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Articles 4021 - 4050 of 11180

Full-Text Articles in Artificial Intelligence and Robotics

Training Simulation Scenario Generation Based On Particle Swarm Optimization, Jianxing Gong, Zimu Wang, Qilong Yang Sep 2023

Training Simulation Scenario Generation Based On Particle Swarm Optimization, Jianxing Gong, Zimu Wang, Qilong Yang

Journal of System Simulation

Abstract: The training effect in the training simulation scenario is not ideal. Therefore, in order to obtain the training simulation scenario with a better training effect, the training simulation scenario is optimized, and a training simulation scenario generation method based on the PSO algorithm is proposed. A fitness function is constructed based on the improved situation assessment method of the power field model, and the ability weight parameters are determined by combining the improved AHP with computer simulation software; by instantiating particles with the attributes of the combat platform, the particle swarm optimization algorithm is improved to solve the optimization …

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Survey On Intelligent Wargaming: Tactical & Campaign Wargame And Strategic Game From Game-Theoretic Perspective, Junren Luo, Wanpeng Zhang, Fengtao Xiang, Chaoyuan Jiang, Jing Chen Sep 2023

Survey On Intelligent Wargaming: Tactical & Campaign Wargame And Strategic Game From Game-Theoretic Perspective, Junren Luo, Wanpeng Zhang, Fengtao Xiang, Chaoyuan Jiang, Jing Chen

Journal of System Simulation

Abstract: Wargaming is a pre-practice activity to study national security and competition, military conflict and war, crisis management, and other major strategic issues. An intelligent wargaming system needs the ability of artificial intelligence technology. This paper briefly summarizes the research progress of intelligent game, the evolution of wargaming, intelligent wargaming, and strategic gaming methods. From the perspective of game theory, it analyzes the game problem model for intelligent wargaming and sorts out the application mode of intelligent wargaming and the organization mode of a strategic game. An intelligent wargaming service-oriented architecture based on cloud native is proposed. Pre-training method for …

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Deceptive Path Planning In Fog Of War, Dejun Chen, Zihao Fang, Yunxiu Zeng, Kai Xu Sep 2023

Deceptive Path Planning In Fog Of War, Dejun Chen, Zihao Fang, Yunxiu Zeng, Kai Xu

Journal of System Simulation

Abstract: Computer generated forces (CGFs) are virtual combat force objects created by computers and critical elements in the field of military simulation. Deceptive path planning is a basic method of deceptive behavior, which is important for improving the intelligence and competitiveness of CGFs. However, the current combination of deceptive behavior and military simulation is insufficient, and classical path planning methods cannot effectively take advantage of the partial observability of the battlefield and achieve better deceptive effects. To solve these problems, we propose four new deceptive path planning methods by re-defining a single circular fog road network based on road networks …

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Formation Obstacle Avoidance Algorithm Based On Joint Virtual Sub-Target And Boundary Force, Man Wang, Dapeng Li, Lianghui Ding, Tianlin Zhu Sep 2023

Formation Obstacle Avoidance Algorithm Based On Joint Virtual Sub-Target And Boundary Force, Man Wang, Dapeng Li, Lianghui Ding, Tianlin Zhu

Journal of System Simulation

Abstract: In view of the formation control of leader-follower swarm and obstacle avoidance in artificial potential field method in unmanned aerial vehicle (UAV) swarm formation system under complex environmental conditions, an obstacle avoidance algorithm for UAV swarm formation based on joint virtual sub-target and boundary force (JVBF) is proposed. The leader-follower method based on a virtual sub-target is used, and the modified force function is optimized to realize the formation control of the UAV swarm, so as to help the follower UAV to recover the formation quickly; the artificial potential field method based on boundary force is used for local …

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Research On Artificial Population Generation And Application Based On Genetic Algorithm, Hongli Zhang, Jingshuang Deng Sep 2023

