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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Elena Trunz Sebastian Merzbach Jonathan Klein Thomas Schulze Michael Weinmann Reinhard Klein Institute of Computer Science II University of Bonn

ISBN: (纸本)9781728132945

The analysis and modeling of cloth has received a lot of attention in recent years. While recent approaches are focused on woven cloth, we present a novel practical approach for the inference of more complex knitwear structures as well as the respective knitting instructions from only a single image without attached annotations. Knitwear is produced by repeating instances of the same pattern, consisting of grid-like arrangements of a small set of basic stitch types. Our framework addresses the identification and localization of the occurring stitch types, which is challenging due to huge appearance variations. The resulting coarsely localized stitch types are used to infer the underlying grid structure as well as for the extraction of the knitting instruction of pattern repeats, taking into account principles of Gestalt theory. Finally, the derived instructions allow the reproduction of the knitting structures, either as renderings or by actual knitting, as demonstrated in several examples.

关键词： Knitted fabrics Knitting Gestalt Psychology woven garment Rendering grid structures instruction Textile fabrics Modeling pattern repeat

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A Practical and Scalable Privacy-preserving Framework

A Practical and Scalable Privacy-preserving Framework

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Cyber Security and Resilience (CSR), IEEE International Conference on

作者： Nikos Avgerinos Salvatore D'Antonio Irene Kamara Christos Kotselidis Ioannis Lazarou Teresa Mannarino Georgios Meditskos Konstantina Papachristopoulou Angelos Papoutsis Paolo Roccetti Martin Zuber Diadikasia Business Consulting S.A Athens Greece TRUSTUP Naples Italy Tilburg Institute for Law Technology and Society Tilburg Law School Tilburg The Netherlands Department of Computer Science The University of Manchester Manchester United Kingdom EXUS Athens Greece Department of Advanced Biomedical Sciences University of Naples Federico II Naples Italy School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Eight Bells Ltd Athens Greece Information Technologies Institute CERTH Thessaloniki Greece R&D Labs Engineering Ingegneria Informatica Rome Italy Universite Paris-Saclay CEA List Palaiseau France

ENCRYPT is an EU funded research initiative, working towards the development of a scalable, practical, adaptable privacy-preserving framework, allowing researchers and developers to process data stored in federated cross-border data spaces in a GDPR-compliant way. ENCRYPT proposes an intelligent and user-centric platform for the confidential processing of privacy-sensitive data via configurable, optimizable, and verifiable privacy-preserving techniques. Research and development activities leverage, improve, and complement technologies and cryptographic schemes that represent the current state-of-the-art in the field of data-in-use protection. Hence, ENCRYPT builds on top of cutting-edge technologies such as Fully Homomorphic Encryption, Secure Multi-Party Computation, Differential Privacy, Trusted Execution Environment, GPU acceleration, knowledge graphs, and AI-based recommendation systems, making them configurable in terms of security and, most importantly, performance. The ENCRYPT framework is being designed taking into consideration the needs and preferences of relevant actors and will be validated in realistic use cases provided by consortium partners in three sectors, namely healthcare (oncology domain), fintech, and cyber threat intelligence domain. This position paper provides an overview of ENCRYPT by presenting project objectives, use cases, and technology pillars.

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A Low-Temperature Process for Device Quality Si/SiO 2 Interfaces On Si(111)

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MRS Online Proceedings Library 2020年第1期315卷 375-380页

作者： T. Yasuda D. R. Lee C. H. Bjorkman Y. Ma G. Lucovsky U. Emmerichs C. Meyer K. Leo H. Kurz Departments of Physics Materials Science and Engineering and Electrical and Computer Engineering North Carolina State University Raleigh USA Institute of Semiconductor Electronics II Rheinisch-Westfälishe Technische Hochschule Aachen Germany

