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Aspect-based sentiment analysis (ABSA) involves identifying sentiment towards specific aspect terms in a sentence and allows us to uncover nuanced perspectives and attitudes on particular aspects of a product, service, or topic. However, the scarcity of labeled data poses a significant challenge to training high-quality models. To address this issue, we explore the potential of data augmentation using ChatGPT, a well-performing large language model (LLM), to enhance the sentiment classification performance towards aspect terms. Specifically, we explore three data augmentation strategies based on ChatGPT: context-focused, aspect-focused, and context-aspect data augmentation techniques. Context-focused data augmentation focuses on changing the word expression of context words in the sentence while keeping aspect terms unchanged. In contrast, aspect-focused data augmentation aims to change aspect terms but keep context words unchanged. Context-Aspect data augmentation integrates the above two data augmentations to generate augmented samples. Furthermore, we incorporate contrastive learning into the ABSA tasks to improve performance. Extensive experiments show that all three data augmentation techniques lead to performance improvements, with the context-aspect data augmentation strategy performing best and surpassing the performance of the baseline models., Comment: 8 pages, 3 figures

The convergence rate is a crucial issue in opinion dynamics, which characterizes how quickly opinions reach a consensus and tells when the collective behavior can be formed. However, the key factors that determine the convergence rate of opinions are elusive, especially when individuals interact with complex interaction types such as friend/foe, ally/adversary, or trust/mistrust. In this paper, using random matrix theory and low-rank perturbation theory, we present a new body of theory to comprehensively study the convergence rate of opinion dynamics. First, we divide the complex interaction types into five typical scenarios: mutual trust $(+/+)$, mutual mistrust $(-/-)$, trust$/$mistrust $(+/-)$, unilateral trust $(+/0)$, and unilateral mistrust $(-/0)$. For diverse interaction types, we derive the mathematical expression of the convergence rate, and further establish the direct connection between the convergence rate and population size, the density of interactions (network connectivity), and individuals' self-confidence level. Second, taking advantage of these connections, we prove that for the $(+/+)$, $(+/-)$, $(+/0)$, and random mixture of different interaction types, the convergence rate is proportional to the population size and network connectivity, while it is inversely proportional to the individuals' self-confidence level. However, for the $(-/-)$ and $(-/0)$ scenarios, we draw the exact opposite conclusions. Third, for the $(+/+,-/-)$ and $(-/-,-/0)$ scenarios, we derive the optimal proportion of different interaction types to ensure the fast convergence of opinions. Finally, simulation examples are provided to illustrate the effectiveness and robustness of our theoretical findings.

Despite the promising performance of current video segmentation models on existing benchmarks, these models still struggle with complex scenes. In this paper, we introduce the 6th Large-scale Video Object Segmentation (LSVOS) challenge in conjunction with ECCV 2024 workshop. This year's challenge includes two tasks: Video Object Segmentation (VOS) and Referring Video Object Segmentation (RVOS). In this year, we replace the classic YouTube-VOS and YouTube-RVOS benchmark with latest datasets MOSE, LVOS, and MeViS to assess VOS under more challenging complex environments. This year's challenge attracted 129 registered teams from more than 20 institutes across over 8 countries. This report include the challenge and dataset introduction, and the methods used by top 7 teams in two tracks. More details can be found in our homepage https://lsvos.github.io/., Comment: ECCV 2024 LSVOS Challenge Report: https://lsvos.github.io/

Satellite imagery, due to its long-range imaging, brings with it a variety of scale-preferred tasks, such as the detection of tiny/small objects, making the precise localization and detection of small objects of interest a challenging task. In this article, we design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction. Renormalized connection (RC) on the KDN enables ``synergistic focusing'' of multi-scale features. Based on our observations of KDN, we abstract a class of RCs with different connection strengths, called n21C, and generalize it to FPN-based multi-branch detectors. In a series of FPN experiments on the scale-preferred tasks, we found that the ``divide-and-conquer'' idea of FPN severely hampers the detector's learning in the right direction due to the large number of large-scale negative samples and interference from background noise. Moreover, these negative samples cannot be eliminated by the focal loss function. The RCs extends the multi-level feature's ``divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks, and enables synergistic effects of multi-level features on the specific learning goal. In addition, interference activations in two aspects are greatly reduced and the detector learns in a more correct direction. Extensive experiments of 17 well-designed detection architectures embedded with n21s on five different levels of scale-preferred tasks validate the effectiveness and efficiency of the RCs. Especially the simplest linear form of RC, E421C performs well in all tasks and it satisfies the scaling property of RGT. We hope that our approach will transfer a large number of well-designed detectors from the computer vision community to the remote sensing community., Comment: 24 pages, 14 figures Journal

Developers usually use TPLs to facilitate the development of the projects to avoid reinventing the wheels, however, the vulnerable TPLs indeed cause severe security threats. The majority of existing research only considered whether projects used vulnerable TPLs but neglected whether the vulnerable code of the TPLs was indeed used by the projects, which inevitably results in false positives and further requires additional patching efforts and maintenance costs. To address this, we propose VAScanner, which can effectively identify vulnerable root methods causing vulnerabilities in TPLs and further identify all vulnerable APIs of TPLs used by Java projects. Specifically, we first collect the initial patch methods from the patch commits and extract accurate patch methods by employing a patch-unrelated sifting mechanism, then we further identify the vulnerable root methods for each vulnerability by employing an augmentation mechanism. Based on them, we leverage backward call graph analysis to identify all vulnerable APIs for each vulnerable TPL version and construct a database consisting of 90,749 (2,410,779 with library versions) vulnerable APIs with 1.45% false positive proportion with a 95% CI of [1.31%, 1.59%] from 362 TPLs with 14,775 versions. Our experiments show VAScanner eliminates 5.78% false positives and 2.16% false negatives owing to the proposed sifting and augmentation mechanisms. Besides, it outperforms the state-of-the-art method-level tool in analyzing direct dependencies, Eclipse Steady, achieving more effective detection of vulnerable APIs. Furthermore, in a large-scale analysis of 3,147 projects using vulnerable TPLs, we find only 21.51% of projects (with 1.83% false positive proportion and a 95% CI of [0.71%, 4.61%]) were threatened through vulnerable APIs by vulnerable TPLs, demonstrating that VAScanner can potentially reduce false positives significantly., Comment: 15 pages, 4 figures

