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Missouri University of Science and Technology

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Articles 601 - 630 of 1938

Full-Text Articles in Computer Sciences

Improving Error-Bounded Compression For Cosmological Simulation, Sihuan Li, Sheng Di, Xin Liang, Zizhong Chen, Franck Cappello Nov 2018

Improving Error-Bounded Compression For Cosmological Simulation, Sihuan Li, Sheng Di, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Cosmological simulations may produce extremely large amount of data, such that its successful run depends on large storage capacity and huge I/O bandwidth, especially in the exascale computing scale. Effective error-bounded lossy compressors with both high compression ratios and low data distortion can significantly reduce the total data size while guaranteeing the data valid for post-analysis. In this poster, we propose a novel, efficient compression model for cosmological N-body simulation framework, by combining the advantages of both space-based compression and time-based compression. The evaluation with a well-known cosmological simulation code shows that our proposed solution can get much higher compression …

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Multicellular Models Bridging Intracellular Signaling And Gene Transcription To Population Dynamics, Mohammad Aminul Islam, Satyaki Roy, Sajal K. Das, Dipak Barua Nov 2018

Multicellular Models Bridging Intracellular Signaling And Gene Transcription To Population Dynamics, Mohammad Aminul Islam, Satyaki Roy, Sajal K. Das, Dipak Barua

Computer Science Faculty Research & Creative Works

Cell signaling and gene transcription occur at faster time scales compared to cellular death, division, and evolution. Bridging these multiscale events in a model is computationally challenging. We introduce a framework for the systematic development of multiscale cell population models. Using message passing interface (MPI) parallelism, the framework creates a population model from a single-cell biochemical network model. It launches parallel simulations on a single-cell model and treats each stand-alone parallel process as a cell object. MPI mediates cell-to-cell and cell-to-environment communications in a server-client fashion. In the framework, model-specific higher level rules link the intracellular molecular events to cellular …

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Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li Nov 2018

Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li

Engineering Management and Systems Engineering Faculty Research & Creative Works

Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and "linear inseparability" of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher's Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of "potential" inverse …

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Phase Contrast Time-Lapse Microscopy Datasets With Automated And Manual Cell Tracking Annotations, Dai Fei Elmer Ker, Zhaozheng Yin, For Full List Of Authors, See Publisher's Website. Nov 2018

Phase Contrast Time-Lapse Microscopy Datasets With Automated And Manual Cell Tracking Annotations, Dai Fei Elmer Ker, Zhaozheng Yin, For Full List Of Authors, See Publisher's Website.

Computer Science Faculty Research & Creative Works

Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these …

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A Sensitivity Analysis For Mixed Criticality: Trading Criticality With Computational Resource, Luca Santinelli, Zhishan Guo Oct 2018

A Sensitivity Analysis For Mixed Criticality: Trading Criticality With Computational Resource, Luca Santinelli, Zhishan Guo

Computer Science Faculty Research & Creative Works

Mixing workloads with multiple criticality levels raises challenges both in timing analysis and schedulability analysis. The timing models have to characterize the different behaviors that real-time tasks can experience under the various criticality modes. Instead, the schedulability analysis has to combine every task and task interactions providing several guarantees, depending on the criticality level demanded at runtime. With this work, at first, we propose representations to model every possible system criticality mode as a combination of task criticality modes. A set of bounding functions is obtained, a bound for each mode combination thus corresponding to a system criticality level. Secondly, …

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Off-Policy Integral Reinforcement Learning For Semi-Global Constrained Output Regulation Of Continuous-Time Linear Systems, Yongliang Yang, Xianzhong Chen, Yixin Yin, Donald C. Wunsch Oct 2018

Off-Policy Integral Reinforcement Learning For Semi-Global Constrained Output Regulation Of Continuous-Time Linear Systems, Yongliang Yang, Xianzhong Chen, Yixin Yin, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a data-driven method based on off-policy integral reinforcement learning to solve the semi-global output regulation of continuous-time linear systems with input saturation. A family of state feedback laws for the input constrained output regulation problem is designed based on solving an algebraic Riccati equation. In contrast to the existing methods, complete knowledge of the system dynamics is no longer required in this paper. Instead, the data collected from online implementation is efficiently utilized to design the controller. Therefore, the controller design in this paper is data driven. It is shown that the presented method can find feedback …

