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arXiv 2024年

作者： Alfradique, Viviane Castro, Tiago Marra, Valerio Quartin, Miguel Giocoli, Carlo Monaco, Pierluigi Instituto de Física Universidade Federal do Rio de Janeiro RJ Rio de Janeiro21941-972 Brazil Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 RJ Rio de Janeiro22290-180 Brazil INAF - Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy INFN - Sezione di Trieste TriesteI-34100 Italy IFPU - Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Departamento de Física Universidade Federal do Espírito Santo ES Vitória29075-910 Brazil PPGCosmo Universidade Federal do Espírito Santo ES Vitória29075-910 Brazil Observatório do Valongo Universidade Federal do Rio de Janeiro RJ Rio de Janeiro20080-090 Brazil INAF - Osservatorio Astronomico di Bologna via Piero Gobetti 93/3 BolognaI-40129 Italy INFN Sezione di Bologna viale Berti Pichat 6/2 BolognaI-40127 Italy Università di Trieste Dipartimento di Fisica via Tiepolo 11 Trieste34143 Italy

Gravitational lensing is a crucial tool for exploring cosmic phenomena, providing insights into galaxy clustering, dark matter, and dark energy. Given the substantial computational demands of N-body simulations, approximate methods like PINOCCHIO and turboGL have been proposed as viable alternatives for simulating lensing probability density functions (PDFs). This paper evaluates these methods and their effectiveness across both weak and strong lensing regimes, with a focus in the context where baryonic effects are negligible. Our comparative analysis reveals that these methods are effective for applications where lensing is mild, such as the majority of sources of electromagnetic and gravitational waves. However, both PINOCCHIO and turboGL break down for large values of convergence and magnification due to their loss of accuracy in capturing small-scale nonlinear matter fields, owing to oversimplified assumptions about internal halo structures and reliance on perturbation theory. PINOCCHIO yields second-to-fourth moments of the lensing PDFs, which are 6-10% smaller than those resulting from N-body simulations in regimes where baryonic effects are minimal. These findings aim to inform future studies on gravitational lensing of point sources, which are increasingly relevant with upcoming supernova and gravitational wave datasets. © 2024, CC BY.

关键词： Probability density function

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Poisoning attack against estimating from pairwise comparisons

arXiv

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arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

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Transferring knowledge distillation for multilingual social event detection

arXiv

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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

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PHD: A Deep Learning Based Human Detection Framework for Panoramic Videos

PHD: A Deep Learning Based Human Detection Framework for Pan...

引用

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Jinting Tang Zhenhui Chen Yongkai Huo Peichang Zhang National Engineering Laboratory for Big Data System Computing Technology Research Institute for Future Media Computing School of Computer Science and Software Engineering Shenzhen University Shenzhen China The College of Information Engineering Shenzhen University Shenzhen China

Panoramic video has attracted substantial research attention as the coming video format. It is capable of providing 360 degree immersive experience of omnidirectional visual information. State-of-the-art detection networks may fail to detect humans on spherical images, which are normally represented in deformed rectangular shapes. In this paper, we propose a socalled Panoramic Human Detection (PHD) scheme to address the task of human detection in panoramic videos. Moreover, the PHD method is designed to detect humans by extracting multiple overlapping sub-images from each integral spherical image, where three-dimensional rotation of spherical images is employed to ensure consistency of sub-images. Two detection box filters are designed for removing redundant boxes. Our PHD method is capable of accomplishing the task of human detection in various panoramic video types. Experiments prove that our PHD method outperforms the baseline by 35% and 48.6% in terms of precision and recall, respectively.

关键词： Videos Task analysis Feature extraction Matrices Visualization Strain Transmission line matrix methods

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A Deep Temporal Collaborative Filtering Recommendation Framework via Joint Learning from Long and Short-Term Effects

A Deep Temporal Collaborative Filtering Recommendation Frame...

