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There has been a paradigm shift from accommodations to the identification of levels of accessibility (e.g., universal features, designated features, and accommodations). A consequence of this shift is that many students with disabilities who previously were assigned accommodations now access many of them as universal or designated features. This Brief highlights what states and Individualized Education Program (IEP) teams can do to avoid possible unintended consequences of the paradigm shift.

Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers' expertise and idiosyncratic factors, preventing teachers from getting timely and frequent feedback. Different from prior research that focuses on low-inference instructional practices, this paper presents the first study that leverages Natural Language Processing (NLP) techniques to assess multiple high-inference instructional practices in two distinct educational settings: in-person K-12 classrooms and simulated performance tasks for pre-service teachers. This is also the first study that applies NLP to measure a teaching practice that has been demonstrated to be particularly effective for students with special needs. We confront two challenges inherent in NLP-based instructional analysis, including noisy and long input data and highly skewed distributions of human ratings. Our results suggest that pretrained Language Models (PLMs) demonstrate performances comparable to the agreement level of human raters for variables that are more discrete and require lower inference, but their efficacy diminishes with more complex teaching practices. Interestingly, using only teachers' utterances as input yields strong results for student-centered variables, alleviating common concerns over the difficulty of collecting and transcribing high-quality student speech data in in-person teaching settings. Our findings highlight both the potential and the limitations of current NLP techniques in the education domain, opening avenues for further exploration.

Chorus is one of the strongest electromagnetic emissions naturally occurring in space, and can cause hazardous radiations to humans and satellites1-3. Although chorus has attracted extreme interest and been intensively studied for decades4-7, its generation and evolution remain highly debated, due to the complexity of the underlying physics and the limited capacity of previous spacecraft missions7. Chorus has also been believed to be governed by planetary magnetic dipolar fields5,7. Contrary to such conventional expectation, here we report unexpected observations of chorus in the terrestrial neutral sheet where magnetic dipolar effect is absent. Using unprecedentedly high-cadence data from the Magnetospheric Multiscale Mission, we present the first, ultrafast measurements of the wave dispersion relation and electron three-dimensional distributions within the waves, showing smoking-gun evidences for chorus-electron interactions and development of electron holes in the wave phase space. We estimate field-particle energy transfer inside the waves and find that the waves were extracting energy from local thermal electrons, in line with the wave positive growth rate derived from instability analysis. Our observations, opening new pathways for resolving long-standing controversies regarding the chorus emissions, are crucial for understanding nonlinear energy transport ubiquitously observed in space and astrophysical environments., Comment: under review; comments and suggestions are welcomed

Class imbalance significantly impacts the performance of multi-label classifiers. Oversampling is one of the most popular approaches, as it augments instances associated with less frequent labels to balance the class distribution. Existing oversampling methods generate feature vectors of synthetic samples through replication or linear interpolation and assign labels through neighborhood information. Linear interpolation typically generates new samples between existing data points, which may result in insufficient diversity of synthesized samples and further lead to the overfitting issue. Deep learning-based methods, such as AutoEncoders, have been proposed to generate more diverse and complex synthetic samples, achieving excellent performance on imbalanced binary or multi-class datasets. In this study, we introduce AEMLO, an AutoEncoder-guided Oversampling technique specifically designed for tackling imbalanced multi-label data. AEMLO is built upon two fundamental components. The first is an encoder-decoder architecture that enables the model to encode input data into a low-dimensional feature space, learn its latent representations, and then reconstruct it back to its original dimension, thus applying to the generation of new data. The second is an objective function tailored to optimize the sampling task for multi-label scenarios. We show that AEMLO outperforms the existing state-of-the-art methods with extensive empirical studies.

Order flow modeling stands as the most fundamental and essential financial task, as orders embody the minimal unit within a financial market. However, current approaches often result in unsatisfactory fidelity in generating order flow, and their generation lacks controllability, thereby limiting their application scenario. In this paper, we advocate incorporating controllability into the market generation process, and propose a Diffusion Guided meta Agent(DiGA) model to address the problem. Specifically, we utilize a diffusion model to capture dynamics of market state represented by time-evolving distribution parameters about mid-price return rate and order arrival rate, and define a meta agent with financial economic priors to generate orders from the corresponding distributions. Extensive experimental results demonstrate that our method exhibits outstanding controllability and fidelity in generation. Furthermore, we validate DiGA's effectiveness as generative environment for downstream financial applications.

Video Moment Retrieval (VMR) aims to retrieve relevant moments of an untrimmed video corresponding to the query. While cross-modal interaction approaches have shown progress in filtering out query-irrelevant information in videos, they assume the precise alignment between the query semantics and the corresponding video moments, potentially overlooking the misunderstanding of the natural language semantics. To address this challenge, we propose a novel model called \textit{QD-VMR}, a query debiasing model with enhanced contextual understanding. Firstly, we leverage a Global Partial Aligner module via video clip and query features alignment and video-query contrastive learning to enhance the cross-modal understanding capabilities of the model. Subsequently, we employ a Query Debiasing Module to obtain debiased query features efficiently, and a Visual Enhancement module to refine the video features related to the query. Finally, we adopt the DETR structure to predict the possible target video moments. Through extensive evaluations of three benchmark datasets, QD-VMR achieves state-of-the-art performance, proving its potential to improve the accuracy of VMR. Further analytical experiments demonstrate the effectiveness of our proposed module. Our code will be released to facilitate future research., Comment: 9 pages, 4 figures, 4 tables

In this study, we conducted simulations to find the geometric aberrations expected for images taken by the Main Survey Camera (MSC) of the Chinese Space Station Telescope (CSST) due to its motion. As anticipated by previous work, our findings indicate that the geometric distortion of light impacts the focal plane's apparent scale, with a more pronounced influence as the size of the focal plane increases. Our models suggest that the effect consistently influences the pixel scale in both the vertical and parallel directions. The apparent scale variation follows a sinusoidal distribution throughout one orbit period. Simulations reveal that the effect is particularly pronounced in the center of the Galaxy and gradually diminishes along the direction of ecliptic latitude. At low ecliptic latitudes, the total aberration leads to about 0.94 pixels offset (a 20-minute exposure) and 0.26 pixels offset (a 300-second exposure) at the edge of the field of view, respectively. Appropriate processings for the geometric effect during the CSST pre- and post-observation phases are presented., Comment: 13 pages, 8 figures; accepted by RAA

