Search

In the field of 3D Human Pose Estimation from monocular videos, the presence of diverse occlusion types presents a formidable challenge. Prior research has made progress by harnessing spatial and temporal cues to infer 3D poses from 2D joint observations. This paper introduces a Dual Transformer Fusion (DTF) algorithm, a novel approach to obtain a holistic 3D pose estimation, even in the presence of severe occlusions. Confronting the issue of occlusion-induced missing joint data, we propose a temporal interpolation-based occlusion guidance mechanism. To enable precise 3D Human Pose Estimation, our approach leverages the innovative DTF architecture, which first generates a pair of intermediate views. Each intermediate-view undergoes spatial refinement through a self-refinement schema. Subsequently, these intermediate-views are fused to yield the final 3D human pose estimation. The entire system is end-to-end trainable. Through extensive experiments conducted on the Human3.6M and MPI-INF-3DHP datasets, our method's performance is rigorously evaluated. Notably, our approach outperforms existing state-of-the-art methods on both datasets, yielding substantial improvements. The code is available here: https://github.com/MehwishG/DTF.

We introduce a novel type of neutron interference mirrors based on a chemically homogeneous BxC (x>4) matrix with internal high precision 10B/11B isotope modulation. Simulations predict that these mirrors exhibit very high neutron reflectivities for a small number of bilayer periods. This is experimentally confirmed by neutron reflectivity measurements of mirrors synthesized by ion-assisted magnetron sputter deposition. For example, a 120 nm thick multilayer consisting of just 20 bilayer periods of 10B5.7C/11B5.7C, with a periodicity of 61.5 {\AA}, exhibits a neutron reflectivity of 13 percent at an incidence angle of 4.7 deg for neutrons with a wavelength of 4.825 {\AA}. This is attributed to a high scattering length density contrast between the layers with an interface width <5 {\AA}. Structural analyses by X-ray diffraction, X-ray reflectivity, and transmission electron microscopy demonstrate that the 10B5.7C/11B5.7C multilayer mirrors are composed of amorphous, chemically homogenous B5.7C, without any internal chemical modulation. The data show that 10BxC/11BxC multilayer mirrors have the potential for higher neutron reflectivities at higher q-values using fewer and thinner layers, compared to todays state-of-the-art chemically modulated neutron multilayer mirrors.

Polarized neutron scattering is a very important analysis technique for studies of magnetism, spintronics, and high-sensitivity measurements, among others, offering invaluable information. Yet, the efficiency of such experiments rely on the performance of the polarizing neutron optics to provide high reflectivity and polarization. Presently, state-of-the-art polarizers like Fe/Si supermirrors are not able to polarize neutrons at low scattering angles in a monochromatic beam or able to polarize neutrons with a variety of wavelengths as for a non-monochromatic beam. To overcome this limitation, it is suggested to use Co/Ti multilayers on Ti substrates owing to their favorable scattering length density characteristics. It is shown that this approach enables a wavelength bandwidth several times larger than achievable with state-of-the-art materials on Si or glass substrates. Consequently, enabling the possibility of polarizing neutrons across an extended wavelength range, including those with very high wavelengths.

We present the first report on fabrication of Hafnium aluminum nitride chiral sculptured thin films (CSTFs) using reactive magnetron sputtering in a glancing angle deposition configuration, and the analysis of its optical polarization properties. The resulting CSTFs were designed to give interference extrema or so-called circular Bragg (CB) resonances at desired wavelengths in the region from 370 to 690 nm. This was achieved by tailoring the growth of the chiral thin films to obtain a dielectric pitch between 87 and 260.9 nm. The spectral positions of the obtained CB resonances were compared to values from analytical expressions. Contrary to the common case where the dielectric pitch is half of the growth-related rotational pitch due to a 180{\deg} symmetry, this pitch was shown to be the same as the rotational pitch. It is concluded that this is due to the c-axis of the CSTF being tilted about 45{\deg} from the substrate normal. The morphology and crystallographic characterizations were done using scanning electron microscopy and X-ray diffraction, respectively, while the tilt of the crystal lattice was corroborated using X-ray diffraction pole figures. The optical response from the CSTFs was analyzed using Mueller matrix spectroscopic ellipsometry from which the degree of circular polarization at the CB resonances was obtained. In addition, a strong non-reciprocal reflection was observed which could be attributed to the helicoidal morphology and the intrinsic crystal tilt. An optical layered model of the chiral structure including azimuthal twist and using the Cauchy dispersion relations was used to simulate the Mueller matrix elements and compare with the ellipsometry measurements. The correlation between the simulated and experimental data gave information of the morphological parameters of the CSTF and its optical properties.

