Physical Sciences and Mathematics Commons^™

Cellmarkerpipe: Cell Marker Identification And Evaluation Pipeline In Single Cell Transcriptomes, Yinglu Jia, Pengchong Ma, Qiuming Yao

School of Computing: Faculty Publications

Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe (https:// github. com/ yao- labor atory/ cellM arker Pipe), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker’s overall reliable performance …

Nonlinear Classifiers For Wet-Neuromorphic Computing Using Gene Regulatory Neural Network, Adrian Ratwatte, Samitha Somathilaka, Sasitharan Balasubramaniam, Assaf A. Gilad

School of Computing: Faculty Publications

The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an artificial neural network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where the TF concentration relative to half-maximal RNA concentration and gene product copy number influences transcription and translation via weighted multiplication before undergoing a nonlinear activation function. This process yields protein concentration as the …

Data-Driven Evidence-Based Syntactic Sugar Design, David Obrien, Robert Dyer, Tien N. Nguyen, Hridesh Rajan

School of Computing: Faculty Publications

Programming languages are essential tools for developers, and their evolution plays a crucial role in supporting the activities of developers. One instance of programming language evolution is the introduction of syntactic sugars, which are additional syntax elements that provide alternative, more readable code constructs. However, the process of designing and evolving a programming language has traditionally been guided by anecdotal experiences and intuition. Recent advances in tools and methodologies for mining open-source repositories have enabled developers to make datadriven software engineering decisions. In light of this, this paper proposes an approach for motivating data-driven programming evolution by applying frequent subgraph …

Scalable Relational Analysis Via Relational Bound Propagation, Clay Stevens, Hamid Bagheri

School of Computing: Faculty Publications

Bounded formal analysis techniques (such as bounded model checking) are incredibly powerful tools for today’s software engineers. However, such techniques often suffer from scalability challenges when applied to large-scale, real-world systems. It can be very difficult to ensure the bounds are set properly, which can have a profound impact on the performance and scalability of any bounded formal analysis. In this paper, we propose a novel approach—relational bound propagation—which leverages the semantics of the underlying relational logic formula encoded by the specification to automatically tighten the bounds for any relational specification. Our approach applies two sets of semantic rules to …

Micrornas In Pancreatic Cancer: Advances In Biomarker Discovery And Therapeutic Implications, Roland Madadjim, Thuy An, Juan Cui

School of Computing: Faculty Publications

Pancreatic cancer remains a formidable malignancy characterized by high mortality rates, primarily attributable to late-stage diagnosis and a dearth of effective therapeutic interventions. The identification of reliable biomarkers holds paramount importance in enhancing early detection, prognostic evaluation, and targeted treatment modalities. Small non-coding RNAs, particularly microRNAs, have emerged as promising candidates for pancreatic cancer biomarkers in recent years. In this review, we delve into the evolving role of cellular and circulating miRNAs, including exosomal miRNAs, in the diagnosis, prognosis, and therapeutic targeting of pancreatic cancer. Drawing upon the latest research advancements in omics data-driven biomarker discovery, we also perform a …

Securing Synchrophasors Using Data Provenance In The Quantum Era, Kashif Javed, Mansoor Ali Khan, Mukhtar Ullah, Muhammad Naveed Aman, Biplab Sikdar

School of Computing: Faculty Publications

Trust in the fidelity of synchrophasor measurements is crucial for the correct operation of modern power grids. While most of the existing research on data provenance focuses on the Internet of Things, there is a significant need for effective malicious data detection in power systems. Current methods either fail to detect malicious data modifications or require certain Phasor Measurement Units (PMUs) to be physically secured. To solve these issues, this paper presents a new protocol to establish data provenance in synchrophasor networks. The proposed protocol is based on Physically Unclonable Functions (PUFs) and harnesses the principles of quantum unreality and …

Intermittent-Aware Design Exploration Of Systolic Array Using Various Non-Volatile Memory: A Comparative Study, Nedasadat Taheri, Sepehr Tabrizchi, Arman Roohi