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From Tag To Protect: A Tag-Driven Policy Recommender System For Image Sharing, Anna Cinzia Squicciarini, Andrea Novelli, Dan Lin, Cornelia Caragea, Haoti Zhong Sep 2018

From Tag To Protect: A Tag-Driven Policy Recommender System For Image Sharing, Anna Cinzia Squicciarini, Andrea Novelli, Dan Lin, Cornelia Caragea, Haoti Zhong

Computer Science Faculty Research & Creative Works

Sharing images on social network sites has become a part of daily routine for more and more online users. However, in face of the considerable number of images shared online, it is not a trivial task for a person to manually configure proper privacy settings for each of the images that he/she uploaded. The lack of proper privacy protection during image sharing could raise many potential privacy breaches of people's private lives that they are not aware of. In this work, we propose a privacy setting recommender system to help people effortlessly set up the privacy settings for their online …

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Multi-Task Allocation In Mobile Crowd Sensing With Individual Task Quality Assurance, Jiangtao Wang, Yasha Wang, Daqing Zhang, Feng Wang, Haoyi Xiong, Chao Chen, Qin Lv, Zhaopeng Qiu Sep 2018

Multi-Task Allocation In Mobile Crowd Sensing With Individual Task Quality Assurance, Jiangtao Wang, Yasha Wang, Daqing Zhang, Feng Wang, Haoyi Xiong, Chao Chen, Qin Lv, Zhaopeng Qiu

Computer Science Faculty Research & Creative Works

Task allocation is a fundamental research issue in mobile crowd sensing. While earlier research focused mainly on single tasks, recent studies have started to investigate multi-task allocation, which considers the interdependency among multiple tasks. A common drawback shared by existing multi-task allocation approaches is that, although the overall utility of multiple tasks is optimized, the sensing quality of individual tasks may become poor as the number of tasks increases. To overcome this drawback, we re-define the multi-task allocation problem by introducing task-specific minimal sensing quality thresholds, with the objective of assigning an appropriate set of tasks to each worker such …

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A Distributed Semi-Supervised Platform For Dnase-Seq Data Analytics Using Deep Generative Convolutional Networks, Shayan Shams, Richard Platania, Joohyun Kim, Jian Zhang, Kisung Lee, Seungwon Yang, Seung Jong Park Aug 2018

A Distributed Semi-Supervised Platform For Dnase-Seq Data Analytics Using Deep Generative Convolutional Networks, Shayan Shams, Richard Platania, Joohyun Kim, Jian Zhang, Kisung Lee, Seungwon Yang, Seung Jong Park

Computer Science Faculty Research & Creative Works

A deep learning approach for analyzing DNase-seq datasets is presented, which has promising potentials for unraveling biological underpinnings on transcription regulation mechanisms. Further understanding of these mechanisms can lead to important advances in life sciences in general and drug, biomarker discovery, and cancer research in particular. Motivated by recent remarkable advances in the field of deep learning, we developed a platform, Deep Semi-Supervised DNase-seq Analytics (DSSDA). Primarily empowered by deep generative Convolutional Networks (ConvNets), the most notable aspect is the capability of semi-supervised learning, which is highly beneficial for common biological settings often plagued with a less sufficient number of …

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Work-In-Progress: Rws - A Roulette Wheel Scheduler For Preventing Execution Pattern Leakage, Ying Zhang, Lingxiang Wang, Wei Jiang, Zhishan Guo Aug 2018

Work-In-Progress: Rws - A Roulette Wheel Scheduler For Preventing Execution Pattern Leakage, Ying Zhang, Lingxiang Wang, Wei Jiang, Zhishan Guo

Computer Science Faculty Research & Creative Works

Many real-time systems are safety-critical, where reliability is crucial. Under traditional scheduling mechanism, the execution patterns of the tasks on such system can be easily derived from side-channel attacks, such that attackers can launch short high-priority tasks at critical instants which may cause deadline miss for high-critical tasks. In order to protect the system from such kind of attacks, this paper proposes the roulette wheel scheduler (RWS) to randomize the task execution pattern. Under RWS, probabilities will be assigned to each task at predefined scheduling points, and the choice for execution is randomized, such that the execution pattern is no …

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Gpu-Accelerated Large-Scale Genome Assembly, Sayan Goswami, Kisung Lee, Shayan Shams, Seung Jong Park Aug 2018

