arXiv subjects

Mahmudul Hasan

Publications and source records attributed to Mahmudul Hasan.

At least 19 recordsLinked to original sources

arXiv2026

Surface-mediated frequency aging beyond quality-factor saturation in an AlScN-on-silicon resonator

Vacuum package integrity in micro- and nanoelectromechanical resonators is commonly assessed through the quality factor Q, although Q probes residual-gas damping rather than the surface-state evolution that can govern frequency aging. Here, we disentangle the pressure responses of Q and the resonance frequency f0 in a 64.21 MHz Al0.7Sc0.3N-on-silicon cross-sectional Lam\'e-mode resonator between 0.01 to 760 Torr. Measurements at 25 {\deg}C and at the 68.8{\deg}C frequency turnover, where first-order thermal sensitivity is suppressed, reveal widely separated equilibration timescales. Following each pressure step, Q reaches a reversible, history-independent steady value on the pressure-control timescale and approaches a fitted pressure-independent ceiling of 8.5e4 below approximately 1 Torr. By contrast, f0 responds measurably down to 1e-5 Torr and remains history-dependent, relaxing for hours at fixed pressure. The transients follow stretched-exponential kinetics, consistent with a broad distribution of surface relaxation rates, and individual pressure steps produce fractional frequency shifts as large as 1.9 ppm. Ten-hour phase-locked measurements show a common short-term time-deviation floor near 1e-10 s from 0.01 to 100 Torr, whereas residual deterministic relaxation dominates at long averaging times; at 760 Torr, gas damping degrades short-term tracking through the reduced Q. These results establish gas-damping equilibrium and frequency equilibrium as distinct states. Quality-factor saturation alone is therefore insufficient to qualify vacuum packaging for precision mechanical frequency references; package specifications must also constrain surface-mediated frequency aging.

cond-mat.mes-hall↗

arXiv2026

GET: Generative Embedding Translation for Medical Image Segmentation

Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-to-mask translation must preserve target structure while remaining computationally efficient. We propose Generative Embedding Translation (GET), a structured embedding-translation framework that progressively transforms image embeddings into mask embeddings within the frozen latent space of a Stable Diffusion VAE. GET uses a U-Net-style Embedding Translation Network with 1.07M trainable parameters, combining Mobile Bottleneck Convolutions, Subsampled Self-Attention, and Multi-scale Feature Enrichment for local modeling, global context, and multi-scale refinement. Across five medical segmentation datasets, GET outperforms generative, CNN, and Transformer baselines. Compared with the strongest generative baseline, GMS, GET improves average Dice and IoU by 0.93% and 1.26%, reduces HD95 by 0.81 pixels, and uses 31.41% fewer trainable parameters. Under bidirectional BUS-BUSI domain shift, GET further improves Dice and IoU by 3.51% and 3.39%, while reducing HD95 by 27.37 pixels. Our code is available at: https://github.com/maklachur/GET.

eess.IV↗

arXiv2026

Costs of Arbitrary Real Matrix Factorizations for Pure-DP Continual Counting

Let $T_n$ be the lower-triangular prefix-sum matrix and let $c_{\mathrm{F}}(T_n)$ and $c_2(T_n)$ be the factorization costs that govern the mean and maximum per-coordinate squared error of the Laplace matrix mechanism under pure $\varepsilon$-differential privacy, for $\varepsilon>0$. We prove $c_{\mathrm{F}}(T_n),c_2(T_n)=\Theta((\log(n+1))^{3/2})$ with no sign, sparsity, or squareness restriction and with arbitrary finite inner dimension. Consequently, within the pure-$\varepsilon$-DP matrix-mechanism class, the optimized maximum and mean squared errors are both $\Theta(\varepsilon^{-2}\log^3(n+1))$. Under the factorization contract of Arkhipov and Kalinin (arXiv:2607.08963v1), who prove the matching lower order for factors with entries in $\{0,1\}$ and state the arbitrary-factor extension as open, the theorem below establishes the order for arbitrary real factors. The lower bound runs through a $p$-nuclear obstruction: an aggregate column-width estimate $D_k(T_n)\asymp n^{3/2}k^{-1/2}$, valid in the low-rank range $1\leq k\leq n/16$, for the prefix chain, fed into the classical approximation-space conversion of Pietsch and Hinrichs--Pietsch, becomes harmonic at the critical exponent $p=2/3$, and H\"older's inequality transfers it to both factorization costs. The same computation determines $\mathfrak{n}_p(T_n)$ for each fixed $0<p<1$: order $n$ below $2/3$, $n\log n$ at $2/3$, and $n^{3p/2}$ above. A Fenwick interval factorization supplies matching upper bounds. The claims are confined to pure-$\varepsilon$-DP Laplace matrix mechanisms and the two stated squared-error criteria; they do not cover non-matrix continual mechanisms, approximate-DP sensitivity, or expected maxima across coordinates.