Research On Artificial Population Generation And Application Based On Genetic Algorithm, Hongli Zhang, Jingshuang Deng

Journal of System Simulation

Abstract: High-precision micro-population data are one of the key basic data for simulation systems such as disease spread, traffic travel, and emergency events. In reality, computer-generated artificial populations are often used for simulation. Due to computational efficiency and standardization of generation steps, the iterative proportional fitting method is currently used for artificial population synthesis. However, it has strict requirements on basic data and faces zero-unit and data representational deviation problems, and it fails to guarantee the fitting at the individual and family levels at the same time. In order to overcome this deficiency, an improved genetic algorithm using a simulated …

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Research On Intelligent Statistical Analysis Of Wargaming Data Based On Nl2sql, Laixiang Yin, Zhiqiang Li, Qiongying Fu Sep 2023

Research On Intelligent Statistical Analysis Of Wargaming Data Based On Nl2sql, Laixiang Yin, Zhiqiang Li, Qiongying Fu

Journal of System Simulation

Abstract: In the face of massive wargaming data, the traditional interface query method can no longer meet the commander's requirements, i. e., fast, comprehensive, and accurate data querying. Through indepth analysis of the characteristics of wargaming data and the defects of the mainstream natural language to struct query language (NL2SQL) model, a set of solutions for the intelligent statistical query of wargaming data is presented. Due to the lack of datasets, a wargaming dataset construction scheme based on human-machine assistance and dynamic iteration is provided. In order to solve the timesensitive problem of wargaming querying, time expression recognition and standardization …

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Construction And Application Of Digital Twin System For Optical Fiber Secondary Coating Workshop, Biao Yuan, Yourui Huang, Shanyong Xu, Xue Rong Sep 2023

Construction And Application Of Digital Twin System For Optical Fiber Secondary Coating Workshop, Biao Yuan, Yourui Huang, Shanyong Xu, Xue Rong

Journal of System Simulation

Abstract: In order to solve the problem that the current optical fiber secondary coating workshop has inferior intelligence and low digital degree, a three-dimensional (3D) visual monitoring and fault diagnosis method for the optical fiber secondary coating workshop based on digital twin (DT) is proposed. In view of the equipment in the optical fiber secondary coating workshop, combined with the workshop production process and equipment operation mechanism, the digital modeling of all physical properties of the optical fiber secondary coating workshop is carried out, and the virtual twin scene is constructed. Real-time mapping between the virtual workshop and the physical …

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Method For Extracting Data During Flight Phase Of Ski Jumping Based On Monocular Video, Ziyi Shen, Meng Yang, Chao Yang, Weidi Tang, Xie Wu, Yu Liu, Bin Sheng Sep 2023

Method For Extracting Data During Flight Phase Of Ski Jumping Based On Monocular Video, Ziyi Shen, Meng Yang, Chao Yang, Weidi Tang, Xie Wu, Yu Liu, Bin Sheng

Journal of System Simulation

Abstract: To solve the problem of the high difficulty factor of ski jumping and the difficulty of extracting data of this sport due to the danger of invasive devices such as wearable sensors and high price, a method for extracting data during the flight phase of ski jumping based on monocular video is proposed. The distortion and background clutter of the monocular video are preprocessed. The distortion of the captured images is corrected by calibrating camera parameters, and the background is removed by the inter-frame difference method. The human pose recognition library, namely OpenPose is used to initially identify the …

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Style Transfer Network For Generating Opera Makeup Details, Fengquan Zhang, Duo Cao, Xiaohan Ma, Baijun Chen, Jiangxiao Zhang Sep 2023

Style Transfer Network For Generating Opera Makeup Details, Fengquan Zhang, Duo Cao, Xiaohan Ma, Baijun Chen, Jiangxiao Zhang