Gpu-Accelerated Large-Scale Genome Assembly, Sayan Goswami, Kisung Lee, Shayan Shams, Seung Jong Park

Computer Science Faculty Research & Creative Works

Spurred by a widening gap between hardware accelerators and traditional processors, numerous bioinformatics applications have harnessed the computing power of GPUS and reported substantial performance improvements compared to their CPU-based counterparts. However, most of these GPU-based applications only focus on the read alignment problem, while the field of de novo assembly still relies mostly on CPU-based solutions. This is primarily due to the nature of the assembly workload which is not only compute-intensive but also extremely data-intensive. Such workloads require large memories, making it difficult to adapt them to use GPUS with their limited memory capacities. To the best of …

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Algorithms Cs2500, Simone Silvestri, Ken Goss, Zhishan Guo, Ashikahmed Bhuiyan Aug 2018

Algorithms Cs2500, Simone Silvestri, Ken Goss, Zhishan Guo, Ashikahmed Bhuiyan

AOER Course Materials

No abstract provided.

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Optical Lens Attack On Monocular Depth Estimation For Autonomous Driving, Ce Zhou, Qiben Yan, Daniel Kent, Guangjing Wang, Weikang Ding, Ziqi Zhang, Hayder Radha Aug 2018

Optical Lens Attack On Monocular Depth Estimation For Autonomous Driving, Ce Zhou, Qiben Yan, Daniel Kent, Guangjing Wang, Weikang Ding, Ziqi Zhang, Hayder Radha

Computer Science Faculty Research & Creative Works

Monocular Depth Estimation (MDE) is a pivotal component of vision-based Autonomous Driving (AD) systems, enabling vehicles to estimate the depth of surrounding objects using a single camera image. This estimation guides essential driving decisions, such as braking before an obstacle or changing lanes to avoid collisions. In this paper, we explore vulnerabilities of MDE algorithms in AD systems, presenting 𝐿𝑒𝑛𝑠𝐴𝑡𝑡𝑎𝑐𝑘, a novel physical attack that strategically places optical lenses on the camera of an autonomous vehicle to manipulate the perceived object depths. 𝐿𝑒𝑛𝑠𝐴𝑡𝑡𝑎𝑐𝑘 encompasses two attack formats: concave lens attack and convex lens attack, each utilizing different optical lenses to …

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A Study Of The Genetic Algorithm Parameters For Solving Multi-Objective Travelling Salesman Problem, Romit S. Beed, Sunita Sarkar, Arindam Roy, Shubham Chatterjee Jul 2018

A Study Of The Genetic Algorithm Parameters For Solving Multi-Objective Travelling Salesman Problem, Romit S. Beed, Sunita Sarkar, Arindam Roy, Shubham Chatterjee

Computer Science Faculty Research & Creative Works

The objective of this work is to present a solution to a multiple-objective optimization problem using genetic algorithms (GA). Generally, the objectives (minimizing cost, maximizing performance, reducing carbon footprints, maximizing profit) are conflicting for multiple-objective problems, hindering concurrent optimization of each objective. A bi-objective traditional combinatorial optimization of Travelling Salesman Problem is undertaken named as the Multi-Objective Travelling Salesman Problem (MTSP). The two objectives are minimization of the distance travelled by the salesman and minimization of the time taken to travel. The purpose of this paper is to model the problem as a single objective optimization problem using the weighted …

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Mac Layer Misbehavior Detection Using Time Series Analysis, Maggie X. Cheng, Yi Ling, Wei Biao Wu Jul 2018

Mac Layer Misbehavior Detection Using Time Series Analysis, Maggie X. Cheng, Yi Ling, Wei Biao Wu

Computer Science Faculty Research & Creative Works

This paper presents a solution to the real-time detection of MAC layer misbehaviors in IEEE 802.11 networks. Among the wide range of misbehaviors, we focus on the sender side selfish behavior that creates a channel- capturing effect by using favorable parameters, and the receiver side selfish behavior that does not respond with CTS and ACK upon receiving RTS and data packets, which clears the channel for itself and causes its sender to waste resources. These misbehaviors are subtle to detect and yet can undermine the performance of the well-behaved nodes significantly. This paper shows a powerful real-time detection method that …

