Miracle-dz/UniFAR-FTUs huggingface dataset:
10 Reasons to Get lnterested in Graph Drawing

T his is an invitation to the research area of graph drawing. lt encompasses basic research such as graph theory, complexity theory, data structurcs, a.nd graph algorithms as wcll as applicd rcscarch such as soft ware libraries, implementations, and applications. Application domains include areas within computer science (e
Unconventional Charge Density Wave Order in the Pnictide Superconductor Ba(Ni_{1-x}Co_{x})_{2}As_{2}.

Ba(Ni_{1-x}Co_{x})_{2}As_{2} is a structural homologue of the pnictide high temperature superconductor, Ba(Fe_{1-x}Co_{x})_{2}As_{2}, in which the Fe atoms are replaced by Ni. Superconductivity is highly suppressed in this system, reaching a maximum T_{c}=2.3 K, compared to 24 K in its iron-based cousin, and the origin of this T_{c} suppression is not known. Using x-ray scattering, we show that Ba(Ni_{1-x}Co_{x})_{2}As_{2} exhibits a unidirectional charge density wave (CDW) at its triclinic phase transition. The CDW is incommensurate, exhibits a sizable lattice distortion, and is accompanied by the appearance of α Fermi surface pockets in photoemission [B. Zhou et al., Phys. Rev. B 83, 035110 (2011)PRBMDO1098-012110.1103/PhysRevB.83. 035110], suggesting it forms by an unconventional mechanism. Co doping suppresses the CDW, paralleling the behavior of antiferromagnetism in iron-based superconductors. Our study demonstrates that pnictide superconductors can exhibit competing CDW order, which may be the origin of T_{c} suppression in this system.
Unfocused images removal of z-axis overlapping Mie scattering particles by using three-dimensional nonlinear diffusion based on digital holography

We propose a three-dimensional nonlinear diffusion method to implement the similar autofocusing function of multiple micro-objects and simultaneously remove the defocused images, which can distinguish the locations of certain sized scattering particles that are overlapping along z-axis. It is applied to all of the reconstruction slices that are generated from the captured hologram after each back propagation. For certain small sized particles, the maxima of maximum gradient magnitude of each reconstruction slice appears at the ground truth z position after applying the proposed scheme when the reconstruction range along z-axis is sufficiently long and the reconstruction depth spacing is sufficiently fine. Therefore, the reconstructed image at ground truth z position is remained, while the defocused images are diffused out. The results demonstrated that the proposed scheme can diffuse out the defocused images which are 20 um away from the ground truth z position in spite of that several scattering particles with different diameters are completely overlapping along z-axis with a distance of 800 um when the hologram pixel pitch is 2 um. It also demonstrated that the sparsity distribution of the ground truth z slice cannot be affected by the sparsity distribution of corresponding defocused images when the diameter of the particle is not more than 35um and the reconstruction depth spacing is not less than 20 um.
Horseshoe Regularisation for Machine Learning in Complex and Deep Models 1

Since the advent of the horseshoe priors for regularisation, global–local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high‐dimensional regression and classification problems. They have achieved remarkable success in computation and enjoy strong theoretical support. Most of the existing literature has focused on the linear Gaussian case; for which systematic surveys are available. The purpose of the current article is to demonstrate that the horseshoe regularisation is useful far more broadly, by reviewing both methodological and computational developments in complex models that are more relevant to machine learning applications. Specifically, we focus on methodological challenges in horseshoe regularisation in non‐linear and non‐Gaussian models, multivariate models and deep neural networks. We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.
Prognostic Factors of Functional Outcome Assessed by Using the Modified Rankin Scale in Subacute Ischemic Stroke

Background Most stroke survivors spent their lifetime with disability which not only affects the clients themselves and the family but also brings economic cost to the country. Therefore, this retrospective cohort study aimed to identify independent prognostic determinants associated with functional recovery in ischemic stroke within 6 months after onset. Methods Data from all first-onset ischemic stroke patients admitted to the acute stroke unit of the tertiary, university hospital were reviewed for 5 years consecutively. The functional outcome of the patients was recorded during 6-month follow-up by using the modified Rankin Scale (mRS). Baseline characteristics, motor assessment and all stroke-related variables were assessed during first week after stroke and 6-month follow-up. In order to derive clinical predictors, the backward stepwise multivariable risk regression analyses were used with the generalized linear model. Results The result revealed that in the 358 patients recruited into this study, 255 (71.2%) were in the functional recovery group (mRS score of 1 - 3) within 6 months after onset. The final model of multivariable risk regression analysis, with generalized linear model, demonstrated that the independent variables of functional recovery were leg score with a risk ratio (RR = 1.92, 95% confidence interval (CI): 1.14 - 3.21, P = 0.013), arm score (RR = 1.75, 95% CI: 1.02 - 3.01, P = 0.042) and age older than 75 years (RR = 1.36, 95% CI: 1.04 - 1.77, P = 0.025). Conclusions Achieving functional recovery during 6 months post stroke was related to age and motor improvement. With limited resources, continuity of rehabilitation training in the community system or allocation of caregiver training should be a part of discharge planning to promote recovery.
OperatorNet: Recovering 3D Shapes From Difference Operators