Chart Question Answering (CQA) aims at answering questions based on the visual chart content, which plays an important role in chart sumarization, business data analysis, and data report generation. CQA is a challenging multi-modal task because of the strong context dependence and complex reasoning requirement. The former refers to answering this question strictly based on the analysis of the visual content or internal data of the given chart, while the latter emphasizes the various logical and numerical reasoning involved in answer prediction process. In this paper, we pay more attention on the complex reasoning in CQA task, and propose a novel Graph-of-Thought (GoT) guided compositional reasoning model called GoT-CQA to overcome this problem. At first, we transform the chart-oriented question into a directed acyclic GoT composed of multiple operator nodes, including localization, numerical and logical operator. It intuitively reflects the human brain's solution process to this question. After that, we design an efficient auto-compositional reasoning framework guided by the GoT, to excute the multi-step reasoning operations in various types of questions. Comprehensive experiments on ChartQA and PlotQA-D datasets show that GoT-CQA achieves outstanding performance, especially in complex human-written and reasoning questions, comparing with the latest popular baselines.

Charts are widely used for data visualization across various fields, including education, research, and business. Chart Question Answering (CQA) is an emerging task focused on the automatic interpretation and reasoning of data presented in charts. However, chart images are inherently difficult to interpret, and chart-related questions often involve complex logical and numerical reasoning, which hinders the performance of existing models. This paper introduces VProChart, a novel framework designed to address these challenges in CQA by integrating a lightweight Visual Perception Alignment Agent (VPAgent) and a Programmatic Solution Reasoning approach. VPAgent aligns and models chart elements based on principles of human visual perception, enhancing the understanding of chart context. The Programmatic Solution Reasoning approach leverages large language models (LLMs) to transform natural language reasoning questions into structured solution programs, facilitating precise numerical and logical reasoning. Extensive experiments on benchmark datasets such as ChartQA and PlotQA demonstrate that VProChart significantly outperforms existing methods, highlighting its capability in understanding and reasoning with charts.

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

Chart understanding enables automated data analysis for humans, which requires models to achieve highly accurate visual comprehension. While existing Visual Language Models (VLMs) have shown progress in chart understanding, the lack of high-quality training data and comprehensive evaluation benchmarks hinders VLM chart comprehension. In this paper, we introduce EvoChart, a novel self-training method for generating synthetic chart data to enhance VLMs' capabilities in real-world chart comprehension. We also propose EvoChart-QA, a noval benchmark for measuring models' chart comprehension abilities in real-world scenarios. Specifically, EvoChart is a unique self-training data synthesis approach that simultaneously produces high-quality training corpus and a high-performance chart understanding model. EvoChart-QA consists of 650 distinct real-world charts collected from 140 different websites and 1,250 expert-curated questions that focus on chart understanding. Experimental results on various open-source and proprietary VLMs tested on EvoChart-QA demonstrate that even the best proprietary model, GPT-4o, achieves only 49.8% accuracy. Moreover, the EvoChart method significantly boosts the performance of open-source VLMs on real-world chart understanding tasks, achieving 54.2% accuracy on EvoChart-QA.

Video Object Segmentation (VOS) presents several challenges, including object occlusion and fragmentation, the dis-appearance and re-appearance of objects, and tracking specific objects within crowded scenes. In this work, we combine the strengths of the state-of-the-art (SOTA) models SAM2 and Cutie to address these challenges. Additionally, we explore the impact of various hyperparameters on video instance segmentation performance. Our approach achieves a J\&F score of 0.7952 in the testing phase of LSVOS challenge VOS track, ranking third overall., Comment: arXiv admin note: text overlap with arXiv:2406.03668

When simulating hyperbolic conservation laws with discontinuous solutions, high-order linear numerical schemes often produce undesirable spurious oscillations. In this paper, we propose a jump filter within the discontinuous Galerkin (DG) method to mitigate these oscillations. This filter operates locally based on jump information at cell interfaces, targeting high-order polynomial modes within each cell. Besides its localized nature, our proposed filter preserves key attributes of the DG method, including conservation, $L^2$ stability, and high-order accuracy. We also explore its compatibility with other damping techniques, and demonstrate its seamless integration into a hybrid limiter. In scenarios featuring strong shock waves, this hybrid approach, incorporating this jump filter as the low-order limiter, effectively suppresses numerical oscillations while exhibiting low numerical dissipation. Additionally, the proposed jump filter maintains the compactness of the DG scheme, which greatly aids in efficient parallel computing. Moreover, it boasts an impressively low computational cost, given that no characteristic decomposition is required and all computations are confined to physical space. Numerical experiments validate the effectiveness and performance of our proposed scheme, confirming its accuracy and shock-capturing capabilities., Comment: 39 pages

The coldness of the universe is a thermodynamic resource that has largely remained untapped for renewable energy generation. Recently, a growing interest in this area has led to a number of studies with the aim to realize the potential of tapping this vast resource for energy generation. While the theoretical calculation based on thermodynamic principles places an upper limit in the power density at the level of 6000 mW/m$^2$, most experimental demonstrations so far result in much lower power density at the level of tens of mW/m$^2$. Here we demonstrate, through design optimization involving the tailoring of the thermal radiation spectrum, the minimization of parasitic heat leakage, and the maximum conversion of heat to electricity, an energy generation system harvesting electricity from the thermal radiation of the ambient heat to the cold universe that achieves a sustained power density of 350 mW/m$^2$. We further demonstrate a power density at the 1000 mW/m$^2$ level using an additional heat source or heat storage that provides access to heat at a temperature above ambient. Our work here shows that the coldness of the universe can be harvested to generate renewable energy at the power density level that approaches the established bound.

