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Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data

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arXiv 2024年

作者： Chen, Aokun Li, Qian Huang, Yu Li, Yongqiu Chuang, Yu-Neng Hu, Xia Guo, Serena Wu, Yonghui Guo, Yi Bian, Jiang Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida 1889 Museum Rd Suite 7000 GainesvilleFL32610 United States Department of Computer Science George R. Brown School of Engineering Rice University 6100 Main St. HoustonTX77005 United States Department of Pharmaceutical Outcomes & Policy College of Pharmacy University of Florida 1225 Center Drive GainesvilleFL32610 United States

A comprehensive view of factors associated with AD/ADRD will significantly aid in studies to develop new treatments for AD/ADRD and identify high-risk populations and patients for prevention efforts. In our study, we summarized the risk factors for AD/ADRD by reviewing existing meta-analyses and review articles on risk and preventive factors for AD/ADRD. In total, we extracted 477 risk factors in 10 categories from 537 studies. We constructed an interactive knowledge map to disseminate our study results. Most of the risk factors are accessible from structured Electronic Health Records (EHRs), and clinical narratives show promise as information sources. However, evaluating genomic risk factors using RWD remains a challenge, as genetic testing for AD/ADRD is still not a common practice and is poorly documented in both structured and unstructured EHRs. Considering the constantly evolving research on AD/ADRD risk factors, literature mining via NLP methods offers a solution to automatically update our knowledge map. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

Efficient Computing of Disaster-Disjoint Paths: Greedy and Beyond

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Efficient Computing of Disaster-Disjoint Paths: Greedy and B...

IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Balázs Vass Erika Bérczi-Kovács Péter Gyimesi János Tapolcai Department of Telecommunication and Media Informatics Faculty of Electrical Engineering and Informatics (VIK) Budapest University of Technology and Economics (BME) and HUN-REN-BME Information Systems Research Group and MTA-BME Future Internet Research Group Faculty of Mathematics and Computer Science Babeş-Bolyai University Cluj Napoca Romania ELTE Eötvös Loránd University Budapest Hungary HUN-REN-ELTE Egerváry Research Group on Combinatorial Optimization

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

In a network topology $G$ , we say a set of st-paths are disaster-disjoint if no disaster strikes more than one path. In this poster, we explore the basic capabilities and limitations of greedy and more advanced algorithms for computing maximal collections of such paths in planar networks. An algorithm is greedy if it generates consecutive paths $P_{1}, P_{2},\ldots$ according to a simple rule. In the simplest setting, the only rule is that $P_{i+1}$ is the closest clockwise disaster-disjoint from $P_{i}$ . We find that the simplest greedy may fail even when 1) $G$ is planar, 2) each disaster region is connected, and 3) each node failure (apart from s and $t$ ) is considered possible. Adding a simple rule explained in [1] yields a correct polynomial-time algorithm for the above problem. Finally, we digest a recent related near-linear runtime algorithm of [2] solving a more general problem and discuss the underlying relations among the foundations of these algorithms.

关键词： Greedy algorithms Visualization Runtime Network topology Disasters Conferences

SPP-CNN: An Efficient Framework for Network Robustness Prediction

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： Complex networks

An Active Speaker Detection Method in Videos using Standard Deviations of Color Histogram

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An Active Speaker Detection Method in Videos using Standard ...

science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Adekunle A. Akinrinmade Emmanuel Adetiba Joke A. Badejo Oluwadamilola Oshin Department of Electrical and Information Engineering (Computer Engineering) College of Engineering Covenant University Covenant University Ota Ogun Nigeria Department of Electrical and Information Engineering College of Engineering Covenant University Ota Ogun Nigeria HRA Institute for Systems Science Durban University South Africa Department of Electrical and Information Engineering College of Engineering Covenant University Covenant University Ota Ogun Nigeria

Active Speaker Detection (ASD) is a process that predicts who the speaker is amongst those whose faces appear in a video (if any) at any given point in time within the recorded video. This work presents a novel algorithm capable of detecting the active speakers in each video using the standard deviations of Color Histograms (CHs) computed at the mouth region from one frame to another. This paper relies on the assumption that the lips of an active speaker are in motion. They open and close and thus reveal the inner parts of the mouth, like the tongue, teeth, and the vocal cavity which are of diverse colors in the process of talking. It is possible to use already existing algorithms to detect the mouth region. This region can be analyzed during the speaking process for the changes in color activity, and this can be used to predict whether a user is speaking or not. If a person is not speaking, the lips are at rest the CH of such mouth regions such candidates would be stable. As a result, the standard deviations of such regions would be negligible. A threshold can be experimentally determined which is thus capable of predicting if a person is speaking or otherwise. This paper explores 53 online videos from Channels TV station, these videos were employed in the creation of 250 video clips. Each clip is between 15 to 60 seconds with a total of 3.6 hours. Each video contained the faces of at most two speakers in no particular order. Sometimes, only one of the speakers' faces appears, at other times both appear in the duration of the video. The status of the speakers whether active or not was manually labeled to be used for the performance evaluation of the proposed algorithm. This method was able to predict the active speakers with an accuracy of 99.19%.

