Digital Commons Network^™

Flexible Job-Shop Scheduling Via Graph Neural Network And Deep Reinforcement Learning, Wen Song, Xinyang Chen, Qiqiang Li, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching rules (PDRs) for solving complex scheduling problems. However, the existing works face challenges in dealing with flexibility, which allows an operation to be scheduled on one out of multiple machines and is often required in practice. Such one-to-many relationship brings additional complexity in both decision making and state representation. This article considers the well-known flexible job-shop scheduling problem and addresses these issues by proposing a novel DRL method to learn high-quality PDRs end to end. The operation selection and the machine assignment are combined as a composite decision. …

Search For Gamma-Ray And Neutrino Coincidences Using Hawc And Antares Data, H. A. Ayala Solares, S. Coutu, D. Cowen, D. B. Fox, T. Grégoire, F. Mcbride, M. Mostafá, K. Murase, S. Wissel, A. Albert, S. Alves, M. André, M. Ardid, S. Ardid, J. J. Aubert, J. Aublin, B. Baret, S. Basa, B. Belhorma, M. Bendahman, F. Benfenati, V. Bertin, S. Biagi, M. Bissinger, J. Boumaaza, M. Bouta, M. C. Bouwhuis, H. Branzas, R. Bruijn, J. Brunner, J. Busto, B. Caiffi, D. Calvo, A. Capone, L. Caramete, J. Carr, V. Carretero, S. Celli, M. Chabab, T. N. Chau, R. Cherkaoui El Moursli, T. Chiarusi, M. Circella, J. A. B. Coelho, A. Coleiro, R. Coniglione, P. Coyle, A. Creusot, A. F. Diaz, G. De Wasseige, B. De Martino, C. Distefano, I. Di Palma, A. Domi, C. Donzaud, D. Dornic, D. Drouhin, T. Eberl, T. Van Eeden, D. Van Eijk, N. El Khayati, A. Enzenhofer, P. Fermani, G. Ferrara, F. Filippini, L. Fusco, J. Garcia, P. Gay, H. Glotin, R. Gozzini, R. Gracia Ruiz, K. Graf, C. Guidi, S. Hallmann, H. Van Haren, A. J. Heijboer, Y. Hello, J. J. Hernandez-Rey, J. Hobl, J. Hofestadt, F. Huang, G. Illuminati, C. W. James, B. Jisse-Jung, M. De Jong, P. De Jong, M. Kadler, O. Kalekin, U. Katz, A. Kouchner, I. Kreykenbohm, V. Kulikovskiy, R. Lahmann, M. Lamoureux, R. Le Breton, D. Lefevre, E. Leonora, G. Levi, S. Le Stum, D. Lopez-Coto, S. Loucatos, L. Maderer, J. Manczak, M. Marcelin, A. Margiotta, A. Marinelli, J. A. Martinez-Mora, K. Melis, P. Migliozzi, A. Moussa, R. Muller, L. Nauta, S. Navas, E. Nezri, B. O Fearraigh, A. Paun, G. E. Pavalas, C. Pellegrino, M. Perrin-Terrin, V. Pestel, P. Piattelli, C. Pieterse, C. Piore, V. Popa, T. Pradier, N. Randazzo, D. Real, S. Reck, G. Riccobene, A. Romanov, A. Sanchez-Losa, D. F. E. Samtleben, M. Sanguineti, P. Sapienza, J. Schnabel, J. Schumann, F. Schussler, J. Seneca, M. Spurio, Th. Stolarczyk, M. Taiuti, Y. Tayalati, S. J. Tingay, B. Vallage, V. Van Elewyck, F. Versari, S. Viola, D. Vivolo, J. Wilms, S. Zavatarelli, A. Zegarelli, J. D. Zornoza, J. Zuniga, A. Albert, C. Alvarez, J. C. Arteaga-Velazquez, R. Babu, E. Belmont-Moreno, K. S. Caballero-Mora, T. Capistran, A. Carraminana, S. Casanova, U. Cotti, O. Chaparro-Amaro, J. Cotzomi, S. Coutino De Leon, E. De La Fuenta, C. De Leon, R. Diaz Hernandez, M. A. Duvernois, M. Durocher, J. C. Diaz-Velez, K. Engel, C. Espinoza, K. L. Fan, M. Fernandez Alonso, N. Fraija, J. A. Garcia-Gonzalez, F. Garfias, M. M. Gonzalez, J. A. Goodman, J. P. Harding, S. Hernandez, D. Huang, F. Hueyotl-Zahuantitla, P. Huntemeyer, A. Iriarte, V. Joshi, S. Kaufmann, A. Lara, H. Leon Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, I. Martinez-Castellanos, J. Martinez-Castro, J. A. Matthews, P. Miranda-Romagnoli, J. A. Morales-Soto, E. Moreno, A. Nayerhoda, L. Nellen, M. U. Nisa, R. Noriega-Papaqui, N. Omodei, A. Peisker, Y. Perez Araujo, E. G. Perez-Perez, C. D. Rho, D. Rosa-Gonzalez, E. Ruiz-Velasco, H. Salazar, F. Salesa Greus, A. Sandoval, M. Schneider, A. J. Smith, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, R. Turner, F. Urena-Mena, E. Varela, Xiaojie Wang, K. Whitaker, E. Willox, A. Zepeda, H. Zhou

