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The Skinned Multi-Person Linear (SMPL) model plays a crucial role in 3D human pose estimation, providing a streamlined yet effective representation of the human body. However, ensuring the validity of SMPL configurations during tasks such as human mesh regression remains a significant challenge , highlighting the necessity for a robust human pose prior capable of discerning realistic human poses. To address this, we introduce MOPED: \underline{M}ulti-m\underline{O}dal \underline{P}os\underline{E} \underline{D}iffuser. MOPED is the first method to leverage a novel multi-modal conditional diffusion model as a prior for SMPL pose parameters. Our method offers powerful unconditional pose generation with the ability to condition on multi-modal inputs such as images and text. This capability enhances the applicability of our approach by incorporating additional context often overlooked in traditional pose priors. Extensive experiments across three distinct tasks-pose estimation, pose denoising, and pose completion-demonstrate that our multi-modal diffusion model-based prior significantly outperforms existing methods. These results indicate that our model captures a broader spectrum of plausible human poses.

Offline imitation learning enables learning a policy solely from a set of expert demonstrations, without any environment interaction. To alleviate the issue of distribution shift arising due to the small amount of expert data, recent works incorporate large numbers of auxiliary demonstrations alongside the expert data. However, the performance of these approaches rely on assumptions about the quality and composition of the auxiliary data. However, they are rarely successful when those assumptions do not hold. To address this limitation, we propose Robust Offline Imitation from Diverse Auxiliary Data (ROIDA). ROIDA first identifies high-quality transitions from the entire auxiliary dataset using a learned reward function. These high-reward samples are combined with the expert demonstrations for weighted behavioral cloning. For lower-quality samples, ROIDA applies temporal difference learning to steer the policy towards high-reward states, improving long-term returns. This two-pronged approach enables our framework to effectively leverage both high and low-quality data without any assumptions. Extensive experiments validate that ROIDA achieves robust and consistent performance across multiple auxiliary datasets with diverse ratios of expert and non-expert demonstrations. ROIDA effectively leverages unlabeled auxiliary data, outperforming prior methods reliant on specific data assumptions.

We report on the NuSTAR observations of the accreting pulsars IGR~J17480-2446 and IGR~J17511-3057 performed on March 4, 2023, and April 8, 2015, respectively. We describe the continuum emission of IGR~J17480-2446 with a combination of two soft thermal components and an additional hard X-ray emission described by a power-law. We suggest that the spectral properties of IGR~J17480-2446 are consistent with a soft state, different from many other accreting X-ray millisecond pulsars usually found in the hard spectral state. The source IGR~J17511-3057 exhibits a hard spectrum characterized by a Comptonized emission from the corona. The X-ray spectrum of both sources shows evidence of disc reflection. For the first time, we employ the self-consistent reflection models ({\tt relxill} and {\tt relxillNS}) to fit the reflection features in the NuSTAR spectrum. From the best-fit spectral model, we find an inner disc radius is precisely constrained to $(1.99-2.68)\:R_{ISCO}$ and inclination to $30\pm 1$ degree for IGR~J17480-2446. We determine an inner disc radius of $\lesssim 1.3\;R_{ISCO}$ and inclination of $44\pm 3$ degree for IGR~J17511-3057. A low inclination angle of the system is required for both sources. We further place an upper limit on the magnetic field strength of the sources, considering the disc is truncated at the magnetospheric radius., Comment: 23 pages, 11 figures, Submitted to Journal of High Energy Astrophysics (JHEAP). arXiv admin note: text overlap with arXiv:2408.06193

Nano-positioning plays a very important role in applications such as scanning probe microscopy and optics. We report the development of a compact inertial nanopositioner along with fully computer interfaced electronics operating down to 2 K, and its use in our fully automated needle-anvil type Point Contact Andreev Reflection (PCAR) apparatus. We also present the fully automated operational procedures using LabVIEW interface with our home-built electronics. The point contact spectroscopy probe has been successfully used to perform PCAR measurements on elemental superconductors at low temperatures. The small footprint of our nano-positioner makes it ideally suited for incorporation in low temperature scanning probe microscopes and makes this design versatile for various research and industrial purposes.

