Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic gravitational-wave background in quantum gravity

Among all cosmological quantum-gravity or quantum-gravity-inspired scenarios, only very few predict a blue-tilted primordial tensor spectrum. We explore five of them and check whether they can generate a stochastic gravitational-wave background detectable by present and future interferometers: non-local quantum gravity, string-gas cosmology, new ekpyrotic scenario, Brandenberger-Ho non-commutative inflation and multi-fractional spacetimes. We show that non-local quantum gravity is unobservable, while all the other models can reach the strain sensitivity of DECIGO but not that of LIGO-Virgo-KAGRA, LISA or Einstein Telescope. Other quantum-gravity models with red-tilted spectra (most loop quantum cosmologies) or with exceptionally tiny quantum corrections (Wheeler-DeWitt quantum cosmology) are found to be non-detectable.
Cross-modal registration using point clouds and graph-matching in the context of correlative microscopies

Correlative microscopy aims at combining two or more modalities to gain more information than the one provided by one modality on the same biological structure. Registration is needed at different steps of correlative microscopies workflows. Biologists want to select the image content used for registration not to introduce bias in the correlation of unknown structures. Intensity-based methods might not allow this selection and might be too slow when the images are very large. We propose an approach based on point clouds created from selected content by the biologist. These point clouds may be prone to big differences in densities but also missing parts and outliers. In this paper we present a method of registration for point clouds based on graph building and graph matching, and compare the method to iterative closest point based methods.
A 3 − 2 /b ( G ) - APPROXIMATION ALGORITHM FOR THE GRAPH BURNING PROBLEM

The graph burning problem is an NP-Hard optimization problem that models the sequential spread of information over a network. Due to its nature, this problem has been approached through approximation algorithms and heuristics. This paper introduces a new 3 − 2 /b ( G ) -approximation algorithm for the graph burning problem over general graphs, where b ( G ) ∈ Z + is the size of an optimal solution. This algorithm, named burning greedy permutation algorithm, is based on greedy permutations and is easy to implement. According to a brief empirical analysis, the proposed algorithm generates competitive solutions, some of which are better than the best previously known
A branch-and-bound algorithm based on NSGAII for multi-objective mixed integer nonlinear optimization problems

Solving Multi-Objective Mixed Integer NonLinear Programming (MO-MINLP) problems is a point of interest for many researchers as they appear in several real-world applications, especially in the mechanical engineering field. Many researchers have proposed using hybrids of metaheuristics with mono-objective branch and bound. Others have suggested using heuristics with Multi-Criteria Branch and Bound (MCBB). A general hybrid approach is proposed based on MCBB and Non-dominated Sorting Genetic Algorithm 2 (NSGAII) to enhance the approximated Pareto front of MO-MINLP problems. A computational experiment based on statistical assessment is presented to compare the performance of the proposed algorithm (BnB-NSGAII) with NSGAII using well-known metrics from the literature. To evaluate the computational efficiency, a new metric, the Investment Ratio (IR), is proposed that relates the quality of solution to the consumed effort. Experimental results on five real-world mechanical engineering problems and two mathematical ones indicate that BnB-NSGAII could be a competitive alternative for solving MO-MINLP problems.
Clustering of CODEX clusters

