Reviewerly/exHarmony huggingface dataset:
Monogamy and polygamy for generalized <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>W</mml:mi></mml:math> -class states using Rényi- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math> entropy

We investigate monogamy relations and upper bounds for generalized $W$-class states related to the R\'{e}nyi-$\alpha$ entropy. First, we present an analytical formula on R\'{e}nyi-$\alpha$ entanglement (R$\alpha$E) and R\'{e}nyi-$\alpha$ entanglement of assistance (REoA) of a reduced density matrix for a generalized $W$-class states. According to the analytical formula, we show monogamy and polygamy relations for generalized $W$-class states in terms of R$\alpha$E and REoA. Then we give the upper bounds for generalized $W$-class states in terms of R$\alpha$E. Next, we provide tighter monogamy relations for generalized $W$-class states in terms of concurrence and convex-roof extended negativity and obtain the monogamy relations for R$\alpha$E by the analytical expression between R$\alpha$E and concurrence. Finally, we apply our results into quantum games and present a new bound of the nonclassicality of quantum games restricting to generalized $W$-class states.
Sharma–Mittal quantum discord

We demonstrate a generalization of quantum discord using a generalized definition of von-Neumann entropy, which is Sharma-Mittal entropy; and the new definition of discord is called Sharma-Mittal quantum discord. Its analytic expressions are worked out for two qubit quantum states as well as Werner, isotropic, and pointer states as special cases. The R{\'e}nyi, Tsallis, and von-Neumann entropy based quantum discords can be expressed as limiting cases for of Sharma-Mittal quantum discord. We also numerically compare all these discords and entanglement negativity.
Neural network‐based semantic segmentation model for robot perception of driverless vision

Driverless vision is one of the important applications of robot perception. With the development of driverless vehicles, the perception and understanding of the surrounding environment are becoming more and more important. When the types of surrounding objects are too complex, the ability of the computer to recognise the environment is poor. To improve the recognition accuracy of the computer and enhance the ability of segmentation, in this study, depth estimation is used to predict depth information to assist semantic segmentation, and then edge features of objects are introduced to enhance the contour of objects. A neural network-based semantic segmentation model is proposed. Finally, the intrinsic mechanism of attention is used to increase the correlation between channels. The experimental results on the CamVid data set show that this model can obtain better evaluation results and improve the segmentation accuracy of images compared with other models.
Single-shot energetic-based estimator for entanglement in a half-parity measurement setup

Producing and certifying entanglement between distant qubits is a highly desirable skill for quantum information technologies. Here we propose a new strategy to monitor and characterize entanglement genesis in a half parity measurement setup, that relies on the continuous readout of an energetic observable which is the half-parity observable itself. Based on a quantum-trajectory approach, we theoretically analyze the statistics of energetic fluctuations for a pair of continuously monitored qubits. We quantitatively relate these energetic fluctuations to the rate of entanglement produced between the qubits, and build an energetic-based estimator to assess the presence of entanglement in the circuit. Remarkably, this estimator is valid at the single-trajectory level and shows to be robust against finite detection efficiency. Our work paves the road towards a fundamental understanding of the stochastic energetic processes associated with entanglement genesis, and opens new perspectives for witnessing quantum correlations thanks to quantum thermodynamic quantities.
Algorithmic Classification and Statistical Modelling of Coastal Settlement Patterns in Mesolithic South-Eastern Norway

This paper presents and contrasts procedures and conceptual underpinnings associated with statistical modelling and machine learning in the study of past locational patterns. This was done by applying the methods of logistic regression and random forest to a case study of coastal Mesolithic settlement patterns in southern Norway—a context that has not been subject to formal locational pattern analysis in the past. While the predictive accuracy of the the two methods was comparable, the different strengths and weaknesses associated with the methods offered a firmer foundation on which to both draw and moderate substantive conclusions. The main findings were that among the considered variables, the exposure of sites was the most important driver of Mesolithic site location in the region, and while there are some small indications of diachronic variation, the differences detected appear to both be of a different and far more modest nature compared to that which has previously been proposed. All employed data, the Python script used to run the analyses in GRASS GIS, as well as the R script used for subsequent statistical analysis is freely available in online repositories, allowing for a complete scrutiny of the steps taken.
Grounding and propositional identity

Abstract I show that standard grounding conditions contradict standard conditions for the identities of propositions.
Problems and solutions from the fourth International Students’ Olympiad in Cryptography (NSUCRYPTO)

