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Articles 21091 - 21120 of 291657

Full-Text Articles in Physical Sciences and Mathematics

Context-Aware Adapter Tuning For Few-Shot Relation Learning In Knowledge Graphs, Ran Liu, Zhongzhou Liu, Xiaoli Li, Yuan Fang Nov 2024

Context-Aware Adapter Tuning For Few-Shot Relation Learning In Knowledge Graphs, Ran Liu, Zhongzhou Liu, Xiaoli Li, Yuan Fang

Research Collection School Of Computing and Information Systems

Knowledge graphs (KGs) are instrumental in various real-world applications, yet they often suffer from incompleteness due to missing relations. To predict instances for novel relations with limited training examples, few-shot relation learning approaches have emerged, utilizing techniques such as meta-learning. However, the assumption is that novel relations in meta-testing and base relations in meta-training are independently and identically distributed, which may not hold in practice. To address the limitation, we propose RelAdapter, a context-aware adapter for few-shot relation learning in KGs designed to enhance the adaptation process in meta-learning. First, RelAdapter is equipped with a lightweight adapter module that facilitates …

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Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang Nov 2024

Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang

Research Collection School Of Computing and Information Systems

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task’s labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether …

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Pcqpr : Proactive Conversational Question Planning With Reflection, Shasha Guo, Lizi Liao, Jing Zhang, Cuiping Li, Hong Cheng Nov 2024

Pcqpr : Proactive Conversational Question Planning With Reflection, Shasha Guo, Lizi Liao, Jing Zhang, Cuiping Li, Hong Cheng

Research Collection School Of Computing and Information Systems

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task’s labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether …

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Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang Nov 2024

Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Current research in food analysis primarily concentrates on tasks such as food recognition, recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a significant gap in exploring the impact of food intake on physiological indicators (e.g., weight) over time. This paper addresses this gap by introducing the DietDiary dataset, which encompasses daily dietary diaries and corresponding weight measurements of real users. Furthermore, we propose a novel task of weight prediction with a dietary diary that aims to leverage historical food intake and weight to predict future weights. To tackle this task, we propose a model-agnostic time series …

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Class Name Guided Out-Of-Scope Intent Classification, Chandan Gautam, Sethupathy Parameswaran, Aditya Kane, Yuan Fang, Savitha Ramasamy, Suresh Sundaram, Sunil Kumar Sahu, Xiaoli Li Nov 2024

Class Name Guided Out-Of-Scope Intent Classification, Chandan Gautam, Sethupathy Parameswaran, Aditya Kane, Yuan Fang, Savitha Ramasamy, Suresh Sundaram, Sunil Kumar Sahu, Xiaoli Li

Research Collection School Of Computing and Information Systems

The paper introduces Semantics of Class Labelbased Unsupervised Out of Scope Intent Detection (SCOOS), a novel method aimed at enhancing out-of-scope (OOS) intent classification in task-oriented dialogue systems. Unlike prior approaches that rely solely on indomain (ID) data features, SCOOS leverages semantic cues embedded in class labels to improve classification accuracy. The method entails forming a compact feature space centered around the semantics of class labels by minimizing losses between ID features and class names. SCOOS achieves this by creating a compact feature space centered around class label semantics, achieved through minimizing losses between in-domain (ID) features and class names. …

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Defending Large Language Models Against Jailbreak Attacks Via Layer-Specific Editing, Wei Zhao, Zhe Li, Yige Li, Jun Sun, Jun Sun Nov 2024

Defending Large Language Models Against Jailbreak Attacks Via Layer-Specific Editing, Wei Zhao, Zhe Li, Yige Li, Jun Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are increasingly being adopted in a wide range of realworld applications. Despite their impressive performance, recent studies have shown that LLMs are vulnerable to deliberately crafted adversarial prompts even when aligned via Reinforcement Learning from Human Feedback or supervised fine-tuning. While existing defense methods focus on either detecting harmful prompts or reducing the likelihood of harmful responses through various means, defending LLMs against jailbreak attacks based on the inner mechanisms of LLMs remains largely unexplored. In this work, we investigate how LLMs respond to harmful prompts and propose a novel defense method termed Layer-specific Editing (LED) …

