Miracle-dz/UniFAR-FTUs huggingface dataset:
A physics-aware machine to predict extreme events in turbulence

We propose a physics-aware machine learning method to time-accurately predict extreme events in a turbulent flow. The method combines two radically different approaches: empirical modelling based on reservoir computing, which learns the chaotic dynamics from data only, and physical modelling based on conservation laws. We show that the combination of the two approaches is able to predict the occurrence and amplitude of extreme events in the self-sustaining process in turbulence-the abrupt transitions from turbulent to quasi-laminar states-which cannot be achieved by using either approach separately. This opens up new possibilities for enhancing synergistically data-driven methods with physical knowledge for the accurate prediction of extreme events in chaotic dynamical systems.
Minimal Solutions for Relative Pose With a Single Affine Correspondence

In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases. It is shown, that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and a least-squares solution, a closed-form solution for unknown focal length and the case of a known vertical direction. These algorithms can be used efficiently for outlier detection within a RANSAC loop and for initial motion estimation. All the methods are evaluated on both synthetic data and real-world datasets from the KITTI benchmark. The experimental results demonstrate that our methods outperform comparable state-of-the-art methods in accuracy with the benefit of a reduced number of needed RANSAC iterations.
A Unified Framework for X-shaped Radio Galaxies

We propose a radically different picture for X-shaped radio galaxies compared with existing models as transition objects between cold-mode accreting, low-spinning retrograde black holes and low-spinning prograde black holes. This model explains their smaller average black hole masses; their general aversion for cluster compared with isolated environments; the negligible difference in spectral index between primary and secondary jets, despite a time difference in their formation; their absence among the most powerful radio quasars and radio galaxies; and their connection to the elusive Fanaroff–Riley I quasar class, among others. The key to their formation is cold gas accretion onto Schwarzschild black holes.
End-Point Detection with State Transition Model based on Chunk-Wise Classification

A state transition model (STM) based on chunk-wise classification was proposed for end-point detection (EPD). In general, EPD is developed using frame-wise voice activity detection (VAD) with additional STM, in which the state transition is conducted based on VAD's frame-level decision (speech or non-speech). However, VAD errors frequently occur in noisy environments, even though we use state-of-the-art deep neural network based VAD, which causes the undesired state transition of STM. In this work, to build robust STM, a state transition is conducted based on chunk-wise classification as EPD does not need to be conducted in frame-level. The chunk consists of multiple frames and the classification of chunk between speech and non-speech is done by aggregating the decisions of VAD for multiple frames, so that some undesired VAD errors in a chunk can be smoothed by other correct VAD decisions. Finally, the model was evaluated in both qualitative and quantitative measures including phone error rate.
Two Way Adversarial Unsupervised Word Translation

Word translation is a problem in machine translation that seeks to build models that recover word level correspondence between languages. Recent approaches to this problem have shown that word translation models can learned with very small seeding dictionaries, and even without any starting supervision. In this paper we propose a method to jointly find translations between a pair of languages. Not only does our method learn translations in both directions but it improves accuracy of those translations over past methods.
Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation

Motivated by their broad applications in reinforcement learning, we study the linear two-time-scale stochastic approximation, an iterative method using two different step sizes for finding the solutions of a system of two equations. Our main focus is to characterize the finite-time complexity of this method under time-varying step sizes and Markovian noise. In particular, we show that the mean square errors of the variables generated by the method converge to zero at a sublinear rate $\Ocal(k^{2/3})$, where $k$ is the number of iterations. We then improve the performance of this method by considering the restarting scheme, where we restart the algorithm after every predetermined number of iterations. We show that using this restarting method the complexity of the algorithm under time-varying step sizes is as good as the one using constant step sizes, but still achieving an exact converge to the desired solution. Moreover, the restarting scheme also helps to prevent the step sizes from getting too small, which is useful for the practical implementation of the linear two-time-scale stochastic approximation.
Analysis of the Hands in Egocentric Vision: A Survey

Egocentric vision (a.k.a. first-person vision–FPV) applications have thrived over the past few years, thanks to the availability of affordable wearable cameras and large annotated datasets. The position of the wearable camera (usually mounted on the head) allows recording exactly what the camera wearers have in front of them, in particular hands and manipulated objects. This intrinsic advantage enables the study of the hands from multiple perspectives: localizing hands and their parts within the images; understanding what actions and activities the hands are involved in; and developing human-computer interfaces that rely on hand gestures. In this survey, we review the literature that focuses on the hands using egocentric vision, categorizing the existing approaches into: localization (where are the hands or parts of them? ); interpretation (what are the hands doing? ); and application (e.g., systems that used egocentric hand cues for solving a specific problem). Moreover, a list of the most prominent datasets with hand-based annotations is provided.
Gemini-GRACES high-quality spectra of Kepler evolved stars with transiting planets

