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Multilingual automatic speech recognition (ASR) in the medical domain serves as a foundational task for various downstream applications such as speech translation, spoken language understanding, and voice-activated assistants. This technology enhances patient care by enabling efficient communication across language barriers, alleviating specialized workforce shortages, and facilitating improved diagnosis and treatment, particularly during pandemics. In this work, we introduce MultiMed, a collection of small-to-large end-to-end ASR models for the medical domain, spanning five languages: Vietnamese, English, German, French, and Mandarin Chinese, together with the corresponding real-world ASR dataset. To our best knowledge, MultiMed stands as the largest and the first multilingual medical ASR dataset, in terms of total duration, number of speakers, diversity of diseases, recording conditions, speaker roles, unique medical terms, accents, and ICD-10 codes. Secondly, we establish the empirical baselines, present the first reproducible study of multilinguality in medical ASR, conduct a layer-wise ablation study for end-to-end ASR training, and provide the first linguistic analysis for multilingual medical ASR. All code, data, and models are available online https://github.com/leduckhai/MultiMed/tree/master/MultiMed, Comment: Preprint

To protect patient safety, modern pharmaceutical complexity demands strict prescription verification. We offer a new approach - Rx Strategist - that makes use of knowledge graphs and different search strategies to enhance the power of Large Language Models (LLMs) inside an agentic framework. This multifaceted technique allows for a multi-stage LLM pipeline and reliable information retrieval from a custom-built active ingredient database. Different facets of prescription verification, such as indication, dose, and possible drug interactions, are covered in each stage of the pipeline. We alleviate the drawbacks of monolithic LLM techniques by spreading reasoning over these stages, improving correctness and reliability while reducing memory demands. Our findings demonstrate that Rx Strategist surpasses many current LLMs, achieving performance comparable to that of a highly experienced clinical pharmacist. In the complicated world of modern medications, this combination of LLMs with organized knowledge and sophisticated search methods presents a viable avenue for reducing prescription errors and enhancing patient outcomes., Comment: 17 Pages, 6 Figures, Under Review

This paper presents novel algorithms for the Fermat-Weber Location Problem, guiding an autonomous agent to the point that minimizes the weighted sum of Euclidean distances to some beacons using only bearing measurements. The existing results address only the simple scenario where the beacons are stationary and the agent is modeled by a single integrator. In this paper, we propose a number of bearing-only algorithms that let the agent, which can be modeled as either a single-integrator or a double-integrator, follow the Fermat-Weber point of a group of stationary or moving beacons. The theoretical results are rigorously proven using Lyapunov theory and supported with simulation examples., Comment: 14 pages (double-column), 7 figures, submitted to a journal

We report observations of direct evidence of energetic protons being accelerated above ~400 keV within the reconnection exhaust of a heliospheric current sheet (HCS) crossing by NASA's Parker Solar Probe (PSP) at a distance of ~16.25 solar radii (Rs) from the Sun. Inside the extended exhaust, both the reconnection-generated plasma jets and the accelerated protons propagated toward the Sun, unambiguously establishing their origin from HCS reconnection sites located beyond PSP. Within the core of the exhaust, PSP detected stably trapped energetic protons up to ~400 keV, which is approximately 1000 times greater than the available magnetic energy per particle. The differential energy spectrum of the accelerated protons behaved as a pure power-law with spectral index of about -5. Supporting simulations using the kglobal model suggest that the trapping and acceleration of protons up to ~400 keV in the reconnection exhaust is likely facilitated by merging magnetic islands with a guide field between ~0.2-0.3 of the reconnecting magnetic field, consistent with the observations. These new results, enabled by PSP's proximity to the Sun, demonstrate that magnetic reconnection in the HCS is a significant new source of energetic particles in the near-Sun solar wind. The discovery of in-situ particle acceleration via magnetic reconnection at the HCS provides valuable insights into this fundamental process which frequently converts the large magnetic field energy density in the near-Sun plasma environment and may be responsible for heating the sun's atmosphere, accelerating the solar wind, and energizing charged particles to extremely high energies in solar flares., Comment: 24 pages, 4 figures

With the oncologist acting as the ``game leader'', we employ a Stackelberg game-theoretic model involving multiple populations to study prostate cancer. We refine the drug dosing schedule using an empirical Bayes feed-forward analysis, based on clinical data that reflects each patient's prostate-specific drug response. Our methodology aims for a quantitative grasp of the parameter landscape of this adaptive multi-population model, focusing on arresting the growth of drug-resistant prostate cancer by promoting competition across drug-sensitive cancer cell populations. Our findings indicate that not only is it is feasible to considerably extend cancer suppression duration through careful optimization, but even transform metastatic prostate cancer into a chronic condition instead of an acute one for most patients, with supporting clinical and analytical evidence.