This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices. To this end we introduce a novel neural architecture, called OperatorNet, which takes as input a set of linear operators representing a shape and produces its 3D embedding. We demonstrate that this approach significantly outperforms previous purely geometric methods for the same problem. Furthermore, we introduce a novel functional operator, which encodes the extrinsic or pose-dependent shape information, and thus complements purely intrinsic pose-oblivious operators, such as the classical Laplacian. Coupled with this novel operator, our reconstruction network achieves very high reconstruction accuracy, even in the presence of incomplete information about a shape, given a soft or functional map expressed in a reduced basis. Finally, we demonstrate that the multiplicative functional algebra enjoyed by these operators can be used to synthesize entirely new unseen shapes, in the context of shape interpolation and shape analogy applications.
Exponentially Decaying Extended Emissions Following Short Gamma-Ray Bursts with a Possible Luminosity–E-folding Time Correlation

The origin of extended emissions following prompt emissions of short gamma-ray bursts (SGRBs) is a mystery. The long-term activity of the extended emission is responsible for promising electromagnetic counterparts to gravitational waves, thus it may be a key to uncovering the progenitor of SGRBs. We investigate the early X-ray light curves of 26 SGRBs with known redshifts observed with the X-Ray Telescope on board the Neil Gehrels Swift Observatory (Swift). We find that the exponential temporal decay model is able to describe the extended emission comprehensively, with a rest-frame e-folding time of 20–200 s. We also estimate the isotropic equivalent energies of the extended emission with the exponential decay model and of the prompt emission, and compare with those of the prompt emission. Then, it is revealed that the extended emission is 0–3 orders of magnitude less powerful than the prompt emission. We find a strong correlation between the expected maximum luminosity and e-folding time, which can be described by a power law with an index of −3.3 and whose chance probability is 8.2 × 10−6 if there is no observation bias of Swift. The exponential temporal decay may be interpreted as coming from the spin-down timescale of the rotation energy of a highly magnetized neutron star, and/or fallback accretion onto a disk surrounding a black hole with an exponentially decaying magnetic flux by magnetic reconnection.
Synthetic Aperture Radar: Systems and Signal Processing

The Radar Equation. The Matched Filter and Pulse Compression. Imaging and the Rectangular Algorithm. Ancillary Processes in Image Formation. SAR Flight System. Radiometric Calibration of SAR Data. Geometric Calibration of SAR Data. The SAR Ground System. Other Imaging Algorithms. Appendices. List of Acronyms. Index.
Second critical temperature in conventional superconductors

We demonstrate that in a BCS superconductor, superconducting gap survives at surfaces at higher temperatures than uniform superconductivity in the bulk. We show that by revising the Caroli-De Gennes-Matricon theory of a superconductor-vacuum boundary. We describe analytically the effect of enhancement of superconducting gap on the surfaces and find inhomogeneous solutions of the BCS gap equation that exist near boundaries above the temperature of the bulk phase transition. The analytical results are confirmed by numerical solutions of the microscopic model.
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised

Credit card has become popular mode of payment for both online and offline purchase, which leads to increasing daily fraud transactions. An Efficient fraud detection methodology is therefore essential to maintain the reliability of the payment system. In this study, we perform a comparison study of credit card fraud detection by using various supervised and unsupervised approaches. Specifically, 6 supervised classification models, i.e., Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), as well as 4 unsupervised anomaly detection models, i.e., One-Class SVM (OCSVM), Auto-Encoder (AE), Restricted Boltzmann Machine (RBM), and Generative Adversarial Networks (GAN), are explored in this study. We train all these models on a public credit card transaction dataset from Kaggle website, which contains 492 frauds out of 284,807 transactions. The labels of the transactions are used for supervised learning models only. The performance of each model is evaluated through 5-fold cross validation in terms of Area Under the Receiver Operating Curves (AUROC). Within supervised approaches, XGB and RF obtain the best performance with AUROC = 0.989 and AUROC = 0.988, respectively. While for unsupervised approaches, RBM achieves the best performance with AUROC = 0.961, followed by GAN with AUROC = 0.954. The experimental results show that supervised models perform slightly better than unsupervised models in this study. Anyway, unsupervised approaches are still promising for credit card fraud transaction detection due to the insufficient annotation and the data imbalance issue in real-world applications.
Low-Rank Tucker Approximation of a Tensor From Streaming Data

