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Showing 1–50 of 427 results for author: Cheng, G

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  1. arXiv:2403.18826  [pdf

    q-bio.QM eess.IV eess.SY

    SAM-dPCR: Real-Time and High-throughput Absolute Quantification of Biological Samples Using Zero-Shot Segment Anything Model

    Authors: Yuanyuan Wei, Shanhang Luo, Changran Xu, Yingqi Fu, Qingyue Dong, Yi Zhang, Fuyang Qu, Guangyao Cheng, Yi-Ping Ho, Ho-Pui Ho, Wu Yuan

    Abstract: Digital PCR (dPCR) has revolutionized nucleic acid diagnostics by enabling absolute quantification of rare mutations and target sequences. However, current detection methodologies face challenges, as flow cytometers are costly and complex, while fluorescence imaging methods, relying on software or manual counting, are time-consuming and prone to errors. To address these limitations, we present SAM… ▽ More

    Submitted 22 January, 2024; originally announced March 2024.

    Comments: 23 pages, 6 figures

  2. arXiv:2403.18216  [pdf, other

    stat.ML cs.CY cs.LG math.ST

    Minimax Optimal Fair Classification with Bounded Demographic Disparity

    Authors: Xianli Zeng, Guang Cheng, Edgar Dobriban

    Abstract: Mitigating the disparate impact of statistical machine learning methods is crucial for ensuring fairness. While extensive research aims to reduce disparity, the effect of using a \emph{finite dataset} -- as opposed to the entire population -- remains unclear. This paper explores the statistical foundations of fair binary classification with two protected groups, focusing on controlling demographic… ▽ More

    Submitted 26 March, 2024; originally announced March 2024.

  3. arXiv:2403.16331  [pdf, other

    cs.SD cs.LG eess.AS

    Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models

    Authors: Hanzhi Yin, Gang Cheng, Christian J. Steinmetz, Ruibin Yuan, Richard M. Stern, Roger B. Dannenberg

    Abstract: We describe a novel approach for developing realistic digital models of dynamic range compressors for digital audio production by analyzing their analog prototypes. While realistic digital dynamic compressors are potentially useful for many applications, the design process is challenging because the compressors operate nonlinearly over long time scales. Our approach is based on the structured stat… ▽ More

    Submitted 24 March, 2024; originally announced March 2024.

  4. arXiv:2403.12187  [pdf, ps, other

    stat.ML cs.LG math.ST

    Approximation of RKHS Functionals by Neural Networks

    Authors: Tian-Yi Zhou, Namjoon Suh, Guang Cheng, Xiaoming Huo

    Abstract: Motivated by the abundance of functional data such as time series and images, there has been a growing interest in integrating such data into neural networks and learning maps from function spaces to R (i.e., functionals). In this paper, we study the approximation of functionals on reproducing kernel Hilbert spaces (RKHS's) using neural networks. We establish the universality of the approximation… ▽ More

    Submitted 18 March, 2024; originally announced March 2024.

  5. arXiv:2403.07780  [pdf, other

    stat.ML cs.LG

    FairRR: Pre-Processing for Group Fairness through Randomized Response

    Authors: Xianli Zeng, Joshua Ward, Guang Cheng

    Abstract: The increasing usage of machine learning models in consequential decision-making processes has spurred research into the fairness of these systems. While significant work has been done to study group fairness in the in-processing and post-processing setting, there has been little that theoretically connects these results to the pre-processing domain. This paper proposes that achieving group fairne… ▽ More

    Submitted 12 March, 2024; originally announced March 2024.

  6. arXiv:2403.07056  [pdf, other

    hep-th gr-qc quant-ph

    Gravitational back-reaction is the Holographic Dual of Magic

    Authors: ChunJun Cao, Gong Cheng, Alioscia Hamma, Lorenzo Leone, William Munizzi, Savatore F. E. Oliviero

    Abstract: We study interplay between magic and entanglement in quantum many-body systems. We show that non-local magic which is supported by the quantum correlations is lower bounded by the flatness of entanglement spectrum and upper bounded by the amount of entanglement in the system. We then argue that a smoothed version of non-local magic bounds the hardness of classical simulations for incompressible st… ▽ More

    Submitted 11 March, 2024; originally announced March 2024.

    Comments: 61 pages, 20 figures

  7. arXiv:2403.06642  [pdf, other

    cs.IR cs.AI cs.CL

    KELLMRec: Knowledge-Enhanced Large Language Models for Recommendation

    Authors: Weiqing Luo, Chonggang Song, Lingling Yi, Gong Cheng

    Abstract: The utilization of semantic information is an important research problem in the field of recommender systems, which aims to complement the missing parts of mainstream ID-based approaches. With the rise of LLM, its ability to act as a knowledge base and its reasoning capability have opened up new possibilities for this research area, making LLM-based recommendation an emerging research direction. H… ▽ More

    Submitted 11 March, 2024; originally announced March 2024.