Large language models (LLMs) have demonstrated potential applications in medicine, yet data privacy and computational burden limit their deployment in healthcare institutions. Open-source and lightweight versions of LLMs emerge as potential solutions, but their performance, particularly in pediatric settings remains underexplored. In this cross-sectional study, 250 patient consultation questions were randomly selected from a public online medical forum, with 10 questions from each of 25 pediatric departments, spanning from December 1, 2022, to October 30, 2023. Two lightweight open-source LLMs, ChatGLM3-6B and Vicuna-7B, along with a larger-scale model, Vicuna-13B, and the widely-used proprietary ChatGPT-3.5, independently answered these questions in Chinese between November 1, 2023, and November 7, 2023. To assess reproducibility, each inquiry was replicated once. We found that ChatGLM3-6B demonstrated higher accuracy and completeness than Vicuna-13B and Vicuna-7B (P < .001), but all were outperformed by ChatGPT-3.5. ChatGPT-3.5 received the highest ratings in accuracy (65.2%) compared to ChatGLM3-6B (41.2%), Vicuna-13B (11.2%), and Vicuna-7B (4.4%). Similarly, in completeness, ChatGPT-3.5 led (78.4%), followed by ChatGLM3-6B (76.0%), Vicuna-13B (34.8%), and Vicuna-7B (22.0%) in highest ratings. ChatGLM3-6B matched ChatGPT-3.5 in readability, both outperforming Vicuna models (P < .001). In terms of empathy, ChatGPT-3.5 outperformed the lightweight LLMs (P < .001). In safety, all models performed comparably well (P > .05), with over 98.4% of responses being rated as safe. Repetition of inquiries confirmed these findings. In conclusion, Lightweight LLMs demonstrate promising application in pediatric healthcare. However, the observed gap between lightweight and large-scale proprietary LLMs underscores the need for continued development efforts., Comment: 27 pages in total with 17 pages of main manuscript and 10 pages of supplementary materials; 4 figures in the main manuscript and 2 figures in supplementary material

Optical tweezer arrays of laser-cooled and individual controlled particles have revolutionized the atomic, molecular and optical physics, and they afford exquisite capabilities for applications in quantum simulation of many-body physics, quantum computation and quantum sensing. Underlying this development is the technical maturity of generating scalable optical beams, enabled by active components and high numerical aperture objective. However, such a complex combination of bulk optics outside the vacuum chamber is very sensitive to any vibration and drift. Here we demonstrate the generation of 3*3 static tweezer array with a single chip-scale multifunctional metasurface element in vacuum, replacing the meter-long free space optics. Fluorescence counts on the camera validates the successfully trapping of the atomic ensemble array. Further, we discuss the strategy to achieve low scattering and crosstalk, where a metasurface design featuring dual-wavelength independent control is included. Our results, together with other recent development in integrated photonics for cold atoms, could pave the way for compact and portable quantum sensors and simulators in platforms of neutral atom arrays.

Understanding 3D scenes is a crucial challenge in computer vision research with applications spanning multiple domains. Recent advancements in distilling 2D vision-language foundation models into neural fields, like NeRF and 3DGS, enables open-vocabulary segmentation of 3D scenes from 2D multi-view images without the need for precise 3D annotations. While effective, however, the per-pixel distillation of high-dimensional CLIP features introduces ambiguity and necessitates complex regularization strategies, adding inefficiencies during training. This paper presents MaskField, which enables fast and efficient 3D open-vocabulary segmentation with neural fields under weak supervision. Unlike previous methods, MaskField distills masks rather than dense high-dimensional CLIP features. MaskFields employ neural fields as binary mask generators and supervise them with masks generated by SAM and classified by coarse CLIP features. MaskField overcomes the ambiguous object boundaries by naturally introducing SAM segmented object shapes without extra regularization during training. By circumventing the direct handling of high-dimensional CLIP features during training, MaskField is particularly compatible with explicit scene representations like 3DGS. Our extensive experiments show that MaskField not only surpasses prior state-of-the-art methods but also achieves remarkably fast convergence, outperforming previous methods with just 5 minutes of training. We hope that MaskField will inspire further exploration into how neural fields can be trained to comprehend 3D scenes from 2D models., Comment: 16 pages, 7 figures

Multi-wavelength switchable single-frequency microlasers in a broad spectral range are highly desirable for integrated photonic applications due to their dynamic switching functionality, narrow linewidth, and high side-mode-suppression-ratio (SMSR). Here, a strategy based on highly efficient successive excitation of different stimulated multi-photon hyper-Raman scattering (SMPHRS) processes is proposed to generate multi-wavelength switchable single-frequency hyper-Raman microlasers. This is achieved through collective precise dispersion management for arranging excitation wavelengths to trigger different phase-matched SMPHRS processes in order, mode-hopping-free tuning of the pump wavelength within a wide range of 0.75 nm by leveraging strong thermo-optical broadening of ultra-high Q modes, and simultaneously suppressing harmonics generation in a lithium niobate microcavity with high second-order nonlinearity. As a result, under continuous-wave laser pump at a low level of only 3.9 mW, SMPHRS processes from two- to five-photons emerged step by step and almost depleted previously generated multi-photon Raman signal. Consequently, four-wavelength dynamically switchable single-mode lasing from near infrared (857 nm) to ultraviolet (350 nm) spanning beyond the record range (~500 nm) with high SMSRs >35 dB is reported., Comment: 17 pages,5 figures, and 1 table

