Tootfinder

VIXEN: Visual Text Comparison Network for Image Difference Captioning
Alexander Black, Jing Shi, Yifei Fai, Tu Bui, John Collomosse
https://arxiv.org/abs/2402.19119

VIXEN: Visual Text Comparison Network for Image Difference Captioning
We present VIXEN - a technique that succinctly summarizes in text the visual differences between a pair of images in order to highlight any content manipulation present. Our proposed network linearly maps image features in a pairwise manner, constructing a soft prompt for a pretrained large language model. We address the challenge of low volume of training data and lack of manipulation variety in existing image difference captioning (IDC) datasets by training on synthetically manipulated images…

Evaluating Quantized Large Language Models
Shiyao Li, Xuefei Ning, Luning Wang, Tengxuan Liu, Xiangsheng Shi, Shengen Yan, Guohao Dai, Huazhong Yang, Yu Wang
https://arxiv.org/abs/2402.18158

Evaluating Quantized Large Language Models
Post-training quantization (PTQ) has emerged as a promising technique to reduce the cost of large language models (LLMs). Specifically, PTQ can effectively mitigate memory consumption and reduce computational overhead in LLMs. To meet the requirements of both high efficiency and performance across diverse scenarios, a comprehensive evaluation of quantized LLMs is essential to guide the selection of quantization methods. This paper presents a thorough evaluation of these factors by evaluating th…

Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging
Hongcheng Guo, Wei Zhang, Anjie Le, Jian Yang, Jiaheng Liu, Zhoujun Li, Tieqiao Zheng, Shi Xu, Runqiang Zang, Liangfan Zheng, Bo Zhang
https://arxiv.org/abs/2402.18205

Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging
Logs produced by extensive software systems are integral to monitoring system behaviors. Advanced log analysis facilitates the detection, alerting, and diagnosis of system faults. Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics. Existing log parsers fail to identify the correct templates due to reliance on human-made rules. Besides, These methods focus on statistical features while ignoring seman…

Minimizing End-to-End Latency for Joint Source-Channel Coding Systems
Kaiyi Chi, Qianqian Yang, Yuanchao Shu, Zhaohui Yang, Zhiguo Shi
https://arxiv.org/abs/2403.20198

Minimizing End-to-End Latency for Joint Source-Channel Coding Systems
While existing studies have highlighted the advantages of deep learning (DL)-based joint source-channel coding (JSCC) schemes in enhancing transmission efficiency, they often overlook the crucial aspect of resource management during the deployment phase. In this paper, we propose an approach to minimize the transmission latency in an uplink JSCC-based system. We first analyze the correlation between end-to-end latency and task performance, based on which the end-to-end delay model for each devi…

Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks
Luoyu Wang, Yitian Tao, Qing Yang, Yan Liang, Siwei Liu, Hongcheng Shi, Dinggang Shen, Han Zhang
https://arxiv.org/abs/2403.20058

Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks
Simultaneous functional PET/MR (sf-PET/MR) presents a cutting-edge multimodal neuroimaging technique. It provides an unprecedented opportunity for concurrently monitoring and integrating multifaceted brain networks built by spatiotemporally covaried metabolic activity, neural activity, and cerebral blood flow (perfusion). Albeit high scientific/clinical values, short in hardware accessibility of PET/MR hinders its applications, let alone modern AI-based PET/MR fusion models. Our objective is to…

MambaStock: Selective state space model for stock prediction
Zhuangwei Shi
https://arxiv.org/abs/2402.18959 https://arxiv.org/pdf/240…

MambaStock: Selective state space model for stock prediction
The stock market plays a pivotal role in economic development, yet its intricate volatility poses challenges for investors. Consequently, research and accurate predictions of stock price movements are crucial for mitigating risks. Traditional time series models fall short in capturing nonlinearity, leading to unsatisfactory stock predictions. This limitation has spurred the widespread adoption of neural networks for stock prediction, owing to their robust nonlinear generalization capabilities. …

Compton scattering in Bandos-Lechner-Sorokin-Townsend nonlinear electrodynamics
Yang Shi, Towe Wang
https://arxiv.org/abs/2403.20044 https://

Compton scattering in Bandos-Lechner-Sorokin-Townsend nonlinear electrodynamics
The nonlinear electrodynamics proposed by Bandos, Lechner, Sorokin and Townsend is a remarkable theory that unifies Maxwell, Bialynicki-Birula and ModMax theories, which are known theories invariant under conformal transformations and electromagnetic duality transformations. In the Bandos-Lechner-Sorokin-Townsend nonlinear electrodynamics, we calculate the energy flux density, dispersion relations, refractive indices, phase and group velocities of plane waves as well as the changes of the photo…

An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT
Haikuo Shao, Huihong Shi, Wendong Mao, Zhongfeng Wang
https://arxiv.org/abs/2403.20230

An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT
Vision Transformers (ViTs) have achieved significant success in computer vision. However, their intensive computations and massive memory footprint challenge ViTs' deployment on embedded devices, calling for efficient ViTs. Among them, EfficientViT, the state-of-the-art one, features a Convolution-Transformer hybrid architecture, enhancing both accuracy and hardware efficiency. Unfortunately, existing accelerators cannot fully exploit the hardware benefits of EfficientViT due to its unique arch…

GARA: A novel approach to Improve Genetic Algorithms' Accuracy and Efficiency by Utilizing Relationships among Genes
Zhaoning Shi, Meng Xiang, Zhaoyang Hai, Xiabi Liu, Yan Pei
https://arxiv.org/abs/2404.18955

GARA: A novel approach to Improve Genetic Algorithms' Accuracy and Efficiency by Utilizing Relationships among Genes
Genetic algorithms have played an important role in engineering optimization. Traditional GAs treat each gene separately. However, biophysical studies of gene regulatory networks revealed direct associations between different genes. It inspires us to propose an improvement to GA in this paper, Gene Regulatory Genetic Algorithm (GRGA), which, to our best knowledge, is the first time to utilize relationships among genes for improving GA's accuracy and efficiency. We design a directed multipartite…

Prospect Personalized Recommendation on Large Language Model-based Agent Platform
Jizhi Zhang, Keqin Bao, Wenjie Wang, Yang Zhang, Wentao Shi, Wanhong Xu, Fuli Feng, Tat-Seng Chua
https://arxiv.org/abs/2402.18240

Prospect Personalized Recommendation on Large Language Model-based Agent Platform
The new kind of Agent-oriented information system, exemplified by GPTs, urges us to inspect the information system infrastructure to support Agent-level information processing and to adapt to the characteristics of Large Language Model (LLM)-based Agents, such as interactivity. In this work, we envisage the prospect of the recommender system on LLM-based Agent platforms and introduce a novel recommendation paradigm called Rec4Agentverse, comprised of Agent Items and Agent Recommender. Rec4Agent…

Joint Beamforming Design and Stream Allocation for Non-Coherent Joint Transmission in Cell-Free MIMO Networks
Xi Wang, Xiaotong Zhao, Juncheng Wang, You Li, Qingjiang Shi
https://arxiv.org/abs/2402.18231

Joint Beamforming Design and Stream Allocation for Non-Coherent Joint Transmission in Cell-Free MIMO Networks
We consider joint beamforming and stream allocation to maximize the weighted sum rate (WSR) for non-coherent joint transmission (NCJT) in user-centric cell-free MIMO networks, where distributed access points (APs) are organized in clusters to transmit different signals to serve each user equipment (UE). We for the first time consider the common limits of maximum number of receive streams at UEs in practical networks, and formulate a joint beamforming and transmit stream allocation problem for W…

Induced Gravitational Wave interpretation of PTA data: a complete study for general equation of state
Guillem Dom\`enech, Shi Pi, Ao Wang, Jianing Wang
https://arxiv.org/abs/2402.18965

Induced Gravitational Wave interpretation of PTA data: a complete study for general equation of state
We thoroughly study the induced gravitational wave interpretation of the possible gravitational wave background reported by PTA collaborations, considering the unknown equation of state $w$ of the early universe. We perform a Bayesian analysis of the NANOGrav data using the publicly available \textsc{PTArcade} code together with \textsc{SIGWfast} for the numerical integration of the induced gravitational wave spectrum. We focus on two cases: a monochromatic and a log-normal primordial spectrum …

Agentive Permissions in Multiagent Systems
Qi Shi
https://arxiv.org/abs/2404.17053 https://arxiv.org/pdf/2404.17053

Agentive Permissions in Multiagent Systems
This paper proposes to distinguish four forms of agentive permissions in multiagent settings. The main technical results are the complexity analysis of model checking, the semantic undefinability of modalities that capture these forms of permissions through each other, and a complete logical system capturing the interplay between these modalities.

