arXiv subjects

Rahul Sharma

Publications and source records attributed to Rahul Sharma.

At least 55 records · Page 3Linked to original sources

arXiv2024

Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern

The timely identification of significant memory concern (SMC) is crucial for proactive cognitive health management, especially in an aging population. Detecting SMC early enables timely intervention and personalized care, potentially slowing cognitive disorder progression. This study presents a state-of-the-art review followed by a comprehensive evaluation of machine learning models within the randomized neural networks (RNNs) and hyperplane-based classifiers (HbCs) family to investigate SMC diagnosis thoroughly. Utilizing the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset, 111 individuals with SMC and 111 healthy older adults are analyzed based on T1W magnetic resonance imaging (MRI) scans, extracting rich features. This analysis is based on baseline structural MRI (sMRI) scans, extracting rich features from gray matter (GM), white matter (WM), Jacobian determinant (JD), and cortical thickness (CT) measurements. In RNNs, deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) emerge as the best classifiers in terms of performance metrics in the identification of SMC. In HbCs, Kernelized pinball general twin support vector machine (Pin-GTSVM-K) excels in CT and WM features, whereas Linear Pin-GTSVM (Pin-GTSVM-L) and Linear intuitionistic fuzzy TSVM (IFTSVM-L) performs well in the JD and GM features sets, respectively. This comprehensive evaluation emphasizes the critical role of feature selection and model choice in attaining an effective classifier for SMC diagnosis. The inclusion of statistical analyses further reinforces the credibility of the results, affirming the rigor of this analysis. The performance measures exhibit the suitability of this framework in aiding researchers with the automated and accurate assessment of SMC. The source codes of the algorithms and datasets used in this study are available at https://github.com/mtanveer1/SMC.

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arXiv2024

Energy dependence of Quasi-periodic oscillations in accreting X-ray pulsars

We present the results from an investigation of the energy dependence of Quasi-Periodic Oscillations (QPOs) exhibited by accreting X-ray pulsars using data from archival \textit{XMM-Newton}, \textit{NuSTAR}, \textit{RXTE}, and \textit{NICER} observations. In a search for the presence of QPOs in 99 \textit{XMM-Newton} and \textit{NuSTAR} observations, we detected QPOs in eleven observations of five sources, viz., 4U 1626--67 (48 mHz), IGR J19294+1816 (30 mHz), V 0332+53 (2, 18 and 40 mHz), Cen X--3 (30 mHz), and XTE J1858+034 (180 mHz). A positive correlation of the QPO rms amplitude with energy is exhibited by 4U 1626--67, IGR J19294+1816, Cen X--3 and XTE J1858+034, while no energy dependence is observed in V 0332+53. We also analysed the energy spectrum to decouple thermal (soft-excess) from non-thermal emission and determine if the soft-excess has different QPO properties. We found no evidence for different QPO characteristics of the soft excess. The \textit{NuSTAR} observations of V 0332+53 during the Type-I outburst in 2016 show the presence of twin QPOs at 2.5 mHz and 18 mHz, while the \textit{XMM-Newton} and \textit{NuSTAR} observations during the Type-II outburst in 2015 show a QPO at 40 mHz. We review the observed QPO properties in the context of QPOs found in other types of accreting sources and the models usually used to explain the QPOs in accreting X-ray pulsars.

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arXiv2024

Enabling Memory Safety of C Programs using LLMs

Memory safety violations in low-level code, written in languages like C, continues to remain one of the major sources of software vulnerabilities. One method of removing such violations by construction is to port C code to a safe C dialect. Such dialects rely on programmer-supplied annotations to guarantee safety with minimal runtime overhead. This porting, however, is a manual process that imposes significant burden on the programmer and, hence, there has been limited adoption of this technique. The task of porting not only requires inferring annotations, but may also need refactoring/rewriting of the code to make it amenable to such annotations. In this paper, we use Large Language Models (LLMs) towards addressing both these concerns. We show how to harness LLM capabilities to do complex code reasoning as well as rewriting of large codebases. We also present a novel framework for whole-program transformations that leverages lightweight static analysis to break the transformation into smaller steps that can be carried out effectively by an LLM. We implement our ideas in a tool called MSA that targets the CheckedC dialect. We evaluate MSA on several micro-benchmarks, as well as real-world code ranging up to 20K lines of code. We showcase superior performance compared to a vanilla LLM baseline, as well as demonstrate improvement over a state-of-the-art symbolic (non-LLM) technique.

