arXiv subjects

Praveen Kumar

Publications and source records attributed to Praveen Kumar.

At least 37 records · Page 2Linked to original sources

arXiv2025

Exploiting solute segregation and partitioning to the deformation-induced planar defects and nano-martensite in designing ultra-strong Co-Ni base alloys

Single-phase, multi-elements (three or more) with high concentrations show exceptional tensile strength up to ~ 0.8-1.2 GPa. However, they possess a very low 0.2% yield strength (YS), i.e., they can be permanently deformed at very low-stress levels of 300 to 600 MPa. Here, we reveal by exploiting atomic-scale solute interactions with the deformation-induced structures to design ultra-strong single-phase alloys with YS > 2 GPa. This was achieved by controlled thermomechanical processing that introduces stacking-faults (SFs), nano-twins (NTs), and nano-martensite ε-laths (NMLs) during cold deformation followed by facilitating solute segregation/partitioning to them by tempering at intermediate temperature. We demonstrate the phenomena in a low stacking faulty energy multi-component (face-centered-cubic, fcc structured) Co-33Ni-24Cr alloy (all in at.%) containing 5at.% Mo as a solute. It is also shown that the degree of strengthening after tempering scales up with the fraction of these structures (before tempering) in the alloy microstructure that can be tuned by the amount and temperature of cold deformation. Cold-rolling with 45% and 65% thickness reduction, followed by tempering at 600°C for 4 hours, led to an YS of 1.5 GPa and 2 GPa with elongation to fracture (%El) 14% and 7%, respectively. The YS is further enhanced to ~ 2.2 GPa without reduction in %El upon cryo-rolling followed by tempering. The alloy microstructure is stable at 600°C up to 100 hours and also retains an YS of ~ 1.5 GPa with %El of 18% during tensile test at 600°C. The derived high YS and high-temperature stability are critically a consequence of solute partitioning to the NMLs that we termed as Solute-Partitioned NMLs (SP-NMLs) in the microstructure.

cond-mat.mtrl-sci↗

arXiv2025

Unsupervised Latent Pattern Analysis for Estimating Type 2 Diabetes Risk in Undiagnosed Populations

The global prevalence of diabetes, particularly type 2 diabetes mellitus (T2DM), is rapidly increasing, posing significant health and economic challenges. T2DM not only disrupts blood glucose regulation but also damages vital organs such as the heart, kidneys, eyes, nerves, and blood vessels, leading to substantial morbidity and mortality. In the US alone, the economic burden of diagnosed diabetes exceeded \$400 billion in 2022. Early detection of individuals at risk is critical to mitigating these impacts. While machine learning approaches for T2DM prediction are increasingly adopted, many rely on supervised learning, which is often limited by the lack of confirmed negative cases. To address this limitation, we propose a novel unsupervised framework that integrates Non-negative Matrix Factorization (NMF) with statistical techniques to identify individuals at risk of developing T2DM. Our method identifies latent patterns of multimorbidity and polypharmacy among diagnosed T2DM patients and applies these patterns to estimate the T2DM risk in undiagnosed individuals. By leveraging data-driven insights from comorbidity and medication usage, our approach provides an interpretable and scalable solution that can assist healthcare providers in implementing timely interventions, ultimately improving patient outcomes and potentially reducing the future health and economic burden of T2DM.

cs.LG↗

arXiv2025

Harnessing Layer-Controlled Two-dimensional Semiconductors for Photoelectrochemical Energy Storage via Quantum Capacitance and Band Nesting

Two-dimensional (2D) transition metal dichalcogenides like molybdenum diselenide (MoSe$_2$) have shown great potential in optoelectronics and energy storage due to their layer-dependent bandgap. However, producing high-quality 2D MoSe$_2$ layers in a scalable and controlled manner remains challenging. Traditional methods, such as hydrothermal and liquid-phase exfoliation, lack precision and understanding at the nanoscale, limiting further applications. Atmospheric pressure chemical vapor deposition (APCVD) offers a scalable solution for growing high-quality, large-area, layer-controlled 2D MoSe$_2$. Despite this, the photoelectrochemical performance of APCVD-grown 2D MoSe$_2$, particularly in energy storage, has not been extensively explored. This study addresses this by examining MoSe$_2$'s layer-dependent quantum capacitance and photo-induced charge storage properties. Using a three-electrode setup in 0.5M H$_2$SO$_4$, we observed a layer-dependent increase in areal capacitance under both dark and illuminated conditions. A six-layer MoSe$_2$ film exhibited the highest capacitance, reaching $96 μ\mathrm{F/cm^2}$ in the dark and $115 μ\mathrm{F/cm^2}$ under illumination at a current density of $5 μ\mathrm{A/cm^2}$. Density Functional Theory (DFT) and Many-Body Perturbation Theory calculations reveal that Van Hove singularities and band nesting significantly enhance optical absorption and quantum capacitance. These results highlight APCVD-grown 2D MoSe$_2$'s potential as light-responsive, high-performance energy storage electrodes, paving the way for innovative energy storage systems.

