Search

We introduce LLM4AD, a unified Python platform for algorithm design (AD) with large language models (LLMs). LLM4AD is a generic framework with modularized blocks for search methods, algorithm design tasks, and LLM interface. The platform integrates numerous key methods and supports a wide range of algorithm design tasks across various domains including optimization, machine learning, and scientific discovery. We have also designed a unified evaluation sandbox to ensure a secure and robust assessment of algorithms. Additionally, we have compiled a comprehensive suite of support resources, including tutorials, examples, a user manual, online resources, and a dedicated graphical user interface (GUI) to enhance the usage of LLM4AD. We believe this platform will serve as a valuable tool for fostering future development in the merging research direction of LLM-assisted algorithm design.

The study takes the social media industry as its research subject and examines the impact of scientific innovation capabilities on profit distribution within the value chain of the social media industry. It proposes a specific solution to the profit distribution problem using an improved Shapley value method. Additionally, the AHP (Analytic Hierarchy Process) is employed to evaluate the profit distribution model, allowing the improved Shapley value method to better address the issue of profit allocation within the value chain of the social media industry. This approach ensures that each member receives a fair share of the profits, fostering strong cooperative relationships among members. Moreover, it compensates for the shortcomings of the traditional Shapley value method in addressing such problems to a certain extent.

This paper studies a sub-connected six-dimensional movable antenna (6DMA)-aided multi-user communication system. In this system, each sub-array is connected to a dedicated radio frequency chain and collectively moves and rotates as a unit within specific local regions. The movement and rotation capabilities of 6DMAs enhance design flexibility, facilitating the capture of spatial variations for improved communication performance. To fully characterize the effect of antenna position and orientation on wireless channels between the base station (BS) and users, we develop a field-response-based 6DMA channel model to account for the antenna radiation pattern and polarization. We then maximize the sum rate of multiple users, by jointly optimizing the digital and unit-modulus analog beamformers given the transmit power budget as well as the positions and orientations of sub-arrays within given movable and rotatable ranges at the BS. Due to the highly coupled variables, the formulated optimization problem is non-convex and thus challenging to solve. We develop a fractional programming-aided alternating optimization framework that integrates the Lagrange multiplier method, manifold optimization, and gradient descent to solve the problem. Numerical results demonstrate that the proposed 6DMA-aided sub-connected structure achieves a substantial sum-rate improvement over various benchmark schemes with less flexibility in antenna movement and can even outperform fully-digital beamforming systems that employ antenna position or orientation adjustments only. The results also highlight the necessity of considering antenna polarization for optimally adjusting antenna orientation., Comment: The conference version of this paper has been accepted for Globecom 2024 Workshop

Assembling increasingly larger-scale defect-free optical tweezer-trapped atom arrays is essential for quantum computation and quantum simulations based on atoms. Here, we propose an AI-enabled, rapid, constant-time-overhead rearrangement protocol, and we experimentally assemble defect-free 2D and 3D atom arrays with up to 2024 atoms with a constant time cost of 60 ms. The AI model calculates the holograms for real-time atom rearrangement. With precise controls over both position and phase, a high-speed spatial light modulator moves all the atoms simultaneously. This protocol can be readily used to generate defect-free arrays of tens of thousands of atoms with current technologies, and become a useful toolbox for quantum error correction.

Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-stationary environment. To achieve high reliability and scalability in deploying this paradigm in distributed systems, it is essential to conquer challenges stemming from both spatial and temporal dimensions, manifesting as distribution shifts, catastrophic forgetting, heterogeneity, and privacy issues. This survey focuses on a comprehensive examination of the development of the non-centralized continual learning algorithms and the real-world deployment across distributed devices. We begin with an introduction to the background and fundamentals of non-centralized learning and continual learning. Then, we review existing solutions from three levels to represent how existing techniques alleviate the catastrophic forgetting and distribution shift. Additionally, we delve into the various types of heterogeneity issues, security, and privacy attributes, as well as real-world applications across three prevalent scenarios. Furthermore, we establish a large-scale benchmark to revisit this problem and analyze the performance of the state-of-the-art NCCL approaches. Finally, we discuss the important challenges and future research directions in NCCL.

The growing concern over data privacy, the benefits of utilizing data from diverse sources for model training, and the proliferation of networked devices with enhanced computational capabilities have all contributed to the rise of federated learning (FL). The clients in FL collaborate to train a global model by uploading gradients computed on their private datasets without collecting raw data. However, a new attack surface has emerged from gradient sharing, where adversaries can restore the label distribution of a victim's private data by analyzing the obtained gradients. To mitigate this privacy leakage, existing lightweight defenses restrict the sharing of gradients, such as encrypting the final-layer gradients or locally updating the parameters within. In this paper, we introduce a novel attack called Gradient Bridge (GDBR) that recovers the label distribution of training data from the limited gradient information shared in FL. GDBR explores the relationship between the layer-wise gradients, tracks the flow of gradients, and analytically derives the batch training labels. Extensive experiments show that GDBR can accurately recover more than 80% of labels in various FL settings. GDBR highlights the inadequacy of restricted gradient sharing-based defenses and calls for the design of effective defense schemes in FL.

This paper investigates movable antenna (MA) aided non-orthogonal multiple access (NOMA) for multi-user downlink communication, where the base station (BS) is equipped with a fixed-position antenna (FPA) array to serve multiple MA-enabled users. An optimization problem is formulated to maximize the minimum achievable rate among all the users by jointly optimizing the MA positioning of each user, the precoding matrix at the BS, and the successive interference cancellation (SIC) decoding indicator matrix at the users, subject to a set of constraints including the limited movement area of the MAs, the maximum transmit power of the BS, and the SIC decoding condition. To solve this non-convex problem, we propose a two-loop iterative optimization algorithm that combines the hippopotamus optimization (HO) method with the alternating optimization (AO) method to obtain a suboptimal solution efficiently. Specifically, in the inner loop, the complex-valued precoding matrix and the binary decoding indicator matrix are optimized alternatively by the successive convex approximation (SCA) technique with customized greedy search to maximize the minimum achievable rate for the given positions of the MAs. In the outer loop, each user's antenna position is updated using the HO algorithm, following a novel nature-inspired intelligent optimization framework. Simulation results show that the proposed algorithms can effectively avoid local optimum for highly coupled variables and significantly improve the rate performance of the NOMA system compared to the conventional FPA system as well as other benchmark schemes.

