Search

The rise of social media and short-form video (SFV) has facilitated a breeding ground for misinformation. With the emergence of large language models, significant research has gone into curbing this misinformation problem with automatic false claim detection for text. Unfortunately, the automatic detection of misinformation in SFV is a more complex problem that remains largely unstudied. While text samples are monomodal (only containing words), SFVs comprise three different modalities: words, visuals, and non-linguistic audio. In this work, we introduce Video Masked Autoencoders for Misinformation Guarding (ViMGuard), the first deep-learning architecture capable of fact-checking an SFV through analysis of all three of its constituent modalities. ViMGuard leverages a dual-component system. First, Video and Audio Masked Autoencoders analyze the visual and non-linguistic audio elements of a video to discern its intention; specifically whether it intends to make an informative claim. If it is deemed that the SFV has informative intent, it is passed through our second component: a Retrieval Augmented Generation system that validates the factual accuracy of spoken words. In evaluation, ViMGuard outperformed three cutting-edge fact-checkers, thus setting a new standard for SFV fact-checking and marking a significant stride toward trustworthy news on social platforms. To promote further testing and iteration, VimGuard was deployed into a Chrome extension and all code was open-sourced on GitHub., Comment: 7 pages, 2 figures

Propagation of interplanetary (IP) shocks, particularly those driven by coronal mass ejections (CMEs), is still an outstanding question in heliophysics and space weather forecasting. Here we address effects of the ambient solar wind on the propagation of two similar CME-driven shocks from the Sun to Earth. The two shock events (CME03: April 3, 2010 and CME12: July 12, 2012) have the following properties: Both events (1) were driven by a halo CME (i.e., source location is near the Sun-Earth line), (2) had a CME source in the southern hemisphere, (3) had a similar transit time (~2 days) to Earth, (4) occurred in a non-quiet solar period, and (5) led to a severe geomagnetic storm. The initial (near the Sun) propagation speed, as measured by coronagraph images, was slower (by ~300 km/s) for CME03 than CME12, but it took about the same amount of traveling time for both events to reach Earth. According to the in-situ solar wind observations from the Wind spacecraft, the CME03-driven shock was associated with a faster solar wind upstream of the shock than the CME12-driven shock. This is also demonstrated in our global MHD simulations. Analysis of our simulation result indicates that the drag force indirectly plays an important role in the shock propagation. The present study suggests that in addition to the initial CME propagation speed near the Sun the shock speed (in the inertial frame) and the ambient solar wind condition, in particular the solar wind speed, are the key to timing the arrival of CME-driven-shock events., Comment: in press

We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu et al., 2023), designed to improve the large language model (LLM) generation. Specifically, it guides an LLM to fulfill an input instruction by simulating multiple experts, aggregating their responses, and selecting the best among individual and aggregated responses. This process is performed in a single chain of thoughts through our seven carefully designed subtasks derived from the Nominal Group Technique (Ven and Delbecq, 1974), a well-established decision-making framework. Our evaluations demonstrate that Multi-expert Prompting significantly outperforms ExpertPrompting and comparable baselines in enhancing the truthfulness, factuality, informativeness, and usefulness of responses while reducing toxicity and hurtfulness. It further achieves state-of-the-art truthfulness by outperforming the best baseline by 8.69% with ChatGPT. Multi-expert Prompting is efficient, explainable, and highly adaptable to diverse scenarios, eliminating the need for manual prompt construction., Comment: EMNLP 2024 Main Conference

Motivated by the surge of interest in Koopman operator theory, we propose a machine-learning alternative based on a functional Bayesian perspective for operator-theoretic modeling of unknown, data-driven, nonlinear dynamical systems. This formulation is directly done in an infinite-dimensional space of linear operators or Hilbert space with universal approximation property. The theory of reproducing kernel Hilbert space (RKHS) allows the lifting of nonlinear dynamics to a potentially infinite-dimensional space via linear embeddings, where a general nonlinear function is represented as a set of linear functions or operators in the functional space. This allows us to apply classical linear Bayesian methods such as the Kalman filter directly in the Hilbert space, yielding nonlinear solutions in the original input space. This kernel perspective on the Koopman operator offers two compelling advantages. First, the Hilbert space can be constructed deterministically, agnostic to the nonlinear dynamics. The Gaussian kernel is universal, approximating uniformly an arbitrary continuous target function over any compact domain. Second, Bayesian filter is an adaptive, linear minimum-variance algorithm, allowing the system to update the Koopman operator and continuously track the changes across an extended period of time, ideally suited for modern data-driven applications such as real-time machine learning using streaming data. In this paper, we present several practical implementations to obtain a finite-dimensional approximation of the functional Bayesian filter (FBF). Due to the rapid decay of the Gaussian kernel, excellent approximation is obtained with a small dimension. We demonstrate that this practical approach can obtain accurate results and outperform finite-dimensional Koopman decomposition.

