Numerical Analysis and Scientific Computing Commons^™

Predicting Switch-Like Behavior In Proteins Using Logistic Regression On Sequence-Based Descriptors, Benjamin Strauss

Master's Projects

Ligands can bind at specific protein locations, inducing conformational changes such as those involving secondary structure. Identifying these possible switches from sequence, including homology, is an important ongoing area of research. We attempt to predict possible secondary structure switches from sequence in proteins using machine learning, specifically a logistic regression approach with 48 N-acetyltransferases as our learning set and 5 sirtuins as our test set. Validated residue binary assignments of 0 (no change in secondary structure) and 1 (change in secondary structure) were determined (DSSP) from 3D X-ray structures for sets of virtually identical chains crystallized under different conditions. Our …

Mathematical And Computer Simulation Of The Processes Of Two-Phase Joint Gas Filtration And Water In A Porous Environment, Elmira Nazirova

Bulletin of TUIT: Management and Communication Technologies

A mathematical model, methods and algorithms for the numerical solution of problems of joint gas-water filtration in porous media are considered. The mathematical model of the process of non-stationary joint gas-water filtration in a porous medium is described by a system of nonlinear differential equations of parabolic type. In the numerical solution of the boundary value problem of gas displacement by water in a porous medium, the differential sweeping method is used for systems of differential-difference equations. The system of differential-difference equations with respect to the gas pressure function is nonlinear, therefore, an iterative method is used for it, based …

Mathematics And Programming Exercises For Educational Robot Navigation, Ronald I. Greenberg

Computer Science: Faculty Publications and Other Works

This paper points students towards ideas they can use towards developing a convenient library for robot navigation, with examples based on Botball primitives, and points educators towards mathematics and programming exercises they can suggest to students, especially advanced high school students.

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

An Intelligent Platform With Automatic Assessment And Engagement Features For Active Online Discussions, Michelle L. F. Cheong, Yun-Chen Chen, Bing Tian Dai

Research Collection School Of Computing and Information Systems

In a universitycontext, discussion forums are mostly available in Learning and ManagementSystems (LMS) but are often ineffective in encouraging participation due topoorly designed user interface and the lack of motivating factors toparticipate. Our integrated platform with the Telegram mobile app and aweb-based forum, is capable of automatic thoughtfulness assessment of questionsand answers posted, using text mining and Natural Language Processing (NLP)methodologies. We trained and applied the Random Forest algorithm to provideinstant thoughtfulness score prediction for the new posts contributed by thestudents, and prompted the students to improve on their posts, thereby invokingdeeper thinking resulting in better quality contributions. In addition, …

Modeling Intra-Relation In Math Word Problems With Different Functional Multi-Head Attentions, Jierui Li, Lei Wang, Jipeng Zhang, Yan Wang, Bing Tian Dai, Dongxiang Zhang

Research Collection School Of Computing and Information Systems

Several deep learning models have been proposed for solving math word problems (MWPs) automatically. Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs. To utilize the merits of deep learning models with simultaneous consideration of MWPs’ specific features, we propose a group attention mechanism to extract global features, quantity-related features, quantity-pair features and question-related features in MWPs respectively. The experimental results show that the proposed approach performs significantly better than previous state-of-the-art methods, and boost performance from 66.9% to 69.5% on Math23K with training-test split, from …

Volumetric Optimization Of Freight Cargo Loading: Case Study Of A Smu Forwarder, Tristan Lim, Michael Ser Chong Ping, Mark Goh, Shi Ying Jacelyn Tan

Research Collection School Of Computing and Information Systems

Purpose: Freight forwarders faces a challenging environment of high market volatility and margin compression risks. Hence, strategic consideration is given to undertaking capacity management and transport asset ownership to achieve longer term cost leadership. Doing so will also help to address management issues, such as better control of potential transport disruptions, improve scheduling flexibility and efficiency, and provide service level enhancement.Design/methodology/approach: The case company currently hastruck resource which is unprofitable, and the firm’s schedulers are having difficulty optimizing the loading capacity. We apply Genetic Algorithm (GA) to undertake volumetric optimization of truckcapacity and to build an easy-to-use platform to help …

Exploiting Mobility For Predictive Urban Analytics & Operations, Kasthuri Jayarajah

Dissertations and Theses Collection (Open Access)

As cities worldwide invest heavily in smart city infrastructure, it invites opportunities for a next wave of urban analytics. Unlike its predecessors, urban analytics applications and services can now be real-time and proactive -- they can (a) leverage situational data from large deployments of connected sensors, (b) capture attributes of a variety of entities that make up the urban fabric (e.g., people and their social relationships, transport nodes, utilities, etc.), and (c) use predictive insights to both proactively optimize urban operations (e.g., HVAC systems in smart buildings, buses in the transportation network, crowd-workers, etc.) and promote smarter policy decisions (e.g., …

Making Sense Of Crowd-Generated Content In Domain-Specific Settings, Agus Sulistya

Dissertations and Theses Collection (Open Access)

