Miracle-dz/UniFAR-FTUs huggingface dataset:
The Swift bulge survey: motivation, strategy, and first X-ray results

Very faint X-ray transients (VFXTs) are X-ray transients with peak X-ray luminosities (LX) of L$_X \lesssim 10^{36}$ erg s−1, which are not well understood. We carried out a survey of 16 deg2 of the Galactic Bulge with the Swift Observatory, using short (60 s) exposures, and returning every 2 weeks for 19 epochs in 2017–18 (with a gap from 2017 November to 2018 February, when the Bulge was in sun-constraint). Our main goal was to detect and study VFXT behaviour in the Galactic Bulge across various classes of X-ray sources. In this work, we explain the observing strategy of the survey, compare our results with the expected number of source detections per class, and discuss the constraints from our survey on the Galactic VFXT population. We detected 91 X-ray sources, 25 of which have clearly varied by a factor of at least 10. In total, 45 of these X-ray sources have known counterparts: 17 chromospherically active stars, 12 X-ray binaries, 5 cataclysmic variables (and 4 candidates), 3 symbiotic systems, 2 radio pulsars, 1 active galactic nuclei, and a young star cluster. The other 46 are of previously undetermined nature. We utilize X-ray hardness ratios, searches for optical/infrared counterparts in published catalogues, and flux ratios from quiescence to outburst to constrain the nature of the unknown sources. Of these 46, 7 are newly discovered hard transients, which are likely VFXT X-ray binaries. Furthermore, we find strong new evidence for a symbiotic nature of four sources in our full sample, and new evidence for accretion power in six X-ray sources with optical counterparts. Our findings indicate that a large subset of VXFTs is likely made up of symbiotic systems.
Dynamics and decay of a spherical region of turbulence in free space

Abstract We perform direct numerical simulation and large-eddy simulation of an initially spherical region of turbulence evolving in free space. The computations are performed with a lattice Green's function method, which allows the exact free-space boundary conditions to be imposed on a compact vortical region. Large-eddy simulations are conducted with the stretched vortex subgrid stress model. The initial condition is spherically windowed, isotropic homogeneous incompressible turbulence. We study the spectrum and statistics of the decaying turbulence and compare the results with decaying isotropic turbulence, including cases representing different low-wavenumber behaviour of the energy spectrum (i.e. $k^2$ versus $k^4$). At late times the turbulent sphere expands with both mean radius and integral scale showing similar timewise growth exponents. The low-wavenumber behaviour has little effect on the inertial scales, and we find that decay rates follow the predictions of Saffman (J. Fluid Mech., vol. 27, 1967, pp. 581–593) in both cases, at least until approximately $400$ initial eddy turnover times. The boundary of the spherical region develops intermittency and features ejections of vortex rings. These are shown to occur at the integral scale of the initial turbulence field and are hypothesized to occur due to a local imbalance of impulse on this scale.
Constrained global optimization of functions with low effective dimensionality using multiple random embeddings

We consider the bound-constrained global optimization of functions with low effective dimensionality, that are constant along an (unknown) linear subspace and only vary over the effective (complement) subspace. We aim to implicitly explore the intrinsic low dimensionality of the constrained landscape using feasible random embeddings, in order to understand and improve the scalability of algorithms for the global optimization of these special-structure problems. A reduced subproblem formulation is investigated that solves the original problem over a random low-dimensional subspace subject to affine constraints, so as to preserve feasibility with respect to the given domain. Under reasonable assumptions, we show that the probability that the reduced problem is successful in solving the original, full-dimensional problem is positive. Furthermore, in the case when the objective's effective subspace is aligned with the coordinate axes, we provide an asymptotic bound on this success probability that captures its algebraic dependence on the effective and, surprisingly, ambient dimensions. We then propose X-REGO, a generic algorithmic framework that uses multiple random embeddings, solving the above reduced problem repeatedly, approximately and possibly, adaptively. Using the success probability of the reduced subproblems, we prove that X-REGO converges globally, with probability one, and linearly in the number of embeddings, to an $\epsilon$-neighbourhood of a constrained global minimizer. Our numerical experiments on special structure functions illustrate our theoretical findings and the improved scalability of X-REGO variants when coupled with state-of-the-art global - and even local - optimization solvers for the subproblems.
Stability of \mathrm{HK_\phi} and \mathrm{\PHI_\phi} for symmetric jump processes on metric measure spaces with atoms