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arXiv2024

A comprehensive study of orbital evolution of LMC X-4: Existence of a second derivative of the orbital period

We report here results from pulse arrival time delay analysis of the eclipsing high mass X-ray binary pulsar LMC X-4 using observations made with the Rossi X-ray Timing Explorer, XMM-Newton, NuSTAR and AstroSat. Combining the orbital parameters determined from these observations with the historical measurements dating back to 1998, we have extended the $T_{π/2}$ epoch history of LMC X-4 by about 4600 binary orbits spanning about 18 years. We also report mid-eclipse time measurements ($T_{ecl}$) using data obtained from wide-field X-ray monitors of MAXI-GSC and Swift-BAT. Combining the new $T_{π/2}$ and $T_{ecl}$ estimates with all the previously reported values, we have significantly improved the orbital evolution measurement, which indicates that the orbital period is evolving at a time scale ($P_{\rm orb}/\dot{P}_{\rm orb}$ ) of about 0.8 Myr. For the first time in an accreting X-ray pulsar system, we confirm the existence of a second derivative of the orbital period, having an evolution time scale ($\dot{P}_{orb}/\ddot{P}_{orb}$) of about 55 yr. Detection of a second derivative of the orbital period in LMC X-4 makes its orbital evolution timescale more uncertain, which may also be true for other HMXBs. Independent solutions for the orbital evolution measurement using the mid-eclipse data and the pulse timing data are consistent with each other, and help us put an upper limit of 0.009 on the eccentricity of the binary system.

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arXiv2024

Flares during Eclipses of High Mass X-ray Binary Systems Vela X-1, 4U 1700-37, and LMC X-4

In eclipsing X-ray binary systems, the direct X-ray emission is blocked by the companion star during the eclipse. We observe only reprocessed emission that contains clues about the environment of the compact object and its chemical composition, ionization levels, etc. We have found flares in some X-ray binaries during their eclipses. The study of eclipse flares provides additional clues regarding the size of the reprocessing region and helps distinguish between different components of the X-ray spectrum observed during the eclipse. In the archival data, we searched for flares during eclipses of high-mass X-ray binaries and found flares in three sources: Vela X-1, LMC X-4, and 4U 1700-37. Comparing spectral properties of the eclipse flare and non-flare data, we found changes in the power-law photon index in all three sources and multiple emission lines in Vela X-1 and 4U 1700-37. The fluxes of prominent emission lines showed a similar increase as the overall X-ray flux during the eclipse flare, suggesting the lines originate in the binary environment and not in the interstellar medium. We also observed a soft excess in 4U 1700-37 that remains unchanged during both eclipse flare and non-flare states. Our analysis suggests that this emission originates from the extremely thin shell of the stellar wind surrounding the photosphere of its companion star. The detection of short (100-200 seconds) count-rate doubling timescale in 4U 1700-37 and LMC X-4 indicates that the eclipse reprocessing occurs in a region larger than, but comparable to the size of the companion star.

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arXiv2024

Detection of a cyclotron line in the Be X-ray pulsar IGR J06074$+$2205

IGR J0607.4+2205 is a transient Be X-ray binary discovered two decades ago. IGR J0607.4+2205 underwent an outburst in 2023 during which it was observed twice with \textit{NuSTAR}. The main goal of this work is to model the broadband X-ray spectrum of IGR J0607.4+2205 during the outburst and to study the variations of the spectral and timing features at different intensities. We extracted the light curve and spectrum of the source from the two \textit{NuSTAR} observations carried out during the recent outburst in the energy range of 3$-$78 keV. We used the epoch folding technique to find pulsation from the source and to study the changes in emission characteristics from the source with energy across an order of magnitude variation in source luminosity. IGR J0607.4+2205 shows pulsations with a period of $\sim$347.6 s during both the observations, with a pulse fraction of $\geq$50\%. The broadband spectrum of the source was modelled using a power-law continuum with a high-energy cutoff. During the first observation, a cyclotron absorption line at $\sim$51 keV was also present in the source with an optical depth of $\sim$1.3. However, no cyclotron line feature was detected in the second observation when the source was an order of magnitude fainter. Additionally, soft excess was detected in the second observation, which was modelled with a black body component emerging from close to the neutron star (NS). We report the first ever detection of a cyclotron line in the broadband spectrum of IGR J0607.4+2205 centred at 51$\pm$1 keV. The magnetic field strength of the NS is estimated to be $\sim$4$\times$$10^{12}$ G from the centroid energy of the absorption line. A significant change is observed in the pulse profile with luminosity during the decay of the outburst, indicating an associated change in the beaming pattern.

