1,803 results on '"Sun, Ke"'
Search Results
2. While GitHub Copilot Excels at Coding, Does It Ensure Responsible Output?
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Cheng, Wen, Sun, Ke, Zhang, Xinyu, and Wang, Wei
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Computer Science - Computation and Language ,Computer Science - Cryptography and Security - Abstract
The rapid development of large language models (LLMs) has significantly advanced code completion capabilities, giving rise to a new generation of LLM-based Code Completion Tools (LCCTs). Unlike general-purpose LLMs, these tools possess unique workflows, integrating multiple information sources as input and prioritizing code suggestions over natural language interaction, which introduces distinct security challenges. Additionally, LCCTs often rely on proprietary code datasets for training, raising concerns about the potential exposure of sensitive data. This paper exploits these distinct characteristics of LCCTs to develop targeted attack methodologies on two critical security risks: jailbreaking and training data extraction attacks. Our experimental results expose significant vulnerabilities within LCCTs, including a 99.4% success rate in jailbreaking attacks on GitHub Copilot and a 46.3% success rate on Amazon Q. Furthermore, We successfully extracted sensitive user data from GitHub Copilot, including 54 real email addresses and 314 physical addresses associated with GitHub usernames. Our study also demonstrates that these code-based attack methods are effective against general-purpose LLMs, such as the GPT series, highlighting a broader security misalignment in the handling of code by modern LLMs. These findings underscore critical security challenges associated with LCCTs and suggest essential directions for strengthening their security frameworks. The example code and attack samples from our research are provided at https://github.com/Sensente/Security-Attacks-on-LCCTs.
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- 2024
3. StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model
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Zhou, Ziyin, Sun, Ke, Chen, Zhongxi, Kuang, Huafeng, Sun, Xiaoshuai, and Ji, Rongrong
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
The rapid progress in generative models has given rise to the critical task of AI-Generated Content Stealth (AIGC-S), which aims to create AI-generated images that can evade both forensic detectors and human inspection. This task is crucial for understanding the vulnerabilities of existing detection methods and developing more robust techniques. However, current adversarial attacks often introduce visible noise, have poor transferability, and fail to address spectral differences between AI-generated and genuine images. To address this, we propose StealthDiffusion, a framework based on stable diffusion that modifies AI-generated images into high-quality, imperceptible adversarial examples capable of evading state-of-the-art forensic detectors. StealthDiffusion comprises two main components: Latent Adversarial Optimization, which generates adversarial perturbations in the latent space of stable diffusion, and Control-VAE, a module that reduces spectral differences between the generated adversarial images and genuine images without affecting the original diffusion model's generation process. Extensive experiments show that StealthDiffusion is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries with frequency spectra similar to genuine images. These forgeries are classified as genuine by advanced forensic classifiers and are difficult for humans to distinguish.
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- 2024
4. Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments
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Sun, Ke, Kong, Linglong, Zhu, Hongtu, and Shi, Chengchun
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Economics - Econometrics - Abstract
Time series experiments, in which experimental units receive a sequence of treatments over time, are frequently employed in many technological companies to evaluate the performance of a newly developed policy, product, or treatment relative to a baseline control. Many existing A/B testing solutions assume a fully observable experimental environment that satisfies the Markov condition, which often does not hold in practice. This paper studies the optimal design for A/B testing in partially observable environments. We introduce a controlled (vector) autoregressive moving average model to capture partial observability. We introduce a small signal asymptotic framework to simplify the analysis of asymptotic mean squared errors of average treatment effect estimators under various designs. We develop two algorithms to estimate the optimal design: one utilizing constrained optimization and the other employing reinforcement learning. We demonstrate the superior performance of our designs using a dispatch simulator and two real datasets from a ride-sharing company.
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- 2024
5. Few-Shot, Robust Calibration of Single Qubit Gates Using Bayesian Robust Phase Estimation
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Hurant, Travis, Sun, Ke, Jia, Zhubing, Kim, Jungsang, and Brown, Kenneth R.