We introduce the Cuntz-Thomsen picture of $\mathcal{C}$-equivariant Kasparov theory, denoted $\mathrm{KK}^\mathcal{C}$, for a unitary tensor category $\mathcal{C}$ with countably many isomorphism classes of simple objects. We use this description of $\mathrm{KK}^\mathcal{C}$ to prove the stable uniqueness theorem in this setting.

Global magnetohydrodynamic (MHD) models play an important role in the infrastructure of space weather forecasting. Validating such models commonly utilizes in situ solar wind measurements made near the orbit of the Earth. The purpose of this study is to test the performance of G3DMHD (a data driven, time-dependent, 3-D MHD model of the solar wind) with Parker Solar Probe (PSP) measurements. Since its launch in August 2018, PSP has traversed the inner heliosphere at different radial distances sunward of the Earth (the closest approach ~13.3 solar radii), thus providing a good opportunity to study evolution of the solar wind and to validate heliospheric models of the solar wind. The G3DMHD model simulation is driven by a sequence of maps of photospheric field extrapolated to the assumed source surface (2.5 Rs) using the potential field model from 2018 to 2022, which covers the first 15 PSP orbits. The Pearson correlation coefficient (cc) and the mean absolute squared error (MASE) are used as the metrics to evaluate the model performance. It is found that the model performs better for both magnetic intensity (cc = 0.75; MASE = 0.60) and the solar wind density (cc = 0.73; MASE = 0.50) than for the solar wind speed (cc = 0.15; MASE = 1.29) and temperature (cc = 0.28; MASE = 1.14). This is due primarily to lack of accurate boundary conditions. The well-known underestimate of the magnetic field in solar minimum years is also present. Assuming that the radial magnetic field becomes uniformly distributed with latitude at or below 18 Rs (the inner boundary of the computation do-main), the agreement in the magnetic intensity significantly improves (cc = 0.83; MASE = 0.49)., Comment: in press

We analytically study isothermal spheres in the light of nonextensive statistical mechanics. The equations for the isothermal spheres are derived from the grand partition function of the gravitating particle system in the Tsallis statistical mechanics. The effect of nonextensive statistics appears in relatively dense state, which appears at the center of the isothermal sphere. The stability of the isothermal sphere in the general relativistic system is found to be sensitive to the parameter q in the Tsallis statistics., Comment: 16 pages, 10 figures

We propose an efficient decomposition scheme for a quantum receiver that attains the Helstrom bound in the low-photon regime for discriminating binary coherent states. Our method, which avoids feedback as used in Dolinar's case, breaks down nonlinear operations into basic gates used in continuous-variable quantum computation. We account for realistic conditions by examining the impact of photon loss and imperfect photon detection, including the presence of dark counts, while presenting squeezing as a technique to mitigate these noise sources and maintain the advantage over SQL. Our scheme motivates testing quantum advantages with cubic-phase gates and designing photonic quantum computers to optimize symbol-by-symbol measurements in optical communication., Comment: 8 pages, 7 figures

Optical circuit systems, unlike other systems, have the potential to perform quantum information processing (QIP) at higher clock rate than conventional processing. The approach utilizing the electromagnetic field of light allows deterministic QIP by feedforward process, which counteracts the quantum randomness by performing adaptive quantum operation according to the measurement result of an entangled state. However, conventional feedforward with electronic measurement devices has limited the clock rate of the QIP down to around 100 MHz. In this paper, we demonstrate a variable squeezing gate with a clock rate of 1.3 THz by all-optical measurement-device-free feedforward. We utilize a periodically poled lithium niobate (PPLN) waveguide as an optical parametric amplifier, which eliminates the need for electronic measuring devices and enables ultra-fast feedforward. Experimental results demonstrate that our all-optical QIP operates at a THz clock rate, representing a major step toward a true optical quantum computer which opens the curtain to a new era of ultra-fast information processing., Comment: 12 pages, 4 figures

We introduce DataTales, a novel benchmark designed to assess the proficiency of language models in data narration, a task crucial for transforming complex tabular data into accessible narratives. Existing benchmarks often fall short in capturing the requisite analytical complexity for practical applications. DataTales addresses this gap by offering 4.9k financial reports paired with corresponding market data, showcasing the demand for models to create clear narratives and analyze large datasets while understanding specialized terminology in the field. Our findings highlights the significant challenge that language models face in achieving the necessary precision and analytical depth for proficient data narration, suggesting promising avenues for future model development and evaluation methodologies.