Context. The clustering of galaxy clusters links the spatial nonuniformity of dark matter halos to the growth of the primordial spectrum of perturbations. The amplitude of the clustering signal is widely used to estimate the halo mass of astrophysical objects. The advent of cluster mass calibrations enables using clustering in cosmological studies. Aims. We analyze the autocorrelation function of a large contiguous sample of galaxy clusters, the Constrain Dark Energy with X-ray (CODEX) sample, in which we take particular care of cluster definition. These clusters were X-ray selected using the ROentgen SATellite All-Sky Survey and then identified as galaxy clusters using the code redMaPPer run on the photometry of the Sloan Digital Sky Survey. We develop methods for precisely accounting for the sample selection effects on the clustering and demonstrate their robustness using numerical simulations. Methods. Using the clean CODEX sample, which was obtained by applying a redshift-dependent richness selection, we computed the two-point autocorrelation function of galaxy clusters in the 0.1 < z < 0.3 and 0.3 < z < 0.5 redshift bins. We compared the bias in the measured correlation function with values obtained in numerical simulations using a similar cluster mass range. Results. By fitting a power law, we measured a correlation length r0 = 18.7 ± 1.1 and slope γ = 1.98 ± 0.14 for the correlation function in the full redshift range. By fixing the other cosmological parameters to their nine-year Wilkinson Microwave Anisotropy Probe values, we reproduced the observed shape of the correlation function under the following cosmological conditions: Ωm0 = 0.22−0.03+0.04 and S8 = σ8(Ωm0/0.3)0.5 = 0.85−0.08+0.10 with estimated additional systematic errors of σΩm0 = 0.02 and σS8 = 0.20. We illustrate the complementarity of clustering constraints by combining them with CODEX cosmological constraints based on the X-ray luminosity function, deriving Ωm0 = 0.25 ± 0.01 and σ8 = 0.81−0.02+0.01 with an estimated additional systematic error of σΩm0 = 0.07 and σσ8 = 0.04. The mass calibration and statistical quality of the mass tracers are the dominant source of uncertainty.
SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge

Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain. The SeMantic AnsweR Type prediction task (SMART) was part of ISWC 2020 challenges. Question type and answer type prediction can play a key role in knowledge base question answering systems providing insights that are helpful to generate correct queries or rank the answer candidates. More concretely, given a question in natural language, the task of SMART challenge is, to predict the answer type using a target ontology (e.g., DBpedia or Wikidata).
The splashback boundary of haloes in hydrodynamic simulations

The splashback radius, $R_{\rm sp}$, is a physically motivated halo boundary that separates infalling and collapsed matter of haloes. We study $R_{\rm sp}$ in the hydrodynamic and dark matter only IllustrisTNG simulations. The most commonly adopted signature of $R_{\rm sp}$ is the radius at which the radial density profiles are steepest. Therefore, we explicitly optimise our density profile fit to the profile slope and find that this leads to a $\sim5\%$ larger radius compared to other optimisations. We calculate $R_{\rm sp}$ for haloes with masses between $10^{13-15}{\rm M}_{\odot}$ as a function of halo mass, accretion rate and redshift. $R_{\rm sp}$ decreases with mass and with redshift for haloes of similar $M_{\rm200m}$ in agreement with previous work. We also find that $R_{\rm sp}/R_{\rm200m}$ decreases with halo accretion rate. We apply our analysis to dark matter, gas and satellite galaxies associated with haloes to investigate the observational potential of $R_{\rm sp}$. The radius of steepest slope in gas profiles is consistently smaller than the value calculated from dark matter profiles. The steepest slope in galaxy profiles, which are often used in observations, tends to agree with dark matter profiles but is lower for less massive haloes. We compare $R_{\rm sp}$ in hydrodynamic and N-body dark matter only simulations and do not find a significant difference caused by the addition of baryonic physics. Thus, results from dark matter only simulations should be applicable to realistic haloes.
Charging a quantum battery with linear feedback control

Energy storage is a basic physical process with many applications. When considering this task at the quantum scale, it becomes important to optimise the non-equilibrium dynamics of energy transfer to the storage device or battery. Here, we tackle this problem using the methods of quantum feedback control. Specifically, we study the deposition of energy into a quantum battery via an auxiliary charger. The latter is a driven-dissipative two-level system subjected to a homodyne measurement whose output signal is fed back linearly into the driving field amplitude. We explore two different control strategies, aiming to stabilise either populations or quantum coherences in the state of the charger. In both cases, linear feedback is shown to counteract the randomising influence of environmental noise and allow for stable and effective battery charging. We analyse the effect of realistic control imprecisions, demonstrating that this good performance survives inefficient measurements and small feedback delays. Our results highlight the potential of continuous feedback for the control of energetic quantities in the quantum regime.
A Multi-Intersection Vehicular Cooperative Control Based on End-Edge-Cloud Computing

Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control within a small area around an unsignalized intersection. This paper expands the control domain to a large area with multiple intersections. In particular, a Multi-intersection Vehicular Cooperative Control (MiVeCC) is proposed to enable cooperation among vehicles in a large area with multiple unsignalized intersections. Firstly, a vehicular end-edge-cloud computing framework is proposed to facilitate end-edge-cloud vertical cooperation and horizontal cooperation among vehicles. Under the framework, a two-stage reinforcement learning is implemented to obtain the optimal policy for vehicle control. To support RL in the proposed framework, a multi-vehicle state representation method and a safety-oriented value representation method are designed. The former structures the collected vehicle information, and the latter evaluates the traffic condition. A multi-intersection simulation platform is developed to evaluate the proposed scheme. Simulation results show that the proposed MiVeCC can improve travel efficiency at multiple intersections by up to 4.59 times without collision compared with benchmark methods.
Measuring Young Stars in Space and Time. I. The Photometric Catalog and Extinction Properties of N44

In order to better understand the role of high-mass stellar feedback in regulating star formation in giant molecular clouds, we carried out a Hubble Space Telescope (HST) Treasury Program Measuring Young Stars in Space and Time (MYSST) targeting the star-forming complex N44 in the Large Magellanic Cloud (LMC). Using the F555W and F814W broadband filters of both the ACS and WFC3/UVIS, we built a photometric catalog of 461,684 stars down to m F555W ≃ 29 mag and m F814W ≃ 28 mag, corresponding to the magnitude of an unreddened 1 Myr pre-main-sequence star of ≈ 0.09 M ☉ at the LMC distance. In this first paper we describe the observing strategy of MYSST and the data reduction procedure and present the photometric catalog. We identify multiple young stellar populations tracing the gaseous rim of N44's superbubble, together with various contaminants belonging to the LMC field population. We also determine the reddening properties from the slope of the elongated red clump (RC) feature by applying the machine-learning algorithm RANSAC, and we select a set of upper-main-sequence stars as primary probes to build an extinction map, deriving a relatively modest median extinction A F555W ≃ 0.77 mag. The same procedure applied to the RC provides A F555W ≃ 0.68 mag.
Byzantine Eventual Consistency and the Fundamental Limits of Peer-to-Peer Databases

Sybil attacks, in which a large number of adversary-controlled nodes join a network, are a concern for many peer-to-peer database systems, necessitating expensive countermeasures such as proof-of-work. However, there is a category of database applications that are, by design, immune to Sybil attacks because they can tolerate arbitrary numbers of Byzantine-faulty nodes. In this paper, we characterize this category of applications using a consistency model we call Byzantine Eventual Consistency (BEC). We introduce an algorithm that guarantees BEC based on Byzantine causal broadcast, prove its correctness, and demonstrate near-optimal performance in a prototype implementation.
White dwarf–main sequence star collisions from wide triples in the field

Multiple star systems interact strongly with Galactic field stars when the outer semi-major axis of a triple or multiple star is > 10 3 AU. Stable triples composed of two white-dwarfs (WD) and a low mass main sequence (MS) star in a wide outer orbit can thus be destabilized by gravitational interactions with random field stars. Such interactions excite the eccentricity of the distant third star sufficiently so that it begins to interact significantly with the inner binary. When this occurs the triple undergoes multiple binary-single resonant encounters. These encounters may result either in a collision between the nondegenerate component and a WD, or the breakup of the triple into a compact binary and a third object which is ejected. The compact binary can be either a MS-WD pair which survives, or collides, or a double WD which may inspiral through gravitational wave emission. We calculate the collision rate between a MS and WD star, and the merger rate of double WDs. Additionally, we describe the prospects of detectability of such a collision, which may resemble a sub-luminous SN event.
Particle image velocimetry analysis with simultaneous uncertainty quantification using Bayesian neural networks