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Quantum Physics - Abstract
Accurate calibration of control parameters in quantum gates is crucial for high-fidelity operations, yet it represents a significant time and resource challenge, necessitating periods of downtime for quantum computers. Robust Phase Estimation (RPE) has emerged as a practical and effective calibration technique aimed at tackling this challenge. It combines a provably efficient number of control pulses with a classical post-processing algorithm to estimate the phase accumulated by a quantum gate. We introduce Bayesian Robust Phase Estimation (BRPE), an innovative approach that integrates Bayesian parameter estimation into the classical post-processing phase to reduce the sampling overhead. Our numerical analysis shows that BRPE markedly reduces phase estimation errors, requiring approximately $50\%$ fewer samples than standard RPE. Specifically, in an ideal, noise-free setting, it achieves up to a $96\%$ reduction in average absolute estimation error for a fixed sample cost of $88$ shots when compared to RPE. Under a depolarizing noise model, it attains up to a $47\%$ reduction for a fixed cost of $176$ shots. Additionally, we adapt BRPE for Ramsey spectroscopy applications and successfully implement it experimentally in a trapped ion system., Comment: 10 pages, 5 figures
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- 2024
6. OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person
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Sun, Ke, Cao, Jian, Wang, Qi, Tian, Linrui, Zhang, Xindi, Zhuo, Lian, Zhang, Bang, Bo, Liefeng, Zhou, Wenbo, Zhang, Weiming, and Gao, Daiheng
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Virtual Try-On (VTON) has become a transformative technology, empowering users to experiment with fashion without ever having to physically try on clothing. However, existing methods often struggle with generating high-fidelity and detail-consistent results. While diffusion models, such as Stable Diffusion series, have shown their capability in creating high-quality and photorealistic images, they encounter formidable challenges in conditional generation scenarios like VTON. Specifically, these models struggle to maintain a balance between control and consistency when generating images for virtual clothing trials. OutfitAnyone addresses these limitations by leveraging a two-stream conditional diffusion model, enabling it to adeptly handle garment deformation for more lifelike results. It distinguishes itself with scalability-modulating factors such as pose, body shape and broad applicability, extending from anime to in-the-wild images. OutfitAnyone's performance in diverse scenarios underscores its utility and readiness for real-world deployment. For more details and animated results, please see \url{https://humanaigc.github.io/outfit-anyone/}., Comment: 10 pages, 13 figures
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- 2024
7. DVPE: Divided View Position Embedding for Multi-View 3D Object Detection
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Wang, Jiasen, Li, Zhenglin, Sun, Ke, Liu, Xianyuan, and Zhou, Yang
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics - Abstract
Sparse query-based paradigms have achieved significant success in multi-view 3D detection for autonomous vehicles. Current research faces challenges in balancing between enlarging receptive fields and reducing interference when aggregating multi-view features. Moreover, different poses of cameras present challenges in training global attention models. To address these problems, this paper proposes a divided view method, in which features are modeled globally via the visibility crossattention mechanism, but interact only with partial features in a divided local virtual space. This effectively reduces interference from other irrelevant features and alleviates the training difficulties of the transformer by decoupling the position embedding from camera poses. Additionally, 2D historical RoI features are incorporated into the object-centric temporal modeling to utilize highlevel visual semantic information. The model is trained using a one-to-many assignment strategy to facilitate stability. Our framework, named DVPE, achieves state-of-the-art performance (57.2% mAP and 64.5% NDS) on the nuScenes test set. Codes will be available at https://github.com/dop0/DVPE.
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- 2024
8. Mistral-C2F: Coarse to Fine Actor for Analytical and Reasoning Enhancement in RLHF and Effective-Merged LLMs
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Zheng, Chen, Sun, Ke, and Zhou, Xun
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Computer Science - Computation and Language - Abstract
Despite the advances in Large Language Models (LLMs), exemplified by models like GPT-4 and Claude, smaller-scale LLMs such as Llama and Mistral often struggle with generating in-depth and coherent dialogues. This paper presents a novel two-step Coarse-to-Fine Actor model to address the inherent limitations in conversational and analytical capabilities of small-sized LLMs. Our approach begins with the Policy-based Coarse Actor, employing a technique we term "Continuous Maximization". The Coarse Actor establishes an enhanced, knowledge-rich pool adept at aligning with human preference styles in analysis and reasoning. Through the RLHF process, it employs Continuous Maximization, a strategy that dynamically and adaptively extends the output length limit, enabling the generation of more detailed and analytical content. Subsequently, the Fine Actor refines this analytical content, addressing the generation of excessively redundant information from the Coarse Actor. We introduce a "Knowledge Residue Merger" approach, refining the content from the Coarse Actor and merging it with an existing Instruction model to improve quality, correctness, and reduce redundancies. We applied our methodology to the popular Mistral model, creating Mistral-C2F, which has demonstrated exceptional performance across 11 general language tasks and the MT-Bench Dialogue task, outperforming similar-scale models and even larger models with 13B and 30B parameters. Our model has significantly improved conversational and analytical reasoning abilities.
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- 2024
9. Quantum Simulation of Spin-Boson Models with Structured Bath
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Sun, Ke, Kang, Mingyu, Nuomin, Hanggai, Schwartz, George, Beratan, David N., Brown, Kenneth R., and Kim, Jungsang
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Quantum Physics ,Physics - Atomic Physics - Abstract
The spin-boson model, involving spins interacting with a bath of quantum harmonic oscillators, is a widely used representation of open quantum systems. Trapped ions present a natural platform for simulating the quantum dynamics of such models, thanks to the presence of both high quality internal qubit states and the motional modes of the ions that can simulate the relevant quantum degrees of freedom. In our work, we extend the previous body of work that focused on coherent coupling of the spins and bosons to perform quantum simulations with structured dissipative baths using the motional states of trapped ions. We demonstrate the capability for adjusting the bath's temperature and continuous spectral density by adding randomness to fully programmable control parameters. Subsequently, we simulate the dynamics of various spin-boson models with noise spectral densities constructed from coupling to several dissipative harmonic oscillator modes. The experimental outcomes closely align with theoretical predictions, indicating successful simulation of open quantum systems using a trapped-ion system., Comment: 11 pages, 7 figures
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- 2024
10. DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis
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Chen, Zhongxi, Sun, Ke, Zhou, Ziyin, Lin, Xianming, Sun, Xiaoshuai, Cao, Liujuan, and Ji, Rongrong
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Computer Science - Computer Vision and Pattern Recognition - Abstract
The rapid progress in deep learning has given rise to hyper-realistic facial forgery methods, leading to concerns related to misinformation and security risks. Existing face forgery datasets have limitations in generating high-quality facial images and addressing the challenges posed by evolving generative techniques. To combat this, we present DiffusionFace, the first diffusion-based face forgery dataset, covering various forgery categories, including unconditional and Text Guide facial image generation, Img2Img, Inpaint, and Diffusion-based facial exchange algorithms. Our DiffusionFace dataset stands out with its extensive collection of 11 diffusion models and the high-quality of the generated images, providing essential metadata and a real-world internet-sourced forgery facial image dataset for evaluation. Additionally, we provide an in-depth analysis of the data and introduce practical evaluation protocols to rigorously assess discriminative models' effectiveness in detecting counterfeit facial images, aiming to enhance security in facial image authentication processes. The dataset is available for download at \url{https://github.com/Rapisurazurite/DiffFace}.