To efficiently select optimal dataset combinations for enhancing multi-task learning (MTL) performance in large language models, we proposed a novel framework that leverages a neural network to predict the best dataset combinations. The framework iteratively refines the selection, greatly improving efficiency, while being model-, dataset-, and domain-independent. Through experiments on 12 biomedical datasets across four tasks - named entity recognition, relation extraction, event extraction, and text classification-we demonstrate that our approach effectively identifies better combinations, even for tasks that may seem unpromising from a human perspective. This verifies that our framework provides a promising solution for maximizing MTL potential., Comment: 14 pages, 5 figures, 4 tables

The effective utilization of unlicensed spectrum is regarded as an important direction to enable the massive access and broad coverage for next-generation wireless local area network (WLAN). Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face huge challenges in realizing massive access and interference cancellation. To address this issue, in this paper we develop a six-dimensional movable antenna (6DMA) enhanced multi-AP coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP's antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and both of the online and offline 6DMA schemes can attain considerable performance improvement compared to the conventional FA-based schemes., Comment: 13 pages, 9 figures, submitted to an IEEE journal for possible publication

The effectiveness of large language models (LLMs) is closely tied to the design of prompts, making prompt optimization essential for enhancing their performance across a wide range of tasks. Many existing approaches to automating prompt engineering rely exclusively on textual feedback, refining prompts based solely on inference errors identified by large, computationally expensive LLMs. Unfortunately, smaller models struggle to generate high-quality feedback, resulting in complete dependence on large LLM judgment. Moreover, these methods fail to leverage more direct and finer-grained information, such as gradients, due to operating purely in text space. To this end, we introduce GReaTer, a novel prompt optimization technique that directly incorporates gradient information over task-specific reasoning. By utilizing task loss gradients, GReaTer enables self-optimization of prompts for open-source, lightweight language models without the need for costly closed-source LLMs. This allows high-performance prompt optimization without dependence on massive LLMs, closing the gap between smaller models and the sophisticated reasoning often needed for prompt refinement. Extensive evaluations across diverse reasoning tasks including BBH, GSM8k, and FOLIO demonstrate that GReaTer consistently outperforms previous state-of-the-art prompt optimization methods, even those reliant on powerful LLMs. Additionally, GReaTer-optimized prompts frequently exhibit better transferability and, in some cases, boost task performance to levels comparable to or surpassing those achieved by larger language models, highlighting the effectiveness of prompt optimization guided by gradients over reasoning. Code of GReaTer is available at https://github.com/psunlpgroup/GreaTer., Comment: 32 pages, 8 figures

Fairness in multi-document summarization (MDS) measures whether a system can generate a summary fairly representing information from documents with different social attribute values. Fairness in MDS is crucial since a fair summary can offer readers a comprehensive view. Previous works focus on quantifying summary-level fairness using Proportional Representation, a fairness measure based on Statistical Parity. However, Proportional Representation does not consider redundancy in input documents and overlooks corpus-level unfairness. In this work, we propose a new summary-level fairness measure, Equal Coverage, which is based on coverage of documents with different social attribute values and considers the redundancy within documents. To detect the corpus-level unfairness, we propose a new corpus-level measure, Coverage Parity. Our human evaluations show that our measures align more with our definition of fairness. Using our measures, we evaluate the fairness of thirteen different LLMs. We find that Claude3-sonnet is the fairest among all evaluated LLMs. We also find that almost all LLMs overrepresent different social attribute values.

Document content extraction is crucial in computer vision, especially for meeting the high-quality data needs of large language models (LLMs) and retrieval-augmented generation (RAG) technologies. However, current document parsing methods suffer from significant limitations in terms of diversity and comprehensive evaluation. To address these challenges, we introduce OmniDocBench, a novel multi-source benchmark designed to advance automated document content extraction. OmniDocBench includes a meticulously curated and annotated high-quality evaluation dataset comprising nine diverse document types, such as academic papers, textbooks, slides, among others. Our benchmark provides a flexible and comprehensive evaluation framework with 19 layout category labels and 14 attribute labels, enabling multi-level assessments across entire datasets, individual modules, or specific data types. Using OmniDocBench, we perform an exhaustive comparative analysis of existing modular pipelines and multimodal end-to-end methods, highlighting their limitations in handling document diversity and ensuring fair evaluation. OmniDocBench establishes a robust, diverse, and fair evaluation standard for the document content extraction field, offering crucial insights for future advancements and fostering the development of document parsing technologies. The codes and dataset is available in https://github.com/opendatalab/OmniDocBench.

Vision-and-Language Navigation (VLN) is a challenging task that requires an agent to navigate through photorealistic environments following natural-language instructions. One main obstacle existing in VLN is data scarcity, leading to poor generalization performance over unseen environments. Tough data argumentation is a promising way for scaling up the dataset, how to generate VLN data both diverse and world-consistent remains problematic. To cope with this issue, we propose the world-consistent data generation (WCGEN), an efficacious data-augmentation framework satisfying both diversity and world-consistency, targeting at enhancing the generalizations of agents to novel environments. Roughly, our framework consists of two stages, the trajectory stage which leverages a point-cloud based technique to ensure spatial coherency among viewpoints, and the viewpoint stage which adopts a novel angle synthesis method to guarantee spatial and wraparound consistency within the entire observation. By accurately predicting viewpoint changes with 3D knowledge, our approach maintains the world-consistency during the generation procedure. Experiments on a wide range of datasets verify the effectiveness of our method, demonstrating that our data augmentation strategy enables agents to achieve new state-of-the-art results on all navigation tasks, and is capable of enhancing the VLN agents' generalization ability to unseen environments.