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IEEE International Conference on big data and Cloud computing (BdCloud)

作者： Qianqian Ji Xiaoyu Shi Mingsheng Shang Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Recommendations based on deep learning technologies are attracting increasingly attentions from academia and industry recently, due to it has the powerful ability of data representation learning, and can capture the complex and non-linear user-item interaction patterns. Previous studies on deep learning based recommenders heavily emphasize on learning the short-term or long-term temporal effects in separate ways. Furthermore, most of the existing recommenders studied on temporal dynamics hidden in user-item interactions by using ratings or review texts solely, without utilizing these heterogeneous side information in a comprehensive manner. As a result, they own a limited ability to exploit all of available user-item interaction data. To address the above issues, we propose a temporal collaborative filtering recommendation framework with utilizing deep learning technologies, which can capture the drift of users' preferences and items' attributes over time from available heterogeneous feedback. For studying the rich sentiment information about particular item features hidden in reviews and capturing the short-term temporal dynamics in user preference, a parallel recurrent neural network (RNN) coupled in the last layer is presented. With consideration of the life cycle of each item, we further propose an item-based time evolution model as a supplement to enrich the long-term temporal effects when recommendation. In addition, a modified cosine similarity function is incorporated to produce the personalized candidate list for each user. Finally, extensive experimental are conducted on Amazon's three datasets. They indicate that the proposed method outperforms the other state-of-the-art recommenders in terms of precision and recall when doing the Top-n recommendation task.

关键词： Hidden Markov models Recommender systems Analytical models Deep learning Predictive models Collaboration Bidirectional control

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Relation-Guided Representation Learning

arXiv

引用

arXiv 2020年

作者： Kang, Zhao Lu, Xiao Liang, Jian Bai, Kun Xu, Zenglin School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Cloud and Smart Industries Group Tencent Beijing China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Center for Artificial Intelligence Peng Cheng Lab Shenzhen China

Deep auto-encoders (DAEs) have achieved great success in learning data representations via the powerful representability of neural networks. But most DAEs only focus on the most dominant structures which are able to reconstruct the data from a latent space and neglect rich latent structural information. In this work, we propose a new representation learning method that explicitly models and leverages sample relations, which in turn is used as supervision to guide the representation learning. Different from previous work, our framework well preserves the relations between samples. Since the prediction of pairwise relations themselves is a fundamental problem, our model adaptively learns them from data. This provides much flexibility to encode real data manifold. The important role of relation and representation learning is evaluated on the clustering task. Extensive experiments on benchmark data sets demonstrate the superiority of our approach. By seeking to embed samples into subspace, we further show that our method can address the large-scale and out-of-sample problem. Our source code is publicly available at: https://***/nbShawnLu/RGRL. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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SPINBIS: Spintronics based Bayesian Inference System with Stochastic computing

arXiv

引用

arXiv 2019年

作者： Jia, Xiaotao Yang, Jianlei Dai, Pengcheng Liu, Runze Chen, Yiran Zhao, Weisheng Advanced Innovation Center for Big Data and Brain Computing Fert Beijing Research Institute School of microelectronics Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Fert Beijing Research Institute School of Computer Science and Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering Duke University NC Durham27708 United States

Bayesian inference is an effective approach for solving statistical learning problems, especially with uncertainty and incompleteness. However, Bayesian inference is a computing-intensive task whose efficiency is physically limited by the bottlenecks of conventional computing platforms. In this work, a spintronics based stochastic computing approach is proposed for efficient Bayesian inference. The inherent stochastic switching behaviors of spintronic devices are exploited to build stochastic bitstream generator (SBG) for stochastic computing with hybrid CMOS/MTJ circuits design. Aiming to improve the inference efficiency, an SBG sharing strategy is leveraged to reduce the required SBG array scale by integrating a switch network between SBG array and stochastic computing logic. A device-to-architecture level framework is proposed to evaluate the performance of spintronics based Bayesian inference system (SPINBIS). Experimental results on data fusion applications have shown that SPINBIS could improve the energy efficiency about 12× than MTJ-based approach with 45% design area overhead and about 26× than FPGA-based approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Tracking the consumption junction: Temporal dependencies between articles and advertisements in dutch newspapers

arXiv

引用

arXiv 2019年

作者： Wevers, Melvin Gao, Jianbo Nielbo, Kristoffer L. Digital Humanities Lab KNAW Humanities Cluster Netherlands Institute of Complexity Science and Big Data Technology Guangxi University China Center for Humanities Computing Aarhus Aarhus University Denmark