Mathematical problems and their solutions of the Fourth International Students' Olympiad in cryptography NSUCRYPTO'2017 are presented. We consider problems related to attacks on ciphers and hash functions, cryptographic Boolean functions, the linear branch number, addition chains, error correction codes, etc. We discuss several open problems on algebraic structure of cryptographic functions, useful proof-of-work algorithms, the Boolean hidden shift problem and quantum computings.
MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems

Analyzing the gaze accuracy characteristics of an eye tracker is a critical task as its gaze data is frequently affected by non-ideal operating conditions in various consumer eye tracking applications. In this study, gaze error patterns produced by a commercial eye tracking device were studied with the help of machine learning algorithms, such as classifiers and regression models. Gaze data were collected from a group of participants under multiple conditions that commonly affect eye trackers operating on desktop and handheld platforms. These conditions (referred here as error sources) include user distance, head pose, and eye-tracker pose variations, and the collected gaze data were used to train the classifier and regression models. It was seen that while the impact of the different error sources on gaze data characteristics were nearly impossible to distinguish by visual inspection or from data statistics, machine learning models were successful in identifying the impact of the different error sources and predicting the variability in gaze error levels due to these conditions. The objective of this study was to investigate the efficacy of machine learning methods towards the detection and prediction of gaze error patterns, which would enable an in-depth understanding of the data quality and reliability of eye trackers under unconstrained operating conditions. Coding resources for all the machine learning methods adopted in this study were included in an open repository named MLGaze to allow researchers to replicate the principles presented here using data from their own eye trackers.
Revisiting Explicit Negation in Answer Set Programming

Abstract A common feature in Answer Set Programming is the use of a second negation, stronger than default negation and sometimes called explicit, strong or classical negation. This explicit negation is normally used in front of atoms, rather than allowing its use as a regular operator. In this paper we consider the arbitrary combination of explicit negation with nested expressions, as those defined by Lifschitz, Tang and Turner. We extend the concept of reduct for this new syntax and then prove that it can be captured by an extension of Equilibrium Logic with this second negation. We study some properties of this variant and compare to the already known combination of Equilibrium Logic with Nelson’s strong negation.
Phase diffusion and the small-noise approximation in linear amplifiers: Limitations and beyond

The phase of an optical field inside a linear amplifier is widely known to diffuse with a diffusion coefficient that is inversely proportional to the photon number. The same process occurs in lasers which limits its intrinsic linewidth and makes the phase uncertainty difficult to calculate. The most commonly used simplification is to assume a narrow photon-number distribution for the optical field (which we call the small-noise approximation). For coherent light, this condition is determined by the average photon number. The small-noise approximation relies on (i) the input to have a good signal-to-noise ratio, and (ii) that such a signal-to-noise ratio can be maintained throughout the amplification process. Here we ask: For a coherent input, how many photons must be present in the input to a quantum linear amplifier for the phase noise at the output to be amenable to a small-noise analysis? We address these questions by showing how the phase uncertainty can be obtained without recourse to the small-noise approximation. It is shown that for an ideal linear amplifier (i.e. an amplifier most favourable to the small-noise approximation), the small-noise approximation breaks down with only a few photons on average. Interestingly, when the input strength is increased to tens of photons, the small-noise approximation can be seen to perform much better and the process of phase diffusion permits a small-noise analysis. This demarcates the limit of the small-noise assumption in linear amplifiers as such an assumption is less true for a nonideal amplifier.
How to identify the roots of broad research topics and fields? The introduction of RPYS sampling using the example of climate change research

Since the introduction of the reference publication year spectroscopy (RPYS) method and the corresponding program CRExplorer, many studies have been published revealing the historical roots of topics, fields, and researchers. The application of the method was restricted up to now by the available memory of the computer used for running the CRExplorer. Thus, many users could not perform RPYS for broader research fields or topics. In this study, we present various sampling methods to solve this problem: random, systematic, and cluster sampling. We introduce the script language of the CRExplorer which can be used to draw many samples from the population dataset. Based on a large dataset of publications from climate change research, we compare RPYS results using population data with RPYS results using different sampling techniques. From our comparison with the full RPYS (population spectrogram), we conclude that the cluster sampling performs worst and the systematic sampling performs best. The random sampling also performs very well but not as well as the systematic sampling. The study therefore demonstrates the fruitfulness of the sampling approach for applying RPYS.
Coherent optical communications enhanced by machine intelligence

