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Researcher profile

Rahul Gupta

Rahul Gupta contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computation and LanguageMachine Learningastro-ph.HEArtificial Intelligencemath.AGcond-mat.mtrl-sciCryptography and Securityphysics.opticsSystems and Controlastro-ph.SRcs.CYeess.ASeess.SYInformation Retrievalquant-phSoundastro-ph.IMcond-mat.mes-hallcond-mat.othercond-mat.quant-gashep-phHuman-Computer InteractionLogic in Computer Sciencephysics.atom-ph

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Published work

47 published item(s)

preprint2026arXiv

Cavity-Driven Multispectral Gain for High-Sensitivity NV Center Magnetometers

We report a cavity-enabled solid-state magnetometer based on an NV ensemble coupled with a dielectric cavity, achieving 12 pT/$\sqrt{\rm{Hz}}$ sensitivity and a nearly threefold gain from multispectral features. The features originate from cavity-induced splitting of the NV hyperfine levels and leverages robust quantum coherence in the doubly dressed states of the system to achieve high sensitivity. We project simulated near-term sensitivities approaching 100 fT/$\sqrt{\rm{Hz}}$, close to the Johnson-Nyquist limit. Our results establish frequency multiplexing as a new operational paradigm, offering a robust and scalable quantum resource for metrology under ambient conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

DECOR: Auditing LLM Deception via Information Manipulation Theory

Large language models can deceive by subtly manipulating truthful information -- omitting key facts, shifting focus, or obscuring meaning -- making such behavior difficult to detect. Existing black-box methods rely on coarse-grained judgments, offering limited interpretability and failing to pinpoint which facts were distorted and how. We introduce DECOR, a multi-agent framework grounded in Information Manipulation Theory for fine-grained auditing of strategic deception in LLM responses. DECOR decomposes input contexts into atomic informational units and scores each unit against the response across four dimensions of manipulation, producing interpretable manipulation profiles that are aggregated into a global deception index. We comprehensively evaluate DECOR on both single-turn and multi-turn deception detection benchmarks spanning real-world domains, and show that DECOR achieves state-of-the-art performance on both, outperforming competitive baselines. The framework generalizes across 15 frontier models, and ablation studies confirm the contribution of each key design component. Our findings demonstrate that fine-grained, theory-grounded auditing of information manipulation offers an effective and interpretable path for LLM deception detection.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Embedded AI Companion System on Edge Devices

Computational resource constraints on edge devices make it difficult to develop a fully embedded AI companion system with a satisfactory user experience. AI companion and memory systems detailed in existing literature cannot be directly used in such an environment due to lack of compute resources and latency concerns. In this paper, we propose a memory paradigm that alternates between active and inactive phases: during phases of user activity, the system performs low-latency, real-time dialog using lightweight retrieval over existing memories and context; whereas during phases of user inactivity, it conducts more computationally intensive extraction, consolidation, and maintenance of memories across full conversation sessions. This design minimizes latency while maintaining long-term personalization under the tight constraints of embedded hardware. We also introduce an AI Companion benchmark designed to holistically evaluate the AI Companion across both its conversational quality and memory capabilities. In our experiments, we found that our system (using a very weak model: Qwen2.5-7B-Instruct quantized int4) outperforms the equivalent raw LLM without memory across most metrics, and performs comparably to GPT-3.5 with 16k context window.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SWAN: Semantic Watermarking with Abstract Meaning Representation

We introduce SWAN (Semantic Watermarking with Abstract Meaning Representation), a novel framework that embeds watermark signatures into the semantic structure of a sentence using Abstract Meaning Representation (AMR). In contrast to existing watermarking methods, which typically encode signatures by adjusting token selection preferences during text generation, SWAN embeds the signature directly in the sentence's semantic representation. As the signature is encoded at the semantic structure level, any paraphrase that preserves meaning automatically preserves the signature. SWAN is training-free: watermark injection is achieved by prompting an LLM to generate sentences guided by a selected AMR template while maintaining contextual coherence, and detection uses an off-the-shelf AMR parser followed by a simple one-proportion z-test. Empirical evaluation on the RealNews benchmark shows SWAN matches state-of-the-art detection performance on unaltered watermarked text, while significantly improving robustness against paraphrasing, increasing detection AUC by up to 13.9 percentage points compared to prior methods. These results demonstrate that SWAN's approach of anchoring watermarks in AMR semantic structures provides a simple, effective, and prompt-based method for robust text provenance verification under paraphrasing, opening new avenues for semantic-level watermarking research.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Tame class field theory over local fields

For a quasi-projective scheme $X$ admitting a smooth compactification over a local field of residue characteristic $p > 0$, we construct a continuous reciprocity homomorphism from a tame class group to the abelian tame etale fundamental group of $X$. We describe the prime-to-$p$ parts of its kernel and cokernel. This generalizes the higher dimensional unramified class field theory over local fields by Jannsen-Saito and Forre. We also prove a finiteness theorem for the geometric part of the abelian tame etale fundamental group, generalizing the results of Grothendieck and Yoshida for the unramified fundamental group.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Cavity Optomechanical Quantum Memory for Twisted Photons Using a Ring BEC

We theoretically propose a photonic orbital angular momentum (OAM) quantum memory platform based on an atomic Bose-Einstein condensate confined in a ring trap and placed inside a Fabry-Perot cavity driven by Laguerre-Gaussian beams. In contrast to electromagnetically induced transparency-based protocols, our memory does not require change of internal atomic levels. The optical states are instead stored in the large Hilbert space of topologically protected and long-lived motional states (persistent currents) of the condensate, yielding a storage time three orders of magnitude better than presently available. Further, the use of a cavity provides orders of magnitude more resonances, and hence bandwidth, for reading and writing than internal atomic transitions. Finally, the analogy to cavity optomechanics suggests a natural path to wavelength conversion, OAM transduction, and nondestructive readout of the memory.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Faithful Model Evaluation for Model-Based Metrics

