Reviewerly/exHarmony huggingface dataset:
van der Waals-corrected density functional study of electric field noise heating in ion traps caused by electrode surface adsorbates

Abstract In order to realize the full potential of ion trap quantum computers, an improved understanding is required of the motional heating that trapped ions experience. Experimental studies of the temperature-, frequency-, and ion–electrode distance-dependence of the electric field noise responsible for motional heating, as well as the noise before and after ion bombardment cleaning of trap electrodes, suggest that fluctuations of adsorbate dipoles are a likely source of so-called ‘anomalous heating,’ or motional heating of the trapped ions at a rate much higher than the Johnson noise limit. Previous computational studies have investigated how the fluctuation of model adsorbate dipoles affects anomalous heating. However, the way in which specific adsorbates affect the electric field noise has not yet been examined, and an electric dipole model employed in previous studies is only accurate for a small subset of possible adsorbates. Here, we analyze the behavior of both in-plane and out-of-plane vibrational modes of twenty-one adsorbate–electrode combinations within the independent fluctuating dipole model, utilizing accurate first principles computational methods to determine the surface-induced dipole moments. We find the chemical specificity of the adsorbate can change the electric field noise by seven orders of magnitude and specifically that soft in-plane modes of weakly-adsorbed hydrocarbons produce the greatest noise and ion heating. We discuss the dynamics captured by the fluctuating dipole model, namely the adsorbate-dependent turn-on temperature and electric field noise magnitude, and also discuss the model’s failure to reproduce the measured 1/ ω noise frequency scaling with a single adsorbate species. We suggest future research directions for improved, quantitatively predictive models based on extensions of the present framework to multiple interacting adsorbates.
Scattering as a Quantum Metrology Problem: A Quantum Walk Approach

We address the scattering of a quantum particle by a one-dimensional barrier potential over a set of discrete positions. We formalize the problem as a continuous-time quantum walk on a lattice with an impurity and use the quantum Fisher information as a means to quantify the maximal possible accuracy in the estimation of the height of the barrier. We introduce suitable initial states of the walker and derive the reflection and transmission probabilities of the scattered state. We show that while the quantum Fisher information is affected by the width and central momentum of the initial wave packet, this dependency is weaker for the quantum signal-to-noise ratio. We also show that a dichotomic position measurement provides a nearly optimal detection scheme.
Robustness of quantum Fourier transform interferometry

We analyze the effect of decoherence and noise on quantum Fourier transform interferometry, in which a boson sampling photonic network is used to measure optical phase gradients. This novel type of metrology is shown to be robust against phase decoherence. One can also measure gradients using lower-order correlations without substantial degradation. Our results involve the estimation of up to a 100×100 matrix permanent.
On the Sparsity of Neural Machine Translation Models

Modern neural machine translation (NMT) models employ a large number of parameters, which leads to serious over-parameterization and typically causes the underutilization of computational resources.In response to this problem, we empirically investigate whether the redundant parameters can be reused to achieve better performance.Experiments and analyses are systematically conducted on different datasets and NMT architectures.We show that: 1) the pruned parameters can be rejuvenated to improve the baseline model by up to +0.8 BLEU points; 2) the rejuvenated parameters are reallocated to enhance the ability of modeling low-level lexical information.
RUSSE'2020: FINDINGS OF THE FIRST TAXONOMY ENRICHMENT TASK FOR THE RUSSIAN LANGUAGE

This paper describes the results of the first shared task on taxonomy enrichment for the Russian language. The participants were asked to extend an existing taxonomy with previously unseen words: for each new word their systems should provide a ranked list of possible (candidate) hypernyms. In comparison to the previous tasks for other languages, our competition has a more realistic task setting: new words were provided without definitions. Instead, we provided a textual corpus where these new terms occurred. For this evaluation campaign, we developed a new evaluation dataset based on unpublished RuWordNet data. The shared task features two tracks: “nouns” and “verbs”. 16 teams participated in the task demonstrating high results with more than a half of them outperforming the provided baseline.
On the Analysis of a Multipartite Entanglement Distribution Switch