cond-mat.mtrl-sci↗

arXiv2025

Energy reconstruction and calibration techniques of the DUNE LArTPC

The Liquid Argon Time Projection Chamber (LArTPC) technology is currently a preferred choice for neutrino experiments and beyond the standard model physics searches such as nucleon decay and dark matter. The Deep Underground Neutrino Experiment (DUNE) will employ the LArTPC technology at a large scale for physics programs, benefiting from its large target mass and excellent imaging, tracking, and particle identification capabilities. In DUNE, accurate energy reconstruction is important for precisely measuring CP violation, determining neutrino mass ordering and fully utilising the detector's potential. The energy calibration techniques developed for the DUNE far detector (FD) horizontal drift are presented here, utilising stopping cosmic-ray muons and validating the methods with the stopping pions and protons. The study demonstrates the versatility of the calibration techniques, applicable to other LArTPC, and valid for different particles. The electromagnetic shower energy reconstruction from $π^{0} \rightarrow 2γ$ events and the subsequent reconstruction of $π^{0}$ mass are also presented. These are important calibrations which address the measurement of energy loss in the DUNE FD volume and are critical for achieving the exciting physics goals of the experiment.

hep-ex↗

arXiv2024

Measuring Network Dynamics of Opioid Overdose Deaths in the United States

The US opioid overdose epidemic has been a major public health concern in recent decades. There has been increasing recognition that its etiology is rooted in part in the social contexts that mediate substance use and access; however, reliable statistical measures of social influence are lacking in the literature. We use Facebook's social connectedness index (SCI) as a proxy for real-life social networks across diverse spatial regions that help quantify social connectivity across different spatial units. This is a measure of the relative probability of connections between localities that offers a unique lens to understand the effects of social networks on health outcomes. We use SCI to develop a variable, called "deaths in social proximity", to measure the influence of social networks on opioid overdose deaths (OODs) in US counties. Our results show a statistically significant effect size for deaths in social proximity on OODs in counties in the United States, controlling for spatial proximity, as well as demographic and clinical covariates. The effect size of standardized deaths in social proximity in our cluster-robust linear regression model indicates that a one-standard-deviation increase, equal to 11.70 more deaths per 100,000 population in the social proximity of ego counties in the contiguous United States, is associated with thirteen more deaths per 100,000 population in ego counties. To further validate our findings, we performed a series of robustness checks using a network autocorrelation model to account for social network effects, a spatial autocorrelation model to capture spatial dependencies, and a two-way fixed-effect model to control for unobserved spatial and time-invariant characteristics. These checks consistently provide statistically robust evidence of positive social influence on OODs in US counties.

cs.SI↗

arXiv2024

Optimized Search for a Binary Black Hole Merger Population in LIGO-Virgo O3 Data

Maximizing the number of detections in matched filter searches for compact binary coalescence (CBC) gravitational wave (GW) signals requires a model of the source population distribution. In previous searches using the PyCBC framework, sensitivity to the population of binary black hole (BBH) mergers was improved by restricting the range of filter template mass ratios and use of a simple one-dimensional population model. However, this approach does not make use of our full knowledge of the population and cannot be extended to a full parameter space search. Here, we introduce a new ranking method, based on kernel density estimation (KDE) with adaptive bandwidth, to accurately model the probability distributions of binary source parameters over a template bank, both for signals and for noise events. We demonstrate this ranking method by conducting a search over LIGO-Virgo O3 data for BBH with unrestricted mass ratio, using a signal model derived from previous significant detected events. We achieve over 10% increase in sensitive volume for a simple power-law simulated signal population, compared to the previous BBH search. Correspondingly, with the new ranking, 8 additional candidate events above an inverse false alarm rate (IFAR) threshold 0.5 yr are identified.

