Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Accurate and Realistic Virtual Try-on Through Shape Matching and Multiple Warps

A virtual try-on method takes a product image and an image of a model and produces an image of the model wearing the product. Most methods essentially compute warps from the product image to the model image and combine using image generation methods. However, obtaining a realistic image is challenging because the kinematics of garments is complex and because outline, texture, and shading cues in the image reveal errors to human viewers. The garment must have appropriate drapes; texture must be warped to be consistent with the shape of a draped garment; small details (buttons, collars, lapels, pockets, etc.) must be placed appropriately on the garment, and so on. Evaluation is particularly difficult and is usually qualitative. This paper uses quantitative evaluation on a challenging, novel dataset to demonstrate that (a) for any warping method, one can choose target models automatically to improve results, and (b) learning multiple coordinated specialized warpers offers further improvements on results. Target models are chosen by a learned embedding procedure that predicts a representation of the products the model is wearing. This prediction is used to match products to models. Specialized warpers are trained by a method that encourages a second warper to perform well in locations where the first works poorly. The warps are then combined using a U-Net. Qualitative evaluation confirms that these improvements are wholesale over outline, texture shading, and garment details.
A new calibration method of sub-halo orbital evolution for semi-analytic models

Understanding the non-linear dynamics of satellite halos (a.k.a. "sub-halos") is important for predicting the abundance and distribution of dark matter substructures and satellite galaxies, and for distinguishing among microphysical dark matter models using observations. Typically, modeling these dynamics requires large N-body simulations with high resolution. Semi-analytic models can provide a more efficient way to describe the key physical processes such as dynamical friction, tidal mass loss, and tidal heating, with only a few free parameters. In this work, we present a fast Monte Carlo Markov Chain fitting approach to explore the parameter space of such a sub-halo non-linear evolution model. We use the dynamical models described in an earlier work and calibrate the models to two sets of high-resolution cold dark matter N-body simulations, ELVIS and Caterpillar. Compared to previous calibrations that used manual parameter tuning, our approach provides a more robust way to determine the best-fit parameters and their posterior probabilities. We find that jointly fitting for the sub-halo mass and maximum velocity functions can break the degeneracy between tidal stripping and tidal heating parameters, as well as providing better constraints on the strength of dynamical friction. We show that our semi-analytic simulation can accurately reproduce N-body simulations statistics, and that the calibration results for the two sets of N-body simulations agree at 95% confidence level. Dynamical models calibrated in this work will be important for future dark matter substructure studies.
Emergence of generalized hydrodynamics in the non-local Luttinger model

We propose the Luttinger model with finite-range interactions as a simple tractable example in 1+1 dimensions to analytically study the emergence of Euler-scale hydrodynamics in a quantum many-body system. This non-local Luttinger model is an exactly solvable quantum field theory somewhere between conformal and Bethe-ansatz integrable models. Applying the recent proposal of generalized hydrodynamics, we show that the model allows for fully explicit yet non-trivial solutions of the resulting Euler-scale hydrodynamic equations. Comparing with exact analytical non-equilibrium results valid at all time and length scales, we show perfect agreement at the Euler scale when the interactions are short range. A formal proof of the emergence of generalized hydrodynamics in the non-local Luttinger model is also given, and effects of long-range interactions are briefly discussed.
An Isolated White Dwarf with 317 s Rotation and Magnetic Emission

We report the discovery of short-period photometric variability and modulated Zeeman-split hydrogen emission in SDSSJ125230.93−023417.72 (EPIC 228939929), a variable white dwarf star observed at long cadence in K2 Campaign 10. The behavior is associated with a magnetic (B = 5.0 MG) spot on the stellar surface, making the 317.278 s period a direct measurement of the stellar rotation rate. This object is therefore the fastest-rotating, apparently isolated (without a stellar companion) white dwarf yet discovered and the second found to exhibit chromospheric Balmer emission after GD 356, in which the emission has been attributed to a unipolar inductor mechanism driven by a possible rocky planet. We explore the properties and behavior of this object, and consider whether its evolution may hold implications for white dwarf mergers and their remnants.
Defect dynamics in growing bacterial colonies.

