Physical Sciences and Mathematics Commons^™

Posmlp-Video: Spatial And Temporal Relative Position Encoding For Efficient Video Recognition, Yanbin Hao, Diansong Zhou, Zhicai Wang, Chong-Wah Ngo, Xiangnan He, Meng Wang

Research Collection School Of Computing and Information Systems

In recent years, vision Transformers and MLPs have demonstrated remarkable performance in image understanding tasks. However, their inherently dense computational operators, such as self-attention and token-mixing layers, pose significant challenges when applied to spatio-temporal video data. To address this gap, we propose PosMLP-Video, a lightweight yet powerful MLP-like backbone for video recognition. Instead of dense operators, we use efficient relative positional encoding (RPE) to build pairwise token relations, leveraging small-sized parameterized relative position biases to obtain each relation score. Specifically, to enable spatio-temporal modeling, we extend the image PosMLP’s positional gating unit to temporal, spatial, and spatio-temporal variants, namely PoTGU, …

Anomaly Detection Under Distribution Shift, Tri Cao, Jiawen Zhu, Guansong Pang

Research Collection School Of Computing and Information Systems

Anomaly detection (AD) is a crucial machine learning task that aims to learn patterns from a set of normal training samples to identify abnormal samples in test data. Most existing AD studies assume that the training and test data are drawn from the same data distribution, but the test data can have large distribution shifts arising in many real-world applications due to different natural variations such as new lighting conditions, object poses, or background appearances, rendering existing AD methods ineffective in such cases. In this paper, we consider the problem of anomaly detection under distribution shift and establish performance benchmarks …

Visilience: An Interactive Visualization Framework For Resilience Analysis Using Control-Flow Graph, Hailong Jiang, Shaolun Ruan, Bo Fang, Yong Wang, Qiang Guan

Research Collection School Of Computing and Information Systems

Soft errors have become one of the main concerns for the resilience of HPC applications, as these errors can cause HPC applications to generate serious outcomes such as silent data corruption (SDC). Many approaches have been proposed to analyze the resilience of HPC applications. However, existing studies rarely address the challenges of analysis result perception. Specifically, resilience analysis techniques often produce a massive volume of unstructured data, making it difficult for programmers to perform resilience analysis due to non-intuitive raw data. Furthermore, different analysis models produce diverse results with multiple levels of detail, which can create obstacles to compare and …

Homomorphism Obstructions For Satellite Maps, Allison N. Miller

Mathematics & Statistics Faculty Works

A knot in a solid torus defines a map on the set of (smooth or topological) concordance classes of knots in S³. This set admits a group structure, but a conjecture of Hedden suggests that satellite maps never induce interesting homomorphisms: we give new evidence for this conjecture in both categories. First, we use Casson-Gordon signatures to give the first obstruction to a slice pattern inducing a homomorphism on the topological concordance group, constructing examples with every winding number besides ± 1. We then provide subtle examples of satellite maps which map arbitrarily deep into the n-solvable filtration …

Understanding The Role Of The Jet Streams And Gulf Stream Eddies On The Northwest Atlantic Marine Heatwaves, Lydia Rose Duncan Sims

Theses and Dissertations

The Northwest (NW) Atlantic is one of the fastest warming regions in the global ocean and in the recent decade has experienced several extreme temperature events. These extreme anomalous temperature events are known as Marine Heatwaves (MHWs), which are forced by a variety of physical processes that affect the heat source and sink of the water column. These MHWs have been increasing globally in duration and frequency due to anthropogenic warming, and have increased ecological damage seen in mass mortality events of economically viable species. Within the NW Atlantic, several key processes encourage the formation of MHWs, such as the …

Computerized Psychological Testing: Designing And Developing An Efficient Test Suite Using Hci And Reinforcement Learning Techniques, William Henry Hoskins

Theses and Dissertations

In this work we discuss the design and development of the Carolina Automated Reading Evaluation (CARE), created to facilitate the finding of deficits in the reading ability of children from four to nine years of age. Designed to automate the process of screening for reading deficits, the CARE is an interactive computer-based tool that helps eliminate the need for one-on-one evaluations of pupils to detect dyslexia and other reading deficits and facilitates the creation of new reading tests within the platform. While other tests collect specific data points in order to determine whether a pupil has dyslexia, they typically focus …

Figured Worlds Of Women Mathematics Education Scholars, Lili Zhou, Ricki L. Geller-Mckee, Brooke Max, Hyunyi Jung, Bima Sapkota, Jill Newton, Lindsay M. Keazer