The open world is inherently dynamic, characterized by ever-evolving concepts and distributions. Continual learning (CL) in this dynamic open-world environment presents a significant challenge in effectively generalizing to unseen test-time classes. To address this challenge, we introduce a new practical CL setting tailored for open-world visual representation learning. In this setting, subsequent data streams systematically introduce novel classes that are disjoint from those seen in previous training phases, while also remaining distinct from the unseen test classes. In response, we present Dynamic Prompt and Representation Learner (DPaRL), a simple yet effective Prompt-based CL (PCL) method. Our DPaRL learns to generate dynamic prompts for inference, as opposed to relying on a static prompt pool in previous PCL methods. In addition, DPaRL jointly learns dynamic prompt generation and discriminative representation at each training stage whereas prior PCL methods only refine the prompt learning throughout the process. Our experimental results demonstrate the superiority of our approach, surpassing state-of-the-art methods on well-established open-world image retrieval benchmarks by an average of 4.7% improvement in Recall@1 performance., Comment: ECCV 2024

The accreting millisecond X-ray pulsar IGR J17498-2921 went into X-ray outburst on April 13-15, 2023, for the first time since its discovery on August 11, 2011. Here, we report on the first follow-up \nustar{} observation of the source, performed on April 23, 2023, around ten days after the peak of the outburst. The \nustar{} spectrum of the persistent emission ($3-60$ \kev{} band) is well described by an absorbed blackbody with a temperature of $kT_{bb}=1.61\pm 0.04$\kev{}, most likely arising from the NS surface and a Comptonization component with power-law index $\Gamma=1.79\pm0.02$, arising from a hot corona at $kT_{e}=16\pm 2$ keV. The X-ray spectrum of the source shows robust reflection features which have not been observed before. We use a couple of self-consistent reflection models, {\tt relxill} and {\tt relxillCp}, to fit the reflection features. We find an upper limit to the inner disc radius of $ 6\: R_{ISCO}$ and $ 9\: R_{ISCO}$ from {\tt relxill} and {\tt relxillCp} model, respectively. The inclination of the system is estimated to be $\simeq 40\degr$ from both reflection models. Assuming magnetic truncation of the accretion disc, the upper limit of magnetic field strength at the pole of the NS is found to be $B\lesssim 1.8\times 10^{8}$ G. Furthermore, the \nustar{} observation revealed two type I X-ray bursts and the burst spectroscopy confirms the thermonuclear nature of the burst. The blackbody temperature reaches nearly $2.2$ keV at the peak of the burst., Comment: 13 pages, 17 figures, Accepted for publication in MNRAS main journal

Existing algorithms for human body part segmentation have shown promising results on challenging datasets, primarily relying on end-to-end supervision. However, these algorithms exhibit severe performance drops in the face of domain shifts, leading to inaccurate segmentation masks. To tackle this issue, we introduce POSTURE: \underline{Po}se Guided Un\underline{s}upervised Domain Adap\underline{t}ation for H\underline{u}man Body Pa\underline{r}t S\underline{e}gmentation - an innovative pseudo-labelling approach designed to improve segmentation performance on the unlabeled target data. Distinct from conventional domain adaptive methods for general semantic segmentation, POSTURE stands out by considering the underlying structure of the human body and uses anatomical guidance from pose keypoints to drive the adaptation process. This strong inductive prior translates to impressive performance improvements, averaging 8\% over existing state-of-the-art domain adaptive semantic segmentation methods across three benchmark datasets. Furthermore, the inherent flexibility of our proposed approach facilitates seamless extension to source-free settings (SF-POSTURE), effectively mitigating potential privacy and computational concerns, with negligible drop in performance.

