Miracle-dz/UniFAR-FTUs huggingface dataset:
Connectivity, throughput, and end-to-end latency in infrastructureless wireless Networks with beamforming-enabled devices

Infrastructureless wireless networks are an important class of wireless networks best fitted to operational situations with temporary, localized demand for communication ability. These networks are composed of wireless communication devices that autonomously form a network without the need for pre-deployed infrastructure such as wireless base-stations and access points. Significant research and development has been devoted to mobile ad hoc wireless networks (MANETs) in the past decade, a particular infrastructureless wireless network architecture. While MANETs are capable of autonomous network formation and multihop routing, the practical adoption of this technology has been limited since these networks are not designed to support more than about thirty users or to provide the quality of service (QoS) assurance required by many of the envisioned driving applications for infrastructureless wireless networks. In particular, communication during disaster relief efforts or tactical military operations requires guaranteed network service capabilities for mission-critical, time-sensitive data and applications. MANETs may be frequently disconnected due to device mobility and mismatches between routing and transport layer protocols, making them unsuitable for these scenarios. Network connectivity is fundamentally important to a network designed to provide QoS guarantees to the end-user. Without network connectivity, at least one pair of devices in the network experiences zero sustainable data rate and infinite end-to-end message delay, a catastrophic condition during a search and rescue mission or in a battlefield. We consider the use of wireless devices equipped with beamforming-enabled antennas to expand deployment regimes in which there is a high probability of instantaneous connectivity and desirable network scalability. Exploiting the increased communication reach of directional antennas and electronic beam steering techniques in fixed rate systems, we characterize the probability of instantaneous connectivity for a finite number of nodes operating in a bounded region and identify required conditions to achieve an acceptably high probability of connectivity. Our analysis shows significant improvements to highly-connected regimes of operation with added antenna directivity. Following the characterization of instantaneous network connectivity, we analyze the achievable network throughput and scalability of both fixed and variable rate beamforming-enabled power-limited networks operating in a bounded region. Our study of the scaling behavior of the network is concerned with three QoS metrics of central importance for a system designed to provide service assurance to the end-user: achievable throughput, end-to-end delay (which we quantify as the number of end-to-end hops), and network energy consumption. We find that the infrastructureless wireless network can achieve scalable performance that is independent of end-user device density with high probability, as well as identify the existence of a system characteristic…
Effects of 22Ne sedimentation and metallicity on the local 40 pc white dwarf luminosity function

Aims. We analyzed the effect of the sedimentation of 22Ne on the local white dwarf luminosity function by studying scenarios under different Galactic metallicity models. Methods. We use an advanced population synthesis code based on Monte Carlo techniques to derive the synthetic luminosity function. The code incorporates the most recent and reliable cooling sequences and an accurate modeling of the observational biases under different scenarios. We first analyzed the case for a model with constant solar metallicity and compared the models with and without 22Ne sedimentation with the observed luminosity function for a pure thin-disk population. Then we analyzed the possible effects of a thick-disk contribution. We also studied model scenarios with different metallicities, including 22Ne sedimentation. The analysis was quantified from a statistical χ2-test value for the complete and also for the most significant regions of the white dwarf luminosity function. Finally, a best-fit model along with a disk age estimate was derived. Results. Models with constant solar metallicity cannot simultaneously reproduce the peak and cutoff of the white dwarf luminosity function. The additional release of energy due to 22Ne sedimentation piles up more objects in brighter bins of the faint end of the luminosity function. The contribution of a single-burst thick-disk population increases the number of stars in the magnitude interval centered around Mbol = 15.75. The metallicity model that follows a Twarog profile is disposable. Our best-fit model was obtained when a dispersion in metallicities of about solar metallicity was considered along with a 22Ne sedimentation model, a thick-disk contribution, and an age of the thin disk of 8.8 ± 0.2 Gyr. Conclusions. Our population synthesis model is able to reproduce the local white dwarf luminosity function with a high degree of precision when a dispersion in metallicities around a model with solar values is adopted. Although the effects of 22Ne sedimentation are only marginal and the contribution of a thick-disk population is minor, both of them help in better fitting the peak and the cutoff regions of the white dwarf luminosity function.
Monolayer 
VTe2
: Incommensurate Fermi surface nesting and suppression of charge density waves

