Miracle-dz/UniFAR-FTUs huggingface dataset:
The Evolution of the Galaxy Luminosity Function from z=6-10 in the Context of Hierarchical Structure Formation

The galaxy luminosity function (LF) is a convolution of the mass function of dark matter halos and the galaxy-formation physics that maps halo mass to galaxy luminosity. We consider the observed strong evolution in the LF of Lyman-break galaxies from z=6-10 in this context. Given the rapidly changing halo mass function, we determine the corresponding variation in the parameters that describe the physics of galaxy formation. While our model allows halos of a given mass to host galaxies with a range of luminosities, we find that the average brightness increases by about 0.5 dex from z=6 to 8. We also consider recent measurements at z~10 and find that the paucity of detected galaxies is consistent with no evolution in the average luminosity at fixed halo mass; the evolution in the LF can be completely accounted for by the behavior of the halo mass function. We show that these results can be understood with theoretical models of galaxy growth rate for a star formation efficiency of ~10%.
Spontaneous formation of electric current sheets and the origin of solar flares

It is demonstrated that the continuous boundary motion of a sheared magnetic field in a tenuous plasma with an infinite electrical conductivity can induce the formation of multiple electric current sheets in the interior plasma. In response to specific footpoint displacements, the quadrupolar magnetic field considered is shown to require the formation of multiple electric current sheets as it achieves a force-free state. Some of the current sheets are found to be of finite length, running along separatrix lines of force which separate lobes of magnetic flux. It is suggested that current sheets in the form of infinitely thin magnetic shear layers may be unstable to resistive tearing, a process which may have application to solar flares. 33 references.
Solar soft X-ray variations in 22$^{nd}$ and 23$^d$ solar cycles

The oscillations in solar daily soft X-rays (SXR) have been analyzed for 22$^{nd}$ and 23$^d$ solar cycles. The SXR in the 1–8Å passband have been recorded by the GOES-6,8 satellites from January 1986 to April 2003 year. For 22$^{nd}$ cycle the revealed periodicities 24, 43, 54, 65 and 73 days coincide with the 23$^d$ cycle ones within data uncertainty (1 day) and their values may be explained by the differential rotation of the solar chromosphere and corona.To search for other articles by the author(s) go to: http://adsabs.harvard. edu/abstract_service. html
Advanced Deep Transfer Learning Using Ensemble Models for COVID-19 Detection from X-ray Images

: The pandemic of Coronavirus disease (COVID-19) has become one of the main causes of mortality over the world. In this paper, we employ a transfer learning-based method using five pre-trained deep convolutional neural networks (CNN) architectures fine-tuned with an X-ray image dataset to detect COVID-19. Hence, we use VGG-16, ResNet50, InceptionV3, ResNet101 and Inception-ResNetV2 models in order to classify the input images into three classes (COVID-19 / Healthy / Other viral pneumonia). The results of each model are presented in detail using 10-fold cross-validation and comparative analysis has been given among these models by taking into account different elements in order to find the more suitable model. To further enhance the performance of single models, we propose to combine the obtained predictions of these models using the majority vote strategy. The proposed method has been validated on a publicly available chest X-ray image database that contains more than one thousand images per class. Evaluation measures of the classification performance have been reported and discussed in detail. Promising results have been achieved compared to state-of-the-art methods where the proposed ensemble model achieved higher performance than using any single model. This study gives more insights to researchers for choosing the best models to accurately detect the COVID-19 virus.
Improving the Accuracy of Tracker by Linearized Transformer