cs.CR↗

arXiv2026

Coherence Under Commitment: Probing Generalization and Vacuous Memorization in LLM Logical Reasoning

Large language models (LLMs) deployed for logical reasoning in knowledge-intensive domains exhibit a subtle but critical failure: coherence can be vacuously achieved through systematic abstention. A model that withholds commitment to either entailment or refutation satisfies negation consistency while providing no utility. We introduce Coherence Under Commitment (CUC), a dual-query evaluation paradigm that jointly measures consistency and decisiveness. CUC contributes three innovations: (1) a commitment score $c(\varphi) = p(\varphi) + p(\lnot\varphi)$ quantifying probability mass allocated to decisive outcomes; (2) a \textbf{deterministic elicitation protocol} via normalized YES/NO log probabilities, eliminating sampling variance; and (3) a 3-way decision framework (True/False/Uncertain) operationalizing the coherence-commitment trade-off into metrics. Experiments on four open-weight LLMs (1B-3B) across 204 FOLIO examples expose a sharp frontier. Qwen2.5-3B achieves near-zero contradiction ($\mathbb{E}[v_{\mathrm{neg}}]{=}0.025$) but only $7.4\%$ coverage, while TinyLlama-1.1B reaches $79.4\%$ coverage with violations on every example. Coherence-only evaluation would rank the abstaining model first; CUC exposes this as vacuous, and the frontier generalizes to LogiQA~v2 ($\rho{=}0.97$). We argue that evaluation must report both coherence and non-vacuous commitment and release a toolkit for standardized assessment.

cs.AI↗

arXiv2026

Semantic Grading of Written Answers in Low-Resource Language Bangla Using a Fine-Tuned Lightweight Language Model

Bangla is among the world's most widely spoken languages, yet it remains underserved in educational NLP research. In many remote and rural regions, access to qualified subject teachers is limited, and written answers are consequently graded largely by hand, restricting timely and consistent feedback. Automatic assessment is challenging because semantically correct responses can vary substantially in surface form. We present a bilingual (Bangla-English) evaluation system designed for low-resource educational settings that prioritizes semantic correctness over lexical overlap. Our approach fine-tunes a lightweight language model to grade each response using the question, reference answer, and student answer, producing a numeric score and concise, context-grounded feedback suitable for classroom deployment. We also construct a synthetic bilingual dataset to enable controlled training and evaluation. Across proprietary and open-source LLMs evaluated under a unified protocol, our QLoRA-tuned Qwen3-8B confirms consistent improvement by producing the most leakage-resistant feedback (RoRa = 0.819) in synthetic evaluation and the strongest agreement with human scores (rho = 0.936, MAE = 0.725) in a dedicated human study.

cs.CL↗

arXiv2026

A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset

Accurate short-term forecasting of agricultural commodity prices is critical for food security planning and smallholder income stabilisation in developing economies, yet machine-learning-ready datasets for this purpose remain scarce in South Asia. This paper makes two contributions. First, we introduce AgriPriceBD, a benchmark dataset of 1,779 daily retail mid-prices for five Bangladeshi commodities - garlic, chickpea, green chilli, cucumber, and sweet pumpkin - spanning July 2020 to June 2025, extracted from government reports via an LLM-assisted digitisation pipeline. Second, we evaluate seven forecasting approaches spanning classical models - na\"{i}ve persistence, SARIMA, and Prophet - and deep learning architectures - BiLSTM, Transformer, Time2Vec-enhanced Transformer, and Informer - with Diebold-Mariano statistical significance tests. Commodity price forecastability is fundamentally heterogeneous: na\"{i}ve persistence dominates on near-random-walk commodities. Time2Vec temporal encoding provides no statistically significant advantage over fixed sinusoidal encoding and causes catastrophic degradation on green chilli (+146.1% MAE, p<0.001). Prophet fails systematically, attributable to discrete step-function price dynamics incompatible with its smooth decomposition assumptions. Informer produces erratic predictions (variance up to 50x ground-truth), confirming sparse-attention Transformers require substantially larger training sets than small agricultural datasets provide. All code, models, and data are released publicly to support replication and future forecasting research on agricultural commodity markets in Bangladesh and similar developing economies.

cs.LG↗

arXiv2026

COVERT: Trojan Detection in COTS Hardware via Statistical Activation of Microarchitectural Events