Peaks and troughs in the subsynchronous impedance spectrum of a distribution feeder may be a useful indication of oscillation risk, or more importantly lack of oscillation risk, if inverter-based resource (IBR) deployments are increased on that feeder. GridSweep is a new instrument for measuring the subsynchronous impedance spectra of distribution feeders. It combines an active probing device that modulates a 120-volt 1-kW load sinusoidally at a user-selected GPS-phase locked frequency from 1.0 to 40.0 Hz, and with a recorder that takes ultra-high-precision continuous point-on-wave (CPOW) 120-volt synchrowaveforms at 4 kHz. This paper presents a computer simulation of GridSweep's probing and measurement capability. We construct an electromagnetic transient (EMT) simulation of a single-phase distribution feeder equipped with multiple inverter-based resources (IBRs). We include a model of the GridSweep probing device, then demonstrate the model's capability to measure the subsynchronous apparent impedance spectrum of the feeder. Peaks in that spectrum align with the system's dominant oscillation modes caused by IBRs., Comment: 10 pages, 18 figures

The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and accurate vision systems. Physics-based vision aims to invert the processes to recover scene properties such as shape, reflectance, light distribution, and medium properties from images. In recent years, deep learning has shown promising improvements for various vision tasks, and when combined with physics-based vision, these approaches can enhance the robustness and accuracy of vision systems. This technical report summarizes the outcomes of the Physics-Based Vision Meets Deep Learning (PBDL) 2024 challenge, held in CVPR 2024 workshop. The challenge consisted of eight tracks, focusing on Low-Light Enhancement and Detection as well as High Dynamic Range (HDR) Imaging. This report details the objectives, methodologies, and results of each track, highlighting the top-performing solutions and their innovative approaches., Comment: CVPR 2024 PBDL Challenges: https://pbdl-ws.github.io/pbdl2024/challenge/index.html

Recently, generative AI (GAI), with their emerging capabilities, have presented unique opportunities for augmenting and revolutionizing industrial recommender systems (Recsys). Despite growing research efforts at the intersection of these fields, the integration of GAI into industrial Recsys remains in its infancy, largely due to the intricate nature of modern industrial Recsys infrastructure, operations, and product sophistication. Drawing upon our experiences in successfully integrating GAI into several major social and e-commerce platforms, this survey aims to comprehensively examine the underlying system and AI foundations, solution frameworks, connections to key research advancements, as well as summarize the practical insights and challenges encountered in the endeavor to integrate GAI into industrial Recsys. As pioneering work in this domain, we hope outline the representative developments of relevant fields, shed lights on practical GAI adoptions in the industry, and motivate future research.

Video object segmentation approaches primarily rely on large-scale pixel-accurate human-annotated datasets for model development. In Dense Video Object Segmentation (DVOS) scenarios, each video frame encompasses hundreds of small, dense, and partially occluded objects. Accordingly, the labor-intensive manual annotation of even a single frame often takes hours, which hinders the development of DVOS for many applications. Furthermore, in videos with dense patterns, following a large number of objects that move in different directions poses additional challenges. To address these challenges, we proposed a semi-self-supervised spatiotemporal approach for DVOS utilizing a diffusion-based method through multi-task learning. Emulating real videos' optical flow and simulating their motion, we developed a methodology to synthesize computationally annotated videos that can be used for training DVOS models; The model performance was further improved by utilizing weakly labeled (computationally generated but imprecise) data. To demonstrate the utility and efficacy of the proposed approach, we developed DVOS models for wheat head segmentation of handheld and drone-captured videos, capturing wheat crops in fields of different locations across various growth stages, spanning from heading to maturity. Despite using only a few manually annotated video frames, the proposed approach yielded high-performing models, achieving a Dice score of 0.82 when tested on a drone-captured external test set. While we showed the efficacy of the proposed approach for wheat head segmentation, its application can be extended to other crops or DVOS in other domains, such as crowd analysis or microscopic image analysis.

Neural Radiance Fields (NeRF) have been successfully applied in various aerial scenes, yet they face challenges with sparse views due to limited supervision. The acquisition of dense aerial views is often prohibitive, as unmanned aerial vehicles (UAVs) may encounter constraints in perspective range and energy constraints. In this work, we introduce Multiplane Prior guided NeRF (MPNeRF), a novel approach tailored for few-shot aerial scene rendering-marking a pioneering effort in this domain. Our key insight is that the intrinsic geometric regularities specific to aerial imagery could be leveraged to enhance NeRF in sparse aerial scenes. By investigating NeRF's and Multiplane Image (MPI)'s behavior, we propose to guide the training process of NeRF with a Multiplane Prior. The proposed Multiplane Prior draws upon MPI's benefits and incorporates advanced image comprehension through a SwinV2 Transformer, pre-trained via SimMIM. Our extensive experiments demonstrate that MPNeRF outperforms existing state-of-the-art methods applied in non-aerial contexts, by tripling the performance in SSIM and LPIPS even with three views available. We hope our work offers insights into the development of NeRF-based applications in aerial scenes with limited data., Comment: 17 pages, 8 figures, accepted at CVPR 2024

