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Asian Americans as a group are overrepresented among STEM college graduates and have the highest average college enrollment rate of any racial or ethnic category. Thus, Asian Americans are typically excluded from educational interventions directed at improving STEM education for Students of Color because they are not considered to be underrepresented minorities. However, statistics obscure the individual needs of the more than 20 ethnic subgroups that fall under the umbrella term Asian Americans. Using a participatory action research approach, this paper documents the institutional and sociocultural factors that push out HMoob (or Hmong) American college students from STEM programs at one large, predominantly White university; and the coordinate processes of gatekeeping and transactional advising that either redirect those students toward non-STEM programs or force them out of the university completely.

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.

The vision of a broadly capable and goal-directed agent, such as an Internet-browsing agent in the digital world and a household humanoid in the physical world, has rapidly advanced, thanks to the generalization capability of foundation models. Such a generalist agent needs to have a large and diverse skill repertoire, such as finding directions between two travel locations and buying specific items from the Internet. If each skill needs to be specified manually through a fixed set of human-annotated instructions, the agent's skill repertoire will necessarily be limited due to the quantity and diversity of human-annotated instructions. In this work, we address this challenge by proposing Proposer-Agent-Evaluator, an effective learning system that enables foundation model agents to autonomously discover and practice skills in the wild. At the heart of PAE is a context-aware task proposer that autonomously proposes tasks for the agent to practice with context information of the environment such as user demos or even just the name of the website itself for Internet-browsing agents. Then, the agent policy attempts those tasks with thoughts and actual grounded operations in the real world with resulting trajectories evaluated by an autonomous VLM-based success evaluator. The success evaluation serves as the reward signal for the agent to refine its policies through RL. We validate PAE on challenging vision-based web navigation, using both real-world and self-hosted websites from WebVoyager and WebArena.To the best of our knowledge, this work represents the first effective learning system to apply autonomous task proposal with RL for agents that generalizes real-world human-annotated benchmarks with SOTA performances. Our open-source checkpoints and code can be found in https://yanqval.github.io/PAE/

The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliable, and highly interconnected solutions. However, such development has also brought challenges to network optimizations. Thanks to the emergence of Large Language Models (LLMs) in recent years, tools including Retrieval Augmented Generation (RAG) have been developed and applied in various fields including networking, and have shown their effectiveness. Taking one step further, the integration of knowledge graphs into RAG frameworks further enhanced the performance of RAG in networking applications such as Intent-Driven Networks (IDNs) and spectrum knowledge maps by providing more contextually relevant responses through more accurate retrieval of related network information. This paper introduces the RAG framework that integrates knowledge graphs in its database and explores such framework's application in networking. We begin by exploring RAG's applications in networking and the limitations of conventional RAG and present the advantages that knowledge graphs' structured knowledge representation brings to the retrieval and generation processes. Next, we propose a detailed GraphRAG-based framework for networking, including a step-by-step tutorial on its construction. Our evaluation through a case study on channel gain prediction demonstrates GraphRAG's enhanced capability in generating accurate, contextually rich responses, surpassing traditional RAG models. Finally, we discuss key future directions for applying knowledge-graphs-empowered RAG frameworks in networking, including robust updates, mitigation of hallucination, and enhanced security measures for networking applications., Comment: 9 pages, 4 figures

In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We conduct a thorough evaluation of Hunyuan-Large's superior performance across various benchmarks including language understanding and generation, logical reasoning, mathematical problem-solving, coding, long-context, and aggregated tasks, where it outperforms LLama3.1-70B and exhibits comparable performance when compared to the significantly larger LLama3.1-405B model. Key practice of Hunyuan-Large include large-scale synthetic data that is orders larger than in previous literature, a mixed expert routing strategy, a key-value cache compression technique, and an expert-specific learning rate strategy. Additionally, we also investigate the scaling laws and learning rate schedule of mixture of experts models, providing valuable insights and guidances for future model development and optimization. The code and checkpoints of Hunyuan-Large are released to facilitate future innovations and applications. Codes: https://github.com/Tencent/Hunyuan-Large Models: https://huggingface.co/tencent/Tencent-Hunyuan-Large, Comment: 17 pages, 4 Figures

This article clarifies historical and sociocultural factors that impact the role of STEM in the racialization of Asian Americans. Drawing on critical race and other theories of Asian American racialization, and a review of empirical research on the experiences of Asian American college students in STEM, we develop a conceptual framework called "STEM Asianization" that highlights the role of STEM ideology in the model minority racialization of Asian Americans. Consequences for Asian American students include (1) erasure of the intersectional experiences of minoritized Asian American students; (2) dehumanization of Asian Americans and establishment of a bamboo ceiling; (3) representation of Asian Americans as a perpetual foreigner/Yellow Peril during times of cultural and political crisis; and (4) representation of Asian Americans who cannot or do not conform to the STEM achievement narrative as a failed minority. We argue that STEM Asianization reproduces White supremacy by ideologically reinforcing the colorblind meritocracy of STEM institutions in the United States. [Additional funding provided by the Wisconsin Louis Stokes Alliance for Minority Participation.]

Macroscopic entanglement, as critical quantum resources in quantum information science, has been extensively studied in optomechanical systems with purely dispersive coupling over the past decades. However, quantum entanglement, induced by purely dissipative coupling, remains unexplored. In this work, we study quantum entanglement in a Michelson-Sagnac interferometer, where the dispersive and the dissipative coupling can be arbitrarily switched on and off. With current experimental parameters, we demonstrate that the steady-state mechanical displacement exhibits a nonlinear (linear) dependence on the driving power with the purely dispersive (dissipative) coupling. Further, we find that quantum entanglement generated by purely dissipative coupling is significantly stronger and more noise-tolerant than that generated by purely dispersive coupling. When both couplings coexist, entanglement is weakened due to interference. Our findings provide a promising path to engineer strong and noise-tolerant quantum entanglement in purely dissipative quantum systems., Comment: 7 pages, 5 figures

Networked cybernetic and physical systems of the Internet of Things (IoT) immerse civilian and industrial infrastructures into an interconnected and dynamic web of hybrid and mobile devices. The key feature of such systems is the hybrid and tight coupling of mobile and pervasive discrete communications in a continuously evolving environment (discrete computations with predominant continuous dynamics). In the aim of ensuring the correctness and reliability of such heterogeneous infrastructures, we introduce the hybrid {\pi}-calculus (HpC), to formally capture both mobility, pervasiveness and hybridisation in infrastructures where the network topology and its communicating entities evolve continuously in the physical world. The {\pi}-calculus proposed by Robin Milner et al. is a process calculus that can model mobile communications and computations in a very elegant manner. The HpC we propose is a conservative extension of the classical {\pi}-calculus, i.e., the extension is ``minimal'', and yet describes mobility, time and physics of systems, while allowing to lift all theoretical results (e.g. bisimulation) to the context of that extension. We showcase the HpC by considering a realistic handover protocol among mobile devices., Comment: The published version of this article will be available in the ACM Digital Library as part of the Proceedings of the ACM on Programming Languages issue for SPLASH/OOPSLA 2025. This extended version contains additional appendices, proofs and case studies

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80\% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40\%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55\%, enabling scalable, low-latency LLM services in resource-constrained edge environments., Comment: 8 pages, 5 figures

Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains.

