Physical Sciences and Mathematics Commons^™

Mask2former With Improved Query For Semantic Segmentation In Remote-Sensing Images, Shichen Guo, Qi Wang, Shiming Xiang, Shuwen Wang, Xuezhi Wang

Computer Science Faculty Publications and Presentations

Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS …

Self-Optimizing Feature Generation Via Categorical Hashing Representation And Hierarchical Reinforcement Crossing, Wangyang Ying, Dongjie Wang, Kunpeng Liu, Leilei Sun, Yanjie Fu

Computer Science Faculty Publications and Presentations

Feature generation aims to generate new and meaningful features to create a discriminative representation space. A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities. We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, …

Constrained Multiview Representation For Self-Supervised Contrastive Learning, Siyuan Dai, Kai Ye, Kun Zhao, Ge Cui, Haoteng Tang, Liang Zhan

Computer Science Faculty Publications and Presentations

Representation learning constitutes a pivotal cornerstone in contemporary deep learning paradigms, offering a conduit to elucidate distinctive features within the latent space and interpret the deep models. Nevertheless, the inherent complexity of anatomical patterns and the random nature of lesion distribution in medical image segmentation pose significant challenges to the disentanglement of representations and the understanding of salient features. Methods guided by the maximization of mutual information, particularly within the framework of contrastive learning, have demonstrated remarkable success and superiority in decoupling densely intertwined representations. However, the effectiveness of contrastive learning highly depends on the quality of the positive and …

Efficient High-Resolution Time Series Classification Via Attention Kronecker Decomposition, Aosong Feng, Jialin Chen, Juan Garza, Brooklyn Berry, Francisco Salazar, Yifeng Gao, Rex Ying, Leandros Tassiulas

Computer Science Faculty Publications and Presentations

The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains. To tackle this challenge effectively, it is imperative that the state-of-theart attention model is scalable to accommodate the growing sequence lengths typically encountered in highresolution time series data, while also demonstrating robustness in handling the inherent noise prevalent in such datasets. To address this, we propose to hierarchically encode the long time series into multiple levels based on the interaction ranges. By capturing relationships at different levels, we can build more robust, expressive, and efficient models that are capable of …

A Simple Proof That Ricochet Robots Is Pspace-Complete, Jose Balanza-Martinez, Angel A. Cantu, Robert Schweller, Tim Wylie

Computer Science Faculty Publications and Presentations

In this paper, we seek to provide a simpler proof that the relocation problem in Ricochet Robots (Lunar Lockout with fixed geometry) is PSPACE-complete via a reduction from Finite Function Generation (FFG). Although this result was originally proven in 2003, we give a simpler reduction by utilizing the FFG problem, and put the result in context with recent publications showing that relocation is also PSPACE-complete in related models.

Multitasking Scheduling With Shared Processing, Bin Fu, Yumei Huo, Hairong Zhao

Computer Science Faculty Publications and Presentations

Recently, the problem of multitasking scheduling has raised a lot of interest in the service industries. Hall et al. (Discrete Applied Mathematics, 2016) proposed a shared processing multitasking scheduling model which allows a team to continue to work on the primary tasks while processing the routinely scheduled activities as they occur. With a team being modeled as a single machine, the processing sharing of the machine is achieved by allocating a fraction of the processing capacity to routine jobs and the remaining fraction, which we denote as sharing ratio, to the primary jobs. In this paper, we generalize this model …

Janus: Toward Preventing Counterfeits In Supply Chains Utilizing A Multi-Quorum Blockchain, Vika Crossland, Connor Dellwo, Golam Bashar, Gaby G. Dagher

Computer Science Faculty Publications and Presentations

The modern pharmaceutical supply chain lacks transparency and traceability, resulting in alarming rates of counterfeit products entering the market. These illegitimate products cause harm to end users and wreak havoc on the supply chain itself, costing billions of dollars in profit loss. In this paper, in response to the Drug Supply Chain Security Act (DSCSA), we introduce Janus, a novel pharmaceutical track-and-trace system that utilizes blockchain and cloning-resistant hologram tags to prevent counterfeits from entering the pharmaceutical supply chain. We design a multi-quorum consensus protocol that achieves load balancing across the network. We perform a security analysis to show robustness …