gr-qc↗

arXiv2024

ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting

Unmanned Aerial Vehicles (UAVs) have gained popularity in data harvesting (DH) and coverage path planning (CPP) to survey a given area efficiently and collect data from aerial perspectives, while data harvesting aims to gather information from various Internet of Things (IoT) sensor devices, coverage path planning guarantees that every location within the designated area is visited with minimal redundancy and maximum efficiency. We propose the ARDDQN (Attention-based Recurrent Double Deep Q Network), which integrates double deep Q-networks (DDQN) with recurrent neural networks (RNNs) and an attention mechanism to generate path coverage choices that maximize data collection from IoT devices and to learn a control scheme for the UAV that generalizes energy restrictions. We employ a structured environment map comprising a compressed global environment map and a local map showing the UAV agent's locate efficiently scaling to large environments. We have compared Long short-term memory (LSTM), Bi-directional long short-term memory (Bi-LSTM), Gated recurrent unit (GRU) and Bidirectional gated recurrent unit (Bi-GRU) as recurrent neural networks (RNN) to the result without RNN We propose integrating the LSTM with the Attention mechanism to the existing DDQN model, which works best on evolution parameters, i.e., data collection, landing, and coverage ratios for the CPP and data harvesting scenarios.

cs.LG↗

arXiv2024

Positive Unlabeled Learning Selected Not At Random (PULSNAR): class proportion estimation when the SCAR assumption does not hold

Positive and Unlabeled (PU) learning is a type of semi-supervised binary classification where the machine learning algorithm differentiates between a set of positive instances (labeled) and a set of both positive and negative instances (unlabeled). PU learning has broad applications in settings where confirmed negatives are unavailable or difficult to obtain, and there is value in discovering positives among the unlabeled (e.g., viable drugs among untested compounds). Most PU learning algorithms make the \emph{selected completely at random} (SCAR) assumption, namely that positives are selected independently of their features. However, in many real-world applications, such as healthcare, positives are not SCAR (e.g., severe cases are more likely to be diagnosed), leading to a poor estimate of the proportion, $α$, of positives among unlabeled examples and poor model calibration, resulting in an uncertain decision threshold for selecting positives. PU learning algorithms vary; some estimate only the proportion, $α$, of positives in the unlabeled set, while others calculate the probability that each specific unlabeled instance is positive, and some can do both. We propose two PU learning algorithms to estimate $α$, calculate calibrated probabilities for PU instances, and improve classification metrics: i) PULSCAR (positive unlabeled learning selected completely at random), and ii) PULSNAR (positive unlabeled learning selected not at random). PULSNAR employs a divide-and-conquer approach to cluster SNAR positives into subtypes and estimates $α$ for each subtype by applying PULSCAR to positives from each cluster and all unlabeled. In our experiments, PULSNAR outperformed state-of-the-art approaches on both synthetic and real-world benchmark datasets.

cs.LG↗

arXiv2023

Differentiable modeling to unify machine learning and physical models and advance Geosciences

Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift. For decades, PBM offered benefits in interpretability and physical consistency but struggled to efficiently leverage large datasets. ML methods, especially deep networks, presented strong predictive skills yet lacked the ability to answer specific scientific questions. While various methods have been proposed for ML-physics integration, an important underlying theme -- differentiable modeling -- is not sufficiently recognized. Here we outline the concepts, applicability, and significance of differentiable geoscientific modeling (DG). "Differentiable" refers to accurately and efficiently calculating gradients with respect to model variables, critically enabling the learning of high-dimensional unknown relationships. DG refers to a range of methods connecting varying amounts of prior knowledge to neural networks and training them together, capturing a different scope than physics-guided machine learning and emphasizing first principles. Preliminary evidence suggests DG offers better interpretability and causality than ML, improved generalizability and extrapolation capability, and strong potential for knowledge discovery, while approaching the performance of purely data-driven ML. DG models require less training data while scaling favorably in performance and efficiency with increasing amounts of data. With DG, geoscientists may be better able to frame and investigate questions, test hypotheses, and discover unrecognized linkages.

