arXiv subjects

Aditya Shukla

Publications and source records attributed to Aditya Shukla.

17 recordsLinked to original sources

arXiv2026

Geometrically necessary boundaries accommodate the residual elastic strain in cold-rolled Fe-3%Si

The relationship between plastic deformation accommodation structures and residual elastic strain fields in deformed metals is poorly understood at the intragranular scale, largely because no experimental technique has provided simultaneous, three-dimensional, bulk-sensitive access to both fields at the length scale of dislocation boundaries. Here we use dark-field X-ray microscopy (DFXM) to map intragranular misorientation and residual elastic strain simultaneously in three dimensions within a grain of 50% cold-rolled Fe 3%Si alloy. We resolve geometrically necessary boundaries (GNBs) and incidental dislocation boundary (IDB) cell structures in the bulk non-destructively. Correlating the elastic strain field with the segmented plastically deformed substructure reveals that GNBs act as the primary carriers and distributors of long range residual elastic strain. GNBs separate subdomains of distinct mean d-spacing, across the grain volume. The plastic misorientation associated with IDBs and dislocation cells develops within GNB-delimited subdomains that carry comparatively similar values of elastic strain. This supports a mechanistic picture in which GNBs accommodate nearly all the long-range residual elastic strain in the deformed state, while plastic slip propagates into GNB interiors to organize into IDB cells with similar strain levels. The three-dimensional misorientation and strain gradients quantified here provide direct experimental input for recovery and recrystallization modelling in ferritic steels, such as electrical steels.

cond-mat.mtrl-sci↗

arXiv2026

3D Mapping of Intragranular Residual Strain and Microstructure in Recrystallized Iron Using Dark-Field X-ray Microscopy

Grain growth is a key process in the thermomechanical treatment of metals. Recently, the presence of local residual stresses within fully recrystallized grains has attracted increasing interest in connection with shear-coupled grain boundary migration mechanisms. In this work, we provide the first direct experimental measurements of residual elastic strain variations in fully recrystallized commercial-purity iron, on the order of $10^{-4}$. Using dark-field X-ray microscopy (DFXM), we performed non-destructive three-dimensional measurements of strain and orientation variations within individual grains. Our results reveal heterogeneous strain distributions across all measured grains. In one case, we observed several isolated dislocations accommodating two second-phase particles, exhibiting a localized strain signature with no detectable long-range effect. The formation mechanisms of intragranular residual strains and their potential influence on grain boundary migration during subsequent grain growth are discussed. This work highlights the importance of accounting for such residual elastic strains in future grain growth models.

cond-mat.mtrl-sci↗

arXiv2026

Benchmarking LLM Summaries of Multimodal Clinical Time Series for Remote Monitoring

Large language models (LLMs) can generate fluent clinical summaries of remote therapeutic monitoring time series. However, it remains unclear whether these narratives faithfully capture clinically significant events, such as sustained abnormalities. Existing evaluation metrics primarily focus on semantic similarity and linguistic quality, leaving event-level correctness largely unmeasured. To address this gap, we introduce an event-based evaluation framework for multimodal time-series summarization using the Technology-Integrated Health Management (TIHM)-1.5 dementia monitoring dataset. Clinically grounded daily events are derived through rule-based abnormal thresholds and temporal persistence criteria. Model-generated summaries are then aligned with these structured facts. Our evaluation protocol measures abnormality recall, duration recall, measurement coverage, and hallucinated event mentions. We benchmark three approaches: zero-shot prompting, statistical prompting, and a vision-based pipeline that uses rendered time-series visualizations. The results reveal a striking decoupling between conventional metrics and clinical event fidelity. Models that achieve high semantic similarity scores often exhibit near-zero abnormality recall. In contrast, the vision-based approach demonstrates the strongest event alignment, achieving 45.7% abnormality recall and 100% duration recall. These findings underscore the importance of event-aware evaluation to ensure reliable clinical time-series summarization.

