Miracle-dz/UniFAR-FTUs huggingface dataset:
Atomistic simulation of finite-temperature magnetism of nanoparticles: Application to cobalt clusters on Au(111)

We developed a technique to determine suitable spin models for small embedded clusters of arbitrary geometry by combining the spin-cluster expansion with the relativistic disordered local moment scheme. We present results for uncovered and covered hexagonal Co clusters on Au(111) surface, and use classical Monte Carlo simulations to study the temperature dependent properties of the systems. To test the new method we compare the calculated spin-model parameters of the uncovered clusters with those of a Co monolayer deposited on Au(111). In general, the isotropic and DM interactions are larger between atoms at the perimeter than at the center of the clusters. For Co clusters covered by Au, both the contribution to the magnetic anisotropy and the easy axis direction of the perimeter atoms differ from those of the inner atoms due to reduced symmetry. We investigate the spin reversals of the covered clusters with perpendicular magnetic anisotropy and by measuring the magnetization variance in the easy direction we suggest a technique to determine the blocking temperature of superparamagnetic particles. We also determine the Neel relaxation time from the Monte Carlo simulations and find that it satisfies the Neel--Arrhenius law with an energy barrier close to the magnetic anisotropy energy of the clusters.
The coherent structure of the energy cascade in shear turbulence

The cascade of energy in turbulent flows, i.e., the transfer of kinetic energy from large to small flow motions (forward cascade) or vice versa (backward cascade), is the cornerstone of most theories and models of turbulence since the 1940s. Yet, understanding the spatial organization of kinetic energy transfer remains an outstanding challenge. Particularly interesting is the energy cascade in turbulence subjected to a mean shear, which is relevant for most geophysical and industrial flows. Here, we unveil the three-dimensional structure of regions of intense kinetic energy transfer at different scales using numerical data of shear turbulence. We show that forward and backward cascades are paired side-by-side in the spanwise direction and that this organization is self-similar across the flow scales. We further establish that the characteristic flow structure associated with the energy transfer is an inverted hairpin followed by an upright hairpin. The asymmetry between the forward and backward cascade arises from the opposite flow circulation within the hairpins. These results provide the most complete characterization of the flow structure surrounding the cascading process to date.
Pushing the Envelope for RGB-Based Dense 3D Hand Pose Estimation via Neural Rendering

Estimating 3D hand meshes from single RGB images is challenging, due to intrinsic 2D-3D mapping ambiguities and limited training data. We adopt a compact parametric 3D hand model that represents deformable and articulated hand meshes. To achieve the model fitting to RGB images, we investigate and contribute in three ways: 1) Neural rendering: inspired by recent work on human body, our hand mesh estimator (HME) is implemented by a neural network and a differentiable renderer, supervised by 2D segmentation masks and 3D skeletons. HME demonstrates good performance for estimating diverse hand shapes and improves pose estimation accuracies. 2) Iterative testing refinement: Our fitting function is differentiable. We iteratively refine the initial estimate using the gradients, in the spirit of iterative model fitting methods like ICP. The idea is supported by the latest research on human body. 3) Self-data augmentation: collecting sized RGB-mesh (or segmentation mask)-skeleton triplets for training is a big hurdle. Once the model is successfully fitted to input RGB images, its meshes i.e. shapes and articulations, are realistic, and we augment view-points on top of estimated dense hand poses. Experiments using three RGB-based benchmarks show that our framework offers beyond state-of-the-art accuracy in 3D pose estimation, as well as recovers dense 3D hand shapes. Each technical component above meaningfully improves the accuracy in the ablation study.
VQD: Visual Query Detection In Natural Scenes

We propose a new visual grounding task called Visual Query Detection (VQD). In VQD, the task is to localize a variable number of objects in an image where the objects are specified in natural language. VQD is related to visual referring expression comprehension, where the task is to localize only one object. We propose the first algorithms for VQD, and we evaluate them on both visual referring expression datasets and our new VQDv1 dataset.
Quantum entanglement, two-sided spacetimes and the thermodynamic arrow of time

We investigate the emergence of thermodynamic arrow of time in the context of AdS/CFT correspondence. We show that, on the CFT side, if the two copies of the field theory are not initially correlated the entropy can only increase such that a definite orientation for the thermodynamic arrow of time is imposed. Conversely, in a high-correlation environment, the entropy can either increase or decrease, such that there is no opportunity for the dominance of one direction of time over the other. On the gravity side, we construct the structure of geometric dual by considering the notion of spacetime sidedness and time-orientability. Accordingly, we conjecture that the entanglement of the CFTs in the thermofield double state, impose the connection of the two sides of the spacetime forming a one sided spacetime. In addition, disentangling the degrees of freedom of the two CFTs, results in disconnecting the two sides of the spacetime. In essence, the maximal entanglement between the two copies of CFT builds, in the geometric dual, a connection between the two sides of spacetime.
Improved Speaker-Dependent Separation for CHiME-5 Challenge