Physics Faculty Research & Creative Works

In the quest for high-energy neutrino sources, the Astrophysical Mult messenger Observatory Network has implemented a new search by combining data from the High-Altitude Water Cherenkov (HAWC) Observatory and the Astronomy with a Neutrino Telescope and Abyss environmental RESearch (ANTARES) neutrino telescope. Using the same analysis strategy as in a previous detector combination of HAWC and IceCube data, we perform a search for coincidences in HAWC and ANTARES events that are below the threshold for sending public alerts in each individual detector. Data were collected between 2015 July and 2020 February with a live time of 4.39 yr. Over this …

Correlated Dynamics Of Collective Droplet Excitations In A One-Dimensional Harmonic Trap, I. A. Englezos, Simeon I. Mistakidis, P. Schmelcher

Physics Faculty Research & Creative Works

We Address The Existence And Dynamics Of One-Dimensional Harmonically Confined Quantum Droplets Appearing In Two-Component Mixtures By Deploying A Nonperturbative Approach. We Find That, In Symmetric Homonuclear Settings, Beyond-Lee-Huang-Yang Correlations Result In Flat-Top Droplet Configurations For Either Decreasing Intercomponent Attraction Or Larger Atom Number. Asymmetric Mixtures Feature Spatial Mixing Among The Involved Components With The More Strongly Interacting Or Heavier One Exhibiting Flat-Top Structures. Applying Quenches On The Harmonic Trap We Trigger The Lowest-Lying Collective Droplet Excitations. The Interaction-Dependent Breathing Frequency, Being Slightly Reduced In The Presence Of Correlations, Shows A Decreasing Trend For Stronger Attractions. Semianalytical Predictions Are Also …

Coreflooding Evaluation Of Fiber-Assisted Recrosslinkable Preformed Particle Gel Using An Open Fracture Model, Shuda Zhao, Ali Al Brahim, Junchen Liu, Baojun Bai, Thomas P. Schuman

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Recrosslinkable Preformed Particle Gels (RPPGs) Have Been Used to Treat the Problem of Void Space Conduits (VSC) and repair the "Short-Circuited" Waterflood in Alaska's West Sak Field. Field Results Showed a 23% Increase in Success Rates over Typical Preformed Particle Gel (PPG) Treatments. in This Paper, We Evaluated Whether Adding Fiber into RPPGs Can Increase the RPPG Plugging Efficiency and Thus Further Improve the Success Rate. We Designed Open Fracture Models to Represent vs.C and Investigated the Effect of Swelling Ratio (SR), Fracture Size, and Fiber Concentration on Gel Injection Pressure, Water Breakthrough Pressure, and Permeability Reduction. Results Show that …

Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation Of Irregular Networks, Anand K. Nambisan, Akanksha Maurya, Norsang Lama, Thanh Phan, Gehana Patel, Keith Miller, Binita Lama, Jason Hagerty, Ronald Stanley, William V. Stoecker

