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Innovative staffing models are promising, but challenging to scale up. What does the work of leading strategic staffing involve, and what could make scaling up easier? This report digs deep into the many challenges system leaders face when scaling up innovative staffing solutions. These leaders are trying to address longstanding teacher shortages and retention challenges by rethinking everything, including who they hire and how they design the job, provide support, build trust, and uproot old assumptions about the teaching role. The early results are promising: these leaders report fewer vacancies, higher staff satisfaction, and improved student learning experiences. This work is "so hard, but so rewarding"--and it could be much more manageable if policymakers, technical assistance providers, and researchers stepped up to share the load.

In this work, we proposed a new 3D integration technology: the Flip 3D integration (F3D), consisting of the 3D transistor stacking, the 3D dual-sided interconnects, the 3D die-to-die stacking and the dual-sided Monolithic 3D (M3D). Based on a 32-bit FFET RISCV core, besides the scaling benefits of the Flip FET (FFET), the dual-sided signal routing shows even more routing flexibility with 6.8% area reduction and 5.9% EDP improvement. Novel concepts of Multi-Flipping processes (Double Flips and Triple Flips) were proposed to relax the thermal budget constraints in the F3D and thus support the dual-sided M3D in the F3D. The core's EDP and frequency are improved by up to 3.2% and 2.3% respectively, after BEOL optimizations based on the Triple Flips compared with unoptimized ones., Comment: Accepted by EDTM 2025

We analyze the second order perturbations of the Deser-Woodard II (DWII), Vardanyan-Akrami-Amendola-Silvestri (VAAS) and Amendola-Burzilla-Nersisyan (ABN) nonlocal gravity models in an attempt to extract their associated gravitational wave energy-momentum fluxes. In Minkowski spacetime, the gravitational spatial momentum density is supposed to scale at most as $1/r^{2}$, in the $r \rightarrow \infty$ limit, where $r$ is the observer-source spatial distance. The DWII model has a divergent flux because its momentum density goes as $1/r$; though this can be avoided when we set to zero the first derivative of its distortion function at the origin. Meanwhile, the ABN model also suffers from a divergent flux because its momentum density goes as $r^{2}$. The momentum density from the VAAS model was computed on a cosmological background expressed in a Fermi-Normal-Coordinate system, and was found to scale as $r$. For generic parameters, therefore, none of these three Dark Energy models appear to yield well-defined gravitational wave energies, as a result of their nonlocal gravitational self-interactions., Comment: 23 pages, 1 figure

Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGraph), which brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. On the top of our framework is a toy graph vector library that we established, which captures key attributes, such as features and task-specific label information. During inference, the RAGraph adeptly retrieves similar toy graphs based on key similarities in downstream tasks, integrating the retrieved data to enrich the learning context via the message-passing prompting mechanism. Our extensive experimental evaluations demonstrate that RAGraph significantly outperforms state-of-the-art graph learning methods in multiple tasks such as node classification, link prediction, and graph classification across both dynamic and static datasets. Furthermore, extensive testing confirms that RAGraph consistently maintains high performance without the need for task-specific fine-tuning, highlighting its adaptability, robustness, and broad applicability., Comment: NeurIPS 2024

An unsolved issue in widely used methods such as Support Vector Data Description (SVDD) and Small Sphere and Large Margin SVM (SSLM) for anomaly detection is their nonconvexity, which hampers the analysis of optimal solutions in a manner similar to SVMs and limits their applicability in large-scale scenarios. In this paper, we introduce a novel convex SSLM formulation which has been demonstrated to revert to a convex quadratic programming problem for hyperparameter values of interest. Leveraging the convexity of our method, we derive numerous results that are unattainable with traditional nonconvex approaches. We conduct a thorough analysis of how hyperparameters influence the optimal solution, pointing out scenarios where optimal solutions can be trivially found and identifying instances of ill-posedness. Most notably, we establish connections between our method and traditional approaches, providing a clear determination of when the optimal solution is unique -- a task unachievable with traditional nonconvex methods. We also derive the {\nu}-property to elucidate the interactions between hyperparameters and the fractions of support vectors and margin errors in both positive and negative classes.

We discuss the transition between black strings and fundamental strings in the presence of a compact dimension, $\mathbb{S}^1_z$. In particular, we study the Horowitz-Polchinski effective field theory in $\mathbb{R}^d\times\mathbb{S}^1_z$, with a reduction on the Euclidean time circle $\mathbb{S}_\tau^1$. The classical solution of this theory describes a bound state of self-gravitating strings, known as a ``string star'', in Lorentzian spacetime. By analyzing non-uniform perturbations to the uniform solution, we identify the critical mass at which the string star becomes unstable towards non-uniformity along the spatial circle (i.e., Gregory-Laflamme instability) and determine the order of the associated phase transition. For $3\le d<4$, we argue that at the critical mass, the uniform string star can transition into a localized black hole. More generally, we describe the sequence of transitions from a large uniform black string as its mass decreases, depending on the number of dimensions $d$. Additionally, using the $SL(2)_k/U(1)$ model in string theory, we show that for sufficiently large $d$, the uniform black string is stable against non-uniformity before transitioning into fundamental strings. We also present a novel solution that exhibits double winding symmetry breaking in the asymptotically $\mathbb{R}^d\times\mathbb{S}^1_\tau\times\mathbb{S}^1_z$ Euclidean spacetime., Comment: 37+8 pages, 18 figures

