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In recent times, there has been a surge of interest in utilizing manganite systems for oxide-based heterostructures that host non-trivial spin textures, primarily driven by the Dzyaloshinskii-Moriya interaction (DMI). In these systems, the Topological Hall Effect (THE) manifests as humps or peaks in anomalous Hall Effect (AHE) loops near the coercive field. Numerous studies have reported THE in ruthenate/iridate and ruthenate/manganite heterostructures. Among the manganites, the Nd0.6Sr0.4MnO3 (NSMO) system remains relatively less explored. The strontium ruthenate (SrRuO3 - SRO) system exhibits excellent structural compatibility with NSMO, making NSMO/SRO heterostructures a model system for investigating non-trivial spin textures at oxide-based interfaces. In this work, we have studied the proximity effect of NSMO with SRO through magnetic and magnetotransport studies, varying the SRO layer thicknesses in the NSMO/SRO heterostructures.

Interfaces in FeCoB/MgO/FeCoB magnetic tunnel junction play a vital role in controlling their magnetic and transport properties for various applications in spintronics and magnetic recording media. In this work, interface structures of a few nm thick FeCoB layers in FeCoB/MgO and MgO/FeCoB bilayers are comprehensively studied using x-ray standing waves (XSW) generated by depositing bilayers between Pt waveguide structures. High interface selectivity of nuclear resonance scattering (NRS) under the XSW technique allowed measuring structure and magnetism at the two interfaces, namely FeCoB-on-MgO and MgO-on-FeCoB, yielding an interesting result that electron density and hyperfine fields are not symmetric at both interfaces. The formation of a high-density FeCoB layer at the MgO/FeCoB (FeCoB-on-MgO) interface with an increased hyperfine field (~34.65 T) is attributed to the increasing volume of FeCo at the interface due to boron diffusion from 57FeCoB to the MgO layer. Furthermore, it caused unusual angular-dependent magnetic properties in MgO/FeCoB bilayer, whereas FeCoB/MgO is magnetically isotropic. In contrast to the literature, where the unusual angular dependent in FeCoB based system is explained in terms of in-plane magnetic anisotropy, present findings attributed the same to the interlayer exchange coupling between bulk and interface layer within the FeCoB layer.

We study the quantum transduction of a superconducting qubit to an optical photon in electro-optomechanical and electro-optomagnonical systems. The electro-optomechanical system comprises a flux-tunable transmon qubit coupled to a suspended mechanical beam, which then couples to an optical cavity. Similarly, in an electro-optomagnonical system, a flux-tunable transmon qubit is coupled to an optical whispering gallery mode via a magnon excitation in a YIG ferromagnetic sphere. In both systems, the transduction process is done in sequence. In the first sequence, the qubit states are encoded in coherent excitations of phonon/magnon modes through the phonon/magnon-qubit interaction, which is non-demolition in the qubit part. We then measure the phonon/magnon excitations, which reveal the qubit states, by counting the average number of photons in the optical cavities. The measurement of the phonon/magnon excitations can be performed at a regular intervals of time., Comment: 8 pages, 12 figures

In this work, we study the phenomena of quantum interference assisted magnon blockade and magnon antibunching in a weakly interacting hybrid ferromagnet-superconductor system. The magnon excitations in two yttrium iron garnet spheres are indirectly coupled to a superconducting qubit through microwave cavity modes of two mutually perpendicular cavities. We find that when one of the magnon mode is driven by a weak optical field, the destructive interference between more than two distinct transition pathways restricts simultaneous excitation of two magnons. We analyze the magnon correlations in the driven magnon mode for the case of zero detunings as well as finite detunings of the magnon modes and the qubit. We show that the magnon antibunching can be tuned by changing the magnon-qubit coupling strength ratio and the driving detuning. Our work proposes a possible scheme which have significant role in the construction of single magnon generating devices., Comment: 9 pages, 7 figures