The concept of scattering length density tuning for improved polarization is investigated for Fe/11B4CTi multilayers and compared to the commonly used Fe/Si system in polarizing multilayer neutron optics. X-ray and neutron reflectivity, magnetization, and neutron polarization have been measured on such multilayers, highlighting differences from conventional Fe/Si multilayers. The multilayer systems were deposited with 25 {\AA} period thickness, a layer thickness ratio of 0.35 and 20 periods, using ion-assisted DC magnetron sputtering. Replacing Si with 11B4CTi for these multilayers showed an increase in reflectivity due to a reduction in interface width. By tuning the ratio between 11B4C and Ti in the non-magnetic layers, a broad range of scattering length density values was achieved, facilitating scattering length density contrast matching between layers for spin-down neutrons, thereby enhancing polarization. These findings demonstrate the potential of Fe/11B4CTi multilayers as a promising option for polarizing neutron optics and highlight the concept of scattering length density tuning in a large range using 11B4CTi.

State-of-the-art Ni/Ti supermirror neutron optics have limited reflected intensity and a restricted neutron energy range due to the interface width. Incorporating low-neutron-absorbing 11B4C enhances reflectivity and allows for thinner layers to be deposited, with which more efficient supermirrors with higher m-values can be realized. However, incorporating 11B4C reduces the optical contrast, limiting the attainable reflectivity at low scattering vectors, making this approach infeasible. This study explores various approaches to optimize the material design of 11B4C-containing Ni/Ti supermirrors to maintain high reflectivity at low scattering vectors and achieve low interface widths at large scattering vectors. The scattering length density contrast versus interface width is investigated for multilayer periods of 30 {\AA}, 48 {\AA}, and 84 {\AA}, for designs involving pure Ni/Ti multilayers, multilayers with 11B4C co-deposited in Ni and Ti layers, multilayers with 11B4C co-deposited only in Ni layers, and multilayers with 11B4C as thin interlayers between Ni and Ti layers. Our results suggest that a depth-graded hybrid material design by incorporating 11B4C inside the Ni and Ti layers, below approximately 26 {\AA}, and introducing 1.5 {\AA} 11B4C interlayers between the thicker Ni and Ti layers can achieve a higher reflectivity than state-of-the-art Ni/Ti multilayers over the entire scattering vector range.

This paper proposed a cutting-edge multiclass teeth segmentation architecture that integrates an M-Net-like structure with Swin Transformers and a novel component named Teeth Attention Block (TAB). Existing teeth image segmentation methods have issues with less accurate and unreliable segmentation outcomes due to the complex and varying morphology of teeth, although teeth segmentation in dental panoramic images is essential for dental disease diagnosis. We propose a novel teeth segmentation model incorporating an M-Net-like structure with Swin Transformers and TAB. The proposed TAB utilizes a unique attention mechanism that focuses specifically on the complex structures of teeth. The attention mechanism in TAB precisely highlights key elements of teeth features in panoramic images, resulting in more accurate segmentation outcomes. The proposed architecture effectively captures local and global contextual information, accurately defining each tooth and its surrounding structures. Furthermore, we employ a multiscale supervision strategy, which leverages the left and right legs of the U-Net structure, boosting the performance of the segmentation with enhanced feature representation. The squared Dice loss is utilized to tackle the class imbalance issue, ensuring accurate segmentation across all classes. The proposed method was validated on a panoramic teeth X-ray dataset, which was taken in a real-world dental diagnosis. The experimental results demonstrate the efficacy of our proposed architecture for tooth segmentation on multiple benchmark dental image datasets, outperforming existing state-of-the-art methods in objective metrics and visual examinations. This study has the potential to significantly enhance dental image analysis and contribute to advances in dental applications.

