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Food insecurity is widespread among college students in the United States. Food benefits delivered through the CalFresh program, California's version of the federal Supplemental Nutrition Assistance Program (SNAP), can reduce hunger by helping students pay for groceries, but may not reach all eligible students. To date, higher education systems have lacked good estimates of the share of their students who are eligible for CalFresh and the share who actually receive benefits. To address this information gap, the California Policy Lab (CPL) partnered with the California Community College (CCC) Chancellor's Office, the University of California Office of the President (UCOP), the California Department of Social Services (CDSS), and the California Student Aid Commission (CSAC) to build a linked database of student-level administrative data on college enrollment, financial aid, and CalFresh participation. This database covers all students enrolled at CCC or UC campuses from academic years 2010-11 through 2021-22, along with corresponding FAFSA submissions and CalFresh participation. Using these data, researchers are able to measure how many college students are likely eligible for CalFresh, and of those how many participate. This report was updated in August 2024 with updated eligibility statistics that reflect results from a refined simulation of student eligibility for CalFresh. Using the improved simulation, the researchers found that among California Community College students, 20% of students were eligible for CalFresh (an increase from the 16% estimate in the June version of this report), among University of California (UC) undergraduates, 33% were eligible (an increase from 31% in the June report), and among UC graduate students, 7% were eligible (an increase from 6%).

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

The existing single-pulse search algorithms for fast radio bursts (FRBs) do not adequately consider the frequency bandpass pattern of the pulse, rendering them incomplete for the relatively narrow-spectrum detection of pulses. We present a new search algorithm for narrow-band pulses to update the existing standard pipeline, Bandpass-Adaptive Single-pulse SEarch Toolkit (BASSET). The BASSET employs a time-frequency correlation analysis to identify and remove the noise involved by the zero-detection frequency band, thereby enhancing the signal-to-noise ratio (SNR) of the pulses. The BASSET algorithm was implemented on the FAST real dataset of FRB 20190520B, resulting in the discovery of additional 79 pulses through reprocessing. The new detection doubles the number of pulses compared to the previously known 75 pulses, bringing the total number of pulses to 154. In conjunction with the pulse calibration and the Markov Chain Monte Carlo (MCMC) simulated injection experiments, this work updates the quantified parameter space of the detection rate. Moreover, a parallel-accelerated version of the BASSET code was provided and evaluated through simulation. BASSET has the capacity of enhancing the detection sensitivity and the SNR of the narrow-band pulses from the existing pipeline, offering high performance and flexible applicability. BASSET not only enhances the completeness of the low-energy narrow-band pulse detection in a more robust mode, but also has the potential to further elucidate the FRB luminosity function at a wider energy scale., Comment: 22 pages, 11 figures, submitted to ApJS

Semantic communications have been explored to perform downstream intelligent tasks by extracting and transmitting essential information. In this paper, we introduce a large model-empowered streaming semantic communication system for speech translation across various languages, named LaSC-ST. Specifically, we devise an edge-device collaborative semantic communication architecture by offloading the intricate semantic extraction module to edge servers, thereby reducing the computational burden on local devices. To support multilingual speech translation, pre-trained large speech models are utilized to learn unified semantic features from speech in different languages, breaking the constraint of a single input language and enhancing the practicality of the LaSC-ST. Moreover, the input speech is sequentially streamed into the developed system as short speech segments, which enables low transmission latency without the degradation in speech translation quality. A novel dynamic speech segmentation algorithm is proposed to further minimize the transmission latency by adaptively adjusting the duration of speech segments. According to simulation results, the LaSC-ST provides more accurate speech translation and achieves streaming transmission with lower latency compared to existing non-streaming semantic communication systems.

The InSb/CdTe heterojunction structure, characterized by low effective mass and high electron mobility, exhibits interfacial energy band bending, leading to the Rashba spin-orbit coupling effect and nonreciprocal transport, which makes its suitable for spintronic devices with broad applications in logic and storage fields. However, the complex heterojunction interfaces of InSb/CdTe, composed of group III-V and group II-VI semiconductors, are prone to interdiffusion. Therefore, characterization and study of the interfacial properties of InSb/CdTe heterojunctions are crucial for the growth improvement of the InSb/CdTe material system as well as its application in the field of spintronics. In this study, a novel scanning probe microscope, called a scanning noise microscope, was applied to visualize hot electron scattering in InSb/CdTe nano-devices. The results demonstrated that the near-field signal originates from the Coulomb scattering of charged ions on electrons at the interface of the embedded layer heterojunction. This real-space, nondestructive characterization of the heterojunction interface properties offers a new tool for enhancing the performance of heterojunctions.

In this paper, we propose a high-fidelity scheme for generating entangled states in a system of two and three giant atoms coupled to the coupled resonator waveguide. Our approach leverages the bound state in the continuum, which is robust against waveguide disorder. Specifically, we achieve a fidelity exceeding $98\%$ for Bell state generation, overcoming the limitations of conventional decoherence-free interaction mechanisms. This scheme can be readily extended to a three-giant-atom system for generating W states. In both the two- and three-atom setups, the maximally entangled states are generated in a short time and remain stable even as time approaches infinity. Our proposal is feasible for implementation on state-of-the-art solid-state quantum platforms and significantly broadens the applications of giant atoms and waveguide QED system in quantum information processing., Comment: 7 Pages, 5 Figures

The correlation of topology and disorder has attracted great intention due to appropriate disorder could induce the phase transition between trivial and nontrivial topological states. While it is widely recognized that strong disorder can produce rich phase diagrams in topological nontrivial states, moderate disorder has been proposed to induce transitions into topologically nontrivial phases counter-intuitively, leading to the concept of topological Anderson insulators. This phenomenon has been theoretically explored and simulated in various systems, yet experimental realization in solid state systems has remained elusive due to challenges in controlling disorder. Here, we report the experimental observation of Chern insulator state signed by the coexistence of quantized Hall plateau and zero longitudinal resistance in monolayer MnBi$_4$Te$_7$ Hall bar device, which originally hosts a trivial insulating state with Chern number $C$ = 0 in clean limit. We demonstrate that the observed trivial to nontrivial transition in this monolayer device can be attributed to disorder, evidenced by universal conductance fluctuations. Our findings substantiate the existence of a long-sought topological Anderson Chern insulator in real materials, a unique variant of the topological Anderson insulator characterized by broken time-reversal-symmetry., Comment: 45 pages, 4 main figures, 5 extended data figures, 5 supplementary figures