    Comments: 9 pages, 1 figure

  8. arXiv:2403.03099  [pdf, other

    stat.ME stat.CO

    Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure

    Authors: Traymon E. Beavers, Ge Cheng, Yajie Duan, Javier Cabrera, Mariusz Lubomirski, Dhammika Amaratunga, Jeffrey E. Teigler

    Abstract: Big data, with NxP dimension where N is extremely large, has created new challenges for data analysis, particularly in the realm of creating meaningful clusters of data. Clustering techniques, such as K-means or hierarchical clustering are popular methods for performing exploratory analysis on large datasets. Unfortunately, these methods are not always possible to apply to big data due to memory o… ▽ More

    Submitted 5 March, 2024; originally announced March 2024.

    Comments: 31 pages and 8 figures

    MSC Class: 62:0

  9. arXiv:2402.16792  [pdf, other

    stat.ML cs.CR cs.LG

    Rate-Optimal Rank Aggregation with Private Pairwise Rankings

    Authors: Shirong Xu, Will Wei Sun, Guang Cheng

    Abstract: In various real-world scenarios like recommender systems and political surveys, pairwise rankings are commonly collected and utilized for rank aggregation to obtain an overall ranking of items. However, preference rankings can reveal individuals' personal preferences, underscoring the need to protect them before releasing for downstream analysis. In this paper, we address the challenge of preservi… ▽ More

    Submitted 26 February, 2024; originally announced February 2024.

  10. arXiv:2402.12692  [pdf, other

    cs.CL

    FormulaQA: A Question Answering Dataset for Formula-Based Numerical Reasoning

    Authors: Xiao Li, Sichen Liu, Bolin Zhu, Yin Zhu, Yiwei Liu, Gong Cheng

    Abstract: The application of formulas is a fundamental ability of humans when addressing numerical reasoning problems. However, existing numerical reasoning datasets seldom explicitly indicate the formulas employed during the reasoning steps. To bridge this gap, we propose a question answering dataset for formula-based numerical reasoning called FormulaQA, from junior high school physics examinations. We fu… ▽ More

    Submitted 20 February, 2024; v1 submitted 19 February, 2024; originally announced February 2024.

    Comments: 17 pages, 9 figures, 7 tables

  11. arXiv:2402.12001  [pdf, other

    cs.AI cs.DB cs.IR cs.SI

    A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions

    Authors: Xiaxia Wang, Gong Cheng

    Abstract: With the continuous growth of large Knowledge Graphs (KGs), extractive KG summarization becomes a trending task. Aiming at distilling a compact subgraph with condensed information, it facilitates various downstream KG-based tasks. In this survey paper, we are among the first to provide a systematic overview of its applications and define a taxonomy for existing methods from its interdisciplinary s… ▽ More

    Submitted 19 February, 2024; originally announced February 2024.

    Comments: 9 pages, 13 figures, submitted to the IJCAI 2024 Survey Track

  12. arXiv:2402.11016  [pdf, other

    hep-th astro-ph.CO gr-qc

    Holographic phenomenology via overlapping degrees of freedom

    Authors: Oliver Friedrich, ChunJun Cao, Sean M. Carroll, Gong Cheng, Ashmeet Singh

    Abstract: The holographic principle suggests that regions of space contain fewer physical degrees of freedom than would be implied by conventional quantum field theory. Meanwhile, in Hilbert spaces of large dimension $2^n$, it is possible to define $N \gg n$ Pauli algebras that are nearly anti-commuting (but not quite) and which can be thought of as "overlapping degrees of freedom". We propose to model the… ▽ More

    Submitted 5 March, 2024; v1 submitted 16 February, 2024; originally announced February 2024.

    Comments: 46 pages + appendix; code and data available at https://github.com/OliverFHD/GPUniverse

  13. arXiv:2402.07175  [pdf, other

    astro-ph.SR

    A magnetic reconnection model for the hot explosion with both ultraviolet and Hα wing emissions

    Authors: Guanchong Cheng, Lei Ni, Yajie Chen, Jun Lin

    Abstract: Ellerman bombs (EBs) with significant H$α$ wing emissions and ultraviolet bursts (UV bursts) with strong Si IV emissions are two kinds of small transient brightening events that occur in the low solar atmosphere.We numerically investigated the magnetic reconnection process between the emerging arch magnetic field and the lower atmospheric background magnetic field. We aim to find out if the hot UV… ▽ More

    Submitted 20 February, 2024; v1 submitted 11 February, 2024; originally announced February 2024.

