Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Hypothesis Approach to Color Constancy

Contemporary approaches frame the color constancy problem as learning camera specific illuminant mappings. While high accuracy can be achieved on camera specific data, these models depend on camera spectral sensitivity and typically exhibit poor generalisation to new devices. Additionally, regression methods produce point estimates that do not explicitly account for potential ambiguities among plausible illuminant solutions, due to the ill-posed nature of the problem. We propose a Bayesian framework that naturally handles color constancy ambiguity via a multi-hypothesis strategy. Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate a set of corrected images. Secondly, we estimate, for each corrected image, the likelihood of the light source being achromatic using a camera-agnostic CNN. Finally, our method explicitly learns a final illumination estimate from the generated posterior probability distribution. Our likelihood estimator learns to answer a camera-agnostic question and thus enables effective multi-camera training by disentangling illuminant estimation from the supervised learning task. We extensively evaluate our proposed approach and additionally set a benchmark for novel sensor generalisation without re-training. Our method provides state-of-the-art accuracy on multiple public datasets (up to 11% median angular error improvement) while maintaining real-time execution.
Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning

According to the World Health Organization(WHO), it is estimated that approximately 1.3 billion people live with some forms of vision impairment globally, of whom 36 million are blind. Due to their disability, engaging these minority into the society is a challenging problem. The recent rise of smart mobile phones provides a new solution by enabling blind users' convenient access to the information and service for understanding the world. Users with vision impairment can adopt the screen reader embedded in the mobile operating systems to read the content of each screen within the app, and use gestures to interact with the phone. However, the prerequisite of using screen readers is that developers have to add natural-language labels to the image-based components when they are developing the app. Unfortunately, more than 77% apps have issues of missing labels, according to our analysis of 10,408 Android apps. Most of these issues are caused by developers' lack of awareness and knowledge in considering the minority. And even if developers want to add the labels to UI components, they may not come up with concise and clear description as most of them are of no visual issues. To overcome these challenges, we develop a deep-learning based model, called Labeldroid, to automatically predict the labels of image-based buttons by learning from large-scale commercial apps in Google Play. The experimental results show thatour model can make accurate predictions and the generated labels are of higher quality than that from real Android developers.
Ascertaining price formation in cryptocurrency markets with DeepLearning

The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using deep learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a deep learning approach to predict the direction of the mid-price changes on the upcoming tick. We monitored live tick-level data from $8$ cryptocurrency pairs and applied both statistical and machine learning techniques to provide a live prediction. We reveal that promising results are possible for cryptocurrencies, and in particular, we achieve a consistent $78\%$ accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs US dollars.
Regularity results for shortest billiard trajectories in convex bodies in $\mathbb{R}^n$

We derive properties of closed billiard trajectories in convex bodies in $\mathbb{R}^n$. Building on techniques introduced by K. and D. Bezdek we establish two regularity results for length minimizing closed billiard trajectories: one for billiard trajectories in general convex bodies, the other for billiard trajectories in the special case of acute convex polytopes. Moreover, we attach particular importance to various examples, also including examples which show the sharpness of the first regularity result. Finally, we show how our results can be used in order to calculate (analytically and by computer) length minimizing closed regular billiard trajectories in convex polytopes.
Longitudinal Evaluation of Open-Source Software Maintainability

We present a longitudinal study on the long-term evolution of maintainability in open-source software. Quality assessment remains at the forefront of both software research and practice, with many models and assessment methodologies proposed and used over time. Some of them helped create and shape standards such as ISO 9126 and 25010, which are well established today. Both describe software quality in terms of characteristics such as reliability, security or maintainability. An important body of research exists linking these characteristics with software metrics, and proposing ways to automate quality assessment by aggregating software metric values into higher-level quality models. We employ the Maintainability Index, technical debt ratio and a maintainability model based on the ARiSA Compendium. Our study covers the entire 18 year development history and all released versions for three complex, open-source applications. We determine the maintainability for each version using the proposed models, we compare obtained results and use manual source code examination to put them into context. We examine the common development patterns of the target applications and study the relation between refactoring and maintainability. Finally, we study the strengths and weaknesses of each maintainability model using manual source code examination as the baseline.
A Rapid Change in X-Ray Variability and a Jet Ejection in the Black Hole Transient MAXI J1820+070

