Early Lane Change Prediction For Mixed Traffic With V2x Communication,
2025
Technological University Dublin
Early Lane Change Prediction For Mixed Traffic With V2x Communication, Muhammed Fatih Koc, Nouman Ashraf, Pramod Pathak, Sachin Sharma
SAML-25 Workshop on Statistical and Machine Learning
Lane change prediction is essential for ensuring road safety and effective decision-making in autonomous vehicles (AVs). AVs will probably take several decades to penetrate new vehicle sales. As AVs and human-driven vehicles (HDVs) will coexist in traffic for the long term, AVs must understand the lane change intentions of surrounding HDVs. Lane changing is a critical manoeuvre that can cause a crash if it is performed late or if incorrect lane adjustments are made. Therefore, forecasting surrounding vehicles’ lane change intentions in advance is essential to ensure safe driving in mixed traffic environments having both AVs and HDVs. The unpredictability …
Interpretable Ai In Education: A Comparison Of Glass-Box Models For Predicting Student Success,
2025
Hogeschool Utrecht
Interpretable Ai In Education: A Comparison Of Glass-Box Models For Predicting Student Success, Jan Glazenborg
SAML-25 Workshop on Statistical and Machine Learning
This Master’s thesis addresses early identification of first-year Computer Science students at risk of underperformance by comparing inherently interpretable (“glass-box”) predictive models with the existing Naïve Bayes–based PreSS tool. The PreSS dataset was originally compiled by Quille & Bergin from 692 first-year CS1 students across eleven institutions in Ireland and Denmark, who completed surveys on programming and mathematics backgrounds, gaming habits and a short programming test four to six hours into the course. Seventeen normalized features capturing demographic, academic and behavioural factors were extracted. In this thesis, four machine learning models are evaluated: Naïve Bayes, explainable boosting machines, automatic piecewise …
Pros & Cons Of Reinforcement Learning - Illustrated By The Problem Of Controlling Gantry Robots,
2025
Darmstadt University of Applied Sciences
Pros & Cons Of Reinforcement Learning - Illustrated By The Problem Of Controlling Gantry Robots, Horst Zisgen
SAML-25 Workshop on Statistical and Machine Learning
In this talk a solution for the dynamic scheduling of flexible flow shop systems using gantry robots for material handling by means of simulation and Reinforcement Learning (RL) is presented. Subsequently the pros and cons of a RL approach are briefly discussed and illustrated at the robot control problem.
Survival Predictions From Classification Algorithms – Concepts And Application To Graft And Patient Survival After Kidney Transplantation,
2025
Darmstadt University of Applied Sciences
Survival Predictions From Classification Algorithms – Concepts And Application To Graft And Patient Survival After Kidney Transplantation, Antje Jahn
SAML-25 Workshop on Statistical and Machine Learning
Clinical prediction models are developed to predict long-term patient outcomes following medical interventions. One example motivating this research is the prediction of graft and patient survival after kidney transplantation, using data from the German organ transplantation registry. A practical issue in this context is to deal with incomplete information due to right-censoring, which arises when patients are lost to follow-up or enter the study at different times, resulting in varying durations of observation. This is particularly relevant in the registry data, where follow-up is frequently incomplete or irregular. While traditional survival analysis methods handle censoring by modeling the hazard function, …
Pathology’S Place In Understanding The Bias And Inequalities In Women’S Healthcare,
2025
Technological University Dublin
Pathology’S Place In Understanding The Bias And Inequalities In Women’S Healthcare, Andrea Heaney, Eugene Hickey, Emma Murphy
SAML-25 Workshop on Statistical and Machine Learning
Women’s healthcare is a complex, multifaceted issue with both historic and implicit biases, along with biological differences between men and women. With the advancement of AI tools in healthcare and the potential for biased data to create biased models, it is vital to consider how women are represented in data. Previously conducted semi-structured semantic interviews with clinicians were analysed via Braun and Clark’s method of thematic analysis. The analysis of these interviews yielded the following themes: Gender Influencing Health, Pregnancy, Social Factors, General Health, Treatment, Training, and Research. These themes highlight that context is key to understanding the biases in …
Impact Of Spatial Diversity And Subject Variability On Wifi-Based Human Activity Recognition,
2025