Multiple stationary, localized structures were recently found for inclined porous medium convection with constant-temperature boundaries. We analyze traveling asymmetric, localized convective structures, consisting of 1 to 5 pulses, in a 2D inclined porous layer with fixed temperature at the bottom and an imperfectly conducting boundary at the top, such that midplane reflection symmetry is broken. Direct numerical simulations (DNS) are performed with different Biot numbers at the top boundary. The drift velocity $c$ of pulses is measured for different values of the symmetry parameter $\kappa\geq0$ based on the Biot number, with perfect midplane reflection symmetry and $c=0$ at $\kappa=0$. In small domains, the drift velocity $c>0$ (upslope), increases monotonically with $\kappa$, while in large domains $c$ changes sign depending on parameters. We show that pulse tails, controlling interactions, depend on the dominant spatial eigenvalues, whose real part is closest to zero, with a transition at $\kappa_c>0$: below $\kappa_c$, both dominant eigenvalues are complex, and the tails are oscillatory. Above $\kappa_c$, the dominant spatial eigenvalue with a positive real part becomes real, and the downslope tail transitions from oscillatory to monotonic. Below $\kappa_c$, bound states with different numbers of pulses exist whose collisions are studied. Well above $\kappa_c$, adjacent pulses repel each other, spreading out and becoming equispaced in the domain. A reduced model is proposed based on the interaction via tails of adjacent pulses, reproducing the repulsion and collisions from DNS. The model shows that the transition from bound states to equidistant spreading occurs when the monotonic/oscillatory tails have the same slope. This study elucidates the motion of localized patterns in moderate-Rayleigh number convection in an inclined porous layer with an imperfectly conducting boundary.

Large language models have revolutionized data processing in numerous domains, with their ability to handle extended context reasoning receiving notable recognition. To speed up inference, maintaining a key-value (KV) cache memory is essential. Nonetheless, the growing demands for KV cache memory create significant hurdles for efficient implementation. This work introduces a novel architecture, Cross-Layer Latent Attention (CLLA), aimed at compressing the KV cache to less than 2% of its original size while maintaining comparable performance levels. CLLA integrates multiple aspects of KV cache compression, including attention head/dimension reduction, layer sharing, and quantization techniques, into a cohesive framework. Our extensive experiments demonstrate that CLLA achieves lossless performance on most tasks while utilizing minimal KV cache, marking a significant advancement in practical KV cache compression.

Quantum networks (QNs) promise to enhance the performance of various quantum technologies in the near future by distributing entangled states over long distances. The first step towards this is to develop novel entanglement measures that are both informative and computationally tractable at large scales. While numerous such entanglement measures exist for discrete-variable (DV) systems, a comprehensive exploration for experimentally preferred continuous-variable (CV) systems is lacking. Here, we introduce a class of CV entanglement measures, among which we identify a non-convex entanglement monotone -- the ratio negativity, which possesses a simple, scalable form for characterizing the optimal entanglement swapping of pure Gaussian states. This characterization opens avenues for leveraging statistical physics tools to analyze swapping-protocol-based CV QNs.

To broaden the dissemination of scientific knowledge to diverse audiences, scientific document summarization must simultaneously control multiple attributes such as length and empirical focus. However, existing research typically focuses on controlling single attributes, leaving the compositional control of multiple attributes underexplored. To address this gap, we introduce CCSBench, a benchmark for compositional controllable summarization in the scientific domain. Our benchmark enables fine-grained control over both explicit attributes (e.g., length), which are objective and straightforward, and implicit attributes (e.g., empirical focus), which are more subjective and conceptual. We conduct extensive experiments on GPT-4, LLaMA2, and other popular LLMs under various settings. Our findings reveal significant limitations in large language models' ability to balance trade-offs between control attributes, especially implicit ones that require deeper understanding and abstract reasoning.

Iterative refinement has emerged as an effective paradigm for enhancing the capabilities of large language models (LLMs) on complex tasks. However, existing approaches typically implement iterative refinement at the application or prompting level, relying on autoregressive (AR) modeling. The sequential token generation in AR models can lead to high inference latency. To overcome these challenges, we propose Context-Wise Order-Agnostic Language Modeling (COrAL), which incorporates iterative refinement directly into the LLM architecture while maintaining computational efficiency. Our approach models multiple token dependencies within manageable context windows, enabling the model to perform iterative refinement internally during the generation process. Leveraging the order-agnostic nature of COrAL, we introduce sliding blockwise order-agnostic decoding, which performs multi-token forward prediction and backward reconstruction within context windows. This allows the model to iteratively refine its outputs in parallel in the sliding block, effectively capturing diverse dependencies without the high inference cost of sequential generation. Empirical evaluations on reasoning tasks demonstrate that COrAL improves performance and inference speed, respectively, achieving absolute accuracy gains of $4.6\%$ on GSM8K and $4.0\%$ on LogiQA, along with inference speedups of up to $3.9\times$ over next-token baselines. Preliminary results on code generation indicate a drop in pass rates due to inconsistencies in order-agnostic outputs, highlighting the inherent quality--speed trade-off. Our code is publicly available at https://github.com/YuxiXie/COrAL., Comment: 12 pages, 7 figures, 3 tables (23 pages, 9 figures, 4 tables including references and appendices)