This paper summarizes several follow-up contributions for improving our submitted NWPU speaker-dependent system for CHiME-5 challenge, which aims to solve the problem of multi-channel, highly-overlapped conversational speech recognition in a dinner party scenario with reverberations and non-stationary noises. We adopt a speaker-aware training method by using i-vector as the target speaker information for multi-talker speech separation. With only one unified separation model for all speakers, we achieve a 10\% absolute improvement in terms of word error rate (WER) over the previous baseline of 80.28\% on the development set by leveraging our newly proposed data processing techniques and beamforming approach. With our improved back-end acoustic model, we further reduce WER to 60.15\% which surpasses the result of our submitted CHiME-5 challenge system without applying any fusion techniques.
Gradients of Fear and Anger in the Social Media Response to Terrorism

Research suggests that public fear and anger in wake of a terror attack can each uniquely contribute to policy attitudes and risk-avoidance behaviors. Given the importance of these negative-valanced emotions, there is value in studying how terror events can incite fear and anger at various times and locations relative to an attack. We analyze 36,259 Twitter posts authored in response to the 2016 Orlando nightclub shooting and examined how fearand anger-related language varied with time and distance from the attack. Fear-related words sharply decreased over time, though the trend was strongest at locations near the attack, while anger-related words slightly decreased over time and increased with distance from Orlando. Comparing these results to users’ pre-attack emotional language suggested that distant users remained both angry and fearful after the shooting, while users close to the attack remained angry but quickly reduced expressions of fear to pre-attack levels.
Private Firm Valuation in the Technology Sector: Illuminating the Interaction Between Multiple Performance and Peer Pool Setting

Prior research, investigating the absolute performance of multiples as well as the relative superiority of different types of multiples, yields contradictory results that might be attributed to varying peer pool settings. This paper emphasizes on the technology sector and extends existing research, in its entirety being limited to trading multiples on listed companies, to transaction multiples on private firms. Employing a set of 22,967 observations on private market transactions of technology firms collected from 2000 until 2018, I examine the systematic impact of peer pooling on (i) the relative superiority of cross-sectoral multiples, (ii) the absolute superiority of sectoral multiples and, (iii) the absolute superiority of cross-sectoral multiples being segmented by various country-specific high-tech indicators. The multiples employed capture both, enterprise value and equity value multiples. The performance of the multiples in the various peer pool settings is evaluated according to bias as well as accuracy, utilizing the standard holdout routine on the transactions. The results indicate that (i) contradictory results in prior research on multiple’s bias may be strongly attributed to the varying peer pools employed, (ii) the enterprise value to total assets multiple clearly dominates across all peer pools on a cross-sectoral basis, indicating that contradictory results on multiple’s accuracy may not be attributed to the varying peer pools employed and, (iii) the performance of sectoral multiples depends on the value driver employed, showing only a weak relationship with the peer pool setting. Therefore, valuation analysts are recommended to utilize larger peer pools when employing cross-sectoral multiples, to emphasize on the enterprise value to total assets multiple, to further break down the high-tech sector into sub-sectors and, to employ sectoral multiples or multiples segmented according to country-specific high-tech indicators alternately.
Why Do I Rate? - Shedding Light on the Factors Influencing the Participation on Physician Rating Websites

Nowadays it is possible for consumers to exchange consumption experiences with everybody on the internet. For almost every product or service people form communities or visit dedicated rating websites to exchange information. Concerning medical treatment services, physician ratings have emerged as a field of interest for both patients and physicians. However, little is known about why people even spend time and effort to rate their physician. We conducted a study in which we asked patients about their intention to use physician rating websites, building on the social exchange theory. The results suggest that the main motivations to rate are to help other patients finding the right doctor or trying to influence the doctor for better treatment in the future.
Systems of quotients and Martindale-like quotients of Jordan–Lie triple systems

Abstract This article is devoted to introducing the definition of systems of quotients of Jordan–Lie triple systems and surveying the vital properties which can be lifted from a Jordan–Lie triple system to systems of quotients of Jordan–Lie triple system. Furthermore, the notions of systems of Martindale-like quotients of a Jordan–Lie triple system with respect to a power filter of sturdy ideals are introduced and the relationship between the systems of quotients of a Jordan–Lie triple system and the systems of Martindale-like quotients of a Jordan–Lie triple system are investigated. What’s more, main propositions about algebras of quotients of δ Jordan–Lie algebras are presented and the relationship between algebras of quotients of Jordan–Lie algebras and systems of quotients of Jordan–Lie triple systems are highlighted. Besides, some connections between systems of Martindale-like quotients of a Jordan–Lie triple system T and algebras of Martindale-like quotients of its standard embedding Jordan algebra L(T) are considered, too.
Minimum Enclosing Ball Revisited: Stability and Sub-linear Time Algorithms

