Reviewerly/exHarmony huggingface dataset:
Figure of merit for single-photon generation based on cavity quantum electrodynamics

We investigate a tradeoff relation between the internal generation efficiency and the escape efficiency for single-photon generation based on cavity quantum electrodynamics, where cavity internal loss is treated explicitly. Consequently, we analytically derive an upper bound on the overall efficiency. The bound is expressed only with an internal cooperativity, introduced here as the cooperativity parameter with respect to the cavity internal loss rate. This result means that the internal cooperativity is a figure of merit for single-photon generation based on cavity QED. The bound is derived by optimizing the cavity external loss rate, which can be experimentally controlled by designing or tuning the transmissivity of the output coupler. The model here is general enough to treat various cavity-QED effects, such as the Purcell effect, on-resonant or off-resonant cavity-enhanced Raman scattering, and vacuum-stimulated Raman adiabatic passage. For typical optical systems, we additionally take into account a ``reexcitation'' process, where the atom is reexcited after its decay to the initial ground state.
Hyperprior Induced Unsupervised Disentanglement of Latent Representations

We address the problem of unsupervised disentanglement of latent representations learnt via deep generative models. In contrast to current approaches that operate on the evidence lower bound (ELBO), we argue that statistical independence in the latent space of VAEs can be enforced in a principled hierarchical Bayesian manner. To this effect, we augment the standard VAE with an inverse-Wishart (IW) prior on the covariance matrix of the latent code. By tuning the IW parameters, we are able to encourage (or discourage) independence in the learnt latent dimensions. Extensive experimental results on a range of datasets (2DShapes, 3DChairs, 3DFaces and CelebA) show our approach to outperform the β-VAE and is competitive with the state-of-the-art FactorVAE. Our approach achieves significantly better disentanglement and reconstruction on a new dataset (CorrelatedEllipses) which introduces correlations between the factors of variation.
Model-Based Learning from Preference Data

Preference data occur when assessors express comparative opinions about a set of items, by rating, ranking, pair comparing, liking, or clicking. The purpose of preference learning is to ( a) infer on the shared consensus preference of a group of users, sometimes called rank aggregation, or ( b) estimate for each user her individual ranking of the items, when the user indicates only incomplete preferences; the latter is an important part of recommender systems. We provide an overview of probabilistic approaches to preference learning, including the Mallows, Plackett–Luce, and Bradley–Terry models and collaborative filtering, and some of their variations. We illustrate, compare, and discuss the use of these methods by means of an experiment in which assessors rank potatoes, and with a simulation. The purpose of this article is not to recommend the use of one best method but to present a palette of different possibilities for different questions and different types of data.
Trajectory and velocity planning of the robot for sphere-pipe intersection hole cutting with single-Y welding groove

The influence of welding preparation and surface treatment of the weldment on welding quality is very important. Single Y-groove technique is a common surface treatment technique in intersecting curve welding. This paper presents a trajectory and velocity planning method for robot to machine a spherical single Y-groove. First, the geometrical models of the sphere-pipe intersection and the single Y-groove are established. This model can cover most of the intersection modes and parameterizes the groove angle and root face height to obtain the optimum process parameters. The above model forms the basis of robot trajectory planning and is the core innovations of this paper. Based on this model and the principle of three-dimensional tool compensation, our paper designs the interface of cutting robot tool compensation. Through this interface, the operators can flexibly change the radius to adapt to different tools or cutting torches, which can fulfill the requirements of dynamic compensation. As the goal of our paper, this method gives a general trajectory description of the robot and introduces two algorithms for the velocity planning to ensure the stability of the robot velocity. Finally, this paper takes the plasma cutting as example, and gives the relevant algorithm to solve the optimal start position and orientation of the plasma torch, which can avoid the cutting defects.
All-Optical Storage of Phase-Sensitive Quantum States of Light

We experimentally demonstrate storage and on-demand release of phase-sensitive, photon-number superposition states of the form $\ensuremath{\alpha}|0⟩+\ensuremath{\beta}{e}^{i\ensuremath{\theta}}|1⟩$ for an optical quantized oscillator mode. For this purpose, we newly developed a phase-probing mechanism compatible with a storage system composed of two concatenated optical cavities, which was previously employed for storage of phase-insensitive single-photon states [Phys. Rev. X 3, 041028 (2013)]. This is the first demonstration of all-optically storing highly nonclassical and phase-sensitive quantum states of light. The strong nonclassicality of the states after storage becomes manifest as a negative region in the corresponding Wigner function shifted away from the origin in phase space. This negativity is otherwise, without the phase information of the memory system, unobtainable. While our scheme includes the possibility of optical storage, on-demand release and synchronization of arbitrary single-rail qubit states, it is not limited to such states. In fact, our technique is extendible to more general phase-sensitive states such as multiphoton superposition or entangled states, and thus it represents a significant step toward advanced optical quantum information processing, where highly nonclassical states are utilized as resources.
Face recognition using nonnegative matrix factorization with fractional power inner product kernel