Journal of System Simulation

Abstract: To address the problem of the loss of local style details in cross-domain image simulation, a ChinOperaGAN network framework suitable for opera makeup is designed from the perspective of protecting the excellent traditional culture. In order to solve the style translation of differences in two image domains, multiple overlapping local adversarial discriminators are proposed in the generative adversarial network. Since paired opera makeup data are difficult to obtain, a synthetic image is generated by combining the source image makeup mapping to effectively guide the transfer of local makeup details between images. In view of the characteristics of opera makeup …

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Energy-Aware Path Planning For Fixed-Wing Seaplane Uavs, Benjamin Atkinson Wolsieffer Sep 2023

Energy-Aware Path Planning For Fixed-Wing Seaplane Uavs, Benjamin Atkinson Wolsieffer

Dartmouth College Master’s Theses

Fixed-wing unmanned aerial vehicles (UAVs) are commonly used for remote sensing applications over water bodies, such as monitoring water quality or tracking harmful algal blooms. However, there are some types of measurements that are difficult to accurately obtain from the air. In existing work, water samples have been collected in situ either by hand, with an unmanned surface vehicle (USV), or with a vertical takeoff and landing (VTOL) UAV such as a multirotor. We propose a path planner, landing control algorithm, and energy estimator that will allow a low-cost and energy efficient fixed-wing UAV to carry out a combined remote …

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Effects Of Weight Initialization Methods On Ffn's, Ida K. Karem Sep 2023

Effects Of Weight Initialization Methods On Ffn's, Ida K. Karem

The Cardinal Edge

Weight initialization is the method of determining starting values of weights in a neural network. The way this method is done can have massive effects on the network[2, 3, 6, 9] and can halt training if not handled properly. On the other hand, if initialization is chosen tactfully it can improve training and accuracy greatly. The initialization method usually called Normalized Xavier will be referred to as Nox in this paper to avoid confusion with the Xavier initialization method. This study analyzes five methods of weight initialization(Nox, He, Xavier, Plutonian, and Self-Root), two of them …

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Sentiment Analysis Of Public Perception Towards Elon Musk On Reddit (2008-2022), Daniel Maya Bonilla, Samuel Iradukunda, Pamela Thomas Sep 2023

Sentiment Analysis Of Public Perception Towards Elon Musk On Reddit (2008-2022), Daniel Maya Bonilla, Samuel Iradukunda, Pamela Thomas

The Cardinal Edge

As Elon Musk’s influence in technology and business continues to expand, it becomes crucial to comprehend public sentiment surrounding him in order to gauge the impact of his actions and statements. In this study, we conducted a comprehensive analysis of comments from various subreddits discussing Elon Musk over a 14-year period, from 2008 to 2022. Utilizing advanced sentiment analysis models and natural language processing techniques, we examined patterns and shifts in public sentiment towards Musk, identifying correlations with key events in his life and career. Our findings reveal that public sentiment is shaped by a multitude of factors, including his …

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Overview Of The Clef-2023 Checkthat! Lab Task 4 On Factuality Of Reporting Of News Media, Preslav Nakov, Firoj Alam, Giovanni Da San Martino, Maram Hasanain, Dilshod Azizov, Rabindra Nath Nandi, Panayotov Panayot Sep 2023

Overview Of The Clef-2023 Checkthat! Lab Task 4 On Factuality Of Reporting Of News Media, Preslav Nakov, Firoj Alam, Giovanni Da San Martino, Maram Hasanain, Dilshod Azizov, Rabindra Nath Nandi, Panayotov Panayot

Natural Language Processing Faculty Publications

We present an overview of the CLEF-2023 CheckThat! lab Task 4, which focused on predicting the factuality of reporting of entire news outlets. This is a different level of granularity compared to previous efforts, which focused on fact-checking, where the target is a claim, or fake news detection, where the target is an article. We briefly summarize the participating systems and discuss the dataset, the task, and the evaluation setup. The task attracted a large number of registrations, and eventually five teams made submissions. All participants improved over the baseline by a margin using both deep learning and traditional machine …

