arXiv subjects

Muhammad Imran

Publications and source records attributed to Muhammad Imran.

At least 37 records · Page 2Linked to original sources

arXiv2025

Halide Perovskite-Chalcohalide Nanocrystal Heterostructures as a Platform for the Synthesis and Investigation of the CsPbCl3-CsPbI3 Epitaxial Interface

Halide exchange in lead-based halide perovskites has been studied extensively. While mixed Cl-Br or Br-I alloy compositions can be formed with no miscibility gaps, this is precluded for mixed Cl-I compositions, due to the large difference in Cl and I ionic radii. Here, we exploit perovskite-chalcohalide CsPbCl3-Pb4S3Cl2 heterostructures to study the Cl-I exchange and isolate new types of intermediate structures. The epitaxial interface between the Pb4S3Cl2 chalcohalide and the CsPbCl3 perovskite domain significantly influences the intermediate stages of halide exchange in the perovskite domain, leading to coexisting CsPbCl3 and CsPbI3 domains, thereby delivering segmented CsPbI3-CsPbCl3-Pb4S3Cl2 energetically favorable heterostructures, with partial iodide alloying of the CsPbCl3 domain and at the perovskite-chalcohalide interface. The I:CsPbCl3 domain between CsPbI3 and Pb4S3Cl2 enables a gradual lattice expansion across the heterostructure. This design accommodates interfacial strain, with a 5.6% mismatch at the CsPbCl3-CsPbI3 interface and a 3.4% mismatch at the perovskite-chalcohalide interface. Full halide exchange leads to CsPbI3-Pb4S3Cl2 heterostructures. Both in intermediate and fully exchanged heterostructures, the CsPbI3 domain is emissive. In the intermediate structures, the band alignment between the two perovskite domains is type-I, with carriers photogenerated in the CsPbCl3 domain quickly transferring to the CsPbI3 domain, where they can recombine radiatively.

cond-mat.mtrl-sci↗

arXiv2025

On the Classical Hardness of the Semidirect Discrete Logarithm Problem in Finite Groups

The semidirect discrete logarithm problem (SDLP) in finite groups was proposed as a foundation for post-quantum cryptographic protocols, based on the belief that its non-abelian structure would resist quantum attacks. However, recent results have shown that SDLP in finite groups admits efficient quantum algorithms, undermining its quantum resistance. This raises a fundamental question: does the SDLP offer any computational advantages over the standard discrete logarithm problem (DLP) against classical adversaries? In this work, we investigate the classical hardness of SDLP across different finite group platforms. We establish that the group-case SDLP can be reformulated as a generalized discrete logarithm problem, enabling adaptation of classical algorithms to study its complexity. We present a concrete adaptation of the Baby-Step Giant-Step algorithm for SDLP, achieving time and space complexity $O(\sqrt{r})$ where $r$ is the period of the underlying cycle structure. Through theoretical analysis and experimental validation in SageMath, we demonstrate that the classical hardness of SDLP is highly platform-dependent and does not uniformly exceed that of standard DLP. In finite fields $\mathbb{F}_p^*$, both problems exhibit comparable complexity. Surprisingly, in elliptic curves $E(\mathbb{F}_p)$, the SDLP becomes trivial due to the bounded automorphism group, while in elementary abelian groups $\mathbb{F}_p^n$, the SDLP can be harder than DLP, with complexity varying based on the eigenvalue structure of the automorphism. Our findings reveal that the non-abelian structure of semidirect products does not inherently guarantee increased classical hardness, suggesting that the search for classically hard problems for cryptographic applications requires more careful consideration of the underlying algebraic structures.

cs.CR↗

arXiv2025

Rare-Earth Engineering of NaAlO3 Perovskites Unlocks Unified Optoelectronic, Thermoelectric, and Spintronic Functionalities