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arXiv2024

Evaluating Atypical Gaze Patterns through Vision Models: The Case of Cortical Visual Impairment

A wide range of neurological and cognitive disorders exhibit distinct behavioral markers aside from their clinical manifestations. Cortical Visual Impairment (CVI) is a prime example of such conditions, resulting from damage to visual pathways in the brain, and adversely impacting low- and high-level visual function. The characteristics impacted by CVI are primarily described qualitatively, challenging the establishment of an objective, evidence-based measure of CVI severity. To study those characteristics, we propose to create visual saliency maps by adequately prompting deep vision models with attributes of clinical interest. After extracting saliency maps for a curated set of stimuli, we evaluate fixation traces on those from children with CVI through eye tracking technology. Our experiments reveal significant gaze markers that verify clinical knowledge and yield nuanced discriminability when compared to those of age-matched control subjects. Using deep learning to unveil atypical visual saliency is an important step toward establishing an eye-tracking signature for severe neurodevelopmental disorders, like CVI.

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arXiv2024

Ranking LLM-Generated Loop Invariants for Program Verification

Synthesizing inductive loop invariants is fundamental to automating program verification. In this work, we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable of synthesizing loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariants. This can lead to a large number of calls to a program verifier to establish an invariant. To address this issue, we propose a {\it re-ranking} approach for the generated results of LLMs. We have designed a ranker that can distinguish between correct inductive invariants and incorrect attempts based on the problem definition. The ranker is optimized as a contrastive ranker. Experimental results demonstrate that this re-ranking mechanism significantly improves the ranking of correct invariants among the generated candidates, leading to a notable reduction in the number of calls to a verifier. The source code and the experimental data for this paper are available in \url{https://github.com/microsoft/NeuralInvariantRanker}.

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arXiv2023

$\textit{Insights}$ into the phase-dependent cyclotron line feature in XTE J1946+274: An $\textit{AstroSat}$ and $\textit{Insight}$-HXMT view

XTE J1946+274 is a Be/X-ray binary with a 15.8s spin period and 172 d orbital period. Using $\textit{RXTE/PCA}$ data of the 1998 outburst, a cyclotron line around 37 keV was reported. The presence of this line, its dependence on the pulse phase, and its variation with luminosity have been of some debate since. In this work, we present the reanalysis of two $\textit{AstroSat}$ observations: one made during the rising phase of the 2018 outburst and the other during the declining phase of the 2021 outburst. We also present a new analysis of the $\textit{Insight}$-HXMT observations of the source at the peak of the 2018 outburst. We find the source to be spinning up over the course of the outburst and spinning down between the two outbursts. We report the presence of a higher cyclotron line energy using the 2018 $\textit{AstroSat}$ observation ($\sim 45$ keV) and 2018 $\textit{Insight}$-HXMT observation ($\sim$ 50 keV) and a line at $\sim$ 40 keV during the declining phase of the 2021 outburst using data from $\textit{AstroSat}$. We also investigate the pulse phase dependence of the cyclotron line parameters and find that the line is significantly detected in all the phases of both $\textit{AstroSat}$ observations, along with showing variation with the pulse phase. This differs from the previous results reported using $\textit{BeppoSAX}$ and $\textit{NuSTAR}$. We explain this behaviour of the cyclotron line to be due to photon spawning and different accretion column radii at the two poles of this neutron star.

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arXiv2023

Development and Validation of a Dynamic Operating Envelopes-enabled Demand Response Scheme in Low-voltage Distribution Networks

Dynamic operating envelopes (DOEs) offer an attractive solution for maintaining network integrity amidst increasing penetration of distributed energy resources (DERs) in low-voltage (LV) networks. Currently, the focus of DOEs primarily revolves around active power exports of rooftop photovoltaic (PV) generation, often neglecting the impact of demand response (DR). This paper presents a two-stage, coordinated approach for residential DR participation in electricity markets under the DOE framework. In the first stage, the distribution network service provider (DNSP) adopts a convex hull technique to establish DOEs at each customer point-of-connection (POC). In the second stage, the demand response aggregator (DRA) utilises DOEs assigned by the DNSP to develop a hierarchical control scheme for tracking a load set-point signal without jeopardising network statutory limits. To assess the effectiveness of the proposed control scheme in a practical setting, software-in-the-loop (SIL) tests are performed in a grid simulator, considering a real residential feeder with realistic household load and generation profiles. Simulation validations suggest that the DRA can provide precise DR while honouring network statutory limits and maintaining end-user thermal comfort. Furthermore, the overall approach is compliant with the market dispatch interval and preserves end-user data privacy.

