Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of entrepreneurial education on entrepreneurial intention: The case of Vietnamese

Article history: Received: October 9, 2019 Received in revised format: November 25 2019 Accepted: December 31, 2019 Available online: January 1, 2020 The objective of this paper was to assess the impact of entrepreneurial education on entrepreneurial intention on students in the North of Vietnam, through two mediating variables; namely entrepreneurship passion and entrepreneurial self-efficacy. The study was conducted on 688 students from both economic and technical university in the Northern region of Vietnam. Data collected using SPSS 22 and Smart PLS 3.0 software shows that entrepreneurial education had a strong impact on entrepreneurial intention in the North of Vietnam. At the same time, entrepreneurial education also had a positive impact on entrepreneurship passion and Entrepreneurial self-efficacy. Time and Team Cooperation maintained a statistically significant moderate role on the relationship between entrepreneurial education and entrepreneurial intention of students in the North of Vietnam. © 2020 by the authors; licensee Growing Science, Canada
Spatio-Temporal Usage Patterns of Dockless Bike-Sharing Service Linking to a Metro Station: A Case Study in Shanghai, China

The dockless bike-sharing (DLBS) system serves as a link between metro stations and travelers’ destinations (or originations). This paper aims to uncover spatio-temporal usage patterns of dockless bike-sharing service linking to metro stations for supporting scientific planning and management of the dockless bike-sharing system. A powerful visualization tool was used to analyze the differences in usage patterns in workdays and weekends. The travel distance distributions of using dockless bike-sharing near metro stations were investigated to shed light on the service area of the dockless bike-sharing system. Agglomerative hierarchical clustering was applied to analyze differences in usage patterns of metro stations located in different areas. The results show that the usage patterns of dockless bike-sharing on weekends are different from those on workdays. The average travel distance using the dockless bike-sharing system at weekends is significantly larger than that of workdays. The travel distance distribution could be nicely fitted by the Fréchet distribution of the Generalized Extreme Value (GEV) distribution family. The usage characteristics of shared bikes are correlated with land use and population density around metro stations. No matter in urban or suburban areas, there is a great demand for bike-sharing in densely populated areas with intensive land development, such as university towns in suburban areas. This study improves the understandings regarding the usage patterns of the DLBS system serving as a link between the final destinations (or originations) and metro stations. The results can be helpful to the operation and demand management of DLBS.
Stability and applications

We give a brief overview of Bridgeland's theory of stability conditions, focusing on applications to algebraic geometry. We sketch the basic ideas in Bayer's proof of the Brill--Noether Theorem and in the authors' proof of a theorem by Gruson--Peskine and Harris on the genus of space curves. This note originated from the lecture of the first author at the conference From Algebraic Geometry to Vision and AI: A Symposium Celebrating the Mathematical Work of David Mumford, held at the Center of Mathematical Sciences and Applications, Harvard University, August 18-20, 2018.
Primordial perturbations in the Dapor-Liegener model of hybrid loop quantum cosmology

In this work, we extend the formalism of hybrid loop quantum cosmology for primordial perturbations around a flat, homogeneous, and isotropic universe to the new treatment of Friedmann-Lemaitre-Robertson-Walker geometries proposed recently by Dapor and Liegener, based on an alternative regularization of the Hamiltonian constraint. In fact, our discussion is applicable also to other possible regularization schemes for loop quantum cosmology, although we specialize our analysis to the Dapor-Liegener proposal and construct explicitly all involved quantum operators for that case.
Spectral Structure and Many-Body Dynamics of Ultracold Bosons in a Double-Well

We examine the spectral structure and many-body dynamics of two and three repulsively interacting bosons trapped in a one-dimensional double-well, for variable barrier height, inter-particle interaction strength, and initial conditions. By exact diagonalization of the many-particle Hamiltonian, we specifically explore the dynamical behavior of the particles launched either at the single-particle ground state or saddle-point energy, in a time-independent potential. We complement these results by a characterization of the cross-over from diabatic to quasi-adiabatic evolution under finite-time switching of the potential barrier, via the associated time evolution of a single particle’s von Neumann entropy. This is achieved with the help of the multiconfigurational time-dependent Hartree method for indistinguishable particles (MCTDH-X)—which also allows us to extrapolate our results for increasing particle numbers.
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware

Markov chain Monte Carlo (MCMC) is widely regarded as one of the most important algorithms of the 20th century. Its guarantees of asymptotic convergence, stability, and estimator-variance bounds using only unnormalized probability functions make it indispensable to probabilistic programming. In this paper, we introduce the TensorFlow Probability MCMC toolkit, and discuss some of the considerations that motivated its design.
Deep Learning for Highly Accelerated Diffusion Tensor Imaging