cs.AI↗

arXiv2025

Relaxation-based dynamical Ising machines for discrete tomography

Dynamical Ising machines are continuous dynamical systems that evolve from a generic initial state to a state strongly related to the ground state of the classical Ising model. We show that such a machine driven by the V${}_2$ dynamical model can solve exactly discrete tomography problems about reconstructing a binary image from the pixel sums along a discrete set of rays. In contrast to usual applications of Ising machines, targeting approximate solutions to optimization problems, the randomly initialized V${}_2$ model converges with high probability ($P_{\mathrm{succ}} \approx 1$) to an image precisely satisfying the tomographic data. For the problems with at most two rays intersecting at each pixel, the V${}_2$ model converges in internal machine time that depends only weakly on the image size. Our consideration is an example of how specific dynamical systems can produce exact solutions to highly non-trivial data processing tasks. Crucially, this solving capability arises from the dynamical features of the V${}_2$ model itself, in particular its equations of motion that enable non-local transitions of the discrete component of the relaxed spin beyond Hamming-neighborhood constraints, rather than from merely recasting the tomography problem in spin form.

cs.ET↗

arXiv2025

Financial Instruction Following Evaluation (FIFE)

Language Models (LMs) struggle with complex, interdependent instructions, particularly in high-stakes domains like finance where precision is critical. We introduce FIFE, a novel, high-difficulty benchmark designed to assess LM instruction-following capabilities for financial analysis tasks. FIFE comprises 88 human-authored prompts and employs a verification system with chainable, verifiable constraints for fine-grained reward signals. We evaluate 53 models (proprietary, open-weight, open-source) in a zero-shot setting. Our key findings reveal a clear performance hierarchy: the top open-weight model (76.1 strict / 79.5 loose) surpasses the leading proprietary system (65.9 strict / 70.5 loose), while the best open-source models lag significantly (45.5 strict / 48.9 loose). However, even top-performing models struggle with FIFE's complex requirements, failing to achieve perfect compliance. We release our dataset and code as an open-source resource to promote research in Reinforcement Learning for the financial domain.

cs.LG↗

arXiv2025

Bridging Grain Mapping and Dark Field X-ray Microscopy for Multiscale Diffraction Imaging

Resolving how defects emerge and interact within the hierarchical structure of polycrystalline materials remains a core challenge in materials science. Grain-mapping methods such as three-dimensional X-ray diffraction (3DXRD) and diffraction contrast tomography (DCT) provide essential mesoscale context but lack the resolution to image lattice defects. Conversely, high-resolution methods like Dark Field X-ray Microscopy (DFXM) capture lattice distortions but not the surrounding microstructure. Here, we introduce a transferable framework that unifies these complementary approaches into a single, non-destructive workflow. Enabled by open-source software, the method translates grain orientation and position data into precise goniometer settings for DFXM imaging without dismounting or reorienting the sample. Applied to an iron polycrystal containing 1100 grains, DFXM motor positions were calculated for all grains within seconds, enabling on-the-fly targeting of specific grains. This allows reproducible zooming from the millimetre-scale aggregate to individual dislocations. We resolve three-dimensional misorientation fields across grain boundaries with 36 nm pixel size, directly capturing grain-grain interactions within their microstructural context. Finally, we show transferability from LabDCT to synchrotron and XFEL platforms, enabling new ways of studying defect interactions across scales.

physics.app-ph↗

arXiv2025

A high-temperature furnace for multi-modal synchrotron-based X-ray microscopy and diffraction imaging

The design, calibration, and initial application of a non-contact high-temperature furnace developed for in situ synchrotron X-ray experiments are presented. The system enables a stable operation up to 1000 {\deg}C, with heating rates exceeding 6000 {\deg}C/min and thermal stability better than {\pm}2 {\deg}C. Temperature calibration was performed using (i) direct measurements with a thermocouple to characterize heating and cooling ramp rates and map temperature gradients along the x, y, and z axes, and (ii) synchrotron X-ray diffraction to track the ferrite-to-austenite (BCC to FCC) phase transition in an iron grain under beamline conditions. The furnace's contactless geometry provides full translational and rotational freedom, with 360{\deg} rotation and wide tilt capabilities, making it fully compatible with a range of diffraction and imaging techniques. Its 3D-printed modular body includes closable apertures for auxiliary functions such as active cooling or X-ray fluorescence. The design is easily customizable for diverse experimental requirements and can be adapted to most beamlines. The furnace has been implemented at the ID03 beamline of the European Synchrotron Radiation Facility (ESRF) which supports Dark field X-ray Microscopy (DFXM), 3D X-ray Diffraction (3DXRD), magnified topotomography (MTT), phase-contrast tomography (PCT) and diffraction contrast tomography (DCT). As a first application, a DFXM case study on a cold-rolled Al1050 sample during isothermal annealing is presented. The imaging of a selected grain before and after the heat treatment reveals strain relaxation and grain growth. This furnace offers a robust and flexible platform for high-temperature synchrotron studies across materials science, including metals, ceramics, and energy materials. It is now part of the ESRF sample environment pool and is available to all users.