Given a hypergraph, influence maximization (IM) is to discover a seed set containing $k$ vertices that have the maximal influence. Although the existing vertex-based IM algorithms perform better than the hyperedge-based algorithms by generating random reverse researchable (RR) sets, they are inefficient because (i) they ignore important structural information associated with hyperedges and thus obtain inferior results, (ii) the frequently-used sampling methods for generating RR sets have low efficiency because of a large number of required samplings along with high sampling variances, and (iii) the vertex-based IM algorithms have large overheads in terms of running time and memory costs. To overcome these shortcomings, this paper proposes a novel approach, called \emph{HyperIM}. The key idea behind \emph{HyperIM} is to differentiate structural information of vertices for developing stratified sampling combined with highly-efficient strategies to generate the RR sets. With theoretical guarantees, \emph{HyperIM} is able to accelerate the influence spread, improve the sampling efficiency, and cut down the expected running time. To further reduce the running time and memory costs, we optimize \emph{HyperIM} by inferring the bound of the required number of RR sets in conjunction with stratified sampling. Experimental results on real-world hypergraphs show that \emph{HyperIM} is able to reduce the number of required RR sets and running time by orders of magnitude while increasing the influence spread by up to $2.73X$ on average, compared to the state-of-the-art IM algorithms., Comment: 15 pages,10figures

Existing efforts are dedicated to designing many topologies and graph-aware strategies for the graph Transformer, which greatly improve the model's representation capabilities. However, manually determining the suitable Transformer architecture for a specific graph dataset or task requires extensive expert knowledge and laborious trials. This paper proposes an evolutionary graph Transformer architecture search framework (EGTAS) to automate the construction of strong graph Transformers. We build a comprehensive graph Transformer search space with the micro-level and macro-level designs. EGTAS evolves graph Transformer topologies at the macro level and graph-aware strategies at the micro level. Furthermore, a surrogate model based on generic architectural coding is proposed to directly predict the performance of graph Transformers, substantially reducing the evaluation cost of evolutionary search. We demonstrate the efficacy of EGTAS across a range of graph-level and node-level tasks, encompassing both small-scale and large-scale graph datasets. Experimental results and ablation studies show that EGTAS can construct high-performance architectures that rival state-of-the-art manual and automated baselines., Comment: This work has been accepted by IEEE Transactions on Neural Networks and Learning Systems. Copyright may be transferred without notice, after which this version may no longer be accessible

Spatial point process models are widely applied to point pattern data from various fields in the social and environmental sciences. However, a serious hurdle in fitting point process models is the presence of duplicated points, wherein multiple observations share identical spatial coordinates. This often occurs because of decisions made in the geo-coding process, such as assigning representative locations (e.g., aggregate-level centroids) to observations when data producers lack exact location information. Because spatial point process models like the Log-Gaussian Cox Process (LGCP) assume unique locations, researchers often employ {\it ad hoc} solutions (e.g., jittering) to address duplicated data before analysis. As an alternative, this study proposes a Modified Minimum Contrast (MMC) method that adapts the inference procedure to account for the effect of duplicates without needing to alter the data. The proposed MMC method is applied to LGCP models, with simulation results demonstrating the gains of our method relative to existing approaches in terms of parameter estimation. Interestingly, simulation results also show the effect of the geo-coding process on parameter estimates, which can be utilized in the implementation of the MMC method. The MMC approach is then used to infer the spatial clustering characteristics of conflict events in Afghanistan (2008-2009).

Recently, topolectrical circuits (TECs) boom in studying the topological states of matter. The resemblance between circuit Laplacians and tight-binding models in condensed matter physics allows for the exploration of exotic topological phases on the circuit platform. In this review, we begin by presenting the basic equations for the circuit elements and units, along with the fundamentals and experimental methods for TECs. Subsequently, we retrospect the main literature in this field, encompassing the circuit realization of (higher-order) topological insulators and semimetals. Due to the abundant electrical elements and flexible connections, many unconventional topological states like the non-Hermitian, nonlinear, non-Abelian, non-periodic, non-Euclidean, and higher-dimensional topological states that are challenging to observe in conventional condensed matter physics, have been observed in circuits and summarized in this review. Furthermore, we show the capability of electrical circuits for exploring the physical phenomena in other systems, such as photonic and magnetic ones. Importantly, we highlight TEC systems are convenient for manufacture and miniaturization because of their compatibility with the traditional integrated circuits. Finally, we prospect the future directions in this exciting field, and connect the emerging TECs with the development of topology physics, (meta)material designs, and device applications.

Existing diffusion-based video editing methods have achieved impressive results in motion editing. Most of the existing methods focus on the motion alignment between the edited video and the reference video. However, these methods do not constrain the background and object content of the video to remain unchanged, which makes it possible for users to generate unexpected videos. In this paper, we propose a one-shot video motion editing method called Edit-Your-Motion that requires only a single text-video pair for training. Specifically, we design the Detailed Prompt-Guided Learning Strategy (DPL) to decouple spatio-temporal features in space-time diffusion models. DPL separates learning object content and motion into two training stages. In the first training stage, we focus on learning the spatial features (the features of object content) and breaking down the temporal relationships in the video frames by shuffling them. We further propose Recurrent-Causal Attention (RC-Attn) to learn the consistent content features of the object from unordered video frames. In the second training stage, we restore the temporal relationship in video frames to learn the temporal feature (the features of the background and object's motion). We also adopt the Noise Constraint Loss to smooth out inter-frame differences. Finally, in the inference stage, we inject the content features of the source object into the editing branch through a two-branch structure (editing branch and reconstruction branch). With Edit-Your-Motion, users can edit the motion of objects in the source video to generate more exciting and diverse videos. Comprehensive qualitative experiments, quantitative experiments and user preference studies demonstrate that Edit-Your-Motion performs better than other methods.