This study presents an automatic differentiation (AD)-based optimization framework for flow control in compressible turbulent channel flows. We developed a fully differentiable boundary condition framework that allows for the precise calculation of gradients with respect to boundary control variables. This facilitates the efficient optimization of flow control methods. The framework's adaptability and effectiveness are demonstrated using two boundary conditions: opposition control and tunable permeable walls. Various optimization targets are evaluated, including wall friction and turbulent kinetic energy (TKE), across different time horizons. In each optimization, there were around $4\times10^4$ control variables and $3\times10^{9}$ state variables in a single episode. Results indicate that TKE-targeted opposition control achieves a more stable and significant reduction in drag, with effective suppression of turbulence throughout the channel. In contrast, strategies that focus directly on minimizing wall friction were found to be less effective, exhibiting instability and increased turbulence in the outer region. The tunable permeable walls also show potential to achieve stable drag reduction through a `flux-inducing' mechanism. This study demonstrates the advantages of AD-based optimization in complex flow control scenarios and provides physical insight into the choice of quantity of interest for improved optimization performance.

We report on the reactions $\psi(3770)\to \bar{\Omega}^+ \bar{K} \Xi $ and $\psi(3770)\to \bar{\Omega}^+ \bar{K}\Xi^*(1530)~(\Xi^*(1530)\to \pi\Xi $), and calculate the mass distributions $\frac{{\rm d}\Gamma}{{\rm d}M_\text{inv}(\bar{K}\Xi)}$ and $\frac{{\rm d}\Gamma}{{\rm d}M_\text{inv}(\bar{K}\Xi^*)}$, respectively. We obtain clear peaks corresponding to the $\Omega(2012)$. From the decay of $\psi(3770)\to \bar{\Omega}^+ \bar{K}\Xi^*$, we also get a second, broader, peak around $2035\,\rm MeV$, which comes from the $\Omega(2012)$ decay to $\bar{K}\Xi^*$. This second peak is closely tied to the molecular picture of the $\Omega(2012)$ with the $\bar{K}\Xi^*(1530)$ and $\eta\Omega$ components. Its observation would provide a boost to the molecular picture of the $\Omega(2012)$., Comment: 6 pages, 5 figures

This paper investigates distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenario under transportation cyber-physical system. Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ramp merging scenario, where the trajectories of all vehicles are jointly optimized. To get rid of the reliance on a central controller and reduce computation time, a distributed solution to this problem implemented among CAVs through Vehicles-to-Everything (V2X) communication is proposed. Unlike existing method, our method can distribute the computational task among CAVs and carry out parallel solving through V2X communication. Then, a multi-vehicles model predictive control (MPC) problem aimed at maximizing system stability and minimizing control input is formulated based on the solution of the first problem subject to strict safety constants and input limits. Due to these complex constraints, this problem becomes high-dimensional, centralized, and non-convex. To solve it in a short time, a decomposition and convex reformulation method, namely distributed cooperative iterative model predictive control (DCIMPC), is proposed. This method leverages the communication capability of CAVs to decompose the problem, making full use of the computational resources on vehicles to achieve fast solutions and distributed control. The two above problems with their corresponding solving methods form the systemic framework of the V2X assisted distributed computing and control. Simulations have been conducted to evaluate the framework's convergence, safety, and solving speed. Additionally, extra experiments are conducted to validate the performance of DCIMPC. The results show that our method can greatly improve computation speed without sacrificing system performance., Comment: This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/V2X-Assisted-Distributed-Computing-and-Control-Framework-for-Connected-and-Automated-Vehicles.git

Autonomous mobile app interaction has become increasingly important with growing complexity of mobile applications. Developing intelligent agents that can effectively navigate and interact with mobile apps remains a significant challenge. In this paper, we propose an Explainable Behavior Cloning LLM Agent (EBC-LLMAgent), a novel approach that combines large language models (LLMs) with behavior cloning by learning demonstrations to create intelligent and explainable agents for autonomous mobile app interaction. EBC-LLMAgent consists of three core modules: Demonstration Encoding, Code Generation, and UI Mapping, which work synergistically to capture user demonstrations, generate executable codes, and establish accurate correspondence between code and UI elements. We introduce the Behavior Cloning Chain Fusion technique to enhance the generalization capabilities of the agent. Extensive experiments on five popular mobile applications from diverse domains demonstrate the superior performance of EBC-LLMAgent, achieving high success rates in task completion, efficient generalization to unseen scenarios, and the generation of meaningful explanations., Comment: 20 pages