Colonies of rod-shaped bacteria constitute a system of colloidal active matter with nematic properties. As a single initial bacterium multiplies through repeated divisions, the resulting colony quickly loses long-range orientational order, but retains locally ordered domains. At the boundaries of these domains, topological defects emerge, that themselves move around randomly as the colony grows. In both experiments and simulations, we find that these defects are created at a rate that corresponds to the exponential growth of the colony, resulting in a stable defect density. We also characterize the geometric and topological properties of bacterial colonies, from which we find that the aspect ratio of the rod-shaped particles is the main regulator of both the correlation length and the defect density. Moreover, we find that the defect dynamics are well described by a Gamma distribution, which is due to repeated divisions and subsequent re-orientations of the bacteria.
Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection

Deep Learning has achieved state of the art performance in medical imaging. However, these methods for disease detection focus exclusively on improving the accuracy of classification or predictions without quantifying uncertainty in a decision. Knowing how much confidence there is in a computer-based medical diagnosis is essential for gaining clinicians trust in the technology and therefore improve treatment. Today, the 2019 Coronavirus (SARS-CoV-2) infections are a major healthcare challenge around the world. Detecting COVID-19 in X-ray images is crucial for diagnosis, assessment and treatment. However, diagnostic uncertainty in the report is a challenging and yet inevitable task for radiologist. In this paper, we investigate how drop-weights based Bayesian Convolutional Neural Networks (BCNN) can estimate uncertainty in Deep Learning solution to improve the diagnostic performance of the human-machine team using publicly available COVID-19 chest X-ray dataset and show that the uncertainty in prediction is highly correlates with accuracy of prediction. We believe that the availability of uncertainty-aware deep learning solution will enable a wider adoption of Artificial Intelligence (AI) in a clinical setting.
MaskFlownet: Asymmetric Feature Matching With Learnable Occlusion Mask

Feature warping is a core technique in optical flow estimation; however, the ambiguity caused by occluded areas during warping is a major problem that remains unsolved. In this paper, we propose an asymmetric occlusion-aware feature matching module, which can learn a rough occlusion mask that filters useless (occluded) areas immediately after feature warping without any explicit supervision. The proposed module can be easily integrated into end-to-end network architectures and enjoys performance gains while introducing negligible computational cost. The learned occlusion mask can be further fed into a subsequent network cascade with dual feature pyramids with which we achieve state-of-the-art performance. At the time of submission, our method, called MaskFlownet, surpasses all published optical flow methods on the MPI Sintel, KITTI 2012 and 2015 benchmarks. Code is available at https://github.com/microsoft/MaskFlownet.
Adversarial Perturbations Fool Deepfake Detectors

This work uses adversarial perturbations to enhance deepfake images and fool common deepfake detectors. We created adversarial perturbations using the Fast Gradient Sign Method and the Carlini and Wagner L2 norm attack in both blackbox and whitebox settings. Detectors achieved over 95% accuracy on unperturbed deepfakes, but less than 27% accuracy on perturbed deepfakes. We also explore two improvements to deep-fake detectors: (i) Lipschitz regularization, and (ii) Deep Image Prior (DIP). Lipschitz regularization constrains the gradient of the detector with respect to the input in order to increase robustness to input perturbations. The DIP defense removes perturbations using generative convolutional neural networks in an unsupervised manner. Regularization improved the detection of perturbed deepfakes on average, including a 10% accuracy boost in the blackbox case. The DIP defense achieved 95% accuracy on perturbed deepfakes that fooled the original detector while retaining 98% accuracy in other cases on a 100 image subsample.
Large-P Variable Selection in Two-Stage Models

Model selection in the large-P small-N scenario is discussed in the framework of two-stage models. Two specific models are considered, namely, two-stage least squares (TSLS) involving instrumental variables (IVs), and mediation models. In both cases, the number of putative variables (e.g. instruments or mediators) is large, but only a small subset should be included in the two-stage model. We use two variable selection methods which are designed for high-dimensional settings, and compare their performance in terms of their ability to find the true IVs or mediators. Our approach is demonstrated via simulations and case studies.
A time series method to analyze incidence pattern and estimate reproduction number of COVID-19

The ongoing pandemic of Coronavirus disease (COVID-19) emerged in Wuhan, China in the end of 2019. It has already affected more than 300,000 people, with the number of deaths nearing 13000 across the world. As it has been posing a huge threat to global public health, it is of utmost importance to identify the rate at which the disease is spreading. In this study, we propose a time series model to analyze the trend pattern of the incidence of COVID-19 outbreak. We also incorporate information on total or partial lockdown, wherever available, into the model. The model is concise in structure, and using appropriate diagnostic measures, we showed that a time-dependent quadratic trend successfully captures the incidence pattern of the disease. We also estimate the basic reproduction number across different countries, and find that it is consistent except for the United States of America. The above statistical analysis is able to shed light on understanding the trends of the outbreak, and gives insight on what epidemiological stage a region is in. This has the potential to help in prompting policies to address COVID-19 pandemic in different countries.
A Mildly Relativistic Outflow from the Energetic, Fast-rising Blue Optical Transient CSS161010 in a Dwarf Galaxy

