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Segregated interactions in urban and online spaces

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arXiv 2019年

作者： Dong, Xiaowen Morales, Alfredo J. Jahani, Eaman Moro, Esteban Lepri, Bruno Bozkaya, Burcin Sarraute, Carlos Bar-Yam, Yaneer Pentland, Alex Department of Engineering Science University of Oxford Oxford United Kingdom MIT Media Lab CambridgeMA United States New England Complex Systems Institute CambridgeMA United States MIT Institute for Data Systems and Society CambridgeMA United States Department of Mathematics and GISC Universidad Carlos III de Madrid Leganés Spain Mobile and Social Computing Lab Fondazione Bruno Kessler Trento Italy Behavioral Analytics & Visualization Lab Sabancı University Istanbul Turkey New College of Florida SarasotaFL United States Grandata Labs Buenos Aires Argentina

Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both shopping activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric, i.e., the amount of interaction from poorer to wealthier neighborhoods is considerably larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications. Copyright © 2019, The Authors. All rights reserved.

关键词： Geographical distribution

CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

Understanding adversarial attacks on deep learning based medical image analysis systems

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arXiv 2019年

作者： Ma, Xingjun Niu, Yuhao Gu, Lin Wang, Yisen Zhao, Yitian Bailey, James Lu, Feng State Key Laboratory of VR Technology and Systems School of CSE Beihang University Beijing China School of Computing and Information Systems The University of Melbourne ParkvilleVIC3010 Australia Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China National Institute of Informatics Tokyo101-8430 Japan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Cixi Instuitue of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep learning systems can be compromised by carefully-engineered adversarial examples/attacks with small imperceptible perturbations. This raises safety concerns about the deployment of these systems in clinical settings. In this paper, we provide a deeper understanding of adversarial examples in the context of medical images. We find that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints. Surprisingly, we also find that medical adversarial attacks can be easily detected, i.e., simple detectors can achieve over 98% detection AUC against state-of-the-art attacks, due to fundamental feature differences compared to normal examples. We believe these findings may be a useful basis to approach the design of more explainable and secure medical deep learning systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Parallel computing TEDA for high frequency streaming data clustering 2nd

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Parallel computing TEDA for high frequency streaming data cl...

2nd International Neural Network Society Conference on Big data, INNS 2016

作者： Gu, Xiaowei Angelov, Plamen P. Gutierrez, German Iglesias, Jose Antonio Sanchis, Araceli Data Science Group School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom CAOS Research Group Computer Science Department Carlos III University of Madrid Leganes28911 Spain

ISBN: (纸本)9783319478975

In this paper, a novel online clustering approach called Parallel TEDA is introduced for processing high frequency streaming data. This newly proposed approach is developed within the recently introduced TEDA theory and inherits all advantages from it. In the proposed approach, a number of data stream processors are involved, which collaborate with each other efficiently to achieve parallel computation as well as a much higher processing speed. A fusion center is involved to gather the key information from the processors which work on chunks of the whole data stream and generate the overall output. The quality of the generated clusters is being monitored within the data processors all the time and stale clusters are being removed to ensure the correctness and timeliness of the overall clustering results. This, in turn, gives the proposed approach a stronger ability of handling shifts/drifts that may take place in live data streams. The numerical experiments performed with the proposed new approach Parallel TEDA on benchmark datasets present higher performance and faster processing speed when compared with the alternative well-known approaches. The processing speed has been demonstrated to fall exponentially with more data processors involved. This new online clustering approach is very suitable and promising for real-time high frequency streaming processing and data analytics. © Springer International Publishing AG 2017.

关键词： Clustering algorithms

Digital is the New Blood: Enabling the Present and the Future

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Alzheimer's & Dementia 2023年第S21期19卷

作者： Rhoda Au Zachary T Popp Spencer Low Phillip H Hwang Ileana De Anda-Duran Stephanie Li Salman Rahman Huitong Ding Akwaugo Igwe Cody Karjadi Ting Fang Alvin Ang Sherral A. Devine Ashita S Gurnani Jesse B. Mez Lindsay A. Farrer Preeti Sunderaraman Honghuang Lin Vijaya B. Kolachalama Boston University School of Public Health Boston MA USA Framingham Heart Study Boston University School of Medicine Boston MA USA Davos Alzheimer’s Collaborative Philadelphia PA USA Department of Epidemiology Boston University School of Public Health Boston MA USA Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Neurology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University Alzheimer’s Disease Research Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Anatomy and Neurobiology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University School of Public Heatlh Boston MA USA Tulane School of Public Health and Tropical Medicine New Orleans LA USA Slone Epidemiology Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framigham Heart Study - Boston University Framingham MA USA Boston University Bioinformatics Program Boston MA USA Departments of Medicine (Biomedical Genetics) Neurology Ophthalmology Biostatistics and Epidemiology Boston University Schools of Medicine and Public Health Boston MA USA Department of Neurology - Boston University Chobanian & Avedisian School of Medicine Boston MA USA University of Massachusetts Medical School Worcester MA USA Framingham Heart Study/Boston University BOSTON MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA College of Arts & Sciences Boston University Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Boston University Alzheimer’s Disease Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Whitaker Cardiovascular Institute Boston University Chobanian & Avedisian School of Medicine Boston MA USA