Self-supervised learning (SSL) has achieved impressive results across several computer vision tasks, even rivaling supervised methods. However, its performance degrades on real-world datasets with long-tailed distributions due to difficulties in capturing inherent class imbalances. Although supervised long-tailed learning offers significant insights, the absence of labels in SSL prevents direct transfer of these strategies.To bridge this gap, we introduce Adaptive Paradigm Synergy (APS), a cross-paradigm objective that seeks to unify the strengths of both paradigms. Our approach reexamines contrastive learning from a spatial structure perspective, dynamically adjusting the uniformity of latent space structure through adaptive temperature tuning. Furthermore, we draw on a re-weighting strategy from supervised learning to compensate for the shortcomings of temperature adjustment in explicit quantity perception.Extensive experiments on commonly used long-tailed datasets demonstrate that APS improves performance effectively and efficiently. Our findings reveal the potential for deeper integration between supervised and self-supervised learning, paving the way for robust models that handle real-world class imbalance., Comment: 11 pages, 3 figures

This work proposes DOFS, a pilot dataset of 3D deformable objects (DOs) (e.g., elasto-plastic objects) with full spatial information (i.e., top, side, and bottom information) using a novel and low-cost data collection platform with a transparent operating plane. The dataset consists of active manipulation action, multi-view RGB-D images, well-registered point clouds, 3D deformed mesh, and 3D occupancy with semantics, using a pinching strategy with a two-parallel-finger gripper. In addition, we trained a neural network with the down-sampled 3D occupancy and action as input to model the dynamics of an elasto-plastic object. Our dataset and all CADs of the data collection system will be released soon on our website., Comment: 5 pages, 6 figures, 2024 CoRL Workshop on Learning Robot Fine and Dexterous Manipulation: Perception and Control

In this paper, we prove an optimal systolic inequality and the corresponding rigidity in the equality case on closed manifolds with positive bi-Ricci curvature, which generalizes the work of Bray-Brendle-Neves. The proof is given in all dimensions based on the method of minimal surfaces under the Generic Regularity Hypothesis, which is known to be true up to dimension ten., Comment: 23 pages, no figure

Thermoelectricity in telluride materials is often improved by replacing telluride with selenium in its crystal. Most work, however, focuses on bulk crystal and leaves the 2D thin films intact. In this paper, we optimize the fabrication of selenium-doped bismuth telluride (Bi$_2$Te$_{3-\rm{x}}$Se$_{\rm{x}}$) thin films using a 3-source thermal co-evaporation. Thermoelectric properties, including the Seebeck coefficient and electrical resistivity, are systematically characterized to evaluate the material's performance for thermoelectric applications near room temperature. The thin films were deposited under carefully controlled conditions, with the evaporation rates of bismuth, tellurium, and selenium precisely monitored to achieve the desired stoichiometry and crystalline phase. Finally, thermoelectricity in Bi$_2$Te$_{3-\rm{x}}$Se$_{\rm{x}}$ at the ultra-thin regime is investigated. We consistently obtain films with thickness near 30 nm with a Seebeck coefficient of 400 $\mu$V/K and a power factor of 1 mW/mK$^2$.

We consider Bayesian algorithm execution (BAX), a framework for efficiently selecting evaluation points of an expensive function to infer a property of interest encoded as the output of a base algorithm. Since the base algorithm typically requires more evaluations than are feasible, it cannot be directly applied. Instead, BAX methods sequentially select evaluation points using a probabilistic numerical approach. Current BAX methods use expected information gain to guide this selection. However, this approach is computationally intensive. Observing that, in many tasks, the property of interest corresponds to a target set of points defined by the function, we introduce PS-BAX, a simple, effective, and scalable BAX method based on posterior sampling. PS-BAX is applicable to a wide range of problems, including many optimization variants and level set estimation. Experiments across diverse tasks demonstrate that PS-BAX performs competitively with existing baselines while being significantly faster, simpler to implement, and easily parallelizable, setting a strong baseline for future research. Additionally, we establish conditions under which PS-BAX is asymptotically convergent, offering new insights into posterior sampling as an algorithm design paradigm., Comment: Published as a conference paper at the 38th Conference on Neural Information Processing Systems (NeurIPS 2024)

One-shot Federated Learning (OFL) significantly reduces communication costs in FL by aggregating trained models only once. However, the performance of advanced OFL methods is far behind the normal FL. In this work, we provide a causal view to find that this performance drop of OFL methods comes from the isolation problem, which means that local isolatedly trained models in OFL may easily fit to spurious correlations due to the data heterogeneity. From the causal perspective, we observe that the spurious fitting can be alleviated by augmenting intermediate features from other clients. Built upon our observation, we propose a novel learning approach to endow OFL with superb performance and low communication and storage costs, termed as FuseFL. Specifically, FuseFL decomposes neural networks into several blocks, and progressively trains and fuses each block following a bottom-up manner for feature augmentation, introducing no additional communication costs. Comprehensive experiments demonstrate that FuseFL outperforms existing OFL and ensemble FL by a significant margin. We conduct comprehensive experiments to show that FuseFL supports high scalability of clients, heterogeneous model training, and low memory costs. Our work is the first attempt using causality to analyze and alleviate data heterogeneity of OFL.