  14. arXiv:2402.03760  [pdf, other

    cs.NI

    DeMarking: A Defense for Network Flow Watermarking in Real-Time

    Authors: Yali Yuan, Jian Ge, Guang Cheng

    Abstract: The network flow watermarking technique associates the two communicating parties by actively modifying certain characteristics of the stream generated by the sender so that it covertly carries some special marking information. Some curious users communicating with the hidden server as a Tor client may attempt de-anonymization attacks to uncover the real identity of the hidden server by using this… ▽ More

    Submitted 6 February, 2024; v1 submitted 6 February, 2024; originally announced February 2024.

  15. arXiv:2402.02817  [pdf, other

    stat.ML cs.CY cs.LG

    Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing

    Authors: Xianli Zeng, Guang Cheng, Edgar Dobriban

    Abstract: Machine learning algorithms may have disparate impacts on protected groups. To address this, we develop methods for Bayes-optimal fair classification, aiming to minimize classification error subject to given group fairness constraints. We introduce the notion of \emph{linear disparity measures}, which are linear functions of a probabilistic classifier; and \emph{bilinear disparity measures}, which… ▽ More

    Submitted 6 February, 2024; v1 submitted 5 February, 2024; originally announced February 2024.

    Comments: This paper replaces the preprint "Bayes-optimal classifiers under group fairness" by Xianli Zeng, Edgar Dobriban, and Guang Cheng (arXiv:2202.09724)

  16. arXiv:2402.00743  [pdf, other

    cs.LG cs.CL stat.ML

    Benefits of Transformer: In-Context Learning in Linear Regression Tasks with Unstructured Data

    Authors: Yue Xing, Xiaofeng Lin, Namjoon Suh, Qifan Song, Guang Cheng

    Abstract: In practice, it is observed that transformer-based models can learn concepts in context in the inference stage. While existing literature, e.g., \citet{zhang2023trained,huang2023context}, provide theoretical explanations on this in-context learning ability, they assume the input $x_i$ and the output $y_i$ for each sample are embedded in the same token (i.e., structured data). However, in reality,… ▽ More

    Submitted 1 February, 2024; originally announced February 2024.

  17. arXiv:2401.15248  [pdf, other

    cs.LG stat.ML

    Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective

    Authors: Yue Xing, Xiaofeng Lin, Qifan Song, Yi Xu, Belinda Zeng, Guang Cheng

    Abstract: Pre-training is known to generate universal representations for downstream tasks in large-scale deep learning such as large language models. Existing literature, e.g., \cite{kim2020adversarial}, empirically observe that the downstream tasks can inherit the adversarial robustness of the pre-trained model. We provide theoretical justifications for this robustness inheritance phenomenon. Our theoreti… ▽ More

    Submitted 26 January, 2024; originally announced January 2024.

    Comments: To appear in AISTATS2024

  18. arXiv:2401.14547  [pdf

    cond-mat.str-el cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.other physics.app-ph

    Discovery of a Topological Charge Density Wave

    Authors: Maksim Litskevich, Md Shafayat Hossain, Songbo Zhang, Zi-Jia Cheng, Satya N. Guin, Nitesh Kumar, Chandra Shekhar, Zhiwei Wang, Yongkai Li, Guoqing Chang, Jia-Xin Yin, Qi Zhang, Guangming Cheng, Yu-Xiao Jiang, Tyler A. Cochran, Nana Shumiya, Xian P. Yang, Daniel Multer, Xiaoxiong Liu, Nan Yao, Yugui Yao, Claudia Felser, Titus Neupert, M. Zahid Hasan

    Abstract: Charge density waves (CDWs) appear in numerous condensed matter platforms, ranging from high-Tc superconductors to quantum Hall systems. Despite such ubiquity, there has been a lack of direct experimental study on boundary states that can uniquely stem from the charge order. Here, using scanning tunneling microscopy, we directly visualize the bulk and boundary phenomenology of CDW in a topological… ▽ More

    Submitted 25 January, 2024; originally announced January 2024.

    Comments: Nature Physics (2024); in press

  19. arXiv:2401.11776  [pdf, other

    cs.RO

    On the impact of robot personalization on human-robot interaction: A review

    Authors: Jinyu Yang, Camille Vindolet, Julio Rogelio Guadarrama Olvera, Gordon Cheng

    Abstract: This study reviews the impact of personalization on human-robot interaction. Firstly, the various strategies used to achieve personalization are briefly described. Secondly, the effects of personalization known to date are discussed. They are presented along with the personalized parameters, personalized features, used technology, and use case they relate to. It is observed that various positive e… ▽ More

    Submitted 22 January, 2024; originally announced January 2024.