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Revisiting Conversation Discourse For Dialogue Disentanglement, Bobo Li, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Yinwei Wei, Tat-Seng Chua, Donghong Ji Nov 2024

Revisiting Conversation Discourse For Dialogue Disentanglement, Bobo Li, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Yinwei Wei, Tat-Seng Chua, Donghong Ji

Research Collection School Of Computing and Information Systems

Dialogue disentanglement aims to detach the chronologically ordered utterances into several independent sessions. Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attribute. In this article, we propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. First of all, in feature encoding stage, we construct the heterogeneous graph representations to model the various dialogue-specific discourse structural features, including the static speaker-role structures (i.e., speaker-utterance and speaker-mentioning structure) and the dynamic contextual structures (i.e., the utterance-distance and partial-replying structure). We then …

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Cirp: Cross‑Item Relational Pre‑Training For Multimodal Product Bundling, Yunshan Ma, Yingzhi He, Wenjun Zhong, Xiang Wang, Roger Zimmermann, Tat-Seng Chua Nov 2024

Cirp: Cross‑Item Relational Pre‑Training For Multimodal Product Bundling, Yunshan Ma, Yingzhi He, Wenjun Zhong, Xiang Wang, Roger Zimmermann, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Product bundling has been a prevailing marketing strategy that is beneficial in the online shopping scenario. Effective product bundling methods depend on high-quality item representations capturing both the individual items' semantics and cross-item relations. However, previous item representation learning methods, either feature fusion or graph learning, suffer from inadequate cross-modal alignment and struggle to capture the cross-item relations for cold-start items. Multimodal pre-train models could be the potential solutions given their promising performance on various multimodal downstream tasks. However, the cross-item relations have been under-explored in the current multimodal pre-train models.To bridge this gap, we propose a novel and simple …

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Mm‑Forecast: A Multimodal Approach To Temporal Event Forecasting With Large Language Models, Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, Tat-Seng Chua Nov 2024

Mm‑Forecast: A Multimodal Approach To Temporal Event Forecasting With Large Language Models, Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of utilizing images for temporal event forecasting has not been fully explored, especially in the era of large language models (LLMs). To bridge this gap, we are particularly interested in two key questions of: 1) why images will help in temporal event forecasting, and 2) how to integrate images into the LLM-based forecasting framework. To answer these research questions, we propose to identify two essential functions that images play in the scenario of temporal event …

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Eyegraph : Modularity-Aware Spatio Temporal Graph Clustering For Continuous Event-Based Eye Tracking, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra, Ila Gokarn, Archan Misra Nov 2024

Eyegraph : Modularity-Aware Spatio Temporal Graph Clustering For Continuous Event-Based Eye Tracking, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra, Ila Gokarn, Archan Misra

Research Collection School Of Computing and Information Systems

Continuous tracking of eye movement dynamics plays a significant role in developing a broad spectrum of human-centered applications, such as cognitive skills (visual attention and working memory) modeling, human-machine interaction, biometric user authentication, and foveated rendering. Recently neuromorphic cameras have garnered significant interest in the eye-tracking research community, owing to their sub-microsecond latency in capturing intensity changes resulting from eye movements. Nevertheless, the existing approaches for event-based eye tracking suffer from several limitations: dependence on RGB frames, label sparsity, and training on datasets collected in controlled lab environments that do not adequately reflect real-world scenarios. To address these limitations, in …

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On Spatiotemporal Trends In Meter-Scale Gris Surface Roughness And The Development Of An On-Ice Laser Distance Meter, Jamie C. Good Nov 2024

