Miracle-dz/UniFAR-FTUs huggingface dataset:
Green Human Resource Management in Chinese Enterprises

The need to integrate human resource management (HRM) with environmental management (EM) has been increasingly recognized over the last decade (Orlitzky & Swanson, 2006; Renwick et al., 2013; Tang et al., 2018). Against this background, the concept ‘green HRM’ has emerged and is defined as ‘HRM practices that are implemented by firms to realize corporate green strategies through providing opportunities to employees and motivating employees to become involved in environmental activities’ (Dumont et al., 2016; Renwick et al., 2013; Shen et al., 2018; Tang et al., 2018). It can be interchangeably used with environmentally friendly HRM and its practices include green recruitment and selection, green training, green performance management, green pay and rewards, and green involvement (Dumont et al., 2016; Renwick et al., 2013; Shen et al., 2018; Tang et al., 2018). Green HRM research is still considered piecemeal, with current research mainly conducted in Western countries (Renwick et al., 2013; Tang et al., 2018). Recently, there has been growing research interest in green HRM in Asia, such as Malaysia (e.g. Ooi, Amran, Goh, & Nejati, 2017; Yusoff et al., 2015) and India (e.g. Bose & Gupta, 2017; Mishra, 2017; Pandey, Viswanathan, & Kamboj, 2016). The studies of Ooi et al. (2017) and Yusoff et al. (2015) reveal that although green HRM is recognized by organizations as an important element of human resources (HR), its implementation is still in the introductory stages in the Malaysian financial service industry. In India, the potential of green HRM practices used to encourage employee proenvironmental behavior in firms has not yet been explored (Mishra, 2017). As argued by Renwick et al. (2013), ‘given the importance of Asian economic development for environmental management, this is an important gap for future studies to reduce’ (p.11). Using both interview and survey techniques the current research investigates what type and how green HRM practices have been implemented in 16 companies in China. Of these firms, 13 are indigenous Chinese firms and three are wholly foreignowned multinational enterprises. The results of this research demonstrated that Chinese and foreign companies based in China have already begun actively implementing a range of green HRM practices. The results also revealed a range of motivations and obstacles that these
Research on Factors Influencing Core Competitiveness of Internet Enterprises Based on AMOS Model

—This article comprehensively considers the operating characteristics of the Internet industry, selecting 158 Internet companies as the research object and using the structural equation model, with AMOS software as the analysis tool. By building the core competitiveness verification model of Internet enterprises and selecting 10 observational variables like total R&D expenditure, total assets and asset-liability ratio to explore the differences between the impact of latent variables and observation variables on core competitiveness of Internet enterprises. This article empirically studies what effect the correlation between variables and scale competitiveness, innovation risk competitiveness and operational competitiveness would have on improving the entity's core competitiveness, and puts forward countermeasures and suggestions for improvements, which provides theoretical basis for the improvement of core competitiveness of Internet enterprises.
Environmental responsibility disclosure of publicly listed companies in Finland

Environmental challenges such as climate change have increased environmental awareness of the public around the world. This has resulted in increasing societal demand for environmental sustainability of commercial organizations and in increased policy pressure for environmental regulation. Many of the regulation programs are addressing corporations’ disclosure of environmental information. Different company stakeholders such as investors, employees and customers are increasingly demanding social and environmental sustainability and transparency. Also, environmental sustainability has great economic potential for companies. The purpose of this thesis was to evaluate the extent of environmental responsibility disclosure of publicly listed companies on Helsinki Stock Exchange. Thus, the aim was to increase understanding on how extensively the companies included in the research are communicating on the effects that their operations are causing to the environment and to compare the results with different company characteristics which in this research were the companies’ industry, size, profitability, board size and share of women on the board of directors. There are total of 129 companies listed in the Helsinki Stock Exchange of which 53 companies released the GRI report in 2017. Thus, 41% of the companies listed in Helsinki Stock Exchange are included in the study. To evaluate and quantify the environmental responsibility disclosure transparency, a disclosure index was created for each company by examining the share of disclosed GRI indices. The theoretical framework is based on earlier research on corporate social responsibility and corporate environmental sustainability. Hypotheses of the study were developed with the help of the utilized theories. The hypotheses which were concerning the relationship between the extent of environmental disclosure and different company characteristics were evaluated by utilizing correlation analysis and regression analysis. The findings suggest that Finnish publicly listed companies are disclosing relatively little amount of information on their environmental sustainability and performance. Of the five hypotheses one was accepted. The company characteristic which positively affected the level of environmental disclosure was company size. The main contribution of this thesis to the existing knowledge is that it elicits information on the subject in the Finnish context and with multiple different industries. The results can be used to compare environmental reporting of commercial organizations between different countries.
The expansion of stripped-envelope stars: Consequences for supernovae and gravitational-wave progenitors