We present X-ray and radio observations of the Fast Blue Optical Transient CRTS-CSS161010 J045834−081803 (CSS161010 hereafter) at t = 69–531 days. CSS161010 shows luminous X-ray (Lx ∼ 5 × 1039 erg s−1) and radio (Lν ∼ 1029 erg s−1 Hz−1) emission. The radio emission peaked at ∼100 days post-transient explosion and rapidly decayed. We interpret these observations in the context of synchrotron emission from an expanding blast wave. CSS161010 launched a mildly relativistic outflow with velocity Γβc ≥ 0.55c at ∼100 days. This is faster than the non-relativistic AT 2018cow (Γβc ∼ 0.1c) and closer to ZTF18abvkwla (Γβc ≥ 0.3c at 63 days). The inferred initial kinetic energy of CSS161010 (Ek ≳ 1051 erg) is comparable to that of long gamma-ray bursts, but the ejecta mass that is coupled to the mildly relativistic outflow is significantly larger ( ). This is consistent with the lack of observed γ-rays. The luminous X-rays were produced by a different emission component to the synchrotron radio emission. CSS161010 is located at ∼150 Mpc in a dwarf galaxy with stellar mass M* ∼ 107 M⊙ and specific star formation rate sSFR ∼ 0.3 Gyr−1. This mass is among the lowest inferred for host galaxies of explosive transients from massive stars. Our observations of CSS161010 are consistent with an engine-driven aspherical explosion from a rare evolutionary path of a H-rich stellar progenitor, but we cannot rule out a stellar tidal disruption event on a centrally located intermediate-mass black hole. Regardless of the physical mechanism, CSS161010 establishes the existence of a new class of rare (rate < 0.4% of the local core-collapse supernova rate) H-rich transients that can launch mildly relativistic outflows.
KFNet: Learning Temporal Camera Relocalization Using Kalman Filtering

Temporal camera relocalization estimates the pose with respect to each video frame in sequence, as opposed to one-shot relocalization which focuses on a still image. Even though the time dependency has been taken into account, current temporal relocalization methods still generally underperform the state-of-the-art one-shot approaches in terms of accuracy. In this work, we improve the temporal relocalization method by using a network architecture that incorporates Kalman filtering (KFNet) for online camera relocalization. In particular, KFNet extends the scene coordinate regression problem to the time domain in order to recursively establish 2D and 3D correspondences for the pose determination. The network architecture design and the loss formulation are based on Kalman filtering in the context of Bayesian learning. Extensive experiments on multiple relocalization benchmarks demonstrate the high accuracy of KFNet at the top of both one-shot and temporal relocalization approaches.
COHERENT Collaboration data release from the first detection of coherent elastic neutrino-nucleus scattering on argon

Release of COHERENT collaboration data from the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on argon. This release corresponds with the results of "Analysis A" published in Akimov et al., arXiv:2003.10630 [nucl-ex]. Data is shared in a binned, text-based format representing both "signal" and "backgrounds" along with associated uncertainties such that the included data can be used to perform independent analyses. This document describes the contents of the data release as well as guidance on the use of the data. Included example code in C++ (ROOT) and Python show one possible use of the included data.
Rate optimality of adaptive finite element methods with respect to the overall computational costs

We consider adaptive finite element methods for second-order elliptic PDEs, where the arising discrete systems are not solved exactly. For contractive iterative solvers, we formulate an adaptive algorithm which monitors and steers the adaptive mesh-refinement as well as the inexact solution of the arising discrete systems. We prove that the proposed strategy leads to linear convergence with optimal algebraic rates. Unlike prior works, however, we focus on convergence rates with respect to the overall computational costs. In explicit terms, the proposed adaptive strategy thus guarantees quasi-optimal computational time. In particular, our analysis covers linear problems, where the linear systems are solved by an optimally preconditioned CG method as well as nonlinear problems with strongly monotone nonlinearity which are linearized by the so-called Zarantonello iteration.
ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