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arXiv2023

Finding Inductive Loop Invariants using Large Language Models

Loop invariants are fundamental to reasoning about programs with loops. They establish properties about a given loop's behavior. When they additionally are inductive, they become useful for the task of formal verification that seeks to establish strong mathematical guarantees about program's runtime behavior. The inductiveness ensures that the invariants can be checked locally without consulting the entire program, thus are indispensable artifacts in a formal proof of correctness. Finding inductive loop invariants is an undecidable problem, and despite a long history of research towards practical solutions, it remains far from a solved problem. This paper investigates the capabilities of the Large Language Models (LLMs) in offering a new solution towards this old, yet important problem. To that end, we first curate a dataset of verification problems on programs with loops. Next, we design a prompt for exploiting LLMs, obtaining inductive loop invariants, that are checked for correctness using sound symbolic tools. Finally, we explore the effectiveness of using an efficient combination of a symbolic tool and an LLM on our dataset and compare it against a purely symbolic baseline. Our results demonstrate that LLMs can help improve the state-of-the-art in automated program verification.

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arXiv2023

Discretizing Numerical Attributes: An Analysis of Human Perceptions

Machine learning (ML) has employed various discretization methods to partition numerical attributes into intervals. However, an effective discretization technique remains elusive in many ML applications, such as association rule mining. Moreover, the existing discretization techniques do not reflect best the impact of the independent numerical factor on the dependent numerical target factor. This research aims to establish a benchmark approach for numerical attribute partitioning. We conduct an extensive analysis of human perceptions of partitioning a numerical attribute and compare these perceptions with the results obtained from our two proposed measures. We also examine the perceptions of experts in data science, statistics, and engineering by employing numerical data visualization techniques. The analysis of collected responses reveals that $68.7\%$ of human responses approximately closely align with the values generated by our proposed measures. Based on these findings, our proposed measures may be used as one of the methods for discretizing the numerical attributes.

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arXiv2023

AstroSat observation of the magnetar SGR J1830$-$0645 during its first detected X-ray outburst

We present here timing and spectral analyses of SGR J1830$-$0645 based on an AstroSat observation carried out on 2020 October 16, about a week after the onset of its first detected X-ray outburst. Using data taken with the Soft X-ray Telescope (SXT) and Large Area X-ray Proportional Counter (LAXPC), we have detected 0.9$-$10 keV coherent pulsations at a period of $\sim$10.4 s. The pulse profiles were single-peaked, asymmetric and consisted of minor peaks attributable to hotspots on the neutron star surface. The pulsed fraction evolved significantly with energy, increasing to energies around 5 keV with a steep drop thereafter. The 0.9--25 keV SXT--LAXPC energy spectrum is best described with two thermal components having temperatures $\sim$0.46 and $\sim$1.1 keV (emission radii of $\sim$2.4 and $\sim$0.65 km, respectively, assuming a distance of 4 kpc) along with a power-law component having a photon index of $\sim$0.39. We report the detection of 67 X-ray bursts having an average duration of $\sim$33 ms. The brightest burst lasted for about 90 ms and had a 3--25 keV fluence of $\sim 5 \times 10^{-9}$ erg cm$^{-2}$.

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arXiv2023

Probing spectral and timing properties of the X-ray pulsar RX J0440.9+4431 in the giant outburst of 2022-2023

The X-ray pulsar RX J0440.9+4431 went through a giant outburst in 2022 and reached a record-high flux of 2.3 Crab, as observed by Swift/BAT. We study the evolution of different spectral and timing properties of the source using NICER observations. The pulse period is found to decrease from 208 s to 205 s, and the pulse profile evolves significantly with energy and luminosity. The hardness ratio and hardness intensity diagram (HID) show remarkable evolution during the outburst. The HID turns towards the diagonal branch from the horizontal branch above a transition (critical) luminosity, suggesting the presence of two accretion modes. Each NICER spectrum can be described using a cutoff power law with a blackbody component and a Gaussian at 6.4 keV. At higher luminosities, an additional Gaussian at 6.67 keV is used. The observed photon index shows negative and positive correlations with X-ray flux below and above the critical luminosity, respectively. The evolution of spectral and timing parameters suggests a possible change in the emission mechanism and beaming pattern of the pulsar depending on the spectral transition to sub- and super-critical accretion regimes. Based on the critical luminosity, the magnetic field of the neutron star can be estimated in the order of 10$^{12}$ or 10$^{13}$ G, assuming different theoretical models. Moreover, the observed iron emission line evolves from a narrow to a broad feature with luminosity. Two emission lines originating from neutral and highly ionized Fe atoms were evident in the spectra around 6.4 keV and 6.67 keV (higher luminosities).

