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In this tutorial paper, we provide an introduction to the briskly expanding research field of Majorana fermions in topological superconductors. We discuss several aspects of topological superconductivity and the advantages it brings to quantum computing. Mathematical derivation of the Kitaev model and BdG Hamiltonian is carried out to explain the phenomena of superconductivity and Majorana fermions. The Majorana fermions and the Non-Abelian statistics are described in detail along with their significance for quantum engineers. The theory provided led towards the engineering of the topological qubits using Majoranas., Comment: More proper mathematical equations are needed to be added to provided comprehensive information and re modify some section

We present a method that reconstructs and animates a 3D head avatar from a single-view portrait image. Existing methods either involve time-consuming optimization for a specific person with multiple images, or they struggle to synthesize intricate appearance details beyond the facial region. To address these limitations, we propose a framework that not only generalizes to unseen identities based on a single-view image without requiring person-specific optimization, but also captures characteristic details within and beyond the face area (e.g. hairstyle, accessories, etc.). At the core of our method are three branches that produce three tri-planes representing the coarse 3D geometry, detailed appearance of a source image, as well as the expression of a target image. By applying volumetric rendering to the combination of the three tri-planes followed by a super-resolution module, our method yields a high fidelity image of the desired identity, expression and pose. Once trained, our model enables efficient 3D head avatar reconstruction and animation via a single forward pass through a network. Experiments show that the proposed approach generalizes well to unseen validation datasets, surpassing SOTA baseline methods by a large margin on head avatar reconstruction and animation.

Transferring the pose of a reference avatar to stylized 3D characters of various shapes is a fundamental task in computer graphics. Existing methods either require the stylized characters to be rigged, or they use the stylized character in the desired pose as ground truth at training. We present a zero-shot approach that requires only the widely available deformed non-stylized avatars in training, and deforms stylized characters of significantly different shapes at inference. Classical methods achieve strong generalization by deforming the mesh at the triangle level, but this requires labelled correspondences. We leverage the power of local deformation, but without requiring explicit correspondence labels. We introduce a semi-supervised shape-understanding module to bypass the need for explicit correspondences at test time, and an implicit pose deformation module that deforms individual surface points to match the target pose. Furthermore, to encourage realistic and accurate deformation of stylized characters, we introduce an efficient volume-based test-time training procedure. Because it does not need rigging, nor the deformed stylized character at training time, our model generalizes to categories with scarce annotation, such as stylized quadrupeds. Extensive experiments demonstrate the effectiveness of the proposed method compared to the state-of-the-art approaches trained with comparable or more supervision. Our project page is available at https://jiashunwang.github.io/ZPT, Comment: CVPR 2023

Quantum technology is an emerging cutting-edge field which offers a new paradigm for computation and research in the field of physics, mathematics and other scientific disciplines. This technology is of strategic importance to governments globally and heavy investments and budgets are being sanctioned to gain competitive advantage in terms of military, space and education. Due to this, it is important to understand the educational and research needs required to implement this technology at a large scale. Here, we propose a novel universal quantum technology master's curriculum which comprises a balance between quantum hardware and software skills to enhance the employability of professionals thereby reducing the skill shortage faced by the academic institutions and organizations today. The proposed curriculum holds the potential to revolutionize the quantum education ecosystem by reducing the pressure of hiring PhDs faced by startups and promoting the growth of a balanced scientific mindset in quantum research., Comment: 6 pages, 1 table

Herein, we report a minireview to give a brief introduction of applications of nanomaterials in the field of forensic science. The materials that have their size in nanoscale (1 - 100 nm) comes under the category of nanomaterials. Nanomaterials possess various applications in different fields like cosmetic production, medical, photoconductivity etc. because of their physio-chemical, electrical and magnetic properties. Due to the different characteristic property that nanomaterials have, they are widely employed in diverse domains. In various fields of forensic science such as fingerprints, toxicology, medicine, serology, nanomaterials are being used extensively. Large surface area to volume ratio of the materials in nano-regime makes the nanomaterials suitable for all these application with high efficiency. This review article briefs about the nanomaterials, their advantages and their novel applications in various fields, focusing especially in the field of forensic science. The basic idea of different areas of forensic science such as development of fingerprints, detection of drugs, estimating the time since death, analysis of GSR, detection of various explosives and for the extraction of DNA etc. has also been provided., Comment: 16 Pages, 3 Figures, 42 References

Modern avatar generators allow anyone to synthesize photorealistic real-time talking avatars, ushering in a new era of avatar-based human communication, such as with immersive AR/VR interactions or videoconferencing with limited bandwidths. Their safe adoption, however, requires a mechanism to verify if the rendered avatar is trustworthy: does it use the appearance of an individual without their consent? We term this task avatar fingerprinting. To tackle it, we first introduce a large-scale dataset of real and synthetic videos of people interacting on a video call, where the synthetic videos are generated using the facial appearance of one person and the expressions of another. We verify the identity driving the expressions in a synthetic video, by learning motion signatures that are independent of the facial appearance shown. Our solution, the first in this space, achieves an average AUC of 0.85. Critical to its practical use, it also generalizes to new generators never seen in training (average AUC of 0.83). The proposed dataset and other resources can be found at: https://research.nvidia.com/labs/nxp/avatar-fingerprinting/., Comment: 26 pages, 8 figures

