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arXiv 2021年

作者： Lyu, Bo Wang, Shengbo Wen, Shiping Shi, Kaibo Yang, Yin Zeng, Lingfang Huang, Tingwen Zhejiang Lab Hangzhou311121 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia School of Information Science and Engineering Chengdu University Chengdu611040 China College of Science and Engineering Hamad Bin Khalifa University Doha5855 Qatar Science Program Texas A & M University at Qatar Doha23874 Qatar

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., Social Networks, Knowledge Graphs) on traditional computing architectures (CPU, GPU, or TPU). But the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy. To implement the computation or storage of large-scale or batch graphs on memristive crossbars, a natural assumption is that a large-scale crossbar is demanded, but with low utilization. Some recent works question this assumption, to avoid the waste of storage and computational resource, the fixed-size or progressively scheduled "block partition" schemes are proposed. However, these methods are coarse-grained or static, and are not effectively sparsity-aware. This work proposes the dynamic sparsity-aware mapping scheme generating method that models the problem with a sequential decision-making model, and optimizes it by reinforcement learning (RL) algorithm (REINFORCE). Our generating model (LSTM, combined with the dynamic-fill scheme) generates remarkable mapping performance on a small-scale graph/matrix data (complete mapping costs 43% area of the original matrix) and two large-scale matrix data (costing 22.5% area on qh882 and 17.1% area on qh1484). Our method may be extended to sparse graph computing on other PIM architectures, not limited to the memristive device-based platforms. Copyright © 2021, The Authors. All rights reserved.

关键词： Memory architecture

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Correction: Unbiased and multilevel methods for a class of diffusions partially observed via marked point processes

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Statistics and Computing 2024年第6期34卷 1-1页

作者： Alvarez, Miguel Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia School of Data Science The Chinese University of Hong Kong Shenzhen China

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Self-supervised approach for facial movement based optical flow

arXiv

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arXiv 2021年

作者： Alkaddour, Muhannad Tariq, Usman Dhall, Abhinav Graduate Program in Mechatronics Engineering American University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering American University of Sharjah Sharjah United Arab Emirates Department of Human Centred Computing Monash University Melbourne Australia Department of Computer Science and Engineering Indian Institute of Technology Ropar Rupnagar India

Computing optical flow is a fundamental problem in computer vision. However, deep learning-based optical flow techniques do not perform well for non-rigid movements such as those found in faces, primarily due to lack of the training data representing the fine facial motion. We hypothesize that learning optical flow on face motion data will improve the quality of predicted flow on faces. The aim of this work is threefold: (1) exploring self-supervised techniques to generate optical flow ground truth for face images;(2) computing baseline results on the effects of using face data to train Convolutional Neural Networks (CNN) for predicting optical flow;and (3) using the learned optical flow in micro-expression recognition to demonstrate its effectiveness. We generate optical flow ground truth using facial key-points in the BP4D-Spontaneous dataset. The generated optical flow is used to train the FlowNetS architecture to test its performance on the generated dataset. The performance of FlowNetS trained on face images surpassed that of other optical flow CNN architectures, demonstrating its usefulness. Our optical flow features are further compared with other methods using the STSTNet micro-expression classifier, and the results indicate that the optical flow obtained using this work has promising applications in facial expression analysis. © 2021, CC BY.

关键词： Optical flows

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A micromirror array with annular partitioning for high-speed random-access axial focusing

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Light(Science & Applications) 2020年第1期9卷 241-255页

作者： Nathan Tessema Ersumo Cem Yalcin Nick Antipa Nicolas Pegard Laura Waller Daniel Lopez Rikky Muller The University of California Berkeley and University of CaliforniaSan Francisco Graduate Program in BioengineeringBerkeleyCA 94720USA Department of Electrical Engineering&Computer Sciences University of CaliforniaBerkeleyCA 94720USA Department of Applied Physical Sciences University of North Carolina at Chapel HillChapel HillNC 27514USA Chan Zuckerberg Biohub San FranciscoCA 94158USA Physical Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA

Dynamic axial focusing functionality has recently experienced widespread incorporation in microscopy,augmented/virtual reality(AR/VR),adaptive optics and material ***,the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such *** varifocal tools that are the least burdensome to operate(*** crystal,elastomeric or optofluidic lenses)suffer from low(≈100 Hz)refresh ***,the fastest devices sacrifice either critical capabilities such as their dwelling capacity(*** gradient lenses or monolithic micromechanical mirrors)or low operating overhead(*** mirrors).Here,we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed,dwelling capacity and lightweight drive by employing lowrigidity micromirrors that exploit the robustness of defocusing phase ***,the device consists of an 8.2mm diameter array of piston-motion and 48-μm-pitch micromirror pixels that provide 2πphase shifting for wavelengths shorter than 1100 nm with 10-90% settling in 64.8μs(i.e.,15.44 kHz refresh rate).The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires<30 V per *** experiments demonstrated the array’s wide focusing range with a measured ability to target 29 distinct resolvable depth ***,the features of the proposed array offer the potential for compact,straightforward methods of tackling bottlenecked applications,including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR.