Recently, a wide range of memory-efficient LLM training algorithms have gained substantial popularity. These methods leverage the low-rank structure of gradients to project optimizer states into a subspace using projection matrix found by singular value decomposition (SVD). However, convergence of these algorithms is highly dependent on the update rules of their projection matrix. In this work, we provide the \emph{first} convergence guarantee for arbitrary update rules of projection matrix. This guarantee is generally applicable to optimizers that can be analyzed with Hamiltonian Descent, including most common ones, such as LION, Adam. Inspired by our theoretical understanding, we propose Online Subspace Descent, a new family of subspace descent optimizer without SVD. Instead of updating the projection matrix with eigenvectors, Online Subspace Descent updates the projection matrix with online PCA. Online Subspace Descent is flexible and introduces only minimum overhead to training. We show that for the task of pretraining LLaMA models ranging from 60M to 7B parameters on the C4 dataset, Online Subspace Descent achieves lower perplexity and better downstream tasks performance than state-of-the-art low-rank training methods across different settings and narrows the gap with full-rank baselines., Comment: Code is available at https://github.com/kyleliang919/Online-Subspace-Descent

With the rapid development of deepfake technology, especially the deep audio fake technology, misinformation detection on the social media scene meets a great challenge. Social media data often contains multimodal information which includes audio, video, text, and images. However, existing multimodal misinformation detection methods tend to focus only on some of these modalities, failing to comprehensively address information from all modalities. To comprehensively address the various modal information that may appear on social media, this paper constructs a comprehensive multimodal misinformation detection framework. By employing corresponding neural network encoders for each modality, the framework can fuse different modality information and support the multimodal misinformation detection task. Based on the constructed framework, this paper explores the importance of the audio modality in multimodal misinformation detection tasks on social media. By adjusting the architecture of the acoustic encoder, the effectiveness of different acoustic feature encoders in the multimodal misinformation detection tasks is investigated. Furthermore, this paper discovers that audio and video information must be carefully aligned, otherwise the misalignment across different audio and video modalities can severely impair the model performance.

The interplay between strong electron-electron interaction and symmetry breaking can have profound influence on the topological properties of materials. In magic angle twisted bilayer graphene (MATBG), the flat band with a single SU(4) flavor associated with the spin and valley degrees of freedom gains non-zero Chern number when C2z symmetry or C2zT symmetry is broken. Electron-electron interaction can further lift the SU(4) degeneracy, leading to the Chern insulator states. Here we report a complete sequence of zero-field Chern insulators at all odd integer fillings (v = +-1, +-3) with different chirality (C = 1 or -1) in hBN aligned MATBG which structurally breaks C2z symmetry. The Chern states at hole fillings (v = -1, -3), which are firstly observed in this work, host an opposite chirality compared with the electron filling scenario. By slightly doping the v = +-3 states, we have observed new correlated insulating states at incommensurate moir\'e fillings which is highly suggested to be intrinsic Wigner crystals according to our theoretical calculations. Remarkably, we have observed prominent Streda-formula violation around v = -3 state. By doping the Chern gap at v = -3 with notable number of electrons at finite magnetic field, the Hall resistance Ryx robustly quantizes to ~ h/e2 whereas longitudinal resistance Rxx vanishes, indicating that the chemical potential is pinned within a Chern gap, forming an incommensurate Chern insulator. By providing the first experimental observation of zero-field Chern insulators in the flat valence band, our work fills up the overall topological framework of MATBG with broken C2z symmetry. Our findings also demonstrate that doped topological flat band is an ideal platform to investigate exotic incommensurate correlated topological states., Comment: 28 pages. 5 figures

Pretrained language models like BERT and T5 serve as crucial backbone encoders for dense retrieval. However, these models often exhibit limited generalization capabilities and face challenges in improving in domain accuracy. Recent research has explored using large language models (LLMs) as retrievers, achieving SOTA performance across various tasks. Despite these advancements, the specific benefits of LLMs over traditional retrievers and the impact of different LLM configurations, such as parameter sizes, pretraining duration, and alignment processes on retrieval tasks remain unclear. In this work, we conduct a comprehensive empirical study on a wide range of retrieval tasks, including in domain accuracy, data efficiency, zero shot generalization, lengthy retrieval, instruction based retrieval, and multi task learning. We evaluate over 15 different backbone LLMs and non LLMs. Our findings reveal that larger models and extensive pretraining consistently enhance in domain accuracy and data efficiency. Additionally, larger models demonstrate significant potential in zero shot generalization, lengthy retrieval, instruction based retrieval, and multi task learning. These results underscore the advantages of LLMs as versatile and effective backbone encoders in dense retrieval, providing valuable insights for future research and development in this field., Comment: Submitted to EMNLP24

This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques. We begin by introducing fundamental concepts, traditional methods, and benchmark datasets, then examine the various roles ML plays in improving CFD. The literature systematically reviews papers in recent five years and introduces a novel classification for forward modeling: Data-driven Surrogates, Physics-Informed Surrogates, and ML-assisted Numerical Solutions. Furthermore, we also review the latest ML methods in inverse design and control, offering a novel classification and providing an in-depth discussion. Then we highlight real-world applications of ML for CFD in critical scientific and engineering disciplines, including aerodynamics, combustion, atmosphere & ocean science, biology fluid, plasma, symbolic regression, and reduced order modeling. Besides, we identify key challenges and advocate for future research directions to address these challenges, such as multi-scale representation, physical knowledge encoding, scientific foundation model and automatic scientific discovery. This review serves as a guide for the rapidly expanding ML for CFD community, aiming to inspire insights for future advancements. We draw the conclusion that ML is poised to significantly transform CFD research by enhancing simulation accuracy, reducing computational time, and enabling more complex analyses of fluid dynamics. The paper resources can be viewed at https://github.com/WillDreamer/Awesome-AI4CFD., Comment: 22 pages, 6 figures

Matrix reordering permutes the rows and columns of a matrix to reveal meaningful visual patterns, such as blocks that represent clusters. A comprehensive collection of matrices, along with a scoring method for measuring the quality of visual patterns in these matrices, contributes to building a benchmark. This benchmark is essential for selecting or designing suitable reordering algorithms for specific tasks. In this paper, we build a matrix reordering benchmark, ReorderBench, with the goal of evaluating and improving matrix reordering techniques. This is achieved by generating a large set of representative and diverse matrices and scoring these matrices with a convolution- and entropy-based method. Our benchmark contains 2,835,000 binary matrices and 5,670,000 continuous matrices, each featuring one of four visual patterns: block, off-diagonal block, star, or band. We demonstrate the usefulness of ReorderBench through three main applications in matrix reordering: 1) evaluating different reordering algorithms, 2) creating a unified scoring model to measure the visual patterns in any matrix, and 3) developing a deep learning model for matrix reordering., Comment: Submitted to IEEE TVCG