Chemistry Faculty Research & Creative Works

Deep Learning Has Achieved Significant Success in Malignant Melanoma Diagnosis. These Diagnostic Models Are Undergoing a Transition into Clinical Use. However, with Melanoma Diagnostic Accuracy in the Range of Ninety Percent, a Significant Minority of Melanomas Are Missed by Deep Learning. Many of the Melanomas Missed Have Irregular Pigment Networks Visible using Dermoscopy. This Research Presents an Annotated Irregular Network Database and Develops a Classification Pipeline that Fuses Deep Learning Image-Level Results with Conventional Hand-Crafted Features from Irregular Pigment Networks. We Identified and Annotated 487 Unique Dermoscopic Melanoma Lesions from Images in the ISIC 2019 Dermoscopic Dataset to Create a …

Laboratory-Scale Investigation On Shear Behavior Of Non-Persistent Joints And Joint Infill Using Lattice-Spring-Based Synthetic Rock Mass Model, Mariam Al-E'Bayat, Taghi Sherizadeh, Dogukan Guner

Mining Engineering Faculty Research & Creative Works

Discontinuities Mainly Control the Mechanical Behavior of Rock Mass and Cause a Significant Reduction in the Rock Mass Strength. Joint Persistency and Joint Infill Conditions Are Considered the Most Significant Joint Parameters that Control the Mechanical Response of Rock Mass. in This Study, Numerical and Statistical Analyses Were Performed on Pre-Cracked Specimens with Two Flaws to Investigate the Effect of Joint Persistence Parameters on Shear Strength. in Addition, an Extensive Study Was Conducted to Explore the Effect of Infilled Mineral Strength, Infill Thickness, and Infill Wall Roughness on Shear Strength. the Lattice-Spring-Based Synthetic Rock Mass (LS-SRM) Approach Was Utilized to …

Utilizing Design Thinking To Enhance Wellbeing In Interdisciplinary Courses: A Positive Social Psychology Approach, Areej Elsayary

All Works

Design thinking is considered to be a positive social psychology intervention. Students who practice using the design thinking process during their coursework will most likely solve personal problems that improve their well-being. This study investigates the impact of using the design thinking process in interdisciplinary courses to enhance students' well-being through developing mindsets that enable them to solve their problems and set personal goals unconsciously. A modified PERMA-PH model is used to guide this study. The PERMA included Positive Emotions, Negative Emotions, Engagement, Relationship, Meaning and Purpose, and Accomplishments, the "PH" (future plans and health) was added to the PE …

Seismic Azimuthal Anisotropy Beneath A Fast Moving Ancient Continent: Constraints From Shear Wave Splitting Analysis In Australia, Kailun Ba, Stephen S. Gao, Jianguo Song, Kelly H. Liu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Seismic Azimuthal Anisotropy Beneath Australia is Investigated using Splitting of the Teleseismic PKS, SKKS, and SKS Phases to Delineate Asthenospheric Flow and Lithospheric Deformation Beneath One of the Oldest and Fast-Moving Continents on Earth. in Total 511 Pairs of High-Quality Splitting Parameters Were Observed at 116 Seismic Stations. Unlike Other Stable Continental Areas in Africa, East Asia, and North America, Where Spatially Consistent Splitting Parameters Dominate, the Fast Orientations and Splitting Times Observed in Australia Show a Complex Pattern, with a Slightly Smaller Than Normal Average Splitting Time of 0.85 ± 0.33 S. on the North Australian Craton, the Fast …

A Systematic Design Approach For Bulk Gel Treatments Based On Gel Volume-Concentration Ratio In Field Projects, Munqith Aldhaheri, Mingzhen Wei, Baojun Bai

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Controlling excessive water production in mature oil fields has always been a desired objective of the oil and gas industry. This objective calls for planning of more effective water-control gel treatments with optimized designs to obtain more attractive outcomes. Unfortunately, planning such effective treatments remains a dilemma for reservoir engineers due to the lack of methodical design tools in the industry. This paper presents a novel systematic design approach for polyacrylamide-Based bulk gel treatments by classifying their field projects according to the gel volume-concentration ratio (VCR) into three design types. in terms of one another, the approach estimates either the …

Prediction Of Wilms’ Tumor Susceptibility To Preoperative Chemotherapy Using A Novel Computer-Aided Prediction System, Israa Sharaby, Ahmed Alksas, Ahmed Nashat, Hossam Magdy Balaha, Mohamed Shehata, Mallorie Gayhart, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Rasha T. Abouelkheir, Ahmed Elmahdy, Ahmed Abdelhalim, Ahmed Mosbah, Ayman El-Baz