Object tracking, especially animal tracking, is one of the key topics that attract a lot of attention due to its benefits of animal behavior understanding and monitoring. Recent state-of-the-art tracking methods are founded on deep learning architectures for object detection, appearance feature extraction and track association. Despite the good tracking performance, these methods are trained and evaluated on common objects such as human and cars. To perform on the animal, there is a need to create large datasets of different types in multiple conditions. The dataset construction comprises of data collection and data annotation. In this work, we put more focus on the latter task. Particularly, we renovate the well-known tool, LabelMe, so as to assist common user with or without in-depth knowledge about computer science to annotate the data with less effort. The new tool named as TrackMe inherits the simplicity, high compatibility with varied systems, minimal hardware requirement and convenient feature utilization from the predecessor. TrackMe is an upgraded version with essential features for multiple object tracking annotation.

Auditory attention decoding (AAD) is the process of identifying the attended speech in a multi-talker environment using brain signals, typically recorded through electroencephalography (EEG). Over the past decade, AAD has undergone continuous development, driven by its promising application in neuro-steered hearing devices. Most AAD algorithms are relying on the increase in neural entrainment to the envelope of attended speech, as compared to unattended speech, typically using a two-step approach. First, the algorithm predicts representations of the attended speech signal envelopes; second, it identifies the attended speech by finding the highest correlation between the predictions and the representations of the actual speech signals. In this study, we proposed a novel end-to-end neural network architecture, named AADNet, which combines these two stages into a direct approach to address the AAD problem. We compare the proposed network against the traditional approaches, including linear stimulus reconstruction, canonical correlation analysis, and an alternative non-linear stimulus reconstruction using two different datasets. AADNet shows a significant performance improvement for both subject-specific and subject-independent models. Notably, the average subject-independent classification accuracies from 56.1 % to 82.7 % with analysis window lengths ranging from 1 to 40 seconds, respectively, show a significantly improved ability to generalize to data from unseen subjects. These results highlight the potential of deep learning models for advancing AAD, with promising implications for future hearing aids, assistive devices, and clinical assessments., Comment: 11 pages, 6 figures

In this paper we propose that a restricted version of logical inference can be implemented with self-attention networks. We are aiming at showing that LLMs (Large Language Models) constructed with transformer networks can make logical inferences. We would reveal the potential of LLMs by analyzing self-attention networks, which are main components of transformer networks. Our approach is not based on semantics of natural languages but operations of logical inference. %point of view. We show that hierarchical constructions of self-attention networks with feed forward networks (FFNs) can implement top-down derivations for a class of logical formulae. We also show bottom-up derivations are also implemented for the same class. We believe that our results show that LLMs implicitly have the power of logical inference., Comment: Presented at NeLaMKRR@KR, 2024 (arXiv:2410.05339)

Statutory law retrieval is a typical problem in legal language processing, that has various practical applications in law engineering. Modern deep learning-based retrieval methods have achieved significant results for this problem. However, retrieval systems relying on semantic and lexical correlations often exhibit limitations, particularly when handling queries that involve real-life scenarios, or use the vocabulary that is not specific to the legal domain. In this work, we focus on overcoming this weaknesses by utilizing the logical reasoning capabilities of large language models (LLMs) to identify relevant legal terms and facts related to the situation mentioned in the query. The proposed retrieval system integrates additional information from the term--based expansion and query reformulation to improve the retrieval accuracy. The experiments on COLIEE 2022 and COLIEE 2023 datasets show that extra knowledge from LLMs helps to improve the retrieval result of both lexical and semantic ranking models. The final ensemble retrieval system outperformed the highest results among all participating teams in the COLIEE 2022 and 2023 competitions., Comment: Presented at NeLaMKRR@KR, 2024 (arXiv:2410.05339)

