arXiv subjects

Abhishek Singh

Publications and source records attributed to Abhishek Singh.

At least 37 records · Page 2Linked to original sources

arXiv2025

Investigation of Performance and Scalability of a Quantum-Inspired Evolutionary Optimizer (QIEO) on NVIDIA GPU

Quantum inspired evolutionary optimization leverages quantum computing principles like superposition, interference, and probabilistic representation to enhance classical evolutionary algorithms with improved exploration and exploitation capabilities. Implemented on NVIDIA Tesla V100 SXM2 GPUs, this study systematically investigates the performance and scalability of a GPU-accelerated Quantum Inspired Evolutionary Optimizer applied to large scale 01 Knapsack problems. By exploiting CUDA`s parallel processing capabilities, particularly through optimized memory management and thread configuration, significant speedups and efficient utilization of GPU resources is demonstrated. The analysis covers various problem sizes, kernel launch configurations, and memory models including constant, shared, global, and pinned memory, alongside extensive scaling studies. The results reveal that careful tuning of memory strategies and kernel configurations is essential for maximizing throughput and efficiency, with constant memory providing superior performance up to hardware limits. Beyond these limits, global memory and strategic tiling become necessary, albeit with some performance trade offs. The findings highlight both the promise and the practical constraints of applying QIEO on GPUs for complex combinatorial optimization, offering actionable insights for future large scale metaheuristic implementations.

cs.CE↗

arXiv2025

Evolution of AI Agent Registry Solutions: Centralized, Enterprise, and Distributed Approaches

Autonomous AI agents now operate across cloud, enterprise, and decentralized domains, creating demand for registry infrastructures that enable trustworthy discovery, capability negotiation, and identity assurance. We analyze five prominent approaches: (1) MCP Registry (centralized publication of mcp.json descriptors), (2) A2A Agent Cards (decentralized self-describing JSON capability manifests), (3) AGNTCY Agent Directory Service (IPFS Kademlia DHT content routing extended for semantic taxonomy-based content discovery, OCI artifact storage, and Sigstore-backed integrity), (4) Microsoft Entra Agent ID (enterprise SaaS directory with policy and zero-trust integration), and (5) NANDA Index AgentFacts (cryptographically verifiable, privacy-preserving fact model with credentialed assertions). Using four evaluation dimensions: security, authentication, scalability, and maintainability, we surface architectural trade-offs between centralized control, enterprise governance, and distributed resilience. We conclude with design recommendations for an emerging Internet of AI Agents requiring verifiable identity, adaptive discovery flows, and interoperable capability semantics.

cs.NI↗

arXiv2025

Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts

The Internet is poised to host billions to trillions of autonomous AI agents that negotiate, delegate, and migrate in milliseconds and workloads that will strain DNS-centred identity and discovery. In this paper, we describe the NANDA index architecture, which we envision as a means for discoverability, identifiability and authentication in the internet of AI agents. We present an architecture where a minimal lean index resolves to dynamic, cryptographically verifiable AgentFacts that supports multi-endpoint routing, load balancing, privacy-preserving access, and credentialed capability assertions. Our architecture design delivers five concrete guarantees: (1) A quilt-like index proposal that supports both NANDA-native agents as well as third party agents being discoverable via the index, (2) rapid global resolution for newly spawned AI agents, (3) sub-second revocation and key rotation, (4) schema-validated capability assertions, and (5) privacy-preserving discovery across organisational boundaries via verifiable, least-disclosure queries. We formalize the AgentFacts schema, specify a CRDT-based update protocol, and prototype adaptive resolvers. The result is a lightweight, horizontally scalable foundation that unlocks secure, trust-aware collaboration for the next generation of the Internet of AI agents, without abandoning existing web infrastructure.

cs.NI↗

arXiv2025

Upgrade or Switch: Do We Need a Next-Gen Trusted Architecture for the Internet of AI Agents?