On Spatiotemporal Trends In Meter-Scale Gris Surface Roughness And The Development Of An On-Ice Laser Distance Meter, Jamie C. Good

Dartmouth College Master’s Theses

Surface roughness is a critical component of the energy and mass balance of the Greenland Ice Sheet (GrIS). However, roughness is often oversimplified in predictive models due to its inherent scale dependency. Accurate quantification of roughness, including its recent changes and driving factors, is critically important for understanding and predicting GrIS surface dynamics. Using multi- and single-scale methods of roughness analysis, I assess spatiotemporal trends in GrIS meter-scale surface roughness from 2009 to 2019 with Operation IceBridge’s Airborne Topographic Mapper ILATM2 product. Additionally, with data from on-ice automated weather stations, I employ machine learning techniques to identify primary climatic controls …

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Advancing Crispr-Based Solutions For Covid-19 Diagnosis And Therapeutics, Roaa Hadi, Abhishek Poddar, Shivakuma Sonnaila, Venkata Suryanarayana Murthy Bhavaraju, Shilpi Agrawal Nov 2024

Advancing Crispr-Based Solutions For Covid-19 Diagnosis And Therapeutics, Roaa Hadi, Abhishek Poddar, Shivakuma Sonnaila, Venkata Suryanarayana Murthy Bhavaraju, Shilpi Agrawal

Chemistry & Biochemistry Faculty Publications and Presentations

Since the onset of the COVID-19 pandemic, a variety of diagnostic approaches, including RT-qPCR, RAPID, and LFA, have been adopted, with RT-qPCR emerging as the gold standard. However, a significant challenge in COVID-19 diagnostics is the wide range of symptoms presented by patients, necessitating early and accurate diagnosis for effective management. Although RT-qPCR is a precise molecular technique, it is not immune to false-negative results. In contrast, CRISPR-based detection methods for SARS-CoV-2 offer several advantages: they are cost-effective, time-efficient, highly sensitive, and specific, and they do not require sophisticated instruments. These methods also show promise for scalability, enabling diagnostic tests. …

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Lithicone-Protected Lithium Metal Anodes For Lithium Metal Batteries With Nickel-Rich Cathode Materials, Ridwan A. Ahmed, Kevin V. Carballo, Krishna P. Koirala, Qian Zhao, Peiyuan Gao, Ju-Myung Kim, Cassidy S. Anderson, Xiangbo Meng, Chongmin Wang, Ji-Guang Zhang, Wu Xu Nov 2024

Lithicone-Protected Lithium Metal Anodes For Lithium Metal Batteries With Nickel-Rich Cathode Materials, Ridwan A. Ahmed, Kevin V. Carballo, Krishna P. Koirala, Qian Zhao, Peiyuan Gao, Ju-Myung Kim, Cassidy S. Anderson, Xiangbo Meng, Chongmin Wang, Ji-Guang Zhang, Wu Xu

Mechanical Engineering Faculty Publications and Presentations

The high energy density advantage of lithium (Li) metal batteries (LMBs) makes them increasingly desirable; however, problems such as strong reactivity and dendrite growth of Li metal anode limit their practical uses. In this work, a novel Li-containing glycerol (LiGL) or lithicone protection layer on a 50 μm thick Li metal anode is employed for improving the performance of LMBs. This LiGL layer was accurately deposited via a molecular layer deposition (MLD) process at 150°C, using lithium tert-butoxide and glycerol as precursors. The as-formed LiGL coating layer is highly tunable in its thickness by simply adjusting MLD cycles and shows …

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Tackling Toxicity And Harassment In Online Environments Through The Use Of Artificial Intelligence, Heba Saleous Nov 2024

Tackling Toxicity And Harassment In Online Environments Through The Use Of Artificial Intelligence, Heba Saleous