关键词： Histograms Visualization Video on demand Tongue Lips Mouth Color

Feverous: Fact extraction and verification over unstructured and structured information

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arXiv 2021年

作者： Aly, Rami Guo, Zhijiang Schlichtkrull, Michael Thorne, James Vlachos, Andreas Christodoulopoulos, Christos Cocarascu, Oana Mittal, Arpit Department of Computer Science and Technology University of Cambridge Amazon Alexa Department of Informatics King’s College London United Kingdom Facebook

Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation. Existing large-scale benchmarks for this task have focused mostly on textual sources, i.e. unstructured information, and thus ignored the wealth of information available in structured formats, such as tables. In this paper we introduce a novel dataset and benchmark, Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS), which consists of 87,026 verified claims. Each claim is annotated with evidence in the form of sentences and/or cells from tables in Wikipedia, as well as a label indicating whether this evidence supports, refutes, or does not provide enough information to reach a verdict. Furthermore, we detail our efforts to track and minimize the biases present in the dataset and could be exploited by models, e.g. being able to predict the label without using evidence. Finally, we develop a baseline for verifying claims against text and tables which predicts both the correct evidence and verdict for 18% of the claims. © 2021, CC BY-SA.

关键词： Natural language processing systems

Verifiable Blind Quantum Computing with Trapped Ions and Single Photons

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Physical Review Letters 2024年第15期132卷 150604-150604页

作者： P. Drmota D. P. Nadlinger D. Main B. C. Nichol E. M. Ainley D. Leichtle A. Mantri E. Kashefi R. Srinivas G. Araneda C. J. Ballance D. M. Lucas Department of Physics University of Oxford Clarendon Laboratory Parks Road Oxford OX1 3PU United Kingdom Laboratoire d’Informatique de Paris 6 CNRS Sorbonne Université Paris 75005 France Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland USA School of Informatics University of Edinburgh Edinburgh EH8 9AB United Kingdom

We report the first hybrid matter-photon implementation of verifiable blind quantum computing. We use a trapped-ion quantum server and a client-side photonic detection system networked via a fiber-optic quantum link. The availability of memory qubits and deterministic entangling gates enables interactive protocols without postselection—key requirements for any scalable blind server, which previous realizations could not provide. We quantify the privacy at ≲0.03 leaked classical bits per qubit. This experiment demonstrates a path to fully verified quantum computing in the cloud.

关键词： Measurement-based quantum computing Quantum computation Quantum information with atoms & light Quantum information with hybrid systems Quantum information with trapped ions Quantum verification

A survey for recognizing sign language for deaf-mute people using image-based hand recognition

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AIP Conference Proceedings 2024年第1期2742卷

作者： S. Balakrishnan B. Naveen Kumar S. Kiran Subramanian R. Nithiskumar Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India

Sign language recognition is an image-based recognition approach that uses hand gestures, and individuals all around the world have created various sign languages one of the non-verbal communication methods used in Sign language is the hand gesture. This is how deaf-mute people who have hearing and speech problems communicate with one another and with non-deaf people. This is an optical communication language due to its distinctive syntax. The major goal of this article is to break down the barrier in the conversation between deaf-mute people and normal people, with an emphasis on increasing communication for physically challenged persons (Deaf and Dumb people) and teaching normal people to recognize deaf-mute people’s sign language.

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Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

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arXiv 2022年

作者： Immer, Alexander van der Ouderaa, Tycho F.A. Rätsch, Gunnar Fortuin, Vincent van der Wilk, Mark Department of Computer Science ETH Zurich Switzerland Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computing Imperial College London United Kingdom Department of Engineering University of Cambridge United Kingdom

Data augmentation is commonly applied to improve performance of deep learning by enforcing the knowledge that certain transformations on the input preserve the output. Currently, the data augmentation parameters are chosen by human effort and costly cross-validation, which makes it cumbersome to apply to new datasets. We develop a convenient gradient-based method for selecting the data augmentation without validation data during training of a deep neural network. Our approach relies on phrasing data augmentation as an invariance in the prior distribution on the functions of a neural network, which allows us to learn it using Bayesian model selection. This has been shown to work in Gaussian processes, but not yet for deep neural networks. We propose a differentiable Kronecker-factored Laplace approximation to the marginal likelihood as our objective, which can be optimised without human supervision or validation data. We show that our method can successfully recover invariances present in the data, and that this improves generalisation and data efficiency on image datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Environment-Driven Online LiDAR-Camera Extrinsic Calibration

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data