The Causal Impact of Dean’s List Recognition on Academic Performance: Evidence from a Regression Discontinuity Design

This study examines the causal impact of being placed on the Dean’s List, a positive education incentive, on future student performance using a regression discontinuity design. The results suggest that for students with low prior academic performance and who are native English speakers, there is a positive impact of being on the Dean’s List on the probability of getting onto the Dean’s List in the following year. However, being on the Dean’s List does not appear to have a statistically significant effect on subsequent GPA, total credits taken, dropout rates, or the probability of graduating within four years. These findings suggest that a place on the Dean’s List may not be a strong motivator for students to improve their academic performance and achieve better outcomes

GarmentIQ: Automated Garment Measurement for Fashion Retail

Online fashion retail has revolutionized shopping but faces persistent sizing issues, driving return rates above 25%, costing fashion retailers billions of dollar annually, and increasing textile waste and carbon emissions. We present GarmentIQ, an end-to-end computer vision system combining garment classification, high-resolution segmentation, and landmark detection for precise, template-free measurements across nine categories. An interactive web interface lets users define custom measurement points and export structured JSON and PDF instructions. We used a 23,266-image dataset from Nordstrom and Myntra, and our tinyViT classifier achieves 95.76% accuracy, demonstrating superior generalization after fine-tuning on Zara data. BiRefNet produces high-quality segmentation, and HRNet-based landmark extraction attains high precision, with customized landmark derivation. GarmentIQ\u27s modular, user-friendly design streamlines workflows, reduces returns, and promotes sustainability, laying the groundwork for future automated fashion analysis

GarmentIQ: Automated Garment Measurement for Fashion Retail

Online fashion retail has revolutionized shopping but faces persistent sizing issues, driving return rates above 25%, costing fashion retailers billions of dollar annually, and increasing textile waste and carbon emissions. We present GarmentIQ, an end-to-end computer vision system combining garment classification, high-resolution segmentation, and landmark detection for precise, template-free measurements across nine categories. An interactive web interface lets users define custom measurement points and export structured JSON and PDF instructions. We used a 23,266-image dataset from Nordstrom and Myntra, and our tinyViT classifier achieves 95.76% accuracy, demonstrating superior generalization after fine-tuning on Zara data. BiRefNet produces high-quality segmentation, and HRNet-based landmark extraction attains high precision, with customized landmark derivation. GarmentIQ\u27s modular, user-friendly design streamlines workflows, reduces returns, and promotes sustainability, laying the groundwork for future automated fashion analysis

Time Series of Analysis Annual Temperature Anomalies (1850–2021) for the Northern Hemisphere

Today, climate change is one of the most substantial issues in the world. Therefore, annual temperature anomalies are the problem we are concerned about, since anomalies are how we see how the climate changes. Our data set is about the annual temperature anomalies (1850–2021) for the northern hemisphere [1]. A temperature anomaly means a deviation from a reference value or long-term average. The data set contains two columns which are Year and Temperature Anomalies. Based on our knowledge of time series, we want to predict the trend of Temperature Anomalies in the future. As we know, a sequence captured at successive, equally spaced points in time is referred to as a time series. Since Temperature Anomalies are taken over time, which is Years, it is a time series. Because of prior climatic history and its effects on humans, we believe that it is vital to analyze and forecast future anomalies. To better prepare and safeguard the environment, it is crucial to anticipate what these potential future anomalies might be. People can then prepare to mitigate it based on how severe it is

Data Efficient Dense Cross-Lingual Information Retrieval

Cross-Lingual Information Retrieval (CIR) remains challenging due to limited annotated data and linguistic diversity, especially for low-resource languages. While dense retrieval models have significantly advanced retrieval performance, their reliance on large-scale training datasets hampers their effectiveness in multilingual settings. In this work, we propose two complementary strategies to improve data efficiency and robustness in CIR model fine- tuning. First, we introduce a paraphrase-based query augmentation pipeline leveraging large language models (LLMs) to enrich scarce training data, thereby promoting more robust and language-agnostic representations. Second, we present a weighted InfoNCE loss that emphasizes underrepresented languages, ensuring balanced optimization across heterogeneous linguistic inputs. Experiments on cross-lingual benchmark datasets demonstrate that our combined approaches yield substantial gains in retrieval quality, outperforming standard training protocols on small and imbalanced datasets. These results underscore the potential of targeted data augmentation and reweighted objectives to build more inclusive and effective CIR systems, even under resource constraints