The emerging Internet of AI Agents challenges existing web infrastructure designed for human-scale, reactive interactions. Unlike traditional web resources, autonomous AI agents initiate actions, maintain persistent state, spawn sub-agents, and negotiate directly with peers: demanding millisecond-level discovery, instant credential revocation, and cryptographic behavioral proofs that exceed current DNS/PKI capabilities. This paper analyzes whether to upgrade existing infrastructure or implement purpose-built index architectures for autonomous agents. We identify critical failure points: DNS propagation (24-48 hours vs. required milliseconds), certificate revocation unable to scale to trillions of entities, and IPv4/IPv6 addressing inadequate for agent-scale routing. We evaluate three approaches: (1) Upgrade paths, (2) Switch options, (3) Hybrid index/registries. Drawing parallels to dialup-to-broadband transitions, we find that agent requirements constitute qualitative, and not incremental, changes. While upgrades offer compatibility and faster deployment, clean-slate solutions provide better performance but require longer for adoption. Our analysis suggests hybrid approaches will emerge, with centralized indexes for critical agents and federated meshes for specialized use cases.

cs.NI↗

arXiv2025

Quantum noise spectroscopy of superconducting dynamics in thin film Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$

Characterizing the low-energy dynamics of quantum materials is crucial to our understanding of strongly correlated electronic states. Yet, it remains experimentally challenging to investigate such dynamics with high spectroscopic resolution in both frequency and momentum space, particularly in two-dimensional correlated systems. Here, we leverage Nitrogen-Vacancy (NV) centers in diamond as a powerful and non-invasive tool to study thin-film Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ (BSCCO), revealing several distinct dynamical phenomena across the superconducting phase diagram. At zero magnetic field and low temperatures, NV depolarization ($T_1$) noise spectroscopy captures the low-frequency (GHz-scale) magnetic noise generated by nodal superconducting quasiparticle excitations, in agreement with Bardeen-Cooper-Schrieffer (BCS) mean-field theory. Near the critical temperature $T_c \approx 90$ K, supercurrent-fluctuation-induced noise leads to a sharp reduction of the NV $T_1$. By carefully analyzing the temperature-scaling of $T_1$, we observe clear deviations from the BCS prediction, reflecting the importance of order parameter fluctuations and enabling the determination of both static and dynamical critical exponents. When a small field is applied, we detect a broad and asymmetric reduction of NV $T_1$ near $T_c$; the field-induced smearing of the transition unveils the presence of a vortex liquid phase. Finally, NV decoherence ($T_2$) noise spectroscopy allows us to characterize magnetic noise at even lower MHz-scale frequencies and obtain evidence for complex vortex-solid fluctuations well below $T_c$. Our results establish quantum noise spectroscopy as a versatile platform for probing dynamical phenomena in superconductors, with frequency and length scales complementary to existing techniques.

cond-mat.supr-con↗

arXiv2025

Anomalies in the electronic, magnetic and thermal behavior near the Invar compositions of Fe-Ni alloys

The structural and magnetic properties of Fe$_{1-x}$Ni$_x$~($x$ = 0.32, 0.36, 0.40, 0.50) alloys have been investigated using synchrotron based x-ray diffraction (XRD) technique with x-rays of wavelength 0.63658 Å down to 50 K temperature, magnetic measurement using superconducting quantum interference device (SQUID) magnetometer and high resolution x-ray photoelectron spectroscopy (XPS) with monochromatic AlK$_α$ radiation. The XRD studies suggest a single phase with fcc structure for $x$ = 0.36, 0.40, and 0.50 ~alloys and a mixed phase for $x$ = 0.32 alloy containing both bcc and fcc structures. The lattice parameter of the alloys exhibits a linear dependence on temperature giving rise to a temperature independent coefficient of thermal expansion (CTE). The lowest CTE is observed for $x$ = 0.36 Invar alloy as expected while $x$ = 0.50 alloy exhibits the highest CTE among the alloys studied. The CTE of the fcc component of mixed phase alloy is close to that of Invar alloy. The temperature dependence of magnetization of the alloys down to 2 K reveals an overall antiferromagnetic interactions within the ferromagnetic phase causing the magnetization decreasing with cooling. The field cooled and zero field cooled data show larger differences for the Invar compositions; this is also manifested in the magnetic hysteresis data at 2 K and 300 K.

cond-mat.str-el↗

arXiv2024

Uncovering Semantics and Topics Utilized by Threat Actors to Deliver Malicious Attachments and URLs

