Miracle-dz/UniFAR-FTUs huggingface dataset:
How Educators Design Group Learning Scenarios in Higher Education?

Collaborative learning (CL) processes are not always effective and the inadequate design of CL scenarios is one of the main causes of their unsuccess. An essential problem is that designing CL scenarios is a complex task, since it involves countless requirements and constraints that affect learning process and hence learning outcomes. Consequently, CL scenarios are usually inappropriately structured. This paper is particularly interested in analyzing the complexities inherent to the process of designing CL scenarios. Its goal is to investigate and obtain information regarding how educators perform designing of CL scenarios while teaching an undergraduate course. In order to accomplish this, a set of 12 design principles was defined. Moreover, an exploratory study (semi-structured interviews with 30 educators of a university) was conducted and the principles were used to analyze the interviews' data. The results show that educators, in general, perform an inappropriate design of CL scenarios - failing to specify essential parameters and processes, mainly regarding the guidance of the students' actions and evaluation of their learning - therefore, indicating the need to provide educators with proper and useful guidance.
Intellectual Approach to the Design of Fuzzy Systems Based on Multi-objective Evolutionary Modeling

Over the past decades, fuzzy systems (FSs) have become widespread in many areas of practical application due to their universal approximating properties, as well as their ability to process inaccuracies and uncertainties in the behavior of complex systems without requiring accurate mathematical models. However, the design of such systems is not a simple objective since there is a lack of sufficiently complete expert information about the object being modeled, and designing an FS involves automated design methods based on experimental data. In this regard, for the FS design the authors propose a new intellectual approach based on the development of the theory of multi-objective evolutionary modeling which is based on the Pareto-optimality principle involving evidence combining theory or Dempster-Shafer (DS) theory) and provides selecting from the Pareto-optimal sets the solutions that best suit the multi-objective design aims, and increase the objectivity of the selection among the best solutions in multi-objective algorithms. The results of experiments in solving the problem in the field of railway automation related to the prediction of the cut rolling speeds in splitting up of train on a hump yard are presented.
SIW Bow-tie Slot Cavity Backed Antenna with Improved Gain Characteristics

A bow-tie slot cavity backed antenna using substrate integrated waveguide technology is developed in this communication. A proposed antenna designed and investigated for band application, operating in the frequency ranging from 10.1 GHz to 10.7 GHz. The simulated result shows that 7.1 dBi and 6.1 dBi peak gain at resonant frequencies of 10.2 and 10.7 GHz with an impedance bandwidth of 7%. Moreover, the antenna is single layered, small-footprint, light-weight and can be easily integrated and fabricated with planar circuits. The proposed antenna is fabricated using PCB process. For the proposed antenna, a50 $\Omega$ microstrip feed line is used with tapered transition maintaining printed circuit board (PCB) level circuitry.
Application of Entropy Filter in the Analysis of X-Ray Images

The problem of automatic analysis of X-ray images of large reinforced concrete structures is considered. The aim is to improve the accuracy of the evaluation of the shape and size of projections of internal cracks in the armature. The analysis is based on the classification of image elements by brightness threshold. In this case, it is complicated by the fact that the image has low contrast. To increase it, it is proposed to use an entropy filter whose window size is consistent with the noise level in the image. The study, including a computational experiment, confirmed the effectiveness of the proposed method.
obust fragments-based tracking with adaptive eature selection

ingqing Lv ue Zhou hanghai Jiao Tong University nstitute of Image Processing and Pattern Recognition hanghai 200240, China Abstract. Representing an object with multiple image fragments or patches for target tracking in a video has proved to be able to maintain the spatial information. The major challenges in visual tracking are effectiveness and robustness. We propose a robust fragments-based tracking algorithm with adaptive feature selection. The best discriminate feature is used for tracking, which can improve tracking effectiveness. A set of likelihood images corresponding to the most discriminative features are fused to divide the object into some fragments, which can maintain the spatial information. By weighting the fragment and background colors, more robust target and candidate models are built. Given these advantages, the novel tracking algorithm can provide more accurate performance and can be directly extended to a multiple object-tracking system. © 2010 Society of Photo-Optical Instrumentation Engineers. DOI: 10.1117/1.3481118
p75NTR as a Molecular Memory Switch