Perovskite oxides are promising for energy and quantum technologies, but wide-gap hosts such as NaAlO3 suffer from deep-UV absorption and limited carrier transport. Using first-principles GGA+U+SOC calculations, we investigate Eu3+-, Gd3+-, and Tb3+-doped NaAlO3 and evaluate their electronic, optical, elastic, and thermoelectric properties. Rare-earth substitution is thermodynamically favorable (formation energies 1.2-1.6 eV) and induces strong f-p hybridization, reducing the pristine band gap (about 6.2 eV) to about 3.1 eV for Tb. Spin-resolved band structures reveal Gd-driven half-metallicity, Eu-induced spin-selective metallicity, and Tb-stabilized p-type semiconducting behavior. The optical spectra show a red-shifted absorption edge (about 2.0-2.2 eV), a large static dielectric response (epsilon1(0) about 95 for Eu), and plasmonic resonances near 4 eV, enabling visible-light harvesting. Elastic analysis indicates mild lattice softening with preserved ductility (Pugh ratio B/G about 1.56-1.57). Thermoelectric performance is enhanced, with Seebeck coefficients greater than 210 uV/K for Eu and Tb and ZT about 0.45 at 500 K. These results identify rare-earth-doped NaAlO3 as a multifunctional perovskite platform for photovoltaics, photocatalysis, thermoelectrics, and spintronics.

cond-mat.mtrl-sci↗

arXiv2025

Multi-Modal Interpretability for Enhanced Localization in Vision-Language Models

Recent advances in vision-language models have significantly expanded the frontiers of automated image analysis. However, applying these models in safety-critical contexts remains challenging due to the complex relationships between objects, subtle visual cues, and the heightened demand for transparency and reliability. This paper presents the Multi-Modal Explainable Learning (MMEL) framework, designed to enhance the interpretability of vision-language models while maintaining high performance. Building upon prior work in gradient-based explanations for transformer architectures (Grad-eclip), MMEL introduces a novel Hierarchical Semantic Relationship Module that enhances model interpretability through multi-scale feature processing, adaptive attention weighting, and cross-modal alignment. Our approach processes features at multiple semantic levels to capture relationships between image regions at different granularities, applying learnable layer-specific weights to balance contributions across the model's depth. This results in more comprehensive visual explanations that highlight both primary objects and their contextual relationships with improved precision. Through extensive experiments on standard datasets, we demonstrate that by incorporating semantic relationship information into gradient-based attribution maps, MMEL produces more focused and contextually aware visualizations that better reflect how vision-language models process complex scenes. The MMEL framework generalizes across various domains, offering valuable insights into model decisions for applications requiring high interpretability and reliability.

cs.CV↗

arXiv2025

Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation

Denoising diffusion probabilistic models (DDPMs) have achieved unprecedented success in computer vision. However, they remain underutilized in medical imaging, a field crucial for disease diagnosis and treatment planning. This is primarily due to the high computational cost associated with (1) the use of large number of time steps (e.g., 1,000) in diffusion processes and (2) the increased dimensionality of medical images, which are often 3D or 4D. Training a diffusion model on medical images typically takes days to weeks, while sampling each image volume takes minutes to hours. To address this challenge, we introduce Fast-DDPM, a simple yet effective approach capable of improving training speed, sampling speed, and generation quality simultaneously. Unlike DDPM, which trains the image denoiser across 1,000 time steps, Fast-DDPM trains and samples using only 10 time steps. The key to our method lies in aligning the training and sampling procedures to optimize time-step utilization. Specifically, we introduced two efficient noise schedulers with 10 time steps: one with uniform time step sampling and another with non-uniform sampling. We evaluated Fast-DDPM across three medical image-to-image generation tasks: multi-image super-resolution, image denoising, and image-to-image translation. Fast-DDPM outperformed DDPM and current state-of-the-art methods based on convolutional networks and generative adversarial networks in all tasks. Additionally, Fast-DDPM reduced the training time to 0.2x and the sampling time to 0.01x compared to DDPM. Our code is publicly available at: https://github.com/mirthAI/Fast-DDPM.