    Report number: CONCATENATE/2023/14 ACM Class: I.2.9

  20. arXiv:2401.07187  [pdf, ps, other

    stat.ML cs.LG math.ST

    A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models

    Authors: Namjoon Suh, Guang Cheng

    Abstract: In this article, we review the literature on statistical theories of neural networks from three perspectives. In the first part, results on excess risks for neural networks are reviewed in the nonparametric framework of regression or classification. These results rely on explicit constructions of neural networks, leading to fast convergence rates of excess risks, in that tools from the approximati… ▽ More

    Submitted 13 January, 2024; originally announced January 2024.

    Comments: 33 pages, no figures,Invited for review in Annual Review of Statistics and Its Application (In review)

  21. arXiv:2401.01770  [pdf, other

    math.OC

    Legendre-Moment Transform for Linear Ensemble Control and Computation

    Authors: Xin Ning, Gong Cheng, Wei Zhang, Jr-Shin Li

    Abstract: Ensemble systems, pervasive in diverse scientific and engineering domains, pose challenges to existing control methods due to their massive scale and underactuated nature. This paper presents a dynamic moment approach to addressing theoretical and computational challenges in systems-theoretic analysis and control design for linear ensemble systems. We introduce the Legendre-moments and Legendre-mo… ▽ More

    Submitted 3 January, 2024; originally announced January 2024.

    MSC Class: 93B05; 93B28; 93B51

  22. arXiv:2401.01233  [pdf, other

    cs.LG

    Graph Elimination Networks

    Authors: Shuo Wang, Ge Cheng, Yun Zhang

    Abstract: Graph Neural Networks (GNNs) are widely applied across various domains, yet they perform poorly in deep layers. Existing research typically attributes this problem to node over-smoothing, where node representations become indistinguishable after multiple rounds of propagation. In this paper, we delve into the neighborhood propagation mechanism of GNNs and discover that the real root cause of GNNs'… ▽ More

    Submitted 2 January, 2024; originally announced January 2024.

    Comments: Includes 8 pages of main text and 4 pages of appendices

  23. arXiv:2401.00974  [pdf, other

    cs.LG cs.AI

    Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models

    Authors: Yinan Cheng, Chi-Hua Wang, Vamsi K. Potluru, Tucker Balch, Guang Cheng

    Abstract: Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance. Existing studies focused on the utility of a single family of generative models. They provided limited insights on how synthetic data practitioners select the best family generative models for synthetic training tasks given a specific combination of machine learning model… ▽ More

    Submitted 1 January, 2024; originally announced January 2024.

    Comments: The following article has been accepted by ICAIF22, Synthetic Data for AI in Finance; see https://sites.google.com/view/icaif-synthetic-2022/program

  24. arXiv:2401.00965  [pdf, other

    cs.LG

    Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective

    Authors: Din-Yin Hsieh, Chi-Hua Wang, Guang Cheng

    Abstract: Exploring generative model training for synthetic tabular data, specifically in sequential contexts such as credit card transaction data, presents significant challenges. This paper addresses these challenges, focusing on attaining both high fidelity to actual data and optimal utility for machine learning tasks. We introduce five pre-processing schemas to enhance the training of the Conditional Pr… ▽ More

    Submitted 1 January, 2024; originally announced January 2024.

    Comments: The following article has been accepted by 2nd Workshop on Synthetic Data for AI in Finance; see https://sites.google.com/view/icaif-synthetic/home

  25. arXiv:2312.16248  [pdf, other

    cs.LG cs.AI cs.DL

    XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

    Authors: Wenzhang Liu, Wenzhe Cai, Kun Jiang, Guangran Cheng, Yuanda Wang, Jiawei Wang, Jingyu Cao, Lele Xu, Chaoxu Mu, Changyin Sun

    Abstract: In this paper, we present XuanCe, a comprehensive and unified deep reinforcement learning (DRL) library designed to be compatible with PyTorch, TensorFlow, and MindSpore. XuanCe offers a wide range of functionalities, including over 40 classical DRL and multi-agent DRL algorithms, with the flexibility to easily incorporate new algorithms and environments. It is a versatile DRL library that support… ▽ More

    Submitted 25 December, 2023; originally announced December 2023.

    Comments: 16 pages, 4 figures, 32 conferences

  26. arXiv:2312.15862  [pdf

    cond-mat.str-el cond-mat.mes-hall cond-mat.mtrl-sci quant-ph

    Discovery of a topological exciton insulator with tunable momentum order

    Authors: Md Shafayat Hossain, Tyler A. Cochran, Yu-Xiao Jiang, Songbo Zhang, Huangyu Wu, Xiaoxiong Liu, Xiquan Zheng, Byunghoon Kim, Guangming Cheng, Qi Zhang, Maksim Litskevich, Junyi Zhang, Zi-Jia Cheng, Jinjin Liu, Jia-Xin Yin, Xian P. Yang, Jonathan Denlinger, Massimo Tallarida, Ji Dai, Elio Vescovo, Anil Rajapitamahuni, Hu Miao, Nan Yao, Yingying Peng, Yugui Yao , et al. (4 additional authors not shown)

    Abstract: Topology and correlations are fundamental concepts in modern physics, but their simultaneous occurrence within a single quantum phase is exceptionally rare. In this study, we present the discovery of such a phase of matter in Ta2Pd3Te5, a semimetal where the Coulomb interaction between electrons and holes leads to the spontaneous formation of excitonic bound states below T=100 K. Our spectroscopy… ▽ More

    Submitted 25 December, 2023; originally announced December 2023.

