arXiv subjects

Tanmoy Debnath

Publications and source records attributed to Tanmoy Debnath.

10 recordsLinked to original sources

arXiv2026

SWARM-LLM: Collaborative Inference for Edge-based Small Language Models

Large language models (LLMs) provide strong performance across a wide range of tasks but are typically hosted on centralised cloud infrastructure, incurring significant bandwidth, latency, and privacy costs. In contrast, small language models (SLMs) can run on edge devices but have limited capability and robustness. This paper introduces SWARM-LLM, a routing and collaboration layer that coordinates a small swarm of edge-hosted SLMs with an optional cloud foundation model (FM). SWARM-LLM decides, for each query, whether to answer locally, collaborate with peer SLMs, or "summon" a cloud FM, using lightweight uncertainty estimates and safety signals. We implement a working prototype on commodity hardware with three heterogeneous SLMs and a 70B-parameter cloud FM accessed via API, and evaluate it on a controlled study workload of easy, hard, and safety-oriented queries. Our results show that SWARM-LLM substantially improves performance on hard questions compared to an edge-only deployment, while limiting cloud usage to roughly one quarter of queries, illustrating a practical trade-off between accuracy, latency, and cost for privacy-conscious edge deployments. The implementation code is available at the GitHub repository https://github.com/mdahshan/swarm_llm.

cs.DC↗

arXiv2026

A Sparse-Attention Deep Learning Model Integrating Heterogeneous Multimodal Features for Parkinson's Disease Severity Profiling

Characterising the heterogeneous presentation of Parkinson's disease (PD) requires integrating biological and clinical markers within a unified predictive framework. While multimodal data provide complementary information, many existing computational models struggle with interpretability, class imbalance, or effective fusion of high-dimensional imaging and tabular clinical features. To address these limitations, we propose the Class-Weighted Sparse-Attention Fusion Network (SAFN), an interpretable deep learning framework for robust multimodal profiling. SAFN integrates MRI cortical thickness, MRI volumetric measures, clinical assessments, and demographic variables using modality-specific encoders and a symmetric cross-attention mechanism that captures nonlinear interactions between imaging and clinical representations. A sparsity-constrained attention-gating fusion layer dynamically prioritises informative modalities, while a class-balanced focal loss (beta = 0.999, gamma = 1.5) mitigates dataset imbalance without synthetic oversampling. Evaluated on 703 participants (570 PD, 133 healthy controls) from the Parkinson's Progression Markers Initiative using subject-wise five-fold cross-validation, SAFN achieves an accuracy of 0.98 plus or minus 0.02 and a PR-AUC of 1.00 plus or minus 0.00, outperforming established machine learning and deep learning baselines. Interpretability analysis shows a clinically coherent decision process, with approximately 60 percent of predictive weight assigned to clinical assessments, consistent with Movement Disorder Society diagnostic principles. SAFN provides a reproducible and transparent multimodal modelling paradigm for computational profiling of neurodegenerative disease.

cs.LG↗

arXiv2025

PALQA: A Novel Parameterized Position-Aware Lossy Quantum Autoencoder using LSB Control Qubit for Efficient Image Compression

With the growing interest in quantum computing, quantum image processing technology has become a vital research field due to its versatile applications and ability to outperform classical computing. A quantum autoencoder approach has been used for compression purposes. However, existing autoencoders are limited to small-scale images, and the mechanisms of state compression remain unclear. There is also a need for efficient quantum autoencoders using standard representation approaches and for studying parameterized position-aware control qubits and their corresponding quality measurement metrics. This work introduces a novel parameterized position-aware lossy quantum autoencoder (PALQA) circuit that utilizes the least significant bit control qubit for image compression. The PALQA circuit employs a transformed coefficient block-based modified state connection approach to efficiently compress images at various resolutions. The method leverages compression opportunities in the state-label connection by applying position-aware least significant control qubit. Compared to JPEG and other enhanced quantum representation-based quantum autoencoders, the PALQA circuit demonstrates superior performance in terms of the number of gates required and PSNR metrics.

