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IEEE International Conference on Big data

作者： John Paisley Sebastian Rowland Jeremiah Zhe Liu Brent Coull Marianthi-Anna Kioumourtzoglou Dept. of Electrical Engineering Data Science Institute Columbia University New York NY USA Environmental Health Sciences Columbia University New York NY USA Google Research San Francisco CA USA Department of Biostatistics Harvard University Boston MA USA

ISBN: (纸本)9781665480468

Bayesian nonparametric methods provide a convenient and well-founded framework for constructing spatio-temporally evolving ensemble models. They not only provide a flexible way to weight different underlying models in an ensemble according to the strengths of each model on different regions of the input space, but also allow for quantification of the ensemble’s uncertainty. However, computational costs can pose a challenge to kernel-based ensemble methods when spatio-temporal resolution is high, such as environmental models. We propose a Bayesian Nonparametric Ensemble (BNE) method for spatio-temporal ensemble learning based on the Gaussian process. Our ensemble relies on theoretically well-founded linearized approximations to the Gaussian process to adaptively weight the underlying models in a way that is scalable and amenable to stochastic learning methods. We investigate both the random Fourier feature and Nystrom approaches in this setting. We demonstrate the practicality and usefulness of the approximate model on the problem of air pollution prediction across the contiguous USA over a 6 year period.

关键词： Adaptation models Atmospheric modeling Computational modeling Gaussian processes Predictive models Big data Air pollution

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Crystal structure prediction by joint equivariant diffusion 23

Crystal structure prediction by joint equivariant diffusion

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Rui Jiao Wenbing Huang Peijia Lin Jiaqi Han Pin Chen Yutong Lu Yang Liu Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University and Institute for AIR Tsinghua University Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University Stanford University

Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion models), this task encounters unique challenges owing to the symmetric geometry of crystal structures—the invariance of translation, rotation, and periodicity. To incorporate the above symmetries, this paper proposes DiffCSP, a novel diffusion model to learn the structure distribution from stable crystals. To be specific, DiffCSP jointly generates the lattice and atom coordinates for each crystal by employing a periodic-E(3)-equivariant denoising model, to better model the crystal geometry. Notably, different from related equivariant generative approaches, DiffCSP leverages fractional coordinates other than Cartesian coordinates to represent crystals, remarkably promoting the diffusion and the generation process of atom positions. Extensive experiments verify that our DiffCSP significantly outperforms existing CSP methods, with a much lower computation cost in contrast to DFT-based methods. Moreover, the superiority of DiffCSP is also observed when it is extended for ab initio crystal generation. Code is available at https://***/jiaor17/DiffCSP.

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Intratumoral heterogeneity of second-harmonic generation scattering from tumor collagen and its effects on metastatic risk prediction 21

Intratumoral heterogeneity of second-harmonic generation sca...

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Multiphoton Microscopy in the Biomedical sciences XXI 2021

作者： Desa, Danielle E. Wu, Wencheng Hill, Robert L. Martens, J.W.M. Strawderman, Robert L. Turner, Bradley M. Brown, Edward B. Dept. of Biomedical Engineering University of Rochester 201 Goergen Hall RochesterNY14627 United States Goergen Institute for Data Science University of Rochester 1209 Wegmans Hall RochesterNY14627 United States Harmonigenic Corp. RochesterNY United States Dept. of Medical Oncology Erasmus University Medical Center PO Box 5201 Rotterdam3008 AE Netherlands Dept. of Biostatistics and Computational Biology University of Rochester Medical Center 265 Crittenden Blvd. RochesterNY14642 United States Dept. of Pathology and Laboratory Medicine University of Rochester Medical Center 601 Elmwood Ave. RochesterNY14642 United States