Student Future Academic Performance after Being Placed on Dean’s List

This study examines the causal impact of being placed on the Dean\u27s List, a positive education incentive, on future student performance using a regression discontinuity design. The results suggest that for students with low prior academic performance and who are native English speakers, there is a positive impact of being on the Dean\u27s List on the probability of getting onto the Dean\u27s List in the following year. However, being on the Dean\u27s List does not appear to have a statistically significant effect on subsequent GPA, total credits taken, dropout rates, or the probability of graduating within four years. These findings suggest that a place on the Dean\u27s List may not be a strong motivator for students to improve their academic performance and achieve better outcomes

Predictive Modeling of Blood Pressure Categories: Integrating Demographic and Dietary Factors for Personalized Management

This study delves into predictive modeling of blood pressure levels, focusing on the United States, addressing the global health concern of hypertension. Mainly utilizing demographic and dietary data from the Centers for Disease Control and Prevention (CDC) National Health and Nutrition Examination Survey (NHANES) 2017-2018, aims to craft personalized management strategies. Drawing on research emphasizing the multifaceted determinants of hypertension, we leverage the multinomial regression model with lasso regularization as a baseline. Furthermore, the study advances to the eXtreme Gradient Boosting (XGBoost) algorithm, achieving a better performance than multinomial regression. Evaluation metrics include accuracy and Area Under the ROC Curve (ROC-AUC) in a 10-fold cross validation framework. The study provides possible personal blood pressure management solution

Curriculum Learning For Autonomous Vehicles

This study investigates how the sequence of training environments affects performance in simple driving tasks for an autonomous driving agent. By training agents solely through interaction with maps of varying difficulty, we demonstrate that transfer learning enhances performance within single-environment driving scenarios. However, we find that agents struggle to master advanced driving capabilities and fail to generalize well to new environments, regardless of the sequence of training data. We conclude by looking at areas to build on this work such by combining imitation learning with curriculum learning and developing curriculum-specific MDP

Predictive Modeling of H5N1 Bird Flu in United States of America: A 2022-2023 Analysis

This research uniquely focuses on predicting the likelihood of H5N1 outbreaks in the United States at the county level. Unlike previous studies, which either excluded the United States or used outdated data, we utilized diverse statistical techniques and publicly available H5N1-related data from January 2022 to March 2023. Employing logistic regression, regularization methods, cross-validation, and eXtreme Gradient Boosting (XGBoost), our models demonstrated remarkable predictive efficacy. Notably, the XGBoost model, trained with 10-fold cross-validation, outperformed others in terms of ROC-AUC. This research provides valuable epidemiological insights, proposes intervention strategies for H5N1 in the United States, and suggests future research directions

Unveiling Color Dynamics and Value of Andy Warhol's "Shot Marilyns": A Study on Visual Variations and Perception

This study investigates the visual dynamics and value of Andy Warhol’s "Shot Marilyns" series through the innovative application of statistical techniques, including entropy, K-Means clustering, and K-Nearest Neighbors, alongside traditional analytical methods. This approach provides a comprehensive analysis of color distribution, regional variation, and the overall value of the masterpiece. The results reveal significant inter-painting variations and uncover intricate color dependencies that challenge assumptions of uniformity. Furthermore, the restoration of the damaged region in "Blue Marilyn" highlights the complexity of Warhol\u27s color choices and contributes to the ongoing discussion about the potential value of the series. These findings offer new insights into Warhol’s aesthetic decisions, deepening our understanding of the role of color perception in contemporary art