Oblique/normal sequential deposition technique is used to create Co based unique multilayer structure [Co-oblique(4.4nm)/Co-normal (4.2 nm)]x10, where each Co-oblique layer is deposited at an oblique angle of 75deg, to induce large in-plane uniaxial magnetic anisotropy (UMA). Compared to the previous ripple, stress and oblique angle deposition (OAD) related studies on Cobalt in literature, one-order higher UMA with the easy axis of magnetization along the projection of the tilted nano-columns in the multilayer plane is observed. The multilayer retains magnetic anisotropy even after annealing at 450C. The in-plane UMA in this multilayer is found to be the combination of shape, and magneto-crystalline anisotropy (MCA) confirmed by the temperature-dependent grazing incidence small angle X-ray scattering (GISAXS), in situ reflection high energy electron diffraction (RHEED) and grazing incidence X-ray diffraction (GIXRD) measurements. The crystalline texturing of hcp Co in the multilayer minimizes spin-orbit coupling energy along the column direction, which couples with the shape anisotropy energies and results in preferential orientation of the easy magnetic axis along the projection of the columns in the multilayer plane. Reduction in UMA after annealing is attributed to diffusion/merging of columns and annihilating crystallographic texturing. The obtained one-order high UMA demonstrates the potential application of the unique structure engineering technique, which may have far-reaching advantages in magnetic thin films/multilayers and spintronic devices., Comment: 22 pages, 10 figures

Growth of Co film on SiO2 substrates with the surface roughness of 0.5 nm and 1.6 nm has been studied in situ using the magneto-optical Kerr effect (MOKE) and four probe resistivity measurements. In-situ measurements jointly suggest the Volmer-Weber growth process and proceed via a nonmagnetic, superparamagnetic and ferromagnetic phase formation on both substrates. Islands are found to coalesce at film thicknesses of 0.6 nm and at 1.5 nm with continuous film formation around film thicknesses of 1.5 nm and 3.0 nm for smooth and rough substrates, respectively. Ferromagnetic long-range ordering i.e., the appearance of a magnetic hysteresis loop in both films, is observed just after coalescing stage. Observed azimuthal angular dependence of coercivity confirmed the presence of a weak uniaxial magnetic anisotropy in both films, whereas the difference in uniaxial magnetic anisotropy with substrate roughness is interpreted in terms of the combined effect of domain wall pinning and internal stresses in the films. The origin of much higher UMA in the case of the Co film deposited on a ripple-patterned substrate of similar root means square roughness is attributed to the modified long-range dipolar stray fields on the surface.

We investigate the magnetic and magnetotransport properties of nanostructured Nd0.6Sr0.4MnO3 (NSMO) thin films grown on (100) oriented SrTiO3 (STO) substrates. The thin films fabricated using the pulsed laser deposition technique have been found to possess two distinct surface morphologies: granular and nano rod type. Magnetization measurements have revealed that the films with rod-type morphology exhibit improved in-plane magnetic anisotropy. Magnetotransport studies have revealed that the granular thin films display a characteristic butterfly-shaped low-field magneto-resistive (LFMR) behavior. Furthermore, we investigate the anisotropic magneto-resistive (AMR) phenomenon in the samples and we find that morphology greatly affects AMR. Thin films with rod-type morphology show an enhanced AMR %. Such morphology dependent tunability in magnetoresistance properties over a wide temperature range is potentially interesting for developing oxide-based sensors and devices.