Device-quality Si/SiO2 interfaces with an interface trap density as low as ~2 × 10{sp10} cm{sp−2}·eV{sp−1} were formed on Si(111) surfaces by a low-temperature, two-step oxidation/deposition process using a remote plasma enhanced CVD system. The micromorphology of the initial Si(111) surface was changed i) by controlling the roughness of the initial Si surface through the pH of HF/NH4F treatments, and ii) by an introduction of surface steps using Si(111) wafers that were cut off the axis in the [112] direction. When the Si wafer was subjected to a pre-deposition rinse in a 40 wt-% NH4F solution to develop an atomically smooth surface, the SiO2/Si(111) interface displayed a midgap interface trap density, Dit, of 2~3 × 10{sp10} cm{sp−2}·eV{sp−1} with Al as an electrode material. The Dit values increased systematically up to about 1 × 10{sp11} cm{sp−2}·eV{sp−1} as the pH of the HF/NH4F treatment was decreased. The density of surface steps had a lesser effect on Dit. MOS capacitors with an n-type poly-Si electrode also showed Dit of ~2 × 10{sp10} cm{sp−2}·eV{sp−1}. The effects of the high-temperature processing associated with poly-Si deposition and doping are discussed in conjunction with our recent study on second harmonic generation from the SiO2/Si interfaces.

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Semi-supervised Detector Training with Prototypes for Vehicle Detection

Semi-supervised Detector Training with Prototypes for Vehicl...

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International Conference on Intelligent Transportation

作者： Georg Waltner Michael Opitz Georg Krispel Horst Possegger Horst Bischof Institute of Computer Graphics and Vision (ICG) Faculty of Computer Science Graz University of Technology Graz Austria

Adapting detectors to new datasets is needed in scenarios where a user has a specific dataset that contains novel classes or is recorded in a setting where a pretrained detector fails. While detectors based on Convolutional Neural Networks (CNNs) are state-of-the-art and nowadays publicly available, they suffer from bad generalization capabilities when applied on datasets that notably differ from the one they were trained on. Finetuning the detector is only possible if the dataset is large enough to not destroy the underlying feature representation. We propose a method where only a few prototypes are labeled for training in a semi-supervised manner. In particular, we separate the detection from the classification step to avoid impairing the bounding box proposal generation. Our trained prototype classification network provides labels to automatically source a large dataset containing 20 to 30 times more samples without further supervision, which we then use to train a more powerful network. We evaluate our method on a private vehicle dataset with six classes and show that evaluating on a previously unseen recording site we can gain an accuracy increase of 9% at same precision and recall levels. We further show that finetuning with as few as 25 labeled samples per class doubles accuracy compared to directly using pretrained features for nearest neighbor classification.

关键词： Detectors Proposals Prototypes Training Object detection Feature extraction Task analysis

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Fast decomposable submodular function minimization using constrained total variation 33

Fast decomposable submodular function minimization using con...

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33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019

作者： Sesh Kumar, K.S. Bach, Francis Pock, Thomas Data Science Institute Imperial College London United Kingdom INRIA Ecole Normale Superieure PSL Research University Paris France Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

We consider the problem of minimizing the sum of submodular set functions assuming minimization oracles of each summand function. Most existing approaches reformulate the problem as the convex minimization of the sum of the corresponding Lovász extensions and the squared Euclidean norm, leading to algorithms requiring total variation oracles of the summand functions;without further assumptions, these more complex oracles require many calls to the simpler minimization oracles often available in practice. In this paper, we consider a modified convex problem requiring a constrained version of the total variation oracles that can be solved with significantly fewer calls to the simple minimization oracles. We support our claims by showing results on graph cuts for 2D and 3D graphs. © 2019 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

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Predicting the role of inequalities on human mobility patterns

arXiv

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arXiv 2024年

作者： Boldini, Alain De Lellis, Pietro Imperatore, Salvatore Das, Rishita Ceferino, Luis Heitor, Manuel Porfiri, Maurizio Department of Mechanical Engineering New York Institute of Technology College of Engineering and Computer Science Old WestburyNY11568 United States Center for Urban Science and Progress Department of Mechanical and Aerospace Engineering New York United States University Tandon School of Engineering BrooklynNY11201 United States Department of Electrical Engineering and Information Technology University of Naples Federico II NA Naples80125 Italy Center for Urban Science and Progress Department of Mechanical and Aerospace Engineering New York University Tandon School of Engineering BrooklynNY11201 United States Department of Aerospace Engineering Indian Institute of Science Karnataka Bengaluru560012 India Center for Urban Science and Progress Department of Civil and Urban Engineering New York University Tandon School of Engineering BrooklynNY11201 United States Department of Civil and Environmental Engineering College of Engineering University of California Berkeley BerkeleyCA94720 United States Center for Urban Science and Progress Marron Institute of Urban Management New York United States Center for Innovation Technology and Policy Research Instituto Superior Técnico Technical University of Lisbon Lisbon1049-001 Portugal Center for Urban Science and Progress Department of Mechanical and Aerospace Engineering Department of Biomedical Engineering New York University Tandon School of Engineering BrooklynNY11201 United States