Evaluating Digital Creativity Support For Children: A Systematic Literature Review, Marte Hoff Hagen, Daniela Soares Cruzes, Letizia Jaccheri, Jerry Alan Fails

Computer Science Faculty Publications and Presentations

Creativity, the process of creating something new and valuable, benefits children by improving their skills and development, encouraging interaction and engagement, and enabling the generation and expression of novel ideas. In recent years, interactive digital tools have emerged to support the user’s creativity in the open-ended creation of new artifacts. However, the question of evaluating the creativity happening in the interplay between children, digital tools, and products is still open. This systematic literature review investigated the evaluations of digital creativity support tools for children and identified 81 peer-reviewed relevant articles from the last 10 years. This research contributes to practitioners …

Gated Recurrent Units For Blockage Mitigation In Mmwave Wireless, Ahmed H. Almutairi, Alireza Keshavarz-Haddad, Ehsan Aryafar

Computer Science Faculty Publications and Presentations

Millimeter-Wave (mmWave) communication is susceptible to blockages, which can significantly reduce the signal strength at the receiver. Mitigating the negative impacts of blockages is a key requirement to ensure reliable and high throughput mmWave communication links. Previous research on blockage mitigation has introduced several model and protocol based blockage mitigation solutions that focus on one technique at a time, such as handoff to a different base station or beam adaptation to the same base station. In this paper, we address the overarching problem: what blockage mitigation method should be employed? and what is the optimal sub-selection within that method? To …

Preventing Inferences Through Data Dependencies On Sensitive Data, Primal Pappachan, Shufan Zhang, Xi He, Sharad Mehrotra

Computer Science Faculty Publications and Presentations

Simply restricting the computation to non-sensitive part of the data may lead to inferences on sensitive data through data dependencies. Inference control from data dependencies has been studied in the prior work. However, existing solutions either detect and deny queries which may lead to leakage – resulting in poor utility, or only protects against exact reconstruction of the sensitive data – resulting in poor security. In this paper, we present a novel security model called full deniability. Under this stronger security model, any information inferred about sensitive data from non-sensitive data is considered as a leakage. We describe algorithms for …

Parameterized Complexity Of Feature Selection For Categorical Data Clustering, Sayan Bandyapadhyay, Fedor V. Fomin, Petr A. Golovach, Kirill Simonov

Computer Science Faculty Publications and Presentations

We develop new algorithmic methods with provable guarantees for feature selection in regard to categorical data clustering. While feature selection is one of the most common approaches to reduce dimensionality in practice, most of the known feature selection methods are heuristics. We study the following mathematical model. We assume that there are some inadvertent (or undesirable) features of the input data that unnecessarily increase the cost of clustering. Consequently, we want to select a subset of the original features from the data such that there is a small-cost clustering on the selected features. More precisely, for given integers ℓ (the …

A Comprehensive Survey Of Complex Brain Network Representation, Haoteng Tang, Guixiang Ma, Yanfu Zhang, Kai Ye, Lei Guo, Guodong Liu, Qi Huang, Yalin Wang, Olusola Ajilore, Alex D. Leow

Computer Science Faculty Publications and Presentations

Highlights

• Major traditional and deep learning methods on brain network representation are overviewed.

• Brain network datasets and algorithm implementation tools are summarized.

• Promising research directions in brain network analysis are discussed.

Abstract

Recent years have shown great merits in utilizing neuroimaging data to understand brain structural and functional changes, as well as its relationship to different neurodegenerative diseases and other clinical phenotypes. Brain networks, derived from different neuroimaging modalities, have attracted increasing attention due to their potential to gain system-level insights to characterize brain dynamics and abnormalities in neurological conditions. Traditional methods aim to pre-define multiple topological features of brain …

A Fast And Responsive Web-Based Framework For Visualizing Hpc Application Usage, Ved Arora, Nayeli Gurrola, Amiya K. Maji, Guangzhen Jin

Computer Science Faculty Publications and Presentations

Insights about applications and user environments can help HPC center staff make data-driven decisions about cluster operations. In this paper, we present a fast and responsive web-based visualization framework for analyzing HPC application usage. By leveraging XALT, a powerful tool for tracking application and library usage, we collected tens of millions of data points on a national supercomputer. The portable visualization framework created with Plotly Dash can be easily launched as a container and accessed from a web browser. The presented visualizations take a deep dive into the XALT data, analyzing application use, compiler usage, library usage, and even user-specific …

