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Bulk-boundary correspondence is a foundational principle underlying the electronic band structure and physical behavior of topological quantum materials. Although it has been rigorously tested in topological systems where the physical properties involve charge currents, it remains unclear whether bulk-boundary correspondence should also hold for non-conserved spin currents. We study charge-to-spin conversion in a canonical topological insulator, Bi$_{1-x}$Sb$_x$, to address this fundamentally unresolved question. We use spin-torque ferromagnetic resonance measurements to accurately probe the charge-to-spin conversion efficiency in epitaxial Bi$_{1-x}$Sb$_x$~thin films of high structural quality spanning the entire range of composition, including both trivial and topological band structures, as verified using {\it in vacuo} angle-resolved photoemission spectroscopy. From these measurements, we deduce the effective spin Hall conductivity (SHC) and find excellent agreement with the values predicted by tight-binding calculations for the intrinsic SHC of the bulk bands. These results provide strong evidence that the strong spin-orbit entanglement of bulk states well below the Fermi energy connects directly to the SHC in epitaxial Bi$_{1-x}$Sb$_x$~films interfaced with a metallic ferromagnet. The excellent agreement between theory and experiment points to the generic value of analyses focused entirely on bulk properties, even for topological systems involving non-conserved spin currents.

The $^{111,112,113}$Sn isotopes have been studied with ($p,d \gamma$), ($p,p^{\prime} \gamma$), and ($d,p \gamma$) reactions to extract the nuclear level densities (NLDs) and $\gamma$-ray strength functions (GSFs) of these nuclei below the neutron separation energy by means of the Oslo method. The experimental NLDs for all three nuclei demonstrate a trend compatible with the constant-temperature model below the neutron separation energy while also being in good agreement with the NLDs of neighboring Sn isotopes, obtained previously with the Oslo-type and neutron evaporation experiments. The extracted microcanonical entropies yield $\approx 1.5$ $k_B$ entropy of a valence neutron in both $^{111}$Sn and $^{113}$Sn. Moreover, the deduced microcanonical temperatures indeed suggest a clear constant-temperature behavior above $\approx$ 3 MeV in $^{111,113}$Sn and above $\approx$ 4.5 MeV in $^{112}$Sn. We observe signatures for the first broken neutron pairs between 2 and 4 MeV in all three nuclei. The GSFs obtained with the Oslo method are found to be in good agreement below the neutron threshold with the strengths of $^{112,114}$Sn extracted in the ($p,p^{\prime}$) Coulomb excitation experiments., Comment: 13 pages, 9 figures

We investigate whether there is a relationship between the discrete Bakry-\'{E}mery curvature of a graph and the smooth curvature of an ambient surface into which the graph is embedded geodesically. As we used weighted graphs as test objects, we developed a program for the calculation of the discrete curvature and with the help of this calculator, we observed some indications of such a relationship., Comment: 18 pages, 12 figures, 1 table

Detecting events by using social media has been an active research problem. In this work, we investigate and compare the performance of two methods for event detection in Twitter by using Apache Storm as the stream processing infrastructure. The first event detection method is based on identifying uncommonly common words inside tweet blocks, and the second one is based on clustering tweets to detect a cluster as an event. Each of the methods has its own characteristics. Uncommonly common word based method relies on the burst of words and hence is not affected from concurrency problems in distributed environment. On the other hand, clustering based method includes a finer grained analysis, but it is sensitive to the concurrent processing. We investigate the effect of stream processing and concurrency handling support provided by Apace Storm on event detection by these methods.