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- 2024
11. Balancing Enhancement, Harmlessness, and General Capabilities: Enhancing Conversational LLMs with Direct RLHF
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Zheng, Chen, Sun, Ke, Wu, Hang, Xi, Chenguang, and Zhou, Xun
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Computer Science - Computation and Language - Abstract
In recent advancements in Conversational Large Language Models (LLMs), a concerning trend has emerged, showing that many new base LLMs experience a knowledge reduction in their foundational capabilities following Supervised Fine-Tuning (SFT). This process often leads to issues such as forgetting or a decrease in the base model's abilities. Moreover, fine-tuned models struggle to align with user preferences, inadvertently increasing the generation of toxic outputs when specifically prompted. To overcome these challenges, we adopted an innovative approach by completely bypassing SFT and directly implementing Harmless Reinforcement Learning from Human Feedback (RLHF). Our method not only preserves the base model's general capabilities but also significantly enhances its conversational abilities, while notably reducing the generation of toxic outputs. Our approach holds significant implications for fields that demand a nuanced understanding and generation of responses, such as customer service. We applied this methodology to Mistral, the most popular base model, thereby creating Mistral-Plus. Our validation across 11 general tasks demonstrates that Mistral-Plus outperforms similarly sized open-source base models and their corresponding instruct versions. Importantly, the conversational abilities of Mistral-Plus were significantly improved, indicating a substantial advancement over traditional SFT models in both safety and user preference alignment.
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- 2024
12. SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic
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Liu, Qi, Li, Xingyu, Sun, Ke, Li, Yufeng, and Liu, Yanchen
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Computer Science - Cryptography and Security ,Computer Science - Machine Learning ,Computer Science - Networking and Internet Architecture - Abstract
Scalable service-Oriented Middleware over IP (SOME/IP) is an Ethernet communication standard protocol in the Automotive Open System Architecture (AUTOSAR), promoting ECU-to-ECU communication over the IP stack. However, SOME/IP lacks a robust security architecture, making it susceptible to potential attacks. Besides, random hardware failure of ECU will disrupt SOME/IP communication. In this paper, we propose SISSA, a SOME/IP communication traffic-based approach for modeling and analyzing in-vehicle functional safety and cyber security. Specifically, SISSA models hardware failures with the Weibull distribution and addresses five potential attacks on SOME/IP communication, including Distributed Denial-of-Services, Man-in-the-Middle, and abnormal communication processes, assuming a malicious user accesses the in-vehicle network. Subsequently, SISSA designs a series of deep learning models with various backbones to extract features from SOME/IP sessions among ECUs. We adopt residual self-attention to accelerate the model's convergence and enhance detection accuracy, determining whether an ECU is under attack, facing functional failure, or operating normally. Additionally, we have created and annotated a dataset encompassing various classes, including indicators of attack, functionality, and normalcy. This contribution is noteworthy due to the scarcity of publicly accessible datasets with such characteristics.Extensive experimental results show the effectiveness and efficiency of SISSA.
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- 2024
13. Tradeoffs of Diagonal Fisher Information Matrix Estimators
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Soen, Alexander and Sun, Ke
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Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
The Fisher information matrix characterizes the local geometry in the parameter space of neural networks. It elucidates insightful theories and useful tools to understand and optimize neural networks. Given its high computational cost, practitioners often use random estimators and evaluate only the diagonal entries. We examine two such estimators, whose accuracy and sample complexity depend on their associated variances. We derive bounds of the variances and instantiate them in regression and classification networks. We navigate trade-offs of both estimators based on analytical and numerical studies. We find that the variance quantities depend on the non-linearity with respect to different parameter groups and should not be neglected when estimating the Fisher information.
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- 2024
14. ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers
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Zheng, Chen, Sun, Ke, Tang, Da, Ma, Yukun, Zhang, Yuyu, Xi, Chenguang, and Zhou, Xun
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Computer Science - Computation and Language - Abstract
The emergence of Large Language Models (LLMs) such as ChatGPT and LLaMA encounter limitations in domain-specific tasks, with these models often lacking depth and accuracy in specialized areas, and exhibiting a decrease in general capabilities when fine-tuned, particularly analysis ability in small sized models. To address these gaps, we introduce ICE-GRT, utilizing Reinforcement Learning from Human Feedback (RLHF) grounded in Proximal Policy Optimization (PPO), demonstrating remarkable ability in in-domain scenarios without compromising general task performance. Our exploration of ICE-GRT highlights its understanding and reasoning ability to not only generate robust answers but also to provide detailed analyses of the reasons behind the answer. This capability marks a significant progression beyond the scope of Supervised Fine-Tuning models. The success of ICE-GRT is dependent on several crucial factors, including Appropriate Data, Reward Size Scaling, KL-Control, Advantage Normalization, etc. The ICE-GRT model exhibits state-of-the-art performance in domain-specific tasks and across 12 general Language tasks against equivalent size and even larger size LLMs, highlighting the effectiveness of our approach. We provide a comprehensive analysis of the ICE-GRT, underscoring the significant advancements it brings to the field of LLM.