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

It has been observed that flattening the mean velocity profile of pipe flow by body force can laminarise turbulence, a promising means to reduce frictional drag substantially. To explore whether there is a more efficient body force to eliminate turbulence, we consider time-independent active body forces with varying spatial dependencies. Results confirm that when using an active force, a flattened forced laminar profile is needed to eliminate turbulence, and that a purely streamwise body force is best for laminarisation. While these results required an expensive nonlinear optimisation, it was also observed that the optimal forced profile exhibits reduced linear transient growth (TG). To determine whether the reduction of linear TG alone is a sufficient target for the laminarisation of turbulence, a linear Lagrange Multiplier technique is used to minimise TG of perturbations to the forced laminar profile. The optimal velocity profiles reveal that TG for each azimuthal wavenumber strongly depends on the radial velocity gradient at some specific radial interval. The optimal velocity profile obtained by minimising TG of perturbations of azimuthal wavenumber m = 1 is shown to be able to eliminate turbulence at Re = 2400, but a more effective reduction of TG, including perturbations of higher m, is needed for laminarisation at higher Reynolds number Re = 3000. The streaks formed with the flattened forced laminar profile reveal the mechanism of laminarisation: it is found that the lift-up mechanism in the more flattened forced laminar profile creates a lower streak (i.e. closer to the wall) that is more stable, so that turbulence is harder to maintain through streak instability. Further numerical experiments by disturbing a periodic orbit validated that the breakdown of turbulence self-sustaining mechanisms is mainly caused by suppression of the formation of streaks.

Model editing has become an increasingly popular alternative for efficiently updating knowledge within language models. Current methods mainly focus on reliability, generalization, and locality, with many methods excelling across these criteria. Some recent works disclose the pitfalls of these editing methods such as knowledge distortion or conflict. However, the general abilities of post-edited language models remain unexplored. In this paper, we perform a comprehensive evaluation on various editing methods and different language models, and have following findings. (1) Existing editing methods lead to inevitable performance deterioration on general benchmarks, indicating that existing editing methods maintain the general abilities of the model within only a few dozen edits. When the number of edits is slightly large, the intrinsic knowledge structure of the model is disrupted or even completely damaged. (2) Instruction-tuned models are more robust to editing, showing less performance drop on general knowledge after editing. (3) Language model with large scale is more resistant to editing compared to small model. (4) The safety of the edited model, is significantly weakened, even for those safety-aligned models. Our findings indicate that current editing methods are only suitable for small-scale knowledge updates within language models, which motivates further research on more practical and reliable editing methods. The details of code and reproduction can be found in https://github.com/lqinfdim/EditingEvaluation., Comment: NeurIPS 2024 https://github.com/lqinfdim/EditingEvaluation

The spatial distribution of spurious dots in SiMOS single-electron transistors (SETs), fabricated on an industrial 300 mm process line, has been statistically analyzed. To have a deeper understanding of the origin of these spurious dots, we analyzed SETs with three different oxide thicknesses: 8 nm, 12 nm and 20 nm. By combining spurious dot triangulation cryo-measurement with simulations of strain, gate bias, and location of the electron wave function, we demonstrate that most spurious dots are formed through the combined effects of strain and gate bias, leading to variations in the conduction band energy. Despite the similar thermal expansion coefficients of polycrystalline silicon gates and single-crystalline silicon substrates, strain remains a crucial factor in spurious dots formation. This learning can be use to optimize the device design and the oxide thickness, to reduce the density of spurious dot while keeping quantum dot tunability., Comment: 10 pages, 9 figures

Photothermal microscopy is an emerging tool for measuring light-matter interactions with single-molecule sensitivity. It is generally believed that the spectral acquisition speed in photothermal microscopy is limited by the slow thermal diffusion process. Here, we demonstrate mid-infrared energy deposition (MIRED) spectroscopy, which offers both microsecond-scale temporal resolution and sub-micron spatial resolution. In this approach, the photothermal process is optically probed while the infrared pulses from a quantum cascade laser array are rapidly tuned. Based on Newton's law, the energy deposition corresponds to the first derivative of local temperature rise over time and provides the instantaneous infrared absorption. By employing time-resolved measurement of transient energy deposition, the upper limit for spectrum encoding shifts to the vibrational relaxation level, which occurs on the picosecond scale. This method significantly increases the detection bandwidth while maintaining the sensitivity and resolution advantages of photothermal detection.

In this work, we propose a unified representation for Super-Resolution (SR) and Image Compression, termed **Factorized Fields**, motivated by the shared principles between these two tasks. Both SISR and Image Compression require recovering and preserving fine image details--whether by enhancing resolution or reconstructing compressed data. Unlike previous methods that mainly focus on network architecture, our proposed approach utilizes a basis-coefficient decomposition to explicitly capture multi-scale visual features and structural components in images, addressing the core challenges of both tasks. We first derive our SR model, which includes a Coefficient Backbone and Basis Swin Transformer for generalizable Factorized Fields. Then, to further unify these two tasks, we leverage the strong information-recovery capabilities of the trained SR modules as priors in the compression pipeline, improving both compression efficiency and detail reconstruction. Additionally, we introduce a merged-basis compression branch that consolidates shared structures, further optimizing the compression process. Extensive experiments show that our unified representation delivers state-of-the-art performance, achieving an average relative improvement of 204.4% in PSNR over the baseline in Super-Resolution (SR) and 9.35% BD-rate reduction in Image Compression compared to the previous SOTA., Comment: Project page: https://jayisaking.github.io/FIPER/