Tokenization is associated with many poorly understood shortcomings in language models (LMs), yet remains an important component for long sequence scaling purposes. This work studies how tokenization impacts model performance by analyzing and comparing the stochastic behavior of tokenized models with their byte-level, or token-free, counterparts. We discover that, even when the two models are statistically equivalent, their predictive distributions over the next byte can be substantially different, a phenomenon we term as "tokenization bias''. To fully characterize this phenomenon, we introduce the Byte-Token Representation Lemma, a framework that establishes a mapping between the learned token distribution and its equivalent byte-level distribution. From this result, we develop a next-byte sampling algorithm that eliminates tokenization bias without requiring further training or optimization. In other words, this enables zero-shot conversion of tokenized LMs into statistically equivalent token-free ones. We demonstrate its broad applicability with two use cases: fill-in-the-middle (FIM) tasks and model ensembles. In FIM tasks where input prompts may terminate mid-token, leading to out-of-distribution tokenization, our method mitigates performance degradation and achieves an approximately 18% improvement in FIM coding benchmarks, consistently outperforming the standard token healing fix. For model ensembles where each model employs a distinct vocabulary, our approach enables seamless integration, resulting in improved performance (up to 3.7%) over individual models across various standard baselines in reasoning, knowledge, and coding.

Chick sexing, the process of determining the gender of day-old chicks, is a critical task in the poultry industry due to the distinct roles that each gender plays in production. While effective traditional methods achieve high accuracy, color, and wing feather sexing is exclusive to specific breeds, and vent sexing is invasive and requires trained experts. To address these challenges, we propose a novel approach inspired by facial gender classification techniques in humans: facial chick sexing. This new method does not require expert knowledge and aims to reduce training time while enhancing animal welfare by minimizing chick manipulation. We develop a comprehensive system for training and inference that includes data collection, facial and keypoint detection, facial alignment, and classification. We evaluate our model on two sets of images: Cropped Full Face and Cropped Middle Face, both of which maintain essential facial features of the chick for further analysis. Our experiment demonstrates the promising viability, with a final accuracy of 81.89%, of this approach for future practices in chick sexing by making them more universally applicable.

We propose a quantitative direct method of proving the local stability for the trivial solution of a rough differential equation and of its regular discretization scheme. Using Doss-Sussmann technique and stopping time analysis, we prove that the trivial solution of the rough system is exponentially stable as long as the noise is small. The same conclusions hold for the regular discretization scheme with small noise and small step size. Our results are significantly stronger than \cite[Theorem 14]{garrido-atienzaetal} and \cite[Theorem 18]{GABSch18} and can be applied to non-flat bounded or linear noises.

General one-loop formulas for loop-induced processes $\gamma \gamma \rightarrow \phi_i\phi_j$ with $\phi_i\phi_j = hh,~hH,~HH$ are presented in the paper. Analytic expressions evaluated in this work are valid for a class of Higgs Extensions of the Standard Models, e.g. Inert Doublet Higgs Models, Two Higgs Doublet Models, Zee-Babu Models as well as Triplet Higgs Models, etc. Analytic expressions for one-loop form factors are written in terms of the basic scalar one-loop two-, three- and four-point functions following the output format of both the packages~{\tt LoopTools} and {\tt Collier}. Physical results can be hence evaluated numerically by using one of the mentioned packages. Analytic results are tested by several checks such as the ultraviolet finiteness, infrared finiteness of the one-loop amplitudes. Furthermore, the amplitudes also obey the ward identity due to the on-shell initial photons. This identity is also verified numerically in this works. In the applications, we present the phenomenological results for Zee-Babu model as a typical example in this report. Production cross-section for the processes $\gamma \gamma\rightarrow hh$ are scanned over the parameter space of the Zee-Babu Models., Comment: 44 pages, 3 table and 11 Figures