Melting of a solid is one of the most ubiquitous phenomena observed in nature. Most solids, when heated, melt from a crystalline state to an isotropic liquid at a characteristic temperature. There are however situations where increase in temperature can induce a transition to a more ordered state. Broadly termed as "inverse melting", experimental realisations of such situations are rare. Here, we report such a phenomenon in the 2-dimensional vortex liquid that forms in a moderately pinned amorphous Re6Zr (a-ReZr) thin film, from direct imaging of the vortex lattice using a scanning tunnelling microscope. At low temperature and magnetic fields, we find that the vortices form a "pinned liquid" , that is characterised by a low mobility of the vortices and vortex density that is spatially inhomogeneous. As the temperature or magnetic field is increased the vortices become more ordered, eventually forming a nearly perfectly ordered vortex lattice. Above this temperature/magnetic field, the ordered vortex lattice melts again into a vortex liquid. This re-entrant transformation from a liquid to solid-like state and then back to a liquid also leaves distinct signature in the magnetotransport properties of the superconductor.

We introduce the Reality-linked 3D Scenes (R3DS) dataset of synthetic 3D scenes mirroring the real-world scene arrangements from Matterport3D panoramas. Compared to prior work, R3DS has more complete and densely populated scenes with objects linked to real-world observations in panoramas. R3DS also provides an object support hierarchy, and matching object sets (e.g., same chairs around a dining table) for each scene. Overall, R3DS contains 19K objects represented by 3,784 distinct CAD models from over 100 object categories. We demonstrate the effectiveness of R3DS on the Panoramic Scene Understanding task. We find that: 1) training on R3DS enables better generalization; 2) support relation prediction trained with R3DS improves performance compared to heuristically calculated support; and 3) R3DS offers a challenging benchmark for future work on panoramic scene understanding.

Test time adaptation is the process of adapting, in an unsupervised manner, a pre-trained source model to each incoming batch of the test data (i.e., without requiring a substantial portion of the test data to be available, as in traditional domain adaptation) and without access to the source data. Since it works with each batch of test data, it is well-suited for dynamic environments where decisions need to be made as the data is streaming in. Current test time adaptation methods are primarily focused on a single source model. We propose the first completely unsupervised Multi-source Test Time Adaptation (MeTA) framework that handles multiple source models and optimally combines them to adapt to the test data. MeTA has two distinguishing features. First, it efficiently obtains the optimal combination weights to combine the source models to adapt to the test data distribution. Second, it identifies which of the source model parameters to update so that only the model which is most correlated to the target data is adapted, leaving the less correlated ones untouched; this mitigates the issue of "forgetting" the source model parameters by focusing only on the source model that exhibits the strongest correlation with the test batch distribution. Experiments on diverse datasets demonstrate that the combination of multiple source models does at least as well as the best source (with hindsight knowledge), and performance does not degrade as the test data distribution changes over time (robust to forgetting)., Comment: Under Review

The capability to accurately estimate 3D human poses is crucial for diverse fields such as action recognition, gait recognition, and virtual/augmented reality. However, a persistent and significant challenge within this field is the accurate prediction of human poses under conditions of severe occlusion. Traditional image-based estimators struggle with heavy occlusions due to a lack of temporal context, resulting in inconsistent predictions. While video-based models benefit from processing temporal data, they encounter limitations when faced with prolonged occlusions that extend over multiple frames. This challenge arises because these models struggle to generalize beyond their training datasets, and the variety of occlusions is hard to capture in the training data. Addressing these challenges, we propose STRIDE (Single-video based TempoRally contInuous occlusion Robust 3D Pose Estimation), a novel Test-Time Training (TTT) approach to fit a human motion prior for each video. This approach specifically handles occlusions that were not encountered during the model's training. By employing STRIDE, we can refine a sequence of noisy initial pose estimates into accurate, temporally coherent poses during test time, effectively overcoming the limitations of prior methods. Our framework demonstrates flexibility by being model-agnostic, allowing us to use any off-the-shelf 3D pose estimation method for improving robustness and temporal consistency. We validate STRIDE's efficacy through comprehensive experiments on challenging datasets like Occluded Human3.6M, Human3.6M, and OCMotion, where it not only outperforms existing single-image and video-based pose estimation models but also showcases superior handling of substantial occlusions, achieving fast, robust, accurate, and temporally consistent 3D pose estimates.

