检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Li, Ziyue Yan, Hao Zhang, Chen Tsung, Fugee Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S Mill Ave TempeAZ85281 United States Industrial Engineering Tsinghua University Beijing China Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology

Spatiotemporal data is very common in many applications, such as manufacturing systems and transportation systems. It is typically difficult to be accurately predicted given intrinsic complex spatial and temporal correlations. Most of the existing methods based on var-ious statistical models and regularization terms, fail to preserve innate features in data alongside their complex correlations. In this paper, we focus on a tensor-based prediction and propose several practical techniques to improve prediction. For long-term prediction specifically, we propose the "Tensor Decomposition + 2-Dimensional Auto-Regressive Moving Average (2D-ARMA)" model, and an effective way to update prediction real-time;For short-term prediction, we propose to conduct tensor completion based on tensor clustering to avoid oversimplifying and ensure accuracy. A case study based on the metro passenger flow data is conducted to demonstrate the improved performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Matrix Factorization with Interval-Valued Data

Matrix Factorization with Interval-Valued Data

引用

International Conference on Data engineering

作者： Mao-Lin Li Francesco Di Mauro K Selçuk Candan Maria Luisa Sapino School of Computing Informatics and Decision System Engineering Arizona State University Tempe United States Department of Computer Science University of Torino Turin Italy

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

With many applications relying on multi-dimensional datasets for decision making, matrix factorization (or decomposition) is becoming the basis for many knowledge discovery and machine learning tasks, from clustering, trend detection, anomaly detection, to correlation analysis. Unfortunately, a major shortcoming of matrix analysis operations is that, despite their effectiveness when the data is scalar, these operations become difficult to apply in the presence of non-scalar data, as they are not designed for data that include non-scalar observations, such as intervals. In this paper, we propose matrix decomposition techniques that consider the existence of interval-valued data. We show that naive ways to deal with such imperfect data may introduce errors in analysis and present factorization techniques that are especially effective when the amount of imprecise information is large.

关键词： Matrix decomposition Semantics Face Probabilistic logic Collaboration Conferences Data engineering

来源：评论

学校读者我要写书评

暂无评论

Small Cohort of Epilepsy Patients Showed Increased Activity on Facebook before Sudden Unexpected Death

arXiv

引用

arXiv 2022年

作者： Wood, Ian B. Correia, Rion Brattig Miller, Wendy R. Rocha, Luis M. Center for Social and Biomedical Complexity Luddy School of Informatics Computing & Engineering Indiana University BloomingtonIN47408 United States Instituto Gulbenkian de Ciência Oeiras2780-156 Portugal CAPES Foundation Ministry of Education of Brazil DF Brasília Brazil School of Nursing Indiana University IndianapolisIN46202 United States Department of Systems Science and Industrial Engineering Binghamton University BinghamtonNY13902 United States

Sudden Unexpected Death in Epilepsy (SUDEP) remains a leading cause of death in people with epilepsy. Despite the constant risk for patients and bereavement to family members, to date the physiological mechanisms of SUDEP remain unknown. Here we explore the potential to identify putative predictive signals of SUDEP from online digital behavioral data using text and sentiment analysis. Specifically, we analyze Facebook timelines of six epilepsy patients deceased due to SUDEP, donated by surviving family members. We find preliminary evidence for behavioral changes detectable by text and sentiment analysis tools. Namely, in the months preceding their SUDEP event patient social media timelines show: i) increase in verbosity;ii) increased use of functional words;and iii) sentiment shifts as measured by different sentiment analysis tools. Combined, these results suggest that social media engagement, as well as its sentiment, may serve as possible early-warning signals for SUDEP in people with epilepsy. While the small sample of patient timelines analyzed in this study prevents generalization, our preliminary investigation demonstrates the potential of social media data as complementary data in larger studies of SUDEP and *** Codes 62P10 (Primary) 92D50, 68U15, 92D30 (Secondary) © 2022, CC BY-SA.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Multi-step predictions for adaptive sampling using proximal ADMM

TechRxiv

引用

TechRxiv 2021年

作者： Le, Viet-Anh Nguyen, Linh Nghiem, Truong X. The School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011 United States The School of Engineering Information Technology and Physical Sciences Federation University Australia Churchill VIC3842 Australia Northern Arizona University United States

This paper presents a novel approach, using multi-step predictions, to the adaptive sampling problem for efficient monitoring of environmental spatial phenomena in a resource- and obstacle-constrained mobile sensor network. We employ a Gaussian process (GP) to represent the spatial field of interest, which can then be used to predict the field at unmeasured locations. The adaptive sampling problem aims to drive the mobile sensors to optimally navigate the environment where the sensors adaptively take measurements of the spatial phenomena at each sampling step. To this end, an optimal sampling criterion based on conditional entropy is proposed, which aims to minimize the prediction uncertainty of the GP model. By predicting the measurements the mobile sensors potentially take in a finite horizon of multiple future sampling steps and exploiting the chain rule of the conditional entropy, a multi-step-ahead adaptive sampling optimization problem is formulated. The objective of the optimization problem is to find the optimal sampling paths for the mobile sensors in multiple sampling steps ahead. Compared with the single-step-ahead approach typically adopted in the literature, our approach provides better navigation, deployment and data collection with more informative sensor readings. However, this optimization problem is non-convex and highly complex. Even more challenging is that it is a mixed-integer problem, due to collision avoidance constraints. We propose to employ the proximal alternating direction method of multipliers algorithm to efficiently solve this problem. More importantly, the solution obtained by the proposed algorithm is theoretically guaranteed to converge to a stationary value. The effectiveness of our proposed approach was extensively validated by simulation using a real-world dataset, which showed highly promising results. © 2021, CC BY.