We study a quantum switch that distributes maximally entangled multipartite states to sets of users. The entanglement switching process requires two steps: first, each user attempts to generate bipartite entanglement between itself and the switch; and second, the switch performs local operations and a measurement to create multipartite entanglement for a set of users. In this work, we study a simple variant of this system, wherein the switch has infinite memory and the links that connect the users to the switch are identical. Further, we assume that all quantum states, if generated successfully, have perfect fidelity and that decoherence is negligible. This problem formulation is of interest to several distributed quantum applications, while the technical aspects of this work result in new contributions within queueing theory. Via extensive use of Lyapunov functions, we derive necessary and sufficient conditions for the stability of the system and closed-form expressions for the switch capacity and the expected number of qubits in memory.
NOAA Marine Geophysical Data and a GEBCO Grid for the Topographical Analysis of Japanese Archipelago by Means of GRASS GIS and GDAL Library

This article analyzes topographical and geological settings in the Japan Archipelago for comparative raster data processing using GRASS GIS. Data include bathymetric and geological grids in NetCDF format: GEBCO, EMAG2, GlobSed, marine free-air gravity anomaly and EGM96. Data were imported to GRASS by gdalwarp utility of GDAL and projected via PROJ library. Methodology includes data processing (projecting and import), mapping and spatial analysis. Visualization was done by shell scripting using a sequence of GRASS modules: ‘d.shade’ for relief mapping, ‘r.slope.aspect’; for modelling based on DEM, ‘r.contour’ for plotting isolines, ‘r.mapcalc’ for classification, ‘r.category’ for associating labels, and auxiliary modules (d.vect, d.rast, d.grid, d.legend).The results of the geophysical visualization show that marine free-air gravitational anomalies vary in the Sea of Japan (–30 to above 40 mGal) reflecting density inhomogeneities of the tectonic structure, and correlating with the geological structure of the seafloor. Dominating values of geoid model are 30–45 m reflecting West Pacific rise, determined by deep density inhomogeneities associated with the mantle convention. Sediment thickness varies across the sea reflecting its geological development with density of 2 km in its deepest part and thinner in central part (Yamato Rise). The aspect map and reclassified map express gradient of the steepest descent.
Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor

Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become a study in the field of data science. Forecasting that learns through data in the light age is able to solve problems with large-scale data or big data. With the big data, the performance of the k-Nearest Neighbor (k-NN) method can be tested with several accuracy measurements. Generally, accuracy measurement uses MAPE so it is necessary to conduct sensitivity on MAPE by combining it with the detection rate which is the difference technique. In addition, the k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper using the crude palm oil (CPO) price dataset, it is able to forecast and become a future model and apply it to Business Intelligence and analysis. In the final stage of this paper, the accuracy value in doing big data forecasting on CPO prices with MAPE is 0.013526% and MAPE sensitivity combined with a detection rate of 0.000361% so that future processes using different methods need to involve detection rates.
Bengali Abstractive News Summarization (BANS): A Neural Attention Approach

Abstractive summarization is the process of generating novel sentences based on the information extracted from the original text document while retaining the context. Due to abstractive summarization's underlying complexities, most of the past research work has been done on the extractive summarization approach. Nevertheless, with the triumph of the sequence-to-sequence (seq2seq) model, abstractive summarization becomes more viable. Although a significant number of notable research has been done in the English language based on abstractive summarization, only a couple of works have been done on Bengali abstractive news summarization (BANS). In this article, we presented a seq2seq based Long Short-Term Memory (LSTM) network model with attention at encoder-decoder. Our proposed system deploys a local attention-based model that produces a long sequence of words with lucid and human-like generated sentences with noteworthy information of the original document. We also prepared a dataset of more than 19k articles and corresponding human-written summaries collected from bangla.bdnews24.com1 which is till now the most extensive dataset for Bengali news document summarization and publicly published in Kaggle2. We evaluated our model qualitatively and quantitatively and compared it with other published results. It showed significant improvement in terms of human evaluation scores with state-of-the-art approaches for BANS.
RuBQ: A Russian Dataset for Question Answering over Wikidata

The paper presents RuBQ, the first Russian knowledge base question answering (KBQA) dataset. The high-quality dataset consists of 1,500 Russian questions of varying complexity, their English machine translations, SPARQL queries to Wikidata, reference answers, as well as a Wikidata sample of triples containing entities with Russian labels. The dataset creation started with a large collection of question-answer pairs from online quizzes. The data underwent automatic filtering, crowd-assisted entity linking, automatic generation of SPARQL queries, and their subsequent in-house verification. The freely available dataset will be of interest for a wide community of researchers and practitioners in the areas of Semantic Web, NLP, and IR, especially for those working on multilingual question answering. The proposed dataset generation pipeline proved to be efficient and can be employed in other data annotation projects.
Efficient Path Planing for Articulated Vehicles in Cluttered Environments