physics.ins-det↗

arXiv2025

Pink-Beam Dark Field X-ray Microscopy: Expanding 3D/4D Imaging for Complex and Deformed Microstructures

Dark Field X-ray Microscopy (DFXM) has advanced 3D non-destructive, high-resolution imaging of strain and orientation in crystalline materials, enabling the study of embedded structures in bulk. However, the photon-intensive nature of monochromatic DFXM limits its applicability to highly deformed or weakly crystalline structures and constrains time-resolved studies in industrially relevant materials. We present pink-beam DFXM (\pDFXM) at the ID03 beamline of ESRF, achieving a 27-fold increase in diffracted intensity while maintaining 100 nm spatial resolution. We validate \pDFXM{} by imaging a partially recrystallized aluminum grain, confirming sufficient angular resolution for microstructure mapping. The increased flux significantly enhances the diffracted signal, enabling the resolution of subgrain structures. Additionally, we image a highly deformed ferritic iron grain, previously inaccessible in monochromatic mode without focusing optics. Beyond static imaging, \pDFXM{} enables real-time tracking of grain growth during annealing, achieving hundred-millisecond temporal resolution. By combining high photon flux with non-destructive, high-resolution 3D mapping, \pDFXM{} expands diffraction-contrast imaging to poorly diffracting crystals, unlocking new opportunities for studying grain growth, fatigue, and corrosion in bulk materials.

physics.app-ph↗

arXiv2024

Non-binary dynamical Ising machines for combinatorial optimization

Dynamical Ising machines achieve accelerated solving of complex combinatorial optimization problems by remapping the convergence to the ground state of the classical spin networks to the evolution of specially constructed continuous dynamical systems. The main adapted principle of constructing such systems is based on requiring that, on the one hand, the system converges to a binary state and, on the other hand, the system's energy in such states mimics the classical Ising Hamiltonian. The emergence of binary-like states is regarded to be an indispensable feature of dynamical Ising machines as it establishes the relation between the machine's continuous terminal state and the inherently discrete solution of a combinatorial optimization problem. This is emphasized by problems where the unknown quantities are represented by spin complexes, for example, the graph coloring problem. In such cases, an imprecise mapping of the continuous states to spin configurations may lead to invalid solutions requiring intensive post-processing. In contrast to such an approach, we show that there exists a class of non-binary dynamical Ising machines without the incongruity between the continuous character of the machine's states and the discreteness of the spin states. We demonstrate this feature by applying such a machine to the problems of finding proper graph coloring, constructing Latin squares, and solving Sudoku puzzles. Thus, we demonstrate that the information characterizing discrete states can be unambiguously presented in essentially continuous dynamical systems. This opens new opportunities in the realization of scalable electronic accelerators of combinatorial optimization.

cs.ET↗

arXiv2023

Self-contained relaxation-based dynamical Ising machines

Dynamical Ising machines are based on continuous dynamical systems evolving from a generic initial state to a state strongly related to the ground state of the classical Ising model on a graph. Reaching the ground state is equivalent to finding the maximum (weighted) cut of the graph, which presents the Ising machines as an alternative way to solving and investigating NP-complete problems. Among the dynamical models, relaxation-based models are distinguished by their relations with guarantees of performance achieved in time scaling polynomially with the problem size. However, the terminal states of such machines are essentially non-binary, necessitating special post-processing relying on disparate computing. We show that an Ising machine implementing a special continuous dynamical system (called the V${}_2$ model) solves the rounding problem dynamically. We prove that the V${}_2$ model, starting from an arbitrary non-binary state, terminates in a state that trivially rounds to a binary state with the cut at least as big as obtained by optimal rounding of the initial state. Besides showing that relaxation-based dynamical Ising machines can be made self-contained, this result presents a non-Boolean realization of solving a non-trivial information processing task on Ising machines. Moreover, we prove that if the initial state of the V${}_2$-machine is a random limited amplitude perturbation of a binary state, the machine progresses to a state with at least as high cut as that of the initial binary state. Since the probability of improving the cut is finite, this shows that the V${}_2$-machine with random agitations converges to a maximum cut state almost surely.