Particle image velocimetry (PIV) is an effective tool in experimental fluid mechanics for extracting flow fields from images. Recently, convolutional neural networks (CNNs) have been used to perform PIV analysis with accuracy on par with classical methods. Here we extend the use of CNNs to analyze PIV data while providing simultaneous uncertainty quantification on the inferred flow field. The method we apply in this paper is a Bayesian convolutional neural network (BCNN) which learns distributions of the CNN weights through variational Bayes. In order to demonstrate the utility of BCNNs for the PIV task, we compare the performance of three distinct BCNN models with simple architectures. The first network estimates flow velocity from image interrogation regions only. Our second model learns to infer velocity from both the image interrogation regions and interrogation region cross-correlation maps. Finally, our best performing network infers velocities from interrogation region cross-correlation maps only. We find that BCNNs using interrogation region cross-correlation maps as inputs perform better than those using interrogation windows only as inputs and discuss reasons why this may be the case. Additionally, we test the best performing BCNN on a full synthetic test image pair and a real image pair from the 1st International PIV Challenge. We show that ∼98% of true particle displacements from the full synthetic image pair can be captured within the BCNN’s 95% confidence intervals, and that the BCNN’s performance on the real image pair is quantitatively similar to that of algorithms tested in the 1st International PIV Challenge. Finally, we show that BCNNs can be generalized to be used with multi-pass PIV algorithms with a moderate loss in accuracy, which may be overcome by future work on finetuning and training schemes. To our knowledge, this is the first use of Bayesian neural networks to perform PIV.
One-Pixel Attack Deceives Automatic Detection of Breast Cancer

In this article we demonstrate that a state-of-the-art machine learning model predicting whether a whole slide image contains mitosis can be fooled by changing just a single pixel in the input image. Computer vision and machine learning can be used to automate various tasks in cancer diagnostic and detection. If an attacker can manipulate the automated processing, the results can be devastating and in the worst case lead to wrong diagnostic and treatments. In this research one-pixel attack is demonstrated in a real-life scenario with a real tumor dataset. The results indicate that a minor one-pixel modification of a whole slide image under analysis can affect the diagnosis. The attack poses a threat from the cyber security perspective: the one-pixel method can be used as an attack vector by a motivated attacker.
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification

Recent advances in person re-identification (ReID) obtain impressive accuracy in the supervised and unsupervised learning settings. However, most of the existing methods need to train a new model for a new domain by accessing data. Due to public privacy, the new domain data are not always accessible, leading to a limited applicability of these methods. In this paper, we study the problem of multi-source domain generalization in ReID, which aims to learn a model that can perform well on unseen domains with only several labeled source domains. To address this problem, we propose the Memory-based Multi-Source Meta-Learning (M3L) framework to train a generalizable model for unseen domains. Specifically, a meta-learning strategy is introduced to simulate the train-test process of domain generalization for learning more generalizable models. To overcome the unstable meta-optimization caused by the parametric classifier, we propose a memory-based identification loss that is non-parametric and harmonizes with meta-learning. We also present a meta batch normalization layer (MetaBN) to diversify meta-test features, further establishing the advantage of meta-learning. Experiments demonstrate that our M3L can effectively enhance the generalization ability of the model for unseen domains and can outperform the state-of-the-art methods on four large-scale ReID datasets.
Convergence analysis of a fully discrete energy-stable numerical scheme for the Q-tensor flow of liquid crystals

