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Strong quantum nonlocality without entanglement in multipartite quantum systems

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arXiv 2020年

作者： Yuan, Pei Tian, Guojing Sun, Xiaoming CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

In this paper, we generalize the concept of strong quantum nonlocality from two aspects. Firstly in Cd ⊗ Cd ⊗ Cd quantum system, we present a construction of strongly nonlocal quantum states containing 6(d−1)2 orthogonal product states, which is one order of magnitude less than the number of basis states d3. Secondly, we give the explicit form of strongly nonlocal orthogonal product basis in C3 ⊗ C3 ⊗ C3 ⊗ C3 quantum system, where four is the largest known number of subsystems in which there exists strong quantum nonlocality up to now. Both the two results positively answer the open problems in [Halder, et al., PRL, 122, 040403 (2019)], that is, there do exist and even smaller number of quantum states can demonstrate strong quantum nonlocality without entanglement. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum communication

Unveiling latent behaviors of video viewers with cross-platform information 27

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Unveiling latent behaviors of video viewers with cross-platf...

27th ACM Workshop on network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2017

作者： Gu, Xuhong Zhou, Yipeng Wu, Di Chan, Terence H. Chen, Min School of Data and Computer Science Sun Yat-sen University China Institute for Telecommunications Research University of South Australia Australia Guangdong Province Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781450350037

The online video streaming service is of huge market values with billions of worldwide users. For online video providers, e.g., Net-flix, Youku, the crucial question is how to understand users' view behaviors and preferences because this knowledge is important for their business operation. Existing solutions mainly rely on analyzing users' historical view records, which however are not always available, especially for new videos and unprovided videos. Different from existing solutions, we propose to infer user behaviors and preferences by jointly analyzing data collected from multiple platforms (e.g., video streaming systems, video databases, etc.). In particular, we use the movie data crawled from a leading video streaming system (i.e., Youku), and a well-known video database in China (i.e, Douban) for this study. Our investigation points out that movie quality (evaluated in terms of Douban scores) and release date jointly influence viewers' preferences. In addition, we reveal a series of user behaviors, e.g., users are reluctant to post comments or ratings for movies they do not like, and user eyeballs are heavily captured by new movies. Understanding of these user behaviors covered by this study is essential for video recommendation and video popularity prediction which can benefit video procurement and advertisement campaign. © 2017 ACM.

关键词： Video streaming

Link Prediction on n-ary relational data based on relatedness evaluation

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arXiv 2021年

作者： Guan, Saiping Jin, Xiaolong Guo, Jiafeng Wang, Yuanzhuo Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China

With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to fill in missing facts for a long time. However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity). In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction on n-ary relational data. It has even proven that they may cause loss of structure information. To overcome these problems, in this paper, we represent each n-ary relational fact as a set of its role and role-value pairs. We then propose a method called NaLP to conduct link prediction on n-ary relational data, which explicitly models the relatedness of all the role and role-value pairs in an n-ary relational fact. We further extend NaLP by introducing type constraints of roles and role-values without any external type-specific supervision, and proposing a more reasonable negative sampling mechanism. Experimental results validate the effectiveness and merits of the proposed methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

Few-Shot Stance Detection via Target-Aware Prompt Distillation

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arXiv 2022年

作者： Jiang, Yan Gao, Jinhua Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Stance detection aims to identify whether the author of a text is in favor of, against, or neutral to a given target. The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets. Existing works mainly focus on solving the second issue by designing attention-based models or introducing noisy external knowledge, while the first issue remains under-explored. In this paper, inspired by the potential capability of pre-trained language models (PLMs) serving as knowledge bases and few-shot learners, we propose to introduce prompt-based fine-tuning for stance detection. PLMs can provide essential contextual information for the targets and enable few-shot learning via prompts. Considering the crucial role of the target in stance detection task, we design target-aware prompts and propose a novel verbalizer. Instead of mapping each label to a concrete word, our verbalizer maps each label to a vector and picks the label that best captures the correlation between the stance and the target. Moreover, to alleviate the possible defect of dealing with varying targets with a single hand-crafted prompt, we propose to distill the information learned from multiple prompts. Experimental results show the superior performance of our proposed model in both full-data and few-shot scenarios. © 2022, CC BY.

关键词： Distillation

Uncertainty calibration for ensemble-based debiasing methods

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arXiv 2021年

作者： Xiong, Ruibin Chen, Yimeng Pang, Liang Cheng, Xueqi Ma, Zhiming Lan, Yanyan CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Baidu Inc Academy of Mathematics and Systems Science Chinese Academy of Sciences Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Institute for AI Industry Research Tsinghua University

Ensemble-based debiasing methods have been shown effective in mitigating the reliance of classifiers on specific dataset bias, by exploiting the output of a bias-only model to adjust the learning target. In this paper, we focus on the bias-only model in these ensemble-based methods, which plays an important role but has not gained much attention in the existing literature. Theoretically, we prove that the debiasing performance can be damaged by inaccurate uncertainty estimations of the bias-only model. Empirically, we show that existing bias-only models fall short in producing accurate uncertainty estimations. Motivated by these findings, we propose to conduct calibration on the bias-only model, thus achieving a three-stage ensemble-based debiasing framework, including bias modeling, model calibrating, and debiasing. Experimental results on NLI and fact verification tasks show that our proposed three-stage debiasing framework consistently outperforms the traditional two-stage one in out-of-distribution accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Calibration

Information and Sensing Beamforming Optimization for Multi-User Multi-Target MIMO ISAC Systems 48

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Information and Sensing Beamforming Optimization for Multi-U...