quant-ph↗

arXiv2023

Efficient quantum image representation and compression circuit using zero-discarded state preparation approach

Quantum image computing draws a lot of attention due to storing and processing image data faster than classical. With increasing the image size, the number of connections also increases, leading to the circuit complex. Therefore, efficient quantum image representation and compression issues are still challenging. The encoding of images for representation and compression in quantum systems is different from classical ones. In quantum, encoding of position is more concerned which is the major difference from the classical. In this paper, a novel zero-discarded state connection novel enhance quantum representation (ZSCNEQR) approach is introduced to reduce complexity further by discarding '0' in the location representation information. In the control operational gate, only input '1' contribute to its output thus, discarding zero makes the proposed ZSCNEQR circuit more efficient. The proposed ZSCNEQR approach significantly reduced the required bit for both representation and compression. The proposed method requires 11.76\% less qubits compared to the recent existing method. The results show that the proposed approach is highly effective for representing and compressing images compared to the two relevant existing methods in terms of rate-distortion performance.

quant-ph↗

arXiv2022

Block-wise quantum grayscale image representation and compression scheme using state connection

Quantum computing draws huge attention due to its faster computational capability compared to classical computing to represent and compress the classical image data into the quantum domain. The main idea of quantum domain representation is to convert pixel intensities and their coordinates i.e. state label preparation using quantum bits i.e. Qubits. For a bigger size image, the state label preparation takes more Qubits. To address more Qubits issues, a novel SCMNEQR (State Connection Modification Novel Enhanced Quantum Representation) approach has been proposed that uses fewer qubits to map the arbitrary size of the grayscale image using block-wise state label preparation. The proposed SCMNEQR approach introduces the state connection using a reset gate rather than repeating the use of the Toffoli gate used in the existing approach. The experimental results show that the proposed approach outperforms the existing methods in terms of compression.

quant-ph↗

arXiv2022

A novel state connection strategy for quantum computing to represent and compress digital images

Quantum image processing draws a lot of attention due to faster data computation and storage compared to classical data processing systems. Converting classical image data into the quantum domain and state label preparation complexity is still a challenging issue. The existing techniques normally connect the pixel values and the state position directly. Recently, the EFRQI (efficient flexible representation of the quantum image) approach uses an auxiliary qubit that connects the pixel-representing qubits to the state position qubits via Toffoli gates to reduce state connection. Due to the twice use of Toffoli gates for each pixel connection still it requires a significant number of bits to connect each pixel value. In this paper, we propose a new SCMFRQI (state connection modification FRQI) approach for further reducing the required bits by modifying the state connection using a reset gate rather than repeating the use of the same Toffoli gate connection as a reset gate. Moreover, unlike other existing methods, we compress images using block-level for further reduction of required qubits. The experimental results confirm that the proposed method outperforms the existing methods in terms of both image representation and compression points of view.

quant-ph↗

arXiv2022

Advance quantum image representation and compression using DCTEFRQI approach

In recent year, quantum image processing got a lot of attention in the field of image processing due to opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimension to locate and process the image data faster. Moreover, several researches show that, the computational time of quantum process is faster than classical computer. By encoding and compressing the image in quantum domain is still challenging issue. From literature survey, we have proposed a DCTEFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently which save computational time and minimize the complexity of preparation. The objective of this work is to represent and compress various gray image size in quantum computer using DCT(Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approach together. Quirk simulation tool is used to design corresponding quantum image circuit. Due to limitation of qubit, total 16 numbers of qubit are used to represent the gray scale image among those 8 are used to map the coefficient values and the rest 8 are used to generate the corresponding coefficient position. Theoretical analysis and experimental result show that, proposed DCTEFRQI scheme provides better representation and compression compare to DCT-GQIR, DWT-GQIR and DWT-EFRQI in terms of PSNR(Peak Signal to Noise Ratio) and bit rate..