Aims. Kepler-278 and Kepler-391 are two of the three evolved stars known to date on the red giant branch (RGB) to host multiple short-period transiting planets. Moreover, the planets orbiting Kepler-278 and Kepler-391 are among the smallest discovered around RGB stars. Here we present a detailed stellar and planetary characterization of these remarkable systems. Methods. Based on high-quality spectra from Gemini-GRACES for Kepler-278 and Kepler-391, we obtained refined stellar parameters and precise chemical abundances for 25 elements. Nine of these elements and the carbon isotopic ratios, 12C∕13C, had not previously been measured. Also, combining our new stellar parameters with a photodynamical analysis of the Kepler light curves, we determined accurate planetary properties of both systems. Results. Our revised stellar parameters agree reasonably well with most of the previous results, although we find that Kepler-278 is ~15% less massive than previously reported. The abundances of C, N, O, Na, Mg, Al, Si, S, Ca, Sc, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, Sr, Y, Zr, Ba, and Ce, in both stars, are consistent with those of nearby evolved thin disk stars. Kepler-391 presents a relatively high abundance of lithium (A(Li)NLTE = 1.29 ± 0.09 dex), which is likely a remnant from the main-sequence phase. The precise spectroscopic parameters of Kepler-278 and Kepler-391, along with their high 12C∕13C ratios, show that both stars are just starting their ascent on the RGB. The planets Kepler-278b, Kepler-278c, and Kepler-391c are warm sub-Neptunes, whilst Kepler-391b is a hot sub-Neptune that falls in the hot super-Earth desert and, therefore, it might be undergoing photoevaporation of its outer envelope. The high-precision obtained in the transit times allowed us not only to confirm Kepler-278c’s TTV signal, but also to find evidence of a previously undetected TTV signal for the inner planet Kepler-278b. From the presence of gravitational interaction between these bodies we constrain, for the first time, the mass of Kepler-278b (Mp = 56 −13+37 M⊕) and Kepler-278c (Mp = 35 −21+9.9 M⊕). The mass limits, coupled with our precise determinations of the planetary radii, suggest that their bulk compositions are consistent with a significant amount of water content and the presence of H2 gaseous envelopes. Finally, our photodynamical analysis also shows that the orbits of both planets around Kepler-278 are highly eccentric (e ~ 0.7) and, surprisingly, coplanar. Further observations (e.g., precise radial velocities) of this system are needed to confirm the eccentricity values presented here.
On The Effect Of Code Review On Code Smells

Code smells are symptoms of poor design quality. Since code review is a process that also aims at improving code quality, we investigate whether and how code review influences the severity of code smells. In this study, we analyze more than 21,000 code reviews belonging to seven Java open-source projects; we find that active and participated code reviews have a significant influence on the likelihood of reducing the severity of code smells. This result seems to confirm the expectations around code review's influence on code quality. However, by manually investigating 365 cases in which the severity of a code smell in a file was reduced with a review, we found that-in 95% of the cases-the reduction was a side effect of changes that reviewers requested on matters unrelated to code smells. Data and materials [this https URL].
NaïveRole: Author-Contribution Extraction and Parsing from Biomedical Manuscripts

Information about the contributions of individual authors to scientific publications is important for assessing authors' achievements. Some biomedical publications have a short section that describes authors' roles and contributions. It is usually written in natural language and hence author contributions cannot be trivially extracted in a machine-readable format. In this paper, we present 1) A statistical analysis of roles in author contributions sections, and 2) Na\"iveRole, a novel approach to extract structured authors' roles from author contribution sections. For the first part, we used co-clustering techniques, as well as Open Information Extraction, to semi-automatically discover the popular roles within a corpus of 2,000 contributions sections from PubMed Central. The discovered roles were used to automatically build a training set for Na\"iveRole, our role extractor approach, based on Na\"ive Bayes. Na\"iveRole extracts roles with a micro-averaged precision of 0.68, recall of 0.48 and F1 of 0.57. It is, to the best of our knowledge, the first attempt to automatically extract author roles from research papers. This paper is an extended version of a previous poster published at JCDL 2018.
Using Prolog for Transforming XML-Documents