We present Neutron Star Interior Composition Explorer X-ray and Arcminute Microkelvin Imager Large Array radio observations of a rapid hard-to-soft state transition in the black hole X-ray transient MAXI J1820+070. During the transition from the hard state to the soft state a switch between two particular types of quasiperiodic oscillations (QPOs) was seen in the X-ray power density spectra, from type-C to type-B, along with a drop in the strength of the broadband X-ray variability and a brief flare in the 7–12 keV band. Soon after this switch (∼2–2.5 hr) a strong radio flare was observed that corresponded to the launch of superluminal ejecta. Although hints of a connection between QPO transitions and radio flares have been seen in other black hole X-ray transients, our observations constitute the strongest observational evidence to date for a link between the appearance of type-B QPOs and the launch of discrete jet ejections.
Enumerating Hardware-Software Splits with Program Rewriting

A core problem in hardware-software codesign is in the sheer size of the design space. Without a set ISA to constrain the hardware-software interface, the design space explodes. This work presents a strategy for managing the massive hardware-software design space within the domain of machine learning inference workloads and accelerators. We first propose EngineIR, a new language for representing machine learning hardware and software in a single program. Then, using equality graphs -- a data structure from the compilers literature -- we suggest a method for efficiently enumerating the design space by performing rewrites over our representation.
Rethinking Fully Convolutional Networks for the Analysis of Photoluminescence Wafer Images

The manufacturing of light-emitting diodes is a complex semiconductor-manufacturing process, interspersed with different measurements. Among the employed measurements, photoluminescence imaging has several advantages, namely being a non-destructive, fast and thus cost-effective measurement. On a photoluminescence measurement image of an LED wafer, every pixel corresponds to an LED chip's brightness after photo-excitation, revealing chip performance information. However, generating a chip-fine defect map of the LED wafer, based on photoluminescence images, proves challenging for multiple reasons: on the one hand, the measured brightness values vary from image to image, in addition to local spots of differing brightness. On the other hand, certain defect structures may assume multiple shapes, sizes and brightness gradients, where salient brightness values may correspond to defective LED chips, measurement artefacts or non-defective structures. In this work, we revisit the creation of chip-fine defect maps using fully convolutional networks and show that the problem of segmenting objects at multiple scales can be improved by the incorporation of densely connected convolutional blocks and atrous spatial pyramid pooling modules. We also share implementation details and our experiences with training networks with small datasets of measurement images. The proposed architecture significantly improves the segmentation accuracy of highly variable defect structures over our previous version.
Permutation Invariant Graph Generation via Score-Based Generative Modeling

Learning generative models for graph-structured data is challenging because graphs are discrete, combinatorial, and the underlying data distribution is invariant to the ordering of nodes. However, most of the existing generative models for graphs are not invariant to the chosen ordering, which might lead to an undesirable bias in the learned distribution. To address this difficulty, we propose a permutation invariant approach to modeling graphs, using the recent framework of score-based generative modeling. In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph (a.k.a., the score function). This permutation equivariant model of gradients implicitly defines a permutation invariant distribution for graphs. We train this graph neural network with score matching and sample from it with annealed Langevin dynamics. In our experiments, we first demonstrate the capacity of this new architecture in learning discrete graph algorithms. For graph generation, we find that our learning approach achieves better or comparable results to existing models on benchmark datasets.
THE KIM–PILLAY THEOREM FOR ABSTRACT ELEMENTARY CATEGORIES

Abstract We introduce the framework of AECats (abstract elementary categories), generalizing both the category of models of some first-order theory and the category of subsets of models. Any AEC and any compact abstract theory (“cat”, as introduced by Ben-Yaacov) forms an AECat. In particular, we find applications in positive logic and continuous logic: the category of (subsets of) models of a positive or continuous theory is an AECat. The Kim–Pillay theorem for first-order logic characterizes simple theories by the properties dividing independence has. We prove a version of the Kim–Pillay theorem for AECats with the amalgamation property, generalizing the first-order version and existing versions for positive logic.
Upper Confidence Primal-Dual Optimization: Stochastically Constrained Markov Decision Processes with Adversarial Losses and Unknown Transitions

We consider online learning for episodic stochastically constrained Markov decision processes (CMDP), which plays a central role in ensuring the safety of reinforcement learning. Here the loss function can vary arbitrarily across the episodes, whereas both the loss received and the budget consumption are revealed at the end of each episode. Previous works solve this problem under the restrictive assumption that the transition model of the Markov decision processes (MDP) is known a priori and establish regret bounds that depend polynomially on the cardinalities of the state space $\mathcal{S}$ and the action space $\mathcal{A}$. In this work, we propose a new \emph{upper confidence primal-dual} algorithm, which only requires the trajectories sampled from the transition model. In particular, we prove that the proposed algorithm achieves $\widetilde{\mathcal{O}}(L|\mathcal{S}|\sqrt{|\mathcal{A}|T})$ upper bounds of both the regret and the constraint violation, where $L$ is the length of each episode. Our analysis incorporates a new high-probability drift analysis of Lagrange multiplier processes into the celebrated regret analysis of upper confidence reinforcement learning, which demonstrates the power of "optimism in the face of uncertainty" in constrained online learning.
CCD VI time-series of the extremely metal-poor globular cluster M92: revisiting its variable star population