All Works

Wilms’ tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of Wilms’ tumor is multimodal, including surgery, chemotherapy, and occasionally, radiation therapy. Preoperative chemotherapy is used routinely in European studies and in select indications in North American trials. The objective of this study was to build a novel computer-aided prediction system for preoperative chemotherapy response in Wilms’ tumors. A total of 63 patients (age range: 6 months–14 years) were included in this study, after receiving their guardians’ informed consent. We incorporated contrast-enhanced computed tomography imaging to extract the texture, shape, and functionality-based …

Newsroom, Georgia Southern University

Newsroom (2006-2023)

Georgia Southern Partners with Bulloch County High Schools, Introduces Public Health Pathway for Students

Georgia Southern Starting New Software Engineering Degree

New Data Shows Real Value of On-Campus Student Engagement

Employee Wellness Program Recognizes American Heart Month During February

Review Of Feature Selection Methods Using Optimization Algorithm (Review Paper For Optimization Algorithm), Zana O. Hamad

Polytechnic Journal

Many works have been done to reduce complexity in terms of time and memory space. The feature selection process is one of the strategies to reduce system complexity and can be defined as a process of selecting the most important feature among feature space.Therefore,themostuseful features will be kept,and the less useful features will be eliminated.In the fault classification and diagnosis field, feature selection plays an important role in reducing dimensionality and sometimes might lead to having a high classification rate. In this paper, a comprehensive review is presented about feature selection processing and how it can be done.The primary goal …

Modeling And Forecasting Realized Volatility With The Fractional Ornstein-Uhlenbeck Process, Xiaohu Wang, Weilin Xiao, Jun Yu

Research Collection School Of Economics

This paper proposes to model and forecast realized volatility (RV) using the fractional Ornstein-Uhlenbeck (fO-U) process with a general Hurst parameter, H. A two-stage method is introduced for estimating parameters in the fO-U process based on discrete-sampled observations. In the first stage, H is estimated based on the ratio of two second-order differences of observations from different frequencies. In the second stage, with the estimated , the other parameters of the model are estimated by the method of moments. All estimators have closed-form expressions and are easy to implement. A large sample theory of the proposed estimators is derived. Extensive …

Scalable And Globally Optimal Generalized L1 K-Center Clustering Via Constraint Generation In Mixed Integer Linear Programming, Aravinth Chembu, Scott Sanner, Hassan Khurran, Akshat Kumar

Research Collection School Of Computing and Information Systems

The k-center clustering algorithm, introduced over 35 years ago, is known to be robust to class imbalance prevalent in many clustering problems and has various applications such as data summarization, document clustering, and facility location determination. Unfortunately, existing k-center algorithms provide highly suboptimal solutions that can limit their practical application, reproducibility, and clustering quality. In this paper, we provide a novel scalable and globally optimal solution to a popular variant of the k-center problem known as generalized L_1 k-center clustering that uses L_1 distance and allows the selection of arbitrary vectors as cluster centers. We show that this clustering objective …

Mitigating Popularity Bias For Users And Items With Fairness-Centric Adaptive Recommendation, Zhongzhou Liu, Yuan Fang, Min Wu

Research Collection School Of Computing and Information Systems

Recommendation systems are popular in many domains. Researchers usually focus on the effectiveness of recommendation (e.g., precision) but neglect the popularity bias that may affect the fairness of the recommendation, which is also an important consideration that could influence the benefits of users and item providers. A few studies have been proposed to deal with the popularity bias, but they often face two limitations. Firstly, most studies only consider fairness for one side - either users or items, without achieving fairness jointly for both. Secondly, existing methods are not sufficiently tailored to each individual user or item to cope with …

Learning To Count Isomorphisms With Graph Neural Networks, Xingtong Yu, Zemin Liu, Yuan Fang, Xinming Zhang

Research Collection School Of Computing and Information Systems

Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational costs. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting. Moreover, on …

On Generalized Degree Fairness In Graph Neural Networks, Zemin Liu, Trung Kien Nguyen, Yuan Fang