48th IEEE International Conference on Acoustics, Speech and Signal Processing, IcasSP 2023

作者： Zhu, Minghe Li, Lei Xia, Shuqiang Chang, Tsung-Hui The Chinese University of Hong Kong School of Science & Engineering Shenzhen China Shenzhen Research Institute of Big Data China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China

ISBN: (纸本)9781728163277

In this paper, we consider the joint beamforming design for simultaneous sensing and communication in a wireless multi-user system. Different from the existing works that mostly are for single target, we consider sensing the channel parameters of multiple targets while communicating with multiple users. The design goal is to minimize a weighted sum of the Cramer-Rao bounds (CRB) of target parameters subject to the communication sum rate and transmission power constraints. While the classical weighted minimum mean square error (WMMSE) and semidefinite relaxation (SDR) can be used to handle the problem, we propose to reformulate the problem into a max-min form, by leveraging the tightness of SDR, and solve it by a low-complex first-order method. Numerical results not only demonstrate the computation efficiency of the proposed algorithm but also its effectiveness in enhancing the sensing performance in practice. © 2023 IEEE.

关键词： Mean square error

Adaptive information seeking for open-domain question answering

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arXiv 2021年

作者： Zhu, Yunchang Pang, Liang Lan, Yanyan Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Institute for AI Industry Research Tsinghua University

Information seeking is an essential step for open-domain question answering to efficiently gather evidence from a large corpus. Recently, iterative approaches have been proven to be effective for complex questions, by recursively retrieving new evidence at each step. However, almost all existing iterative approaches use predefined strategies, either applying the same retrieval function multiple times or fixing the order of different retrieval functions, which cannot fulfill the diverse requirements of various questions. In this paper, we propose a novel adaptive information-seeking strategy for open-domain question answering, namely AISO. Specifically, the whole retrieval and answer process is modeled as a partially observed Markov decision process, where three types of retrieval operations (e.g., BM25, DPR, and hyperlink) and one answer operation are defined as actions. According to the learned policy, AISO could adaptively select a proper retrieval action to seek the missing evidence at each step, based on the collected evidence and the reformulated query, or directly output the answer when the evidence set is sufficient for the question. Experiments on SQuAD Open and HotpotQA fullwiki, which serve as single-hop and multi-hop open-domain QA benchmarks, show that AISO outperforms all baseline methods with predefined strategies in terms of both retrieval and answer evaluations. © 2021, CC BY.

关键词： Hypertext systems

MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching

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arXiv 2019年

作者： Guo, Jiafeng Fan, Yixing Ji, Xiang Cheng, Xueqi University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Beijing Institute of Technology University Beijing China

Text matching is the core problem in many natural language processing (NLP) tasks, such as information retrieval, question answering, and conversation. Recently, deep leaning technology has been widely adopted for text matching, making neural text matching a new and active research domain. With a large number of neural matching models emerging rapidly, it becomes more and more difficult for researchers, especially those newcomers, to learn and understand these new models. Moreover, it is usually difficult to try these models due to the tedious data pre-processing, complicated parameter configuration, and massive optimization tricks, not to mention the unavailability of public codes sometimes. Finally, for researchers who want to develop new models, it is also not an easy task to implement a neural text matching model from scratch, and to compare with a bunch of existing models. In this paper, therefore, we present a novel system, namely MatchZoo, to facilitate the learning, practicing and designing of neural text matching models. The system consists of a powerful matching library and a user-friendly and interactive studio, which can help researchers: 1) to learn state-of-the-art neural text matching models systematically, 2) to train, test and apply these models with simple configurable steps;and 3) to develop their own models with rich APIs and assistance. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

Interactive key-Value Memory-augmented Attention for Image Paragraph Captioning 28

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Interactive Key-Value Memory-augmented Attention for Image P...

28th International Conference on Computational Linguistics, COLING 2020

作者： Xu, Chunpu Li, Yu Li, Chengming Ao, Xiang Yang, Min Tian, Jinwen Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Huazhong University of Science and Technology China Shanghai Jiao Tong University China Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781952148279

Image paragraph captioning (IPC) aims to generate a fine-grained paragraph to describe the visual content of an image. Significant progress has been made by deep neural networks, in which the attention mechanism plays an essential role. However, conventional attention mechanisms tend to ignore the past alignment information, which often results in problems of repetitive captioning and incomplete captioning. In this paper, we propose an Interactive key-value Memory-augmented Attention model for image Paragraph captioning (IMAP) to keep track of the attention history (salient objects coverage information) along with the update-chain of the decoder state and therefore avoid generating repetitive or incomplete image descriptions. In addition, we employ an adaptive attention mechanism to realize adaptive alignment from image regions to caption words, where an image region can be mapped to an arbitrary number of caption words while a caption word can also attend to an arbitrary number of image regions. Extensive experiments on a benchmark dataset (i.e., Stanford) demonstrate the effectiveness of our IMAP model. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Deep neural networks