This paper investigates the application of machine learning models, Long Short-Term Memory (LSTM), one-dimensional Convolutional Neural Networks (1D CNN), and Logistic Regression (LR), for predicting stock trends based on fundamental analysis. Unlike most existing studies that predominantly utilize technical or sentiment analysis, we emphasize the use of a company's financial statements and intrinsic value for trend forecasting. Using a dataset of 269 data points from publicly traded companies across various sectors from 2019 to 2023, we employ key financial ratios and the Discounted Cash Flow (DCF) model to formulate two prediction tasks: Annual Stock Price Difference (ASPD) and Difference between Current Stock Price and Intrinsic Value (DCSPIV). These tasks assess the likelihood of annual profit and current profitability, respectively. Our results demonstrate that LR models outperform CNN and LSTM models, achieving an average test accuracy of 74.66% for ASPD and 72.85% for DCSPIV. This study contributes to the limited literature on integrating fundamental analysis into machine learning for stock prediction, offering valuable insights for both academic research and practical investment strategies. By leveraging fundamental data, our approach highlights the potential for long-term stock trend prediction, supporting portfolio managers in their decision-making processes., Comment: 10 pages

We study differentiable strongly quasiconvex functions for providing new properties for algorithmic and monotonicity purposes. Furthemore, we provide insights into the decreasing behaviour of strongly quasiconvex functions, applying this for establishing exponential convergence for first- and second-order gradient systems without relying on the usual Lipschitz continuity assumption on the gradient of the function. The explicit discretization of the first-order dynamical system leads to the gradient descent method while discretization of the second-order dynamical system with viscous damping recovers the heavy ball method. We establish the linear convergence of both methods under suitable conditions on the parameters as well as comparisons with other classes of nonconvex functions used in the gradient descent literature.

Integral curves have been widely used to represent and analyze various vector fields. In this paper, we propose a Curve Segment Neighborhood Graph (CSNG) to capture the relationships between neighboring curve segments. This graph representation enables us to adapt the fast community detection algorithm, i.e., the Louvain algorithm, to identify individual graph communities from CSNG. Our results show that these communities often correspond to the features of the flow. To achieve a multi-level interactive exploration of the detected communities, we adapt a force-directed layout that allows users to refine and re-group communities based on their domain knowledge. We incorporate the proposed techniques into an interactive system to enable effective analysis and interpretation of complex patterns in large-scale integral curve datasets., Comment: This paper has been accepted by IEEE VIS 2024 Short Papers

Recent advancements in Code Large Language Models (CodeLLMs) have predominantly focused on open-ended code generation tasks, often neglecting the critical aspect of code understanding and comprehension. To bridge this gap, we present CodeMMLU, a comprehensive multiple-choice question-answer benchmark designed to evaluate the depth of software and code understanding in LLMs. CodeMMLU includes over 10,000 questions sourced from diverse domains, encompassing tasks such as code analysis, defect detection, and software engineering principles across multiple programming languages. Unlike traditional benchmarks, CodeMMLU assesses models's ability to reason about code rather than merely generate it, providing deeper insights into their grasp of complex software concepts and systems. Our extensive evaluation reveals that even state-of-the-art models face significant challenges with CodeMMLU, highlighting deficiencies in comprehension beyond code generation. By underscoring the crucial relationship between code understanding and effective generation, CodeMMLU serves as a vital resource for advancing AI-assisted software development, ultimately aiming to create more reliable and capable coding assistants.

The study of magnetoresistance (MR) phenomena has been pivotal in advancing magnetic sensors and spintronic devices. Helimagnets present an intriguing avenue for spintronics research. Theoretical predictions suggest that MR magnitude in the helimagnetic (HM) regime surpasses that in the ferromagnetic (FM) regime by over an order of magnitude. However, in metallic helimagnets like manganese phosphide, MR in the HM phase remains modest (10%), limiting its application in MR devices. Here, a groundbreaking approach is presented to achieve a giant low field MR effect in nanostructured manganese phosphide films by leveraging confinement and strain effects along with spin helicity. Unlike the modest MR observed in bulk manganese phosphide single crystals and large grain polycrystalline films, which exhibit a small negative MR in the FM region (2%) increasing to 8% in the HM region across 10-300 K, a grain size-dependent giant positive MR (90%) is discovered near FM to HM transition temperature (110 K), followed by a rapid decline to a negative MR below 55 K in manganese phosphide nanocrystalline films. These findings illuminate a novel strain-mediated spin helicity phenomenon in nanostructured helimagnets, presenting a promising pathway for the development of high-performance MR sensors and spintronic devices through the strategic utilization of confinement and strain effects.