—Diffusion tensor imaging (DTI) is widely used to examine the human brain white matter structures, including their microarchitecture integrity and spatial fiber tract trajectories, with clinical applications in several neurological disorders and neurosurgical guidance. However, a major factor that prevents DTI from being incorporated in clinical routines is its long scan time due to the acquisition of a large number (typically 30 or more) of diffusion-weighted images (DWIs) required for reliable tensor estimation. Here, a deep learning-based technique is developed to obtain diffusion tensor images with only six DWIs, resulting in a significant reduction in imaging time. The method uses deep convolutional neural networks to learn the highly nonlinear relationship between DWIs and several tensor-derived maps, bypassing the conventional tensor fitting procedure, which is well known to be highly susceptible to noises in DWIs. The performance of the method was evaluated using DWI datasets from the Human Connectome Project and patients with ischemic stroke. Our results demonstrate that the proposed technique is able to generate quantitative maps of good quality fractional anisotropy (FA) and mean diffusivity (MD), as well as the fiber tractography from as few as six DWIs. The proposed method achieves a quantification error of less than 5% in all regions of interest of the brain, which is the rate of in vivo reproducibility of diffusion tensor imaging. Tractography reconstruction is also comparable to the ground truth obtained from 90 DWIs. In addition, we also demonstrate that the neural network trained on healthy volunteers can be directly applied/tested on stroke patients' DWIs data without compromising the lesion detectability. Such a significant reduction in scan time will allow inclusion of DTI into clinical routine for many potential applications.
Properties of Chebyshev polynomials

Ordinary differential equations and boundary value problems arise in many aspects of mathematical physics. Chebyshev differential equation is one special case of the Sturm-Liouville boundary value problem. Generating function, recursive formula, orthogonality, and Parseval's identity are some important properties of Chebyshev polynomials. Compared with a Fourier series, an interpolation function using Chebyshev polynomials is more accurate in approximating polynomial functions. -------- Des equations differentielles ordinaires et des problemes de valeurs limites se posent dans de nombreux aspects de la physique mathematique. L'equation differentielle de Chebychev est un cas particulier du probleme de la valeur limite de Sturm-Liouville. La fonction generatrice, la formule recursive, l'orthogonalite et l'identite de Parseval sont quelques proprietes importantes du polynome de Chebyshev. Par rapport a une serie de Fourier, une fonction d'interpolation utilisant des polynomes de Chebyshev est plus precise dans l'approximation des fonctions polynomiales.
Towards a Fast Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI)

Steady-state visual evoked potentials (SSVEP) brain-computer interface (BCI) provides reliable responses leading to high accuracy and information throughput. But achieving high accuracy typically requires a relatively long time window of one second or more. Various methods were proposed to improve sub-second response accuracy through subject-specific training and calibration. Substantial performance improvements were achieved with tedious calibration and subject-specific training; resulting in the user's discomfort. So, we propose a training-free method by combining spatial-filtering and temporal alignment (CSTA) to recognize SSVEP responses in sub-second response time. CSTA exploits linear correlation and non-linear similarity between steady-state responses and stimulus templates with complementary fusion to achieve desirable performance improvements. We evaluated the performance of CSTA in terms of accuracy and Information Transfer Rate (ITR) in comparison with both training-based and training-free methods using two SSVEP data-sets. We observed that CSTA achieves the maximum mean accuracy of 97.43$\pm$2.26 % and 85.71$\pm$13.41 % with four-class and forty-class SSVEP data-sets respectively in sub-second response time in offline analysis. CSTA yields significantly higher mean performance (p<0.001) than the training-free method on both data-sets. Compared with training-based methods, CSTA shows 29.33$\pm$19.65 % higher mean accuracy with statistically significant differences in time window less than 0.5 s. In longer time windows, CSTA exhibits either better or comparable performance though not statistically significantly better than training-based methods. We show that the proposed method brings advantages of subject-independent SSVEP classification without requiring training while enabling high target recognition performance in sub-second response time.
Properties of ∆ -modular simplicies and ”close” polyhedrons. O ( ∆ polylog ∆ ) -algorithm for knapsack, proximity and sparsity.