Kernel nonnegative matrix factorization (KNMF) algorithms have been widely used to extract features for face recognition. The choice of kernel function is vital to facial feature extraction. The polynomial kernel function has been commonly used in KNMF. The power of the polynomial kernel is required to be a positive integer, thereby ensuring that the kernel generates a positive semi-definite matrix. In this paper, we investigate a new type of inner-product kernel that has a fractional power. The new kernel offers us flexibility in data representation as the power can be any positive real number. Based on the fractional power inner-product kernel, we present a novel KNMF algorithm called fractional power inner-product KNMF (FPKNMF). The FPKNMF algorithm is theoretically and experimentally validated to be convergent. The experimental results confirm that our algorithm exhibits a performance superior to the state-of-the-art methods in terms of facial representation and recognition accuracy.
Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions

Machine Reading Comprehension (MRC) with multiple-choice questions requires the machine to read given passage and select the correct answer among several candidates. In this paper, we propose a novel approach called Convolutional Spatial Attention (CSA) model which can better handle the MRC with multiple-choice questions. The proposed model could fully extract the mutual information among the passage, question, and the candidates, to form the enriched representations. Furthermore, to merge various attention results, we propose to use convolutional operation to dynamically summarize the attention values within the different size of regions. Experimental results show that the proposed model could give substantial improvements over various state-of-the-art systems on both RACE and SemEval-2018 Task11 datasets.
Majority vote feature selection algorithm in software fault prediction

Identification and location of defects in software projects is an important task to improve software quality and to reduce software test effort estimation cost. In software fault prediction domain, it is known that 20% of the modules will in general contain about 80% of the faults. In order to minimize cost and effort, it is considerably important to identify those most error prone modules precisely and correct them in time. Machine Learning (ML) algorithms are frequently used to locate error prone modules automatically. Furthermore, the performance of the algorithms is closely related to determine the most valuable software metrics. The aim of this research is to develop a Majority Vote based Feature Selection algorithm (MVFS) to identify the most valuable software metrics. The core idea of the method is to identify the most influential software metrics with the collaboration of various feature rankers. To test the efficiency of the proposed method, we used CM1, JM1, KC1, PC1, Eclipse Equinox, Eclipse JDT datasets and J48, NB, K-NN (IBk) ML algorithms. The experiments show that the proposed method is able to find out the most significant software metrics that enhances defect prediction performance.
PIRM2018 Challenge on Spectral Image Super-Resolution: Methods and Results

In this paper, we describe the Perceptual Image Restoration and Manipulation (PIRM) workshop challenge on spectral image super-resolution, motivate its structure and conclude on results obtained by the participants. The challenge is one of the first of its kind, aiming at leveraging modern machine learning techniques to achieve spectral image super-resolution. It comprises of two tracks. The first of these (Track 1) is about example-based single spectral image super-resolution. The second one (Track 2) is on colour-guided spectral image super-resolution. In this manner, Track 1 focuses on the problem of super-resolving the spatial resolution of spectral images given training pairs of low and high spatial resolution spectral images. Track 2, on the other hand, aims to leverage the inherently higher spatial resolution of colour (RGB) cameras and the link between spectral and trichromatic images of the scene. The challenge in both tracks is then to recover a super-resolved image making use of low-resolution imagery at the input. We also elaborate upon the methods used by the participants, summarise the results and discuss their rankings.
A multi-sensor dataset of human-human handover

The article describes a multi-sensor dataset of human-human handovers composed of over 1000 recordings collected from 18 volunteers. The recordings refer to 76 test configurations, which consider different volunteer׳s starting positions and roles, objects to pass and motion strategies. In all experiments, we acquire 6-axis inertial data from two smartwatches, the 15-joint skeleton model of one volunteer with an RGB-D camera and the upper-body model of both persons using a total of 20 motion capture markers. The recordings are annotated with videos and questionnaires about the perceived characteristics of the handover.
Joint Model Feature Regression and Topic Learning for Global Citation Recommendation