As LLM-based agents exhibit exceptional capabilities in addressing complex problems, there is a growing focus on developing coding agents to tackle increasingly sophisticated tasks. Despite their promising performance, these coding agents often produce programs or modifications that contain runtime errors, which can cause code failures and are difficult for static analysis tools to detect. Enhancing the ability of coding agents to statically identify such errors could significantly improve their overall performance. In this work, we introduce Execution-free Runtime Error Detection for COding Agents (REDO), a method that integrates LLMs with static analysis tools to detect runtime errors for coding agents, without code execution. Additionally, we propose a benchmark task, SWE-Bench-Error-Detection (SWEDE), based on SWE-Bench (lite), to evaluate error detection in repository-level problems with complex external dependencies. Finally, through both quantitative and qualitative analyses across various error detection tasks, we demonstrate that REDO outperforms current state-of-the-art methods by achieving a 11.0% higher accuracy and 9.1% higher weighted F1 score; and provide insights into the advantages of incorporating LLMs for error detection., Comment: 27 pages, 13 figures, 6 tables

Virtual reality (VR) is increasingly used as a social platform for users to interact and build connections with one another in an immersive virtual environment. Reflecting on the empirical progress in this area of study, a comprehensive review of how VR could be used to support social interaction is required to consolidate existing practices and identify research gaps to inspire future studies. In this work, we conducted a systematic review of 94 publications in the HCI field to examine how VR is designed and evaluated for social purposes. We found that VR influences social interaction through self-representation, interpersonal interactions, and interaction environments. We summarized four positive effects of using VR for socializing, which are relaxation, engagement, intimacy, and accessibility, and showed that it could also negatively affect user social experiences by intensifying harassment experiences and amplifying privacy concerns. We introduce an evaluation framework that outlines the key aspects of social experience: intrapersonal, interpersonal, and interaction experiences. According to the results, we uncover several research gaps and propose future directions for designing and developing VR to enhance social experience., Comment: 36 pages, 5 tables, 3 images

The locate-then-edit paradigm has shown significant promise for knowledge editing (KE) in Large Language Models (LLMs). While previous methods perform well on single-hop fact recall tasks, they consistently struggle with multi-hop factual recall tasks involving newly edited knowledge. In this paper, leveraging tools in mechanistic interpretability, we first identify that in multi-hop tasks, LLMs tend to retrieve implicit subject knowledge from deeper MLP layers, unlike single-hop tasks, which rely on earlier layers. This distinction explains the poor performance of current methods in multi-hop queries, as they primarily focus on editing shallow layers, leaving deeper layers unchanged. To address this, we propose IFMET, a novel locate-then-edit KE approach designed to edit both shallow and deep MLP layers. IFMET employs multi-hop editing prompts and supplementary sets to locate and modify knowledge across different reasoning stages. Experimental results demonstrate that IFMET significantly improves performance on multi-hop factual recall tasks, effectively overcoming the limitations of previous locate-then-edit methods., Comment: 21 pages

Planet-disk interactions can produce kinematic signatures in protoplanetary disks. While recent observations have detected non-Keplerian gas motions in disks, their origins are still being debated. To explore this, we conduct 3D hydrodynamic simulations using the code FARGO3D to study non-axisymmetric kinematic perturbations at 2 scale heights induced by Jovian planets in protoplanetary disks, followed by examinations of detectable signals in synthetic CO emission line observations at millimeter wavelengths. We advocate for using residual velocity or channel maps, generated by subtracting an azimuthally averaged background of the disk, to identify planet-induced kinematic perturbations. We investigate the effects of two basic simulation parameters, simulation duration and numerical resolution, on the simulation results. Our findings suggest that a short simulation (e.g., 100 orbits) is insufficient to establish a steady velocity pattern given our chosen viscosity ($\alpha=10^{-3}$), and displays plenty of fluctuations on orbital timescale. Such transient features could be detected in observations. By contrast, a long simulation (e.g., 1,000 orbits) is required to reach steady state in kinematic structures. At 1,000 orbits, the strongest and detectable velocity structures are found in the spiral wakes close to the planet. Through numerical convergence tests, we find hydrodynamics results converge in spiral regions at a resolution of 14 cells per disk scale height (CPH) or higher. Meanwhile, synthetic observations produced from hydrodynamic simulations at different resolutions are indistinguishable with 0.1$^{\prime\prime}$ angular resolution and 10 hours of integration time on ALMA., Comment: Accepted for publication in APJ, 18 pages, 16 figures