ISBN: (纸本)9781510641310

Metastases are the leading cause of breast cancer-related deaths. Tumor metastasis is affected by the microenvironment, including the extracellular matrix (ECM). Fibrillar collagen in the ECM produces an intrinsic optical signal called secondharmonic generation (SHG). The ratio of forward- to backward-scattered SHG photons (F/B) is sensitive to the collagen fiber internal structure and has been shown to be an independent prognostic indicator of metastasis-free survival time. We evaluated the effects of heterogeneity in the tumor ECM on F/B's prognostic ability. Using SHG imaging we identified two distinct regions within 95 untreated primary tumor excisions: the tumor bulk and surrounding collagenous tumorstroma interface. We found that F/B measured in the tumor-stroma interface, but not tumor bulk, is prognostic of metastasis-free survival time (MFS) using both an intensity threshold selected by a blinded observer and adaptive thresholding. We calculated a 21-gene recurrence score (surrogate OncotypeDX®, or S-ODX) for each patient and using a Random Survival Forest method we found that F/B from the tumor-stroma interface, but not tumor bulk, and S-ODX contribute to predicting MFS in this patient cohort. These results suggest that F/B from the tumor-stroma interface of primary tumor excisions may provide information independent of genomic methods to stratify patients by metastatic risk and identify need for post-operative treatment. © 2021 SPIE. All rights reserved.

关键词： Harmonic generation

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Advanced Battery Management for Electric Vehicles: A Deep Dive into Estimation Techniques Based on Deep Learning for the State of Health and State of Charge of Lithium-Ion Batteries

Advanced Battery Management for Electric Vehicles: A Deep Di...

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Electrical, Electronic and Information sciences (eStream), Open Conference of

作者： Saad El Fallah Jaouad Kharbach Jonas Vanagas Živile Vilkelytė Sonata Tolvaišienė Ghita Ikmel Assia Redouani Saloua Lagnaoui Khaoula Boumais Mohammed Ouazzani Jamil Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences (FSI) Private University of Fez (UPF) Fez Morocco Université Sidi Mohamed Ben Abdellah Faculté des Sciences Dhar El Mahraz Laboratoire de Physique de Solide Fez Morocco Dept. Electrical Engineering Vilnius TECH Vilnius Lithuania Signals Systems Components Laboratory (LSSC) Faculty of Science and Technology Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco Artificial Intelligence Data Science and Emerging Systems (IASSE) ENSA Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798350352412

ISBN: (纸本)9798350352429

The precision of state of charge (SoC) prediction prediction of the SoC is necessary to avoid deep discharging and remains an important challenge in the field of electric vehicles and overcharging, which can damage batteries and shorten their life. the energy storage industry. The SoC is the percentage of energy The degradation of lithium-ion batteries is a key area of research, available in a battery relative to its total capacity. A precise as these type of batteries are extensively employed in many sectors, in particular electric vehicles, electronic devices and renewable energies. Evaluation systems based on deep learning, like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), are becoming increasingly popular for their ability to process complex data and make accurate predictions. As lithium-ion batteries degrade over time, their reliability in storing and delivering energy is diminishing. To effectively monitor and manage this degradation, researchers are turning to evaluation systems based on deep learning. These approaches enable the prediction of battery state of health (SoH) and SoC, making it easier to optimize battery use and extend battery life. This article presents various techniques for predicting the SoH and SoC of batteries to evaluate the degradation of cells.

关键词： Lithium-ion batteries Degradation Deep learning Recurrent neural networks Estimation Battery management systems Electric vehicles

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Decision-Focused Learning: Through the Lens of Learning to Rank

arXiv

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arXiv 2021年

作者： Mandi, Jayanta Bucarey, Víctor Mulamba, Maxime Guns, Tias Data Analytics Laboratory Vrije Universiteit Brussel Belgium Institute of Engineering Sciences Universidad de O’Higgins Rancagua Chile Dept. Computer Science KU Leuven Belgium

In the last years, decision-focused learning, also known as predict-and-optimize, has received increasing attention. In this setting, the predictions of a machine learning model are used as estimated cost coefficients in the objective function of a discrete combinatorial optimization problem for decision making. Decision-focused learning proposes to train the ML models, often neural network models, by directly optimizing the quality of decisions made by the optimization solvers. Based on a recent work that proposed a noise contrastive estimation loss over a subset of the solution space, we observe that decision-focused learning can more generally be seen as a learning-to-rank problem, where the goal is to learn an objective function that ranks the feasible points correctly. This observation is independent of the optimization method used and of the form of the objective function. We develop pointwise, pairwise and listwise ranking loss functions, which can be differentiated in closed form given a subset of solutions. We empirically investigate the quality of our generic methods compared to existing decision-focused learning approaches with competitive results. Furthermore, controlling the subset of solutions allows controlling the runtime considerably, with limited effect on regret. © 2021, CC BY.