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arXiv2023

4U 1626-67 Returns to Spin-Down: Timing features toe the line

We present a comprehensive analysis of X-ray pulsar 4U 1626-67 during its current spin-down (2SD) state, following a recent torque reversal. Since its discovery, this ultra-compact binary has experienced multiple torque states, transitioning from spin-up (1SU) during 1977-1990 to spin-down (1SD) during 1990-2008, and again spin-up (2SU) until 2023. From NuSTAR observation of May 2023, we have investigated the timing and spectral properties of this pulsar during its 2SD phase, while also comparing them with previous spin-up-down states. For energies upto 8 keV, a distinct bi-horned pulse profile was observed during the spin-up phase, while several sub-structures emerged during spin-down. Beyond 8 keV, a broad asymmetric peak was consistently observed across all torque states. The pulse fraction during the 2SD phase was higher than that during 2SU phase. A prominent ~46.8 mHz quasi-periodic oscillation has been exclusively detected during the spin-down phase. The broadband spectrum during the 2SD phase is described by empirical NPEX model, cyclotron absorption feature and its first harmonic. The spectrum during 2SU phase requires an additional blackbody component and asymmetry in the cyclotron absorption line. A significant flux drop by a factor of ~3 in the 2SD was observed.

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arXiv2023

StaticFixer: From Static Analysis to Static Repair

Static analysis tools are traditionally used to detect and flag programs that violate properties. We show that static analysis tools can also be used to perturb programs that satisfy a property to construct variants that violate the property. Using this insight we can construct paired data sets of unsafe-safe program pairs, and learn strategies to automatically repair property violations. We present a system called \sysname, which automatically repairs information flow vulnerabilities using this approach. Since information flow properties are non-local (both to check and repair), \sysname also introduces a novel domain specific language (DSL) and strategy learning algorithms for synthesizing non-local repairs. We use \sysname to synthesize strategies for repairing two types of information flow vulnerabilities, unvalidated dynamic calls and cross-site scripting, and show that \sysname successfully repairs several hundred vulnerabilities from open source {\sc JavaScript} repositories, outperforming neural baselines built using {\sc CodeT5} and {\sc Codex}. Our datasets can be downloaded from \url{http://aka.ms/StaticFixer}.

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arXiv2023

Numerical Association Rule Mining: A Systematic Literature Review

Numerical association rule mining is a widely used variant of the association rule mining technique, and it has been extensively used in discovering patterns and relationships in numerical data. Initially, researchers and scientists integrated numerical attributes in association rule mining using various discretization approaches; however, over time, a plethora of alternative methods have emerged in this field. Unfortunately, the increase of alternative methods has resulted into a significant knowledge gap in understanding diverse techniques employed in numerical association rule mining -- this paper attempts to bridge this knowledge gap by conducting a comprehensive systematic literature review. We provide an in-depth study of diverse methods, algorithms, metrics, and datasets derived from 1,140 scholarly articles published from the inception of numerical association rule mining in the year 1996 to 2022. In compliance with the inclusion, exclusion, and quality evaluation criteria, 68 papers were chosen to be extensively evaluated. To the best of our knowledge, this systematic literature review is the first of its kind to provide an exhaustive analysis of the current literature and previous surveys on numerical association rule mining. The paper discusses important research issues, the current status, and future possibilities of numerical association rule mining. On the basis of this systematic review, the article also presents a novel discretization measure that contributes by providing a partitioning of numerical data that meets well human perception of partitions.

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arXiv2023

Training with Mixed-Precision Floating-Point Assignments

When training deep neural networks, keeping all tensors in high precision (e.g., 32-bit or even 16-bit floats) is often wasteful. However, keeping all tensors in low precision (e.g., 8-bit floats) can lead to unacceptable accuracy loss. Hence, it is important to use a precision assignment -- a mapping from all tensors (arising in training) to precision levels (high or low) -- that keeps most of the tensors in low precision and leads to sufficiently accurate models. We provide a technique that explores this memory-accuracy tradeoff by generating precision assignments for convolutional neural networks that (i) use less memory and (ii) lead to more accurate convolutional networks at the same time, compared to the precision assignments considered by prior work in low-precision floating-point training. We evaluate our technique on image classification tasks by training convolutional networks on CIFAR-10, CIFAR-100, and ImageNet. Our method typically provides > 2x memory reduction over a baseline precision assignment while preserving training accuracy, and gives further reductions by trading off accuracy. Compared to other baselines which sometimes cause training to diverge, our method provides similar or better memory reduction while avoiding divergence.

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