: Visual object tracking seeks to correctly estimate the target’s bounding box, which is difficult due to occlusion, illumination variation, background clutters, and camera motion. Recently, Siamese-based approaches have demonstrated promising visual tracking capability. However, most modern Siamese-based methods compute target and search image features independently, then use correlation to acquire correlation information from two feature maps. The correlation operation is a straightforward fusion technique that considers the similarity between the template and the search region. This may be the limiting factor in the development of high-precision tracking algorithms. This research offers a Siamese refinement network for visual tracking that enhances and fuses template and search patch information directly without needing a correlation operation. This approach can boost any tracker performance and produces boxes without any postprocessing. Extensive experiments on visual tracking benchmarks such as VOT2018, UAV123, OTB100, and LaSOT with DiMP50 base tracker demonstrate that our method achieves state-of-the-art results. For example, on the VOT2018, LaSOT, and UAV123 test sets, our method obtains a significant improvement of 5.3% (EAO), 3.5% (AUC), and 2.9% (AUC) over the base tracker. Our network runs at approximately 30 FPS on GPU RTX 3070.
Observations of T Tauri Stars Using the Hubble Space Telescope GHRS. II. Optical and Near-Ultraviolet Lines

We have analyzed GHRS data of eight classical T Tauri stars (CTTSs) and one weak T Tauri star (WTTS). The GHRS data consist of a spectral range 40 Å wide centered on 2800 Å. For four of the CTTSs, we have nearly simultaneous optical observations that contain Hα, Hβ, He I, Na D, and the Ca II infrared triplet. The Mg II resonance doublet is the strongest feature in the 2800 Å range. This line has a fairly wide and symmetric emission component (~200-300 km s-1 for the CTTSs), with a narrow central absorption and a wide blueshifted absorption superimposed to it. The narrow central absorption width and equivalent width are inconsistent with being due only to interstellar medium clouds described in the literature, which leads us to conclude that it is partially due to non-LTE processes in the emission-line region itself. The emission profile closely follows Hα. Its large width in CTTSs cannot be due to the Stark effect, and we suggest that it is due to supersonic turbulence. All the stars show blueshifted absorptions that are evidence of outflows (terminal velocities ~300 km s-1), with multiple flows observed in two stars. We show evidence that the wind is not spherical, with wind signatures being stronger for lower inclinations at a given accretion rate. We briefly compare other optical lines with the hot transition region lines observed in CTTS.
Generalized Torsion-Curvature Scale Space Descriptor for 3-Dimensional Curves

: In this paper, we propose a new method for representing 3D curves called the Generalized Torsion Curvature Scale Space (GTCSS) descriptor. This method is based on the calculation of curvature and torsion measures at different scales, and it is invariant under rigid transformations. To address the challenges associated with estimating these measures, we employ a multi-scale technique in our approach. We evaluate the effectiveness of our method through experiments, where we extract space curves from 3D objects and apply our method to pose estimation tasks. Our results demonstrate the effectiveness of the GTCSS descriptor for representing 3D curves and its potential for use in a variety of computer vision applications.
Mobilities of Atomic and Molecular Ions in the Noble Gases

A method is described for measuring the mobilities of ions of near-thermal energy. A pulse of ions is generated in a discharge region and passes through a grid into a drift region where a uniform electric field causes the ions to move to a collector electrode. The mobilities are determined from measurements of the transit time of the ions in crossing the drift space. The experimental mobility values for thermal energy ions moving in their parent gases at 300\ifmmode^\circ\else\textdegree\fi{}K and a gas density of 2.69\ifmmode\times\else\texttimes\fi{}${10}^{19}$ atoms/cc are ${\ensuremath{\mu}}_{0}=10.5$ ${\mathrm{cm}}^{2}$/volt-sec (${\mathrm{He}}^{+}$), 20.3 (${\mathrm{He}}_{2}^{+}$), 4.0 (${\mathrm{Ne}}^{+}$), 6.5 (${\mathrm{Ne}}_{2}^{+}$), 1.60 (${\mathrm{A}}^{+}$), 2.65 (${\mathrm{A}}_{2}^{+}$), 0.90 (${\mathrm{Kr}}^{+}$), 1.21 (${\mathrm{Kr}}_{2}^{+}$), 0.58 (${\mathrm{Xe}}^{+}$), and 0.79 (${\mathrm{Xe}}_{2}^{+}$). These values are in good agreement with available theoretical results. Our measurements join smoothly at higher ion energies with the measurements made by Hornbeck and by Varney except in the case of ${\mathrm{A}}_{2}^{+}$. In addition, our results indicate that earlier measurements at near-thermal energy by Tyndall and collaborators refer to the molecular ions in helium and neon, and to the atomic ions in krypton and xenon.
Concept Explainability for Plant Diseases Classification