Underwater vision is crucial for autonomous underwater vehicles (AUVs), and enhancing degraded underwater images in real-time on a resource-constrained AUV is a key challenge due to factors like light absorption and scattering, or the sufficient model computational complexity to resolve such factors. Traditional image enhancement techniques lack adaptability to varying underwater conditions, while learning-based methods, particularly those using convolutional neural networks (CNNs) and generative adversarial networks (GANs), offer more robust solutions but face limitations such as inadequate enhancement, unstable training, or mode collapse. Denoising diffusion probabilistic models (DDPMs) have emerged as a state-of-the-art approach in image-to-image tasks but require intensive computational complexity to achieve the desired underwater image enhancement (UIE) using the recent UW-DDPM solution. To address these challenges, this paper introduces UW-DiffPhys, a novel physical-based and diffusion-based UIE approach. UW-DiffPhys combines light-computation physical-based UIE network components with a denoising U-Net to replace the computationally intensive distribution transformation U-Net in the existing UW-DDPM framework, reducing complexity while maintaining performance. Additionally, the Denoising Diffusion Implicit Model (DDIM) is employed to accelerate the inference process through non-Markovian sampling. Experimental results demonstrate that UW-DiffPhys achieved a substantial reduction in computational complexity and inference time compared to UW-DDPM, with competitive performance in key metrics such as PSNR, SSIM, UCIQE, and an improvement in the overall underwater image quality UIQM metric. The implementation code can be found at the following repository: https://github.com/bachzz/UW-DiffPhys

We study the ground state energy of a gas of spin $1/2$ fermions with repulsive short-range interactions. We derive an upper bound that agrees, at low density $\rho$, with the Huang-Yang conjecture. The latter captures the first three terms in an asymptotic low-density expansion, and in particular the Huang-Yang correction term of order $\rho^{7/3}$. Our trial state is constructed using an adaptation of the bosonic Bogoliubov theory to the Fermi system, where the correlation structure of fermionic particles is incorporated by quasi-bosonic Bogoliubov transformations. In the latter, it is important to consider a modified zero-energy scattering equation that takes into account the presence of the Fermi sea, in the spirit of the Bethe-Goldstone equation., Comment: 48 pages

We discuss in this article the creation and organization of a national core facility for bioimage analysis, based on a distributed team. F-BIAS federates bioimage analysts across France and relies on them to deliver services to the researchers of this territory. The main challenge in implementing this structure is to provide significant scientific value to its members, thereby encouraging their active participation and persuading their respective host teams to support their involvement. F-BIAS accomplished this by creating a professional network that mitigates the negative effects of isolation experienced by its members, who are often the sole bioimage analyst within their local teams, and fosters the development of their technical skills. In a second phase we capitalized on F-BIAS to create a virtual, remotely-operating core facility for bioimage analysis, offering consultations and collaborative project services to the scientific community of France. The services are organized so that they also contribute to the technical proficiency of the analysts. To promote the creation of similar structures, we present and discuss here the organization of this nationally distributed bioimage analysis service core, highlighting successes and challenges.

The capability of generating high-quality source code using large language models (LLMs) reduces software development time and costs. However, they often introduce security vulnerabilities due to training on insecure open-source data. This highlights the need for ensuring secure and functional code generation. This paper introduces PromSec, an algorithm for prom optimization for secure and functioning code generation using LLMs. In PromSec, we combine 1) code vulnerability clearing using a generative adversarial graph neural network, dubbed as gGAN, to fix and reduce security vulnerabilities in generated codes and 2) code generation using an LLM into an interactive loop, such that the outcome of the gGAN drives the LLM with enhanced prompts to generate secure codes while preserving their functionality. Introducing a new contrastive learning approach in gGAN, we formulate code-clearing and generation as a dual-objective optimization problem, enabling PromSec to notably reduce the number of LLM inferences. PromSec offers a cost-effective and practical solution for generating secure, functional code. Extensive experiments conducted on Python and Java code datasets confirm that PromSec effectively enhances code security while upholding its intended functionality. Our experiments show that while a state-of-the-art approach fails to address all code vulnerabilities, PromSec effectively resolves them. Moreover, PromSec achieves more than an order-of-magnitude reduction in operation time, number of LLM queries, and security analysis costs. Furthermore, prompts optimized with PromSec for a certain LLM are transferable to other LLMs across programming languages and generalizable to unseen vulnerabilities in training. This study is a step in enhancing the trustworthiness of LLMs for secure and functional code generation, supporting their integration into real-world software development., Comment: 15 pages, 19 figures, CCS 2024