Dissertations

With the increase in popularity of online communities, such as social media platforms, online games, and chatroom servers, there is a need to improve chat and content moderation. Platforms have reported an increase in the prevalence of toxic behavior and hate speech. Meanwhile, moderators are reporting difficulties in keeping up with the amount of data to check as well and the type of content they are exposed to, which further harms their own mental health. The main objective of this work is to address the challenges that exist within online communities with the rising prevalence of hate speech. Additionally, some …

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Enhancedbert: A Python Software Tailored For Arabic Word Sense Disambiguation, Sanaa Kaddoura, Reem Nassar Nov 2024

Enhancedbert: A Python Software Tailored For Arabic Word Sense Disambiguation, Sanaa Kaddoura, Reem Nassar

All Works

EnhancedBERT is a software framework designed to disambiguate Arabic polysemous terms using advanced natural language processing techniques. It integrates transformer architectures with ensemble methods to achieve high performance in understanding and processing Arabic text. The framework provides a flexible pipeline that can be directly utilized or fine-tuned according to specific needs. EnhancedBERT stands out for its ease of use, leveraging transformer-based models combined with ensemble strategies to provide superior contextual understanding. This contextual awareness makes it an invaluable tool for researchers and practitioners tackling complexities in Arabic language processing.

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The Digital Renaissance In Education: Adapting Generative Ai In Pre-Service Teacher And Provider Strategies, Jennifer J. Lesh, Jévaughn J. Lancaster Nov 2024

The Digital Renaissance In Education: Adapting Generative Ai In Pre-Service Teacher And Provider Strategies, Jennifer J. Lesh, Jévaughn J. Lancaster

Faculty and Staff Publications & Presentations

Dr. Lesh's second presentation, "The Digital Renaissance in Education: Adapting Generative AI in Pre-Service Teacher and Provider Strategies," offered insights into the transformative role of generative AI in teacher education. Collaborating with Dr. JeVaughn Lancaster virtually, Lesh and Lancaster shared data from a recent study examining teachers' perceptions of AI in academic research. Findings underscored the potential for AI to enhance educational efficiency while also identifying ethical considerations that must be addressed. Lesh and Lancaster advocated for responsible AI training, stressing that generative AI should augment, not replace, educators' expertise and critical thinking.

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The Implementation Of Stream In Mathematics Classrooms In Abu Dhabi Primary Schools: Prospects, Priorities, Processes, And Problems, Nadeia Rashed Alalawi Nov 2024

The Implementation Of Stream In Mathematics Classrooms In Abu Dhabi Primary Schools: Prospects, Priorities, Processes, And Problems, Nadeia Rashed Alalawi

Dissertations

This study shed light on implementing science, technology, reading and writing, engineering, art, and mathematics (STREAM) in mathematics classrooms in Abu Dhabi primary schools. The study aimed to explore mathematics teachers’ views on the prospects, priorities, processes, and problems of the implementation of STREAM in their classrooms. The study employed qualitative methods to collect and analyze data to support the findings using interviews, classroom observations, and document analysis. Fifteen in-service mathematics teachers were interviewed to explore their views about the application of STREAM in their classrooms regarding prospects, priorities, processes, and problems (4 Ps). Then, three of them were observed …

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A Study To Examine Vessel Traffic Services In The Context Of Maritime Autonomous Surface Ships : Strait Of Malacca As A Case Study, Nadarajan Perumal Nov 2024

A Study To Examine Vessel Traffic Services In The Context Of Maritime Autonomous Surface Ships : Strait Of Malacca As A Case Study, Nadarajan Perumal

World Maritime University Dissertations

No abstract provided.

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Use Of Environmental Dna To Examine Seagrass Ecosystem Functionality, Kathy Ann Young Nov 2024

Use Of Environmental Dna To Examine Seagrass Ecosystem Functionality, Kathy Ann Young

World Maritime University Dissertations

No abstract provided.

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Critical Valuation Of The Implementation Of Imo’S Guidelines For The Reduction Of Underwater Radiated Noise : A Kenyan Perspective, Swabra Mohamed Abdulrahman Nov 2024

Critical Valuation Of The Implementation Of Imo’S Guidelines For The Reduction Of Underwater Radiated Noise : A Kenyan Perspective, Swabra Mohamed Abdulrahman

World Maritime University Dissertations

No abstract provided.