School of Computing: Faculty Publications

This paper conducts a comprehensive study on intermittent computing within IoT environments, emphasizing the interplay between different dataflows—row, weight, and output—and a variety of non-volatile memory technologies. We then delve into the architectural optimization of these systems using a spatial architecture, namely IDEA, with their processing elements efficiently arranged in a rhythmic pattern, providing enhanced performance in the presence of power failures. This exploration aims to highlight the diverse advantages and potential applications of each combination, offering a comparative perspective. In our findings, using IDEA for the row stationary dataflow with AlexNet on the CIFAR10 dataset, we observe a power …

Command Injection Attacks In Smart Grids: A Survey, Muhammad Usama, Muhammad Naveed Aman

School of Computing: Faculty Publications

Cybersecurity is important in the realization of various smart grid technologies. Several studies have been conducted to discuss different types of cyberattacks and provide their countermeasures. The false command injection attack (FCIA) is considered one of the most critical attacks that have been studied. Various techniques have been proposed in the literature to detect FCIAs on different components of smart grids. The predominant focus of current surveys lies on FCIAs and detection techniques for such attacks. This article presents a survey of existing works on FCIAs and classifies FCIAs in smart grids according to the targeted component. The impacts of …

Unveiling The Connection Between Malware And Pirated Software In Southeast Asian Countries: A Case Study, Asif Iqbal, Muhammad Naveed Aman, Ramkumar Rejendran, Biplab Sikdar

School of Computing: Faculty Publications

Pirated software is an attractive choice for cybercriminals seeking to spread malicious software, known as malware. This paper attempts to quantify the occurrence of malware concealed within pirated software.We collected samples of pirated software from various sources from Southeast Asian countries, including hard disk drives, optical discs purchased in eight different countries, and online platforms using peerto- peer services. Our dataset comprises a total of 750 pirated software samples. To analyze these samples, we employed seven distinct antivirus (AV) engines. The malware identified by the AV engines was classified into four categories: adware, Trojans, viruses, and a miscellaneous category termed …

Strategyproof Mechanisms For Group-Fair Obnoxious Facility Location Problems, Jiaqian Li, Minming Li, Hau Chan

School of Computing: Faculty Publications

We study the group-fair obnoxious facility location problems from the mechanism design perspective where agents belong to different groups and have private location preferences on the undesirable locations of the facility. Our main goal is to design strategyproof mechanisms that elicit the true location preferences from the agents and determine a facility location that approximately optimizes several group-fair objectives. We first consider the maximum total and average group cost (group-fair) objectives. For these objectives, we propose deterministic mechanisms that achieve 3-approximation ratios and provide matching lower bounds. We then provide the characterization of 2-candidate strategyproof randomized mechanisms. Leveraging the characterization, …

Altruism In Facility Location Problems, Houyu Zhou, Hau Chan, Minming Li

School of Computing: Faculty Publications

We study the facility location problems (FLPs) with altruistic agents who act to benefit others in their affiliated groups. Our aim is to design mechanisms that elicit true locations from the agents in different overlapping groups and place a facility to serve agents to approximately optimize a given objective based on agents’ costs to the facility. Existing studies of FLPs consider myopic agents who aim to minimize their own costs to the facility.We mainly consider altruistic agents with well-motivated group costs that are defined over costs incurred by all agents in their groups. Accordingly, we define Pareto strategyproofness to account …

Relative Comparison Of Modern Computing To Computer Technology Of Ages, Iwasan D. Kejawa Dr., Hailly Rubio Ms.

School of Computing: Faculty Publications

Abstract

Abstract

Are there differences and similarities between the computer technology of today and yesterdays. Research had shown that there had been tremendous improvements from the computers of ages (traditional Computers) as we enter the 21st century. Both the physicality and the functionalities of computers have changed but some remain the same. The memory capacity and functions have changed, but all are still based on the old concepts of yesteryears.