Understanding The Contribution Of Recommendation Algorithms On Misinformation Recommendation And Misinformation Dissemination On Social Networks, Royal Pathak, Francesca Spezzano, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

Social networks are a platform for individuals and organizations to connect with each other and inform, advertise, spread ideas, and ultimately influence opinions. These platforms have been known to propel misinformation. We argue that this could be compounded by the recommender algorithms that these platforms use to suggest items potentially of interest to their users, given the known biases and filter bubbles issues affecting recommender systems. While much has been studied about misinformation on social networks, the potential exacerbation that could result from recommender algorithms in this environment is in its infancy. In this manuscript, we present the result of …

Sublinear Time Motif Discovery From Multiple Sequences, Bin Fu, Yunhui Fu, Yuan Xue

Computer Science Faculty Publications and Presentations

In this paper, a natural probabilistic model for motif discovery has been used to experimentally test the quality of motif discovery programs. In this model, there are k background sequences, and each character in a background sequence is a random character from an alphabet, Σ. A motif G = g1g2 . . . gm is a string of m characters. In each background sequence is implanted a probabilistically-generated approximate copy of G. For a probabilistically-generated approximate copy b1b2 . . . bm of G, every character, bi , is probabilistically generated, such that the probability for bi 6= gi is …

Complexity Of Reconfiguration In Surface Chemical Reaction Networks, Robert M. Alaniz, Josh Brunner, Michael Coulombe, Erik D. Demaine, Yevhenii Diomidov, Ryan Knobel, Timothy Gomez, Elise Grizzell, Jayson Lynch, Andrew Rodriguez, Robert Schweller, Tim Wylie

Computer Science Faculty Publications and Presentations

We analyze the computational complexity of basic reconfiguration problems for the recently introduced surface Chemical Reaction Networks (sCRNs), where ordered pairs of adjacent species nondeterministically transform into a different ordered pair of species according to a predefined set of allowed transition rules (chemical reactions). In particular, two questions that are fundamental to the simulation of sCRNs are whether a given configuration of molecules can ever transform into another given configuration, and whether a given cell can ever contain a given species, given a set of transition rules. We show that these problems can be solved in polynomial time, are NP-complete, …

Effective Entity Augmentation By Querying External Data Sources, Christopher Buss, Jasmin Mousavi, Mikhail Tokarev, Arash Termehchy, David Maier, Stefan Lee

Computer Science Faculty Publications and Presentations

Users often want to augment and enrich entities in their datasets with relevant information from external data sources. As many external sources are accessible only via keyword-search interfaces, a user usually has to manually formulate a keyword query that extract relevant information for each entity. This approach is challenging as many data sources contain numerous tuples, only a small fraction of which may contain entity-relevant information. Furthermore, different datasets may represent the same information in distinct forms and under different terms (e.g., different data source may use different names to refer to the same person). In such cases, it is …

Auxiliary Features-Guided Super Resolution For Monte Carlo Rendering, Qiqi Hou, Feng Liu

Computer Science Faculty Publications and Presentations

This paper investigates super-resolution to reduce the number of pixels to render and thus speed up Monte Carlo rendering algorithms. While great progress has been made to super-resolution technologies, it is essentially an ill-posed problem and cannot recover high-frequency details in renderings. To address this problem, we exploit high-resolution auxiliary features to guide super-resolution of low-resolution renderings. These high-resolution auxiliary features can be quickly rendered by a rendering engine and at the same time provide valuable high-frequency details to assist super-resolution. To this end, we develop a cross-modality transformer network that consists of an auxiliary feature branch and a low-resolution …

Formalizing Stack Safety As A Security Property, Sean Noble Anderson, Roberto Blanco, Leonidas Lampropoulos, Benjamin C. Pierce, Andrew Tolmach

Computer Science Faculty Publications and Presentations

The term stack safety is used to describe a variety of compiler, runtime, and hardware mechanisms for protecting stack memory. Unlike “the heap,” the ISA-level stack does not correspond to a single high-level language concept: different compilers use it in different ways to support procedural and functional abstraction mechanisms from a wide range of languages. This protean nature makes it difficult to nail down what it means to correctly enforce stack safety.