The increasing complexity of computing systems places a tremendous burden on optimizing compilers, requiring ever more accurate and aggressive optimizations. Machine learning offers significant benefits for constructing optimization heuristics but there remains a gap between what state-of-the-art methods achieve and the performance of an optimal heuristic. Closing this gap requires improvements in two key areas: a representation that accurately captures the semantics of programs, and a model architecture with sufficient expressiveness to reason about this representation. We introduce ProGraML - Program Graphs for Machine Learning - a novel graph-based program representation using a low level, language agnostic, and portable format; and machine learning models capable of performing complex downstream tasks over these graphs. The ProGraML representation is a directed attributed multigraph that captures control, data, and call relations, and summarizes instruction and operand types and ordering. Message Passing Neural Networks propagate information through this structured representation, enabling whole-program or per-vertex classification tasks. ProGraML provides a general-purpose program representation that equips learnable models to perform the types of program analysis that are fundamental to optimization. To this end, we evaluate the performance of our approach first on a suite of traditional compiler analysis tasks: control flow reachability, dominator trees, data dependencies, variable liveness, and common subexpression detection. On a benchmark dataset of 250k LLVM-IR files covering six source programming languages, ProGraML achieves an average 94.0 F1 score, significantly outperforming the state-of-the-art approaches. We then apply our approach to two high-level tasks - heterogeneous device mapping and program classification - setting new state-of-the-art performance in both.
Spiral magnetic field and vortex clustering in noncentrosymmetric superconductors

We study magnetic response and vortex states in noncentrosymmetric superconductors with $O$ or $T$ symmetry. We microscopically derive Ginzburg-Landau free energy, which exhibits a substantial temperature dependence. For some materials the later leads to a crossover from type-1 superconductivity at elevated temperature to vortex states at lower temperature. Next we show that magnetic field can be solved in terms of complex force free fields. Using that we uncover that magnetic field of a vortex decays in spirals. Due to that intervortex and vortex-boundary interaction becomes non-monotonic with multiple minima. This implies that vortices form bound states with other vortices, antivortices and boundary.
Extension theorem and representation formula in non-axially-symmetric domains for slice regular functions

Slice analysis is a generalization of the theory of holomorphic functions of one complex variable to quaternions. Among the new phenomena which appear in this context, there is the fact that the convergence domain of $f(q)=\Sigma_{n\in\mathbb{N}}(q-p)^{*n} a_n$, given by a $\sigma$-ball $\Sigma(p,r)$, is not open in $\mathbb{H}$ unless $p\in\mathbb{R}$. This motivates us to investigate, in this article, what is a natural topology for slice regular functions. It turns out that the natural topology is the so-called slice topology, which is different from the Euclidean topology and nicely adapts to the slice structure of quaternions. We extend the function theory of slice regular functions to any domains in the slice topology. Many fundamental results in the classical slice analysis for axially symmetric domains fail in our general setting. We can even construct a counterexample to show that a slice regular function in a domain cannot be extended to an axially symmetric domain. In order to provide positive results we need to consider the so-called path-slice functions instead of slice functions. Along this line, we can establish an extension theorem and a representation formula in a slice-domain.
Broad Area Search and Detection of Surface-to-Air Missile Sites Using Spatial Fusion of Component Object Detections From Deep Neural Networks

Here, we demonstrate how deep neural network (DNN) detections of multiple constitutive or component objects that are part of a larger, more complex, and encompassing feature can be spatially fused to improve the search, detection, and retrieval (ranking) of the larger complex feature. First, scores computed from a spatial clustering algorithm are normalized to a reference space so that they are independent of image resolution and DNN input chip size. Then, multiscale DNN detections from various component objects are fused to improve the detection and retrieval of DNN detections of a larger complex feature. We demonstrate the utility of this approach for broad area search and detection of surface-to-air missile (SAM) sites that have a very low occurrence rate (only 16 sites) over a <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>90 000 km<sup>2</sup> study area in SE China. The results demonstrate that spatial fusion of multiscale component-object DNN detections can reduce the detection error rate of <italic>SAM Sites</italic> by <inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>85% while still maintaining a 100% recall. The novel spatial fusion approach demonstrated here can be easily extended to a wide variety of other challenging object search and detection problems in large-scale remote sensing image datasets.
Teachers who make a difference An investigation into teachers as change agents in primary education

This dissertation reports on four consecutive studies, which focus on the identification and deeper understanding of teachers as change agents in primary education. Teachers as change agents can be described as teachers who are able to cope with change and new, often external requirements related to the teaching profession. Today’s schools need teachers who are capable to function as change agents. However, the descriptions in the literature of teachers as change agents are very general and provide only a fragmented picture of these teachers. Moreover, limited research has been done on teachers who fit these descriptions derived from the literature. Little is furthermore known about the extent to which teachers perceive themselves as change agents, how they act accordingly in their educational practice, and by what personal(ity) and contextual factors these are influenced. This dissertation intends to contribute to knowledge of teachers as change agents by addressing these aspects.