Cross-identity Video Motion Retargeting with Joint Transformation and Synthesis

In this paper, we propose a novel dual-branch Transformation-Synthesis network (TS-Net), for video motion retargeting. Given one subject video and one driving video, TS-Net can produce a new plausible video with the subject appearance of the subject video and motion pattern of the driving video. TS-Net consists of a warp-based transformation branch and a warp-free synthesis branch. The novel design of dual branches combines the strengths of deformation-grid-based transformation and warp-free generation for better identity preservation and robustness to occlusion in the synthesized videos. A mask-aware similarity module is further introduced to the transformation branch to reduce computational overhead. Experimental results on face and dance datasets show that TS-Net achieves better performance in video motion retargeting than several state-of-the-art models as well as its single-branch variants. Our code is available at https://github.com/nihaomiao/WACV23_TSNet.Comment: WACV 202

Assessing First Visits By Physicians To Medicare Patients Discharged To Skilled Nursing Facilities

In this study of postacute care, more than 10% of Medicare skilled nursing facility (SNF) stays included no visit from a physician or advanced practitioner. Of stays with visits, about half of initial assessments occurred within a day of admission, and nearly 80% occurred within four days. Patients who did not receive a visit from a physician or advanced practitioner were nearly twice as likely to be readmitted to a hospital (28%) or to die (14%) within 30 days of SNF admission than patients who had an initial visit

Topological triply-degenerate point with double Fermi arcs

Unconventional chiral particles have recently been predicted to appear in certain three dimensional (3D) crystal structures containing three- or more-fold linear band degeneracy points (BDPs). These BDPs carry topological charges, but are distinct from the standard twofold Weyl points or fourfold Dirac points, and cannot be described in terms of an emergent relativistic field theory. Here, we report on the experimental observation of a topological threefold BDP in a 3D phononic crystal. Using direct acoustic field mapping, we demonstrate the existence of the threefold BDP in the bulk bandstructure, as well as doubled Fermi arcs of surface states consistent with a topological charge of 2. Another novel BDP, similar to a Dirac point but carrying nonzero topological charge, is connected to the threefold BDP via the doubled Fermi arcs. These findings pave the way to using these unconventional particles for exploring new emergent physical phenomena

Coordinate Translator for Learning Deformable Medical Image Registration

The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images. This, however, requires the convolutional kernels in the CNN to not only extract intensity features from the inputs but also understand image coordinate systems. We argue that the latter task is challenging for traditional CNNs, limiting their performance in registration tasks. To tackle this problem, we first introduce Coordinate Translator, a differentiable module that identifies matched features between the fixed and moving image and outputs their coordinate correspondences without the need for training. It unloads the burden of understanding image coordinate systems for CNNs, allowing them to focus on feature extraction. We then propose a novel deformable registration network, im2grid, that uses multiple Coordinate Translator's with the hierarchical features extracted from a CNN encoder and outputs a deformation field in a coarse-to-fine fashion. We compared im2grid with the state-of-the-art DL and non-DL methods for unsupervised 3D magnetic resonance image registration. Our experiments show that im2grid outperforms these methods both qualitatively and quantitatively

The Modification Strategies for Enhancing the Metabolic Stabilities and Pharmacokinetics of Aptamer Drug Candidates

Aptamers are single-stranded DNA or RNA that can mimic the functional properties of monoclonal antibodies. Aptamers have high affinity and specificity for their target molecules, which can make them a promising alternative to therapeutic antibodies or peptide ligands. However, many aptamer drug candidates in clinical development have been discontinued due to suboptimal metabolic stabilities and pharmacokinetics. To address these issues, chemical modification can be used to enhance the metabolic stability and prolong the half-life of aptamer candidates. The chapter reviewed published data regarding the metabolic stability and pharmacokinetics of aptamer drug candidates from preclinical and clinical studies. The benefits and possible shortcomings of current modification strategies used in these aptamers were briefly discussed

Exploring Vision Transformers as Diffusion Learners

Score-based diffusion models have captured widespread attention and funded fast progress of recent vision generative tasks. In this paper, we focus on diffusion model backbone which has been much neglected before. We systematically explore vision Transformers as diffusion learners for various generative tasks. With our improvements the performance of vanilla ViT-based backbone (IU-ViT) is boosted to be on par with traditional U-Net-based methods. We further provide a hypothesis on the implication of disentangling the generative backbone as an encoder-decoder structure and show proof-of-concept experiments verifying the effectiveness of a stronger encoder for generative tasks with ASymmetriC ENcoder Decoder (ASCEND). Our improvements achieve competitive results on CIFAR-10, CelebA, LSUN, CUB Bird and large-resolution text-to-image tasks. To the best of our knowledge, we are the first to successfully train a single diffusion model on text-to-image task beyond 64x64 resolution. We hope this will motivate people to rethink the modeling choices and the training pipelines for diffusion-based generative models