Sequential Recommendation (SR) plays a pivotal role in recommender systems by tailoring recommendations to user preferences based on their non-stationary historical interactions. Achieving high-quality performance in SR requires attention to both item representation and diversity. However, designing an SR method that simultaneously optimizes these merits remains a long-standing challenge. In this study, we address this issue by integrating recent generative Diffusion Models (DM) into SR. DM has demonstrated utility in representation learning and diverse image generation. Nevertheless, a straightforward combination of SR and DM leads to sub-optimal performance due to discrepancies in learning objectives (recommendation vs. noise reconstruction) and the respective learning spaces (non-stationary vs. stationary). To overcome this, we propose a novel framework called DimeRec (\textbf{Di}ffusion with \textbf{m}ulti-interest \textbf{e}nhanced \textbf{Rec}ommender). DimeRec synergistically combines a guidance extraction module (GEM) and a generative diffusion aggregation module (DAM). The GEM extracts crucial stationary guidance signals from the user's non-stationary interaction history, while the DAM employs a generative diffusion process conditioned on GEM's outputs to reconstruct and generate consistent recommendations. Our numerical experiments demonstrate that DimeRec significantly outperforms established baseline methods across three publicly available datasets. Furthermore, we have successfully deployed DimeRec on a large-scale short video recommendation platform, serving hundreds of millions of users. Live A/B testing confirms that our method improves both users' time spent and result diversification.

Multi-behavior recommendation systems enhance effectiveness by leveraging auxiliary behaviors (such as page views and favorites) to address the limitations of traditional models that depend solely on sparse target behaviors like purchases. Existing approaches to multi-behavior recommendations typically follow one of two strategies: some derive initial node representations from individual behavior subgraphs before integrating them for a comprehensive profile, while others interpret multi-behavior data as a heterogeneous graph, applying graph neural networks to achieve a unified node representation. However, these methods do not adequately explore the intricate patterns of behavior among users and items. To bridge this gap, we introduce a novel algorithm called Behavior Pattern mining-based Multi-behavior Recommendation (BPMR). Our method extensively investigates the diverse interaction patterns between users and items, utilizing these patterns as features for making recommendations. We employ a Bayesian approach to streamline the recommendation process, effectively circumventing the challenges posed by graph neural network algorithms, such as the inability to accurately capture user preferences due to over-smoothing. Our experimental evaluation on three real-world datasets demonstrates that BPMR significantly outperforms existing state-of-the-art algorithms, showing an average improvement of 268.29% in Recall@10 and 248.02% in NDCG@10 metrics. The code of our BPMR is openly accessible for use and further research at https://github.com/rookitkitlee/BPMR.

The emergence of Large Language Models (LLMs) and multimodal foundation models (FMs) has generated heightened interest in their applications that integrate vision and language. This paper investigates the capabilities of ChatGPT-4V and Gemini Pro for Street View Imagery, Built Environment, and Interior by evaluating their performance across various tasks. The assessments include street furniture identification, pedestrian and car counts, and road width measurement in Street View Imagery; building function classification, building age analysis, building height analysis, and building structure classification in the Built Environment; and interior room classification, interior design style analysis, interior furniture counts, and interior length measurement in Interior. The results reveal proficiency in length measurement, style analysis, question answering, and basic image understanding, but highlight limitations in detailed recognition and counting tasks. While zero-shot learning shows potential, performance varies depending on the problem domains and image complexities. This study provides new insights into the strengths and weaknesses of multimodal foundation models for practical challenges in Street View Imagery, Built Environment, and Interior. Overall, the findings demonstrate foundational multimodal intelligence, emphasizing the potential of FMs to drive forward interdisciplinary applications at the intersection of computer vision and language.

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.

The advent of the Segment Anything Model (SAM) marks a significant milestone for interactive segmentation using generalist models. As a late fusion model, SAM extracts image embeddings once and merges them with prompts in later interactions. This strategy limits the models ability to extract detailed information from the prompted target zone. Current specialist models utilize the early fusion strategy that encodes the combination of images and prompts to target the prompted objects, yet repetitive complex computations on the images result in high latency. The key to these issues is efficiently synergizing the images and prompts. We propose SAM-REF, a two-stage refinement framework that fully integrates images and prompts globally and locally while maintaining the accuracy of early fusion and the efficiency of late fusion. The first-stage GlobalDiff Refiner is a lightweight early fusion network that combines the whole image and prompts, focusing on capturing detailed information for the entire object. The second-stage PatchDiff Refiner locates the object detail window according to the mask and prompts, then refines the local details of the object. Experimentally, we demonstrated the high effectiveness and efficiency of our method in tackling complex cases with multiple interactions. Our SAM-REF model outperforms the current state-of-the-art method in most metrics on segmentation quality without compromising efficiency.

In various domains, Sequential Recommender Systems (SRS) have become essential due to their superior capability to discern intricate user preferences. Typically, SRS utilize transformer-based architectures to forecast the subsequent item within a sequence. Nevertheless, the quadratic computational complexity inherent in these models often leads to inefficiencies, hindering the achievement of real-time recommendations. Mamba, a recent advancement, has exhibited exceptional performance in time series prediction, significantly enhancing both efficiency and accuracy. However, integrating Mamba directly into SRS poses several challenges. Its inherently unidirectional nature may constrain the model's capacity to capture the full context of user-item interactions, while its instability in state estimation can compromise its ability to detect short-term patterns within interaction sequences. To overcome these issues, we introduce a new framework named Selective Gated Mamba (SIGMA) for Sequential Recommendation. This framework leverages a Partially Flipped Mamba (PF-Mamba) to construct a bidirectional architecture specifically tailored to improve contextual modeling. Additionally, an input-sensitive Dense Selective Gate (DS Gate) is employed to optimize directional weights and enhance the processing of sequential information in PF-Mamba. For short sequence modeling, we have also developed a Feature Extract GRU (FE-GRU) to efficiently capture short-term dependencies. Empirical results indicate that SIGMA outperforms current models on five real-world datasets. Our implementation code is available at https://github.com/ziwliu-cityu/SIMGA to ease reproducibility.