Statistical significance testing is used in natural language processing (NLP) to determine whether the results of a study or experiment are likely to be due to chance or if they reflect a genuine relationship. A key step in significance testing is the estimation of confidence interval which is a function of sample variance. Sample variance calculation is straightforward when evaluating against ground truth. However, in many cases, a metric model is often used for evaluation. For example, to compare toxicity of two large language models, a toxicity classifier is used for evaluation. Existing works usually do not consider the variance change due to metric model errors, which can lead to wrong conclusions. In this work, we establish the mathematical foundation of significance testing for model-based metrics. With experiments on public benchmark datasets and a production system, we show that considering metric model errors to calculate sample variances for model-based metrics changes the conclusions in certain experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A decomposition theorem for 0-cycles and applications

We prove a decomposition theorem for the cohomological Chow group of 0-cycles on the double of a quasi-projective $R_1$-scheme over a field along a closed subscheme, in terms of the Chow groups, with and without modulus, of the scheme. This yields a significant generalization of the decomposition theorem of Binda-Krishna. As applications, we prove a moving lemma for Chow groups with modulus and an analogue of Bloch's formula for 0-cycles with modulus on singular surfaces. The latter extends a previous result of Binda-Krishna-Saito.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks. In particular, we train a 20 billion parameter multilingual seq2seq model called Alexa Teacher Model (AlexaTM 20B) and show that it achieves state-of-the-art (SOTA) performance on 1-shot summarization tasks, outperforming a much larger 540B PaLM decoder model. AlexaTM 20B also achieves SOTA in 1-shot machine translation, especially for low-resource languages, across almost all language pairs supported by the model (Arabic, English, French, German, Hindi, Italian, Japanese, Marathi, Portuguese, Spanish, Tamil, and Telugu) on Flores-101 dataset. We also show in zero-shot setting, AlexaTM 20B outperforms GPT3 (175B) on SuperGLUE and SQuADv2 datasets and provides SOTA performance on multilingual tasks such as XNLI, XCOPA, Paws-X, and XWinograd. Overall, our results present a compelling case for seq2seq models as a powerful alternative to decoder-only models for Large-scale Language Model (LLM) training.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

An Efficient DP-SGD Mechanism for Large Scale NLP Models

Recent advances in deep learning have drastically improved performance on many Natural Language Understanding (NLU) tasks. However, the data used to train NLU models may contain private information such as addresses or phone numbers, particularly when drawn from human subjects. It is desirable that underlying models do not expose private information contained in the training data. Differentially Private Stochastic Gradient Descent (DP-SGD) has been proposed as a mechanism to build privacy-preserving models. However, DP-SGD can be prohibitively slow to train. In this work, we propose a more efficient DP-SGD for training using a GPU infrastructure and apply it to fine-tuning models based on LSTM and transformer architectures. We report faster training times, alongside accuracy, theoretical privacy guarantees and success of Membership inference attacks for our models and observe that fine-tuning with proposed variant of DP-SGD can yield competitive models without significant degradation in training time and improvement in privacy protection. We also make observations such as looser theoretical $ε, δ$ can translate into significant practical privacy gains.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Canary Extraction in Natural Language Understanding Models

Natural Language Understanding (NLU) models can be trained on sensitive information such as phone numbers, zip-codes etc. Recent literature has focused on Model Inversion Attacks (ModIvA) that can extract training data from model parameters. In this work, we present a version of such an attack by extracting canaries inserted in NLU training data. In the attack, an adversary with open-box access to the model reconstructs the canaries contained in the model's training set. We evaluate our approach by performing text completion on canaries and demonstrate that by using the prefix (non-sensitive) tokens of the canary, we can generate the full canary. As an example, our attack is able to reconstruct a four digit code in the training dataset of the NLU model with a probability of 0.5 in its best configuration. As countermeasures, we identify several defense mechanisms that, when combined, effectively eliminate the risk of ModIvA in our experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Coordinated Day-ahead Dispatch of Multiple Power Distribution Grids hosting Stochastic Resources: An ADMM-based Framework

This work presents an optimization framework to aggregate the power and energy flexibilities in an interconnected power distribution systems. The aggregation framework is used to compute the day-ahead dispatch plans of multiple and interconnected distribution grids operating at different voltage levels. Specifically, the proposed framework optimizes the dispatch plan of an upstream medium voltage (MV) grid accounting for the flexibility offered by downstream low voltage (LV) grids and the knowledge of the uncertainties of the stochastic resources. The framework considers grid, i.e., operational limits on the nodal voltages, lines, and transformer capacity using a linearized grid model, and controllable resources' constraints. The dispatching problem is formulated as a stochastic-optimization scheme considering uncertainty on stochastic power generation and demands and the voltage imposed by the upstream grid. The problem is solved by a distributed optimization method relying on the Alternating Direction Method of Multipliers (ADMM) that splits the main problem into an aggregator problem (solved at the MV-grid level) and several local problems (solved at the MV-connected-controllable-resources and LV-grid levels). The use of distributed optimization enables a decentralized dispatch computation where the centralized aggregator is agnostic about the parameters/models of the participating resources and downstream grids. The framework is validated for interconnected CIGRE medium- and low-voltage networks hosting heterogeneous stochastic and controllable resources.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Core-collapse supernova from a possible progenitor star of 100 M$_{\odot}$

In this work, we study the synthetic explosions of a massive star. We take a 100 M$_{\odot}$ zero--age main--sequence (ZAMS) star and evolve it until the onset of core-collapse using {\tt MESA}. Then, the resulting star model is exploded using the publicly available stellar explosion code, {\tt STELLA}. The outputs of {\tt STELLA} calculations provide us the bolometric light curve and photospheric velocity evolution along with other physical properties of the underlying supernova. In this paper, the effects of having large Hydrogen-envelope on the supernova light curve have been explored. We also explore the effects of the presence of different amounts of nickel mass and the effect of changing the explosion energy of the resulting supernovae from such heavy progenitors, on their bolometric light curves and photospheric velocities.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Differentially Private Decoding in Large Language Models

Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart. In practice, the pre-trained model is adapted to a wide array of tasks via fine-tuning on task-specific datasets. LLMs, while effective, have been shown to memorize instances of training data thereby potentially revealing private information processed during pre-training. The potential leakage might further propagate to the downstream tasks for which LLMs are fine-tuned. On the other hand, privacy-preserving algorithms usually involve retraining from scratch, which is prohibitively expensive for LLMs. In this work, we propose a simple, easy to interpret, and computationally lightweight perturbation mechanism to be applied to an already trained model at the decoding stage. Our perturbation mechanism is model-agnostic and can be used in conjunction with any LLM. We provide theoretical analysis showing that the proposed mechanism is differentially private, and experimental results showing a privacy-utility trade-off.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Federated Learning with Noisy User Feedback