In semi-supervised learning, methods that rely on confidence learning to generate pseudo-labels have been widely proposed. However, increasing research finds that when faced with noisy and biased data, the model's representation network is more reliable than the classification network. Additionally, label generation methods based on model predictions often show poor adaptability across different datasets, necessitating customization of the classification network. Therefore, we propose a Hierarchical Dynamic Labeling (HDL) algorithm that does not depend on model predictions and utilizes image embeddings to generate sample labels. We also introduce an adaptive method for selecting hyperparameters in HDL, enhancing its versatility. Moreover, HDL can be combined with general image encoders (e.g., CLIP) to serve as a fundamental data processing module. We extract embeddings from datasets with class-balanced and long-tailed distributions using pre-trained semi-supervised models. Subsequently, samples are re-labeled using HDL, and the re-labeled samples are used to further train the semi-supervised models. Experiments demonstrate improved model performance, validating the motivation that representation networks are more reliable than classifiers or predictors. Our approach has the potential to change the paradigm of pseudo-label generation in semi-supervised learning.

Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence. Delving into deeper factors that affect the fairness of DNNs is paramount and serves as the foundation for mitigating model biases. However, current methods are limited in accurately predicting DNN biases, relying solely on the number of training samples and lacking more precise measurement tools. Here, we establish a geometric perspective for analyzing the fairness of DNNs, comprehensively exploring how DNNs internally shape the intrinsic geometric characteristics of datasets-the intrinsic dimensions (IDs) of perceptual manifolds, and the impact of IDs on the fairness of DNNs. Based on multiple findings, we propose Intrinsic Dimension Regularization (IDR), which enhances the fairness and performance of models by promoting the learning of concise and ID-balanced class perceptual manifolds. In various image recognition benchmark tests, IDR significantly mitigates model bias while improving its performance., Comment: 8pages, 6figures, Submitted to TPAMI

The longitudinal nonreciprocal charge transport (NCT) in crystalline materials is a highly non-trivial phenomenon, motivating the design of next generation two-terminal rectification devices (e.g., semiconductor diodes beyond PN junctions). The practical application of such devices is built upon crystalline materials whose longitudinal NCT occurs at room temperature and under low magnetic field. However, materials of this type are rather rare and elusive, and theory guiding the discovery of these materials is lacking. Here, we develop such a theory within the framework of semiclassical Boltzmann transport theory. By symmetry analysis, we classify the complete 122 magnetic point groups with respect to the longitudinal NCT phenomenon. The symmetry-adapted Hamiltonian analysis further uncovers a previously overlooked mechanism for this phenomenon. Our theory guides the first-principles prediction of longitudinal NCT in multiferroic \epsilon-Fe2O3 semiconductor that possibly occurs at room temperature, without the application of external magnetic field. These findings advance our fundamental understandings of longitudinal NCT in crystalline materials, and aid the corresponding materials discoveries., Comment: 6 pages, 2 figures

Positron Emission Tomography (PET) is a crucial tool in medical imaging, particularly for diagnosing diseases like cancer and Alzheimer's. The advent of Positronium Lifetime Imaging (PLI) has opened new avenues for assessing the tissue micro-environment, which is vital for early-stage disease detection. In this study, we introduce a two-component reconstruction model for PLI in Time-of-Flight (TOF) PET, incorporating both ortho-positronium and para-positronium decays. Our model enhances the accuracy of positronium imaging by providing a more detailed representation of the tissue environment. We conducted simulation studies to evaluate the performance of our model and compared it with existing single-component models. The results demonstrate the superiority of the two-component model in capturing the intricacies of the tissue micro-environment, thus paving the way for more precise and informative PET diagnostics.

Parker Solar Probe (PSP) observed sub-Alfvenic solar wind intervals during encounters 8 - 14, and low-frequency magnetohydrodynamic turbulence in these regions may differ from that in super-Alfvenic wind. We apply a new mode-decomposition analysis (Zank et al 2023) to the sub-Alfv\'enic flow observed by PSP on 2021 April 28, identifying and characterizing entropy, magnetic islands, forward and backward Alfv\'en waves, including weakly/non-propagating Alfv\'en vortices, forward and backward fast and slow magnetosonic modes. Density fluctuations are primarily and almost equally entropy and backward propagating slow magnetosonic modes. The mode-decomposition provides phase information (frequency and wavenumber k) for each mode. Entropy-density fluctuations have a wavenumber anisotropy k_{||} >> k_{perp} whereas slow mode density fluctuations have k_{perp} > k_{||}. Magnetic field fluctuations are primarily magnetic island modes (delta B^i) with an O(1) smaller contribution from uni-directionally propagating Alfven waves (delta B^{A+}) giving a variance anisotropy of <{\delta B^i}^2> / = 4.1. Incompressible magnetic fluctuations dominate compressible contributions from fast and slow magnetosonic modes. The magnetic island spectrum is Kolmogorov-like k_{perp}^{-1.6} in perpendicular wavenumber and the uni-directional Alfven wave spectra are k_{||}^{-1.6} and k_{perp}^{-1.5}. Fast magnetosonic modes propagate at essentially the Alfv\'en speed with anti-correlated transverse velocity and magnetic field fluctuations and are almost exclusively magnetic due to beta_p<<1. Transverse velocity fluctuations are the dominant velocity component in fast magnetosonic modes and longitudinal fluctuations dominate in slow modes. Mode-decomposition is an effective tool in identifying the basic building blocks of MHD turbulence and provides detailed phase information about each of the modes.