Vision Transformers (ViTs) are increasingly being adopted in various sensitive vision applications - like medical diagnosis, facial recognition, etc. To improve the interpretability of such models, many approaches attempt to forward-align them with carefully annotated abstract, human-understandable semantic entities - concepts. Concepts provide global rationales to the model predictions and can be quickly understood/intervened on by domain experts. Most current research focuses on designing model-agnostic, plug-and-play generic concept-based explainability modules that do not incorporate the inner workings of foundation models (e.g., inductive biases, scale invariance, etc.) during training. To alleviate this issue for ViTs, in this paper, we propose a novel Concept Representation Alignment Module (CRAM) which learns both scale and position-aware representations from multi-scale feature pyramids and patch representations respectively. CRAM further aligns these representations with concept annotations through an attention matrix. The proposed CRAM module improves the predictive performance of ViT architectures and also provides accurate and robust concept explanations as demonstrated on five datasets - including three widely used benchmarks (CUB, Pascal APY, Concept-MNIST) and 2 real-world datasets (AWA2, KITS).

Reinforcement Learning (RL) has shown excellent performance in solving decision-making and control problems of autonomous driving, which is increasingly applied in diverse driving scenarios. However, driving is a multi-attribute problem, leading to challenges in achieving multi-objective compatibility for current RL methods, especially in both policy execution and policy iteration. On the one hand, the common action space structure with single action type limits driving flexibility or results in large behavior fluctuations during policy execution. On the other hand, the multi-attribute weighted single reward function result in the agent's disproportionate attention to certain objectives during policy iterations. To this end, we propose a Multi-objective Ensemble-Critic reinforcement learning method with Hybrid Parametrized Action for multi-objective compatible autonomous driving. Specifically, a parameterized action space is constructed to generate hybrid driving actions, combining both abstract guidance and concrete control commands. A multi-objective critics architecture is constructed considering multiple attribute rewards, to ensure simultaneously focusing on different driving objectives. Additionally, uncertainty-based exploration strategy is introduced to help the agent faster approach viable driving policy. The experimental results in both the simulated traffic environment and the HighD dataset demonstrate that our method can achieve multi-objective compatible autonomous driving in terms of driving efficiency, action consistency, and safety. It enhances the general performance of the driving while significantly increasing training efficiency., Comment: 12 pages, 9 figures, 5 tables

Traditional Mixture-of-Experts (MoE) networks benefit from utilizing multiple smaller expert models as opposed to a single large network. However, these experts typically operate independently, leaving a question open about whether interconnecting these models could enhance the performance of MoE networks. In response, we introduce GRAPHMOE, a novel method aimed at augmenting the cognitive depth of language models via a self-rethinking mechanism constructed on Pseudo GraphMoE networks. GRAPHMOE employs a recurrent routing strategy to simulate iterative thinking steps, thereby facilitating the flow of information among expert nodes. We implement the GRAPHMOE architecture using Low-Rank Adaptation techniques (LoRA) and conduct extensive experiments on various benchmark datasets. The experimental results reveal that GRAPHMOE outperforms other LoRA based models, achieving state-of-the-art (SOTA) performance. Additionally, this study explores a novel recurrent routing strategy that may inspire further advancements in enhancing the reasoning capabilities of language models., Comment: 10 pages

Recently, significant progress has been made in the study of black hole (BH) perturbations, both within the framework of general modified gravity theories and in complex environments. However, a well-established conclusion regarding the boundary conditions of the perturbed fields remains elusive. In this paper, we investigate the boundary conditions for a general perturbation at spatial infinity and the event horizon of a black hole (BH) described by a generic metric that is stationary, axisymmetric, asymptotically flat, and respects the condition of circularity. Our analysis is independent of any specific BH model or the nature of the perturbed field. In particular, by extending the formulation introduced by Teukolsky and utilizing purely geometric principles, we derive a universal expression for the boundary condition at the horizon. This expression is elegantly formulated in terms of the physical quantities at the event horizon, specifically the BH's surface gravity and angular velocity. The results presented in this work may provide valuable insights for the calculation of quasinormal modes and the gravitational waves generated by extreme-mass-ratio inspirals, extending beyond the standard Kerr case., Comment: 10 pages

The Johannsen-Psaltis (JP) metric provides a robust framework for testing the no-hair theorem of astrophysical black holes due to the regular spacetime configuration around JP black holes. Verification of this theorem through electromagnetic spectra often involves analyzing the photon sphere near black holes, which is intrinsically linked to black hole shadows. Investigating the shadow of JP black holes offers an effective approach for assessing the validity of theorem. Since the Hamilton-Jacobi equation in the JP metric does not permit exact variable separation, an approximate analytical approach is employed to calculate the shadow, while the backward ray-tracing numerical method serves as a rigorous alternative. For JP black holes with closed event horizons, the approximate analytical approach reliably reproduces results obtained through the numerical computation. However, significant discrepancies emerge for black holes with non-closed event horizons. As the deviation parameter $\epsilon_3$ increases, certain regions of the critical curve transition into non-smooth configurations. Analysis of photon trajectories in these regions reveals chaotic dynamics, which accounts for the failure of the approximate analytical method to accurately describe the shadows of JP black holes with non-closed event horizons., Comment: 37 pages, 10 figures