Machine Learning (ML) systems are getting increasingly popular, and drive more and more applications and services in our daily life. This has led to growing concerns over user privacy, since human interaction data typically needs to be transmitted to the cloud in order to train and improve such systems. Federated learning (FL) has recently emerged as a method for training ML models on edge devices using sensitive user data and is seen as a way to mitigate concerns over data privacy. However, since ML models are most commonly trained with label supervision, we need a way to extract labels on edge to make FL viable. In this work, we propose a strategy for training FL models using positive and negative user feedback. We also design a novel framework to study different noise patterns in user feedback, and explore how well standard noise-robust objectives can help mitigate this noise when training models in a federated setting. We evaluate our proposed training setup through detailed experiments on two text classification datasets and analyze the effects of varying levels of user reliability and feedback noise on model performance. We show that our method improves substantially over a self-training baseline, achieving performance closer to models trained with full supervision.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks

Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks. Federated learning (FL) provides promising approaches for a large number of clients (e.g., personal devices or organizations) to collaboratively learn a shared global model to benefit all clients while allowing users to keep their data locally. Despite interest in studying FL methods for NLP tasks, a systematic comparison and analysis is lacking in the literature. Herein, we present the FedNLP, a benchmarking framework for evaluating federated learning methods on four different task formulations: text classification, sequence tagging, question answering, and seq2seq. We propose a universal interface between Transformer-based language models (e.g., BERT, BART) and FL methods (e.g., FedAvg, FedOPT, etc.) under various non-IID partitioning strategies. Our extensive experiments with FedNLP provide empirical comparisons between FL methods and helps us better understand the inherent challenges of this direction. The comprehensive analysis points to intriguing and exciting future research aimed at developing FL methods for NLP tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hard X-ray polarization catalog for a 5-year sample of Gamma-Ray Bursts using AstroSat CZT-Imager

Cadmium Zinc Telluride Imager (CZTI) aboard AstroSat has been regularly detecting Gamma-Ray Bursts (GRBs) since its launch in 2015. Its sensitivity to polarization measurements at energies above 100 keV allows CZTI to attempt spectro-polarimetric studies of GRBs. Here, we present the first catalog of GRB polarization measurements made by CZTI during its first five years of operation. This presents the time integrated polarization measurements of the prompt emission of 20 GRBs in the energy range 100-600 keV. The sample includes the bright GRBs which were detected within an angle range of 0-60 degree and 120-180 degree where the instrument has useful polarization sensitivity and is less prone to systematics. We implement a few new modifications in the analysis to enhance polarimetric sensitivity of the instrument. Majority of the GRBs in the sample are found to possess less / null polarization across the total bursts' duration in contrast to a small fraction of five GRBs exhibiting high polarization. The low polarization across the bursts can be speculated to be either due to the burst being intrinsically weakly polarized or due to varying polarization angle within the burst even when it is highly polarized. In comparison to POLAR measurements, CZTI has detected a larger number of cases with high polarization. This may be a consequence of the higher energy window of CZTI observations which results in the sampling of smaller duration of burst emissions in contrast to POLAR, thereby, probing emissions of less temporal variations of polarization properties.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Learnings from Federated Learning in the Real world

Federated Learning (FL) applied to real world data may suffer from several idiosyncrasies. One such idiosyncrasy is the data distribution across devices. Data across devices could be distributed such that there are some "heavy devices" with large amounts of data while there are many "light users" with only a handful of data points. There also exists heterogeneity of data across devices. In this study, we evaluate the impact of such idiosyncrasies on Natural Language Understanding (NLU) models trained using FL. We conduct experiments on data obtained from a large scale NLU system serving thousands of devices and show that simple non-uniform device selection based on the number of interactions at each round of FL training boosts the performance of the model. This benefit is further amplified in continual FL on consecutive time periods, where non-uniform sampling manages to swiftly catch up with FL methods using all data at once.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Measuring Fairness of Text Classifiers via Prediction Sensitivity

With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have been explored in the recent literature, there is lack of consensus on which metrics most accurately reflect the fairness of a system. In this work, we propose a new formulation : ACCUMULATED PREDICTION SENSITIVITY, which measures fairness in machine learning models based on the model's prediction sensitivity to perturbations in input features. The metric attempts to quantify the extent to which a single prediction depends on a protected attribute, where the protected attribute encodes the membership status of an individual in a protected group. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness. It also correlates well with humans' perception of fairness. We conduct experiments on two text classification datasets : JIGSAW TOXICITY, and BIAS IN BIOS, and evaluate the correlations between metrics and manual annotations on whether the model produced a fair outcome. We observe that the proposed fairness metric based on prediction sensitivity is statistically significantly more correlated with human annotation than the existing counterfactual fairness metric.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal

Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However, these models can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. Therefore, knowledge distillation without any fairness constraints may preserve or exaggerate the teacher model's biases onto the distilled model. To this end, we present a novel approach to mitigate gender disparity in text generation by learning a fair model during knowledge distillation. We propose two modifications to the base knowledge distillation based on counterfactual role reversal$\unicode{x2014}$modifying teacher probabilities and augmenting the training set. We evaluate gender polarity across professions in open-ended text generated from the resulting distilled and finetuned GPT$\unicode{x2012}$2 models and demonstrate a substantial reduction in gender disparity with only a minor compromise in utility. Finally, we observe that language models that reduce gender polarity in language generation do not improve embedding fairness or downstream classification fairness.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Model-less Robust Voltage Control in Active Distribution Networks using Sensitivity Coefficients Estimated from Measurements

