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arXiv 2024年

作者： Dieny, Bernard Aggarwal, Sanjeev Naik, Vinayak Bharat Couet, Sebastien Coughlin, Thomas Fukami, Shunsuke Garello, Kevin Guedj, Jack Incorvia, Jean Anne C. Lebrun, Laurent Lee, Kyung-Jin Leonelli, Daniele Noh, Yonghwan Salimy, Siamak Soss, Steven Thomas, Luc Wang, Weigang Worledge, Daniel Univ. Grenoble Alpes CEA CNRS SPINTEC Grenoble38000 France Everspin Technologies 5670 W Chandler Blvd Suite 130 AZ85226 United States GlobalFoundries 400 Stone Break Rd Extension MaltaNY12020 United States Imec Leuven3001 Belgium Coughlin Associates AtascaderoCA United States Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Center for Science and Innovation in Spintronics Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan NUMEM SunnyvaleCA United States Dept. of Electrical and Computer Engineering The University of Texas at Austin AustinTX United States Hprobe 4 Rue Irène Joliot Curie Eybens38320 France Daejeon Korea Republic of Huawei Technologies R&D Belgium N.V. Leuven Belgium Netsol Dongtan-daero 23-gil Hwaseong-si Gyeonggi-do Korea Republic of Applied Materials Santa ClaraCA95054-3299 United States Department of Physics Department of Electrical & Computer Engineering The University of Arizona TucsonAZ85721 United States IBM Almaden Research Center San JoseCA United States

This application note discusses the working principle of spin-transfer torquemagnetoresistive random access memory (STT-MRAM) and the impact that magneticfields can have on STT-MRAM operation. Sources of magnetic field and typicalmagnitudes of magnetic fields are given. Based on the magnitude of commonlyencountered external magnetic fields, we show below that magnetic immunity ofSTT-MRAM is sufficient for most uses once the chip is mounted on a printedcircuit board (PCB) or inserted in its working environment. This statement issupported by the experience acquired during 60 years of use of magnetic harddisk drives (HDD) including 20 years of HDD with readers comprising magnetictunnel junctions, 20+ years of use of magnetic field sensors as positionencoders in automotive industry and 15+ years of use of MRAM. Mainly duringchip handling does caution need to be exercised to avoid exposing the chip toexcessively high magnetic fields. © 2024, CC BY.

关键词： Automotive industry

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computer vision for focus calibration of photo-polymerization systems

Computer vision for focus calibration of photo-polymerizatio...

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2018 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing

作者： Ingwersen, Christian K. Mortensen, Harald L. Ribo, Macarena M. Danielak, Anna H. Eiriksson, Eythor R. Nielsen, Allan A. Pedersen, David B. Dept. of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark Dept. of Mechanical Engineering Technical University of Denmark Kgs. Lyngby Denmark

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Differentially private k-means with constant multiplicative error

arXiv

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arXiv 2018年

作者： Kaplan, Haim Stemmer, Uri School of Computer Science Tel Aviv University and Google Dept. of Computer Science and Applied Math Weizmann Institute of Science

We design new differentially private algorithms for the Euclidean k-means problem, both in the centralized model and in the local model of differential privacy. In both models, our algorithms achieve significantly improved error guarantees than the previous state-of-the-art. In addition, in the local model, our algorithm significantly reduces the number of interaction rounds. Although the problem has been widely studied in the context of differential privacy, all of the existing constructions achieve only super constant approximation factors. We present-for the first time-efficient private algorithms for the problem with constant multiplicative error. Furthermore, we show how to modify our algorithms so they compute private corsets for k-means clustering in both models. Copyright © 2018, The Authors. All rights reserved.

关键词： Errors

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Semantic Synergy Exploration in Interface Description Models of Heterogeneus Vehicle Frameworks: Towards Automotive Meta Interface Description Model

Semantic Synergy Exploration in Interface Description Models...

