Miracle-dz/UniFAR-FTUs huggingface dataset:
Absolute dimensions of the low-mass eclipsing binary system NSVS 10653195

Context. Low-mass stars in eclipsing binary systems show radii larger and effective temperatures lower than theoretical stellar models predict for isolated stars with the same masses. Eclipsing binaries with low-mass components are hard to find due to their low luminosity. As a consequence, the analysis of the known low-mass eclipsing systems is key to understand this behavior. Aims. We aim to investigate the mass–radius relation for low-mass stars and the cause of the deviation of the observed radii in low-mass detached eclipsing binary stars (LMDEB) from theoretical stellar models. Methods. We developed a physical model of the LMDEB system NSVS 10653195 to accurately measure the masses and radii of the components. We obtained several high-resolution spectra in order to fit a spectroscopic orbit. Standardized absolute photometry was obtained to measure reliable color indices and to measure the mean Teff of the system in out-of-eclipse phases. We observed and analyzed optical VRI and infrared JK band differential light-curves which were fitted using PHOEBE. A Markov chain Monte-Carlo (MCMC) simulation near the solution found provides robust uncertainties for the fitted parameters. Results. NSVS 10653195 is a detached eclipsing binary composed of two similar stars with masses of M1 = 0.6402 ± 0.0052 M⊙ and M2 = 0.6511 ± 0.0052 M⊙ and radii of R1 = 0.687+0.017−0.024 R⊙ and R2 = 0.672+0.018−0.022 R⊙. Spectral types were estimated to be K6V and K7V. These stars rotate in a circular orbit with an orbital inclination of i = 86.22 ± 0.61 degrees and a period of P = 0.5607222(2) d. The distance to the system is estimated to be d = 135.2+7.6−7.9 pc, in excellent agreement with the value from Gaia. If solar metallicity were assumed, the age of the system would be older than log (age) ∼ 8 based on the Mbol–log Teff diagram. Conclusions. NSVS 10653195 is composed of two oversized and active K stars. While their radii is above model predictions their Teff are in better agreement with models.
Generative approach to unsupervised deep local learning

Abstract. Most existing feature learning methods optimize inflexible handcrafted features and the affinity matrix is constructed by shallow linear embedding methods. Different from these conventional methods, we pretrain a generative neural network by stacking convolutional autoencoders to learn the latent data representation and then construct an affinity graph with them as a prior. Based on the pretrained model and the constructed graph, we add a self-expressive layer to complete the generative model and then fine-tune it with a new loss function, including the reconstruction loss and a deliberately defined locality-preserving loss. The locality-preserving loss designed by the constructed affinity graph serves as prior to preserve the local structure during the fine-tuning stage, which in turn improves the quality of feature representation effectively. Furthermore, the self-expressive layer between the encoder and the decoder is based on the assumption that each latent feature is a linear combination of other latent features, so the weighted combination coefficients of the self-expressive layer are used to construct a new refined affinity graph for representing the data structure. We conduct experiments on four datasets to demonstrate the superiority of the representation ability of our proposed model over the state-of-the-art methods.
Crop Lodging Prediction From UAV-Acquired Images of Wheat and Canola Using a DCNN Augmented With Handcrafted Texture Features

Lodging, the permanent bending over of food crops, leads to poor plant growth and development. Consequently, lodging results in reduced crop quality, lowers crop yield, and makes harvesting difficult. Plant breeders routinely evaluate several thousand breeding lines, and therefore, automatic lodging detection and prediction is of great value aid in selection. In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials. Also, using transfer learning, we trained 10 lodging detection models using well-established deep convolutional neural network architectures. Our proposed model outperforms the state-of-the-art lodging detection methods in the literature that use only handcrafted features. In comparison to 10 DCNN lodging detection models, our proposed model achieves comparable results while having a substantially lower number of parameters. This makes the proposed model suitable for applications such as real-time classification using inexpensive hardware for high-throughput phenotyping pipelines. The GitHub repository at https://github. com/FarhadMaleki/LodgedNet contains code and models.
Agnostic data debiasing through a local sanitizer learnt from an adversarial network approach

The widespread use of automated decision processes in many areas of our society raises serious ethical issues concerning the fairness of the process and the possible resulting discriminations. In this work, we propose a novel approach called \gansan whose objective is to prevent the possibility of \emph{any} discrimination i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our sanitization algorithm \gansan is partially inspired by the powerful framework of generative adversarial networks (in particuler the Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible and thus preserving the interpretability of the sanitized data. As a consequence, once the sanitizer is trained, it can be applied to new data, such as for instance, locally by an individual on his profile before releasing it. Finally, experiments on a real dataset demonstrate the effectiveness of the proposed approach as well as the achievable trade-off between fairness and utility.
Counting elliptic curves with an isogeny of degree three