Contemporary large-scale visual language models (VLMs) exhibit strong representation capacities, making them ubiquitous for enhancing image and text understanding tasks. They are often trained in a contrastive manner on a large and diverse corpus of images and corresponding text captions scraped from the internet. Despite this, VLMs often struggle with compositional reasoning tasks which require a fine-grained understanding of the complex interactions of objects and their attributes. This failure can be attributed to two main factors: 1) Contrastive approaches have traditionally focused on mining negative examples from existing datasets. However, the mined negative examples might not be difficult for the model to discriminate from the positive. An alternative to mining would be negative sample generation 2) But existing generative approaches primarily focus on generating hard negative texts associated with a given image. Mining in the other direction, i.e., generating negative image samples associated with a given text has been ignored. To overcome both these limitations, we propose a framework that not only mines in both directions but also generates challenging negative samples in both modalities, i.e., images and texts. Leveraging these generative hard negative samples, we significantly enhance VLMs' performance in tasks involving multimodal compositional reasoning. Our code and dataset are released at https://ugorsahin.github.io/enhancing-multimodal-compositional-reasoning-of-vlm.html., Comment: Accepted to WACV

In the pursuit of accurate experimental and computational data while minimizing effort, there is a constant need for high-fidelity results. However, achieving such results often requires significant computational resources. To address this challenge, this paper proposes a deep operator learning-based framework that requires a limited high-fidelity dataset for training. We introduce a novel physics-guided, bi-fidelity, Fourier-featured Deep Operator Network (DeepONet) framework that effectively combines low and high-fidelity datasets, leveraging the strengths of each. In our methodology, we began by designing a physics-guided Fourier-featured DeepONet, drawing inspiration from the intrinsic physical behavior of the target solution. Subsequently, we train this network to primarily learn the low-fidelity solution, utilizing an extensive dataset. This process ensures a comprehensive grasp of the foundational solution patterns. Following this foundational learning, the low-fidelity deep operator network's output is enhanced using a physics-guided Fourier-featured residual deep operator network. This network refines the initial low-fidelity output, achieving the high-fidelity solution by employing a small high-fidelity dataset for training. Notably, in our framework, we employ the Fourier feature network as the Trunk network for the DeepONets, given its proficiency in capturing and learning the oscillatory nature of the target solution with high precision. We validate our approach using a well-known 2D benchmark cylinder problem, which aims to predict the time trajectories of lift and drag coefficients. The results highlight that the physics-guided Fourier-featured deep operator network, serving as a foundational building block of our framework, possesses superior predictive capability for the lift and drag coefficients compared to its data-driven counterparts., Comment: 24 pages, 10 figures, 5 tables- submitted to Fluid

Control of components in integrated photonic circuits is crucial in achieving programmable devices. Operation bandwidth of a plasmonic device cannot be generally tuned once it is manufactured, especially in the visible regime. Here, we demonstrate the electrical control of such a device for extraordinary optical transmission~(EOT) in the visible regime. (i) Operation frequency of the EOT device can be tuned via a bias voltage applied through nanowires. (ii) Or, at a given frequency, the EOT signal (normalized to the incident field) can be tuned continuously, e.g., between $10^{-4}$ and $0.4$. This corresponds to a 3-orders of magnitude modulation depth. We utilize Fano resonances induced by a quantum emitter~(QE) that is embedded into the nanoholes. The external bias-voltage tunes QE's resonance. We also discuss the lifetime extensions of surface plasmon polaritons as a response to an ultra-short optical pulse. Our proposed method provides the active electronic control of EOT signal which makes it a feasible and compact element in integrated photonic circuits, for bio-sensing, high resolution imaging, and molecular spectroscopy applications., Comment: 9 pages, 8 figures

Continuous optimization based motion planners require specifying a maneuver class before calculating the optimal trajectory for that class. In traffic, the intentions of other participants are often unclear, presenting multiple maneuver options for the autonomous vehicle. This uncertainty can make it difficult for the vehicle to decide on the best option. This work introduces a continuous optimization based motion planner that combines multiple maneuvers by weighting the trajectory of each maneuver according to the vehicle's preferences. In this way, the planner eliminates the need for committing to a single maneuver. To maintain safety despite this increased complexity, the planner considers uncertainties ranging from perception to prediction, while ensuring the feasibility of a chance-constrained emergency maneuver. Evaluations in both driving experiments and simulation studies show enhanced interaction capabilities and comfort levels compared to conventional planners, which consider only a single maneuver.