eess.IV↗

arXiv2025

Evaluating Compositional Approaches for Focus and Sentiment Analysis

This paper summarizes the results of evaluating a compositional approach for Focus Analysis (FA) in Linguistics and Sentiment Analysis (SA) in Natural Language Processing (NLP). While quantitative evaluations of compositional and non-compositional approaches in SA exist in NLP, similar quantitative evaluations are very rare in FA in Linguistics that deal with linguistic expressions representing focus or emphasis such as "it was John who left". We fill this gap in research by arguing that compositional rules in SA also apply to FA because FA and SA are closely related meaning that SA is part of FA. Our compositional approach in SA exploits basic syntactic rules such as rules of modification, coordination, and negation represented in the formalism of Universal Dependencies (UDs) in English and applied to words representing sentiments from sentiment dictionaries. Some of the advantages of our compositional analysis method for SA in contrast to non-compositional analysis methods are interpretability and explainability. We test the accuracy of our compositional approach and compare it with a non-compositional approach VADER that uses simple heuristic rules to deal with negation, coordination and modification. In contrast to previous related work that evaluates compositionality in SA on long reviews, this study uses more appropriate datasets to evaluate compositionality. In addition, we generalize the results of compositional approaches in SA to compositional approaches in FA.

cs.CL↗

arXiv2025

Millimeter-Wave Communication Testbed Using Digital Coding Dynamic Metasurface Antenna: Practical Design and Implementation

Dynamic Metasurface Antennas (DMAs) are transforming reconfigurable antenna technology by enabling energy-efficient, cost-effective beamforming through programmable meta-elements, eliminating the need for traditional phase shifters and delay lines. This breakthrough technology is emerging to revolutionize beamforming for next-generation wireless communication and sensing networks. In this paper, we present the design and real-world implementation of a DMA-assisted wireless communication platform operating in the license-free 60 GHz millimeter-wave (mmWave) band. Our system employs high-speed binary-coded sequences generated via a field-programmable gate array (FPGA), enabling real-time beam steering for spatial multiplexing and independent data transmission. A proof-of-concept experiment successfully demonstrates high-definition quadrature phase-shift keying (QPSK) modulated video transmission at 62 GHz. Furthermore, leveraging the DMA's multi-beam capability, we simultaneously transmit video to two spatially separated receivers, achieving accurate demodulation. We envision the proposed mmWave testbed as a platform for enabling the seamless integration of sensing and communication by allowing video transmission to be replaced with sensing data or utilizing an auxiliary wireless channel to transmit sensing information to multiple receivers. This synergy paves the way for advancing integrated sensing and communication (ISAC) in beyond-5G and 6G networks. Additionally, our testbed demonstrates potential for real-world use cases, including mmWave backhaul links and massive multiple-input multiple-output (MIMO) mmWave base stations.

eess.SP↗

arXiv2025

Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages

Code-switching presents a complex challenge for syntactic analysis, especially in low-resource language settings where annotated data is scarce. While recent work has explored the use of large language models (LLMs) for sequence-level tagging, few approaches systematically investigate how well these models capture syntactic structure in code-switched contexts. Moreover, existing parsers trained on monolingual treebanks often fail to generalize to multilingual and mixed-language input. To address this gap, we introduce the BiLingua Parser, an LLM-based annotation pipeline designed to produce Universal Dependencies (UD) annotations for code-switched text. First, we develop a prompt-based framework for Spanish-English and Spanish-Guaraní data, combining few-shot LLM prompting with expert review. Second, we release two annotated datasets, including the first Spanish-Guaraní UD-parsed corpus. Third, we conduct a detailed syntactic analysis of switch points across language pairs and communicative contexts. Experimental results show that BiLingua Parser achieves up to 95.29% LAS after expert revision, significantly outperforming prior baselines and multilingual parsers. These results show that LLMs, when carefully guided, can serve as practical tools for bootstrapping syntactic resources in under-resourced, code-switched environments. Data and source code are available at https://github.com/N3mika/ParsingProject

cs.CL↗

arXiv2025

Quantum Random Number Generator (QRNG): Theoretical and Experimental Investigations

