Theory and Algorithms Commons^™

Wang Tilings In Arbitrary Dimensions, Ian Tassin

Rose-Hulman Undergraduate Mathematics Journal

This paper makes a new observation about arbitrary dimensional Wang Tilings,
demonstrating that any d -dimensional tile set that can tile periodically along d − 1 axes must be able to tile periodically along all axes.
This work also summarizes work on Wang Tiles up to the present day, including
definitions for various aspects of Wang Tilings such as periodicity and the validity of a tiling. Additionally, we extend the familiar 2D definitions for Wang Tiles and associated properties into arbitrary dimensional spaces. While there has been previous discussion of arbitrary dimensional Wang Tiles in other works, it has been …

Sepsis Treatment: Reinforced Sequential Decision-Making For Saving Lives, Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu, Vaneet Aggarwal

Graduate Industrial Research Symposium

Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications. Existing machine learning methods for managing sepsis struggle in offline scenarios, exhibiting suboptimal performance with survival rates below 50%. Our project introduces the "PosNegDM: Reinforcement Learning with Positive and Negative Demonstrations for Sequential Decision-Making" framework utilizing an innovative transformer-based model and a feedback reinforcer to replicate expert actions while considering individual patient characteristics. A mortality classifier with 96.7% accuracy guides treatment decisions towards positive outcomes. The PosNegDM framework significantly improves patient survival, saving 97.39% of patients and outperforming established machine learning …

Online Class-Incremental Learning For Real-World Food Image Classification, Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu

Graduate Industrial Research Symposium

Food image classification is essential for monitoring health and tracking dietary in image-based dietary assessment methods. However, conventional systems often rely on static datasets with fixed classes and uniform distribution. In contrast, real-world food consumption patterns, shaped by cultural, economic, and personal influences, involve dynamic and evolving data. Thus, it requires the classification system to cope with continuously evolving data. Online Class Incremental Learning (OCIL) addresses the challenge of learning continuously from a single-pass data stream while adapting to the new knowledge and reducing catastrophic forgetting. Experience Replay (ER) based OCIL methods store a small portion of previous data and …

A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes

Graduate Industrial Research Symposium

The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute it into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects, but does not necessarily indicate the initial point of interference within the network. The objective of this project is to take advantage of large scale and genome-wide perturbational datasets by using them to train a tuned machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of …

Screening Through A Broad Pool: Towards Better Diversity For Lexically Constrained Text Generation, Changsen Yuan, Heyan Huang, Yixin Cao, Qianwen Cao

Research Collection School Of Computing and Information Systems

Lexically constrained text generation (CTG) is to generate text that contains given constrained keywords. However, the text diversity of existing models is still unsatisfactory. In this paper, we propose a lightweight dynamic refinement strategy that aims at increasing the randomness of inference to improve generation richness and diversity while maintaining a high level of fluidity and integrity. Our basic idea is to enlarge the number and length of candidate sentences in each iteration, and choose the best for subsequent refinement. On the one hand, different from previous works, which carefully insert one token between two words per action, we insert …

Active Discovering New Slots For Task-Oriented Conversation, Yuxia Wu, Tianhao Dai, Zhedong Zheng, Lizi Liao

Research Collection School Of Computing and Information Systems

Existing task-oriented conversational systems heavily rely on domain ontologies with pre-defined slots and candidate values. In practical settings, these prerequisites are hard to meet, due to the emerging new user requirements and ever-changing scenarios. To mitigate these issues for better interaction performance, there are efforts working towards detecting out-of-vocabulary values or discovering new slots under unsupervised or semi-supervised learning paradigms. However, overemphasizing on the conversation data patterns alone induces these methods to yield noisy and arbitrary slot results. To facilitate the pragmatic utility, real-world systems tend to provide a stringent amount of human labeling quota, which offers an authoritative way …

Conditional Neural Heuristic For Multiobjective Vehicle Routing Problems, Mingfeng Fan, Yaoxin Wu, Zhiguang Cao, Wen Song, Guillaume Sartoretti, Huan Liu, Guohua Wu

Research Collection School Of Computing and Information Systems

Existing neural heuristics for multiobjective vehicle routing problems (MOVRPs) are primarily conditioned on instance context, which failed to appropriately exploit preference and problem size, thus holding back the performance. To thoroughly unleash the potential, we propose a novel conditional neural heuristic (CNH) that fully leverages the instance context, preference, and size with an encoder–decoder structured policy network. Particularly, in our CNH, we design a dual-attention-based encoder to relate preferences and instance contexts, so as to better capture their joint effect on approximating the exact Pareto front (PF). We also design a size-aware decoder based on the sinusoidal encoding to explicitly …