关键词： Decision making

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PERSISTENT ANIMAL IDENTIFICATION LEVERAGING NON-VISUAL MARKERS

arXiv

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arXiv 2021年

作者： Camilleri, Michael P.J. Zhang, Li Bains, Rasneer S. Zisserman, Andrew Williams, Christopher K.I. School of Informatics University of Edinburgh United Kingdom School of Data Science Fudan University China Mary Lyon Centre MRC Harwell United Kingdom Dept. of Engineering Science University of Oxford United Kingdom

Our objective is to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time, as a precursor to automated behaviour recognition for biological research. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion, making standard visual tracking approaches unusable. However, a coarse estimate of each mouse’s location is available from a unique RFID implant, so there is the potential to optimally combine information from (weak) tracking with coarse information on identity. To achieve our objective, we make the following key contributions: (a) the formulation of the object identification problem as an assignment problem (solved using Integer Linear Programming), (b) a novel probabilistic model of the affinity between tracklets and RFID data, and (c) a curated dataset with per-frame Bounding Box (BB) and regularly-spaced ground-truth annotations for evaluating the models. The latter is a crucial part of the model, as it provides a principled probabilistic treatment of object detections given coarse localisation. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden. Copyright © 2021, The Authors. All rights reserved.

关键词： Radio frequency identification (RFID)

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Detection of careless mistakes during programming learning using a simple electroencephalograph 15

Detection of careless mistakes during programming learning u...

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15th International Conference on Computer science and Education, ICCSE 2020

作者： Umezawa, Katsuyuki Nakazawa, Makoto Kobayashi, Manabu Ishii, Yutaka Nakano, Michiko Hirasawa, Shigeichi Shonan Institute of Technology Dept. of Information Science Kanagawa Japan College of Aizu Dept. of Industrial Information Science Fukushima Japan Waseda University Center for Data Science Tokyo Japan Chiba University Faculty of Education Chiba Japan Waseda University Faculty of Education and Integrated Arts and Sciences Tokyo Japan Waseda University Research Institute for Science and Engineering Tokyo Japan

ISBN: (纸本)9781728172675

There are several difficulties encountered by learners during learning such as good or bad learning content, the difficulty level of learning content, and the degree of learning proficiency. It is possible to detect these difficulties by measuring the browsing history, editing history, and biological information such as brain waves or eye-tracking information. In this paper, we measure electroencephalograph (EEG) information during programming learning. We focus on the relationship between task response time and EEG, and try to detect careless mistakes due to the lack of attention. The results show that careless mistakes during programming learning can be detected by experiments. © 2020 IEEE.

关键词： Eye tracking

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Monitoring System of Seismo Dynamics of Rocks for Recording Earthquake Precursors 15

Monitoring System of Seismo Dynamics of Rocks for Recording ...

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15th International Scientific-Technical Conference on Actual Problems of Electronic Instrument engineering, APEIE 2021

作者： Zhmud, V.A. Dimitrov, L.V. Mansurova, M.E. Semibalamut, V.M. Fomin, Yu. N. Stukach, O.V. Novosibirsk State Technical University Institute of Laser Physics SB RAS Russia Dept. of Automation Novosibirsk Russia Technical University of Sofia Mechanical Engineering Faculty Sofia Bulgaria Al-Farabi Kazakh National University Department of Artificial Intelligence and Big Data Almaty Kazakhstan Siberian Branch of the Federal State Budgetary Institution of Science of the Federal Research Center Unified Geophysical Service of the Russian Academy of Sciences Novosibirsk Russia Novosibirsk State Technical University Dept. of Information Defense Novosibirsk Russia

