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Reinforcement Learning-Based Integrated Risk Aware Dynamic Treatment Strategy for Consumer-Centric Next-Gen Healthcare

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IEEE Transactions on Consumer Electronics 2025年

作者： Nimma, Divya Rao, Pinapati Lakshmana Ramesh, Janjhyam Venkata Naga Dahan, Fadl Reddy, Desidi Narsimha Selvakumar, Venkatachalam Ilkhamova, Yodgorkhon Jangir, Pradeep UMMC United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Vaddeswaram522302 India Graphic Era Hill University Department of CSE Dehradun248002 India Graphic Era Deemed To Be University Department of CSE Uttarakhand Dehradun248002 India Prince Sattam Bin Abdulaziz University Department of Management Information Systems College of Business Administration Hawtat Bani Tamim Al-Kharj11942 Saudi Arabia Soniks consulting LLC Soniks consulting Data Consultant Telangana E park blvd India Bhavan’s Vivekananda College of Science Humanities and Commerce Department of Maths and Statistics Telangana Hyderabad India Tashkent State University of Economics Department of Digital Economy Tashkent Uzbekistan Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai602 105 India Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan

Reinforcement learning (RL) has gained prominence in healthcare due to its ability to optimize treatment strategies without relying on predefined mathematical models. However, existing approaches face critical challenges: (1) the optimality of learned strategies is assessed without considering treatment risks, potentially leading to unsafe recommendations;(2) distribution shift issues cause learned strategies to diverge from physician decisions;and (3) past observational data and treatment history are often overlooked, leading to suboptimal state representations. To address these limitations, we propose a Dynamic Treatment Strategy Generation Model that integrates Dead Ends with an Offline Supervised Actor-Critic approach (DOSAC-DTR). Our model incorporates Dead Ends into the Actor-Critic framework to evaluate risks associated with recommended treatments. Additionally, physician oversight is embedded to mitigate distribution shift and align the learned strategy with expert decisions while maximizing expected outcomes. To enhance state representation, we employ an LSTM-based encoder-decoder model to capture essential patient history, ensuring robust decision-making. Experimental results on real-world datasets (MIMIC-III) demonstrate that DOSAC-DTR significantly reduces mortality rates (Sepsis: 3.51%, Ventilation: 13.74%) and improves treatment alignment with physicians (Jaccard similarity: 0.362, 0.126) compared to baseline models. These findings underscore the potential of reinforcement learning in personalized healthcare, improving both treatment efficacy and patient safety. © 1975-2011 IEEE.

关键词： Risk assessment

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Complex interplay of magnetic ordering and spin-lattice coupling in orthochromite Nd0.5Dy0.5CrO3

arXiv

引用

arXiv 2021年

作者： Anas, M. Balasubramanian, Padmanabhan Vikram, K. Singh, Ankita Kumar, C.M.N. Hoser, Andreas Rusinek, Dariusz Sinha, A.K. Srihari, V. Singh, Ranjan K. Kumar, Rinku Gupta, Mukul Maitra, T. Malik, V.K. Department of Physics I.I.T. Roorkee247667 India Graphic Era University Dehradun248002 India Institute of Solid State Physics Vienna University of Technology Wiedner Hauptstraße 810 Vienna1040 Austria AGH University of Science and Technology Faculty of Physics and Applied Computer Science Karaków30-059 Poland Helmholtz-Zentrum Berlin Germany National Center for Nuclear Research Otwock-Swierk Poland Indus-2 Raja Ramanna Centre for Advanced Technology Indore India Department of Physics Banaras Hindu University Uttar Pradesh India Institute Instrumentation Centre I.I.T. Roorkee247667 India UGC-DAE Consortium for Scientific Research University Campus Khandwa Road Indore452001 India Department of Condensed Matter Physics and Materials Science Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India Department of Physics School of Engineering UPES Dehradun248007 India