Quantum Random Number Generators (QRNGs) emerged as a promising solution for generating truly random numbers. In the present article, we give an overview of QRNGs highlighting the merits and demerits of various strategies briefly. Then opting for the best-case scenario, we present the in-depth experimental explorations for building and characterizing QRNG using the homodyne detection technique to measure the quadrature amplitude of quantum vacuum fluctuations. Since entropy assessment plays a fundamental role in authenticating the true randomness, a comprehensive description of entropy and how it evaluates the quality of randomness of the source is illustrated. Our experimental setup, apart from the hardware, includes a diverse set of testing techniques including NIST statistical/entropy suites, Dieharder tests battery, and autocorrelation coefficient to verify the randomness and statistical properties of the generated random numbers. We believe that our experimental investigations provide a valuable resource for building QRNGs for a wide range of applications.

quant-ph↗

arXiv2025

The Observation of hyperradiance accompanied by enhanced entanglement in a hybrid optomechanical system

We have theoretically investigated an optomechanical system and presented the scenario of significantly enhanced bipartite photon-phonon entanglement for two qubits coupled to the single mode of the cavity. And results are compared with the one qubit case for reference. The tripartite atoms-photon-phonon interaction is considered as only three-body resonant interaction while the two-body actions are ignored under some potential approximations. Furthermore, we have studied the phenomenon of hyperradiance in which the well-known Dicke superradiant ($N^2$ scaling law) can be surpassed due to the inter-atomic correlations. Jointly, a parameter regime is explored to observe the entanglement of photon-phonon pairs and their hyperradiance simultaneously. As it is important to show that the generation of photons and phonons are antibunched, the equal time second-order correlation function $g^{(2)}(0)$ is characterized as witness. This system can be realized in Circuit Cavity Quantum Electrodynamics (CCQED) in which the direct coupling of the atom and mechanical resonator is possible.We have theoretically investigated an optomechanical system and presented the scenario of significantly enhanced bipartite photon-phonon entanglement for two qubits coupled to the single mode of the cavity.

quant-ph↗

arXiv2025

Prostate Cancer Screening with Artificial Intelligence-Enhanced Micro-Ultrasound: A Comparative Study with Traditional Methods

Background and objective: Micro-ultrasound (micro-US) is a novel imaging modality with diagnostic accuracy comparable to MRI for detecting clinically significant prostate cancer (csPCa). We investigated whether artificial intelligence (AI) interpretation of micro-US can outperform clinical screening methods using PSA and digital rectal examination (DRE). Methods: We retrospectively studied 145 men who underwent micro-US guided biopsy (79 with csPCa, 66 without). A self-supervised convolutional autoencoder was used to extract deep image features from 2D micro-US slices. Random forest classifiers were trained using five-fold cross-validation to predict csPCa at the slice level. Patients were classified as csPCa-positive if 88 or more consecutive slices were predicted positive. Model performance was compared with a classifier using PSA, DRE, prostate volume, and age. Key findings and limitations: The AI-based micro-US model and clinical screening model achieved AUROCs of 0.871 and 0.753, respectively. At a fixed threshold, the micro-US model achieved 92.5% sensitivity and 68.1% specificity, while the clinical model showed 96.2% sensitivity but only 27.3% specificity. Limitations include a retrospective single-center design and lack of external validation. Conclusions and clinical implications: AI-interpreted micro-US improves specificity while maintaining high sensitivity for csPCa detection. This method may reduce unnecessary biopsies and serve as a low-cost alternative to PSA-based screening. Patient summary: We developed an AI system to analyze prostate micro-ultrasound images. It outperformed PSA and DRE in detecting aggressive cancer and may help avoid unnecessary biopsies.

