Reviewerly/exHarmony huggingface dataset:
Fast FRFT-based method for estimating physical parameters from Newton’s rings

By analyzing Newton’s rings, often encountered in interferometry, using fractional Fourier transform (FRFT), we can estimate the physical parameters, such as the curvature radius of the spherical surface and the rings’ center. However, parameter estimation from large images using FRFT consumes considerable time. We introduce an improved method that resamples the image before applying FRFT. Because Newton’s rings are sparse in the FRFT domain, this method reduces the computational time without decreasing the accuracy. Experimental results show that compared to traditional FRFT-based algorithms, this method can estimate parameters in about 1.3 s when processing 1920×1080 pixel images.
Dangerous Objects Detection of X-Ray Images Using Convolution Neural Network

Object detection is a popular research direction in the field of traditional visual research. It has a wide range of applications in our real life. Because the deep learning model training for object detection requires a large amount of training data, the research on object detection until now is mostly based on natural images datasets. While natural image object recognition techniques are already very mature, the research on X-ray images is still in its infancy. This paper applies X-ray image dataset for object detection experiments and in particular aims to build an X-ray security system which can be widely used in airports, subway stations and high security conference venues. Our experiments mainly focus on three kinds of dangerous objects: scissor, knife and bottle. The results show that we achieve mean average precision of 86.41% and recall rate of 87.70%.
Efficient variational approach to dynamics of a spatially extended bosonic Kondo model

An efficient variational approach to study the dynamics of a localized quantum spin coupled to a bath of mobile spinful bosons is developed. A recently proposed impurity-decoupling transformation with the variational Gaussian ansatz for bosons is used to analyze coherent time evolution. This formalism can be applied potentially to a wide class of challenging quantum impurity problems.
Semi-supervised image attribute editing using generative adversarial networks

Image attribute editing is a challenging problem that has been recently studied by many researchers using generative networks. The challenge is in the manipulation of selected attributes of images while preserving the other details. The method to achieve this goal is to find an accurate latent vector representation of an image and a direction corresponding to the attribute. Almost all the works in the literature use labeled datasets in a supervised setting for this purpose. In this study, we introduce an architecture called Cyclic Reverse Generator (CRG), which allows learning the inverse function of the generator accurately via an encoder in an unsupervised setting by utilizing cyclic cost minimization. Attribute editing is then performed using the CRG models for finding desired attribute representations in the latent space. In this work, we use two arbitrary reference images, with and without desired attributes, to compute an attribute direction for editing. We show that the proposed approach performs better in terms of image reconstruction compared to the existing end-to-end generative models both quantitatively and qualitatively. We demonstrate state-of-the-art results on both real images and generated images in CelebA dataset.
Rapid population transfer of a two-level system by a polychromatic driving field

Abstract We propose a simple exact analytical solution for a model consisting of a two-level system and a polychromatic driving field. It helps us to realize a rapid complete population transfer from the ground state to the excited state, and the system can be stable at the excited state for an extremely long time. A combination of the mechanism and the Rydberg atoms successfully prepares the Bell state and multipartite W state, and the experimental feasibility is discussed via the current experimental parameters. Finally, the simple exact analytical solution is generalized into a three-level system, which leads to a significant enhancement of the robustness against dissipation.
Machine-learning-based three-qubit gate design for the Toffoli gate and parity check in transmon systems

We use machine learning techniques to design a 50 ns three-qubit flux-tunable controlled-controlled-phase gate with fidelity of >99.99% for nearest-neighbor coupled transmons in circuit quantum electrodynamics architectures. We explain our gate design procedure where we enforce realistic constraints, and analyze the new gate's robustness under decoherence, distortion, and random noise. Our controlled-controlled-phase gate in combination with two single-qubit gates realizes a Toffoli gate which is widely used in quantum circuits, logic synthesis, quantum error correction, and quantum games.
Entanglement of genuinely entangled subspaces and states: Exact, approximate, and numerical results

Genuinely entangled subspaces (GESs) are those subspaces of multipartite Hilbert spaces that consist only of genuinely multiparty entangled pure states. They are natural generalizations of the well-known notion of completely entangled subspaces, which by definition are void of fully product vectors. Entangled subspaces are an important tool of quantum information theory as they directly lead to constructions of entangled states, since any state supported on such a subspace is automatically entangled. Moreover, they have also proven useful in the area of quantum error correction. In our recent contribution [M. Demianowicz and R. Augusiak, Phys. Rev. A 98, 012313 (2018)], we have studied the notion of a GES qualitatively in relation to so-called nonorthogonal unextendible product bases and provided a few constructions of such subspaces. The main aim of the present work is to perform a quantitative study of the entanglement properties of GESs. First, we show how one can attempt to compute analytically the subspace entanglement, defined as the entanglement of the least-entangled vector from the subspace, of a GES and illustrate our method by applying it to a new class of GESs. Second, we show that certain semidefinite programming relaxations can be exploited to estimate the entanglement of a GES and apply this observation to a few classes of GESs revealing that in many cases the method provides the exact results. Finally, we study the entanglement of certain states supported on GESs, which is compared to the obtained values of the entanglement of the corresponding subspaces, and find the white-noise robustness of several GESs. In our study we use the (generalized) geometric measure as the quantifier of entanglement.
Blind Deblurring of Text Images Using a Text-Specific Hybrid Dictionary