Indexing is an important step towards strong performance in retrieval-augmented generation (RAG) systems. However, existing methods organize data based on either semantic similarity (similarity) or related information (relatedness), but do not cover both perspectives comprehensively. Our analysis reveals that modeling only one perspective results in insufficient knowledge synthesis, leading to suboptimal performance on complex tasks requiring multihop reasoning. In this paper, we propose SiReRAG, a novel RAG indexing approach that explicitly considers both similar and related information. On the similarity side, we follow existing work and explore some variances to construct a similarity tree based on recursive summarization. On the relatedness side, SiReRAG extracts propositions and entities from texts, groups propositions via shared entities, and generates recursive summaries to construct a relatedness tree. We index and flatten both similarity and relatedness trees into a unified retrieval pool. Our experiments demonstrate that SiReRAG consistently outperforms state-of-the-art indexing methods on three multihop datasets (MuSiQue, 2WikiMultiHopQA, and HotpotQA), with an average 1.9% improvement in F1 scores. As a reasonably efficient solution, SiReRAG enhances existing reranking methods significantly, with up to 7.8% improvement in average F1 scores.

Six-dimensional movable antenna (6DMA) is a promising solution for enhancing wireless network capacity through the adjustment of both three-dimensional (3D) positions and 3D rotations of distributed antenna surfaces. Previous works mainly consider 6DMA surfaces composed of active antenna elements, thus termed as active 6DMA. In this letter, we propose a new passive 6DMA system consisting of distributed passive intelligent reflecting surfaces (IRSs) that can be adjusted in terms of 3D position and 3D rotation. Specifically, we study a passive 6DMA-aided multiuser uplink system and aim to maximize the users' achievable sum rate by jointly optimizing the 3D positions, 3D rotations, and reflection coefficients of all passive 6DMA surfaces, as well as the receive beamforming matrix at the base station (BS). To solve this challenging non-convex optimization problem, we propose an alternating optimization (AO) algorithm that decomposes it into three subproblems and solves them alternately in an iterative manner. Numerical results are presented to investigate the performance of the proposed passive 6DMA system under different configurations and demonstrate its superior performance over the traditional fixed-IRS counterpart for both directive and isotropic radiation patterns of passive reflecting elements.

The increasing frequency of mass school shootings in the United States has been raised as a critical concern. Active shooters kill innocent students and educators in schools. These tragic events highlight the urgent need for effective strategies to minimize casualties. This study aims to address the challenge of simulating and assessing potential mitigation measures by developing a multi-agent simulation model. Our model is designed to estimate casualty rates and evacuation efficiency during active shooter scenarios within school buildings. The simulation evaluates the impact of a gun detection system on safety outcomes. By simulating school shooting incidents with and without this system, we observe a significant improvement in evacuation rates, which increased from 16.6% to 66.6%. Furthermore, the Gun Detection System reduced the average casualty rate from 24.0% to 12.2% within a period of six minutes, based on a simulated environment with 100 students. We conducted a total of 48 simulations across three different floor layouts, varying the number of students and time intervals to assess the system's adaptability. We anticipate that the research will provide a starting point for demonstrating that a gunshot detection system can significantly improve both evacuation rates and casualty reduction., Comment: 10 pages, 6 figures, conference

Wireless sensing technology has become prevalent in healthcare, smart homes, and autonomous driving due to its non-contact operation, penetration capabilities, and cost-effectiveness. As its applications expand, the technology faces mounting security challenges: sensing systems can be attack targets, signals can be weaponized, or signals can function as security shields. Despite these security concerns significantly impacting the technology's development, a systematic review remains lacking. This paper presents the first comprehensive survey of wireless sensing security through a role-based perspective. Analyzing over 200 publications from 2020-2024, we propose a novel classification framework that systematically categorizes existing research into three main classes: (1) wireless systems as victims of attacks, (2) wireless signals as weapons for attacks, and (3) wireless signals as shields for security applications. This role-based classification method is not only intuitive and easy to understand, but also reflects the essential connection between wireless signals and security issues. Through systematic literature review and quantitative analysis, this paper outlines a panoramic view of wireless sensing security, revealing key technological trends and innovation opportunities, thereby helping to promote the development of this field. Project page: \url{https://github.com/Intelligent-Perception-Lab/Awesome-WS-Security}., Comment: 38 pages, 14 figures

In this letter, we propose a six-dimensional movable antenna (6DMA)-aided cell-free massive multiple-input multiple-output (MIMO) system to fully exploit its macro spatial diversity, where a set of distributed access points (APs), each equipped with multiple 6DMA surfaces, cooperatively serve all users in a given area. Connected to a central processing unit (CPU) via fronthaul links, 6DMA-APs can optimize their combining vectors for decoding the users' information based on either local channel state information (CSI) or global CSI shared among them. We aim to maximize the average achievable sum-rate via jointly optimizing the rotation angles of all 6DMA surfaces at all APs, based on the users' spatial distribution. Since the formulated problem is non-convex and highly non-linear, we propose a Bayesian optimization-based algorithm to solve it efficiently. Simulation results show that, by enhancing signal power and mitigating interference through reduced channel cross-correlation among users, 6DMA-APs with optimized rotations can significantly improve the average sum-rate, as compared to the conventional cell-free network with fixed-position antennas and that with only a single centralized AP with optimally rotated 6DMAs, especially when the user distribution is more spatially diverse.

Errors in understanding visual information in images (i.e., visual perception errors) remain a major source of mistakes in Large Vision Language Models (LVLMs). While further analysis is essential, there is a deficiency in datasets for evaluating the visual perception of LVLMs. In this work, we introduce VisOnlyQA, a new dataset designed to directly evaluate the visual perception capabilities of LVLMs on questions about geometric and numerical information in scientific figures. Our dataset enables us to analyze the visual perception of LVLMs for fine-grained visual information, independent of other capabilities such as reasoning. The evaluation set of VisOnlyQA includes 1,200 multiple-choice questions in 12 tasks on four categories of figures. We also provide synthetic training data consisting of 70k instances. Our experiments on VisOnlyQA highlight the following findings: (i) 20 LVLMs we evaluate, including GPT-4o and Gemini 1.5 Pro, work poorly on the visual perception tasks in VisOnlyQA, while human performance is nearly perfect. (ii) Fine-tuning on synthetic training data demonstrates the potential for enhancing the visual perception of LVLMs, but observed improvements are limited to certain tasks and specific models. (iii) Stronger language models improve the visual perception of LVLMs. In summary, our experiments suggest that both training data and model architectures should be improved to enhance the visual perception capabilities of LVLMs. The datasets, code, and model responses are provided at https://github.com/psunlpgroup/VisOnlyQA., Comment: VisOnlyQA dataset, code, and model responses are provided at https://github.com/psunlpgroup/VisOnlyQA

Young's modulus is an important parameter for characterizing the strength of, and wave propagation through, a given material. This study improves the estimation of Young's modulus using the impulse excitation (IE) technique based on an experimental analysis of 19 borosilicate glass bars. Analysis of the frequency equations relating Young's modulus to the out of plane and in plane flexural resonant frequencies of rectangular prisms has been conducted for both the fundamental frequency and its overtones at higher orders of vibration. The Young's modulus of three novaculite rocks with various porosities were then measured up to the seventh order of vibration to validate the optimum shear constant equation for estimating Young's modulus. Young's modulus was found to be nearly frequency independent for these rock samples.