Uncertainty in discriminating between different received coherent signals is integral to the operation of many free-space optical communications protocols, and is often difficult when the receiver measures a weak signal. Here we design an optical communications scheme that uses balanced homodyne detection in combination with an unsupervised generative machine learning and convolutional neural network (CNN) system, and demonstrate its efficacy in a realistic simulated coherent quadrature phase shift keyed (QPSK) communications system. Additionally, we program the neural network system at the transmitter such that it autonomously learns to correct for the noise associated with a weak QPSK signal, which is shared with the network state of the receiver prior to the implementation of the communications. We find that the scheme significantly reduces the overall error probability of the communications system, achieving the classical optimal limit. This communications design is straightforward to build, implement, and scale. We anticipate that these results will allow for a significant enhancement of current classical and quantum coherent optical communications technologies.
Exploration of Interactive Foreign Language Teaching Mode Based on Artificial Intelligence

Traditional foreign language teaching cannot fully meet the students' individualized learning needs in the aspects of teaching resources, cultural communication and teaching evaluation. Foreign language teaching assisted by artificial intelligence technology can effectively solve the problems of limited teaching hours, limited space, limited resources and monotonous evaluation method, etc. This paper explores the application of artificial intelligence technology in foreign language teaching, and estimates its potential utility, in terms of the teaching model, the way of learning and teachers' functions. And then, this paper elaborates the construction of interactive foreign language teaching model based on artificial intelligence, from the perspective of learner modelling, teaching process and evaluation system. Finally, this paper proposes the design of interactive foreign language teaching platform, and introduce the key AI technologies of the platform.
Dynamic pattern matching with Python

Pattern matching allows programs both to extract specific information from complex data types, as well as to branch on the structure of data and thus apply specialized actions to different forms of data. Originally designed for strongly typed functional languages with algebraic data types, pattern matching has since been adapted for object-oriented and even dynamic languages. This paper discusses how pattern matching can be included in the dynamically typed language Python in line with existing features that support extracting values from sequential data structures.
Learning Propagation Rules for Attribution Map Generation

Prior gradient-based attribution-map methods rely on hand-crafted propagation rules for the non-linear/activation layers during the backward pass, so as to produce gradients of the input and then the attribution map. Despite the promising results achieved, such methods are sensitive to the non-informative high-frequency components and lack adaptability for various models and samples. In this paper, we propose a dedicated method to generate attribution maps that allow us to learn the propagation rules automatically, overcoming the flaws of the hand-crafted ones. Specifically, we introduce a learnable plugin module, which enables adaptive propagation rules for each pixel, to the non-linear layers during the backward pass for mask generating. The masked input image is then fed into the model again to obtain new output that can be used as a guidance when combined with the original one. The introduced learnable module can be trained under any auto-grad framework with higher-order differential support. As demonstrated on five datasets and six network architectures, the proposed method yields state-of-the-art results and gives cleaner and more visually plausible attribution maps.
Innovation in Elementary Schools: Engaging Augmented Reality and Balinese Folklore for Science Learning Aids

This study aimed at developing an application for elementary science education that uses Augmented Reality to integrate culture (folklore) and science knowledge.The application was called SABAR (that stands for Satua Bali (Balinese folklore)-Augmented Reality).It was developed by using the ADDIE model.The analysis stage was carried out by a FGD.At the design stage, SABAR blueprint was prepared, science concepts related to the selected folklore were mapped, the content was drafted, and the initial draft was prepared.At the development stage content validation, practitioners consultation, and feasibility testing were carried out.The results of expert judgment were then analyzed using Aiken's validity index.Then a questionnaire was distributed to 10 elementary school teachers and 100 elementary school students from Denpasar City.The implementation stage was carried out in one-shot case study.The try-out involved one class with 30 students, namely the fifth grade of Public Elementary School No 29 Dangin Puri, Denpasar.The variable tested was scientific literacy.The data were analyzed using one-sample t-test.The final stage was evaluation.The result of the content validity test showed that SABAR has high validity coefficient.The responses of the practitioners and students showed that SABAR is easy to use, attractive, and helps students in learning.The analysis of one-sample test showed that p-value = 0.000.In other words, there is a difference in the elementary school students' scientific literacy when they learned science by using the application.This study needs to be continued with a wider range of population.
Multiple Exemplars-Based Hallucination for Face Super-Resolution and Editing