Research Collection School Of Computing and Information Systems

Conventional graph neural networks (GNNs) are often confronted with fairness issues that may stem from their input, including node attributes and neighbors surrounding a node. While several recent approaches have been proposed to eliminate the bias rooted in sensitive attributes, they ignore the other key input of GNNs, namely the neighbors of a node, which can introduce bias since GNNs hinge on neighborhood structures to generate node representations. In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes. In this paper, we first define …

Contrastive Learning Approach To Word-In-Context Task For Low-Resource Languages, Pei-Chi Lo, Yang-Yin Lee, Hsien-Hao Chen, Agus Trisnajaya Kwee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Word in context (WiC) task aims to determine whether a target word’s occurrences in two sentences share the same sense. In this paper, we propose a Contrastive Learning WiC (CLWiC) framework to improve the learning of sentence/word representations and classification of target word senses in the sentence pair when performing WiC on lowresource languages. In representation learning, CLWiC trains a pre-trained language model’s ability to cope with lowresource languages using both unsupervised and supervised contrastive learning. The WiC classifier learning further finetunes the language model with WiC classification loss under two classifier architecture options, SGBERT and WiSBERT, which use single-encoder …

Scalable And Globally Optimal Generalized L1 K-Center Clustering Via Constraint Generation In Mixed Integer Linear Programming, Aravinth Chembu, Scott Sanner, Hassan Khurram, Akshat Kumar

Research Collection School Of Computing and Information Systems

The k-center clustering algorithm, introduced over 35 years ago, is known to be robust to class imbalance prevalent in many clustering problems and has various applications such as data summarization, document clustering, and facility location determination. Unfortunately, existing k-center algorithms provide highly suboptimal solutions that can limit their practical application, reproducibility, and clustering quality. In this paper, we provide a novel scalable and globally optimal solution to a popular variant of the k-center problem known as generalized L1 k-center clustering that uses L1 distance and allows the selection of arbitrary vectors as cluster centers. We show that this clustering objective …

Future Aware Pricing And Matching For Sustainable On-Demand Ride Pooling, Xianjie Zhang, Pradeep Varakantham, Hao Jiang

Research Collection School Of Computing and Information Systems

The popularity of on-demand ride pooling is owing to the benefits offered to customers (lower prices), taxi drivers (higher revenue), environment (lower carbon footprint due to fewer vehicles) and aggregation companies like Uber (higher revenue). To achieve these benefits, two key interlinked challenges have to be solved effectively: (a) pricing – setting prices to customer requests for taxis; and (b) matching – assignment of customers (that accepted the prices) to taxis/cars. Traditionally, both these challenges have been studied individually and using myopic approaches (considering only current requests), without considering the impact of current matching on addressing future requests. In this …

Anomaly-Based Network Intrusion Detection System Using Deep Intelligent Technique, Sardar Kh. Hassan, Muhammadamin A. Daneshwar

Polytechnic Journal

Background and objectives: Computer systems and network infrastructures are still exposed to many security risks and cyber-attack vulnerabilities despite advancements of information security. Traditional signature-based intrusion detection systems and security solutions by matching rule-based mechanism and prior knowledge are insufficient of fully protecting computer networks against novel attacks. For this purpose, Anomaly-based Network Intrusion Detection System (A-NIDS) as cyber security tool is considered for identifying and detecting anomalous behavior in the flow-based network traffic alongside with firewalls and other security measures. The main objective of the research is to improve the detection rate and reduce false-positive rates of the classifier …

Social Bot Detection Using Machine Learning Algorithms: A Survey And Research Challenges, Kayhan Zrar Ghafoor

Polytechnic Journal

In the past decade social media platforms growing rapidly and they are part of our routine life. Each platform has its own specification which uses for specific purposes. After this widely spread, those SMPs were targeted by the cybercriminals to cast their malicious activities. There are many different malicious activities in SMPs such as spamming, phishing, fake account. In these papers, Bots activities in SMPs one of those threats which include fake accounts, fake friends/followers, spreading misinformation by purpose, and many more. At the beginning of our work, we explain all terminology related to this topic to have a clear …

Economic Land Utilization Optimization Model, Ossama A. Hosny, Elkhayam M. Dorra, Khaled A. Tarabieh, Ahmed El Eslamboly

Faculty Journal Articles

No abstract provided.