quant-ph↗

arXiv2021

MAVIDH Score: A COVID-19 Severity Scoring using Chest X-Ray Pathology Features

The application of computer vision for COVID-19 diagnosis is complex and challenging, given the risks associated with patient misclassifications. Arguably, the primary value of medical imaging for COVID-19 lies rather on patient prognosis. Radiological images can guide physicians assessing the severity of the disease, and a series of images from the same patient at different stages can help to gauge disease progression. Hence, a simple method based on lung-pathology interpretable features for scoring disease severity from Chest X-rays is proposed here. As the primary contribution, this method correlates well to patient severity in different stages of disease progression with competitive results compared to other existing, more complex methods. An original data selection approach is also proposed, allowing the simple model to learn the severity-related features. It is hypothesized that the resulting competitive performance presented here is related to the method being feature-based rather than reliant on lung involvement or opacity as others in the literature. A second contribution comes from the validation of the results, conceptualized as the scoring of patients groups from different stages of the disease. Besides performing such validation on an independent data set, the results were also compared with other proposed scoring methods in the literature. The results show that there is a significant correlation between the scoring system (MAVIDH) and patient outcome, which could potentially help physicians rating and following disease progression in COVID-19 patients.

eess.IV↗

arXiv2021

Potential Features of ICU Admission in X-ray Images of COVID-19 Patients

X-ray images may present non-trivial features with predictive information of patients that develop severe symptoms of COVID-19. If true, this hypothesis may have practical value in allocating resources to particular patients while using a relatively inexpensive imaging technique. The difficulty of testing such a hypothesis comes from the need for large sets of labelled data, which need to be well-annotated and should contemplate the post-imaging severity outcome. This paper presents an original methodology for extracting semantic features that correlate to severity from a data set with patient ICU admission labels through interpretable models. The methodology employs a neural network trained to recognise lung pathologies to extract the semantic features, which are then analysed with low-complexity models to limit overfitting while increasing interpretability. This analysis points out that only a few features explain most of the variance between patients that developed severe symptoms. When applied to an unrelated larger data set with pathology-related clinical notes, the method has shown to be capable of selecting images for the learned features, which could translate some information about their common locations in the lung. Besides attesting separability on patients that eventually develop severe symptoms, the proposed methods represent a statistical approach highlighting the importance of features related to ICU admission that may have been only qualitatively reported. While handling limited data sets, notable methodological aspects are adopted, such as presenting a state-of-the-art lung segmentation network and the use of low-complexity models to avoid overfitting. The code for methodology and experiments is also available.

eess.IV↗

arXiv2020

Hyperspectral Imaging to detect Age, Defects and Individual Nutrient Deficiency in Grapevine Leaves

Hyperspectral (HS) imaging was successfully employed in the 380 nm to 1000 nm wavelength range to investigate the efficacy of detecting age, healthiness and individual nutrient deficiency of grapevine leaves collected from vineyards located in central west NSW, Australia. For age detection, the appearance of many healthy grapevine leaves has been examined. Then visually defective leaves were compared with healthy leaves. Control leaves and individual nutrient-deficient leaves (e.g. N, K and Mg) were also analysed. Several features were employed at various stages in the Ultraviolet (UV), Visible (VIS) and Near Infrared (NIR) regions to evaluate the experimental data: mean brightness, mean 1st derivative brightness, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD). Experiment results demonstrate that these features could be utilised with a high degree of effectiveness to compare age, identify unhealthy samples and not only to distinguish from control and nutrient deficiency but also to identify individual nutrient defects. Therefore, our work corroborated that HS imaging has excellent potential as a non-destructive as well as a non-contact method to detect age, healthiness and individual nutrient deficiencies of grapevine leaves

q-bio.TO↗