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- 2024
15. A General Framework for Nonconvex Sparse Mean-CVaR Portfolio Optimization Via ADMM
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Sun, Ke-Xin, Wu, Zhong-Ming, and Wan, Neng
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- 2024
- Full Text
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16. PI3K/AKT/mTOR signaling pathway: an important driver and therapeutic target in triple-negative breast cancer
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Zhang, Huan-ping, Jiang, Rui-yuan, Zhu, Jia-yu, Sun, Ke-na, Huang, Yuan, Zhou, Huan-huan, Zheng, Ya-bing, and Wang, Xiao-jia
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- 2024
- Full Text
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17. Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation
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Hu, Li, Gao, Xin, Zhang, Peng, Sun, Ke, Zhang, Bang, and Bo, Liefeng
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Character Animation aims to generating character videos from still images through driving signals. Currently, diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. However, challenges persist in the realm of image-to-video, especially in character animation, where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper, we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image, we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity, we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data, our approach can animate arbitrary characters, yielding superior results in character animation compared to other image-to-video methods. Furthermore, we evaluate our method on benchmarks for fashion video and human dance synthesis, achieving state-of-the-art results., Comment: Page: https://humanaigc.github.io/animate-anyone/
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- 2023
18. Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
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Chang, Jung-Woo, Sun, Ke, Heydaribeni, Nasimeh, Hidano, Seira, Zhang, Xinyu, and Koushanfar, Farinaz
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Computer Science - Cryptography and Security ,Computer Science - Artificial Intelligence - Abstract
Machine Learning (ML) has been instrumental in enabling joint transceiver optimization by merging all physical layer blocks of the end-to-end wireless communication systems. Although there have been a number of adversarial attacks on ML-based wireless systems, the existing methods do not provide a comprehensive view including multi-modality of the source data, common physical layer components, and wireless domain constraints. This paper proposes Magmaw, the first black-box attack methodology capable of generating universal adversarial perturbations for any multimodal signal transmitted over a wireless channel. We further introduce new objectives for adversarial attacks on ML-based downstream applications. The resilience of the attack to the existing widely used defense methods of adversarial training and perturbation signal subtraction is experimentally verified. For proof-of-concept evaluation, we build a real-time wireless attack platform using a software-defined radio system. Experimental results demonstrate that Magmaw causes significant performance degradation even in the presence of the defense mechanisms. Surprisingly, Magmaw is also effective against encrypted communication channels and conventional communications.
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- 2023
19. Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy
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Zhang, Zheng, Zheng, Chen, Tang, Da, Sun, Ke, Ma, Yukun, Bu, Yingtong, Zhou, Xun, and Zhao, Liang
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
This paper introduces a multifaceted methodology for fine-tuning and evaluating large language models (LLMs) for specialized monetization tasks. The goal is to balance general language proficiency with domain-specific skills. The methodology has three main components: 1) Carefully blending in-domain and general-purpose data during fine-tuning to achieve an optimal balance between general and specialized capabilities; 2) Designing a comprehensive evaluation framework with 45 questions tailored to assess performance on functionally relevant dimensions like reliability, consistency, and business impact; 3) Analyzing how model size and continual training influence metrics to guide efficient resource allocation during fine-tuning. The paper details the design, data collection, analytical techniques, and results validating the proposed frameworks. It aims to provide businesses and researchers with actionable insights on effectively adapting LLMs for specialized contexts. We also intend to make public the comprehensive evaluation framework, which includes the 45 tailored questions and their respective scoring guidelines, to foster transparency and collaboration in adapting LLMs for specialized tasks.
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- 2023
20. Data-driven model identification and control of the quasi-zero-stiffness system
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Jiang, Jiyuan, Tang, Jie, Sun, Ke, Chen, Huatao, Li, Yinghui, and Cao, Dengqing
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- 2024
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21. Effects of Zr and Sr on microstructure and mechanical properties of cast Al–Si–Cu–Ni–Mg alloy
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Zhang, Ning, Feng, Yicheng, Sun, Ke, Zhao, Sicong, and Fu, Yuanke
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- 2024
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22. Seeking a quantum advantage with trapped-ion quantum simulations of condensed-phase chemical dynamics
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Kang, Mingyu, Nuomin, Hanggai, Chowdhury, Sutirtha N., Yuly, Jonathon L., Sun, Ke, Whitlow, Jacob, Valdiviezo, Jesús, Zhang, Zhendian, Zhang, Peng, Beratan, David N., and Brown, Kenneth R.
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- 2024
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23. Metabolic Heterogeneity and Potential Immunotherapeutic Responses Revealed by Single-Cell Transcriptomics of Breast Cancer
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Tang, Shicong, Wang, Qing, Sun, Ke, Song, Ying, Liu, Rui, Tan, Xin, Li, Huimeng, Lv, Yafeng, Yang, Fuying, Zhao, Jiawen, Li, Sijia, Bi, Pingping, Yang, Jiali, Zhu, Zhengna, Chen, Dong, Chuan, Zhirui, Luo, Xiaomao, Hu, Zaoxiu, Liu, Ying, Li, Zhenhui, Ke, Tengfei, Jiang, Dewei, Zheng, Kai, Yang, Rirong, Chen, Kai, and Guo, Rong
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- 2024
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24. Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore
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Zhang, Ming, Tang, Chao, Wang, Zichun, Chen, Shanchuan, Zhang, Dan, Li, Kaiju, Sun, Ke, Zhao, Changjian, Wang, Yu, Xu, Mengying, Dai, Lunzhi, Lu, Guangwen, Shi, Hubing, Ren, Haiyan, Chen, Lu, and Geng, Jia
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- 2024
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25. Illuminating spin-crossover octanuclear metal-organic cages
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Liu, Zhi-Kun, Starikova, Alyona A., Li, Yu-Xia, Sun, Ke, Yu, Meng, Yao, Zi-Shuo, and Tao, Jun
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- 2024
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26. A New Perspective in the Treatment of Ischemic Stroke: Ferroptosis
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Zhang, Lei, Bai, Xin Yue, Sun, Ke Yao, Li, Xuan, Zhang, Zhao Qi, Liu, Yi Ding, Xiang, Yang, and Liu, Xiao Long
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- 2024
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27. Continual Face Forgery Detection via Historical Distribution Preserving
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Sun, Ke, Chen, Shen, Yao, Taiping, Sun, Xiaoshuai, Ding, Shouhong, and Ji, Rongrong
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Face forgery techniques have advanced rapidly and pose serious security threats. Existing face forgery detection methods try to learn generalizable features, but they still fall short of practical application. Additionally, finetuning these methods on historical training data is resource-intensive in terms of time and storage. In this paper, we focus on a novel and challenging problem: Continual Face Forgery Detection (CFFD), which aims to efficiently learn from new forgery attacks without forgetting previous ones. Specifically, we propose a Historical Distribution Preserving (HDP) framework that reserves and preserves the distributions of historical faces. To achieve this, we use universal adversarial perturbation (UAP) to simulate historical forgery distribution, and knowledge distillation to maintain the distribution variation of real faces across different models. We also construct a new benchmark for CFFD with three evaluation protocols. Our extensive experiments on the benchmarks show that our method outperforms the state-of-the-art competitors.