Background Digital is the new blood, where collection, use, and storage of raw digital data streams enabled by technology-embedded sensors are replacing analog methods. Analyses of digital data extend beyond current applications, analogous to previously stored blood currently be used for omics/biomarker research. Today digital technologies are used to measure Alzheimer’s disease (AD)-related clinical symptoms (e.g., cognition, function, and behavior), but future possibilities remain to be determined. Importantly, digital data collection can go beyond current intermittent sampling by employing internet-connected applications and devices that allow real-time monitoring and accurate detection of changes, even within a widely heterogeneous spectrum. Method Beginning in 2005, the Framingham Heart Study (FHS) began digitally recording participants’ spoken responses to neuropsychological (NP) test questions, and in 2011 extended to a digital pen to record written responses. The emergence of wearable devices and smartphone applications has led to an expanded use of digital technologies beyond recordings during NP assessments. The Boston University Alzheimer’s Disease Research Center (BU ADRC) leverages digital sensors in multiple internet-connected devices that longitudinally collect digital data, in both in-person and remote settings. Result The first publication involving FHS digital data was in 2016; the remaining (n> 17) have been from 2020-current. Digital data measuring cognition, function and behavior are being collected through the BU ADRC, where results to date indicate nearly 100% and 75% uptake of active and passive engagement applications, respectively, and 95% uptake for wearable devices (n = 60). Longitudinal assessments include > 3 months for ∼50 participants; > 1 year for n∼21 participants. Conclusion Opportunities for improved trial outcome measurement are evident in the broad range of outcomes digital sensors capture beyond traditional research methodologi

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Making the most of data: Quantum monte carlo post-analysis revisited

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arXiv 2019年

作者： Ichibha, Tom Hongo, Kenta Maezono, Ryo Thom, Alex J.W. School of Information Science JAIST 1-1 Asahidai Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan Department of Chemistry University of Cambridge Lensfield Road CambridgeCB2 1EW United Kingdom

In quantum Monte Carlo (QMC) methods, energy estimators are calculated as the statistical average of the Markov chain sampling of energy estimator along with an associated statistical error. This error estimation is not straightforward and there are several choices of the error estimation methods. We evaluate the performance of three methods, Straatsma, an autoregressive model, and a blocking analysis based on von Neumann's ratio test for randomness, for the energy time-series given by Diffusion Monte Carlo, Full Configuration Interaction Quantum Monte Carlo and Coupled Cluster Monte Carlo methods. From these analyses we describe a hybrid analysis method which provides reliable error estimates for series of all lengths. Equally important is the estimation of the appropriate start point of the equilibrated phase, and two heuristic schemes are tested, establishing that MSER (mean squared error rule) gives reasonable and constant estimations independent of the length of time-series. Copyright © 2019, The Authors. All rights reserved.