In recent years, traffic flow prediction has played a crucial role in the management of intelligent transportation systems. However, traditional prediction methods are often limited by static spatial modeling, making it difficult to accurately capture the dynamic and complex relationships between time and space, thereby affecting prediction accuracy. This paper proposes an innovative traffic flow prediction network, SFADNet, which categorizes traffic flow into multiple traffic patterns based on temporal and spatial feature matrices. For each pattern, we construct an independent adaptive spatio-temporal fusion graph based on a cross-attention mechanism, employing residual graph convolution modules and time series modules to better capture dynamic spatio-temporal relationships under different fine-grained traffic patterns. Extensive experimental results demonstrate that SFADNet outperforms current state-of-the-art baselines across four large-scale datasets., Comment: Accepted by 2025 lEEE International Conference on Acoustics, speech, and signal Processing (lCASSP2025)

In recent years, traffic flow prediction has played a crucial role in the management of intelligent transportation systems. However, traditional forecasting methods often model non-Euclidean low-dimensional traffic data as a simple graph with single-type nodes and edges, failing to capture similar trends among nodes of the same type. To address this limitation, this paper proposes MHGNet, a novel framework for modeling spatiotemporal multi-heterogeneous graphs. Within this framework, the STD Module decouples single-pattern traffic data into multi-pattern traffic data through feature mappings of timestamp embedding matrices and node embedding matrices. Subsequently, the Node Clusterer leverages the Euclidean distance between nodes and different types of limit points to perform clustering with O(N) time complexity. The nodes within each cluster undergo residual subgraph convolution within the spatiotemporal fusion subgraphs generated by the DSTGG Module, followed by processing in the SIE Module for node repositioning and redistribution of weights. To validate the effectiveness of MHGNet, this paper conducts extensive ablation studies and quantitative evaluations on four widely used benchmarks, demonstrating its superior performance., Comment: Accepted by 2025 lEEE International Conference on Acoustics, speech, and signal Processing (lCASSP2025)

Information-theoretic metrics, such as mutual information, have been widely used to evaluate privacy leakage in dynamic systems. However, these approaches are typically limited to stochastic systems and face computational challenges. In this paper, we introduce a novel volumetric framework for analyzing privacy in systems affected by unknown but bounded noise. Our model considers a dynamic system comprising public and private states, where an observation set of the public state is released. An adversary utilizes the observed public state to infer an uncertainty set of the private state, referred to as the inference attack. We define the evolution dynamics of these inference attacks and quantify the privacy level of the private state using the volume of its uncertainty sets. For linear scalar systems, we derive an explicit formulation of the uncertainty set. For multi-dimensional linear systems, we develop an approximate computation method leveraging interval analysis. We investigate the properties of the proposed volumetric privacy measure and demonstrate that it is bounded by the information gain derived from the observation set. Furthermore, we propose an optimization approach to designing privacy filter using randomization and linear programming based on the proposed privacy measure. The effectiveness of the optimal privacy filter design is evaluated through a production-inventory case study, illustrating its robustness against the inference attack.

In this work, we predict the emergence of a quantum spin Hall insulator (QSHI) in conventional semiconductors, specifically InAs quantum wells, driven by a built-in polarization field. We propose QSHI InAs quantum wells as a platform to engineer topological field effect devices. More precisely, we first present a novel topological logic device that operates without a topological phase transition. Subsequently, we design a high-performance topological transistor due to the presence of edge states. Our approach provides a potential framework for harnessing the unique features of QSHI in device design, paving the way for future topological devices.

We conduct a comprehensive analysis of the large-space and long-time asymptotics of kink-soliton gases in the sine-Gordon (sG) equation, addressing an important open problem highlighted in the recent work [Phys. Rev. E 109 (2024) 061001]. We focus on kink-soliton gases modeled within a Riemann-Hilbert framework and characterized by two types of generalized reflection coefficients, each defined on the interval $[\eta_1, \eta_2]$: $r_0(\lambda) = (\lambda - \eta_1)^{\beta_1} (\eta_2 - \lambda)^{\beta_2} |\lambda - \eta_0|^{\beta_0} \gamma(\lambda)$ and $r_c(\lambda) = (\lambda - \eta_1)^{\beta_1} (\eta_2 - \lambda)^{\beta_2} \chi_c(\lambda) \gamma(\lambda)$, where $0 < \eta_1 < \eta_0 < \eta_2$ and $\beta_j > -1$, \(\gamma(\lambda)\) is a continuous, strictly positive function defined on $[\eta_1, \eta_2]$. The function \(\chi_c(\lambda)\) demonstrates a step-like behavior: it is given by \(\chi_c(\lambda) = 1\) for \(\lambda \in [\eta_1, \eta_0)\) and \(\chi_c(\lambda) = c^2\) for \(\lambda \in (\eta_0, \eta_2]\), with \(c\) as a positive constant distinct from one. To rigorously derive the asymptotic results, we leverage the Deift-Zhou steepest descent method. A central component of this approach is constructing an appropriate \(g\)-function for the conjugation process. Unlike in the KdV equation, the sG presents unique challenges for \(g\)-function formulation, particularly concerning the singularity at the origin. The Riemann-Hilbert problem also requires carefully constructed local parametrices near endpoints \(\eta_j\) and the singularity \(\eta_0\). At the endpoints \(\eta_j\), we employ a modified Bessel parametrix of the first kind. For the singularity \(\eta_0\), the parametrix selection depends on the reflection coefficient: the second kind of modified Bessel parametrix is used for \(r_0(\lambda)\), while a confluent hypergeometric parametrix is applied for \(r_c(\lambda)\)., Comment: 54 pages, 11 figures

We explore the $N_{\infty}$-soliton asymptotics for the modified Camassa-Holm (mCH) equation with linear dispersion and boundaries vanishing at infinity: $m_t+(m(u^2-u_x^2)^2)_x+\kappa u_x=0,\quad m=u-u_{xx}$ with $\lim_{x\rightarrow \pm \infty }u(x,t)=0$. We mainly analyze the aggregation state of $N$-soliton solutions of the mCH equation expressed by the solution of the modified Riemann-Hilbert problem in the new $(y,t)$-space when the discrete spectra are located in different regions. Starting from the modified RH problem, we find that i) when the region is a quadrature domain with $\ell=n=1$, the corresponding $N_{\infty}$-soliton is the one-soliton solution which the discrete spectral point is the center of the region; ii) when the region is a quadrature domain with $\ell=n$, the corresponding $N_{\infty}$-soliton is an $n$-soliton solution; iii) when the discrete spectra lie in the line region, we provide its corresponding Riemann-Hilbert problem,; and iv) when the discrete spectra lie in an elliptic region, it is equivalent to the case of the line region., Comment: 43 pages, 4 figures