Proponents of the programming language Prolog share the opinion Prolog is more appropriate for transforming XML-documents as other well-established techniques and languages like XSLT. In order to clarify this position this work proposes a tuProlog-styled interpreter for parsing XML-documents into Prolog-internal lists and vice versa for serialising lists into XML-documents. Based on this implementation a comparison between XSLT and Prolog follows. First, criteria are researched, such as considered language features of XSLT, usability and expressibility. These criteria are validated. Second, it is assessed when Prolog distinguishes between input and output parameters towards reversible transformation.
Neural Outlier Rejection for Self-Supervised Keypoint Learning

Identifying salient points in images is a crucial component for visual odometry, Structure-from-Motion or SLAM algorithms. Recently, several learned keypoint methods have demonstrated compelling performance on challenging benchmarks. However, generating consistent and accurate training data for interest-point detection in natural images still remains challenging, especially for human annotators. We introduce IO-Net (i.e. InlierOutlierNet), a novel proxy task for the self-supervision of keypoint detection, description and matching. By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and improve keypoint matching. Second, we introduce KeyPointNet, a keypoint-network architecture that is especially amenable to robust keypoint detection and description. We design the network to allow local keypoint aggregation to avoid artifacts due to spatial discretizations commonly used for this task, and we improve fine-grained keypoint descriptor performance by taking advantage of efficient sub-pixel convolutions to upsample the descriptor feature-maps to a higher operating resolution. Through extensive experiments and ablative analysis, we show that the proposed self-supervised keypoint learning method greatly improves the quality of feature matching and homography estimation on challenging benchmarks over the state-of-the-art.
Convolutional Neural Networks: A Binocular Vision Perspective

It is arguable that whether the single camera captured (monocular) image datasets are sufficient enough to train and test convolutional neural networks (CNNs) for imitating the biological neural network structures of the human brain. As human visual system works in binocular, the collaboration of the eyes with the two brain lobes needs more investigation for improvements in such CNN-based visual imagery analysis applications. It is indeed questionable that if respective visual fields of each eye and the associated brain lobes are responsible for different learning abilities of the same scene. There are such open questions in this field of research which need rigorous investigation in order to further understand the nature of the human visual system, hence improve the currently available deep learning applications. This position paper analyses a binocular CNNs architecture that is more analogous to the biological structure of the human visual system than the conventional deep learning techniques. While taking a structure called optic chiasma into account, this architecture consists of basically two parallel CNN structures associated with each visual field and the brain lobe, fully connected later possibly as in the primary visual cortex (V1). Experimental results demonstrate that binocular learning of two different visual fields leads to better classification rates on average, when compared to classical CNN architectures.
BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale

A massive number of biological entities, such as genes and mutations, are mentioned in the biomedical literature. The capturing of the semantic relatedness of biological entities is vital to many biological applications, such as protein-protein interaction prediction and literature-based discovery. Concept embeddings—which involve the learning of vector representations of concepts using machine learning models—have been employed to capture the semantics of concepts. To develop concept embeddings, named-entity recognition (NER) tools are first used to identify and normalize concepts from the literature, and then different machine learning models are used to train the embeddings. Despite multiple attempts, existing biomedical concept embeddings generally suffer from suboptimal NER tools, small-scale evaluation, and limited availability. In response, we employed high-performance machine learning-based NER tools for concept recognition and trained our concept embeddings, BioConceptVec, via four different machine learning models on ~30 million PubMed abstracts. BioConceptVec covers over 400,000 biomedical concepts mentioned in the literature and is of the largest among the publicly available biomedical concept embeddings to date. To evaluate the validity and utility of BioConceptVec, we respectively performed two intrinsic evaluations (identifying related concepts based on drug-gene and gene-gene interactions) and two extrinsic evaluations (protein-protein interaction prediction and drug-drug interaction extraction), collectively using over 25 million instances from nine independent datasets (17 million instances from six intrinsic evaluation tasks and 8 million instances from three extrinsic evaluation tasks), which is, by far, the most comprehensive to our best knowledge. The intrinsic evaluation results demonstrate that BioConceptVec consistently has, by a large margin, better performance than existing concept embeddings in identifying similar and related concepts. More importantly, the extrinsic evaluation results demonstrate that using BioConceptVec with advanced deep learning models can significantly improve performance in downstream bioinformatics studies and biomedical text-mining applications. Our BioConceptVec embeddings and benchmarking datasets are publicly available at https://github.com/ncbi-nlp/BioConceptVec.
J-PLUS: Tools to identify compact planetary nebulae in the Javalambre and southern photometric local Universe surveys