In this work we consider properties of square and “close”-square ∆ - modular systems of linear inequalities Ax ≤ b . The system called ∆ -modular if absolute values of all rank minors of A are at most ∆ . First part of results is close to classical results of R. Gomory for systems of the type Ax = b, x ≥ 0. We observe that results of R. Gomory and our paper are much easier to proof, when the system has the type Ax ≤ b and “normalized”. More precisely, we study some class P of polyhedrons deﬁned by ∆ -modular systems of the type Ax ≤ b that includes simplicies, simple cones, parallelotopes (aﬃne images of cubes) and some more general polyhedrons. We show that for P ∈ P the Integer Linear Programming (the ILP) problem max { c ⊤ x : x ∈ P ∩ Z n } can be solved by an algorithm with the complexity O ( ∆ · log ∆ · M + poly( n, s )) , where M = ( m − n ) · mult(log ∆ ) + mult(log k c k ∞ ), s is input size and mult( t ) is complexity of t -bit integers multiplication. Let v be an optimal point of the Linear Programming (the LP) problem { c ⊤ x : x ∈ P } and z be an optimal point of the related ILP problem. Additionally, we show that k v − z k ∞ ≤ ∆ , k z k 0 ≤ log ∆ , k v − z k 1 ≤ ∆ log 2 ∆ and z leads on some face of P whose dimension is bounded by log 2 ∆ . Finally, we show that for ﬁxed A with high probability the system Ax ≤ b deﬁnes polyhedron from P and all mentioned properties are
Ant Routing scalability for the Lightning Network

The ambition of the Lightning Network is to provide a second layer to the Bitcoin network to enable transactions confirmed instantly, securely and anonymously with a world scale capacity using a decentralized protocol. Some of the current propositions and implementations present some difficulties in anonymity, scaling and decentalization. The Ant Routing algorithm for the Lightning Network was proposed in [7] for maximal decentralization, anonymity and potential scaling. It solves several problems of current implementation, such as channel information update and centralization by beacon nodes. Ant Routing nodes play all the same role and don't require any extra information on the network topology beside for their immediate neighbors. The goal of LN transactions are completed instantaneously and anonymously. We study the scaling of the Ant Routing protocol. We propose a precise implementation, with efficient memory management using AVL trees. We evaluate the efficiency of the algorithm and we estimate the memory usage of nodes by local node workload simulations. We prove that the number of transactions per second that Ant Routing can sustain is of the order of several thousands which is enough for a global payment network.
New approach for FIB-preparation of atom probe specimens for aluminum alloys

Site-specific atom probe tomography (APT) from aluminum alloys has been limited by sample preparation issues. Indeed, Ga, which is conventionally used in focused-ion beam (FIB) preparations, has a high affinity for Al grain boundaries and causes their embrittlement. This leads to high concentrations of Ga at grain boundaries after specimen preparation, unreliable compositional analyses and low specimen yield. Here, to tackle this problem, we propose to use cryo-FIB for APT specimen preparation specifically from grain boundaries in a commercial Al-alloy. We demonstrate how this setup, easily implementable on conventional Ga-FIB instruments, is efficient to prevent Ga diffusion to grain boundaries. Specimens were prepared at room temperature and at cryogenic temperature (below approx. 90K) are compared, and we confirm that at room temperature, a compositional enrichment above 15 at. % of Ga is found at the grain boundary, whereas no enrichment could be detected for the cryo-prepared sample. We propose that this is due to the decrease of the diffusion rate of Ga at low temperature. The present results could have a high impact on the understanding of aluminum and Al-alloys.
Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks

Acoustic anomaly detection aims at distinguishing abnormal acoustic signals from the normal ones. It suffers from the class imbalance issue and the lacking in the abnormal instances. In addition, collecting all kinds of abnormal or unknown samples for training purpose is impractical and timeconsuming. In this paper, a novel Gaussian Mixture Generative Adversarial Network (GMGAN) is proposed under semi-supervised learning framework, in which the underlying structure of training data is not only captured in spectrogram reconstruction space, but also can be further restricted in the space of latent representation in a discriminant manner. Experiments show that our model has clear superiority over previous methods, and achieves the state-of-the-art results on DCASE dataset.
Baryon acoustic oscillations signature in the three-point angular correlation function from the SDSS-DR12 quasar survey

The clustering properties of the Universe at large scales are currently being probed at various redshifts through several cosmological tracers and with diverse statistical estimators. Here we use the three-point angular correlation function (3PACF) to probe the baryon acoustic oscillation (BAO) features in the quasars catalogue from the Sloan Digital Sky Survey Data Release 12, with mean redshift $\overline{z} = 2.225$, detecting the BAO imprint with a statistical significance of $2.9 \sigma$, obtained using lognormal mocks. Following a quasi-model-independent approach for the 3PACF, we find the BAO transversal signature for triangles with sides θ1 = $1{^{\circ}_{. }}0$ and θ2 = $1{^{\circ}_{. }}5$ and the angle between them of α = 1.59 ± 0.17 rad, a value that corresponds to the angular BAO scale $\theta_{\rm BAO}=1{^{\circ}_{. }}82 \pm 0{^{\circ}_{. }}21$, in excellent agreement with the value found in a recent work ($\theta_{\rm BAO}=1{^{\circ}_{.}}77 \pm 0{^{\circ}_{.}}31$) applying the two-point angular correlation function (2PACF) to similar data. Moreover, we performed two types of test: one to confirm the robustness of the BAO signal in the 3PACF through random displacements in the data set, and the other to verify the suitability of our random samples, a null test that in fact does not show any signature that could bias our results.
Code Duplication on Stack Overflow