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Drawing on the concept of figured worlds (Holland et al., 1998), this project focuses on addressing, responding to, and understanding the self within the figured world of the mathematics education community. Specifically, we examine a group of women with diverse backgrounds in terms of race, class, and cultural contexts, who are engaged in various roles as mathematics education scholars, including teachers, teacher educators, and researchers. Using a dialogical self approach, we facilitate both internal and external discourses, exploring personal histories, narratives, and the development of evolving identities. Our findings reveal that culture and social positions, such as gender, class, and …

Semidefinite Programming Bounds For Distance Distribution Of Spherical Codes, Oleg R. Musin

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

We present an extension of known semidefinite and linear programming upper bounds for spherical codes. We apply the main result for the distance distribution of a spherical code and show that this method can work effectively In particular, we get a shorter solution to the kissing number problem in dimension 4.

Elementary Mathematics Curriculum: State Policy, Covid-19, And Teachers’ Control, Mona Baniahmadi, Bima Sapkota, Amy M. Olson

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In the U.S., state guidance to schools in response to the COVID-19 pandemic was politicized. We used state-level political affiliation to explore whether access to curricular resources differed pre-pandemic or during pandemic remote teaching and teachers' reported control over curricular resources during pandemic teaching. We found that pre-pandemic the percentage of teachers in Republican states reported higher levels of resources overall, and use of core and teacher-created curricular resources in particular. They also reported having greater control over their curricular decision-making during the pandemic. There were no state-level differences in teachers’ level of preparation for pandemic teaching, but teachers in …

Convergence Of The Two-Point Modulus-Based Matrix Splitting Iteration Method, Ximing Fang, Ze Gu, Zhijun Qiao

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this paper, we discuss the convergence of the two-point modulus-based matrix splitting iteration method for solving the linear complementarity problem. Some convergence conditions are presented from the spectral radius and the matrix norm when the system matrix is a -matrix. Besides, the quasi-optimal cases of the method are studied. Numerical experiments are provided to show the presented results.

Rogue Waves In The Massive Thirring Model, Junchao Chen, Bo Yang, Bao-Feng Feng

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this paper, general rogue wave solutions in the massive Thirring (MT) model are derived by using the Kadomtsev–Petviashvili (KP) hierarchy reduction method and these rational solutions are presented explicitly in terms of determinants whose matrix elements are elementary Schur polynomials. In the reduction process, three reduction conditions including one index- and two dimension-ones are proved to be consistent by only one constraint relation on parameters of tau-functions of the KP-Toda hierarchy. It is found that the rogue wave solutions in the MT model depend on two background parameters, which influence their orientation and duration. Differing from many other coupled …

Online Aircraft System Identification Using A Novel Parameter Informed Reinforcement Learning Method, Nathan Schaff

Doctoral Dissertations and Master's Theses

This thesis presents the development and analysis of a novel method for training reinforcement learning neural networks for online aircraft system identification of multiple similar linear systems, such as all fixed wing aircraft. This approach, termed Parameter Informed Reinforcement Learning (PIRL), dictates that reinforcement learning neural networks should be trained using input and output trajectory/history data as is convention; however, the PIRL method also includes any known and relevant aircraft parameters, such as airspeed, altitude, center of gravity location and/or others. Through this, the PIRL Agent is better suited to identify novel/test-set aircraft.

First, the PIRL method is applied to …

Rigid Body Constrained Motion Optimization And Control On Lie Groups And Their Tangent Bundles, Brennan S. Mccann

Doctoral Dissertations and Master's Theses

Rigid body motion requires formulations where rotational and translational motion are accounted for appropriately. Two Lie groups, the special orthogonal group SO(3) and the space of quaternions H, are commonly used to represent attitude. When considering rigid body pose, that is spacecraft position and attitude, the special Euclidean group SE(3) and the space of dual quaternions DH are frequently utilized. All these groups are Lie groups and Riemannian manifolds, and these identifications have profound implications for dynamics and controls. The trajectory optimization and optimal control problem on Riemannian manifolds presents significant opportunities for theoretical development. Riemannian optimization is an attractive …

Constructing Cyber-Physical System Testing Suites Using Active Sensor Fuzzing, Fan. Zhang, Qianmei. Wu, Bohan. Xuan, Yuqi. Chen, Wei. Lin, Christopher M. Poskitt, Jun Sun, Binbin. Chen