Evolving from massive multiple-input multiple-output (MIMO) in current 5G communications, ultra-massive MIMO emerges as a seminal technology for fulfilling more stringent requirements of future 6G communications. However, widely-utilized phased arrays relying on active components make the implementation of ultra-massive MIMO in practice increasingly prohibitive from both cost and power consumption perspectives. In contrast, the development of reconfigurable holographic surface (RHS) provides a new paradigm to solve the above issue without the need of costly hardware components. By leveraging the holographic principle, the RHS serves as an ultra-thin and lightweight surface antenna integrated with the transceiver, which is a promising alternative to phased arrays for realizing ultra-massive MIMO. In this paper, we provide a comprehensive overview of the RHS, especially the RHS-aided communication and sensing. We first describe the basic concepts of RHS, and introduce its working principle and unique practical constraints. Moreover, we show how to utilize the RHS to achieve cost-efficient and high-performance wireless communication and sensing, and introduce the key technologies. In particular, we present the implementation of RHS with a wireless communication prototype, and report the experimental measurement results based on it. Finally, we outline some open challenges and potential future directions in this area.

Robotic grasping in the open world is a critical component of manufacturing and automation processes. While numerous existing approaches depend on 2D segmentation output to facilitate the grasping procedure, accurately determining depth from 2D imagery remains a challenge, often leading to limited performance in complex stacking scenarios. In contrast, techniques utilizing 3D point cloud data inherently capture depth information, thus enabling adeptly navigating and manipulating a diverse range of complex stacking scenes. However, such efforts are considerably hindered by the variance in data capture devices and the unstructured nature of the data, which limits their generalizability. Consequently, much research is narrowly concentrated on managing designated objects within specific settings, which confines their real-world applicability. This paper presents a novel pipeline capable of executing object grasping tasks in open-world scenarios even on previously unseen objects without the necessity for training. Additionally, our pipeline supports the flexible use of different 3D point cloud segmentation models across a variety of scenes. Leveraging the segmentation results, we propose to engage a training-free binary clustering algorithm that not only improves segmentation precision but also possesses the capability to cluster and localize unseen objects for executing grasping operations. In our experiments, we investigate a range of open-world scenarios, and the outcomes underscore the remarkable robustness and generalizability of our pipeline, consistent across various environments, robots, cameras, and objects. The code will be made available upon acceptance of the paper.

Low-resolution fine-grained image classification has recently made significant progress, largely thanks to the super-resolution techniques and knowledge distillation methods. However, these approaches lead to an exponential increase in the number of parameters and computational complexity of models. In order to solve this problem, in this letter, we propose a Vision Mamba Distillation (ViMD) approach to enhance the effectiveness and efficiency of low-resolution fine-grained image classification. Concretely, a lightweight super-resolution vision Mamba classification network (SRVM-Net) is proposed to improve its capability for extracting visual features by redesigning the classification sub-network with Mamba modeling. Moreover, we design a novel multi-level Mamba knowledge distillation loss boosting the performance, which can transfer prior knowledge obtained from a High-resolution Vision Mamba classification Network (HRVM-Net) as a teacher into the proposed SRVM-Net as a student. Extensive experiments on seven public fine-grained classification datasets related to benchmarks confirm our ViMD achieves a new state-of-the-art performance. While having higher accuracy, ViMD outperforms similar methods with fewer parameters and FLOPs, which is more suitable for embedded device applications. Code is available at https://github.com/boa2004plaust/ViMD.

\textbf{Objective:} We aimed to develop an advanced multi-task large language model (LLM) framework to extract multiple types of information about dietary supplements (DS) from clinical records. \textbf{Methods:} We used four core DS information extraction tasks - namely, named entity recognition (NER: 2,949 clinical sentences), relation extraction (RE: 4,892 sentences), triple extraction (TE: 2,949 sentences), and usage classification (UC: 2,460 sentences) as our multitasks. We introduced a novel Retrieval-Augmented Multi-task Information Extraction (RAMIE) Framework, including: 1) employed instruction fine-tuning techniques with task-specific prompts, 2) trained LLMs for multiple tasks with improved storage efficiency and lower training costs, and 3) incorporated retrieval augmentation generation (RAG) techniques by retrieving similar examples from the training set. We compared RAMIE's performance to LLMs with instruction fine-tuning alone and conducted an ablation study to assess the contributions of multi-task learning and RAG to improved multitasking performance. \textbf{Results:} With the aid of the RAMIE framework, Llama2-13B achieved an F1 score of 87.39 (3.51\% improvement) on the NER task and demonstrated outstanding performance on the RE task with an F1 score of 93.74 (1.15\% improvement). For the TE task, Llama2-7B scored 79.45 (14.26\% improvement), and MedAlpaca-7B achieved the highest F1 score of 93.45 (0.94\% improvement) on the UC task. The ablation study revealed that while MTL increased efficiency with a slight trade-off in performance, RAG significantly boosted overall accuracy. \textbf{Conclusion:} This study presents a novel RAMIE framework that demonstrates substantial improvements in multi-task information extraction for DS-related data from clinical records. Our framework can potentially be applied to other domains.

Recent progress in knowledge graph completion (KGC) has focused on text-based approaches to address the challenges of large-scale knowledge graphs (KGs). Despite their achievements, these methods often overlook the intricate interconnections between entities, a key aspect of the underlying topological structure of a KG. Stochastic blockmodels (SBMs), particularly the latent feature relational model (LFRM), offer robust probabilistic frameworks that can dynamically capture latent community structures and enhance link prediction. In this paper, we introduce a novel framework of sparse latent feature models for KGC, optimized through a deep variational autoencoder (VAE). Our approach not only effectively completes missing triples but also provides clear interpretability of the latent structures, leveraging textual information. Comprehensive experiments on the WN18RR, FB15k-237, and Wikidata5M datasets show that our method significantly improves performance by revealing latent communities and producing interpretable representations.