Multi-modal Large Language Models have recently experienced rapid developments and excel in various multi-modal tasks. However, they still struggle with mathematical geometric problem solving, which requires exceptional visual perception proficiency. Existing MLLMs mostly optimize the LLM backbone to acquire geometric reasoning capabilities, while rarely emphasizing improvements in visual comprehension. In this paper, we first investigate the visual perception performance of MLLMs when facing geometric diagrams. Our findings reveal that current MLLMs severely suffer from inaccurate geometric perception and hallucinations. To address these limitations, we propose EAGLE, a novel two-stage end-to-end visual enhancement MLLM framework designed to ElevAte Geometric reasoning through LLM-Empowered visual instruction tuning. Specifically, in the preliminary stage, we feed geometric image-caption pairs into our MLLM that contains a fully fine-tuning CLIP ViT and a frozen LLM, aiming to endow our model with basic geometric knowledge. In the subsequent advanced stage, we incorporate LoRA modules into the vision encoder and unfreeze the LLM backbone. This enables the model to leverage the inherent CoT rationales within question-answer pairs, guiding the MLLM to focus on nuanced visual cues and enhancing its overall perceptual capacity. Moreover, we optimize the cross-modal projector in both stages to foster adaptive visual-linguistic alignments. After the two-stage visual enhancement, we develop the geometry expert model EAGLE-7B. Extensive experiments on popular benchmarks demonstrate the effectiveness of our model. For example, on the GeoQA benchmark, EAGLE-7B not only surpasses the exemplary G-LLaVA 7B model by 2.9%, but also marginally outperforms the larger G-LLaVA 13B model. On the MathVista benchmark, EAGLE-7B achieves remarkable 3.8% improvements compared with the proprietary model GPT-4V.

Large-scale data collection is essential for developing personalized training data, mitigating the shortage of training data, and fine-tuning specialized models. However, creating high-quality datasets quickly and accurately remains a challenge due to annotation errors, the substantial time and costs associated with human labor. To address these issues, we propose Automatic Dataset Construction (ADC), an innovative methodology that automates dataset creation with negligible cost and high efficiency. Taking the image classification task as a starting point, ADC leverages LLMs for the detailed class design and code generation to collect relevant samples via search engines, significantly reducing the need for manual annotation and speeding up the data generation process. Despite these advantages, ADC also encounters real-world challenges such as label errors (label noise) and imbalanced data distributions (label bias). We provide open-source software that incorporates existing methods for label error detection, robust learning under noisy and biased data, ensuring a higher-quality training data and more robust model training procedure. Furthermore, we design three benchmark datasets focused on label noise detection, label noise learning, and class-imbalanced learning. These datasets are vital because there are few existing datasets specifically for label noise detection, despite its importance. Finally, we evaluate the performance of existing popular methods on these datasets, thereby facilitating further research in the field.

The Tsinghua University--Ma Huateng Telescopes for Survey (TMTS) started to monitor the LAMOST plates in 2020, leading to the discovery of numerous short-period eclipsing binaries, peculiar pulsators, flare stars, and other variable objects. Here, we present the uninterrupted light curves for a sample of 64 cataclysmic variables (CVs) observed/discovered using the TMTS during its first three-year observations, and we introduce new CVs and new light-variation periods (from known CVs) revealed through the TMTS observations. Thanks to the high-cadence observations of TMTS, diverse light variations, including superhumps, quasi-periodic oscillations, large-amplitude orbital modulations, and rotational modulations, are able to be detected in our CV samples, providing key observational clues for understanding the fast-developing physical processes in various CVs. All of these short-timescale light-curve features help further classify the subtypes of CV systems. We highlight the light-curve features observed in our CV sample and discuss further implications of minute-cadence light curves for CV identifications and classifications. Moreover, we examine the H$\alpha$ emission lines in the spectra from our nonmagnetic CV samples (i.e., dwarf novae and nova-like subclasses) and find that the distribution of H$\alpha$ emission strength shows significant differences between the sources with orbital periods above and below the period gap, which agrees with the trend seen from the SDSS nonmagnetic CV sample., Comment: 27 pages, 12 figures in main text, accepted for the publication in Universe

Medical abstractive summarization faces the challenge of balancing faithfulness and informativeness. Current methods often sacrifice key information for faithfulness or introduce confabulations when prioritizing informativeness. While recent advancements in techniques like in-context learning (ICL) and fine-tuning have improved medical summarization, they often overlook crucial aspects such as faithfulness and informativeness without considering advanced methods like model reasoning and self-improvement. Moreover, the field lacks a unified benchmark, hindering systematic evaluation due to varied metrics and datasets. This paper addresses these gaps by presenting a comprehensive benchmark of six advanced abstractive summarization methods across three diverse datasets using five standardized metrics. Building on these findings, we propose uMedSum, a modular hybrid summarization framework that introduces novel approaches for sequential confabulation removal followed by key missing information addition, ensuring both faithfulness and informativeness. Our work improves upon previous GPT-4-based state-of-the-art (SOTA) medical summarization methods, significantly outperforming them in both quantitative metrics and qualitative domain expert evaluations. Notably, we achieve an average relative performance improvement of 11.8% in reference-free metrics over the previous SOTA. Doctors prefer uMedSum's summaries 6 times more than previous SOTA in difficult cases where there are chances of confabulations or missing information. These results highlight uMedSum's effectiveness and generalizability across various datasets and metrics, marking a significant advancement in medical summarization., Comment: 12 pages

Mental disorders, such as anxiety and depression, have become a global issue that affects the regular lives of people across different ages. Without proper detection and treatment, anxiety and depression can hinder the sufferer's study, work, and daily life. Fortunately, recent advancements of digital and AI technologies provide new opportunities for better mental health care and many efforts have been made in developing automatic anxiety and depression assessment techniques. However, this field still lacks a publicly available large-scale dataset that can facilitate the development and evaluation of AI-based techniques. To address this limitation, we have constructed a new large-scale \textbf{M}ulti-\textbf{M}odal \textbf{Psy}chological assessment corpus (MMPsy) on anxiety and depression assessment of Mandarin-speaking adolescents. The MMPsy contains audios and extracted transcripts of responses from automated anxiety or depression assessment interviews along with the self-reported anxiety or depression evaluations of the participants using standard mental health assessment questionnaires. Our dataset contains over 7,700 post-processed recordings of interviews for anxiety assessment and over 4,200 recordings for depression assessment. Using this dataset, we have developed a novel deep-learning based mental disorder estimation model, named \textbf{Mental-Perceiver}, to detect anxious/depressive mental states from recorded audio and transcript data. Extensive experiments on our MMPsy and the commonly-used DAIC-WOZ datasets have shown the effectiveness and superiority of our proposed Mental-Perceiver model in anxiety and depression detection. The MMPsy dataset will be made publicly available later to facilitate the research and development of AI-based techniques in the mental health care field.

Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model uncertainties, parameterization simplifications, and inadequate representation of complex climate phenomena. Traditional bias correction methods, which rely on historical observation data and statistical techniques, often neglect unobserved confounders, leading to biased results. This paper proposes a novel bias correction approach to utilize both GCM and observational data to learn a factor model that captures multi-cause latent confounders. Inspired by recent advances in causality based time series deconfounding, our method first constructs a factor model to learn latent confounders from historical data and then applies them to enhance the bias correction process using advanced time series forecasting models. The experimental results demonstrate significant improvements in the accuracy of precipitation outputs. By addressing unobserved confounders, our approach offers a robust and theoretically grounded solution for climate model bias correction.

Large language models (LLMs) like ChatGPT and Gemini have significantly advanced natural language processing, enabling various applications such as chatbots and automated content generation. However, these models can be exploited by malicious individuals who craft toxic prompts to elicit harmful or unethical responses. These individuals often employ jailbreaking techniques to bypass safety mechanisms, highlighting the need for robust toxic prompt detection methods. Existing detection techniques, both blackbox and whitebox, face challenges related to the diversity of toxic prompts, scalability, and computational efficiency. In response, we propose ToxicDetector, a lightweight greybox method designed to efficiently detect toxic prompts in LLMs. ToxicDetector leverages LLMs to create toxic concept prompts, uses embedding vectors to form feature vectors, and employs a Multi-Layer Perceptron (MLP) classifier for prompt classification. Our evaluation on various versions of the LLama models, Gemma-2, and multiple datasets demonstrates that ToxicDetector achieves a high accuracy of 96.39\% and a low false positive rate of 2.00\%, outperforming state-of-the-art methods. Additionally, ToxicDetector's processing time of 0.0780 seconds per prompt makes it highly suitable for real-time applications. ToxicDetector achieves high accuracy, efficiency, and scalability, making it a practical method for toxic prompt detection in LLMs., Comment: Accepted by the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE 2024)

Mixers play a crucial role in superconducting quantum computing, primarily by facilitating frequency conversion of signals to enable precise control and readout of quantum states. However, imperfections, particularly carrier leakage and unwanted sideband signal, can significantly compromise control fidelity. To mitigate these defects, regular and precise mixer calibrations are indispensable, yet they pose a formidable challenge in large-scale quantum control. Here, we introduce an in situ calibration technique and outcome-focused mixer calibration scheme using superconducting qubits. Our method leverages the qubit's response to imperfect signals, allowing for calibration without modifying the wiring configuration. We experimentally validate the efficacy of this technique by benchmarking single-qubit gate fidelity and qubit coherence time., Comment: 9 pages, 7 figures

Federated Graph Learning (FGL) is an emerging technology that enables clients to collaboratively train powerful Graph Neural Networks (GNNs) in a distributed manner without exposing their private data. Nevertheless, FGL still faces the challenge of the severe non-Independent and Identically Distributed (non-IID) nature of graphs, which possess diverse node and edge structures, especially across varied domains. Thus, exploring the knowledge inherent in these structures becomes significantly crucial. Existing methods, however, either overlook the inherent structural knowledge in graph data or capture it at the cost of significantly increased resource demands (e.g., FLOPs and communication bandwidth), which can be detrimental to distributed paradigms. Inspired by this, we propose FedDense, a novel FGL framework that optimizes the utilization efficiency of inherent structural knowledge. To better acquire knowledge of diverse and underexploited structures, FedDense first explicitly encodes the structural knowledge inherent within graph data itself alongside node features. Besides, FedDense introduces a Dual-Densely Connected (DDC) GNN architecture that exploits the multi-scale (i.e., one-hop to multi-hop) feature and structure insights embedded in the aggregated feature maps at each layer. In addition to the exploitation of inherent structures, we consider resource limitations in FGL, devising exceedingly narrow layers atop the DDC architecture and adopting a selective parameter sharing strategy to reduce resource costs substantially. We conduct extensive experiments using 15 datasets across 4 different domains, demonstrating that FedDense consistently surpasses baselines by a large margin in training performance, while demanding minimal resources.

In high-energy physics, anti-neutrons ($\bar{n}$) are fundamental particles that frequently appear as final-state particles, and the reconstruction of their kinematic properties provides an important probe for understanding the governing principles. However, this confronts significant challenges instrumentally with the electromagnetic calorimeter (EMC), a typical experimental sensor but recovering the information of incident $\bar{n}$ insufficiently. In this study, we introduce Vision Calorimeter (ViC), a baseline method for anti-neutron reconstruction that leverages deep learning detectors to analyze the implicit relationships between EMC responses and incident $\bar{n}$ characteristics. Our motivation lies in that energy distributions of $\bar{n}$ samples deposited in the EMC cell arrays embody rich contextual information. Converted to 2-D images, such contextual energy distributions can be used to predict the status of $\bar{n}$ ($i.e.$, incident position and momentum) through a deep learning detector along with pseudo bounding boxes and a specified training objective. Experimental results demonstrate that ViC substantially outperforms the conventional reconstruction approach, reducing the prediction error of incident position by 42.81% (from 17.31$^{\circ}$ to 9.90$^{\circ}$). More importantly, this study for the first time realizes the measurement of incident $\bar{n}$ momentum, underscoring the potential of deep learning detectors for particle reconstruction. Code is available at https://github.com/yuhongtian17/ViC.