Measurement-rich power distribution networks may enable distribution system operators (DSOs) to adopt model-less and measurement-based monitoring and control of distributed energy resources (DERs) for mitigating grid issues such as over/under voltages and lines congestions. However, measurement-based monitoring and control applications may lead to inaccurate control decisions due to measurement errors. In particular, estimation models relying on regression-based schemes result in significant errors in the estimates (e.g., nodal voltages) especially for measurement devices with high Instrument Transformer (IT) classes. The consequences are detrimental to control performance since this may lead to infeasible decisions. This work proposes a model-less robust voltage control accounting for the uncertainties of measurement-based estimated voltage sensitivity coefficients. The coefficients and their uncertainties are obtained using a recursive least squares (RLS)-based online estimation, updated whenever new measurements are available. This formulation is applied to control distributed controllable photovoltaic (PV) generation in a distribution network to restrict the voltage within prescribed limits. The proposed scheme is validated by simulating a CIGRE low-voltage system interfacing multiple controllable PV plants.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations

Multiple metrics have been introduced to measure fairness in various natural language processing tasks. These metrics can be roughly categorized into two categories: 1) \emph{extrinsic metrics} for evaluating fairness in downstream applications and 2) \emph{intrinsic metrics} for estimating fairness in upstream contextualized language representation models. In this paper, we conduct an extensive correlation study between intrinsic and extrinsic metrics across bias notions using 19 contextualized language models. We find that intrinsic and extrinsic metrics do not necessarily correlate in their original setting, even when correcting for metric misalignments, noise in evaluation datasets, and confounding factors such as experiment configuration for extrinsic metrics. %al

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Probing into emission mechanisms of GRB 190530A using time-resolved spectra and polarization studies: Synchrotron Origin?

Multi-pulsed GRB 190530A, detected by the GBM and LAT onboard \fermi, is the sixth most fluent GBM burst detected so far. This paper presents the timing, spectral, and polarimetric analysis of the prompt emission observed using \AstroSat and \fermi to provide insight into the prompt emission radiation mechanisms. The time-integrated spectrum shows conclusive proof of two breaks due to peak energy and a second lower energy break. Time-integrated (55.43 $\pm$ 21.30 \%) as well as time-resolved polarization measurements, made by the Cadmium Zinc Telluride Imager (CZTI) onboard \AstroSat, show a hint of high degree of polarization. The presence of a hint of high degree of polarization and the values of low energy spectral index ($α_{\rm pt}$) do not run over the synchrotron limit for the first two pulses, supporting the synchrotron origin in an ordered magnetic field. However, during the third pulse, $α_{\rm pt}$ exceeds the synchrotron line of death in few bins, and a thermal signature along with the synchrotron component in the time-resolved spectra is observed. Furthermore, we also report the earliest optical observations constraining afterglow polarization using the MASTER (P $<$ 1.3 \%) and the redshift measurement ($z$= 0.9386) obtained with the 10.4m GTC telescopes. The broadband afterglow can be described with a forward shock model for an ISM-like medium with a wide jet opening angle. We determine a circumburst density of $n_{0} \sim$ 7.41, kinetic energy $E_{\rm K} \sim$ 7.24 $\times 10^{54}$ erg, and radiated $γ$-ray energy $E_{\rm γ, iso} \sim$ 6.05 $\times 10^{54}$ erg, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Prompt emission and early optical afterglow of VHE detected GRB 201015A and GRB 201216C: onset of the external forward shock

We present a detailed prompt emission and early optical afterglow analysis of the two very high energy (VHE) detected bursts GRB 201015A and GRB 201216C, and their comparison with a subset of similar bursts. Time-resolved spectral analysis of multi-structured GRB 201216C using the Bayesian binning algorithm revealed that during the entire duration of the burst, the low energy spectral index ($α_{\rm pt}$) remained below the limit of the synchrotron line of death. However, statistically some of the bins supported the additional thermal component. Additionally, the evolution of spectral parameters showed that both peak energy (Ep) and $α_{\rm pt}$ tracked the flux. These results were further strengthened using the values of the physical parameters obtained by synchrotron modeling of the data. Our earliest optical observations of both bursts using FRAM-ORM and BOOTES robotic telescopes displayed a smooth bump in their early optical light curves, consistent with the onset of the afterglow due to synchrotron emission from an external forward shock. Using the observed optical peak, we constrained the initial bulk Lorentz factors of GRB 201015A and GRB 201216C to $Γ_0$ = 204 and $Γ_0$ = 310, respectively. The present early optical observations are the earliest known observations constraining outflow parameters and our analysis indicate that VHE-detected bursts could have a diverse range of observed luminosity within the detectable redshift range of present VHE facilities.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Reciprocity for Kato-Saito idele class group with modulus

We introduce an etale fundamental group with modulus and construct a reciprocity homomorphism from the Kato-Saito idele class group with modulus to this fundamental group. This is the K-theoretic analogue of the reciprocity for the cycle-theoretic idele class group with modulus due to Kerz-Saito, and plays a central role in showing the isomorphism between the two idele class groups. It also provides a new interpretation of the already known etale fundamental group with modulus due to Deligne and Laumon.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SN 2016iyc: A Type IIb supernova arising from a low-mass progenitor

In this work, photometric and spectroscopic analyses of a very low-luminosity Type IIb supernova (SN) 2016iyc have been performed. SN 2016iyc lies near the faint end among the distribution of similar supernovae (SNe). Given lower ejecta mass ($M_{\rm ej}$) and low nickel mass ($M_{\rm Ni}$) from the literature, combined with SN 2016iyc lying near the faint end, one-dimensional stellar evolution models of 9 - 14 M$_{\odot}$ zero-age main-sequence (ZAMS) stars as the possible progenitors of SN 2016iyc have been performed using the publicly available code MESA. Moreover, synthetic explosions of the progenitor models have been simulated using the hydrodynamic evolution codes STELLA and SNEC. The bolometric luminosity light curve and photospheric velocities produced through synthetic explosions of ZAMS stars of mass in the range 12 - 13 M$_{\odot}$ having a pre-supernova radius $R_{\mathrm{0}} =$ (240 - 300) R$_{\odot}$, with $M_{\rm ej} =$ (1.89 - 1.93) M$_{\odot}$, explosion energy $E_{\rm exp} = $ (0.28 - 0.35) $\times 10^{51}$ erg, and $M_{\rm Ni} < 0.09$ M$_{\odot}$, are in good agreement with observations; thus, SN 2016iyc probably exploded from a progenitor near the lower mass limits for SNe IIb. Finally, hydrodynamic simulations of the explosions of SN 2016gkg and SN 2011fu have also been performed to compare intermediate- and high-luminosity examples among well-studied SNe IIb. The results of progenitor modelling and synthetic explosions for SN 2016iyc, SN 2016gkg, and SN 2011fu exhibit a diverse range of mass for the possible progenitors of SNe IIb.