Consider a (continuous-time) symmetric Markovian jump process $\{X_t\}$ on a metric measure space $(M, d, \mu)$. If $(M, d, \mu)$ satisfies the \textit{volume doubling} ($\mathrm{VD}$) and \textit{reverse volume doubling} ($\mathrm{RVD}$) properties, then \textit{two-sided heat kernel estimates} ($\mathrm{HK}_\phi$) and the \textit{parabolic Harnack inequality} ($\mathrm{PHI}_\phi$) are both known to be stable under bounded perturbations of the jumping measure. However, if the underlying metric measure space is a graph (or if it contains any atoms), then $\mathrm{RVD}$ does not hold. We define a new volume-growth condition $\QRVD$, which is weaker than $\mathrm{RVD}$ and can be thought of as "$\mathrm{RVD}$ at sufficiently large scales. " We show that $\mathrm{VD} + \QRVD$ is enough to establish the stability of $\mathrm{HK}_\phi$ and $\mathrm{PHI}_\phi$. We show that connected graphs of infinite diameter satisying $\mathrm{VD}$ also satisfy $\QRVD$. As a corollary, $\mathrm{HK}_\phi$ and $\mathrm{PHI}_\phi$ are stable on graphs of infinite diameter satisfying $\mathrm{VD}$.
Convergence and Error Estimates for the Conservative Spectral Method for Fokker-Planck-Landau Equations

Error estimates are rigorously derived for a semi-discrete version of a conservative spectral method for approximating the space-homogeneous Fokker-Planck-Landau (FPL) equation associated to hard potentials. The analysis included shows that the semi-discrete problem has a unique solution with bounded moments. In addition, the derivatives of such a solution up to any order also remain bounded in $L^2$ spaces globally time, under certain conditions. These estimates, combined with control of the spectral projection, are enough to obtain error estimates to the analytical solution and convergence to equilibrium states. It should be noted that this is the first time that an error estimate has been produced for any numerical method which approximates FPL equations associated to any range of potentials.
MonoClothCap: Towards Temporally Coherent Clothing Capture from Monocular RGB Video

We present a method to capture temporally coherent dynamic clothing deformation from a monocular RGB video input. In contrast to the existing literature, our method does not require a pre-scanned personalized mesh template, and thus can be applied to in-the-wild videos. To constrain the output to a valid deformation space, we build statistical deformation models for three types of clothing: T- shirt, short pants and long pants. A differentiable renderer is utilized to align our captured shapes to the input frames by minimizing the difference in both silhouette, segmentation, and texture. We develop a UV texture growing method which expands the visible texture region of the clothing sequentially in order to minimize drift in deformation tracking. We also extract fine-grained wrinkle detail from the input videos by fitting the clothed surface to the normal maps estimated by a convolutional neural network. Our method produces temporally coherent reconstruction of body and clothing from monocular video. We demonstrate successful clothing capture results from a variety of challenging videos. Extensive quantitative experiments demonstrate the effectiveness of our method on metrics including body pose error and surface reconstruction error of the clothing.
Learning Image Labels On-the-fly for Training Robust Classification Models

Current deep learning paradigms largely benefit from the tremendous amount of annotated data. However, the quality of the annotations often varies among labelers. Multi-observer studies have been conducted to study these annotation variances (by labeling the same data for multiple times) and its effects on critical applications like medical image analysis. This process indeed adds an extra burden to the already tedious annotation work that usually requires professional training and expertise in the specific domains. On the other hand, automated annotation methods based on NLP algorithms have recently shown promise as a reasonable alternative, relying on the existing diagnostic reports of those images that are widely available in the clinical system. Compared to human labelers, different algorithms provide labels with varying qualities that are even noisier. In this paper, we show how noisy annotations (e.g., from different algorithm-based labelers) can be utilized together and mutually benefit the learning of classification tasks. Specifically, the concept of attention-on-label is introduced to sample better label sets on-the-fly as the training data. A meta-training based label-sampling module is designed to attend the labels that benefit the model learning the most through additional back-propagation processes. We apply the attention-on-label scheme on the classification task of a synthetic noisy CIFAR-10 dataset to prove the concept, and then demonstrate superior results (3-5% increase on average in multiple disease classification AUCs) on the chest x-ray images from a hospital-scale dataset (MIMIC-CXR) and hand-labeled dataset (OpenI) in comparison to regular training paradigms.
All-dielectric silicon metalens for two-dimensional particle manipulation in optical tweezers