The mixed rare-earth orthochromite Nd0.5Dy0.5CrO3has a Néel temperature (TN) of ∼ 175K, resulting in the G-type antiferromagnetic ordering of Cr3+spins. The inverse susceptibility shows a deviation from Curie-Weiss law at 230K, with a large effective paramagnetic moment of 8.8 μB. The ZFC-FC magnetization bifurcate just above TNand show a distinct signature of spin reorientation near 60K. Neutron diffraction show that below TN, the Cr3+spins align in Γ2representation as (Fx, Gz). Below 60K, due to spin reorientation, the magnetic structure is in Γ1(Gy) configuration. The neutron diffraction does not show any signature of rare-earth ordering even at 1.5K. First principles density functional theory calculations within GGA+U and GGA+U+SO approximations reveal that the G-type antiferromagnetic order is the ground state magnetic structure of Cr sublattice and the spin-reorientation of Cr3+spins can happen in the absence of 3d-4f interactions unlike in the case of orthoferrites. The specific heat shows a 'λ' anomaly at TN, while at low temperature two distinct Schottky anomalies are observed;a Schottky peak at 2K and an additional step-like feature above 10K. Above TN, the magnetic transition is preceded by structural anomalies as seen in our x-ray diffraction and Raman measurements. The deviation of structural parameters near Néel temperature is smaller. The phonon frequencies show deviation from the standard anharmonic behaviour: first near 250K, due to magneto-volume effects while the second deviation occurs near 200K due to spin-phonon coupling. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic structure

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Simultaneous spectral-spatial feature selection and extraction for hyperspectral images

arXiv

引用

arXiv 2019年

作者： Zhang, Lefei Zhang, Qian Du, Bo Huang, Xin Tang, Yuan Yan Tao, Dacheng State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China Beijing Samsung Telecom R&D Center Beijing100028 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430072 China Department of Computer and Information Science University of Macau Macau999078 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectralspatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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Accurate Q-learning 25th

Accurate Q-learning

引用

25th International Conference on Neural Information Processing, ICONIP 2018

作者： Hu, Zhihui Jiang, Yubin Ling, Xinghong Liu, Quan School of Computer Science and Technology Soochow University SuzhouJiangsu215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210000 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9783030041816

In order to solve the problem that Q-learning can suffer from large overestimations in some stochastic environments, we first propose a new form of Q-learning, which proves that it is equivalent to the incremental form and analyze the reasons why the convergence rate of Q-learning will be affected by positive bias. We generalize the new form for the purpose of easy adaptations. By using the current value instead of the bias term, we present an accurate Q-learning algorithm and show that the new algorithm converges to an optimal policy. Experimentally, the new algorithm can avoid the effect of positive bias and the convergence rate is faster than Q-learning and its variants on several MDP problems. © Springer Nature Switzerland AG 2018.

关键词： Reinforcement learning

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Energetic electron precipitation driven by electromagnetic ion cyclotron waves from ELFIN’s low altitude perspective