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Asset-Based Approaches To Multilingual Students’ Computer Science Identity Development, Sharin Rawhiya Jacob, Mark Warschauer Sep 2023

Asset-Based Approaches To Multilingual Students’ Computer Science Identity Development, Sharin Rawhiya Jacob, Mark Warschauer

Journal of Computer Science Integration

While computer science identity development has been examined in several studies, there is much to learn about the development of multilingual students’ computer science (CS) identities. To develop strong CS identities, multilingual students must engage in culturally and linguistically sustaining curriculum, pedagogy, and interaction that draws from their rich and varied resources. This theoretical paper is grounded in a justice-centered, asset-based framework that views the traditions and practices in students’ cultures and communities as strong contributors to knowledge construction in STEM. We draw on multiple studies exploring multilingual student CS identity development to better understand how their personal, familial, community-based, …

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Wind River Elementary Computer Science Collaborative: Connecting Computer Science And Indigenous Identities And Knowledges On The Wind River Reservation, Joseph P. Wilson, Kathryn M. Rich, Jared O'Leary, Veronica Miller Sep 2023

Wind River Elementary Computer Science Collaborative: Connecting Computer Science And Indigenous Identities And Knowledges On The Wind River Reservation, Joseph P. Wilson, Kathryn M. Rich, Jared O'Leary, Veronica Miller

Journal of Computer Science Integration

Three Northern Arapaho and Eastern Shoshone–serving districts formed a researcher–practitioner partnership with the Wyoming Department of Education, the American Institutes for Research®, and BootUp Professional Development to advance the computer science (CS) education of their elementary students in ways that strengthen their Indigenous identities and knowledges. In this paper, we share experiences from 2019 to 2022 with our curriculum development, professional development (PD), and classroom implementation. The researcher–practitioner partnership developed student and teacher materials to support elementary CS lessons aligned to Wyoming’s CS standards and “Indian Education for All” social studies standards. Indigenous community members served as experts to codesign …

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Near Peer Pedagogies For Culturally Sustaining Computer Science And Mathematics, Cliff Freeman, Eli Tucker-Raymond, Juan Gutierrez Sep 2023

Near Peer Pedagogies For Culturally Sustaining Computer Science And Mathematics, Cliff Freeman, Eli Tucker-Raymond, Juan Gutierrez

Journal of Computer Science Integration

In this study, College STEM Literacy Workers act as co-teachers in 9th and 10th grade classrooms alongside mathematics teachers as they both learn to integrate a 30-hour integrated computer science/mathematics curriculum. As part of the curriculum for educator learning, we adapted the Algebra Project’s “Model of Excellence” for culturally relevant-sustaining pedagogies. We used the framework to explore how teachers understand College STEM Literacy Workers’ contributions in the model as well as College STEM Literacy Workers’ own experiences in the classroom. We found that while teachers and College STEM Literacy Workers did not individually instantiate the model of excellence, they were …

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Codesigning A Big Data Analytic Tool For Girl Child Learner Drop Out From Eastern Cape Province -South Africa, Nobert Rangarirai Jere, Nosipho Carol Mavuso, Nelly Sharpley Sep 2023

Codesigning A Big Data Analytic Tool For Girl Child Learner Drop Out From Eastern Cape Province -South Africa, Nobert Rangarirai Jere, Nosipho Carol Mavuso, Nelly Sharpley

African Conference on Information Systems and Technology

Developing sustainable solutions is critical for adoption of digital solutions. As the high number of learners dropping out of school continues to increase, it is critical to find innovative ways of predicting and preventing high drop out. Current literature has documented a number of factors that influence learner drop out. Innovative ideas, techniques and activities have been undertaken to motivate learners to stay at school. It is unfortunate that most of the initiatives have not helped to avoid drop out of learners. The study is based on a mixed approached that was used targeting female learns from Oliver Tambo District …