Foundation models that bridge vision and language have made significant progress, inspiring numerous life-enriching applications. However, their potential for misuse to introduce new threats remains largely unexplored. This paper reveals that vision-language models (VLMs) can be exploited to overcome longstanding limitations in gradient inversion attacks (GIAs) within federated learning (FL), where an FL server reconstructs private data samples from gradients shared by victim clients. Current GIAs face challenges in reconstructing high-resolution images, especially when the victim has a large local data batch. While focusing reconstruction on valuable samples rather than the entire batch is promising, existing methods lack the flexibility to allow attackers to specify their target data. In this paper, we introduce Geminio, the first approach to transform GIAs into semantically meaningful, targeted attacks. Geminio enables a brand new privacy attack experience: attackers can describe, in natural language, the types of data they consider valuable, and Geminio will prioritize reconstruction to focus on those high-value samples. This is achieved by leveraging a pretrained VLM to guide the optimization of a malicious global model that, when shared with and optimized by a victim, retains only gradients of samples that match the attacker-specified query. Extensive experiments demonstrate Geminio's effectiveness in pinpointing and reconstructing targeted samples, with high success rates across complex datasets under FL and large batch sizes and showing resilience against existing defenses.

The data-driven method for infrared small target detection (IRSTD) has achieved promising results. However, due to the small scale of infrared small target datasets and the limited number of pixels occupied by the targets themselves, it is a challenging task for deep learning methods to directly learn from these samples. Utilizing human expert knowledge to assist deep learning methods in better learning is worthy of exploration. To effectively guide the model to focus on targets' spatial features, this paper proposes the Local Contrast Attention Enhanced infrared small target detection Network (LCAE-Net), combining prior knowledge with data-driven deep learning methods. LCAE-Net is a U-shaped neural network model which consists of two developed modules: a Local Contrast Enhancement (LCE) module and a Channel Attention Enhancement (CAE) module. The LCE module takes advantages of prior knowledge, leveraging handcrafted convolution operator to acquire Local Contrast Attention (LCA), which could realize background suppression while enhance the potential target region, thus guiding the neural network to pay more attention to potential infrared small targets' location information. To effectively utilize the response information throughout downsampling progresses, the CAE module is proposed to achieve the information fusion among feature maps' different channels. Experimental results indicate that our LCAE-Net outperforms existing state-of-the-art methods on the three public datasets NUDT-SIRST, NUAA-SIRST, and IRSTD-1K, and its detection speed could reach up to 70 fps. Meanwhile, our model has a parameter count and Floating-Point Operations (FLOPs) of 1.945M and 4.862G respectively, which is suitable for deployment on edge devices., Comment: 16 pages, 8 figures

Recently, Text-to-Image (T2I) synthesis technology has made tremendous strides. Numerous representative T2I models have emerged and achieved promising application outcomes, such as DALL-E, Stable Diffusion, Imagen, etc. In practice, it has become increasingly popular for model developers to selectively adopt various pre-trained text encoders and conditional diffusion models from third-party platforms, integrating them to build customized (personalized) T2I models. However, such an adoption approach is vulnerable to backdoor attacks. In this work, we propose a Combinational Backdoor Attack against Customized T2I models (CBACT2I) targeting this application scenario. Different from previous backdoor attacks against T2I models, CBACT2I embeds the backdoor into the text encoder and the conditional diffusion model separately. The customized T2I model exhibits backdoor behaviors only when the backdoor text encoder is used in combination with the backdoor conditional diffusion model. These properties make CBACT2I more stealthy and flexible than prior backdoor attacks against T2I models. Extensive experiments demonstrate the effectiveness of CBACT2I with different backdoor triggers and different backdoor targets on the open-sourced Stable Diffusion model. This work reveals the backdoor vulnerabilities of customized T2I models and urges countermeasures to mitigate backdoor threats in this scenario.

Graph clustering is an unsupervised machine learning method that partitions the nodes in a graph into different groups. Despite achieving significant progress in exploiting both attributed and structured data information, graph clustering methods often face practical challenges related to data isolation. Moreover, the absence of collaborative methods for graph clustering limits their effectiveness. In this paper, we propose a collaborative graph clustering framework for attributed graphs, supporting attributed graph clustering over vertically partitioned data with different participants holding distinct features of the same data. Our method leverages a novel technique that reduces the sample space, improving the efficiency of the attributed graph clustering method. Furthermore, we compare our method to its centralized counterpart under a proximity condition, demonstrating that the successful local results of each participant contribute to the overall success of the collaboration. We fully implement our approach and evaluate its utility and efficiency by conducting experiments on four public datasets. The results demonstrate that our method achieves comparable accuracy levels to centralized attributed graph clustering methods. Our collaborative graph clustering framework provides an efficient and effective solution for graph clustering challenges related to data isolation., Comment: 16 pages, 3 figures

Efficient acquisition of real-world embodied data has been increasingly critical. However, large-scale demonstrations captured by remote operation tend to take extremely high costs and fail to scale up the data size in an efficient manner. Sampling the episodes under a simulated environment is a promising way for large-scale collection while existing simulators fail to high-fidelity modeling on texture and physics. To address these limitations, we introduce the RoboGSim, a real2sim2real robotic simulator, powered by 3D Gaussian Splatting and the physics engine. RoboGSim mainly includes four parts: Gaussian Reconstructor, Digital Twins Builder, Scene Composer, and Interactive Engine. It can synthesize the simulated data with novel views, objects, trajectories, and scenes. RoboGSim also provides an online, reproducible, and safe evaluation for different manipulation policies. The real2sim and sim2real transfer experiments show a high consistency in the texture and physics. Moreover, the effectiveness of synthetic data is validated under the real-world manipulated tasks. We hope RoboGSim serves as a closed-loop simulator for fair comparison on policy learning. More information can be found on our project page https://robogsim.github.io/ .