We propose UnCLe, a standardized benchmark for Unsupervised Continual Learning of a multimodal depth estimation task: Depth completion aims to infer a dense depth map from a pair of synchronized RGB image and sparse depth map. We benchmark depth completion models under the practical scenario of unsupervised learning over continuous streams of data. Existing methods are typically trained on a static, or stationary, dataset. However, when adapting to novel non-stationary distributions, they "catastrophically forget" previously learned information. UnCLe simulates these non-stationary distributions by adapting depth completion models to sequences of datasets containing diverse scenes captured from distinct domains using different visual and range sensors. We adopt representative methods from continual learning paradigms and translate them to enable unsupervised continual learning of depth completion. We benchmark these models for indoor and outdoor and investigate the degree of catastrophic forgetting through standard quantitative metrics. Furthermore, we introduce model inversion quality as an additional measure of forgetting. We find that unsupervised continual learning of depth completion is an open problem, and we invite researchers to leverage UnCLe as a development platform., Comment: Preprint

Graphs are widely used for modeling relational data in real-world scenarios, such as social networks and urban computing. Existing LLM-based graph analysis approaches either integrate graph neural networks (GNNs) for specific machine learning tasks, limiting their transferability, or rely solely on LLMs' internal reasoning ability, resulting in suboptimal performance. To address these limitations, we take advantage of recent advances in LLM-based agents, which have shown capabilities of utilizing external knowledge or tools for problem solving. By simulating human problem-solving strategies such as analogy and collaboration, we propose a multi-agent system based on LLMs named GraphTeam, for graph analysis. GraphTeam consists of five LLM-based agents from three modules, and the agents with different specialities can collaborate with each other to address complex problems. Specifically, (1) input-output normalization module: the question agent extracts and refines four key arguments from the original question, facilitating the problem understanding, and the answer agent organizes the results to meet the output requirement; (2) external knowledge retrieval module: we first build a knowledge base consisting of relevant documentation and experience information, and then the search agent retrieves the most relevant entries for each question. (3) problem-solving module: given the retrieved information from search agent, the coding agent uses established algorithms via programming to generate solutions, and in case the coding agent does not work, the reasoning agent will directly compute the results without programming. Extensive experiments on six graph analysis benchmarks demonstrate that GraphTeam achieves state-of-the-art performance with an average 25.85% improvement over the best baseline in terms of accuracy. The code and data are available at https://github.com/BUPT-GAMMA/GraphTeam.

Sparse Mixture of Experts (MoE) models, while outperforming dense Large Language Models (LLMs) in terms of performance, face significant deployment challenges during inference due to their high memory demands. Existing offloading techniques, which involve swapping activated and idle experts between the GPU and CPU, often suffer from rigid expert caching mechanisms. These mechanisms fail to adapt to dynamic routing, leading to inefficient cache utilization, or incur prohibitive costs for prediction training. To tackle these inference-specific challenges, we introduce ExpertFlow, a comprehensive system specifically designed to enhance inference efficiency by accommodating flexible routing and enabling efficient expert scheduling between CPU and GPU. This reduces overhead and boosts system performance. Central to our approach is a predictive routing path-based offloading mechanism that utilizes a lightweight predictor to accurately forecast routing paths before computation begins. This proactive strategy allows for real-time error correction in expert caching, significantly increasing cache hit ratios and reducing the frequency of expert transfers, thereby minimizing I/O overhead. Additionally, we implement a dynamic token scheduling strategy that optimizes MoE inference by rearranging input tokens across different batches. This method not only reduces the number of activated experts per batch but also improves computational efficiency. Our extensive experiments demonstrate that ExpertFlow achieves up to 93.72\% GPU memory savings and enhances inference speed by 2 to 10 times compared to baseline methods, highlighting its effectiveness and utility as a robust solution for resource-constrained inference scenarios., Comment: Mixture-of-Experts, Inference, Offloading

The intersection of LLMs (Large Language Models) and UAV (Unoccupied Aerial Vehicles) technology represents a promising field of research with the potential to enhance UAV capabilities significantly. This study explores the application of LLMs in UAV control, focusing on the opportunities for integrating advanced natural language processing into autonomous aerial systems. By enabling UAVs to interpret and respond to natural language commands, LLMs simplify the UAV control and usage, making them accessible to a broader user base and facilitating more intuitive human-machine interactions. The paper discusses several key areas where LLMs can impact UAV technology, including autonomous decision-making, dynamic mission planning, enhanced situational awareness, and improved safety protocols. Through a comprehensive review of current developments and potential future directions, this study aims to highlight how LLMs can transform UAV operations, making them more adaptable, responsive, and efficient in complex environments. A template development framework for integrating LLMs in UAV control is also described. Proof of Concept results that integrate existing LLM models and popular robotic simulation platforms are demonstrated. The findings suggest that while there are substantial technical and ethical challenges to address, integrating LLMs into UAV control holds promising implications for advancing autonomous aerial systems.

