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Physics-informed Inverse Design of Multi-bit Programmable Metasurfaces

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arXiv 2024年

作者： Xu, Yucheng Yang, Jia-Qi Fan, Kebin Wang, Sheng Wu, Jingbo Zhang, Caihong Zhan, De-Chuan Padilla, Willie J. Jin, Biaobing Chen, Jian Wu, Peiheng Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE School of Electronic Science and Engineering Nanjing University Nanjing210023 China Purple Mountain Laboratories Nanjing211111 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Electrical and Computer Engineering Duke University Box 90291 DurhamNC27708 United States

Emerging reconfigurable metasurfaces offer various possibilities in programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270o, due to the design constraints with time-intensive forward design methodologies. Here, we demonstrate a multi-bit programmable metasurface capable of terahertz beam steering, facilitated by a developed physics-informed inverse design (PIID) approach. Through integrating a modified coupled mode theory (MCMT) into residual neural networks, our PIID algorithm not only significantly increases the design accuracy compared to conventional neural networks but also elucidates the intricate physical relations between the geometry and the modes. Without decreasing the reflection intensity, our method achieves the enhanced phase tuning as large as 300o. Additionally, we experimentally validate the inverse designed programmable beam steering metasurface, which is adaptable across 1-bit, 2-bit, and tri-state coding schemes, yielding a deflection angle up to 68o and broadened steering coverage. Our demonstration provides a promising pathway for rapidly exploring advanced metasurface devices, with potentially great impact on communication and imaging technologies. © 2024, CC0.

关键词： Tuning

Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond

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arXiv 2023年

作者： Zhang, Lijun Bai, Haomin Zhao, Peng Yang, Tianbao Zhou, Zhi-Hua National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Department of Computer Science and Engineering Texas A&M University College Station United States

This paper investigates group distributionally robust optimization (GDRO) with the goal of learning a model that performs well over m different distributions. First, we formulate GDRO as a stochastic convex-concave saddle-point problem, which is then solved by stochastic mirror descent (SMD) with m samples in each iteration, and attain a nearly optimal sample complexity. To reduce the number of samples required in each round from m to 1, we cast GDRO as a two-player game, where one player conducts SMD and the other executes an online algorithm for non-oblivious multi-armed bandits, maintaining the same sample complexity. Next, we extend GDRO to address scenarios involving imbalanced data and heterogeneous distributions. In the first scenario, we introduce a weighted variant of GDRO, enabling distribution-dependent convergence rates that rely on the number of samples from each distribution. We design two strategies to meet the sample budget: one integrates non-uniform sampling into SMD, and the other employs the stochastic mirror-prox algorithm with mini-batches, both of which deliver faster rates for distributions with more samples. In the second scenario, we propose to optimize the average top-k risk instead of the maximum risk, thereby mitigating the impact of outlier distributions. Similar to the case of vanilla GDRO, we develop two stochastic approaches: one uses m samples per iteration via SMD, and the other consumes k samples per iteration through an online algorithm for non-oblivious combinatorial semi-bandits. Copyright © 2023, The Authors. All rights reserved.

关键词： Budget control

Parameterized Inapproximability Hypothesis under ETH

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arXiv 2023年

作者： Guruswami, Venkatesan Lin, Bingkai Ren, Xuandi Sun, Yican Wu, Kewen Simons Institute for the Theory of Computing Departments of EECS and Mathematics UC Berkeley United States State Key Laboratory for Novel Software Technology Nanjing University China Department of EECS UC Berkeley United States School of Computer Science Peking University China

The Parameterized Inapproximability Hypothesis (PIH) asserts that no fixed parameter tractable (FPT) algorithm can distinguish a satisfiable CSP instance, parameterized by the number of variables, from one where every assignment fails to satisfy an ϵ fraction of constraints for some absolute constant ϵ > 0. PIH plays the role of the PCP theorem in parameterized complexity. However, PIH has only been established under Gap-ETH, a very strong assumption with an inherent gap. In this work, we prove PIH under the Exponential Time Hypothesis (ETH). This is the first proof of PIH from a gap-free assumption. Our proof is self-contained and elementary. We identify an ETH-hard CSP whose variables take vector values, and constraints are either linear or of a special parallel structure. Both kinds of constraints can be checked with constant soundness via a "parallel PCP of proximity" based on the Walsh-Hadamard code. © 2023, CC BY.

关键词： Parameterization

Policy learning from tutorial books via understanding, rehearsing and introspecting 24

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Policy learning from tutorial books via understanding, rehea...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xiong-Hui Chen Ziyan Wang Yali Du Shengyi Jiang Meng Fang Yang Yu Jun Wang National Key Laboratory for Novel Software Technology Nanjing University China & School of Artificial Intelligence Nanjing University China Cooperative AI Lab Department of Informatics King's College London The University of Hong Kong University of Liverpool AI Centre Department of Computer Science University College London

ISBN: (纸本)9798331314385

When humans need to learn a new skill, we can acquire knowledge through written books, including textbooks, tutorials, etc. However, current research for decisionmaking, like reinforcement learning (RL), has primarily required numerous real interactions with the target environment to learn a skill, while failing to utilize the existing knowledge already summarized in the text. The success of Large Language Models (LLMs) sheds light on utilizing such knowledge behind the books. In this paper, we discuss a new policy learning problem called Policy Learning from tutorial Books (PLfB) upon the shoulders of LLMs' systems, which aims to leverage rich resources such as tutorial books to derive a policy network. Inspired by how humans learn from books, we solve the problem via a three-stage framework: Understanding, Rehearsing, and Introspecting (URI). In particular, it first rehearses decision-making trajectories based on the derived knowledge after understanding the books, then introspects about the imaginary dataset to distill a policy network. We build two benchmarks for PLfB based on Tic-Tac-Toe and Football games. In the experiment, URI's policy achieves a minimum of 44% net winning rate against GPT-based agents without any real data. In the much more complex football game, URI's policy beat the built-in AIs with a 37% winning rate while GPT-based agents can only achieve a 6% winning rate. The project page: https://***/.

