arXiv subjects

Muhammad Arif

Publications and source records attributed to Muhammad Arif.

At least 19 recordsLinked to original sources

arXiv2026

Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation

This paper describes the Rosetta system for Subtask 1 (Context-Aware English-to-Dialectal Arabic Dialogue Translation) of the AlexandriaX shared task, participating in both constrained and unconstrained tracks. The approach fine-tunes a LoRA adapter on NileChat-3B using structured system/user prompts that condition generation on dialect and dialogue context. For the unconstrained track, the adapter is additionally pretrained on MADAR and PADIC. Rosetta ranked 4th in the constrained track (spBLEU 26.10) and 5th in the unconstrained track (spBLEU 25.09). The experimental results demonstrate that external pretraining helps only two of thirteen dialects while slightly hurting overall performance, suggesting negative transfer.

cs.CL↗

arXiv2026

EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages

Secure patient-provider messages contain clinically important communication behaviors that are difficult to characterize manually at scale. The Electronic Patient-Provider Communication (EPPC) framework provides an ontology for coding these behaviors, but automated extraction remains challenging because predictions must preserve fine-grained code/sub-code structure while grounding annotations in message text. We developed EPPC-OASIS, an ontology-aware adaptation approach for structured EPPC extraction, and combined it with deployable inference-refinement procedures designed to improve the coherence of final annotations. EPPC-OASIS augments supervised fine-tuning with a Wasserstein alignment objective that encourages alignment between model representation neighborhoods and EPPC ontology-derived neighborhoods, while inference refinement uses verification, self-consistency, hybrid correction, and selection or ensembling to address residual prediction errors. We evaluated the framework on a de-identified corpus of secure patient-provider messages against prompting, supervised fine-tuning, preference-based, and robustness-oriented baselines across multiple open-weight language models. Across model families, the best deployable pipeline achieved 77.13% Code+Sub-code F1 and 63.83% Triplet F1, corresponding to modest but consistent absolute gains of +1.39 and +2.12 F1 points over the strongest supervised fine-tuning baseline. These results suggest that ontology-aware adaptation with structured inference refinement can support scalable retrospective EPPC mining, although external validation is needed before operational use.

cs.AI↗

arXiv2025

A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression

Predictive maintenance (PdM) has become a crucial element of modern industrial practice. PdM plays a significant role in operational dependability and cost management by decreasing unforeseen downtime and optimizing asset life cycle management. Machine learning and deep learning have enabled more precise forecasts of equipment failure and remaining useful life (RUL). Although many studies have been conducted on PdM, there has not yet been a standalone comparative study between regression- and classification-based approaches. In this review, we look across a range of PdM methodologies, while focusing more strongly on the comparative use of classification and regression methods in prognostics. While regression-based methods typically provide estimates of RUL, classification-based methods present a forecast of the probability of failure across defined time intervals. Through a comprehensive analysis of recent literature, we highlight key advancements, challenges-such as data imbalance and high-dimensional feature spaces-and emerging trends, including hybrid approaches and AI-enabled prognostic systems. This review aims to provide researchers and practitioners with an awareness of the strengths and compromises of various PdM methods and to help identify future research and build more robust, directed adaptive maintenance systems. Future work may include a systematic review of practical aspects such as public datasets, benchmarking platforms, and open-source tools to support the advancement of PdM research.

cs.LG↗

arXiv2025

EDU-NER-2025: Named Entity Recognition in Urdu Educational Texts using XLM-RoBERTa with X (formerly Twitter)

Named Entity Recognition (NER) plays a pivotal role in various Natural Language Processing (NLP) tasks by identifying and classifying named entities (NEs) from unstructured data into predefined categories such as person, organization, location, date, and time. While extensive research exists for high-resource languages and general domains, NER in Urdu particularly within domain-specific contexts like education remains significantly underexplored. This is Due to lack of annotated datasets for educational content which limits the ability of existing models to accurately identify entities such as academic roles, course names, and institutional terms, underscoring the urgent need for targeted resources in this domain. To the best of our knowledge, no dataset exists in the domain of the Urdu language for this purpose. To achieve this objective this study makes three key contributions. Firstly, we created a manually annotated dataset in the education domain, named EDU-NER-2025, which contains 13 unique most important entities related to education domain. Second, we describe our annotation process and guidelines in detail and discuss the challenges of labelling EDU-NER-2025 dataset. Third, we addressed and analyzed key linguistic challenges, such as morphological complexity and ambiguity, which are prevalent in formal Urdu texts.

