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arXiv 2024年

作者： Miao, Juzheng Chen, Cheng Zhang, Keli Chuai, Jie Li, Quanzheng Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Center for Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School BostonMA United States Huawei Noah’s Ark Lab Shenzhen China Data Science Office Massachusetts General Brigham BostonMA United States Institute of Medical Intelligence and XR The Chinese University of Hong Kong Hong Kong Hong Kong

One-shot detection of anatomical landmarks is gaining significant attention for its efficiency in using minimal labeled data to produce promising results. However, the success of current methods heavily relies on the employment of extensive unlabeled data to pre-train an effective feature extractor, which limits their applicability in scenarios where a substantial amount of unlabeled data is unavailable. In this paper, we propose the first foundation model-enabled one-shot landmark detection (FM-OSD) framework for accurate landmark detection in medical images by utilizing solely a single template image without any additional unlabeled data. Specifically, we use the frozen image encoder of visual foundation models as the feature extractor, and introduce dual-branch global and local feature decoders to increase the resolution of extracted features in a coarse-to-fine manner. The introduced feature decoders are efficiently trained with a distance-aware similarity learning loss to incorporate domain knowledge from the single template image. Moreover, a novel bidirectional matching strategy is developed to improve both robustness and accuracy of landmark detection in the case of scattered similarity map obtained by foundation models. We validate our method on two public anatomical landmark detection datasets. By using solely a single template image, our method demonstrates significant superiority over strong state-of-the-art one-shot landmark detection methods. Code is available at: https://***/JuzhengMiao/FM-OSD. © 2024, CC BY.

关键词： Foundations

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Exponential Lower Bounds for Locally Decodable and Correctable Codes for Insertions and Deletions

arXiv

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arXiv 2021年

作者： Blocki, Jeremiah Cheng, Kuan Grigorescu, Elena Li, Xin Zheng, Yu Zhu, Minshen Department of Computer Science Purdue University United States Center on Frontiers of Computing Studies Peking University China Advanced Institute of Information Technology Peking University China Department of Computer Science Johns Hopkins University United States

Locally Decodable Codes (LDCs) are error-correcting codes for which individual message symbols can be quickly recovered despite errors in the codeword. LDCs for Hamming errors have been studied extensively in the past few decades, where a major goal is to understand the amount of redundancy that is necessary and sufficient to decode from large amounts of error, with small query complexity. Motivated by new progress in DNA-storage technologies (Banal et al., Nature Materials, 2021), in this work we study LDCs for insertion and deletion errors, called Insdel LDCs. Their study was initiated by Ostrovsky and Paskin-Cherniavsky (Information Theoretic Security, 2015), who gave a reduction from Hamming LDCs to Insdel LDCs with a small blowup in the code parameters. On the other hand, the only known lower bounds for Insdel LDCs come from those for Hamming LDCs, thus there is no separation between them. Here we prove new, strong lower bounds for the existence of Insdel LDCs. In particular, we show that 2-query linear Insdel LDCs do not exist, and give an exponential lower bound for the length of all q-query Insdel LDCs with constant q. For q ≥ 3 our bounds are exponential in the existing lower bounds for Hamming LDCs. Furthermore, our exponential lower bounds continue to hold for adaptive decoders, and even in private-key settings where the encoder and decoder share secret randomness. This exhibits a strict separation between Hamming LDCs and Insdel LDCs. Our strong lower bounds also hold for the related notion of Insdel LCCs (except in the private-key setting), due to an analogue to the Insdel notions of a reduction from Hamming LCCs to LDCs. Our techniques are based on a delicate design and analysis of hard distributions of insertion and deletion errors, which depart significantly from typical techniques used in analyzing Hamming LDCs. © 2021, CC BY.