Commercial Off-The-Shelf (COTS) hardware, such as microprocessors, are widely adopted in system design due to their ability to reduce development time and cost compared to custom solutions. However, supply chain entities involved in the design and fabrication of COTS components are considered untrusted from the consumer's standpoint due to the potential insertion of hidden malicious logic or hardware Trojans (HTs). Existing solutions to detect Trojans are largely inapplicable for COTS components due to their black-box nature and lack of access to a golden model. A few studies that apply require expensive equipment, lack scalability, and apply to a limited class of Trojans. In this work, we present a novel golden-free trust verification framework, COVERT for COTS microprocessors, which can efficiently test the presence of hardware Trojan implants by identifying microarchitectural rare events and transferring activation knowledge from existing processor designs to trigger highly susceptible internal nodes. COVERT leverages Large Language Models to automatically generate test programs that trigger rare microarchitectural events, which may be exploited to develop Trojan trigger conditions. By deriving these events from publicly available Register Transfer Level implementations, COVERT can verify a wide variety of COTS microprocessors that inherit the same Instruction Set Architecture. We have evaluated the proposed framework on open-source RISC-V COTS microprocessors and demonstrated its effectiveness in activating combinational and sequential Trojan triggers with high coverage, highlighting the efficiency of the trust verification. By pruning rare microarchitectural events from mor1kx Cappuccino OpenRISC processor design, COVERT has been able to achieve more than 80% trigger coverage for the rarest 5% of events in or1k Marocchino and PicoRV32 as COTS processors.

cs.ET↗

arXiv2026

Evolving CNN Architectures: From Custom Designs to Deep Residual Models for Diverse Image Classification and Detection Tasks

This paper presents a comparative study of a custom convolutional neural network (CNN) architecture against widely used pretrained and transfer learning CNN models across five real-world image datasets. The datasets span binary classification, fine-grained multiclass recognition, and object detection scenarios. We analyze how architectural factors, such as network depth, residual connections, and feature extraction strategies, influence classification and localization performance. The results show that deeper CNN architectures provide substantial performance gains on fine-grained multiclass datasets, while lightweight pretrained and transfer learning models remain highly effective for simpler binary classification tasks. Additionally, we extend the proposed architecture to an object detection setting, demonstrating its adaptability in identifying unauthorized auto-rickshaws in real-world traffic scenes. Building upon a systematic analysis of custom CNN architectures alongside pretrained and transfer learning models, this study provides practical guidance for selecting suitable network designs based on task complexity and resource constraints.

cs.CV↗

arXiv2025

Time-domain measurement of Auger electron dynamics in xenon atoms after giant resonant photoionization

Time-resolved measurement of Auger-Meitner (AM) decay [Nature 419, 803 (2002)] marked a milestone in the development of attosecond science. To date, the time constants for the AM decay processes obtained from the time-domain experiments were found to be consistent with the values deduced from conventional energy-domain measurements. One of the main factors limiting the temporal resolution of these studies is the unlocked carrier-envelope-phase (CEP) of the laser pulses used to probe the electronic dynamics triggered by inner-shell photoabsorption. In this work, we report time-resolved inner-shell electron spectroscopy of xenon and krypton using attosecond soft X-ray (atto-SXR) pulses centered at 130 eV in combination with CEP-stabilized few-cycle Yb laser pulses. We observed that the N$_{4,5}$OO Auger electrons from xenon exhibit a clear streaking pattern, but with an unexpected time shift of $\sim$ 1.32 fs relative to the 4$d$ photoelectrons. Furthermore, the energy-integrated yield of streaked Auger electrons from xenon exhibits a pronounced minimum at a pump-probe time delay of 4 fs. Neither of these observations can be explained by current streaking theories and both are inconsistent with lifetimes inferred from energy-domain measurements. The M$_{4,5}$NN Auger electrons from krypton partly overlap in energy with the 3$d$ inner-shell photoelectrons and do not show these anomalous features. This study offers new insights into the inner-shell electron dynamics of heavy atoms in the giant dipole resonance region, laying the groundwork for attosecond soft X-ray spectroscopy of molecular systems containing iodine or bromine atoms.

physics.atom-ph↗

arXiv2025

The CHSH Game, Tsirelson's Bound, and Causal Locality

We reformulate the CHSH game in terms of indivisible stochastic processes. Using Barandes's stochastic-quantum correspondence and its associated definition of causal locality, we present a novel proof of the Tsirelson bound. In particular, we show that unlike the no-signaling principle alone, the postulates defining causally local, indivisible stochastic processes are precisely strong enough to allow for violations of the Bell inequality up to, but not beyond, the Tsirelson bound.

