arXiv subjects

Sanjay Kumar

Publications and source records attributed to Sanjay Kumar.

At least 19 recordsLinked to original sources

arXiv2026

Exploring an X1.8 flare and associated quasi-periodic pulsations using SDO and Aditya-L1 observations, alongside NFFF extrapolation

The paper seeks to explore the initiation of an X1.8 class solar flare (SOL2024-10-09T0124) and the related quasi-periodic pulsations (QPPs), which are situated within NOAA active region 13848. To achieve this, we perform a multi-wavelength analysis of the event utilizing data from the Solar Dynamic Observatory (SDO) and Aditya-L1. Importantly, the observations indicate the emergence of a sigmoidal brightening at the onset of the flare, alongside the evolution of a complex morphology of flare brightenings and ribbons around the peak time of the flare. Furthermore, QPPs are detected during the impulsive phase of the flare, exhibiting a dominant period of approximately 4 minutes in the hard X-ray light curve. In order to comprehend the initiation of the flare, we construct the coronal magnetic field at a pre-flare stage by employing the non-force-free-field (NFFF) extrapolation model. The extrapolated magnetic field illustrates the existence of various significant magnetic features, including a magnetic flux rope, a three-dimensional (3D) null, sheared arcade, and quasi- separatrix layers (QSLs) within the flaring region. The recurrent reconnection occurring at these features is proposed to play a role in the initiation of the flare as well as the associated QPPs.

astro-ph.SR↗

arXiv2026

Meromorphic Solutions of Certain Kind of Non-Linear Differential equation with finite sums of Exponential Functions

We study the existence and nonexistence of meromorphic solutions of nonlinear differential equations involving differential polynomials and finite sums of exponential-type functions with entire coefficients of controlled growth. Employ ing Nevanlinna theory, we obtain structural results showing that any admissible meromorphic solution must reduce to an exponential-type function governed by the dominant exponential term. The obtained lemma and main result strengthen several existing results in nonlinear complex differential equations with exponential terms.

math.CV↗

arXiv2026

Implementation and Performance Evaluation of CMOS-integrated Memristor-driven Flip-flop Circuits

In this work, we report implementation and performance evaluation of memristor-driven fundamental logic gates, including NOT, AND, NAND, OR, NOR, and XOR, and novel and optimized design of the sequential logic circuits, such as D flip-flop, T-flip-flop, JK-flip-flop, and SR-flip-flop. The design, implementation, and optimization of these logic circuits were performed in SPECTRE in Cadence Virtuoso and integrated with 90 nm CMOS technology node. Additionally, we discuss an optimized design of memristor-driven logic gates and sequential logic circuits, and draw a comparative analysis with the other reported state-of-the-art work on sequential circuits. Moreover, the utilized memristor framework was experimentally pre-validated with the experimental data of Y2O3-based memristive devices, which shows significantly low values of variability during switching in both device-to-device (D2D) and cycle-to-cycle (C2C) operation. The performance metrics were calculated in terms of area, power, and delay of these sequential circuits and were found to be reduced by more than ~24%, 60%, and 58%, respectively, as compared to the other state-of-the-art work on sequential circuits. Therefore, the implemented memristor-based design significantly improves the performance of various logic designs, which makes it more area and power-efficient and shows the potential of memristor in designing various low-power, low-cost, ultrafast, and compact circuits.

cs.AR↗

arXiv2026

Smart Railway Obstruction Detection System using IoT and Computer Vision

Railway track intrusions pose a critical safety challenge for Indian Railways, encompassing wildlife incursions and deliberate malicious obstructions. The December 2025 collision in Assam, in which seven elephants were killed by the Rajdhani Express, underscores the urgency of effective real-time detection. Existing solutions such as the optical fiber-based Gajraj system suffer from prohibitive costs (\$1000/km) and high false alarm rates, limiting deployment to only 20 of India's 101 elephant corridors. This paper proposes NETRA, a cost-effective, internet-independent intrusion detection system deployed on Raspberry Pi Zero W and Raspberry Pi 4 edge platforms. NETRA employs probabilistic sensor fusion integrating a PIR motion sensor and an HC-SR04 ultrasonic distance sensor with a tunable threshold (tau_c = 0.65), enabling event-driven camera activation that reduces unnecessary visual processing by 52%. Upon confirmed intrusion, edge-AI classification using MobileNet-SSD (Pi Zero) or YOLOv5 ONNX (Pi 4) identifies threats including humans, large animals, and track obstructions. Confirmed threats are transmitted via LoRa (868 MHz) to alert the locomotive driver within 2.4 seconds end-to-end. Experimental evaluation across 113 motion events demonstrated 95% detection accuracy with zero false alarms through probabilistic fusion, compared to 85% for binary methods. Raspberry Pi 4 with YOLOv5 achieved 83.5% elephant F1-score, a 5.6x improvement over Pi Zero's heuristic approach (14.8%). LoRa communication achieved 100% packet delivery across 1-2 km in field trials. NETRA reduces deployment cost by 75% (\$247/km vs \$1000/km for Gajraj) while providing unified detection of both wildlife and obstruction threats.