In recent years, many molecular and environmental factors have been studied to understand how synaptic plasticity is modulated. Sleep, as an evolutionary conserved biological function, has shown to be a critical player for the consolidation and filtering of synaptic circuitry underlying memory traces. Although sleep disturbances do not alter normal memory consolidation, they may reflect fundamental circuit malfunctions that can play a significant role in exacerbating diseases, such as autism and Alzheimer’s disease. Very recently, scientists sought to answer part of this enigma and they identified p75 neurotrophic receptor (p75NTR) as a critical player in mediating impairments in hippocampal-dependent associative plasticity upon sleep deprivation. This paper will review the role of the p75NTR, critically discuss the impact and implications of this research as the bridge for sleep research and neurological diseases.
A Cycle-GAN Approach to Model Natural Perturbations in Speech for ASR Applications

Naturally introduced perturbations in audio signal, caused by emotional and physical states of the speaker, can significantly degrade the performance of Automatic Speech Recognition (ASR) systems. In this paper, we propose a front-end based on Cycle-Consistent Generative Adversarial Network (CycleGAN) which transforms naturally perturbed speech into normal speech, and hence improves the robustness of an ASR system. The CycleGAN model is trained on non-parallel examples of perturbed and normal speech. Experiments on spontaneous laughter-speech and creaky-speech datasets show that the performance of four different ASR systems improve by using speech obtained from CycleGAN based front-end, as compared to directly using the original perturbed speech. Visualization of the features of the laughter perturbed speech and those generated by the proposed front-end further demonstrates the effectiveness of our approach.
Empirical constraints on the high-density equation of state from multimessenger observables

We search for possible correlations between neutron star observables and thermodynamic quantities that characterize high density nuclear matter. We generate a set of model-independent equations of state describing stellar matter from a Taylor expansion around saturation density. Each equation of state which is a functional of the nuclear matter parameters is thermodynamically consistent, causal and compatible with astrophysical observations. We find that the neutron star tidal deformability and radius are strongly correlated with the pressure, the energy density and the sound velocity at different densities. Similar correlations are also exhibited by a large set of mean-field models based on non-relativistic and relativistic nuclear energy density functionals. These model independent correlations can be employed to constrain the equation of state at different densities above saturation from measurements of NS properties with multi-messenger observations. In particular, precise constraints on the radius of PSR J0030+0451 thanks to NICER observations would allow to better infer the properties of matter around two times the nuclear saturation density.
$p$-adic Banach space representations of $SL_2({\mathbb Q}_p)$

Let $F/{\mathbb Q}_p$ be a finite extension. This paper is about continuous admissible $p$-adic Banach space representations $\Pi$ of $G = GL_n(F)$ and their restriction to $H = SL_n(F)$. We first show that the restriction of any such absolutely irreducible $G$-representation decomposes as a finite direct sum of irreducible $H$-representations. Then we consider the restriction to $SL_2({\mathbb Q}_p)$ of certain $p$-adic unitary Banach space representations of $GL_2({\mathbb Q}_p)$, relying on the work of Colmez and Colmez-Dospinescu-Paskūnas. It is shown that if $\Pi$ is associated to an irreducible trianguline de Rham representation with distinct Hodge-Tate weights, then $\Pi|_H$ decomposes non-trivially if and only if this is true for the subrepresentation $(\Pi^{\rm lalg})|_H$ of locally algebraic vectors.
mRMR-DNN with Transfer Learning for IntelligentFault Diagnosis of Rotating Machines