This study extended noncanonical warm inflation to the nonminimal derivative coupling scenario. The fundamental equations, including the evolution equations and the slow roll equations of this new framework, were derived. The enlarged damping term, which encompasses both gravitationally enhanced friction and thermal damping, resulted in a well overdamped inflationary process, ensuring that the slow roll approximations can be satisfactorily satisfied. A linear stability analysis corroborated the viability of this approach, yielding significantly relaxed slow roll conditions within the context of noncanonical warm inflation with nonminimal derivative coupling. Subsequently, the density fluctuations in this new framework were analyzed, leading to approximately analytic results for the power spectrum, spectral index, and related quantities. Both the energy scale at horizon crossing and the tensor-to-scalar ratio decreased considerably because of the effects of thermal damping and nonminimal derivative coupling. The upper bound for field excursion remained safely sub-Planckian in this inflationary scenario. Thus we reached a successful and meaningful model to broad the scope of warm inflation., Comment: 18 pages, 0 figures

Metastasis is a leading cause of cancer-related mortality and remains challenging to detect during early stages. Accurate identification of cancers likely to metastasize can improve treatment strategies and patient outcomes. This study leverages publicly available gene expression profiles from primary cancers, with and without distal metastasis, to build predictive models. We utilize elastic net regularization within a hierarchical classification framework to predict both the tissue of origin and the metastasis status of primary tumors. Our elastic net-based hierarchical classification achieved a tissue-of-origin prediction accuracy of 97%, and a metastasis prediction accuracy of 90%. Notably, mitochondrial gene expression exhibited significant negative correlations with metastasis, providing potential biological insights into the underlying mechanisms of cancer progression.

Multimodal Large Language Models (MLLMs) utilize multimodal contexts consisting of text, images, or videos to solve various multimodal tasks. However, we find that changing the order of multimodal input can cause the model's performance to fluctuate between advanced performance and random guessing. This phenomenon exists in both single-modality (text-only or image-only) and mixed-modality (image-text-pair) contexts. Furthermore, we demonstrate that popular MLLMs pay special attention to certain multimodal context positions, particularly the beginning and end. Leveraging this special attention, we place key video frames and important image/text content in special positions within the context and submit them to the MLLM for inference. This method results in average performance gains of 14.7% for video-caption matching and 17.8% for visual question answering tasks. Additionally, we propose a new metric, Position-Invariant Accuracy (PIA), to address order bias in MLLM evaluation. Our research findings contribute to a better understanding of Multi-Modal In-Context Learning (MMICL) and provide practical strategies for enhancing MLLM performance without increasing computational costs.

The $\sigma_k(A_g)$ curvature and the boundary $\mathcal{B}^k_g$ curvature arise naturally from the Chern--Gauss--Bonnet formula for manifolds with boundary. In this paper, we prove a Liouville theorem for the equation $\sigma_k(A_g)=1$ in $\overline{\mathbb{R}^n_+}$ with the boundary condition $\mathcal{B}^k_g=c$ on $\partial\mathbb{R}^n_+$, where $g=e^{2v}|dx|^2$ and $c$ is some nonnegative constant. This extends an earlier result of Wei, which assumes the existence of $\lim_{|x|\to\infty}(v(x)+2\log|x|)$. In addition, we establish a local gradient estimate for solutions of such equations, assuming an upper bound on the solution $v$.

The prevalent high intrinsic absorption in the crystalline state of phase-change materials (PCMs), typically leads to a decline in modulation efficiency for phase-change metasurfaces, underutilizing their potential for quasi-continuous phase-state tuning. This research introduces a concise design approach that maximizes the exploitation of the quasi-continuous phase transition properties of PCM, achieving high-efficiency spatial light modulation. By optimizing the metasurface design, the phase modulation process is strategically localized in a low crystallization ratio state, significantly reducing the impact of material absorption on modulation efficiency. Utilizing GSST as an example, numerical simulations demonstrate a minimum reflectance of 46.5% at the target wavelength of 1550 nm, with a phase modulation depth of 246.6{\deg}. The design is fruther extended to dynamic polarization modulation by incorporating structural anisotropy, enabling independent control of amplitude and phase modulation in orthogonal polarization directions. This strategy not only circumvents the efficiency limitations of crystalline states but also harnesses the full potential of PCMs for multifunctional photonic applications.

We study from the theoretical point of view the $\Lambda_c^+\to \pi^+ \eta \Lambda$ reaction, recently measured by the Belle and BESIII Collaborations, where clear signals are observed for $a_0(980)$, $\Lambda(1670)$, and $\Sigma(1385)$ excitation. By considering the $a_0(980)$ and $\Lambda(1670)$ as dynamically generated resonances from the meson meson and meson baryon interaction, respectively, we are able to determine their relative production strength in the reaction, which is also tied to the strength of the $\pi^+ \eta \Lambda$ tree level contribution. We observe that this latter strength is very big and there are large destructive interferences between the tree level and the rescattering terms where the $a_0(980)$ and $\Lambda(1670)$ are generated. The $\Sigma(1385)$ contribution is included by means of a free parameter, the only one of the theory, up to a global normalization, when one considers only external emission, and we observe that the spin flip part of this term, usually ignored in theoretical and experimental works, plays an important role determining the shape of the mass distributions. Internal emission is also considered and it is found to play a minor role.

