arXiv subjects

Divyanshu Singh

Publications and source records attributed to Divyanshu Singh.

7 recordsLinked to original sources

arXiv2026

Altruistic Ride Sharing: A Framework for Fair and Sustainable Urban Mobility via Peer-to-Peer Incentives

Urban mobility systems face persistent challenges of congestion, underutilized vehicles, and rising emissions driven by private point-to-point commuting. Although ride-sharing platforms exist, their profit-driven incentive structures often fail to align individual participation with broader community benefit. We introduce Altruistic Ride Sharing (ARS), a decentralized peer-to-peer mobility framework in which commuters alternate between driver and rider roles using altruism points, a non-monetary credit mechanism that rewards providing rides and discourages persistent free-riding. To enable scalable coordination among agents, ARS formulates ride-sharing as a multi-agent reinforcement learning problem and introduces ORACLE (One-Network Actor-Critic for Learning in Cooperative Environments), a shared-parameter learning architecture for decentralized rider selection. We evaluate ARS using real-world New York City Taxi and Limousine Commission (TLC) trajectory data under varying agent populations and behavioral dynamics. Across simulations, ARS reduces total travel distance and associated carbon emissions by approximately 20%, reduces urban traffic density by up to 30%, and doubles vehicle utilization relative to no-sharing baselines while maintaining balanced participation across agents. These results demonstrate that altruism-based incentives combined with decentralized learning can provide a scalable and equitable alternative to profit-driven ride-sharing systems.

cs.MA↗

arXiv2026

Hydrodynamical simulations of helium-ignited binary white dwarf mergers

Type Ia supernovae (SNe Ia) are common luminous astrophysical transients. SNe Ia serve as distance indicators for measuring the expansion rate of the universe and play important roles in galactic nucleosynthesis. However, ambiguities persist regarding the nature of their stellar progenitors and explosion mechanisms. The recent discovery of \textit{Gaia} hypervelocity white dwarfs (WDs) has provided direct evidence in support of helium-ignited double degenerate SNe Ia. In this study, we investigate the outcomes of helium-ignited double-degenerate WD mergers by performing a set of 3D hydrodynamical simulations with two different codes: \texttt{AREPO} and \texttt{FLASH}. We consider two distinct binary WD systems close to helium ignition, evolving each with both codes while keeping initial conditions fixed. The first binary WD model produces a double detonation of the primary WD and the hypervelocity ejection of the surviving secondary, similar to the canonical dynamically driven double degenerate double detonation (D6) scenario. In the second model, the secondary also undergoes a core detonation, resulting in the complete disruption of both WDs. Notably, despite utilizing distinct numerical solvers, nuclear reaction networks, and mesh strategies, \texttt{AREPO} and \texttt{FLASH} produce broadly consistent outcomes for both sets of initial conditions. While the nucleosynthetic yields differ due to the different nuclear reaction networks employed, the overall agreement between the simulations demonstrates the robustness of the numerical modeling of this scenario. Our results strongly support the viability of both the D6 and quadruple detonation channels for at least some SNe Ia. We explore the prospective observational signatures of this channel, including in the X-rays using \textit{XRISM's} \textit{RESOLVE}.

astro-ph.HE↗

arXiv2026

DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery

The acceleration of materials discovery requires digital platforms that go beyond data repositories to embed learning, optimization, and decision-making directly into research workflows. We introduce DataScribe, an AI-native, cloud-based materials discovery platform that unifies heterogeneous experimental and computational data through ontology-backed ingestion and machine-actionable knowledge graphs. The platform integrates FAIR-compliant metadata capture, schema and unit harmonization, uncertainty-aware surrogate modeling, and native multi-objective multi-fidelity Bayesian optimization, enabling closed-loop propose-measure-learn workflows across experimental and computational pipelines. DataScribe functions as an application-layer intelligence stack, coupling data governance, optimization, and explainability rather than treating them as downstream add-ons. We validate the platform through case studies in electrochemical materials and high-entropy alloys, demonstrating end-to-end data fusion, real-time optimization, and reproducible exploration of multi-objective trade spaces. By embedding optimization engines, machine learning, and unified access to public and private scientific data directly within the data infrastructure, and by supporting open, free use for academic and non-profit researchers, DataScribe functions as a general-purpose application-layer backbone for laboratories of any scale, including self-driving laboratories and geographically distributed materials acceleration platforms, with built-in support for performance, sustainability, and supply-chain-aware objectives.