关键词： Decoding

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Prediction of the final rank of Players in PUBG with the optimal number of features

arXiv

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arXiv 2021年

作者： Sen, Diptakshi Roy, Rupam Kumar Majumdar, Ritajit Chatterjee, Kingshuk Ganguly, Debayan Department of Computer Science and Engineering University of Calcutta India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India Department of Computer Science and Engineering Government College of Engineering and Ceramic Technology India Department of Computer Science and Engineering Government College of Engineering and Leather Technology India

PUBG is an online video game that has become very popular among the youths in recent years. Final rank, which indicates the performance of a player, is one of the most important feature for this game. This paper focuses on predicting the final rank of the players based on their skills and abilities. In this paper we have used different machine learning algorithms to predict the final rank of the players on a dataset obtained from kaggle which has 29 features. Using the correlation heatmap,we have varied the number of features used for the model. Out of these models GBR and LGBM have given the best result with the accuracy of 91.63% and 91.26% respectively for 14 features and the accuracy of 90.54% and 90.01% for 8 features. Although the accuracy of the models with 14 features is slightly better than 8 features, the empirical time taken by 8 features is 1.4x lesser than 14 features for LGBM and 1.5x lesser for GBR. Furthermore, reducing the number of features any more significantly hampers the performance of all the ML models. Therefore, we conclude that 8 is the optimal number of features that can be used to predict the final rank of a player in PUBG with high accuracy and low run-time. © 2021, CC0.

关键词： Forecasting

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L-SPEX: LOCALIZED TARGET SPEAKER EXTRACTION

arXiv

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arXiv 2022年

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Kuaishou Technology Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan The Chinese University of Hong Kong Shenzhen China

Speaker extraction aims to extract the target speaker’s voice from a multi-talker speech mixture given an auxiliary reference utterance. Recent studies show that speaker extraction benefits from the location or direction of the target speaker. However, these studies assume that the target speaker’s location is known in advance or detected by an extra visual cue, e.g., face image or video. In this paper, we propose an end-to-end localized target speaker extraction on pure speech cues, that is called L-SpEx. Specifically, we design a speaker localizer driven by the target speaker’s embedding to extract the spatial features, including direction-of-arrival (DOA) of the target speaker and beamforming output. Then, the spatial cues and target speaker’s embedding are both used to form a top-down auditory attention to the target speaker. Experiments on the multi-channel reverberant dataset called MCLibri2Mix show that our L-SpEx approach significantly outperforms the baseline system. Copyright © 2022, The Authors. All rights reserved.

关键词： Beamforming

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Automating gamification personalization: To the user and beyond

arXiv

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arXiv 2021年

作者： Rodrigues, Luiz Toda, Armando M. Oliveira, Wilk Palomino, Paula T. Vassileva, Julita Isotani, Seiji Laboratory of Applied Computing to Education and Advanced Social Technology Institute of Mathematics and Computer Science University of São Paulo São Carlos Brazil Lab Department of Computer Science University of Saskatchewan Saskatoon Canada

Personalized gamification explores knowledge about the users to tailor gamification designs to improve one-size-fits-all gamification. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity to be done and geographic location), which leads to several occasions to tailor. Consequently, tools for automating gamification personalization are needed. The problems that emerge are that which of those characteristics are relevant and how to do such tailoring are open questions, and that the required automating tools are lacking. We tackled these problems in two steps. First, we conducted an exploratory study, collecting participants’ opinions on the game elements they consider the most useful for different learning activity types (LAT) via survey. Then, we modeled opinions through conditional decision trees to address the aforementioned tailoring process. Second, as a product from the first step, we implemented a recommender system that suggests personalized gamification designs (which game elements to use), addressing the problem of automating gamification personalization. Our findings i) present empirical evidence that LAT, geographic locations, and other user characteristics affect users’ preferences, ii) enable defining gamification designs tailored to user and contextual features simultaneously, and iii) provide technological aid for those interested in designing personalized gamification. The main implications are that demographics, game-related characteristics, geographic location, and LAT to be done, as well as the interaction between different kinds of information (user and contextual characteristics), should be considered in defining gamification designs and that personalizing gamification designs can be improved with aid from our recommender system. © 2021, CC BY.