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- 2023
28. Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On
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Gao, Daiheng, Chen, Xu, Zhang, Xindi, Wang, Qi, Sun, Ke, Zhang, Bang, Bo, Liefeng, and Huang, Qixing
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virtual try-on, digitalization of 2D clothes into 3D apparel, and cloth animation. It thus necessitates a simple and straightforward pipeline to obtain high-quality texture from simple input, such as 2D reference images. Since traditional warping-based texture generation methods require a significant number of control points to be manually selected for each type of garment, which can be a time-consuming and tedious process. We propose a novel method, called Cloth2Tex, which eliminates the human burden in this process. Cloth2Tex is a self-supervised method that generates texture maps with reasonable layout and structural consistency. Another key feature of Cloth2Tex is that it can be used to support high-fidelity texture inpainting. This is done by combining Cloth2Tex with a prevailing latent diffusion model. We evaluate our approach both qualitatively and quantitatively and demonstrate that Cloth2Tex can generate high-quality texture maps and achieve the best visual effects in comparison to other methods. Project page: tomguluson92.github.io/projects/cloth2tex/, Comment: 15 pages, 15 figures
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- 2023
29. Towards General Visual-Linguistic Face Forgery Detection
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Sun, Ke, Chen, Shen, Yao, Taiping, Yang, Haozhe, Sun, Xiaoshuai, Ding, Shouhong, and Ji, Rongrong
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We argue that such supervisions lack semantic information and interpretability. To address this issues, in this paper, we propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation. Since text annotations are not available in current deepfakes datasets, VLFFD first generates the mixed forgery image with corresponding fine-grained prompts via Prompt Forgery Image Generator (PFIG). Then, the fine-grained mixed data and coarse-grained original data and is jointly trained with the Coarse-and-Fine Co-training framework (C2F), enabling the model to gain more generalization and interpretability. The experiments show the proposed method improves the existing detection models on several challenging benchmarks. Furthermore, we have integrated our method with multimodal large models, achieving noteworthy results that demonstrate the potential of our approach. This integration not only enhances the performance of our VLFFD paradigm but also underscores the versatility and adaptability of our method when combined with advanced multimodal technologies, highlighting its potential in tackling the evolving challenges of deepfake detection.
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- 2023
30. The Power of Telemetry: Uncovering Software-Based Side-Channel Attacks on Apple M1/M2 Systems
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Chawla, Nikhil, Liu, Chen, Chakraborty, Abhishek, Chervatyuk, Igor, Sun, Ke, Hamasaki, Thais Moreira, and Kawakami, Henrique
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Computer Science - Cryptography and Security - Abstract
Power analysis is a class of side-channel attacks, where power consumption data is used to infer sensitive information and extract secrets from a system. Traditionally, such attacks required physical access to the target, as well as specialized devices to measure the power consumption with enough precision. The PLATYPUS attack has shown that on-chip power meter capabilities exposed to a software interface might form a new class of power side-channel attacks. This paper presents a software-based power side-channel attack on Apple Silicon M1/M2 platforms, exploiting the System Management Controller (SMC) and its power-related keys, which provides access to the on-chip power meters through a software interface to user space software. We observed data-dependent power consumption reporting from such keys and analyzed the correlations between the power consumption and the processed data. Our work also demonstrated how an unprivileged user mode application successfully recovers bytes from an AES encryption key from a cryptographic service supported by a kernel mode driver in macOS. Furthermore, we discuss the impact of software-based power side-channels in the industry, possible countermeasures, and the overall implications of software interfaces for modern on-chip power management systems., Comment: 6 pages, 4 figures, 5 tables
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- 2023
31. Hydrophobically gated memristive nanopores for neuromorphic applications
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Paulo, Gonçalo, Sun, Ke, di Muccio, Giovanni, Gubbiotti, Alberto, della Rocca, Blasco Morozzo, Geng, Jia, Maglia, Giovanni, Chinappi, Mauro, and Giacomello, Alberto
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Condensed Matter - Mesoscale and Nanoscale Physics ,Condensed Matter - Soft Condensed Matter - Abstract
Brain-inspired computing has the potential to revolutionise the current von Neumann architecture, advancing machine learning applications. Signal transmission in the brain relies on voltage-gated ion channels, which exhibit the electrical behaviour of memristors, resistors with memory. State-of-the-art technologies currently employ semiconductor-based neuromorphic approaches, which have already demonstrated their efficacy in machine learning systems. However, these approaches still cannot match performance achieved by biological neurons in terms of energy efficiency and size. In this study, we utilise molecular dynamics simulations, continuum models, and electrophysiological experiments to propose and realise a bioinspired hydrophobically gated memristive nanopore. Our findings indicate that hydrophobic gating enables memory through an electrowetting mechanism, and we establish simple design rules accordingly. Through the engineering of a biological nanopore, we successfully replicate the characteristic hysteresis cycles of a memristor \tr{and construct a synaptic device capable of learning and forgetting}. This advancement offers a promising pathway for the realization of nanoscale, cost- and energy-effective, and adaptable bioinspired memristors.