Historians have regularly debated whether advertisements can be used as a viable source to study the past. Their main concern centered on the question of agency. Were advertisements a reflection of historical events and societal debates, or were ad makers instrumental in shaping society and the ways people interacted with consumer goods? Using techniques from econometrics (Granger causality test) and complexity science (Adaptive Fractal Analysis), this paper analyzes to what extent advertisements shaped or reflected society. We found evidence that indicate a fundamental difference between the dynamic behavior of word use in articles and advertisements published in a century of Dutch newspapers. Articles exhibit persistent trends that are likely to be reflective of communicative memory. Contrary to this, advertisements have a more irregular behavior characterized by short bursts and fast decay, which, in part, mirrors the dynamic through which advertisers introduced terms into public discourse. On the issue of whether advertisements shaped or reflected society, we found particular product types that seemed to be collectively driven by a causality going from advertisements to articles. Generally, we found support for a complex interaction pattern dubbed the consumption junction. Finally, we discovered noteworthy patterns in terms of causality and long-range dependencies for specific product groups. All in, this study shows how methods from econometrics and complexity science can be applied to humanities data to improve our understanding of complex cultural-historical phenomena such as the role of advertising in society. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistical tests

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Open domain question answering with character-level deep learning models

Open domain question answering with character-level deep lea...

引用

作者： Lei, Kai Deng, Yang Zhang, Bing Shen, Ying Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology and Applications School of Electronic and Computer Engineering Peking University Shenzhen518055 China

ISBN: (纸本)9781538636749

Single-relation factoid question answering (QA) is strongly supported by rich sources of facts from knowledge bases (KB). However, there are many irrelevant information in questions and overwhelming number of facts in knowledge bases, making it difficult to capture goal entity and relation involved in a question. In order to settle these issues, firstly, a state-of-the-art sequence tagging model (BiLSTM-CRF) is adopted to detect the entity mention in a question. Then, we propose a n-gram match (NGM) algorithm with Chinese-specific rules and an attention-based siamese bidirectional long-short term memory (ASBLSTM) model to measure the lexical and semantic similarity between questions and candidate facts. Our whole method requires no hand-crafted template or feature engineering. In addition, character-level models are proved to be effective in solving the out of vocabulary (OOV) issue and improving the accuracy in Chinese KBQA task. Experiment results show that our system outperforms the best system with deep learning models in the KBQA share task of the Conference on Natural Language Processing and Chinese computing (NLPCC) 2016 and our system achieves an AverageF1 measure of 80.97% and 37.18% on test dataset in NLPCC 2016 and 2017 respectively. © 2017 IEEE.

关键词： Entity extraction Entity linking Knowledge base question answering Relation recognition

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A Low Noise Operational Amplifier With Cascode Frequency Compensation Technique for Voice Detection and Recognition

A Low Noise Operational Amplifier With Cascode Frequency Com...

引用

International Conference on Integrated Circuits and Microsystems (ICICM)

作者： Yang Liu Zhaohao Wang Jianlei Yang Weisheng Zhao Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Beijing China Fert Beijing Research Institute Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

This paper proposes a low noise operational amplifier for voice detection and recognition. The transistor level low noise technologies and the cascode frequency compensation are presented in this design. This work has been implemented in a 180 nm CMOS process and simulated with a 2 V power supply, a 200 kΩ resistive load and a 220 pF capacitive load. The proposed amplifier consumes an area of 282 fm x 332 fm and a total quiescent current of 109.258 μA. And it achieves a closed loop gain of 2.96205 dB, a unity gain frequency of 3.18 MHz with 40.2° phase margin and an input integrated noise of -109.585 dBV within 20 Hz - 20 kHz (an input referred noise at 1 kHz of 28.61 nV/sqrt(Hz)).

关键词： Conferences Integrated circuits Micromechanical devices

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