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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.

Metal carbides, nitrides, or carbonitrides of early transition metals, better known as MXenes, possess notable structural, electrical, and magnetic properties. Analyzing electronic structures by calculating structural stability, band structure, density of states, Bader charge transfer, and work functions utilizing first principle calculations, we revealed that titanium nitride Mxenes, namely Ti$_2$N and Ti$_3$N$_2$, have excess anionic electrons in their pseudo-atomic structure inside the crystal lattice, making them MXene electrides. Bulk Ti$_3$N$_2$ has competing antiferromagnetic (AFM) and ferromagnetic(FM) configurations with slightly more stable AFM configurations, while the Ti$_2$N MXene is nonmagnetic. Although Ti$_3$N$_2$ favors AFM configurations with hexagonal crystal systems having $6/mmm$ point group symmetry, Ti$_3$N$_2$ does not support altermagnetism. The monolayer of the Ti$_3$N$_2$ MXene is a ferromagnetic electride. These unique properties of having non-nuclear interstitial anionic electrons in the electronic structure of titanium nitride MXene have not yet been reported in the literature. Density functional theory calculations show TiN is neither an electride, MXene, or magnetic.

In actinide systems, the 5f electrons experience a uniquely delicate balance of effects and interactions having similar energy scales, which are often difficult to properly disentangle. This interplay of factors such as the dual nature of 5f-states, strong electronic correlations, and strong spin-orbit coupling results in electronically unusual and intriguing behavior such as multi-k antiferromagnetic ordering, multipolar ordering, mott-physics, mixed valence configurations, and more. Despite the inherent allure of their exotic properties, the exploratory science of even the more basic, binary systems like the actinide oxides has been limited due to their toxicity, radioactivity, and reactivity. In this article, we provide an overview of the available synthesis techniques for selected binary actinide oxides, including the actinide dioxides, sesquioxides, and a selection of higher oxides. For these oxides, we also review and evaluate the current state of knowledge of their crystal structures and magnetic properties. In many aspects, substantial knowledge gaps exist in the current body of research on actinide oxides related to understanding their electronic ground states. Bridging these gaps is vital for improving not only a fundamental understanding of these systems but also of future nuclear technologies. To this end, we note the experimental techniques and necessary future investigations which may aid in better elucidating the nature of these fascinating systems.

Video Anomaly Detection (VAD) is an open-set recognition task, which is usually formulated as a one-class classification (OCC) problem, where training data is comprised of videos with normal instances while test data contains both normal and anomalous instances. Recent works have investigated the creation of pseudo-anomalies (PAs) using only the normal data and making strong assumptions about real-world anomalies with regards to abnormality of objects and speed of motion to inject prior information about anomalies in an autoencoder (AE) based reconstruction model during training. This work proposes a novel method for generating generic spatio-temporal PAs by inpainting a masked out region of an image using a pre-trained Latent Diffusion Model and further perturbing the optical flow using mixup to emulate spatio-temporal distortions in the data. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting by learning three types of anomaly indicators, namely reconstruction quality, temporal irregularity and semantic inconsistency. Extensive experiments on four VAD benchmark datasets namely Ped2, Avenue, ShanghaiTech and UBnormal demonstrate that our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting. Our analysis also examines the transferability and generalisation of PAs across these datasets, offering valuable insights by identifying real-world anomalies through PAs., Comment: Accepted in CVPRW 2024 - VAND Workshop

N and $\Delta$ baryons hold an important place towards understanding the quark dynamics inside hadrons. The hypercentral Constituent Quark Model (hCQM) has been employed in various studies ranging from light to heavy hadrons. In the present article, screened potential has been used to study light baryon resonances. The Regge trajectories have been plotted alongwith the details of slopes and intercepts. The strong decay widths to pion have been calculated for some channels using the present masses., Comment: 15 pages, 6 figures, Suggestions are welcome

The effect of proximity of the magnetism of the Pr-based manganite (Pr0.6Sr0.4MnO3) on the superconductivity of Bi-based high-temperature superconductor (Bi1.75Pb0.25Sr2Ca2Cu3O10+d) was studied based on the results obtained from the magnetotransport and magnetization measurements. Decrease in the values of the upper critical field (HC2(0)) and an increase in the width of the superconducting transition (Delta TC) of Bi1.75Pb0.25Sr2Ca2Cu3O10+d were observed in proximity with the manganite. The combined effect of magnetic exchange interaction arising from the manganite, the leakage of Cooper-pairs from the superconductor into the manganite, and the diffusion and transport of spin-polarized electrons from the manganite into the superconductor were found to modify the superconducting properties of Bi1.75Pb0.25Sr2Ca2Cu3O10+d. The stacking sequence of the individual layers in these heterostructures was found to dictate the ground state properties of the heterostructure. As a consequence of the proximity effect, the colossal-magnetoresistance (CMR) ratio as high as ~ 99 % observed in the heterostructure makes the thin film heterostructures promising candidates for potential technological applications., Comment: 29 pages, 14 figures

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.

Computationally expensive training strategies make self-supervised learning (SSL) impractical for resource constrained industrial settings. Techniques like knowledge distillation (KD), dynamic computation (DC), and pruning are often used to obtain a lightweightmodel, which usually involves multiple epochs of fine-tuning (or distilling steps) of a large pre-trained model, making it more computationally challenging. In this work we present a novel perspective on the interplay between SSL and DC paradigms. In particular, we show that it is feasible to simultaneously learn a dense and gated sub-network from scratch in a SSL setting without any additional fine-tuning or pruning steps. The co-evolution during pre-training of both dense and gated encoder offers a good accuracy-efficiency trade-off and therefore yields a generic and multi-purpose architecture for application specific industrial settings. Extensive experiments on several image classification benchmarks including CIFAR-10/100, STL-10 and ImageNet-100, demonstrate that the proposed training strategy provides a dense and corresponding gated sub-network that achieves on-par performance compared with the vanilla self-supervised setting, but at a significant reduction in computation in terms of FLOPs, under a range of target budgets (td )., Comment: Accepted in BMVC 2023

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

In the work of Rostami et al., the Bogomolov multiplier of a Lie algebra $L$ over a field $\Omega$ is defined as a particular factor of a subalgebra of the exterior product $L \wedge L$. If $L$ is finite dimensional, we identify this object as a certain subgroup of the second cohomology group $H^2(L, \Omega)$ by deriving a Hopf-Type formula. As an application, we affirmatively answer two questions posed by Kunyavskii regarding the invariance of the Bogomolov multiplier under isoclinism of Lie algebras and the existence of a family of Lie algebras with Bogomolov multipliers of unbounded dimension., Comment: 10 pages

The task of Generic Event Boundary Detection (GEBD) aims to detect moments in videos that are naturally perceived by humans as generic and taxonomy-free event boundaries. Modeling the dynamically evolving temporal and spatial changes in a video makes GEBD a difficult problem to solve. Existing approaches involve very complex and sophisticated pipelines in terms of architectural design choices, hence creating a need for more straightforward and simplified approaches. In this work, we address this issue by revisiting a simple and effective self-supervised method and augment it with a differentiable motion feature learning module to tackle the spatial and temporal diversities in the GEBD task. We perform extensive experiments on the challenging Kinetics-GEBD and TAPOS datasets to demonstrate the efficacy of the proposed approach compared to the other self-supervised state-of-the-art methods. We also show that this simple self-supervised approach learns motion features without any explicit motion-specific pretext task., Comment: Accepted in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023