Humans perform visual perception at multiple levels, including low-level object recognition and high-level semantic interpretation such as behavior understanding. Subtle differences in low-level details can lead to substantial changes in high-level perception. For example, substituting the shopping bag held by a person with a gun suggests violent behavior, implying criminal or violent activity. Despite significant advancements in various multimodal tasks, Large Visual-Language Models (LVLMs) remain unexplored in their capabilities to conduct such multi-level visual perceptions. To investigate the perception gap between LVLMs and humans, we introduce MVP-Bench, the first visual-language benchmark systematically evaluating both low- and high-level visual perception of LVLMs. We construct MVP-Bench across natural and synthetic images to investigate how manipulated content influences model perception. Using MVP-Bench, we diagnose the visual perception of 10 open-source and 2 closed-source LVLMs, showing that high-level perception tasks significantly challenge existing LVLMs. The state-of-the-art GPT-4o only achieves an accuracy of $56\%$ on Yes/No questions, compared with $74\%$ in low-level scenarios. Furthermore, the performance gap between natural and manipulated images indicates that current LVLMs do not generalize in understanding the visual semantics of synthetic images as humans do. Our data and code are publicly available at https://github.com/GuanzhenLi/MVP-Bench.

Convection is a ubiquitous process driving geophysical/astrophysical fluid flows, which are typically strongly constrained by planetary rotation on large scales. A celebrated model of such flows, rapidly rotating Rayleigh-B\'enard convection, has been extensively studied in direct numerical simulations (DNS) and laboratory experiments, but the parameter values attainable by state-of-the-art methods are limited to moderately rapid rotation (Ekman numbers $Ek\gtrsim10^{-8}$), while realistic geophysical/astrophysical $Ek$ are significantly smaller. Asymptotically reduced equations of motion, the nonhydrostatic quasi-geostrophic equations (NHQGE), describing the flow evolution in the limit $Ek\to 0$, do not apply at finite rotation rates. The geophysical/astrophysical regime of small but finite $Ek$ therefore remains currently inaccessible. Here, we introduce a new, numerically advantageous formulation of the Navier-Stokes-Boussinesq equations informed by the scalings valid for $Ek\to0$, the \textit{rescaled incompressible Navier-Stokes equations} (RiNSE). We solve the RiNSE using a spectral quasi-inverse method resulting in a sparse, fast algorithm to perform efficient DNS in this previously unattainable parameter regime. We validate our results against the literature across a range of $Ek$, and demonstrate that the algorithmic approaches taken remain accurate and numerically stable at $Ek$ as low as $10^{-15}$. Like the NHQGE, the RiNSE derive their efficiency from adequate conditioning, eliminating spurious growing modes that otherwise induce numerical instabilities at small $Ek$. We show that the time derivative of the mean temperature is inconsequential for accurately determining the Nusselt number in the stationary state, significantly reducing the required simulation time, and demonstrate that full DNS using RiNSE agree with the NHQGE at very small $Ek$.

As the dielectric film thickness shrinks to ~10 nm, some traditional wurtzite piezoelectric materials demonstrate ferroelectricity through element doping. Among them, Sc doped AlN and Mg doped ZnO are the most famous examples. While it is widely acknowledged that the dopant atoms effectively reduce the coercive field, enabling ferroelectric polarization switching, the material genome of these wurtzite (WZ) ferroelectrics is still less understood. In this work, we analyze the features of WZ ferroelectrics, ascribing them to five-coordination (5C) ferroelectrics, which may be compared with 6C ferroelectrics (perovskite-type) and 7C ferroelectrics (hafnia-like). In particular, the exact reason for their adopting the hexagonal WZ structure instead of the zinc blende structure is studied. Emphasis is paid to the degree of ionicity in promoting the hexagonal arrangement, and the phenomenon of layer distance compression is discovered and explained in WZ ferroelectrics. The role of element doping in coercive field reduction is understood within this context., Comment: 5 figures

This paper explores the growing need for task-oriented communications in warehouse logistics, where traditional communication Key Performance Indicators (KPIs)-such as latency, reliability, and throughput-often do not fully meet task requirements. As the complexity of data flow management in large-scale device networks increases, there is also a pressing need for innovative cross-system designs that balance data compression, communication, and computation. To address these challenges, we propose a task-oriented, edge-assisted framework for cooperative data compression, communication, and computing in Unmanned Ground Vehicle (UGV)-enhanced warehouse logistics. In this framework, two UGVs collaborate to transport cargo, with control functions-navigation for the front UGV and following/conveyance for the rear UGV-offloaded to the edge server to accommodate their limited on-board computing resources. We develop a Deep Reinforcement Learning (DRL)-based two-stage point cloud data compression algorithm that dynamically and collaboratively adjusts compression ratios according to task requirements, significantly reducing communication overhead. System-level simulations of our UGV logistics prototype demonstrate the framework's effectiveness and its potential for swift real-world implementation.

