arXiv subjects

Akash Kumar

Publications and source records attributed to Akash Kumar.

At least 55 records · Page 3Linked to original sources

arXiv2025

Bulk spin-orbit torque-driven spin Hall nano-oscillators using PtBi alloys

Spin-orbit-torque-driven auto-oscillations in spin Hall nano-oscillators (SHNOs) offer a transformative pathway toward energy-efficient, nanoscale microwave devices for next-generation neuromorphic computing and high-frequency technologies. A key requirement for achieving robust, sustained oscillations is reducing the threshold current ($I_{\text{th}}$), strongly governed by spin Hall efficiency ($θ_{\text{SH}}$). However, conventional strategies to enhance $θ_{\text{SH}}$ face trade-offs, including high longitudinal resistivity, interfacial effects, and symmetry-breaking torques that limit performance. Here, we demonstrate a substantial enhancement of the bulk spin Hall effect in PtBi alloys, achieving over a threefold increase in $θ_{\text{SH}}$, from 0.07 in pure Pt to 0.24 in Pt$_{94.0}$Bi$_{6.0}$ and 0.19 in Pt$_{91.3}$Bi$_{8.7}$, as extracted from DC-bias spin-torque ferromagnetic resonance. The enhanced $θ_{\text{SH}}$ originates from bulk-dominated, extrinsic side-jump scattering across all PtBi compositions. Correspondingly, we observe a 42\% and 32\% reduction in $I_{\text{th}}$ in 100 nm SHNOs based on Co$_{40}$Fe$_{40}$B$_{20}$(3 nm)/Pt$_{94.0}$Bi$_{6.0}$(4 nm) and Co$_{40}$Fe$_{40}$B$_{20}$(3 nm)/Pt$_{91.3}$Bi$_{8.7}$(4 nm), respectively. Structural characterization reveals reduced Pt crystallinity, along with emergence of preferred crystallographic orientations upon introducing higher Bi concentrations. Together, these results position PtBi alloys as a compelling alternative to conventional 5$d$ transition metals, enabling enhanced $θ_{\text{SH}}$ and significantly lower $I_{\text{th}}$, thus opening new avenues for energy-efficient neuromorphic computing and magnetic random access memory.

cond-mat.mes-hall↗

arXiv2025

A composite of the effects of major sudden stratospheric warming events on carbon dioxide radiative cooling in the mesosphere-lower-thermosphere

The major sudden stratospheric warming (SSW) events strongly influence the mean structure of the entire atmosphere, from the troposphere to the thermosphere. These events disrupt the compositional and thermal structure of the mesosphere and lower thermosphere (MLT), causing spatiotemporal variations in the concentration of trace species of this region. Currently, the role of dynamical changes during SSW events on radiative cooling in the MLT region is not well understood. An investigation of the SSW-induced changes in CO$_2$ radiative cooling in the MLT region is presented by examining the changes in the dynamics and transport of key species, such as CO$_2$ and atomic oxygen (O). A composite analysis has been performed to understand these changes during the major SSW events that occurred between 2005 and 2020. The variation of trace species is found to be associated with the change in vertical residual circulation. The results also show that CO$_2$ radiative cooling decreases during the mesospheric cooling that occurs during the stratospheric warming over the polar region. During the recovery stage of the SSW event, the CO$_2$ radiative cooling enhances in the mesosphere. These variations in CO$_2$ radiative cooling are mainly caused by temperature perturbations and oxygen transport in the MLT region. The contribution of temperature change and transport have also been investigated in detail.

physics.space-ph↗

arXiv2025

A Large-Scale Analysis on Contextual Self-Supervised Video Representation Learning

Self-supervised learning has emerged as a powerful paradigm for label-free model pretraining, particularly in the video domain, where manual annotation is costly and time-intensive. However, existing self-supervised approaches employ diverse experimental setups, making direct comparisons challenging due to the absence of a standardized benchmark. In this work, we establish a unified benchmark that enables fair comparisons across different methods. Additionally, we systematically investigate five critical aspects of self-supervised learning in videos: (1) dataset size, (2) model complexity, (3) data distribution, (4) data noise, and (5) feature representations. To facilitate this study, we evaluate six self-supervised learning methods across six network architectures, conducting extensive experiments on five benchmark datasets and assessing performance on two distinct downstream tasks. Our analysis reveals key insights into the interplay between pretraining strategies, dataset characteristics, pretext tasks, and model architectures. Furthermore, we extend these findings to Video Foundation Models (ViFMs), demonstrating their relevance in large-scale video representation learning. Finally, leveraging these insights, we propose a novel approach that significantly reduces training data requirements while surpassing state-of-the-art methods that rely on 10% more pretraining data. We believe this work will guide future research toward a deeper understanding of self-supervised video representation learning and its broader implications.