The diffusion transformer (DiT) architecture has attracted significant attention in image generation, achieving better fidelity, performance, and diversity. However, most existing DiT - based image generation methods focus on global - aware synthesis, and regional prompt control has been less explored. In this paper, we propose a coarse - to - fine generation pipeline for regional prompt - following generation. Specifically, we first utilize the powerful large language model (LLM) to generate both high - level descriptions of the image (such as content, topic, and objects) and low - level descriptions (such as details and style). Then, we explore the influence of cross - attention layers at different depths. We find that deeper layers are always responsible for high - level content control, while shallow layers handle low - level content control. Various prompts are injected into the proposed regional cross - attention control for coarse - to - fine generation. By using the proposed pipeline, we enhance the controllability of DiT - based image generation. Extensive quantitative and qualitative results show that our pipeline can improve the performance of the generated images., Comment: Accepted by ICASSP 2025, Github: https://github.com/ZhenXiong-dl/ICASSP2025-RCAC

Multi-modal Large Language Models (MLLMs) exhibit impressive capabilities in 2D tasks, yet encounter challenges in discerning the spatial positions, interrelations, and causal logic in scenes when transitioning from 2D to 3D representations. We find that the limitations mainly lie in: i) the high annotation cost restricting the scale-up of volumes of 3D scene data, and ii) the lack of a straightforward and effective way to perceive 3D information which results in prolonged training durations and complicates the streamlined framework. To this end, we develop pipeline based on open-source 2D MLLMs and LLMs to generate high-quality 3D-text pairs and construct 3DS-160K , to enhance the pre-training process. Leveraging this high-quality pre-training data, we introduce the 3UR-LLM model, an end-to-end 3D MLLM designed for precise interpretation of 3D scenes, showcasing exceptional capability in navigating the complexities of the physical world. 3UR-LLM directly receives 3D point cloud as input and project 3D features fused with text instructions into a manageable set of tokens. Considering the computation burden derived from these hybrid tokens, we design a 3D compressor module to cohesively compress the 3D spatial cues and textual narrative. 3UR-LLM achieves promising performance with respect to the previous SOTAs, for instance, 3UR-LLM exceeds its counterparts by 7.1\% CIDEr on ScanQA, while utilizing fewer training resources. The code and model weights for 3UR-LLM and the 3DS-160K benchmark are available at 3UR-LLM., Comment: Accepted to IEEE Transactions on Multimedia (TMM)

In this paper, we focus on the two-time correlation function (TTCF) of the cavity optomechanical system, which serves as the most popular tool in precision detection technologies. We utilize the stochastic Schrodinger equation approach to study TTCF for the cavity optomechanical system in the long-time steady state TTCF and time-dependent case. Our numerical simulations support two major conclusions: (1) long-time steady states in Markovian and non-Markovian regimes are different, resulting in the distinct TTCF, and (2) the time-dependent TTCF can reveal more information about the environment, rather than the traditional spectral function method., Comment: arXiv admin note: text overlap with arXiv:2212.13362

On top of Segment Anything Model (SAM), SAM 2 further extends its capability from image to video inputs through a memory bank mechanism and obtains a remarkable performance compared with previous methods, making it a foundation model for video segmentation task. In this paper, we aim at making SAM 2 much more efficient so that it even runs on mobile devices while maintaining a comparable performance. Despite several works optimizing SAM for better efficiency, we find they are not sufficient for SAM 2 because they all focus on compressing the image encoder, while our benchmark shows that the newly introduced memory attention blocks are also the latency bottleneck. Given this observation, we propose EdgeTAM, which leverages a novel 2D Spatial Perceiver to reduce the computational cost. In particular, the proposed 2D Spatial Perceiver encodes the densely stored frame-level memories with a lightweight Transformer that contains a fixed set of learnable queries. Given that video segmentation is a dense prediction task, we find preserving the spatial structure of the memories is essential so that the queries are split into global-level and patch-level groups. We also propose a distillation pipeline that further improves the performance without inference overhead. As a result, EdgeTAM achieves 87.7, 70.0, 72.3, and 71.7 J&F on DAVIS 2017, MOSE, SA-V val, and SA-V test, while running at 16 FPS on iPhone 15 Pro Max., Comment: Code will be released at https://github.com/facebookresearch/EdgeTAM

Large Language Models (LLMs) have demonstrated impressive capabilities in role-playing scenarios, particularly in simulating domain-specific experts using tailored prompts. This ability enables LLMs to adopt the persona of individuals with specific backgrounds, offering a cost-effective and efficient alternative to traditional, resource-intensive user studies. By mimicking human behavior, LLMs can anticipate responses based on concrete demographic or professional profiles. In this paper, we evaluate the effectiveness of LLMs in simulating individuals with diverse backgrounds and analyze the consistency of these simulated behaviors compared to real-world outcomes. In particular, we explore the potential of LLMs to interpret and respond to discharge summaries provided to patients leaving the Intensive Care Unit (ICU). We evaluate and compare with human responses the comprehensibility of discharge summaries among individuals with varying educational backgrounds, using this analysis to assess the strengths and limitations of LLM-driven simulations. Notably, when LLMs are primed with educational background information, they deliver accurate and actionable medical guidance 88% of the time. However, when other information is provided, performance significantly drops, falling below random chance levels. This preliminary study shows the potential benefits and pitfalls of automatically generating patient-specific health information from diverse populations. While LLMs show promise in simulating health personas, our results highlight critical gaps that must be addressed before they can be reliably used in clinical settings. Our findings suggest that a straightforward query-response model could outperform a more tailored approach in delivering health information. This is a crucial first step in understanding how LLMs can be optimized for personalized health communication while maintaining accuracy.

This paper introduces a cost-effective robotic handwriting system designed to replicate human-like handwriting with high precision. Combining a Raspberry Pi Pico microcontroller, 3D-printed components, and a machine learning-based handwriting generation model implemented via TensorFlow, the system converts user-supplied text into realistic stroke trajectories. By leveraging lightweight 3D-printed materials and efficient mechanical designs, the system achieves a total hardware cost of approximately \$56, significantly undercutting commercial alternatives. Experimental evaluations demonstrate handwriting precision within $\pm$0.3 millimeters and a writing speed of approximately 200 mm/min, positioning the system as a viable solution for educational, research, and assistive applications. This study seeks to lower the barriers to personalized handwriting technologies, making them accessible to a broader audience., Comment: This is an updated version of a paper originally presented at the 2024 IEEE Long Island Systems, Applications and Technology Conference (LISAT)