Instruction data is crucial for improving the capability of Large Language Models (LLMs) to align with human-level performance. Recent research LIMA demonstrates that alignment is essentially a process where the model adapts instructions' interaction style or format to solve various tasks, leveraging pre-trained knowledge and skills. Therefore, for instructional data, the most important aspect is the task it represents, rather than the specific semantics and knowledge information. The latent representations of instructions play roles for some instruction-related tasks like data selection and demonstrations retrieval. However, they are always derived from text embeddings, encompass overall semantic information that influences the representation of task categories. In this work, we introduce a new concept, instruction embedding, and construct Instruction Embedding Benchmark (IEB) for its training and evaluation. Then, we propose a baseline Prompt-based Instruction Embedding (PIE) method to make the representations more attention on tasks. The evaluation of PIE, alongside other embedding methods on IEB with two designed tasks, demonstrates its superior performance in accurately identifying task categories. Moreover, the application of instruction embeddings in four downstream tasks showcases its effectiveness and suitability for instruction-related tasks., Comment: NeurIPS 2024

Service Level Objectives (SLOs) aim to set threshold for service time in cloud services to ensure acceptable quality of service (QoS) and user satisfaction. Currently, many studies consider SLOs as a system resource to be allocated, ensuring QoS meets the SLOs. Existing microservice auto-scaling frameworks that rely on SLO resources often utilize complex and computationally intensive models, requiring significant time and resources to determine appropriate resource allocation. This paper aims to rapidly allocate SLO resources and minimize resource costs while ensuring application QoS meets the SLO requirements in a dynamically changing microservice environment. We propose MSARS, a framework that leverages meta-learning to quickly derive SLO resource allocation strategies and employs reinforcement learning for adaptive scaling of microservice resources. It features three innovative components: First, MSARS uses graph convolutional networks to predict the most suitable SLO resource allocation scheme for the current environment. Second, MSARS utilizes meta-learning to enable the graph neural network to quickly adapt to environmental changes ensuring adaptability in highly dynamic microservice environments. Third, MSARS generates auto-scaling policies for each microservice based on an improved Twin Delayed Deep Deterministic Policy Gradient (TD3) model. The adaptive auto-scaling policy integrates the SLO resource allocation strategy into the scheduling algorithm to satisfy SLOs. Finally, we compare MSARS with state-of-the-art resource auto-scaling algorithms that utilize neural networks and reinforcement learning, MSARS takes 40% less time to adapt to new environments, 38% reduction of SLO violations, and 8% less resources cost., Comment: 10 pages, 6 figures, IEEE ISPA 2024

Federated Domain Generalization (FedDG) aims to train the global model for generalization ability to unseen domains with multi-domain training samples. However, clients in federated learning networks are often confined to a single, non-IID domain due to inherent sampling and temporal limitations. The lack of cross-domain interaction and the in-domain divergence impede the learning of domain-common features and limit the effectiveness of existing FedDG, referred to as the single-source FedDG (sFedDG) problem. To address this, we introduce the Federated Global Consistent Augmentation (FedGCA) method, which incorporates a style-complement module to augment data samples with diverse domain styles. To ensure the effective integration of augmented samples, FedGCA employs both global guided semantic consistency and class consistency, mitigating inconsistencies from local semantics within individual clients and classes across multiple clients. The conducted extensive experiments demonstrate the superiority of FedGCA., Comment: 6 pages, 7 figures, conference

The analytical expressions for the electromagnetic potential generated from a focusing charged particle beam are indispensable in various beam physics problems. In this article, we review the theory in detail and point out the necessary assumptions made in the derivation.

Current Large Language Models (LLMs) exhibit limited ability to understand table structures and to apply precise numerical reasoning, which is crucial for tasks such as table question answering (TQA) and table-based fact verification (TFV). To address these challenges, we introduce our Tool-Augmented Reasoning framework for Tables (TART), which integrates LLMs with specialized tools. TART contains three key components: a table formatter to ensure accurate data representation, a tool maker to develop specific computational tools, and an explanation generator to maintain explainability. We also present the TOOLTAB dataset, a new benchmark designed specifically for training LLMs in table-tool integration. Our experiments indicate that TART achieves substantial improvements over existing methods (e.g., Chain-of-Thought) by improving both the precision of data processing and the clarity of the reasoning process. Notably, TART paired with CodeLlama achieves 90.0% of the accuracy of the closed-sourced LLM GPT-3.5-turbo, highlighting its robustness in diverse real-world scenarios. All the code and data are available at https://github.com/XinyuanLu00/TART., Comment: technical report

Fluorite-type $\mathrm{HfO_2}$-based ferroelectric (FE) oxides have rekindled interest in FE memories due to their compatibility with silicon processing and potential for high-density integration. The polarization characteristics of FE devices are governed by the dynamics of metastable domain structure evolution. Insightful design of FE devices for encoding and storage necessitates a comprehensive understanding of the internal structural evolution. Here, we demonstrate the evolution of domain structures through a transient polar orthorhombic (O)-$Pmn2_1$-like configuration via $in$-$situ$ biasing on $\mathrm{TiN/Hf_{0.5}Zr_{0.5}O_2/TiN}$ capacitors within spherical aberration-corrected transmission electron microscope, combined with theoretical calculations. Furthermore, it is directly evidenced that the non-FE O-$Pbca$ transforms into the FE O-$Pca2_1$ phase under electric field, with the polar axis of the FE-phase aligning towards the bias direction through ferroelastic transformation, thereby enhancing FE polarization. As cycling progresses further, however, the polar axis collapses, leading to FE degradation. These novel insights into the intricate structural evolution path under electrical field cycling facilitate optimization and design strategies for $\mathrm{HfO_2}$-based FE memory devices., Comment: 16 pages, 4 figures for the main text