We present a fully discrete convergent finite difference scheme for the Q-tensor flow of liquid crystals based on the energy-stable semi-discrete scheme by Zhao, Yang, Gong, and Wang (Comput. Methods Appl. Mech. Engrg. 2017). We prove stability properties of the scheme and show convergence to weak solutions of the Q-tensor flow equations. We demonstrate the performance of the scheme in numerical simulations.
Weakly-Supervised Arbitrary-Shaped Text Detection with Expectation-Maximization Algorithm

Arbitrary-shaped text detection is an important and challenging task in computer vision. Most existing methods require heavy data labeling efforts to produce polygon-level text region labels for supervised training. In order to reduce the cost in data labeling, we study weakly-supervised arbitrary-shaped text detection for combining various weak supervision forms (e.g., image-level tags, coarse, loose and tight bounding boxes), which are far easier for annotation. We propose an Expectation-Maximization (EM) based weakly-supervised learning framework to train an accurate arbitrary-shaped text detector using only a small amount of polygon-level annotated data combined with a large amount of weakly annotated data. Meanwhile, we propose a contour-based arbitrary-shaped text detector, which is suitable for incorporating weakly-supervised learning. Extensive experiments on three arbitrary-shaped text benchmarks (CTW1500, Total-Text and ICDAR-ArT) show that (1) using only 10% strongly annotated data and 90% weakly annotated data, our method yields comparable performance to state-of-the-art methods, (2) with 100% strongly annotated data, our method outperforms existing methods on all three benchmarks. We will make the weakly annotated datasets publicly available in the future.
On the Northcott property for special values of L-functions

We propose an investigation on the Northcott, Bogomolov and Lehmer properties for special values of $L$-functions. We first introduce an axiomatic approach to these three properties. We then focus on the Northcott property for special values of $L$-functions. We prove that such a property holds for the special value at zero of Dedekind zeta functions of number fields. In the case of $L$-functions of pure motives, we prove a Northcott property for special values located at the left of the critical strip, assuming the validity of the functional equation.
Debunking Generalization Error or: How I Learned to Stop Worrying and Love My Training Set.

We aim to determine some physical properties of distant galaxies (for example, stellar mass, star formation history, or chemical enrichment history) from their observed spectra, using supervised machine learning methods. We know that different astrophysical processes leave their imprint in various regions of the spectra with characteristic signatures. Unfortunately, identifying a training set for this problem is very hard, because labels are not readily available - we have no way of knowing the true history of how galaxies have formed. One possible approach to this problem is to train machine learning models on state-of-the-art cosmological simulations. However, when algorithms are trained on the simulations, it is unclear how well they will perform once applied to real data. In this paper, we attempt to model the generalization error as a function of an appropriate measure of distance between the source domain and the application domain. Our goal is to obtain a reliable estimate of how a model trained on simulations might behave on data.
Fast Class-Wise Updating for Online Hashing

Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this end, most existing works exploit this problem under a supervised setting, i.e., using class labels to boost the hashing performance, which suffers from the defects in both adaptivity and efficiency: First, large amounts of training batches are required to learn up-to-date hash functions, which leads to poor online adaptivity. Second, the training is time-consuming, which contradicts with the core need of online learning. In this paper, a novel supervised online hashing scheme, termed Fast Class-wise Updating for Online Hashing (FCOH), is proposed to address the above two challenges by introducing a novel and efficient inner product operation. To achieve fast online adaptivity, a class-wise updating method is developed to decompose the binary code learning and alternatively renew the hash functions in a class-wise fashion, which well addresses the burden on large amounts of training batches. Quantitatively, such a decomposition further leads to at least 75 percent storage saving. To further achieve online efficiency, we propose a semi-relaxation optimization, which accelerates the online training by treating different binary constraints independently. Without additional constraints and variables, the time complexity is significantly reduced. Such a scheme is also quantitatively shown to well preserve past information during updating hashing functions. We have quantitatively demonstrated that the collective effort of class-wise updating and semi-relaxation optimization provides a superior performance comparing to various state-of-the-art methods, which is verified through extensive experiments on three widely-used datasets.