arXiv

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arXiv 2022年

作者： Angelopoulos, V. Zhang, X.-J. Artemyev, A.V. Mourenas, D. Tsai, E. Wilkins, C. Runov, A. Liu, J. Turner, D.L. Li, W. Khurana, K. Wirz, R.E. Sergeev, V.A. Meng, X. Wu, J. Hartinger, M.D. Raita, T. Shen, Y. An, X. Shi, X. Bashir, M.F. Shen, X. Gan, L. Qin, M. Capannolo, L. Ma, Q. Russell, C.L. Masongsong, E.V. Caron, R. He, I. Iglesias, L. Jha, S. King, J. Kumar, S. Le, K. Mao, J. McDermott, A. Nguyen, K. Norris, A. Palla, A. Roosnovo Tam, J. Xie, E. Yap, R.C. Ye, S. Young, C. Adair, L.A. Shaffer, C. Chung, M. Cruce, P. Lawson, M. Leneman, D. Allen, M. Anderson, M. Arreola-Zamora, M. Artinger, J. Asher, J. Branchevsky, D. Cliffe, M. Colton, K. Costello, C. Depe, D. Domae, B.W. Eldin, S. Fitzgibbon, L. Flemming, A. Frederick, D.M. Gilbert, A. Hesford, B. Krieger, R. Lian, K. McKinney, E. Miller, J.P. Pedersen, C. Qu, Z. Rozario, R. Rubly, M. Seaton, R. Subramanian, A. Sundin, S.R. Tan, A. Thomlinson, D. Turner, W. Wing, G. Wong, C. Zarifian, A. Earth Planetary and Space Sciences Department Institute of Geophysics and Planetary Physics University of California Los Angeles Los AngelesCA90095 United States University of Texas at Dallas RichardsonTX75080 United States CEA DAM DIF Arpajon France Atmospheric and Oceanic Sciences Departments University of California Los AngelesCA United States Johns Hopkins University Applied Physics Laboratory LaurelMD United States Department of Astronomy Center for Space Physics Boston University BostonMA United States Mechanical and Aerospace Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States School of Mechanical Industrial Manufacturing Engineering Oregon State University CorvallisOR97331 United States University of St. Petersburg St. Petersburg Russia Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Space Science Institute BoulderCO80301 United States Sodankylä Geophysical Observatory University of Oulu Sodankylä Finland Materials Science and Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Deloitte Consulting New YorkNY10112 United States Computer Science Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Microsoft RedmondWA98052 United States Physics and Astronomy Department University of California Los AngelesCA90095 United States Department of Astronomy and Astrophysics The University of Chicago ChicagoIL60637 United States Raybeam Inc. Mountain ViewCA94041 United States SpaceX HawthorneCA90250 United States Reliable Robotics Corporation Mountain ViewCA94043 United States Los Alamos National Laboratory Los AlamosNM87545 United States Mathematics Department University of California Los AngelesCA90095 United States Planet Labs PBC San FranciscoCA94107 United States KSAT Inc. De

We review comprehensive observations of electromagnetic ion cyclotron (EMIC) wave-driven energetic electron precipitation using data from the energetic electron detector on the Electron Losses and Fields InvestigatioN (ELFIN) mission, two polar-orbiting low-altitude spinning CubeSats, measuring 50-5000 keV electrons with good pitch-angle and energy resolution. EMIC wave-driven precipitation exhibits a distinct signature in energy-spectrograms of the precipitating-to-trapped flux ratio: peaks at >0.5 MeV which are abrupt (bursty) (lasting ∼17s, or ∆L ∼ 0.56) with significant substructure (occasionally down to sub-second timescale). We attribute the bursty nature of the precipitation to the spatial extent and structuredness of the wave field at the equator. Multiple ELFIN passes over the same MLT sector allow us to study the spatial and temporal evolution of the EMIC wave - electron interaction region. Case studies employing conjugate ground-based or equatorial observations of the EMIC waves reveal that the energy of moderate and strong precipitation at ELFIN approximately agrees with theoretical expectations for cyclotron resonant interactions in a cold plasma. Using 2 years of ELFIN data uniformly distributed in local time, we assemble a statistical database of ∼50 events of strong EMIC wave-driven precipitation. Most reside at L ∼ 5 − 7 at dusk, while a smaller subset exists at L ∼ 8 − 12 at post-midnight. The energies of the peak-precipitation ratio and of the half-peak precipitation ratio (our proxy for the minimum resonance energy) exhibit an L-shell dependence in good agreement with theoretical estimates based on prior statistical observations of EMIC wave power spectra. The precipitation ratio’s spectral shape for the most intense events has an exponential falloff away from the peak (i.e., on either side of ∼ 1.45 MeV). It too agrees well with quasi-linear diffusion theory based on prior statistics of wave spectra. It should be noted though that this diffusive

关键词： Radiation belts

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Citizen science and the United Nations Sustainable Development Goals (vol 2, pg 922, 2019)

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NATURE SUSTAINABILITY 2019年第11期2卷 1063-1063页