We present an analysis of \emph{VI} CCD time-series photometry of the Oo II type globular cluster M92. The variable star population of the cluster is studied with the aim of revising their classifications, identifications, frequency spectra and to select indicators of the parental cluster metallicity and distance. The Fourier decomposition of RR Lyrae light curves lead to the estimation of mean [Fe/H]$_{\rm spec}=-2.20\pm 0.18$, and distance of $8.3\pm0.2$ kpc. Four new variables are reported; one RRd (V40), a multimode SX Phe (V41), a SR (V42) and one RRc (F1) that is most likely not a cluster member. The AC nature of V7 is confirmed. The double mode nature of the RRc star V11 is not confirmed and its amplitude modulations are most likely due to the Blazhko effect. Two modes are found in the known RRc variable V13. It is argued that the variable V30, previously classified as RRab is, in fact, an BL Her-type star not belonging to the cluster. Making use of the $Gaia$-DR2 proper motions, we identified 5012 stars in the field of the cluster, that are very likely cluster members, and for which we possess photometry, enabling the production of a refined Colour-Magnitude Diagram. This also allowed us to identify a few variable stars that do not belong to the cluster. The RR Lyrae pulsation modes on the HB are cleanly separated by the first overtone red edge, a common feature in all Oo II type clusters.
Code Renewability for Native Software Protection

Software protection aims at safeguarding assets embedded in software by preventing and delaying reverse engineering and tampering attacks. This article presents an architecture and supporting tool flow to renew parts of native applications dynamically. Renewed and diversified code and data belonging to either the original application or to linked-in protections are delivered from a secure server to a client on demand. This results in frequent changes to the software components when they are under attack, thus making attacks harder. By supporting various forms of diversification and renewability, novel protection combinations become available and existing combinations become stronger. The prototype implementation is evaluated on several industrial use cases.
Double X/Peanut structures in barred galaxies – insights from an N-body simulation

Boxy, peanut- or X-shaped "bulges" are observed in a large fraction of barred galaxies viewed in, or close to, edge-on projection, as well as in the Milky Way. They are the product of dynamical instabilities occurring in stellar bars, which cause the latter to buckle and thicken vertically. Recent studies have found nearby galaxies that harbour two such features arising at different radial scales, in a nested configuration. In this paper we explore the formation of such double peanuts, using a collisionless N-body simulation of a pure disc evolving in isolation within a live dark matter halo, which we analyse in a completely analogous way to observations of real galaxies. In the simulation we find a stable double configuration consisting of two X/peanut structures associated to the same galactic bar - rotating with the same pattern speed - but with different morphology, formation time, and evolution. The inner, conventional peanut-shaped structure forms early via the buckling of the bar, and experiences little evolution once it stabilises. This feature is consistent in terms of size, strength and morphology, with peanut structures observed in nearby galaxies. The outer structure, however, displays a strong X, or "bow-tie", morphology. It forms just after the inner peanut, and gradually extends in time (within 1 to 1.5 Gyr) to almost the end of the bar, a radial scale where ansae occur. We conclude that, although both structures form, and are dynamically coupled to, the same bar, they are supported by inherently different mechanisms.
GPU-Accelerated Mobile Multi-view Style Transfer

An estimated 60% of smartphones sold in 2018 were equipped with multiple rear cameras, enabling a wide variety of 3D-enabled applications such as 3D Photos. The success of 3D Photo platforms (Facebook 3D Photo, Holopix, etc) depend on a steady influx of user generated content. These platforms must provide simple image manipulation tools to facilitate content creation, akin to traditional photo platforms. Artistic neural style transfer, propelled by recent advancements in GPU technology, is one such tool for enhancing traditional photos. However, naively extrapolating single-view neural style transfer to the multi-view scenario produces visually inconsistent results and is prohibitively slow on mobile devices. We present a GPU-accelerated multi-view style transfer pipeline which enforces style consistency between views with on-demand performance on mobile platforms. Our pipeline is modular and creates high quality depth and parallax effects from a stereoscopic image pair.
A Dynamic Epidemic Model for Rumor Spread in Multiplex Network with Numerical Analysis

