Miracle-dz/UniFAR-FTUs huggingface dataset:
How Does an Enterprise System Implementation Change Interpersonal Relationships in Organizations

PurposeAlthough research has suggested that enterprise system (ES) implementations have major impacts on employee job characteristics and outcomes, there has been limited research that has examined the impacts of ES implementations on interpersonal relationships over time. Building on and extending recent studies that have examined changes in employee job characteristics and outcomes during an ES implementation, this research examined the nature, extent, determinants and outcomes of changes in an important interpersonal relationship construct—coworker exchange (CWX)—following an ES implementation. CWX is considered a critical aspect of employees' job and an important determinant of their success in the workplace. Drawing on social exchange theory (SET), the authors theorize that employees will perceive a change in CWX following an ES implementation.Design/methodology/approachA longitudinal field study over a period of 6 months among 249 employees was conducted. Latent growth modeling was used to analyze the data.FindingsThe authors found that employees' work process characteristics, namely perceived process complexity, perceived process rigidity and perceived process radicalness, significantly explained change, i.e. decline in our case, in CWX during the shakedown phase of an ES implementation. The decreasing trajectory of change in CWX led to declining job performance and job satisfaction.Originality/valueThe role of CWX and its importance in the context of ES implementations is a key novel element of this work.
A Toolkit for Computing Constrained Optimal Policy Projections (COPPs)

This paper presents a toolkit for generating optimal policy projections. It makes five contributions. First, the toolkit requires a minimal set of inputs: only a baseline projection for target and instrument variables and impulse responses of those variables to policy shocks. Second, it solves optimal policy projections under commitment, limited-time commitment, and discretion. Third, it handles multiple policy instruments. Fourth, it handles multiple constraints on policy instruments such as a lower bound on the policy rate and an upper bound on asset purchases. Fifth, it allows alternative approaches to address the forward guidance puzzle. The toolkit that accompanies this paper is Dynare compatible, which facilitates its use. Examples replicate existing results in the optimal monetary policy literature and illustrate the usefulness of the toolkit for highlighting policy trade-offs. We use the toolkit to analyse US monetary policy at the height of the Great Financial Crisis. Given the Fed’s early-2009 baseline macroeconomic projections, we find the Fed’s planned use of the policy rate was close to optimal whereas a more aggressive QE program would have been beneficial.
Dynamic Pricing in a Non-stationary Growing Environment

Many emerging businesses and services encounter growing environments where the stochastic demand gradually increases. The increases are often not only in the expectation of the demand but also in the variance. Because of the growing environments, sequential decision making problems raise additional challenges when model parameters are unknown and need to be dynamically learned from sequentially observed data. In this work, we use a single-product dynamic pricing problem to illustrate how the non-stationary growing environment inﬂuences policy design and policy performance such as regret. We prove matching upper and lower bounds on regret and design near-optimal pricing policies. We then demonstrate how the growth rate of demand variance aﬀects the best achievable policy performance as well as the near-optimal policy design. In the analysis, we also prove that that whether the seller knows the length of time horizon in advance or not render diﬀerent optimal regret orders.
Silica Raman scattering probe for absolute calibration of Thomson scattering spectrometers

We have developed a solid state probe for an absolute irradiance calibration of the Thomson scattering system on the Large Plasma Device (LAPD), based on Raman scattering off silica. Measurements performed with a triple-grating spectrometer have investigated the intensities of a pulsed laser beam Raman scattered off crystalline and amorphous silica over a range of temperatures of relevance to the LAPD (299–498 K). The data were compared with Rayleigh and Raman scattering intensities in gaseous nitrogen. The measurements show that Raman scattering off quartz allows rapid and accurate alignment and calibration of Thomson scattering systems in plasma physics experiments that cannot be calibrated using conventional methods.
How Residence Permits Affect the Labor Market Attachment of Foreign Workers: Evidence from a Migration Lottery in Liechtenstein

We analyze the impact of obtaining a residence permit on foreign workers' labor market and residential attachment. To overcome the usually severe selection issues, we exploit a unique migration lottery that randomly assigns access to otherwise restricted residence permits in Liechtenstein (situated between Austria and Switzerland). Using an instrumental variable approach, our results show that lottery compliers (whose migration behavior complies with the assignment in their first lottery) raise their employment probability in Liechtenstein by on average 24 percentage points across outcome periods (2008 to 2018) as a result of receiving a permit. Relatedly, their activity level and employment duration in Liechtenstein increase by on average 20 percentage points and 1.15 years, respectively, over the outcome window. These substantial and statistically significant effects are mainly driven by individuals not (yet) working in Liechtenstein prior to the lottery rather than by previous cross-border commuters. Moreover, we find both the labor market and residential effects to be persistent even several years after the lottery with no sign of fading out. These results suggest that granting resident permits to foreign workers can be effective to foster labor supply even beyond the effect of cross-border commuting from adjacent regions.
Kerr-Schild double copy of the Coulomb solution in three dimensions