cs.LG↗

arXiv2025

Fault-Tolerant Implementation of the Deutsch-Jozsa Algorithm

We show that one can implement the Deutsch-Josza algorithm, one of the first and simplest quantum algorithms, in a fault-tolerant manner using the smallest quantum error-detecting code -- the $[[4,2,2]]$ code -- without any ancillae. We implemented the algorithm on a trapped-ion quantum computer with and without fault-tolerant encoding and compared the results. With approximately $99 \%$ confidence, we found that the fault-tolerant implementation provided a noise reduction for all oracles. Averaged across all oracles, the reduction in error rate was nearly $90 \%$.

quant-ph↗

arXiv2025

EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion

Voice Conversion research in recent times has increasingly focused on improving the zero-shot capabilities of existing methods. Despite remarkable advancements, current architectures still tend to struggle in zero-shot cross-lingual settings. They are also often unable to generalize for speakers of unseen languages and accents. In this paper, we adopt a simple yet effective approach that combines discrete speech representations from self-supervised models with a non-autoregressive Diffusion-Transformer based conditional flow matching speech decoder. We show that this architecture allows us to train a voice-conversion model in a purely textless, self-supervised fashion. Our technique works without requiring multiple encoders to disentangle speech features. Our model also manages to excel in zero-shot cross-lingual settings even for unseen languages. For Demo: https://ez-vc.github.io/EZ-VC-Demo/

cs.SD↗

arXiv2024

Automated Classification of Cybercrime Complaints using Transformer-based Language Models for Hinglish Texts

The rise in cybercrime and the complexity of multilingual and code-mixed complaints present significant challenges for law enforcement and cybersecurity agencies. These organizations need automated, scalable methods to identify crime types, enabling efficient processing and prioritization of large complaint volumes. Manual triaging is inefficient, and traditional machine learning methods fail to capture the semantic and contextual nuances of textual cybercrime complaints. Moreover, the lack of publicly available datasets and privacy concerns hinder the research to present robust solutions. To address these challenges, we propose a framework for automated cybercrime complaint classification. The framework leverages Hinglish-adapted transformers, such as HingBERT and HingRoBERTa, to handle code-mixed inputs effectively. We employ the real-world dataset provided by Indian Cybercrime Coordination Centre (I4C) during CyberGuard AI Hackathon 2024. We employ GenAI open source model-based data augmentation method to address class imbalance. We also employ privacy-aware preprocessing to ensure compliance with ethical standards while maintaining data integrity. Our solution achieves significant performance improvements, with HingRoBERTa attaining an accuracy of 74.41% and an F1-score of 71.49%. We also develop ready-to-use tool by integrating Django REST backend with a modern frontend. The developed tool is scalable and ready for real-world deployment in platforms like the National Cyber Crime Reporting Portal. This work bridges critical gaps in cybercrime complaint management, offering a scalable, privacy-conscious, and adaptable solution for modern cybersecurity challenges.

cs.CR↗

arXiv2020

Speaker Recognition using SincNet and X-Vector Fusion

In this paper, we propose an innovative approach to perform speaker recognition by fusing two recently introduced deep neural networks (DNNs) namely - SincNet and X-Vector. The idea behind using SincNet filters on the raw speech waveform is to extract more distinguishing frequency-related features in the initial convolution layers of the CNN architecture. X-Vectors are used to take advantage of the fact that this embedding is an efficient method to churn out fixed dimension features from variable length speech utterances, something which is challenging in plain CNN techniques, making it efficient both in terms of speed and accuracy. Our approach uses the best of both worlds by combining X-vector in the later layers while using SincNet filters in the initial layers of our deep model. This approach allows the network to learn better embedding and converge quicker. Previous works use either X-Vector or SincNet Filters or some modifications, however we introduce a novel fusion architecture wherein we have combined both the techniques to gather more information about the speech signal hence, giving us better results. Our method focuses on the VoxCeleb1 dataset for speaker recognition, and we have used it for both training and testing purposes.

cs.CL↗