Lossy Kernelization Of Same-Size Clustering, Sayan Bandyapadhyay, Fedor V. Fomin, Petr A. Golovach, Nidhi Purohit, Kirill Simonov

Computer Science Faculty Publications and Presentations

In this work, we study the k-median clustering problem with an additional equal-size constraint on the clusters from the perspective of parameterized preprocessing. Our main result is the first lossy (2-approximate) polynomial kernel for this problem parameterized by the cost of clustering. We complement this result by establishing lower bounds for the problem that eliminate the existence of an (exact) kernel of polynomial size and a PTAS.

Classification Of Drainage Crossings On High-Resolution Digital Elevation Models: A Deep Learning Approach, Di Wu, Ruopu Li, Banafsheh Rekabdar, Claire Talbert, Michael Edidem, Guangxing Wang

Computer Science Faculty Publications and Presentations

High-Resolution Digital Elevation Models (HRDEMs) have been used to delineate fine-scale hydrographic features in landscapes with relatively level topography. However, artificial flow barriers associated with roads are known to cause incorrect modeled flowlines, because these barriers substantially increase the terrain elevation and often terminate flowlines. A common practice is to breach the elevation of roads near drainage crossing locations, which, however, are often unavailable. Thus, developing a reliable drainage crossing dataset is essential to improve the HRDEMs for hydrographic delineation. The purpose of this research is to develop deep learning models for classifying the images that contain the locations of …

Streaming Approximation Scheme For Minimizing Total Completion Time On Parallel Machines Subject To Varying Processing Capacity, Bin Fu, Yumei Huo, Hairong Zhao

Computer Science Faculty Publications and Presentations

We study the problem of minimizing total completion time on parallel machines subject to varying processing capacity. In this paper, we develop an approximation scheme for the problem under the data stream model where the input data is massive and cannot fit into memory and thus can only be scanned a few times. Our algorithm can compute an approximate value of the optimal total completion time in one pass and output the schedule with the approximate value in two passes.

A Survey On Security Analysis Of Machine Learning-Oriented Hardware And Software Intellectual Property, Ashraful Tauhid, Lei Xu, Mostafizur Rahman, Emmett Tomai

Computer Science Faculty Publications and Presentations

Intellectual Property (IP) includes ideas, innovations, methodologies, works of authorship (viz., literary and artistic works), emblems, brands, images, etc. This property is intangible since it is pertinent to the human intellect. Therefore, IP entities are indisputably vulnerable to infringements and modifications without the owner’s consent. IP protection regulations have been deployed and are still in practice, including patents, copyrights, contracts, trademarks, trade secrets, etc., to address these challenges. Unfortunately, these protections are insufficient to keep IP entities from being changed or stolen without permission. As for this, some IPs require hardware IP protection mechanisms, and others require software …

The X-Ray Variation Of M81* Resolved By Chandra And Nustar, Shu Niu, Fu-Guo Xie, Q. Daniel Wang, Li Ji, Feng Yuan, Min Long

Computer Science Faculty Publications and Presentations

Despite advances in our understanding of low-luminosity active galactic nuclei (LLAGNs), the fundamental details about the mechanisms of radiation and flare/outburst in hot accretion flow are still largely missing. We have systematically analysed the archival Chandra and NuSTAR X-ray data of the nearby LLAGN M81*, whose L_bol ∼ 10⁻⁵L_Edd. Through a detailed study of X-ray light curve and spectral properties, we find that the X-ray continuum emission of the power-law shape more likely originates from inverse Compton scattering within the hot accretion flow. In contrast to Sgr A*, flares are rare in M81*. Low-amplitude variation …

Quantum Multi-Solution Bernoulli Search With Applications To Bitcoin’S Post-Quantum Security, Alexandru Cojocaru, Juan Garay, Fang Song, Petros Wallden