TI-ASU: Toward Robust Automatic Speech Understanding through Text-to-speech Imputation Against Missing Speech Modality
Tiantian Feng, Xuan Shi, Rahul Gupta, Shrikanth S. Narayanan
https://arxiv.org/abs/2404.17983

TI-ASU: Toward Robust Automatic Speech Understanding through Text-to-speech Imputation Against Missing Speech Modality
Automatic Speech Understanding (ASU) aims at human-like speech interpretation, providing nuanced intent, emotion, sentiment, and content understanding from speech and language (text) content conveyed in speech. Typically, training a robust ASU model relies heavily on acquiring large-scale, high-quality speech and associated transcriptions. However, it is often challenging to collect or use speech data for training ASU due to concerns such as privacy. To approach this setting of enabling ASU whe…

MIKO: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery
Feihong Lu, Weiqi Wang, Yangyifei Luo, Ziqin Zhu, Qingyun Sun, Baixuan Xu, Haochen Shi, Shiqi Gao, Qian Li, Yangqiu Song, Jianxin Li
https://arxiv.org/abs/2402.18169

MIKO: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery
Social media has become a ubiquitous tool for connecting with others, staying updated with news, expressing opinions, and finding entertainment. However, understanding the intention behind social media posts remains challenging due to the implicitness of intentions in social media posts, the need for cross-modality understanding of both text and images, and the presence of noisy information such as hashtags, misspelled words, and complicated abbreviations. To address these challenges, we presen…

Distribution Properties of the 6.7 GHz Methanol Masers and Their Surrounding Gases in the Milky Way
Tian Yang, Xi Chen, Yan-Kun Zhang, Xu-Jia Ouyang, Shi-Min Song, Jia-Liang Chen, Ying Lu
https://arxiv.org/abs/2402.19206

Distribution Properties of the 6.7 GHz Methanol Masers and Their Surrounding Gases in the Milky Way
An updated catalog consisting of 1092 6.7-GHz methanol maser sources was reported in this work. Additionally, the NH3 (1, 1), NH3 (2, 2), and NH3 (3, 3) transitions were observed towards 214 star forming regions using the Shanghai Tianma radio telescope (TMRT) in order to examine the differences in physical environments, such as excitation temperature and column density of molecular clouds associated with methanol masers on the Galactic scale. Statistical results reveal that the number of 6.7 G…

Autonomous Shuttle Operation for Vulnerable Populations: Lessons and Experiences
Ren Zhong, Zhaofeng Tian, Jinghui Liao, Weisong Shi
https://arxiv.org/abs/2402.17593

Autonomous Shuttle Operation for Vulnerable Populations: Lessons and Experiences
The increasing shortage of drivers poses a significant threat to vulnerable populations, particularly seniors and disabled individuals who heavily depend on public transportation for accessing healthcare services and social events. Autonomous Vehicles (AVs) emerge as a promising alternative, offering potential improvements in accessibility and independence for these groups. However, current designs and studies often overlook the unique needs and experiences of these populations, leading to pote…

Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications
Quan Zhang, Binqi Zeng, Chijin Zhou, Gwihwan Go, Heyuan Shi, Yu Jiang
https://arxiv.org/abs/2404.17196 …

Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications
Presently, with the assistance of advanced LLM application development frameworks, more and more LLM-powered applications can effortlessly augment the LLMs' knowledge with external content using the retrieval augmented generation (RAG) technique. However, these frameworks' designs do not have sufficient consideration of the risk of external content, thereby allowing attackers to undermine the applications developed with these frameworks. In this paper, we reveal a new threat to LLM-powered appl…

Gate-tunable quantum acoustoelectric transport in graphene
Yicheng Mou, Haonan Chen, Jiaqi Liu, Qing Lan, Jiayu Wang, Chuanxin Zhang, Yuxiang Wang, Jiaming Gu, Tuoyu Zhao, Xue Jiang, Wu Shi, Cheng Zhang
https://arxiv.org/abs/2403.20248

Gate-tunable quantum acoustoelectric transport in graphene
Transport probes the motion of quasiparticles in response to external excitations. Apart from the well-known electric and thermoelectric transport, acoustoelectric transport induced by traveling acoustic waves has been rarely explored. Here, by adopting a hybrid nanodevices integrated with piezoelectric substrates, we establish a simple design of acoustoelectric transport with gate tunability. We fabricate dual-gated acoustoelectric devices based on BN-encapsuled graphene on LiNbO3. Longitudina…

EDA-Driven Preprocessing for SAT Solving
Zhengyuan Shi, Tiebing Tang, Sadaf Khan, Hui-Ling Zhen, Mingxuan Yuan, Zhufei Chu, Qiang Xu
https://arxiv.org/abs/2403.19446

EDA-Driven Preprocessing for SAT Solving
Effective formulation of problems into Conjunctive Normal Form (CNF) is critical in modern Boolean Satisfiability (SAT) solving for optimizing solver performance. Addressing the limitations of existing methods, our Electronic Design Automation (EDA)-driven preprocessing framework introduces a novel methodology for preparing SAT instances, leveraging both circuit and CNF formats for enhanced flexibility and efficiency. Central to our approach is the integration of a new logic synthesis technique…

Three-dimensional Moir\'e Crystal
Ce Wang, Chao Gao, Jing Zhang, Hui Zhai, Zhe-Yu Shi
https://arxiv.org/abs/2404.19608 https://ar…

Three-dimensional Moiré Crystal
The work intends to extend the moiré physics to three dimensions. Three-dimensional moiré patterns can be realized in ultracold atomic gases by coupling two spin states in spin-dependent optical lattices with a relative twist, a structure currently unachievable in solid-state materials. We give the commensurate conditions under which the three-dimensional moiré pattern features a periodic structure, termed a three-dimensional moiré crystal. We emphasize a key distinction of three-dimensiona…

Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks
Luoyu Wang, Yitian Tao, Qing Yang, Yan Liang, Siwei Liu, Hongcheng Shi, Dinggang Shen, Han Zhang
https://arxiv.org/abs/2403.20058

Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks
Simultaneous functional PET/MR (sf-PET/MR) presents a cutting-edge multimodal neuroimaging technique. It provides an unprecedented opportunity for concurrently monitoring and integrating multifaceted brain networks built by spatiotemporally covaried metabolic activity, neural activity, and cerebral blood flow (perfusion). Albeit high scientific/clinical values, short in hardware accessibility of PET/MR hinders its applications, let alone modern AI-based PET/MR fusion models. Our objective is to…

Understanding the Cluster LP for Correlation Clustering
Nairen Cao, Vincent Cohen-Addad, Euiwoong Lee, Shi Li, Alantha Newman, Lukas Vogl
https://arxiv.org/abs/2404.17509

Understanding the Cluster LP for Correlation Clustering
In the classic Correlation Clustering problem introduced by Bansal, Blum, and Chawla~(FOCS 2002), the input is a complete graph where edges are labeled either $+$ or $-$, and the goal is to find a partition of the vertices that minimizes the sum of the +edges across parts plus the sum of the -edges within parts. In recent years, Chawla, Makarychev, Schramm and Yaroslavtsev~(STOC 2015) gave a 2.06-approximation by providing a near-optimal rounding of the standard LP, and Cohen-Addad, Lee, Li, an…

An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT
Haikuo Shao, Huihong Shi, Wendong Mao, Zhongfeng Wang
https://arxiv.org/abs/2403.20230

An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT
Vision Transformers (ViTs) have achieved significant success in computer vision. However, their intensive computations and massive memory footprint challenge ViTs' deployment on embedded devices, calling for efficient ViTs. Among them, EfficientViT, the state-of-the-art one, features a Convolution-Transformer hybrid architecture, enhancing both accuracy and hardware efficiency. Unfortunately, existing accelerators cannot fully exploit the hardware benefits of EfficientViT due to its unique arch…

RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices
Junshuo Liu, Yunlong Huang, Wei Yang, Zhe Li, Rujing Xiong, Tiebin Mi, Xin Shi, Robert C. Qiu
https://arxiv.org/abs/2402.17277

RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices
Human activity recognition (HAR) is integral to smart homes, security, and healthcare. Existing systems face challenges such as the absence of line-of-sight links, insufficient details regarding the sensing subject, and inefficiencies in noise reduction and feature extraction from channel state information. To address these issues, this study builds a reconfigurable intelligent surface (RIS)-assisted passive human activity recognition (RISAR) system, compatible with commercial Wi-Fi devices. RI…

Mechanochemical bistability of intestinal organoids enables robust morphogenesis
Shi-Lei Xue, Qiutan Yang, Prisca Liberali, Edouard Hannezo
https://arxiv.org/abs/2403.19900

Mechanochemical bistability of intestinal organoids enables robust morphogenesis
How pattern and form are generated in a reproducible manner during embryogenesis remains poorly understood. Intestinal organoid morphogenesis involves a number of mechanochemical regulators, including cell-type specific cytoskeletal forces and osmotically-driven lumen volume changes. However, whether and how these forces are coordinated in time and space via feedbacks to ensure robust morphogenesis remains unclear. Here, we propose a minimal physical model of organoid morphogenesis with local c…