Whether in search of better trade opportunities or escaping wars, humans have always been on the move. For almost a century, mathematical models of human mobility have been instrumental in the quantification of commuting patterns and migratory fluxes. Equity is a common premise of most of these mathematical models, such that living conditions and job opportunities are assumed to be equivalent across cities. Growing inequalities in modern urban economy and pressing effects of climate change significantly strain this premise. Here, we propose a mobility model that is aware of inequalities across cities in terms of living conditions and job opportunities. Comparing results with real datasets, we show that the proposed model outperforms the state-of-the-art in predicting migration patterns in South Sudan and commuting fluxes in the United States. This model paves the way to critical research on resilience and sustainability of urban systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AiiRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AiiRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AiiRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AiiRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AiiRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AiiRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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Duality in vector optimization with domination structures

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and Optimization 2019年第3期1卷 319-335页

作者： Bao, Truong Quang Le, Thanh T.A.M. Tammer, Christiane Tuan, Vu A.N.H. Department of Mathematics and Computer Science Northern Michigan University United States Institute of Mathematics Faculty of Natural Sciences II Martin-Luther-University Halle-Wittenberg Germany Faculty of Basic Sciences University of Transport and Communications Hanoi Viet Nam

In this paper, we study duality schemes for vector optimization problems with respect to variable domination structures by using an appropriate extension of the so-called Gerstewitz nonlinear scalarization function for dealing with weakly nondominated solutions. We apply these results to multiobjective location problems with variable domination structures. For nondominated solutions, we use a general scalarization function which is strictly monotone with respect to the domination structure. The strict monotonicity of our proposed function is also discussed. © 2019 Applied Set-Valued Analysis and Optimization.

关键词： Multiobjective optimization

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Automatic normal orientation in point clouds of building interiors

arXiv

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arXiv 2019年

作者： Ochmann, Sebastian Klein, Reinhard Institute of Computer Science II University of Bonn Germany

Orienting surface normals correctly and consistently is a fundamental problem in geometry processing. Applications such as visualization, feature detection, and geometry reconstruction often rely on the availability of correctly oriented normals. Many existing approaches for automatic orientation of normals on meshes or point clouds make severe assumptions on the input data or the topology of the underlying object which are not applicable to real-world measurements of urban scenes. In contrast, our approach is specifically tailored to the challenging case of unstructured indoor point cloud scans of multi-story, multi-room buildings. We evaluate the correctness and speed of our approach on multiple real-world point cloud datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

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Generation of simple, connected, non-isomorphic random graphs 6th

Generation of simple, connected, non-isomorphic random graph...

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6th International Doctoral Symposium on Applied Computation and Security Systems, ACSS 2019

作者： Chakraborty, Maumita Chowdhury, Sumon Pal, Rajat Kumar Department of Information Technology Institute of Engineering and Management Salt Lake Sector V Kolkata700091 India Tata Consultancy Services Gitanjali Park Action Area II New Town Kolkata700156 India Department of Computer Science and Engineering University of Calcutta JD-2 Sector III Salt Lake Kolkata700106 India

ISBN: (纸本)9789811389610

In graph theory, generation of random graphs finds a wide range of applications in different scheduling problems, approximation algorithms, problems involving modeling and simulation, different database applications, and obviously to test the performance of any algorithm. The algorithm, which has been devised in this paper, is mainly for the purpose of providing test bed for checking performance of other algorithms. It generates different non-isomorphic graph instances of a given order and having unique number of edges. The number of such instances possible for a graph of given order has also been subsequently formulated. Different such graph instances of different orders, generated in a uniform computing environment, and the computing time required for such generations have also been included in this paper. The simplicity and efficiency of the algorithm, subsequently proved in the paper, give us a new insight in the area of random graph generation and have called for further research scope in the domain. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Graph theory

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