Accurate solar power forecasting is pivotal for the global transition towards sustainable energy systems. This study conducts a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models for solar power production forecasting. The primary objective is to evaluate the potential advantages of QLSTMs, leveraging their exponential representational capabilities, in capturing the intricate spatiotemporal patterns inherent in renewable energy data. Through controlled experiments on real-world photovoltaic datasets, our findings reveal promising improvements offered by QLSTMs, including accelerated training convergence and substantially reduced test loss within the initial epoch compared to classical LSTMs. These empirical results demonstrate QLSTM's potential to swiftly assimilate complex time series relationships, enabled by quantum phenomena like superposition. However, realizing QLSTM's full capabilities necessitates further research into model validation across diverse conditions, systematic hyperparameter optimization, hardware noise resilience, and applications to correlated renewable forecasting problems. With continued progress, quantum machine learning can offer a paradigm shift in renewable energy time series prediction, potentially ushering in an era of unprecedented accuracy and reliability in solar power forecasting worldwide. This pioneering work provides initial evidence substantiating quantum advantages over classical LSTM models while acknowledging present limitations. Through rigorous benchmarking grounded in real-world data, our study illustrates a promising trajectory for quantum learning in renewable forecasting., Comment: 21 pages, 9 figures

The utilization of polarized neutrons is of great importance in scientific disciplines spanning materials science, physics, biology, and chemistry. Polarization analysis offers insights into otherwise unattainable sample information such as magnetic domains and structures, protein crystallography, composition, orientation, ion-diffusion mechanisms, and relative location of molecules in multicomponent biological systems. State-of-the-art multilayer polarizing neutron optics have limitations, particularly low specular reflectivity and polarization at higher scattering vectors/angles, and the requirement of high external magnetic fields to saturate the polarizer magnetization. Here, we show that by incorporating 11B4C into Fe/Si multilayers, amorphization and smooth interfaces can be achieved, yielding higher neutron reflectivity, less diffuse scattering and higher polarization. Magnetic coercivity is eliminated, and magnetic saturation can be reached at low external fields (>2 mT). This approach offers prospects for significant improvement in polarizing neutron optics, enabling; nonintrusive positioning of the polarizer, enhanced flux, increased data accuracy, and further polarizing/analyzing methods at neutron scattering facilities.

Multilayer neutron optics require precise control of interface morphology for optimal performance. In this work, we investigate the effects of different growth conditions on the interface morphology of Ni/Ti based multilayers, with a focus on incorporating low-neutron-absorbing 11B4C and using different ion assistance schemes. Grazing incidence small angle X-ray scattering was used to probe the structural and morphological details of buried interfaces, revealing that the layers become more strongly correlated and the interfaces form mounds with increasing amounts of 11B4C. Applying high flux ion assistance during growth can reduce mound formation but lead to interface mixing, while a high flux modulated ion assistance scheme with an initial buffer layer grown at low ion energy and the top layer at higher ion energy prevents intermixing. The optimal condition was found to be adding 26.0 at.% 11B4C combined with high flux modulated ion assistance. A multilayer with a period of 48.2 {\AA} and 100 periods was grown under these conditions, and coupled fitting to neutron and X-ray reflectivity data revealed an average interface width of only 2.7 {\AA}, a significant improvement over the current state-of-the-art commercial Ni/Ti multilayers. Overall, our study demonstrates that the addition of 11B4C and the use of high flux modulated ion assistance during growth can significantly improve the interface morphology of Ni/Ti multilayers, leading to improved neutron optics performance., Comment: 11 pages

Soft robotics holds tremendous potential for various applications, especially in unstructured environments such as search and rescue operations. However, the lack of autonomy and teleoperability, limited capabilities, absence of gait diversity and real-time control, and onboard sensors to sense the surroundings are some of the common issues with soft-limbed robots. To overcome these limitations, we propose a spatially symmetric, topologically-stable, soft-limbed tetrahedral robot that can perform multiple locomotion gaits. We introduce a kinematic model, derive locomotion trajectories for different gaits, and design a teleoperation mechanism to enable real-time human-robot collaboration. We use the kinematic model to map teleoperation inputs and ensure smooth transitions between gaits. Additionally, we leverage the passive compliance and natural stability of the robot for toppling and obstacle navigation. Through experimental tests, we demonstrate the robot's ability to tackle various locomotion challenges, adapt to different situations, and navigate obstructed environments via teleoperation.