cs.ET↗

arXiv2022

Scalable almost-linear dynamical Ising machines

The past decade has seen the emergence of Ising machines targeting hard combinatorial optimization problems by minimizing the Ising Hamiltonian with spins represented by continuous dynamical variables. However, capabilities of these machines at larger scales are yet to be fully explored. We investigate an Ising machine based on a network of almost-linearly coupled analog spins. We show that such networks leverage the computational resource similar to that of the semidefinite positive relaxation of the Ising model. We estimate the expected performance of the almost-linear machine and benchmark it on a set of {0,1}-weighted graphs. We show that the running time of the investigated machine scales polynomially (linearly with the number of edges in the connectivity graph). As an example of the physical realization of the machine, we present a CMOS-compatible implementation comprising an array of vertices efficiently storing the continuous spins on charged capacitors and communicating externally via analog current.

cs.ET↗

arXiv2022

On computational capabilities of Ising machines based on nonlinear oscillators,

Dynamical Ising machines are actively investigated from the perspective of finding efficient heuristics for NP-hard optimization problems. However, the existing data demonstrate super-polynomial scaling of the running time with the system size, which is incompatible with large NP-hard problems. We show that oscillator networks implementing the Kuramoto model of synchronization are capable of demonstrating polynomial scaling. The dynamics of these networks is related to the semidefinite programming relaxation of the Ising model ground state problem. Consequently, such networks, as we numerically demonstrate, are capable of producing the best possible approximation in polynomial time. To reach such performance, however, the reconstruction of the binary Ising state (rounding) must be specially addressed. We demonstrate that commonly implemented forced collapse to a close-to-Ising state may diminish the computational capabilities up to their complete invalidation. Therefore, consistent treatment of rounding may cardinally improve various operation metrics of already existing and upcoming dynamical Ising machines.

cond-mat.stat-mech↗

arXiv2020

Linear Delay-cell Design for Low-energy Delay Multiplication and Accumulation

A practical deep neural network's (DNN) evaluation involves thousands of multiply-and-accumulate (MAC) operations. To extend DNN's superior inference capabilities to energy constrained devices, architectures and circuits that minimize energy-per-MAC must be developed. In this respect, analog delay-based MAC is advantageous due to reasons both extrinsic and intrinsic to the MAC implementation - (1) lower fixed-point precision requirement for a DNN's evaluation, (2) better dynamic range than charge-based accumulation, for smaller technology nodes, and (3) simpler analog-digital interfacing. Implementing DNNs using delay-based MAC requires mixed-signal delay multipliers that accept digitally stored weights and analog voltages as arguments. To this end, a novel, linearly tune-able delay-cell is proposed, wherein, the delay is realized using an inverted MOS capacitor's (C*) steady discharge from a linearly input-voltage dependent initial charge. The cell is analytically modeled, constraints for its functional validity are determined, and jitter-models are developed. Multiple cells with scaled delays, corresponding to each bit of the digital argument, must be cascaded to form the multiplier. To realize such bit-wise delay-scaling of the cells, a biasing circuit is proposed that generates sub-threshold gate-voltages to scale C*'s discharging rate, and thus area-expensive transistor width-scaling is avoided. For 130nm CMOS technology, the theoretical constraints and limits on jitter are used to find the optimal design-point and quantify the jitter versus bits-per-multiplier trade-off. Schematic-level simulations show a worst-case energy-consumption close to the state-of-art, and thus, feasibility of the cell.

cs.ET↗

arXiv2019

A framework for the extraction of Deep Neural Networks by leveraging public data

Machine learning models trained on confidential datasets are increasingly being deployed for profit. Machine Learning as a Service (MLaaS) has made such models easily accessible to end-users. Prior work has developed model extraction attacks, in which an adversary extracts an approximation of MLaaS models by making black-box queries to it. However, none of these works is able to satisfy all the three essential criteria for practical model extraction: (1) the ability to work on deep learning models, (2) the non-requirement of domain knowledge and (3) the ability to work with a limited query budget. We design a model extraction framework that makes use of active learning and large public datasets to satisfy them. We demonstrate that it is possible to use this framework to steal deep classifiers trained on a variety of datasets from image and text domains. By querying a model via black-box access for its top prediction, our framework improves performance on an average over a uniform noise baseline by 4.70x for image tasks and 2.11x for text tasks respectively, while using only 30% (30,000 samples) of the public dataset at its disposal.