Massive binaries that merge as compact objects are the progenitors of gravitational-wave sources. Most of these binaries experience one or more phases of mass transfer, during which one of the stars loses all or part of its outer envelope and becomes a stripped-envelope star. The evolution of the size of these stripped stars is crucial in determining whether they experience further interactions and understanding their ultimate fate. We present new calculations of stripped-envelope stars based on binary evolution models computed with MESA. We use these to investigate their radius evolution as a function of mass and metallicity. We further discuss their pre-supernova observable characteristics and potential consequences of their evolution on the properties of supernovae from stripped stars. At high metallicity, we find that practically all of the hydrogen-rich envelope is removed, which is in agreement with earlier findings. Only progenitors with initial masses below 10 M⊙ expand to large radii (up to 100 R⊙), while more massive progenitors remain compact. At low metallicity, a substantial amount of hydrogen remains and the progenitors can, in principle, expand to giant sizes (> 400 R⊙) for all masses we consider. This implies that they can fill their Roche lobe anew. We show that the prescriptions commonly used in population synthesis models underestimate the stellar radii by up to two orders of magnitude. We expect that this has consequences for the predictions for gravitational-wave sources from double neutron star mergers, particularly with regard to their metallicity dependence.
Robust High-Transparency Haptic Exploration for Dexterous Telemanipulation

Robotic teleoperation provides human-in-the-loop capabilities of complex manipulation tasks in dangerous or remote environments, such as for planetary exploration or nuclear decommissioning. This work proposes a novel telemanipulation architecture using a passive Fractal Impedance Controller (FIC), which does not depend upon an active viscous component for guaranteeing stability. Compared to a traditional impedance controller in ideal conditions (no delays and maximum communication bandwidth), our proposed method yields higher transparency in interaction and demonstrates superior dexterity and capability in our telemanipulation test scenarios. We also validate its performance with extreme delays up to 1 s and communication bandwidths as low as 10 Hz. All results validate a consistent stability when using the proposed controller in challenging conditions, regardless of operator expertise.
A Hybrid Lagrangian-Eulerian Method for Topology Optimization

We propose LETO, a new hybrid Lagrangian-Eulerian method for topology optimization. At the heart of LETO lies in a hybrid particle-grid Material Point Method (MPM) to solve for elastic force equilibrium. LETO transfers density information from freely movable Lagrangian carrier particles to a fixed set of Eulerian quadrature points. The quadrature points act as MPM particles embedded in a lower-resolution grid and enable sub-cell resolution of intricate structures with a reduced computational cost. By treating both densities and positions of the carrier particles as optimization variables, LETO reparameterizes the Eulerian solution space of topology optimization in a Lagrangian view. LETO also unifies the treatment for both linear and non-linear elastic materials. In the non-linear deformation regime, the resulting scheme naturally permits large deformation and buckling behaviors. Additionally, LETO explores contact-awareness during optimization by incorporating a fictitious domain-based contact model into the static equilibrium solver, resulting in the discovery of novel structures. We conduct an extensive set of experiments. By comparing against a representative Eulerian scheme, LETO's objective achieves an average quantitative improvement of 20% (up to 40%) in 3D and 2% in 2D (up to 12%). Qualitatively, LETO also discovers novel non-linear functional structures and conducts self-contact-aware structural explorations.
Separable MSE-Based Design of Two-Way Multiple-Relay Cooperative MIMO 5G Networks