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A Social Profile-Based E-Learning Model, Xola Ntlangula Sep 2023

A Social Profile-Based E-Learning Model, Xola Ntlangula

African Conference on Information Systems and Technology

Many High Education Institutions (HEIs) have migrated to blended or complete online learning to cater for less interruption with learning. As such, there is a growing demand for personalized e-learning to accommodate the diversity of students' needs. Personalization can be achieved using recommendation systems powered by artificial intelligence. Although using student data to personalize learning is not a new concept, collecting and identifying appropriate data is necessary to determine the best recommendations for students. By reviewing the existing data collection capabilities of the e-learning platforms deployed by public universities in South Africa, we were able to establish the readiness of …

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Bare-Bones Based Salp Swarm Algorithm For Text Document Clustering, Mohammed Azmi Al-Betar, Ammar Kamal Abasi, Ghazi Al-Naymat, Kamran Arshad, Sharif Naser Makhadmeh Sep 2023

Bare-Bones Based Salp Swarm Algorithm For Text Document Clustering, Mohammed Azmi Al-Betar, Ammar Kamal Abasi, Ghazi Al-Naymat, Kamran Arshad, Sharif Naser Makhadmeh

Machine Learning Faculty Publications

Text Document Clustering (TDC) is a challenging optimization problem in unsupervised machine learning and text mining. The Salp Swarm Algorithm (SSA) has been found to be effective in solving complex optimization problems. However, the SSA’s exploitation phase requires improvement to solve the TDC problem effectively. In this paper, we propose a new approach, known as the Bare-Bones Salp Swarm Algorithm (BBSSA), which leverages Gaussian search equations, inverse hyperbolic cosine control strategies, and greedy selection techniques to create new individuals and guide the population towards solving the TDC problem. We evaluated the performance of the BBSSA on six benchmark datasets from …

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A Study On Feature Selection Using Multi-Domain Feature Extraction For Automated K-Complex Detection, Yabing Li, Xinglong Dong, Kun Song, Xiangyun Bai, Hongye Li, Fakhreddine Karray Sep 2023

A Study On Feature Selection Using Multi-Domain Feature Extraction For Automated K-Complex Detection, Yabing Li, Xinglong Dong, Kun Song, Xiangyun Bai, Hongye Li, Fakhreddine Karray

Machine Learning Faculty Publications

Background: K-complex detection plays a significant role in the field of sleep research. However, manual annotation for electroencephalography (EEG) recordings by visual inspection from experts is time-consuming and subjective. Therefore, there is a necessity to implement automatic detection methods based on classical machine learning algorithms. However, due to the complexity of EEG signal, current feature extraction methods always produce low relevance to k-complex detection, which leads to a great performance loss for the detection. Hence, finding compact yet effective integrated feature vectors becomes a crucially core task in k-complex detection. Method: In this paper, we first extract multi-domain features based …

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Disease Progression Modelling Of Alzheimer's Disease Using Probabilistic Principal Components Analysis, Martin Saint-Jalmes, Victor Fedyashov, Daniel Beck, Timothy Baldwin, Noel G. Faux, Pierrick Bourgeat, Jurgen Fripp, Colin L. Masters, Benjamin Goudey Sep 2023

Disease Progression Modelling Of Alzheimer's Disease Using Probabilistic Principal Components Analysis, Martin Saint-Jalmes, Victor Fedyashov, Daniel Beck, Timothy Baldwin, Noel G. Faux, Pierrick Bourgeat, Jurgen Fripp, Colin L. Masters, Benjamin Goudey

Natural Language Processing Faculty Publications

The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the …

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Overview Of The Clef-2023 Checkthat! Lab Task 1 On Check-Worthiness Of Multimodal And Multigenre Content, Firoj Alam, Alberto Barrón-Cedeño, Gullal S. Cheema, Gautam Kishore Shahi, Sherzod Hakimov, Maram Hasanain, Chengkai Li, Rubén Míguez, Hamdy Mubarak, Wajdi Zaghouani, Preslav Nakov Sep 2023

