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Identification of Social and Racial Disparities in Risk of HIV Infection in Florida using Causal AI Methods

Identification of Social and Racial Disparities in Risk of H...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mattia Prosperi Jie Xu Jingchuan Serena Guo Jiang Bian Wei-Han William Chen Shantrel Canidate Simone Marini Mo Wang Department of Epidemiology College of Public Health and Health Professions University of Florida Gainesville FL USA Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Gainesville FL USA Department of Pharmaceutical Outcomes and Policy College of Pharmacy University of Florida Gainesville FL USA Department of Management Warrington College of Business University of Florida Gainesville FL USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Florida -the 3 rd most populous state in the USA– has the highest rates of Human Immunodeficiency Virus (HIV) infections and of unfavorable HIV outcomes, with marked social and racial disparities. In this work, we leveraged large-scale, real-world data, i.e., statewide surveillance records and publicly available data resources encoding social determinants of health (SDoH), to identify social and racial disparities contributing to individuals’ risk of HIV infection. We used the Florida Department of Health’s Syndromic Tracking and Reporting System (STARS) database (including 100,000+ individuals screened for HIV infection and their partners), and a novel algorithmic fairness assessment method –the Fairness-Aware Causal paThs decompoSition (FACTS)– merging causal inference and artificial intelligence. FACTS deconstructs disparities based on SDoH and individuals’ characteristics, and can discover novel mechanisms of inequity, quantifying to what extent they could be reduced by interventions. We paired the deidentified demographic information (age, gender, drug use) of 44,350 individuals in STARS –with non-missing data on interview year, county of residence, and infection status– to eight SDoH, including access to healthcare facilities, % uninsured, median household income, and violent crime rate. Using an expert-reviewed causal graph, we found that the risk of HIV infection for African Americans was higher than for non- African Americans (both in terms of direct and total effect), although a null effect could not be ruled out. FACTS identified several paths leading to racial disparity in HIV risk, including multiple SDoH: education, income, violent crime, drinking, smoking, and rurality.

关键词： Surveillance Merging Stars Medical services Information age inference algorithms Human immunodeficiency virus

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Unsupervised energy disaggregation with factorial hidden Markov models based on generalized backfitting algorithm

Unsupervised energy disaggregation with factorial hidden Mar...

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IEEE Region 10 International Conference TENCON

作者： Lanruo Wang Xianjue Luo Wei Zhang School of Electrical Engineering Xi'an Jiaotong University Xi'an China

This paper proposes an approximate inference and parameter estimation in factorial hidden Markov models (FHMMs), a generalization of hidden Markov models (HMMs) in which the state is factored into multiple Markov chains. Earlier research has proved exact inference and parameter estimation can be computationally intractable. The proposed method in this paper considers FHMMs to be an alternative generalization of generalized additive models (GAMs). We use backfitting algorithm to estimate the parameters in FHMMs instead of exact but complex derivation and make the approximate inference more efficient. This method is motivated by the problem of energy disaggregation which is the process of decomposing a whole household's electricity consumption into individual appliances in order to improve energy utility efficiency both in load terminal and electricity supplier. Numerical simulations indicate the effectiveness of the proposed method for energy disaggregation.

关键词： Home appliances Hidden Markov models Markov processes Parameter estimation Approximation algorithms inference algorithms Additives

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Dynamic Social Influence Analysis through Time-Dependent Factor Graphs

Dynamic Social Influence Analysis through Time-Dependent Fac...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Chi Wang Jie Tang Jimeng Sun Jiawei Han University of Illinois at Urbana-Champaign USA Tsinghua University China IBM TJ Watson Research Center USA

Social influence, the phenomenon that the actions of a user can induce her/his friends to behave in a similar way, plays a key role in many (online) social systems. For example, a company wants to market a new product through the effect of "word of mouth'' in the social network. It wishes to find and convince a small number of influential users to adopt the product, and the goal is to trigger a large cascade of further adoptions. Fundamentally, we need to answer the following question: how to quantify the influence between two users in a large social network? To address this question, we propose a pair wise factor graph (PFG) model to model the social influence in social networks. An efficient algorithm is designed to learn the model and make inference. We further propose a dynamic factor graph (DFG) model to incorporate the time information. Experimental results on three different genres of data sets show that the proposed approaches can efficiently infer the dynamic social influence. The results are applied to the influence maximization problem, which aims to find a small subset of nodes (users) in a social network that could maximize the spread of influence. Experiments show that the proposed approach can facilitate the application.