作者： Fritz, Steffen See, Linda Carlson, Tyler Haklay, Mordechai (Muki) Oliver, Jessie L. Fraisl, Dilek Mondardini, Rosy Brocklehurst, Martin Shanley, Lea A. Schade, Sven Wehn, Uta Abrate, Tommaso Anstee, Janet Arnold, Stephan Billot, Matthew Campbell, Jillian Espey, Jessica Gold, Margaret Hager, Gerid He, Shan Hepburn, Libby Hsu, Angel Long, Deborah Maso, Joan McCallum, Ian Muniafu, Maina Moorthy, Inian Obersteiner, Michael Parker, Alison J. Weisspflug, Maike West, Sarah Ecosystem Services and Management Program International Institute for Applied Systems Analysis Laxenburg Austria School of Resource and Environmental Management Simon Fraser University Burnaby Canada University College London London UK School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia Australian Citizen Science Association c/o Faculty of Science Partner Engagement and Outreach University of Sydney Sydney Australia University of Natural Resources and Life Sciences Vienna Austria Competence Center Citizen Science ETH Zurich and University of Zurich Zurich Switzerland Citizen Science Global Partnership Washington DC USA European Citizen Science Association Museum für Naturkunde Berlin Berlin Germany Nelson Institute University of Wisconsin at Madison Madison USA European Commission Joint Research Center Ispra Italy IHE Delft Institute for Water Education Delft The Netherlands World Meteorological Organization Geneva Switzerland Commonwealth Scientific and Industrial Research Organisation Canberra Australia Federal Statistical Office – Destatis Wiesbaden Germany UN Environment Programme Nairobi Kenya United Nations Sustainable Development Solutions Network New York USA CitizenScience.Asia Hong Kong China Yale-NUS College Singapore Singapore University of Dundee Dundee UK Scottish Environment LINK Perth UK Grumets Research Group CREAF Universitat Autònoma de Barcelona Barcelona Spain African Citizen Science Association Nairobi Kenya Woodrow Wilson Center Washington DC USA Museum für Naturkunde Berlin Berlin Germany Stockholm Environment Institute University of York York UK

Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditional data source that is already making a contribution. In this Perspective, we present a roadmap that outlines how citizen science can be integrated into the formal Sustainable Development Goals reporting mechanisms. Success will require leadership from the United Nations, innovation from National Statistical Offices and focus from the citizen-science community to identify the indicators for which citizen science can make a real contribution.

关键词： Planning

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Automatic detection of passable roads after floods in remote sensed and social media data

arXiv

引用

arXiv 2019年

作者： Ahmad, Kashif Pogorelov, Konstantin Riegler, Michael Ostroukhova, Olga Halvorsen, Pål Conci, Nicola Dahyot, Rozenn Adapt Centre School of Computer Science and Statistics Trinity College Dublin Ireland Simula Research Laboratory University of Oslo Norway Simula Metropolitan Center for Digital Engineering University of Oslo Norway Research Institute of Multiprocessor Computation Systems n.a. A.V. Kalyaev Russia University of Trento Italy

This paper addresses the problem of floods classification and floods aftermath detection utilizing both social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods are carried out on the large-scale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Floods

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CEPC Technical Design Report