Breathers have been experimentally and theoretically found in many physical systems -- in particular, in integrable nonlinear-wave models. A relevant problem is to study the \textit{breather gas}, which is the limit, for $N\rightarrow \infty $, of $N$-breather solutions. In this paper, we investigate the breather gas in the framework of the focusing nonlinear Schr\"{o}dinger (NLS) equation with nonzero boundary conditions, using the inverse scattering transform and Riemann-Hilbert problem. We address aggregate states in the form of $N$-breather solutions, when the respective discrete spectra are concentrated in specific domains. We show that the breather gas coagulates into a single-breather solution whose spectral eigenvalue is located at the center of the circle domain, and a multi-breather solution for the higher-degree quadrature concentration domain. These coagulation phenomena in the breather gas are called \textit{breather shielding}. In particular, when the nonzero boundary conditions vanish, the breather gas reduces to an $n$-soliton solution. When the discrete eigenvalues are concentrated on a line, we derive the corresponding Riemann-Hilbert problem. When the discrete spectrum is uniformly distributed within an ellipse, it is equivalent to the case of the line domain. These results may be useful to design experiments with breathers in physical settings., Comment: 23 pages, 1 figure

Combining first-principles calculations and tight-binding Hamiltonians, we study the stack-dependent behaviour of electronic and topological properties of layered antiferromagnet MnBi$_2$Te$_4$. Lateral shift of top septuple-layer greatly modify electronic properties, and even induce topological phase transition between quantum anomalous Hall (QAH) insulators with $C=1$ and trivial magnetic insulators with $C=0$. The local energy minimum of ``incorrect" stacking order exhibits thickness-dependent topology opposite to the usual stacking order, which is attribute to relatively weakened interlayer Te-Te interaction in ``incorrect" stacking configuration. Our effective model analysis provides a comprehensive understanding of the underlying mechanisms involved, and we also propose two optical setups that can effectively differentiate between different stacking configurations. Our findings underscores the nuanced and profound influence that interlayer sliding in magnetic topological materials can have on the macroscopic quantum states, opening new avenues for the design and engineering of topological quantum materials., Comment: 7 pages, 4 figures

Membership inference attacks have emerged as a significant privacy concern in the training of deep learning models, where attackers can infer whether a data point was part of the training set based on the model's outputs. To address this challenge, we propose a novel defense mechanism, AdaMixup. AdaMixup employs adaptive mixup techniques to enhance the model's robustness against membership inference attacks by dynamically adjusting the mixup strategy during training. This method not only improves the model's privacy protection but also maintains high performance. Experimental results across multiple datasets demonstrate that AdaMixup significantly reduces the risk of membership inference attacks while achieving a favorable trade-off between defensive efficiency and model accuracy. This research provides an effective solution for data privacy protection and lays the groundwork for future advancements in mixup training methods., Comment: 6 pages, 2 figures

We present the HI surface density ($\Sigma_{\rm HI}$) radial distributions based on total-power HI images obtained by FAST in the FEASTS program, for 35 galaxies with inclinations lower than 72 degree. We derive the HI radius $R_{001}$, which is the radius for the 0.01 $\,M_{\odot}\,{\rm pc}^{-2}$ ($\sim10^{18.1}\,{\rm cm}^{-2}$) iso-density level, 100 times deeper than the 1 $\,M_{\odot}\,{\rm pc}^{-2}$ level previously commonly used to measure $R_1$. The profile shapes show a large diversity at a given radius in units of kpc, group virial radius, and $R_1$, but align more tightly with radius normalized by $R_{001}$. The universal HI profile has a scatter of $\sim0.2$ dex, and a scale-length of $\sim0.11R_{001}$ in the outer region. We derive a new $R_{001}$-$M_{\rm HI}$ relation, which has a scatter of 0.02 dex, and similar slope of $\sim$0.5 as the previously known $R_1$-$M_{\rm HI}$ relation. Excluding strongly tidal-interacting galaxies, the ratio $R_{001}/R_1$ (anti-)correlate strongly and significantly with the HI-to-stellar mass ratio and sSFR, but not with the stellar mass, $M_{\rm HI}$, dark matter mass, or SFR. The strongly tidal-interacting galaxies tend to show deviations from these trends, and have the most flattened profiles. These results imply that in absence of major tidal interactions, physical processes must cooperate so that $\Sigma_{\rm HI}$ distributes in a self-similar way in the outer region down to the 0.01$\,M_{\odot}\,{\rm pc}^{-2}$ level. Moreover, they may drive gas flows in such a way, that HI-richer galaxies have HI disks not only extend further, but also transport HI inward more efficiently from $R_{001}$ to $R_1$., Comment: 15 pages, 8 figures. ApJ in press

Employing LLMs for visual generation has recently become a research focus. However, the existing methods primarily transfer the LLM architecture to visual generation but rarely investigate the fundamental differences between language and vision. This oversight may lead to suboptimal utilization of visual generation capabilities within the LLM framework. In this paper, we explore the characteristics of visual embedding space under the LLM framework and discover that the correlation between visual embeddings can help achieve more stable and robust generation results. We present IAR, an Improved AutoRegressive Visual Generation Method that enhances the training efficiency and generation quality of LLM-based visual generation models. Firstly, we propose a Codebook Rearrangement strategy that uses balanced k-means clustering algorithm to rearrange the visual codebook into clusters, ensuring high similarity among visual features within each cluster. Leveraging the rearranged codebook, we propose a Cluster-oriented Cross-entropy Loss that guides the model to correctly predict the cluster where the token is located. This approach ensures that even if the model predicts the wrong token index, there is a high probability the predicted token is located in the correct cluster, which significantly enhances the generation quality and robustness. Extensive experiments demonstrate that our method consistently enhances the model training efficiency and performance from 100M to 1.4B, reducing the training time by half while achieving the same FID. Additionally, our approach can be applied to various LLM-based visual generation models and adheres to the scaling law, providing a promising direction for future research in LLM-based visual generation.