This paper describes a new algorithm for computing a low-Tucker-rank approximation of a tensor. The method applies a randomized linear map to the tensor to obtain a sketch that captures the important directions within each mode, as well as the interactions among the modes. The sketch can be extracted from streaming or distributed data or with a single pass over the tensor, and it uses storage proportional to the degrees of freedom in the output Tucker approximation. The algorithm does not require a second pass over the tensor, although it can exploit another view to compute a superior approximation. The paper provides a rigorous theoretical guarantee on the approximation error. Extensive numerical experiments show that that the algorithm produces useful results that improve on the state of the art for streaming Tucker decomposition.
Unsupervised Adversarial Domain Adaptation Based on The Wasserstein Distance For Acoustic Scene Classification

A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions. In this paper we focus on the acoustic scene classification (ASC) task and propose an adversarial deep learning method to allow adapting an acoustic scene classification system to deal with a new acoustic channel resulting from data captured with a different recording device. We build upon the theoretical model of ℋΔℋ-distance and previous adversarial discriminative deep learning method for ASC unsupervised domain adaptation, and we present an adversarial training based method using the Wasserstein distance. We improve the state-of-the-art mean accuracy on the data from the unseen conditions from 32% to 45%, using the TUT Acoustic Scenes dataset.
Galveston Bay and Coastal Ocean Optical‐Geochemical Response to Hurricane Harvey From VIIRS Ocean Color

Dissolved and particulate organic carbon, suspended particulate matter concentrations, and their optical proxies colored dissolved organic matter absorption and backscattering coefficients were studied in Galveston Bay, Texas, following the extreme flooding of Houston and surrounding areas due to Hurricane Harvey (25–29 August 2017) using field and ocean color observations. A three‐step empirical‐semianalytic algorithm for determination of colored dissolved organic matter absorption and backscattering coefficients revealed the dynamics of dissolved organic carbon and particle distribution from Visible and Infrared Imaging Radiometric Suite ocean color. Environmental drivers, especially floodwater discharge and winds, strongly influenced the spatiotemporal distribution of dissolved/particulate material in the bay and shelf waters following the hurricane passage. Over 10 days during/following the hurricane, ~25.2 × 106 kg C of total organic carbon and ~314.7 × 106 kg of suspended particulate matter were rapidly exported from Galveston Bay (representing ~0.65% and 0.27% of respective annual Mississippi River fluxes to the Gulf of Mexico), with potential for ecological impacts to shelf waters.
Levomilnacipran for the treatment of major depressive disorder: a review

: Levomilnacipran (LVM, Fetzima ® ) was recently approved by the US Food and Drug Administration for the treatment of major depressive disorder. It is a unique dual neurotransmitter reuptake inhibitor. In contrast with other selective serotonin norepinephrine reuptake inhibitors, including duloxetine, venlafaxine, and desvenlafaxine, it has greater selectivity for inhibiting norepinephrine reuptake than serotonin reuptake. Our review focuses on the efficacy, safety, and tolerability data for five double-blind, placebo-controlled, short-term studies and two long-term studies. In the short-term studies, LVM was found to be more effective than placebo in reducing depression (Montgomery-Åsberg Depression Rating Scale) scores as well as improving functional impairment (Sheehan Disability Scale) scores. Long-term studies found LVM to be similarly effective but in the only placebo-controlled long-term study, LVM was not significantly superior to placebo. LVM is fairly well tolerated, with the most common adverse events being nausea, headache, dry mouth, hyperhidrosis, and constipation. Discontinuation rates were mildly increased in those being treated with LVM (9%) versus placebo (3%). Adverse events were not dose-related except for urinary hesitancy and erectile dysfunction. LVM was weight neutral, was not toxic to the liver, and did not cause clinically significant QTc prolongation. Consistent with being a predominant potentiator of norepinephrine, pulse and blood pressure were significantly elevated by LVM but rarely induced tachycardia or hypertension. LVM is a relatively safe alternative antidepressant treatment with minimal drug–drug interactions. It is the only antidepressant that has in its labeling that it is not only effective in improving depression but also effective in improving impaired functioning. Whether this important effect on functioning is unique to LVM must be researched. In addition, whether LVM might be effective in norepinephrine-deficit depression, refractory depression, atypical depression, or seasonal depression is yet to be evaluated. Ultimately, head-to-head studies comparing LVM with other antidepressants will determine the place of LM in antidepressant treatment.
Determinants of contractual restraints in franchise contracting

Although an efficient design of franchise contracts requires from the franchisor to choose a bundle of contractual restraints as safeguarding and control mechanism, previous research has not explored the antecedents of contractual restraints as a bundle of contractual clauses. To address this gap, the aim of this study is to explain the determinants of the most important contractual restraints (i.e., exclusive dealing, exclusive territory, tying, resale price maintenance, call option, leasing, alienation, and noncompetition clauses), using transaction cost and relational governance reasoning. The regression results based on primary data from German and Swiss franchise systems provide support of hypotheses.
Exact Exponent for Soft Covering

