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Rapid synthesis of Pd single‐atom/cluster as highly active catalysts for Suzuki coupling reactions

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Chinese Journal of Catalysis 2022年第4期43卷 1058-1065页

作者： Hehe Wei Xiaoyang Li Bohan Deng Jialiang Lang Ya Huang Xingyu Hua Yida Qiao Binghui Ge Jun Ge Hui Wu State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Key Lab for Industrial Biocatalysis Ministry of EducationDepartment of Chemical EngineeringTsinghua UniversityBeijing 100084China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601AnhuiChina Key Laboratory for Advanced Materials Center for Computational Chemistry and Research Institute of Industrial CatalysisEast China University of Science and TechnologyShanghai 200237China

Palladium(Pd)‐based catalysts are essential to drive high‐performance Suzuki coupling reactions,which are powerful tools for the synthesis of functional organic ***,we developed a solution‐rapid‐annealing process to stabilize nitrogen‐mesoporous carbon supported Pd single‐atom/cluster(Pd/NMC)material,which provided a catalyst with superior performance for Suzuki coupling *** comparison with commercial palladium/carbon(Pd/C)catalysts,the Pd/NMC catalyst exhibited significantly boosted activity(100%selectivity and 95%yield)and excellent stability(almost no decay in activity after 10 reuse cycles)for the Suzuki coupling reactions of chlorobenzenes,together with superior yield and excellent selectivity in the fields of the board scope of the ***,our newly developed rapid annealing process of precursor solutions is applied as a generalized method to stabilize metal clusters(***,Pt,Ru),opening new possibilities in the construction of efficient highly dispersed metal atom and sub‐nanometer cluster catalysts with high performance.

关键词： Pd single‐atom/cluster catalyst Suzuki coupling reactions Solution rapid annealing Energy barrier High yield

Quantifying robustness to adversarial word substitutions

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arXiv 2022年

作者： Yang, Yuting Huang, Pei Ma, Fei Fei Cao, Juan Zhang, Meishan Zhang, Jian Li, Jintao Key Lab Of Intelligent Information Processing Institute Of Computing Technology Chinese Academy Of Sciences Beijing100190 China University Of Chinese Academy Of Sciences Beijing100049 China Beijing100190 China Laboratory Of Parallel Software And Computational Science ISCAS Beijing100190 China China

Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations. Before they are widely adopted, the fundamental issues of robustness need to be addressed. Along this line, we propose a formal framework to evaluate word-level robustness. First, to study safe regions for a model, we introduce robustness radius which is the boundary where the model can resist any perturbation. As calculating the maximum robustness radius is computationally hard, we estimate its upper and lower bound. We repurpose attack methods as ways of seeking upper bound and design a pseudo-dynamic programming algorithm for a tighter upper bound. Then verification method is utilized for a lower bound. Further, for evaluating the robustness of regions outside a safe radius, we reexamine robustness from another view: quantification. A robustness metric with a rigorous statistical guarantee is introduced to measure the quantification of adversarial examples, which indicates the model's susceptibility to perturbations outside the safe radius. The metric helps us figure out why state-of-the-art models like BERT can be easily fooled by a few word substitutions, but generalize well in the presence of real-world noises. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

Hierarchical Object-to-Zone Graph for Object Navigation

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Hierarchical Object-to-Zone Graph for Object Navigation

International Conference on Computer Vision (ICCV)

作者： Sixian Zhang Xinhang Song Yubing Bai Weijie Li Yakui Chu Shuqiang Jiang Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing University of Chinese Academy of Sciences Beijing Huawei Application Innovate Laboratory Beijing Institute of Intelligent Computing Technology Suzhou CAS

ISBN: (纸本)9781665428132

The goal of object navigation is to reach the expected objects according to visual information in the unseen environments. Previous works usually implement deep models to train an agent to predict actions in real-time. However, in the unseen environment, when the target object is not in egocentric view, the agent may not be able to make wise decisions due to the lack of guidance. In this paper, we propose a hierarchical object-to-zone (HOZ) graph to guide the agent in a coarse-to-fine manner, and an online-learning mechanism is also proposed to update HOZ according to the real-time observation in new environments. In particular, the HOZ graph is composed of scene nodes, zone nodes and object nodes. With the pre-learned HOZ graph, the real-time observation and the target goal, the agent can constantly plan an optimal path from zone to zone. In the estimated path, the next potential zone is regarded as sub-goal, which is also fed into the deep reinforcement learning model for action prediction. Our methods are evaluated on the AI2-Thor simulator. In addition to widely used evaluation metrics SR and SPL, we also propose a new evaluation metric of SAE that focuses on the effective action rate. Experimental results demonstrate the effectiveness and efficiency of our proposed method. The code is available at https://***/sx-zhang/***.