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- 2023
- Full Text
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32. CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models
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Chen, Zhongxi, Sun, Ke, Lin, Xianming, and Ji, Rongrong
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. Existing COD methods primarily employ semantic segmentation, which suffers from overconfident incorrect predictions. In this paper, we propose a new paradigm that treats COD as a conditional mask-generation task leveraging diffusion models. Our method, dubbed CamoDiffusion, employs the denoising process of diffusion models to iteratively reduce the noise of the mask. Due to the stochastic sampling process of diffusion, our model is capable of sampling multiple possible predictions from the mask distribution, avoiding the problem of overconfident point estimation. Moreover, we develop specialized learning strategies that include an innovative ensemble approach for generating robust predictions and tailored forward diffusion methods for efficient training, specifically for the COD task. Extensive experiments on three COD datasets attest the superior performance of our model compared to existing state-of-the-art methods, particularly on the most challenging COD10K dataset, where our approach achieves 0.019 in terms of MAE.
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- 2023
33. Gloss-Free End-to-End Sign Language Translation
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Lin, Kezhou, Wang, Xiaohan, Zhu, Linchao, Sun, Ke, Zhang, Bang, and Yang, Yi
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Computation and Language - Abstract
In this paper, we tackle the problem of sign language translation (SLT) without gloss annotations. Although intermediate representation like gloss has been proven effective, gloss annotations are hard to acquire, especially in large quantities. This limits the domain coverage of translation datasets, thus handicapping real-world applications. To mitigate this problem, we design the Gloss-Free End-to-end sign language translation framework (GloFE). Our method improves the performance of SLT in the gloss-free setting by exploiting the shared underlying semantics of signs and the corresponding spoken translation. Common concepts are extracted from the text and used as a weak form of intermediate representation. The global embedding of these concepts is used as a query for cross-attention to find the corresponding information within the learned visual features. In a contrastive manner, we encourage the similarity of query results between samples containing such concepts and decrease those that do not. We obtained state-of-the-art results on large-scale datasets, including OpenASL and How2Sign. The code and model will be available at https://github.com/HenryLittle/GloFE., Comment: ACL 2023 Main Conference (Oral)
- Published
- 2023
34. Seeking a quantum advantage with trapped-ion quantum simulations of condensed-phase chemical dynamics
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Kang, Mingyu, Nuomin, Hanggai, Chowdhury, Sutirtha N., Yuly, Jonathon L., Sun, Ke, Whitlow, Jacob, Valdiviezo, Jesús, Zhang, Zhendian, Zhang, Peng, Beratan, David N., and Brown, Kenneth R.
- Subjects
Quantum Physics - Abstract
Simulating the quantum dynamics of molecules in the condensed phase represents a longstanding challenge in chemistry. Trapped-ion quantum systems may serve as a platform for the analog-quantum simulation of chemical dynamics that is beyond the reach of current classical-digital simulation. To identify a 'quantum advantage' for these simulations, performance analysis of both analog-quantum simulation on noisy hardware and classical-digital algorithms is needed. In this Review, we make a comparison between a noisy analog trapped-ion simulator and a few choice classical-digital methods on simulating the dynamics of a model molecular Hamiltonian with linear vibronic coupling. We describe several simple Hamiltonians that are commonly used to model molecular systems, which can be simulated with existing or emerging trapped-ion hardware. These Hamiltonians may serve as stepping stones toward the use of trapped-ion simulators for systems beyond the reach of classical-digital methods. Finally, we identify dynamical regimes where classical-digital simulations seem to have the weakest performance compared to analog-quantum simulations. These regimes may provide the lowest hanging fruit to exploit potential quantum advantages., Comment: 27 pages, 6 figures. v2) Box 1 and Subsection "LVCM beyond the simple model: seeking a quantum advantage" added. v3) Fig. 1 revised
- Published
- 2023
- Full Text
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35. Quantum Simulation of Polarized Light-induced Electron Transfer with A Trapped-ion Qutrit System
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Sun, Ke, Fang, Chao, Kang, Mingyu, Zhang, Zhendian, Zhang, Peng, Beratan, David N., Brown, Kenneth R., and Kim, Jungsang
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Quantum Physics ,Physics - Atomic Physics - Abstract
Electron transfer within and between molecules is crucial in chemistry, biochemistry, and energy science. This study describes a quantum simulation method that explores the influence of light polarization on the electron transfer between two molecules. By implementing precise and coherent control among the quantum states of trapped atomic ions, we can induce quantum dynamics that mimic the electron transfer dynamics in molecules. We use $3$-level systems (qutrits), rather than traditional two-level systems (qubits) to enhance the simulation efficiency and realize high-fidelity simulations of electron transfer dynamics. We treat the quantum interference between the electron coupling pathways from a donor with two degenerate excited states to an acceptor and analyze the transfer efficiency. We also examine the potential error sources that enter the quantum simulations. The trapped ion systems have favorable scalings with system size compared to those of classical computers, promising access to electron-transfer simulations of increasing richness., Comment: 9 pages, 6 figures
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- 2023
- Full Text
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36. InterFormer: Real-time Interactive Image Segmentation
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Huang, You, Yang, Hao, Sun, Ke, Zhang, Shengchuan, Cao, Liujuan, Jiang, Guannan, and Ji, Rongrong
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Human-Computer Interaction - Abstract