A major bottleneck in imitation learning is the requirement of a large number of expert demonstrations, which can be expensive or inaccessible. Learning from supplementary demonstrations without strict quality requirements has emerged as a powerful paradigm to address this challenge. However, previous methods often fail to fully utilize their potential by discarding non-expert data. Our key insight is that even demonstrations that fall outside the expert distribution but outperform the learned policy can enhance policy performance. To utilize this potential, we propose a novel approach named imitation learning via meta-learning an action ranker (ILMAR). ILMAR implements weighted behavior cloning (weighted BC) on a limited set of expert demonstrations along with supplementary demonstrations. It utilizes the functional of the advantage function to selectively integrate knowledge from the supplementary demonstrations. To make more effective use of supplementary demonstrations, we introduce meta-goal in ILMAR to optimize the functional of the advantage function by explicitly minimizing the distance between the current policy and the expert policy. Comprehensive experiments using extensive tasks demonstrate that ILMAR significantly outperforms previous methods in handling suboptimal demonstrations. Code is available at https://github.com/F-GOD6/ILMAR.

Data centers have become one of the major energy consumers, making their low-carbon operations critical to achieving global carbon neutrality. Although distributed data centers have the potential to reduce costs and emissions through cooperation, they are facing challenges due to uncertainties. This paper proposes an online approach to co-optimize the workload, energy, and temperature strategies across distributed data centers, targeting minimal total cost, controlled carbon emissions, and adherence to operational constraints. Lyapunov optimization technique is adopted to derive a parametric real-time strategy that accommodates uncertainties in workload demands, ambient temperature, electricity prices, and carbon intensities, without requiring prior knowledge of their distributions. A theoretical upper bound for the optimality gap is derived, based on which a linear programming problem is proposed to optimize the strategy parameters, enhancing performance while ensuring operational constraints. Case studies and method comparison validate the proposed method's effectiveness in reducing costs and carbon emissions., Comment: accepted by The 4th Energy Conversion and Economics Annual Forum

We investigate the properties of nucleons at finite temperature and density using a two-flavor quark meson model with Gaussian fluctuations that extend beyond the mean-field approximation. Our findings suggest that Gaussian fluctuations lead to a non-monotonic behavior of the nucleon mass as a function of temperature and density, which may play an important role in the study of the hadronization process of relativistic heavy-ion collisions. Moreover, we observe an increase in the nucleon radius due to Gaussian fluctuations, suggesting an effective repulsive force akin to the Casimir effect, as observed in the gold-bromobenzene-silica system. This study offers new insights into how temperature, density, and quantum fluctuations affect the structure and properties of nucleons under extreme conditions., Comment: 12 pages, 6 figures

We introduce the vector meson $\omega$ into the Quark Meson model, and study the impact of vector interactions on the properties of static hadrons using the mean-field approximation. The short-range repulsive force associated with vector interactions leads to an expansion of the root mean square radius of nucleons. While the mass of hadrons increases, the gap between this mass and the energy of the three free constituent quarks decreases, resulting in the instability of hadrons. Our study of nucleon mass and radius at finite temperature and density has potential applications for particle yield in heavy ion collisions and the mass-radius relationship in compact stars., Comment: 8 pages, 6 figures

Objective: Extracting PICO elements -- Participants, Intervention, Comparison, and Outcomes -- from clinical trial literature is essential for clinical evidence retrieval, appraisal, and synthesis. Existing approaches do not distinguish the attributes of PICO entities. This study aims to develop a named entity recognition (NER) model to extract PICO entities with fine granularities. Materials and Methods: Using a corpus of 2,511 abstracts with PICO mentions from 4 public datasets, we developed a semi-supervised method to facilitate the training of a NER model, FinePICO, by combining limited annotated data of PICO entities and abundant unlabeled data. For evaluation, we divided the entire dataset into two subsets: a smaller group with annotations and a larger group without annotations. We then established the theoretical lower and upper performance bounds based on the performance of supervised learning models trained solely on the small, annotated subset and on the entire set with complete annotations, respectively. Finally, we evaluated FinePICO on both the smaller annotated subset and the larger, initially unannotated subset. We measured the performance of FinePICO using precision, recall, and F1. Results: Our method achieved precision/recall/F1 of 0.567/0.636/0.60, respectively, using a small set of annotated samples, outperforming the baseline model (F1: 0.437) by more than 16\%. The model demonstrates generalizability to a different PICO framework and to another corpus, which consistently outperforms the benchmark in diverse experimental settings (p-value \textless0.001). Conclusion: This study contributes a generalizable and effective semi-supervised approach to named entity recognition leveraging large unlabeled data together with small, annotated data. It also initially supports fine-grained PICO extraction.

In moir\'e systems, the impact of lattice relaxation on electronic band structures is significant, yet the computational demands of first-principles relaxation are prohibitively high due to the large number of atoms involved. To address this challenge, We introduce a robust methodology for the construction of machine learning potentials specifically tailored for moir\'e structures and present an open-source software package DPmoire designed to facilitate this process. Utilizing this package, we have developed machine learning force fields (MLFFs) for MX$_2$ (M = Mo, W; X = S, Se, Te) materials. Our approach not only streamlines the computational process but also ensures accurate replication of the detailed electronic and structural properties typically observed in density functional theory (DFT) relaxations. The MLFFs were rigorously validated against standard DFT results, confirming their efficacy in capturing the complex interplay of atomic interactions within these layered materials. This development not only enhances our ability to explore the physical properties of moir\'e systems with reduced computational overhead but also opens new avenues for the study of relaxation effects and their impact on material properties in two-dimensional layered structures., Comment: 16 pages, 19 figures

Group Re-identification (G-ReID) faces greater complexity than individual Re-identification (ReID) due to challenges like mutual occlusion, dynamic member interactions, and evolving group structures. Prior graph-based approaches have aimed to capture these dynamics by modeling the group as a single topological structure. However, these methods struggle to generalize across diverse group compositions, as they fail to fully represent the multifaceted relationships within the group. In this study, we introduce a Hierarchical Multi-Graphs Learning (HMGL) framework to address these challenges. Our approach models the group as a collection of multi-relational graphs, leveraging both explicit features (such as occlusion, appearance, and foreground information) and implicit dependencies between members. This hierarchical representation, encoded via a Multi-Graphs Neural Network (MGNN), allows us to resolve ambiguities in member relationships, particularly in complex, densely populated scenes. To further enhance matching accuracy, we propose a Multi-Scale Matching (MSM) algorithm, which mitigates issues of member information ambiguity and sensitivity to hard samples, improving robustness in challenging scenarios. Our method achieves state-of-the-art performance on two standard benchmarks, CSG and RoadGroup, with Rank-1/mAP scores of 95.3%/94.4% and 93.9%/95.4%, respectively. These results mark notable improvements of 1.7% and 2.5% in Rank-1 accuracy over existing approaches.