We investigated the electronic structure of monolayer ${\mathrm{VTe}}_{2}$ grown on bilayer graphene by angle-resolved photoemission spectroscopy (ARPES). We found that monolayer ${\mathrm{VTe}}_{2}$ takes the octahedral $1T$ structure in contrast to the monoclinic one in the bulk, as evidenced by the good agreement in the Fermi surface topology between ARPES results and first-principles band calculations for octahedral monolayer $1T\text{\ensuremath{-}}{\mathrm{VTe}}_{2}$. We have revealed that monolayer $1T\text{\ensuremath{-}}{\mathrm{VTe}}_{2}$ at low temperatures is characterized by a metallic state whereas the nesting condition is better than that of isostructural monolayer ${\mathrm{VSe}}_{2}$ which undergoes a charge density wave (CDW) transition to an insulator at low temperatures. The present result suggests an importance of the Fermi surface topology for characterizing the CDW properties of monolayer transition-metal dichalcogenides.
Localization and free energy asymptotics in disordered statistical mechanics and random growth models

This dissertation develops, for several families of statistical mechanical and random growth models, techniques for analyzing infinite-volume asymptotics. In the statistical mechanical setting, we focus on the low-temperature phases of spin glasses and directed polymers, wherein the ensembles exhibit localization which is physically phenomenological. We quantify this behavior in several ways and establish connections to properties of the limiting free energy. We also consider two popular zero-temperature polymer models, namely first- and last-passage percolation. For these random growth models, we investigate the order of fluctuations in their growth rates, which are analogous to free energy.
Quantum motional state tomography with nonquadratic potentials and neural networks

We propose to use the complex quantum dynamics of a massive particle in a non-quadratic potential to reconstruct an initial unknown motional quantum state. We theoretically show that the reconstruction can be efficiently done by measuring the mean value and the variance of the position quantum operator at different instances of time in a quartic potential. We train a neural network to successfully solve this hard regression problem. We discuss the experimental feasibility of the method by analyzing the impact of decoherence and uncertainties in the potential.
Griffith energies as small strain limit of nonlinear models for nonsimple brittle materials

We consider a nonlinear, frame indifferent Griffith model for nonsimple brittle materials where the elastic energy also depends on the second gradient of the deformations. In the framework of free discontinuity and gradient discontinuity problems, we prove existence of minimizers for boundary value problems. We then pass to a small strain limit in terms of suitably rescaled displacement fields and show that the nonlinear energies can be identified with a linear Griffith model in the sense of $\Gamma$-convergence. This complements the study in [Arch. Ration. Mech. Anal. 225 (2017), 425-467] by providing a linearization result in arbitrary space dimensions.
Teaching English to Young Learners: Some Points to be Considered

Teaching English as a foreign language to young learners needs special approach since young learners have special characteristics. It is stated that the improvement of abilities to learn foreign language is started from the early age. If teachers don’t use appropriate teaching methods to teach English to young learners, the achievement of learning process may not work well. That is way, in order to be successful in teaching English for young learners, it is quite necessary for teachers to comprehend young learners’ characteristics. It is important because it will influence how a teacher creates a lesson appropriate with young learners’ characteristics. This paper tries to elaborate some points to be considered by teachers of young learners in teaching English as a foreign language. Keywords— teaching English, young learners _________________________________________________________________________________
Understanding Attention and Generalization in Graph Neural Networks

We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness. We particularly focus on the ability of attention GNNs to generalize to larger, more complex or noisy graphs. Motivated by insights from the work on Graph Isomorphism Networks, we design simple graph reasoning tasks that allow us to study attention in a controlled environment. We find that under typical conditions the effect of attention is negligible or even harmful, but under certain conditions it provides an exceptional gain in performance of more than 60% in some of our classification tasks. Satisfying these conditions in practice is challenging and often requires optimal initialization or supervised training of attention. We propose an alternative recipe and train attention in a weakly-supervised fashion that approaches the performance of supervised models, and, compared to unsupervised models, improves results on several synthetic as well as real datasets. Source code and datasets are available at this https URL.
Development of an alternative PSO‐based algorithm for simulation of endurance time excitation functions

This paper presents a particle swarm optimizer for production of endurance time excitation functions (ETEFs). These excitations are intensifying acceleration time histories that are used as input motions in endurance time (ET) method. The accuracy of the ET methods heavily depends on the accuracy of ET excitations. Unconstrained nonlinear optimization is employed to simulate these excitations. Particle swarm optimization (PSO) method as an evolutionary algorithm is examined in this paper to achieve a more accurate ETEF, where optimal parameters of the PSO are first determined using a parametric study on the involved variables. The proposed method is verified and compared with the trust‐region‐reflective method as a classical optimization method and imperialist competitive algorithm as a recently developed evolutionary method. Results show that the proposed method leads to more accurate ET excitations.
Signatures from a merging galaxy cluster and its AGN population: LOFAR observations of Abell 1682