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Towards Distributed Cyberinfrastructure For Smart Cities Using Big Data And Deep Learning Technologies, Shayan Shams, Sayan Goswami, Kisung Lee, Seungwon Yang, Seung Jong Park Jul 2018

Towards Distributed Cyberinfrastructure For Smart Cities Using Big Data And Deep Learning Technologies, Shayan Shams, Sayan Goswami, Kisung Lee, Seungwon Yang, Seung Jong Park

Computer Science Faculty Research & Creative Works

Recent advances in big data and deep learning technologies have enabled researchers across many disciplines to gain new insight into large and complex data. For example, deep neural networks are being widely used to analyze various types of data including images, videos, texts, and time-series data. In another example, various disciplines such as sociology, social work, and criminology are analyzing crowd-sourced and online social network data using big data technologies to gain new insight from a plethora of data. Even though many different types of data are being generated and analyzed in various domains, the development of distributed city-level cyberinfrastructure …

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The Automated Design Of Probabilistic Selection Methods For Evolutionary Algorithms, Samuel N. Richter, Daniel R. Tauritz Jul 2018

The Automated Design Of Probabilistic Selection Methods For Evolutionary Algorithms, Samuel N. Richter, Daniel R. Tauritz

Computer Science Faculty Research & Creative Works

Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population of individuals, by regulating the probability that an individual's genes survive, typically based on fitness. Various conventional fitness based selection methods exist, each providing a unique relationship between the fitnesses of individuals in a population and their chances of selection. However, the full space of selection algorithms is only limited by max algorithm size, and each possible selection algorithm is optimal for some EA configuration applied to a particular problem class. Therefore, improved performance may be expected by tuning an EA's selection algorithm to the problem at …

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Evolution Of Network Enumeration Strategies In Emulated Computer Networks, Sean Harris, Eric Michalak, Kevin Schoonover, Adam Gausmann, Hannah Reinbolt, Joshua Herman, Daniel R. Tauritz, Chris Rawlings, Aaron Scott Pope Jul 2018

Evolution Of Network Enumeration Strategies In Emulated Computer Networks, Sean Harris, Eric Michalak, Kevin Schoonover, Adam Gausmann, Hannah Reinbolt, Joshua Herman, Daniel R. Tauritz, Chris Rawlings, Aaron Scott Pope

Computer Science Faculty Research & Creative Works

Successful attacks on computer networks today do not often owe their victory to directly overcoming strong security measures set up by the defender. Rather, most attacks succeed because the number of possible vulnerabilities are too large for humans to fully protect without making a mistake. Regardless of the security elsewhere, a skilled attacker can exploit a single vulnerability in a defensive system and negate the benefits of those security measures. This paper presents an evolutionary framework for evolving attacker agents in a real, emulated network environment using genetic programming, as a foundation for coevolutionary systems which can automatically discover and …

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Automated Design Of Network Security Metrics, Aaron Scott Pope, Daniel R. Tauritz, Robert Morning, Alexander D. Kent Jul 2018

Automated Design Of Network Security Metrics, Aaron Scott Pope, Daniel R. Tauritz, Robert Morning, Alexander D. Kent

Computer Science Faculty Research & Creative Works

Many abstract security measurements are based on characteristics of a graph that represents the network. These are typically simple and quick to compute but are often of little practical use in making real-world predictions. Practical network security is often measured using simulation or real-world exercises. These approaches better represent realistic outcomes but can be costly and time-consuming. This work aims to combine the strengths of these two approaches, developing efficient heuristics that accurately predict attack success. Hyper-heuristic machine learning techniques, trained on network attack simulation training data, are used to produce novel graph-based security metrics. These low-cost metrics serve as …

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A Network Tomography Approach For Traffic Monitoring In Smart Cities, Ruoxi Zhang, Sara Newman, Marco Ortolani, Simone Silvestri Jul 2018

A Network Tomography Approach For Traffic Monitoring In Smart Cities, Ruoxi Zhang, Sara Newman, Marco Ortolani, Simone Silvestri

Computer Science Faculty Research & Creative Works

Traffic monitoring is a key enabler for several planning and management activities of a Smart City. However, traditional techniques are often not cost efficient, flexible, and scalable. This paper proposes an approach to traffic monitoring that does not rely on probe vehicles, nor requires vehicle localization through GPS. Conversely, it exploits just a limited number of cameras placed at road intersections to measure car end-to-end traveling times. We model the problem within the theoretical framework of network tomography, in order to infer the traveling times of all individual road segments in the road network. We specifically deal with the potential …