关键词： Social network services inference algorithms Heuristic algorithms Algorithm design and analysis Sum product algorithm Joints Analytical models

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Meta-cognitive interval type-2 fuzzy controller for quadcopter flight control- an EEG based approach

Meta-cognitive interval type-2 fuzzy controller for quadcopt...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： A. K. Das Tan Teck Leong S. Suresh N. Sundararajan School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781509006274

This paper presents a method for a hands-off noninvasive brain computer interface (BCI) to control the movement of a quadcopter. The quadcopter model used in this paper is the *** 2.0 and the non-invasive BCI device used is the Emotiv EPOC. A framework is developed to convert the raw EEG signals into commands to control the flight of quadcopter, *** 2.0 through a wireless interface. The common spatial pattern algorithm is used to extract features from the EEG signals. The signals are classified using the meta-cognitive interval type-2 neuro-fuzzy inference system. The decoded intentions of the user, commands (move left, move right) are relayed to the quad over a wireless connection. Experiments show that the user is able to fly the quadcopter successfully without using hands.

关键词： Electroencephalography Feature extraction Electrodes Covariance matrices Brain modeling Headphones inference algorithms

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Analysis of evolutionary computation research through IEEE conferences

Analysis of evolutionary computation research through IEEE c...

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IEEE International Conference on Systems, Man and Cybernetics

作者： T. Murata H. Takagi Department of Informatics Kansai University Takatsuki Osaka Japan Department of Art and Information Design Kyushu Institute of Design Fukuoka Japan

Recently a large number of applications of evolutionary computation (EC) have been proposed. In this paper, we examine the number of papers relating to evolutionary computation submitted to the World Congress on Computational Intelligence (WCCI) and the Int. Conf. on Systems, Man and Cybernetics (SMC). We examine applications of EC In five fields: (1) hybridization of EC with neural networks (NN) or fuzzy systems (FS), (2) application of EC In classification, recognition, and data mining, (3) application of Interaction EC (EEC), (4) enhancing technique for EC, and (5) practical application of EC. We analyze the data and make EC research trends clear.

关键词： Evolutionary computation Sliding mode control Neural networks Genetic programming Societies Informatics Art Computer networks inference algorithms Uncertainty

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Modern Soft Computing Techniques and Their Various Applications

Modern Soft Computing Techniques and Their Various Applicati...

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International Conference for Convergence of Technology (I2CT)

作者： Kamal Saluja Sunil Gupta Jatin Arora Naveen Kumar Yogesh Arora Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Engineering and Technology GD Goenka University Haryana India

In contrast to traditional computing, soft computing can be used to adjust prediction models and locate answers to difficult, real-world issues. Soft computing is more forgiving of ambiguity than classical computing. For soft computing, the gold standard is the human brain. Soft computing includes innovations like machine learning, artificial neural networks, genetic algorithms, fuzzy logic, and expert systems. First proposed in the 1980s, soft computing is now a central focus of academic inquiry across disciplines. Soft computing is widely used in commercial, industrial, and household settings due to its low cost and high performance. Soft computing is finding more and more uses today. This article compares and contrasts the benefits of traditional computing with those of contemporary soft computing.

关键词： Fuzzy logic Technological innovation Costs Computational modeling Machine learning Predictive models inference algorithms

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FUN: optimization of fuzzy rule based systems using neural networks

FUN: optimization of fuzzy rule based systems using neural n...

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International Conference on Neural Networks

作者： S.M. Sulzberger N. Tschichold-Gurman S.J. Vestli Institute of Robotics ETH Zurich Zurich Switzerland

A method for optimization of fuzzy rule based systems using neural networks is described. A neural network model with special neurons has been developed so that the translation of fuzzy rules and membership functions into the network is possible. The performance of this network, and hence the quality of the original rule base, is then improved by training the network using a combination of neural network learning algorithms. The optimized rules and membership functions can be extracted from the net and used in normal fuzzy inference tools. This net has been tested on the WallJumperOver and the problem of local navigation for mobile robots.< >

关键词： Fuzzy neural networks Fuzzy systems Knowledge based systems Neural networks Optimization methods Neurons inference algorithms Testing Navigation Mobile robots

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Poster: An Algorithm and Tool to Infer Practical Postconditions

Poster: An Algorithm and Tool to Infer Practical Postconditi...