Overview Of The Clef-2023 Checkthat! Lab Task 1 On Check-Worthiness Of Multimodal And Multigenre Content, Firoj Alam, Alberto Barrón-Cedeño, Gullal S. Cheema, Gautam Kishore Shahi, Sherzod Hakimov, Maram Hasanain, Chengkai Li, Rubén Míguez, Hamdy Mubarak, Wajdi Zaghouani, Preslav Nakov

Natural Language Processing Faculty Publications

We present an overview of CheckThat! Lab’s 2023 Task 1, which is part of CLEF-2023. Task 1 asks to determine whether a text item, or a text coupled with an image, is check-worthy. This task places a special emphasis on COVID-19, political debates and transcriptions, and it is conducted in three languages: Arabic, English, and Spanish. A total of 15 teams participated, and most submissions managed to achieve significant improvements over the baselines using Transformer-based models. Out of these, seven teams participated in the multimodal subtask (1A), and 12 teams participated in the Multigenre subtask (1B), collectively submitting 155 official …

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Gpachov At Checkthat! 2023: A Diverse Multi-Approach Ensemble For Subjectivity Detection In News Articles, Georgi Pachov, Dimitar Dimitrov, Ivan Koychev, Preslav Nakov Sep 2023

Gpachov At Checkthat! 2023: A Diverse Multi-Approach Ensemble For Subjectivity Detection In News Articles, Georgi Pachov, Dimitar Dimitrov, Ivan Koychev, Preslav Nakov

Natural Language Processing Faculty Publications

The wide-spread use of social networks has given rise to subjective, misleading, and even false information on the Internet. Thus, subjectivity detection can play an important role in ensuring the objectiveness and the quality of a piece of information. This paper presents the solution built by the Gpachov team for the CLEF-2023 CheckThat! lab Task 2 on subjectivity detection. Three different research directions are explored. The first one is based on fine-tuning a sentence embeddings encoder model and dimensionality reduction. The second one explores a sample-efficient few-shot learning model. The third one evaluates fine-tuning a multilingual transformer on an altered …

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Enriched Pre-Trained Transformers For Joint Slot Filling And Intent Detection, Momchil Hardalov, Ivan Koychev, Preslav Nakov Sep 2023

Enriched Pre-Trained Transformers For Joint Slot Filling And Intent Detection, Momchil Hardalov, Ivan Koychev, Preslav Nakov

Natural Language Processing Faculty Publications

Detecting the user's intent and finding the corresponding slots among the utterance's words are important tasks in natural language understanding. Their interconnected nature makes their joint modeling a standard part of training such models. Moreover, data scarceness and specialized vocabularies pose additional challenges. Recently, the advances in pre-trained language models, namely contextualized models such as ELMo and BERT have revolutionized the field by tapping the potential of training very large models with just a few steps of fine-tuning on a task-specific dataset. Here, we leverage such models, and we design a novel architecture on top of them. Moreover, we propose …

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Grammatical Error Correction: A Survey Of The State Of The Art, Christopher Bryant, Zheng Yuan, Muhammad Reza Qorib, Hannan Cao, Hwee Tou Ng, Ted Briscoe Sep 2023

Grammatical Error Correction: A Survey Of The State Of The Art, Christopher Bryant, Zheng Yuan, Muhammad Reza Qorib, Hannan Cao, Hwee Tou Ng, Ted Briscoe

Natural Language Processing Faculty Publications

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject–verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors, respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems, which represent the current dominant state of the art. In this survey paper, we condense …

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Advances In Quaternion-Valued Neural Networks, Jeremiah P. Bill Sep 2023