Recent advances recognize that the viscoelastic properties of epithelial structures play important roles in biology and disease modeling. However, accessing the viscoelastic properties of multicellular structures in mechanistic or drug-screening applications face challenges in repeatability, accuracy, and practical implementation. Here, we present a microfluidic platform that leverages elastohydrodynamic phenomena, sensed by graphene strain sensors, to measure the viscoelasticity of cellular monolayer in situ, without using labels or specialized equipment. We demonstrate platform utility with two systems: cell dissociation following trypsinization, where viscoelastic properties change over minutes, and an epithelial-to-mesenchymal transition, where changes occur over days. These cellular events could only be resolved with our platforms higher signal-to-noise ratio: relaxation times of 14.5 plus or minus 0.4 s-1 for intact epithelial monolayers versus 13.4 plus or minus 15.0 s-1 in other platforms. By rapidly assessing combined contributions from cell stiffness and intercellular interactions, we anticipate that the platform will hasten translation of new mechanical biomarkers.

The intracluster medium (ICM) in the centers of galaxy clusters is heavily influenced by the ``feedback'' from supermassive black holes (SMBHs). Feedback can drive turbulence in the ICM and turbulent dissipation can potentially be an important source of heating. Due to the limited spatial and spectral resolutions of X-ray telescopes, direct observations of turbulence in the hot ICM have been challenging. Recently, we developed a new method to measure turbulence in the ICM using multiphase filaments as tracers. These filaments are ubiquitous in cluster centers and can be observed at very high resolution using optical and radio telescopes. We study the kinematics of the filaments by measuring their velocity structure functions (VSFs) over a wide range of scales in the centers of $\sim 10$ galaxy clusters. We find features of the VSFs that correlate with the SMBHs activities, suggesting that SMBHs are the main driver of gas motions in the centers of galaxy clusters. In all systems, the VSF is steeper than the classical Kolmogorov expectation and the slopes vary from system to system. One theoretical explanation is that the VSFs we have measured so far mostly reflect the motion of the driver (jets and bubbles) rather than the cascade of turbulence. We show that in Abell 1795, the VSF of the outer filaments far from the SMBH flattens on small scales to a Kolmogorov slope, suggesting that the cascade is only detectable farther out with the current telescope resolution. The level of turbulent heating computed at small scales is typically an order of magnitude lower than that estimated at the driving scale. Even though SMBH feedback heavily influences the kinematics of the ICM in cluster centers, the level of turbulence it drives is rather low, and turbulent heating can only offset $\lesssim10\%$ of the cooling loss, consistent with the findings of numerical simulations., Comment: 21 pages, 4 figures, accepted for publication in Frontiers in Astronomy and Space Sciences: Thermal Imbalance and Multiphase Plasmas Across Scales: From the Solar Corona to the Intracluster Medium

Water crisis is a crucial concern around the globe. Appropriate and timely maintenance of water pumps in drought-hit countries is vital for communities relying on the well. In this paper, we analyze and apply a sequential attentive deep neural architecture, TabNet, for predicting water pump repair status in Tanzania. The model combines the valuable benefits of tree-based algorithms and neural networks, enabling end-to-end training, model interpretability, sparse feature selection, and efficient learning on tabular data. Finally, we compare the performance of TabNet with popular gradient tree-boosting algorithms like XGBoost, LightGBM,CatBoost, and demonstrate how we can further uplift the performance by choosing focal loss as the objective function while training on imbalanced data., Comment: 9 pages, 5 figures, 2 tables

We present a diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image. Our model samples from the distribution of possible renderings consistent with the input and, even in the presence of ambiguity, is capable of rendering diverse and plausible novel views. To achieve this, our method makes use of existing 2D diffusion backbones but, crucially, incorporates geometry priors in the form of a 3D feature volume. This latent feature field captures the distribution over possible scene representations and improves our method's ability to generate view-consistent novel renderings. In addition to generating novel views, our method has the ability to autoregressively synthesize 3D-consistent sequences. We demonstrate state-of-the-art results on synthetic renderings and room-scale scenes; we also show compelling results for challenging, real-world objects., Comment: Project page: https://nvlabs.github.io/genvs

Scaling up weakly-supervised datasets has shown to be highly effective in the image-text domain and has contributed to most of the recent state-of-the-art computer vision and multimodal neural networks. However, existing large-scale video-text datasets and mining techniques suffer from several limitations, such as the scarcity of aligned data, the lack of diversity in the data, and the difficulty of collecting aligned data. Currently popular video-text data mining approach via automatic speech recognition (ASR) used in HowTo100M provides low-quality captions that often do not refer to the video content. Other mining approaches do not provide proper language descriptions (video tags) and are biased toward short clips (alt text). In this work, we show how recent advances in image captioning allow us to pre-train high-quality video models without any parallel video-text data. We pre-train several video captioning models that are based on an OPT language model and a TimeSformer visual backbone. We fine-tune these networks on several video captioning datasets. First, we demonstrate that image captioning pseudolabels work better for pre-training than the existing HowTo100M ASR captions. Second, we show that pre-training on both images and videos produces a significantly better network (+4 CIDER on MSR-VTT) than pre-training on a single modality. Our methods are complementary to the existing pre-training or data mining approaches and can be used in a variety of settings. Given the efficacy of the pseudolabeling method, we are planning to publicly release the generated captions.