Context. From the approximately 3500 planetary nebulae (PNe) discovered in our Galaxy, only 14 are known to be members of the Galactic halo. Nevertheless, a systematic search for halo PNe has never been performed. Aims. In this study, we present new photometric diagnostic tools to identify compact PNe in the Galactic halo by making use of the novel 12-filter system projects, Javalambre Photometric Local Universe Survey (J-PLUS) and Southern-Photometric Local Universe Survey (S-PLUS). Methods. We reconstructed the Isaac Newton Telescope Photometric Hα Survey of the Northern Galactic Plane diagnostic diagram and propose four new ones using (i) the J-PLUS and S-PLUS synthetic photometry for a grid of photo-ionisation models of halo PNe, (ii) several observed halo PNe, as well as (iii) a number of other emission-line objects that resemble PNe. All colour–colour diagnostic diagrams are validated using two known halo PNe observed by J-PLUS during the scientific verification phase and the first data release (DR1) of S-PLUS and the DR1 of J-PLUS. Results. By applying our criteria to the DR1s (~1190 deg2), we identified one PN candidate. However, optical follow-up spectroscopy proved it to be a H II region belonging to the UGC 5272 galaxy. Here, we also discuss the PN and two H II galaxies recovered by these selection criteria. Finally, the cross-matching with the most updated PNe catalogue (HASH) helped us to highlight the potential of these surveys, since we recover all the known PNe in the observed area. Conclusions. The tools here proposed to identify PNe and separate them from their emission-line contaminants proved to be very efficient thanks to the combination of many colours, even when applied – like in the present work – to an automatic photometric search that is limited to compact PNe.
A New Ensemble Method for Concessively Targeted Multi-model Attack

It is well known that deep learning models are vulnerable to adversarial examples crafted by maliciously adding perturbations to original inputs. There are two types of attacks: targeted attack and non-targeted attack, and most researchers often pay more attention to the targeted adversarial examples. However, targeted attack has a low success rate, especially when aiming at a robust model or under a black-box attack protocol. In this case, non-targeted attack is the last chance to disable AI systems. Thus, in this paper, we propose a new attack mechanism which performs the non-targeted attack when the targeted attack fails. Besides, we aim to generate a single adversarial sample for different deployed models of the same task, e.g. image classification models. Hence, for this practical application, we focus on attacking ensemble models by dividing them into two groups: easy-to-attack and robust models. We alternately attack these two groups of models in the non-targeted or targeted manner. We name it a bagging and stacking ensemble (BAST) attack. The BAST attack can generate an adversarial sample that fails multiple models simultaneously. Some of the models classify the adversarial sample as a target label, and other models which are not attacked successfully may give wrong labels at least. The experimental results show that the proposed BAST attack outperforms the state-of-the-art attack methods on the new defined criterion that considers both targeted and non-targeted attack performance.
Privacy Attacks on Network Embeddings

Data ownership and data protection are increasingly important topics with ethical and legal implications, e.g., with the right to erasure established in the European General Data Protection Regulation (GDPR). In this light, we investigate network embeddings, i.e., the representation of network nodes as low-dimensional vectors. We consider a typical social network scenario with nodes representing users and edges relationships between them. We assume that a network embedding of the nodes has been trained. After that, a user demands the removal of his data, requiring the full deletion of the corresponding network information, in particular the corresponding node and incident edges. In that setting, we analyze whether after the removal of the node from the network and the deletion of the vector representation of the respective node in the embedding significant information about the link structure of the removed node is still encoded in the embedding vectors of the remaining nodes. This would require a (potentially computationally expensive) retraining of the embedding. For that purpose, we deploy an attack that leverages information from the remaining network and embedding to recover information about the neighbors of the removed node. The attack is based on (i) measuring distance changes in network embeddings and (ii) a machine learning classifier that is trained on networks that are constructed by removing additional nodes. Our experiments demonstrate that substantial information about the edges of a removed node/user can be retrieved across many different datasets. This implies that to fully protect the privacy of users, node deletion requires complete retraining - or at least a significant modification - of original network embeddings. Our results suggest that deleting the corresponding vector representation from network embeddings alone is not sufficient from a privacy perspective.