Interfaces in the MgO-FeCoB-MgO trilayer have been studied with grazing incident nuclear resonance scattering (GINRS) using the x-ray standing waves (XSW) technique. High depth selectivity of the present method allows one to measure magnetism and structure at the two interfaces of FeCoB, namely, FeCoB-on-MgO and MgO-on-FeCoB, independently, yielding an intriguing result that both interfaces are not symmetric. A high-density layer with an increased magnetic hyperfine field at the FeCoB-on-MgO interface suggests different growth mechanisms at the two interfaces. The azimuthal angle-dependent magneto-optic Kerr effect measurements reveal the presence of unusual uniaxial magnetic anisotropy (UMA) in the trilayer. An in-situ temperature-dependent study discovered that this UMA systematically reduces with temperature. The trilayer becomes isotropic at 450C with an order-of-magnitude increase in coercivity. The asymmetry at the interfaces is, in turn, explained by boron diffusion from the FeCoB interface layer into the nearby MgO layer. Stress-induced UMA is observed in the boron-deficient FeCoB layer, superimposed with the bulk FeCoB layer, and found to be responsible for unusual UMA. The temperature-dependent variation in the UMA and coercivity can be understood in terms of variations in the internal stresses and coupling between FeCoB bulk and the interface layer.

Manganite thin films are promising candidates for studying the strongly correlated electron systems. Understanding the growth-and morphology-driven changes in the physical properties of manganite thin films is vital for their applications in oxitronics. This work reports the morphological, structural, and electrical transport properties of nanostructured Nd0.6Sr0.4MnO3 (NSMO) thin films fabricated using the pulsed laser deposition technique. Scanning electron microscopy (SEM) imaging of the thin films revealed two prominent surface morphologies: a granular and a unique crossed-nano-rod-type morphology. From X-ray diffraction (XRD) and atomic force microscopy (AFM) analysis, we found that the observed nanostructures resulted from altered growth modes occurring on the terraced substrate surface. Furthermore, investigations on the electrical-transport properties of thin films revealed that the films with crossed-nano-rod type morphology showed a sharp resistive transition near the metal-to-insulator transition (MIT). An enhanced temperature coefficient of resistance (TCR) of up to one order of magnitude was also observed compared to the films with granular morphology. Such enhancement in TCR % by tuning the morphology makes these thin films promising candidates for developing oxide-based temperature sensors and detectors., Comment: Main article : page 1-23 , Supplementary information: page 23-27

Group-fairness in classification aims for equality of a predictive utility across different sensitive sub-populations, e.g., race or gender. Equality or near-equality constraints in group-fairness often worsen not only the aggregate utility but also the utility for the least advantaged sub-population. In this paper, we apply the principles of Pareto-efficiency and least-difference to the utility being accuracy, as an illustrative example, and arrive at the Rawls classifier that minimizes the error rate on the worst-off sensitive sub-population. Our mathematical characterization shows that the Rawls classifier uniformly applies a threshold to an ideal score of features, in the spirit of fair equality of opportunity. In practice, such a score or a feature representation is often computed by a black-box model that has been useful but unfair. Our second contribution is practical Rawlsian fair adaptation of any given black-box deep learning model, without changing the score or feature representation it computes. Given any score function or feature representation and only its second-order statistics on the sensitive sub-populations, we seek a threshold classifier on the given score or a linear threshold classifier on the given feature representation that achieves the Rawls error rate restricted to this hypothesis class. Our technical contribution is to formulate the above problems using ambiguous chance constraints, and to provide efficient algorithms for Rawlsian fair adaptation, along with provable upper bounds on the Rawls error rate. Our empirical results show significant improvement over state-of-the-art group-fair algorithms, even without retraining for fairness., Comment: 24 figures, 19 figures

This work proposes a supervised multi-channel time-series learning framework for financial stock trading. Although many deep learning models have recently been proposed in this domain, most of them treat the stock trading time-series data as 2-D image data, whereas its true nature is 1-D time-series data. Since the stock trading systems are multi-channel data, many existing techniques treating them as 1-D time-series data are not suggestive of any technique to effectively fusion the information carried by the multiple channels. To contribute towards both of these shortcomings, we propose an end-to-end supervised learning framework inspired by the previously established (unsupervised) convolution transform learning framework. Our approach consists of processing the data channels through separate 1-D convolution layers, then fusing the outputs with a series of fully-connected layers, and finally applying a softmax classification layer. The peculiarity of our framework - SuperDeConFuse (SDCF), is that we remove the nonlinear activation located between the multi-channel convolution layers and the fully-connected layers, as well as the one located between the latter and the output layer. We compensate for this removal by introducing a suitable regularization on the aforementioned layer outputs and filters during the training phase. Specifically, we apply a logarithm determinant regularization on the layer filters to break symmetry and force diversity in the learnt transforms, whereas we enforce the non-negativity constraint on the layer outputs to mitigate the issue of dead neurons. This results in the effective learning of a richer set of features and filters with respect to a standard convolutional neural network. Numerical experiments confirm that the proposed model yields considerably better results than state-of-the-art deep learning techniques for real-world problem of stock trading., Comment: Accepted in Elsevier Expert Systems With Applications 2020