A quantum network provides an infrastructure connecting quantum devices with revolutionary computing, sensing, and communication capabilities. As the best-known application of a quantum network, quantum key distribution (QKD) shares secure keys guaranteed by the laws of quantum mechanics. A quantum satellite constellation offers a solution to facilitate the quantum network on a global scale. The Micius satellite has verified the feasibility of satellite quantum communications, however, scaling up quantum satellite constellations is challenging, requiring small lightweight satellites, portable ground stations and real-time secure key exchange. Here we tackle these challenges and report the development of a quantum microsatellite capable of performing space-to-ground QKD using portable ground stations. The quantum microsatellite features a payload weighing approximately 23 kg, while the portable ground station weighs about 100 kg. These weights represent reductions by more than an order and two orders of magnitude, respectively, compared to the Micius satellite. Additionally, we multiplex bidirectional satellite-ground optical communication with quantum communication, enabling key distillation and secure communication in real-time. Using the microsatellite and the portable ground stations, we demonstrate satellite-based QKD with multiple ground stations and achieve the sharing of up to 0.59 million bits of secure keys during a single satellite pass. The compact quantum payload can be readily assembled on existing space stations or small satellites, paving the way for a satellite-constellation-based quantum and classical network for widespread real-life applications., Comment: 40 pages, 8 figures

Large language models (LLMs) have advanced financial applications, yet they often lack sufficient financial knowledge and struggle with tasks involving multi-modal inputs like tables and time series data. To address these limitations, we introduce \textit{Open-FinLLMs}, a series of Financial LLMs. We begin with FinLLaMA, pre-trained on a 52 billion token financial corpus, incorporating text, tables, and time-series data to embed comprehensive financial knowledge. FinLLaMA is then instruction fine-tuned with 573K financial instructions, resulting in FinLLaMA-instruct, which enhances task performance. Finally, we present FinLLaVA, a multimodal LLM trained with 1.43M image-text instructions to handle complex financial data types. Extensive evaluations demonstrate FinLLaMA's superior performance over LLaMA3-8B, LLaMA3.1-8B, and BloombergGPT in both zero-shot and few-shot settings across 19 and 4 datasets, respectively. FinLLaMA-instruct outperforms GPT-4 and other Financial LLMs on 15 datasets. FinLLaVA excels in understanding tables and charts across 4 multimodal tasks. Additionally, FinLLaMA achieves impressive Sharpe Ratios in trading simulations, highlighting its robust financial application capabilities. We will continually maintain and improve our models and benchmarks to support ongoing innovation in academia and industry., Comment: 33 pages, 13 figures

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices

The DLVO theory and electrical double layer (EDL) theory are the foundation of colloid and interface science. With the invention and development of surface forces apparatus (SFA) and atomic force microscope (AFM), the measurements and interpretations of DLVO forces (i.e., mainly measuring the EDL force (electrostatic force) FEDL and van der Waals force FvdW, and interpreting the potential {\psi}, charge density {\sigma}, and Hamaker constant H) can be greatly facilitated by various surface force measurement techniques, and would have been very promising in advancing the DLVO theory, EDL theory, and colloid and interface science. However, although numerous studies have been conducted, pervasive anomalous results can be identified throughout the literature, main including: (1) the fitted {\psi}/{\sigma} is normally extremely small ({\psi} can be close to or (much) smaller than {\psi}{\zeta} (zeta potential)) and varies greatly; (2) the fitted {\psi}/{\sigma} can exceed the allowable range of calculation; and (3) the measured FvdW and the fitted H vary greatly. Based on rigorous and comprehensive arguments, we have reasonably explained the pervasive anomalous results in the literature and further speculated that, the pervasive anomalous results are existing but not noticed and questioned owing to the two important aspects: (1) the pervasive unreasonable understandings of EDL theory and (2) the commonly neglected systematic errors. Consequently, we believe that the related studies have been seriously hampered. We therefore call for re-examination and re-analysis of related experimental results and theoretical understandings by careful consideration of the EDL theory and systematic errors. On these bases, we can interpret the experimental results properly and promote the development of EDL theory, colloid and interface science, and many related fields., Comment: 71 pages, 18 figures

In the current era of Artificial Intelligence Generated Content (AIGC), a Low-Rank Adaptation (LoRA) method has emerged. It uses a plugin-based approach to learn new knowledge with lower parameter quantities and computational costs, and it can be plugged in and out based on the specific sub-tasks, offering high flexibility. However, the current application schemes primarily incorporate LoRA into the pre-introduced conditional parts of the speech models. This fixes the position of LoRA, limiting the flexibility and scalability of its application. Therefore, we propose the Exploring Efficient and Extensible LoRA Integration in Emotional Text-to-Speech (EELE) method. Starting from a general neutral speech model, we do not pre-introduce emotional information but instead use the LoRA plugin to design a flexible adaptive scheme that endows the model with emotional generation capabilities. Specifically, we initially train the model using only neutral speech data. After training is complete, we insert LoRA into different modules and fine-tune the model with emotional speech data to find the optimal insertion scheme. Through experiments, we compare and test the effects of inserting LoRA at different positions within the model and assess LoRA's ability to learn various emotions, effectively proving the validity of our method. Additionally, we explore the impact of the rank size of LoRA and the difference compared to directly fine-tuning the entire model.

In the field of deepfake detection, previous studies focus on using reconstruction or mask and prediction methods to train pre-trained models, which are then transferred to fake audio detection training where the encoder is used to extract features, such as wav2vec2.0 and Masked Auto Encoder. These methods have proven that using real audio for reconstruction pre-training can better help the model distinguish fake audio. However, the disadvantage lies in poor interpretability, meaning it is hard to intuitively present the differences between deepfake and real audio. This paper proposes a noval feature extraction method via color quantisation which constrains the reconstruction to use a limited number of colors for the spectral image-like input. The proposed method ensures reconstructed input differs from the original, which allows for intuitive observation of the focus areas in the spectral reconstruction. Experiments conducted on the ASVspoof2019 dataset demonstrate that the proposed method achieves better classification performance compared to using the original spectral as input and pretraining the recolor network can also benefit the fake audio detection., Comment: accepted by ISCSLP2024

The escalating prevalence of encryption protocols has led to a concomitant surge in the number of malicious attacks that hide in encrypted traffic. Power grid systems, as fundamental infrastructure, are becoming prime targets for such attacks. Conventional methods for detecting malicious encrypted packets typically use a static pre-trained model. We observe that these methods are not well-suited for blockchain-based power grid systems. More critically, they fall short in dynamic environments where new types of encrypted attacks continuously emerge. Motivated by this, in this paper we try to tackle these challenges from two aspects: (1) We present a novel framework that is able to automatically detect malicious encrypted traffic in blockchain-based power grid systems and incrementally learn from new malicious traffic. (2) We mathematically derive incremental learning losses to resist the forgetting of old attack patterns while ensuring the model is capable of handling new encrypted attack patterns. Empirically, our method achieves state-of-the-art performance on three different benchmark datasets. We also constructed the first malicious encrypted traffic dataset for blockchain-based power grid scenario. Our code and dataset are available at https://github.com/PPPmzt/ETGuard, hoping to inspire future research.

Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by concatenating collaborative features with text tokens into a unified sequence input and then fine-tuning to align these features with LLM's input space. Although effective, in this work, we identify two limitations when adapting LLMs to recommendation tasks, which hinder the integration of general knowledge and collaborative information, resulting in sub-optimal recommendation performance. (1) Fine-tuning LLM with recommendation data can undermine its inherent world knowledge and fundamental competencies, which are crucial for interpreting and inferring recommendation text. (2) Incorporating collaborative features into textual prompts disrupts the semantics of the original prompts, preventing LLM from generating appropriate outputs. In this paper, we propose a new paradigm, CoRA (an acronym for Collaborative LoRA), with a collaborative weights generator. Rather than input space alignment, this method aligns collaborative information with LLM's parameter space, representing them as incremental weights to update LLM's output. This way, LLM perceives collaborative information without altering its general knowledge and text inference capabilities. Specifically, we employ a collaborative filtering model to extract user and item embeddings, converting them into collaborative weights with low-rank properties through the collaborative weights generator. We then merge the collaborative weights into LLM's weights, enabling LLM to perceive the collaborative signals and generate personalized recommendations without fine-tuning or extra collaborative tokens in prompts. Extensive experiments confirm that CoRA effectively integrates collaborative information into LLM, enhancing recommendation performance.

Video segmentation aims at partitioning video sequences into meaningful segments based on objects or regions of interest within frames. Current video segmentation models are often derived from image segmentation techniques, which struggle to cope with small-scale or class-imbalanced video datasets. This leads to inconsistent segmentation results across frames. To address these issues, we propose a training strategy Masked Video Consistency, which enhances spatial and temporal feature aggregation. MVC introduces a training strategy that randomly masks image patches, compelling the network to predict the entire semantic segmentation, thus improving contextual information integration. Additionally, we introduce Object Masked Attention (OMA) to optimize the cross-attention mechanism by reducing the impact of irrelevant queries, thereby enhancing temporal modeling capabilities. Our approach, integrated into the latest decoupled universal video segmentation framework, achieves state-of-the-art performance across five datasets for three video segmentation tasks, demonstrating significant improvements over previous methods without increasing model parameters.

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

Open-world 3D reconstruction models have recently garnered significant attention. However, without sufficient 3D inductive bias, existing methods typically entail expensive training costs and struggle to extract high-quality 3D meshes. In this work, we introduce MeshFormer, a sparse-view reconstruction model that explicitly leverages 3D native structure, input guidance, and training supervision. Specifically, instead of using a triplane representation, we store features in 3D sparse voxels and combine transformers with 3D convolutions to leverage an explicit 3D structure and projective bias. In addition to sparse-view RGB input, we require the network to take input and generate corresponding normal maps. The input normal maps can be predicted by 2D diffusion models, significantly aiding in the guidance and refinement of the geometry's learning. Moreover, by combining Signed Distance Function (SDF) supervision with surface rendering, we directly learn to generate high-quality meshes without the need for complex multi-stage training processes. By incorporating these explicit 3D biases, MeshFormer can be trained efficiently and deliver high-quality textured meshes with fine-grained geometric details. It can also be integrated with 2D diffusion models to enable fast single-image-to-3D and text-to-3D tasks. Project page: https://meshformer3d.github.io, Comment: 20 pages, 9 figures

Open-world 3D generation has recently attracted considerable attention. While many single-image-to-3D methods have yielded visually appealing outcomes, they often lack sufficient controllability and tend to produce hallucinated regions that may not align with users' expectations. In this paper, we explore an important scenario in which the input consists of one or a few unposed 2D images of a single object, with little or no overlap. We propose a novel method, SpaRP, to reconstruct a 3D textured mesh and estimate the relative camera poses for these sparse-view images. SpaRP distills knowledge from 2D diffusion models and finetunes them to implicitly deduce the 3D spatial relationships between the sparse views. The diffusion model is trained to jointly predict surrogate representations for camera poses and multi-view images of the object under known poses, integrating all information from the input sparse views. These predictions are then leveraged to accomplish 3D reconstruction and pose estimation, and the reconstructed 3D model can be used to further refine the camera poses of input views. Through extensive experiments on three datasets, we demonstrate that our method not only significantly outperforms baseline methods in terms of 3D reconstruction quality and pose prediction accuracy but also exhibits strong efficiency. It requires only about 20 seconds to produce a textured mesh and camera poses for the input views. Project page: https://chaoxu.xyz/sparp., Comment: ECCV 2024

Combinatorial assembly uses standardized unit primitives to build objects that satisfy user specifications. Lego is a widely used platform for combinatorial assembly, in which people use unit primitives (ie Lego bricks) to build highly customizable 3D objects. This paper studies sequence planning for physical combinatorial assembly using Lego. Given the shape of the desired object, we want to find a sequence of actions for placing Lego bricks to build the target object. In particular, we aim to ensure the planned assembly sequence is physically executable. However, assembly sequence planning (ASP) for combinatorial assembly is particularly challenging due to its combinatorial nature, ie the vast number of possible combinations and complex constraints. To address the challenges, we employ deep reinforcement learning to learn a construction policy for placing unit primitives sequentially to build the desired object. Specifically, we design an online physics-aware action mask that efficiently filters out invalid actions and guides policy learning. In the end, we demonstrate that the proposed method successfully plans physically valid assembly sequences for constructing different Lego structures. The generated construction plan can be executed in real.

Pre-trained language models are increasingly being used in multi-document summarization tasks. However, these models need large-scale corpora for pre-training and are domain-dependent. Other non-neural unsupervised summarization approaches mostly rely on key sentence extraction, which can lead to information loss. To address these challenges, we propose a lightweight yet effective unsupervised approach called GLIMMER: a Graph and LexIcal features based unsupervised Multi-docuMEnt summaRization approach. It first constructs a sentence graph from the source documents, then automatically identifies semantic clusters by mining low-level features from raw texts, thereby improving intra-cluster correlation and the fluency of generated sentences. Finally, it summarizes clusters into natural sentences. Experiments conducted on Multi-News, Multi-XScience and DUC-2004 demonstrate that our approach outperforms existing unsupervised approaches. Furthermore, it surpasses state-of-the-art pre-trained multi-document summarization models (e.g. PEGASUS and PRIMERA) under zero-shot settings in terms of ROUGE scores. Additionally, human evaluations indicate that summaries generated by GLIMMER achieve high readability and informativeness scores. Our code is available at https://github.com/Oswald1997/GLIMMER., Comment: 19 pages, 7 figures. Accepted by ECAI 2024