eess.IV↗

arXiv2025

Detecting Actionable Requests and Offers on Social Media During Crises Using LLMs

Natural disasters often result in a surge of social media activity, including requests for assistance, offers of help, sentiments, and general updates. To enable humanitarian organizations to respond more efficiently, we propose a fine-grained hierarchical taxonomy to systematically organize crisis-related information about requests and offers into three critical dimensions: supplies, emergency personnel, and actions. Leveraging the capabilities of Large Language Models (LLMs), we introduce Query-Specific Few-shot Learning (QSF Learning) that retrieves class-specific labeled examples from an embedding database to enhance the model's performance in detecting and classifying posts. Beyond classification, we assess the actionability of messages to prioritize posts requiring immediate attention. Extensive experiments demonstrate that our approach outperforms baseline prompting strategies, effectively identifying and prioritizing actionable requests and offers.

cs.IR↗

arXiv2025

"Actionable Help" in Crises: A Novel Dataset and Resource-Efficient Models for Identifying Request and Offer Social Media Posts

During crises, social media serves as a crucial coordination tool, but the vast influx of posts--from "actionable" requests and offers to generic content like emotional support, behavioural guidance, or outdated information--complicates effective classification. Although generative LLMs (Large Language Models) can address this issue with few-shot classification, their high computational demands limit real-time crisis response. While fine-tuning encoder-only models (e.g., BERT) is a popular choice, these models still exhibit higher inference times in resource-constrained environments. Moreover, although distilled variants (e.g., DistilBERT) exist, they are not tailored for the crisis domain. To address these challenges, we make two key contributions. First, we present CrisisHelpOffer, a novel dataset of 101k tweets collaboratively labelled by generative LLMs and validated by humans, specifically designed to distinguish actionable content from noise. Second, we introduce the first crisis-specific mini models optimized for deployment in resource-constrained settings. Across 13 crisis classification tasks, our mini models surpass BERT (also outperform or match the performance of RoBERTa, MPNet, and BERTweet), offering higher accuracy with significantly smaller sizes and faster speeds. The Medium model is 47% smaller with 3.8% higher accuracy at 3.5x speed, the Small model is 68% smaller with a 1.8% accuracy gain at 7.7x speed, and the Tiny model, 83% smaller, matches BERT's accuracy at 18.6x speed. All models outperform existing distilled variants, setting new benchmarks. Finally, as a case study, we analyze social media posts from a global crisis to explore help-seeking and assistance-offering behaviours in selected developing and developed countries.

cs.CL↗

arXiv2025

Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://aortaseg24.grand-challenge.org.

eess.IV↗

arXiv2024

Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features

The widespread use of microblogging platforms like X (formerly Twitter) during disasters provides real-time information to governments and response authorities. However, the data from these platforms is often noisy, requiring automated methods to filter relevant information. Traditionally, supervised machine learning models have been used, but they lack generalizability. In contrast, Large Language Models (LLMs) show better capabilities in understanding and processing natural language out of the box. This paper provides a detailed analysis of the performance of six well-known LLMs in processing disaster-related social media data from a large-set of real-world events. Our findings indicate that while LLMs, particularly GPT-4o and GPT-4, offer better generalizability across different disasters and information types, most LLMs face challenges in processing flood-related data, show minimal improvement despite the provision of examples (i.e., shots), and struggle to identify critical information categories like urgent requests and needs. Additionally, we examine how various linguistic features affect model performance and highlight LLMs' vulnerabilities against certain features like typos. Lastly, we provide benchmarking results for all events across both zero- and few-shot settings and observe that proprietary models outperform open-source ones in all tasks.