Context-aware methods have achieved remarkable advancements in supervised scene text recognition by leveraging semantic priors from words. Considering the heterogeneity of text and background in STR, we propose that such contextual priors can be reinterpreted as the relations between textual elements, serving as effective self-supervised labels for representation learning. However, textual relations are restricted to the finite size of the dataset due to lexical dependencies, which causes over-fitting problem, thus compromising the representation quality. To address this, our work introduces a unified framework of Relational Contrastive Learning and Masked Image Modeling for STR (RCMSTR), which explicitly models the enriched textual relations. For the RCL branch, we first introduce the relational rearrangement module to cultivate new relations on the fly. Based on this, we further conduct relational contrastive learning to model the intra- and inter-hierarchical relations for frames, sub-words and words. On the other hand, MIM can naturally boost the context information via masking, where we find that the block masking strategy is more effective for STR. For the effective integration of RCL and MIM, we also introduce a novel decoupling design aimed at mitigating the impact of masked images on contrastive learning. Additionally, to enhance the compatibility of MIM with CNNs, we propose the adoption of sparse convolutions and directly sharing the weights with dense convolutions in training. The proposed RCMSTR demonstrates superior performance in various evaluation protocols for different STR-related downstream tasks, outperforming the existing state-of-the-art self-supervised STR techniques. Ablation studies and qualitative experimental results further validate the effectiveness of our method. The code and pre-trained models will be available at https://github.com/ThunderVVV/RCMSTR ., Comment: arXiv admin note: text overlap with arXiv:2308.00508

Machine learning (ML) defenses protect against various risks to security, privacy, and fairness. Real-life models need simultaneous protection against multiple different risks which necessitates combining multiple defenses. But combining defenses with conflicting interactions in an ML model can be ineffective, incurring a significant drop in the effectiveness of one or more defenses being combined. Practitioners need a way to determine if a given combination can be effective. Experimentally identifying effective combinations can be time-consuming and expensive, particularly when multiple defenses need to be combined. We need an inexpensive, easy-to-use combination technique to identify effective combinations. Ideally, a combination technique should be (a) accurate (correctly identifies whether a combination is effective or not), (b) scalable (allows combining multiple defenses), (c) non-invasive (requires no change to the defenses being combined), and (d) general (is applicable to different types of defenses). Prior works have identified several ad-hoc techniques but none satisfy all the requirements above. We propose a principled combination technique, Def\Con, to identify effective defense combinations. Def\Con meets all requirements, achieving 90% accuracy on eight combinations explored in prior work and 81% in 30 previously unexplored combinations that we empirically evaluate in this paper.

Fluid antenna system (FAS)/movable antenna (MA) has emerged as a promising technology to fully exploit the spatial degrees of freedom (DoFs). In this paper, we propose a new rotatable antenna (RA) model, as a simplified implementation of six-dimensional movable antenna (6DMA), to improve the performance of wireless communication systems. Different from conventional fixed antenna, the proposed RA system can independently and flexibly change the three-dimensional (3D) orientation/boresight of each antenna by adjusting its deflection angles to achieve desired channel realizations. Specifically, we study an RA-enabled uplink communication system, where the receive beamforming and the deflection angles of all RAs are jointly optimized to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the users. In the special single-user and free-space propagation setup, the optimal deflection angles are derived in closed form with the maximum-ratio combining (MRC) beamformer applied at the base station (BS). In the general multi-user and multi-path setup, we propose an alternating optimization (AO) algorithm to alternately optimize the receive beamforming and the deflection angles in an iterative manner. Simulation results are provided to demonstrate that the proposed RA-enabled system can significantly outperform other benchmark schemes., Comment: 7 pages, 6 figures

Although Large Language Models (LLMs) have demonstrated their strong capabilities in various tasks, recent work has revealed LLMs also exhibit undesirable behaviors, such as hallucination and toxicity, limiting their reliability and broader adoption. In this paper, we discover an understudied type of undesirable behavior of LLMs, which we term Verbosity Compensation (VC), similar to the hesitation behavior of humans under uncertainty, where they respond with excessive words such as repeating questions, introducing ambiguity, or providing excessive enumeration. We present the first work that defines and analyzes Verbosity Compensation, explores its causes, and proposes a simple mitigating approach. Our experiments, conducted on five datasets of knowledge and reasoning-based QA tasks with 14 newly developed LLMs, reveal three conclusions. 1) We reveal a pervasive presence of VC across all models and all datasets. Notably, GPT-4 exhibits a VC frequency of 50.40%. 2) We reveal the large performance gap between verbose and concise responses, with a notable difference of 27.61% on the Qasper dataset. We also demonstrate that this difference does not naturally diminish as LLM capability increases. Both 1) and 2) highlight the urgent need to mitigate the frequency of VC behavior and disentangle verbosity with veracity. We propose a simple yet effective cascade algorithm that replaces the verbose responses with the other model-generated responses. The results show that our approach effectively alleviates the VC of the Mistral model from 63.81% to 16.16% on the Qasper dataset. 3) We also find that verbose responses exhibit higher uncertainty across all five datasets, suggesting a strong connection between verbosity and model uncertainty. Our dataset and code are available at https://github.com/psunlpgroup/VerbosityLLM., Comment: 22 pages, 7 figures

Let $a(n)$ be the $n$-th Dirichlet coefficient of the automorphic $L$-function or the Rankin--Selberg $L$-function. We investigate the cancellation of $a(n)$ over specific sequences linked to the Waring--Goldbach problem, by establishing a nontrivial bound for the additive twisted sums over primes on ${\mathrm{GL}}_m$ and ${\mathrm{GL}}_m\times{\mathrm{GL}}_m .$ The bound does not depend on the generalized Ramanujan conjecture or the nonexistence of Landau--Siegel zeros. Furthermore, we present an application associated with the Sato--Tate conjecture and propose a conjecture about Goldbach's conjecture on average bound., Comment: 33 pages. Comments welcome

This paper presents an overview on intelligent reflecting surface (IRS)-enabled sensing and communication for the forthcoming sixth-generation (6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication (S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's (BS's) non-line-of-sight (NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication (ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work., Comment: 22 pages,7 figures

In this paper, we propose a radio-based passive target tracking algorithm using multipath measurements, including the angle of arrival and relative distance. We focus on a scenario in which a mobile receiver continuously receives radio signals from a transmitter located at an unknown position. The receiver utilizes multipath measurements extracted from the received signal to jointly localize the transmitter and the scatterers over time, with scatterers comprising a moving target and stationary objects that can reflect signals within the environment. We develop a comprehensive probabilistic model for the target tracking problem, incorporating the localization of the transmitter and scatterers, the identification of false alarms and missed detections in the measurements, and the association between scatterers and measurements. We employ a belief propagation approach to compute the posterior distributions of the positions of the scatterers and the transmitter. Additionally, we introduce a particle implementation for the belief propagation method. Simulation results demonstrate that our proposed algorithm outperforms existing benchmark methods in terms of target tracking accuracy.