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preprint2022arXiv

Tale of GRB 171010A/SN 2017htp and GRB 171205A/SN 2017iuk: Magnetar origin?

We present late-time optical follow-up observations of GRB 171010A/SN 2017htp ($z$ = 0.33) and low-luminosity GRB 171205A/SN 2017iuk ($z$ = 0.037) acquired using the 4K$\times$4K CCD Imager mounted at the 3.6m Devasthal Optical Telescope (3.6m DOT) along with the prompt emission data analysis of these two interesting bursts. The prompt characteristics (other than brightness) such as spectral hardness, T$_{90}$, and minimum variability time-scale are comparable for both the bursts. The isotropic $X$-ray and kinetic energies of the plateau phase of GRB 171205A are found to be less than the maximum energy budget of magnetars, supporting magnetar as a central engine powering source. The new optical data of SN 2017htp and SN 2017iuk presented here, along with published ones, indicate that SN 2017htp is one of the brightest and SN 21017iuk is among the faintest GRB associated SNe (GRB-SNe). Semi-analytical light-curve modelling of SN 2017htp, SN 2017iuk and only known GRB associated superluminous supernova (SLSN 2011kl) are performed using the $\texttt{MINIM}$ code. The model with a spin-down millisecond magnetar as a central engine powering source nicely reproduced the bolometric light curves of all three GRB-SNe mentioned above. The magnetar central engines for SN 2017htp, SN 2017iuk, and SLSN 2011kl exhibit values of initial spin periods higher and magnetic fields closer to those observed for long GRBs and H-deficient SLSNe. Detection of these rare events at such late epochs also demonstrates the capabilities of the 3.6m DOT for deep imaging considering longitudinal advantage in the era of time-domain astronomy.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The long-active afterglow of GRB 210204A: Detection of the most delayed flares in a Gamma-Ray Burst

We present results from extensive broadband follow-up of GRB 210204A over the period of thirty days. We detect optical flares in the afterglow at 7.6 x 10^5 s and 1.1 x 10^6 s after the burst: the most delayed flaring ever detected in a GRB afterglow. At the source redshift of 0.876, the rest-frame delay is 5.8 x 10^5 s (6.71 d). We investigate possible causes for this flaring and conclude that the most likely cause is a refreshed shock in the jet. The prompt emission of the GRB is within the range of typical long bursts: it shows three disjoint emission episodes, which all follow the typical GRB correlations. This suggests that GRB 210204A might not have any special properties that caused late-time flaring, and the lack of such detections for other afterglows might be resulting from the paucity of late-time observations. Systematic late-time follow-up of a larger sample of GRBs can shed more light on such afterglow behaviour. Further analysis of the GRB 210204A shows that the late time bump in the light curve is highly unlikely due to underlying SNe at redshift (z) = 0.876 and is more likely due to the late time flaring activity. The cause of this variability is not clearly quantifiable due to the lack of multi-band data at late time constraints by the bad weather conditions. The flare of GRB 210204A is the latest flare detected to date.

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preprint2022arXiv

Training Mixed-Domain Translation Models via Federated Learning

Training mixed-domain translation models is a complex task that demands tailored architectures and costly data preparation techniques. In this work, we leverage federated learning (FL) in order to tackle the problem. Our investigation demonstrates that with slight modifications in the training process, neural machine translation (NMT) engines can be easily adapted when an FL-based aggregation is applied to fuse different domains. Experimental results also show that engines built via FL are able to perform on par with state-of-the-art baselines that rely on centralized training techniques. We evaluate our hypothesis in the presence of five datasets with different sizes, from different domains, to translate from German into English and discuss how FL and NMT can mutually benefit from each other. In addition to providing benchmarking results on the union of FL and NMT, we also propose a novel technique to dynamically control the communication bandwidth by selecting impactful parameters during FL updates. This is a significant achievement considering the large size of NMT engines that need to be exchanged between FL parties.

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preprint2021arXiv

ADePT: Auto-encoder based Differentially Private Text Transformation

Privacy is an important concern when building statistical models on data containing personal information. Differential privacy offers a strong definition of privacy and can be used to solve several privacy concerns (Dwork et al., 2014). Multiple solutions have been proposed for the differentially-private transformation of datasets containing sensitive information. However, such transformation algorithms offer poor utility in Natural Language Processing (NLP) tasks due to noise added in the process. In this paper, we address this issue by providing a utility-preserving differentially private text transformation algorithm using auto-encoders. Our algorithm transforms text to offer robustness against attacks and produces transformations with high semantic quality that perform well on downstream NLP tasks. We prove the theoretical privacy guarantee of our algorithm and assess its privacy leakage under Membership Inference Attacks(MIA) (Shokri et al., 2017) on models trained with transformed data. Our results show that the proposed model performs better against MIA attacks while offering lower to no degradation in the utility of the underlying transformation process compared to existing baselines.

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preprint2021arXiv

BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation

Recent advances in deep learning techniques have enabled machines to generate cohesive open-ended text when prompted with a sequence of words as context. While these models now empower many downstream applications from conversation bots to automatic storytelling, they have been shown to generate texts that exhibit social biases. To systematically study and benchmark social biases in open-ended language generation, we introduce the Bias in Open-Ended Language Generation Dataset (BOLD), a large-scale dataset that consists of 23,679 English text generation prompts for bias benchmarking across five domains: profession, gender, race, religion, and political ideology. We also propose new automated metrics for toxicity, psycholinguistic norms, and text gender polarity to measure social biases in open-ended text generation from multiple angles. An examination of text generated from three popular language models reveals that the majority of these models exhibit a larger social bias than human-written Wikipedia text across all domains. With these results we highlight the need to benchmark biases in open-ended language generation and caution users of language generation models on downstream tasks to be cognizant of these embedded prejudices.