Direct observation of anisotropic Cooper pairing in kagome superconductor CsV3Sb5
Akifumi Mine, Yigui Zhong, Jinjin Liu, Takeshi Suzuki, Sahand Najafzadeh, Takumi Uchiyama, Jia-Xin Yin, Xianxin Wu, Xun Shi, Zhiwei Wang, Yugui Yao, Kozo Okazaki
https://arxiv.org/abs/2404.18472

Direct observation of anisotropic Cooper pairing in kagome superconductor CsV3Sb5
In the recently discovered kagome superconductor AV3Sb5 (A = K, Rb, and Cs), the superconductivity is intertwined with an unconventional charge density wave order. Its pairing symmetry remains elusive owing to the lack of direct measurement of the superconducting gap in the momentum space. In this letter, utilizing laser-based ultra-high-resolution and low-temperature angle-resolved photoemission spectroscopy, we observe anisotropic Cooper pairing in kagome superconductor CsV3Sb5. We detect a h…

Metasurface spectrometers beyond resolution-sensitivity constraints
Feng Tang, Jingjun Wu, Tom Albrow-Owen, Hanxiao Cui, Fujia Chen, Yaqi Shi, Lan Zou, Jun Chen, Xuhan Guo, Yijun Sun, Jikui Luo, Bingfeng Ju, Jing Huang, Shuangli Liu, Bo Li, Liming Yang, Eric Anthony Munro, Wanguo Zheng, Hannah J. Joyce, Hongsheng Chen, Lufeng Che, Shurong Dong, Tawfique Hasan, Xin Ye, Yihao Yang, Zongyin Yang

Metasurface spectrometers beyond resolution-sensitivity constraints
Optical spectroscopy plays an essential role across scientific research and industry for non-contact materials analysis1-3, increasingly through in-situ or portable platforms4-6. However, when considering low-light-level applications, conventional spectrometer designs necessitate a compromise between their resolution and sensitivity7,8, especially as device and detector dimensions are scaled down. Here, we report on a miniaturizable spectrometer platform where light throughput onto the detector…

Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering
Long Shi, Lei Cao, Yunshan Ye, Yu Zhao, Badong Chen
https://arxiv.org/abs/2403.18393

Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering
Graph learning is widely recognized as a crucial technique in multi-view clustering. Existing graph learning methods typically involve constructing an adaptive neighbor graph based on probabilistic neighbors and then learning a consensus graph to for clustering, however, they are confronted with two limitations. Firstly, they often rely on Euclidean distance to measure similarity when constructing the adaptive neighbor graph, which proves inadequate in capturing the intrinsic structure among da…

Exploration of Adapter for Noise Robust Automatic Speech Recognition
Hao Shi, Tatsuya Kawahara
https://arxiv.org/abs/2402.18275 https://

Exploration of Adapter for Noise Robust Automatic Speech Recognition
Adapting a robust automatic speech recognition (ASR) system to tackle unseen noise scenarios is crucial. Integrating adapters into neural networks has emerged as a potent technique for transfer learning. This paper thoroughly investigates adapter-based noise-robust ASR adaptation. We conducted the experiments using the CHiME--4 dataset. The results show that inserting the adapter in the shallow layer yields superior effectiveness, and there is no significant difference between adapting solely w…

Unsupervised Learning of High-resolution Light Field Imaging via Beam Splitter-based Hybrid Lenses
Jianxin Lei, Chengcai Xu, Langqing Shi, Junhui Hou, Ping Zhou
https://arxiv.org/abs/2402.19020

Unsupervised Learning of High-resolution Light Field Imaging via Beam Splitter-based Hybrid Lenses
In this paper, we design a beam splitter-based hybrid light field imaging prototype to record 4D light field image and high-resolution 2D image simultaneously, and make a hybrid light field dataset. The 2D image could be considered as the high-resolution ground truth corresponding to the low-resolution central sub-aperture image of 4D light field image. Subsequently, we propose an unsupervised learning-based super-resolution framework with the hybrid light field dataset, which adaptively settle…

LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
Ming Wang, Yuanzhong Liu, Xiaoming Zhang, Songlian Li, Yijie Huang, Chi Zhang, Daling Wang, Shi Feng, Jigang Li
https://arxiv.org/abs/2402.16929

LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
LLMs have demonstrated commendable performance across diverse domains. Nevertheless, formulating high-quality prompts to effectively instruct LLMs poses a challenge for non-AI experts. Existing research in prompt engineering suggests somewhat fragmented optimization principles and designs empirically dependent prompt optimizers. Unfortunately, these endeavors lack a structured design template, incurring high learning costs and resulting in low reusability. Inspired by structured reusable progra…

A Hierarchical Dataflow-Driven Heterogeneous Architecture for Wireless Baseband Processing
Limin Jiang, Yi Shi, Haiqin Hu, Qingyu Deng, Siyi Xu, Yintao Liu, Feng Yuan, Si Wang, Yihao Shen, Fangfang Ye, Shan Cao, Zhiyuan Jiang
https://arxiv.org/abs/2402.18070

A Hierarchical Dataflow-Driven Heterogeneous Architecture for Wireless Baseband Processing
Wireless baseband processing (WBP) is a key element of wireless communications, with a series of signal processing modules to improve data throughput and counter channel fading. Conventional hardware solutions, such as digital signal processors (DSPs) and more recently, graphic processing units (GPUs), provide various degrees of parallelism, yet they both fail to take into account the cyclical and consecutive character of WBP. Furthermore, the large amount of data in WBPs cannot be processed qu…

Worpitzky-compatible sets and the freeness of arrangements between Shi and Catalan
Takuro Abe, Tan Nhat Tran
https://arxiv.org/abs/2403.17274 https://

Worpitzky-compatible sets and the freeness of arrangements between Shi and Catalan
Given an irreducible root system, the Worpitzky-compatible subsets are defined by a geometric property of the alcoves inside the fundamental parallelepiped of the root system. This concept is motivated and mainly understood through a lattice point counting formula concerning the characteristic and Ehrhart quasi-polynomials. In this paper, we show that the Worpitzky-compatibility has a simple combinatorial characterization in terms of roots. As a byproduct, we obtain a complete characterization …

EDA-Driven Preprocessing for SAT Solving
Zhengyuan Shi, Tiebing Tang, Sadaf Khan, Hui-Ling Zhen, Mingxuan Yuan, Zhufei Chu, Qiang Xu
https://arxiv.org/abs/2403.19446

EDA-Driven Preprocessing for SAT Solving
Effective formulation of problems into Conjunctive Normal Form (CNF) is critical in modern Boolean Satisfiability (SAT) solving for optimizing solver performance. Addressing the limitations of existing methods, our Electronic Design Automation (EDA)-driven preprocessing framework introduces a novel methodology for preparing SAT instances, leveraging both circuit and CNF formats for enhanced flexibility and efficiency. Central to our approach is the integration of a new logic synthesis technique…

OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining
Fanjin Zhang, Shijie Shi, Yifan Zhu, Bo Chen, Yukuo Cen, Jifan Yu, Yelin Chen, Lulu Wang, Qingfei Zhao, Yuqing Cheng, Tianyi Han, Yuwei An, Dan Zhang, Weng Lam Tam, Kun Cao, Yunhe Pang, Xinyu Guan, Huihui Yuan, Jian Song, Xiaoyan Li, Yuxiao Dong, Jie Tang
https://arx…

OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining
With the rapid proliferation of scientific literature, versatile academic knowledge services increasingly rely on comprehensive academic graph mining. Despite the availability of public academic graphs, benchmarks, and datasets, these resources often fall short in multi-aspect and fine-grained annotations, are constrained to specific task types and domains, or lack underlying real academic graphs. In this paper, we present OAG-Bench, a comprehensive, multi-aspect, and fine-grained human-curated…

Two atoms in a harmonic trap with spin-orbital-angular-momentum coupling
Xiao-Long Chen, Aixi Chen, Shi-Guo Peng
https://arxiv.org/abs/2404.19293 https://<…

Two atoms in a harmonic trap with spin-orbital-angular-momentum coupling
We study the problem of two harmonically trapped atoms in the presence of spin-orbital-angular-momentum coupling. The two-body energy spectrum is numerically calculated by utilizing the exact diagonalization method. We analyze how the degeneracy of energy levels is lifted under the interplay between the interatomic interaction and spin-orbital-angular-momentum coupling. The exact numerical results show an excellent agreement with that of perturbation theory in the weak-interaction limit, as wel…

Quasi-intermediate value theorem and outflanking arc theorem for plane maps
Jiehua Mai, Enhui Shi, Kesong Yan, Fanping Zeng
https://arxiv.org/abs/2402.16076