    Report number: Journal submission on 7th December 23

  27. arXiv:2312.09487  [pdf

    cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph quant-ph

    Transport response of topological hinge modes in $α$-Bi$_4$Br$_4$

    Authors: Md Shafayat Hossain, Qi Zhang, Zhiwei Wang, Nikhil Dhale, Wenhao Liu, Maksim Litskevich, Brian Casas, Nana Shumiya, Jia-Xin Yin, Tyler A. Cochran, Yongkai Li, Yu-Xiao Jiang, Ying Yang, Guangming Cheng, Zi-Jia Cheng, Xian P. Yang, Nan Yao, Titus Neupert, Luis Balicas, Yugui Yao, Bing Lv, M. Zahid Hasan

    Abstract: Electronic topological phases are renowned for their unique properties, where conducting surface states exist on the boundary of an insulating three-dimensional bulk. While the transport response of the surface states has been extensively studied, the response of the topological hinge modes remains elusive. Here, we investigate a layered topological insulator $α$-Bi$_4$Br$_4$, and provide the firs… ▽ More

    Submitted 14 February, 2024; v1 submitted 14 December, 2023; originally announced December 2023.

    Comments: Nature Physics, in press (2023)

  28. arXiv:2312.06423  [pdf, other

    cs.CR cs.AI cs.LG

    MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks

    Authors: Yuyang Zhou, Guang Cheng, Zongyao Chen, Shui Yu

    Abstract: Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid proliferation of malware attacks. However, recent studies have revealed the inherent vulnerabilities of ML-based detection systems to evasion attacks. While efforts have been made to address this critical issue, many of the existing defensive methods encounter cha… ▽ More

    Submitted 11 December, 2023; originally announced December 2023.

    Comments: 14 pages; In submission

    MSC Class: 62 ACM Class: I.2.1

  29. arXiv:2312.02844  [pdf

    eess.SP

    Modeling of SCADA and PMU Measurement Chains

    Authors: Gang Cheng, Yuzhang Lin

    Abstract: In this document, the supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurement chain modeling will be studied, where the measurement error sources of each component in the SCADA and PMU measurement chains and the reasons leading to measurement errors exhibiting non-zero-mean, non-Gaussian, and time-varying statistical characteristic are summarized and analyzed… ▽ More

    Submitted 27 February, 2024; v1 submitted 5 December, 2023; originally announced December 2023.

  30. arXiv:2312.01573  [pdf

    eess.IV cs.CV

    Survey on deep learning in multimodal medical imaging for cancer detection

    Authors: Yan Tian, Zhaocheng Xu, Yujun Ma, Weiping Ding, Ruili Wang, Zhihong Gao, Guohua Cheng, Linyang He, Xuran Zhao

    Abstract: The task of multimodal cancer detection is to determine the locations and categories of lesions by using different imaging techniques, which is one of the key research methods for cancer diagnosis. Recently, deep learning-based object detection has made significant developments due to its strength in semantic feature extraction and nonlinear function fitting. However, multimodal cancer detection r… ▽ More

    Submitted 3 December, 2023; originally announced December 2023.

    Journal ref: Neural Computing and Applications. 2023 Nov 29:1-6

  31. arXiv:2312.01389  [pdf

    physics.app-ph

    Deep Learning Assisted Raman Spectroscopy for Rapid Identification of 2D Materials

    Authors: Yaping Qi, Dan Hu, Zhenping Wu, Ming Zheng, Guanghui Cheng, Yucheng Jiang, Yong P. Chen

    Abstract: Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extracti… ▽ More

    Submitted 3 December, 2023; originally announced December 2023.

  32. arXiv:2311.18473  [pdf, other

    cs.RO

    DGMem: Learning Visual Navigation Policy without Any Labels by Dynamic Graph Memory

    Authors: Wenzhe Cai, Teng Wang, Guangran Cheng, Lele Xu, Changyin Sun

    Abstract: In recent years, learning-based approaches have demonstrated significant promise in addressing intricate navigation tasks. Traditional methods for training deep neural network navigation policies rely on meticulously designed reward functions or extensive teleoperation datasets as navigation demonstrations. However, the former is often confined to simulated environments, and the latter demands sub… ▽ More

    Submitted 30 November, 2023; originally announced November 2023.