cs.CV↗

arXiv2026

Network exploration by random walks: A large deviation perspective

We study exploration properties of a random walk on a network. For a fully connected network we find that the problem can be mapped to the well known coupon collector problem, thus allowing us to estimate form of $P(S,t)$: the distribution of number of distinct nodes $S$ visited by the random walk upto time $t$. From a practical point of view, however, both the fully connected network and hops taking place after fixed intervals are an idealization. We solve this problem by introducing the formalism of continuous time random walks wherein the random walk spends a random amount of time a node before hopping to its neighboring node. The formalism allows us to study the large deviation limit of $P(S,t)$ under very mild conditions that the distribution of waiting times $ψ(τ)$ exhibits analyticity at small times. Furthermore, we find that at small times, the properties of $P(S,t)$ are largely independent of the network topology, and are governed solely by the waiting time characteristics.

physics.soc-ph↗

arXiv2026

Magnetic Reconnection at Hyperbolic Flux Tube associated with a Confined Flare in NOAA Active Region 12268

In this paper, we identify the magnetic reconnections at the hyperbolic flux tube (HFT), aided by slipping reconnection at quasi-separatrix layers (QSLs), which are pivotal to the occurrence of a confined M2.1 class flare in NOAA active region 12268. The magnetic field topology before the flare's onset is obtained through a non-force-free-field extrapolation scheme that accommodates a non-zero Lorentz force. A key aspect is the presence of an HFT in the computational domain above the flaring region, along with two QSLs at the lower boundary. To simulate the dynamics of the active region, we conduct a data-constrained magnetohydrodynamics (MHD) simulation initiated by the extrapolated field. The dynamics captured in the simulation document the formation of a current sheet within the HFT configuration, leading to magnetic reconnection at the HFT. Additionally, we observe the slipping motion of the footpoints of the magnetic field lines in the QSLs at the bottom boundary, which indicates the occurrence of slipping reconnection in the QSLs. Importantly, the magnetic reconnection at the HFT is suggested to be the primary driver in the development of the intricate flare brightenings and the flare ribbons.

astro-ph.SR↗

arXiv2026

FLUX: Data Worth Training On

Modern large language model training is no longer limited by data availability, but by the inability of existing preprocessing pipelines to simultaneously achieve massive scale and high data quality. Current approaches are forced to sacrifice one for the other: either aggressively filtering to improve quality at the cost of severe token loss, or retaining large volumes of data while introducing substantial noise. In this work, we introduce FLUX, a preprocessing pipeline specifically designed to break this long-standing trade-off by maximizing token retention while enforcing rigorous quality control. Models trained on FLUX-curated data consistently outperform prior methods. A 3B-parameter model trained on 60B tokens with FLUX achieves 32.14% MMLU accuracy, surpassing the previous state-of-the-art pipeline DCLM (31.98%) and significantly outperforming FineWeb (29.88%). FLUX achieves the same aggregate score as a model trained on DCLM data using only 39B tokens, resulting in a 34.4% reduction in training compute. At the data level, FLUX extracts 50B usable tokens from a single dump (CC-MAIN-2025-51), compared to 40B from DCLM (+25% retention). FLUX-Base yields 192B tokens, exceeding FineWeb's 170B while still maintaining superior quality. Overall, FLUX establishes a new state of the art in web-scale data preprocessing by demonstrating that high retention, strong quality control, and computational efficiency can be achieved simultaneously, redefining the limits of scalable dataset construction for modern language models.

cs.CL↗

arXiv2025

Blu-WERP (Web Extraction and Refinement Pipeline): A Scalable Pipeline for Preprocessing Large Language Model Datasets