Advances In Quaternion-Valued Neural Networks, Jeremiah P. Bill

Theses and Dissertations

This dissertation investigates the construction, optimization, and application of quaternion neural networks (QNNs) to Department of Defense (DoD) related problem sets. QNNs are a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. This work provides a critical evaluation of the myriad different quaternion backpropagation derivations that exist in the literature, testing the performance of each on a range of regression problem sets. The optimization dynamics of QNNs are explored, presenting visualizations of QNN loss surfaces and a novel method for assessing the “smoothness” of these loss surfaces. Finally, this dissertation presents …

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Perceptions And Barriers To Adopting Artificial Intelligence In K-12 Education: A Survey Of Educators In Fifty States, Karen Woodruff, James Hutson, Kathryn Arnone Sep 2023

Perceptions And Barriers To Adopting Artificial Intelligence In K-12 Education: A Survey Of Educators In Fifty States, Karen Woodruff, James Hutson, Kathryn Arnone

Faculty Scholarship

Artificial Intelligence (AI) is making significant strides in the field of education, offering new opportunities for personalized learning and access to education for a more diverse population. Despite this potential, the adoption of AI in K-12 education is limited, and educators’ express hesitancy towards its integration due to perceived technological barriers and misconceptions. The purpose of this study is to examine the perceptions of K-12 educators in all 50 states of the USA towards AI, policies, training, and resources related to technology and AI, their comfort with technology, willingness to adopt new technologies for classroom instruction, and needs assessment for …

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Thermodynamics Of Learning With Parametric Probabilistic Models, Shervin Sadat Parsi Sep 2023

Thermodynamics Of Learning With Parametric Probabilistic Models, Shervin Sadat Parsi

Dissertations, Theses, and Capstone Projects

This study delves into the learning process within the Probabilistic Parametric Models (PPMs) framework from a unique thermodynamic perspective. By exploring the core concepts of thermodynamics and its innate connection with information theory, we showcase how this interdisciplinary approach can effectively contribute to the domain of machine learning. In the initial chapter, we establish the link between the learning problem in PPMs and a thermodynamic process by reframing various elements of the learning process within the context of thermodynamics. We introduce novel information-theoretic measurements that provide insights into the information learned in both the parameter space and the overall performance …

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Mpla Case: How Do You Lead As A Lead Physicist?, Patricia Sansourekidou, Leonard Kim, Lee Xu, Mary Gronberg, Cassandra Stambaugh, Dongxu Wang Sep 2023

Mpla Case: How Do You Lead As A Lead Physicist?, Patricia Sansourekidou, Leonard Kim, Lee Xu, Mary Gronberg, Cassandra Stambaugh, Dongxu Wang

Faculty, Staff and Student Publications

This work of fiction is part of a case study series developed by the Medical Physics Leadership Academy (MPLA). It is intended to facilitate the discussion of the managerial and leadership challenges faced by a clinical medical physicist. In this case, a physicist David used to work in a clinic where he thrived and felt like a leader, despite not having the title. After a job change, he is now officially the "Lead Physicist" at a hospital newly affiliated with a large academic healthcare system. He believes he will be equally successful. Yet he struggles to bring about changes and …

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Out-Of-Distribution Generalization Of Deep Learning To Illuminate Dark Protein Functional Space, Tian Cai Sep 2023

Out-Of-Distribution Generalization Of Deep Learning To Illuminate Dark Protein Functional Space, Tian Cai

Dissertations, Theses, and Capstone Projects

Dark protein illumination is a fundamental challenge in drug discovery where majority human proteins are understudied, i.e. with only known protein sequence but no known small molecule binder. It's a major road block to enable drug discovery paradigm shift from single-targeted which looks to identify a single target and design drug to regulate the single target to multi-targeted in a Systems Pharmacology perspective. Diseases such as Alzheimer's and Opioid-Use-Disorder plaguing millions of patients call for effective multi-targeted approach involving dark proteins. Using limited protein data to predict dark protein property requires deep learning systems with OOD generalization capacity. Out-of-Distribution (OOD) …

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