Automated red teaming can discover rare model failures and generate challenging examples that can be used for training or evaluation. However, a core challenge in automated red teaming is ensuring that the attacks are both diverse and effective. Prior methods typically succeed in optimizing either for diversity or for effectiveness, but rarely both. In this paper, we provide methods that enable automated red teaming to generate a large number of diverse and successful attacks. Our approach decomposes the task into two steps: (1) automated methods for generating diverse attack goals and (2) generating effective attacks for those goals. While we provide multiple straightforward methods for generating diverse goals, our key contributions are to train an RL attacker that both follows those goals and generates diverse attacks for those goals. First, we demonstrate that it is easy to use a large language model (LLM) to generate diverse attacker goals with per-goal prompts and rewards, including rule-based rewards (RBRs) to grade whether the attacks are successful for the particular goal. Second, we demonstrate how training the attacker model with multi-step RL, where the model is rewarded for generating attacks that are different from past attempts further increases diversity while remaining effective. We use our approach to generate both prompt injection attacks and prompts that elicit unsafe responses. In both cases, we find that our approach is able to generate highly-effective and considerably more diverse attacks than past general red-teaming approaches.

Image dehazing, particularly with learning-based methods, has gained significant attention due to its importance in real-world applications. However, relying solely on the RGB color space often fall short, frequently leaving residual haze. This arises from two main issues: the difficulty in obtaining clear textural features from hazy RGB images and the complexity of acquiring real haze/clean image pairs outside controlled environments like smoke-filled scenes. To address these issues, we first propose a novel Structure Guided Dehazing Network (SGDN) that leverages the superior structural properties of YCbCr features over RGB. It comprises two key modules: Bi-Color Guidance Bridge (BGB) and Color Enhancement Module (CEM). BGB integrates a phase integration module and an interactive attention module, utilizing the rich texture features of the YCbCr space to guide the RGB space, thereby recovering clearer features in both frequency and spatial domains. To maintain tonal consistency, CEM further enhances the color perception of RGB features by aggregating YCbCr channel information. Furthermore, for effective supervised learning, we introduce a Real-World Well-Aligned Haze (RW$^2$AH) dataset, which includes a diverse range of scenes from various geographical regions and climate conditions. Experimental results demonstrate that our method surpasses existing state-of-the-art methods across multiple real-world smoke/haze datasets. Code and Dataset: \textcolor{blue}{\url{https://github.com/fiwy0527/AAAI25_SGDN.}}

Accreting X-ray pulsars, located in X-ray binaries, are neutron stars with magnetic fields as strong as $B\sim10^{12\text{--}13}$ G. This review offers a concise overview of the accretion and radiation processes of X-ray pulsars and summarizes their rich observational features, particularly focusing on complex and variable temporal phenomena, spectral properties, and evolution, the new window for X-ray polarimetry and multi-wavelength advances. We also briefly discuss other related systems, i.e., gamma-ray binaries and pulsating ultraluminous X-ray sources., Comment: Universe 2024, 10(12), 453

The magnetic topological phases attract much interest, such as the axion insulator, higher-order topology, Weyl semimetals, and the quantum anomalous Hall effect (QAHE). Here, we predict that the axion insulator phase, magnetic Weyl points, and QAHE can be achieved in Eu3In2As4. Recently, the single-crystal Eu3In2As4 has been successfully synthesized, which exhibits an antiferromagnetic (AFM) ground state. Our first-principles calculations show that it lies on the phase boundary between multiple magnetic topological phases, and the magnetic anisotropy is weak, with an energy difference less than 1 meV. In the AFM state, it can be tuned to an axion insulator by tensile strain. The quantized axion angle $\theta = \pi$ and the magnetic higher-order topology are characterized by the parity index $Z_4 = 2$. By applying an external magnetic field, the induced ferromagnetic (FM) state becomes an ideal magnetic topological semimetal with a single pair of Weyl points or a nodal ring. The QAHE can be achieved in FM multilayer films of Eu3In2As4 on a magnetic insulating substrate., Comment: 7 pages, 4 figures, submitted. The experimental results can be found in arXiv:2403:07637 (2024)

Natural Language Processing (NLP) is widely used in fields like machine translation and sentiment analysis. However, traditional NLP models struggle with accuracy and efficiency. This paper introduces Deep Convolutional Neural Networks (DCNN) into NLP to address these issues. By integrating DCNN, machine learning (ML) algorithms, and generative adversarial networks (GAN), the study improves language understanding, reduces ambiguity, and enhances task performance. The high-performance NLP model shows a 10% improvement in segmentation accuracy and a 4% increase in recall rate compared to traditional models. This integrated approach excels in tasks such as word segmentation, part-of-speech tagging, machine translation, and text classification, offering better recognition accuracy and processing efficiency.

Can Large Language Models (LLMs) accurately predict election outcomes? While LLMs have demonstrated impressive performance in healthcare, legal analysis, and creative applications, their capabilities in election forecasting remain uncertain. Notably, election prediction poses unique challenges: limited voter-level data, evolving political contexts, and the complexity of modeling human behavior. In the first part of this paper, we explore and introduce a multi-step reasoning framework for election prediction, which systematically integrates demographic, ideological, and time-sensitive factors. Validated on 2016 and 2020 real-world data and extensive synthetic personas, our approach adapts to changing political landscapes, reducing bias and significantly improving predictive accuracy. We further apply our pipeline to the 2024 U.S. presidential election, illustrating its ability to generalize beyond observed historical data. Beyond enhancing accuracy, the second part of the paper provides insights into the broader implications of LLM-based election forecasting. We identify potential political biases embedded in pretrained corpora, examine how demographic patterns can become exaggerated, and suggest strategies for mitigating these issues. Together, this project, a large-scale LLM empirical study, advances the accuracy of election predictions and establishes directions for more balanced, transparent, and context-aware modeling in political science research and practice., Comment: arXiv admin note: substantial text overlap with arXiv:2411.03321

Heterogeneous computing, which incorporates GPUs, NPUs, and FPGAs, is increasingly utilized to improve the efficiency of computer systems. However, this shift has given rise to significant security and privacy concerns, especially when the execution platform is remote. One way to tackle these challenges is to establish a trusted and isolated environment for remote program execution, while maintaining minimal overhead and flexibility. While CPU-based trusted execution has been extensively explored and found commercial success, extension to heterogeneous computing systems remains a challenge. This paper proposes a practical trusted execution environment design for ARM/FPGA System-on-Chip platforms, leveraging TrustZone's unique characteristics. The design features a dedicated security controller within the ARM TrustZone, overseeing FPGA reconfiguration and managing communication between CPU cores and FPGA fabrics. This design involves a provisioning service that enables application users to establish trust in the FPGA fabric within cloud-based computing resources provided by the platform owner, running applications developed by third-party developers and hardware manufactured by the device manufacturer. To ensure the security of our proposed system, we employ an automated protocol verifier, ProVerif, to validate its compliance with essential security requirements. Furthermore, we demonstrate the practicality of our system model by implementing a prototype application on the Xilinx MPSoC development board., Comment: 13 pages, 6 figures