The differences in H2O2 production between conventional (CONV) and ultra-high dose rate (UHDR) irradiations in water radiolysis are still not fully understood. The lower levels of this radiolytic species, as a critical end product of water radiolysis, are particularly relevant for investigating the connection between the high-density energy deposition during short-duration physical events (ionizations or excitations) and biological responses of the FLASH effect. In this study, we developed a new Geant4-DNA chemistry model to simulate radiolysis considering the time structure of the irradiation pulse at different absorbed doses to liquid water of 0.01, 0.1, 1, and 2 Gy under 1 MeV electron irradiation. The model allows the description of the beam's temporal structure, including the pulse duration, the pulse repetition frequency, and the pulse amplitude for the different beam irradiation conditions through a wide dose rate range, from 0.01 Gy/s up to about 105 Gy/s, at various oxygen concentrations. The preliminary results indicate a correlation between the temporal structure of the pulses and a significant reduction in the production of reactive oxygen species (ROS) at different dose rates., Comment: 27 pages, 14 figures including 3 figures in appendix

We study the size and shape statistics of ground state fuzzy spheres when projected onto the transverse plane, utilizing the regularized SU(N=2) matrix model in D=(1+3)-dimensional spacetime. We show that they appear as ellipses, from matrix/membrane correspondence. With our numerical and analytical approximation for the ground state wavefunction, we provide estimations for their expected surface areas, perimeters, eccentricities, and shape-parameters. These geometric constants of quantum membranes deviate drastically from classical mechanics.

Anomalous metallic behavior is often observed near superconductor-insulator quantum critical points and, if persistent to zero temperature, poses a challenge to current theories of metals. One explanation for this behavior could be incomplete thermal equilibrium between the sample and the cryostat. However, despite decades of study, the temperature of an anomalous metal has not yet been measured. We introduce a new experimental probe by capturing microwave radiation emitted from the anomalous metal, using a two-dimensional array of superconductor-semiconductor hybrid Josephson junctions as a model system. The total emitted radiation exceeds the limits of thermodynamic equilibrium, and is well described by an elevated sample temperature. The extracted sample temperature matches the onset of anomalous metallic behavior. Additionally, we discover scaling behavior of radiative noise in the presence of an applied bias. Elements of our noise-scaling observations were predicted based on nonlinear critical field theories and gauge-gravity duality between current noise and Hawking radiation from a black hole. This work shows that, in a prominent model system, anomalous metallic behavior is a non-equilibrium effect, and opens a new frontier in the study of universal, non-equilibrium phenomena near quantum criticality., Comment: 7+8 pages, 4+11 figures

This paper studies a class of linear parabolic equations in non-divergence form in which the leading coefficients are measurable and they can be singular or degenerate as a weight belonging to the $A_{1+\frac{1}{n}}$ class of Muckenhoupt weights. Krylov-Safonov Harnack inequality for solutions is proved under some smallness assumption on a weighted mean oscillation of the weight. To prove the result, we introduce a class of generic weighted parabolic cylinders and the smallness condition on the weighted mean oscillation of the weight through which several growth lemmas are established. Additionally, a perturbation method is used and the parabolic Aleksandrov-Bakelman-Pucci type maximum principle is crucially applied to suitable barrier functions to control the solutions. As corollaries, H\"{o}lder regularity estimates of solutions with respect to a quasi-distance, and a Liouville type theorem are obtained in the paper., Comment: 46 pages; edited here and there; version submitted to a journal for publication

This paper introduces SGCode, a flexible prompt-optimizing system to generate secure code with large language models (LLMs). SGCode integrates recent prompt-optimization approaches with LLMs in a unified system accessible through front-end and back-end APIs, enabling users to 1) generate secure code, which is free of vulnerabilities, 2) review and share security analysis, and 3) easily switch from one prompt optimization approach to another, while providing insights on model and system performance. We populated SGCode on an AWS server with PromSec, an approach that optimizes prompts by combining an LLM and security tools with a lightweight generative adversarial graph neural network to detect and fix security vulnerabilities in the generated code. Extensive experiments show that SGCode is practical as a public tool to gain insights into the trade-offs between model utility, secure code generation, and system cost. SGCode has only a marginal cost compared with prompting LLMs. SGCode is available at: https://sgcode.codes/.