Technological University Dublin
Impact Of Spatial Diversity And Subject Variability On Wifi-Based Human Activity Recognition, Amany Elkelany, Robert J. Ross, Susan Mckeever
SAML-25 Workshop on Statistical and Machine Learning
In recent years, WiFi-based Human Activity Recognition (HAR) has gained substantial attention due to the ubiquity of WiFi infrastructure and advancements in wireless communication. Unlike camera-based systems that raise privacy concerns or wearable sensors that require user compliance, WiFi-based HAR provides a noninvasive and practical alternative that operates seamlessly with existing infrastructure. WiFi-based HAR leverages fluctuations in wireless signals, particularly Channel State Information (CSI), to passively detect and classify human activities. WiFi-based HAR models often achieve high accuracy in a single environment but suffer significant performance drops when applied to new environments due to variations in spatial settings, human movement, …
Optimising Ai For Chemical Imaging: Benchmarking Performance Against Foundation Models For Task-Specific Applications In Histopathology,
2025
TU Dublin
Optimising Ai For Chemical Imaging: Benchmarking Performance Against Foundation Models For Task-Specific Applications In Histopathology, Rahul Suresh, Mohd Rifqi Rafsanjani, Karin Jirstrom, Arman Rahman, William M. Gallagher, Aidan Meade
SAML-25 Workshop on Statistical and Machine Learning
The integration of chemical imaging with artificial intelligence presents a compelling route toward fully digital, label-free histopathology, yet it also introduces notable challenges. While deep learning models from domains like machine vision, digital pathology, and remote sensing are readily accessible, they frequently struggle to generalize effectively to chemical imaging data, as highlighted in recent research [1]. Additionally, although foundational pathological models show potential for advancing AI-based histopathological diagnostics and prognostics, our preliminary assessments suggest they may fall short in addressing the broad spectrum of classification tasks encountered in clinical settings. In this presentation, we highlight some recent published work from …
Automated Approaches To Interpreting And Explaining Machine Learning Models,
2025
[email protected]
Automated Approaches To Interpreting And Explaining Machine Learning Models, Tilak Chandrashekar, Tarry Singh, Maged Shaban
SAML-25 Workshop on Statistical and Machine Learning
This paper proposes a novel framework for automating ML model interpretation and explainability across different applications with an emphasis on transparency, trust, and human-centric decisionmaking assistance. Although ML models, particularly advanced structures such as deep neural networks, possess superior predictive powers, their interpretability tends to be obscure, and thus their application in sensitive or regulated domains is impeded. Current XAI techniques, though promising, tend to be post-hoc and are not scalable for real-time or large-scale deployments. This study addresses such concerns by presenting an automated, modular pipeline where interpretation techniques are embedded in the process of developing the ML model. …
Tracking The Kinetics Cellular Glycolysis And Glutaminolysis Pathways Using Vibrational Spectroscopy, Combined With Multivariate Statistical And Machine Learning Approaches For Data Mining,
2025
Technological University Dublin
Tracking The Kinetics Cellular Glycolysis And Glutaminolysis Pathways Using Vibrational Spectroscopy, Combined With Multivariate Statistical And Machine Learning Approaches For Data Mining, Zohreh Mirveis, Nithin Patil, Hugh Byrne
SAML-25 Workshop on Statistical and Machine Learning
Understanding dynamic metabolic processes within living cells is crucial for gaining insights into cellular function and disease mechanisms. The kinetics of glycolysis and glutaminolysis pathways play significant roles, as alterations in their activity have been linked to various disorders, including cancer and mental health conditions such as bipolar disorder. These pathways therefore hold potential as biomarkers for disease diagnosis and therapy. However, real-time monitoring of their kinetics remains challenging due to the lack of suitable non-invasive techniques. Current gold-standard fluxomics approaches, such as mass spectrometry, are destructive to cells and thus unsuitable for time-resolved studies. In this study, we evaluate …
Benchmarking Energy And Performance Of Parallel Machine Learning Models Using Hardware And Software Power Meters,
2025
Technological University Dublin
Benchmarking Energy And Performance Of Parallel Machine Learning Models Using Hardware And Software Power Meters, Urooj Asgher, Tania Malik
SAML-25 Workshop on Statistical and Machine Learning