This paper focuses on the design and systematic evaluation of fabric-based, bellow-type soft pneumatic actuators to assist with flexion and extension of the elbow, intended for use in infant wearable devices. Initially, the performance of a range of actuator variants was explored via simulation. The actuator variants were parameterized based on the shape, number, and size of the cells present. Subsequently, viable actuator variants identified from the simulations were fabricated and underwent further testing on a physical model based on an infant's body anthropometrics. The performance of these variants was evaluated based on kinematic analyses using metrics including movement smoothness, path length, and elbow joint angle. Internal pressure of the actuators was also attained. Taken together, results reported herein provide valuable insights about the suitability of several actuator designs to serve as components for pediatric wearable assistive devices.

We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption. The proposed architecture leverages the energy efficiency of transistors operating in the subthreshold region, eliminating the need for analog-to-digital converters (ADC) and static random access memory (SRAM). The system design includes a novel analog sequential Multiply-Accumulate (MAC) circuit that mitigates process, supply voltage, and temperature variations. Experimental evaluations on PhysioNet's MIT-BIH and PTB Diagnostics datasets demonstrate the effectiveness of the proposed method, achieving average balanced accuracy of 95% and 94.25% for intra-patient arrhythmia classification and myocardial infarction (MI) classification, respectively. This innovative approach presents a promising avenue for developing low-power arrhythmia classification systems with enhanced accuracy and transferability in biomedical applications., Comment: Accepted on IEEE Biomedical Circuits and Systems (BioCAS) 2023

Many attempts have been made at estimating discrete emotions (calmness, anxiety, boredom, surprise, anger) and continuous emotional measures commonly used in psychology, namely `valence' (The pleasantness of the emotion being displayed) and `arousal' (The intensity of the emotion being displayed). Existing methods to estimate arousal and valence rely on learning from data sets, where an expert annotator labels every image frame. Access to an expert annotator is not always possible, and the annotation can also be tedious. Hence it is more practical to obtain self-reported arousal and valence values directly from the human in a real-time Human-Robot collaborative setting. Hence this paper provides an emotion data set (HRI-AVC) obtained while conducting a human-robot interaction (HRI) task. The self-reported pair of labels in this data set is associated with a set of image frames. This paper also proposes a spatial and temporal attention-based network to estimate arousal and valence from this set of image frames. The results show that an attention-based network can estimate valence and arousal on the HRI-AVC data set even when Arousal and Valence values are unavailable per frame.

In this study, the structural, electronic and vibrational properties of thinnest possible Cadmium crystal are investigated by performing first-principle calculations. Total energy optimization and dynamic stability calculations reveal that the thinnest possible crystal structure is a double-layered structure consisting of alternating layers formed by trigonal arrangements of Cd atoms. Excessive softening of optical phonons occurs as a result of structural relaxation when going from a bulk to a single layer (SL) structure. It is also shown that thinnest structure obtained from bulk Cd crystals maintains its metallic feature despite the dimensional crossover. In addition, it is predicted through calculations that the SL Cd crystal strongly interacts with oxygen and that the oxidized regions even undergo chemical transformation to form the CdO crystal. In the double-layer CdO crystal resulting from the oxidation of individual Cd layers, the layers are connected to each other with strong covalent bonds, and this structure is a semiconductor with a band gap of 2.10 eV. While the robust metallic structure of the thinnest possible Cd crystal provides flexibility in terms of its use in nanoscale applications, on the other hand, the fact that its electronic properties can be changed by oxidation is important for optoelectronic device applications., Comment: Some missing information due to response