Superlubricity, a state where friction between two contact surfaces is nearly zero, has a great potential to revolutionize various mechanical systems by significantly reducing energy dissipation and enhancing efficiency. It can be realized either by structural incommensurate contact between crystalline surfaces or by creating highly passive interfaces to cancel out the adhesive forces. However, fabricating and maintaining such superlubric surfaces still present challenges, often disabled by surface structural defects or susceptibility to humid atmospheres, which renders superlubricity fragile. Here, we propose a novel strategy of photoelectron superlubricity (PESL), where robust superlubricity can be achieved in humid atmospheres by in-situ laser-irradiating the contact interface of an amorphous carbon film sliding against sapphire ball. We demonstrate that PESL not only exhibits a high resistance to environmental disturbances but also features rapid response. The formation of PESL originates from the laser-irradiating induced formation of nanographene-layered interface and enrichment of photoelectrons at the interface, resulting in a repulsive electric field between the nanographene layers. The discovery of PESL opens a new avenue for achieving superlubricity, and also provides novel insights for smart friction, mechanical motion control and light manipulation.

While holding great promise for improving and facilitating healthcare, large language models (LLMs) struggle to produce up-to-date responses on evolving topics due to outdated knowledge or hallucination. Retrieval-augmented generation (RAG) is a pivotal innovation that improves the accuracy and relevance of LLM responses by integrating LLMs with a search engine and external sources of knowledge. However, the quality of RAG responses can be largely impacted by the rank and density of key information in the retrieval results, such as the "lost-in-the-middle" problem. In this work, we aim to improve the robustness and reliability of the RAG workflow in the medical domain. Specifically, we propose a map-reduce strategy, BriefContext, to combat the "lost-in-the-middle" issue without modifying the model weights. We demonstrated the advantage of the workflow with various LLM backbones and on multiple QA datasets. This method promises to improve the safety and reliability of LLMs deployed in healthcare domains.

Numerous well-annotated human key-point datasets are publicly available to date. However, annotating human poses for newly collected images is still a costly and time-consuming progress. Pose distributions from different datasets share similar pose hinge-structure priors with different geometric transformations, such as pivot orientation, joint rotation, and bone length ratio. The difference between Pose distributions is essentially the difference between the transformation distributions. Inspired by this fact, we propose a method to calibrate a pre-trained pose generator in which the pose prior has already been learned to an adapted one following a new pose distribution. We treat the representation of human pose joint coordinates as skeleton image and transfer a pre-trained pose annotation generator with only a few annotation guidance. By fine-tuning a limited number of linear layers that closely related to the pose transformation, the adapted generator is able to produce any number of pose annotations that are similar to the target poses. We evaluate our proposed method, FlexPose, on several cross-dataset settings both qualitatively and quantitatively, which demonstrates that our approach achieves state-of-the-art performance compared to the existing generative-model-based transfer learning methods when given limited annotation guidance., Comment: Accepted by AAAI25, 12 pages, 10 figures

We report an infrared spectroscopy study of the antiferromagnetic (AFM) insulator EuZn$_2$As$_2$ over a broad frequency range, spanning temperatures both above and below the AFM transition $T_{\rm N} \simeq$ 20 K. The optical response reveals an insulating behavior, featuring two prominent infrared-active phonon modes at around 95 and 190 cm$^{-1}$, and two subtle absorption peaks at around 130 ($\alpha$ peak) and 2700 cm$^{-1}$ ($\beta$ peak), along with a strong absorption edge rising around 9000 cm$^{-1}$ ($\gamma$ peak). Significantly, the temperature-dependent changes in these peaks show noticeable anomalies across the AFM transition, particularly the emergence of the $\alpha$ peak and an unusual redshift of the $\gamma$ peak, suggesting a strong interaction between the charge excitations and the AFM order. Band structure calculations reveal that these anomalies arise from magnetization-induced band renormalizations, including shifts and foldings. Additionally, both phonon modes feature asymmetric Fano line shapes at low temperatures, with the 95 cm$^{-1}$ phonon mode exhibiting strong coupling to the fluctuations of Eu spins. These findings highlight a complex interplay of spin, charge, and lattice degrees of freedom in EuZn$_2$As$_2$., Comment: 6 pages, 4 figures

In this paper, we investigate the large-order asymptotics of multi-rational solitons of the focusing complex modified Korteweg-de Vries (c-mKdV) equation with nonzero background via the Riemann-Hilbert problems. First, based on the Lax pair, inverse scattering transform, and a series of deformations, we construct a multi-rational soliton of the c-mKdV equation via a solvable Riemann-Hilbert problem (RHP). Then, through a scale transformation, we construct a RHP corresponding to the limit function which is a new solution of the c-mKdV equation in the rescaled variables $X,\,T$, and prove the existence and uniqueness of the RHP's solution. Moreover, we also find that the limit function satisfies the ordinary differential equations (ODEs) with respect to space $X$ and time $T$, respectively. The ODEs with respect to space $X$ are identified with certain members of the Painlev\'{e}-III hierarchy. We study the large $X$ and transitional asymptotic behaviors of near-field limit solutions, and we provide some part results for the case of large $T$. These results will be useful to understand and apply the large-order rational solitons in the nonlinear wave equations., Comment: 52 pages, 10 figures

Human motion generation involves creating natural sequences of human body poses, widely used in gaming, virtual reality, and human-computer interaction. It aims to produce lifelike virtual characters with realistic movements, enhancing virtual agents and immersive experiences. While previous work has focused on motion generation based on signals like movement, music, text, or scene background, the complexity of human motion and its relationships with these signals often results in unsatisfactory outputs. Manifold learning offers a solution by reducing data dimensionality and capturing subspaces of effective motion. In this review, we present a comprehensive overview of manifold applications in human motion generation, one of the first in this domain. We explore methods for extracting manifolds from unstructured data, their application in motion generation, and discuss their advantages and future directions. This survey aims to provide a broad perspective on the field and stimulate new approaches to ongoing challenges.