    Comments: 8 pages, 6 figures

  33. arXiv:2311.18005  [pdf, ps, other

    cond-mat.str-el cond-mat.stat-mech hep-th

    Exact fixed-point tensor network construction for rational conformal field theory

    Authors: Gong Cheng, Lin Chen, Zheng-Cheng Gu, Ling-Yan Hung

    Abstract: The novel concept of entanglement renormalization and its corresponding tensor network renormalization technique have been highly successful in developing a controlled real space renormalization group (RG) scheme for classical 2D systems or $(1+1)$D quantum systems. Numerically approximate fixed-point (FP) tensors are widely used to extract the conformal data of the underlying conformal field theo… ▽ More

    Submitted 20 February, 2024; v1 submitted 29 November, 2023; originally announced November 2023.

    Comments: 12 pages, 13 figures, 1 table

  34. arXiv:2311.13228  [pdf

    cond-mat.supr-con cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.str-el

    Strain mediated phase crossover in Ruddlesden Popper nickelates

    Authors: Ting Cui, Songhee Choi, Ting Lin, Chen Liu, Gang Wang, Ningning Wang, Shengru Chen, Haitao Hong, Dongke Rong, Qianying Wang, Qiao Jin, Jia-Ou Wang, Lin Gu, Chen Ge, Can Wang, Jin Guang Cheng, Qinghua Zhang, Liang Si, Kui-juan Jin, Er-Jia Guo

    Abstract: Recent progress on the signatures of pressure-induced high temperature superconductivity in Ruddlesden Popper (RP) nickelates (Lan+1NinO3n+1) has attracted growing interest in both theoretical calculations and experimental efforts. The fabrication of high-quality single crystalline RP nickelate thin films is critical for possible reducing the superconducting transition pressure and advancing appli… ▽ More

    Submitted 22 November, 2023; originally announced November 2023.

    Comments: 29 pages, 5 figures, one supplementary materials

  35. arXiv:2311.03074  [pdf, other

    eess.IV cs.CV

    A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection

    Authors: Wenxin Wang, Zhuo-Xu Cui, Guanxun Cheng, Chentao Cao, Xi Xu, Ziwei Liu, Haifeng Wang, Yulong Qi, Dong Liang, Yanjie Zhu

    Abstract: Accurate detection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution. In this paper, we propose a novel framework Two-Stage Generative Model (TSGM) that combines Cyc… ▽ More

    Submitted 6 November, 2023; originally announced November 2023.

    Comments: 11 pages,9 figures,3 tables

  36. arXiv:2311.02927  [pdf

    eess.IV physics.bio-ph

    Auto-ICell: An Accessible and Cost-Effective Integrative Droplet Microfluidic System for Real-Time Single-Cell Morphological and Apoptotic Analysis

    Authors: Yuanyuan Wei, Meiai Lin, Shanhang Luo, Syed Muhammad Tariq Abbasi, Liwei Tan, Guangyao Cheng, Bijie Bai, Yi-Ping Ho, Scott Wu Yuan, Ho-Pui Ho

    Abstract: The Auto-ICell system, a novel, and cost-effective integrated droplet microfluidic system, is introduced for real-time analysis of single-cell morphology and apoptosis. This system integrates a 3D-printed microfluidic chip with image analysis algorithms, enabling the generation of uniform droplet reactors and immediate image analysis. The system employs a color-based image analysis algorithm in th… ▽ More

    Submitted 6 November, 2023; originally announced November 2023.

    Comments: 22 pages, 5 figures

  37. arXiv:2310.20564  [pdf, other

    astro-ph.CO gr-qc hep-ph

    Constraints on ultra-slow-roll inflation with the NANOGrav 15-Year Dataset

    Authors: Bo Mu, Jing Liu, Gong Cheng, Zong-Kuan Guo

    Abstract: Ultra-slow-roll~(USR) inflation predicts an exponential amplification of scalar perturbations at small scales, which leads to a stochastic gravitational wave background~(SGWB) through the coupling of the scalar and tensor modes at the second-order expansion of the Einstein equation. In this work, we search for such a scalar-induced SGWB from the NANOGrav 15-year (NG15) dataset, and find that the S… ▽ More

    Submitted 31 October, 2023; originally announced October 2023.

    Comments: 6 pages, 4 figures

  38. arXiv:2310.15479  [pdf, other

    stat.ML cs.AI cs.LG

    AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing

    Authors: Namjoon Suh, Xiaofeng Lin, Din-Yin Hsieh, Merhdad Honarkhah, Guang Cheng

    Abstract: Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesis. In this paper, we leverage the power of diffusion model for generating synthetic tabular data. The heterogeneous features in tabular data have been main obstacles in tabular data synthesis, and we tackle this problem… ▽ More

    Submitted 16 November, 2023; v1 submitted 23 October, 2023; originally announced October 2023.