High-quality training data is fundamental to large language model (LLM) performance, yet existing preprocessing pipelines often struggle to effectively remove noise and unstructured content from web-scale corpora. This paper presents Blu-WERP, a novel data preprocessing pipeline designed to optimize the quality of Common Crawl WARC files for LLM training. We demonstrate that Blu-WERP significantly outperforms established baselines including DCLM across multiple model scales and evaluation benchmarks. Our pipeline processes CC WARC dumps, implementing advanced filtering and quality assessment mechanisms. We conducted comprehensive evaluations using models with 150M, 400M, 530M, 750M, and 1B parameters, testing against nine standard benchmarks categorized as World Knowledge & Reasoning, Language Understanding, and Commonsense Reasoning. Results show Blu-WERP consistently achieved superior performance across all model scales. At the 1B parameter scale, Relatively Blu-WERP demonstrates a 4.0% and 9.5% aggregate improvement over DCLM and Fineweb respectively, while achieving quality-per-token efficiency gain. Categorical analysis reveals 2.4% improvement in World Knowledge & Reasoning, 6.2% improvement in Language Understanding, and 4.2% improvement in Commonsense Reasoning. These results establish Blu-WERP as a state-of-the-art preprocessing pipeline that substantially improves LLM training data quality and downstream model performance with reduced computational cost. Our findings contribute to the growing body of research on data-centric AI, demonstrating that preprocessing pipeline design significantly impacts LLM capabilities. The Blu-WERP pipeline represents a practical advancement in data quality optimization, offering researchers and practitioners an effective solution for improving LLM training efficiency and model performance.

cs.CL↗

arXiv2025

Evaluating the Impact of Weather-Induced Sensor Occlusion on BEVFusion for 3D Object Detection

Accurate 3D object detection is essential for automated vehicles to navigate safely in complex real-world environments. Bird's Eye View (BEV) representations, which project multi-sensor data into a top-down spatial format, have emerged as a powerful approach for robust perception. Although BEV-based fusion architectures have demonstrated strong performance through multimodal integration, the effects of sensor occlusions, caused by environmental conditions such as fog, haze, or physical obstructions, on 3D detection accuracy remain underexplored. In this work, we investigate the impact of occlusions on both camera and Light Detection and Ranging (LiDAR) outputs using the BEVFusion architecture, evaluated on the nuScenes dataset. Detection performance is measured using mean Average Precision (mAP) and the nuScenes Detection Score (NDS). Our results show that moderate camera occlusions lead to a 41.3% drop in mAP (from 35.6% to 20.9%) when detection is based only on the camera. On the other hand, LiDAR sharply drops in performance only under heavy occlusion, with mAP falling by 47.3% (from 64.7% to 34.1%), with a severe impact on long-range detection. In fused settings, the effect depends on which sensor is occluded: occluding the camera leads to a minor 4.1% drop (from 68.5% to 65.7%), while occluding LiDAR results in a larger 26.8% drop (to 50.1%), revealing the model's stronger reliance on LiDAR for the task of 3D object detection. Our results highlight the need for future research into occlusion-aware evaluation methods and improved sensor fusion techniques that can maintain detection accuracy in the presence of partial sensor failure or degradation due to adverse environmental conditions.

cs.CV↗

arXiv2025

Occluded nuScenes: A Multi-Sensor Dataset for Evaluating Perception Robustness in Automated Driving

Robust perception in automated driving requires reliable performance under adverse conditions, where sensors may be affected by partial failures or environmental occlusions. Although existing autonomous driving datasets inherently contain sensor noise and environmental variability, very few enable controlled, parameterised, and reproducible degradations across multiple sensing modalities. This gap limits the ability to systematically evaluate how perception and fusion architectures perform under well-defined adverse conditions. To address this limitation, we introduce the Occluded nuScenes Dataset, a novel extension of the widely used nuScenes benchmark. For the camera modality, we release both the full and mini versions with four types of occlusions, two adapted from public implementations and two newly designed. For radar and LiDAR, we provide parameterised occlusion scripts that implement three types of degradations each, enabling flexible and repeatable generation of occluded data. This resource supports consistent, reproducible evaluation of perception models under partial sensor failures and environmental interference. By releasing the first multi-sensor occlusion dataset with controlled and reproducible degradations, we aim to advance research on robust sensor fusion, resilience analysis, and safety-critical perception in automated driving.