We present LOFAR data from 110–180 MHz of the merging galaxy cluster Abell 1682, alongside archival optical, radio, and X-ray data. Our images of 6 arcsec in resolution at low frequencies reveal new structures associated with numerous radio galaxies in the cluster. At a resolution of 20 arcsec we see diffuse emission throughout the cluster over hundreds of kiloparsecs, indicating particle acceleration mechanisms are in play as a result of the cluster merger event and powerful active galactic nuclei. We show that a significant part of the cluster emission is from an old radio galaxy with very steep spectrum emission (having a spectral index of α < −2.5). Furthermore, we identify a new region of diffuse steep-spectrum emission (α < −1.1) as a candidate for a radio halo which is co-spatial with the centre of the cluster merger. We suggest its origin as a population of old and mildly relativistic electrons left over from radio galaxies throughout the cluster which have been re-accelerated to higher energies by shocks and turbulence induced by the cluster merger event. We also note the discovery of six new giant radio galaxies in the vicinity of Abell 1682.
A joint XMM-NuSTAR observation of the galaxy cluster Abell 523: Constraints on inverse Compton emission

Aims. We present the results of a joint XMM-Newton and NuSTAR observation (200 ks) of the galaxy cluster Abell 523 at z = 0.104. The peculiar morphology of the cluster radio halo and its outlier position in the radio power P(1.4 GHz) – X-ray luminosity plane make it an ideal candidate for the study of radio and X-ray correlations and for the search of inverse Compton (IC) emission. Methods. We constructed bi-dimensional maps for the main thermodynamic quantities (i.e., temperature, pressure and entropy) derived from the XMM observations to describe the physical and dynamical state of the cluster’s intracluster medium (ICM) in detail. We performed a point-to-point comparison in terms of surface brightness between the X-ray and radio emissions to quantify their morphological discrepancies. Making use of NuSTAR’s unprecedented hard X-ray focusing capability, we looked for IC emission both globally and locally after properly modeling the purely thermal component with a multi-temperature description. Results. The thermodynamic maps obtained from the XMM observation suggest the presence of a secondary merging process that could be responsible for the peculiar radio halo morphology. This hypothesis is supported by the comparison between the X-ray and radio surface brightnesses, which shows a broad intrinsic scatter and a series of outliers from the best-fit relation, corresponding to those regions that could be influenced by a secondary merger. The global NuSTAR spectrum can be explained by purely thermal gas emission, and there is no convincing evidence that an IC component is needed. The 3σ upper limit on the IC flux in the 20−80 keV band is in the [2.2−4.0] × 10−13 erg s−1 cm−2 range, implying a lower limit on the magnetic field strength in the B > [0.23 − 0.31] μG range. Locally, we looked for IC emission in the central region of the cluster radio halo finding a 3σ upper limit on the 20−80 keV nonthermal flux of 3.17 × 10−14 erg s−1 cm−2, corresponding to a lower limit on the magnetic field strength of B ≳ 0.81 μG.
Cloud-based Image Classification Service Is Not Robust To Simple Transformations: A Forgotten Battlefield

Many recent works demonstrated that Deep Learning models are vulnerable to adversarial examples.Fortunately, generating adversarial examples usually requires white-box access to the victim model, and the attacker can only access the APIs opened by cloud platforms. Thus, keeping models in the cloud can usually give a (false) sense of security.Unfortunately, cloud-based image classification service is not robust to simple transformations such as Gaussian Noise, Salt-and-Pepper Noise, Rotation and Monochromatization. In this paper,(1) we propose one novel attack method called Image Fusion(IF) attack, which achieve a high bypass rate,can be implemented only with OpenCV and is difficult to defend; and (2) we make the first attempt to conduct an extensive empirical study of Simple Transformation (ST) attacks against real-world cloud-based classification services. Through evaluations on four popular cloud platforms including Amazon, Google, Microsoft, Clarifai, we demonstrate that ST attack has a success rate of approximately 100% except Amazon approximately 50%, IF attack have a success rate over 98% among different classification services. (3) We discuss the possible defenses to address these security challenges.Experiments show that our defense technology can effectively defend known ST attacks.
What is this thing called theory of constraints and how should it be implemented

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Drop Impact on Two-Tier Monostable Superrepellent Surfaces.