In this study, we analyze the observational images of a Konoplya-Zhidenko rotating non-Kerr black hole, wherein a thin accretion disk, serving as the sole background light source, is situated on the equatorial plane of the black hole. The inner boundary of the thin accretion disk extends to the event horizon, and the accretion material in the disk exhibits two different motion behaviors, that is, it moves along the critical plunging orbit inside the innermost stable circular orbit (ISCO) and follows the Keplerian orbit outside the ISCO. The shadow image is captured on the imaging plane of a zero angular momentum observer utilizing advanced fisheye camera ray-tracing techniques. The results demonstrate that an image consistently reveals a dark region encircled by a narrow photon ring, which is called the inner shadow. At low observation inclination angles, the observation intensity is highly concentrated, with the lensed image of accretion disk being superimposed on the direct image. As observation inclination angle increases, the direct and lensed images gradually separate, becoming distinctly distinguishable and forming a hat-like structure. Furthermore, variations in the parameter space and observation angle will influence pertinent image characteristics, including image symmetry, the range or deformation degree of the inner shadow. We further examined the distinctive characteristics of images observed in both prograde and retrograde accretion disk scenarios. Subsequently, we also examined the redshift distribution on the disk. The findings indicate that while variations in relevant parameters do influence the redshift distribution, the primary factor is the change in observational inclination. The observer can detect both redshift and blueshift phenomena on the screen when viewed at a higher observation angle., Comment: 32 pages, 13 figures

Neural implicit representations have recently shown promising progress in dense Simultaneous Localization And Mapping (SLAM). However, existing works have shortcomings in terms of reconstruction quality and real-time performance, mainly due to inflexible scene representation strategy without leveraging any prior information. In this paper, we introduce SP-SLAM, a novel neural RGB-D SLAM system that performs tracking and mapping in real-time. SP-SLAM computes depth images and establishes sparse voxel-encoded scene priors near the surfaces to achieve rapid convergence of the model. Subsequently, the encoding voxels computed from single-frame depth image are fused into a global volume, which facilitates high-fidelity surface reconstruction. Simultaneously, we employ tri-planes to store scene appearance information, striking a balance between achieving high-quality geometric texture mapping and minimizing memory consumption. Furthermore, in SP-SLAM, we introduce an effective optimization strategy for mapping, allowing the system to continuously optimize the poses of all historical input frames during runtime without increasing computational overhead. We conduct extensive evaluations on five benchmark datasets (Replica, ScanNet, TUM RGB-D, Synthetic RGB-D, 7-Scenes). The results demonstrate that, compared to existing methods, we achieve superior tracking accuracy and reconstruction quality, while running at a significantly faster speed.

Understanding the dynamics of photoexcited carriers is essential for advancing photoelectronic device design. Photon absorption generates electron-hole pairs, and subsequent scatterings can induce ultrafast thermalization within a picosecond, forming a quasi-equilibrium distribution with overheated electrons. The high-energy tail of this distribution enables carriers to overcome energy barriers, thereby enhancing quantum efficiency--a phenomenon known as photo-thermionic emission (PTE). Despite its importance, the onset and mechanisms of PTE remain under debate. Using real-time time-dependent density functional theory (rt-TDDFT), we investigate ultrafast carrier thermalization in two-dimensional materials graphene and PtTe2, and the results reveal distinct differences. In graphene, both electrons and holes thermalize into Fermi-Dirac distributions with good agreement to experiment, while PtTe2 exhibits anomalous high-energy tails for both electrons and holes, deviating significantly from Fermi-Dirac behavior. We attribute this anomaly to differences in orbital coupling between the two materials, from which we derive design principles for identifying optimal PTE candidates and, ultimately, improving photodetector performance., Comment: 12 pages, 5 figures, 1 table

Remote brightening (RB) is compact brightening at footpoints of magnetic loops, which are remotely-connecting to and confining an eruption in the solar atmosphere. Here, we report on observations of an RB resulting from an EUV jet with a speed of about 90\,km\,s$^{-1}$. The loops connecting the RB and the jet have an apparent length of about 59\,Mm. Intriguingly, the RB exhibits at least two episodes of brightenings, as characterised by two peaks in its lightcurve. The energies, which sustain the first and second peaks of the RB, are $6.3\times10^{26}$\,erg and $8.4\times10^{26}$\,erg, respectively, and take a significant proportion of the total energy. The first peak of the RB brightenings coincides with the jet's peak with a time delay of 12 seconds, while the second peak lags behind by 108 seconds. Besides the flows of the ejecta, we have identified two additional flows originating from the eruption site. One is relatively cool with a temperature of $log_{10}T/K=5.8-6.1$ and has a speed of about $275\pm15$\,km\,s$^{-1}$. The other is hot with a temperature of $log_{10}T/K=7.0-7.3$ and travels much faster with a speed of about 750$\pm$\,70\,km\,s$^{-1}$. We attribute the second peak of RB directly to this hot flow, which our numerical experiments suggest is the result of a slow shock wave. Considering the minimal time delay between the first peak of RB and the eruption, we infer this first episode is due to heating by nonthermal electrons. Our research demonstrates that the dynamics in an RB can offer vital insights into the nature of the corresponding eruption and help understand how the energy is distributed throughout the solar atmosphere.

Restless multi-armed bandits (RMABs) have been highly successful in optimizing sequential resource allocation across many domains. However, in many practical settings with highly scarce resources, where each agent can only receive at most one resource, such as healthcare intervention programs, the standard RMAB framework falls short. To tackle such scenarios, we introduce Finite-Horizon Single-Pull RMABs (SPRMABs), a novel variant in which each arm can only be pulled once. This single-pull constraint introduces additional complexity, rendering many existing RMAB solutions suboptimal or ineffective. %To address this, we propose using dummy states to duplicate the system, ensuring that once an arm is activated, it transitions exclusively within the dummy states. To address this shortcoming, we propose using \textit{dummy states} that expand the system and enforce the one-pull constraint. We then design a lightweight index policy for this expanded system. For the first time, we demonstrate that our index policy achieves a sub-linearly decaying average optimality gap of $\tilde{\mathcal{O}}\left(\frac{1}{\rho^{1/2}}\right)$ for a finite number of arms, where $\rho$ is the scaling factor for each arm cluster. Extensive simulations validate the proposed method, showing robust performance across various domains compared to existing benchmarks., Comment: 17 Pages, 8 figures. Accepted by AAMAS 2025

To defend against Advanced Persistent Threats on the endpoint, threat hunting employs security knowledge such as cyber threat intelligence to continuously analyze system audit logs through retrospective scanning, querying, or pattern matching, aiming to uncover attack patterns/graphs that traditional detection methods (e.g., recognition for Point of Interest) fail to capture. However, existing threat hunting systems based on provenance graphs face challenges of high false negatives, high false positives, and low efficiency when confronted with diverse attack tactics and voluminous audit logs. To address these issues, we propose a system called Actminer, which constructs query graphs from descriptive relationships in cyber threat intelligence reports for precise threat hunting (i.e., graph alignment) on provenance graphs. First, we present a heuristic search strategy based on equivalent semantic transfer to reduce false negatives. Second, we establish a filtering mechanism based on causal relationships of attack behaviors to mitigate false positives. Finally, we design a tree structure to incrementally update the alignment results, significantly improving hunting efficiency. Evaluation on the DARPA Engagement dataset demonstrates that compared to the SOTA POIROT, Actminer reduces false positives by 39.1%, eliminates all false negatives, and effectively counters adversarial attacks.