3dgaunet: 3d Generative Adversarial Networks With A 3d U-Net Based Generator To Achieve The Accurate And Effective Synthesis Of Clinical Tumor Image Data For Pancreatic Cancer, Yu Shi, Hannah Tang, Michael J. Baine, Michael A. Hollingsworth, Huijing Du, Dandan Zheng, Chi Zhang, Hongfeng Yu

School of Computing: Faculty Publications

Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to represent a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D …

Osc-Co2: Coattention And Cosegmentation Framework For Plant State Change With Multiple Features, Rubi Quiñones, Ashok Samal, Sruti Das Choudhury, Francisco Muñoz-Arriola

School of Computing: Faculty Publications

Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO²) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object’s pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and …

Rapid: Region-Based Pointer Disambiguation, Khushboo Chitre, Piyus Kedia, Rahul Purandare

School of Computing: Faculty Publications

Interprocedural alias analyses often sacrifice precision for scalability. Thus, modern compilers such as GCC and LLVM implement more scalable but less precise intraprocedural alias analyses. This compromise makes the compilers miss out on potential optimization opportunities, affecting the performance of the application. Modern compilers implement loop-versioning with dynamic checks for pointer disambiguation to enable the missed optimizations. Polyhedral access range analysis and symbolic range analysis enable O(1) range checks for non-overlapping of memory accesses inside loops. However, these approaches work only for the loops in which the loop bounds are loop invariants. To address this limitation, researchers proposed a …

Perceptual Cue-Guided Adaptive Image Downscaling For Enhanced Semantic Segmentation On Large Document Images, Chulwoo Pack, Leen-Kiat Soh, Elizabeth Lorang

School of Computing: Faculty Publications

Image downscaling is an essential operation to reduce spatial complexity for various applications and is becoming increasingly important due to the growing number of solutions that rely on memory-intensive approaches, such as applying deep convolutional neural networks to semantic segmentation tasks on large images. Although conventional content-independent image downscaling can efficiently reduce complexity, it is vulnerable to losing perceptual details, which are important to preserve. Alternatively, existing content-aware downscaling severely distorts spatial structure and is not effectively applicable for segmentation tasks involving document images. In this paper, we propose a novel image downscaling approach that combines the strengths of both …

Revealing Gene Regulation-Based Neural Network Computing In Bacteria, Samitha S. Somathilaka, Sasitharan Balasubramaniam, Daniel P. Martins, Xu Li

School of Computing: Faculty Publications

Bacteria are known to interpret a range of external molecular signals that are crucial for sensing environmental conditions and adapting their behaviors accordingly. These external signals are processed through a multitude of signaling transduction networks that include the gene regulatory network (GRN). From close observation, the GRN resembles and exhibits structural and functional properties that are similar to artificial neural networks. An in-depth analysis of gene expression dynamics further provides a new viewpoint of characterizing the inherited computing properties underlying the GRN of bacteria despite being non-neuronal organisms. In this study, we introduce a model to quantify the gene-to-gene interaction …

Mfa-Dvr: Direct Volume Rendering Of Mfa Models, Jianxin Sun, David Lenz, Hongfeng Yu, Tom Peterka

School of Computing: Faculty Publications

3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumetric data can make efficiently generating high-quality volume rendering results a challenging task. Multivariate functional approximation (MFA) is a new data model that addresses some of the critical challenges: high-order evaluation of both value and derivative anywhere in the spatial domain, compact representation for largescale volumetric data, and uniform representation of both structured and unstructured data. In this paper, we present MFA-DVR, the first direct volume rendering pipeline utilizing the MFA model, for both structured …

A Roadmap For The Human Gut Cell Atlas, Matthias Zilbauer, Kylie R. James, Mandeep Kaur, Sebastian Pott, Zhixin Li, Albert Burger, Jay R. Thiagarajah, Joseph Burclaff, Frode L. Jahnsen, Francesca Perrone, Alexander D. Ross, Gianluca Matteoli, Nathalie Stakenborg, Tomohisa Sujino, Andreas Moor, Raquel Bartolome-Casado, Espen S. Bækkevold, Ran Zhou, Bingqing Xie, Ken S. Lau, Shahida Din, Scott T. Magness, Qiuming Yao, Semir Beyaz, Mark Arends, Alexandre Denadai-Souza, Lori A. Coburn, Jellert T. Gaublomme, Richard Baldock, Irene Papatheodorou, Jose Ordovas-Montanes, Guy Boeckxstaens, Anna Hupalowska, Sarah A. Teichmann