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Multi-Objective Optimization Based Allocation Of Heterogeneous Spatial Crowdsourcing Tasks, Liang Wang, Zhiwen Yu, Qi Han, Bin Guo, Haoyi Xiong Jul 2018

Multi-Objective Optimization Based Allocation Of Heterogeneous Spatial Crowdsourcing Tasks, Liang Wang, Zhiwen Yu, Qi Han, Bin Guo, Haoyi Xiong

Computer Science Faculty Research & Creative Works

With the rapid development of mobile networks and the proliferation of mobile devices, spatial crowdsourcing, which refers to recruiting mobile workers to perform location-based tasks, has gained emerging interest from both research communities and industries. In this paper, we consider a spatial crowdsourcing scenario: in addition to specific spatial constraints, each task has a valid duration, operation complexity, budget limitation, and the number of required workers. Each volunteer worker completes assigned tasks while conducting his/her routine tasks. The system has a desired task probability coverage and budget constraint. Under this scenario, we investigate an important problem, namely heterogeneous spatial crowdsourcing …

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Improving Performance Of Iterative Methods By Lossy Checkponting, Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, Franck Cappello Jun 2018

Improving Performance Of Iterative Methods By Lossy Checkponting, Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Iterative methods are commonly used approaches to solve large, sparse linear systems, which are fundamental operations for many modern scientific simulations. When the large-scale iterative methods are running with a large number of ranks in parallel, they have to checkpoint the dynamic variables periodically in case of unavoidable fail-stop errors, requiring fast I/O systems and large storage space. To this end, significantly reducing the checkpointing overhead is critical to improving the overall performance of iterative methods. Our contribution is fourfold. (1) We propose a novel lossy checkpointing scheme that can significantly improve the checkpointing performance of iterative methods by leveraging …

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Message From The Program Chairs - Volume 1, Jiannong Cao, Stelvio Cimato, Yasuo Okabe, Sahra Sedighsarvestani Jun 2018

Message From The Program Chairs - Volume 1, Jiannong Cao, Stelvio Cimato, Yasuo Okabe, Sahra Sedighsarvestani

Electrical and Computer Engineering Faculty Research & Creative Works

No abstract provided.

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Identity-Adaptive Facial Expression Recognition Through Expression Regeneration Using Conditional Generative Adversarial Networks, Huiyuan Yang, Zheng Zhang, Lijun Yin Jun 2018

Identity-Adaptive Facial Expression Recognition Through Expression Regeneration Using Conditional Generative Adversarial Networks, Huiyuan Yang, Zheng Zhang, Lijun Yin

Computer Science Faculty Research & Creative Works

Subject variation is a challenging issue for facial expression recognition, especially when handling unseen subjects with small-scale labeled facial expression databases. Although transfer learning has been widely used to tackle the problem, the performance degrades on new data. In this paper, we present a novel approach (so-called IA-gen) to alleviate the issue of subject variations by regenerating expressions from any input facial images. First of all, we train conditional generative models to generate six prototypic facial expressions from any given query face image while keeping the identity related information unchanged. Generative Adversarial Networks are employed to train the conditional generative …

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Design Of Robust And Efficient Topology Using Enhanced Gene Regulatory Networks, Satyaki Roy, Vijay K. Shah, Sajal K. Das Jun 2018

Design Of Robust And Efficient Topology Using Enhanced Gene Regulatory Networks, Satyaki Roy, Vijay K. Shah, Sajal K. Das

Computer Science Faculty Research & Creative Works

Biological networks are characterized by their inherent robustness against component failures. Gene regulatory networks (GRNs) are biological networks with graph properties contributing to their innate functional robustness. In this paper, we first propose a three-tier topological characterization to study the graph properties of GRN, namely scale free out-degree distribution, low graph density, and abundance of subgraphs, called motifs. We then present a novel edge rewiring mechanism, consisting of edge addition and deletion algorithms, to remedy its vulnerability against failure of well-connected nodes while preserving its graph properties. We discuss the preferential attachment growth-based edge addition and greedy edge deletion. We …