Quasi-intermediate value theorem and outflanking arc theorem for plane maps
For a disk $D$ in the plane $\mathbb R^2$ and a plane map $f$, we give several conditions on the restriction of $f$ to the boundary $\partial D$ of $D$ which imply the existence of a fixed point of $f$ in some specified domain in $D$. These conditions are similar to those appeared in the intermediate value theorem for maps on the real line. As an application of the main results, we establish a fixed point theorem for plane maps having an outflanking arc, which extends the famous theorem due to …

Magnetar as the Central Engine of AT2018cow: Optical, Soft X-Ray, and Hard X-Ray Emission
Long Li, Shu-Qing Zhong, Di Xiao, Zi-Gao Dai, Shi-Feng Huang, Zhen-Feng Sheng
https://arxiv.org/abs/2402.15067

Magnetar as the Central Engine of AT2018cow: Optical, Soft X-Ray, and Hard X-Ray Emission
AT2018cow is the most extensively observed and widely studied fast blue optical transient to date; its unique observational properties challenge all existing standard models. In this paper, we model the luminosity evolution of the optical, soft X-ray, and hard X-ray emission, as well as the X-ray spectrum of AT2018cow with a magnetar-centered engine model. We consider a two-zone model with a striped magnetar wind in the interior and an expanding ejecta outside. The soft and hard X-ray emission …

This https://arxiv.org/abs/2402.11902 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Affleck-Dine leptogenesis scenario for resonant production of sterile neutrino dark matter
Sterile neutrino is a fascinating candidate for dark matter. In this paper, we examine the Affleck-Dine (AD) leptogenesis scenario generating a large lepton asymmetry, which can induce the resonant production of sterile neutrino dark matter via the Shi-Fuller (SF) mechanism. We also revisit the numerical calculation of the SF mechanism and the constraints from current X-ray and Lyman-$α$ forest observations. We find that the AD leptogenesis scenario can explain the production of sterile neutri…

On generalizations of Iwasawa's theorem
Jiangtao Shi, Fanjie Xu, Mengjiao Shan
https://arxiv.org/abs/2403.17960 https://arxiv.org…

On generalizations of Iwasawa's theorem
Iwasawa's theorem indicates that a finite group $G$ is supersolvable if and only if all maximal chains of the identity in $G$ have the same length. As generalizations of Iwasawa's theorem, we provide some characterizations of the structure of a finite group $G$ in which all maximal chains of every minimal subgroup have the same length. Moreover, let $δ(G)$ be the number of subgroups of $G$ all of whose maximal chains in $G$ do not have the same length, we prove that $G$ is a non-solvable group…

An Analysis of Switchback Designs in Reinforcement Learning
Qianglin Wen, Chengchun Shi, Ying Yang, Niansheng Tang, Hongtu Zhu
https://arxiv.org/abs/2403.17285

An Analysis of Switchback Designs in Reinforcement Learning
This paper offers a detailed investigation of switchback designs in A/B testing, which alternate between baseline and new policies over time. Our aim is to thoroughly evaluate the effects of these designs on the accuracy of their resulting average treatment effect (ATE) estimators. We propose a novel "weak signal analysis" framework, which substantially simplifies the calculations of the mean squared errors (MSEs) of these ATEs in Markov decision process environments. Our findings suggest that …

Long-time dynamics of a competition model with nonlocal diffusion and free boundaries: Chances of successful invasion
Yihong Du, Wenjie Ni, Linfei Shi
https://arxiv.org/abs/2403.19134

Long-time dynamics of a competition model with nonlocal diffusion and free boundaries: Chances of successful invasion
This is a continuation of our work \cite{dns-part1} to investigate the long-time dynamics of a two species competition model of Lotka-Volterra type with nonlocal diffusions, where the territory (represented by the real line $\R$) of a native species with density $v(t,x)$, is invaded by a competitor with density $u(t,x)$, via two fronts, $x=g(t)$ on the left and $x=h(t)$ on the right. So the population range of $u$ is the evolving interval $[g(t), h(t)]$ and the reaction-diffusion equation for $…

Sensitivity-Improved Polarization Maps at 40 GHz with CLASS and WMAP data
Rui Shi, John W. Appel, Charles L. Bennett, Ricardo Bustos, David T. Chuss, Sumit Dahal, Jullianna Denes Couto, Joseph R. Eimer, Thomas Essinger-Hileman, Kathleen Harrington, Jeffrey Iuliano, Yunyang Li, Tobias A. Marriage, Matthew A. Petroff, Karwan Rostem, Zeya Song, Deniz A. N. Valle, Duncan J. Watts, Janet L. Weiland, Edward J. Wollack, Zhilei Xu

Sensitivity-Improved Polarization Maps at 40 GHz with CLASS and WMAP data
Improved polarization measurements at frequencies below 70 GHz with degree-level angular resolution are crucial for advancing our understanding of the Galactic synchrotron radiation and the potential polarized anomalous microwave emission and ultimately benefiting the detection of primordial $B$ modes. In this study, we present sensitivity-improved 40 GHz polarization maps obtained by combining the CLASS 40 GHz and WMAP $Q$-band data through a weighted average in the harmonic domain. The decisi…

Doubly robust causal inference through penalized bias-reduced estimation: combining non-probability samples with designed surveys
Jiacong Du, Xu Shi, Donglin Zeng, Bhramar Mukherjee
https://arxiv.org/abs/2403.18039

Doubly robust causal inference through penalized bias-reduced estimation: combining non-probability samples with designed surveys
Causal inference on the average treatment effect (ATE) using non-probability samples, such as electronic health records (EHR), faces challenges from sample selection bias and high-dimensional covariates. This requires considering a selection model alongside treatment and outcome models that are typical ingredients in causal inference. This paper considers integrating large non-probability samples with external probability samples from a design survey, addressing moderately high-dimensional conf…

Deep CSI Compression for Dual-Polarized Massive MIMO Channels with Disentangled Representation Learning
Suhang Fan, Wei Xu, Renjie Xie, Shi Jin, Derrick Wing Kwan Ng, Naofal Al-Dhahir
https://arxiv.org/abs/2403.19185

Deep CSI Compression for Dual-Polarized Massive MIMO Channels with Disentangled Representation Learning
Channel state information (CSI) feedback is critical for achieving the promised advantages of enhancing spectral and energy efficiencies in massive multiple-input multiple-output (MIMO) wireless communication systems. Deep learning (DL)-based methods have been proven effective in reducing the required signaling overhead for CSI feedback. In practical dual-polarized MIMO scenarios, channels in the vertical and horizontal polarization directions tend to exhibit high polarization correlation. To f…

LpQcM: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising
Menghua Xia, Huidong Xie, Qiong Liu, Bo Zhou, Hanzhong Wang, Biao Li, Axel Rominger, Kuangyu Shi, Georges EI Fakhri, Chi Liu
https://arxiv.org/abs/2404.17994 https://arxiv.org/pdf/2404.17994
arXiv:2404.17994v1 Announce Type: new
Abstract: Deep learning-based positron emission tomography (PET) image denoising offers the potential to reduce radiation exposure and scanning time by transforming low-count images into high-count equivalents. However, existing methods typically blur crucial details, leading to inaccurate lesion quantification. This paper proposes a lesion-perceived and quantification-consistent modulation (LpQcM) strategy for enhanced PET image denoising, via employing downstream lesion quantification analysis as auxiliary tools. The LpQcM is a plug-and-play design adaptable to a wide range of model architectures, modulating the sampling and optimization procedures of model training without adding any computational burden to the inference phase. Specifically, the LpQcM consists of two components, the lesion-perceived modulation (LpM) and the multiscale quantification-consistent modulation (QcM). The LpM enhances lesion contrast and visibility by allocating higher sampling weights and stricter loss criteria to lesion-present samples determined by an auxiliary segmentation network than lesion-absent ones. The QcM further emphasizes accuracy of quantification for both the mean and maximum standardized uptake value (SUVmean and SUVmax) across multiscale sub-regions throughout the entire image, thereby enhancing the overall image quality. Experiments conducted on large PET datasets from multiple centers and vendors, and varying noise levels demonstrated the LpQcM efficacy across various denoising frameworks. Compared to frameworks without LpQcM, the integration of LpQcM reduces the lesion SUVmean bias by 2.92% on average and increases the peak signal-to-noise ratio (PSNR) by 0.34 on average, for denoising images of extremely low-count levels below 10%.