cs.CL↗

arXiv2024

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study

The generation of high-quality medical time series data is essential for advancing healthcare diagnostics and safeguarding patient privacy. Specifically, synthesizing realistic phonocardiogram (PCG) signals offers significant potential as a cost-effective and efficient tool for cardiac disease pre-screening. Despite its potential, the synthesis of PCG signals for this specific application received limited attention in research. In this study, we employ and compare three state-of-the-art generative models from different categories - WaveNet, DoppelGANger, and DiffWave - to generate high-quality PCG data. We use data from the George B. Moody PhysioNet Challenge 2022. Our methods are evaluated using various metrics widely used in the previous literature in the domain of time series data generation, such as mean absolute error and maximum mean discrepancy. Our results demonstrate that the generated PCG data closely resembles the original datasets, indicating the effectiveness of our generative models in producing realistic synthetic PCG data. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs, in order to address the current scarcity of abnormal data. We hope to improve the robustness and accuracy of diagnostic tools in cardiology, enhancing their effectiveness in detecting heart murmurs.

cs.LG↗

arXiv2023

Joy Learning: Smartphone Application For Children With Parkinson Disease

Parkinson's is a Neurologic disorder that not only affects the human body but also their social and personal life. Especially children having the Parkinson's disease come up with infinite difficulties in different areas of life mostly in social interaction, communication, connectedness, and other skills such as thinking, reasoning, learning, remembering. This study gives the solution to learning social skills by using smartphone applications. The children having Parkinson's disease (juvenile) can learn to solve social and common problems by observing real-life situations that cannot be explained properly by instructors. The result shows that the application will enhance their involvement in learning and solving a complex problem.

cs.CY↗

arXiv2022

Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning

Given the current social distance restrictions across the world, most individuals now use social media as their major medium of communication. Millions of people suffering from mental diseases have been isolated due to this, and they are unable to get help in person. They have become more reliant on online venues to express themselves and seek advice on dealing with their mental disorders. According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, etc., are immensely common and have affected an individuals' physical health. Recently Artificial Intelligence (AI) methods have been presented to help mental health providers, including psychiatrists and psychologists, in decision making based on patients' authentic information (e.g., medical records, behavioral data, social media utilization, etc.). AI innovations have demonstrated predominant execution in numerous real-world applications broadening from computer vision to healthcare. This study analyzes unstructured user data on the Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD. We trained traditional machine learning, deep learning, and transfer learning multi-class models to detect mental disorders of individuals. This effort will benefit the public health system by automating the detection process and informing appropriate authorities about people who require emergency assistance.

cs.LG↗

arXiv2020

Ultrasonic Actuation of a Fine-Needle Improves Biopsy Yield

Despite the ubiquitous use over the past 150 years, the functions of the current medical needle are facilitated only by mechanical shear and cutting by the needle tip,i.e.the lancet. In this study, we demonstrate how nonlinear ultrasonics (NLU) extends the functionality of the medical needle far beyond its present capability. The NLU actions were found to be localized to the proximity of the needle tip, the SonoLancet, but the effects extend several millimeters from the physical needle boundary. The observed nonlinear phenomena, transient cavitation, fluid streams, translation of micro- and nanoparticles and atomization, were quantitatively characterized. In the fine-needle biopsy application, the SonoLancet contributed to obtaining tissue cores with increase in tissue yield by 3-6x in different tissue types compared to conventional needle biopsy technique using the same 21G needle. In conclusion, the SonoLancet could be of interest to several other medical applications, including drug or gene delivery, cell modulation, and minimally invasive surgical procedures.