Interactive image segmentation enables annotators to efficiently perform pixel-level annotation for segmentation tasks. However, the existing interactive segmentation pipeline suffers from inefficient computations of interactive models because of the following two issues. First, annotators' later click is based on models' feedback of annotators' former click. This serial interaction is unable to utilize model's parallelism capabilities. Second, in each interaction step, the model handles the invariant image along with the sparse variable clicks, resulting in a process that's highly repetitive and redundant. For efficient computations, we propose a method named InterFormer that follows a new pipeline to address these issues. InterFormer extracts and preprocesses the computationally time-consuming part i.e. image processing from the existing process. Specifically, InterFormer employs a large vision transformer (ViT) on high-performance devices to preprocess images in parallel, and then uses a lightweight module called interactive multi-head self attention (I-MSA) for interactive segmentation. Furthermore, the I-MSA module's deployment on low-power devices extends the practical application of interactive segmentation. The I-MSA module utilizes the preprocessed features to efficiently response to the annotator inputs in real-time. The experiments on several datasets demonstrate the effectiveness of InterFormer, which outperforms previous interactive segmentation models in terms of computational efficiency and segmentation quality, achieve real-time high-quality interactive segmentation on CPU-only devices. The code is available at https://github.com/YouHuang67/InterFormer., Comment: ICCV2023
- Published
- 2023
37. Seabed Liquefaction Around a Composite Breakwater with Dynamic Soil Resistance Under the Wave and Current Actions
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Sun, Ke, Zhang, Bing, Gao, Yuan, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Tajima, Yoshimitsu, editor, Aoki, Shin-ichi, editor, and Sato, Shinji, editor
- Published
- 2024
- Full Text
- View/download PDF
38. Research on High Frequency Current Measurement Method for the SiC MOSFET
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Liang, Shuai, Liu, Yuming, Miao, Chunhui, Sun, Ke, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Yang, Jianwei, editor, Liu, Zhigang, editor, Diao, Lijun, editor, and An, Min, editor
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- 2024
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39. Research on the Structure Design of Body Clothing for Elderly Women
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Xu, Tian-tian, Sun, Ke-ke, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Lin, Jerry Chun-Wei, editor, Shieh, Chin-Shiuh, editor, Horng, Mong-Fong, editor, and Chu, Shu-Chuan, editor
- Published
- 2024
- Full Text
- View/download PDF
40. Late holocene soil evolution and environment change in the southeast suburbs of Beijing, China
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Liang, Jia, Zhao, Ye, Song, Mengjie, Li, Fangfang, Liu, Xitao, Sun, Ke, Chen, Lei, and Li, Gary
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- 2024
- Full Text
- View/download PDF
41. Improve meat production and virus resistance by simultaneously editing multiple genes in livestock using Cas12iMax
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Ren, Jilong, Hai, Tang, Chen, Yangcan, Sun, Ke, Han, Zhiqiang, Wang, Jing, Li, Chongyang, Wang, Qingwei, Wang, Leyun, Zhu, Huabing, Yu, Dawei, Li, Wei, and Zhao, Shanjiang
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- 2024
- Full Text
- View/download PDF
42. Renal outcomes in IgA nephropathy following inactivated SARS-CoV-2 vaccination
- Author
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Sun, Ke, Shang, Da, Hao, Chuanming, and Lai, LingYun
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- 2024
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- View/download PDF
43. Clinical phenotypes and prognosis of IgG4-related diseases accompanied by deteriorated kidney function: a retrospective study
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Guo, Luying, Yang, Zhenzhen, Cheng, Yamei, Wang, Xingxia, Ren, Xue, Wang, Meifang, Yan, Pengpeng, Shen, Jia, Sun, Ke, Wang, Huiping, Wu, Jianyong, Chen, Jianghua, and Wang, Rending
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- 2024
- Full Text
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44. FineIBT: Fine-grain Control-flow Enforcement with Indirect Branch Tracking
- Author
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Gaidis, Alexander J., Moreira, Joao, Sun, Ke, Milburn, Alyssa, Atlidakis, Vaggelis, and Kemerlis, Vasileios P.
- Subjects
Computer Science - Cryptography and Security - Abstract
We present the design, implementation, and evaluation of FineIBT: a CFI enforcement mechanism that improves the precision of hardware-assisted CFI solutions, like Intel IBT, by instrumenting program code to reduce the valid/allowed targets of indirect forward-edge transfers. We study the design of FineIBT on the x86-64 architecture, and implement and evaluate it on Linux and the LLVM toolchain. We designed FineIBT's instrumentation to be compact, incurring low runtime and memory overheads, and generic, so as to support different CFI policies. Our prototype implementation incurs negligible runtime slowdowns ($\approx$0%-1.94% in SPEC CPU2017 and $\approx$0%-1.92% in real-world applications) outperforming Clang-CFI. Lastly, we investigate the effectiveness/security and compatibility of FineIBT using the ConFIRM CFI benchmarking suite, demonstrating that our instrumentation provides complete coverage in the presence of modern software features, while supporting a wide range of CFI policies with the same, predictable performance., Comment: Accepted at RAID 2023. Errata (reported by Lucas Becker): Section 2.4.1: "in which every bit represents 8 bytes of (virtual) memory" -> "in which two bits represent 16 bytes of (virtual) memory"
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- 2023
45. Mathematical Challenges in Deep Learning
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Nia, Vahid Partovi, Zhang, Guojun, Kobyzev, Ivan, Metel, Michael R., Li, Xinlin, Sun, Ke, Hemati, Sobhan, Asgharian, Masoud, Kong, Linglong, Liu, Wulong, and Chen, Boxing
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Mathematics - Statistics Theory ,Statistics - Machine Learning - Abstract
Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challenges to this field with applications in cell phones, personal computers, autonomous cars, and wireless base stations. Here we list a set of problems, ranging from training, inference, generalization bound, and optimization with some formalism to communicate these challenges with mathematicians, statisticians, and theoretical computer scientists. This is a subjective view of the research questions in deep learning that benefits the tech industry in long run.