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPSs) automating critical public infrastructure face a pervasive threat of attack, motivating research into different types of countermeasures. Assessing the effectiveness of these countermeasures is challenging, however, as benchmarks are difficult to construct manually, existing automated testing solutions often make unrealistic assumptions, and blindly fuzzing is ineffective at finding attacks due to the enormous search spaces and resource requirements. In this work, we propose active sensor fuzzing , a fully automated approach for building test suites without requiring any a prior knowledge about a CPS. Our approach employs active learning techniques. Applied to a real-world water treatment system, …

Objectfusion: Multi-Modal 3d Object Detection With Object-Centric Fusion, Q. Cai, Y. Pan, T. Yao, Chong-Wah Ngo, T. Mei

Research Collection School Of Computing and Information Systems

Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space. Nevertheless, it is not trivial to perform camera-to-BEV transformation due to the inherently ambiguous depth estimation of each pixel, resulting in spatial misalignment between these two multi-modal features. Moreover, such transformation also inevitably leads to projection distortion of camera image features in BEV space. In this paper, we propose a novel Object-centric Fusion (ObjectFusion) paradigm, which completely gets rid of camera-to-BEV transformation during fusion to align object-centric features across different modalities for …

Experiences Of Autistic Twitch Livestreamers: “I Have Made Easily The Most Meaningful And Impactful Relationships”, Terrance Mok, Anthony Tang, Adam Mccrimmon, Lora Oehlberg

Research Collection School Of Computing and Information Systems

We present perspectives from 10 autistic Twitch streamers regarding their experiences as livestreamers and how autism uniquely colors their experiences. Livestreaming offers a social online experience distinct from in-person, face-to-face communication, where autistic people tend to encounter challenges. Our reflexive thematic analysis of interviews with 10 participants showcases autistic livestreamers’ perspectives in their own words. Our findings center on the importance of having streamers establishing connections with other, sharing autistic identities, controlling a space for social interaction, personal growth, and accessibility challenges. In our discussion, we highlight the crucial value of having a medium for autistic representation, as well as …

Ubisurface: A Robotic Touch Surface For Supporting Mid-Air Planar Interactions In Room-Scale Vr, Ryota Gomi, Kazuki Takashima, Yuki Onishi, Kazuyuki Fujita, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

Room-scale VR has been considered an alternative to physical office workspaces. For office activities, users frequently require planar input methods, such as typing or handwriting, to quickly record annotations to virtual content. However, current off-The-shelf VR HMD setups rely on mid-Air interactions, which can cause arm fatigue and decrease input accuracy. To address this issue, we propose UbiSurface, a robotic touch surface that can automatically reposition itself to physically present a virtual planar input surface (VR whiteboard, VR canvas, etc.) to users and to permit them to achieve accurate and fatigue-less input while walking around a virtual room. We design …

Residual Pattern Learning For Pixel-Wise Out-Of-Distribution Detection In Semantic Segmentation, Y Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Semantic segmentation models classify pixels into a set of known ("in-distribution") visual classes. When deployed in an open world, the reliability of these models depends on their ability to not only classify in-distribution pixels but also to detect out-of-distribution (OoD) pixels. Historically, the poor OoD detection performance of these models has motivated the design of methods based on model re-training using synthetic training images that include OoD visual objects. Although successful, these re-trained methods have two issues: 1) their in-distribution segmentation accuracy may drop during re-training, and 2) their OoD detection accuracy does not generalise well to new contexts (e.g., …

Feature Prediction Diffusion Model For Video Anomaly Detection, Cheng Yan, Shiyu Zhang, Yang Liu, Guansong Pang, Wenjun Wang

Research Collection School Of Computing and Information Systems

Anomaly detection in the video is an important research area and a challenging task in real applications. Due to the unavailability of large-scale annotated anomaly events, most existing video anomaly detection (VAD) methods focus on learning the distribution of normal samples to detect the substantially deviated samples as anomalies. To well learn the distribution of normal motion and appearance, many auxiliary networks are employed to extract foreground object or action information. These high-level semantic features effectively filter the noise from the background to decrease its influence on detection models. However, the capability of these extra semantic models heavily affects the …

Deep Video Demoireing Via Compact Invertible Dyadic Decomposition, Yuhui Quan, Haoran Huang, Shengfeng He, Ruotao Xu