Motion planning and control for articulated logistic vehicles such as tugger trains is a challenging problem in service robotics. The case of tugger trains presents particular difficulties due to the kinematic complexity of these multiarticulated vehicles. Sampling-based motion planners offer a motion planning solution that can take into account the kinematics and dynamics of the vehicle. However, their planning times scale poorly for high dimensional systems, such as these articulated vehicles moving in a big map. To improve the efficiency of the sampling-based motion planners, some approaches combine these methods with discrete search techniques. The goal is to direct the sampling phase with heuristics provided by a faster, precociously ran, discrete search planner. However, sometimes these heuristics can mislead the search towards unfeasible solutions, because the discrete search planners do not take into account the kinematic and dynamic restrictions of the vehicle. In this paper we present a solution adapted for articulated logistic vehicles that uses a kinodynamic discrete planning to bias the sampling-based algorithm. The whole system has been applied in two different towing tractors (a tricycle and a quadricycle) with two different trailers (simple trailer and synchronized shaft trailer).
Augmented and Virtual Reality Object Repository for Rapid Prototyping

Augmented Reality (AR) and Virtual Reality (VR) are technologies on the rise and as their market grows, speeding up the process of developing new ideas becomes even more important. In achieving rapid development, rapid prototyping plays a very important role. To support rapid prototyping, this demo paper presents an approach that relies on an object repository. It enables scanning, editing, storing and publishing of virtual objects that can be reused in different augmented and virtual reality applications. To show the benefits of the approach, an exemplary scenario is illustrated by prototyping an interior design application.
Towards Repairing Neural Networks Correctly

Neural networks are increasingly applied to support decision-making in safety-critical applications (like autonomous cars, unmanned aerial vehicles, and face recognition-based authentication). While many impressive static verification techniques have been proposed to tackle the correctness problem of neural networks, existing static verification techniques still do not answer the natural question: what is the subsequent measure that one should take if the DNN is not verified? In this work, we propose a runtime repairing method to ensure the correctness of neural networks within certain input regions. Given a neural network and a safety property, we first adopt state-of-the-art static verification techniques to verify the neural networks. In the case that the verification fails, we strategically identify locations to introduce additional gates which “correct” neural network behaviors at runtime whilst keeping the modifications small. Experiment results show that our approach effectively generates neural networks which are guaranteed to satisfy the properties, whilst being consistent with the original neural network most of the time.
Moderating role of top management support in electronic procurement usage of Jordanian firms

The use of e-procurement aids organisations in reducing business costs, broadening their market access and simplifying purchasing processes. However, in Jordan, only 27.6% of firms utilise e-procurement systems. Hence, the usage of e-procurement systems by Jordanian firms is still in its infancy. In this work, the technology, organisation and environment (TOE) factors affecting the use of e-procurement systems by large firms in Jordan were investigated. Previous studies have reported inconsistent findings with regard to the antecedent factors that may affect e-procurement usage. Therefore, this work aimed to determine the factors related to e-procurement usage and whether or not top management support moderates the relationship between TOE factors and e-procurement usage. Results revealed that all TOE factors used in this study influenced the e-procurement usage of the firms. Moreover, the moderating role of top management support was found to be positively related to relative advantages and organisational readiness and negatively related to complexity.
Concerns and Trade-Offs in Information Technology Acceptance: The Balance between the Requirement for Privacy and the Desire for Safety

In this paper, we construct a new motivation model by bridging self-determination theory (SDT) with the unified theory of acceptance and use of technology (UTAUT).Using an explorative approach, we study how human motivational determinants influence the trade-off between safety and privacy in technology acceptance.We take the Scandinavian healthcare context as our empirical outset and explore how older Danish adults perceive sensor-based e-health monitor technology that monitors their health status.Danish municipalities have begun to use these technologies to identify early warning signs and, thereby, improve the quality of care and life by making people more self-reliant and reducing unnecessary hospitalization.However, one needs to consider ethical issues concerning privacy versus safety when implementing these technologies.After monitoring 21 respondents (mean age: 85) living independently at home over nine weeks, we interviewed them about their concerns regarding privacy and safety.We found that the respondents were willing to compromise their privacy if their autonomy and personal integrity were respected and if the benefits of sensor-based monitoring outweighed health-related threats.We used these findings and the theoretical outset to create a novel model that takes human motivation into account when using UTAUT.
Doubly-fed Deep Learning Method for Bad Data Identification in Linear State Estimation