School of Computing: Faculty Publications

The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human …

Agris: Wind-Adaptive Wideband Reconfigurable Intelligent Surfaces For Resilient Wireless Agricultural Networks At Millimeter-Wave Spectrum, Shuai Nie, M. C. Vuran

School of Computing: Faculty Publications

Wireless networks in agricultural environments are unique in many ways. Recent measurements reveal that the dynamics of crop growth impact wireless propagation channels with a long-term seasonal pattern. Additionally, short-term environmental factors, such as strong wind, result in variations in channel statistics. Next-generation agricultural fields, populated by autonomous tractors, drones, and high-throughput sensing systems, require high-throughput connectivity infrastructure, resulting in the future deployment of high-frequency networks, where they have not been deployed before. More specifically, when millimeter-wave (mmWave) communication systems, a viable candidate for 5G and 6G high-throughput solutions, are deployed for higher throughput, these issues become more prominent due …

Next-Generation Sequencing Data-Based Association Testing Of A Group Of Genetic Markers For Complex Responses Using A Generalized Linear Model Framework, Zheng Xu, Song Yan, Cong Wu, Qing Duan, Sixia Chen, Yun Li

School of Computing: Faculty Publications

To study the relationship between genetic variants and phenotypes, association testing is adopted; however, most association studies are conducted by genotype-based testing. Testing methods based on next-generation sequencing (NGS) data without genotype calling demonstrate an advantage over testing methods based on genotypes in the scenarios when genotype estimation is not accurate. Our objective was to develop NGS data-based methods for association studies to fill the gap in the literature. Single-variant testing methods based on NGS data have been proposed, including our previously proposed single-variant NGS data-based testing method, i.e., UNC combo method. The NGS data-based group testing method has been …

Next-Generation Sequencing Data-Based Association Testing Of A Group Of Genetic Markers For Complex Responses Using A Generalized Linear Model Framework, Zheng Xu, Song Yan, Cong Wu, Qing Duan, Sixia Chen, Yun Li

School of Computing: Faculty Publications

To study the relationship between genetic variants and phenotypes, association testing is adopted; however, most association studies are conducted by genotype-based testing. Testing methods based on next-generation sequencing (NGS) data without genotype calling demonstrate an advantage over testing methods based on genotypes in the scenarios when genotype estimation is not accurate. Our objective was to develop NGS data-based methods for association studies to fill the gap in the literature. Single-variant testing methods based on NGS data have been proposed, including our previously proposed single-variant NGS data-based testing method, i.e., UNC combo method. The NGS data-based group testing method has been …

Realizing Molecular Machine Learning Through Communications For Biological Ai: Future Directions And Challenges, Sasitharan Balasubramaniam, Samitha Somathilaka, Sehee Sun, Adrian Ratwatte, Massimiliano Pierobon

School of Computing: Faculty Publications

Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various types of devices, we benefit from their use into energy-efficient algorithms for low powered devices. In this paper, we investigate a scale and medium that is far smaller than conventional devices as we move towards molecular systems that can be utilized to perform machine learning functions, i.e., Molecular Machine Learning (MML). Fundamental to the operation of MML is the …

Dynamic Resource Optimization For Energy-Efficient 6g-Iot Ecosystems, James Adu Ansere, Mohsin Kamal, Izaz Ahmad Khan, Muhammad Naveed Aman

School of Computing: Faculty Publications

The problem of energy optimization for Internet of Things (IoT) devices is crucial for two reasons. Firstly, IoT devices powered by renewable energy sources have limited energy resources. Secondly, the aggregate energy requirement for these small and low-powered devices is translated into significant energy consumption. Existing works show that a significant portion of an IoT device’s energy is consumed by the radio sub-system. With the emerging sixth generation (6G), energy efficiency is a major design criterion for significantly increasing the IoT network’s performance. To solve this issue, this paper focuses on maximizing the energy efficiency of the radio sub-system. In …

A Generalization Of The Chomsky-Halle Phonetic Representation Using Real Numbers For Robust Speech Recognition In Noisy Environments, Peter Z. Revesz