关键词： Sensor networks

来源：评论

学校读者我要写书评

暂无评论

Investigating RNA dynamics from single molecule transcriptomes

引用

Trends in genetics : TIG 2025年

作者： Sahiti Somalraju Doaa Hassan Salem Sarath Chandra Janga Department of Biomedical Engineering and Informatics Luddy School of Informatics Computing and Engineering Indiana University Indianapolis (IUI) Indianapolis IN USA. Department of Biomedical Engineering and Informatics Luddy School of Informatics Computing and Engineering Indiana University Indianapolis (IUI) Indianapolis IN USA Computers and Systems Department National Telecommunication Institute Cairo Egypt. Department of Medical and Molecular Genetics Indiana University School of Medicine Medical Research and Library Building 975 West Walnut Street Indianapolis IN USA Centre for Computational Biology and Bioinformatics Indiana University School of Medicine 5021 Health Information and Translational Sciences (HITS) 410 West 10th Street Indianapolis IN USA. Electronic address: scjanga@iu.edu.

Investigating RNA dynamics is crucial for uncovering fundamental mechanisms, such as alternative splicing, RNA stability, and post-transcriptional modifications, all processes with implications for identifying therapeutic targets and advancing knowledge of cellular function and regulation. Advances in long-read sequencing technologies, particularly from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), offer unprecedented insights into RNA dynamics at single molecule and single nucleotide resolutions. In this review, we examine protocols and methods for analyzing RNA dynamics, focusing on isoform detection, poly(A) tail length quantification, and mapping of RNA modifications. We envision that these high-throughput, transcriptome-wide data sets, combined with integrated software systems, will transform workflows for studying single molecule RNA dynamics. Such advances will help unravel the complexities of gene regulation and deepen our understanding of cellular processes.

关键词： RNA bioinformatics RNA isoform RNA modifications poly(A) tail single molecule sequencing transcriptomics

来源：评论

学校读者我要写书评

暂无评论

Real-time detection of clustered events in video-imaging data with applications to additive manufacturing

arXiv

引用

arXiv 2020年

作者： Yan, Hao Grasso, Marco Paynabar, Kamran Colosimo, Bianca Maria School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Dipartimento di Meccanica Politecnico di Milano Italy H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States

The use of video-imaging data for in-line process monitoring applications has become more and more popular in the industry. In this framework, spatio-temporal statistical process monitoring methods are needed to capture the relevant information content and signal possible out-of-control states. Video-imaging data are characterized by a spatio-temporal variability structure that depends on the underlying phenomenon, and typical out-of-control patterns are related to the events that are localized both in time and space. In this paper, we propose an integrated spatio-temporal decomposition and regression approach for anomaly detection in video-imaging data. Out-of-control events are typically sparse spatially clustered and temporally consistent. Therefore, the goal is to not only detect the anomaly as quickly as possible ("when") but also locate it ("where"). The proposed approach works by decomposing the original spatio-temporal data into random natural events, sparse spatially clustered and temporally consistent anomalous events, and random noise. Recursive estimation procedures for spatio-temporal regression are presented to enable the real-time implementation of the proposed methodology. Finally, a likelihood ratio test procedure is proposed to detect when and where the hotspot happens. The proposed approach was applied to the analysis of video-imaging data to detect and locate local over-heating phenomena ("hotspots") during the layer-wise process in a metal additive manufacturing process. Copyright © 2020, The Authors. All rights reserved.

关键词： Real time control

来源：评论

学校读者我要写书评

暂无评论

Learning Latent Perception Graphs for Personalized Unknowns Recommendation

Learning Latent Perception Graphs for Personalized Unknowns ...