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preprint2021arXiv

Co$_2$FeAl full Heusler compound based spintronic terahertz emitter

To achieve a large terahertz (THz) amplitude from a spintronic THz emitter (STE), materials with 100\% spin polarisation such as Co-based Heusler compounds as the ferromagnetic layer are required. However, these compounds are known to loose their half-metallicity in the ultrathin film regime, as it is difficult to achieve L2$_1$ ordering, which has become a bottleneck for the film growth. Here, the successful deposition using room temperature DC sputtering of the L2$_1$ and B2 ordered phases of the Co$_2$FeAl full Heusler compound is reported. Co$_2$FeAl is used as ferromagnetic layer together with highly orientated Pt as non-ferromagnetic layer in the Co$_2$FeAl/Pt STE, where an MgO(10 nm) seed layer plays an important role to achieve the L2$_1$ and B2 ordering of Co$_2$FeAl. The generation of THz radiation in the CFA/Pt STE is presented, which has a bandwidth in the range of 0.1-4 THz. The THz electric field amplitude is optimized with respect to thickness, orientation, and growth parameters using a thickness dependent model considering the optically induced spin current, superdiffusive spin current, inverse spin Hall effect and the attenuation of THz radiation in the layers. This study, based on the full Heusler Co$_2$FeAl compound opens up a plethora possibilities in STE research involving full Heusler compounds.

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preprint2021arXiv

ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification

Practical sequence classification tasks in natural language processing often suffer from low training data availability for target classes. Recent works towards mitigating this problem have focused on transfer learning using embeddings pre-trained on often unrelated tasks, for instance, language modeling. We adopt an alternative approach by transfer learning on an ensemble of related tasks using prototypical networks under the meta-learning paradigm. Using intent classification as a case study, we demonstrate that increasing variability in training tasks can significantly improve classification performance. Further, we apply data augmentation in conjunction with meta-learning to reduce sampling bias. We make use of a conditional generator for data augmentation that is trained directly using the meta-learning objective and simultaneously with prototypical networks, hence ensuring that data augmentation is customized to the task. We explore augmentation in the sentence embedding space as well as prototypical embedding space. Combining meta-learning with augmentation provides upto 6.49% and 8.53% relative F1-score improvements over the best performing systems in the 5-shot and 10-shot learning, respectively.

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preprint2021arXiv

Revealing nature of GRB 210205A, ZTF21aaeyldq (AT2021any), and follow-up observations with the 4K$\times$4K CCD Imager+3.6m DOT

Optical follow-up observations of optical afterglows of gamma-ray bursts are crucial to probe the geometry of outflows, emission mechanisms, energetics, and burst environments. We performed the follow-up observations of GRB 210205A and ZTF21aaeyldq (AT2021any) using the 3.6m Devasthal Optical Telescope (DOT) around one day after the burst to deeper limits due to the longitudinal advantage of the place. This paper presents our analysis of the two objects using data from other collaborative facilities, i.e., 2.2m Calar Alto Astronomical Observatory (CAHA) and other archival data. Our analysis suggests that GRB 210205A is a potential dark burst once compared with the X-ray afterglow data. Also, comparing results with other known and well-studied dark GRBs samples indicate that the reason for the optical darkness of GRB 210205A could either be intrinsic faintness or a high redshift event. Based on our analysis, we also found that ZTF21aaeyldq is the third known orphan afterglow with a measured redshift except for ZTF20aajnksq (AT2020blt) and ZTF19abvizsw (AT2019pim). The multiwavelength afterglow modelling of ZTF21aaeyldq using the afterglowpy package demands a forward shock model for an ISM-like ambient medium with a rather wider jet opening angle. We determine circumburst density of $n_{0}$ = 0.87 cm$^{-3}$, kinetic energy $E_{k}$ = 3.80 $\times 10^{52}$ erg and the afterglow modelling also indicates that ZTF21aaeyldq is observed on-axis ($θ_{obs} < θ_{core}$) and a gamma-ray counterpart was missed by GRBs satellites. Our results emphasize that the 3.6m DOT has a unique capability for deep follow-up observations of similar and other new transients for deeper observations as a part of time-domain astronomy in the future.

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preprint2021arXiv

SN 2020ank: a bright and fast-evolving H-deficient superluminous supernova

We investigate the observational properties of a hydrogen-deficient superluminous supernova (SLSN) SN 2020ank (at z = 0.2485), with the help of early phase observations carried out between $-$21 and +52 d since $g$-band maximum. Photometrically, SN 2020ank is one of the brightest SLSN ($M_{g,peak}$ $\sim$ $-$21.84 $\pm$ 0.10 mag), having fast pre-peak rising and post-peak decaying rates. The bolometric light curve of SN 2020ank exhibits a higher peak luminosity ($L_{max}$) of $\sim$(3.9 $\pm$ 0.7) $\times$ 10$^{44}$ erg s$^{-1}$ and appears to be symmetric around the peak with $L^{rise}_{max}$/e $\approx$ $L^{fall}_{max}$/e $\approx$ 15 d. The semi-analytical light-curve modelling using the MINIM code suggests a spin down millisecond magnetar with $P_i$ $\sim$2.2 $\pm$ 0.5 ms and $B$ $\sim$(2.9 $\pm$ 0.1) $\times$ $10^{14}$ G as a possible powering source for SN 2020ank. The possible magnetar origin and excess ultraviolet flux at early epochs indicate a central-engine based powering source for SN 2020ank. Near-peak spectra of SN 2020ank are enriched with the W-shaped O II features but with the weaker signatures of C II and Fe III. Using the estimated rise time of $\sim$27.9 d and the photospheric velocity of $\sim$12050 km s$^{-1}$, we constrain the ejecta mass to $\sim$7.2 $M_{\odot}$ and the kinetic energy of $\sim$6.3 $\times$ 10$^{51}$ erg. The near-peak spectrum of SN 2020ank exhibits a close spectral resemblance with that of fast-evolving SN 2010gx. The absorption features of SN 2020ank are blueshifted compared to Gaia16apd, suggesting a higher expansion velocity. The spectral similarity with SN 2010gx and comparatively faster spectral evolution than PTF12dam (a slow-evolving SLSN) indicate the fast-evolving behavior of SN 2020ank.