cs.CV↗

arXiv2025

STPro: Spatial and Temporal Progressive Learning for Weakly Supervised Spatio-Temporal Grounding

In this work we study Weakly Supervised Spatio-Temporal Video Grounding (WSTVG), a challenging task of localizing subjects spatio-temporally in videos using only textual queries and no bounding box supervision. Inspired by recent advances in vision-language foundation models, we investigate their utility for WSTVG, leveraging their zero-shot grounding capabilities. However, we find that a simple adaptation lacks essential spatio-temporal grounding abilities. To bridge this gap, we introduce Tubelet Referral Grounding (TRG), which connects textual queries to tubelets to enable spatio-temporal predictions. Despite its promise, TRG struggles with compositional action understanding and dense scene scenarios. To address these limitations, we propose STPro, a novel progressive learning framework with two key modules: (1) Sub-Action Temporal Curriculum Learning (SA-TCL), which incrementally builds compositional action understanding, and (2) Congestion-Guided Spatial Curriculum Learning (CG-SCL), which adapts the model to complex scenes by spatially increasing task difficulty. STPro achieves state-of-the-art results on three benchmark datasets, with improvements of 1.0% on VidSTG-Declarative and 3.0% on HCSTVG-v1.

cs.CV↗

arXiv2025

Symmetry Enhanced Unconventional Spin Current Anisotropy in a Collinear Antiferromagnet

Spin-orbit torque (SOT) presents a promising avenue for energy-efficient spintronics devices, surpassing the limitations of spin transfer torque. While extensively studied in heavy metals, SOT in antiferromagnetic quantum materials remains largely unexplored. Here, we investigate SOT in epitaxial FeSn, a collinear antiferromagnet with a kagome lattice. FeSn exhibits intriguing topological quantum features, including two-dimensional flat bands and Dirac-like surface states, making it an ideal platform for investigating emergent SOT properties. Using spin-torque ferromagnetic resonance, we uncover a six-fold symmetric damping-like SOT in epitaxial-FeSn/Py heterostructures, reflecting the six-fold symmetry of the epitaxial [0001]-oriented FeSn films. Additionally, we observe a substantial unconventional field-like torque, originating from spin currents with out-of-plane spin polarization. This torque exhibits a unique angular dependence-a superposition of six-fold crystalline symmetry and uniaxial symmetry associated with the antiferromagnetic spin Hall effect. Notably, the unconventional field-like torque is enhanced when the RF current flows along the Neel vector in FeSn. Our findings reveal an unconventional spin current anisotropy tunable by crystalline and magnetic symmetry, offering a novel approach for controlling SOT in antiferromagnetic spintronics.

cond-mat.mes-hall↗

arXiv2025

Giant Spin Pumping at Polymer/Ferromagnet Interfaces for Hybrid Spintronic Devices

While the growing utilization of polymers in flexible electronic devices has sparked significant interest in polymer/metal interfaces, spintronic studies of such interfaces remain limited. Here, we systematically study spin pumping across a polymer/ferromagnet metal interface between hydrogen silsesquioxane (HSQ) oligomer layers ($t_\mathit{HSQ} = 30, 36, 48$ nm) and NiFe ($t_\mathit{NiFe} = 4, 5, 7, 10$ nm) thin films. Using ferromagnetic resonance measurements, we observe strong spin pumping (large linewidth broadening) and a giant spin mixing conductance, reaching 19.8~${\rm nm^{-2}}$ for HSQ = 48 nm, \emph{i.e.}~comparable to that of heavy metals. Our results suggest efficient spin transfer across the HSQ/NiFe interface, possibly originating from a combination of spin and orbital pumping, and provide valuable insights for designing self-powered and flexible spintronic devices utilizing polymers in combination with ferromagnetic materials.

cond-mat.mes-hall↗

arXiv2025

Retrospective: A CORDIC Based Configurable Activation Function for NN Applications