<p>We count by height the number of elliptic curves over <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="double-struck upper Q"> <mml:semantics> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="double-struck">Q</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">\mathbb {Q}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> that possess an isogeny of degree <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="3"> <mml:semantics> <mml:mn>3</mml:mn> <mml:annotation encoding="application/x-tex">3</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. </p>
Integrated Visualization of Patient Data

The efficient and timely access to patient data is essential for successful patient treatment in clinical wards. Even though, most of the patient data is stored electronically, e.g. in the electronic health record (EHR), relevant patient information is frequently captured, processed and passed in non-electronic form in day-to-day ward routines, e.g. during ward rounds or at shift handover. Following a disruptive design mode, we present a design and development approach comprising a fundamental visualization concept that is refined in a feedback-loop between computer scientists, sociologists and physicians in order to support peri-operative collaborative workflows on a neurosurgical hospital ward. The resulting prototype realizes on-patient visualization methods using an anatomical avatar. It allows for the visual access of medical data with spatial reference relevant for spinal disc herniation and it handles various quantitative and qualitative medical data related to the same anatomical structure as well as referring to hidden and/or small anatomical structures with limited detectability. Furthermore, temporal changes of medical data are made accessible.
The Cinderella Complex: Word Embeddings Quantify Gender Stereotypes in Movies and Books

Gender norms and stereotypes are deeply rooted in society and are reinforced through the recreation of cultural products. Our analysis of thousands of novels, movie synopsis, and movie scripts reveals how these culture products promote gender stereotypes in a quiet but strong way. We start from demonstrating how the happiness of Cinderella depends on the Prince but not vice versa. This analysis, based on the word embedding technique, provides a novel approach to identify how gender stereotypes in our case, men are women’s way to happiness are created in stories through a carefully designed narrative structure. This structure of narrative builds up and enhances “Cinderella complex”, women's fear of independence and unconscious desire to be taken care of by others. The used computational techniques allows us to scale up analysis to include 1,327 novels, 6,657 movie synopsis, and 1,109 movie scripts, which covers a substantial portion of collective narratives as both the consequence and the cause of how society thinks. The observed widely existence of “Cinderella complex” across periods and cultures reminds the necessary to combat against it through the creation of new narrative in education, policy, and beyond.
Robust Clustering Using Tau-Scales

K means is a popular non-parametric clustering procedure introduced by Steinhaus (1956) and further developed by MacQueen (1967). It is known, however, that K means does not perform well in the presence of outliers. Cuesta-Albertos et al (1997) introduced a robust alternative, trimmed K means, which can be tuned to be robust or efficient, but cannot achieve these two properties simultaneously in an adaptive way. To overcome this limitation we propose a new robust clustering procedure called K Tau Centers, which is based on the concept of Tau scale introduced by Yohai and Zamar (1988). We show that K Tau Centers performs well in extensive simulation studies and real data examples. We also show that the centers found by the proposed method are consistent estimators of the "true" centers defined as the minimizers of the the objective function at the population level.
Wasserstein Adversarial Imitation Learning

Imitation Learning describes the problem of recovering an expert policy from demonstrations. While inverse reinforcement learning approaches are known to be very sample-efficient in terms of expert demonstrations, they usually require problem-dependent reward functions or a (task-)specific reward-function regularization. In this paper, we show a natural connection between inverse reinforcement learning approaches and Optimal Transport, that enables more general reward functions with desirable properties (e.g., smoothness). Based on our observation, we propose a novel approach called Wasserstein Adversarial Imitation Learning. Our approach considers the Kantorovich potentials as a reward function and further leverages regularized optimal transport to enable large-scale applications. In several robotic experiments, our approach outperforms the baselines in terms of average cumulative rewards and shows a significant improvement in sample-efficiency, by requiring just one expert demonstration.
Survey of nuclear pasta in the intermediate-density regime: Shapes and energies

Background: Nuclear pasta, emerging due to the competition between the long-range Coulomb force and the short-range strong force, is believed to be present in astrophysical scenarios, such as neutron stars and core-collapse supernovae. Its structure can have a high impact e.g. on neutrino transport or the tidal deformability of neutron stars. Purpose: We study several possible pasta configurations, all of them minimal surface configurations, which are expected to appear in the mid-density regime of nuclear pasta, i.e. around 40% of the nuclear saturation density. In particular we are interested in the energy spectrum for different pasta configurations considered. Method: Employing the density functional theory (DFT) approach, we calculate the binding energy of the different configurations for three values of the proton content XP = 1/10, 1/3 and 1/2, by optimizing their periodic length. We study finite temperature effects and the impact of electron screening. Results: Nuclear pasta lowers the energy significantly compared to uniform matter, especially for $X_P \geq 1/3$. However, the different configurations have very similar binding energies. For large proton content, $X_P \geq 1/3$, the pasta configurations are very stable, for lower proton content temperatures of a few MeV are enough for the transition to uniform matter. Electron screening has a small influence on the binding energy of nuclear pasta, but increases its periodic length. Conclusion: Nuclear pasta in the mid-density regime lowers the energy of the matter for all proton fractions under study. It can survive even large temperatures of several MeV. Since various configurations have very similar energy, it is to expected that many configurations can coexist simultaneously already at small temperatures.
Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring

Computing the gradient of an image is a common step in computer vision pipelines. The image gradient quantifies the magnitude and direction of edges in an image and is used in creating features for downstream machine learning tasks. Typically, the image gradient is represented as a grayscale image. This paper introduces directional pseudo-coloring, an approach to color the image gradient in a deliberate and coherent manner. By pseudo-coloring the image gradient magnitude with the image gradient direction, we can enhance the visual quality of image edges and achieve an artistic transformation of the original image. Additionally, we present a simple style transfer pipeline which learns a color map from a style image and then applies that color map to color the edges of a content image through the directional pseudo-coloring technique. Code for the algorithms and experiments is available at this https URL.
Optical follow-up study of 32 high-redshift galaxy cluster candidates from Planck with the William Herschel Telescope

The Planck satellite has detected cluster candidates via the Sunyaev Zel’dovich (SZ) effect, but the optical follow-up required to confirm these candidates is still incomplete, especially at high redshifts and for SZ detections at low significance. In this work, we present our analysis of optical observations obtained for 32 Planck cluster candidates using ACAM on the 4.2-m William Herschel Telescope. These cluster candidates were pre-selected using SDSS, WISE, and Pan-STARRS images to likely represent distant clusters at redshifts z ≳ 0.7. We obtain photometric redshift and richness estimates for all of the cluster candidates from a red-sequence analysis of r-, i-, and z-band imaging data. In addition, long-slit observations allow us to measure the redshifts of a subset of the clusters spectroscopically. The optical richness is often lower than expected from the inferred SZ mass when compared to scaling relations previously calibrated at low redshifts. This likely indicates the impact of Eddington bias and projection effects or noise-induced detections, especially at low-SZ significance. Thus, optical follow-up not only provides redshift measurements, but also an important independent verification method. We find that 18 (7) of the candidates at redshifts z > 0.5 (z > 0.8) are at least half as rich as expected from scaling relations, thereby clearly confirming these candidates as massive clusters. While the complex selection function of our sample due to our pre-selection hampers its use for cosmological studies, we do provide a validation of massive high-redshift clusters particularly suitable for further astrophysical investigations.
Learning data representation using modified autoencoder for the integrative analysis of multi-omics data.

In integrative analyses of omics data, it is often of interest to extract data embedding from one data type that best reflect relations with another data type. This task is traditionally fulfilled by linear methods such as canonical correlation and partial least squares. However, information contained in one data type pertaining to the other data type may not be in the linear form. Deep learning provides a convenient alternative to extract nonlinear information. Here we develop a method Autoencoder-based Integrative Multi-omics data Embedding (AIME) to extract such information. Using a real gene expression - methylation dataset, we show that AIME extracted meaningful information that the linear approach could not find. The R implementation is available at this http URL.
LIA: Latently Invertible Autoencoder with Adversarial Learning

Deep generative models play an increasingly important role in machine learning and computer vision. However there are two fundamental issues hindering real-world applications of these techniques: the learning difficulty of variational inference in Variational AutoEncoder (VAE) and the functional absence of encoding samples in Generative Adversarial Network (GAN). In this paper, we manage to address these issues in one framework by proposing a novel algorithm named Latently Invertible Autoencoder (LIA). A deep invertible network and its inverse mapping are symmetrically embedded in the latent space of VAE. Thus the partial encoder first transforms inputs to be feature vectors and then the distribution of these feature vectors is reshaped to approach a prior by the invertible network. The decoder proceeds in reverse order of composite mappings of the complete encoder. The two-stage stochasticity-free training is devised to train LIA via adversarial learning, in the sense that we first train a standard GAN whose generator is the decoder of LIA and then an autoencoder in the adversarial manner by detaching the invertible network from LIA. Experiments conducted on the FFHQ dataset validate the effectiveness of LIA for inference and generation tasks.
Safe Testing