Research Collection School Of Computing and Information Systems

Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, which thus are easily confused with moire patterns and can be significantly erased in the removal process. By interpreting video demoireing as a multi-frame decomposition problem, we propose a compact invertible dyadic network called CIDNet that progressively decouples latent frames and the moire patterns from an input video sequence. Using a dyadic cross-scale coupling structure with coupling layers tailored for multi-scale processing, CIDNet aims at disentangling …

Towards An Effective And Interpretable Refinement Approach For Dnn Verification, Jiaying Li, Guangdong Bai, Long H. Pham, Jun Sun

Research Collection School Of Computing and Information Systems

Recently, several abstraction refinement techniques have been proposed to improve the verification precision for deep neural networks (DNNs). However, these techniques usually take many refinement steps to verify a property and the refinement decision in each step is hard to interpret, thus hindering their analysis, reasoning and optimization.In this work, we propose SURGEON, a novel DNN verification refinement approach that is both effective and interpretable, allowing analyst to understand why and how each refinement decision is made. The main insight is to leverage the ‘interpretable’ nature of debugging processes and formulate the verification refinement problem as a debugging problem. Given …

Toward Intention Discovery For Early Malice Detection In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu

Research Collection School Of Computing and Information Systems

Cryptocurrency’s pseudo-anonymous nature makes it vulnerable to malicious activities. However, existing deep learning solutions lack interpretability and only support retrospective analysis of specific malice types. To address these challenges, we propose Intention-Monitor for early malice detection in Bitcoin. Our model, utilizing Decision-Tree based feature Selection and Complement (DT-SC), builds different feature sets for different malice types. The Status Proposal Module (SPM) and hierarchical self-attention predictor provide real-time global status and address label predictions. A survival module determines the stopping point and proposes the status sequence (intention). Our model detects various malicious activities with strong interpretability, outperforming state-of-the-art methods in extensive …

Owner-Free Distributed Symmetric Searchable Encryption Supporting Conjunctive Queries, Qiuyun Tong, Xinghua Li, Yinbin Miao, Yunwei Wang, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Symmetric Searchable Encryption (SSE), as an ideal primitive, can ensure data privacy while supporting retrieval over encrypted data. However, existing multi-user SSE schemes require the data owner to share the secret key with all query users or always be online to generate search tokens. While there are some solutions to this problem, they have at least one weakness, such as non-supporting conjunctive query, result decryption assistance of the data owner, and unauthorized access. To solve the above issues, we propose an Owner-free Distributed Symmetric searchable encryption supporting Conjunctive query (ODiSC). Specifically, we first evaluate the Learning-Parity-with-Noise weak Pseudorandom Function (LPN-wPRF) …

Instance-Specific Algorithm Configuration Via Unsupervised Deep Graph Clustering, Wen Song, Yi Liu, Zhiguang Cao, Yaoxin Wu, Qiqiang Li

Research Collection School Of Computing and Information Systems

Instance-specific Algorithm Configuration (AC) methods are effective in automatically generating high-quality algorithm parameters for heterogeneous NP-hard problems from multiple sources. However, existing works rely on manually designed features to describe training instances, which are simple numerical attributes and cannot fully capture structural differences. Targeting at Mixed-Integer Programming (MIP) solvers, this paper proposes a novel instances-specific AC method based on end-to-end deep graph clustering. By representing an MIP instance as a bipartite graph, a random walk algorithm is designed to extract raw features with both numerical and structural information from the instance graph. Then an auto-encoder is designed to learn dense …

H5n1 Highly Pathogenic Avian Influenza Clade 2.3.4.4b In Wild And Domestic Birds: Introductions Into The United States And Reassortments, December 2021–April 2022, Sungsu Youk, Mia Kim Torchetti, Kristina Lantz, Julianna B. Lenoch, Mary Lea Killian, Christina Leyson, Sarah N. Bevins, Krista Dilione, Hon S. Ip, David E. Stallknecht, Rebecca L. Poulson, David L. Suarez, David E. Swayne, Mary J. Pantin-Jackwood

USDA Wildlife Services: Staff Publications

Highly pathogenic avian influenza viruses (HPAIVs) of the A/goose/Guangdong/1/1996 lineage H5 clade 2.3.4.4b continue to have a devastating effect on domestic and wild birds. Full genome sequence analyses using 1369 H5N1 HPAIVs detected in the United States (U.S.) in wild birds, commercial poultry, and backyard flocks from December 2021 to April 2022, showed three phylogenetically distinct H5N1 virus introductions in the U.S. by wild birds. Unreassorted Eurasian genotypes A1 and A2 entered the Northeast Atlantic states, whereas a genetically distinct A3 genotype was detected in Alaska. The A1 genotype spread westward via wild bird migration and reassorted with North American …