Contrastive learning has achieved great success in skeleton-based representation learning recently. However, the prevailing methods are predominantly negative-based, necessitating additional momentum encoder and memory bank to get negative samples, which increases the difficulty of model training. Furthermore, these methods primarily concentrate on learning a global representation for recognition and retrieval tasks, while overlooking the rich and detailed local representations that are crucial for dense prediction tasks. To alleviate these issues, we introduce a Unified Skeleton-based Dense Representation Learning framework based on feature decorrelation, called USDRL, which employs feature decorrelation across temporal, spatial, and instance domains in a multi-grained manner to reduce redundancy among dimensions of the representations to maximize information extraction from features. Additionally, we design a Dense Spatio-Temporal Encoder (DSTE) to capture fine-grained action representations effectively, thereby enhancing the performance of dense prediction tasks. Comprehensive experiments, conducted on the benchmarks NTU-60, NTU-120, PKU-MMD I, and PKU-MMD II, across diverse downstream tasks including action recognition, action retrieval, and action detection, conclusively demonstrate that our approach significantly outperforms the current state-of-the-art (SOTA) approaches. Our code and models are available at https://github.com/wengwanjiang/USDRL., Comment: Accepted by AAAI 2025

We report on an instance in quantum gravity where a topological expansion resums into an effective description on a single geometry. The original theory whose gravitational path integral we study is JT quantum gravity with one asymptotic boundary at nonperturbatively low temperatures. The effective theory we derive is a deformation of JT gravity by a highly quantum and nonlocal interaction for the dilaton, evaluated only on a disk topology. This emergent description addresses a strongly quantum gravitational regime where all genera contribute at the same order, successfully capturing the doubly nonperturbative physics of the original theory., Comment: 5 pages, 2 figures, 1 geometry

Vehicle telematics provides granular data for dynamic driving risk assessment, but current methods often rely on aggregated metrics (e.g., harsh braking counts) and do not fully exploit the rich time-series structure of telematics data. In this paper, we introduce a flexible framework using continuous-time hidden Markov model (CTHMM) to model and analyze trip-level telematics data. Unlike existing methods, the CTHMM models raw time-series data without predefined thresholds on harsh driving events or assumptions about accident probabilities. Moreover, our analysis is based solely on telematics data, requiring no traditional covariates such as driver or vehicle characteristics. Through unsupervised anomaly detection based on pseudo-residuals, we identify deviations from normal driving patterns -- defined as the prevalent behaviour observed in a driver's history or across the population -- which are linked to accident risk. Validated on both controlled and real-world datasets, the CTHMM effectively detects abnormal driving behaviour and trips with increased accident likelihood. In real-data analysis, higher anomaly levels in longitudinal and lateral accelerations consistently correlate with greater accident risk, with classification models using this information achieving ROC-AUC values as high as 0.86 for trip-level analysis and 0.78 for distinguishing drivers with claims. Furthermore, the methodology reveals significant behavioural differences between drivers with and without claims, offering valuable insights for insurance applications, accident analysis, and prevention.

Despite being empowered with alignment mechanisms, large language models (LLMs) are increasingly vulnerable to emerging jailbreak attacks that can compromise their alignment mechanisms. This vulnerability poses significant risks to the real-world applications. Existing work faces challenges in both training efficiency and generalization capabilities (i.e., Reinforcement Learning from Human Feedback and Red-Teaming). Developing effective strategies to enable LLMs to resist continuously evolving jailbreak attempts represents a significant challenge. To address this challenge, we propose a novel defensive paradigm called GuidelineLLM, which assists LLMs in recognizing queries that may have harmful content. Before LLMs respond to a query, GuidelineLLM first identifies potential risks associated with the query, summarizes these risks into guideline suggestions, and then feeds these guidelines to the responding LLMs. Importantly, our approach eliminates the necessity for additional safety fine-tuning of the LLMs themselves; only the GuidelineLLM requires fine-tuning. This characteristic enhances the general applicability of GuidelineLLM across various LLMs. Experimental results demonstrate that GuidelineLLM can significantly reduce the attack success rate (ASR) against the LLMs (an average reduction of 34.17\% ASR) while maintaining the helpfulness of the LLMs in handling benign queries. Code is available at https://github.com/sqzhang-lazy/GuidelineLLM.

We introduce the Concept Bottleneck Large Language Model (CB-LLM), a pioneering approach to creating inherently interpretable Large Language Models (LLMs). Unlike traditional black-box LLMs that rely on post-hoc interpretation methods with limited neuron function insights, CB-LLM sets a new standard with its built-in interpretability, scalability, and ability to provide clear, accurate explanations. We investigate two essential tasks in the NLP domain: text classification and text generation. In text classification, CB-LLM narrows the performance gap with traditional black-box models and provides clear interpretability. In text generation, we show how interpretable neurons in CB-LLM can be used for concept detection and steering text generation. Our CB-LLMs enable greater interaction between humans and LLMs across a variety of tasks -- a feature notably absent in existing LLMs. Our code is available at https://github.com/Trustworthy-ML-Lab/CB-LLMs., Comment: arXiv admin note: substantial text overlap with arXiv:2407.04307

Business intelligence (BI) transforms large volumes of data within modern organizations into actionable insights for informed decision-making. Recently, large language model (LLM)-based agents have streamlined the BI workflow by automatically performing task planning, reasoning, and actions in executable environments based on natural language (NL) queries. However, existing approaches primarily focus on individual BI tasks such as NL2SQL and NL2VIS. The fragmentation of tasks across different data roles and tools lead to inefficiencies and potential errors due to the iterative and collaborative nature of BI. In this paper, we introduce DataLab, a unified BI platform that integrates a one-stop LLM-based agent framework with an augmented computational notebook interface. DataLab supports a wide range of BI tasks for different data roles by seamlessly combining LLM assistance with user customization within a single environment. To achieve this unification, we design a domain knowledge incorporation module tailored for enterprise-specific BI tasks, an inter-agent communication mechanism to facilitate information sharing across the BI workflow, and a cell-based context management strategy to enhance context utilization efficiency in BI notebooks. Extensive experiments demonstrate that DataLab achieves state-of-the-art performance on various BI tasks across popular research benchmarks. Moreover, DataLab maintains high effectiveness and efficiency on real-world datasets from Tencent, achieving up to a 58.58% increase in accuracy and a 61.65% reduction in token cost on enterprise-specific BI tasks.