引用

Radiation Detection Technology and Methods 2024年第1期8卷 I0003-I0016,1-1091页

作者： Waleed Abdallah Tiago Carlos Adorno de Freitas Konstantin Afanaciev Shakeel Ahmad Ijaz Ahmed Xiaocong Ai Abid Aleem Wolfgang Altmannshofer Fabio Alves Weiming An Rui An Daniele Paolo Anderle Stefan Antusch Yasuo Arai Andrej Arbuzov Abdesslam Arhrib Mustafa Ashry Sha Bai Yu Bai Yang Bai Vipul Bairathi Csaba Balazs Philip Bambade Yong Ban Tripamo Bandyopadhyay Shou-Shan Bao Desmond P.Barber Ayse Bat Varvara Batozskaya Subash Chandra Behera Alexander Belyaev Michele Bertucci Xiao-Jun Bi Yuanjie Bi Tianjian Bian Fabrizio Bianchi Thomas Biekotter Michela Biglietti Shalva Bilanishvili Deng Binglin Denis Bodrov Anton Bogomyagkov Serge Bondarenko Stewart Boogert Maarten Boonekamp Marcello Borri Angelo Bosotti Vincent Boudry Mohammed Boukidi Igor Boyko Ivanka Bozovic Giuseppe Bozzi Jean-Claude Brient Anastasiia Budzinskaya Masroor Bukhari Vladimir Bytev Giacomo Cacciapaglia Hua Cai Wenyong Cai Wujun Cai Yijian Cai Yizhou Cai Yuchen Cai Haiying Cai Huacheng Cai Lorenzo Calibbi Junsong Cang Guofu Cao Jianshe Cao Antoine Chance Xuejun Chang Yue Chang Zhe Chang Xinyuan Chang Wei Chao Auttakit Chatrabhuti Yimin Che Yuzhi Che Bin Chen Danping Chen Fuqing Chen Fusan Chen Gang Chen Guoming Chen Hua-Xing Chen Huirun Chen Jinhui Chen Ji-Yuan Chen Kai Chen Mali Chen Mingjun Chen Mingshui Chen Ning Chen Shanhong Chen Shanzhen Chen Shao-Long Chen Shaomin Chen Shiqiang Chen Tianlu Chen Wei Chen Xiang Chen Xiaoyu Chen Xin Chen Xun Chen Xurong Chen Ye Chen Ying Chen Yukai Chen Zelin Chen Zilin Chen Gang Chen Boping Chen Chunhui Chen Hok Chuen Cheng Huajie Cheng Shan Cheng Tongguang Cheng Yunlong Chi Pietro Chimenti Wen Han Chiu Guk Cho Ming-Chung Chu Xiaotong Chu Ziliang Chu Guglielmo Coloretti Andreas Crivellin Hanhua Cui Xiaohao Cui Zhaoyuan Cui Brunella D'Anzi Ling-Yun Dai Xinchen Dai Xuwen Dai Antonio De Maria Nicola De Filippis Christophe De La Taille Francesca De Mori Chiara De Sio Elisa Del Core Shuangxue Deng Wei-Tian Deng Zhi Deng Ziyan Deng Bhupal Dev Tang Dewen Biagio Di Micco Ran Ding Siqin Dingl Yadong Ding Haiyi Dong Jianin AGH University of Science and Technology Faculty of Physics and Applied Computer ScienceKrakow Anhui University HefeiAnhui Applied Physics Department Federal Urdu University of ArtsScience and TechnologyIslamabad Banaras Hindu University Institute of ScienceVaranasi Bandirma Onyedi Eylul University Bandirma Beihang University Beijing Beijing Computational Science Research Center Beijing Beijing Conveyi LTD Beijing Beijing Glass Research Institute Co. LtdBeijing Beijing Institute of Technology Beijing Beijing Normal University Beijing Brown University ProvidenceRI Budker Institute of Nuclear Physics Novosibirsk Cadi Ayyad University Laboratory of Fundamental and Applied PhysicsPolydisciplinary FacultyMarrakech Cairo University Giza Center for Theory and Computation National Tsing Hua UniversityHsinchu Central China Normal University WuhanHubei Central South University ChangshaHunan Centro de Fisica Teorica de Particulas Universidade Tecnica de LisboaLisboa Chengdu KaitengSifang Digital Radio&TV Equipment Co. LtdChengduSichuan China academy of engineering physics MianyangSichuan China building materials academy Beijing China Center of Advanced Science and Technology Beijing China Institute of Atomic Energy Beijing China Jiliang University HangzhouZhejiang China Nuclear Instrument CO. LTDBeijing China University of Geosciences Beijing China University of Geosciences WuhanHubei China University of Mining and Technology XuzhouJiangsu China West Normal University NanchongSichuan Chongqing University Department of PhysicsChongqing Chulalongkorn University Bangkok City University of Hong Kong Department of PhysicsHong Kong Cockcroft Institute Daresbury Cornell University IthacaNY CPPM(AMU/IPhU&CNRS/INP) Marseille Dalian Minzu University DalianLiaoning Dalian University of Technology Department of PhysicsDalian Deutsches Elektronen-Synchrotron DESY Hamburg Dzhelepov Laboratory of Nuclear Problems of Joint Institute for Nuclear Research(DLNP JINR) Dubna East China University Of Science And Technol