In recent years, intelligent condition-based monitoring of rotary machinery systems has become a major research focus of machine fault diagnosis. In condition-based monitoring, it is challenging to form a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation. Along with that, the generated data have a large number of redundant features which degraded the performance of the machine learning models. To overcome this, we have utilized the advantages of minimum redundancy maximum relevance (mRMR) and transfer learning with deep learning model. In this work, mRMR is combined with deep learning and deep transfer learning framework to improve the fault diagnostics performance in term of accuracy and computational complexity. The mRMR reduces the redundant information from data and increases the deep learning performance, whereas transfer learning, reduces a large amount of data dependency for training the model. In the proposed work, two frameworks, i.e., mRMR with deep learning and mRMR with deep transfer learning, have explored and validated on CWRU and IMS rolling element bearings datasets. The analysis shows that the proposed frameworks are able to obtain better diagnostic accuracy in comparison of existing methods and also able to handle the data with a large number of features more quickly.
Singular Value Decomposition in Sobolev Spaces: Part II

Under certain conditions, an element of a tensor product space can be identified with a compact operator and the singular value decomposition (SVD) applies to the latter. These conditions are not fulfilled in Sobolev spaces. In the previous part of this work (part I), we introduced some preliminary notions in the theory of tensor product spaces. We analyzed low-rank approximations in H1 and the error of the SVD performed in the ambient L2 space. In this work (part II), we continue by considering variants of the SVD in norms stronger than the L2-norm. Overall and, perhaps surprisingly, this leads to a more difficult control of the H1-error. We briefly consider an isometric embedding of H1 that allows direct application of the SVD to H1-functions. Finally, we provide a few numerical examples that support our theoretical findings.
Neural Shape Parsers for Constructive Solid Geometry

Constructive solid geometry (CSG) is a geometric modeling technique that defines complex shapes by recursively applying boolean operations on primitives such as spheres and cylinders. We present CSGNet, a deep network architecture that takes as input a 2D or 3D shape and outputs a CSG program that models it. Parsing shapes into CSG programs is desirable as it yields a compact and interpretable generative model. However, the task is challenging since the space of primitives and their combinations can be prohibitively large. CSGNet uses a convolutional encoder and recurrent decoder based on deep networks to map shapes to modeling instructions in a feed-forward manner and is significantly faster than bottom-up approaches. We investigate two architectures for this task—a vanilla encoder (CNN) - decoder (RNN) and another architecture that augments the encoder with an explicit memory module based on the program execution stack. The stack augmentation improves the reconstruction quality of the generated shape and learning efficiency. Our approach is also more effective as a shape primitive detector compared to a state-of-the-art object detector. Finally, we demonstrate CSGNet can be trained on novel datasets without program annotations through policy gradient techniques.
Two-Layer Volt/VAR Control in Unbalanced Active Distribution Systems: Efficient Optimization and Accurate Tracking

This paper proposes a novel two-layer Volt/VAR control (VVC) framework to regulate the voltage profiles across an unbalanced active distribution system, which achieves both the efficient open-loop optimization and accurate closed-loop tracking. In the upper layer, the conventional voltage regulation devices with discrete and slow-response characteristics are optimally scheduled to regulate voltage profiles in an hourly timescale while improving economic operations based on the receding horizon optimization (RHO) in a centralized manner. A generalized linearized branch flow model (G-LBFM) is developed to incorporate tap changers into branches, which significantly reduces the computational complexity compared to the original mixed-integer non-convex case. In the lower layer, we develop an integral-like control algorithm rather than resorting to the droop-based rules for real-time reactive power dispatch of distributed energy resources (DERs) to achieve accurate voltage tracking and mitigate fast voltage fluctuations in a decentralized (purely local) fashion. Further, a sufficient stability condition of the integral rule is presented to guarantee the closed-loop stability. Case studies are carried out on the unbalanced IEEE 123-Node Test Feeder to validate the effectiveness of the proposed method.
Hot Subdwarf Stars Identified in Gaia DR2 with Spectra of LAMOST DR6 and DR7. I. Single-lined Spectra