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- 2023
46. Transformed Distribution Matching for Missing Value Imputation
- Author
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Zhao, He, Sun, Ke, Dezfouli, Amir, and Bonilla, Edwin
- Subjects
Computer Science - Machine Learning - Abstract
We study the problem of imputing missing values in a dataset, which has important applications in many domains. The key to missing value imputation is to capture the data distribution with incomplete samples and impute the missing values accordingly. In this paper, by leveraging the fact that any two batches of data with missing values come from the same data distribution, we propose to impute the missing values of two batches of samples by transforming them into a latent space through deep invertible functions and matching them distributionally. To learn the transformations and impute the missing values simultaneously, a simple and well-motivated algorithm is proposed. Our algorithm has fewer hyperparameters to fine-tune and generates high-quality imputations regardless of how missing values are generated. Extensive experiments over a large number of datasets and competing benchmark algorithms show that our method achieves state-of-the-art performance., Comment: ICML 2023 camera-ready version, https://openreview.net/forum?id=WBWb1FU8iz
- Published
- 2023
47. Realization of Scalable Cirac-Zoller Multi-Qubit Gates
- Author
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Fang, Chao, Wang, Ye, Sun, Ke, and Kim, Jungsang
- Subjects
Quantum Physics - Abstract
The universality theorem in quantum computing states that any quantum computational task can be decomposed into a finite set of logic gates operating on one and two qubits. However, the process of such decomposition is generally inefficient, often leading to exponentially many gates to realize an arbitrary computational task. Practical processor designs benefit greatly from availability of multi-qubit gates that operate on more than two qubits to implement the desired circuit. In 1995, Cirac and Zoller proposed a method to realize native multi-qubit controlled-$Z$ gates in trapped ion systems, which has a stringent requirement on ground-state cooling of the motional modes utilized by the gate. An alternative approach, the M\o lmer-S\o rensen gate, is robust against residual motional excitation and has been a foundation for many high-fidelity gate demonstrations. This gate does not scale well beyond two qubits, incurring additional overhead when used to construct many target algorithms. Here, we take advantage of novel performance benefits of long ion chains to realize fully programmable and scalable high-fidelity Cirac-Zoller gates., Comment: 11 pages, 6 figures
- Published
- 2023
48. Rare top quark decays in the minimal R-symmetric supersymmetric standard model
- Author
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Sun, Ke-Sheng, Wang, Zhi-Chuan, Yang, Xiu-Yi, Gao, Tie-Jun, and Zhang, Hai-Bin
- Subjects
High Energy Physics - Phenomenology - Abstract
The one-loop contributions to the flavor-changing neutral current decays of the top quark into a light quark and a gauge boson or Higgs boson, namely $t\rightarrow qV,qh$, with $q$ being $u$ or $c$, and $V$ being $\gamma$, $g$, or $Z$, are analyzed in this study within the framework of the minimal R-symmetric supersymmetric standard model. The charginos in this model have been separated into two sets, i.e., $\chi$-chargino and $\rho$-chargino. The numerical results reveal that gluino or $\rho$-chargino predominantly dictate the predictions for BR($t\rightarrow qV,qh$), while the impact of neutralino or $\chi$-chargino contributions is insignificant. By incorporating the constraints imposed by the squark mixing parameters from $\bar{B}\rightarrow X_s\gamma$ and $B^0_{d,s}\rightarrow \mu^+\mu^-$, the theoretical predictions for BR($t\rightarrow qg$) can be significantly augmented to approximately $\mathcal O(10^{-5}-10^{-6})$. On the other hand, the values of BR($t\rightarrow q\gamma,qZ,qh$) are predicted to be at least four orders of magnitude below the current experimental limits., Comment: 26 pages,8 figures
- Published
- 2023
49. Research on High Frequency Current Measurement Method for the SiC MOSFET
- Author
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Liang, Shuai, primary, Liu, Yuming, additional, Miao, Chunhui, additional, and Sun, Ke, additional
- Published
- 2024
- Full Text
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50. Some Properties of the Nash Equilibrium in $2 \times 2$ Zero-Sum Games
- Author
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Sun, Ke
- Subjects
Computer Science - Computer Science and Game Theory - Abstract
In this report, some properties of the set of Nash equilibria (NEs) of $2 \times 2$ zero-sum games are reviewed. In particular, the cardinality of the set of NEs is given in terms of the entries of the payoff matrix. Moreover, closed-form expressions for the NE strategies and the payoff at the NE (the value of the game) are provided in terms of the entries of the payoff matrix. The results presented in this report are not necessarily new knowledge, as they follow from the definition of the NE after some tedious calculations. Nevertheless this synthetic presentation is original in the literature.
- Published
- 2022
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