physics.med-ph↗

arXiv2020

Initial successive coefficients for certain classes of univalent functions involving the exponential function

Let $\mathcal{S}$ denote the family of all functions that are analytic and univalent in the unit disk $\mathbb{D}:=\{z: |z|<1\}$ and satisfy $f(0)=f^{\prime}(0)-1=0$. In the present paper, we consider certain subclasses of univalent functions associated with the exponential function, and obtain the sharp upper bounds on the initial coefficients and the difference of initial successive coefficients for functions belonging to these classes.

math.CV↗

arXiv2019

A study of multivalent q-starlike functions connected with circular domain

In the present article, our aim is to examine some useful problems including the convolution problem, sufficiency criteria, coefficient estimates and Fekete-Szego type inequalities for a new subfamily of analytic and multivalent functions associated with circular domain. In addition, we also define and study a Bernardi integral operator in its $q$-extension for multivalent functions.

math.CA↗

arXiv2019

Open-source RANs in practice: an over-the-air deployment for 5G MEC

Edge computing that leverages cloud resources to the proximity of user devices is seen as the future infrastructure for distributed applications. However, developing and deploying edge applications, that rely on cellular networks, is burdensome. Such network infrastructures are often based on proprietary components, each with unique programming abstractions and interfaces. To facilitate straightforward deployment of edge applications, we introduce OSS based RAN on OTA commercial spectrum with DevOps capabilities. OSS allows software modifications and integrations of the system components, e.g., EPC and edge hosts running applications, required for new data pipelines and optimizations not addressed in standardization. Such an OSS infrastructure enables further research and prototyping of novel end-user applications in an environment familiar to software engineers without telecommunications background. We evaluated the presented infrastructure with E2E OTA testing, resulting in 7.5MB/s throughput and latency of 21ms, which shows that the presented infrastructure provides low latency for edge applications.

cs.NI↗

arXiv2017

Increased interference fringe visibility from the post fabrication heat treatment of a perfect crystal silicon neutron interferometer

Construction of silicon neutron interferometers requires a perfect crystal silicon ingot (5 cm to 30 cm long) be machined such that Bragg diffracting "blades" protrude from a common base. Leaving the interferometer blades connected to the same base preserves Bragg plane alignment, but if the interferometer contains crystallographic misalignments of greater than about 10 nrad between the blades, interference fringe visibility begins to suffer. Additionally, the parallelism, thickness, and distance between the blades must be machined to micron tolerances. Traditionally, interferometers do not exhibit usable interference fringe visibility until 30 $\mu$m to 60 $\mu$m of machining surface damage is chemically etched away. However, if too much material is removed, the uneven etch rates across the interferometer cause the shape of the crystal blades to be outside of the required tolerances. As a result, the ultimate interference fringe visibility varies widely among neutron interferometers that are created under similar conditions. We find that annealing a previously etched interferometer at $800^\circ \mathrm{C}$ dramatically increased interference fringe visibility from 23 % to 90 %. The Bragg plane misalignments were also measured before and after annealing using neutron rocking curve interference peaks, showing that Bragg plane alignment was improved across the interferometer after annealing. This suggests that current interferometers with low fringe visibility may be salvageable and that annealing may become an important step in the fabrication process of future neutron interferometers, leading to less need for chemical etching and larger, more exotic neutron interferometers.

physics.ins-det↗

arXiv2017

Scene Recognition by Combining Local and Global Image Descriptors

Object recognition is an important problem in computer vision, having diverse applications. In this work, we construct an end-to-end scene recognition pipeline consisting of feature extraction, encoding, pooling and classification. Our approach simultaneously utilize global feature descriptors as well as local feature descriptors from images, to form a hybrid feature descriptor corresponding to each image. We utilize DAISY features associated with key points within images as our local feature descriptor and histogram of oriented gradients (HOG) corresponding to an entire image as a global descriptor. We make use of a bag-of-visual-words encoding and apply Mini- Batch K-Means algorithm to reduce the complexity of our feature encoding scheme. A 2-level pooling procedure is used to combine DAISY and HOG features corresponding to each image. Finally, we experiment with a multi-class SVM classifier with several kernels, in a cross-validation setting, and tabulate our results on the fifteen scene categories dataset. The average accuracy of our model was 76.4% in the case of a 40%-60% random split of images into training and testing datasets respectively. The primary objective of this work is to clearly outline the practical implementation of a basic screne-recognition pipeline having a reasonable accuracy, in python, using open-source libraries. A full implementation of the proposed model is available in our github repository.