引用

IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Lo Pang-Yun Ting Shan-Yun Teng Suhang Wang Kun-Ta Chuang Huan Liu National Cheng Kung University Tainan Taiwan College of Information Sciences and Technology Penn State University PA USA School of Computing Informatics and Decision Systems Engineering Arizona State University AZ USA

ISBN: (数字)9781728141442

ISBN: (纸本)9781728141459

The fast-growing online-learning platforms, which are very convenient and contain rich course resources, have attracted many users to explore new knowledge online. However, the learning quality of online-learning is generally not as effective as offline classes. In offline studies in classrooms, teachers can interact with students and teach students in accordance with personal aptitude from students' feedback in classes. Without such real-time interaction, it is difficult for users to be aware of personal unknowns. In this paper, we consider an important issue to discover “user unknowns” from the question-giving process in online-learning platforms. A novel personalized learning framework, called PagBay, is devised to recommend user unknowns in the iterative round-by-round strategy, which contributes to applications such as a conversational bot. The flow enables users to progressively discover their weakness and to help them progress. However, discovering personal unknowns is quite challenging in online-learning platforms. Even though solving the problem with previous recommender algorithms provides solutions, they often lead to suboptimal results for unknowns recommendation as they simply rely on the user ratings and contextual features of questions. Generally, questions are associated with perceptions, and mining the relationships among users, questions, and perceptions potentially provide the clue to the better unknowns recommendation. Therefore, in this paper, we develop a novel recommender framework by borrowing strengths from perception-aware graph embedding for learning user unknowns. Our experimental studies on real data show that the proposed framework can effectively discover user unknowns in online learning services.

关键词： Videos Knowledge discovery Task analysis Real-time systems Q measurement Navigation Machine intelligence

来源：评论

学校读者我要写书评

暂无评论

Towards a Bidirectional Machine Translator Generator for Multilingual Communication

Towards a Bidirectional Machine Translator Generator for Mul...

引用

IEEE International Conference on Conversational Data & Knowledge engineering (CDKE)

作者： Ryan Lane Ajay Bansal Arizona State University School of Computing Informatics and Decision Systems Engineering Mesa AZ

ISBN: (纸本)9781728160894

Machine Translation (MT) systems are typically quite complex, especially those used in production environments where high-quality conversational text or speech translation from one language to another is important. As a result, the vast majority of MT systems support translation between a single language pair, often uni-directionally. This research study extends previous work to assess the efficacy of developing a bidirectional translator generator in Prolog programming language using Lexical Functional Grammars. The main research objective is building a machine translator generator for multilingual communication, i.e. developing a system whose inputs are linguistic descriptions of a desired source and target language and whose output is a program that translates between the two natural languages. The implementation of a bidirectional machine translator between English and Hungarian, developed as a proof-of-concept case study, is discussed and assessed in terms of four general classes of translation. The benefits and drawbacks of this approach as generalized to MT systems are also discussed, along with possible areas of future work.

关键词： Syntactics Grammar Linguistics Natural language processing Buildings

来源：评论

学校读者我要写书评

暂无评论

An Energy-Efficient Reconfigurable LSTM Accelerator for Natural Language Processing

An Energy-Efficient Reconfigurable LSTM Accelerator for Natu...

引用

IEEE International Conference on Big Data

作者： Elham Azari Sarma Vrudhula School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Long Short-Term Memory (LSTM) Recurrent Neural network (RNN) is known for its capability in modeling sequence learning tasks such as language modeling. However, due to the large number of model parameters and compute-intensive operations, existing FPGA implementations of LSTMs are not sufficiently energy-efficient as they require large area and exhibit high power consumption. This work describes a substantially different hardware implementation of an LSTM which includes several architectural innovations to achieve high throughput and energy-efficiency. The architectural innovations include (1) an improved design of an approximate multiplier (AM) and its integration with the compute-intensive units of the LSTM; (2) the design of control mechanisms to handle the variable-cycle (data dependent) multiply operations; (3) incorporation of hierarchical pipelining at multiple levels of the design to maximize the overlap of the variable cycle computations. In addition, this work applies a post-training, range-based, linear quantization to the parameters of the model to further improve the performance and energy-efficiency. A python framework is also developed that allows for analysis and fine tuning of the input parameters before mapping the design to hardware. This paper extensively explores the design trade-offs and demonstrates the advantages for one common application - language modeling. Implementation of the design on a Xilinx Zynq XC7Z030 FPGA shows maximum improvement as compared to three recent published works in throughput to be 27.86X, 7.69X and 11.06X and in energy-efficiency to be 45.26X, 14.76X and 16.97X, respectively.

关键词： Field programmable gate arrays Logic gates Computer architecture Computational modeling Throughput Pipeline processing Hardware

来源：评论

学校读者我要写书评

暂无评论

Pricing Implications of Transmission Security Modeling In Electric Energy Markets

Pricing Implications of Transmission Security Modeling In El...

引用

Annual North-American Power Symposium

作者： Mehdi Saleh Karthik Saikumar Kory W. Hedman School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe Arizona

ISBN: (数字)9781728104072

ISBN: (纸本)9781728104089

This paper discusses the pricing implications of modeling transmission security constraints in market clearing models. Most market operators include transmission security constraints in optimization models that are used to determine the commitment and dispatch schedules for both the day ahead and the real time markets. The inclusion of transmission security constraints leads to the rise of a post transmission contingency congestion component in the nodal market clearing prices. Most contemporary market models follow an approach that does not allow for a re-dispatch in generation after the occurrence of a transmission contingency, attempting to arrive at a single market solution that is transmission feasible for both the base case and all modeled post-contingency network models. A theoretical analysis of the pricing implications of transmission security modeling in energy market clearing models (including a model allowing for re-dispatch) is presented by leveraging duality theory.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：