Combining semantic information with behavioral data is a crucial research area in recommender systems. A promising approach involves leveraging external knowledge to enrich behavioral-based recommender systems with abundant semantic information. However, this approach faces two primary challenges: denoising raw external knowledge and adapting semantic representations. To address these challenges, we propose an External Knowledge-Enhanced Recommendation method with LLM Assistance (TRAWL). This method utilizes large language models (LLMs) to extract relevant recommendation knowledge from raw external data and employs a contrastive learning strategy for adapter training. Experiments on public datasets and real-world online recommender systems validate the effectiveness of our approach., Comment: 8 pages, 3 figures

Due to the fact that the locations of base stations (BSs) cannot be changed after they are installed, it is very difficult to communicate directly with remote user equipment (UE), which will directly affect the lifespan of the system. Unmanned aerial vehicles (UAVs) offer a hopeful solution as mobile relays for fifth-generation wireless communications due to the flexible and cost-effective deployment. However, with the limited onboard energy of UAV and slow progress in energy storage technology, it is a key challenge to achieve the energy-efficient communication. Therefore, in this article, we study a wireless communication network using a UAV as a high-altitude relay, and formulate a UAV relay deployment optimization problem (URDOP) to minimize the energy consumption of system by optimizing the deployment of UAV, including the locations and number of UAV hover points. Since the formulated URDOP is a mixed-integer programming problem, it presents a significant challenge for conventional gradient-based approaches. To this end, we propose a self-adaptive differential evolution with a variable population size (SaDEVPS) algorithm to solve the formulated URDOP. The performance of proposed SaDEVPS is verified through simulations, and the results show that it can successfully decrease the energy consumption of system when compared to other benchmark algorithms across multiple instances., Comment: 6 pages, 3 figures, 2 tables

In this research, a unique approach to provide communication service for terrestrial receivers via using unmanned aerial vehicle-enabled visible light communication is investigated. Specifically, we take into account a unmanned aerial vehicle-enabled visible light communication scenario with multiplex transmitters, multiplex receivers, and a single eavesdropper, each of which is equipped with a single photodetector. Then, a unmanned aerial vehicle deployment multi-objective optimization problem is formulated to simultaneously make the optical power received by receiving surface more uniform, minimize the amount of information collected by a eavesdropper, and minimize the energy consumption of unmanned aerial vehicles, while the locations and transmission power of unmanned aerial vehicles are simultaneously optimized under certain constraints. Since the formulated unmanned aerial vehicle deployment multi-objective optimization problem is complex and nonlinear, it is challenging to be tackled by using conventional methods. For the purpose of solving the problem, a multi-objective evolutionary algorithm based on decomposition with chaos initiation and crossover mutation is proposed. Simulation outcomes show that the proposed approach is superior to other approaches, and is efficient at improving the security and energy efficiency of visible light communication system., Comment: 18 pages, 9 tables, 3 tables

The ground fixed base stations (BSs) are often deployed inflexibly, and have high overheads, as well as are susceptible to the damage from natural disasters, making it impractical for them to continuously collect data from sensor devices. To improve the network coverage and performance of wireless communication, unmanned aerial vehicles (UAVs) have been introduced in diverse wireless networks, therefore in this work we consider employing a UAV as an aerial BS to acquire data of agricultural Internet of Things (IoT) devices. To this end, we first formulate a UAV-assisted data collection multi-objective optimization problem (UDCMOP) to efficiently collect the data from agricultural sensing devices. Specifically, we aim to collaboratively optimize the hovering positions of UAV, visit sequence of UAV, speed of UAV, in addition to the transmit power of devices, to simultaneously achieve the maximization of minimum transmit rate of devices, the minimization of total energy consumption of devices, and the minimization of total energy consumption of UAV. Second, the proposed UDCMOP is a non-convex mixed integer nonlinear optimization problem, which indicates that it includes continuous and discrete solutions, making it intractable to be solved. Therefore, we solve it by proposing an improved multi-objective artificial hummingbird algorithm (IMOAHA) with several specific improvement factors, that are the hybrid initialization operator, Cauchy mutation foraging operator, in addition to the discrete mutation operator. Finally, simulations are carried out to testify that the proposed IMOAHA can effectively improve the system performance comparing to other benchmarks., Comment: 13 pages, 7 figures, 4 tables

Polarized light has various potential applications in the communication bands, including optical communication, polarization imaging, quantum emission, and quantum communication. However, optimizing polarization control requires continuous improvements in areas such as dynamic tunability, materials, and efficiency. In this work, we propose a borophene-based structure capable of converting linearly polarized light into arbitrarily polarized light through the coherent excitation of localized surface plasmons (LSPs) in optical communication band. Furthermore, a double-layer borophene structure can be achieved by placing a second borophene array at the top of the first one with a 90{\deg} relative rotation of their crystalline plane. The rotation direction of the polarization state of the reflected light can be switched by independently controlling the carrier concentration of the two-layer borophene. Finally, a dipole source is used to realize the emission of polarized light, which is two orders of magnitude higher than the emission rate in free space, and the polarization state can be dynamically controlled by manipulating the carrier concentration. Our study is simple and compact, with potential applications in the fields of polarizers, polarization detectors, and quantum emitters.