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preprint2020arXiv

Automatic Discovery of Novel Intents & Domains from Text Utterances

One of the primary tasks in Natural Language Understanding (NLU) is to recognize the intents as well as domains of users&#39; spoken and written language utterances. Most existing research formulates this as a supervised classification problem with a closed-world assumption, i.e. the domains or intents to be identified are pre-defined or known beforehand. Real-world applications however increasingly encounter dynamic, rapidly evolving environments with newly emerging intents and domains, about which no information is known during model training. We propose a novel framework, ADVIN, to automatically discover novel domains and intents from large volumes of unlabeled data. We first employ an open classification model to identify all utterances potentially consisting of a novel intent. Next, we build a knowledge transfer component with a pairwise margin loss function. It learns discriminative deep features to group together utterances and discover multiple latent intent categories within them in an unsupervised manner. We finally hierarchically link mutually related intents into domains, forming an intent-domain taxonomy. ADVIN significantly outperforms baselines on three benchmark datasets, and real user utterances from a commercial voice-powered agent.

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preprint2020arXiv

Fast Intent Classification for Spoken Language Understanding

Spoken Language Understanding (SLU) systems consist of several machine learning components operating together (e.g. intent classification, named entity recognition and resolution). Deep learning models have obtained state of the art results on several of these tasks, largely attributed to their better modeling capacity. However, an increase in modeling capacity comes with added costs of higher latency and energy usage, particularly when operating on low complexity devices. To address the latency and computational complexity issues, we explore a BranchyNet scheme on an intent classification scheme within SLU systems. The BranchyNet scheme when applied to a high complexity model, adds exit points at various stages in the model allowing early decision making for a set of queries to the SLU model. We conduct experiments on the Facebook Semantic Parsing dataset with two candidate model architectures for intent classification. Our experiments show that the BranchyNet scheme provides gains in terms of computational complexity without compromising model accuracy. We also conduct analytical studies regarding the improvements in the computational cost, distribution of utterances that egress from various exit points and the impact of adding more complexity to models with the BranchyNet scheme.

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preprint2020arXiv

Joint Multi-Dimensional Model for Global and Time-Series Annotations

Crowdsourcing is a popular approach to collect annotations for unlabeled data instances. It involves collecting a large number of annotations from several, often naive untrained annotators for each data instance which are then combined to estimate the ground truth. Further, annotations for constructs such as affect are often multi-dimensional with annotators rating multiple dimensions, such as valence and arousal, for each instance. Most annotation fusion schemes however ignore this aspect and model each dimension separately. In this work we address this by proposing a generative model for multi-dimensional annotation fusion, which models the dimensions jointly leading to more accurate ground truth estimates. The model we propose is applicable to both global and time series annotation fusion problems and treats the ground truth as a latent variable distorted by the annotators. The model parameters are estimated using the Expectation-Maximization algorithm and we evaluate its performance using synthetic data and real emotion corpora as well as on an artificial task with human annotations

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preprint2020arXiv

Peculiar prompt emission and afterglow in H.E.S.S. detected GRB 190829A

We present the results of a detailed investigation of the prompt and afterglow emission in the HESS detected GRB 190829A. Swift and Fermi observations of the prompt phase of this GRB reveal two isolated sub-bursts or episodes, separated by a quiescent phase. The energetic and the spectral properties of the first episode are in stark contrast to the second. The first episode, which has a higher spectral peak of $\sim 120\:\text{keV}$ and a low isotropic energy $\sim 10^{50}\:\text{erg}$ is an outlier to the Amati correlation and marginally satisfies the Yonetoku correlation. However, the energetically dominant second episode has lower peak energy and is consistent with the above correlations. We compared this GRB to other low luminosity GRBs (LLGRBs). Prompt emission of LLGRBs also indicates a relativistic shock breakout origin of the radiation. For GRB 190829A, some of the properties of a shock breakout origin are satisfied. However, the absence of an accompanying thermal component and energy above the shock breakout critical limit precludes a shock breakout origin. In the afterglow, an unusual long-lasting late time flare of duration $\sim 10^4\:\text{s}$ is observed. We also analyzed the late-time \fermi-LAT emission that encapsulates the H.E.S.S. detection. Some of the LAT photons are likely to be associated with the source. All the above observational facts suggest GRB 190829A is a peculiar low luminosity GRB that is not powered by a shock breakout, and with an unusual rebrightening due to a patchy emission or a refreshed shock during the afterglow. Furthermore, our results show that TeV energy photons seem common in both high luminosity GRBs and LLGRBs.

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preprint2020arXiv

Phase Transition Behavior in Knowledge Compilation

The study of phase transition behaviour in SAT has led to deeper understanding and algorithmic improvements of modern SAT solvers. Motivated by these prior studies of phase transitions in SAT, we seek to study the behaviour of size and compile-time behaviour for random k-CNF formulas in the context of knowledge compilation. We perform a rigorous empirical study and analysis of the size and runtime behavior for different knowledge compilation forms (and their corresponding compilation algorithms): d-DNNFs, SDDs and OBDDs across multiple tools and compilation algorithms. We employ instances generated from the random k-CNF model with varying generation parameters to empirically reason about the expected and median behavior of size and compilation-time for these languages. Our work is similar in spirit to the early work in CSP community on phase transition behavior in SAT/CSP. In a similar spirit, we identify the interesting behavior with respect to different parameters: clause density and solution density, a novel control parameter that we identify for the study of phase transition behavior in the context of knowledge compilation. Furthermore, we summarize our empirical study in terms of two concrete conjectures; a rigorous study of these conjectures will possibly require new theoretical tools.