关键词： mirror axial random

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Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification

arXiv

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arXiv 2021年

作者： Piao, Yinhua Lee, Sangseon Lee, Dohoon Kim, Sun Department of Computer Science and Engineering Seoul National University Korea Republic of Institute of Computer Technology Seoul National University Korea Republic of Bioinformatics Institute Seoul National University Korea Republic of AIGENDRUG Co. Ltd Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Recently, graph neural networks (GNNs) have been widely used for document classification. However, most existing methods are based on static word co-occurrence graphs without sentence-level information, which poses three challenges:(1) word ambiguity, (2) word synonymity, and (3) dynamic contextual dependency. To address these challenges, we propose a novel GNN-based sparse structure learning model for inductive document classification. Specifically, a document-level graph is initially generated by a disjoint union of sentence-level word co-occurrence graphs. Our model collects a set of trainable edges connecting disjoint words between sentences, and employs structure learning to sparsely select edges with dynamic contextual dependencies. Graphs with sparse structures can jointly exploit local and global contextual information in documents through GNNs. For inductive learning, the refined document graph is further fed into a general readout function for graph-level classification and optimization in an end-to-end manner. Extensive experiments on several real-world datasets demonstrate that the proposed model outperforms most state-of-the-art results, and reveal the necessity to learn sparse structures for each document. © 2021, CC BY.

关键词： Graph neural networks

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Exploring ERP User Behaviour in Developing Countries: Integrating Self-efficacy and UTAUT Perspectives

Exploring ERP User Behaviour in Developing Countries: Integr...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Dedy Syamsuar Muhammad Angga Oktaharisetia Deden Witarsyah Ahmad Luthfi Peter Dell Yudistira Sira Permana Darius Antoni Information Systems Department School of Information Systems Bina Nusantara University Indonesia Program Pasca Sarjana Universitas Bina Darma Palembang Indonesia School of Industrial Engineering Telkom University Bandung Indonesia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia School of Management and Marketing Faculty of Business and Law Curtin University Australia Teknik Informatika Politeknik Sekayu Sekayu Indonesia Faculty of Computer Science Universitas Indo Global Mandiri Palembang Indonesia

ERP stands for enterprise resource planning. It is an information system that is all rolled into one, is very flexible and adaptable, and optimizes business operations while also centralizing all of the company's data. This study aims to empirically investigate the elements that affect users' intention and usage of enterprise resource planning (ERP). This research incorporates self-efficacy into the unified theory of acceptance and use of technology (UTAUT2). A quantitative technique was taken, and as a result, 143 replies that might be used were effectively collected. After that, the information was put through a structural equation modelling partial least square (SEM-PLS) analysis. According to the findings of this research, self-efficacy appears to have a significant influence on all endogenous variables (EE, BI, and UB), as indicated by t-values of 3.951, 7.573, and 5.492, respectively. In addition, the analysis also indicated that PE (4.317), EE (2.397), and HM (3.084) are likewise statistically and substantively significant to BI. The result shows that the R2 for BI and UB indicate a moderate impact of 0.603 and 0.521, respectively. It is suggested that the model has valuable applications in academic and real-world contexts.

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Nitrate-mediated anaerobic microorganism-sponge iron system promoting simultaneous nitrogen and phosphate removal from piggery tail water

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Journal of Environmental Sciences 2025年

作者： Qin Liao Cong Jin Ping Li Jinhua Wu Chiqian Zhang Zhiqiang Hu School of Environment and Energy South China University of Technology Guangzhou 510006 China Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling South China University of Technology Guangzhou 510006 China Civil Engineering Program College of Engineering & Computer Science Arkansas State University Jonesboro AR 72467 USA Department of Civil and Environmental Engineering University of Missouri-Columbia 65211 USA