Energy estimation of small-scale jets from the quiet-Sun region
Fanpeng Shi, Dong Li, Zongjun Ning, Jun Xu, Yuxiang Song, Yuzhi Yang
https://arxiv.org/abs/2403.16193

Energy estimation of small-scale jets from the quiet-Sun region
Context. Solar jets play a role in the coronal heating and the supply of solar wind. Aims. This study calculated the energies of 23 small-scale jets emerging from a quiet-Sun region to investigate their contributions for coronal heating. Conclusions. Our observations suggest that although these jets cannot provide sufficient energy for the heating of the whole quiet-Sun coronal region, they are likely to account for a significant portion of the energy demand in the local regions where the jets …

Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
Kexin Shi, Jing Zhang, Linjiajie Fang, Wenjia Wang, Bingyi Jing
https://arxiv.org/abs/2403.19276

Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
In implicit collaborative filtering, hard negative mining techniques are developed to accelerate and enhance the recommendation model learning. However, the inadvertent selection of false negatives remains a major concern in hard negative sampling, as these false negatives can provide incorrect information and mislead the model learning. To date, only a small number of studies have been committed to solve the false negative problem, primarily focusing on designing sophisticated sampling algorit…

Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps
Zhaoyang Shi, Krishnakumar Balasubramanian, Wolfgang Polonik
https://arxiv.org/abs/2402.14985

Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps
We develop nonparametric regression methods for the case when the true regression function is not necessarily smooth. More specifically, our approach is using the fractional Laplacian and is designed to handle the case when the true regression function lies in an $L_2$-fractional Sobolev space with order $s\in (0,1)$. This function class is a Hilbert space lying between the space of square-integrable functions and the first-order Sobolev space consisting of differentiable functions. It contains…

Sensitivity-Improved Polarization Maps at 40 GHz with CLASS and WMAP data
Rui Shi, John W. Appel, Charles L. Bennett, Ricardo Bustos, David T. Chuss, Sumit Dahal, Jullianna Denes Couto, Joseph R. Eimer, Thomas Essinger-Hileman, Kathleen Harrington, Jeffrey Iuliano, Yunyang Li, Tobias A. Marriage, Matthew A. Petroff, Karwan Rostem, Zeya Song, Deniz A. N. Valle, Duncan J. Watts, Janet L. Weiland, Edward J. Wollack, Zhilei Xu

Sensitivity-Improved Polarization Maps at 40 GHz with CLASS and WMAP data
Improved polarization measurements at frequencies below 70 GHz with degree-level angular resolution are crucial for advancing our understanding of the Galactic synchrotron radiation and the potential polarized anomalous microwave emission and ultimately benefiting the detection of primordial $B$ modes. In this study, we present sensitivity-improved 40 GHz polarization maps obtained by combining the CLASS 40 GHz and WMAP $Q$-band data through a weighted average in the harmonic domain. The decisi…

Lattice Boltzmann model for containerless freezing
Jiangxu Huang, Lei Wang, Zhenhua Chai, Baochang Shi
https://arxiv.org/abs/2403.16122 https://

Lattice Boltzmann model for containerless freezing
In this paper, a lattice Boltzmann model is proposed to simulate solid-liquid phase change phenomena in multiphase systems. The model couples the thermal properties of the solidification front with the dynamics of the liquid droplet interface, which enables the description of the complex interfacial changes during solid-liquid phase change process. The model treats the interfaces of gas, liquid, and solid phases using the phase field order parameter and the solid fraction. The volume expansion …

Turing pattern theory on homogeneous and heterogeneous higher-order temporal network system
Junyuan Shi
https://arxiv.org/abs/2402.15736 https://

Turing pattern theory on homogeneous and heterogeneous higher-order temporal network system
Reaction-diffusion processes on networked systems have received mounting attention in the past two decades, and the corresponding network dynamics theories have been continuously enriched with the advancement of network science. Recently, time-varying feature and many-body interactions have been discovered on various and numerous networks in real world like biological and social systems, and the study of contemporary network science has gradually moved away from the historically static network …

Quality Assurance for Artificial Intelligence: A Study of Industrial Concerns, Challenges and Best Practices
Chenyu Wang, Zhou Yang, Ze Shi Li, Daniela Damian, David Lo
https://arxiv.org/abs/2402.16391

Quality Assurance for Artificial Intelligence: A Study of Industrial Concerns, Challenges and Best Practices
Quality Assurance (QA) aims to prevent mistakes and defects in manufactured products and avoid problems when delivering products or services to customers. QA for AI systems, however, poses particular challenges, given their data-driven and non-deterministic nature as well as more complex architectures and algorithms. While there is growing empirical evidence about practices of machine learning in industrial contexts, little is known about the challenges and best practices of quality assurance f…

Unveiling the origin of unconventional moire ferroelectricity
Ruirui Niu, Zhuoxian Li, Xiangyan Han, Qianling Liu, Zhuangzhuang Qu, Zhiyu Wang, Chunrui Han, Kenji Watanabe, Takashi Taniguchi, Kaihui Liu, Jinhai Mao, Wu Shi, Bo Peng, Zheng Vitto Han, Zizhao Gan, Jianming Lu
https://arxiv.org/abs/2403.17326

Unveiling the origin of unconventional moire ferroelectricity
Interfacial ferroelectricity emerges in heterostructures consisting of nonpolar van der Waals (vdW) layers, greatly expanding the scope of two dimensional ferroelectrics. In particular, the unconventional moire ferroelectricity observed in bilayer graphene/boron nitride (BN) heterostructures, exhibits promising functionalities with topological current, superconductivity and synaptic responses. However, the debate about its mechanism - correlation driven charge transfer between two graphene laye…

Markov chains and mappings of distributions on compact spaces II: Numerics and Conjectures
David J. Aldous, Madelyn Cruz, Shi Feng
https://arxiv.org/abs/2403.18153

Markov chains and mappings of distributions on compact spaces II: Numerics and Conjectures
Consider a compact metric space $S$ and a pair $(j,k)$ with $k \ge 2$ and $1 \le j \le k$. For any probability distribution $θ\in P(S)$, define a Markov chain on $S$ by: from state $s$, take $k$ i.i.d. ($θ$) samples, and jump to the $j$'th closest. Such a chain converges in distribution to a unique stationary distribution, say $π_{j,k}(θ)$. This defines a mapping $π_{j,k}: P(S) \to P(S)$. What happens when we iterate this mapping? In particular, what are the fixed points of this mapping? A…

Record Acceleration of the Two-Dimensional Ising Model Using High-Performance Wafer Scale Engine
Dirk Van Essendelf, Hayl Almolyki, Wei Shi, Terry Jordan, Mei-Yu Wang, Wissam A. Saidi
https://arxiv.org/abs/2404.16990

Record Acceleration of the Two-Dimensional Ising Model Using High-Performance Wafer Scale Engine
The versatility and wide-ranging applicability of the Ising model, originally introduced to study phase transitions in magnetic materials, have made it a cornerstone in statistical physics and a valuable tool for evaluating the performance of emerging computer hardware. Here, we present a novel implementation of the two-dimensional Ising model on a Cerebras Wafer-Scale Engine (WSE), a revolutionary processor that is opening new frontiers in computing. In our deployment of the checkerboard algor…

Exact Work Distribution and Jarzynski's Equality of a Relativistic Particle in an Expanding Piston
Xianghang Zhang, Tingzhang Shi, H. T. Quan
https://arxiv.org/abs/2403.15986 …

Exact Work Distribution and Jarzynski's Equality of a Relativistic Particle in an Expanding Piston
We study the non-equilibrium work in a pedagogical model of relativistic ideal gas. We obtain the exact work distribution and verify the Jarzynski's equality. In the non-relativistic limit, our results recover the non-relativistic results [arXiv:cond-mat/0502434]. We also find that, unlike the non-relativistic case, the work distribution no longer has zeros and the number of collisions in this relativistic gas model is finite. In addition, based on an analysis of the experimental parameters, we…

Optimization-based Prompt Injection Attack to LLM-as-a-Judge
Jiawen Shi, Zenghui Yuan, Yinuo Liu, Yue Huang, Pan Zhou, Lichao Sun, Neil Zhenqiang Gong
https://arxiv.org/abs/2403.17710

Optimization-based Prompt Injection Attack to LLM-as-a-Judge
LLM-as-a-Judge is a novel solution that can assess textual information with large language models (LLMs). Based on existing research studies, LLMs demonstrate remarkable performance in providing a compelling alternative to traditional human assessment. However, the robustness of these systems against prompt injection attacks remains an open question. In this work, we introduce JudgeDeceiver, a novel optimization-based prompt injection attack tailored to LLM-as-a-Judge. Our method formulates a p…

Long-time dynamics of a competition model with nonlocal diffusion and free boundaries: Vanishing and spreading of the invader
Yihong Du, Wenjie Ni, Linfei Shi
https://arxiv.org/abs/2403.19131

Long-time dynamics of a competition model with nonlocal diffusion and free boundaries: Vanishing and spreading of the invader
In this work, we investigate the long-time dynamics of a two species competition model of Lotka-Volterra type with nonlocal diffusions. One of the species, with density $v(t,x)$, is assumed to be a native in the environment (represented by the real line $\R$), while the other species, with density $u(t,x)$, is an invading species which invades the territory of $v$ with two fronts, $x=g(t)$ on the left and $x=h(t)$ on the right. So the population range of $u$ is the evolving interval $[g(t), h(t…

Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset
Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Mingjie Zhan, Hongsheng Li
https://arxiv.org/abs/2402.14804

Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset
Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista. However, we observe significant limitations in the diversity of questions and breadth of subjects covered by these benchmarks. To address this issue, we present the MATH-Vision (MATH-V) dataset, a meticulously curated collection of 3,040 high-quality mathematical problems wit…

From Computing to Quantum Mechanics: Accessible and Hands-On Quantum Computing Education for High School Students
Qihong Sun, Shuangxiang Zhou, Ronghang Chen, Guanru Feng, Shi-Yao Hou, Bei Zeng
https://arxiv.org/abs/2403.17485

From Computing to Quantum Mechanics: Accessible and Hands-On Quantum Computing Education for High School Students
This paper outlines an alternative approach to teaching quantum computing at the high school level, tailored for students with limited prior knowledge in advanced mathematics and physics. This approach diverges from traditional methods by building upon foundational concepts in classical computing before gradually introducing quantum mechanics, thereby simplifying the entry into this complex field. The course was initially implemented in a program for gifted high school students under the Hong K…

A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids
Qi Li, Ye Shi, Yuning Jiang, Yuanming Shi, Haoyu Wang, H. Vincent Poor
https://arxiv.org/abs/2403.16402

A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids
The integration of various power sources, including renewables and electric vehicles, into smart grids is expanding, introducing uncertainties that can result in issues like voltage imbalances, load fluctuations, and power losses. These challenges negatively impact the reliability and stability of online scheduling in smart grids. Existing research often addresses uncertainties affecting current states but overlooks those that impact future states, such as the unpredictable charging patterns of…

E(n)-Equivariant Cartesian Tensor Passing Potential
Junjie Wang, Yong Wang, Haoting Zhang, Ziyang Yang, Zhixin Liang, Jiuyang Shi, Hui-Tian Wang, Dingyu Xing, Jian Sun
https://arxiv.org/abs/2402.15286

E(n)-Equivariant Cartesian Tensor Passing Potential
Machine learning potential (MLP) has been a popular topic in recent years for its potential to replace expensive first-principles calculations in some large systems. Meanwhile, message passing networks have gained significant attention due to their remarkable accuracy, and a wave of message passing networks based on Cartesian coordinates has emerged. However, the information of the node in these models is limited to scalars, vectors, and tensors. In this work, we proposed High-order Tensor Pass…

A Survey of Bluetooth Indoor Localization
Taolei Shi, Wei Gong
https://arxiv.org/abs/2404.12529 https://arxiv.org/pdf/2404.12529

A Survey of Bluetooth Indoor Localization
Nowadays, indoor localization has received extensive research interest due to more and more applications' needs for location information to provide a more precise and effective service [1], [2]. There are various wireless techniques and mechanisms that have been proposed; some of them have been studied in depth and come into use, such as Wi-Fi, RFID, and sensor networks. In comparison, the development of Bluetooth location technology is slow and there are not many papers and surveys in this fie…

ContextVis: Envision Contextual Learning and Interaction with Generative Models
Bo Shui, Chufan Shi, Yujiu Yang, Xiaomei Nie
https://arxiv.org/abs/2403.12768

ContextVis: Envision Contextual Learning and Interaction with Generative Models
ContextVis introduces a workflow by integrating generative models to create contextual learning materials. It aims to boost knowledge acquisition through the creation of resources with contextual cues. A case study on vocabulary learning demonstrates the effectiveness of generative models in developing educational resources that enrich language understanding and aid memory retention. The system combines an easy-to-use Dashboard for educators with an interactive Playground for learners, establis…

Interfacial magnetic spin Hall effect in van der Waals Fe3GeTe2/MoTe2 heterostructure
Yudi Dai, Junlin Xiong, Yanfeng Ge, Bin Cheng, Lizheng Wang, Pengfei Wang, Zenglin Liu, Shengnan Yan, Cuiwei Zhang, Xianghan Xu, Youguo Shi, Sang-Wook Cheong, Cong Xiao, Shengyuan A. Yang, Shi-Jun Liang, Feng Miao
https://arxiv.org/abs/2403.181…

Interfacial magnetic spin Hall effect in van der Waals Fe3GeTe2/MoTe2 heterostructure
The spin Hall effect (SHE) allows efficient generation of spin polarization or spin current through charge current and plays a crucial role in the development of spintronics. While SHE typically occurs in non-magnetic materials and is time-reversal even, exploring time-reversal-odd (T-odd) SHE, which couples SHE to magnetization in ferromagnetic materials, offers a new charge-spin conversion mechanism with new functionalities. Here, we report the observation of giant T-odd SHE in Fe3GeTe2/MoTe2…

Performance Analysis of Integrated Sensing and Communication Networks with Blockage Effects
Zezhong Sun, Shi Yan, Ning Jiang, Jiaen Zhou, Mugen Peng
https://arxiv.org/abs/2403.18621

Performance Analysis of Integrated Sensing and Communication Networks with Blockage Effects
Communication-sensing integration represents an up-and-coming area of research, enabling wireless networks to simultaneously perform communication and sensing tasks. However, in urban cellular networks, the blockage of buildings results in a complex signal propagation environment, affecting the performance analysis of integrated sensing and communication (ISAC) networks. To overcome this obstacle, this paper constructs a comprehensive framework considering building blockage and employs a distan…

Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming
Jie Wang, Zhihai Wang, Xijun Li, Yufei Kuang, Zhihao Shi, Fangzhou Zhu, Mingxuan Yuan, Jia Zeng, Yongdong Zhang, Feng Wu
https://arxiv.org/abs/2404.12638

Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming
Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), which formulate many important real-world applications. Cut selection heavily depends on (P1) which cuts to prefer and (P2) how many cuts to select. Although modern MILP solvers tackle (P1)-(P2) by human-designed heuristics, machine learning carries the potential to learn more effective heuristics. However, many existing learning-based methods learn which cuts to prefer, neglecting the importance of l…

Normal ordered grammars
Shi-Mei Ma, Toufik Mansour, Jean Yeh, Yeong-Nan Yeh
https://arxiv.org/abs/2404.15119 https://arxiv.org/pdf/24…

Normal ordered grammars
We introduce the theory of normal ordered grammars, which gives a natural generalization of the normal ordering problem. To illustrate the main idea, we explore normal ordered grammars associated with the Eulerian polynomials and the second-order Eulerian polynomials. In particular, we present a normal ordered grammatical interpretation for the (cdes,cyc) (p,q)-Eulerian polynomials, where cdes and cyc are the cycle descent and cycle statistics, respectively. The exponential generating function …

Stackelberg Game-Theoretic Learning for Collaborative Assembly Task Planning
Yuhan Zhao, Lan Shi, Quanyan Zhu
https://arxiv.org/abs/2404.12570 https://

Stackelberg Game-Theoretic Learning for Collaborative Assembly Task Planning
As assembly tasks grow in complexity, collaboration among multiple robots becomes essential for task completion. However, centralized task planning has become inadequate for adapting to the increasing intelligence and versatility of robots, along with rising customized orders. There is a need for efficient and automated planning mechanisms capable of coordinating diverse robots for collaborative assembly. To this end, we propose a Stackelberg game-theoretic learning approach. By leveraging Stac…

Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models
Yixuan Ren, Yang Zhou, Jimei Yang, Jing Shi, Difan Liu, Feng Liu, Mingi Kwon, Abhinav Shrivastava
https://arxiv.org/abs/2402.14780

Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models
Image customization has been extensively studied in text-to-image (T2I) diffusion models, leading to impressive outcomes and applications. With the emergence of text-to-video (T2V) diffusion models, its temporal counterpart, motion customization, has not yet been well investigated. To address the challenge of one-shot motion customization, we propose Customize-A-Video that models the motion from a single reference video and adapting it to new subjects and scenes with both spatial and temporal v…

MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
Yiqun Chen, Jiaxin Mao, Yi Zhang, Dehong MA, Long Xia, Jun Fan, Daiting Shi, Zhicong Cheng, Dawei Yin
https://arxiv.org/abs/2403.17421

MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
The objective of search result diversification (SRD) is to ensure that selected documents cover as many different subtopics as possible. Existing methods primarily utilize a paradigm of "greedy selection", i.e., selecting one document with the highest diversity score at a time. These approaches tend to be inefficient and are easily trapped in a suboptimal state. In addition, some other methods aim to approximately optimize the diversity metric, such as $α$-NDCG, but the results still remain su…

On the initial spin period distribution of neutron star
Shen-Shi Du, Xiao-Jin Liu, Zu-Cheng Chen, Zhi-Qiang You, Xing-Jiang Zhu, Zong-Hong Zhu
https://arxiv.org/abs/2402.14030

On the initial spin period distribution of neutron star
We derive the initial spin period distribution of neutron stars by studying the population of young pulsars associated with supernova remnants. Our hierarchical Bayesian approach accounts for the measurement uncertainties of individual observations and selection effects. Without correcting for selection effects, as done in previous studies, we find that pulsar initial spin periods follow a Weibull distribution, peaking at 40~ms, which is favoured against the lognormal distribution with a Bayes …