An automated learning method of semantic segmentation for train autonomous driving environment understanding

One of the major reasons for the explosion of autonomous driving in recent years is the great development of computer vision. As one of the most fundamental and challenging problems in autonomous driving, environment understanding has been widely studied. It directly determines whether the entire in-vehicle system can effectively identify surrounding objects of vehicles and make correct path planning. Semantic segmentation is the most important means of environment understanding among the many image recognition algorithms used in autonomous driving. However, the success of semantic segmentation models is highly dependent on human expertise in data preparation and hyperparameter optimization, and the tedious process of training is repeated over and over for each new scene. Automated machine learning (AutoML) is a research area for this problem that aims to automate the development of end-to-end ML models. In this paper, we propose an automatic learning method for semantic segmentation based on reinforcement learning (RL), which can realize automatic selection of training data and guide automatic training of semantic segmentation. The results show that our scheme converges faster and has higher accuracy than researchers manually training semantic segmentation models, while requiring no human involvement

Perovskite quantum dot topological laser

Various topological laser concepts have recently enabled the demonstration of robust light-emitting devices that are immune to structural deformations and tolerant to fabrication imperfections. Current realizations of photonic cavities with topological boundaries are often limited by outcoupling issues or poor directionality and require complex design and fabrication that hinder operation at small wavelengths. Here we propose a topological cavity design based on interface states between two one-dimensional photonic crystals with distinct Zak phases and demonstrate a lithography-free, single-mode perovskite laser emitting in the green. Few monolayers of solution processed all-inorganic cesium lead halide perovskite quantum dots are used as ultrathin gain medium. The topological laser has planar design with large output aperture, akin to vertical-cavity surface-emitting lasers (VCSELs) and is robust against variations of the thickness of the gain medium, from deeply subwavelength to thick quantum dot films. This experimental observation also unveils the topological nature of VCSELs, that is usually overlooked in the description of conventional Fabry-Perot cavity lasers. The design simplicity and topological characteristics make this perovskite quantum dot laser architecture suitable for low-cost and fabrication tolerant vertical emitting lasers operating across the visible spectral region

Observation of photonic antichiral edge states

Chiral edge states are a hallmark feature of two-dimensional topological materials. Such states must propagate along the edges of the bulk either clockwise or counterclockwise, and thus produce oppositely propagating edge states along the two parallel edges of a strip sample. However, recent theories have predicted a counterintuitive picture, where the two edge states at the two parallel strip edges can propagate in the same direction; these anomalous topological edge states are named as antichiral edge states. Here we report the experimental observation of antichiral edge states in a gyromagnetic photonic crystal. The crystal consists of gyromagnetic cylinders in a honeycomb lattice, with the two triangular sublattices magnetically biased in opposite directions. With microwave measurement, unique properties of antichiral edge states have been observed directly, which include the titled dispersion, the chiral-like robust propagation in samples with certain shapes, and the scattering into backward bulk states at certain terminations. These results extend and supplement the current understanding of chiral edge states

Observation of vortex-string chiral modes in metamaterials

As a hypothetical topological defect in the geometry of spacetime, vortex strings play a crucial role in shaping the clusters of galaxies that exist today, and their distinct features can provide observable clues about the early universe's evolution. A key feature of vortex strings is that they can interact with Weyl fermionic modes and support topological chiral-anomaly states with massless dispersions at the core of strings. To date, despite many attempts to detect vortex strings in astrophysics or to emulate them in artificially created systems, observation of these topological vortex-string chiral modes remains experimentally elusive. Here we report the experimental observation of such vortex-string chiral modes using a metamaterial system. This is implemented by inhomogeneous perturbation of a Yang-monopole phononic metamaterial. The measured linear dispersion and modal profiles confirm the existence of topological modes bound to and propagating along the vortex string with the chiral anomaly. Our work not only provides a platform for studying diverse cosmic topological defects in astrophysics but also offers intriguing device applications as topological fibres in signal processing and communication techniques.Comment: 3 Figure