关键词： Time series

Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

Waveform Modelling for the Laser Interferometer Space Antenna

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arXiv 2023年

作者： Afshordi, Niayesh Akçay, Sarp Seoane, Pau Amaro Antonelli, Andrea Aurrekoetxea, Josu C. Barack, Leor Barausse, Enrico Benkel, Robert Bernard, Laura Bernuzzi, Sebastiano Berti, Emanuele Bonetti, Matteo Bonga, Béatrice Bozzola, Gabriele Brito, Richard Buonanno, Alessandra Cárdenas-Avendaño, Alejandro Casals, Marc Chernoff, David F. Chua, Alvin J.K. Clough, Katy Colleoni, Marta Compère, Geoffrey Dhesi, Mekhi Druart, Adrien Durkan, Leanne Faye, Guillaume Ferguson, Deborah Field, Scott E. Gabella, William E. García-Bellido, Juan Gracia-Linares, Miguel Gerosa, Davide Green, Stephen R. Haney, Maria Hannam, Mark Heffernan, Anna Hinderer, Tanja Helfer, Thomas Hughes, Scott A. Husa, Sascha Isoyama, Soichiro Katz, Michael L. Kavanagh, Chris Khanna, Gaurav Kidder, Larry E. Korol, Valeriya Küchler, Lorenzo Laguna, Pablo Larrouturou, François Le Tiec, Alexandre Leather, Benjamin Lim, Eugene A. Lim, Hyun Littenberg, Tyson B. Long, Oliver Lousto, Carlos O. Lovelace, Geoffrey Lukes-Gerakopoulos, Georgios Lynch, Philip Macedo, Rodrigo P. Markakis, Charalampos Maggio, Elisa Mandel, Ilya Maselli, Andrea Mathews, Josh Mourier, Pierre Neilsen, David Nagar, Alessandro Nichols, David A. Novák, Jan Okounkova, Maria O’Shaughnessy, Richard Oshita, Naritaka O’Toole, Conor Pan, Zhen Pani, Paolo Pappas, George Paschalidis, Vasileios Pfeiffer, Harald P. Pompili, Lorenzo Pound, Adam Pratten, Geraint Rüter, Hannes R. Ruiz, Milton Sam, Zeyd Sberna, Laura Shapiro, Stuart L. Shoemaker, Deirdre M. Sopuerta, Carlos F. Spiers, Andrew Sundar, Hari Tamanini, Nicola Thompson, Jonathan E. Toubiana, Alexandre Tsokaros, Antonios Upton, Samuel D. van de Meent, Maarten Vernieri, Daniele Wachter, Jeremy M. Warburton, Niels Wardell, Barry Witek, Helvi Witzany, Vojtěch Yang, Huan Zilhão, Miguel Albertini, Angelica Arun, K.G. Bezares, Miguel Bonilla, Alexander Chapman-Bird, Christian Cownden, Bradley Cunningham, Kevin Devitt, Chris Dolan, Sam Duque, Francisco Dyson, Conor Fryer, Chris L. Gair, Jonathan R. Giacomazzo, Bruno Gupta, Priti Han, Wen-Biao Haas, Roland Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada School of Mathematics and Statistics University College Dublin Belfield Dublin 4 Ireland Universitat Politècnica de València València Spain Max Planck Institute for Extraterrestrial Physics Garching Germany Higgs Centre for Theoretical Physics Edinburgh United Kingdom Kavli Institute for Astronomy and Astrophysics Beijing100871 China William H. Miller III Department of Physics and Astronomy Johns Hopkins University BaltimoreMD21218 United States Astrophysics University of Oxford Denys Wilkinson Building Keble Road OxfordOX1 3RH United Kingdom School of Mathematical Sciences and STAG Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom SISSA Via Bonomea 265 Trieste34136 Italy INFN Sezione di Trieste IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy Max Planck Institute for Gravitational Physics Albert Einstein Institute PotsdamD-14476 Germany Laboratoire Univers et Théories CNRS Observatoire de Paris Université PSL Université Paris Cité Meudon92190 France Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena Jena07743 Germany Dipartimento di Fisica "G. Occhialini" Universitá degli Studi di Milano-Bicocca Piazza della Scienza 3 Milano20126 Italy INFN Sezione di Milano-Bicocca Piazza della Scienza 3 Milano20126 Italy Institute for Mathematics Astrophysics and Particle Physics Radboud University Nijmegen6525 AJ Netherlands The University of Arizona Department of Astronomy TucsonAZ85721 United States CENTRA Departamento de Física Instituto Superior Técnico – IST Universidade de Lisboa – UL Avenida Rovisco Pais 1 Lisboa1049-001 Portugal Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States Institut für Theoretische Physik Universität Leipzig Brüderstraße 16 Leipzig04103 Germany Rio de JaneiroCEP 22290-180 Brazil Department of Astronomy Cornell Univers

LISA, the Laser Interferometer Space Antenna, will usher in a new era in gravitational-wave astronomy. As the first anticipated space-based gravitational-wave detector, it will expand our view to the millihertz gravitational-wave sky, where a spectacular variety of interesting new sources abound: from millions of ultra-compact binaries in our Galaxy, to mergers of massive black holes at cosmological distances;from the beginnings of inspirals that will venture into the ground-based detectors’ view to the death spiral of compact objects into massive black holes, and many sources in between. Central to realising LISA’s discovery potential are waveform models, the theoretical and phenomenological predictions of the pattern of gravitational waves that these sources emit. This white paper is presented on behalf of the Waveform Working Group for the LISA Consortium. It provides a review of the current state of waveform models for LISA sources, and describes the significant challenges that must yet be overcome. © 2023, CC BY.

关键词： Gravity waves

An Algorithm for Describing the Convex and Concave Shape of Protein Surface

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An Algorithm for Describing the Convex and Concave Shape of ...

2018国际计算机前沿大会（原国际青年计算机大会）

作者： Wei Wang Keliang Li Hehe Lv Lin Sun Hongjun Zhang Jinling Shi Shiguang Zhang Yun Zhou Yuan Zhao Jingjing Xv Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining School of Aviation Engineering Anyang University School of International Education Xuchang University

Protein surface plays a key role in many biological *** proteins participate in the life activities of cells via binding to other proteins or ligand molecules. It is an important work to study protein structure and function by analyzing the protein surface shape. Based on the CX algorithm and the 2 D fngerprint-base method, we proposed a FCX method to identify the morphology of bulges and depressions on the protein surface. The experimental results show that the FCX algorithm has a more desirable outcome than CX algorithm. The FCX algorithm has a higher correlation with the convex and concave features than CX values with solvent accessibility, solvent accessibility, and Bfactor's Pearson correlation coefficient. This result shows that the FCX algorithm can describe the shape of the protein surface residues more accurately than the CX algorithm.

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