关键词： Measurement Weight measurement Visualization Computer vision Codes Navigation Reinforcement learning

LMP-GAN: Out-Of-Distribution Detection For Non-Control Data Malware Attacks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wood, David Kapp, David Kebede, Temesgen Hirakawa, Keigo Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples - unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques. Inspired in part by the classical locally most powerful (LMP) test in statistical inferences, the proposed LMP-GAN trains the OOD detector (discriminator) by generating OOD samples that are aimed at making maximal alteration to the inlier samples while evading detection. We experimentally compare the results to the state-of-the-art anomaly detection methods to demonstrate the benefits and the appropriateness of the LMP-GAN OOD detector. © 2025 IEEE.

关键词： Malware

Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance

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arXiv 2024年

作者： Wang, Shengbin Wang, Peng Li, Guihui Zhao, Shubin Zhao, Dongyi Wang, Jing Fang, Yuan Dou, Menghan Gu, Yongjian Wu, Yu-Chun Guo, Guo-Ping Origin Quantum Computing Company Limited Anhui Hefei230026 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China Intelligent Information Sensing and Processing Lab College of Electronic Engineering Ocean University of China Shandong Qingdao266000 China College of Physics and Optoelectronic Engineering Ocean University of China Shandong Qingdao266100 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China

Great efforts have been dedicated in recent years to explore practical applications for noisy intermediate-scale quantum (NISQ) computers, which is a fundamental and challenging problem in quantum computing. As one of the most promising methods, the variational quantum eigensolver (VQE) has been extensively studied. In this paper, VQE is applied to solve portfolio optimization problems in finance by designing two hardware-efficient Dicke state ansatze that reach a maximum of 2n two-qubit 2 gate depth and n2/4 parameters, with n being the number of qubits used. Both ansatze are partitioning-friendly, allowing for the proposal of a highly scalable quantum/classical hybrid distributed computing (HDC) scheme. Combining simultaneous sampling, problem-specific measurement error mitigation, and fragment reuse techniques, we successfully implement the HDC experiments on a superconducting quantum computer with up to 55 qubits. The simulation and experimental results illustrate that the restricted expressibility of the ansatze, induced by the small number of parameters and limited entanglement, is advantageous for solving classical optimization problems with the cost function of the conditional value-at-risk (CVaR) for the NISQ era and beyond. Furthermore, the HDC scheme shows great potential for achieving quantum advantage in the NISQ era. We hope that the heuristic idea presented in this paper can motivate fruitful investigations in current and future quantum computing paradigms. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

Improving the trainability of VQE on NISQ computers for solving portfolio optimization using convex interpolation

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arXiv 2024年

作者： Wang, Shengbin Li, Guihui Chen, Zhaoyun Wang, Peng Dou, Menghan Zheng, Haiyong Wang, Zhimin Gu, Yongjian Wu, Yu-Chun Guo, Guo-Ping Origin Quantum Computing Company Limited Anhui Hefei230026 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China Intelligent Information Sensing and Processing Lab College of Electronic Engineering Ocean University of China Shandong Qingdao266000 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China College of Physics and Optoelectronic Engineering Ocean University of China Shandong Qingdao266100 China

Solving combinatorial optimization problems using variational quantum algorithms (VQAs) represents one of the most promising applications in the NISQ era. However, the limited trainability of VQAs could hinder their scalability to large problem sizes. In this paper, we improve the trainability of variational quantum eigensolver (VQE) by utilizing convex interpolation to solve portfolio optimization. The idea is inspired by the observation that the Dicke state possesses an inherent clustering property. Consequently, the energy of a state with a larger Hamming distance from the ground state intuitively results in a greater energy gap away from the ground state energy in the overall distribution trend. Based on convex interpolation, the location of the ground state can be evaluated by learning the property of a small subset of basis states in the Hilbert space. This enlightens naturally the proposals of the strategies of close-to-solution initialization, regular cost function landscape, and recursive ansatz equilibrium partition. The successfully implementation of a 40-qubit experiment using only 10 superconducting qubits demonstrates the effectiveness of our proposals. Furthermore, the quantum inspiration has also spurred the development of a prototype greedy algorithm. Extensive numerical simulations indicate that the hybridization of VQE and greedy algorithms achieves a mutual complementarity, combining the advantages of both global and local optimization methods. Our proposals can be extended to improve the trainability for solving other large-scale combinatorial optimization problems that are widely used in real applications, paving the way to unleash quantum advantages of NISQ computers in the near future. Copyright © 2024, The Authors. All rights reserved.

