10 citations · 17 across the 17 of their papers we have counts for
18 papers
DCM-SAM: Defect-Conditioned Mixture of LoRA Experts for NPU-Deployed AM Defect Segmentation
Md Mushfiqur Rahaman, Md Mahedi Hasan, Imtiaz Ahmed +1
Metal additive manufacturing parts are inspected by X-ray computed tomography, where labelled data is scarce, the pores and inclusions that matter span a few pixels, and inspection…
Unsupervised Keypoints for Real-Time Fall Detection: Comparative Analysis Under Real-world Conditions with Predictive Bandwidth Reduction
Tasmiah Haque, Jacob Kosinski, Sumit Mohan +2
Falls among older adults are a major safety and health-systems challenge, yet continuous in-person monitoring is difficult to sustain across home and clinical care settings. Video-…
XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation
Md Mahedi Hasan, Md Mushfiqur Rahaman, Alan Pachkovskiy +3
Defect segmentation in additive manufacturing (AM) X-ray computed tomography (XCT) images remains challenging due to severe class imbalance and large distribution shifts across sca…
MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs
Shubhadip Nag, Srinjoy Das, Agniva Saha +5
LLMs can be conveniently adapted to a diverse set of tasks, e.g, prediction, question-answering tasks, etc, using appropriate prompts with few-shot examples. Biased or harmful conc…
Multimodal Transformer Based Generic Mixture Density Network for Scattering Timescale Estimation of Fast Radio Bursts
Bikash Kharel, Emmanuel Fonseca, Srinjoy Das +5
The discovery rate of fast radio bursts (FRBs) continues to increase with the advent of new radio facilities and yet extracting their astrophysical parameters such as scattering ti…
Inference-time Stochastic Refinement of GRU-Normalizing Flow for Real-time Video Motion Transfer
Tasmiah Haque, Srinjoy Das
Real-time video motion transfer applications such as immersive gaming and vision-based anomaly detection require accurate yet diverse future predictions to support realistic synthe…
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness
Md Mushfiqur Rahaman, Elliot Chang, Tasmiah Haque +1
Text classification plays a pivotal role in edge computing applications like industrial monitoring, health diagnostics, and smart assistants, where low latency and high accuracy ar…
Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach
Daniel Campa, Mehdi Saeedi, Ian Colbert +1
Navigation path traces play a crucial role in video game design, serving as a vital resource for both enhancing player engagement and fine-tuning non-playable character behavior. G…
GIF: Generative Inspiration for Face Recognition at Scale
Saeed Ebrahimi, Sahar Rahimi, Ali Dabouei +3
Aiming to reduce the computational cost of Softmax in massive label space of Face Recognition (FR) benchmarks, recent studies estimate the output using a subset of identities. Alth…
Towards Efficient Real-Time Video Motion Transfer via Generative Time Series Modeling
Tasmiah Haque, Md. Asif Bin Syed, Byungheon Jeong +5
Motion Transfer is a technique that synthesizes videos by transferring motion dynamics from a driving video to a source image. In this work we propose a deep learning-based framewo…
LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attention
Hamed Khosravi, Mohammad Reza Shafie, Ahmed Shoyeb Raihan +2
Existing contextual multi-armed bandit (MAB) algorithms fail to effectively capture both long-term trends and local patterns across all arms, leading to suboptimal performance in e…
A Data-Efficient Sequential Learning Framework for Melt Pool Defect Classification in Laser Powder Bed Fusion
Ahmed Shoyeb Raihan, Austin Harper, Israt Zarin Era +4
Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects…
In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers
Israt Zarin Era, Fan Zhou, Ahmed Shoyeb Raihan +5
Directed Energy Deposition (DED) offers significant potential for manufacturing complex and multi-material parts. However, internal defects such as porosity and cracks can compromi…
Enhancing Bandwidth Efficiency for Video Motion Transfer Applications using Deep Learning Based Keypoint Prediction
Xue Bai, Tasmiah Haque, Sumit Mohan +4
We propose a deep learning based novel prediction framework for enhanced bandwidth reduction in motion transfer enabled video applications such as video conferencing, virtual reali…
Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease
Hamed Khosravi, Milad Khanchi, Mobina Noori +3
Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the performance of machine learning models for…
An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything
Israt Zarin Era, Imtiaz Ahmed, Zhichao Liu +1
Foundation models are currently driving a paradigm shift in computer vision tasks for various fields including biology, astronomy, and robotics among others, leveraging user-genera…
Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization
Ahmed Shoyeb Raihan, Hamed Khosravi, Srinjoy Das +1
Advancements in materials play a crucial role in technological progress. However, the process of discovering and developing materials with desired properties is often impeded by su…
Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production
Hamed Khosravi, Sarah Farhadpour, Manikanta Grandhi +3
A significant challenge for predictive maintenance in the pulp-and-paper industry is the infrequency of paper breaks during the production process. In this article, operational dat…