Plant diseases remain a considerable threat to food security and agricultural sustainability. Rapid and early identification of these diseases has become a significant concern motivating several studies to rely on the increasing global digitalization and the recent advances in computer vision based on deep learning. In fact, plant disease classification based on deep convolutional neural networks has shown impressive performance. However, these methods have yet to be adopted globally due to concerns regarding their robustness, transparency, and the lack of explainability compared with their human experts counterparts. Methods such as saliency-based approaches associating the network output to perturbations of the input pixels have been proposed to give insights into these algorithms. Still, they are not easily comprehensible and not intuitive for human users and are threatened by bias. In this work, we deploy a method called Testing with Concept Activation Vectors (TCAV) that shifts the focus from pixels to user-defined concepts. To the best of our knowledge, our paper is the first to employ this method in the field of plant disease classification. Important concepts such as color, texture and disease related concepts were analyzed. The results suggest that concept-based explanation methods can significantly benefit automated plant disease identification.
Special issue overview Nonextensive statistical mechanics: new trends, new perspectives

I t is our pleasure to welcome Jean Pierre Boon and Constantino Tsallis as guests Editors for the present Special Issue of Europhysics News on “Nonextensive Statistical Mechanics”. They did a great job not only in selecting an impressive set of distinguished authors but also in writing the introductory Editorial and in each being a co-author of one of the contributions. The subject is difficult and could not go without a higher proportion of equations than usual in EPN: our thanks go to the EPN designer who had to face a heavier task than usual. It is sometimes necessary to address arduous developments to cover recent progress in Physics. This time, EPN will ask its readers to make an effort. It is always rewarding. The guests Editors were so efficient that the collected material passes largely the size of a standard EPN issue. We are grateful to the Publisher for accepting to accommodate all the articles in a single volume. It will make of this Special Issue the general reference work on “nonextensive statistical mechanics”. Back to the usual mix of wide-ranging Features and News next time! The Editors
The rheology of dilute suspensions of slender rods in weak flows

This paper describes the consequences of pair interactions in dilute suspensions of rigid rods of length 2l and radius a subjected to weak, steady shear flows. The combination of hydrodynamic and Brownian forces increases alignment with the flow, thereby enhancing the shear thinning and strain thickening expected from dilute theories. The theory is asymptotic in Pe [Lt ] 1 and ε = (ln 2l/a)−1 [Lt ] 1 but requires an ad hoc approximation to simplify the form of the hydrodynamic interactions and the rod-rod excluded volume. The theoretical predictions of the Huggins coefficient in simple shear flow are compared with data in the literature for Xanthan gum, a semi-rigid biopolymer. Comparison with semi-dilute theories emphasizes the fundamentally different nature of the interactions in the two regimes and indicates that the transition between the two lies in the range 1.5 [les ][η]0 n [les ] 6.
SWaTEval: An Evaluation Framework for Stateful Web Application Testing

: Web applications are an easily accessible and valuable target for attackers. Therefore, web applications need to be examined for vulnerabilities. Modern web applications usually behave in a stateful manner and hence have an underlying state machine that determines their behavior based on the current state. To thoroughly test a web application, it is necessary to consider all aspects of a web application, including its internal states. In a blackbox setting, which we presuppose for this work, however, the internal state machine must be inferred before it can be used for testing. For state machine inference it is necessary to choose a similarity measure for web pages. Some approaches for automated blackbox stateful testing for web applications have already been proposed. It is, however, unclear how these approaches perform in comparison. We therefore present our evaluation framework for stateful web application testing, SWaTEval. In our evaluation, we show that SWaTEval is able to reproduce evaluation results from literature, demonstrating that SWaTEval is suitable for conducting meaningful evaluations. Further, we use SWaTEval to evaluate various approaches to similarity measures for web pages, including a new method based on the euclidean distance that we propose in this paper. These similarity measures are an important part of the automated state machine inference necessary for stateful blackbox testing. We show that the choice of similarity measure has an impact on the performance of the state machine inference regarding the number of correctly identiﬁed states, and that our newly proposed similarity measure leads to the highest number of correctly identiﬁed states.
Robust receding horizon control of constrained nonlinear systems