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ARCS Workshop 2019;32nd International Conference on Architecture of Computing Systems

作者： Sangita De Michael Niklas Juergen Mottok Premek Brada Corporate System and Technology SW Continental Automotive GmbH Dept. of Electrical Engineering and Information Technology OstBayerische Technical University (OTH) Dept. of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia

ISBN: (纸本)9783800749577

This The vehicle is evolving to a complex network of heterogeneous subsystems of ECUs, sensors and actuators, each with different computational requirements. These sub- systems are connected via bus systems following different communication paradigms like e.g. signal based or service-oriented communications. This has led to the heterogeneous syntax of describing interfaces even though the semantics of the interfaces are similar. The wide variety of Interface Description Languages (IDLs) in automotive industry hinders partly with the efficient collaboration between different suppliers and the OEMs in the automotive industry. Given this wide variety of automotive IDLs, what could be more beneficial, from a software engineering point of view, is a generic automotive domain specific IDL that can satisfy all the fundamental requirements of the heterogeneous subsystems. This paper describes an approach to compare and correlate IDLs based on semantic similarities of the languages, considering the two aspects: application description and underlying message frameworks used in the different domains of given automotive subsystems. With the exploration of semantic synergies between the IDLs, various domain specific and domain-agnostic frameworks can be compared and correlated. The results can be generalized and abstracted to define a generic Meta IDL which could support use cases like e.g. domain-agnostic functional models and migration of software components between different kinds of automotive subsystems.

关键词： Framework Semantic Traits Synergy Meta IDL Component model Vehicle Interface Communication protocol Semantics Communication protocols Frameworks Synergism vehicle interface description personality trait automotive engineering SUB-SYSTEM Interface Interfaces automobile industry Exploration

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InGAN: Capturing and remapping the "DNA" of a natural image

arXiv

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arXiv 2018年

作者： Shocher, Assaf Bagon, Shai Isola, Phillip Irani, Michal Dept. of Computer Science and Applied Math Weizmann Institute of Science Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology

Generative Adversarial Networks (GANs) typically learn a distribution of images in a large image dataset, and are then able to generate new images from this distribution. However, each natural image has its own internal statistics, captured by its unique distribution of patches. In this paper we propose an "Internal GAN" (InGAN) – an image-specific GAN – which trains on a single input image and learns its internal distribution of patches. It is then able to synthesize a plethora of new natural images of significantly different sizes, shapes and aspect-ratios all with the same internal patch-distribution (same "DNA") as the input image. In particular, despite large changes in global size/shape of the image, all elements inside the image maintain their local size/shape. InGAN is fully unsupervised, requiring no additional data other than the input image itself. Once trained on the input image, it can remap the input to any size or shape in a single feedforward pass, while preserving the same internal patch distribution. InGAN provides a unified framework for a variety of tasks, bridging the gap between textures and natural images.1 Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Facial Image Inpainting with Variational Autoencoder

Facial Image Inpainting with Variational Autoencoder

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IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Ching-Ting Tu Yi-Fu Chen Department of Applied Mathematics National Chung Hsing University Taichung City Taiwan Dept. of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan

ISBN: (数字)9781728141923

ISBN: (纸本)9781728141930

This paper proposed a learning-based approach to reveal diversity possible appearances under the missing area of an occluded unseen image. In general, there are a lot of possible facial appearances for the missing area; for example, a male with a scarf, it is difficult to predict he has a beard in the covered area or not? In this paper, we propose a novel method for facial image inpainting, which generates the missing facial appearance by conditioning on the observable appearance. Given a trained standard Variational Autoencoder (VAE) for un-occluded face generation. To be specified, we search for the possible set of VAE coding vector for the current occluded input image, and the predicted coding should be robust to the missing area. The possible facial appearance set is then recovered through the decoder of VAE model. Experiments show that our method successfully predicts recovered results in large missing regions; these results are diverse, and all are reasonable to be consistent with the observable facial area, i.e., both the facial geometry and the personal characteristics are preserved.