Continuous harvesting and storage of multiple fruits in a single operation allow robots to significantly reduce the travel distance required for repetitive back-and-forth movements. Traditional collision-free path planning algorithms, such as Rapidly-Exploring Random Tree (RRT) and A-star (A), often fail to meet the demands of efficient continuous fruit harvesting due to their low search efficiency and the generation of excessive redundant points. This paper presents the Interactive Local Minima Search Algorithm (ILMSA), a fast path-planning method designed for the continuous harvesting of table-top grown strawberries. The algorithm featured an interactive node expansion strategy that iteratively extended and refined collision-free path segments based on local minima points. To enable the algorithm to function in 3D, the 3D environment was projected onto multiple 2D planes, generating optimal paths on each plane. The best path was then selected, followed by integrating and smoothing the 3D path segments. Simulations demonstrated that ILMSA outperformed existing methods, reducing path length by 21.5% and planning time by 97.1% compared to 3D-RRT, while achieving 11.6% shorter paths and 25.4% fewer nodes than the Lowest Point of the Strawberry (LPS) algorithm in 3D environments. In 2D, ILMSA achieved path lengths 16.2% shorter than A, 23.4% shorter than RRT, and 20.9% shorter than RRT-Connect, while being over 96% faster and generating significantly fewer nodes. Field tests confirmed ILMSA's suitability for complex agricultural tasks, having a combined planning and execution time and an average path length that were approximately 58% and 69%, respectively, of those achieved by the LPS algorithm., Comment: Accepted by IEEE Transactions on AgriFood Electronics

Existing methods for safe multi-agent control using logic specifications like Signal Temporal Logic (STL) often face scalability issues. This is because they rely either on single-agent perspectives or on Mixed Integer Linear Programming (MILP)-based planners, which are complex to optimize. These methods have proven to be computationally expensive and inefficient when dealing with a large number of agents. To address these limitations, we present a new scalable approach to multi-agent control in this setting. Our method treats the relationships between agents using a graph structure rather than in terms of a single-agent perspective. Moreover, it combines a multi-agent collision avoidance controller with a Graph Neural Network (GNN) based planner, models the system in a decentralized fashion, and trains on STL-based objectives to generate safe and efficient plans for multiple agents, thereby optimizing the satisfaction of complex temporal specifications while also facilitating multi-agent collision avoidance. Our experiments show that our approach significantly outperforms existing methods that use a state-of-the-art MILP-based planner in terms of scalability and performance. The project website is https://jeappen.com/mastl-gcbf-website/ and the code is at https://github.com/jeappen/mastl-gcbf ., Comment: Accepted to CoRL 2024. arXiv admin note: text overlap with arXiv:2401.14554 by other authors

Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach for specialized domains such as medicine and finance. However, significant challenges remain in identifying optimal adaptation criteria and training strategies across varying data and model configurations. To address these challenges, we introduce FINDAP, a systematic and fine-grained investigation into domain-adaptive post-training of LLMs for the finance domain. Our approach begins by identifying the core capabilities required for the target domain and designing a comprehensive evaluation suite aligned with these needs. We then analyze the effectiveness of key post-training stages, including continual pretraining, instruction tuning, and preference alignment. Building on these insights, we propose an effective training recipe centered on a novel preference data distillation method, which leverages process signals from a generative reward model. The resulting model, Llama-Fin, achieves state-of-the-art performance across a wide range of financial tasks. Our analysis also highlights how each post-training stage contributes to distinct capabilities, uncovering specific challenges and effective solutions, providing valuable insights for domain adaptation of LLMs. Project page: https://github.com/SalesforceAIResearch/FinDap

In photonics, the idea of controlling light in a similar way that magnetic fields control electrons has always been attractive. It can be realized by synthesizing pseudomagnetic fields (PMFs) in photonic crystals (PhCs). Previous works mainly focus on the Landau levels and the robust transport of the chiral states. More versatile control over light using complex nonuniform PMFs such as the flexible splitting and routing of light has been elusive, which hinders their application in practical photonic integrated circuits. Here we propose an universal and systematic methodology to design nonuniform PMFs and arbitrarily control the flow of light in silicon PhCs at telecommunication wavelengths. As proofs of concept, a low-loss S-bend and a highly efficient 50:50 power splitter based on PMFs are experimentally demonstrated. A high-speed data transmission experiment is performed on these devices to prove their applicability in real communication systems. The proposed method offers a new paradigm for the exploration of fundamental physics and the development of novel nanophotonic devices.

The leaky dielectric model is widely used in simulating two-phase electrohydrodynamic (EHD) flows. One critical issue with this classical model is the assumption of Ohmic conduction, which makes it inadequate for describing the newly discovered EHD flows caused by the Onsager-Wien effect [Ryu et al., Phys. Rev. Lett. 104, 104502 (2010)]. In this paper, we proposed a phase-field lattice Boltzmann (LB) method for two-phase electrohydrodynamic flows induced by the Onsager-Wien effect. In this scheme, two LB equations are employed to resolve the incompressible Navier-Stokes equations and the conservative Allen-Cahn equation, while another three LB equations are used for solving the charge conservation equations and the electric potential equation. After we validate the developed LB method, we perform a series of numerical simulations of droplet deformation under EHD conduction phenomena. Our numerical results indicate that the presence of the Onsager-Wien effect has a significant impact on droplet deformation and charge distribution. Also, it is interesting to note that, apart from the heterocharge layers near the electrodes, a charge cloud may form between the droplet interface and the electrode in some cases. To thoroughly understand the droplet dynamics, the effects of the reference length d, the applied voltage {\Delta}{\psi}, the permittivity ratio {\epsilon}r, and the ionic mobility ratio {\mu}r on droplet deformation and charge distribution are all investigated in detail.