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International Conference on Software Engineering - Companion, ICSE, Companion

作者： John L. Singleton Gary T. Leavens Hridesh Rajan David Cok University of Central Florida Orlando FL US Iowa State University Ames IA US GrammaTech Inc. USA

Manually writing pre- and postconditions to document the behavior of a large library is a time-consuming task; what is needed is a way to automatically infer them. Conventional wisdom is that, if one has preconditions, then one can use the strongest postcondition predicate transformer (SP) to infer postconditions. However, we have performed a study using 2,300 methods in 7 popular Java libraries, and found that SP yields postconditions that are exponentially large, which makes them difficult to use, either by humans or by tools. We solve this problem using a novel algorithm and tool for inferring method postconditions, using the SP, and transmuting the inferred postconditions to make them more concise. We applied our technique to infer postconditions for over 2,300 methods in seven popular Java libraries. Our technique was able to infer specifications for 75.7% of these methods. Each of these inferred postconditions was verified using an Extended Static Checker. We also found that 84.6% of resulting specifications were less than 1/4 page (20 lines) in length. Our algorithm was able to reduce the length of SMT proofs needed for verifying implementations by 76.7% and reduced prover execution time by 26.7%.

关键词： Software engineering Tools Libraries inference algorithms Data mining Java

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A Streaming Transformer Accelerator with Efficient On-Chip Normalization

A Streaming Transformer Accelerator with Efficient On-Chip N...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Ning Zhao Jiayuan Chen Jingjing Lv Chenjia Xie Yuan Du Li Du Electronic Science and Engineering Nanjing University Nanjing China China Mobile Research Institute Beijing China

ISBN: (数字)9798350378771

ISBN: (纸本)9798350378788

Transformer has achieved state-of-the-art results in many fields. However, the extensive and dynamic matrix multiplications and normalization operations (Softmax and Layer Normalization (LN)) result in a significant amount of data movement and computation. In this work, a software-architecture co-design method that can effectively addresses the challenges of Transformer inference is proposed. The architecture part implements a low-cost normalization by introducing a fast square root method to enhance the efficiency of LN. Additionally, a hardware-friendly Softmax has been deployed to further reduce the normalization overhead. Benefiting from the low-cost normalization, a streaming transformer inference dataflow has been designed to reduce memory access. The software part ensures the inference accuracy when implementing the proposed low-cost normalization through a two-step fine-tuning method. The proposed accelerator achieves $8.86 \times$ and $6.24 \times$ energy efficiency improvements compared to state-of-the-art attention accelerators ${\mathrm {A}}^{3}$ and SpAtten.

关键词： Accuracy Circuits and systems Asia Transformers CMOS technology Energy efficiency Software inference algorithms System-on-chip IP networks

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Space alternating data augmentation: application to finite mixture of Gaussians and speaker recognition

Space alternating data augmentation: application to finite m...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： A. Doucet S. Senecal T. Matsui Engineering Department University of Cambridge Cambridge UK Institute for Statistical Mathematics Tokyo Japan

The SAGE (space-alternating generalized expectation-maximization) algorithm (Celeux, G. et al., 2001) is one of the most elegant and popular extensions of the EM (expectation maximization) algorithm for performing ML (maximum likelihood) or MAP (maximum a posteriori) parameter estimation. This algorithm updates parameter components by subblocks by alternating missing data spaces. Its efficiency has been reported in numerous simulation studies. We propose here an MCMC (Markov chain Monte Carlo) strategy named SADA (space-alternating data augmentation) which relies on the same principle in order to sample efficiently from (posterior) distributions and we discuss its application to finite mixtures of Gaussians. For this model, we also present an original implementation of the SAGE algorithm. In Monte Carlo simulations and in an application for speaker recognition, these methods, which are straightforward modifications of the standard EM and DA (data augmentation) algorithms, consistently outperform them.

关键词： Gaussian processes Speaker recognition Maximum likelihood estimation inference algorithms Convergence Sampling methods Parameter estimation Monte Carlo methods Gaussian distribution Bayesian methods

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