quant-ph↗

arXiv2025

GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer

Despite Bengali being the sixth most spoken language in the world, handwritten text recognition (HTR) systems for Bengali remain severely underdeveloped. The complexity of Bengali script--featuring conjuncts, diacritics, and highly variable handwriting styles--combined with a scarcity of annotated datasets makes this task particularly challenging. We present GraDeT-HTR, a resource-efficient Bengali handwritten text recognition system based on a Grapheme-aware Decoder-only Transformer architecture. To address the unique challenges of Bengali script, we augment the performance of a decoder-only transformer by integrating a grapheme-based tokenizer and demonstrate that it significantly improves recognition accuracy compared to conventional subword tokenizers. Our model is pretrained on large-scale synthetic data and fine-tuned on real human-annotated samples, achieving state-of-the-art performance on multiple benchmark datasets.

cs.CV↗

arXiv2025

Strong-Field Photoelectron Interferometry with Near-Single-Cycle Yb Lasers

The concept of using photoelectron interferometry in short laser fields to probe electron dynamics and target structures was introduced more than two decades ago. However, the quality of experimental data has remained insufficient for quantitative analysis, largely due to the instability of few-cycle Ti:Sa laser pulses, the current workhorse of short pulses. Here, we report the first systematic strong-field ionization experiments performed with industrial-grade, carrier-envelope-phase (CEP) stabilized, near-single-cycle Yb lasers. By measuring photoelectron momentum distributions in the direct-ionization regime, we show that single-cycle cosine-shaped pulses can separate and enhance both spider-leg and fishbone holographic structures. The spider-leg structure enables extraction of the electron scattering phase from the Ar atomic potential-information typically accessible only through attosecond metrology, while the fishbone structure reveals the orbital-parity contrast between Ar atoms and nitrogen molecules. Our measurements are quantitatively reproduced by both semiclassical Herman-Kluk-propagator and \textit{ab initio} simulations, paving the way for precision studies of electron-molecule scattering with widely accessible industrial-grade lasers.

physics.atom-ph↗

arXiv2025

Bright 25-attosecond light pulses reach the one atomic unit of time

Generating ever-shorter and brighter light pulses has long been a central pursuit in ultrafast science, as it benchmarks our ability to create and manipulate the coherence on the intrinsic timescale of sub-atomic electron motion. The current state-of-the-art in attosecond pulse generation reaches durations of 40-50 attoseconds (1 as = $10^{-18}$ seconds), produced via high-order harmonic generation (HHG) driven by secondary mid-infrared light sources. However, these sources often suffer from low stability and poor HHG conversion efficiency. In this work, we demonstrate the generation of 25$\pm$2 attosecond light pulses, a new world record for the shortest light pulse, driven by a post-compressed, industrial-grade Yb-based laser system. The resulting high-harmonic spectrum spans photon energies from 50 eV to 320 eV, covering the carbon K-edge, with a calibrated photon flux exceeding $10^{12}$ photons per second, approximately three orders of magnitude higher than previous studies. The pulse duration was characterized using an angle-resolved photoelectron streaking camera on helium atoms and systematically optimized through the use of dielectric filters of varying thicknesses to compensate the attochirp. Our study reaches the threshold of one atomic unit of time (24.2 attoseconds), the boundary between atomic and ionic physics, opening the door to resolving exciting ionic quantum dynamics with tabletop lasers.

physics.atom-ph↗

arXiv2025

A Structured Dataset of Disease-Symptom Associations to Improve Diagnostic Accuracy

Disease-symptom datasets are significant and in demand for medical research, disease diagnosis, clinical decision-making, and AI-driven health management applications. These datasets help identify symptom patterns associated with specific diseases, thus improving diagnostic accuracy and enabling early detection. The dataset presented in this study systematically compiles disease-symptom relationships from various online sources, medical literature, and publicly available health databases. The data was gathered through analyzing peer-reviewed medical articles, clinical case studies, and disease-symptom association reports. Only the verified medical sources were included in the dataset, while those from non-peer-reviewed and anecdotal sources were excluded. The dataset is structured in a tabular format, where the first column represents diseases, and the remaining columns represent symptoms. Each symptom cell contains a binary value, indicating whether a symptom is associated with a disease. Thereby, this structured representation makes the dataset very useful for a wide range of applications, including machine learning-based disease prediction, clinical decision support systems, and epidemiological studies. Although there are some advancements in the field of disease-symptom datasets, there is a significant gap in structured datasets for the Bangla language. This dataset aims to bridge that gap by facilitating the development of multilingual medical informatics tools and improving disease prediction models for underrepresented linguistic communities. Further developments should include region-specific diseases and further fine-tuning of symptom associations for better diagnostic performance

cs.CL↗

arXiv2025

Parkinson's Disease Freezing of Gait (FoG) Symptom Detection Using Machine Learning from Wearable Sensor Data