The hexatic phase is an intermediate stage in the melting process of a 2D crystal due to topological defects. Recently, this exotic phase was experimentally identified in the vortex lattice of 2D weakly disordered superconducting MoGe by scanning tunneling microscopic measurements. Here we study this vortex state by the Nernst effect, which is an effective and sensitive tool to detect vortex motion, especially in the superconducting fluctuation regime. We find a surprising Nernst sign reversal at the melting transition of the hexatic phase. We propose that they are a consequence of vortex dislocations in the hexatic state which diffuse preferably from the cold to hot., Comment: 8 pages, 8 figures

The most common allotrope of tungsten, {\alpha}-W, has a superconducting transition at a temperature of ~11 mK. However, two other forms of tungsten have been reported to have superconducting transitions in the temperature range Tc ~ 2-5 K when synthesized as thin films: Crystalline \b{eta}-W and amorphous W (a-W). In this work we carry out a systematic study of W films synthesized using d.c. magnetron sputtering, using transport, low frequency magnetic shielding response, and transmission electron microscopy. Our results show that while a-W is indeed a bulk superconductor, superconductivity in \b{eta}-W probably originates from an amorphous phase that forms along with the \b{eta}-W phase. Our findings reconcile some of the anomalies earlier reported in \b{eta}-W, such the very small superconducting gap and the decrease of Tc with increase in film thickness.

Recently developed terahertz (THz) two-dimensional coherent spectroscopy (2DCS) is a powerful technique to obtain materials information in a fashion qualitatively different from other spectroscopies. Here, we utilized THz 2DCS to investigate the THz nonlinear response of conventional superconductor NbN. Using broad-band THz pulses as light sources, we observed a third-order nonlinear signal whose spectral components are peaked at twice the superconducting gap energy $2\Delta$. With narrow-band THz pulses, a THz nonlinear signal was identified at the driving frequency $\Omega$ and exhibited a resonant enhancement at temperature when $\Omega = 2\Delta$. General theoretical considerations show that such a resonance can only arise from a disorder-activated paramagnetic coupling between the light and the electronic current. This proves that the nonlinear THz response can access processes distinct from the diamagnetic Raman-like density fluctuations, which are believed to dominate the nonlinear response at optical frequencies in metals. Our numerical simulations reveal that even for a small amount of disorder, the $\Omega=2\Delta$ resonance is dominated by the superconducting amplitude mode over the entire investigated disorder range. This is in contrast to other resonances, whose amplitude-mode contribution depends on disorder. Our findings demonstrate the unique ability of THz 2DCS to explore collective excitations inaccessible in other spectroscopies.

Human silhouette extraction is a fundamental task in computer vision with applications in various downstream tasks. However, occlusions pose a significant challenge, leading to incomplete and distorted silhouettes. To address this challenge, we introduce POISE: Pose Guided Human Silhouette Extraction under Occlusions, a novel self-supervised fusion framework that enhances accuracy and robustness in human silhouette prediction. By combining initial silhouette estimates from a segmentation model with human joint predictions from a 2D pose estimation model, POISE leverages the complementary strengths of both approaches, effectively integrating precise body shape information and spatial information to tackle occlusions. Furthermore, the self-supervised nature of \POISE eliminates the need for costly annotations, making it scalable and practical. Extensive experimental results demonstrate its superiority in improving silhouette extraction under occlusions, with promising results in downstream tasks such as gait recognition. The code for our method is available https://github.com/take2rohit/poise.