Normal Fermi Surface in the Nodal Superconductor CeCoIn$_5$ Revealed via Thermal Conductivity
Sangyun Lee, Duk Y. Kim, Priscila F. S. Rosa, Eric D. Bauer, Filip Ronning, J. D. Thompson, Shi-Zeng Lin, Roman Movshovich
https://arxiv.org/abs/2403.19422

Normal Fermi Surface in the Nodal Superconductor CeCoIn$_5$ Revealed via Thermal Conductivity
The thermal conductivity of heavy-fermion superconductor CeCoIn$_5$ was measured with a magnetic field rotating in the tetragonal a-b plane, with the heat current in the anti-nodal direction, $J$ || [100]. We observe a sharp resonance in thermal conductivity for the magnetic field at an angle $θ$ $\sim$ 12$^{\circ}$, measured from the heat current direction [100]. This resonance corresponds to the reported resonance at an angle $θ'$ $\sim$ 33$^{\circ}$ from the direction of the heat…

Probing the pole origin of $X(3872)$ with the coupled-channel dynamics
Jun-Zhang Wang, Zi-Yang Lin, Yan-Ke Chen, Lu Meng, Shi-Lin Zhu
https://arxiv.org/abs/2404.16575

Probing the pole origin of $X(3872)$ with the coupled-channel dynamics
The $X(3872)$, as the first and the most crucial member in the exotic charmoniumlike $XYZ$ family, has been studied for a long time. However, its dynamical origin, whether stemming from a $D\bar{D}^*$ hadronic molecule or the first excited $P$-wave charmonium $χ_{c1}(2P)$, remains controversial. In this Letter, we demonstrate that the $X(3872)$ definitely does not result from the mass shift of the higher bare $χ_{c1}(2P)$ resonance pole in the coupled-channel dynamics involving a short-distan…

Reconstructing Intrinsic Stellar Noise with Stellar Atmospheric Parameters and Chromospheric Activity
Jinghua Zhang, Maosheng Xiang, Jie Yu, Jian Ge, Ji-Wei Xie, Hui Zhang, Yaguang Li, You Wu, Chun-Qian Li, Shaolan Bi, Hong-Liang Yan, Jian-Rong Shi
https://arxiv.org/abs/2404.13850

Reconstructing Intrinsic Stellar Noise with Stellar Atmospheric Parameters and Chromospheric Activity
Accurately characterizing intrinsic stellar photometric noise induced by stellar astrophysics, such as stellar activity, granulation, and oscillations, is of crucial importance for detecting transiting exoplanets. In this study, we investigate the relation between the intrinsic stellar photometric noise, as quantified by the Kepler rrmsCDPP measurement, and the level of stellar chromospheric activity, as indicated by the S-index of Ca II HK lines derived from the LAMOST spectra. Our results rev…

Can points of bounded orbits surround points of unbounded orbits ?
Jiehua Mai, Enhui Shi, Kesong Yan, Fanping Zeng
https://arxiv.org/abs/2404.13642 https:/…

Can points of bounded orbits surround points of unbounded orbits ?
We show a somewhat surprising result: if $E$ is a disk in the plane $\mathbb R^2$, then there is a homeomorphism $h:\mathbb R^2\rightarrow\mathbb R^2$ such that, for every $x\in\partial E$, the orbit $O(x, h)$ is bounded, but for every $y\in {\rm Int}(E)$, the orbit $O(y, h)$ is doubly divergent. To prove this, we define a class of homeomorphisms on the square $[-1, 1]^2$, called normally rising homeomorphisms, and show that a normally rising homeomorphism can have very complex $ω$-limit sets …

Efficiency-improved doubly robust estimation with non-confounding predictive covariates
Shanshan Luo, Mengchen Shi, Wei Li, Xueli Wang, Zhi Geng
https://arxiv.org/abs/2402.14438 <…

Efficiency-improved doubly robust estimation with non-confounding predictive covariates
In observational studies, covariates with substantial missing data are often omitted, despite their strong predictive capabilities. These excluded covariates are generally believed not to simultaneously affect both treatment and outcome, indicating that they are not genuine confounders and do not impact the identification of the average treatment effect (ATE). In this paper, we introduce an alternative doubly robust (DR) estimator that fully leverages non-confounding predictive covariates to en…

Digital control of negative imaginary systems: a discrete-time hybrid integrator-gain system approach
Kanghong Shi, Ian R. Petersen
https://arxiv.org/abs/2403.16046

Digital control of negative imaginary systems: a discrete-time hybrid integrator-gain system approach
A hybrid integrator-gain system (HIGS) is a control element that switches between an integrator and a gain, which overcomes some inherent limitations of linear controllers. In this paper, we consider using discrete-time HIGS controllers for the digital control of negative imaginary (NI) systems. We show that the discrete-time HIGS themselves are step-advanced negative imaginary systems. For a minimal linear NI system, there always exists a HIGS controller that can asymptotically stablize it. An…

Stackelberg Game-Theoretic Learning for Collaborative Assembly Task Planning
Yuhan Zhao, Lan Shi, Quanyan Zhu
https://arxiv.org/abs/2404.12570 https://

Stackelberg Game-Theoretic Learning for Collaborative Assembly Task Planning
As assembly tasks grow in complexity, collaboration among multiple robots becomes essential for task completion. However, centralized task planning has become inadequate for adapting to the increasing intelligence and versatility of robots, along with rising customized orders. There is a need for efficient and automated planning mechanisms capable of coordinating diverse robots for collaborative assembly. To this end, we propose a Stackelberg game-theoretic learning approach. By leveraging Stac…

Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification
Zhan Shi, Jingwei Zhang, Jun Kong, Fusheng Wang
https://arxiv.org/abs/2403.18134

Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification
In digital pathology, the multiple instance learning (MIL) strategy is widely used in the weakly supervised histopathology whole slide image (WSI) classification task where giga-pixel WSIs are only labeled at the slide level. However, existing attention-based MIL approaches often overlook contextual information and intrinsic spatial relationships between neighboring tissue tiles, while graph-based MIL frameworks have limited power to recognize the long-range dependencies. In this paper, we intr…

Energy Efficiency Optimization Method of WDM Visible Light Communication System for Indoor Broadcasting Networks
Dayu Shi, Xun Zhang, Ziqi Liu, Xuanbang Chen, Jianghao Li, Xiaodong Liu, William Shieh
https://arxiv.org/abs/2403.16836

Energy Efficiency Optimization Method of WDM Visible Light Communication System for Indoor Broadcasting Networks
This paper introduces a novel approach to optimize energy efficiency in wavelength division multiplexing (WDM) Visible Light Communication (VLC) systems designed for indoor broadcasting networks. A physics-based LED model is integrated into system energy efficiency optimization, enabling quantitative analysis of the critical issue of VLC energy efficiency: the nonlinear interplay between illumination and communication performance. The optimization jointly incorporates constraints on communicati…

The maximum number of cliques in graphs with given fractional matching number and minimum degree
Chengli Li, Yurui Tang
https://arxiv.org/abs/2404.11268 ht…

The maximum number of cliques in graphs with given fractional matching number and minimum degree
Recently, Ma, Qian and Shi determined the maximum size of an $n$-vertex graph with given fractional matching number $s$ and maximum degree at most $d$. Motivated by this result, we determine the maximum number of $\ell$-cliques in a graph with given fractional matching number and minimum degree, which generalizes Shi and Ma's result about the maximum size of a graph with given fractional matching number and minimum degree at least one. We also determine the maximum number of complete bipartite …

Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems
Yiyang Zhu, Enyu Shi, Ziheng Liu, Jiayi Zhang, Bo Ai
https://arxiv.org/abs/2404.14092

Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems
Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising technique for achieving high spectral efficiency (SE) using multiple distributed access points (APs). However, harsh propagation environments often lead to significant communication performance degradation due to high penetration loss. To overcome this issue, we introduce the reconfigurable intelligent surface (RIS) into the CF mMIMO system as a low-cost and power-efficient solution. In this paper, we focus on optimizi…

Layer Control of Magneto-Optical Effects and Their Quantization in Spin-Valley Splitting Antiferromagnets
Jiaqi Feng, Xiaodong Zhou, Meiling Xu, Jingming Shi, Yinwei Li
https://arxiv.org/abs/2403.16701

Layer Control of Magneto-Optical Effects and Their Quantization in Spin-Valley Splitting Antiferromagnets
Magneto-optical effects (MOE), interfacing the fundamental interplay between magnetism and light, have served as a powerful probe for magnetic order, band topology, and valley index. Here, based on multiferroic and topological bilayer antiferromagnets (AFMs), we propose a layer control of MOE (L-MOE), which is created and annihilated by layer-stacking or an electric field effect. The key character of L-MOE is the sign-reversible response controlled by ferroelectric polarization, the Neel vector…