cs.CV↗

arXiv2025

Bunching of extreme events on complex network

Extreme events such as earthquakes, floods, and power blackouts often display burst phenomena where multiple extreme events occur in quick succession or in bunches. This study examines bunching of extreme events on a complex network using a random walk transport model. We find that in a modular network, a small cluster sparsely connected with the rest, shows bunching and correlations among extreme events. The bunching and correlations emerge naturally in our system. We use several characterization techniques, namely the recurrence time distribution, autocorrelation function, bursty trains, burstiness parameter and memory coefficient to quantify the bunching and correlations of extreme events. Our study shows that the network structure plays a significant role in the bunching of extreme events.

physics.soc-ph↗

arXiv2025

On the Magnetic Reconnection and its Properties during a Flare Using a Magnetohydrodynamics Simulation

We study the magnetic reconnection during a flare by investigating flare ribbon dynamics using observations and data-constrained magnetohydrodynamics (MHD) simulation. In particular, we estimate the reconnection flux and the reconnection flux rates using flare ribbons of an M1.1 flare hosted by the active region 12184 utilizing the technique developed by Qiu et al. (2002, ApJ, 565, 1335). The reconnection flux and corresponding flux rates are found to be 10^20 Mx and 10^18 Mx/s respectively. To understand the flare onset and the origin of flare ribbons, we perform an MHD simulation initiated by the non-force-free-field extrapolation. Importantly, the extrapolated configuration identifies a three-dimensional (3D) magnetic neutral point and a flux rope in the flaring region, which is crucial to the flaring activity. The reconnection initiates at the null point and, subsequently the flux rope rises and appears to reconnect there, which is favorable for the eruption of the filament. The surrounding field lines also seem to take part in the null point reconnection. In later stage, a current sheet is formed below the null point ensuing a secondary reconnection near an X-type topology, further contributing to the energy release process in the flare. We trace the footpoint evolution of the field lines lying over the flare ribbons and find a significant similarity between the observed flare ribbons and the evolution of footpoints computed from the MHD simulation. We estimated induced electric field during the flare and found it to be $\approx$ .52 V/cm, a slight less value, as per many past literatures. Additional findings are the enhancement of vertical current density near the flaring ribbons, a signature of successive reconnections near the null point. Overall, the present work contributes to the understanding of the ribbon formation in a flaring process and the involved magnetic reconnection.

astro-ph.SR↗

arXiv2025

GBSVR: Granular Ball Support Vector Regression

Support Vector Regression (SVR) and its variants are widely used to handle regression tasks, however, since their solution involves solving an expensive quadratic programming problem, it limits its application, especially when dealing with large datasets. Additionally, SVR uses an epsilon-insensitive loss function which is sensitive to outliers and therefore can adversely affect its performance. We propose Granular Ball Support Vector Regression (GBSVR) to tackle problem of regression by using granular ball concept. These balls are useful in simplifying complex data spaces for machine learning tasks, however, to the best of our knowledge, they have not been sufficiently explored for regression problems. Granular balls group the data points into balls based on their proximity and reduce the computational cost in SVR by replacing the large number of data points with far fewer granular balls. This work also suggests a discretization method for continuous-valued attributes to facilitate the construction of granular balls. The effectiveness of the proposed approach is evaluated on several benchmark datasets and it outperforms existing state-of-the-art approaches

cs.LG↗

arXiv2025

Data-Constrained Magnetohydrodynamics Simulation of a Confined X-class Flare in NOAA Active Region 11166

In this paper, we present a magnetohydrodynamics simulation of NOAA active region 11166 to understand the origin of a confined X-class flare that peaked at 23:23 UT on 2011 March 9. The simulation is initiated with a magnetic field extrapolated from the corresponding photospheric magnetogram using a non-force-free-field extrapolation technique. Importantly, the initial magnetic configuration identifies three-dimensional (3D) magnetic nulls and quasi-separatrix layers (QSLs), which nearly agree with the bright structures appeared in multi-wavelength observations. The Lorentz force associated with the extrapolated field self-consistently generates the dynamics that leads to the magnetic reconnections at the 3D nulls and the QSLs. These reconnections are found to contribute to the pre-flare activities and, ultimately, lead to the development of the flare ribbons. Notably, the anchored spine of the 3D null and the complete absence of flux rope in the flaring region are congruent with the confined nature of the flare. Furthermore, the simulation also suggests the role of reconnections at the 3D null with an open spine in the onset of a jet away from the flaring site.