This paper establishes the exact exponents for the soft-covering phenomenon of a memoryless channel under the total variation metric when random (i.i.d. and constant-composition) channel codes are used. The exponents, established herein, are strict improvements in both directions on bounds found in the literature. This complements the recent literature establishing the exact exponents under the relative entropy metric; however, the proof techniques have significant differences, and thus, neither result trivially implies the other. The found exponents imply new and improved bounds for various problems that use soft-covering as their achievability argument, including new lower bounds for the resolvability exponent and the secrecy exponent in the wiretap channel.
Assessing the Influence of Wake Dynamics on the Performance and Aeroelastic Behavior of Wind Turbines

While wind turbine farms are currently rapidly expanding, there are numerous technological challenges that must be overcome before wind energy represents a signi cant contributor to energy generation in the United States. One of the primary challenges is accurately accounting for the aerodynamic environment. This dissertation is focused on improving the aerodynamic modeling through the incorporation of wake e ects. A comprehensive veri cation and validation of the NREL FAST code, which has been enhanced to include a Free Vortex Wake (FVW) model was conducted. The veri cation and validation is carried out through a comparison of wake geometry, blade lift distribution, wind turbine power and force and moment coe cients using a combination of Computational Fluid Dynamics (CFD) and experimental data. The results are also compared against Blade Element Momentum Theory (BEM), and results from an extensive experimental campaign by NREL on the prediction capabilities of wind turbine modeling tools. Results indicate that the enhanced aeroelastic code generally provides improved predictions. However, in several notable cases the predictions are only marginally improved, or even worse, than those generated using Blade Element Momentum Theory aerodynamics. After veri cation and validation of the model, the impact of including the free vortex wake model in the presence of turbulent ow was also examined. The inclusion of turbulence created large di erences between BEM and FVW in predictions of rotor loading and power, however the
Multi-Scale Deep Neural Networks for Real Image Super-Resolution

Single image super-resolution (SR) is extremely difficult if the upscaling factors of image pairs are unknown and different from each other, which is common in real image SR. To tackle the difficulty, we develop two multi-scale deep neural networks (MsDNN) in this work. Firstly, due to the high computation complexity in high-resolution spaces, we process an input image mainly in two different downscaling spaces, which could greatly lower the usage of GPU memory. Then, to reconstruct the details of an image, we design a multi-scale residual network (MsRN) in the downscaling spaces based on the residual blocks. Besides, we propose a multi-scale dense network based on the dense blocks to compare with MsRN. Finally, our empirical experiments show the robustness of MsDNN for image SR when the upscaling factor is unknown. According to the preliminary results of NTIRE 2019 image SR challenge, our team (ZXHresearch@fudan) ranks 21-st among all participants. The implementation of MsDNN is released at: https://github.com/shangqigao/gsq-image-SR
Four Decades of Progress in Monitoring and Modeling of Processes in the Soil-Plant- Atmosphere System: Applications and Challenges TDR-LAB 2.0 Improved TDR Software for soil water content and electrical conductivity measurements

Time Domain Reflectometry is a technique widely used in hydrology and agronomy that allows real time estimation of soil volumetric water content ( ), which is related to soil’s apparent permittivity ( a) and bulk electrical conductivity ( ). This work presents an enhanced release of TDR-Lab, software which controls instrumentation for field measurements of and , enabling a convenient recording and retrieving of data. TDR-Lab 2.0 supports Tektronics 1502C, TDR-100 Campbell Sci. and TRASE Soil-moisture Equipment Corp., which can be connected to a multiplexing system (SDMX50, Campbell Sci) allowing automated scheduled measurements from up to 251 different probes. This new release, when connected to TDR-100, allows increasing the waveform accuracy up to 2048 points/waveform. Multiple comparison of TDR waveform within the same TDR screen is now allowed. Graphical or numerical methods can be used for the estimation of and from soil measurements coming taken with different probes. Additional features to carry out water-surface-level measurements such as matric potential and soil solution electrical conductivity are also available when measurements are made using the corresponding specific probes. Two different versions, a lite and a full release, for field and laboratory applications have been developed. The light version with a reduced set of features (TDR-Lab Lite) was designed to run in low-end ultraportable devices. TDRLab Lite works with XML-files instead of the SQL database engine of the extended TDR-Lab, and has lower system requirements and a faster boot-up time. A robust import/export graphical user interface (GUI) facilitates transferring projects between the centralized SQL database and XML files. A new project manager window has been implemented, where the bar menu has been complemented with a useful set of icons. The display system projects has