Piggery tail water (PTW), the biochemical unit effluent rich in nitrogen and phosphorus, causes eutrophication when directly discharged, threatening the ecological environment. Stricter standards and environmental regulations necessitate the urgent development of green, low-carbon advanced treatment technologies. This study introduces a nitrate-mediated anaerobic microorganism-sponge iron (mic-Fe 0 ) system, which leverages microbial metabolism, in situ iron oxidation, and iron-phosphorus biomineralization to enhance nitrogen and phosphorus removal. A 262-days column experiment was conducted to compare three systems (abiotic sponge iron (s-Fe 0 ), microbial s-Fe 0 , and nitrate-mediated mic-Fe 0 ) and to optimize s-Fe 0 dosage (5 %-15 %, V/V ). The results indicated that when the s-Fe 0 dosage was 10 %, the system achieved 83.55 % nitrate removal and 87.53 % total phosphorus (TP) removal, reducing the effluent TP concentration to 2.55 ± 1.06 mg/L, a 60 % decrease compared to the abiotic s-Fe 0 system (6.33 ± 1.83 mg/L). Several analytical techniques revealed that the nitrate-mediated mic-Fe 0 system exhibited the highest iron corrosion among all experimental groups and generated substantial iron-phosphorus minerals, such as vivianite and strengite. This confirmed that microbial-induced biocorrosion accelerated iron dissolution and promoted phosphorus fixation. Simultaneously, nitrate mediation further enhanced the corrosion process, facilitating the sustained dissolution of s-Fe 0 and thereby achieving efficient phosphorus removal. Additionally, the enrichment of autotrophic and heterotrophic denitrifying bacteria expanded the nitrogen cycling pathway, enabling efficient nitrogen removal under low carbon-to-nitrogen ratios. This study elucidates the mechanism of enhanced nitrogen and phosphorus removal in the nitrate-mediated mic-Fe 0 system, offering a novel strategy for the advanced treatment of PTW.

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Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data

arXiv

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arXiv 2024年

作者： Chen, Shan Gallifant, Jack Guevara, Marco Gao, Yanjun Afshar, Majid Miller, Timothy Dligach, Dmitriy Bitterman, Danielle S. Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Brigham and Women's Hospital Dana-Farber Cancer Institute BostonMA United States Computational Health Informatics Program Boston Children's Hospital Harvard Medical School BostonMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Critical Care Guy's & St Thomas' NHS Trust London United Kingdom Department of Medicine University of Wisconsin School of Medicine and Public Health MadisonWI United States Department of Computer Science Loyola University Chicago ChicagoIL United States

Brief communication 70 words: Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results show feasible applications in such a high-stakes domain. © 2024, CC BY-NC-SA.

关键词： Natural language processing systems

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Observation of Colossal Terahertz Magnetoresistance and Magnetocapacitance in a Perovskite Manganite

arXiv

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arXiv 2023年

作者： Tay, Fuyang Chaudhary, Swati He, Jiaming Peraca, Nicolas Marquez Baydin, Andrey Fiete, Gregory A. Zhou, Jianshi Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics University of Texas at Austin AustinTX78712 United States Department of Physics Northeastern University BostonMA02115 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Materials Science and Engineering Program Mechanical Engineering University of Texas at Austin AustinTX78712 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

We have studied the terahertz response of a bulk single crystal of La0.875Sr0.125MnO3at around its Curie temperature, observing large changes in the real and imaginary parts of the optical conductivity as a function of magnetic field. The terahertz resistance and capacitance extracted from the optical conductivity rapidly increased with increasing magnetic field and did not show any sign of saturation up to 6 T, reaching 60% and 15%, respectively, at 180 K. The observed terahertz colossal magnetoresistance and magnetocapacitance effects can be qualitatively explained by using a two-component model that assumes the coexistence of two phases with vastly different conductivities. These results demonstrate the potential use of perovskite manganites for developing efficient terahertz devices based on magnetic modulations of the amplitude and phase of terahertz waves. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical conductivity

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MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments (vol 18, e1010241, 2022)

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PLOS COMPUTATIONAL BIOLOGY 2022年第9期18卷 e1010241页

作者： Alinejad-Rokny, Hamid Modegh, Rassa Ghavami Rabiee, Hamid R. Sarbandi, Ehsan Ramezani Rezaie, Narges Tam, Kin Tung Forrest, Alistair R. R. Harry Perkins Institute of Medical Research QEII Medical Centre and Centre for Medical Research The University of Western Australia Perth Australia Bio Medical Machine Learning Lab (BML) The Graduate School of Biomedical Engineering UNSW Sydney Sydney Australia Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia Bioinformatics and Computational Biology Lab Department of Computer Engineering Sharif University of Technology Tehran Iran Center for Complex Biological Systems University of California Irvine Irvine California United States of America

Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools including Hi-C significant interaction callers (SIC) and Hi-C loop callers using published Hi-C, capture Hi-C, and Micro-C datasets. Our results demonstrate that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and 3) more likely to link known regulatory features including known functional enhancer-promoter pairs validated by CRISPRi than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distributions only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C, capture Hi-C and Micro-C data.

关键词： Genomics Chromatin Genome-wide association studies Single nucleotide polymorphisms Histones Gene regulation Deoxyribonucleases Insulators

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