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An Evaluation Of The Legal Framework For Seizure And Detention Of Ships For Maritime Law Enforcement In Nigeria, Adetayo Yusuf Adesokan Nov 2024

An Evaluation Of The Legal Framework For Seizure And Detention Of Ships For Maritime Law Enforcement In Nigeria, Adetayo Yusuf Adesokan

World Maritime University Dissertations

No abstract provided.

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The Divergent Responses Of Salinity Generalists To Hyposaline Stress Provide Insights Into The Colonisation Of Freshwaters By Diatoms, Kathryn J. Judy, Eveline Pinseel, Kala M. Downey, Jeffrey A. Lewis, Andrew J. Alverson Nov 2024

The Divergent Responses Of Salinity Generalists To Hyposaline Stress Provide Insights Into The Colonisation Of Freshwaters By Diatoms, Kathryn J. Judy, Eveline Pinseel, Kala M. Downey, Jeffrey A. Lewis, Andrew J. Alverson

Biological Sciences Faculty Publications and Presentations

Environmental transitions, such as the salinity divide separating marine and fresh waters, shape biodiversity over both shallow and deep timescales, opening up new niches and creating opportunities for accelerated speciation and adaptive radiation. Understanding the genetics of environmental adaptation is central to understanding how organisms colonise and subsequently diversify in new habitats. We used time-resolved transcriptomics to contrast the hyposalinity stress responses of two diatoms. Skeletonema marinoi has deep marine ancestry but has recently invaded brackish waters. Cyclotella cryptica has deep freshwater ancestry and can withstand a much broader salinity range. Skeletonema marinoi is less adept at mitigating even mild …

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The Santa Clara, 2024-10-31, Santa Clara University Oct 2024

The Santa Clara, 2024-10-31, Santa Clara University

The Santa Clara

No abstract provided.

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Side-Channel Analysis Platform For A Hardware Implementation Of Fips 203 (Crystals-Kyber), Mohamed Mossad Oct 2024

Side-Channel Analysis Platform For A Hardware Implementation Of Fips 203 (Crystals-Kyber), Mohamed Mossad

USF Tampa Graduate Theses and Dissertations

In 2024, NIST selected the Post-Quantum Cryptography algorithm CRYSTALS-Kyber for standardization as a public-key encryption, key establishment scheme. CRYSTALS-Kyber was standardized under the Federal Information Processing Standard (FIPS), specifically FIPS 203. FIPS standards represent a set of guidelines, developed by NIST, for secure data handling in federal information systems, mandating cryptographic algorithms that protect sensitive information. This highlights the importance of identifying potential vulnerabilities in the algorithm and assessing how CRYSTALS-Kyber implementations react to hardware side channel attacks. Previous research identified several vulnerabilities in implementations of CRYSTALS-Kyber in software, which were addressed in subsequent releases. This thesis focuses on expanding …

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Coloring Trivalent Graphs: A Defect Tft Approach, Amit Kumar Oct 2024

Coloring Trivalent Graphs: A Defect Tft Approach, Amit Kumar

LSU Doctoral Dissertations

We show that the combinatorial matter of graph coloring is, in fact, quantum in the sense of satisfying the sum over all the possible intermediate state properties of a path integral. In our case, the topological field theory (TFT) with defects gives meaning to it. This TFT has the property that when evaluated on a planar trivalent graph, it provides the number of Tait-Coloring of it. Defects can be considered as a generalization of groups. With the Klein-four group as a 1-defect condition, we reinterpret graph coloring as sections of a certain bundle, distinguishing a coloring (global-sections) from a coloring …

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Accumulation Of Road Salt In A Calcareous Fen: Kampoosa Bog, Western Massachusetts, Wayne Ndlovu, Andrew J. Guswa, Amy L. Rhodes Oct 2024