Geophysical and astrophysical fluid flows are typically driven by buoyancy and strongly constrained at large scales by planetary rotation. Rapidly rotating Rayleigh-B\'enard convection (RRRBC) provides a paradigm for experiments and direct numerical simulations (DNS) of such flows, but the accessible parameter space remains restricted to moderately fast rotation rates (Ekman numbers $Ek \gtrsim 10^{-8}$), while realistic $Ek$ for astro-/geophysical applications are orders of magnitude smaller. On the other hand, previously derived reduced equations of motion describing the leading-order behaviour in the limit of very rapid rotation ($Ek\to 0$) cannot capture finite rotation effects, and the physically most relevant part of parameter space with small but finite $Ek$ has remained elusive. Here, we employ the rescaled incompressible Navier-Stokes equations (RiNSE) -- a reformulation of the Navier-Stokes-Boussinesq equations informed by the scalings valid for $Ek\to 0$, recently introduced by Julien et al. (2024) -- to provide full DNS of RRRBC at unprecedented rotation strengths down to $Ek=10^{-15}$ and below, revealing the disappearance of cyclone-anticyclone asymmetry at previously unattainable Ekman numbers ($Ek \approx 10^{-9}$). We also identify an overshoot in the heat transport as $Ek$ is varied at fixed $\widetilde{Ra}= Ra Ek^{4/3}$, associated with dissipation due to ageostrophic motions in the boundary layers. The simulations validate theoretical predictions based on thermal boundary layer theory for RRRBC and show that the solutions of RiNSE agree with the reduced equations at very small $Ek$. These results represent a first foray into the vast, largely unexplored parameter space of very rapidly rotating convection rendered accessible by RiNSE.

High-quality (Q)-factor optical resonators with extreme temporal coherence are of both technological and fundamental importance in optical metrology, continuous-wave lasing, and semiconductor quantum optics. Despite extensive efforts in designing high-Q resonators across different spectral regimes, the experimental realization of very large Q-factors at visible wavelengths remains challenging due to the small feature size that is sensitive to fabrication imperfections, and thus is typically implemented in integrated photonics. In the pursuit of free-space optics with the benefits of large space-bandwidth product and massive parallel operations, here we design and fabricate a visible-wavelength etch-free metasurface with minimized fabrication defects and experimentally demonstrate a million-scale ultrahigh-Q resonance. A new laser-scanning momentum-space-resolved spectroscopy technique with extremely high spectral and angular resolution is developed to characterize the record-high Q-factor as well as the dispersion of the million-Q resonance in free space. By integrating monolayer WSe2 into our ultrahigh-Q meta-resonator, we further demonstrate laser-like highly unidirectional and narrow-linewidth exciton emission, albeit without any operating power density threshold. Under continuous-wave laser pumping, we observe pump-power-dependent linewidth narrowing at room temperature, indicating the potential of our meta-optics platform in controlling coherent quantum light-sources. Our result also holds great promise for applications like optical sensing, spectral filtering, and few-photon nonlinear optics.

All-optical switching (AOS) involves manipulating magnetization using only pulsed laser, presenting a promising approach for next-generation magnetic recording devices. NiCo2O4 (NCO) thin films, a rare-earth-free ferrimagnetic oxide, exhibit a high Curie temperature and strong perpendicular magnetic anisotropy. This study demonstrates AOS in NCO thin films at room temperature using long-duration laser pulses and high repetition rates. Unlike previous findings, the AOS phenomena we report here are helicity-dependent and observable with an ultrashort pulsed laser. Consequently, two distinct types of AOS can be observed in a single NCO thin film, contingent on the characteristics of the laser pulses and temperature., Comment: 5 pages, 4 figures