Traditional test-time adaptation (TTA) methods face significant challenges in adapting to dynamic environments characterized by continuously changing long-term target distributions. These challenges primarily stem from two factors: catastrophic forgetting of previously learned valuable source knowledge and gradual error accumulation caused by miscalibrated pseudo labels. To address these issues, this paper introduces an unsupervised domain change detection method that is capable of identifying domain shifts in dynamic environments and subsequently resets the model parameters to the original source pre-trained values. By restoring the knowledge from the source, it effectively corrects the negative consequences arising from the gradual deterioration of model parameters caused by ongoing shifts in the domain. Our method involves progressive estimation of global batch-norm statistics specific to each domain, while keeping track of changes in the statistics triggered by domain shifts. Importantly, our method is agnostic to the specific adaptation technique employed and thus, can be incorporated to existing TTA methods to enhance their performance in dynamic environments. We perform extensive experiments on benchmark datasets to demonstrate the superior performance of our method compared to state-of-the-art adaptation methods., Comment: WACV 2024

We investigate the low-frequency electrodynamics in the vortex state of two type-II superconducting films, namely, a moderate-to-strongly pinned Niobium Nitride (NbN) and a very weakly pinned amorphous Molybdenum Germanium (a-MoGe). We employ a two-coil mutual inductance technique to extract the complex penetration depth, $\tilde{\lambda}$. The sample response is studied through the temperature variation of $\tilde{\lambda}$ in the mixed state, where we employ a model developed by Coffey and Clem (CC model) to extract the different vortex lattice (VL) parameters such as the restoring pinning force constant (Labusch parameter), VL drag coefficient and pinning potential barrier. We observe that a consistent description of the inductive and dissipative part of the response is only possible when we take the viscous drag on the vortices to be several orders of magnitudes larger than viscous drag estimated from the Bardeen-Stephen model.

Private 5G networks will soon be ubiquitous across the future-generation smart wireless access infrastructures hosting a wide range of performance-critical applications. A high-performing User Plane Function (UPF) in the data plane is critical to achieving such stringent performance goals, as it governs fast packet processing and supports several key control-plane operations. Based on a private 5G prototype implementation and analysis, it is imperative to perform dynamic resource management and orchestration at the UPF. This paper leverages Mobile Edge Cloud-Intelligent Agent (MEC-IA), a logically centralized entity that proactively distributes resources at UPF for various service types, significantly reducing the tail latency experienced by the user requests while maximizing resource utilization. Extending the MEC-IA functionality to MEC layers further incurs data plane latency reduction. Based on our extensive simulations, under skewed uRLLC traffic arrival, the MEC-IA assisted bestfit UPF-MEC scheme reduces the worst-case latency of UE requests by up to 77.8% w.r.t. baseline. Additionally, the system can increase uRLLC connectivity gain by 2.40x while obtaining 40% CapEx savings.

We present the results from the spectral and timing analysis of the accreting neutron star 1A~1744-361 from the \nustar{} observation performed in its 2022 outbursts. The unabsorbed bolometric X-ray luminosity during this observation in the energy band $0.1-100\kev{}$ is $3.9\times 10^{37}$ erg~s$^{-1}$, assuming a distance of $9$ kpc. During this observation, the source was in the banana branch of the atoll track. The source spectrum exhibits relativistic disc reflection and clear absorption features when an absorbed blackbody and cut-off power-law model describes the continuum emission. The $3-50\kev{}$ source spectrum is well fitted using a model combination consisting of an absorbed single-temperature blackbody and a reflection model along with the addition of a warm absorber component. The inner-disk radius, $R_{in}$, obtained from the reflection fit is $\sim(1.61-2.86)R_{ISCO}=(8.4-14.9)R_{g}$ ($17.6-31.2$ km for a $1.4\Msun$ NS). This measurement allowed us to further constrain the magnetic field strength to $B\lesssim 0.94\times 10^{9}$G. The strong absorption features $\sim 6.91\kev{}$ and $\sim 7.99\kev{}$ imply the presence of highly ionized absorbing material with a column density $N_{H}$ of $\sim 3\times 10^{22}$ cm$^{-2}$, emanating from the accretion disk in the form of disc wind with an outflow velocity of $v_{out}\simeq 0.021c\simeq 6300$ km s$^{-1}$., Comment: 10 pages, 10 figures, Accepted for publication in MNRAS main journal

An InAlN/GaN HEMT device was studied using extensive temperature dependent DC IV measurements and CV measurements. Barrier traps in the InAlN layer were characterized using transient analysis. Forward gate current was modelled using analytical equations. RF performance of the device was also studied and device parameters were extracted following small signal equivalent circuit model. Extensive simulations in Silvaco TCAD were also carried out by varying stem height, gate length and incorporating back barrier to optimize the suitability of this device in Ku-band by reducing the detrimental Short Channel Effects (SCEs). In this paper a novel structure i.e., a short length T gate with recess, on thin GaN buffer to achieve high cut-off frequency (f$_T$) and high maximum oscillating frequency (f$_{max}$) apt for Ku-band applications is also proposed.