While the Kerr-Schild double copy of the Coulomb solution in dimensions higher than three is the Schwarzschild black hole, it is known that it should be a non-vacuum solution in three dimensions. We show that the static black hole solution of Einstein-Maxwell theory (with one ghost sign in the action) is the double copy with the correct Newtonian limit, which provides an improvement over the previous construction with a free scalar field that does not vanish at infinity. By considering a negative cosmological constant, we also study the charged Ba\~nados-Teitelboim-Zanelli black hole and find that the single copy gauge field is the Coulomb solution modified by a term which describes an electric field linearly increasing with the radial coordinate, which is the usual behaviour of the Schwarzschild-AdS black hole in higher dimensions when written around a flat background metric.
Kernel Knockoffs Selection for Nonparametric Additive Models

Abstract Thanks to its fine balance between model flexibility and interpretability, the nonparametric additive model has been widely used, and variable selection for this type of model has been frequently studied. However, none of the existing solutions can control the false discovery rate (FDR) unless the sample size tends to infinity. The knockoff framework is a recent proposal that can address this issue, but few knockoff solutions are directly applicable to nonparametric models. In this article, we propose a novel kernel knockoffs selection procedure for the nonparametric additive model. We integrate three key components: the knockoffs, the subsampling for stability, and the random feature mapping for nonparametric function approximation. We show that the proposed method is guaranteed to control the FDR for any sample size, and achieves a power that approaches one as the sample size tends to infinity. We demonstrate the efficacy of our method through intensive simulations and comparisons with the alternative solutions. Our proposal thus, makes useful contributions to the methodology of nonparametric variable selection, FDR-based inference, as well as knockoffs. Supplementary materials for this article are available online.
Algebraic structures in the family of non-Lebesgue measurable sets

In the additive topological group (R,+) of real numbers, we construct families of sets for which elements are not measurable in the Lebesgue sense. The constructed families of sets have algebraic structures of being semigroups (i.e. closed under finite unions of sets), and they are invariant under the action of the group Φ(R) of all translations of R onto itself. Those semigroups are constructed by using Vitali selectors and Bernstein sets on R taken simultaneously differently to what exists in the literature.
The Cynicism of Modern Cybercrime: Automating the Analysis of Surface Web Marketplaces

Cybercrime is continuously growing in numbers and becoming more sophisticated. Currently, there are various monetisation and money laundering methods, creating a huge, underground economy worldwide. A clear indicator of these activities is online marketplaces which allow cybercriminals to trade their stolen assets and services. While traditionally, these marketplaces are available through the dark web, several of them have emerged in the surface web. In this work, we perform a longitudinal analysis of a surface web marketplace. The information was collected through targeted web scrapping that allowed us to identify hundreds of merchants' profiles for the most widely used surface web marketplaces. In this regard, we discuss the products traded in these markets, their prices, their availability, and the exchange currency. This analysis is performed in an automated way through a machine learning-based pipeline, allowing us to quickly and accurately extract the needed information. The outcomes of our analysis evince that illegal practices are leveraged in surface marketplaces and that there are no effective mechanisms towards their takedown at the time of writing.
A Closed-Loop Linear Covariance Framework for Vehicle Path Planning in an Uncertain Obstacle Field

Path planning in uncertain environments is a key enabler of true vehicle autonomy. Over the past two decades, numerous approaches have been developed to account for errors in the vehicle path while navigating complex and often uncertain environments. An important capability of such planning is the prediction of vehicle dispersion covariances about candidate paths. This work develops a new closed-loop linear covariance (CL-LinCov) framework applicable to wide range of autonomous system architectures. Extensions to current CL-LinCov frameworks are made to accommodate 1) the cascaded architecture typical of autonomous vehicles and 2) the dual-use of continuous sensor information for both navigation and control. The closed-loop nature of the framework preserves the important coupling between the system dynamics, exogenous disturbances, and the guidance, navigation, and control algorithms. The developed framework is applied to a simpliﬁed model of an unmanned aerial vehicle and validated by comparison via Monte Carlo analysis. The utility of the CL-LinCov information is illustrated by its application to path planning in an uncertain obstacle ﬁeld via a modiﬁed version of the rapidly exploring random tree algorithm.
Graph Neural Network Based VC Investment Success Prediction