In this paper we proposed a joint reconstruction of \gray events using both extensive air array (EAS) and Imaging air Cherenkov Telescope array (IACT). We considered eight Cherenkov telescopes to be built on the LHAASO (Large High Altitude Air Shower Observatory) site and investigate the improvement in differential sensitivity when combining the information from both IACT and Moun detectors of LHAASO-KM2A. We found that due to the higher cosmic ray background rejection power and higher gamma ray retention ratio provided by muon detectors of LHAASO, such a joint reconstruction can significantly improve the sensitivity of IACTs, especially for extended sources and long exposure time. In this article, we have shown the performance of an eight-telescopes mini array, and our results indicate that above $10~\rm TeV$, the sensitivity can be improved by muon detector from $25\% - 60\%$ in different energy ranges., Comment: 6 pages, 6 figures, receiver by JHEAP

Space-time (ST) beams, ultrafast optical wavepackets with customized spatial and temporal characteristics, present a significant contrast to conventional spatial-structured light and hold the potential to revolutionize our understanding and manipulation of light. However, the progress in ST beam research has been constrained by the absence of a universal framework for their analysis and generation. Here, we introduce the concept of "two-dimensional ST duality", establishing a foundational duality between spatial-structured light and ST beams. We show that breaking the exact balance between paraxial diffraction and narrow-band dispersion is crucial for guiding the dynamics of ST wavepackets. Leveraging this insight, we pioneer a versatile complex-amplitude modulation strategy, enabling the precise crafting of ST beams with an exceptional fidelity exceeding 97%. Furthermore, we uncover a new range of ST wavepackets by harnessing the exact one-to-one relationship between scalar spatial-structured light and ST beams. Our findings suggest a paradigm shift opportunity in ST beam research and may apply to a broader range of wave physics systems.

We present the viewpoint of treating one-dimensional band structures as Riemann surfaces, linking the unique properties of non-Hermiticity to the geometry and topology of the Riemann surface. Branch cuts and branch points play a significant role when this viewpoint is applied to both the open-boundary spectrum and the braiding structure. An open-boundary spectrum is interpreted as branch cuts connecting certain branch points, and its consistency with the monodromy representation severely limits its possible morphology. A braid word for the Brillouin zone can be read off from its intersections with branch cuts, and its crossing number is given by the winding number of the discriminant. These results open new avenues to generate important insights into the physical behaviors of non-Hermitian systems., Comment: 13 pages, 6 figures

Pre-trained large language models (LLMs) have powerful capabilities for generating creative natural text. Evolutionary algorithms (EAs) can discover diverse solutions to complex real-world problems. Motivated by the common collective and directionality of text generation and evolution, this paper illustrates the parallels between LLMs and EAs, which includes multiple one-to-one key characteristics: token representation and individual representation, position encoding and fitness shaping, position embedding and selection, Transformers block and reproduction, and model training and parameter adaptation. By examining these parallels, we analyze existing interdisciplinary research, with a specific focus on evolutionary fine-tuning and LLM-enhanced EAs. Drawing from these insights, valuable future directions are presented for advancing the integration of LLMs and EAs, while highlighting key challenges along the way. These parallels not only reveal the evolution mechanism behind LLMs but also facilitate the development of evolved artificial agents that approach or surpass biological organisms., Comment: A review article under two review

We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world relation extraction. GRD aims to identify unlabeled instances in existing pre-defined relations or discover novel relations by assigning instances to clusters as well as providing specific meanings for these clusters. The key challenges of GRD are how to mitigate the serious model biases caused by labeled pre-defined relations to learn effective relational representations and how to determine the specific semantics of novel relations during classifying or clustering unlabeled instances. We then propose a novel framework, SFGRD, for this task to solve the above issues by learning from semi-factuals in two stages. The first stage is semi-factual generation implemented by a tri-view debiased relation representation module, in which we take each original sentence as the main view and design two debiased views to generate semi-factual examples for this sentence. The second stage is semi-factual thinking executed by a dual-space tri-view collaborative relation learning module, where we design a cluster-semantic space and a class-index space to learn relational semantics and relation label indices, respectively. In addition, we devise alignment and selection strategies to integrate two spaces and establish a self-supervised learning loop for unlabeled data by doing semi-factual thinking across three views. Extensive experimental results show that SFGRD surpasses state-of-the-art models in terms of accuracy by 2.36\% $\sim$5.78\% and cosine similarity by 32.19\%$\sim$ 84.45\% for relation label index and relation semantic quality, respectively. To the best of our knowledge, we are the first to exploit the efficacy of semi-factuals in relation extraction.

In this technical report, we present TeleChat, a collection of large language models (LLMs) with parameters of 3 billion, 7 billion and 12 billion. It includes pretrained language models as well as fine-tuned chat models that is aligned with human preferences. TeleChat is initially pretrained on an extensive corpus containing a diverse collection of texts from both English and Chinese languages, including trillions of tokens. Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe. We evaluate the performance of TeleChat on various tasks, including language understanding, mathematics, reasoning, code generation, and knowledge-based question answering. Our findings indicate that TeleChat achieves comparable performance to other open-source models of similar size across a wide range of public benchmarks. To support future research and applications utilizing LLMs, we release the fine-tuned model checkpoints of TeleChat's 7B and 12B variant, along with code and a portion of our pretraining data, to the public community., Comment: 28 pages, 2 figures

With the rapid growth of cloud computing, a variety of software services have been deployed in the cloud. To ensure the reliability of cloud services, prior studies focus on failure instance (disk, node, and switch, etc.) prediction. Once the output of prediction is positive, mitigation actions are taken to rapidly resolve the underlying failure. According to our real-world practice in Microsoft Azure, we find that the prediction accuracy may decrease by about 9% after retraining the models. Considering that the mitigation actions may result in uncertain positive instances since they cannot be verified after mitigation, which may introduce more noise while updating the prediction model. To the best of our knowledge, we are the first to identify this Uncertain Positive Learning (UPLearning) issue in the real-world cloud failure prediction scenario. To tackle this problem, we design an Uncertain Positive Learning Risk Estimator (Uptake) approach. Using two real-world datasets of disk failure prediction and conducting node prediction experiments in Microsoft Azure, which is a top-tier cloud provider that serves millions of users, we demonstrate Uptake can significantly improve the failure prediction accuracy by 5% on average.