School of Computing: Faculty Publications

Speech recognition is difficult when the speech signal is weak or occurs in a noisy environment. This paper presents an efficient and robust method that can reconstruct the standard pronunciation of English phonemes and words given a weak or noisy signal. The reconstruction is based on a novel representation of the reconstruction task as a problem of data retrieval from a database in two different cases: (1) when the phonemes are represented in the database as binary tuples and the input is also a binary tuple from which deletion errors occur, and (2) when the phonemes are represented in the …

Studying Developer Eye Movements To Measure Cognitive Workload And Visual Effort For Expertise Assessment, Salwa D. Aljehane, Bonita Sharif, Jonathan I. Maletic

School of Computing: Faculty Publications

Eye movement data provides valuable insights that help test hypotheses about a software developer’s comprehension process. The pupillary response is successfully used to assess mental processing effort and attentional focus. Relatively little is known about the impact of expertise level in cognitive effort during programming tasks. This paper presents a quantitative analysis that compares the eye movements of 207 experts and novices collected while solving program comprehension tasks. The goal is to examine changes of developers’ eye movement metrics in accordance with their expertise. The results indicate significant increase in pupil size with the novice group compared to the experts, …

Convolutional Neural Networks Analysis Reveals Three Possible Sources Of Bronze Age Writings Between Greece And India, Shruti Daggumati, Peter Z. Revesz

School of Computing: Faculty Publications

This paper analyzes the relationships among eight ancient scripts from between Greece and India. We used convolutional neural networks combined with support vector machines to give a numerical rating of the similarity between pairs of signs (one sign from each of two different scripts). Two scripts that had a one-to-one matching of their signs were determined to be related. The result of the analysis is the finding of the following three groups, which are listed in chronological order: (1) Sumerian pictograms, the Indus Valley script, and the proto-Elamite script; (2) Cretan hieroglyphs and Linear B; and (3) the Phoenician, Greek, …

Conversion Of Fat To Cellular Fuel—Fatty Acids 𝛽-Oxidation Model, Sylwester M. Kloska, Krzysztof Pałczyński, Tomasz Marciniak, Tomasz Talaśka, Marissa Miller, Beata J. Wysocki, Paul Davis, Tadeusz A. Wysocki

School of Computing: Faculty Publications

𝛽-oxidation of fatty acids plays a significant role in the energy metabolism of the cell. This paper presents a 𝛽-oxidation model of fatty acids based on queueing theory. It uses Michaelis–Menten enzyme kinetics, and literature data on metabolites’ concentration and enzymatic constants. A genetic algorithm was used to optimize the parameters for the pathway reactions. The model enables real-time tracking of changes in the concentrations of metabolites with different carbon chain lengths. Another application of the presented model is to predict the changes caused by system disturbance, such as altered enzyme activity or abnormal fatty acid concentration. The model has …

From Laboratory To Field: Unsupervised Domain Adaptation For Plant Disease Recognition In The Wild, Xinlu Wu, Xijian Fan, Peng Luo, Sruti Das Choudhury, Tardi Tjahjadi, Chunhua Hu

School of Computing: Faculty Publications

Plant disease recognition is of vital importance to monitor plant development and predicting crop production. However, due to data degradation caused by different conditions of image acquisition, e.g., laboratory vs. field environment, machine learning-based recognition models generated within a specific dataset (source domain) tend to lose their validity when generalized to a novel dataset (target domain). To this end, domain adaptation methods can be leveraged for the recognition by learning invariant representations across domains. In this paper, we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via …

Efficient Two-Stage Analysis For Complex Trait Association With Arbitrary Depth Sequencing Data, Zheng Xu, Song Yan, Shuai Yuan, Cong Wu, Sixia Chen, Zifang Guo

School of Computing: Faculty Publications

Sequencing-based genetic association analysis is typically performed by first generating genotype calls from sequence data and then performing association tests on the called genotypes. Standard approaches require accurate genotype calling (GC), which can be achieved either with high sequencing depth (typically available in a small number of individuals) or via computationally intensive multi-sample linkage disequilibrium (LD)-aware methods. We propose a computationally efficient two-stage combination approach for association analysis, in which single-nucleotide polymorphisms (SNPs) are screened in the first stage via a rapid maximum likelihood (ML)-based method on sequence data directly (without first calling genotypes), and then the selected SNPs are …