Despite the unarguable importance of Stack Overflow (SO) for the daily work of many software developers and despite existing knowledge about the impact of code duplication on software maintainability, the prevalence and implications of code clones on SO have not yet received the attention they deserve. In this paper, we motivate why studies on code duplication within SO are needed and how existing studies on code reuse differ from this new research direction. We present similarities and differences between code clones in general and code clones on SO and point to open questions that need to be addressed to be able to make data-informed decisions about how to properly handle clones on this important platform. We present results from a first preliminary investigation, indicating that clones on SO are common and diverse. We further point to specific challenges, including incentives for users to clone successful answers and difficulties with bulk edits on the platform, and conclude with possible directions for future work.Ccs Concepts• Software and its engineering → Maintaining software.
Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation

Target-guided open-domain conversation aims to proactively and naturally guide a dialogue agent or human to achieve specific goals, topics or keywords during open-ended conversations. Existing methods mainly rely on single-turn data-driven learning and simple target-guided strategy without considering semantic or factual knowledge relations among candidate topics/keywords. This results in poor transition smoothness and low success rate. In this work, we adopt a structured approach that controls the intended content of system responses by introducing coarse-grained keywords, attains smooth conversation transition through turn-level supervised learning and knowledge relations between candidate keywords, and drives an conversation towards an specified target with discourse-level guiding strategy. Specially, we propose a novel dynamic knowledge routing network (DRKN) which considers semantic knowledge relations among candidate keywords for accurate next topic prediction of next discourse. With the help of more accurate keyword prediction, our keyword-augmented response retrieval module can achieve better retrieval performance and more meaningful conversations. Besides, we also propose a novel dual discourse-level target-guided strategy to guide conversations to reach their goals smoothly with higher success rate. Furthermore, to push the research boundary of target-guided open-domain conversation to match real-world scenarios better, we introduce a new large-scale Chinese target-guided open-domain conversation dataset (more than 900K conversations) crawled from Sina Weibo. Quantitative and human evaluations show our method can produce meaningful and effective target-guided conversations, significantly improving over other state-of-the-art methods by more than 20% in success rate and more than 0.6 in average smoothness score.
New estimates for network sampling

Network sampling is used around the world for surveys of vulnerable, hard-to-reach populations including people at risk for HIV, opioid misuse, and emerging epidemics. The sampling methods include tracing social links to add new people to the sample. Current estimates from these surveys are inaccurate, with large biases and mean squared errors and unreliable confidence intervals. New estimators are introduced here which eliminate almost all of the bias, have much lower mean squared error, and enable confidence intervals with good properties. The improvement is attained by avoiding unrealistic assumptions about the population network and the design, instead using the topology of the sample network data together with the sampling design actually used. In simulations using the real network of an at-risk population, the new estimates eliminate almost all the bias and have mean squared-errors that are 2 to 92 times lower than those of current estimators. The new estimators are effective with a wide variety of network designs including those with strongly restricted branching such as Respondent-Driven Sampling and freely branching designs such as Snowball Sampling.
Selective Segmentation Networks Using Top-Down Attention

Convolutional neural networks model the transformation of the input sensory data at the bottom of a network hierarchy to the semantic information at the top of the visual hierarchy. Feedforward processing is sufficient for some object recognition tasks. Top-Down selection is potentially required in addition to the Bottom-Up feedforward pass. It can, in part, address the shortcoming of the loss of location information imposed by the hierarchical feature pyramids. We propose a unified 2-pass framework for object segmentation that augments Bottom-Up \convnets with a Top-Down selection network. We utilize the top-down selection gating activities to modulate the bottom-up hidden activities for segmentation predictions. We develop an end-to-end multi-task framework with loss terms satisfying task requirements at the two ends of the network. We evaluate the proposed network on benchmark datasets for semantic segmentation, and show that networks with the Top-Down selection capability outperform the baseline model. Additionally, we shed light on the superior aspects of the new segmentation paradigm and qualitatively and quantitatively support the efficiency of the novel framework over the baseline model that relies purely on parametric skip connections.
Event horizon detecting invariants

Some judiciously chosen local curvature scalars can be used to invariantly characterize event horizons of black holes in $D > 3$ dimensions, but they fail for the three dimensional Banados-Teitelboim-Zanelli (BTZ) black hole since all curvature invariants are constant. Here we provide an invariant characterization of the event horizon of the BTZ black hole using the curvature invariants of codimension one hypersurfaces instead of the full spacetime. Our method is also applicable to black holes in generic dimensions but is most efficient in three, four, and five dimensions. We give four dimensional Kerr, five dimensional Myers-Perry and three dimensional warped-anti-de Sitter, and the three dimensional asymptotically flat black holes as examples.