astro-ph.SR↗

arXiv2025

Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with Multi-Sensor Fusion

Autonomous driving technology is rapidly evolving, offering the potential for safer and more efficient transportation. However, the performance of these systems can be significantly compromised by the occlusion on sensors due to environmental factors like dirt, dust, rain, and fog. These occlusions severely affect vision-based tasks such as object detection, vehicle segmentation, and lane recognition. In this paper, we investigate the impact of various kinds of occlusions on camera sensor by projecting their effects from multi-view camera images of the nuScenes dataset into the Bird's-Eye View (BEV) domain. This approach allows us to analyze how occlusions spatially distribute and influence vehicle segmentation accuracy within the BEV domain. Despite significant advances in sensor technology and multi-sensor fusion, a gap remains in the existing literature regarding the specific effects of camera occlusions on BEV-based perception systems. To address this gap, we use a multi-sensor fusion technique that integrates LiDAR and radar sensor data to mitigate the performance degradation caused by occluded cameras. Our findings demonstrate that this approach significantly enhances the accuracy and robustness of vehicle segmentation tasks, leading to more reliable autonomous driving systems.

cs.CV↗

arXiv2024

Maximum Solar Energy Tracking Leverage High-DoF Robotics System with Deep Reinforcement Learning

Solar trajectory monitoring is a pivotal challenge in solar energy systems, underpinning applications such as autonomous energy harvesting and environmental sensing. A prevalent failure mode in sustained solar tracking arises when the predictive algorithm erroneously diverges from the solar locus, erroneously anchoring to extraneous celestial or terrestrial features. This phenomenon is attributable to an inadequate assimilation of solar-specific objectness attributes within the tracking paradigm. To mitigate this deficiency inherent in extant methodologies, we introduce an innovative objectness regularization framework that compels tracking points to remain confined within the delineated boundaries of the solar entity. By encapsulating solar objectness indicators during the training phase, our approach obviates the necessity for explicit solar mask computation during operational deployment. Furthermore, we leverage the high-DoF robot arm to integrate our method to improve its robustness and flexibility in different outdoor environments.

cs.RO↗

arXiv2024

Effects of Ionic Strength on the Morphology, Scattering, and Mechanical Response of Neurofilament-Derived Protein Brushes

Protein brushes not only play a key role in the functionality of neurofilaments but also have wide applications in biomedical materials. Here, we investigate the effect of ionic strength on the morphology of protein brushes using a continuous-space self-consistent field theory. A coarse-grained multi-block charged macromolecular model is developed to capture the chemical identity of amino acid sequences. For neurofilament heavy (NFH) brushes at pH 2.4, we predict three morphological regimes: swollen brushes, condensed brushes, and coexisting brushes which consist of both a dense inner layer and a diffuse outer layer. The brush height predicted by our theory is in good agreement with experimental data for a wide range of ionic strengths. The dramatic height decrease is a result of the electrostatic screening-induced transition from the overlapping state to the isolated state of the coexisting brushes. We also study the evolution of the scattering and mechanical responses accompanying the morphological change. The oscillation in the reflectivity spectra characterizes the existence and microstructure of the inner condensed layer, whereas the shoulder in the force spectra signifies the swollen morphology.

cond-mat.soft↗

arXiv2024

Classification of All Blood Cell Images using ML and DL Models

Human blood primarily comprises plasma, red blood cells, white blood cells, and platelets. It plays a vital role in transporting nutrients to different organs, where it stores essential health-related data about the human body. Blood cells are utilized to defend the body against diverse infections, including fungi, viruses, and bacteria. Hence, blood analysis can help physicians assess an individual's physiological condition. Blood cells have been sub-classified into eight groups: Neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts, and platelets or thrombocytes on the basis of their nucleus, shape, and cytoplasm. Traditionally, pathologists and hematologists in laboratories have examined these blood cells using a microscope before manually classifying them. The manual approach is slower and more prone to human error. Therefore, it is essential to automate this process. In our paper, transfer learning with CNN pre-trained models. VGG16, VGG19, ResNet-50, ResNet-101, ResNet-152, InceptionV3, MobileNetV2, and DenseNet-20 applied to the PBC dataset's normal DIB. The overall accuracy achieved with these models lies between 91.375 and 94.72%. Hence, inspired by these pre-trained architectures, a model has been proposed to automatically classify the ten types of blood cells with increased accuracy. A novel CNN-based framework has been presented to improve accuracy. The proposed CNN model has been tested on the PBC dataset normal DIB. The outcomes of the experiments demonstrate that our CNN-based framework designed for blood cell classification attains an accuracy of 99.91% on the PBC dataset. Our proposed convolutional neural network model performs competitively when compared to earlier results reported in the literature.

eess.IV↗