The rapid advances of the Web have changed the ways information is distributed and exchanged among individuals and organizations. Various content from different domains are generated daily and contributed by users' daily activities, such as posting messages in a microblog platform, or collaborating in a question and answer site. To deal with such tremendous volume of user generated content, there is a need for approaches that are able to handle the mass amount of available data and to extract knowledge hidden in the user generated content. This dissertation attempts to make sense of the generated content to help in three …

Unsupervised Deep Structured Semantic Models For Commonsense Reasoning, Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Jing Jiang

Research Collection School Of Computing and Information Systems

Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm

Master's Theses

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …

Bincor: An R Package For Estimating The Correlation Between Two Unevenly Spaced Time Series, Josue M. Polanco-Martinez, Martin A. Medina-Elizalde, Maria Fernanda Sanchez Goni, Manfred Mudelsee

The R Journal

This paper presents a computational program named BINCOR (BINned CORrelation) for estimating the correlation between two unevenly spaced time series. This program is also applicable to the situation of two evenly spaced time series not on the same time grid. BINCOR is based on a novel estimation approach proposed by Mudelsee (2010) for estimating the correlation between two climate time series with different timescales. The idea is that autocorrelation (e.g. an AR1 process) means that memory enables values obtained on different time points to be correlated. Binned correlation is performed by resampling the time series under study into time bins …

Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis

The R Journal

In the past 8 months, 1524 new packages were added to the CRAN package repository. 71 packages were unarchived and 302 were archived. The following shows the growth of the number of active packages in the CRAN package repository:

R Foundation News, Torsten Hothorn

The R Journal

Membership fees and donations received between 2019-01-07 and 2019-09-04

R Package For Geometric Shadow Calculations In An Urban Environment, Michael Dorman, Evyatar Erell, Adi Vulkan, Itai Kloog

The R Journal

This paper introduces the shadow package for R. The package provides functions for shadow-related calculations in the urban environment, namely shadow height, shadow footprint and Sky View Factor (SVF) calculations, as well as a wrapper function to estimate solar radiation while taking shadow effects into account. All functions operate on a layer of polygons with a height attribute, also known as “extruded polygons” or 2.5D vector data. Such data are associated with accuracy limitations in representing urban environments. However, unlike 3D models, polygonal layers of building outlines along with their height are abundantly available and their processing does not require …

Indoor Positioning And Fingerprinting: The R Package Ipft, Emilio Sansano, Raúl Montoliu, Óscar Belmonte, Joaquín Torres-Sospedra

The R Journal

Methods based on Received Signal Strength Indicator (RSSI) fingerprinting are in the forefront among several techniques being proposed for indoor positioning. This paper introduces the R package ipft, which provides algorithms and utility functions for indoor positioning using fingerprinting techniques. These functions are designed for manipulation of RSSI fingerprint data sets, estimation of positions, comparison of the performance of different positioning models, and graphical visualization of data. Well-known machine learning algorithms are implemented in this package to perform analysis and estimations over RSSI data sets. The paper provides a description of these algorithms and functions, as well as examples of …

Time-Series Clustering In R Using The Dtwclust Package, Alexis Sardá-Espinosa

The R Journal

Most clustering strategies have not changed considerably since their initial definition. The common improvements are either related to the distance measure used to assess dissimilarity, or the function used to calculate prototypes. Time-series clustering is no exception, with the Dynamic Time Warping distance being particularly popular in that context. This distance is computationally expensive, so many related optimizations have been developed over the years. Since no single clustering algorithm can be said to perform best on all datasets, different strategies must be tested and compared, so a common infrastructure can be advantageous. In this manuscript, a general overview of shape-based …

Editorial, Norm Matloff

The R Journal

On behalf of the Editorial Board, I am pleased to present Volume 10, Issue 1 of the R Journal. This issue contains 36 contributed articles. The majority of which cover new or newly enhanced packages on CRAN.

Optimization Routines For Enforcing One-To-One Matches In Record Linkage Problems, Diego Moretti, Luca Valentino, Tiziana Tuoto

The R Journal

Record linkage aims at quickly and accurately identifying if two records represent the same real world entity. In many applications, we are interested in restricting the linkage results to "1 to 1" links, that is a single record does not appear more than once in the output. This can be dealt with the transport algorithm. The optimization problem, however, grows quadratically in the size of the input, quickly becoming untreatable for cases with a few thousand records. This paper compares different solutions, provided by some R packages for linear programming solvers. The comparison is done in terms of memory usage …

Fclust: An R Package For Fuzzy Clustering, Maria Brigida Ferraro, Paolo Giordani, Alessio Serafini

The R Journal

Fuzzy clustering methods discover fuzzy partitions where observations can be softly assigned to more than one cluster. The package fclust is a toolbox for fuzzy clustering in the R programming language. It not only implements the widely used fuzzy k-means (FkM) algorithm, but also many FkM variants. Fuzzy cluster similarity measures, cluster validity indices and cluster visualization tools are also offered. In the current version, all the functions are rewritten in the C++ language allowing their application in large-size problems. Moreover, new fuzzy relational clustering algorithms for partitioning qualitative/mixed data are provided together with an improved version of the so-called …