关键词： image inpainting variational autoencoder sampling Face Sampling Imagery (Psychotherapy) Image images Facies Beard Scarves decoders occluding

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Recommendations for machine learning validation in biology

arXiv

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arXiv 2020年

作者： Walsh, Ian Fishman, Dmytro Garcia-Gasulla, Dario Titma, Tiina Harrow, Jen Psomopoulos, Fotis E. Tosatto, Silvio C.E. Bioprocessing Technology Institute Agency for Science Technology and Research Singapore Institute of Computer Science University of Tartu Estonia Barcelona Spain School of Information Technologies Tallinn University of Technology Estonia ELIXIR HUB South building Wellcome Genome Campus Hinxton Cambridge United Kingdom Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece Dept. of Biomedical Sciences University of Padua Padua Italy

Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recommendations aiming to help establish standards of machine learning validation in biology. Adopting a structured methods description for machine learning based on DOME (data, optimization, model, evaluation) will allow both reviewers and readers to better understand and assess the performance and limitations of a method or outcome. The recommendations are complemented by a machine learning summary table which can be easily included in the supplementary material of published papers. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Analytic and directional wavelet packets

Analytic and directional wavelet packets

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International Conference on Sampling Theory and Applications (SampTA)

作者： Amir Z. Averbuch Pekka Neittaanmäki Valery A. Zheludev Sch. of Computer Science Tel Aviv University Tel Aviv Israel Dept. of Math. Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9781728137414

ISBN: (纸本)9781728137421

The paper presents a versatile library of analytic and quasi-analytic complex-valued wavelet packets (WPs) which originate from discrete splines of arbitrary orders. The real parts of quasi-analytic WPs are the regular spline-based orthonormal WPs designed in [1]. The imaginary parts are the so-called complementary orthonormal WPs, which, unlike the symmetric regular WPs, are antisymmetric. Tensor products of 1D quasi-analytic WPs provide a diversity of 2D WPs oriented in multiple directions. For example, the set of fourth-level WPs comprise 62 different directions. The designed computational scheme enables extremely fast and easy implementation of the WP transforms.

关键词： Wavelet packets Splines (mathematics) Discrete Fourier transforms Two dimensional displays Transient analysis Radio frequency

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Colorful Helly-type theorems for ellipsoids

arXiv

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arXiv 2019年

作者： Damásdi, Gábor Földvári, Viktória Naszódi, Márton Dept. of Computer Science Eötvös University Budapest Hungary Dept. of Geometry Eötvös University Budapest Hungary Alfréd Rényi Inst. of Math. Dept. of Geometry Eötvös University Budapest Hungary

We prove the following Helly-type result. Let C1,... ,C3d be finite families of convex bodies in Rd. Assume that for any colorful choice of 2d sets, CikG Cikfor each 2d 1 kcontains an ellipsoid fc=1 of volume at least...

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Key components of an E-Learning module in statistics for computer science students 13

Key components of an E-Learning module in statistics for com...

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13th International Conference on computer science and Education, ICCSE 2018

作者： Andersson, Christina Kroisandt, Gerald Dept. of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany Htw Saar School of Engineering Saarbrücken Germany

ISBN: (纸本)9781538654958

The traditionally taught face-to-face course in statistics for undergraduate non-statistics students suffered from several problems: Time and location constraints, different preknowledge in mathematics, lack of motivation, no use of statistical software etc. Therefore, we decided to develop an e-learning course in statistics to reduce the impact of these problems. This paper describes the different components developed for the e-learning course as well as the teaching framework. Preliminary results from the first in-class usage of the e-learning course indicate positive qualitative effects. In addition, we discuss which of the e-learning components the students mainly preferred to use. Furthermore, we show that the exam results from the e-learning course are significantly better than the corresponding results from the face-to-face course in this first in-class usage. © 2018 IEEE.

关键词： Statistics

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