The growing reliance on machine learning algorithms across domains such as healthcare, transportation, and finance has led to their increased deployment on high-performance computing platforms. While performance optimization remains a central concern, energy efficiency is emerging as a critical design consideration, particularly in light of global sustainability goals. This study presents a comparative analysis of the energy consumption and performance of serial and parallel implementations of four machine learning algorithms, K-means clustering, Ant Colony Optimization, Logistic Regression, and Random Search. Experiments were conducted on an HPC testbed using both hardware-based and software-based power meters to measure energy consumption. The results …
Partitioning Around Medoids On Product Spaces: A Clustering Approach For Cylindrical Data,
2025
Universit´e de Carthage
Partitioning Around Medoids On Product Spaces: A Clustering Approach For Cylindrical Data, Yahia Hammami, Houyem Demni, Amor Messaoud, Giovanni C. Porzio
SAML-25 Workshop on Statistical and Machine Learning
Clustering is a common unsupervised task in data analysis and machine learning. It deals with finding clusters of objects that are characterized by the highest similarity within the same cluster and the highest dissimilarity between different clusters. One of the most used algorithms in clustering is the popular Partitioning Around Medoids (PAM), also known as k-medoids [4, 5]. The algorithm imposes the center of clusters to be some of the data points, and it looks for a minimal value of the sum of the dissimilarity to all the objects. One of the recognized properties of such a method is its …
Improving Node Classification For Graphs Withweak Feature Signals: A Similarity-Entropy Aggregation Approach,
2025
University of Cassino and Southern Lazio
Improving Node Classification For Graphs Withweak Feature Signals: A Similarity-Entropy Aggregation Approach, Brian Daniel Bernhardt, Chiara Marciano, Mario Rosario Guarracino
SAML-25 Workshop on Statistical and Machine Learning
Graph Neural Networks (GNNs) have established themselves as powerful tools for graph-structured data. However, when feature separability among nodes is low, conventional neighborhood aggregation strategies often result in performance degradation due to over-smoothing and noisy information propagation. In this work, we introduce a novel GNN framework that refines the aggregation process by integrating feature similarity and neighborhood entropy into node message passing. Unlike standard models that uniformly aggregate neighbor information, this new model dynamically adjusts neighbor influence, prioritizing nodes with high similarity and low entropy. We evaluate the model on synthetic graphs generated using the Stochastic Block Model (SBM), varying …
Dealing With Large Data Sets: The Data Nugget Subset Selection Approach,
2025
University of Cassino and Southern Lazio
Dealing With Large Data Sets: The Data Nugget Subset Selection Approach, Vipin Kumar, Simona Balzano, Giovanni C. Porzio
SAML-25 Workshop on Statistical and Machine Learning
Analysing big data has always been a major issue because its massive volume poses significant challenges for traditional analytical techniques. When the number of instances is extremely large, existing approaches become computationally infeasible due to the complexity of many algorithms, along with memory and time constraints inherent in processing large datasets. In such cases, using a subset of the data is considered a more practical solution, and analyses are typically performed over a simple random sample drawn from the entire dataset. Various subsampling methods have been proposed to address these issues. However, they often fall short in producing representative subsamples …
A Statistical Approach To Portfolio Optimization Using Copula-Garch Models For European Investments,
2025
Institute of Applied Mathematics, Riga Technical University
A Statistical Approach To Portfolio Optimization Using Copula-Garch Models For European Investments, Jegors Fjodorovs
SAML-25 Workshop on Statistical and Machine Learning
This study explores portfolio optimization using copula functions and GARCH models, focusing on the European stock market. Traditional mean-variance methods often miss dynamic dependencies and tail risks. By applying copula-GARCH models—particularly the Student’s t copula with eGARCH—we better capture volatility asymmetries and tail dependencies. Conditional Value at Risk (CVaR) is used to evaluate downside risk across 10,000 simulated portfolios using high-performance computing. Results show that copula- GARCH models, especially eGARCH, consistently outperform traditional methods in risk-adjusted returns, offering improved risk management.