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Modeling Of Cloud-Based Digital Twins For Smart Manufacturing With Mt Connect, Liwen Hu, Ngoc-Tu Nguyen, Wenjin Tao, Ming-Chuan Leu, Xiaoqing Frank Liu, Rakib Shahriar, S M Nahian Al Sunny Jun 2018

Modeling Of Cloud-Based Digital Twins For Smart Manufacturing With Mt Connect, Liwen Hu, Ngoc-Tu Nguyen, Wenjin Tao, Ming-Chuan Leu, Xiaoqing Frank Liu, Rakib Shahriar, S M Nahian Al Sunny

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The common modeling of digital twins uses an information model to describe the physical machines. The integration of digital twins into productive cyber-physical cloud manufacturing (CPCM) systems imposes strong demands such as reducing overhead and saving resources. In this paper, we develop and investigate a new method for building cloud-based digital twins (CBDT), which can be adapted to the CPCM platform. Our method helps reduce computing resources in the information processing center for efficient interactions between human users and physical machines. We introduce a knowledge resource center (KRC) built on a cloud server for information intensive applications. An information model …

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Worker Activity Recognition In Smart Manufacturing Using Imu And Semg Signals With Convolutional Neural Networks, Wenjin Tao, Ze-Hao Lai, Ming-Chuan Leu, Zhaozheng Yin Jun 2018

Worker Activity Recognition In Smart Manufacturing Using Imu And Semg Signals With Convolutional Neural Networks, Wenjin Tao, Ze-Hao Lai, Ming-Chuan Leu, Zhaozheng Yin

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In a smart manufacturing system involving workers, recognition of the worker's activity can be used for quantification and evaluation of the worker's performance, as well as to provide onsite instructions with augmented reality. In this paper, we propose a method for activity recognition using Inertial Measurement Unit (IMU) and surface electromyography (sEMG) signals obtained from a Myo armband. The raw 10-channel IMU signals are stacked to form a signal image. This image is transformed into an activity image by applying Discrete Fourier Transformation (DFT) and then fed into a Convolutional Neural Network (CNN) for feature extraction, resulting in a high-level …

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Heterogeneous Activity Causes A Nonlinear Increase In The Group Energy Use Of Ant Workers Isolated From Queen And Brood, Nolan Ferral, Kyara Holloway, Mingzhong Li, Zhaozheng Yin, Chen Hou Jun 2018

Heterogeneous Activity Causes A Nonlinear Increase In The Group Energy Use Of Ant Workers Isolated From Queen And Brood, Nolan Ferral, Kyara Holloway, Mingzhong Li, Zhaozheng Yin, Chen Hou

Computer Science Faculty Research & Creative Works

Increasing evidence has shown that the energy use of ant colonies increases sublinearly with colony size so that large colonies consume less per capita energy than small colonies. It has been postulated that social environment (e.g., in the presence of queen and brood) is critical for the sublinear group energetics, and a few studies of ant workers isolated from queens and brood observed linear relationships between group energetics and size. In this paper, we hypothesize that the sublinear energetics arise from the heterogeneity of activity in ant groups, that is, large groups have relatively more inactive members than small groups. …

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The Future Possibility Of Consumer-Grade Quantum Computers, Peter Dolan May 2018

The Future Possibility Of Consumer-Grade Quantum Computers, Peter Dolan

Missouri S&T’s Peer to Peer

Quantum computers are rapidly evolving and are on the edge of becoming useful for the first time. The theoretical limit of computational speed for quantum computers would put even small-scale quantum computers well ahead of any classical computer. With more researchers attempting to build their own, it has become a race to see who can create the first truly useful quantum computer. Once such computers become both useful and prevalent, massive advancements in many fields of science can be achieved, leading to a scientific revolution. Advances in quantum computing lead some researchers and consumers to question whether the technology can …

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The Viability Of Quantum Computing, Brennan Michael King May 2018

The Viability Of Quantum Computing, Brennan Michael King

Missouri S&T’s Peer to Peer

Quantum computing is an upcoming computational technology that could be the key to advancing the field and ushering in a new era of innovation. In this paper examines the viability of quantum computing extensively using only highly credible peer-reviewed articles from the last few years. These peer-reviewed articles will provide relevant facts and data from prominent researchers in the field of computer engineering. A growing problem in the field of electronics and computers is the concept of Moore’s law. Moore’s law refers to the doubling of transistors every two years in integrated circuits. Recent research has suggested that electronics may …

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