We present a method for the construction of a robust dual-mode, receding horizon controller which can be employed for a wide class of nonlinear systems with state and control constraints and model error. The controller is dual-mode. In a neighborhood of the origin, the control action is generated by a linear feedback controller designed for the linearized system. Outside this neighborhood, receding horizon control is employed. Existing receding horizon controllers for nonlinear, continuous time systems, which are guaranteed to stabilize the nonlinear system to which they are applied, require the exact solution, at every instant, of an optimal control problem with terminal equality constraints. These requirements are considerably relaxed in the dual-mode receding horizon controller presented in this paper. Stability is achieved by imposing a terminal inequality, rather than an equality, constraint. Only approximate minimization is required. A variable time horizon is permitted. Robustness is achieved by employing conservative state and stability constraint sets, thereby permitting a margin of error. The resultant dual-mode controller requires considerably less online computation than existing receding horizon controllers for nonlinear, constrained systems. >
The Changing Active Galactic Nucleus Population

We investigate how the fraction of broad-line sources in the active galactic nucleus (AGN) population changes with X-ray luminosity and redshift. We first construct the rest-frame hard-energy (2-8 keV) X-ray luminosity function (HXLF) at z = 0.1-1 using Chandra Lockman Hole-Northwest wide-area data, Chandra Deep Field-North 2 Ms data, other Chandra deep field data, and the ASCA Large Sky Survey data. We find that broad-line AGNs dominate above ~3 × 1043 ergs s-1 and have a mean luminosity of 1.3 × 1044 ergs s-1. Type II AGNs can become an important component of the X-ray population only at Seyfert-like X-ray luminosities. We then construct z = 0.1-0.5 and z = 0.5-1 HXLFs and compare them with both the local HXLF measured from HEAO 1 A2 survey data and the z = 1.5-3 HXLF measured from soft-energy (0.5-2 keV) Chandra and ROSAT data. We find that the number density of LX > 1044 ergs s-1 sources (quasars) steadily declines with decreasing redshift, while the number density of LX = 1043-1044 ergs s-1 sources peaks at z = 0.5-1. Strikingly, however, the number density of broad-line AGNs remains roughly constant with redshift while their average luminosities decline at the lower redshifts, showing another example of cosmic downsizing.
The Single Depot Multiple Set Orienteering Problem

: In this article, we present the single Depot multiple Set Orienteering Problem (sDmSOP), a new variant of the classical Set Orienteering Problem (SOP). A significant feature of sDmSOP is the presence of many travelers who set off from the same depot and return there at the end of their journey. The objective of the problem is to maximize the profit while remaining within the budget; hence the challenge at hand involves searching multiple paths among the mutually clustered sets for travelers. A set’s profit can be collected with a single node visit only. Supply-chain management, the bus delivery problem, etc., are just a few examples where the sDmSOP has proven useful. By simulating the instances of the Generalized Traveling Salesman Problem (GTSP) using GAMS 37.1. 0, we determine the optimal profit for GTSP instances for some small and medium instances which follow the triangular and symmetric properties. We find that the use of multiple travelers is beneficial for both service providers and customers, as it allows service providers to offer their services to customers at a lower cost because the service provider gets a significant amount of profit using multiple travelers.
The Identification of New Be Stars in GAUDI