Recent advancements in omnimodal learning have been achieved in understanding and generation across images, text, and speech, though mainly within proprietary models. Limited omnimodal datasets and the inherent challenges associated with real-time emotional speech generation have hindered open-source progress. To address these issues, we propose openomni, a two-stage training method combining omnimodal alignment and speech generation to develop a state-of-the-art omnimodal large language model. In the alignment phase, a pre-trained speech model is further trained on text-image tasks to generalize from vision to speech in a (near) zero-shot manner, outperforming models trained on tri-modal datasets. In the speech generation phase, a lightweight decoder facilitates real-time emotional speech through training on speech tasks and preference learning. Experiments demonstrate that openomni consistently improves across omnimodal, vision-language, and speech-language evaluations, enabling natural, emotion-rich dialogues and real-time emotional speech generation.

Combinatorial optimization problems are foundational challenges in fields such as artificial intelligence, logistics, and network design. Traditional algorithms, including greedy methods and dynamic programming, often struggle to balance computational efficiency and solution quality, particularly as problem complexity scales. To overcome these limitations, we propose a novel and efficient probabilistic optimization framework that integrates true random number generators (TRNGs) based on spin-transfer torque magnetic tunneling junctions (STT-MTJs). The inherent stochastic switching behavior of STT-MTJs enables dynamic configurability of random number distributions, which we leverage to introduce controlled randomness into a probabilistic greedy algorithm. By tuning a temperature parameter, our algorithm seamlessly transitions between deterministic and stochastic strategies, effectively balancing exploration and exploitation. Furthermore, we apply this framework to the traveling salesman problem (TSP), showcasing its ability to consistently produce high-quality solutions across diverse problem scales. Our algorithm demonstrates superior performance in both solution quality and convergence speed compared to classical approaches, such as simulated annealing and genetic algorithms. Specifically, in larger TSP instances involving up to 70 cities, it retains its performance advantage, achieving near-optimal solutions with fewer iterations and reduced computational costs. This work highlights the potential of integrating MTJ-based TRNGs into optimization algorithms, paving the way for future applications in probabilistic computing and hardware-accelerated optimization., Comment: This preprint was originally published on Research Square and is licensed under CC BY 4.0. The original version is available at https://www.researchsquare.com/article/rs-5700548/v1

Face anonymization aims to conceal the visual identity of a face to safeguard the individual's privacy. Traditional methods like blurring and pixelation can largely remove identifying features, but these techniques significantly degrade image quality and are vulnerable to deep reconstruction attacks. Generative models have emerged as a promising solution for anonymizing faces while preserving a natural appearance. However, many still face limitations in visual quality and often overlook the potential to recover the original face from the anonymized version, which can be valuable in specific contexts such as image forensics. This paper proposes a novel framework named iFADIT, an acronym for Invertible Face Anonymization via Disentangled Identity Transform. The framework features a disentanglement architecture coupled with a secure flow-based model: the former decouples identity information from non-identifying attributes, while the latter transforms the decoupled identity into an anonymized version in an invertible manner controlled by a secret key. The anonymized face can then be reconstructed based on a pre-trained StyleGAN that ensures high image quality and realistic facial details. Recovery of the original face (aka de-anonymization) is possible upon the availability of the matching secret, by inverting the anonymization process based on the same set of model parameters. Furthermore, a dedicated secret-key mechanism along with a dual-phase training strategy is devised to ensure the desired properties of face anonymization. Qualitative and quantitative experiments demonstrate the superiority of the proposed approach in anonymity, reversibility, security, diversity, and interpretability over competing methods.

Chain-of-Thought (CoT) Prompting is a dominant paradigm in Large Language Models (LLMs) to enhance complex reasoning. It guides LLMs to present multi-step reasoning, rather than generating the final answer directly. However, CoT encounters difficulties when key information required for reasoning is implicit or missing. This occurs because CoT emphasizes the sequence of reasoning steps while overlooking the early extraction of essential information. We propose a pre-prompting method called Iterative Summarization Pre-Prompting (ISP^2) to refine LLM reasoning when key information is not explicitly provided. First, entities and their corresponding descriptions are extracted to form potential key information pairs. Next, we use a reliability rating to assess these pairs, then merge the two lowest-ranked pairs into a new entity description. This process is repeated until a unique key information pair is obtained. Finally, that pair, along with the original question, is fed into LLMs to produce the answer. Extensive experiments demonstrate a 7.1% improvement compared to existing methods. Unlike traditional prompting, ISP^2 adopts an inductive approach with pre-prompting, offering flexible integration into diverse reasoning frameworks. The code is available at https://github.com/zdhgreat/ISP-2.

The Lin-Kernighan-Helsguan (LKH) heuristic is a classic local search algorithm for the Traveling Salesman Problem (TSP). LKH introduces an $\alpha$-value to replace the traditional distance metric for evaluating the edge quality, which leads to a significant improvement. However, we observe that the $\alpha$-value does not make full use of the historical information during the search, and single guiding information often makes LKH hard to escape from some local optima. To address the above issues, we propose a novel way to extract backbone information during the TSP local search process, which is dynamic and can be updated once a local optimal solution is found. We further propose to combine backbone information, $\alpha$-value, and distance to evaluate the edge quality so as to guide the search. Moreover, we abstract their different combinations to arms in a multi-armed bandit (MAB) and use an MAB model to help the algorithm select an appropriate evaluation metric dynamically. Both the backbone information and MAB can provide diverse guiding information and learn from the search history to suggest the best metric. We apply our methods to LKH and LKH-3, which is an extension version of LKH that can be used to solve about 40 variant problems of TSP and Vehicle Routing Problem (VRP). Extensive experiments show the excellent performance and generalization capability of our proposed method, significantly improving LKH for TSP and LKH-3 for two representative TSP and VRP variants, the Colored TSP (CTSP) and Capacitated VRP with Time Windows (CVRPTW).