Dynamic control of compact chip-scale contactless manipulation of particles for bioscience applications remains a challenging endeavor, which is restrained by the balance between trapping efficiency and scalable apparatus. Metasurfaces offer the implementation of feasible optical tweezers on a planar platform for shaping the exerted optical force by a microscale-integrated device. Here, we design and experimentally demonstrate a highly efficient silicon-based metalens for two-dimensional optical trapping in the near-infrared. Our metalens concept is based on the Pancharatnam-Berry phase, which enables the device for polarization-sensitive particle manipulation. Our optical trapping setup is capable of adjusting the position of both the metasurface lens and the particle chamber freely in three directions, which offers great freedom for optical trap adjustment and alignment. Two-dimensional (2D) particle manipulation is done with a relatively low numerical aperture metalens ($NA_{ML}=0.6$). We experimentally demonstrate both 2D polarization sensitive drag and drop manipulation of polystyrene particles suspended in water and transfer of angular orbital momentum to these particles with a single tailored beam. Our work may open new possibilities for lab-on-a-chip optical trapping for bioscience applications and micro to nanoscale optical tweezers.
Tensor Programs III: Neural Matrix Laws

In a neural network (NN), \emph{weight matrices} linearly transform inputs into \emph{preactivations} that are then transformed nonlinearly into \emph{activations}. A typical NN interleaves multitudes of such linear and nonlinear transforms to express complex functions. Thus, the (pre-)activations depend on the weights in an intricate manner. We show that, surprisingly, (pre-)activations of a randomly initialized NN become \emph{independent} from the weights as the NN's widths tend to infinity, in the sense of \emph{asymptotic freeness} in random matrix theory. We call this the \emph{Free Independence Principle (FIP)}, which has these consequences: 1) It rigorously justifies the calculation of asymptotic Jacobian singular value distribution of an NN in Pennington et al. [36,37], essential for training ultra-deep NNs [48]. 2) It gives a new justification of \emph{gradient independence assumption} used for calculating the \emph{Neural Tangent Kernel} of a neural network. FIP and these results hold for any neural architecture. We show FIP by proving a Master Theorem for any Tensor Program, as introduced in Yang [50,51], generalizing the Master Theorems proved in those works. As warmup demonstrations of this new Master Theorem, we give new proofs of the semicircle and Marchenko-Pastur laws, which benchmarks our framework against these fundamental mathematical results.
Complex Vehicle Routing with Memory Augmented Neural Networks

Complex real-life routing challenges can be modeled as variations of well-known combinatorial optimization problems. These routing problems have long been studied and are difficult to solve at scale. The particular setting may also make exact formulation difficult. Deep Learning offers an increasingly attractive alternative to traditional solutions, which mainly revolve around the use of various heuristics. Deep Learning may provide solutions which are less time-consuming and of higher quality at large scales, as it generally does not need to generate solutions in an iterative manner, and Deep Learning models have shown a surprising capacity for solving complex tasks in recent years. Here we consider a particular variation of the Capacitated Vehicle Routing (CVRP) problem and investigate the use of Deep Learning models with explicit memory components. Such memory components may help in gaining insight into the model’s decisions as the memory and operations on it can be directly inspected at any time, and may assist in scaling the method to such a size that it becomes viable for industry settings.
DISPATCH: Design Space Exploration of Cyber-Physical Systems

Design of cyber-physical systems (CPSs) is a challenging task that involves searching over a large search space of various CPS configurations and possible values of components composing the system. Hence, there is a need for sample-efficient CPS design space exploration to select the system architecture and component values that meet the target system requirements. We address this challenge by formulating CPS design as a multi-objective optimization problem and propose DISPATCH, a two-step methodology for sample-efficient search over the design space. First, we use a genetic algorithm to search over discrete choices of system component values for architecture search and component selection or only component selection and terminate the algorithm even before meeting the system requirements, thus yielding a coarse design. In the second step, we use an inverse design to search over a continuous space to fine-tune the component values and meet the diverse set of system requirements. We use a neural network as a surrogate function for the inverse design of the system. The neural network, converted into a mixed-integer linear program, is used for active learning to sample component values efficiently in a continuous search space. We illustrate the efficacy of DISPATCH on electrical circuit benchmarks: two-stage and three-stage transimpedence amplifiers. Simulation results show that the proposed methodology improves sample efficiency by 5-14x compared to a prior synthesis method that relies on reinforcement learning. It also synthesizes circuits with the best performance (highest bandwidth/lowest area) compared to designs synthesized using reinforcement learning, Bayesian optimization, or humans.
A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearn

Reinforcement learning is a promising model-free and adaptive controller for demand side management, as part of the future smart grid, at the district level. This paper presents the results of the algorithm that was submitted for the CityLearn Challenge, which was hosted in early 2020 with the aim of designing and tuning a reinforcement learning agent to flatten and smooth the aggregated curve of electrical demand of a district of diverse buildings. The proposed solution secured second place in the challenge using a centralised 'Soft Actor Critic' deep reinforcement learning agent that was able to handle continuous action spaces. The controller was able to achieve an averaged score of 0.967 on the challenge dataset comprising of different buildings and climates. This highlights the potential application of deep reinforcement learning as a plug-and-play style controller, that is capable of handling different climates and a heterogenous building stock, for district demand side management of buildings.
The Linear Lasso: a location model resolution.