Accumulation Of Road Salt In A Calcareous Fen: Kampoosa Bog, Western Massachusetts, Wayne Ndlovu, Andrew J. Guswa, Amy L. Rhodes

Geosciences: Faculty Publications

Road salt poses a threat to the quality of soils and water resources. Wetlands located in salt contaminated areas are at risk of experiencing lower plant and animal species diversity. Therefore, it is critical to understand how modifications to salt application rates and hydrological events impact wetland water quality. Here, we use chloride mass flux, discharge, groundwater chloride concentration, meteorological, and salt application data from 2012– 2020 to estimate chloride accumulation and outflux rates in the Kampoosa Bog subwatersheds, located in Stockbridge and Lee, Massachusetts, and bordered by major highways (Interstate-90 and U.S. Route 7). We also investigate the correlation …

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Cp Decomposition Initialization Schemes For Speeding-Up Of Convolutional Neural Networks, Mollee M. Swift Oct 2024

Cp Decomposition Initialization Schemes For Speeding-Up Of Convolutional Neural Networks, Mollee M. Swift

LSU Master's Theses

While machine learning and convolutional neural networks (CNNs) are making strides, a persistent effort remains to optimize classification techniques and target redundancies from a large number of parameters naturally present in CNNs. While CNNs have become more accessible across machine learning, the aim is to make their use optimal for central processing unit (CPU) schemes with varying degrees of computational power. Tensor decomposition methods, specifically canonical polyadic (CP) decomposition, look to reduce parameters by compressing specific layers in a CNN. However, they have certain inconsistencies, and their full potential remains untouched as decomposition research is endless, with many facets one …

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Improving Rice Yield Using Sensor Based Nitrogen Application And Best Management Practices (Bmps) In Rice-Soybean And Rice-Crawfish Rotational Systems, Charles Darnall Oct 2024

Improving Rice Yield Using Sensor Based Nitrogen Application And Best Management Practices (Bmps) In Rice-Soybean And Rice-Crawfish Rotational Systems, Charles Darnall

LSU Master's Theses

Sustainable rice farming requires effective nutrient management and conservation practices to maintain high yields and minimize environmental impact. Implementing best management practices (BMPs) promotes lower fertilizer application rates, increased N use efficiency, improves soil health, and water quality. Demonstration fields established in two different rotational cropping systems (rice-soybean and rice-crawfish) in Kaplan, LA in 2023. In each rotation system, the benefits of BMPs (sensor based mid-season N management and cover cropping) compared to farmer’s practice (FP) were evaluated using yield, yield components, soil and water quality as metrics. The rice-soybean fields in 2023 and 2024 were both planted with PVL03 …

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A Large Scale Multi Institutional Study For Radiomics Driven Machine Learning For Meningioma Grading, Mert Karabacak, Shiv Patil, Rui Feng, Raj K. Shrivastava, Konstantinos Margetis Oct 2024

A Large Scale Multi Institutional Study For Radiomics Driven Machine Learning For Meningioma Grading, Mert Karabacak, Shiv Patil, Rui Feng, Raj K. Shrivastava, Konstantinos Margetis

Department of Medicine Faculty Papers

This study aims to develop and evaluate radiomics-based machine learning (ML) models for predicting meningioma grades using multiparametric magnetic resonance imaging (MRI). The study utilized the BraTS-MEN dataset's training split, including 698 patients (524 with grade 1 and 174 with grade 2-3 meningiomas). We extracted 4872 radiomic features from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. LASSO regression reduced features to 176. The data was split into training (60%), validation (20%), and test (20%) sets. Five ML algorithms (TabPFN, XGBoost, LightGBM, CatBoost, and Random Forest) were employed to build models differentiating low-grade (grade 1) from high-grade …

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2024 October 31 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University Oct 2024

2024 October 31 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

No abstract provided.

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