The emergence of virtual staining technology provides a rapid and efficient alternative for researchers in tissue pathology. It enables the utilization of unlabeled microscopic samples to generate virtual replicas of chemically stained histological slices, or facilitate the transformation of one staining type into another. The remarkable performance of generative networks, such as CycleGAN, offers an unsupervised learning approach for virtual coloring, overcoming the limitations of high-quality paired data required in supervised learning. Nevertheless, large-scale color transformation necessitates processing large field-of-view images in patches, often resulting in significant boundary inconsistency and artifacts. Additionally, the transformation between different colorized modalities typically needs further efforts to modify loss functions and tune hyperparameters for independent training of networks. In this study, we introduce a general virtual staining framework that is adaptable to various conditions. We propose a loss function based on the value mapping constraint to ensure the accuracy of virtual coloring between different pathological modalities, termed the Value Mapping Generative Adversarial Network (VM-GAN). Meanwhile, we present a confidence-based tiling method to address the challenge of boundary inconsistency arising from patch-wise processing. Experimental results on diverse data with varying staining protocols demonstrate that our method achieves superior quantitative indicators and improved visual perception.

Deep Reinforcement Learning (DRL) suffers from uncertainties and inaccuracies in the observation signal in realworld applications. Adversarial attack is an effective method for evaluating the robustness of DRL agents. However, existing attack methods targeting individual sampled actions have limited impacts on the overall policy distribution, particularly in continuous action spaces. To address these limitations, we propose the Distribution-Aware Projected Gradient Descent attack (DAPGD). DAPGD uses distribution similarity as the gradient perturbation input to attack the policy network, which leverages the entire policy distribution rather than relying on individual samples. We utilize the Bhattacharyya distance in DAPGD to measure policy similarity, enabling sensitive detection of subtle but critical differences between probability distributions. Our experiment results demonstrate that DAPGD achieves SOTA results compared to the baselines in three robot navigation tasks, achieving an average 22.03% higher reward drop compared to the best baseline., Comment: 10 pages, 2 figures, 2 tables

For $1

Topological phases are generally characterized by topological invariants denoted by integer numbers. However, different topological systems often require different topological invariants to measure, and theses definition usually fail at critical points. Therefore, it's challenging to predict what would occur during the transformation between two different topological phases. To address these issues, we propose a general definition based on fidelity and trace distance from quantum information theory: manifold distance (MD). This definition does not rely on the berry connection but rather on the information of the two manifolds - their ground state wave functions. Thus, it can measure different topological systems (including traditional band topology models, non-Hermitian systems, and gapless systems, etc.) and exhibit some universal laws during the transformation between two topological phases. Our research demonstrates for different topological manifolds, the change rate (first-order derivative) or susceptibility (second-order derivative) of MD exhibit various divergent behaviors near the critical points. Compared to the strange correlator, which could be used as a diagnosis for short-range entangled states in 1D and 2D, MD is more universal and could be applied to non-Hermitian systems and long-range entangled states. For subsequent studies, we expect the method to be generalized to real-space or non-lattice models, in order to facilitate the study of a wider range of physical platforms such as open systems and many-body localization., Comment: arXiv admin note: substantial text overlap with arXiv:2405.03323

Navigating mobile User Interface (UI) applications using large language and vision models based on high-level goal instructions is emerging as an important research field with significant practical implications, such as digital assistants and automated UI testing. To evaluate the effectiveness of existing models in mobile UI navigation, benchmarks are required and widely used in the literature. Although multiple benchmarks have been recently established for evaluating functional correctness being judged as pass or fail, they fail to address the need for multi-dimensional evaluation of the entire UI navigation process. Furthermore, other exiting related datasets lack an automated and robust benchmarking suite, making the evaluation process labor-intensive and error-prone. To address these issues, in this paper, we propose a new benchmark named Sphinx for multi-dimensional evaluation of existing models in practical UI navigation. Sphinx provides a fully automated benchmarking suite that enables reproducibility across real-world mobile apps and employs reliable evaluators to assess model progress. In addition to functional correctness, Sphinx includes comprehensive toolkits for multi-dimensional evaluation, such as invariant-based verification, knowledge probing, and knowledge-augmented generation to evaluate model capabilities including goal understanding, knowledge and planning, grounding, and instruction following, ensuring a thorough assessment of each sub-process in mobile UI navigation. We benchmark 8 large language and multi-modal models with 13 different configurations on Sphinx. Evaluation results show that all these models struggle on Sphinx, and fail on all test generation tasks. Our further analysis of the multi-dimensional evaluation results underscores the current progress and highlights future research directions to improve a model's effectiveness for mobile UI navigation.

Traffic forecasting is a fundamental task in transportation research, however the scope of current research has mainly focused on a single data modality of loop detectors. Recently, the advances in Artificial Intelligence and drone technologies have made possible novel solutions for efficient, accurate and flexible aerial observations of urban traffic. As a promising traffic monitoring approach, drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks, when combined with existing infrastructure. Therefore, this paper investigates the problem of multi-source traffic speed prediction, simultaneously using drone and loop detector data. A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn spatio-temporal correlations. Detailed analysis shows that predicting accurate segment-level speed is more challenging than the regional speed, especially under high-demand scenarios with heavier congestions and varying traffic dynamics. Utilizing both drone and loop detector data, the prediction accuracy can be improved compared to single-modality cases, when the sensors have lower coverages and are subject to noise. Our simulation study based on vehicle trajectories in a real urban road network has highlighted the added value of integrating drones in traffic forecasting and monitoring.

Link prediction in heterogeneous networks is crucial for understanding the intricacies of network structures and forecasting their future developments. Traditional methodologies often face significant obstacles, including over-smoothing-wherein the excessive aggregation of node features leads to the loss of critical structural details-and a dependency on human-defined meta-paths, which necessitate extensive domain knowledge and can be inherently restrictive. These limitations hinder the effective prediction and analysis of complex heterogeneous networks. In response to these challenges, we propose the Contrastive Heterogeneous grAph Transformer (CHAT). CHAT introduces a novel sampling-based graph transformer technique that selectively retains nodes of interest, thereby obviating the need for predefined meta-paths. The method employs an innovative connection-aware transformer to encode node sequences and their interconnections with high fidelity, guided by a dual-faceted loss function specifically designed for heterogeneous network link prediction. Additionally, CHAT incorporates an ensemble link predictor that synthesizes multiple samplings to achieve enhanced prediction accuracy. We conducted comprehensive evaluations of CHAT using three distinct drug-target interaction (DTI) datasets. The empirical results underscore CHAT's superior performance, outperforming both general-task approaches and models specialized in DTI prediction. These findings substantiate the efficacy of CHAT in addressing the complex problem of link prediction in heterogeneous networks.