We use location model methodology to guide the least squares analysis of the Lasso problem of variable selection and inference. The nuisance parameter is taken to be an indicator for the selection of explanatory variables and the interest parameter is the response variable itself. Recent theory eliminates the nuisance parameter by marginalization on the data space and then uses the resulting distribution for inference concerning the interest parameter. We develop this approach and find: that primary inference is essentially one-dimensional rather than $n$-dimensional; that inference focuses on the response variable itself rather than the least squares estimate (as variables are removed); that first order probabilities are available; that computation is relatively easy; that a scalar marginal model is available; and that ineffective variables can be removed by distributional tilt or shift.
Claraprint: a chord and melody based fingerprint for western classical music cover detection

Cover song detection has been an active field in the Music Information Retrieval (MIR) community during the past decades. Most of the research community focused in solving it for a wide range of music genres with diverse characteristics. Western classical music, a genre heavily based on the recording of "cover songs", or musical works, represents a large heritage, offering immediate application for an efficient fingerprint algorithm. We propose an engineering approach for retrieving a cover song from a reference database thanks to a fingerprint designed for classical musical works. We open a new data set to encourage the scientific community to use it for further researches regarding this genre.
A reduced-precision streaming SpMV architecture for Personalized PageRank on FPGA

Sparse matrix-vector multiplication is often employed in many data-analytic workloads in which low latency and high throughput are more valuable than exact numerical convergence. FPGAs provide quick execution times while offering precise control over the accuracy of the results thanks to reduced-precision fixed-point arithmetic. In this work, we propose a novel streaming implementation of Coordinate Format (COO) sparse matrix-vector multiplication, and study its effectiveness when applied to the Personalized PageR-ank algorithm, a common building block of recommender systems in e-commerce websites and social networks. Our implementation achieves speedups up to 6x over a reference floating-point FPGA architecture and a state-of-the-art multi-threaded CPU implementation on 8 different data-sets, while preserving the numerical fidelity of the results and reaching up to 42x higher energy efficiency compared to the CPU implementation.
Spirals, shadows & precession in HD 100453 – II. The hidden companion

The protoplanetary disc HD 100453 exhibits a curious combination of spirals, shadows and a relative misalignment between the observed outer disc and inferred inner disc. This disc is accompanied by a secondary star on a bound orbit exterior to the disc. Recent observations have suggested there may be an additional low-mass companion residing within the disc inner cavity. In our companion paper the orbit of the secondary was shown to be misaligned by 61 degrees to the plane of the outer disc. Here we investigate the properties of the inner companion and the origin of the misalignment between the inner and outer disc. Using numerical simulations and synthetic observations, we show that the disc structure and kinematics are consistent with a $\lesssim$ 5 Jupiter mass planet located at 15-20au. We find that the disc evolution over around 50 binary orbits (about 10$^5$ yrs) is governed by differential precession and to a lesser extent, the Kozai-Lidov effect. In our proposed model the misalignment observed between the outer and inner disc arises naturally as a result of the misaligned outer companion driving the outer disc to precess more rapidly than the inner disc.
Constraint Programming Algorithms for Route Planning Exploiting Geometrical Information

Problems affecting the transport of people or goods are plentiful in industry and commerce and they also appear to be at the origin of much more complex problems. In recent years, the logistics and transport sector keeps growing supported by technological progress, i.e. companies to be competitive are resorting to innovative technologies aimed at efficiency and effectiveness. This is why companies are increasingly using technologies such as Artificial Intelligence (AI), Blockchain and Internet of Things (IoT). Artificial intelligence, in particular, is often used to solve optimization problems in order to provide users with the most efficient ways to exploit available resources. In this work we present an overview of our current research activities concerning the development of new algorithms, based on CLP techniques, for route planning problems exploiting the geometric information intrinsically present in many of them or in some of their variants. The research so far has focused in particular on the Euclidean Traveling Salesperson Problem (Euclidean TSP) with the aim to exploit the results obtained also to other problems of the same category, such as the Euclidean Vehicle Routing Problem (Euclidean VRP), in the future.
Recent Developments in Factor Models and Applications in Econometric Learning

This article provides a selective overview of the recent developments in factor models and their applications in econometric learning. We focus on the perspective of the low-rank structure of factor models and particularly draw attention to estimating the model from the low-rank recovery point of view. Our survey mainly consists of three parts. The first part is a review of new factor estimations based on modern techniques for recovering low-rank structures of high-dimensional models. The second part discusses statistical inferences of several factor-augmented models and their applications in statistical learning models. The final part summarizes new developments dealing with unbalanced panels from the matrix completion perspective.