First-Principles Calculations On The Electronic Structure, Mechanical, Vibrational, And Thermal Properties Of The Ca₃Bin Antiperovskite Alloy, Reem Maher Mohamad

Theses

Antiperovskites are the counterparts of perovskites. They have diverse properties that make them physically rich and technologically relevant. In the recent years, the alkaline earth based antiperovskites, especially the ones with small semiconductor bandgap, have captured tremendous attention due to their properties which make them efficient to be used in optoelectronic and thermoelectric applications. In this thesis, we are shading light on the alkaline antiperovskite ����₃������ due to its narrow bandgap. Thus, it can be considered as a promising candidate for thermoelectric and optoelectronic applications. Specifically, it is used to harvest waste thermal energy into electricity at low …

Heat Transfer In Mini-Channel Heat Exchanger With Smooth And Pin-Fins Surfaces, Mohammed Molham Daadoua

Theses

This research aims to investigate heat transfer of single-phase and two-phase fluids in mini-channel heat exchanger with smooth and pin-fins surfaces. The main goal of the study is to develop a high-performance heat exchanger for thermal management in various processing applications. This research is carried out using computational fluid dynamics (CFD) modeling and experimentations. The proposed method is to use enhanced structured pin-fins surfaces in a mini-channel heat exchanger to improve heat transfer. The pin-fin microchannel heat exchanger is investigated using a CFD method with single-phase fluid (water, air). The experimental investigations are conducted using single-phase and two-phase fluid involving …

A Fair Incentive Scheme For Community Health Workers, Avinandan Bose, Tracey Li, Arunesh Sinha, Tien Mai

Research Collection School Of Computing and Information Systems

Community health workers (CHWs) play a crucial role in the last mile delivery of essential health services to under-served populations in low-income countries. Many non-governmental organizations (NGOs) provide training and support to enable CHWs to deliver health services to their communities, with no charge to the recipients of the services. This includes monetary compensation for the work that CHWs perform, which is broken down into a series of well-defined tasks. In this work, we partner with a NGO D-Tree International to design a fair monetary compensation scheme for tasks performed by CHWs in the semi-autonomous region of Zanzibar in Tanzania, …

Fa3: Fine-Grained Android Application Analysis, Yan Lin, Weng Onn Wong, Debin Gao

Research Collection School Of Computing and Information Systems

Understanding Android applications' behavior is essential to many security applications, e.g., malware analysis. Although many systems have been proposed to perform such dynamic analysis, they are limited by their applicable analysis environment (on device vs. emulator), transparency to subject apps, applicable runtime (Dalvik vs. ART), applicable system stack, or granularity. In this paper, we propose FA3 (Fine-Grained Android Application Analysis), a novel on-device, non-invasive, and fine-grained analysis platform by leveraging existing profiling mechanisms in the Android Runtime (ART) and kernel to inspect method invocations and control-flow transfers for both Java methods and third-party native libraries. FA3 embeds its tracing capability …

Solving Large-Scale Pursuit-Evasion Games Using Pre-Trained Strategies, Shuxin Li, Xinrun Wang, Youzhi Zhang, Wanqi Xue, Jakub Cerny, Bo An

Research Collection School Of Computing and Information Systems

Pursuit-evasion games on graphs model the coordination of police forces chasing a fleeing felon in real-world urban settings, using the standard framework of imperfect-information extensive-form games (EFGs). In recent years, solving EFGs has been largely dominated by the Policy-Space Response Oracle (PSRO) methods due to their modularity, scalability, and favorable convergence properties. However, even these methods quickly reach their limits when facing large combinatorial strategy spaces of the pursuit-evasion games. To improve their efficiency, we integrate the pre-training and fine-tuning paradigm into the core module of PSRO -- the repeated computation of the best response. First, we pre-train the pursuer's …

Iowa Waste Reduction Center Newsletter, February 2023, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- Energy Trends Workshop
--- IWRC Welcomes New Environmental Intern - Kendall Lieneman
--- ISOSWO Spring Conference Student Scholarship Applications Are Now Open
--- Iowa Minor Source Emissions Inventory Assistance
--- Industry News

Comments On The Draft Final Butte Priority Soils Operable Unit (Bpsou) Temporary Construction Surface Water Performance Standards Variance Request For Future Remedial Elements (Fresow) Projects (Dated December 7, 2022), Nikia Greene

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.