Optimizing Register-Transfer Level (RTL) code is crucial for improving hardware PPA performance. Large Language Models (LLMs) offer new approaches for automatic RTL code generation and optimization. However, existing methods often lack decision interpretability (sufficient, understandable justification for decisions), making it difficult for hardware engineers to trust the generated results, thus preventing these methods from being integrated into the design process. To address this, we propose RTLSquad, a novel LLM-Based Multi-Agent system for interpretable RTL code generation. RTLSquad divides the design process into exploration, implementation, and verification & evaluation stages managed by specialized agent squads, generating optimized RTL code through inter-agent collaboration, and providing decision interpretability through the communication process. Experiments show that RTLSquad excels in generating functionally correct RTL code and optimizing PPA performance, while also having the capability to provide decision paths, demonstrating the practical value of our system.

Chirality, a pervasive phenomenon in nature, is widely studied across diverse fields including the origins of life, chemical catalysis, drug discovery, and physical optoelectronics. The investigations of natural chiral materials have been constrained by their intrinsically weak chiral effects. Recently, significant progress has been made in the fabrication and assembly of low-dimensional micro and nanoscale chiral materials and their architectures, leading to the discovery of novel optoelectronic phenomena such as circularly polarized light emission, spin and charge flip, advocating great potential for applications in quantum information, quantum computing, and biosensing. Despite these advancements, the fundamental mechanisms underlying the generation, propagation, and amplification of chirality in low-dimensional chiral materials and architectures remain largely unexplored. To tackle these challenges, we focus on employing ultrafast spectroscopy to investigate the dynamics of chirality evolution, with the aim of attaining a more profound understanding of the microscopic mechanisms governing chirality generation and amplification. This review thus provides a comprehensive overview of the chiral micro-/nano-materials, including two-dimensional transition metal dichalcogenides (TMDs), chiral halide perovskites, and chiral metasurfaces, with a particular emphasis on the physical mechanism. This review further explores the advancements made by ultrafast chiral spectroscopy research, thereby paving the way for innovative devices in chiral photonics and optoelectronics., Comment: 11 figures

Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance. We derive a closed-form expression for the expectation of the squared ACF of random ISAC signals, considering arbitrary modulation bases and constellation mappings within the Nyquist pulse shaping framework. The structure is metaphorically described as an ``iceberg hidden in the sea", where the ``iceberg'' represents the squared mean of the ACF of random ISAC signals, that is determined by the pulse shaping filter, and the ``sea level'' characterizes the corresponding variance, caused by the randomness of the data payload. Our analysis shows that, for QAM/PSK constellations with Nyquist pulse shaping, Orthogonal Frequency Division Multiplexing (OFDM) achieves the lowest ranging sidelobe level across all lags. Building on these insights, we propose a novel Nyquist pulse shaping design to enhance the sensing performance of random ISAC signals. Numerical results validate our theoretical findings, showing that the proposed pulse shaping significantly reduces ranging sidelobes compared to conventional root-raised cosine (RRC) pulse shaping, thereby improving the ranging performance., Comment: 13 pages, 7 figures, submitted to IEEE for possible publication

Recent Multimodal Large Language Models (MLLMs) have typically focused on integrating visual and textual modalities, with less emphasis placed on the role of speech in enhancing interaction. However, speech plays a crucial role in multimodal dialogue systems, and implementing high-performance in both vision and speech tasks remains a significant challenge due to the fundamental modality differences. In this paper, we propose a carefully designed multi-stage training methodology that progressively trains LLM to understand both visual and speech information, ultimately enabling fluent vision and speech interaction. Our approach not only preserves strong vision-language capacity, but also enables efficient speech-to-speech dialogue capabilities without separate ASR and TTS modules, significantly accelerating multimodal end-to-end response speed. By comparing our method against state-of-the-art counterparts across benchmarks for image, video, and speech tasks, we demonstrate that our model is equipped with both strong visual and speech capabilities, making near real-time vision and speech interaction., Comment: https://github.com/VITA-MLLM/VITA

Electric Vertical Take-off and Landing vehicles (eVTOLs) are driving Advanced Air Mobility (AAM) toward transforming urban transportation by extending travel from congested ground networks to low-altitude airspace. This transition promises to reduce traffic congestion and significantly shorten commute times. To ensure aviation safety, eVTOLs must fly within prescribed flight corridors. These corridors are managed by ground-based Air Traffic Control (ATCo) stations, which oversee air-ground communication and flight scheduling. However, one critical challenge remains: the lack of high rate air-ground communication and safe flight planning within these corridors. The introduction of 6G-oriented Stacked Intelligent Metasurface (SIM) technology presents a high rate communication solution. With advanced phase-shifting capabilities, SIM enables precise wireless signal control and supports beam-tracking communication with eVTOLs. Leveraging this technology, we propose a Composite Potential Field (CPF) approach. This method dynamically integrates target, separation, and communication fields to optimize both SIM communication efficiency and flight safety. Simulation results validate the effectiveness of this DT-based approach. Compared to the potential field flight control benchmark, it improves the transmission rate by 8.3\%. Additionally, it reduces flight distance deviation from the prescribed corridor by 10\% compared to predetermined optimization methods., Comment: 15 pages, 11 figures

A black hole (BH) can tear apart a star that ventures within its tidal radius, producing a luminous flare as the stellar debris falls back, known as a tidal disruption event (TDE). While TDEs in quiescent galaxies are relatively well understood, identifying TDEs in active galactic nuclei (AGN) still remains a significant challenge. We present the discovery of AT2021aeuk, a transient exhibiting dual flares within around three years in a narrow-line Seyfert 1 galaxy. Multi-wavelength observations triggered during the second flare in 2023 revealed an extraordinary X-ray V-shaped light curve, strongly anti-correlated with the optical light curve and accompanied by a lag of $\sim$40 days. This behavior is inconsistent with both supernova and pure AGN origins. In addition, a new broad component emerges in the Balmer lines during the second flare, showing a clear reverberation signal to the continuum variation. We propose that the dual-flare may be linked to a repeating partial TDE (rpTDE), where the second flare results from a collision between the TDE stream and the inner accretion disk, triggering an optical flare while simultaneously partially destroying the X-ray corona. However, other mechanisms, such as a stellar-mass BH (sBH) merger within an accretion disk, could produce similar phenomena, which we cannot entirely rule out. The Vera C. Rubin Observatory will be a powerful tool for further investigating the nature of such events in the future.