In this paper, we revisit the Minimum Enclosing Ball (MEB) problem and its robust version, MEB with outliers, in Euclidean space $\mathbb{R}^d$. Though the problem has been extensively studied before, most of the existing algorithms need at least linear time (in the number of input points $n$ and the dimensionality $d$) to achieve a $(1+\epsilon)$-approximation. Motivated by some recent developments on beyond worst-case analysis, we introduce the notion of stability for MEB (with outliers), which is natural and easy to understand. Roughly speaking, an instance of MEB is stable, if the radius of the resulting ball cannot be significantly reduced by removing a small fraction of the input points. Under the stability assumption, we present two sampling algorithms for computing approximate MEB with sample complexities independent of the number of input points $n$. In particular, the second algorithm has the sample complexity even independent of the dimensionality $d$. Further, we extend the idea to achieve a sub-linear time approximation algorithm for the MEB with outliers problem. Note that most existing sub-linear time algorithms for the problems of MEB and MEB with outliers usually result in bi-criteria approximations, where the "bi-criteria" means that the solution has to allow the approximations on the radius and the number of covered points. Differently, all the algorithms proposed in this paper yield single-criterion approximations (with respect to radius). We expect that our proposed notion of stability and techniques will be applicable to design sub-linear time algorithms for other optimization problems.
Introduction to the Minitrack on Collaboration Via Social Media

Collaborative via Social Media 2019 kicks off its inaugural year by introducing three impactful new articles discussing dimensions of social media’s facilitating role in group processes. This short article sets the stage for the minitrack, introduces the papers, and concludes with areas of future interest. Looking ahead to Collaboration via Social Media 2020, we introduce potential topics for further research.
Developing a User Typology for the Analysis of Participation in Enterprise Collaboration Systems

In this paper, we propose a user typology for Enterprise Collaboration Systems (ECS). We draw on and extend findings from previous research in the area of CSCW and Social Collaboration Analytics. The proposed typology includes: (1) a definition of user types, (2) dimensions of ECS use and (3) a classification of action (event) types. The typology contains the following user types: creator, contributor, lurker, inactive and non-user. These types are characterized by differences in the following dimensions: type of use, frequency of use, variety of use, choice of content type and platform preferences. The definition of user types along these dimensions facilitates the implementation of database queries (scripts) for Social Collaboration Analytics (SCA), with the aim of determining the distribution of types of users in an Enterprise Collaboration System. We present selected results of such SCA for an integrated collaboration platform and discuss the findings. We successfully demonstrate that our classification of user types allows us to draw conclusions on (1) the form and degree of participation of users in the ECS and, derived from that, (2) the likely purpose of the examined communities.
Product Uncertainty in Online Markets: The Influence of Situational Factors and Individual Characteristics on Purchase Decision Reversal

E-commerce has traditionally suffered from significantly higher product return rates than offline retail (30 % online vs. 10 % offline). Product uncertainty at the time of purchase has been identified as one of the key drivers of purchase decision reversals in online markets. In this study we analyze the impact of situational factors (1. Purchase channel choice, 2. Time pressure) and individual differences on product uncertainty and purchase decision reversal. Following the conceptualization of product uncertainty by Hong and Pavlou (2014), we distinguish between product fit uncertainty and product quality uncertainty. To test our hypotheses, we employ a large-scale empirical analysis based on panel data from a large European online fashion retailer. We find that product fit uncertainty is higher for mobile channel users, which is attenuated by prior brand experience. Time pressure leads to lower return rates despite higher product uncertainty.
The Smartphone as the Incumbent "Thing" among the Internet of Things

The smartphone has been the ubiquitous computing platform in the past decade. However, emerging consumer Internet of Things (IoT) technology trends, such as smartwatches and smart speakers, promise the establishment of new ubiquitous platforms. We model two competing horizontally-differentiated platforms that each offer a smartphone and another smart device. This market diverges markedly from standard mixed bundling results when devices from the same vendor have super-additive utility. We show that the degree of a smart device’s differentiation (relative to the smartphone) is the prime factor determining if it is profitable to deepen integration between a smart device with the incumbent smartphone platform. We provide managerial insights for technology strategy.
Towards Real-Time Automatic Portrait Matting on Mobile Devices

We tackle the problem of automatic portrait matting on mobile devices. The proposed model is aimed at attaining real-time inference on mobile devices with minimal degradation of model performance. Our model MMNet, based on multi-branch dilated convolution with linear bottleneck blocks, outperforms the state-of-the-art model and is orders of magnitude faster. The model can be accelerated four times to attain 30 FPS on Xiaomi Mi 5 device with moderate increase in the gradient error. Under the same conditions, our model has an order of magnitude less number of parameters and is faster than Mobile DeepLabv3 while maintaining comparable performance. The accompanied implementation can be found at \url{this https URL}.
What Sustains Individuals' Participation in Crowdsourcing Contests?