关键词： Recommender systems

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Network comparison via encoding, decoding, and causality

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Physical Review Research 2023年第3期5卷 033129-033129页

作者： Yang Tian Hedong Hou Guangzheng Xu Ziyang Zhang Pei Sun Department of Psychology & Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing 100084 China and Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing 100084 China Institut de Mathématiques d'Orsay 14 Rue Joliot-Curie 91190 Gif-sur-Yvette France Department of Computer Science University College London London WC1E 6AE United Kingdom Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing 100084 China Department of Psychology & Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing 100084 China

Quantifying the relations (e.g., similarity) between complex networks paves the way for studying the latent information shared across networks. However, fundamental relation metrics are not well-defined between networks. As a compromise, prevalent techniques measure network relations in data-driven manners, which are inapplicable to analytic derivations in physics. To resolve this issue, we present a theory for obtaining an optimal characterization of network topological properties. We show that a network can be fully represented by a Gaussian variable defined by a function of the Laplacian, which simultaneously satisfies network-topology-dependent smoothness and maximum entropy properties. Based on it, we can analytically measure diverse relations between complex networks. As illustrations, we define encoding (e.g., information divergence and mutual information), decoding (e.g., Fisher information), and causality (e.g., Granger causality and conditional mutual information) between networks. We validate our framework on representative networks (e.g., random networks, protein structures, and chemical compounds) to demonstrate that a series of science and engineering challenges (e.g., network evolution, embedding, and query) can be tackled from a new perspective. An implementation of our theory is released as a multiplatform toolbox.

关键词： Networks & random structures Random graphs Graph theory Information theory

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SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems

arXiv

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arXiv 2024年

作者： Dave, Kshitij Innan, Nouhaila Behera, Bikash K. Mumtaz, Zahid Al-Kuwari, Saif Farouk, Ahmed Institute of Advanced Research Gandhinagar India Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M’sick Hassan II University of Casablanca Morocco NYUAD Research Institute Abu Dhabi United Arab Emirates Pvt. Ltd. WB Mohanpur741246 India London School of Economics and Political Science London United Kingdom Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Department of Computer Science Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt

Sentiment analysis is an essential component of natural language processing, used to analyze sentiments, attitudes, and emotional tones in various contexts. It provides valuable insights into public opinion, customer feedback, and user experiences. Researchers have developed various classical machine learning and neuro-fuzzy approaches to address the exponential growth of data and the complexity of language structures in sentiment analysis. However, these approaches often fail to determine the optimal number of clusters, interpret results accurately, handle noise or outliers efficiently, and scale effectively to high-dimensional data. Additionally, they are frequently insensitive to input variations. In this paper, we propose a novel hybrid approach for sentiment analysis called the Quantum Fuzzy Neural Network (QFNN), which leverages quantum properties and incorporates a fuzzy layer to overcome the limitations of classical sentiment analysis algorithms. In this study, we test the proposed approach on two Twitter datasets: the Coronavirus Tweets Dataset (CVTD) and the General Sentimental Tweets Dataset (GSTD), and compare it with classical and hybrid algorithms. The results demonstrate that QFNN outperforms all classical, quantum, and hybrid algorithms, achieving 100% and 90% accuracy in the case of CVTD and GSTD, respectively. Furthermore, QFNN demonstrates its robustness against six different noise models, providing the potential to tackle the computational complexity associated with sentiment analysis on a large scale in a noisy environment. The proposed approach expedites sentiment data processing and precisely analyses different forms of textual data, thereby enhancing sentiment classification and insights associated with sentiment analysis. © 2024, CC BY.