In this paper, we have characterized an AlGaN/GaN High Electron Mobility Transistor (HEMT) with a short gate length (Lg $\approx$ 0.15$\mu$m). We have studied the effect of short gate length on the small signal parameters, linearity parameters and gm-gd ratio in GaN HEMT devices. To understand how scaling results in the variation of the above-mentioned parameters a comparative study with higher gate length devices on similar heterostructure is also presented here. We have scaled down the gate length but the barrier thickness(t$_{bar}$) remained same which affects the aspect ratio (L$_{g}$/t$_{bar}$) of the device and its inseparable consequences are the prominent short channel effects (SCEs) barring the optimum output performance of the device. These interesting phenomena were studied in detail and explored over a temperature range of -40$^\circ$C to 80$^\circ$C. To the best of our knowledge this paper explores temperature dependence of SCEs of GaN HEMT for the first time. With an approach to reduce the impact of SCEs a simulation study in Silvaco TCAD was carried out and it is observed that a recessed gate structure on conventional heterostructure successfully reduces SCEs and improves RF performance of the device. This work gives an overall view of gate length scaling on conventional AlGaN/GaN HEMTs.

Domain adaptation methods for 2D human pose estimation typically require continuous access to the source data during adaptation, which can be challenging due to privacy, memory, or computational constraints. To address this limitation, we focus on the task of source-free domain adaptation for pose estimation, where a source model must adapt to a new target domain using only unlabeled target data. Although recent advances have introduced source-free methods for classification tasks, extending them to the regression task of pose estimation is non-trivial. In this paper, we present Prior-guided Self-training (POST), a pseudo-labeling approach that builds on the popular Mean Teacher framework to compensate for the distribution shift. POST leverages prediction-level and feature-level consistency between a student and teacher model against certain image transformations. In the absence of source data, POST utilizes a human pose prior that regularizes the adaptation process by directing the model to generate more accurate and anatomically plausible pose pseudo-labels. Despite being simple and intuitive, our framework can deliver significant performance gains compared to applying the source model directly to the target data, as demonstrated in our extensive experiments and ablation studies. In fact, our approach achieves comparable performance to recent state-of-the-art methods that use source data for adaptation., Comment: Accepted at ICCV 2023

Scene understanding using multi-modal data is necessary in many applications, e.g., autonomous navigation. To achieve this in a variety of situations, existing models must be able to adapt to shifting data distributions without arduous data annotation. Current approaches assume that the source data is available during adaptation and that the source consists of paired multi-modal data. Both these assumptions may be problematic for many applications. Source data may not be available due to privacy, security, or economic concerns. Assuming the existence of paired multi-modal data for training also entails significant data collection costs and fails to take advantage of widely available freely distributed pre-trained uni-modal models. In this work, we relax both of these assumptions by addressing the problem of adapting a set of models trained independently on uni-modal data to a target domain consisting of unlabeled multi-modal data, without having access to the original source dataset. Our proposed approach solves this problem through a switching framework which automatically chooses between two complementary methods of cross-modal pseudo-label fusion -- agreement filtering and entropy weighting -- based on the estimated domain gap. We demonstrate our work on the semantic segmentation problem. Experiments across seven challenging adaptation scenarios verify the efficacy of our approach, achieving results comparable to, and in some cases outperforming, methods which assume access to source data. Our method achieves an improvement in mIoU of up to 12% over competing baselines. Our code is publicly available at https://github.com/csimo005/SUMMIT., Comment: 12 pages, 5 figures, 9 tables, ICCV 2023