  39. arXiv:2310.14374  [pdf, other

    cs.CV

    OV-VG: A Benchmark for Open-Vocabulary Visual Grounding

    Authors: Chunlei Wang, Wenquan Feng, Xiangtai Li, Guangliang Cheng, Shuchang Lyu, Binghao Liu, Lijiang Chen, Qi Zhao

    Abstract: Open-vocabulary learning has emerged as a cutting-edge research area, particularly in light of the widespread adoption of vision-based foundational models. Its primary objective is to comprehend novel concepts that are not encompassed within a predefined vocabulary. One key facet of this endeavor is Visual Grounding, which entails locating a specific region within an image based on a corresponding… ▽ More

    Submitted 22 October, 2023; originally announced October 2023.

  40. arXiv:2310.14214  [pdf, other

    cs.CV cs.MM

    TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images

    Authors: Tianyu Yan, Zifu Wan, Pingping Zhang, Gong Cheng, Huchuan Lu

    Abstract: In the remote sensing field, Change Detection (CD) aims to identify and localize the changed regions from dual-phase images over the same places. Recently, it has achieved great progress with the advances of deep learning. However, current methods generally deliver incomplete CD regions and irregular CD boundaries due to the limited representation ability of the extracted visual features. To relie… ▽ More

    Submitted 22 October, 2023; originally announced October 2023.

    Comments: This work is accepted by TGRS2023. It is an extension of our ACCV2022 paper and arXiv:2210.00757

  41. arXiv:2310.01393  [pdf, other

    cs.CV

    DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection

    Authors: Shilin Xu, Xiangtai Li, Size Wu, Wenwei Zhang, Yining Li, Guangliang Cheng, Yunhai Tong, Kai Chen, Chen Change Loy

    Abstract: Open-vocabulary object detection (OVOD) aims to detect the objects beyond the set of classes observed during training. This work presents a simple yet effective strategy that leverages the zero-shot classification ability of pre-trained vision-language models (VLM), such as CLIP, to directly discover proposals of possible novel classes. Unlike previous works that ignore novel classes during traini… ▽ More

    Submitted 23 December, 2023; v1 submitted 2 October, 2023; originally announced October 2023.

  42. arXiv:2309.13266  [pdf, other

    cs.RO cs.AI

    Robust Navigation with Cross-Modal Fusion and Knowledge Transfer

    Authors: Wenzhe Cai, Guangran Cheng, Lingyue Kong, Lu Dong, Changyin Sun

    Abstract: Recently, learning-based approaches show promising results in navigation tasks. However, the poor generalization capability and the simulation-reality gap prevent a wide range of applications. We consider the problem of improving the generalization of mobile robots and achieving sim-to-real transfer for navigation skills. To that end, we propose a cross-modal fusion method and a knowledge transfer… ▽ More

    Submitted 23 September, 2023; originally announced September 2023.

    Comments: Accepted by ICRA 2023

  43. arXiv:2309.12767  [pdf, other

    cs.CL

    Furthest Reasoning with Plan Assessment: Stable Reasoning Path with Retrieval-Augmented Large Language Models

    Authors: Yin Zhu, Zhiling Luo, Gong Cheng

    Abstract: Large Language Models (LLMs), acting as a powerful reasoner and generator, exhibit extraordinary performance across various natural language tasks, such as question answering (QA). Among these tasks, Multi-Hop Question Answering (MHQA) stands as a widely discussed category, necessitating seamless integration between LLMs and the retrieval of external knowledge. Existing methods employ LLM to gener… ▽ More

    Submitted 22 September, 2023; originally announced September 2023.

  44. arXiv:2309.10309  [pdf, other

    cs.RO

    Bridging Zero-shot Object Navigation and Foundation Models through Pixel-Guided Navigation Skill

    Authors: Wenzhe Cai, Siyuan Huang, Guangran Cheng, Yuxing Long, Peng Gao, Changyin Sun, Hao Dong

    Abstract: Zero-shot object navigation is a challenging task for home-assistance robots. This task emphasizes visual grounding, commonsense inference and locomotion abilities, where the first two are inherent in foundation models. But for the locomotion part, most works still depend on map-based planning approaches. The gap between RGB space and map space makes it difficult to directly transfer the knowledge… ▽ More

    Submitted 20 September, 2023; v1 submitted 19 September, 2023; originally announced September 2023.