We propose a line list that may be useful for the abundance analysis of G-type stars in the wavelength range 4080 -- 6780 A. It is expected that the line list will be useful for surveys/libraries with overlapping spectral regions (e.g. ELODIE/SOPHIE libraries, UVES-580 setting of Gaia-ESO), and in particular for the analysis of F- and G-type stars in general. The atomic data are supplemented by detailed references to the sources. We estimated the Solar abundances using stellar lines and the high-resolution Kitt Peak National Observatory (KPNO) spectra of the Sun to determine the uncertainty in the log gf values. By undertaking a systematic search that makes use of the lower excitation potential and gf-values and using revised multiplet table as an initial guide, we identified 363 lines of 24 species that have accurate gf-values and are free of blends in the spectra of the Sun and a Solar analogue star, HD 218209 (G6V), for which accurate and up-to-date abundances were obtained from both ELODIE and PolarBASE spectra of the star. For the common lines with the Gaia-ESO line list v.6 provided by the Gaia-ESO collaboration, we discovered significant inconsistencies in the gf-values for certain lines of varying species., Comment: 17 pages, 4 figures and 5 tables, accepted for publication in Physics and Astronomy Reports

Rate-splitting multiple access (RSMA) is a multiple access scheme to mitigate the effects of the multi-user interference (MUI) in multi-antenna systems. In this study, we leverage the interference management capabilities of RSMA to tackle the issue of inter-carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) waveform. We formulate a sum-rate maximization problem to find the optimal subcarrier and power allocation for downlink transmission in a two-user system using RSMA and OFDM. A weighted minimum mean-square error (WMMSE)-based algorithm is proposed to obtain a solution for the formulated non-convex problem. We show that the marriage of rate-splitting (RS) with OFDM provides complementary strengths to cope with peculiar characteristic of wireless medium and its performance-limiting challenges including inter-symbol interference (ISI), MUI, ICI, and inter-numerology interference (INI). The sum-rate performance of the proposed OFDM-RSMA scheme is numerically compared with that of conventional orthogonal frequency division multiple access (OFDMA) and OFDM-non-orthogonal multiple access (NOMA). It is shown that the proposed OFDM-RSMA outperforms OFDM-NOMA and OFDMA in diverse propagation channel conditions owing to its flexible structure and robust interference management capabilities., Comment: Submitted to IEEE for possible publication

Nuclear quantum effects such as zero-point energy and hydrogen tunnelling play a central role in many biological and chemical processes. The nuclear-electronic orbital (NEO) approach captures these effects by treating selected nuclei quantum mechanically on the same footing as electrons. On classical computers, the resources required for an exact solution of NEO-based models grow exponentially with system size. By contrast, quantum computers offer a means of solving this problem with polynomial scaling. However, due to the limitations of current quantum devices, NEO simulations are confined to the smallest systems described by minimal basis sets whereas realistic simulations beyond the Born--Oppenheimer approximation require more sophisticated basis sets. For this purpose, we herein extend a hardware-efficient ADAPT-VQE method to the NEO framework in the frozen natural orbital (FNO) basis. We demonstrate on H$_2$ and D$_2$ molecules that the NEO-FNO-ADAPT-VQE method reduces the CNOT count by a several orders of magnitude relative to the NEO unitary coupled cluster method with singles and doubles (NEO-UCCSD) while maintaining the desired accuracy. This extreme reduction in the CNOT gate count is sufficient to permit practical computations employing the NEO method -- an important step toward accurate simulations involving non-classical nuclei and non--Born--Oppenheimer effects on near-term quantum devices. We further show that the method can capture isotope effects and we demonstrate that inclusion of correlation energy systematically improves the prediction of $\Delta$ZPE.

In this study, we propose a non-coherent over-the-air computation scheme to calculate the majority vote (MV) reliably in fading channels. The proposed approach relies on modulating the amplitude of the elements of complementary sequences based on the sign of the parameters to be aggregated. Since it does not use channel state information at the nodes, it is compatible with time-varying channels. To demonstrate the efficacy of our method, we employ it in a scenario where an unmanned aerial vehicle is guided by distributed sensors, relying on the MV computed using our proposed scheme. We show that the proposed scheme notably reduces the computation error rate with a longer sequence length in fading channels while maintaining the peak-to-mean-envelope power ratio of the transmitted orthogonal frequency division multiplexing signals to be less than or equal to 3 dB., Comment: 14 pages. arXiv admin note: text overlap with arXiv:2308.06372

