: Fischer, Paul, Willms, Hannah, Schneider, Moritz, Thorwarth, Daniela, Muehlebach, Michael, and Baumgartner, Christian F.
Subjects: Computer Science - Machine Learning
Abstract: Cancer remains a leading cause of death, highlighting the importance of effective radiotherapy (RT). Magnetic resonance-guided linear accelerators (MR-Linacs) enable imaging during RT, allowing for inter-fraction, and perhaps even intra-fraction, adjustments of treatment plans. However, achieving this requires fast and accurate dose calculations. While Monte Carlo simulations offer accuracy, they are computationally intensive. Deep learning frameworks show promise, yet lack uncertainty quantification crucial for high-risk applications like RT. Risk-controlling prediction sets (RCPS) offer model-agnostic uncertainty quantification with mathematical guarantees. However, we show that naive application of RCPS may lead to only certain subgroups such as the image background being risk-controlled. In this work, we extend RCPS to provide prediction intervals with coverage guarantees for multiple subgroups with unknown subgroup membership at test time. We evaluate our algorithm on real clinical planing volumes from five different anatomical regions and show that our novel subgroup RCPS (SG-RCPS) algorithm leads to prediction intervals that jointly control the risk for multiple subgroups. In particular, our method controls the risk of the crucial voxels along the radiation beam significantly better than conventional RCPS., Comment: This work was accepted as a full paper at MICCAI 2024
Published: 2024

50. Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks

Author: Fischer, Stefan M., Felsner, Lina, Osuala, Richard, Kiechle, Johannes, Lang, Daniel M., Peeken, Jan C., and Schnabel, Julia A.
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: In this work, we introduce Progressive Growing of Patch Size, a resource-efficient implicit curriculum learning approach for dense prediction tasks. Our curriculum approach is defined by growing the patch size during model training, which gradually increases the task's difficulty. We integrated our curriculum into the nnU-Net framework and evaluated the methodology on all 10 tasks of the Medical Segmentation Decathlon. With our approach, we are able to substantially reduce runtime, computational costs, and CO2 emissions of network training compared to classical constant patch size training. In our experiments, the curriculum approach resulted in improved convergence. We are able to outperform standard nnU-Net training, which is trained with constant patch size, in terms of Dice Score on 7 out of 10 MSD tasks while only spending roughly 50% of the original training runtime. To the best of our knowledge, our Progressive Growing of Patch Size is the first successful employment of a sample-length curriculum in the form of patch size in the field of computer vision. Our code is publicly available at https://github.com/compai-lab/2024-miccai-fischer., Comment: Accepted at MICCAI2024; Changes for Camera-Ready-Version for MICCAI2024 (missing in this arxiv submission): Replaced T-Test with Wilcoxon Signed Ranked Test, as DSC samples are not normally distributed => now only significant improvements and no significant decreases in performance for PGPS/PGPS+
Published: 2024

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