Time series analysis plays a critical role in numerous applications, supporting tasks such as forecasting, classification, anomaly detection, and imputation. In this work, we present the time series pattern machine (TSPM), a model designed to excel in a broad range of time series tasks through powerful representation and pattern extraction capabilities. Traditional time series models often struggle to capture universal patterns, limiting their effectiveness across diverse tasks. To address this, we define multiple scales in the time domain and various resolutions in the frequency domain, employing various mixing strategies to extract intricate, task-adaptive time series patterns. Specifically, we introduce a general-purpose TSPM that processes multi-scale time series using (1) multi-resolution time imaging (MRTI), (2) time image decomposition (TID), (3) multi-scale mixing (MCM), and (4) multi-resolution mixing (MRM) to extract comprehensive temporal patterns. MRTI transforms multi-scale time series into multi-resolution time images, capturing patterns across both temporal and frequency domains. TID leverages dual-axis attention to extract seasonal and trend patterns, while MCM hierarchically aggregates these patterns across scales. MRM adaptively integrates all representations across resolutions. This method achieves state-of-the-art performance across 8 time series analytical tasks, consistently surpassing both general-purpose and task-specific models. Our work marks a promising step toward the next generation of TSPMs, paving the way for further advancements in time series analysis.

The use of large language models (LLMs) as automated evaluation tools to assess the quality of generated natural language, known as LLMs-as-Judges, has demonstrated promising capabilities and is rapidly gaining widespread attention. However, when applied to pairwise comparisons of candidate responses, LLM-based evaluators often exhibit selection bias. Specifically, their judgments may become inconsistent when the option positions or ID tokens are swapped, compromising the effectiveness and fairness of the evaluation result. To address this challenge, we introduce CalibraEval, a novel label-free method for mitigating selection bias during inference. Specifically, CalibraEval reformulates debiasing as an optimization task aimed at adjusting observed prediction distributions to align with unbiased prediction distributions. To solve this optimization problem, we propose a non-parametric order-preserving algorithm (NOA). This algorithm leverages the partial order relationships between model prediction distributions, thereby eliminating the need for explicit labels and precise mathematical function modeling.Empirical evaluations of LLMs in multiple representative benchmarks demonstrate that CalibraEval effectively mitigates selection bias and improves performance compared to existing debiasing methods. This work marks a step toward building more robust and unbiased automated evaluation frameworks, paving the way for improved reliability in AI-driven assessments, Comment: 13 pages

Existing automated essay scoring (AES) has solely relied on essay text without using explanatory rationales for the scores, thereby forgoing an opportunity to capture the specific aspects evaluated by rubric indicators in a fine-grained manner. This paper introduces Rationale-based Multiple Trait Scoring (RMTS), a novel approach for multi-trait essay scoring that integrates prompt-engineering-based large language models (LLMs) with a fine-tuning-based essay scoring model using a smaller large language model (S-LLM). RMTS uses an LLM-based trait-wise rationale generation system where a separate LLM agent generates trait-specific rationales based on rubric guidelines, which the scoring model uses to accurately predict multi-trait scores. Extensive experiments on benchmark datasets, including ASAP, ASAP++, and Feedback Prize, show that RMTS significantly outperforms state-of-the-art models and vanilla S-LLMs in trait-specific scoring. By assisting quantitative assessment with fine-grained qualitative rationales, RMTS enhances the trait-wise reliability, providing partial explanations about essays.

Reinforcement Learning (RL) is a promising solution, allowing Unmanned Underwater Vehicles (UUVs) to learn optimal behaviors through trial and error. However, existing simulators lack efficient integration with RL methods, limiting training scalability and performance. This paper introduces MarineGym, a novel simulation framework designed to enhance RL training efficiency for UUVs by utilizing GPU acceleration. MarineGym offers a 10,000-fold performance improvement over real-time simulation on a single GPU, enabling rapid training of RL algorithms across multiple underwater tasks. Key features include realistic dynamic modeling of UUVs, parallel environment execution, and compatibility with popular RL frameworks like PyTorch and TorchRL. The framework is validated through four distinct tasks: station-keeping, circle tracking, helical tracking, and lemniscate tracking. This framework sets the stage for advancing RL in underwater robotics and facilitating efficient training in complex, dynamic environments., Comment: Accepted by the 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40)

Low Earth orbit (LEO) satellites are capable of gathering abundant Earth observation data (EOD) to enable different Internet of Things (IoT) applications. However, to accomplish an effective EOD processing mechanism, it is imperative to investigate: 1) the challenge of processing the observed data without transmitting those large-size data to the ground because the connection between the satellites and the ground stations is intermittent, and 2) the challenge of processing the non-independent and identically distributed (non-IID) satellite data. In this paper, to cope with those challenges, we propose an orbit-based spectral clustering-assisted clustered federated self-knowledge distillation (OSC-FSKD) approach for each orbit of an LEO satellite constellation, which retains the advantage of FL that the observed data does not need to be sent to the ground. Specifically, we introduce normalized Laplacian-based spectral clustering (NLSC) into federated learning (FL) to create clustered FL in each round to address the challenge resulting from non-IID data. Particularly, NLSC is adopted to dynamically group clients into several clusters based on cosine similarities calculated by model updates. In addition, self-knowledge distillation is utilized to construct each local client, where the most recent updated local model is used to guide current local model training. Experiments demonstrate that the observation accuracy obtained by the proposed method is separately 1.01x, 2.15x, 1.10x, and 1.03x higher than that of pFedSD, FedProx, FedAU, and FedALA approaches using the SAT4 dataset. The proposed method also shows superiority when using other datasets., Comment: 10 pages, 5 figures