We do experiments on physical agents with dynamic binary ideologies, deep memories of previous probes of neigboring agents, but fixed personalities that interpret the memory content to make ideological decisions. We find experimentally a critical memory depth below which complete ideological polarization of the collective cannot occur, and above which it is inevitable, an emergent symmetry breaking that is memory depth dependent. Depending on the details of the personalities, the polarization can be static or dynamic in time, even in certain cases chaotic due to nonreciprocity in how the agents respond to other agents. Thus, agents with different personalities and depths of memory serve as a physics analog of the ideology dynamics among biased individuals, illuminating how decisions influenced by individual memories of past interactions can shape and influence subsequent polarization. Perhaps such applications of physics-based systems to political systems will help us to understand the ideological instabilities observed today.

Large Language Models (LLMs) have revolutionized software engineering (SE), demonstrating remarkable capabilities in various coding tasks. While recent efforts have produced autonomous software agents based on LLMs for end-to-end development tasks, these systems are typically designed for specific SE tasks. We introduce HyperAgent, a novel generalist multi-agent system designed to address a wide spectrum of SE tasks across different programming languages by mimicking human developers' workflows. Comprising four specialized agents - Planner, Navigator, Code Editor, and Executor. HyperAgent manages the full lifecycle of SE tasks, from initial conception to final verification. Through extensive evaluations, HyperAgent achieves state-of-the-art performance across diverse SE tasks: it attains a 25.01% success rate on SWE-Bench-Lite and 31.40% on SWE-Bench-Verified for GitHub issue resolution, surpassing existing methods. Furthermore, HyperAgent demonstrates SOTA performance in repository-level code generation (RepoExec), and in fault localization and program repair (Defects4J), often outperforming specialized systems. This work represents a significant advancement towards versatile, autonomous agents capable of handling complex, multi-step SE tasks across various domains and languages, potentially transforming AI-assisted software development practices.

Recent advances in deep learning (DL) have significantly impacted motor imagery (MI)-based brain-computer interface (BCI) systems, enhancing the decoding of electroencephalography (EEG) signals. However, most studies struggle to identify discriminative patterns across subjects during MI tasks, limiting MI classification performance. In this article, we propose MixNet, a novel classification framework designed to overcome this limitation by utilizing spectral-spatial signals from MI data, along with a multitask learning architecture named MIN2Net, for classification. Here, the spectral-spatial signals are generated using the filter-bank common spatial patterns (FBCSPs) method on MI data. Since the multitask learning architecture is used for the classification task, the learning in each task may exhibit different generalization rates and potential overfitting across tasks. To address this issue, we implement adaptive gradient blending, simultaneously regulating multiple loss weights and adjusting the learning pace for each task based on its generalization/overfitting tendencies. Experimental results on six benchmark data sets of different data sizes demonstrate that MixNet consistently outperforms all state-of-the-art algorithms in subject-dependent and -independent settings. Finally, the low-density EEG MI classification results show that MixNet outperforms all state-of-the-art algorithms, offering promising implications for Internet of Thing (IoT) applications, such as lightweight and portable EEG wearable devices based on low-density montages., Comment: Supplementary materials and source codes are available on-line at https://github.com/Max-Phairot-A/MixNet