Limited Accumulation And Persistence Of An Influenza A Virus In Tadpole Snails (Physa Spp.), Paul T. Oesterle, J. Jeffrey Root, Darcy S.O. Mora, Heather Schneider, Alan B. Franklin, Kathryn P. Huyvaert

USDA Wildlife Services: Staff Publications

Waterfowl infected with avian influenza A viruses (IAVs) shed infectious virus into aquatic environments, providing a mechanism for transmission among waterfowl, while also exposing the entire aquatic ecosystem to the virus. Aquatic invertebrates such as freshwater snails are likely exposed to IAVs in the water column and sediment. Freshwater snails comprise a significant portion of some waterfowl species’ diets, so this trophic interaction may serve as a novel route of IAV transmission. In these experiments, tadpole snails (Physa spp.) were exposed to a low-pathogenicity IAV (H3N8) to determine whether snails can accumulate the virus and, if so, how long virus …

Modulation Of Electrocatalytic Activity By Tuning Anion Electronegativity: Case Study With Copper Chalcogenides, Harish Singh, David Prendergast, Manashi Nath

Chemistry Faculty Research & Creative Works

Anion-tuning in metallic chalcogenides has been shown to have a significant impact on their electrocatalytic ability for overall water splitting. In this article, copper-based chalcogenides (Cu2 X, X= O, S, Se, and Te) have been systematically studied to examine the effect of decreasing anion electronegativity and increasing covalency on the electrocatalytic performance. Among the copper chalcogenides, Cu2Te has the highest oxygen evolution reaction (OER) activity and can sustain high current density of 10 and 50 mA cm−2 for 12 h. The difference in intrinsic catalytic activity of these chalcogenide surfaces have been also probed through density functional theory calculations, which …

Examination And Application Of Body Condition Methods In Cetaceans, Kira Anne Telford

Theses and Dissertations

Body condition assessments are a valuable tool for evaluating the relative health of a population through various metrics, indexes, or proxies. Long-term data collection can be used to examine the relationship between fluctuations in body condition and natural or anthropogenic drivers. Application of this information is vital for monitoring the success of the conservation management decisions for a species or population. Cetaceans have a variety of methods available to assess body condition, including invasive methods like biopsies and necropsies or observational methods such as photogrammetry. Exploration of the application of these methods in the literature revealed an emphasis on necropsies …

Affine Image Registration Of Arterial Spin Labeling Mri Using Deep Learning Networks, Zongpai Zhang, Huiyuan Yang, Yanchen Guo, Nicolas R. Bolo, Matcheri Keshavan, Eve Derosa, Adam K. Anderson, David C. Alsop, Lijun Yin, Weiying Dai

Computer Science Faculty Research & Creative Works

Convolutional neural networks (CNN) have demonstrated good accuracy and speed in spatially registering high signal-to-noise ratio (SNR) structural magnetic resonance imaging (sMRI) images. However, some functional magnetic resonance imaging (fMRI) images, e.g., those acquired from arterial spin labeling (ASL) perfusion fMRI, are of intrinsically low SNR and therefore the quality of registering ASL images using CNN is not clear. In this work, we aimed to explore the feasibility of a CNN-based affine registration network (ARN) for registration of low-SNR three-dimensional ASL perfusion image time series and compare its performance with that from the state-of-the-art statistical parametric mapping (SPM) algorithm. The …

Catching Elusive Depression Via Facial Micro-Expression Recognition, Xiaohui Chen, Tony Tie (T.) Luo

Computer Science Faculty Research & Creative Works

Depression is a common mental health disorder that can cause consequential symptoms with continuously depressed mood that leads to emotional distress. One category of depression is Concealed Depression, where patients intentionally or unintentionally hide their genuine emotions through exterior optimism, thereby complicating and delaying diagnosis and treatment and leading to unexpected suicides. In this article, we propose to diagnose concealed depression by using facial micro-expressions (FMEs) to detect and recognize underlying true emotions. However, the extremely low intensity and subtle nature of FMEs make their recognition a tough task. We propose a facial landmark-based Region-of-Interest (ROI) approach to address the …