Win: Weight-Decay-Integrated Nesterov Acceleration For Faster Network Training, Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Training deep networks on large-scale datasets is computationally challenging. This work explores the problem of “how to accelerate adaptive gradient algorithms in a general manner", and proposes an effective Weight-decay-Integrated Nesterov acceleration (Win) to accelerate adaptive algorithms. Taking AdamW and Adam as examples, per iteration, we construct a dynamical loss that combines the vanilla training loss and a dynamic regularizer inspired by proximal point method, and respectively minimize the first- and second-order Taylor approximations of dynamical loss to update variable. This yields our Win acceleration that uses a conservative step and an aggressive step to update, and linearly combines these …

Faster Rates For Compressed Federated Learning With Client-Variance Reduction, Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtarik

Research Collection School Of Computing and Information Systems

Due to the communication bottleneck in distributed and federated learning applications, algorithms using communication compression have attracted significant attention and are widely used in practice. Moreover, the huge number, high heterogeneity, and limited availability of clients result in high client -variance. This paper addresses these two issues together by proposing compressed and clientvariance reduced methods COFIG and FRECON. We prove an O( (1+\omega)3/2\surdN+ (1+\omega)N2/3 S\epsilon2 S\epsilon2 ) bound on the number of communication rounds of COFIG in the nonconvex setting, where N is the total number of clients, S is the number of clients participating in each round, \epsilon is …

Music Genre Classification Capabilities Of Enhanced Neural Network Architectures, Joshua Engelkes

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

With the increase of digital music audio uploads, applications that deal with music information have been widely requested by streaming platforms. Automatic music genre classification is an important function of music recommendation and music search applications. Since the music genre categorization criteria continually shift, data-driven methods such as neural networks have been proven especially useful to music information retrieval. An enhanced CNN architecture, the Bottom-up Broadcast Neural Network, uses mel-spectrograms to push music data through a network where important low-level information is preserved. An enhanced RNN architecture, the Independent Recurrent Neural Network for Music Genre Classification, takes advantage of the …

Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework, Ramon Lopes, Rodrigo Alves, Antoine Ledent, Rodrygo L. T. Santos, Marius Kloft

Research Collection School Of Computing and Information Systems

Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous …

Imitate The Good And Avoid The Bad: An Incremental Approach To Safe Reinforcement Learning, Minh Huy Hoang, Mai Anh Tien, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

A popular framework for enforcing safe actions in Reinforcement Learning (RL) is Constrained RL, where trajectory based constraints on expected cost (or other cost measures) are employed to enforce safety and more importantly these constraints are enforced while maximizing expected reward. Most recent approaches for solving Constrained RL convert the trajectory based cost constraint into a surrogate problem that can be solved using minor modifications to RL methods. A key drawback with such approaches is an over or underestimation of the cost constraint at each state. Therefore, we provide an approach that does not modify the trajectory based cost constraint …

Dl-Drl: A Double-Level Deep Reinforcement Learning Approach For Large-Scale Task Scheduling Of Multi-Uav, Xiao Mao, Guohua Wu, Mingfeng Fan, Zhiguang Cao, Witold Pedrycz

Research Collection School Of Computing and Information Systems

Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity recently. To address the underlying task scheduling problem, conventional exact and heuristic algorithms encounter challenges such as rapidly increasing computation time and heavy reliance on domain knowledge, particularly when dealing with large-scale problems. The deep reinforcement learning (DRL) based methods that learn useful patterns from massive data demonstrate notable advantages. However, their decision space will become prohibitively huge as the problem scales up, thus deteriorating the computation efficiency. To alleviate this issue, we propose a double-level deep reinforcement learning (DL-DRL) approach based on a divide and conquer framework …

The Measure Of Efficiency And Effectiveness When Using Artificial Intelligence (Ai) In Radiology, Jordan Watts

Theses, Dissertations and Capstones

Introduction: The use of artificial intelligence in radiology has helped radiologists identify patterns and abnormalities in medical images to diagnose and treat patients. Deep learning and machine learning algorithms have been used to assist physicians in detecting features that are not noticeable to the human eye. The FDA has approved almost 400 AI algorithms for radiology and estimated that the market for AI in medical imaging would grow from $21.48 billion in 2018 to $264.85 billion in 2028.