A CORDIC-based configuration for the design of Activation Functions (AF) was previously suggested to accelerate ASIC hardware design for resource-constrained systems by providing functional reconfigurability. Since its introduction, this new approach for neural network acceleration has gained widespread popularity, influencing numerous designs for activation functions in both academic and commercial AI processors. In this retrospective analysis, we explore the foundational aspects of this initiative, summarize key developments over recent years, and introduce the DA-VINCI AF tailored for the evolving needs of AI applications. This new generation of dynamically configurable and precision-adjustable activation function cores promise greater adaptability for a range of activation functions in AI workloads, including Swish, SoftMax, SeLU, and GeLU, utilizing the Shift-and-Add CORDIC technique. The previously presented design has been optimized for MAC, Sigmoid, and Tanh functionalities and incorporated into ReLU AFs, culminating in an accumulative NEURIC compute unit. These enhancements position NEURIC as a fundamental component in the resource-efficient vector engine for the realization of AI accelerators that focus on DNNs, RNNs/LSTMs, and Transformers, achieving a quality of results (QoR) of 98.5%.

cs.AR↗

arXiv2025

Contextual Self-paced Learning for Weakly Supervised Spatio-Temporal Video Grounding

In this work, we focus on Weakly Supervised Spatio-Temporal Video Grounding (WSTVG). It is a multimodal task aimed at localizing specific subjects spatio-temporally based on textual queries without bounding box supervision. Motivated by recent advancements in multi-modal foundation models for grounding tasks, we first explore the potential of state-of-the-art object detection models for WSTVG. Despite their robust zero-shot capabilities, our adaptation reveals significant limitations, including inconsistent temporal predictions, inadequate understanding of complex queries, and challenges in adapting to difficult scenarios. We propose CoSPaL (Contextual Self-Paced Learning), a novel approach which is designed to overcome these limitations. CoSPaL integrates three core components: (1) Tubelet Phrase Grounding (TPG), which introduces spatio-temporal prediction by linking textual queries to tubelets; (2) Contextual Referral Grounding (CRG), which improves comprehension of complex queries by extracting contextual information to refine object identification over time; and (3) Self-Paced Scene Understanding (SPS), a training paradigm that progressively increases task difficulty, enabling the model to adapt to complex scenarios by transitioning from coarse to fine-grained understanding.

cs.CV↗

arXiv2025

Ultra-large mutually synchronized networks of 10 nm spin Hall nano-oscillators

While mutually interacting spin Hall nano-oscillators (SHNOs) hold great promise for wireless communication, neural networks, neuromorphic computing, and Ising machines, the highest number of synchronized SHNOs remains limited to $N$ = 64. Using ultra-narrow 10 and 20-nm nano-constrictions in W-Ta/CoFeB/MgO trilayers, we demonstrate mutually synchronized SHNO networks of up to $N$ = 105,000. The microwave power and quality factor scale as $N$ with new record values of 9 nW and $1.04 \times 10^6$, respectively. An unexpectedly strong array size dependence of the frequency-current tunability is explained by magnon exchange between nano-constrictions and magnon losses at the array edges, further corroborated by micromagnetic simulations and Brillouin light scattering microscopy. Our results represent a significant step towards viable SHNO network applications in wireless communication and unconventional computing.

cond-mat.mes-hall↗

arXiv2025

Evidence of potential thermospheric overcooling during the May 2024 geomagnetic superstorm

During intense geomagnetic storms, the rapid and significant production of NO followed by its associated infrared radiative emission in lower thermosphere contributes crucially to the energetics of the upper atmosphere. This makes NO infrared radiative cooling a very important phenomenon which needs to be considered for accurate density forecasting in thermosphere. This study reports the investigation of variations in thermospheric density, and NO radiative cooling during the recent geomagnetic superstorm of May 2024. A very rare post-storm thermospheric density depletion of about -23% on May 12 was observed by Swarm-C in northern hemisphere in comparison to the prestorm condition on May 9. This overcooling was observed despite the continuous enhancement in solar EUV (24-36 nm) flux throughout the event. The thermospheric NO infrared radiative emission in the recovery phase of the storm seems to be the plausible cause for this observed post-storm density depletion. The TIMED/SABER observed thermospheric density between 105 and 110 km altitude shows an enhancement during this thermospheric overcooling. Our analysis also suggests an all time high thermospheric NO radiative cooling flux up to 11.84 ergs/cm2/sec during May 2024 geomagnetic superstorm, which has also been compared with famous Halloween storms of October 2003.