Predicting the start-ups that will eventually succeed is essentially important for the venture capital business and worldwide policy makers, especially at an early stage such that rewards can possibly be exponential. Though various empirical studies and data-driven modeling work have been done, the predictive power of the complex networks of stakeholders including venture capital investors, start-ups, and start-ups' managing members has not been thoroughly explored. We design an incremental representation learning mechanism and a sequential learning model, utilizing the network structure together with the rich attributes of the nodes. In general, our method achieves the state-of-the-art prediction performance on a comprehensive dataset of global venture capital investments and surpasses human investors by large margins. Specifically, it excels at predicting the outcomes for start-ups in industries such as healthcare and IT. Meanwhile, we shed light on impacts on start-up success from observable factors including gender, education, and networking, which can be of value for practitioners as well as policy makers when they screen ventures of high growth potentials.
Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation

GuessWhat? is a visual dialog guessing game which incorporates a Questioner agent that generates a sequence of questions, while an Oracle agent answers the respective questions about a target object in an image. Based on this dialog history between the Questioner and the Oracle, a Guesser agent makes a final guess of the target object. While previous work has focused on dialogue policy optimization and visual-linguistic information fusion, most work learns the vision-linguistic encoding for the three agents solely on the GuessWhat? dataset without shared and prior knowledge of vision-linguistic representation. To bridge these gaps, this paper proposes new Oracle, Guesser and Questioner models that take advantage of a pretrained vision-linguistic model, VilBERT. For Oracle model, we introduce a two-way background/target fusion mechanism to understand both intra and inter-object questions. For Guesser model, we introduce a state-estimator that best utilizes VilBERT’s strength in single-turn referring expression comprehension. For the Questioner, we share the state-estimator from pretrained Guesser with Questioner to guide the question generator. Experimental results show that our proposed models outperform state-of-the-art models significantly by 7%, 10%, 12% for Oracle, Guesser and End-to-End Questioner respectively.
Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks

Many research works focus on leveraging the complementary geometric information of indoor depth sensors in vision tasks performed by deep convolutional neural networks, notably semantic segmentation. These works deal with a specific vision task known as "RGB-D Indoor Semantic Segmentation". The challenges and resulting solutions of this task differ from its standard RGB counterpart. This results in a new active research topic. The objective of this paper is to introduce the field of Deep Convolutional Neural Networks for RGB-D Indoor Semantic Segmentation. This review presents the most popular public datasets, proposes a categorization of the strategies employed by recent contributions, evaluates the performance of the current state-of-the-art, and discusses the remaining challenges and promising directions for future works.
Line shapes of electric dipole spin resonance in Pauli spin blockade

Electric dipole spin resonance (EDSR) is a commonly used tool for manipulation and spectroscopy of quantum-dot-based spin qubits. When an EDSR experiment is embedded in a transport setup and Pauli spin blockade is used as means for spin-state read-out, then measured resonant responses in the leakage current indeed carry information about the level structure of the system under study. However, the actual line shape of these current resonances differs substantially from experiment to experiment, varying from being symmetric to asymmetric and from being a peak to a dip, a thorough understanding of which is still lacking. Here, we investigate theoretically the detailed line shape of EDSR-induced resonances in the leakage current in the regime of spin blockade, and we connect different line shapes to the different underlying physical mechanisms that can enable the EDSR. We carry out both numerical and analytical investigations, producing simple analytic expressions that give insight in the physics at play. Our results thus provide a means to extract more information about the detailed system parameters of quantum dots hosting spin qubits from an EDSR experiment than just their level structure based on the location of the resonances.
Small and large scale critical infrastructures detection based on deep learning using high resolution orthogonal images

The detection of critical infrastructures is of high importance in several ﬁelds such as security, anomaly detection, land use planning and land use change detection. However, critical infrastructures detection in aerial and satellite images is still a challenge as each one has completely diﬀerent size and requires diﬀer-ent spacial resolution to be identiﬁed correctly. Heretofore, there are no special datasets for training critical infrastructures detectors. This paper presents a smart dataset as well as a resolution-independent critical infrastructure detection system. In particular, guided by the performance of the detection model, we built a dataset organized into two scales, small and large scale, and designed a two-stage deep learning detection of diﬀerent scale critical infrastructures (DetDSCI) methodology in ortho-images. DetDSCI methodology ﬁrst determines the input image zoom level using a classiﬁcation model, then analyses the input image with the appropriate scale detection model. Our experiments show that DetDSCI methodology achieves up to 37,53% F1 improvement with respect to the baseline detector.
Momentum feedback from marginally resolved H ii regions in isolated disc galaxies