With more data-driven applications introduced in wide-area monitoring systems (WAMS), data quality of phasor measurement units (PMUs) becomes one of the fundamental requirements for ensuring reliable WAMS applications. This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation, which can: 1 identify bad data under both steady states and contingencies; 2 achieve higher accuracy than conventional pre-filtering approaches; 3 reduce iteration burden for linear state estimation; 4 efficiently identify bad data in a parallelizable scheme. The proposed method consists of four key steps: 1 preprocessing filter; 2 online training of short-term deep neural network; 3 offline training of long-term deep neural network; 4 a decision merger. Through delicate design and comprehensive training, the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information. An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method. Multiple test scenarios are applied, which include steady states, three-phase-to-ground faults with (un)successful auto-reclosing, low-frequency oscillation, and low-frequency oscillation with simultaneous three-phase-to-ground faults. The proposed method demonstrates satisfactory performance during both the training session and the testing session.
APMdisc: An online computer program for the geochemical discrimination of siliciclastic sediments from active and passive margins

An online computer program called APMdisc (Active and Passive Margin discrimination) for the discrimination of siliciclastic sediments from active and passive margin settings was written in Java along with the ZK framework. APMdisc calculates four complex discriminant functions (DFilr(A-P)M and DFmlr(A-P)M; DFilr(A-P)MT and DFmlr(A-P)MT), which represent linear combinations of log-ratios of all ten major elements (M) and ten major and six trace elements (MT), respectively. In these equations, ilr and mlr stand for isometric log-ratio and modified log-ratio transformations of the chemical composition of active and passive margin sediments, respectively. The ilr transformation provided the same results as the mlr as documented for five case studies of Quaternary sediments from California, Antarctica, Nigeria, India, and Japan. We also present 9 other case studies (for Neogene to Quaternary sediments from known tectonic settings) and 11 application studies (Tertiary to Neoproterozoic sediments and sedimentary rocks) to show the functioning of the multidimensional discrimination proposed in this paper. In most cases, the results from the APMdisc were consistent with the literature conclusions inferred from different geological and geochemical techniques. Nevertheless, APMdisc provides probability estimates for both tectonic settings, which allows the decision to be made based on the probability concept. We also added a new Robustness module to APMdisc, which allows the user to test the robustness of a sample against field changes, such as weathering and diagenesis, and laboratory analytical errors or uncertainties. This program can therefore be recommended for deciphering the margin type of older terrains. The APMdisc program can be used online by researchers for tectonic discrimination based on sediment composition. The users can process the data file at our web portal http://tlaloc.ier.unam.mx.
Optimal Control Problem Solution with Phase Constraints for Group of Robots by Pontryagin Maximum Principle and Evolutionary Algorithm

A numerical method based on the Pontryagin maximum principle for solving an optimal control problem with static and dynamic phase constraints for a group of objects is considered. Dynamic phase constraints are introduced to avoid collisions between objects. Phase constraints are included in the functional in the form of smooth penalty functions. Additional parameters for special control modes and the terminal time of the control process were introduced. The search for additional parameters and the initial conditions for the conjugate variables was performed by the modified self-organizing migrating algorithm. An example of using this approach to solve the optimal control problem for the oncoming movement of two mobile robots is given. Simulation and comparison with direct approach showed that the problem is multimodal, and it approves application of the evolutionary algorithm for its solution.
A Group Recommendation System of Network Document Resource Based on Knowledge Graph and LSTM in Edge Computing

The Internet has become one of the important channels for users to obtain information and knowledge. It is crucial to work out how to acquire personalized requirement of users accurately and effectively from huge amount of network document resources. Group recommendation is an information system for group participation in common activities that meets the common interests of all members in the group. This paper proposes a group recommendation system for network document resource exploration using the knowledge graph and LSTM in edge computing, which can solve the problem of information overload and resource trek effectively. An extensive system test has been carried out in the field of big data application in packaging industry. The experimental results show that the proposed system recommends network document resource more accurately and further improves recommendation quality using the knowledge graph and LSTM in edge computing. Therefore, it can meet the user’s personalized resource need more effectively.