ISBN: (纸本)9781665434089

Internet of things (IoT) and smart city technologies are closely related to citizen security issues. One of the important components of safety is to reduce damage from man-made and natural disasters. In this area, the most important direction is the continuous monitoring of rock vibrations under the influence of tidal forces. These technologies, with minor changes, can be used to monitor the state of complex technological structures, such as dams, bridges, large buildings. It is known that continuous recording of micro deformations of rocks under the action of gravitational forces from the Sun and the Moon due to a change in their location in relation to the Earth's surface allows one to obtain additional information about the possible future earthquakes. The corresponding meters are based on two helium-neon (He-Ne) lasers. They contain a rather complicated and insufficiently reliable system for automatic phase and frequency tuning of the slave laser to the master. Insufficient accuracy of such phase feedback control generates errors in measurement. This paper proposes to abandon such variants of the devices based on the use of two lasers. The structure based on the only laser has been proposed. This requires metrological justification, as well as the development of appropriate optical schemes, design development and change of the electronic part. Nevertheless, such a modification seems appropriate. The reasons for this lie in a clear increase in reliability and accuracy, a decrease in cost and size. © 2021 IEEE.

关键词： Earthquakes

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Ambidexterity and High-Performance Work Systems: the Roles of knowledge Sharing, Leadership Style, and Social Media

Ambidexterity and High-Performance Work Systems: the Roles o...

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International Conference on Artificial Intelligence and data sciences (AiDAS)

作者： Saide Saide Nurfitria Ningsi Didi Muwardi Dian Jelita Nita Antasari Department of Information Systems Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia PRO Knowledge Pekanbaru Indonesia Dept. Informatics Engineering Universitas Pahlawan Tuanku Tambusai Kampar Indonesia Department of Information Systems Faculty of Information Technology Universitas Sembilanbelas November Kolaka Sulawesi Tenggara Indonesia Department of Agribusiness Riau University Pekanbaru Indonesia Pendidikan Agama Islam Fakultas Tarbiyah dan Tadris Universitas Islam Negeri Fatmawati Sukarno Bengkulu Bengkulu Indonesia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Research on knowledge sharing, participative leadership, and exploitation-exploration has demonstrated its influence on high-performance work systems (HPWS) and the quality of employee performance. In the context of employee ambidexterity, which encompasses both exploitation and exploration innovations, there is a notable gap in research directly addressing these two aspects. data were collected from target employees using an offline questionnaire-based survey, yielding 56 valid responses. This study aims to determine the extent and significance of the supporting variables on employee ambidexterity. Using structural equation modeling, the findings reveal that participative leadership has a positive and significant effect on HPWS and the quality of employee performance. Additionally, exploration has a positive and significant effect on HPWS. The results of this study suggest strategies for enhancing the competitive quality of employee performance, including participative leadership, knowledge sharing, and exploration innovation.

关键词： Surveys Leadership Technological innovation Social networking (online) Mathematical models Artificial intelligence

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Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift

arXiv

引用

arXiv 2022年

作者： Ma, Wenao Chen, Cheng Zheng, Shuang Qin, Jing Zhang, Huimao Dou, Qi Dept. of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Radiology The First Hospital of Jilin University China Jilin Provincial Key Laboratory of Medical Imaging & Big Data China Centre for Smart Health The Hong Kong Polytechnic University Hong Kong

Class distribution plays an important role in learning deep classifiers. When the proportion of each class in the test set differs from the training set, the performance of classification nets usually degrades. Such a label distribution shift problem is common in medical diagnosis since the prevalence of disease vary over location and time. In this paper, we propose the first method to tackle label shift for medical image classification, which effectively adapt the model learned from a single training label distribution to arbitrary unknown test label distribution. Our approach innovates distribution calibration to learn multiple representative classifiers, which are capable of handling different one-dominating-class distributions. When given a test image, the diverse classifiers are dynamically aggregated via the consistency-driven test-time adaptation, to deal with the unknown test label distribution. We validate our method on two important medical image classification tasks including liver fibrosis staging and COVID-19 severity prediction. Our experiments clearly show the decreased model performance under label shift. With our method, model performance significantly improves on all the test datasets with different label shifts for both medical image diagnosis tasks. Code is available at https://***/med-air/TTADC. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

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