Sphaleron electroweak phase transition (EWPT) is calculated in two phase transition stages, thereby showing that the twin (or double) bubble nucleation structure of the phase transition and gravitational wave is in the investigation area of future detectors. With $v^2=v^2_1+v^2_2$ ($v_1$ and $v_2$ are two vacuum average values (VEV)), the parameter $\tan\beta=v_2/v_1$, is the ratio between two VEVs although it does not affect the strength of EWPT but affect the sphaleron energy. However, it only causes this energy to increase slightly. As $a=v^2/v_2^2$ increases, the maximum difference of sphaleron energy in one stage is about $6.15$ TeV. $a$ affects the expansion of bubbles during two phase transitions. The more $a$ increases, the more the expansion of two bubbles is at the same time. This ratio does not greatly affect the sphaleron energy but has an impact on gravitational waves. The larger the masses of the charged Higgs particles are, the greater the gravitational wave energy density ($\Omega h^2$) is. When the frequency is in the range $0-1.2$ mHz, $\Omega h^2$ will has a maximum value in the range $10^{-12}-10^{-11}$ for all values of $a$ so this can be detected in the future., Comment: 26 pages, 13 sub-figures

General expressions for one-loop contributions associated with the lepton-flavor violating decays of the standard model-like Higgs boson $h\to e_b^\pm e_a^\mp$ and gauge boson $Z\to e^\pm_b e_a^\mp$ are introduced in the unitary gauge. The results are used to discuss these decays as new physics signals in a minimal left-right symmetric model containing only one bidoublet Higgs and a $SU(2)_R$ Higgs doublet accommodating data of neutrino oscillations and $(g-2)_{\mu}$. The numerical investigation indicates that some of these decay rates can reach the near future experimental sensitivities., Comment: 39 pages, 4 figures

In this paper, we present the results for one-loop induced processes $\gamma \gamma \rightarrow \phi_i\phi_j$ with CP-even Higgses $\phi_{i,j} \equiv h,~H$ at high energy photon-photon collision, within the frameworks of Inert Higgs Doublet Models and Two Higgs Doublet Models. Total cross-sections are shown as functions of center-of-mass energies. We find that the cross-sections for the computed processes in all the models under investigations are enhanced at around the threshold of singly charged Higgs pair ($\sim 2 M_{H^\pm}$). Furthermore, the enhancement factors defined as the ratio of cross-sections of $\gamma \gamma \rightarrow \phi_i\phi_j$ in the investigated models over the corresponding ones for $\gamma \gamma \rightarrow hh$ in the Standard Model, are examined in the model's parameter space. In the Inert Higgs Doublet Models, the factors are studied in the parameter space of $(M_{H^\pm},~\mu^2_2)$ and $(M_{H^\pm},~\lambda_2)$. In the Two Higgs Doublet Models, the factors are examined in the planes of $(M_{H^\pm},~t_{\beta})$ as well as in the space of charged Higgs mass $M_{H^\pm}$ and the soft-breaking $Z_2$ parameter $m_{12}^2$. Two scenarios of $c_{\beta-\alpha} > 0$ and $c_{\beta-\alpha} < 0$ are studied in further detail. The factors give a different behavior from considering these scenarios. As a result, discriminations for the above-mentioned scenarios can be performed at future colliders., Comment: 46 pages, 21 figures of data, 8 Tables

Predicting program behavior without execution is a crucial and challenging task in software engineering. Traditional models often struggle to capture the dynamic dependencies and interactions within code. This paper introduces a novel machine learning-based framework called CodeFlow, designed to predict code coverage and detect runtime errors through Dynamic Dependencies Learning. By utilizing control flow graphs (CFGs), CodeFlow represents all possible execution paths and the relationships between different statements, providing a comprehensive understanding of program behavior. CodeFlow constructs CFGs to depict execution paths and learns vector representations for CFG nodes, capturing static control-flow dependencies. Additionally, it learns dynamic dependencies through execution traces, which reflect the impacts among statements during execution. This approach enables accurate prediction of code coverage and effective identification of runtime errors. Empirical evaluations demonstrate significant improvements in code coverage prediction accuracy and effective localization of runtime errors, outperforming existing models.

In recent years, the continuous wavelet transform (CWT) has been employed as a spectral feature extractor for acoustic recognition tasks in conjunction with machine learning and deep learning models. However, applying the CWT to each individual audio sample is computationally intensive. This paper proposes an approach that applies the CWT to a subset of samples, spaced according to a specified hop size. Experimental results demonstrate that this method significantly reduces computational costs while maintaining the robust performance of the trained models.