The shot noise in tunneling experiments reflects the Poissonian nature of the tunneling process. The shot noise power is proportional to both the magnitude of the current and the effective charge of the carrier. Shot noise spectroscopy thus enables, in principle, to determine the effective charge q of the charge carriers that tunnel. This can be used to detect electron pairing in superconductors: in the normal state, the noise corresponds to single electron tunneling (q = 1e), while in the paired state, the noise corresponds to q = 2e. Here, we use a newly developed amplifier to reveal that in typical mesoscopic superconducting junctions, the shot noise does not reflect the signatures of pairing and instead stays at a level corresponding to q = 1e. We show that transparency can control the shot noise and this q = 1e is due to the large number of tunneling channels with each having very low transparency. Our results indicate that in typical mesoscopic superconducting junctions one should expect q = 1e noise, and lead to design guidelines for junctions that allow the detection of electron pairing.

We report on the \nustar{} observation of the newly discovered neutron star X-ray binary Swift~J1858.6-0814 taken on 23rd March 2019. The light curve of the source exhibits several large flares during some time intervals of this observation. The source is softer in the high-intensity interval where the large flaring activity mainly occurs. We perform time-resolved spectroscopy on the source by extracting spectra for two different intensity intervals. The source was observed with a $3-79 \kev{}$ luminosity of $\sim 9.68\times 10^{36}$ ergs/s and $\sim 4.78\times 10^{36}$ ergs/s for high and low-intensity interval, respectively assuming a distance of $15$ kpc. We find a large value of the absorbing column density ($\rm{N_{H}}\sim 1.1\times 10^{23}$ cm$^{-2}$), and it appears to be uncorrelated with the observed flux of the source. Each spectrum shows evidence of Fe K$\alpha$ emission in the $5-7$\kev{} energy band, an absorption edge around $\sim 7-8$\kev{}, and a broad Compton hump above $15$\kev{}, indicating the presence of a reflection spectrum. The observed features are well explained by the contribution of a relativistic reflection model and a partially covering absorption model. From the best-fit spectral model, we found an inner disc radius to be $4.87_{-0.96}^{+1.63}\;R_{ISCO}$ (for the high-intensity interval) and $5.68_{-2.78}^{+9.54}\;R_{ISCO}$ (for the low-intensity interval), indicating a significant disc truncation. The disk inclination is found to be relatively low, $i< 33^{0}$. We further place an upper limit on this source's magnetic field strength considering the disc is truncated at the magnetospheric radius., Comment: 13 pages, 14 figures, 3 Tables, Accepted for publication in MNRAS

We report the formation of a pinned vortex liquid spanning a very large region of the magnetic field-temperature parameter space in a 5 nm thick amorphous superconducting Re_xZr (x~6) (a-ReZr) thin film, using a combination of low-temperature scanning tunnelling spectroscopic (STS) imaging and magnetotransport measurements. The nature of the vortex liquid differs significantly from a regular liquid. Analysing series of STS images captured as a function of time, we observe that the interplay of pinning and intervortex interactions produces a very inhomogeneous state, where some vortices remain static, whereas others move forming a percolating network along which vortices are mobile. With increase in temperature or magnetic field this network becomes denser eventually encompassing all vortices. Our results provide key insight on the nature of a pinned vortex liquid and some of the peculiarities in the transport properties of ultrathin superconducting films., Comment: Main text with Supplementary

The Higgs boson plays a central role in the Standard Model, as well as in theories which go beyond it. This article is therefore divided into two parts. The first takes s historical approach and shows how the mass problem entered weak interaction theory from the beginning and how it was solved by invoking the Higgs boson. This is followed by a construction of the Glashow-Salam-Weinberg model, again stressing the role played by the Higgs doublet. This part culminates in the Higgs boson discovery of 2012. The second part first discusses the shortcomings of the Standard Model and then touches upon the major theories which try to improve upon it, mostly with profound consequences on the Higgs sector. This is followed up by short descriptions of a number of popular extensions of the Higgs sector, and culminates in a brief introduction to effective field theory approaches to studying the Higgs sector., Comment: Invited article to appear in a Special Issue of the Indian Journal of Physics