Freezing of gait (FoG) is a special symptom found in patients with Parkinson's disease (PD). Patients who have FoG abruptly lose the capacity to walk as they normally would. Accelerometers worn by patients can record movement data during these episodes, and machine learning algorithms can be useful to categorize this information. Thus, the combination may be able to identify FoG in real time. In order to identify FoG events in accelerometer data, we introduce the Transformer Encoder-Bi-LSTM fusion model in this paper. The model's capability to differentiate between FoG episodes and normal movement was used to evaluate its performance, and on the Kaggle Parkinson's Freezing of Gait dataset, the proposed Transformer Encoder-Bi-LSTM fusion model produced 92.6% accuracy, 80.9% F1 score, and 52.06% in terms of mean average precision. The findings highlight how Deep Learning-based approaches may progress the field of FoG identification and help PD patients receive better treatments and management plans.

cs.CV↗

arXiv2025

Deep Learning for Breast Cancer Detection: Comparative Analysis of ConvNeXT and EfficientNet

Breast cancer is the most commonly occurring cancer worldwide. This cancer caused 670,000 deaths globally in 2022, as reported by the WHO. Yet since health officials began routine mammography screening in age groups deemed at risk in the 1980s, breast cancer mortality has decreased by 40% in high-income nations. Every day, a greater and greater number of people are receiving a breast cancer diagnosis. Reducing cancer-related deaths requires early detection and treatment. This paper compares two convolutional neural networks called ConvNeXT and EfficientNet to predict the likelihood of cancer in mammograms from screening exams. Preprocessing of the images, classification, and performance evaluation are main parts of the whole procedure. Several evaluation metrics were used to compare and evaluate the performance of the models. The result shows that ConvNeXT generates better results with a 94.33% AUC score, 93.36% accuracy, and 95.13% F-score compared to EfficientNet with a 92.34% AUC score, 91.47% accuracy, and 93.06% F-score on RSNA screening mammography breast cancer dataset.

cs.CV↗

arXiv2025

Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity Detection

Deepfake videos, produced through advanced artificial intelligence methods now a days, pose a new challenge to the truthfulness of the digital media. As Deepfake becomes more convincing day by day, detecting them requires advanced methods capable of identifying subtle inconsistencies. The primary motivation of this paper is to recognize deepfake videos using deep learning techniques, specifically by using convolutional neural networks. Deep learning excels in pattern recognition, hence, makes it an ideal approach for detecting the intricate manipulations in deepfakes. In this paper, we consider using MTCNN as a face detector and EfficientNet-B5 as encoder model to predict if a video is deepfake or not. We utilize training and evaluation dataset from Kaggle DFDC. The results shows that our deepfake detection model acquired 42.78% log loss, 93.80% AUC and 86.82% F1 score on kaggle's DFDC dataset.

cs.CV↗

arXiv2025

Automatic Vehicle Detection using DETR: A Transformer-Based Approach for Navigating Treacherous Roads

Automatic Vehicle Detection (AVD) in diverse driving environments presents unique challenges due to varying lighting conditions, road types, and vehicle types. Traditional methods, such as YOLO and Faster R-CNN, often struggle to cope with these complexities. As computer vision evolves, combining Convolutional Neural Networks (CNNs) with Transformer-based approaches offers promising opportunities for improving detection accuracy and efficiency. This study is the first to experiment with Detection Transformer (DETR) for automatic vehicle detection in complex and varied settings. We employ a Collaborative Hybrid Assignments Training scheme, Co-DETR, to enhance feature learning and attention mechanisms in DETR. By leveraging versatile label assignment strategies and introducing multiple parallel auxiliary heads, we provide more effective supervision during training and extract positive coordinates to boost training efficiency. Through extensive experiments on DETR variants and YOLO models, conducted using the BadODD dataset, we demonstrate the advantages of our approach. Our method achieves superior results, and improved accuracy in diverse conditions, making it practical for real-world deployment. This work significantly advances autonomous navigation technology and opens new research avenues in object detection for autonomous vehicles. By integrating the strengths of CNNs and Transformers, we highlight the potential of DETR for robust and efficient vehicle detection in challenging driving environments.

cs.CV↗