Superrepellency is a favorable non-wetting scenario featuring a dramatic reduction of the solid/liquid contact area. The robustness of superhydrophobicity plays a central role in self-cleaning and anti-icing. Drop impacts happen ubiquitously in natural environments and often cause a notable extension of the solid/liquid contact area. This is associated with an enhanced affinity between water and the micro-textures and therefore leads to irreversible breakdowns in the superhydrophobicity. This problem remains a major challenge and limits the practical applications of superrepellent materials. In order to find a solution, in this paper, a repeated Cassie-Wenzel-Cassie wetting state transition is studied at the microscale when a drop impacts a two-tier superhydrophobic surface. In this case, the surface is completely dry without any liquid residue after the drop rebounds. The present results exhibit a striking contrast to the conventional perspective. The influence of geometrical parameters of the textured surface on the spreading and retracting behaviors of the impact drops is quantified, as well as the time-dependence scaling laws. From a practical point of view, it is demonstrated that the self-cleaning and dropwise condensation may significantly benefit from this repeated wetting transition. Dirt particles or small droplets in deep textures are able to be taken away so that the functionality and the robustness of the superhydrophobicity may be significantly strengthened. The results reported in this study facilitate the design of functional superrepellent materials.
The Structure of the Orion Nebula in the Direction of θ1 Ori C

We have used existing optical emission and absorption lines, [C ii] emission lines, and H i absorption lines to create a new model for a central column of material near the Trapezium region of the Orion Nebula. This was necessary because recent high spectral resolution spectra of optical emission lines and imaging spectra in the [C ii] 158 μm line have shown that there are new velocity systems associated with the foreground Veil and the material lying between θ1 Ori C and the main ionization front of the nebula. When a family of models generated with the spectral synthesis code Cloudy were compared with the surface brightness of the emission lines and strengths of the Veil absorption lines seen in the Trapezium stars, distances from θ1 Ori C were derived, with the closest, highest ionization layer being 1.3 pc. The line-of-sight distance of this layer is comparable with the size of the inner Huygens region in the plane of the sky. These layers are all blueshifted with respect to the Orion Nebula Cluster of stars, probably because of the pressure of a hot central bubble created by θ1 Ori C's stellar wind. We find velocity components that are ascribed to both sides of this bubble. Our analysis shows that the foreground [C ii] 158 μm emission is part of a previously identified layer that forms a portion of a recently discovered expanding shell of material covering most of the larger Extended Orion Nebula.
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning. BatchBALD is a greedy linear-time $1 - \frac{1}{e}$-approximate algorithm amenable to dynamic programming and efficient caching. We compare BatchBALD to the commonly used approach for batch data acquisition and find that the current approach acquires similar and redundant points, sometimes performing worse than randomly acquiring data. We finish by showing that, using BatchBALD to consider dependencies within an acquisition batch, we achieve new state of the art performance on standard benchmarks, providing substantial data efficiency improvements in batch acquisition.
Evolution of similarity lengths in anisotropic magnetohydrodynamic turbulence

In an earlier paper (Wan et al., J. Fluid Mech., vol. 697, 2012, pp. 296–315), the authors showed that a similarity solution for anisotropic incompressible three-dimensional magnetohydrodynamic (MHD) turbulence, in the presence of a uniform mean magnetic field $\boldsymbol{B}_{0}$ , exists if the ratio of parallel to perpendicular (with respect to $\boldsymbol{B}_{0}$ ) similarity length scales remains constant in time. This conjecture appears to be a rather stringent constraint on the dynamics of decay of the energy-containing eddies in MHD turbulence. However, we show here, using direct numerical simulations, that this hypothesis is indeed satisfied in incompressible MHD turbulence. After an initial transient period, the ratio of parallel to perpendicular length scales fluctuates around a steady value during the decay of the eddies. We show further that a Taylor–Kármán-like similarity decay holds for MHD turbulence in the presence of a mean magnetic field. The effect of different parameters, including Reynolds number, mean field strength, and cross-helicity, on the nature of similarity decay is discussed.
Barron Spaces and the Compositional Function Spaces for Neural Network Models

One of the key issues in the analysis of machine learning models is to identify the appropriate function space for the model. This is the space of functions that the particular machine learning model can approximate with good accuracy, endowed with a natural norm associated with the approximation process. In this paper, we address this issue for two representative neural network models: the two-layer networks and the residual neural networks. We define Barron space and show that it is the right space for two-layer neural network models in the sense that optimal direct and inverse approximation theorems hold for functions in the Barron space. For residual neural network models, we construct the so-called compositional function space, and prove direct and inverse approximation theorems for this space. In addition, we show that the Rademacher complexity has the optimal upper bounds for these spaces.