cs.CV↗

arXiv2016

Decoupling of a Neutron Interferometer from Temperature Gradients

Neutron interferometry enables precision measurements that are typically operated within elaborate, multi-layered facilities which provide substantial shielding from environmental noise. These facilities are necessary to maintain the coherence requirements in a perfect crystal neutron interferometer which is extremely sensitive to local environmental conditions such as temperature gradients across the interferometer, external vibrations, and acoustic waves. The ease of operation and breadth of applications of perfect crystal neutron interferometry would greatly benefit from a mode of operation which relaxes these stringent isolation requirements. Here, the INDEX Collaboration and National Institute of Standards and Technology demonstrates the functionality of a neutron interferometer in vacuum and characterize the use of a compact vacuum chamber enclosure as a means to isolate the interferometer from spatial temperature gradients and time-dependent temperature fluctuations. The vacuum chamber is found to have no depreciable effect on the performance of the interferometer (contrast) while improving system stability, thereby showing that it is feasible to replace large temperature isolation and control systems with a compact vacuum enclosure for perfect crystal neutron interferometry.

physics.ins-det↗

arXiv2016

Ranking academic institutions on potential paper acceptance in upcoming conferences

The crux of the problem in KDD Cup 2016 involves developing data mining techniques to rank research institutions based on publications. Rank importance of research institutions are derived from predictions on the number of full research papers that would potentially get accepted in upcoming top-tier conferences, utilizing public information on the web. This paper describes our solution to KDD Cup 2016. We used a two step approach in which we first identify full research papers corresponding to each conference of interest and then train two variants of exponential smoothing models to make predictions. Our solution achieves an overall score of 0.7508, while the winning submission scored 0.7656 in the overall results.

cs.AI↗

arXiv2016

Broadband Neutron Interferometer

We demonstrate a two phase-grating, multi-beam neutron interferometer by using a modified Ronchi setup in a far-field regime. The functionality of the interferometer is based on the universal \moire effect that was recently implemented for X-ray phase-contrast imaging in the far-field regime. Interference fringes were achieved with monochromatic, bichromatic, and polychromatic neutron beams; for both continuous and pulsed beams. This far-field neutron interferometry allows for the utilization of the full neutron flux for precise measurements of potential gradients, and expands neutron phase-contrast imaging techniques to more intense polycromatic neutron beams.

physics.ins-det↗

arXiv2016

Spin-Orbit States of Neutron Wavepackets

We propose a method to prepare an entangled spin-orbit state between the spin and the orbital angular momenta of a neutron wavepacket. This spin-orbit state is created by passing neutrons through the center of a quadrupole magnetic field, which provides a coupling between the spin and orbital degrees of freedom. A Ramsey fringe type measurement is suggested as a means of verifying the spin-orbit correlations.

quant-ph↗

arXiv2016

Decoherence Free Neutron Interferometry

Perfect single-crystal neutron interferometers are adversely sensitive to environmental disturbances, particularly mechanical vibrations. The sensitivity to vibrations results from the slow velocity of thermal neutrons and the long measurement time that are encountered in a typical experiment. Consequently, to achieve a good interference solutions for reducing vibration other than those normally used in optical experiments must be explored. Here we introduce a geometry for a neutron interferometer that is less sensitive to low-frequency vibrations. This design may be compared with both dynamical decoupling methods and decoherence-free subspaces that are described in quantum information processing. By removing the need for bulky vibration isolation setups, this design will make it easier to adopt neutron interferometry to a wide range of applications and increase its sensitivity.

quant-ph↗