Citation recommendation has gained increasing attention in recent years.In practice, researchers usually prefer to cite the most topic-relevant articles.Nevertheless, how to model the implicit correlations between topics and citations is still a challenging task.In this paper, we propose a novel citation recommendation model, called TopicCite, which mines such fine-grained correlations.We extract various citation features from citation network, and integrate the learning process of feature regression with topic modeling.At the recommendation stage, we expand the folding-in process by adding the topic influence of papers that correlated with user-provided information.TopicCite can also be considered a technique for extracting topic-related citation features from manually defined citation features, which can essentially improve the granularity of pre-extracted features.In addition, the unsupervised topic model is supervised and mutually reinforced by abundant citation features in TopicCite; thus, the proposed model can also extract more reliable topic distributions from citation data, which brings a new perspective to topic discovery on linked data.The experimental results on the AAN and DBLP datasets demonstrate that our model is competitive with the state-of-the-art methods.
Blended learning of physics in the context of the professional development of teachers

This paper introduces the refresher courses designed for the physics teachers. These courses are based on the blended learning technology combining traditional full-time education with the elements of distance learning supported by LMS Moodle. The courses were tested at the Elabuga Institute of Kazan Federal University in 2016-2018. This paper describes the module-based course structure and methods for applying the e-learning modules. The distinctive feature of the course is that the content of the learning modules was designed to deliver different methods for teaching physics and to improve the general cultural competence of a teacher. The analysis of polling results (poll included 89 physics teachers) allowed assessing the efficiency of designed courses in the context of teacher's professional development, his/her readiness to work in modern learning environment. The research outcomes will be also useful for foreign educational establishments implementing the teacher professional development programs.
Holographic Factorization Machines for Recommendation

Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks. FMs are characterized by its usage of the inner product of factorized parameters to model pairwise feature interactions, making it highly expressive and powerful. This paper proposes Holographic Factorization Machines (HFM), a new novel method of enhancing the representation capability of FMs without increasing its parameter size. Our approach replaces the inner product in FMs with holographic reduced representations (HRRs), which are theoretically motivated by associative retrieval and compressed outer products. Empirically, we found that this leads to consistent improvements over vanilla FMs by up to 4% improvement in terms of mean squared error, with improvements larger at smaller parameterization. Additionally, we propose a neural adaptation of HFM which enhances its capability to handle nonlinear structures. We conduct extensive experiments on nine publicly available datasets for collaborative filtering with explicit feedback. HFM achieves state-of-theart performance on all nine, outperforming strong competitors such as Attentional Factorization Machines (AFM) and Neural Matrix Factorization (NeuMF).
Answer Identification from Product Reviews for User Questions by Multi-Task Attentive Networks

Online Shopping has become a part of our daily routine, but it still cannot offer intuitive experience as store shopping. Nowadays, most e-commerce Websites offer a Question Answering (QA) system that allows users to consult other users who have purchased the product. However, users still need to wait patiently for others’ replies. In this paper, we investigate how to provide a quick response to the asker by plausible answer identification from product reviews. By analyzing the similarity and discrepancy between explicit answers and reviews that can be answers, a novel multi-task deep learning method with carefully designed attention mechanisms is developed. The method can well exploit large amounts of user generated QA data and a few manually labeled review data to address the problem. Experiments on data collected from Amazon demonstrate its effectiveness and superiority over competitive baselines.
Improving One-Class Collaborative Filtering via Ranking-Based Implicit Regularizer

One-class collaborative filtering (OCCF) problems are vital in many applications of recommender systems, such as news and music recommendation, but suffers from sparsity issues and lacks negative examples. To address this problem, the state-of-the-arts assigned smaller weights to unobserved samples and performed low-rank approximation. However, the ground-truth ratings of unobserved samples are usually set to zero but ill-defined. In this paper, we propose a ranking-based implicit regularizer and provide a new general framework for OCCF, to avert the ground-truth ratings of unobserved samples. We then exploit it to regularize a ranking-based loss function and design efficient optimization algorithms to learn model parameters. Finally, we evaluate them on three realworld datasets. The results show that the proposed regularizer significantly improves ranking-based algorithms and that the proposed framework outperforms the state-of-the-art OCCF algorithms.
A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization

There currently exist two dominant strategies to reduce data sizes in analysis and visualization: reducing the precision of the data, e.g., through quantization, or reducing its resolution, e.g., by subsampling. Both have advantages and disadvantages and both face fundamental limits at which the reduced information ceases to be useful. The paper explores the additional gains that could be achieved by combining both strategies. In particular, we present a common framework that allows us to study the trade-off in reducing precision and/or resolution in a principled manner. We represent data reduction schemes as progressive streams of bits and study how various bit orderings such as by resolution, by precision, etc., impact the resulting approximation error across a variety of data sets as well as analysis tasks. Furthermore, we compute streams that are optimized for different tasks to serve as lower bounds on the achievable error. Scientific data management systems can use the results presented in this paper as guidance on how to store and stream data to make efficient use of the limited storage and bandwidth in practice.
Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation

Weighted model counting has recently been extended to weighted model integration, which can be used to solve hybrid probabilistic reasoning problems. Such problems involve both discrete and continuous probability distributions. We show how standard knowledge compilation techniques (to SDDs and d-DNNFs) apply to weighted model integration, and use it in two novel solvers, one exact and one approximate solver. Furthermore, we extend the class of employable weight functions to actual probability density functions instead of mere polynomial weight functions.
3D Volumetric Modeling with Introspective Neural Networks

In this paper, we study the 3D volumetric modeling problem by adopting the Wasserstein introspective neural networks method (WINN) that was previously applied to 2D static images. We name our algorithm 3DWINN which enjoys the same properties as WINN in the 2D case: being simultaneously generative and discriminative. Compared to the existing 3D volumetric modeling approaches, 3DWINN demonstrates competitive results on several benchmarks in both the generation and the classification tasks. In addition to the standard inception score, the Frechet Inception Distance (FID) metric is´ also adopted to measure the quality of 3D volumetric generations. In addition, we study adversarial attacks for volumetric data and demonstrate the robustness of 3DWINN against adversarial examples while achieving appealing results in both classification and generation within a single model. 3DWINN is a general framework and it can be applied to the emerging tasks for 3D object and scene modeling.1
Real-Time Vision through Haze Based on Polarization Imaging

Various gases and aerosols in bad weather conditions can cause severe image degradation, which will seriously affect the detection efficiency of optical monitoring stations for high pollutant discharge systems. Thus, penetrating various gases and aerosols to sense and detect the discharge of pollutants plays an important role in the pollutant emission detection system. Against this backdrop, we recommend a real-time optical monitoring system based on the Stokes vectors through analyzing the scattering characteristics and polarization characteristics of both gases and aerosols in the atmosphere. This system is immune to the effects of various gases and aerosols on the target to be detected and achieves the purpose of real-time sensing and detection of high pollutant discharge systems under bad weather conditions. The imaging system is composed of four polarizers with different polarization directions integrated into independent cameras aligned parallel to the optical axis in order to acquire the Stokes vectors from various polarized azimuth images. Our results show that this approach achieves high-contrast and high-definition images in real time without the loss of spatial resolution in comparison with the performance of conventional imaging techniques.
Supporting Blended Learning Using Mobile Instant Messaging Application: Its Effectiveness and Limitations

This paper discusses the effectiveness of mobile instant messaging (MIM) Line in supporting a blended learning model and elaborates students' opinion regarding the limitations of the MIM.A total of 62 pre-service physics teachers were enrolled in an introductory physics course.Line-supported blended learning was implemented in an experiment group whereas learning management system (LMS)based blended learning was as the control group.Data were collected through test, questionnaire, and interview.The test and questionnaire consisted of 20 multiple choice questions and five open-ended questions, respectively.Meanwhile, the interview was a semi-structured interview performed through a focus group interview.The analysis results have given the information that there is no significant difference in the overall improvement of learning outcomes between the two groups.However, students in experiment group have significantly higher improvement of analy¬sis skills than their counterparts in control group.
Leveraging unpaired out-of-domain data for image captioning

Image captioning is a focal point in the realm of computer vision due to its scientific and practical values. Although recent encoder-decoder models have achieved promising performance, they only leverage data from standard datasets, and the performance is limited to the specific datasets. There're still large amounts of data without any additional annotations on the internet which can't be fully utilized. In this paper, we propose a novel approach to using external unpaired images and texts to enhance the performance of image captioning system. Our method can utilize image and text data scraped from the internet respectively to improve the performance limited in concepts-decoder framework. Our approach can transfer the knowledge learned from web data to the standard dataset. We conduct extensive experiments on MS COCO and Flickr30K datasets. The result demonstrates the effectiveness of our method. On both datasets, our metrics scores are significantly improved compared with other methods.