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The broken inversion symmetry at the ferromagnet (FM)/heavy-metal (HM) interface leads to spin-dependent degeneracy of the energy band, forming spin-polarized surface states. As a result, the interface serves as an effective medium for converting spin accumulation into two-dimensional charge current through the inverse Rashba-Edelstein effect. Exploring and assessing this spin-to-charge conversion (SCC) phenomenon at the FM/HM interface could offer a promising avenue to surpass the presumed limits of SCC in bulk HM layers. We utilize spintronic heterostructures as a platform to measure the spin-to-charge conversion (SCC) experienced by photoexcited spin currents. These heterostructures emit terahertz electric field when illuminated by femtosecond laser pulses, enabling us to quantitatively assess the ultrafast SCC process. Our results demonstrate a robust interfacial spin-to-charge conversion (iSCC) within a synthetic antiferromagnetic heterostructure, specifically for the NiFe/Ru/NiFe configuration, by isolating the SCC contribution originating from the interface itself, separate from the bulk heavy-metal (HM) region. Moreover, the iSCC at the NiFe/Ru interface is discovered to be approximately 27% of the strength observed in the highest spin-Hall conducting heavy-metal, Pt. Our results thus highlight the significance of interfacial engineering as a promising pathway for achieving efficient ultrafast spintronic devices., Comment: 20 Pages, 4 Figures

Femtosecond laser-induced photoexcitation of ferromagnet (FM)/heavy metal (HM) heterostructures have attracted attention by emitting broadband terahertz frequencies. The phenomenon relies on the formation of ultrafast spin current, which is largely attributed to the direct photoexcitation of the FM layer. However, we reveal that during the process, the FM layer also experiences a secondary excitation led by the hot electrons from the HM layer that travel across the FM/HM interface and transfer additional energy in the FM. Thus, the generated secondary spins enhance the total spin current formation and lead to amplified spintronic terahertz emission. The results also emphasize the significance of the secondary spin current, which even exceeds the primary spin currents when FM/HM heterostructures with thicker HM are used. An analytical model is developed to provide deeper insights into the microscopic processes within the individual layers, underlining the generalized ultrafast superdiffusive spin-transport mechanism., Comment: 20 pages, 3 figures

Ginzburg-Landau (GL) parameters formed the basis for Abrikosov discovery of the quantum vortex of a supercurrent in type-II superconductor with a normal core of size $\xi$, the superconductor coherence length and circulating supercurrent induced magnetic field diverging as $log(1/r)$ from the core with a decay length of the London penetration depth, $\lambda_L.$ In 1964, J. Pearl predicted the slowly decaying $(1/r^2)$ field around a vortex spreading out to Pearl length, $P_L=2\lambda_L^2/t$, in a superconductor film of thickness $t < \lambda_L$. However, his quintessential theory failed to predict the existence of giant kinetic inductance (GKI) that arises from the enlarged screening currents of the vortex. Here, we discover giant kinetic inductance in a $high-T_c$ metasurface due to the 1400% expansion of the vortex screening supercurrent from $\lambda_L$ to 14$\lambda_L$ in ultrathin film meta-atom of $\lambda_L/7$ thickness, which leads to the emergence of terahertz superinductance possessing quantum impedance exceeding the resistance quantum limit of $R_Q=h/(2e)^2 =6.47 k\Omega$ by 33%. Our discovery presents a new class of $high-T_c$ superconductor electronic, photonic, and quantum devices enabled through metasurface designed at the Pearl length scales, providing novel applications in quantum circuitry, metrology, and single photon kinetic inductance detectors., Comment: 16 pages, 4 figures

The focus of this work is on Statistical Process Control (SPC) of a manufacturing process based on available measurements. Two important applications of SPC in industrial settings are fault detection and diagnosis (FDD). In this work a deep learning (DL) based methodology is proposed for FDD. We investigate the application of an explainability concept to enhance the FDD accuracy of a deep neural network model trained with a data set of relatively small number of samples. The explainability is quantified by a novel relevance measure of input variables that is calculated from a Layerwise Relevance Propagation (LRP) algorithm. It is shown that the relevances can be used to discard redundant input feature vectors/ variables iteratively thus resulting in reduced over-fitting of noisy data, increasing distinguishability between output classes and superior FDD test accuracy. The efficacy of the proposed method is demonstrated on the benchmark Tennessee Eastman Process., Comment: Under Review. arXiv admin note: text overlap with arXiv:2012.03861

A Deep Neural Network (DNN) based algorithm is proposed for the detection and classification of faults in industrial plants. The proposed algorithm has the ability to classify faults, especially incipient faults that are difficult to detect and diagnose with traditional threshold based statistical methods or by conventional Artificial Neural Networks (ANNs). The algorithm is based on a Supervised Deep Recurrent Autoencoder Neural Network (Supervised DRAE-NN) that uses dynamic information of the process along the time horizon. Based on this network a hierarchical structure is formulated by grouping faults based on their similarity into subsets of faults for detection and diagnosis. Further, an external pseudo-random binary signal (PRBS) is designed and injected into the system to identify incipient faults. The hierarchical structure based strategy improves the detection and classification accuracy significantly for both incipient and non-incipient faults. The proposed approach is tested on the benchmark Tennessee Eastman Process resulting in significant improvements in classification as compared to both multivariate linear model-based strategies and non-hierarchical nonlinear model-based strategies., Comment: Under Review

Speech emotion analysis is an important task which further enables several application use cases. The non-verbal sounds within speech utterances also play a pivotal role in emotion analysis in speech. Due to the widespread use of smartphones, it becomes viable to analyze speech commands captured using microphones for emotion understanding by utilizing on-device machine learning models. The non-verbal information includes the environment background sounds describing the type of surroundings, current situation and activities being performed. In this work, we consider both verbal (speech commands) and non-verbal sounds (background noises) within an utterance for emotion analysis in real-life scenarios. We create an indigenous dataset for this task namely "Indian EmoSpeech Command Dataset". It contains keywords with diverse emotions and background sounds, presented to explore new challenges in audio analysis. We exhaustively compare with various baseline models for emotion analysis on speech commands on several performance metrics. We demonstrate that we achieve a significant average gain of 3.3% in top-one score over a subset of speech command dataset for keyword spotting.

This paper addresses the issues concerning the rescheduling of a static timetable in case of a disaster encountered in a large and complex railway network system. The proposed approach tries to modify the schedule so as to minimise the overall delay of trains. This is achieved by representing the rescheduling problem in the form of a Petri-Net and the highly uncertain disaster recovery times in such a model is handled as Markov Decision Processes (MDP ). For solving the rescheduling problem, a istributed Constraint Optimisation (DCOP ) based strategy involving the use of autonomous agents is used to generate the desired schedule. The proposed approach is evaluated on the actual schedule of the Eastern Railways, India by constructing vari- ous disaster scenarios using the Java Agent DEvelopment Framework (JADE). When compared to the existing approaches, the proposed framework substantially reduces the delay of trains after rescheduling.