The current quantum resource theory for Gaussian steering for continuous-variable systems is flawed and incomplete. Its primary shortcoming stems from an inadequate comprehension of the architecture of Gaussian channels transforming Gaussian unsteerable states into Gaussian unsteerable states, resulting in a restricted selection of free operations. In the present paper, we explore in depth the structure of such $(m+n)$-mode Gaussian channels, and introduce the class of the Gaussian unsteerable channels and the class of maximal Gaussian unsteerable channels, both of them may be chosen as the free operations, which completes the resource theory for Gaussian steering from $A$ to $B$ by Alice's Gaussian measurements. We also propose two quantifications $\mathcal{J}_{j}$ $(j=1,2)$ of $(m+n)$-mode Gaussian steering from $A$ to $B$. The computation of the value of $\mathcal{J}_{j}$ is straightforward and efficient, as it solely relies on the covariance matrices of Gaussian states, eliminating the need for any optimization procedures. Though $\mathcal{J}_{j}$s are not genuine Gaussian steering measures, they have some nice properties such as non-increasing under certain Gaussian unsteerable channels. Additionally, we compare ${\mathcal J}_2$ with the Gaussian steering measure $\mathcal N_3$, which is based on the Uhlmann fidelity, revealing that ${\mathcal J}_2$ is an upper bound of $\mathcal N_3$ at certain class of $(1+1)$-mode Gaussian pure states. As an illustration, we apply $\mathcal J_2$ to discuss the behaviour of Gaussian steering for a special class of $(1+1)$-mode Gaussian states in Markovian environments, which uncovers the intriguing phenomenon of rapid decay in quantum steering., Comment: 14 pages,7 figures

Microorganisms hosted in abiotic structures have led to engineered living materials that can grow, sense and adapt in ways that mimic biological systems. Although porous structures should favor colonization by microorganisms, they have not yet been exploited as abiotic scaffolds for the development of living materials. Here, we report porous ceramics that are colonized by bacteria to form an engineered living material with self-regulated and genetically programmable carbon capture and gas-sensing functionalities. The carbon capture capability is achieved using wild-type photosynthetic cyanobacteria, whereas the gas-sensing function is generated utilizing genetically engineered E. coli. Hierarchical porous clay is used as ceramic scaffold and evaluated in terms of bacterial growth, water uptake and mechanical properties. Using state-of-the-art chemical analysis techniques, we demonstrate the ability of the living porous ceramics to capture CO2 directly from the air and to metabolically turn minute amounts of a toxic gas into a benign scent detectable by humans.

In this paper, we propose and study $r$-minimal codes, a natural extension of minimal codes which have been extensively studied with respect to Hamming metric, rank metric and sum-rank metric. We first propose $r$-minimal codes in a general setting where the ambient space is a finite dimensional left module over a division ring and is supported on a lattice. We characterize minimal subcodes and $r$-minimal codes, derive a general singleton bound, and give existence results for $r$-minimal codes by using combinatorial arguments. We then consider $r$-minimal rank metric codes over a field extension $\mathbb{E}/\mathbb{F}$ of degree $m$, where $\mathbb{E}$ can be infinite. We characterize these codes in terms of cutting $r$-blocking sets, generalized rank weights of the codes and those of the dual codes, and classify codes whose $r$-dimensional subcodes have constant rank support weight. Next, with the help of the evasiveness property of cutting $r$-blocking sets and some upper bounds for the dimensions of evasive subspaces, we derive several lower and upper bounds for the minimal length of $r$-minimal codes. Furthermore, when $\mathbb{E}$ is finite, we establish a general upper bound which generalizes and improves the counterpart for minimal codes in the literature. As a corollary, we show that if $m=3$, then for any $k\geqslant2$, the minimal length of $k$-dimensional minimal codes is equal to $2k$. To the best of our knowledge, when $m\geqslant3$, there was no known explicit formula for the minimal length of $k$-dimensional minimal codes for arbitrary $k$ in the literature.

While recent low-light image enhancement (LLIE) methods have made significant advancements, they still face challenges in terms of low visual quality and weak generalization ability when applied to complex scenarios. To address these issues, we propose a semi-supervised method based on latent mean-teacher and Gaussian process, named LMT-GP. We first design a latent mean-teacher framework that integrates both labeled and unlabeled data, as well as their latent vectors, into model training. Meanwhile, we use a mean-teacher-assisted Gaussian process learning strategy to establish a connection between the latent and pseudo-latent vectors obtained from the labeled and unlabeled data. To guide the learning process, we utilize an assisted Gaussian process regression (GPR) loss function. Furthermore, we design a pseudo-label adaptation module (PAM) to ensure the reliability of the network learning. To demonstrate our method's generalization ability and effectiveness, we apply it to multiple LLIE datasets and high-level vision tasks. Experiment results demonstrate that our method achieves high generalization performance and image quality. The code is available at https://github.com/HFUT-CV/LMT-GP.

There has been much discussion about the initial conditions of the early Universe in the context of quantum theory. In this paper, we construct the wave function and probability distribution by adopting the quantum version of the BPS equation instead of the usual Wheeler-DeWitt equation in a minisuperspace quantum cosmology with spatially uniform scalar fields. Although the model treated in this study is technically valid for a limited form of scalar potential, we show that it is possible to construct a conserved probability current in our model. We also examine classical and quantum aspects of models with the Dirac-Born-Infeld type scalar fields., Comment: 19 pages, 1 figure