cs.LG↗

arXiv2023

Information Flow as an Emergent Property of Divergence in Phase-Space

Recent developments have created the ability to quantify information flow among components that interact in a dynamical system, and have led to significant advances in characterizing the dependence between the variables involved. In particular, they have been used to characterize causal dependency and feedback using observations across diverse fields such as environment, climate, finance, and human health. What causes information flow among coupled components of a dynamical system? This fundamental question has remained unanswered so far. Here it is established that the information flow is an emergent response resulting from the divergence of trajectories in phase-space of a dynamical system. This finding shows that the dynamics encapsulated in the traditional expression of Liouville equation, which neglects this divergence, merely propagates the dependence encoded in the initial conditions. However, when this is not the case, the informational dependence between the components change creating an information flow. This finding has significant implications in a variety of fields, both for the interpretation of observational data for causal inference in natural dynamics, and design of systems with targeted informational dependency.

physics.data-an↗

arXiv2023

Escape dynamics of a self-propelled nanorod from circular confinements with narrow openings

We perform computer simulations to explore the escape dynamics of a self-propelled (active) nanorod from circular confinements with narrow opening(s). Our results clearly demonstrate how the persistent and directed motion of the nanorod helps it to escape. Such escape events are absent if the nanorod is passive. To quantify the escape dynamics, we compute the radial probability density function (RPDF) and mean first escape time (MFET) and show how the activity is responsible for the bimodality of RPDF, which is clearly absent if the nanorod is passive. The broadening of displacement distributions with activity has also been observed. The computed mean first escape time decreases with activity. In contrast, the fluctuations of the first escape times vary in a non-monotonic way. This results high values of the coefficient of variation and indicates the presence of multiple timescales in first escape time distributions and multimodality in uniformity index distributions. We hope our study will help in differentiating activity-driven escape dynamics from purely thermal passive diffusion in confinement.

cond-mat.soft↗

arXiv2023

A Direct Construction of Optimal Symmetrical Z-Complementary Code Sets of Prime Power Lengths

This paper presents a direct construction of an optimal symmetrical Z-complementary code set (SZCCS) of prime power lengths using a multi-variable function (MVF). SZCCS is a natural extension of the Z-complementary code set (ZCCS), which has only front-end zero correlation zone (ZCZ) width. SZCCS has both front-end and tail-end ZCZ width. SZCCSs are used in developing optimal training sequences for broadband generalized spatial modulation systems over frequency-selective channels because they have ZCZ width on both the front and tail ends. The construction of optimal SZCCS with large set sizes and prime power lengths is presented for the first time in this paper. Furthermore, it is worth noting that several existing works on ZCCS and SZCCS can be viewed as special cases of the proposed construction.

cs.IT↗

arXiv2022

Dynamics of self-propelled tracer particles inside a polymer network

Transport of tracer particles through mesh-like environments such as biological hydrogels and polymer matrices is ubiquitous in nature. These tracers could be passive, such as colloids or active (self-propelled), such as synthetic nanomotors or bacteria. Computer simulations in principle should be extremely useful in exploring the mechanism of active (self-propelled) transport of tracer particles through the mesh-like environments. Therefore, we construct a polymer network on a diamond lattice and use computer simulations to investigate the dynamics of spherical self-propelled particles inside the network. Our main objective is to elucidate the effect of the self-propulsion on the dynamics of the tracer particle as a function of tracer size and stiffness of the polymer network. We compute the time-averaged mean-squared displacement (MSD) and the van-Hove correlations of the tracer. On one hand, in the case of the bigger sticky particle, caging caused by the network particles wins over the escape assisted by the self-propulsion. This results intermediate-time subdiffusion. On the other hand, smaller tracers or tracers with high self-propulsion velocities can easily escape from the cages and show intermediate-time superdiffusion. Stiffer the network, slower the dynamics of the tracer, and the bigger tracers exhibit longer lived intermediate time superdiffusion, as the persistence time scales as $\sim σ^3$, where $σ$ is the diameter of the tracer. In intermediate time, non-Gaussianity is more pronounced for active tracers. In the long time, the dynamics of the tracer, if passive or weakly active, becomes Gaussian and diffusive, but remains flat for tracers with high self-propulsion, accounting for their seemingly unrestricted motion inside the network.

cond-mat.soft↗

arXiv2022

Network Security Roadmap

Users may already have some perception of provided security based on experience with earlier generations. To maintain the stability and coherent integration of 5G services, it is imperative that security and privacy features prevalent in earlier generations are also present in 5G. However, it is not sufficient just to provide the same security features as in the legacy systems due to the new threat model introduced by the integration of new technologies like SDN, virtualization and SBA. 5G systems are expected to be more service-oriented. This suggests there will be an additional emphasis on security and privacy requirements that spawn from the new dimension of service-oriented security architecture.