While the combination of multi-antenna and relaying techniques has been extensively studied for Long Term Evolution Advanced (LTE-A) and Internet of Things (IoT) applications, it is expected to still play an important role in 5th Generation (5G) networks. However, the expected benefits of these technologies cannot be achieved without a proper system design. In this paper, we consider the problem of jointly optimizing terminal precoders/decoders and relay forwarding matrices on the basis of the sum mean square error (MSE) criterion in multiple-input multiple-output (MIMO) two-way relay systems, where two multi-antenna nodes mutually exchange information via multi-antenna amplify-and-forward relays. This problem is nonconvex and a local optimal solution is typically found by using iterative algorithms based on alternating optimization. We show how the constrained minimization of the sum-MSE can be relaxed to obtain two separated subproblems which, under mild conditions, admit a closed-form solution. Compared to iterative approaches, the proposed design is more suited to be integrated in 5G networks, since it is computationally more convenient and its performance exhibits a better scaling in the number of relays.
VQ-DRAW: A Sequential Discrete VAE

In this paper, I present VQ-DRAW, an algorithm for learning compact discrete representations of data. VQ-DRAW leverages a vector quantization effect to adapt the sequential generation scheme of DRAW to discrete latent variables. I show that VQ-DRAW can effectively learn to compress images from a variety of common datasets, as well as generate realistic samples from these datasets with no help from an autoregressive prior.
Voice Separation with an Unknown Number of Multiple Speakers

We present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network

We propose a recurrent neural network for a "model-free" simulation of a dynamical system with unknown parameters without prior knowledge. The deep learning model aims to jointly learn the nonlinear time marching operator and the effects of the unknown parameters from a time series dataset. We assume that the time series data set consists of an ensemble of trajectories for a range of the parameters. The learning task is formulated as a statistical inference problem by considering the unknown parameters as random variables. A variational inference method is employed to train a recurrent neural network jointly with a feedforward neural network for an approximately posterior distribution. The approximate posterior distribution makes an inference on a trajectory to identify the effects of the unknown parameters and a recurrent neural network makes a prediction by using the outcome of the inference. In the numerical experiments, it is shown that the proposed variational inference model makes a more accurate simulation compared to the standard recurrent neural networks. It is found that the proposed deep learning model is capable of correctly identifying the dimensions of the random parameters and learning a representation of complex time series data.
Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation

Domain adaptation has recently become a key problem in dialogue systems research. Deep learning, while being the preferred technique for modeling such systems, works best given massive training data. However, in the real-world scenario, such resources aren't available for every new domain, so the ability to train with a few dialogue examples can be considered essential. Pre-training on large data sources and adapting to the target data has become the standard method for few-shot problems within the deep learning framework. In this paper, we present the winning entry at the fast domain adaptation task of DSTC8, a hybrid generative-retrieval model based on GPT-2 fine-tuned to the multi-domain MetaLWOz dataset. Robust and diverse in response generation, our model uses retrieval logic as a fallback, being SoTA on MetaLWOz in human evaluation (>4% improvement over the 2nd place system) and attaining competitive generalization performance in adaptation to the unseen MultiWOZ dataset.
Massively Parallel Algorithms for Distance Approximation and Spanners

Over the past decade, there has been increasing interest in distributed/parallel algorithms for processing large-scale graphs. By now, we have quite fast algorithms---usually sublogarithmic-time and often poly(łogłog n)-time, or even faster---for a number of fundamental graph problems in the massively parallel computation (MPC) model. This model is a widely-adopted theoretical abstraction of MapReduce style settings, where a number of machines communicate in an all-to-all manner to process large-scale data. Contributing to this line of work on MPC graph algorithms, we present poly(łog k) ε poly(łogłog n) round MPC algorithms for computing O(k^1+o(1) )-spanners in the strongly sublinear regime of local memory. To the best of our knowledge, these are the first sublogarithmic-time MPC algorithms for spanner construction. As primary applications of our spanners, we get two important implications, as follows: -For the MPC setting, we get an O(łog^2łog n)-round algorithm for O(łog^1+o(1) n) approximation of all pairs shortest paths (APSP) in the near-linear regime of local memory. To the best of our knowledge, this is the first sublogarithmic-time MPC algorithm for distance approximations. -Our result above also extends to the Congested Clique model of distributed computing, with the same round complexity and approximation guarantee. This gives the first sub-logarithmic algorithm for approximating APSP in weighted graphs in the Congested Clique model.
Full analytical formulas for frequency response of space-based gravitational wave detectors