This research work provides an exhaustive investigation of the viability of different coupled wormhole (WH) geometries with the relativistic matter configurations in the $f(R,G,T)$ extended gravity framework. We consider a specific model in the context of $f(R,G,T)$-gravity for this purpose. Also, we assume a static spherically symmetric space-time geometry and a unique distribution of matter with a set of shape functions ($\beta(r)$) for analyzing different energy conditions (ECs). In addition to this, we examined WH-models in the equilibrium scenario by employing anisotropic fluid. The corresponding results are obtained using numerical methods and then presented using different plots. In this case, $f(R,G,T)$ gravity generates additional curvature quantities, which can be thought of as gravitational objects that maintain irregular WH-situations. Based on our findings, we conclude that in the absence of exotic matter, WH can exist in some specific regions of the parametric space using modified gravity model as, $f(R,G,T) = R +\alpha R^2+\beta G^n+\gamma G\ln(G)+\lambda T$., Comment: Modified, 31 pages, 11 figures

As in-vehicle communication becomes more complex, the automotive community is exploring various architectural options such as centralized and zonal architectures for their numerous benefits. Zonal architecture reduces the wiring cost by physically locating related operations and ECUs near their intended functions and the number of physical ECUs through function consolidation. Centralized architectures consolidate the number of ECUs into few, powerful compute units. Common characteristics of these architectures include the need for high-bandwidth communication and security, which have been elusive with standard automotive architectures. Further, as automotive communication technologies evolve, it is also likely that multiple link-layer technologies such as CAN and Automotive Ethernet will co-exist. These alternative architectures promise to integrate these diverse sets of technologies. However, architectures that allow such co-existence have not been adequately explored. In this work we explore a new network architecture called Named Data Networking (NDN) to achieve multiple goals: provide a foundational security infrastructure and bridge different link layer protocols such as CAN, LIN, and automotive Ethernet into a unified communication system. We created a proof-of-concept bench-top testbed using CAN HATS and Raspberry PIs that replay real traffic over CAN and Ethernet to demonstrate how NDN can provide a secure, high-speed bridge between different automotive link layers. We also show how NDN can support communication between centralized or zonal high-power compute components. Security is achieved through digitally signing all Data packets between these components, preventing unauthorized ECUs from injecting arbitrary data into the network. We also demonstrate NDN's ability to prevent DoS and replay attacks between different network segments connected through NDN.

3D human pose estimation using monocular images is an important yet challenging task. Existing 3D pose detection methods exhibit excellent performance under normal conditions however their performance may degrade due to occlusion. Recently some occlusion aware methods have also been proposed, however, the occlusion handling capability of these networks has not yet been thoroughly investigated. In the current work, we propose an occlusion-guided 3D human pose estimation framework and quantify its occlusion handling capability by using different protocols. The proposed method estimates more accurate 3D human poses using 2D skeletons with missing joints as input. Missing joints are handled by introducing occlusion guidance that provides extra information about the absence or presence of a joint. Temporal information has also been exploited to better estimate the missing joints. A large number of experiments are performed for the quantification of occlusion handling capability of the proposed method on three publicly available datasets in various settings including random missing joints, fixed body parts missing, and complete frames missing, using mean per joint position error criterion. In addition to that, the quality of the predicted 3D poses is also evaluated using action classification performance as a criterion. 3D poses estimated by the proposed method achieved significantly improved action recognition performance in the presence of missing joints. Our experiments demonstrate the effectiveness of the proposed framework for handling the missing joints as well as quantification of the occlusion handling capability of the deep neural networks., Comment: Accepted for publication in IEEE Transaction Multimedia, 2022

There are two phases of Wnt signalling in early vertebrate embryogenesis: very early, maternal Wnt signalling promotes dorsal development, and slightly later, zygotic Wnt signalling promotes ventral and lateral mesoderm induction. However, recent molecular biology analysis has revealed more complexity among the direct Wnt target genes, with at least five classes. Here in order to test the logic and the dynamics of a new Gene Regulatory Network model suggested by these discoveries we use mathematical modelling based on ordinary differential equations (ODEs). Our mathematical modelling of this Gene Regulatory Network reveals that a simplified model, with one "super-gene" for each class is sufficient to a large extent to describe the regulatory behaviour previously observed experimentally.