Double parton scattering (DPS) corresponds to events where two parton-parton scatterings occur in a single hadron-hadron collision. The DPS effects may arise from the spectator scatterings that are somewhat related to soft QCD activities. In this work, we investigate the DPS effects on the $W$-boson mass measurements. Especially, our analysis reveals that the DPS events contribute additional missing transverse momenta from spectator scatterings as well as relevant inclusive cross sections, potentially altering the distribution of total missing transverse momenta. Consequently, the DPS effects have the potential to cause an increase in the measured $W$-boson mass by the CDF detector, which helps to understand the deviation of the CDF-II measurements from other measurements and the predicted value in the Standard Model., Comment: 8 pages, 2 figures, 1 table

The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing powermeasurement-based IRS reflection designs in wideband channels.

The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies. Defining strategies to quickly and efficiently extract useful physical information from this data is mandatory. Among the stellar population parameters for galaxies, their stellar masses and star formation rates (SFRs) are the most fundamental. We develop a novel tool, \textit{Multi-Layer Perceptron for Predicting Galaxy Parameters} (MLP-GaP), that uses a machine-learning (ML) algorithm to accurately and efficiently derive the stellar masses and SFRs from multi-band catalogs. We first adopt a mock dataset generated by the \textit{Code Investigating GALaxy Emission} (CIGALE) for training and testing datasets. Subsequently, we used a multi-layer perceptron model to build MLP-GaP and effectively trained it with the training dataset. The results of the test performed on the mock dataset show that MLP-GaP can accurately predict the reference values. Besides MLP-GaP has a significantly faster processing speed than CIGALE. To demonstrate the science-readiness of the MLP-GaP, we also apply it to a real data sample and compare the stellar masses and SFRs with CIGALE. Overall, the predicted values of MLP-GaP show a very good consistency with the estimated values derived from SED fitting. Therefore, the capability of MLP-GaP to rapidly and accurately predict stellar masses and SFRs makes it particularly well-suited for analyzing huge amounts of galaxies in the era of large sky surveys., Comment: 13 pages, 6 figures, 3 tables. Accepted in Research in Astronomy and Astrophysics

Recently, various types of Text-to-Image (T2I) models have emerged (such as DALL-E and Stable Diffusion), and showing their advantages in different aspects. Therefore, some third-party service platforms collect different model interfaces and provide cheaper API services and more flexibility in T2I model selections. However, this also raises a new security concern: Are these third-party services truly offering the models they claim? To answer this question, we first define the concept of T2I model verification, which aims to determine whether a black-box target model is identical to a given white-box reference T2I model. After that, we propose VerifyPrompt, which performs T2I model verification through a special designed verify prompt. Intuitionally, the verify prompt is an adversarial prompt for the target model without transferability for other models. It makes the target model generate a specific image while making other models produce entirely different images. Specifically, VerifyPrompt utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize the cosine similarity of a prompt's text encoding, generating verify prompts. Finally, by computing the CLIP-text similarity scores between the prompts the generated images, VerifyPrompt can determine whether the target model aligns with the reference model. Experimental results demonstrate that VerifyPrompt consistently achieves over 90\% accuracy across various T2I models, confirming its effectiveness in practical model platforms (such as Hugging Face).

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space., Comment: 204 pages, 100+ figures, 30+ tables

Unimolecular gas phase chemical reactions could be activated by both infrared (IR) radiation and inter-molecular collision in the interstellar environment. Understanding the interplay and competition between the radiation and collision activation mechanisms is crucial for assessing accurate reaction rate constants with an appropriate model. In this work, guided by an extended version of Lindemann theory that considers the contribution of both the radiation-activation and collision-activation to the rate constant of unimolecular reactions, we show that the relative importance of the two mechanisms can be measured by a dimensionless number $PR$ that is the ratio of the collision frequency to the radiation absorption rate of the molecule. The reaction kinetic is dominated by collision-activation or by radiation activation depending on whether $PR$ is larger or smaller than a reference value $PR^*$, which is determined to be $PR^* \approx 10$ based on magnitudes of molecular properties and is verified by detailed calculations of a number of typical interstellar unimolecular reactions. This method of evaluating the relative importance of the two mechanisms is checked against master equation calculations of two interstellar reactions: the dissociation reaction of silicilic acid around the asymptotic giant branch (AGB) star and the methyl association in Titan's atmosphere, and the validity is verified. The method can be used in the future to help determine the appropriate and effective modeling approach for chemical reactions in astrophysical environments.

In this paper, we study the intelligent reflecting surface (IRS) deployment problem where a number of IRSs are optimally placed in a target area to improve its signal coverage with the serving base station (BS). To achieve this, we assume that there is a given set of candidate sites in the target area for deploying IRSs and divide the area into multiple grids of identical size. Then, we derive the average channel power gains from the BS to IRS in each candidate site and from this IRS to any grid in the target area in terms of IRS deployment parameters, including its size, position, height, and orientation. Thus, we are able to approximate the average cascaded channel power gain from the BS to each grid via any IRS, assuming an effective IRS reflection gain based on the large-scale channel knowledge only. Next, we formulate a multi-IRS deployment optimization problem to minimize the total deployment cost by selecting a subset of candidate sites for deploying IRSs and jointly optimizing their heights, orientations, and numbers of reflecting elements while satisfying a given coverage rate performance requirement over all grids in the target area. To solve this challenging combinatorial optimization problem, we first reformulate it as an integer linear programming problem and solve it optimally using the branch-and-bound (BB) algorithm. In addition, we propose an efficient successive refinement algorithm to further reduce computational complexity. Simulation results demonstrate that the proposed lower-complexity successive refinement algorithm achieves near-optimal performance but with significantly reduced running time compared to the proposed optimal BB algorithm, as well as superior performance-cost trade-off than other baseline IRS deployment strategies., Comment: 13 pages