Recent threat reports highlight that email remains the top vector for delivering malware to endpoints. Despite these statistics, detecting malicious email attachments and URLs often neglects semantic cues linguistic features and contextual clues. Our study employs BERTopic unsupervised topic modeling to identify common semantics and themes embedded in email to deliver malicious attachments and call-to-action URLs. We preprocess emails by extracting and sanitizing content and employ multilingual embedding models like BGE-M3 for dense representations, which clustering algorithms(HDBSCAN and OPTICS) use to group emails by semantic similarity. Phi3-Mini-4K-Instruct facilitates semantic and hLDA aid in thematic analysis to understand threat actor patterns. Our research will evaluate and compare different clustering algorithms on topic quantity, coherence, and diversity metrics, concluding with insights into the semantics and topics commonly used by threat actors to deliver malicious attachments and URLs, a significant contribution to the field of threat detection.

cs.LG↗

arXiv2024

AMC: Access to Miss Correlation Prefetcher for Evolving Graph Analytics

Modern memory hierarchies work well with applications that have good spatial locality. Evolving (dynamic) graphs are important applications widely used to model graphs and networks with edge and vertex changes. They exhibit irregular memory access patterns and suffer from a high miss ratio and long miss penalty. Prefetching can be employed to predict and fetch future demand misses. However, current hardware prefetchers can not efficiently predict for applications with irregular memory accesses. In evolving graph applications, vertices that do not change during graph changes exhibit the same access correlation patterns. Current temporal prefetchers use one-to-one or one-to-many correlation to exploit these patterns. Similar patterns are recorded in the same entry, which causes aliasing and can lead to poor prefetch accuracy and coverage. This work proposes a software-assisted hardware prefetcher for evolving graphs. The key idea is to record the correlations between a sequence of vertex accesses and the following misses and then prefetch when the same vertex access sequence occurs in the future. The proposed Access-to-Miss Correlation (AMC) prefetcher provides a lightweight programming interface to identify the data structures of interest and sets the iteration boundary to update the correlation table. For the evaluated applications, AMC achieves a geomean speedup of 1.5x as compared to the best-performing prefetcher in prior work (VLDP). AMC can achieve an average of 62% accuracy and coverage, whereas VLDP has an accuracy of 31% and coverage of 23%.

cs.AR↗

arXiv2024

Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy

Federated Learning (FL) has emerged as a leading paradigm for decentralized, privacy preserving machine learning training. However, recent research on gradient inversion attacks (GIAs) have shown that gradient updates in FL can leak information on private training samples. While existing surveys on GIAs have focused on the honest-but-curious server threat model, there is a dearth of research categorizing attacks under the realistic and far more privacy-infringing cases of malicious servers and clients. In this paper, we present a survey and novel taxonomy of GIAs that emphasize FL threat models, particularly that of malicious servers and clients. We first formally define GIAs and contrast conventional attacks with the malicious attacker. We then summarize existing honest-but-curious attack strategies, corresponding defenses, and evaluation metrics. Critically, we dive into attacks with malicious servers and clients to highlight how they break existing FL defenses, focusing specifically on reconstruction methods, target model architectures, target data, and evaluation metrics. Lastly, we discuss open problems and future research directions.

cs.CR↗

arXiv2024

Microstructured large-area photoconductive terahertz emitters driven at high average power

Emitters based on photoconductive materials excited by ultrafast lasers are well established and popular devices for THz generation. However, so far, these emitters, both photoconductive antennas and large area emitters, were mostly explored using driving lasers with moderate average powers (either fiber lasers with up to hundreds of milliwatts or Ti:Sapphire systems up to few watts). In this paper, we explore the use of high power, MHz repetition rate Ytterbium (Yb) based oscillator for THz emission using a microstructured large area photoconductive emitter, consist of semi insulating GaAs with a 10 by 10 mm2 active area. As a driving source, we use a frequency doubled home built high average power ultrafast Yb oscillator, delivering 22 W of average power, 115 fs pulses with 91 MHz repetition rate at a central wavelength of 516 nm. When applying 9 W of average power (after an optical chopper with a duty cycle of 50 percent) on the structure without optimized heatsinking, we obtain 65 uW THz average power, 4 THz bandwidth; furthermore, we safely apply up to 18 W of power on the structure without observing damage. We investigate the impact of excitation power, bias voltage, optical fluence, and their interplay on the emitter performance and explore in detail the sources of thermal load originating from electrical and optical power. Optical power is found to have a more critical impact on LAE saturation than electrical power, thus optimized heatsinking will allow us to improve the conversion efficiency in the near future towards much higher emitter power. This work paves the way towards achieving hundreds of MHz or even GHz repetition rates, high power THz sources based on photoconductive emitters, that are of great interest for example for future THz imaging applications.

physics.optics↗

arXiv2024

DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images

Neural radiance fields (NeRFs) show potential for transforming images captured worldwide into immersive 3D visual experiences. However, most of this captured visual data remains siloed in our camera rolls as these images contain personal details. Even if made public, the problem of learning 3D representations of billions of scenes captured daily in a centralized manner is computationally intractable. Our approach, DecentNeRF, is the first attempt at decentralized, crowd-sourced NeRFs that require $\sim 10^4\times$ less server computing for a scene than a centralized approach. Instead of sending the raw data, our approach requires users to send a 3D representation, distributing the high computation cost of training centralized NeRFs between the users. It learns photorealistic scene representations by decomposing users' 3D views into personal and global NeRFs and a novel optimally weighted aggregation of only the latter. We validate the advantage of our approach to learn NeRFs with photorealism and minimal server computation cost on structured synthetic and real-world photo tourism datasets. We further analyze how secure aggregation of global NeRFs in DecentNeRF minimizes the undesired reconstruction of personal content by the server.

cs.CV↗

arXiv2024

Generalizable and Stable Finetuning of Pretrained Language Models on Low-Resource Texts

Pretrained Language Models (PLMs) have advanced Natural Language Processing (NLP) tasks significantly, but finetuning PLMs on low-resource datasets poses significant challenges such as instability and overfitting. Previous methods tackle these issues by finetuning a strategically chosen subnetwork on a downstream task, while keeping the remaining weights fixed to the pretrained weights. However, they rely on a suboptimal criteria for sub-network selection, leading to suboptimal solutions. To address these limitations, we propose a regularization method based on attention-guided weight mixup for finetuning PLMs. Our approach represents each network weight as a mixup of task-specific weight and pretrained weight, controlled by a learnable attention parameter, providing finer control over sub-network selection. Furthermore, we employ a bi-level optimization (BLO) based framework on two separate splits of the training dataset, improving generalization and combating overfitting. We validate the efficacy of our proposed method through extensive experiments, demonstrating its superiority over previous methods, particularly in the context of finetuning PLMs on low-resource datasets.

cs.CL↗

arXiv2024

CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models

Federated Learning (FL) enables collaborative optimization of machine learning models across decentralized data by aggregating model parameters. Our approach extends this concept by aggregating "knowledge" derived from models, instead of model parameters. We present a novel framework called CoDream, where clients collaboratively optimize randomly initialized data using federated optimization in the input data space, similar to how randomly initialized model parameters are optimized in FL. Our key insight is that jointly optimizing this data can effectively capture the properties of the global data distribution. Sharing knowledge in data space offers numerous benefits: (1) model-agnostic collaborative learning, i.e., different clients can have different model architectures; (2) communication that is independent of the model size, eliminating scalability concerns with model parameters; (3) compatibility with secure aggregation, thus preserving the privacy benefits of federated learning; (4) allowing of adaptive optimization of knowledge shared for personalized learning. We empirically validate CoDream on standard FL tasks, demonstrating competitive performance despite not sharing model parameters. Our code: https://mitmedialab.github.io/codream.github.io/

cs.LG↗

arXiv2023

EIGEN: Expert-Informed Joint Learning Aggregation for High-Fidelity Information Extraction from Document Images

Information Extraction (IE) from document images is challenging due to the high variability of layout formats. Deep models such as LayoutLM and BROS have been proposed to address this problem and have shown promising results. However, they still require a large amount of field-level annotations for training these models. Other approaches using rule-based methods have also been proposed based on the understanding of the layout and semantics of a form such as geometric position, or type of the fields, etc. In this work, we propose a novel approach, EIGEN (Expert-Informed Joint Learning aGgrEatioN), which combines rule-based methods with deep learning models using data programming approaches to circumvent the requirement of annotation of large amounts of training data. Specifically, EIGEN consolidates weak labels induced from multiple heuristics through generative models and use them along with a small number of annotated labels to jointly train a deep model. In our framework, we propose the use of labeling functions that include incorporating contextual information thus capturing the visual and language context of a word for accurate categorization. We empirically show that our EIGEN framework can significantly improve the performance of state-of-the-art deep models with the availability of very few labeled data instances. The source code is available at https://github.com/ayushayush591/EIGEN-High-Fidelity-Extraction-Document-Images.

cs.CV↗

arXiv2023

Cutting-plane algorithms for preemptive uniprocessor real-time scheduling problems

Fixed-point iteration algorithms like RTA (response time analysis) and QPA (quick processor-demand analysis) are arguably the most popular ways of solving schedulability problems for preemptive uniprocessor FP (fixed-priority) and EDF (earliest-deadline-first) systems. Several IP (integer program) formulations have also been proposed for these problems, but it is unclear whether the algorithms for solving these formulations are related to RTA and QPA. By discovering connections between the problems and the algorithms, we show that RTA and QPA are, in fact, suboptimal cutting-plane algorithms for specific IP formulations of FP and EDF schedulability, where optimality is defined with respect to convergence rate. We propose optimal cutting-plane algorithms for these IP formulations. We compare the new algorithms with RTA and QPA on large collections of synthetic systems to gauge the improvement in convergence rates and running times.

cs.OS↗

arXiv2022

Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic

Containment, the key strategy in quickly halting an epidemic, requires rapid identification and quarantine of the infected individuals, determination of whom they have had close contact with in the previous days and weeks, and decontamination of locations the infected individual has visited. Achieving containment demands accurate and timely collection of the infected individual's location and contact history. Traditionally, this process is labor intensive, susceptible to memory errors, and fraught with privacy concerns. With the recent almost ubiquitous availability of smart phones, many people carry a tool which can be utilized to quickly identify an infected individual's contacts during an epidemic, such as the current 2019 novel Coronavirus crisis. Unfortunately, the very same first-generation contact tracing tools have been used to expand mass surveillance, limit individual freedoms and expose the most private details about individuals. We seek to outline the different technological approaches to mobile-phone based contact-tracing to date and elaborate on the opportunities and the risks that these technologies pose to individuals and societies. We describe advanced security enhancing approaches that can mitigate these risks and describe trade-offs one must make when developing and deploying any mass contact-tracing technology. With this paper, our aim is to continue to grow the conversation regarding contact-tracing for epidemic and pandemic containment and discuss opportunities to advance this space. We invite feedback and discussion.

cs.CR↗

arXiv2022

Scalable Collaborative Learning via Representation Sharing

Privacy-preserving machine learning has become a key conundrum for multi-party artificial intelligence. Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device). In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the clients must release individual cut-layer activations (smashed data) to the server and wait for its response (during both inference and back propagation). While relevant in several settings, both of these schemes have a high communication cost, rely on server-level computation algorithms and do not allow for tunable levels of collaboration. In this work, we present a novel approach for privacy-preserving machine learning, where the clients collaborate via online knowledge distillation using a contrastive loss (contrastive w.r.t. the labels). The goal is to ensure that the participants learn similar features on similar classes without sharing their input data. To do so, each client releases averaged last hidden layer activations of similar labels to a central server that only acts as a relay (i.e., is not involved in the training or aggregation of the models). Then, the clients download these last layer activations (feature representations) of the ensemble of users and distill their knowledge in their personal model using a contrastive objective. For cross-device applications (i.e., small local datasets and limited computational capacity), this approach increases the utility of the models compared to independent learning and other federated knowledge distillation (FD) schemes, is communication efficient and is scalable with the number of clients. We prove theoretically that our framework is well-posed, and we benchmark its performance against standard FD and FL on various datasets using different model architectures.

cs.LG↗

arXiv2022

Learning to Censor by Noisy Sampling

Point clouds are an increasingly ubiquitous input modality and the raw signal can be efficiently processed with recent progress in deep learning. This signal may, often inadvertently, capture sensitive information that can leak semantic and geometric properties of the scene which the data owner does not want to share. The goal of this work is to protect sensitive information when learning from point clouds; by censoring the sensitive information before the point cloud is released for downstream tasks. Specifically, we focus on preserving utility for perception tasks while mitigating attribute leakage attacks. The key motivating insight is to leverage the localized saliency of perception tasks on point clouds to provide good privacy-utility trade-offs. We realize this through a mechanism called Censoring by Noisy Sampling (CBNS), which is composed of two modules: i) Invariant Sampler: a differentiable point-cloud sampler which learns to remove points invariant to utility and ii) Noisy Distorter: which learns to distort sampled points to decouple the sensitive information from utility, and mitigate privacy leakage. We validate the effectiveness of CBNS through extensive comparisons with state-of-the-art baselines and sensitivity analyses of key design choices. Results show that CBNS achieves superior privacy-utility trade-offs on multiple datasets.

cs.CV↗