182 single-lined hot subdwarf stars are identified by using spectra from the sixth and seventh data releases (DR6 and DR7) of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope survey. We classified all the hot subdwarf stars using a canonical classification scheme, and got 89 sdB, 37 sdOB, 26 sdO, 24 He-sdOB, 3 He-sdO, and 3 He-sdB stars, respectively. Among these stars, 108 hot subdwarfs are newly discovered, while 74 stars were reported by previous catalogs. The atmospheric parameters of these stars were obtained by fitting the hydrogen (H) and helium (He) lines with non-local thermodynamic equilibrium model atmospheres. The atmospheric parameters confirm the two He sequences and the two subgroups of He-sdOB stars in our samples, which were found by previous studies in the Teff– diagram. Our results demonstrate different origins of field hot subdwarf stars and extreme horizontal branch stars in globular clusters, and provide strict observational limits on the formation and evolution models of the different sub-types of these evolved objects. Based on the results, we evaluated the completeness of the Geier et al. catalog. We found the fraction of hot subwarf stars is between 10% and 60%, depending on the brightness of the sample. A more accurate estimation for the hot subdwarf fraction can be obtained when similar results from composite spectra will become available.
Algorithms for heavy-tailed statistics: regression, covariance estimation, and beyond

We study polynomial-time algorithms for linear regression and covariance estimation in the absence of strong (Gaussian) assumptions on the underlying distributions of samples, making assumptions instead about only finitely-many moments. We focus on how many samples are required to perform estimation and regression with high accuracy and exponentially-good success probability in the face of heavy-tailed data. For covariance estimation, linear regression, and several other problems in high-dimensional statistics, estimators have recently been constructed whose sample complexities and rates of statistical error match what is possible when the underlying distribution is Gaussian, but known algorithms for these estimators require exponential time. We narrow the gap between the Gaussian and heavy-tailed settings for polynomial-time estimators with: (a) a polynomial-time estimator which takes n samples from a d-dimensional random vector X with covariance Σ and produces Σ such that in spectral norm ||Σ − Σ ||2 ≤ Õ(d 3/4/√n) w.p. 1−2−d where the information-theoretically optimal error bound is Õ(√d/n), while previous approaches to polynomial-time algorithms were stuck at Õ(d/√n) and (b) a polynomial-time algorithm which takes n samples (X i ,Y i ) where Y i = ⟨ u,X i ⟩ + i where both X and have a constant number of bounded moments and produces û such that the loss ||u − û||2 ≤ O(d/n) w.p. 1−2−d for any n ≥ d 3/2 log(d). This (information-theoretically optimal) error is achieved by inefficient algorithms for any n ≫ d, while previous approaches to polynomial-time algorithms suffer loss Ω(d 2/n) and require n ≫ d 2. Our algorithms make crucial use of degree-8 sum-of-squares semidefinite programs. Both apply to any X which has constantly-many certifiably hypercontractive moments. We offer preliminary evidence that improving on these rates of error in polynomial time is not possible in the median of means framework our algorithms employ. Our work introduces new techniques to high-probability estimation, and suggests numerous new algorithmic questions in the following vein: when is it computationally feasible to do statistics in high dimensions with Gaussian-style errors when data is far from Gaussian?
Nitazoxanide for Cryptosporidiosis After Hematopoietic Stem Cell Transplantation: A Case Series and Review of Literature

AbstractDiarrhea caused by the protozoa Cryptosporidium has been well documented and studied in patients with human immunodeficiency virus infection. Cryptosporidium has recently been reported as the causative agent of diarrheal illness in immunosuppressed patients without human immunodeficiency virus infection. In this study, we reported 2 cases of intestinal cryptosporidiosis in hematopoietic stem cell–transplanted patients successfully treated with nitazoxanide and reviewed the literature on previously reported cases.
Length statistics of random multicurves on closed hyperbolic surfaces