cs.CL↗

arXiv2024

RetinaRegNet: A Zero-Shot Approach for Retinal Image Registration

We introduce RetinaRegNet, a zero-shot image registration model designed to register retinal images with minimal overlap, large deformations, and varying image quality. RetinaRegNet addresses these challenges and achieves robust and accurate registration through the following steps. First, we extract features from the moving and fixed images using latent diffusion models. We then sample feature points from the fixed image using a combination of the SIFT algorithm and random point sampling. For each sampled point, we identify its corresponding point in the moving image using a 2D correlation map, which computes the cosine similarity between the diffusion feature vectors of the point in the fixed image and all pixels in the moving image. Second, we eliminate most incorrectly detected point correspondences (outliers) by enforcing an inverse consistency constraint, ensuring that correspondences are consistent in both forward and backward directions. We further remove outliers with large distances between corresponding points using a global transformation based outlier detector. Finally, we implement a two-stage registration framework to handle large deformations. The first stage estimates a homography transformation to achieve global alignment between the images, while the second stage uses a third-order polynomial transformation to estimate local deformations. We evaluated RetinaRegNet on three retinal image registration datasets: color fundus images, fluorescein angiography images, and laser speckle flowgraphy images. Our model consistently outperformed state-of-the-art methods across all datasets. The accurate registration achieved by RetinaRegNet enables the tracking of eye disease progression, enhances surgical planning, and facilitates the evaluation of treatment efficacy. Our code is publicly available at: https://github.com/mirthAI/RetinaRegNet.

cs.CV↗

arXiv2024

CReMa: Crisis Response through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media

During times of crisis, social media platforms play a crucial role in facilitating communication and coordinating resources. In the midst of chaos and uncertainty, communities often rely on these platforms to share urgent pleas for help, extend support, and organize relief efforts. However, the overwhelming volume of conversations during such periods can escalate to unprecedented levels, necessitating the automated identification and matching of requests and offers to streamline relief operations. Additionally, there is a notable absence of studies conducted in multi-lingual settings, despite the fact that any geographical area can have a diverse linguistic population. Therefore, we propose CReMa (Crisis Response Matcher), a systematic approach that integrates textual, temporal, and spatial features to address the challenges of effectively identifying and matching requests and offers on social media platforms during emergencies. Our approach utilizes a crisis-specific pre-trained model and a multi-lingual embedding space. We emulate human decision-making to compute temporal and spatial features and non-linearly weigh the textual features. The results from our experiments are promising, outperforming strong baselines. Additionally, we introduce a novel multi-lingual dataset simulating help-seeking and offering assistance on social media in 16 languages and conduct comprehensive cross-lingual experiments. Furthermore, we analyze a million-scale geotagged global dataset to understand patterns in seeking help and offering assistance on social media. Overall, these contributions advance the field of crisis informatics and provide benchmarks for future research in the area.

cs.CL↗

arXiv2024

Engineering of Hyperentangled Complex Quantum Networks

Hyperentangled states are highly efficient and resource economical. This is because they enhance the quantum information encoding capabilities due to the correlated engagement of more than one degree of freedom of the same quantum entity while keeping the physical resources at their minimum. Therefore, initially the photonic hyperentangled states have been explored extensively but the generation and respective manipulation of the atomic counterpart states are still limited to only few proposals. In this work, we propose a new and feasible scheme to engineer the atomic hyperentangled cluster and ring graph states invoking cavity QED technique for applicative relevance to quantum biology and quantum communications utilizing the complex quantum networks. These states are engineered using both external quantized momenta states and energy levels of neutral atoms under off-resonant and resonant Atomic Bragg Diffraction (ABD) technique. The study of dynamical capacity and potential efficiency have certainly enhanced the range of usefulness of these states. In order to assess the operational behavior of such states when subjected to a realistic noise environment has also been simulated, demonstrating long enough sustainability of the proposed states. Moreover, experimental feasibility of the proposed scheme has also been elucidated under the prevailing cavity-QED research scenario.

quant-ph↗