Metalloborophene, characterized by the presence of metal-centered boron wheels denoted as M\c{opyright}Bn, has garnered considerable attention in recent years due to its versatile properties and potential applications in fields such as electronics, spintronics, and catalysis. However, the experimental verification of metalloborophene has been challenging, mainly due to the unconventional two-dimensional (2D) boron networks. In this study, we employ scanning tunneling microscopy, X-ray photoelectron spectroscopy, low energy electron diffraction, and first-principles calculations to unveil Cu\c{opyright}B8 metalloborophene nanoribbons formed via spontaneous alloying after the deposition of boron on a heated Cu(110) substrate under ultrahigh vacuum condition. The thermodynamically preferred precursor, the anchoring of boron network to metal atoms, and anisotropic lattice mismatch are identified as pivotal factors in the formation of these metalloborophene nanoribbons. This discovery expands the repertoire of 2D materials and offers a potential pathway for the synthesis of other metalloborophenes., Comment: 4 figures

Given the spatial heterogeneity of land use patterns in most cities, large-scale UAM will likely be deployed in specific areas, e.g., inter-transfer traffic between suburbs and city centers. However, large-scale UAM operations connecting multiple origin-destination pairs raise concerns about air traffic safety and efficiency with respect to conflict movements, particularly at large conflict points similar to roadway junctions. In this work, we propose an operational framework that integrates route guidance and collision avoidance to achieve an elegant trade-off between air traffic safety and efficiency. The route guidance mechanism aims to optimize aircraft distribution across both spatial and temporal dimensions by regulating their paths (composed of waypoints). Given the optimized paths, the collision avoidance module aims to generate collision-free aircraft trajectories between waypoints in 3D space. To enable large-scale operations, we develop a fast approximation method to solve the optimal path planning problem and employ the velocity obstacle model for collision avoidance. The proposed route guidance strategy significantly reduces the computational requirements for collision avoidance. As far as we know, this work is one of the first to combine route guidance and collision avoidance for UAM. The results indicate that the framework can enable efficient and flexible UAM operations, such as air traffic assignment, congestion prevention, and dynamic airspace clearance. Compared to the management scheme based on air corridors, the proposed framework has considerable improvements in computational efficiency (433%), average travel speed (70.2%), and trip completion rate (130%). The proposed framework has demonstrated great potential for real-time traffic simulation and management in large-scale UAM systems.

Colorectal cancer is a prevalent form of cancer, and many patients develop colorectal cancer liver metastasis (CRLM) as a result. Early detection of CRLM is critical for improving survival rates. Radiologists usually rely on a series of multi-phase contrast-enhanced computed tomography (CECT) scans done during follow-up visits to perform early detection of the potential CRLM. These scans form unique five-dimensional data (time, phase, and axial, sagittal, and coronal planes in 3D CT). Most of the existing deep learning models can readily handle four-dimensional data (e.g., time-series 3D CT images) and it is not clear how well they can be extended to handle the additional dimension of phase. In this paper, we build a dataset of time-series CECT scans to aid in the early diagnosis of CRLM, and build upon state-of-the-art deep learning techniques to evaluate how to best predict CRLM. Our experimental results show that a multi-plane architecture based on 3D bi-directional LSTM, which we call MPBD-LSTM, works best, achieving an area under curve (AUC) of 0.79. On the other hand, analysis of the results shows that there is still great room for further improvement.

Common Data Elements (CDEs) standardize data collection and sharing across studies, enhancing data interoperability and improving research reproducibility. However, implementing CDEs presents challenges due to the broad range and variety of data elements. This study aims to develop an effective and efficient mapping tool to bridge the gap between local data elements and National Institutes of Health (NIH) CDEs. We propose CDEMapper, a large language model (LLM) powered mapping tool designed to assist in mapping local data elements to NIH CDEs. CDEMapper has three core modules: (1) CDE indexing and embeddings. NIH CDEs were indexed and embedded to support semantic search; (2) CDE recommendations. The tool combines Elasticsearch (BM25 similarity methods) with state of the art GPT services to recommend candidate CDEs and their permissible values; and (3) Human review. Users review and select the NIH CDEs and values that best match their data elements and value sets. We evaluate the tool recommendation accuracy against manually annotated mapping results. CDEMapper offers a publicly available, LLM-powered, and intuitive user interface that consolidates essential and advanced mapping services into a streamlined pipeline. It provides a step by step, quality assured mapping workflow designed with a user-centered approach. The evaluation results demonstrated that augmenting BM25 with GPT embeddings and a ranker consistently enhances CDEMapper mapping accuracy in three different mapping settings across four evaluation datasets. This work opens up the potential of using LLMs to assist with CDE recommendation and human curation when aligning local data elements with NIH CDEs. Additionally, this effort enhances clinical research data interoperability and helps researchers better understand the gaps between local data elements and NIH CDEs., Comment: 11 pages,4 figures

We introduce a general-purpose, human-in-the-loop dual dialogue system to support mental health care professionals. The system, co-designed with care providers, is conceptualized to assist them in interacting with care seekers rather than functioning as a fully automated dialogue system solution. The AI assistant within the system reduces the cognitive load of mental health care providers by proposing responses, analyzing conversations to extract pertinent themes, summarizing dialogues, and recommending localized relevant content and internet-based cognitive behavioral therapy exercises. These functionalities are achieved through a multi-agent system design, where each specialized, supportive agent is characterized by a large language model. In evaluating the multi-agent system, we focused specifically on the proposal of responses to emotionally distressed care seekers. We found that the proposed responses matched a reasonable human quality in demonstrating empathy, showing its appropriateness for augmenting the work of mental health care providers., Comment: Update: Render figures properly and update title

We introduce VIRES, a video instance repainting method with sketch and text guidance, enabling video instance repainting, replacement, generation, and removal. Existing approaches struggle with temporal consistency and accurate alignment with the provided sketch sequence. VIRES leverages the generative priors of text-to-video models to maintain temporal consistency and produce visually pleasing results. We propose the Sequential ControlNet with the standardized self-scaling, which effectively extracts structure layouts and adaptively captures high-contrast sketch details. We further augment the diffusion transformer backbone with the sketch attention to interpret and inject fine-grained sketch semantics. A sketch-aware encoder ensures that repainted results are aligned with the provided sketch sequence. Additionally, we contribute the VireSet, a dataset with detailed annotations tailored for training and evaluating video instance editing methods. Experimental results demonstrate the effectiveness of VIRES, which outperforms state-of-the-art methods in visual quality, temporal consistency, condition alignment, and human ratings. Project page:https://suimuc.github.io/suimu.github.io/projects/VIRES/