Crowdsourcing contests have become widely adopted for idea generation and problem-solving in various companies in different industries. The success of crowdsourcing depends on the sustained participation and quality-submissions of the individuals. Yet, little is known about the factors that influence individuals’ continued participation in these contests. We address this issue, by conducting an empirical study using data from an online crowdsourcing contest platform, Kaggle, which delivers data science and machine learning solutions and models to its clients. The findings show that the community activities and team activities do not contribute to motivating the continued participation, but tenure does significantly affect the continued participation. We also found statistically significant effects of amount of prize, number of competitions, previous team performance, and competition duration on individuals sustained participation in crowdsourcing contests. This research contributes to the literature by identifying the factors influencing individuals’ sustained participation in crowdsourcing contests.
Is the Whole Equal to the Sum of its Parts? Exploring the Impact of Inconsistency on Perceived Helpfulness of a Set of Reviews

Despite a growing literature on the helpfulness of individual reviews, scant attention has been paid to the helpfulness of a set of reviews. Moreover, it is generally assumed that the helpfulness of a review set can be derived from averaging the individual reviews’ helpfulness evaluations. Drawing on bounded rationality theories, we hypothesize that this assumption may not always be valid, and that consumers’ helpfulness perception of a review set is also determined by inter-review consistency. A carefully designed experiment revealed that the whole differs from the sum of its parts when the reviews are consistent with each other or are contradicting each other, but does not differ when the reviews contain mixed, non-contradicting opinions. These findings deepen our understanding of inconsistency in online reviews and have a potential to change how reviews should be presented to the consumers.
Bioprocess optimization of PHB production utilizing agro (sago) waste as a sustainable substrate by the native isolate, Bacillus cereus

Microbial synthesis of polyhydroxyalkanotes (PHA), the eco-friendly biopolymers are gaining more attention towards restricting the environmental pollution by substituting conventional non-biodegradable petroleum-based plastics. The current study was aimed to economize the polyhydroxybutyrate (PHB) production by optimizing the production media using sago waste, as a substrate and cheapest carbon source, by Bacillus cereus a native isolate obtained from soil where sago waste is disposed in and around Salem District, Tamil Nadu, India. PHB production media was optimized by Placket Burman design and Box Benhen design (pH (X1 = 7.07) temperature (X2 = 36.36°C) sago waste (X3 = 19.7g/l)) and observed a maximum OD values of 2.006 in shake flask culture. The dry weight of PHB extracted under optimized condition was also measured by chloroform extraction method and it was found to be 5.2g/l. In addition, Fourier- transform infrared spectroscopy (FTIR), thin layer chromatography (TLC) and Gas Chromatography- Mass Spectrometry (GC-MS) analysis revealed the characteristic features of PHB polymer. Thus this study concludes that the utilization of sage waste for synthesis of value-added products, PHB will not only help in reducing the cost of PHB production process, but also assist to manage the problem associated with solid waste disposal.
Generalized Method of Moment Estimation Method Lagrange Multiplier Test for Simultaneous Spatial of Dynamic Panel Data

In recent developments, the panel data spatial model has penetrated into simultaneous models that do not only move in single models, where the development of this model also indirectly makes the parameter estimation method move in a more complex direction in terms of mathematical proof, also in terms of proof empirically. This paper presents a Generalized Method of Moment (GMM) Lagrange Multiplier (LM) Test for simultaneous spatial modelling for dynamic panel. This is a spatial dependencies test for spatial models.
Fast Spatio-Temporal Residual Network for Video Super-Resolution

Recently, deep learning based video super-resolution (SR) methods have achieved promising performance. To simultaneously exploit the spatial and temporal information of videos, employing 3-dimensional (3D) convolutions is a natural approach. However, straight utilizing 3D convolutions may lead to an excessively high computational complexity which restricts the depth of video SR models and thus undermine the performance. In this paper, we present a novel fast spatio-temporal residual network (FSTRN) to adopt 3D convolutions for the video SR task in order to enhance the performance while maintaining a low computational load. Specifically, we propose a fast spatio-temporal residual block (FRB) that divide each 3D filter to the product of two 3D filters, which have considerably lower dimensions. Furthermore, we design a cross-space residual learning that directly links the low-resolution space and the high-resolution space, which can greatly relieve the computational burden on the feature fusion and up-scaling parts. Extensive evaluations and comparisons on benchmark datasets validate the strengths of the proposed approach and demonstrate that the proposed network significantly outperforms the current state-of-the-art methods.