The Ground-based Asteroseismology Uniform Database Interface (GAUDI) is the result of the preparatory work performed for the COROT satellite. In the data available in GAUDI we discovered 17 B-type stars that show emission in their Balmer lines and were not known to display such emission before, including at least 16 nonsupergiant ones. We thus reclassify those stars as Be stars. These 17 new Be stars increase the number of Be stars in the field of view of COROT by ~25%, which is important for the target selection of the mission. Moreover, ~70% of the discovered Be stars are of late subtypes. Be stars have been mostly found among early subtypes until now, but this could be due to an observational bias. Finally, one of the discovered stars is either a slowly rotating shell Be star or a Herbig Be star with a low v sin i, which makes this star especially interesting.
Loss Reduction in Point Estimation Problems

Abstract When evaluating point estimators by means of general loss functions, the expected loss is not always minimal, similar to the case of mean-biased estimators, whose mean squared error can be reduced by accounting for the mean-bias. Depending on the loss function, the socalled Lehmann-bias can be significantly more important than the mean-bias of an estimator. Although a simple decomposition does not hold for expected losses as it does for the mean squared error, the expected loss can still be reduced by correcting for the Lehmann-bias. An asymptotic and a bootstrap-based correction are suggested and compared in small samples for the exponential distribution by means of Monte Carlo simulation.
Inverse scattering. II. Three dimensions

Assuming that a scattering amplitude, given as a function of the energy and the directions of the incident and scattered particles, is associated with a local potential without spherical symmetry via the Schrodinger equation in three space dimensions, this potential is uniquely reconstructed by two methods. One is based on a generalization of the Marchenko equation; the other, on a generalization of the Gel’fand–Levitan equation.
Examining the Potential for Conversational Exploratory Search using a Smart Speaker Digital Assistant

Online Digital Assistants, such as Amazon Alexa, Google Assistant, Apple Siri are very popular and provide a range or services to their users, a key function is their ability to satisfy user information needs from the sources available to them. Users may often regard these applications as providing search services similar to Google type search engines. However, while it is clear that they are in general able to answer factoid questions effectively, it is much less obvious how well they support less specific or exploratory type search tasks. We describe an investigation examining the behaviour of the standard Amazon Alexa for exploratory search tasks. The results of our study show that it not effective in addressing these types of information needs. We propose extensions to Alexa designed to overcome these shortcomings. Our Custom Alexa application extends Alexa's conversational functionality for exploratory search. A user study shows that our extended Alexa application both enables users to more successfully complete exploratory search tasks and is well accepted by our test users.
New record of six Brachionus species from the South of Iraq

In this survey, six species of Brachionus genus are indexed as new record in the south Iraqi water resource at the first time in Iraq depending on the sequence of 18S rRNA gene, these species were registered as a new record in Iraq and the other two species which are HH1 and HH2 were registered in the National Center for Biotechnology Information (NCBI), The European Nucleotide Archive (ENA) and DNA Data Bank of Japan (DDBJ) sequentially.
Learning Independently from Causality in Multi-Agent Environments

Multi-Agent Reinforcement Learning (MARL) comprises an area of growing interest in the field of machine learning. Despite notable advances, there are still problems that require investigation. The lazy agent pathology is a famous problem in MARL that denotes the event when some of the agents in a MARL team do not contribute to the common goal, letting the teammates do all the work. In this work, we aim to investigate this problem from a causality-based perspective. We intend to create the bridge between the fields of MARL and causality and argue about the usefulness of this link. We study a fully decentralised MARL setup where agents need to learn cooperation strategies and show that there is a causal relation between individual observations and the team reward. The experiments carried show how this relation can be used to improve independent agents in MARL, resulting not only on better performances as a team but also on the rise of more intelligent behaviours on individual agents.
Comparing Conventional and Conversational Search Interaction using Implicit Evaluation Methods

Conversational search applications offer the prospect of improved user experience in information seeking via agent support. However, it is not clear how searchers will respond to this mode of engagement, in comparison to a conventional user-driven search interface, such as those found in a standard web search engine. We describe a laboratory-based study directly comparing user behaviour for a conventional search interface (CSI) with that of an agent-mediated multiview conversational search interface (MCSI) which extends the CSI. User reaction and search outcomes of the two interfaces are compared using implicit evaluation using five analysis methods: claiming to have a better search experience in contrast to a corresponding standard search interface.