    Comments: 8 pages, 5 figures

  45. arXiv:2309.09030  [pdf, other

    cs.LG cs.AI

    Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule

    Authors: Hongyu Zhu, Sichu Liang, Wentao Hu, Fang-Qi Li, Yali yuan, Shi-Lin Wang, Guang Cheng

    Abstract: As a modern ensemble technique, Deep Forest (DF) employs a cascading structure to construct deep models, providing stronger representational power compared to traditional decision forests. However, its greedy multi-layer learning procedure is prone to overfitting, limiting model effectiveness and generalizability. This paper presents an optimized Deep Forest, featuring learnable, layerwise data au… ▽ More

    Submitted 16 September, 2023; originally announced September 2023.

  46. arXiv:2309.06432  [pdf, other

    astro-ph.SR physics.space-ph

    Can the Parker Solar Probe Detect a CME-flare Current Sheet?

    Authors: Yuhao Chen, Zhong Liu, Pengfei Chen, David F. Webb, Qi Hao, Jialiang Hu, Guanchong Cheng, Zhixing Mei, Jing Ye, Qian Wang, Jun Lin

    Abstract: A current sheet (CS) is the central structure in the disrupting magnetic configuration during solar eruptions. More than 90\% of the free magnetic energy (the difference between the energy in the non-potential magnetic field and that in the potential one) stored in the coronal magnetic field beforehand is converted into heating and kinetic energy of the plasma, as well as accelerating charged part… ▽ More

    Submitted 12 September, 2023; originally announced September 2023.

    Comments: 28 pages, 12 figure, accepted by ApJS

  47. arXiv:2309.01384  [pdf

    q-bio.QM eess.IV eess.SY

    Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors

    Authors: Yuanyuan Wei, Sai Mu Dalike Abaxi, Nawaz Mehmood, Luoquan Li, Fuyang Qu, Guangyao Cheng, Dehua Hu, Yi-Ping Ho, Scott Wu Yuan, Ho-Pui Ho

    Abstract: Absolute quantification of biological samples entails determining expression levels in precise numerical copies, offering enhanced accuracy and superior performance for rare templates. However, existing methodologies suffer from significant limitations: flow cytometers are both costly and intricate, while fluorescence imaging relying on software tools or manual counting is time-consuming and prone… ▽ More

    Submitted 4 September, 2023; originally announced September 2023.

    Comments: 23 pages, 6 figures, 1 table

  48. arXiv:2308.16358  [pdf

    physics.plasm-ph cond-mat.mtrl-sci

    Investigation of W-SiC compositionally graded films as a divertor material

    Authors: Zihan Lin, Carlos Monton, Stefan Bringuier, Gregory Sinclair, Guangming Cheng, Eduardo Marin, Zachary Bergstrom, Dmitry Rudakov, Žana Popović, Ulises Losada, Igor Bykov, Evan T. Ostrowski, Shota Abe, Nan Yao, Bruce E. Koel, Tyler Abrams

    Abstract: W-SiC composite material is a promising plasma-facing material candidate alternative to pure W due to the low neutron activation, low impurity radiation, and low tritium diffusivity of SiC while leveraging the high erosion resistance of the W armor. Additionally, W and SiC have high thermomechanical compatibility given their similar thermal expansion rates. The present study addresses the synthesi… ▽ More

    Submitted 15 February, 2024; v1 submitted 30 August, 2023; originally announced August 2023.

    Comments: Published in Journal of Nuclear Materials

    Journal ref: J. Nucl. Mater. 592 (2024) 154942

  49. arXiv:2308.09534  [pdf, other

    cs.CV

    Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning

    Authors: Xiang Yuan, Gong Cheng, Kebing Yan, Qinghua Zeng, Junwei Han

    Abstract: The past few years have witnessed the immense success of object detection, while current excellent detectors struggle on tackling size-limited instances. Concretely, the well-known challenge of low overlaps between the priors and object regions leads to a constrained sample pool for optimization, and the paucity of discriminative information further aggravates the recognition. To alleviate the afo… ▽ More

    Submitted 18 August, 2023; originally announced August 2023.

    Comments: Camera-ready version for ICCV2023. Our code will be available at https://github.com/shaunyuan22/CFINet

    Journal ref: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023: 6317-6327

  50. arXiv:2308.06547  [pdf, other

    eess.AS cs.CL cs.SD

    Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition

    Authors: Han Zhu, Dongji Gao, Gaofeng Cheng, Daniel Povey, Pengyuan Zhang, Yonghong Yan

    Abstract: When labeled data is insufficient, semi-supervised learning with the pseudo-labeling technique can significantly improve the performance of automatic speech recognition. However, pseudo-labels are often noisy, containing numerous incorrect tokens. Taking noisy labels as ground-truth in the loss function results in suboptimal performance. Previous works attempted to mitigate this issue by either fi… ▽ More

    Submitted 12 August, 2023; originally announced August 2023.

    Comments: Accepted by IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 2023