This work proposes an unsupervised fusion framework based on deep convolutional transform learning. The great learning ability of convolutional filters for data analysis is well acknowledged. The success of convolutive features owes to convolutional neural network (CNN). However, CNN cannot perform learning tasks in an unsupervised fashion. In a recent work, we show that such shortcoming can be addressed by adopting a convolutional transform learning (CTL) approach, where convolutional filters are learnt in an unsupervised fashion. The present paper aims at (i) proposing a deep version of CTL; (ii) proposing an unsupervised fusion formulation taking advantage of the proposed deep CTL representation; (iii) developing a mathematically sounded optimization strategy for performing the learning task. We apply the proposed technique, named DeConFuse, on the problem of stock forecasting and trading. Comparison with state-of-the-art methods (based on CNN and long short-term memory network) shows the superiority of our method for performing a reliable feature extraction., Comment: Accepted at EURASIP JASP 2020

This work addresses the problem of analyzing multi-channel time series data %. In this paper, we by proposing an unsupervised fusion framework based on %the recently proposed convolutional transform learning. Each channel is processed by a separate 1D convolutional transform; the output of all the channels are fused by a fully connected layer of transform learning. The training procedure takes advantage of the proximal interpretation of activation functions. We apply the developed framework to multi-channel financial data for stock forecasting and trading. We compare our proposed formulation with benchmark deep time series analysis networks. The results show that our method yields considerably better results than those compared against., Comment: Accepted at EUSIPCO 2020

Personal IoT data is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Typically, marketplaces are centralized systems that raise concerns of privacy, single point of failure, little transparency and involve trusted intermediaries to be fair. Furthermore, the battery-operated IoT devices limit the amount of IoT data to be traded in real-time that affects buyer/seller satisfaction and hence, impacting the sustainability and usability of such a marketplace. This work proposes to utilize blockchain technology to realize a trusted and transparent decentralized marketplace for contract compliance for trading IoT data streams generated by battery-operated IoT devices in real-time. The contribution of this paper is two-fold: (1) we propose an autonomous blockchain-based marketplace equipped with essential functionalities such as agreement framework, pricing model and rating mechanism to create an effective marketplace framework without involving a mediator, (2) we propose a mechanism for selection and allocation of buyers' demands on seller's devices under quality and battery constraints. We present a proof-of-concept implementation in Ethereum to demonstrate the feasibility of the framework. We investigated the impact of buyer's demand on the battery drainage of the IoT devices under different scenarios through extensive simulations. Our results show that this approach is viable and benefits the seller and buyer for creating a sustainable marketplace model for trading IoT data in real-time from battery-powered IoT devices., Comment: Accepted in SmartComp 2020

We demonstrate the excitation and detection of whispering gallery modes in optical microresonators using a "point-and-play", fiber-based, optical nano-antenna. The coupling mechanism is based on cavity-enhanced Rayleigh scattering. Collected spectra exhibit Lorentzian dips, Fano shapes, or Lorentzian peaks, with a coupling efficiency around 13\%. The spectra are characterized by the coupling gap, polarization, and fiber tip position. The coupling method is simple, low-cost and, most importantly, the \textit{Q}-factor can be maintained at $10^8$ over a wide coupling range, thereby making it suitable for metrology, sensing, or cavity quantum electrodynamics (cQED) experiments.