Horizontally Polarized Kink Oscillations Supported by Solar Coronal Loops in an Asymmetric Environment
Mijie Shi, Bo Li, Shengju Yuan
https://arxiv.org/abs/2402.11181

Horizontally Polarized Kink Oscillations Supported by Solar Coronal Loops in an Asymmetric Environment
Kink oscillations are ubiquitously observed in solar coronal loops, their understanding being crucial in the contexts of coronal seismology and atmospheric heating. We study kink modes supported by a straight coronal loop embeded in an asymmetric environment using three-dimensional magnetohydrodynamic (MHD) simulations. We implement the asymmetric effect by setting different exterior densities below and above the loop interior, and initiate the simulation using a kink-like velocity perturbation…

FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
Rajeev Yasarla, Manish Kumar Singh, Hong Cai, Yunxiao Shi, Jisoo Jeong, Yinhao Zhu, Shizhong Han, Risheek Garrepalli, Fatih Porikli
https://arxiv.org/abs/2403.12953

FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training. More specifically, we propose a future prediction network, F-Net, which takes the features of multiple consecutive frames and is trained to predict multi-frame features one time step ahead iteratively. In this way, F-Net learns the underlying motion and correspo…

Baryon Acoustic Oscillation Theory and Modelling Systematics for the DESI 2024 results
Shi-Fan Chen, Cullan Howlett, Martin White, Patrick McDonald, Ashley J. Ross, Hee-Jong Seo, Nikhil Padmanabhan, J. Aguilar, S. Ahlen, S. Alam, O. Alves, R. Blum, D. Brooks, X. Chen, S. Cole, T. M. Davis, K. Dawson, A. de la Macorra, Arjun Dey, Z. Ding, P. Doel, S. Ferraro, A. Font-Ribera, D. Forero-S\'anchez, J. E. Forero-Romero, C. Garcia-Quintero, E. Gazta\~naga, S. Gontcho A Gontcho, M. M. S H…

Baryon Acoustic Oscillation Theory and Modelling Systematics for the DESI 2024 results
This paper provides a comprehensive overview of how fitting of Baryon Acoustic Oscillations (BAO) is carried out within the upcoming Dark Energy Spectroscopic Instrument's (DESI) 2024 results using its DR1 dataset, and the associated systematic error budget from theory and modelling of the BAO. We derive new results showing how non-linearities in the clustering of galaxies can cause potential biases in measurements of the isotropic ($α_{\mathrm{iso}}$) and anisotropic ($α_{\mathrm{ap}}$) BAO …

CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing
Xinyi He, Jiaru Zou, Yun Lin, Mengyu Zhou, Shi Han, Zejian Yuan, Dongmei Zhang
https://arxiv.org/abs/2403.13583

CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing
Large Language Models (LLMs) have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we introduce the CONLINE framework, which enhances code generation by incorporating planned online searches for information retrieval …

Yell At Your Robot: Improving On-the-Fly from Language Corrections
Lucy Xiaoyang Shi, Zheyuan Hu, Tony Z. Zhao, Archit Sharma, Karl Pertsch, Jianlan Luo, Sergey Levine, Chelsea Finn
https://arxiv.org/abs/2403.12910

Yell At Your Robot: Improving On-the-Fly from Language Corrections
Hierarchical policies that combine language and low-level control have been shown to perform impressively long-horizon robotic tasks, by leveraging either zero-shot high-level planners like pretrained language and vision-language models (LLMs/VLMs) or models trained on annotated robotic demonstrations. However, for complex and dexterous skills, attaining high success rates on long-horizon tasks still represents a major challenge -- the longer the task is, the more likely it is that some stage w…

Distributionally Robust Graph-based Recommendation System
Bohao Wang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yang Gao, Yan Feng, Chun Chen, Can Wang
https://arxiv.org/abs/2402.12994

Distributionally Robust Graph-based Recommendation System
With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy often hinges on the assumption that training and testing data share the same distribution (a.k.a. IID assumption), and exhibits significant declines under distribution shifts. Distribution shifts commonly arises in RS, often attributed to the dynamic nature of user preferences or ubiquitous biases during data collection…

Layer Control of Magneto-Optical Effects and Their Quantization in Spin-Valley Splitting Antiferromagnets
Jiaqi Feng, Xiaodong Zhou, Meiling Xu, Jingming Shi, Yinwei Li
https://arxiv.org/abs/2403.16701

Layer Control of Magneto-Optical Effects and Their Quantization in Spin-Valley Splitting Antiferromagnets
Magneto-optical effects (MOE), interfacing the fundamental interplay between magnetism and light, have served as a powerful probe for magnetic order, band topology, and valley index. Here, based on multiferroic and topological bilayer antiferromagnets (AFMs), we propose a layer control of MOE (L-MOE), which is created and annihilated by layer-stacking or an electric field effect. The key character of L-MOE is the sign-reversible response controlled by ferroelectric polarization, the Neel vector…

CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing
Xinyi He, Jiaru Zou, Yun Lin, Mengyu Zhou, Shi Han, Zejian Yuan, Dongmei Zhang
https://arxiv.org/abs/2403.13583

CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing
Large Language Models (LLMs) have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we introduce the CONLINE framework, which enhances code generation by incorporating planned online searches for information retrieval …

Generic 3D Diffusion Adapter Using Controlled Multi-View Editing
Hansheng Chen, Ruoxi Shi, Yulin Liu, Bokui Shen, Jiayuan Gu, Gordon Wetzstein, Hao Su, Leonidas Guibas
https://arxiv.org/abs/2403.12032

Generic 3D Diffusion Adapter Using Controlled Multi-View Editing
Open-domain 3D object synthesis has been lagging behind image synthesis due to limited data and higher computational complexity. To bridge this gap, recent works have investigated multi-view diffusion but often fall short in either 3D consistency, visual quality, or efficiency. This paper proposes MVEdit, which functions as a 3D counterpart of SDEdit, employing ancestral sampling to jointly denoise multi-view images and output high-quality textured meshes. Built on off-the-shelf 2D diffusion mo…

LAVA: Long-horizon Visual Action based Food Acquisition
Amisha Bhaskar, Rui Liu, Vishnu D. Sharma, Guangyao Shi, Pratap Tokekar
https://arxiv.org/abs/2403.12876

LAVA: Long-horizon Visual Action based Food Acquisition
Robotic Assisted Feeding (RAF) addresses the fundamental need for individuals with mobility impairments to regain autonomy in feeding themselves. The goal of RAF is to use a robot arm to acquire and transfer food to individuals from the table. Existing RAF methods primarily focus on solid foods, leaving a gap in manipulation strategies for semi-solid and deformable foods. This study introduces Long-horizon Visual Action (LAVA) based food acquisition of liquid, semisolid, and deformable foods. L…

CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility
Bojia Zi, Shihao Zhao, Xianbiao Qi, Jianan Wang, Yukai Shi, Qianyu Chen, Bin Liang, Kam-Fai Wong, Lei Zhang
https://arxiv.org/abs/2403.12035

CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility
Recent advancements in video generation have been remarkable, yet many existing methods struggle with issues of consistency and poor text-video alignment. Moreover, the field lacks effective techniques for text-guided video inpainting, a stark contrast to the well-explored domain of text-guided image inpainting. To this end, this paper proposes a novel text-guided video inpainting model that achieves better consistency, controllability and compatibility. Specifically, we introduce a simple but …

Java JIT Testing with Template Extraction
Zhiqiang Zang, Fu-Yao Yu, Aditya Thimmaiah, August Shi, Milos Gligoric
https://arxiv.org/abs/2403.11281 https://<…

Java JIT Testing with Template Extraction
We present LeJit, a template-based framework for testing Java just-in-time (JIT) compilers. Like recent template-based frameworks, LeJit executes a template -- a program with holes to be filled -- to generate concrete programs given as inputs to Java JIT compilers. LeJit automatically generates template programs from existing Java code by converting expressions to holes, as well as generating necessary glue code (i.e., code that generates instances of non-primitive types) to make generated temp…

Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation
Haoxiang Ma, Ran Qin, Modi shi, Boyang Gao, Di Huang
https://arxiv.org/abs/2403.11511 htt…

Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation
This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a domain adaptation problem. In this case, we present a global-to-local method to address hybrid domain gaps in RGB and depth data and insufficient multi-modal feature alignment. First, a self-supervised rotation pre-training strategy is adopted to deliver robust initialization for RGB and depth networks. We then propose a global-to-local alignment pipeline with individual global domain classifiers for sce…

Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation
Haoxiang Ma, Ran Qin, Modi shi, Boyang Gao, Di Huang
https://arxiv.org/abs/2403.11511 htt…

Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation
This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a domain adaptation problem. In this case, we present a global-to-local method to address hybrid domain gaps in RGB and depth data and insufficient multi-modal feature alignment. First, a self-supervised rotation pre-training strategy is adopted to deliver robust initialization for RGB and depth networks. We then propose a global-to-local alignment pipeline with individual global domain classifiers for sce…