Wepresent a novel, physically-motivated sub-gridmodel forHII region feedbackwithin themovingmesh codeArepo, accounting for both the radiation pressure-driven and thermal expansion of the ionised gas surrounding young stellar clusters. We apply this framework to isolated disc galaxy simulations with mass resolutions between 103 M and 105 M per gas cell. Each simulation accounts for the self-gravity of the gas, the momentum and thermal energy from supernovae, the injection of mass by stellar winds, and the non-equilibrium chemistry of hydrogen, carbon and oxygen. We reduce the resolution-dependence of our model by grouping those HII regions with overlapping ionisation front radii. The Strömgren radii of the grouped HII regions are at best marginally-resolved, so that the injection of purely-thermal energy within these radii has no effect on the interstellar medium. By contrast, the injection of momentum increases the fraction of cold and molecular gas by more than 50 per cent at mass resolutions of 103 M , and decreases its turbulent velocity dispersion by ∼ 10 kms−1. The mass-loading of galactic outflows is decreased by an order of magnitude. The characteristic lifetime of the least-massive molecular clouds (M/M < ∼ 5.6 × 10 4) is reduced from ∼ 18 Myr to < ∼ 10 Myr, indicating that HII region feedback is effective in destroying these clouds. Conversely, the lifetimes of intermediate-mass clouds (5.6 × 104 < ∼M/M < ∼ 5 × 10 5) are elongated by ∼ 7Myr, likely due to a reduction in supernova clustering. The derived cloud lifetimes span the range from 10-40 Myr, in agreement with observations. All results are independent of whether the momentum is injected from a ‘spherical’ or a ‘blister-type’ HII region.
Mixture of ELM based experts with trainable gating network

Mixture of experts method is a neural network based ensemble learning that has great ability to improve the overall classification accuracy. This method is based on the divide and conquer principle, in which the problem space is divided between several experts by supervisition of gating network. In this paper, we propose an ensemble learning method based on mixture of experts which is named mixture of ELM based experts with trainable gating network (MEETG) to improve the computing cost and to speed up the learning process of ME. The structure of ME consists of multi layer perceptrons (MLPs) as base experts and gating network, in which gradient-based learning algorithm is applied for training the MLPs which is an iterative and time consuming process. In order to overcome on these problems, we use the advantages of extreme learning machine (ELM) for designing the structure of ME. ELM as a learning algorithm for single hidden-layer feed forward neural networks provides much faster learning process and better generalization ability in comparision with some other traditional learning algorithms. Also, in the proposed method a trainable gating network is applied to aggregate the outputs of the experts dynamically according to the input sample. Our experimental results and statistical analysis on 11 benchmark datasets confirm that MEETG has an acceptable performance in classification problems. Furthermore, our experimental results show that the proposed approach outperforms the original ELM on prediction stability and classification accuracy.
Quantum State Transfer Between NV Center -- $13_C$ System Coupled To A CPW Cavity

Quantum state transfer is a very important process in building a quantum network when information from flying Qubit is transferred to the stationary Qubit in a node via a quantum state transfer. NV centers due to their long coherence time and the presence of nearby $13_C$ nuclear spin is an excellent candidate for multi-Qubit quantum memory. Here we propose a theoretical description for such a quantum state transfer from a cavity to a nearest neighbour $13_C$ nuclear spin of a single Nitrogen vacancy center in diamond; it shows great potential in realizing scalable quantum networks and quantum simulation. The full Hamiltonian was considered with the zeroth-order and interaction terms in the Hamiltonian and the theory of effective hamiltonian theory was applied. We study the time evolution of the combined cavity-$13_C$ state through analytical calculation and simulation using QuTip. Graphs for state transfer and fidelity measurement are presented here. We show that our theoretical description verifies a high fidelity quantum state transfer from the cavity to $13_C$ center by choosing suitable system parameters.
SBEVNet: End-to-End Deep Stereo Layout Estimation

Accurate layout estimation is crucial for planning and navigation in robotics applications, such as self-driving. In this paper, we introduce the Stereo Bird’s Eye View Network (SBEVNet) 1, a novel supervised end-to-end framework for estimation of bird’s eye view layout from a pair of stereo images. Although our network reuses some of the building blocks from the state-of-the-art deep learning networks for disparity estimation, we show that explicit depth estimation is neither sufficient nor necessary. Instead, the learning of a good internal bird’s eye view feature representation is effective for layout estimation. Specifically, we first generate a disparity feature volume using the features of the stereo images and then project it to the bird’s eye view coordinates. This gives us coarse-grained information about the scene structure. We also apply inverse perspective mapping (IPM) to map the input images and their features to the bird’s eye view. This gives us fine-grained texture information. Concatenating IPM features with the projected feature volume creates a rich bird’s eye view representation which is useful for spatial reasoning. We use this representation to estimate the BEV semantic map. Additionally, we show that using the IPM features as a supervisory signal for stereo features can give an improvement in performance. We demonstrate our approach on two datasets: the KITTI [5] dataset and a synthetically generated dataset from the CARLA [4] simulator. For both of these datasets, we establish state-of-the-art performance compared to baseline techniques.