Given a really low resolution input image of a face (say \(16\,{\times }\,16\) or \(8\,{\times }\,8\) pixels), the goal of this paper is to reconstruct a high-resolution version thereof. This, by itself, is an ill-posed problem, as the high-frequency information is missing in the low-resolution input and needs to be hallucinated, based on prior knowledge about the image content. Rather than relying on a generic face prior, in this paper we explore the use of a set of exemplars, i.e. other high-resolution images of the same person. These guide the neural network as we condition the output on them. Multiple exemplars work better than a single one. To combine the information from multiple exemplars effectively, we introduce a pixel-wise weight generation module. Besides standard face super-resolution, our method allows to perform subtle face editing simply by replacing the exemplars with another set with different facial features. A user study is conducted and shows the super-resolved images can hardly be distinguished from real images on the CelebA dataset. A qualitative comparison indicates our model outperforms methods proposed in the literature on the CelebA and WebFace datasets.
Assessing Laser Cleaning of a Limestone Monument by Fiber Optics Reflectance Spectroscopy (FORS) and Visible and Near-Infrared (VNIR) Hyperspectral Imaging (HSI)

Fiber optics reflectance spectroscopy (FORS) and visible and near-infrared (VNIR) hyperspectral imaging (HSI) were applied to assess and control the laser cleaning process of a deeply darkened limestone surface collected from the historic entrance gate of Castello Svevo, Bari, Italy. Both techniques enabled us to verify the different degree of removal of a thick deposit of black crust from the surface of the walls. Results obtained were in good agreement with those of previous studies of the elemental composition achieved by application of laser-induced breakdown spectroscopy (LIBS). Coupling FORS and VNIR-HSI provided important information on the optimal conditions to evaluate the conservation status and determine the more appropriate level of cleaning restoration, thus avoiding over- and/or under-cleaning. Imaging spectroscopy was used to obtain maps of areas featuring the same or different spectral characteristics, so to achieve a sufficient removal of unwanted layers, without modifying the surface underneath, and to increase the efficiency of traditional cleaning techniques. The performance of the combined non-invasive approach used in this work shows promise for further applications to other types of rocks and highlights the potential for in situ assessment of the laser cleaning process based on reflectance spectroscopy.
The comparative economics of financial access in gender economic inclusion

Purpose The study has investigated the comparative importance of financial access in promoting gender inclusion in African countries. Design/methodology/approach Gender inclusion is proxied by the female labour participation rate while financial channels include: financial system deposits and private domestic credit. The empirical evidence is based on non-contemporary fixed effects regressions. Findings In order to provide more implications on comparative relevance, the dataset is categorised into income levels (middle income versus (vs.) low income); legal origins (French civil law vs. English common law); religious domination (Islam vs. Christianity); openness to sea (coastal vs. landlocked); resource-wealth (oil-poor vs. oil-rich) and political stability (stable vs. unstable). Six main hypotheses are tested, notably, that middle income, English common law, Christianity, coastal, oil-rich and stable countries enjoy better levels of “financial access”-induced gender inclusion compared to respectively, low income, French civil law, Islam, landlocked, oil-poor and unstable countries. All six tested hypotheses are validated. Originality/value This is the first study on the comparative importance of financial access in gender economic participation.
A Movie Recommendation System Based on Differential Privacy Protection

In the past decades, the ever-increasing popularity of the Internet has led to an explosive growth of information, which has consequently led to the emergence of recommendation systems. A series of cloud-based encryption measures have been adopted in the current recommendation systems to protect users’ privacy. However, there are still many other privacy attacks on the local devices. Therefore, this paper studies the encryption interference of applying a differential privacy protection scheme on the data in the user’s local devices under the assumption of an untrusted server. A dynamic privacy budget allocation method is proposed based on a localized differential privacy protection scheme while taking the specific application scene of movie recommendation into consideration. What is more, an improved user-based collaborative filtering algorithm, which adopts a matrix-based similarity calculation method instead of the traditional vector-based method when computing the user similarity, is proposed. Finally, it was proved by experimental results that the differential privacy-based movie recommendation system (DP-MRE) proposed in this paper could not only protect the privacy of users but also ensure the accuracy of recommendations.
Transforming Gringo Rules into Formulas in a Natural Way

Research on the input language of the ASP grounder gringo uses a translation that converts rules in that language into first-order formulas. That translation often transforms short rules into formulas that are syntactically complex. In this note we identify a class of rules that can be transformed into formulas in a simpler, more natural way. The new translation contributes to our understanding of the relationship between the language of gringo and first-order languages.