Optical metasurfaces have revolutionized analog computing and image processing at sub-wavelength scales with faster speed and lower power consumption. They typically involve spatial differentiation with engineered angular dispersion. Quasi-bound states in the continuum (quasi-BICs) have recently emerged as a powerful tool for tailoring properties of optical resonances. While quasi-BICs have been explored in various applications that require high $Q$-factors and enhanced field confinement, their full potential in image processing remains unexplored. Here, we demonstrate edge detection imaging by leveraging a quasi-BIC in an all-dielectric metasurface. This metasurface, composed of four nanodisks per unit cell, supports a polarization-independent quasi-BIC through structural perturbations, allowing simultaneously engineering $Q$-factor and angular dispersion. Importantly, we find that with suitable parameters, this quasi-BIC metasurface can perform isotropic two-dimensional spatial differentiation, which is the core element for realizing edge detection. Following the theoretical design, we fabricate the metasurfaces on the silicon-on-insulator platform and experimentally validate their capability of high-quality, efficient, and uniform edge detection imaging under different incident polarizations. Our results illuminate the mechanisms of edge detection with quasi-BIC metasurfaces and highlight new opportunities for their application in ultra-compact, low-power optical computing devices., Comment: 17 pages, 5 figures

With exceptionally low magnetic damping, YIG has been extensively applied in the realm of magnetism, encompassing the researches into the spin Seebeck effect. YIG has 20 magnon bands, including 8 higher-energy bands denoted as $\alpha_{1\sim8}$, and 12 lower-energy bands denoted as $\beta_{1\sim12}$. Here, we study the impact of the complex intraband and interband magnon couplings on the transport coefficients of YIG. Four-magnon processes in YIG are considered, and a self-consistent mean-field approximation is made for these interaction terms. We find that the $\beta$ bands exhibit minimal variation with increasing temperature, whereas the $\alpha$ bands undergo a noticeable decline as the temperature rises. These counterintuitive results agree well with the observation of earlier inelastic neutron scattering experiments and the results of the theoretical calculations in recent years. We also find it sufficient to include only the contribution of magnons on the acoustic band $\beta_1$ when studying the spin conductivity ($\sigma_m$). However, when calculating the spin Seebeck coefficient ($S_m$) and the magnon thermal conductivity ($\kappa_m$), the results calculated using only $\beta_{1}$ show noticeable deviations over a large temperature range compared to the full band calculations. These deviations are well mitigated when the $\beta_{2}$ and $\beta_{3}$ bands are considered.

The dynamic nature of open-world scenarios has attracted more attention to class incremental learning (CIL). However, existing CIL methods typically presume the availability of complete ground-truth labels throughout the training process, an assumption rarely met in practical applications. Consequently, this paper explores a more challenging problem of unsupervised class incremental learning (UCIL). The essence of addressing this problem lies in effectively capturing comprehensive feature representations and discovering unknown novel classes. To achieve this, we first model the knowledge of class distribution by exploiting fine-grained prototypes. Subsequently, a granularity alignment technique is introduced to enhance the unsupervised class discovery. Additionally, we proposed a strategy to minimize overlap between novel and existing classes, thereby preserving historical knowledge and mitigating the phenomenon of catastrophic forgetting. Extensive experiments on the five datasets demonstrate that our approach significantly outperforms current state-of-the-art methods, indicating the effectiveness of the proposed method.

Prognosis and diagnosis play an important role in accelerating the development of lithium-ion batteries, as well as reliable and long-life operation. In this work, we answer an important question: What is the minimum amount of data required to extract features for accurate battery prognosis and diagnosis? Based on the first principle, we successfully extracted the best two-point feature (BTPF) for accurate battery prognosis and diagnosis using the fewest data points (only two) and the simplest feature selection method (Pearson correlation coefficient). The BTPF extraction method is tested on 820 cells from 6 open-source datasets (covering five different chemistry types, seven manufacturers, and three data types). It achieves comparable accuracy to state-of-the-art features in both prognosis and diagnosis tasks. This work challenges the cognition of existing studies on the difficulty of battery prognosis and diagnosis tasks, subverts the fixed pattern of establishing prognosis and diagnosis methods for complex dynamic systems through deliberate feature engineering, highlights the promise of data-driven methods for field battery prognosis and diagnosis applications, and provides a new benchmark for future studies.

The manipulation of Rashba-type spin-orbit coupling (SOC) in molecular beam epitaxy-grown Al$_x$In$_{1-x}$Sb/InSb/CdTe quantum well heterostructures is reported. The effective band bending provides robust two-dimensional quantum confinement, while the unidirectional built-in electric field from the asymmetric hetero-interfaces results in pronounced Rashba SOC strength. By tuning the Al concentration in the top Al$_x$In$_{1-x}$Sb barrier layer, the optimal structure with $x = 0.15$ shows the largest Rashba coefficient of 0.23 eV-Angstrom. and the highest low-temperature electron mobility of 4400 cm$^2$/Vs . Quantitative investigations of the weak anti-localization effect further confirm the dominant D'yakonov-Perel (DP) spin relaxation mechanism during charge-to-spin conversion. These findings highlight the significance of quantum well engineering in shaping magneto-resistance responses, and narrow bandgap semiconductor-based heterostructures may offer a reliable platform for energy-efficient spintronic applications.

Few-weight codes have been constructed and studied for many years, since their fascinating relations to finite geometries, strongly regular graphs and Boolean functions. Simplex codes are one-weight Griesmer $[\frac{q^k-1}{q-1},k ,q^{k-1}]_q$-linear codes and they meet all Griesmer bounds of the generalized Hamming weights of linear codes. All the subcodes with dimension $r$ of a $[\frac{q^k-1}{q-1},k ,q^{k-1}]_q$-simplex code have the same subcode support weight $\frac{q^{k-r}(q^r-1)}{q-1}$ for $1\leq r\leq k$. In this paper, we construct linear codes meeting the Griesmer bound of the $r$-generalized Hamming weight, such codes do not meet the Griesmer bound of the $j$-generalized Hamming weight for $1\leq j

Quantum machine learning, which involves running machine learning algorithms on quantum devices, may be one of the most significant flagship applications for these devices. Unlike its classical counterparts, the role of data in quantum machine learning has not been fully understood. In this work, we quantify the performances of quantum machine learning in the landscape of quantum data. Provided that the encoding of quantum data is sufficiently random, the performance, we find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in the number of qubits, which we call "the curse of random quantum data". Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks. Conversely, we highlight that through meticulous design of quantum datasets, it is possible to avoid these curses, thereby achieving efficient convergence and robust generalization. Our conclusions are corroborated by extensive numerical simulations., Comment: 40 pages, 8 figures