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preprint2020arXiv

SN 2010kd: Photometric and Spectroscopic Analysis of a Slow-Decaying Superluminous Supernova

This paper presents data and analysis of SN 2010kd, a low-redshift ($z = 0.101$) H-deficient superluminous supernova (SLSN), based on ultraviolet/optical photometry and optical spectroscopy spanning between $-$28 and +194 days relative to $\mathit{B}$ band maximum light. The $\mathit{B}$ band light curve comparison of SN 2010kd with a subset of well-studied SLSNe I at comparable redshifts indicates that it is a slow-decaying PTF12dam like SLSN. Analytical light-curve modeling using the $\mathtt{Minim}$ code suggests that the bolometric light curve of SN 2010kd favors circumstellar matter interaction for the powering mechanism. $\mathtt{SYNAPPS}$ modeling of the early-phase spectra does not identify broad H or He lines, whereas the photospheric-phase spectra are dominated by O I, O II, C II, C IV and Si II, particularly, presence of both low and high-velocity components of O II and Si II lines. The nebular-phase spectra of SN 2010kd are dominated by O I and Ca II emission lines similar to those seen in other SLSNe I. The line velocities in SN 2010kd exhibit flatter evolution curves similar to SN 2015bn but with comparatively higher values. SN 2010kd shows a higher single-zone local thermodynamic equilibrium temperature in comparison to PTF12dam and SN 2015bn, and it has an upper O I ejected mass limit of $\sim 10~M_\odot$. The host of SN 2010kd is a dwarf galaxy with a high star-formation rate ($\sim 0.18 \pm 0.04~M_\odot$ yr$^{-1}$) and extreme emission lines.

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preprint2020arXiv

Towards classification parity across cohorts

Recently, there has been a lot of interest in ensuring algorithmic fairness in machine learning where the central question is how to prevent sensitive information (e.g. knowledge about the ethnic group of an individual) from adding &#34;unfair&#34; bias to a learning algorithm (Feldman et al. (2015), Zemel et al. (2013)). This has led to several debiasing algorithms on word embeddings (Qian et al. (2019) , Bolukbasi et al. (2016)), coreference resolution (Zhao et al. (2018a)), semantic role labeling (Zhao et al. (2017)), etc. Most of these existing work deals with explicit sensitive features such as gender, occupations or race which doesn&#39;t work with data where such features are not captured due to privacy concerns. In this research work, we aim to achieve classification parity across explicit as well as implicit sensitive features. We define explicit cohorts as groups of people based on explicit sensitive attributes provided in the data (age, gender, race) whereas implicit cohorts are defined as groups of people with similar language usage. We obtain implicit cohorts by clustering embeddings of each individual trained on the language generated by them using a language model. We achieve two primary objectives in this work : [1.] We experimented and discovered classification performance differences across cohorts based on implicit and explicit features , [2] We improved classification parity by introducing modification to the loss function aimed to minimize the range of model performances across cohorts.

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preprint2019arXiv

Engineering of spin mixing conductance in Ru/FeCo/Ru interfaces: Effect of Re Doping

We have deposited polycrystalline Re doped $(Fe_{65}Co_{35})_{100-x}Re_{x}$ (0 $\leq$ x $\leq$ 12.6 at\%) thin films grown under identical conditions and sandwiched between thin layers of Ru in order to study the phenomenon of spin pumping as a function of Re concentration. In-plane and out-of-plane ferromagnetic resonance spectroscopy results show an enhancement of the Gilbert damping with an increase in Re doping. We found evidence of an increase in the real part of effective spin mixing conductance [Re($g^{\uparrow\downarrow}_{eff}$)] with the increase in Re doping of 6.6 at\%, while a decrease is evident at higher Re doping. The increase in Re($g^{\uparrow\downarrow}_{eff}$) can be linked to the Re doping induced change of the interface electronic structure in the non-magnetic Ru layer and the effect interfacial spin-orbit coupling has on the effective spin-mixing conductance. The lowest and highest values of Re($g^{\uparrow\downarrow}_{eff}$) are found to be 9.883(02) $nm^{-2}$ and 19.697(02) $nm^{-2}$ for 0 at\% and 6.6 at\% Re doping, respectively. The saturation magnetization decreases with increasing Re doping, from 2.362(13) T for the undoped film to 1.740(03) T for 12.6 at\% Re doping. This study opens a new direction of tuning the spin-mixing conductance in magnetic heterostructures by doping of the ferromagnetic layerr, which is essential for the realization of energy efficient operation of spintronic devices.

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preprint2019arXiv

Spin pumping and spin torque in interfacial tailored Co2FeAl/\b{eta}-Ta layers

The Heusler ferromagnetic (FM) compound Co2FeAl interfaced with a high-spin orbit coupling non-magnetic (NM) layer is a promising candidate for energy efficient spin logic circuits. The circuit potential depends on the strength of angular momentum transfer across the FM/NM interface; hence, requiring low spin memory loss and high spin-mixing conductance. To highlight this issue, spin pumping and spin-transfer torque ferromagnetic resonance measurements have been performed on Co_2FeAl/β-Ta heterostructures tailored with Cu interfacial layers. The interface tailored structure yields an enhancement of the effective spin-mixing conductance. The interface transparency and spin memory loss corrected values of the spin-mixing conductance, spin Hall angle and spin diffusion length are found to be 3.40 \pm 0.01 \times 10^{19} m^{-2}, 0.029 \pm 0.003, and 2.3 \pm 0.5 nm, respectively. Furthermore, a high current modulation of the effective damping of around 2.1 % has been achieved at an applied current density of 1 \times 10^9 A/m^2 , which clearly indicates the potential of using this heterostructure for energy efficient control in spin devices

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preprint2018arXiv

An ADMM-based Coordination and Control Strategy for PV and Storage to Dispatch Stochastic Prosumers: Theory and Experimental Validation

This paper describes a two-layer control and coordination framework for distributed energy resources. The lower layer is a real-time model predictive control (MPC) executed at 10 s resolution to achieve fine tuning of a given energy set-point. The upper layer is a slower MPC coordination mechanism based on distributed optimization, and solved with the alternating direction method of multipliers (ADMM) at 5 minutes resolution. It is needed to coordinate the power flow among the controllable resources such that enough power is available in real-time to achieve a pre-established energy trajectory in the long term. Although the formulation is generic, it is developed for the case of a battery system and a curtailable PV facility to dispatch stochastic prosumption according to a trajectory at 5 minutes resolution established the day before the operation. The proposed method is experimentally validated in a real-life setup to dispatch the operation of a building with rooftop PV generation (i.e., 101 kW average load, 350 kW peak demand, 82 kW peak PV generation) by controlling a 560 kWh/720 kVA battery and a 13 kW peak curtailable PV facility.

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