Survival analysis is an important research topic with applications in healthcare, business, and manufacturing. One essential tool in this area is the Cox proportional hazards (CPH) model, which is widely used for its interpretability, flexibility, and predictive performance. However, for modern data science challenges such as high dimensionality (both $n$ and $p$) and high feature correlations, current algorithms to train the CPH model have drawbacks, preventing us from using the CPH model at its full potential. The root cause is that the current algorithms, based on the Newton method, have trouble converging due to vanishing second order derivatives when outside the local region of the minimizer. To circumvent this problem, we propose new optimization methods by constructing and minimizing surrogate functions that exploit hidden mathematical structures of the CPH model. Our new methods are easy to implement and ensure monotonic loss decrease and global convergence. Empirically, we verify the computational efficiency of our methods. As a direct application, we show how our optimization methods can be used to solve the cardinality-constrained CPH problem, producing very sparse high-quality models that were not previously practical to construct. We list several extensions that our breakthrough enables, including optimization opportunities, theoretical questions on CPH's mathematical structure, as well as other CPH-related applications., Comment: Accepted into NeurIPS 2024

The codebook-based analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters via low-complexity beam training. However, conventional beamforming codebook design based on array response vectors for narrowband communications may suffer from severe performance loss in wideband systems due to the ``beam squint" effect over frequency. To tackle this issue, we propose in this paper a new codebook design method for analog beamforming in wideband THz systems. In particular, to characterize the analog beamforming performance in wideband systems, we propose a new metric termed wideband beam gain, which is given by the minimum beamforming gain over the entire frequency band given a target angle. Based on this metric, a wideband analog beamforming codebook design problem is formulated for optimally balancing the beamforming gains in both the spatial and frequency domains, and the performance loss of conventional narrowband beamforming in wideband systems is analyzed. To solve the new wideband beamforming codebook design problem, we divide the spatial domain into orthogonal angular zones each associated with one beam, thereby decoupling the codebook design into a zone division sub-problem and a set of beamforming optimization sub-problems each for one zone. For the zone division sub-problem, we propose a bisection method to obtain the optimal boundaries for separating adjacent zones. While for each of the per-zone-based beamforming optimization sub-problems, we further propose an efficient augmented Lagrange method (ALM) to solve it. Numerical results demonstrate the performance superiority of our proposed codebook design for wideband analog beamforming to the narrowband beamforming codebook and also validate our performance analysis., Comment: 14 pages, 8 figures. Accepted for publication by IEEE TWC

Parton distribution functions (PDFs) at large $x$ are challenging to extract from experimental data, yet they are essential for understanding hadron structure and searching for new physics beyond the Standard Model. Within the framework of the large momentum $P^z$ expansion of lattice quasi-PDFs, we investigate large $x$ PDFs, where the matching coefficient is factorized into the hard kernel, related to the active quark momentum $x P^z$, and the threshold soft function, associated with the spectator momentum $(1-x) P^z$. The renormalization group equation of the soft function enables the resummation of the threshold double logarithms $\alpha^{k} \ln^{2k}(1-x)$, which is crucial for a reliable and controllable calculation of large $x$ PDFs. Our analysis with pion valence PDFs indicates that perturbative matching breaks down when the spectator momentum $(1-x)P^z$ approaches $\Lambda_{\rm QCD}$, but remains valid when both $x P^z$ and $(1-x)P^z$ are much larger than $\Lambda_{\rm QCD}$. Additionally, we incorporate leading renormalon resummation within the threshold framework, demonstrating good perturbative convergence in the region where both spectator and active quark momenta are perturbative scales., Comment: 57 pages, 8 figures

Current generative AI models like ChatGPT, Claude, and Gemini are widely used for knowledge dissemination, task decomposition, and creative thinking. However, their linear interaction methods often force users to repeatedly compare and copy contextual information when handling complex tasks, increasing cognitive load and operational costs. Moreover, the ambiguity in model responses requires users to refine and simplify the information further. To address these issues, we developed "Mindalogue", a system using a non-linear interaction model based on "nodes + canvas" to enhance user efficiency and freedom while generating structured responses. A formative study with 11 users informed the design of Mindalogue, which was then evaluated through a study with 16 participants. The results showed that Mindalogue significantly reduced task steps and improved users' comprehension of complex information. This study highlights the potential of non-linear interaction in improving AI tool efficiency and user experience in the HCI field., Comment: 17 pages, 9 figures

In this letter, we propose to employ movable antenna (MA) to enhance covert communications with noise uncertainty, where the confidential data is transmitted from an MA-aided access point (AP) to multiple users with a warden attempting to detect the existence of the legal transmission. To maximize the sum rate of users under covertness constraint, we formulate an optimization problem to jointly design the transmit beamforming and the positions of MAs at the AP. To solve the formulated non-convex optimization problem, we develop a block successive upper-bound minimization (BSUM) based algorithm, where the proximal distance algorithm (PDA) and the successive convex approximation (SCA) are employed to optimize the transmit beamforming and the MAs' positions, respectively. Simulation results show that the proposed MAs-aided system can significantly increase the covert sum rate via antenna position optimization as compared to conventional systems with fixed-position antennas (FPAs)., Comment: 5 pages, 5 figures (subfigures included), submitted to an IEEE journal for possible publication

Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. As the visual-language models (VLMs) can provide essential general knowledge on unseen images, freezing the visual encoder and inserting a domain-agnostic adapter can learn domain-invariant knowledge for DAOD. However, the domain-agnostic adapter is inevitably biased to the source domain. It discards some beneficial knowledge discriminative on the unlabelled domain, i.e., domain-specific knowledge of the target domain. To solve the issue, we propose a novel Domain-Aware Adapter (DA-Ada) tailored for the DAOD task. The key point is exploiting domain-specific knowledge between the essential general knowledge and domain-invariant knowledge. DA-Ada consists of the Domain-Invariant Adapter (DIA) for learning domain-invariant knowledge and the Domain-Specific Adapter (DSA) for injecting the domain-specific knowledge from the information discarded by the visual encoder. Comprehensive experiments over multiple DAOD tasks show that DA-Ada can efficiently infer a domain-aware visual encoder for boosting domain adaptive object detection. Our code is available at https://github.com/Therock90421/DA-Ada., Comment: Accepted by NeurIPS 2024