The discovery of gravitational waves, which are ripples of space-time itself, opened a new window to test general relativity, because it predicts that there are only plus and cross polarizations for gravitational waves. For alternative theories of gravity, there may be up to six polarizations. The measurement of the polarization is one of the major scientific goals for future gravitational wave detectors. To evaluate the capability of the detector, we need to use the frequency dependent response functions averaged over the source direction and polarization angle. We derive the full analytical formulas of the averaged response functions for all six possible polarizations and present their asymptotic behaviors based on these analytical formulas. Compared with the numerical simulation, the full analytical formulas are more efficient and valid for any equal-arm interferometric gravitational wave detector without optical cavities in the arms and for a time-delay-interferometry Michelson combination.
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks

We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazards of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.
Hierarchical Context Enhanced Multi-Domain Dialogue System for Multi-domain Task Completion

Task 1 of the DSTC8-track1 challenge aims to develop an end-to-end multi-domain dialogue system to accomplish complex users' goals under tourist information desk settings. This paper describes our submitted solution, Hierarchical Context Enhanced Dialogue System (HCEDS), for this task. The main motivation of our system is to comprehensively explore the potential of hierarchical context for sufficiently understanding complex dialogues. More specifically, we apply BERT to capture token-level information and employ the attention mechanism to capture sentence-level information. The results listed in the leaderboard show that our system achieves first place in automatic evaluation and the second place in human evaluation.
EGAD! An Evolved Grasping Analysis Dataset for Diversity and Reproducibility in Robotic Manipulation

We present the Evolved Grasping Analysis Dataset (EGAD), comprising over 2000 generated objects aimed at training and evaluating robotic visual grasp detection algorithms. The objects in EGAD are geometrically diverse, filling a space ranging from simple to complex shapes and from easy to difficult to grasp, compared to other datasets for robotic grasping, which may be limited in size or contain only a small number of object classes. Additionally, we specify a set of 49 diverse 3D-printable evaluation objects to encourage reproducible testing of robotic grasping systems across a range of complexity and difficulty. The dataset, code and videos can be found at https://dougsm.github. io/egad/
Differentiable Causal Backdoor Discovery

Discovering the causal effect of a decision is critical to nearly all forms of decision-making. In particular, it is a key quantity in drug development, in crafting government policy, and when implementing a real-world machine learning system. Given only observational data, confounders often obscure the true causal effect. Luckily, in some cases, it is possible to recover the causal effect by using certain observed variables to adjust for the effects of confounders. However, without access to the true causal model, finding this adjustment requires brute-force search. In this work, we present an algorithm that exploits auxiliary variables, similar to instruments, in order to find an appropriate adjustment by a gradient-based optimization method. We demonstrate that it outperforms practical alternatives in estimating the true causal effect, without knowledge of the full causal graph.
A Framework for Interdomain and Multioutput Gaussian Processes

One obstacle to the use of Gaussian processes (GPs) in large-scale problems, and as a component in deep learning system, is the need for bespoke derivations and implementations for small variations in the model or inference. In order to improve the utility of GPs we need a modular system that allows rapid implementation and testing, as seen in the neural network community. We present a mathematical and software framework for scalable approximate inference in GPs, which combines interdomain approximations and multiple outputs. Our framework, implemented in GPflow, provides a unified interface for many existing multioutput models, as well as more recent convolutional structures. This simplifies the creation of deep models with GPs, and we hope that this work will encourage more interest in this approach.
Shape analysis via inconsistent surface registration

In this work, we develop a framework for shape analysis using inconsistent surface mapping. Traditional landmark-based geometric morphometr- ics methods suffer from the limited degrees of freedom, while most of the more advanced non-rigid surface mapping methods rely on a strong assumption of the global consistency of two surfaces. From a practical point of view, given two anatomical surfaces with prominent feature landmarks, it is more desirable to have a method that automatically detects the most relevant parts of the two surfaces and finds the optimal landmark-matching alignment between these parts, without assuming any global 1–1 correspondence between the two surfaces. Our method is capable of solving this problem using inconsistent surface registration based on quasi-conformal theory. It further enables us to quantify the dissimilarity of two shapes using quasi-conformal distortion and differences in mean and Gaussian curvatures, thereby providing a natural way for shape classification. Experiments on Platyrrhine molars demonstrate the effectiveness of our method and shed light on the interplay between function and shape in nature.