Purpose of the Study: The purpose of this research was to evaluate the use of artificial intelligence in radiology to determine its …

Reinforcement Learning For Optimal Kicking Actions In Humanoid Robotics: Advancing Robotic Autonomy And Versatility, Suresh Dodda, Sathish Kumar Chintala, Sukender Reddy Mallreddy, Sharath Chandra Macha, Yashwanth Vasa, Sapan Bharadwaj Bonala, Navin Kamuni, Sujatha Alla

Engineering Management & Systems Engineering Faculty Publications

Acquiring the necessary skills to perform a work effectively and efficiently requires a significant investment of time and computing power. Previous applications of Reinforcement Learning (RL) for action optimization in humanoid robotics have shown how promising this technology is for moving robotics towards true autonomy and versatility. Therefore, this study offers the first use of RL to create an entirely optimal kicking action for the Alderbaran Nao robot. Kicking motions that were steady, precise, quick, and able to kick farther than any existing RoboCup squad were generated by optimizing for a multi-objective reward function. We demonstrate that the ideal kicking …

Advancing Deep Learning With Graph-Based Structural Insights: From Graph Classification To Semantic Segmentation, Xin Ma

Computer Science and Engineering Dissertations - Archive

Deep learning has profoundly transformed machine learning by offering sophisticated data representations, yet effectively incorporating structural information remains a challenge. Structural data, whether explicit or implicit, has the potential to significantly enhance the performance of deep learning tasks. This research investigates the benefits of structural information across three crucial tasks: classification, clustering, and segmentation. For explicit structural data, where inputs are directly represented as graphs, we investigate graph-level classification in brain connectivity networks. We introduce the Multi-resolution Edge Network (MENET), a novel framework designed to identify disease-specific connectomic benchmarks with high discriminatory power across diagnostic categories. MENET leverages graph-level representations …

An Algorithm Based On Priority Rules For Solving A Multi-Drone Routing Problem In Hazardous Waste Collection, Youssef Harrath Dr., Jihene Kaabi Dr.

Research & Publications

This research investigates the problem of assigning pre-scheduled trips to multiple drones to collect hazardous waste from different sites in the minimum time. Each drone is subject to essential restrictions: maximum flying capacity and recharge operation. The goal is to assign the trips to the drones so that the waste is collected in the minimum time. This is done if the total flying time is equally distributed among the drones. An algorithm was developed to solve the problem. The algorithm is based on two main ideas: sort the trips according to a given priority rule and assign the current trip …

Trading Cloud Computing Stocks Using Sma, Xianrong Zheng, Lingyu Li

Information Technology & Decision Sciences Faculty Publications

As cloud computing adoption becomes mainstream, the cloud services market offers vast profits. Moreover, serverless computing, the next stage of cloud computing, comes with huge economic potential. To capitalize on this trend, investors are interested in trading cloud stocks. As high-growth technology stocks, investing in cloud stocks is both rewarding and challenging. The research question here is how a trading strategy will perform on cloud stocks. As a result, this paper employs an effective method—Simple Moving Average (SMA)—to trade cloud stocks. To evaluate its performance, we conducted extensive experiments with real market data that spans over 23 years. Results show …

Mapping Seagrass Distribution And Abundance: Comparing Areal Cover And Biomass Estimates Between Space-Based And Airborne Imagery, Victoria J. Hill, Richard C. Zimmerman, Dorothy A. Byron, Kenneth L. Heck Jr.

OES Faculty Publications

This study evaluated the effectiveness of Planet satellite imagery in mapping seagrass coverage in Santa Rosa Sound, Florida. We compared very-high-resolution aerial imagery (0.3 m) collected in September 2022 with high-resolution Planet imagery (~3 m) captured during the same period. Using supervised classification techniques, we accurately identified expansive, continuous seagrass meadows in the satellite images, successfully classifying 95.5% of the 11.18 km² of seagrass area delineated manually from the aerial imagery. Our analysis utilized an occurrence frequency (OF) product, which was generated by processing ten clear-sky images collected between 8 and 25 September 2022 to determine the frequency with which …

The Feasibility Of Motion Tracking Camera System For Magnetic Suspension Wind Tunnel Tests, Hisham M. Shehata, David Cox, Mark Schoenenberger, Colin Britcher, Eli Shellabarger, Timothy Schott, Brendan Mcgovern

Mechanical & Aerospace Engineering Faculty Publications

The Entry Systems Modeling (ESM) Program at NASA has actively participated in the re-development of the Magnetic Suspension Balance System (MSBS) at the six-inch subsonic wind tunnel at NASA Langley Research Center. This initiative aims to enhance the MSBS system's capabilities, enabling the testing of stingless entry vehicle models at supersonic speeds. To achieve this, control algorithms are required to ensure magnetic levitation control and stability for models during free-oscillation dynamic responses. Currently, the system relies on electromagnetic position sensors to provide real-time 3 degrees of freedom control of a rigid body. While this approach has proven successful for subsonic …