Computer Science Faculty Publications and Presentations

A proof of work (PoW) is an important cryptographic construct which enables a party to convince other parties that they have invested some effort in solving a computational task. Arguably, its main impact has been in the setting of cryptocurrencies such as Bitcoin and its underlying blockchain protocol, which have received significant attention in recent years due to its potential for various applications as well as for solving fundamental distributed computing questions in novel threat models. PoWs enable the linking of blocks in the blockchain data structure, and thus the problem of interest is the feasibility of obtaining a sequence …

Caspi: Collaborative Photon Processing For Active Single-Photon Imaging, Jongho Lee, Atul Ingle, Jenu V. Chacko, Kevin W. Eliceiri, Mohit Gupta

Computer Science Faculty Publications and Presentations

Image sensors capable of capturing individual photons have made tremendous progress in recent years. However, this technology faces a major limitation. Because they capture scene information at the individual photon level, the raw data is sparse and noisy. Here we propose CASPI: Collaborative Photon Processing for Active Single-Photon Imaging, a technology-agnostic, application-agnostic, and training-free photon processing pipeline for emerging high-resolution single-photon cameras. By collaboratively exploiting both local and non-local correlations in the spatio-temporal photon data cubes, CASPI estimates scene properties reliably even under very challenging lighting conditions. We demonstrate the versatility of CASPI with two applications: LiDAR imaging over a …

In-Vitro Validated Methods For Encoding Digital Data In Deoxyribonucleic Acid (Dna), Golam Md Mortuza, Jorge Guerrero, Shoshanna Llewellyn, Michael D. Tobiason, George D. Dickinson, William L. Hughes, Reza Zadegan, Tim Andersen

Computer Science Faculty Publications and Presentations

Deoxyribonucleic acid (DNA) is emerging as an alternative archival memory technology. Recent advancements in DNA synthesis and sequencing have both increased the capacity and decreased the cost of storing information in de novo synthesized DNA pools. In this survey, we review methods for translating digital data to and/or from DNA molecules. An emphasis is placed on methods which have been validated by storing and retrieving real-world data via in-vitro experiments.

Security Attacks And Countermeasures In Smart Homes, Hasibul Alam, Emmett Tomai

Computer Science Faculty Publications and Presentations

The Internet of Things (IoT) application is visible in all aspects of humans’ day-to-day affairs. The demand for IoT is growing at an unprecedented rate, from wearable wristwatches to autopilot cars. The smart home has also seen significant advancements to improve the quality of lifestyle. However, the security and privacy of IoT devices have become primary concerns as data is shared among intelligent devices and over the internet in a smart home network. There are several attacks - node capturing attack, sniffing attack, malware attack, boot phase attack, etc., which are conducted by adversaries to breach the security of smart …

Divergent Directionality Of Immune Cell-Specific Protein Expression Between Bipolar Lithium Responders And Non-Responders Revealed By Enhanced Flow Cytometry, Keming Gao, Nicholas M. Kaye, Marzieh Ayati, Mehmet Koyuturk, Joseph R. Calabrese, Eric Christian, Hillard M. Lazarus, David Kaplan

Computer Science Faculty Publications and Presentations

Background and Objectives: There is no biomarker to predict lithium response. This study used CellPrint™ enhanced flow cytometry to study 28 proteins representing a spectrum of cellular pathways in monocytes and CD4+ lymphocytes before and after lithium treatment in patients with bipolar disorder (BD). Materials and Methods: Symptomatic patients with BD type I or II received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks. Patients were assessed with standard rating scales and divided into two groups, responders (≥50% improvement from baseline) and non-responders. Twenty-eight intracellular proteins in CD4+ lymphocytes and monocytes were analyzed with CellPrint™, an enhanced flow …

Convolution Neural Networks For Phishing Detection, Arun D. Kulkarni

Computer Science Faculty Publications and Presentations

Phishing is one of the significant threats in cyber security. Phishing is a form of social engineering that uses e-mails with malicious websites to solicitate personal information. Phishing e-mails are growing in alarming number. In this paper we propose a novel machine learning approach to classify phishing websites using Convolution Neural Networks (CNNs) that use URL based features. CNNs consist of a stack of convolution, pooling layers, and a fully connected layer. CNNs accept images as input and perform feature extraction and classification. Many CNN models are available today. To avoid vanishing gradient problem, recent CNNs use entropy loss function …