physics.space-ph↗

arXiv2024

Stable Mean Teacher for Semi-supervised Video Action Detection

In this work, we focus on semi-supervised learning for video action detection. Video action detection requires spatiotemporal localization in addition to classification, and a limited amount of labels makes the model prone to unreliable predictions. We present Stable Mean Teacher, a simple end-to-end teacher-based framework that benefits from improved and temporally consistent pseudo labels. It relies on a novel Error Recovery (EoR) module, which learns from students' mistakes on labeled samples and transfers this knowledge to the teacher to improve pseudo labels for unlabeled samples. Moreover, existing spatiotemporal losses do not take temporal coherency into account and are prone to temporal inconsistencies. To address this, we present Difference of Pixels (DoP), a simple and novel constraint focused on temporal consistency, leading to coherent temporal detections. We evaluate our approach on four different spatiotemporal detection benchmarks: UCF101-24, JHMDB21, AVA, and YouTube-VOS. Our approach outperforms the supervised baselines for action detection by an average margin of 23.5% on UCF101-24, 16% on JHMDB21, and 3.3% on AVA. Using merely 10% and 20% of data, it provides competitive performance compared to the supervised baseline trained on 100% annotations on UCF101-24 and JHMDB21, respectively. We further evaluate its effectiveness on AVA for scaling to large-scale datasets and YouTube-VOS for video object segmentation, demonstrating its generalization capability to other tasks in the video domain. Code and models are publicly available.

cs.CV↗

arXiv2024

Mutual synchronization in spin torque and spin Hall nano-oscillators

This chapter reviews the state of the art in mutually synchronized spin-torque and spin Hall nano-oscillator (STNO and SHNO) arrays. After briefly introducing the underlying physics, we discuss different nano-oscillator implementations and their functional properties with respect to frequency range, output power, phase noise, and modulation rates. We then introduce the concepts and the theory of mutual synchronization and discuss the possible coupling mechanisms in spintronic nano-oscillators, such as dipolar, electrical, and spin-wave coupling. We review the experimental literature on mutually synchronized STNOs and SHNOs in one- and two-dimensional arrays and discuss ways to increase the number of mutually synchronized nano-oscillators. Finally, the potential for applications ranging from microwave signal sources/detectors and ultrafast spectrum analyzers to neuromorphic computing elements and Ising machines is discussed together with the specific electronic circuitry that has been designed so far to harness this potential.

cond-mat.mes-hall↗

arXiv2024

Effect of 2009 major SSW event on the mesospheric CO2 cooling

Carbon dioxide (CO2), an important trace species that is gradually increasing in the atmosphere due to anthropogenic activities, causes enhanced warming in the lower atmosphere. The increased concentration of CO2 in the upper atmosphere results in enhanced radiative cooling rates leading to the contraction of the upper atmosphere. Due to its long lifetime and large vertical gradient, CO2 concentration is also influenced by large dynamic events. We report a startling case of variability in CO2 density and its infrared radiative cooling rates in the mesosphere and lower thermospher during a major sudden stratospheric warming (SSW) event. A counter-intuitive connection between CO2 density and resulting CO2 radiative cooling has been observed during the 2009 major SSW event. The behaviour of CO2 cooling rates during such a dramatic events draw attention to our current understanding of CO2 infrared cooling variation and its connection to changes in CO2 concentration. The significance of temperature and atomic oxygen variability in the observed cooling patterns despite changes in CO2 concentration, is also highlighted.

physics.space-ph↗

arXiv2024

Learning Smooth Distance Functions via Queries

In this work, we investigate the problem of learning distance functions within the query-based learning framework, where a learner is able to pose triplet queries of the form: ``Is $x_i$ closer to $x_j$ or $x_k$?'' We establish formal guarantees on the query complexity required to learn smooth, but otherwise general, distance functions under two notions of approximation: $ω$-additive approximation and $(1 + ω)$-multiplicative approximation. For the additive approximation, we propose a global method whose query complexity is quadratic in the size of a finite cover of the sample space. For the (stronger) multiplicative approximation, we introduce a method that combines global and local approaches, utilizing multiple Mahalanobis distance functions to capture local geometry. This method has a query complexity that scales quadratically with both the size of the cover and the ambient space dimension of the sample space.