cs.CR↗

arXiv2022

Interaction of divergence-free deceleration parameter in Weyl-type $f(Q,T)$ gravity

We study an extension of symmetric teleparallel gravity i.e. Weyl-type $f(Q,T)$ gravity and the divergence-free parametrization of the deceleration parameter $q(z) = q_{0}+q_{1}\frac{z(1+z)}{1+z^2}$ ($q_{0}$ and $q_{1}$ are free constants) to explore the evolution of the universe. By considering the above parametric form of $q$, we derive the Hubble solution and further impose it in the Friedmann equations of Weyl-type $f(Q, T)$ gravity. To see whether this model can challenge the $Λ$CDM limits, we computed the constraints on the model parameters using the Bayesian analysis for the Observational Hubble data ($OHD$) and the Pantheon sample ($SNe\,Ia$). Furthermore, the deceleration parameter depicts the accelerating behavior of the universe with the present value $q_0$ and the transition redshift $z_t$ (at which the expansion transits from deceleration to acceleration) with $1-σ$ and $2-σ$ confidence level. We also examine the evolution of the energy density, pressure, and effective equation of state parameters. Finally, we demonstrate that the divergence-free parametric form of the deceleration parameter is consistent with the Weyl-type $f(Q,T)$ gravity.

gr-qc↗

arXiv2022

A Direct Construction of Cross Z-Complementary Sets with Flexible Lengths and Large Zero Correlation Zone

This letter proposes a direct construction for cross Z-complementary sets (CZCSs) with flexible lengths and a large zero correlation zone (ZCZ). CZCS is an extension of the cross Z-complementary pair (CZCP). The maximum possible ZCZ width of a CZCP is half of its sequence length. In this letter, for the first time, a generalized Boolean function based construction of CZCSs with a large number of constituent sequences and a ZCZ ratio of $2/3$ is presented. For integers $m$ and $δ$, the proposed construction produces CZCS with length expressed as $2^{m-1}+2^δ$ ($0 \leq δ<m-1,m\geq 4$), where both odd and even lengths CZCS can be obtained. Additionally, the constructed CZCS also feature a complementary set of the same length. Finally, the proposed construction is compared with the existing works.

cs.IT↗

arXiv2022

A Direct Construction of Cross Z-Complementary Sequence Sets with Large Set Size

This letter presents a direct construction of cross Z-complementary sequence sets (CZCSSs), whose aperiodic correlation sums exhibit zero correlation zones at both the front-end and tail-end shifts. CZCSS can be regarded as an extension of the symmetrical Z-complementary code set (SZCCS). The available construction of SZCCS has a limitation on the set size, with a maximum set size of 8. The proposed generalized Boolean function based construction can generate CZCSS of length in the form of non-power-of-two with variable set size $2^{n+1}$, where each code has $2^{n+1}$ constituent sequences. The proposed construction also yields cross Z-complementary pairs and cross Z-complementary sets with a large number of constituent sequences compared to the existing work.

cs.IT↗

arXiv2022

A Direct Construction of GCP and Binary CCC of Length Non Power of Two

Golay complementary pairs (GCPs) and complete complementary codes (CCCs) have found a wide range of practical applications in coding, signal processing and wireless communication due to their ideal correlation properties. In fact, binary CCCs have special advantages in spread spectrum communication due to their simple modulo-2 arithmetic operation, modulation and correlation simplicity, but they are limited in length. In this paper, we present a direct construction of GCPs, mutually orthogonal complementary sets (MOCSs) and binary CCCs of non-power of two lengths to widen their application in the recent field. First, a generalised Boolean function (GBF) based truncation technique has been used to construct GCPs of non-power of two lengths. Then Complementary sets (CSs) and MOCSs of lengths of the form $2^{m-1}+2^{m-3}$ ($m \geq 5$) and $2^{m-1}+2^{m-2}+2^{m-4}$ ($m \geq 6$) are generated by GBFs. Finally, binary CCCs with desired lengths are constructed using the union of MOCSs. The row and column sequence peak to mean envelope power ratio (PMEPR) has been investigated and compared with existing work. The column sequence PMEPR of resultant CCCs can be effectively upper bounded by $2$.

cs.IT↗