We present a new theory of hypothesis testing. The main concept is the s-value, a notion of evidence which, unlike p-values, allows for effortlessly combining evidence from several tests, even in the common scenario where the decision to perform a new test depends on the previous test outcome: safe tests based on s-values generally preserve Type-I error guarantees under such ‘optional continuation’. S-values exist for completely general testing problems with composite null and alternatives. Their prime interpretation is in terms of gambling or investing, each S-value corresponding to a particular investment. Surprisingly, optimal "GROW" S-values, which lead to fastest capital growth, are fully characterized by the joint information projection (JIPr) between the set of all Bayes marginal distributions on ${\mathcal{H}_0}$ and ${\mathcal{H}_1}$. Thus, optimal s-values also have an interpretation as Bayes factors, with priors given by the JIPr. We illustrate the theory using two classical testing scenarios: the one-sample t-test and the 2 × 2-contingency table. In the t-test setting, GROW S-values correspond to adopting the right Haar prior on the variance, like in Jeffreys’ Bayesian t-test. However, unlike Jeffreys’, the default safe t-test puts a discrete 2-point prior on the effect size, leading to better behaviour in terms of statistical power. Sharing Fisherian, Neymanian and Jeffreys-Bayesian interpretations, S-values and safe tests may provide a methodology acceptable to adherents of all three schools.
Conceptual Graph Analysis: Knowledge Acquisition for Instructional System Design

This paper describes the empirical validation of a knowledge acquisition methodology using conceptual graph analysis. Conceptual graphs are a representational medium used to integrate and organize knowledge obtained from documents, verbal protocols, question probes, and observation of task performance. The method was validated by presenting two groups of subjects with text and graphics on a topic in engineering dynamics. One set of materials was written by a recognized subject matter expert. The other set was developed by subjecting the expert generated text to conceptual graph analysis. The text was first translated into a conceptual graph structure, then the graph was revised via question probes and observation/induction methods. The final graph was returned to a standard text format. Students receiving the knowledge-engineered text solved significantly more problems than students receiving the original text. Conceptual graph analysis is a generalized method that can be used for a broad range of training domains, providing a highly structured means for making explicit the knowledge base to be incorporated into instructional design.
Adversarial Task-Specific Privacy Preservation under Attribute Attack

With the prevalence of machine learning services, crowdsourced data containing sensitive information poses substantial privacy challenges. Existing works focusing on protecting against membership inference attacks under the rigorous notion of differential privacy are susceptible to attribute inference attacks. In this paper, we develop a theoretical framework for task-specific privacy under the attack of attribute inference. Under our framework, we propose a minimax optimization formulation with a practical algorithm to protect a given attribute and preserve utility. We also extend our formulation so that multiple attributes could be simultaneously protected. Theoretically, we prove an information-theoretic lower bound to characterize the inherent tradeoff between utility and privacy when they are correlated. Empirically, we conduct experiments with real-world tasks that demonstrate the effectiveness of our method compared with state-of-the-art baseline approaches.
Les Champs D'application des Représentations Sociales: Le Devenir Interdisciplinaire

Une des questions qui se posent aujourd'hui a propos de la theorie des representations sociales concerne sa place dans les sciences humaines. Cet article vise a explorer cette question en examinant l'etendue des possibles au plan des contenus, des approches et des perspectives scientifiques. En ce qui concerne les contenus, ils font l'objet d'une analyse mettant en evidence la polymorphie des objets approches, ce qui conduit a etudier d'une part les limites a etablir et d'autre part le role des differentes disciplines contributives. Du cote des approches aussi, on retrouve la pluralite des instruments et des methodes ce qui fait d'ailleurs l'objet des interrogations et des pistes de reflexion de l'auteur. Enfin, par la discussion qui est ouverte, cet article tente de mettre en perspective les positions scientifiques qui sont prises et en particulier celles qui font une place privilegiee au pluralisme et au contexte.
Simplex crossover and linkage identification: single-stage evolution vs. multi-stage evolution

Previous studies have proposed simplex crossover for real-coded genetic algorithms (GAs). In this paper, we propose two types of linkage identification for simplex crossover: the linkage identification with single-stage evolution (LISS) and linkage identification with multi-stage evolution (LIMS); and a comparative study is performed. Results show that the LIMS has a more stable performance than LISS.
Signals and Noise in Communication Systems

A circular duct is mounted on the circumferential, bared surface of a buried, coated pipeline so that the duct and pipe surface combine to define a sealed, circumferential chamber. A vertical conduit, defining in and out ports, extends upward from the duct through the dirt fill over the line. The ports provide communication between the ground surface and the duct chamber. A steel tape, having an ultrasonic probe mounted thereon, can be threaded through the assembly to bring the probe into contact with the pipe wall to take wall thickness readings. Scales on the tape serve to locate the position of the hidden probe.