In this note, we determine the length distribution of components of a random multicurve on a fixed hyperbolic surface using Mirzakhani's equidistribution theorem for horospheres and Margulis' thickening technique. We obtain an explicit formula for the resulting lengths statistics and prove, in particular, that it depends only on the topological type of the multicurve and does not depend on the hyperbolic metric. This result generalizes prior result of M. Mirzakhani, providing the length statistics for multicurves decomposing the surface into pairs of pants. Results very close to ours are obtained independently and simultaneously by F. Arana-Herrera.
The Weak Circular Repetition Threshold Over Large Alphabets

The repetition threshold for words on n letters, denoted RT(n), is the infimum of the set of all r such that there are arbitrarily long r-free words over n letters. A repetition threshold for circular words on n letters can be defined in three natural ways, which gives rise to the weak, intermediate, and strong circular repetition thresholds for n letters, denoted CRTW(n), CRTI(n), and CRTS(n), respectively. Currie and the present authors conjectured that CRTI(n) = CRTW(n) = RT(n) for all n ≥ 4. We prove that CRTW(n) = RT(n) for all n ≥ 45, which confirms a weak version of this conjecture for all but finitely many values of n.
Anomalous phase matching through inter sub-cycle interference

Providing efficient nonlinear optical frequency conversion is essential for numerous applications. Fundamentally, dispersion limits the efficiency of such processes through mismatch of the phase velocities and the group velocities of the involved optical waves. We reveal a dispersion condition at which, surprisingly, phase and group velocity mismatch act together to provide efficient optical frequency conversion. We demonstrate this condition numerically for the process of high-harmonic-generation. This finding could be valuable to the theory and practice of nonlinear optical frequency conversion in general.
Evolution of the Accretion Disk–Corona during the Bright Hard-to-soft State Transition: A Reflection Spectroscopic Study with GX 339–4

We present the analysis of several observations of the black hole binary GX 339–4 during its bright intermediate states from two different outbursts (2002 and 2004), as observed by the RXTE/Proportional Counter Array. We perform a consistent study of its reflection spectrum by employing the relxill family of relativistic reflection models to probe the evolutionary properties of the accretion disk including the inner disk radius (Rin), ionization parameter (ξ), temperatures of the inner disk (Tin), corona (kTe), and its optical depth (τ). Our analysis indicates that the disk inner edge approaches the innermost stable circular orbit during the early onset of the bright hard state, and that the truncation radius of the disk remains low (<9 Rg) throughout the transition from hard to soft state. This suggests that the changes observed in the accretion disk properties during the state transition are driven by variation in accretion rate, and not necessarily due to changes in the inner disk’s radius. We compare the aforementioned disk properties in two different outbursts with state transitions occurring at dissimilar luminosities and find identical evolutionary trends in the disk properties, with differences only seen in the corona’s kTe and τ. We also perform an analysis by employing a self-consistent Comptonized accretion disk model accounting for the scatter of disk photons by the corona, and measure the low inner disk truncation radius across the bright intermediate states, using the temperature-dependent values of the spectral hardening factor, thereby independently confirming our results from the reflection analysis.
Hemiballismus That Develops upon Thalamic Hemorrhage: A Case Report

Hemichorea is characterized by sudden, spasmodic, irregular, short-term finger, hand, arm, face, tongue or head movements including one half of body. Ballismus concept defines the high-amplitude, violent, centrifugal or throwing motions. Hemichorea-hemiballismus are the most frequently reported movement disorders in patients with acute stroke. Even if it is believed to occur depending on the effect on contralateral subthalamic nucleus; it has been reported that in the following years there have been choreic and ballistic movements in various lesions intersecting the afferent and efferent subtalamopalidal paths of basal ganglion. In this article; hemiballismus, which has developed after thalamic hemorrhage, of an 63 year old female patient is presented.