This paper focuses on studying and understanding of stochastic dynamics in population composition when the population is subject to rumor spreading. We undertake the study by first developing an individual Susceptible-Exposed-Infectious-Removed (iSEIR) model, an extension of the SEIR model, for summarizing rumor-spreading behaviors of interacting groups in the population. With this iSEIR model, the interacting groups may be regarded as nodes in a multiplex network. Then various properties of the dynamic behaviors of the interacting groups in rumor spreading can be drawn from samples of the multiplex network. The samples are simulated based on the iSEIR model with different settings in terms of population scale, population distribution and transfer rate. Results from the simulation study show that effective control of rumor spreading in the multiplex network entails an efficient management on information flow, which may be achieved by setting appropriate immunization and spreading thresholds in individual behavior dynamics. Under the proposed iSEIR model we also have derived a steady-state result, named the "supersaturation phenomenon", when the rumor spreading process becomes equilibrium, which may help us to make the optimal or better control of information flow in the practice.
Approximate Cross-validation: Guarantees for Model Assessment and Selection

Cross-validation (CV) is a popular approach for assessing and selecting predictive models. However, when the number of folds is large, CV suffers from a need to repeatedly refit a learning procedure on a large number of training datasets. Recent work in empirical risk minimization (ERM) approximates the expensive refitting with a single Newton step warm-started from the full training set optimizer. While this can greatly reduce runtime, several open questions remain including whether these approximations lead to faithful model selection and whether they are suitable for non-smooth objectives. We address these questions with three main contributions: (i) we provide uniform non-asymptotic, deterministic model assessment guarantees for approximate CV; (ii) we show that (roughly) the same conditions also guarantee model selection performance comparable to CV; (iii) we provide a proximal Newton extension of the approximate CV framework for non-smooth prediction problems and develop improved assessment guarantees for problems such as l1-regularized ERM.
Deep Learning on Radar Centric 3D Object Detection

Even though many existing 3D object detection algorithms rely mostly on camera and LiDAR, camera and LiDAR are prone to be affected by harsh weather and lighting conditions. On the other hand, radar is resistant to such conditions. However, research has found only recently to apply deep neural networks on radar data. In this paper, we introduce a deep learning approach to 3D object detection with radar only. To the best of our knowledge, we are the first ones to demonstrate a deep learning-based 3D object detection model with radar only that was trained on the public radar dataset. To overcome the lack of radar labeled data, we propose a novel way of making use of abundant LiDAR data by transforming it into radar-like point cloud data and aggressive radar augmentation techniques.
Dimension-free convergence rates for gradient Langevin dynamics in RKHS

Gradient Langevin dynamics (GLD) and stochastic GLD (SGLD) have attracted considerable attention lately, as a way to provide convergence guarantees in a non-convex setting. However, the known rates grow exponentially with the dimension of the space. In this work, we provide a convergence analysis of GLD and SGLD when the optimization space is an infinite dimensional Hilbert space. More precisely, we derive non-asymptotic, dimension-free convergence rates for GLD/SGLD when performing regularized non-convex optimization in a reproducing kernel Hilbert space. Amongst others, the convergence analysis relies on the properties of a stochastic differential equation, its discrete time Galerkin approximation and the geometric ergodicity of the associated Markov chains.
A new short proof of regularity for local weak solutions for a certain class of singular parabolic equations

We shall establish the interior Holder continuity for locally bounded weak solutions to a class of parabolic singular equations whose prototypes are \begin{equation} u_t= \nabla \cdot \bigg( |\nabla u|^{p-2} \nabla u \bigg), \quad \text{ for } \quad 1<p<2, \end{equation} and \begin{equation} u_{t}- \nabla \cdot ( u^{m-1} | \nabla u |^{p-2} \nabla u ) =0 , \quad \text{for} \quad m+p>3-\frac{p}{N}, \end{equation} via a new and simplified proof using recent techniques on expansion of positivity and $L^{1}$-Harnack estimates.
On Abel-Jacobi maps of moduli of parabolic bundles over a curve

Abstract The Abel-Jacobi maps generalize the usual Jacobi map for curves to the case of nonsingular complex projective varieties. Here, we study certain Abel-Jacobi maps on the moduli space of parabolic stable bundles of fixed rank, determinant and generic weight over a nonsingular complex projective curve. We prove that the maps are split surjections.