关键词： Tweets

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An Intelligent Approach to Segment the Liver Cancer using Machine Learning Method

An Intelligent Approach to Segment the Liver Cancer using Ma...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： L Anand Mahesh Maurya J. Seetha D Nagaraju Ananda Ravuri R G Vidhya Department of networking and communications School of computing SRM Institute of science and Technology K. C. College of Engineering Management and Research University of Mumbai Mith Bunder Road Near Hume Pipe Thane Maharashtra India Department of Computer Science and Business Systems Panimalar Engineering College Chennai Department of CSE Sri Venkatesa Perumal College of Engineering and Technology Puttur Andhra Pradesh India Senior Software Engineer Intel corporation Hillsboro Oregon USA Department of ECE HKBK College of Engineering Bangalore

Cancer Liver cancer is difficult to detect and diagnose early. Machine learning has improved liver cancer diagnosis in recent years. This abstract describes a novel liver cancer detection and classification method using Autoencoder-Extreme Learning Machine (AE-ELM) and Convolutional Neural Network (CNN) technology. AE-ELM models extract features in the suggested method. The autoencoder-extreme learning machine AE-ELM technique learns high-level abstract characteristics from incoming data. AE-ELM extracts liver cancer-specific features by reducing the dimensionality of input data. A CNN model classifies the extracted features. CNNs are good at detecting spatial patterns in medical pictures like CT and MRI scans. The CNN model learns hierarchical representations of input information and classifies them into liver cancer subtypes or stages using various convolutional and pooling processes. AE-ELM and CNN are advantageous. First, AE-ELM decreases input data dimensionality to extract critical features without manual feature engineering. This simplifies feature selection and improves the model’s capacity to handle big data. Second, the CNN model uses hierarchical convolutional processes to capture subtle patterns and spatial correlations in input data, enhancing liver cancer classification accuracy. The proposed liver cancer diagnostic method works in experiments. AE-ELM and CNN models outperform classic machine learning approaches and standalone CNN models. Improved liver cancer detection accuracy, sensitivity, and specificity help healthcare practitioners make quick and precise diagnosis. Finally, AE-ELM and CNN technologies can identify and classify liver cancer. This unique strategy uses both models to extract relevant characteristics and capture complicated patterns, improving liver cancer diagnosis accuracy and efficiency. To evaluate this approach’s therapeutic promise and application in real-world healthcare settings, additional datasets are needed.

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How universal polynomial bases enhance spectral graph neural networks: heterophily, over-smoothing, and over-squashing 24

How universal polynomial bases enhance spectral graph neural...

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Proceedings of the 41st International Conference on Machine Learning

作者： Keke Huang Yu Guang Wang Ming Li Pietro Liò School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China and Shanghai AI Laboratory Shanghai China and School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China and Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge UK

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter-based graph neural network - UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on datasets with a diverse range of heterophily, support the superiority of UniBasis in the universality but also its proficiency in graph explanation.

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Causal Unstructured Pruning in Linear Networks Using Effective Information

Causal Unstructured Pruning in Linear Networks Using Effecti...

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International Conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC

作者： Changyu Zeng Li Liu Haocheng Zhao Yu Zhang Wei Wang Ning Cai Yutao Yue JITRI Institute of Deep Perception Technology Wuxi China Department of Computing School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Department of Computer Science University of Liverpool Liverpool United Kingdom School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

Excessive number of parameters in today’s (deep) neural networks demands tremendous computational resources and slows down training speed. The problem also makes it difficult to deploy these neural network models on capability constrained devices such as mobile devices. To address this challenge, we propose an unstructured pruning method that measures the causal structure of neural networks based on effective information (EI). It introduces an intervention to the input and computes the mutual information between the interference and its corresponding output, within a single linear layer measuring the importance of each weight. In the experiments, we found that the sparsity of EI pruning can reach more than 90%. Only 10% of non-zero parameters in the linear layers were needed compared to the benchmark methods without pruning, while ensuring similar level of accuracy and stable training performance in iterative pruning. In addition, as the invariance of the causal structure of the network is exploited, the network after pruning using EI is highly generalizable and interpretable than other methods.

关键词： Training Deep learning Weight measurement Neurons Interference Knowledge discovery Mobile handsets

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