cs.LG↗

arXiv2024

Mirror Descent on Reproducing Kernel Banach Spaces

Recent advances in machine learning have led to increased interest in reproducing kernel Banach spaces (RKBS) as a more general framework that extends beyond reproducing kernel Hilbert spaces (RKHS). These works have resulted in the formulation of representer theorems under several regularized learning schemes. However, little is known about an optimization method that encompasses these results in this setting. This paper addresses a learning problem on Banach spaces endowed with a reproducing kernel, focusing on efficient optimization within RKBS. To tackle this challenge, we propose an algorithm based on mirror descent (MDA). Our approach involves an iterative method that employs gradient steps in the dual space of the Banach space using the reproducing kernel. We analyze the convergence properties of our algorithm under various assumptions and establish two types of results: first, we identify conditions under which a linear convergence rate is achievable, akin to optimization in the Euclidean setting, and provide a proof of the linear rate; second, we demonstrate a standard convergence rate in a constrained setting. Moreover, to instantiate this algorithm in practice, we introduce a novel family of RKBSs with $p$-norm ($p \neq 2$), characterized by both an explicit dual map and a kernel.

cs.LG↗

arXiv2024

Ultra-fast spin Hall nano-oscillator based microwave spectral analysis

Ultra-fast spectrum analysis concept based on rapidly tuned spintronic nano-oscillators has been under development for the last few years and has already demonstrated promising results. Here, we demonstrate an ultra-fast microwave spectrum analyzer based on a chain of five mutually synchronized nano-constriction spin Hall nano-oscillators (SHNOs). As mutual synchronization affords the chain a much improved signal quality, with linewidths well below 1 MHz at close to a 10 GHz operating frequency, we observe an order of magnitude better frequency resolution bandwidth compared to previously reported spectral analysis based on single magnetic tunnel junction based spin torque nano-oscillators. The high-frequency operation and ability to synchronize long SHNO chains and large arrays make SHNOs ideal candidates for ultra-fast microwave spectral analysis.

cond-mat.mes-hall↗

arXiv2024

QoS-Nets: Adaptive Approximate Neural Network Inference

In order to vary the arithmetic resource consumption of neural network applications at runtime, this work proposes the flexible reuse of approximate multipliers for neural network layer computations. We introduce a search algorithm that chooses an appropriate subset of approximate multipliers of a user-defined size from a larger search space and enables retraining to maximize task performance. Unlike previous work, our approach can output more than a single, static assignment of approximate multiplier instances to layers. These different operating points allow a system to gradually adapt its Quality of Service (QoS) to changing environmental conditions by increasing or decreasing its accuracy and resource consumption. QoS-Nets achieves this by reassigning the selected approximate multiplier instances to layers at runtime. To combine multiple operating points with the use of retraining, we propose a fine-tuning scheme that shares the majority of parameters between operating points, with only a small amount of additional parameters required per operating point. In our evaluation on MobileNetV2, QoS-Nets is used to select four approximate multiplier instances for three different operating points. These operating points result in power savings for multiplications between 15.3% and 42.8% at a Top-5 accuracy loss between 0.3 and 2.33 percentage points. Through our fine-tuning scheme, all three operating points only increase the model's parameter count by only 2.75%.

cs.LG↗

arXiv2024

Seasonal variation in nighttime NO radiative cooling as observed by TIMED/SABER in lower thermosphere during solar maximum and solar minimum

Both composition and temperature play a crucial role in determining the NO radiative cooling in lower thermosphere as observed by TIMED/SABER. In this work, we present a detailed investigation of seasonal variation in thermospheric NO radiative cooling. We have carried forward the investigation of \cite{li2018} regarding the variations in local nighttime peak NO radiative cooling and its altitude during solar maximum and solar minimum conditions. By analyzing latitudinal changes over quiet times for each month in year 2018, it is evident that both the investigative parameters exhibit summer-winter variability. The qualitative contribution of different species (i.e., NO, and O), and temperatures in determining the vertical profile of NO radiative cooling for different latitudes is investigated by utilizing the NRLMSISE-00 estimated parameters, and SNOE observed NO density. The temperature, NO density, meridional wind, and associated compositional variations due to asymmetrical solar heating in both the hemispheres during solar minimum conditions seem to be the dominating factor in controlling the NO radiative cooling during different seasons. The altitudes at which maximum cooling by NO occurs exhibits an inverse correlation with the amount of radiative cooling. The region of enhanced NO densities (polar and summer hemispheric low-mid latitude regions) have larger NO radiative cooling with lower peak altitudes in comparison to other regions (equatorial to winter hemispheric low-mid latitude regions), where NO radiative cooling is low with higher peak altitude values.

physics.space-ph↗