关键词： Ground state

ClickVOS: Click Video Object Segmentation

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arXiv 2024年

作者： Guo, Pinxue Hong, Lingyi Zhou, Xinyu Gao, Shuyong Li, Wanyun Li, Jinglun Chen, Zhaoyu Li, Xiaoqiang Zhang, Wei Zhang, Wenqiang the Shanghai Engineering Research Center of AI&Robotics Academy for Engineering&Technology Fudan University Shanghai China the Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China the School of Computer Engineering and Science Shanghai University Shanghai China Engineering Research Center of AI&Robotics Ministry of Education Academy for Engineering&Technology Fudan University Shanghai China

—Video Object Segmentation (VOS) task aims to segment objects in videos. However, previous settings either require time-consuming manual masks of target objects at the first frame during inference or lack the flexibility to specify arbitrary objects of interest. To address these limitations, we propose the setting named Click Video Object Segmentation (ClickVOS) which segments objects of interest across the whole video according to a single click per object in the first frame. And we provide the extended datasets DAVIS-P and YouTubeVOS-P that with point annotations to support this task. ClickVOS is of significant practical applications and research implications due to its only 1-2 seconds interaction time for indicating an object, comparing annotating the mask of an object needs several minutes. However, ClickVOS also presents increased challenges. To address this task, we propose an end-to-end baseline approach named called Attention Before Segmentation (ABS), motivated by the attention process of humans. ABS utilizes the given point in the first frame to perceive the target object through a concise yet effective segmentation attention. Although the initial object mask is possibly inaccurate, in our ABS, as the video goes on, the initially imprecise object mask can self-heal instead of deteriorating due to error accumulation, which is attributed to our designed improvement memory that continuously records stable global object memory and updates detailed dense memory. In addition, we conduct various baseline explorations utilizing off-the-shelf algorithms from related fields, which could provide insights for the further exploration of ClickVOS. The experimental results demonstrate the superiority of the proposed ABS approach. Extended datasets and codes will be available at https://***/PinxueGuo/ClickVOS. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion compensation

BCIM: Budget and capacity constrained influence maximization in multilayer networks

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arXiv 2024年

作者： Zhang, Su-Su Liu, Chuang Wang, Huijuan Chen, Yang Zhan, Xiu-Xiu Research Center for Complexity Sciences Hangzhou Normal University Hangzhou311121 China College of Media and International Culture Zhejiang University Hangzhou310058 China Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China

Influence maximization (IM), which aims to select a set of seed nodes that can maximize their influence within a network, can be applied to various domains, such as viral marketing, disease control, and political campaigns. One of the extended IM problems, i.e., budgeted Influence Maximization (BIM), adds a budgeted constraint, which considers that different nodes may have different costs. However, the definition of the current BIM problem has limitations, as it only imposes a budget constraint. This may lead to solutions with a large number of low-cost nodes that are not applicable to real-world scenarios. Furthermore, since users can transmit information on multiple social networking platforms, solving the BIM problem across different platforms could better optimize resource usage. To address these issues, we propose a Budget and Capacity Constrained Influence Maximization (BCIM) problem in multilayer networks and design a Multilayer Multi-Population Genetic Algorithm (MMGA) to solve it. In MMGA, a repair module addresses budget constraints, while a multi-population parallel module handles capacity (seed size) constraints. Additionally, the initialization module, which considers multilayer network properties, and the multi-population parallel module significantly enhance the algorithm's efficiency. Extensive experiments upon various synthetic and empirical multilayer networks indicate that MMGA achieves at least a 10% improvement in spreading performance under budget and capacity constraints compared to baseline methods extended from various classical IM problems. The proposed BCIM problem opens up a new direction in influence maximization, and the proposed method offers an effective and efficient framework to the problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Costs

Gaussian-Hermite Moment Invariants of General Multi-Channel Functions

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arXiv 2022年

作者： Mo, Hanlin Li, Hua Zhao, Guoying The Center for Machine Vision and Signal Analysis University of Oulu OuluFI-90014 Finland The Key lab of Intelligent Information Processing The Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China The School of Information and Technology Northwest University Xi'An710069 China

With the development of data acquisition technology, large amounts of multi-channel data are collected and widely used in many fields. Most of them, such as RGB images and vector fields, can be expressed as different types of multi-channel functions. Feature extraction of multi-channel data for identifying interest patterns is a critical but challenging task. This paper focuses on constructing moment-based features of general multi-channel functions. Specifically, we define two transform models, rotation-affine transform and total rotation transform, to describe real deformations of multi-channel data. Then, we design a structural framework to generate Gaussian-Hermite moment invariants for these two transform models systematically. It is the first time that a unified framework has been proposed in the literature to construct orthogonal moment invariants of general multi-channel functions. Given a specific type of multi-channel data, we demonstrate how to utilize the new method to derive all possible invariants and eliminate dependences among them. We obtain independent sets of invariants with low orders and low degrees for RGB images, 2D vector fields and color volume data. Based on synthetic and real multi-channel data, we conduct extensive experiments to evaluate the stability and discriminability of these invariants and their robustness to noise. The results show that new moment invariants significantly outperform previous moment invariants of multi-channel data in RGB image classification and vortex detection in 2D vector fields. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification