arXiv subjects

Vikas Srivastava

Publications and source records attributed to Vikas Srivastava.

At least 19 recordsLinked to original sources

arXiv2026

Ultrafast light-sheet optical tweezers for in situ parallelized biomechanical characterization of cells and soft tissues

Quantitative characterization of the mechanical properties of cells and tissues is essential for understanding disease progression and tissue regeneration. Optical tweezers (OT) enable the direct application of biologically relevant forces; however, OT has been limited to single axial indentations of cells, thereby restricting throughput. Furthermore, the use of quadrant photodiodes is insufficient for assessing the large displacements required for biomechanical characterization of tissues. We present light-sheet optical tweezers as a force transducer (LOFT), an approach that improves the throughput by at least 3x through simultaneous multiparticle trapping and parallelized characterization under sub-nN forces. LOFT is achieved by uniquely integrating light-sheet illumination for extended trapping, femtosecond-pulsed lasers to augment the optical gradient force, and videography-based particle tracking for observation of force transduction. The platform is validated through single-cell indentation experiments. We then apply LOFT to myocardial tissue, revealing significant biomechanical differences between healthy and infarcted regions; the interpretation of which is further supported by quantitative multiphoton imaging using the same optical source and platform. This work represents the first demonstration of OT for the mechanical testing of intact soft tissues, and establishes LOFT as a versatile, multifunctional platform for high-throughput, minimally invasive, mechanical characterization of complex biological systems in situ.

physics.optics↗

arXiv2026

Syndrome Decoding for Silent Data Corruption in Quantized Integer GPU Arithmetic

Quantized neural network inference runs integer matrix multiplications on GPU tensor cores, and the INT32 accumulators inside those cores have neither parity nor ECC. A transient fault in this datapath returns a valid but wrong integer and raises no interrupt. Checksum based Algorithm Based Fault Tolerance (ABFT) can detect such silent data corruptions (SDCs), but its verdict is binary. It cannot identify the corrupted element or its magnitude, and unweighted row and column checksums are blind by construction to errors that cancel on both axes. We present SProbe, a trailing verification kernel that reads the output of an unmodified vendor GEMM. A randomized Freivalds gate with three independent evaluation points in a 61 bit prime field misses a nonzero error with probability at most $2^{-141}$. When the gate fires, per row power sum syndromes over three primes are decoded with the Reed Solomon chain of Berlekamp Massey, Chien search, and Forney, recovering the column and exact magnitude of up to four colliding errors per row. SProbe then repairs the accumulator in place or recomputes the GEMM. On an NVIDIA H100, SProbe detects every injected fault across seven fault classes and four matrix sizes, including constructed patterns that TR-ABFT never detects and patterns that a weighted grid code detects but cannot correct. The gate costs 49% of the cuBLASLt GEMM time at N=16384 and 11% at N=65536. In an INT8 medical LLM, protection eliminates all observed silent corruptions at a 30% throughput cost. Our measurements also show that recomputation is faster than in place recovery in every configuration we tested, that diagnosis rather than repair dominates recovery cost, and we report the defects we found while validating the verifier itself.

cs.DC↗

arXiv2026

Sketching the Error, Not the Product: Post Hoc Fault Recovery for Half Precision GPU Matrix Multiplication

Silent data corruption (SDC) from defective accelerators now interrupts large scale training, yet deployed mitigations act on whole nodes. Algorithm based fault tolerance (ABFT) for a single GEMM has to be fused into the kernel or encode the operands, and it localizes at most one error per checksum. We present FP-Sketch, a verifier that runs after an unmodified tensor core GEMM whose half precision operands are accumulated and delivered at FP32. A sum sketch detects corruption on every call. Hashed first moment sketches, confirmed by independent recomputation, then localize several corrupted entries with no false positives by construction, and each fault yields a coordinate and a magnitude for fleet diagnosis. In floating point, sketch noise rather than bucket collisions limits localization. We measure that noise and find that its constant depends on the BLAS and the operand format and that the bucket count must grow as $n^{2.57}$ for a square product. Sizing the bucket count by measured noise rather than by a fitted power of $n$ raises recovery on eight transformer shapes from 0.402 to 1.000, and measuring the noise at run time adapts the bucket count to the kernel and the model. Instruction level injection with NVBit shows that upsets in a live accumulator are often only 2 to 9% of a typical entry, a population that output side injection cannot produce. Output side injection recovers every fault, while under NVBit the same engine sized for faults of typical magnitude recovers 0.550, and sizing for the measured magnitudes restores 1.000. On Llama-2-7B, guarding the MLP down projections removes 99.4% (BF16) and 99.9% (FP16) of the perplexity damage caused by 2048 bit flips, and the clean path probe costs 0.78 to 3.06 ms against GEMMs of 0.35 to 12.47 ms.

cs.DC↗

arXiv2026

Revisiting Safe Temperature for Environmental Accelerated Aging of Additively Manufactured Polymers

Accelerated aging is widely used to study the long-term behavior of materials within laboratory time scales, particularly for materials exposed to solvent environments over extended periods. This is especially important for additively manufactured (AM) polymers, whose increasing use in naval and commercial undersea applications requires reliable methodologies for assessing durability under in-service conditions. A common approach relies on elevating the temperature below the glass transition or melting temperature to accelerate degradation. However, the temperature limits for accelerated aging of AM polymers remain poorly understood, particularly because temperatures beyond a threshold may activate deformation and degradation mechanisms that are absent under service conditions. To address this gap, this paper investigates fused deposition modeling (FDM) Acrylonitrile Butadiene Styrene (ABS) exposed to saltwater and deionized (DI) water to establish a temperature threshold for accelerated aging in aqueous environments and propose a methodology for determining such thresholds. Controlled geometries and varying print directions were employed to explicitly probe the underlying mechanisms. We show that samples exposed to temperatures above the threshold exhibit pronounced shrinkage and warping along the printing direction due to the relaxation of process-induced internal stresses. These observations establish an accelerated-aging temperature threshold of 50$^\circ$C for ABS, beyond which additional mechanisms absent under service conditions become active. Additionally, the resulting geometric distortions are masked in thick geometries but become highly pronounced in thin structures. Moreover, solvent ionic content strongly influences water uptake, with saltwater reaching saturation in 1 day, whereas DI water did not reach saturation even after 30 days and exhibited greater mass uptake.

cond-mat.soft↗

arXiv2026

Pivot: Proactive and Verifiable Threshold Oblivious Pseudorandom Functions From Isogeny Group Actions

Oblivious pseudorandom functions (OPRFs) allow a client to evaluate a keyed pseudorandom function on a private input without revealing that input to the server. In a threshold OPRF, the secret key is distributed among (n) servers so that any qualified set of at least (t) servers can complete an evaluation, while fewer than (t) shares reveal no information about the key. Existing isogeny-based threshold OPRFs, however, are primarily designed for static corruption models. If the same shares remain valid throughout the lifetime of the service, a mobile adversary can compromise different servers over time, accumulate (t) shares from the same sharing state, and eventually recover the master key. We introduce PIVOT (Proactive Isogeny-based Verifiable Oblivious Threshold PRF), a dealerless threshold VOPRF framework based on effective isogeny group actions. PIVOT periodically refreshes the server shares without changing the master key, public key, or previously generated OPRF outputs. The construction combines Shamir secret sharing, additively homomorphic coefficient commitments, sequential Lagrange-weighted group actions, and joint zero-knowledge relations that link certified shares to their corresponding isogeny actions. It also supports coordinated epoch transitions, publicly verifiable blame, secure erasure, and committee resharing under a possibly different threshold. We formalize the functionality of a long-lived proactive threshold VOPRF, prove the correctness of distributed key generation, threshold evaluation, proactive refresh, and committee resharing, and provide a simulation-based security analysis under the vectorization and one-more hidden-group- action assumptions. As an application, we describe a distributed private lookup service whose encrypted database remains valid across repeated share renewals and committee migrations.

cs.CR↗

arXiv2026

Modeling damage and fracture in additively manufactured polymeric triply periodic minimal surface lattices

Architected triply periodic minimal surface (TPMS) lattices offer superior specific energy absorption, toughness, fatigue strength, and tunability. While recent advancements have established rate-dependent viscoplastic constitutive models to capture the complex nonlinear deformation response of additively manufactured polymeric TPMS structures, predicting fracture and the resulting structural failure remains a significant challenge. We address this by performing systematic experiments on unit cells and lattices of various sizes under tension, compression, and non-monotonic loading. The experiments inform the development of a new constitutive model that captures the damage and fracture behavior of polymeric TPMS lattices. We first implement a high-fidelity viscoplastic deformation constitutive model from Ma et al. (2026) into finite element software Abaqus/Explicit via a user material subroutine. We then propose a damage initiation criterion for amorphous polymers based on stored elastic energy and equivalent plastic strain. The damage model is implemented in Abaqus using gradient-damage framework following Konale and Srivastava(2025). The damage model and numerical simulation capability are quantitatively and qualitatively validated using experimental results for a unit cell under non-monotonic loading and lattices under tension. The proposed damage model and simulation capability enable in silico design of architected polymer structures.

cond-mat.soft↗

arXiv2026

A Post-Quantum Secure Lattice-Based Forward-Secure Identity Based Encryption with Applications to Internet of Things Architecture

The rapid expansion of the Internet of Things (IoT) has led to an unprecedented scale of data exchange across heterogeneous and resource-constrained devices. Ensuring confidentiality and secure key management in such environments is challenging. Traditional public-key infrastructures require heavy certificate-handling overhead. Identity-Based Encryption (IBE) offers a lightweight alternative by deriving public keys directly from device identities, making it attractive for IoT deployments. However, IoT devices are highly vulnerable to side-channel and key-extraction attacks, motivating the need for Forward-Secure IBE(FS-IBE), where the compromise of a current secret key does not threaten past communications. Existing FS-IBE constructions based on classical hardness assumptions are not secure in the era of post-quantum, while the lattice-based (LWE-based) forward-secure scheme suffer from large key and ciphertext sizes, limiting their suitability for constrained IoT systems. Here, we propose a new lattice-based fs-IBE scheme in the ring setting, relying on the RLWE assumption to achieve post-quantum security and significant efficiency gains. Our design uses trapdoor delegation with a minimal-cover mechanism over a binary tree. It results in compact public parameters and efficient per-epoch key updates. Compared to prior LWE-based constructions, our scheme reduces public key, secret key, and ciphertext sizes, and thus, making it better suited for practical IoT environments.

cs.CR↗

arXiv2026

Hierarchical Identity-Based Signature with Designated Aggregator from Lattices

In hierarchical organizations, authenticating data from multiple users can be complex and resource-intensive. Hierarchical Identity-Based Signature with Designated Aggregator (HIBS-DA) provides an efficient solution by allowing users at different levels to generate signatures that can be combined into a single, compact signature. We first introduce the HIBS-DA framework and present the {\em{first}} lattice-based construction of HIBS-DA. Our scheme allows users at different hierarchical levels to generate individual signatures that can be aggregated into a single, compact signature, reducing communication and verification costs. The proposed construction is secure, correct, and resistant to forgery, making it suitable for large-scale environments such as universities, corporations, and government agencies.

cs.CR↗

arXiv2026

A Post-Quantum Secure End-to-End Verifiable E-Voting Protocol Based on Multivariate Polynomials

Voting is a primary democratic activity through which voters select representatives or approve policies. Conventional paper ballot elections have several drawbacks that might compromise the fairness, effectiveness, and accessibility of the voting process. Therefore, there is an increasing need to design safer, effective, and easily accessible alternatives. E-Voting is one such solution that uses digital tools to simplify voting. Existing state-of-the-art designs for secure E-Voting are based on number-theoretic hardness assumptions. These designs are no longer secure due to quantum algorithms such as Shor's algorithm. We present the design and analysis of \textit{first} post-quantum secure end-to-end verifiable E-Voting protocol based on multivariate polynomials to address this issue. The security of our proposed design depends on the hardness of the MQ problem, which is an NP-hard problem. We present a simple yet efficient design involving only standard cryptographic primitives as building blocks.

cs.CR↗

arXiv2026

Isogeny-based Post-Quantum Proxy Signature for Internet of Things

The rapid growth of the Internet of Things (IoT) introduces challenges in secure authentication and delegation due to the limited computational capabilities of devices. Proxy signature schemes offer an effective solution by enabling controlled delegation of signing rights to more capable entities, such as gateway nodes. However, most existing schemes rely on classical assumptions that are likely to be broken by quantum adversaries. In this work, we address these challenges by proposing an isogeny-based post-quantum proxy signature scheme, \textit{CSI-PS}. The scheme leverages the hardness of the Group Action Inverse Problem (GAIP) to ensure quantum-resistant security while maintaining efficiency suitable for resource-constrained environments. We further demonstrate its applicability in IoT architectures through a gateway-based delegation model. Our analysis shows that the proposed scheme strikes an effective balance between security and efficiency in terms of computation and communication overhead, along with provable security under the EUF-CMA notion.

cs.CR↗

arXiv2026

On modeling fracture of soft polymers

Soft polymers are ubiquitous materials in nature and as engineering materials with properties varying from rate-independent to rate-dependent. Current fracture toughness measures are non-unique for rate-dependent soft materials for varying loading profiles and specimen geometries. Works on modeling fracture in rate-dependent soft polymers are limited to specific pre-cracked geometries. There is no generally agreed-upon model for the fracture of soft polymers. We propose and show that a critical value of stress work can be used as a measure of fracture resistance for a certain class of soft polymers. We develop a damage model to predict fracture in soft polymers. In the model, the energetic part of the critical stress work is proposed as a damage initiation criterion that has the ability to capture failure surfaces. The damage growth is modeled through a generalized gradient-damage framework. The fracture model is validated for both elastomers and viscous soft polymers by comparing model predictions against experimental results for different materials (ethylene propylene diene monomer - EPDM, EPS25 vitrimer, styrene butadiene rubber - SBR, natural rubber - NR, and polyborosiloxane - PBS), a variety of specimen geometries, and loading conditions. The model can predict key physical phenomena such as brittle and ductile responses and different fracture profiles. The microstructural quantities, such as subchain dissociation energy during the fracture of polymers, can be predicted from the macroscopic model parameters.

cond-mat.soft↗

arXiv2026

Drug Release Modeling using Physics-Informed Neural Networks

Accurate modeling of drug release is essential for designing and developing controlled-release systems. Classical models (Fick, Higuchi, Peppas) rely on simplifying assumptions that limit their accuracy in complex geometries and release mechanisms. Here, we propose a novel approach using Physics-Informed Neural Networks (PINNs) and Bayesian PINNs (BPINNs) for predicting release from planar, 1D-wrinkled, and 2D-crumpled films. This approach uniquely integrates Fick's diffusion law with limited experimental data to enable accurate long-term predictions from short-term measurements, and is systematically benchmarked against classical drug release models. We embedded Fick's second law into PINN as loss with 10,000 Latin-hypercube collocation points and utilized previously published experimental datasets to assess drug release performance through mean absolute error (MAE) and root mean square error (RMSE), considering noisy conditions and limited-data scenarios. Our approach reduced mean error by up to 40% relative to classical baselines across all film types. The PINN formulation achieved RMSE <0.05 utilizing only the first 6% of the release time data (reducing 94% of release time required for the experiments) for the planar film. For wrinkled and crumpled films, the PINN reached RMSE <0.05 in 33% of the release time data. BPINNs provide tighter and more reliable uncertainty quantification under noise. By combining physical laws with experimental data, the proposed framework yields highly accurate long-term release predictions from short-term measurements, offering a practical route for accelerated characterization and more efficient early-stage drug release system formulation.

cs.LG↗

arXiv2025

A physics-informed neural network for modeling fracture without gradient damage: formulation, application, and assessment

Accurate computational modeling of damage and fracture remains a central challenge in solid mechanics. The finite element method (FEM) is widely used for numerical modeling of fracture problems; however, classical damage models without gradient regularization yield mesh-dependent and usually inaccurate predictions. The use of gradient damage with FEM improves numerical robustness but introduces significant mathematical and numerical implementation complexities. Physics-informed neural networks (PINNs) can encode the governing partial differential equations, boundary conditions, and constitutive models into the loss functions, offering a new method for fracture modeling. Prior applications of PINNs have been limited to small-strain problems and have incorporated gradient damage formulation without a critical evaluation of its necessity. Since PINNs in their basic form are meshless, this work presents a PINN framework for modeling fracture in elastomers undergoing large deformation without the gradient damage formulation. The PINN implementation here does not require training data and utilizes the collocation method to formulate physics-informed loss functions. We have validated the PINN's predictions for various defect configurations using benchmark solutions obtained from FEM with gradient damage formulation. The crack paths obtained using the PINN are approximately insensitive to the collocation point distribution. This study offers new insights into the feasibility of using PINNs without gradient damage and suggests a simplified and efficient computational modeling strategy for fracture problems. The PINN's performance has been evaluated through systematic variations in key neural network parameters to provide an assessment and guidance for future applications. The results motivate the extension of PINN-based approaches to a broader class of materials and damage models in mechanics.

cond-mat.soft↗

arXiv2025

Error Detection Schemes for Barrett Reduction of CT-BU on FPGA in Post Quantum Cryptography

A fault can occur naturally or intentionally. However, intentionally injecting faults into hardware accelerators of Post-Quantum Cryptographic (PQC) algorithms may leak sensitive information. This intentional fault injection in side-channel attacks compromises the reliability of PQC implementations. The recently NIST-standardized key encapsulation mechanism (KEM), Kyber may also leak information at the hardware implementation level. This work proposes three efficient and lightweight recomputation-based fault detection methods for Barrett Reduction in the Cooley-Tukey Butterfly Unit (CT-BU) of Kyber on a Field Programmable Gate Array (FPGA). The CT-BU and Barrett Reduction are fundamental components in structured lattice-based PQC algorithms, including Kyber, NTRU, Falcon, CRYSTALS-Dilithium, etc. This paper introduces a new algorithm, Recomputation with Swapped Operand (RESWO), for fault detection. While Recomputation with Negated Operand (RENO) and Recomputation with Shifted Operand (RESO) are existing methods used in other PQC hardware algorithms. To the best of our knowledge, RENO and RESO have never been used in Barrett Reduction before. The proposed RESWO method consumes a similar number of slices compared to RENO and RESO. However, RESWO shows lesser delay compared to both RENO and RESO. The fault detection efficiency of RESWO, RENO, and RESO is nearly 100%.

cs.CR↗

arXiv2025

A finite element implementation of a large deformation gradient-damage theory for fracture with Abaqus user material subroutines

Recent advancements in computations have enabled the application of various modeling approaches to predict fracture and failure, such as the gradient-damage (phasefield) method. Several existing studies have leveraged the heat equation solver in Abaqus to model gradient-damage, due to its mathematical resemblance to the heat equation. Particular care is required when extending the approach to large deformation scenarios due to differences in the referential and spatial configurations, especially since the heat equation in Abaqus is solved in the spatial configuration, whereas most gradient-damage frameworks are formulated in the referential configuration. This work provides a pedagogic view of an appropriate Abaqus implementation of a gradient-damage theory for fracture in materials undergoing large deformation using Abaqus UMAT and UMATHT user subroutines. Key benchmark problems from the literature are used to demonstrate the robustness of our implementation across various materials exhibiting different constitutive behaviors, such as non-linear elasticity, linear elasticity, and large deformation rate-dependent plasticity, ensuring its applicability regardless of the specific material constitutive choice. The details of the implementation, along with the codes, which are a direct outcome of this work, are also provided.

cond-mat.soft↗

arXiv2025

LaSDVS : A Post-Quantum Secure Compact Strong-Designated Verifier Signature

Digital signatures are fundamental cryptographic primitives that ensure the authenticity and integrity of digital communication. However, in scenarios involving sensitive interactions -- such as e-voting or e-cash -- there is a growing need for more controlled signing mechanisms. Strong-Designated Verifier Signature (SDVS) offers such control by allowing the signer to specify and restrict the verifier of a signature. The existing state-of-the-art SDVS are mostly based on number-theoretic hardness assumptions. Thus, they are not secure against quantum attacks. Moreover, Post-Quantum Cryptography (PQC)-based SDVS are inefficient and have large key and signature sizes. In this work, we address these challenges and propose an efficient post-quantum SDVS (namely, LaSDVS) based on ideal lattices under the hardness assumptions of the Ring-SIS and Ring-LWE problems. LaSDVS achieves advanced security properties including strong unforgeability under chosen-message attacks, non-transferability, non-delegatability, and signer anonymity. By employing the algebraic structure of rings and the gadget trapdoor mechanism of Micciancio et al., we design LaSDVS to minimize computational overhead and significantly reduce key and signature sizes. Notably, our scheme achieves a compact signature size of $\mathcal{O}(n\log q)$, compared to $\mathcal{O}(n^2)$ size, where $n$ is the security parameter, in the existing state-of-the-art PQC designs. To the best of our knowledge, LaSDVS offers the \textit{smallest private key and signature size} among the existing PQC-based SDVS schemes.

cs.CR↗

arXiv2025

Energy-Efficient NTT Sampler for Kyber Benchmarked on FPGA

Kyber is a lattice-based key encapsulation mechanism selected for standardization by the NIST Post-Quantum Cryptography (PQC) project. A critical component of Kyber's key generation process is the sampling of matrix elements from a uniform distribution over the ring Rq . This step is one of the most computationally intensive tasks in the scheme, significantly impacting performance in low-power embedded systems such as Internet of Things (IoT), wearable devices, wireless sensor networks (WSNs), smart cards, TPMs (Trusted Platform Modules), etc. Existing approaches to this sampling, notably conventional SampleNTT and Parse-SPDM3, rely on rejection sampling. Both algorithms require a large number of random bytes, which needs at least three SHAKE-128 squeezing steps per polynomial. As a result, it causes significant amount of latency and energy. In this work, we propose a novel and efficient sampling algorithm, namely Modified SampleNTT, which substantially educes the average number of bits required from SHAKE-128 to generate elements in Rq - achieving approximately a 33% reduction compared to conventional SampleNTT. Modified SampleNTT achieves 99.16% success in generating a complete polynomial using only two SHAKE-128 squeezes, outperforming both state-of-the-art methods, which never succeed in two squeezes of SHAKE-128. Furthermore, our algorithm maintains the same average rejection rate as existing techniques and passes all standard statistical tests for randomness quality. FPGA implementation on Artix-7 demonstrates a 33.14% reduction in energy, 33.32% lower latency, and 0.28% fewer slices compared to SampleNTT. Our results confirm that Modified SampleNTT is an efficient and practical alternative for uniform polynomial sampling in PQC schemes such as Kyber, especially for low-power security processors.

cs.CR↗

arXiv2024

Quantum Secure Protocols for Multiparty Computations

Secure multiparty computation (MPC) schemes allow two or more parties to conjointly compute a function on their private input sets while revealing nothing but the output. Existing state-of-the-art number-theoretic-based designs face the threat of attacks through quantum algorithms. In this context, we present secure MPC protocols that can withstand quantum attacks. We first present the design and analysis of an information-theoretic secure oblivious linear evaluation (OLE), namely ${\sf qOLE}$ in the quantum domain, and show that our ${\sf qOLE}$ is safe from external attacks. In addition, our scheme satisfies all the security requirements of a secure OLE. We further utilize ${\sf qOLE}$ as a building block to construct a quantum-safe multiparty private set intersection (MPSI) protocol.

quant-ph↗