Fixed Points In Linear Regression,
2025
Rochester Institute of Technology
Fixed Points In Linear Regression, David L. Farnsworth
Articles
There is a set of points in the plane whose elements correspond to the observations that are used to generate a simple least-squares regression line. Each value of the independent variable in the observations matches up with one of these points, which are called pivot or fixed points. The coordinates of the fixed points are derived, and the properties of the points are explored. All points in the plane that yield each of the fixed points are found. The role that fixed points play in regression diagnostics is investigated. A new mechanical device that uses linkages to model the role …
Ceno: Non-Uniform, Segment And Parallel Zero-Knowledge Virtual Machine,
2025
Missouri University of Science and Technology
Ceno: Non-Uniform, Segment And Parallel Zero-Knowledge Virtual Machine, Tianyi Liu, Zhenfei Zhang, Yuncong Zhang, Wenqing Hu, Ye Zhang
Mathematics and Statistics Faculty Research & Creative Works
In this paper, we explore a novel Zero-knowledge Virtual Machine (zkVM) framework leveraging succinct, non-interactive zero-knowledge proofs for verifiable computation over any code. Our approach divides the proof of program execution into two stages. In the first stage, the process breaks down program execution into segments, identifying and grouping identical sections. These segments are then proved through data-parallel circuits that allow for varying amounts of duplication. In the subsequent stage, the verifier examines these segment proofs, reconstructing the program's control and data flow based on the segments' duplication number and the original program. The second stage can be further attested …
(R2119) New Algorithms For Independent Component Analysis Based On A General Class Of Dependence Criteria,
2025
Yazd University
(R2119) New Algorithms For Independent Component Analysis Based On A General Class Of Dependence Criteria, Fatemeh Asadi, Hamzeh Torabi, Hossein Nadeb
Applications and Applied Mathematics: An International Journal (AAM)
The objective function of numerous well-established Independent Component Analysis (ICA) algorithms calculate based on specific dependence criteria. This study introduces a distinctive dependence criterion based on the cumulative distribution function (CDF) for characterizing the independence between two random variables and some of its properties are examined. Then, we propose a class of ICA algorithms based on the introduced dependence criterion. The performance of the algorithm is systematically compared to some previous similar algorithms. The results indicate that the suggested algorithm have fruitful performance rather than some similar previous known algorithms. Subsequently, the proposed algorithms are applied to real-time series data, …
"Maine Office Of Behavioral Health’S Section 17 Self-Direction Pilot Program: Executive Summary",
2025
University of Southern Maine, Catherine Cutler Institute
"Maine Office Of Behavioral Health’S Section 17 Self-Direction Pilot Program: Executive Summary", Rachel M. Gallo Mph, Sarah Goan, Emma Schwartz
Publications
The Self-Directed Care (SDC) pilot program in Maine tested a service delivery model that allows individuals with mental health needs to manage a personal budget, supported by a trained broker to purchase goods and services that best support their recovery goals. Funded through the American Rescue Plan Act, the Maine Office of Behavioral Health implemented the nine-month pilot in three counties, partnering with Alpha One, Maine’s Center for Independent Living, to provide reloadable debit cards for participant purchases. The program aimed to promote autonomy, satisfaction, and stability among adults receiving Section 17 Medicaid services while informing decisions about the model’s …
"Maine Office Of Behavioral Health’S Section 17 Self-Direction Pilot Program: Evaluation Report",
2025
University of Southern Maine, Catherine Cutler Institute
"Maine Office Of Behavioral Health’S Section 17 Self-Direction Pilot Program: Evaluation Report", Rachel M. Gallo Mph, Sarah Goan, Emma Schwartz, Aaron Rose
Publications
Maine’s Office of Behavioral Health conducted a nine-month pilot of Self-Directed Care to support adults with serious mental illness in Cumberland, Hancock, and Washington Counties. The program allowed eligible participants receiving MaineCare Section 17 services to manage a personal budget, guided by trained Support Brokers from Alpha One, to purchase goods and services that would advance their recovery goals. The pilot aimed to increase participant choice, autonomy, and flexibility in managing their mental health needs. Support Brokers worked closely with participants and Case Managers to develop and approve individualized purchase plans, monitor expenditures, and ensure alignment with treatment objectives. A …
Statistical Investigations Of Strategies In The Game Ecosystem,
2025
California Polytechnic State University, San Luis Obispo
Statistical Investigations Of Strategies In The Game Ecosystem, Dylan Li
Master's Theses
This work provides a probability-based analysis of strategies in the board game Ecosystem. Ecosystem is a turn-based multiplayer tiling game, where players take turns picking a wildlife card from a limited pool of cards then placing that card on their personal 4x5 grid. The objective of the game is to place the wildlife cards to maximize your score, as each card’s scoring condition depends on the presence or absence of certain cards surrounding it. The goal of this project is to determine optimal strategies for tiling your grid using techniques such as simulation to find optimal grid arrangements and clustering …