cs.LG↗

arXiv2018

A case for multiple and parallel RRAMs as synaptic model for training SNNs

To enable a dense integration of model synapses in a spiking neural networks hardware, various nano-scale devices are being considered. Such a device, besides exhibiting spike-time dependent plasticity (STDP), needs to be highly scalable, have a large endurance and require low energy for transitioning between states. In this work, we first introduce and empirically determine two new specifications for an synapse in SNNs: number of conductance levels per synapse and maximum learning-rate. To the best of our knowledge, there are no RRAMs that meet the latter specification. As a solution, we propose the use of multiple PCMO-RRAMs in parallel within a synapse. While synaptic reading, all PCMO-RRAMs are simultaneously read and for each synaptic conductance-change event, the mechanism for conductance STDP is initiated for only one RRAM, randomly picked from the set. Second, to validate our solution, we experimentally demonstrate STDP of conductance of a PCMO-RRAM and then show that due to a large learning-rate, a single PCMO-RRAM fails to model a synapse in the training of an SNN. As anticipated, network training improves as more PCMO-RRAMs are added to the synapse. Fourth, we discuss the circuit-requirements for implementing such a scheme, to conclude that the requirements are within bounds. Thus, our work presents specifications for synaptic devices in trainable SNNs, indicates the shortcomings of state-of-art synaptic contenders, and provides a solution to extrinsically meet the specifications and discusses the peripheral circuitry that implements the solution.

cs.ET↗

arXiv2017

An On-chip Trainable and Clock-less Spiking Neural Network with 1R Memristive Synapses

Spiking neural networks (SNNs) are being explored in an attempt to mimic brain's capability to learn and recognize at low power. Crossbar architecture with highly scalable Resistive RAM or RRAM array serving as synaptic weights and neuronal drivers in the periphery is an attractive option for SNN. Recognition (akin to reading the synaptic weight) requires small amplitude bias applied across the RRAM to minimize conductance change. Learning (akin to writing or updating the synaptic weight) requires large amplitude bias pulses to produce a conductance change. The contradictory bias amplitude requirement to perform reading and writing simultaneously and asynchronously, akin to biology, is a major challenge. Solutions suggested in the literature rely on time-division-multiplexing of read and write operations based on clocks, or approximations ignoring the reading when coincidental with writing. In this work, we overcome this challenge and present a clock-less approach wherein reading and writing are performed in different frequency domains. This enables learning and recognition simultaneously on an SNN. We validate our scheme in SPICE circuit simulator by translating a two-layered feed-forward Iris classifying SNN to demonstrate software-equivalent performance. The system performance is not adversely affected by a voltage dependence of conductance in realistic RRAMs, despite departing from linearity. Overall, our approach enables direct implementation of biological SNN algorithms in hardware.

cs.NE↗

arXiv2017

A Software-equivalent SNN Hardware using RRAM-array for Asynchronous Real-time Learning

Spiking Neural Network (SNN) naturally inspires hardware implementation as it is based on biology. For learning, spike time dependent plasticity (STDP) may be implemented using an energy efficient waveform superposition on memristor based synapse. However, system level implementation has three challenges. First, a classic dilemma is that recognition requires current reading for short voltage$-$spikes which is disturbed by large voltage$-$waveforms that are simultaneously applied on the same memristor for real$-$time learning i.e. the simultaneous read$-$write dilemma. Second, the hardware needs to exactly replicate software implementation for easy adaptation of algorithm to hardware. Third, the devices used in hardware simulations must be realistic. In this paper, we present an approach to address the above concerns. First, the learning and recognition occurs in separate arrays simultaneously in real$-$time, asynchronously $-$ avoiding non$-$biomimetic clocking based complex signal management. Second, we show that the hardware emulates software at every stage by comparison of SPICE (circuit$-$simulator) with MATLAB (mathematical SNN algorithm implementation in software) implementations. As an example, the hardware shows 97.5 per cent accuracy in classification which is equivalent to software for a Fisher$-$Iris dataset. Third, the STDP is implemented using a model of synaptic device implemented using HfO2 memristor. We show that an increasingly realistic memristor model slightly reduces the hardware performance (85 per cent), which highlights the need to engineer RRAM characteristics specifically for SNN.

cs.NE↗