Tootfinder

Bears' Nahshon Wright pays tribute to late coach John Beam with interception: 'That one was for him'

https://www.cbssports.com/nfl/news/bears-nahshon-wright-pays…

Personalized and Constructive Feedback for Computer Science Students Using the Large Language Model (LLM)
Javed Ali Khan, Muhammad Yaqoob, Mamoona Tasadduq, Hafsa Shareef Dar, Aitezaz Ahsan
https://arxiv.org/abs/2510.11556

Personalized and Constructive Feedback for Computer Science Students Using the Large Language Model (LLM)
The evolving pedagogy paradigms are leading toward educational transformations. One fundamental aspect of effective learning is relevant, immediate, and constructive feedback to students. Providing constructive feedback to large cohorts in academia is an ongoing challenge. Therefore, academics are moving towards automated assessment to provide immediate feedback. However, current approaches are often limited in scope, offering simplistic responses that do not provide students with personalized …

Single layer tiny Co$^4$ outpaces GPT-2 and GPT-BERT
Noor Ul Zain, Mohsin Raza, Ahsan Adeel
https://arxiv.org/abs/2510.08404 https://arxiv.org/pdf/2510.084…

Single layer tiny Co$^4$ outpaces GPT-2 and GPT-BERT
We show that a tiny Co$^4$ machine(Adeel,2025) with a single layer, two heads, and 8M parameters, operating at an approximate cost of $O(N)$ (where $N$ is the number of input tokens), outpaces the BabyLM Challenge baselines GPT-2 (124M, 12 layers, $O(N^2))$ and GPT-BERT (30M, 12 layers, $O(N^2))$ in just two epochs, while both are trained for ten. Co$^4$ achieves orders-of-magnitude greater training efficiency on 10M tokens, demonstrating highly sample efficient pretraining. Using the BabyLM ch…

Replaced article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[1/1]:
- Machine Learning-Driven Insights into Excitonic Effects in 2D Materials
Ahsan Javed, Sajid Ali

Ahshiselsaurus wimani - New duck-billed dinosaur species identified in northwest #NewMexico #Paleontology

InsideOut: An EfficientNetV2-S Based Deep Learning Framework for Robust Multi-Class Facial Emotion Recognition
Ahsan Farabi, Israt Khandaker, Ibrahim Khalil Shanto, Md Abdul Ahad Minhaz, Tanisha Zaman
https://arxiv.org/abs/2510.03066

InsideOut: An EfficientNetV2-S Based Deep Learning Framework for Robust Multi-Class Facial Emotion Recognition
Facial Emotion Recognition (FER) is a key task in affective computing, enabling applications in human-computer interaction, e-learning, healthcare, and safety systems. Despite advances in deep learning, FER remains challenging due to occlusions, illumination and pose variations, subtle intra-class differences, and dataset imbalance that hinders recognition of minority emotions. We present InsideOut, a reproducible FER framework built on EfficientNetV2-S with transfer learning, strong data augme…

Engineering High-Order Harmonic Generation through Gas Confinement at Sub-Millimeter Lengths
Agata Azzolin, Gaia Giovannetti, Oliviero Cannelli, Sabine Rockenstein, Guangyu Fan, Md S. Ahsan, Lorenzo Colaizzi, Erik P. M{\aa}nsson, Noah Tettenborn, Linda Oberti, Davide Faccial\`a, Fabio Frassetto, Anna Gabriella Ciriolo, Dario W. Lodi, Alia Ashraf, Cristian Manzoni, Rebeca Mart\'inez V\'azquez, Michele Devetta, Roberto Osellame, Luca Poletto, Salvatore Stagira, Caterina Vozzi, Te…

Engineering High-Order Harmonic Generation through Gas Confinement at Sub-Millimeter Lengths
Attosecond light sources based on high-order harmonic generation (HHG) constitute to date the only table-top solution for producing coherent broadband radiation covering the spectral range from the extreme ultraviolet to the soft X-rays. The so-called emission cutoff can be extended towards higher photon energies by increasing the driving wavelength at the expense of conversion efficiency. An alternative route is to overdrive the process by using higher laser intensities, with the challenging r…

Next-Generation Event-Driven Architectures: Performance, Scalability, and Intelligent Orchestration Across Messaging Frameworks
Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel, Ahsan Habib Tareq
https://arxiv.org/abs/2510.04404

Next-Generation Event-Driven Architectures: Performance, Scalability, and Intelligent Orchestration Across Messaging Frameworks
Modern distributed systems demand low-latency, fault-tolerant event processing that exceeds traditional messaging architecture limits. While frameworks including Apache Kafka, RabbitMQ, Apache Pulsar, NATS JetStream, and serverless event buses have matured significantly, no unified comparative study evaluates them holistically under standardized conditions. This paper presents the first comprehensive benchmarking framework evaluating 12 messaging systems across three representative workloads: e…

Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI
Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel, Ahsan Habib Tareq, Iftekhar Haider
https://arxiv.org/abs/2510.06259

Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI
Medical AI faces challenges in privacy-preserving collaborative learning while ensuring fairness across heterogeneous healthcare institutions. Current federated learning approaches suffer from static architectures, slow convergence (45-73 rounds), fairness gaps marginalizing smaller institutions, and scalability constraints (15-client limit). We propose Adaptive Fair Federated Learning (AFFL) through three innovations: (1) Adaptive Knowledge Messengers dynamically scaling capacity based on hete…

Constraint Satisfaction Approaches to Wordle: Novel Heuristics and Cross-Lexicon Validation
Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel, Kamrujjaman, Eftakhar Ahmed Arnob, Ahsan Habib Tareq
https://arxiv.org/abs/2510.02855

Constraint Satisfaction Approaches to Wordle: Novel Heuristics and Cross-Lexicon Validation
Wordle presents an algorithmically rich testbed for constraint satisfaction problem (CSP) solving. While existing solvers rely on information-theoretic entropy maximization or frequency-based heuristics without formal constraint treatment, we present the first comprehensive CSP formulation of Wordle with novel constraint-aware solving strategies. We introduce CSP-Aware Entropy, computing information gain after constraint propagation rather than on raw candidate sets, and a Probabilistic CSP fra…