In this paper, cyber-attack prevention for the prosumer-based electric vehicle (EV) charging stations (EVCSs) is investigated, which covers two aspects: 1) cyber-attack detection on prosumers' network traffic (NT) data, and 2) cyber-attack intervention. To establish an effective prevention mechanism, several challenges need to be tackled, for instance, the NT data per prosumer may be non-independent and identically distributed (non-IID), and the boundary between benign and malicious traffic becomes blurred. To this end, we propose an edge-assisted federated prototype knowledge distillation (E-FPKD) approach, where each client is deployed on a dedicated local edge server (DLES) and can report its availability for joining the federated learning (FL) process. Prior to the E-FPKD approach, to enhance accuracy, the Pearson Correlation Coefficient is adopted for feature selection. Regarding the proposed E-FPKD approach, we integrate the knowledge distillation and prototype aggregation technique into FL to deal with the non-IID challenge. To address the boundary issue, instead of directly calculating the distance between benign and malicious traffic, we consider maximizing the overall detection correctness of all prosumers (ODC), which can mitigate the computational cost compared with the former way. After detection, a rule-based method will be triggered at each DLES for cyber-attack intervention. Experimental analysis demonstrates that the proposed E-FPKD can achieve the largest ODC on NSL-KDD, UNSW-NB15, and IoTID20 datasets in both binary and multi-class classification, compared with baselines. For instance, the ODC for IoTID20 obtained via the proposed method is separately 0.3782% and 4.4471% greater than FedProto and FedAU in multi-class classification., Comment: 27 pages, 12 figures

Metallicities of both gas and stars decline toward large radii in spiral galaxies, a trend known as the radial metallicity gradient. We quantify the evolution of the metallicity gradient in the Milky Way as traced by APOGEE red giants with age estimates from machine learning algorithms. Stars up to ages of $\sim$9 Gyr follow a similar relation between metallicity and Galactocentric radius. This constancy challenges current models of Galactic chemical evolution, which typically predict lower metallicities for older stellar populations. Our results favor an equilibrium scenario, in which the gas-phase gradient reaches a nearly constant normalization early in the disk lifetime. Using a fiducial choice of parameters, we demonstrate that one possible origin of this behavior is an outflow that more readily ejects gas from the interstellar medium with increasing Galactocentric radius. A direct effect of the outflow is that baryons do not remain in the interstellar medium for long, which causes the ratio of star formation to accretion, $\dot{\Sigma}_\star / \dot{\Sigma}_\text{in}$, to quickly become constant. This ratio is closely related to the local equilibrium metallicity, since its numerator and denominator set the rates of metal production by stars and hydrogen gained through accretion, respectively. Building in a merger event results in a perturbation that evolves back toward the equilibrium state on $\sim$Gyr timescales. Under the equilibrium scenario, the radial metallicity gradient is not a consequence of the inside-out growth of the disk but instead reflects a trend of declining $\dot{\Sigma}_\star / \dot{\Sigma}_\text{in}$ with increasing Galactocentric radius., Comment: submitted to ApJ, comments welcome

In this paper, we investigate the properties of a symmetric Toeplitz matrix by studying the components of its graph. To this end, we introduce the notion of "weighted Toeplitz graph", which is a weighted graph whose adjacency matrix is a symmetric Toeplitz matrix and whose weight is the corresponding entry of the matrix. By studying the components of a weighted Toeplitz graph, we show that a Frobenius normal form of a symmetric Toeplitz matrix is a direct sum of symmetric irreducible Toeplitz matrices. We design a linear time algorithm which determines the number of blocks in a Frobenius normal form of a symmetric Toeplitz matrix. In the process of deriving results needed to design the algorithm, we contract the residue classes of a given graph to obtain a simpler graph, which is then studied using number-theoretic techniques.

Generative audio models are rapidly advancing in both capabilities and public utilization -- several powerful generative audio models have readily available open weights, and some tech companies have released high quality generative audio products. Yet, while prior work has enumerated many ethical issues stemming from the data on which generative visual and textual models have been trained, we have little understanding of similar issues with generative audio datasets, including those related to bias, toxicity, and intellectual property. To bridge this gap, we conducted a literature review of hundreds of audio datasets and selected seven of the most prominent to audit in more detail. We found that these datasets are biased against women, contain toxic stereotypes about marginalized communities, and contain significant amounts of copyrighted work. To enable artists to see if they are in popular audio datasets and facilitate exploration of the contents of these datasets, we developed a web tool audio datasets exploration tool at https://audio-audit.vercel.app.