A hypothetical gravitating body in the outer Solar System, the so-called Planet Nine, was proposed to explain the unexpected clustering of the Kuiper Belt Objects. As it has not been observed via telescopes, it was conjectured to be a primordial black hole (of the size of a quince) that could be gravitationally detected by laser-launching or solar sailing many small spacecraft. Here, we study various aspects affecting such a search for Planet Nine. Our basic observable is the angular displacement in the trajectory of a small spacecraft which will be mainly affected by the gravity of Planet Nine, augmented with several other 3-body, non-gravitational, post-Newtonian, planetary, and Kuiper Belt effects. First, we calculate the effect of the Sun in the framework of the circular restricted three-body problem of the Sun--Planet Nine-spacecraft for the two particular initial conditions. Then, we study the effects of Kuiper Belt and outer planets, namely Jupiter, Saturn, Uranus, Neptune, as well as non-gravitational perturbations such as magnetic and drag forces exerted by the interstellar medium; and the solar radiation pressure. In addition, we investigate the post-Newtonian general relativistic effects such as the frame-dragging, Schwarzschild effect, and geodetic precession on the spacecraft trajectory. We show that the leading order angular displacement is due to the solar radiation pressure for the lower spacecraft velocities, and the drag force for the higher spacecraft velocities. Among the general relativistic effects, the frame-dragging has the smallest effect; and the Schwarzschild effect due to Sun has the largest effect. However, none of the general relativistic effects produces a meaningful contribution to the detection., Comment: 29 pages, 10 figures, matches the published version

Grammatical Error Detection and Correction (GEC) tools have proven useful for native speakers and second language learners. Developing such tools requires a large amount of parallel, annotated data, which is unavailable for most languages. Synthetic data generation is a common practice to overcome the scarcity of such data. However, it is not straightforward for morphologically rich languages like Turkish due to complex writing rules that require phonological, morphological, and syntactic information. In this work, we present a flexible and extensible synthetic data generation pipeline for Turkish covering more than 20 expert-curated grammar and spelling rules (a.k.a., writing rules) implemented through complex transformation functions. Using this pipeline, we derive 130,000 high-quality parallel sentences from professionally edited articles. Additionally, we create a more realistic test set by manually annotating a set of movie reviews. We implement three baselines formulating the task as i) neural machine translation, ii) sequence tagging, and iii) prefix tuning with a pretrained decoder-only model, achieving strong results. Furthermore, we perform exhaustive experiments on out-of-domain datasets to gain insights on the transferability and robustness of the proposed approaches. Our results suggest that our corpus, GECTurk, is high-quality and allows knowledge transfer for the out-of-domain setting. To encourage further research on Turkish GEC, we release our datasets, baseline models, and the synthetic data generation pipeline at https://github.com/GGLAB-KU/gecturk., Comment: Accepted at Findings of IJCNLP-AACL 2023

Understanding procedural natural language (e.g., step-by-step instructions) is a crucial step to execution and planning. However, while there are ample corpora and downstream tasks available in English, the field lacks such resources for most languages. To address this gap, we conduct a case study on Turkish procedural texts. We first expand the number of tutorials in Turkish wikiHow from 2,000 to 52,000 using automated translation tools, where the translation quality and loyalty to the original meaning are validated by a team of experts on a random set. Then, we generate several downstream tasks on the corpus, such as linking actions, goal inference, and summarization. To tackle these tasks, we implement strong baseline models via fine-tuning large language-specific models such as TR-BART and BERTurk, as well as multilingual models such as mBART, mT5, and XLM. We find that language-specific models consistently outperform their multilingual models by a significant margin across most procedural language understanding (PLU) tasks. We release our corpus, downstream tasks and the baseline models with https://github.com/ GGLAB-KU/turkish-plu., Comment: 9 pages