关键词：

Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization

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arXiv 2023年

作者： Qiu, Zi-Hao Hu, Quanqi Yuan, Zhuoning Zhou, Denny Zhang, Lijun Yang, Tianbao National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Computer Science and Engineering Texas A&M University College Station United States Department of Computer Science The University of Iowa Iowa City United States Google Research United States

In this paper, we aim to optimize a contrastive loss with individualized temperatures in a principled and systematic manner for self-supervised learning. The common practice of using a global temperature parameter τ ignores the fact that "not all semantics are created equal", meaning that different anchor data may have different numbers of samples with similar semantics, especially when data exhibits long-tails. First, we propose a new robust contrastive loss inspired by distributionally robust optimization (DRO), providing us an intuition about the effect of τ and a mechanism for automatic temperature individualization. Then, we propose an efficient stochastic algorithm for optimizing the robust contrastive loss with a provable convergence guarantee without using large mini-batch sizes. Theoretical and experimental results show that our algorithm automatically learns a suitable τ for each sample. Specifically, samples with frequent semantics use large temperatures to keep local semantic structures, while samples with rare semantics use small temperatures to induce more separable features. Our method not only outperforms prior strong baselines (e.g., SimCLR, CLIP) on unimodal and bimodal datasets with larger improvements on imbalanced data but also is less sensitive to hyper-parameters. To our best knowledge, this is the first methodical approach to optimizing a contrastive loss with individualized temperatures. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

Dynamic network embedding via incremental skip-gram with negative sampling

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science China(Information sciences) 2020年第10期63卷 89-107页

作者： Hao PENG Jianxin LI Hao YAN Qiran GONG Senzhang WANG Lin LIU Lihong WANG Xiang REN Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Computer Science University of Southern California

Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. The fundamental problem of continuously capturing the dynamic properties in an efficient way for a dynamic network remains unsolved. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. The first order moment of the objective difference converges in order of O（1/n;）, and the second order moment of the objective difference can be stabilized in order of O（1）. Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up to

关键词： dynamic network embedding bound and convergence analysis multi-label classification link prediction

A Comprehensive Study of OOP-Related Bugs in C++ Compilers

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IEEE Transactions on software Engineering 2025年 1-21页

作者： Wang, Bo Chen, Chong Chen, Junjie Xu, Bowen Ye, Chen Lin, Youfang Dong, Guoliang Sun, Jun Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence China Tianjin University College of Intelligence and Computing Tianjin300350 China North Carolina State University Department of Computer Science NC27695 United States Singapore Management University School of Computing and Information Systems Singapore178902 Singapore

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from GCC and LLVM. Second, derived from the core concepts of OOP and C++, we manually identified a two-level taxonomy of the OOP-related features leading to compiler bugs, which consists of 6 primary categories (e.g., Abstraction & Encapsulation, Inheritance, and Runtime Polymorphism), along with 17 secondary categories (e.g., Constructors & Destructors and Multiple Inheritance). Third, we systematically analyze the root causes, symptoms, fixes, options, and C++ standard versions of these bugs. Our analysis yields 13 key findings, highlighting that features related to the construction and destruction of objects lead to the highest number of bugs, crashes are the most frequent symptom, and while the average time from bug introduction to discovery is 1856 days, fixing the bug once discovered takes only 174 days on average. Additionally, more than half of the bugs can be triggered without any compiler options. These findings offer valuable insights not only for developing new compiler testing approaches but also for improving language design and compiler engineering. Inspired by these findings, we developed a proof-of-concept compiler fuzzer OOPFuzz, specifically targeting OOP-related bugs in C++ compilers. We applied it against the newest release versions

关键词： C++ (programming language)

Verifying Fairness in Quantum Machine Learning

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arXiv 2022年

作者： Guan, Ji Fang, Wang Ying, Mingsheng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Due to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition - any two similar individuals must be treated similarly and are thus unbiased. We show that quantum noise can improve fairness and develop an algorithm to check whether a (noisy) quantum machine learning model is fair. In particular, this algorithm can find bias kernels of quantum data (encoding individuals) during checking. These bias kernels generate infinitely many bias pairs for investigating the unfairness of the model. Our algorithm is designed based on a highly efficient data structure - Tensor Networks - and implemented on Google's TensorFlow Quantum. The utility and effectiveness of our algorithm are confirmed by the experimental results, including income prediction and credit scoring on real-world data, for a class of random (noisy) quantum decision models with 27 qubits (227-dimensional state space) tripling (218 times more than) that of the state-of-the-art algorithms for verifying quantum machine learning models. © 2022, CC BY.

关键词： Quantum noise

Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

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Multi-MedVit: a deep learning approach for the diagnosis of ...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yunjie Cai Zeqi Zheng Shanling Nie Yue Guo Ruijie Zhang Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai PR China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China School of Electronics and Computer Science University of Southampton UK

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study’s 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly.

关键词： COVID-19 Deep learning Training Computed tomography Pulmonary diseases Lung Transformers