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by China,fostered through extensive collaboration with international *** complex comprises four accelerators:a 30 GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs *** its baseline design with synchrotron radiation(SR)power of 30 MW per beam,it can achieve a luminosity of 5×10^(34)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13 ab^(-1)for two IPs over a decade,producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of *** Technical Design Report(TDR)follows the Preliminary Conceptual Design Report(Pre-CDR,2015)and the Conceptual Design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering ***,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection *** is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 *** commencement of experiments and data collection could potentially be initiated in the mid-2030s.

关键词： initiated exceeding precise

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Supervised discriminative sparse PCA for com-characteristic gene selection and tumor classification on multiview biological data

arXiv

引用

arXiv 2019年

作者： Feng, Chun-Mei Xu, Yong Liu, Jin-Xing Gao, Ying-Lian Zheng, Chun-Hou Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Key Laboratory of Network Oriented Intelligent Computation Shenzhen518055 China School of Information Science and Engineering Qufu Normal University Rizhao276826 China Library of Qufu Normal University Qufu Normal University Rizhao276826 China School of Computer Science and Technology Anhui University Hefei230000 China

Principal component analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse PCA, which focuses on seeking sparse loadings. However, the performance of these methods is still far from satisfactory due to their limitation of using unsupervised learning methods;moreover, the class ambiguity within the sample is high. To overcome this problem, this paper developed a new PCA method, which is named the supervised discriminative sparse PCA (SDSPCA). The main innovation of this method is the incorporation of discriminative information and sparsity into the PCA model. Specifically, in contrast to the traditional sparse PCA, which imposes sparsity on the loadings, here, sparse components are obtained to represent the data. Furthermore, via the linear transformation, the sparse components approximate the given label information. On the one hand, sparse components improve interpretability over the traditional PCA, while on the other hand, they are have discriminative abilities suitable for classification purposes. A simple algorithm is developed, and its convergence proof is provided. SDSPCA has been applied to the common-characteristic gene selection and tumor classification on multiview biological data. The sparsity and classification performance of SDSPCA are empirically verified via abundant, reasonable, and effective experiments, and the obtained results demonstrate that SDSPCA outperforms other state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Principal component analysis

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Approximation and competitive algorithms for single-minded selling problem 1

引用

12th International Conference on Algorithmic Aspects in Information and Management, AAIM 2018

作者： Chin, Francis Y. L. Poon, Sheung-Hung Ting, Hing-Fung Xu, Dachuan Yu, Dongxiao Zhang, Yong Department of Computer Science The University of Hong Kong Pok Fu Lam Hong Kong School of Computer Science University of Nottingham Ningbo China Ningbo China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing China Institute of Intelligent Computing School of Computer Science and Technology Shandong University Qingdao266237 China Research Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9783030046187

ISBN: (纸本)9783030046170

The problem of item selling with the objective of maximizing the revenue is studied. Given a seller with k types of items and n single-minded buyers, i.e., each buyer is only interested in a particular bundle of items, to maximize the revenue, the seller must carefully assign some amount of bundles to each buyer with respect to the buyer’s accepted price. Each buyer bi is associated with a value function vi(·) such that vi(x) is the accepted unit bundle price bi is willing to pay for x bundles. We show that the single-minded item selling problem is NP-hard. Moreover, we give an O(√k)-approximation algorithm. For the online version, i.e., the buyers come one by one and the decision on each buyer must be made before the arrival of the next buyer, an O(√k · (log h + log k))- competitive algorithm is achieved, where h is the highest unit item price among all buyers. © Springer Nature Switzerland AG 2018.

关键词： Sales

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