In this paper, we propose a blind text image deblurring algorithm by using a text-specific hybrid dictionary. After careful analysis, we find that the text-specific hybrid dictionary has the great ability of providing powerful contextual information for text image deblurring. Here, it is worth noting that our proposed method is inspired by our observation that an intermediate latent image contains not only sharp regions, but also multiple types of small blurred regions. Based upon our discovery, we propose a prior for text images based on sparse representation, which models the relationship between an intermediate latent image and a desired sharp image. To this end, we carefully collect three different image patch pairs, which are 1) Gaussian blur-sharp, 2) motion blur-sharp, and 3) sharp-sharp, in order to construct the text-specific hybrid dictionary. We also propose a new optimization framework suitable for the task of text image deblurring in this paper. Extensive experiments have been conducted on a challenging dataset of synthetic and real-world text images. Our results demonstrate that the proposed method outperforms the state-of-the-art image deblurring methods both quantitatively and qualitatively.
Estimating linear covariance models with numerical nonlinear algebra

Numerical nonlinear algebra is applied to maximum likelihood estimation for Gaussian models defined by linear constraints on the covariance matrix. We examine the generic case as well as special models (e.g. Toeplitz, sparse, trees) that are of interest in statistics. We study the maximum likelihood degree and its dual analogue, and we introduce a new software package LinearCovarianceModels.jl for solving the score equations. All local maxima can thus be computed reliably. In addition we identify several scenarios for which the estimator is a rational function.
Fabrication of the Impedance-Matched Josephson Parametric Amplifier and the Study of the Gain Profile

We designed and fabricated an impedance-matched Josephson parametric amplifier (JPA) working in the flux-pump mode for the broadband amplification of microwave signals. A simple fabrication method suitable for a small cleanroom was developed. We also studied the phase response, as well as the gain, as a function of signal frequency and pump power at various pump frequencies. The phase response can be explained with the behavior of the nonlinear Duffing oscillator. The observed decrease of the resonance frequency as the pump power increases, as well as the emergence of an unstable bifurcation zone, are the characteristic nonlinear behavior of the Duffing oscillator. The features of the gain profile in the stable zone can be explained with a model adapted from the theoretical model for the two-dimensional gain profile of an impedance-matched, current-pumped JPA. Over 17 dB gain with a bandwidth more than 300 MHz (centered around 6.45 GHz) was observed. Our impedance-matched JPA is used for improving the fast readout fidelity of transmon qubits.
Quasi-inversion of qubit channels

In general quantum operations, or quantum channels cannot be inverted by physical operations, i.e., by completely positive trace-preserving maps. An arbitrary state passing through a quantum channel loses its fidelity with the input. Given a quantum channel $\mathcal{E}$, we discuss the concept of its quasi-inverse as a completely positive trace-preserving map ${\mathcal{E}}^{qi}$ which when composed with $\mathcal{E}$ increases its average input-output fidelity in an optimal way. The channel ${\mathcal{E}}^{qi}$ comes as close as possible to the inverse of a quantum channel. We give a complete classification of such maps for qubit channels and provide quite a few illustrative examples.
LEAP Diarization System for the Second DIHARD Challenge

This paper presents the LEAP System, developed for the Second DIHARD diarization Challenge. The evaluation data in the challenge is composed of multi-talker speech in restaurants, doctor-patient conversations, child language acquisition recordings in home environments and audio extracted YouTube videos. The LEAP system is developed using two types of embeddings, one based on i-vector representations and the other one based on x-vector representations. The initial diarization output obtained using agglomerative hierarchical clustering (AHC) done on the probabilistic linear discriminant analysis (PLDA) scores is refined using the Variational-Bayes hidden Markov model (VB-HMM) model. We propose a modified VB-HMM model with posterior scaling which provides significant improvements in the final diarization error rate (DER). We also use a domain compensation on the i-vector features to reduce the mis-match between training and evaluation conditions.N(s)TN(s)TN(s)T Using the proposed approaches, we obtain relative improvements in DER of about 7.1% relative for the best individual system over the DIHARD baseline system and about 13.7% relative for the final system combination on evaluation set. An analysis performed using the proposed posterior scaling method shows that scaling results in improved discrimination among the HMM states in the VB-HMM.
Human Capital Investment as a Catalyst for Sustainable Economic Development in Nigeria

Human capital development presupposes investments, activities, and processes facilitating the generation of technical and expert knowledge; skills, health or values that are embodied in people. It implies maintaining an appropriate balance and key massive human resource base and providing an encouraging environment for all individuals to be fully engaged and contribute to organizational or national goals. Human capital development is necessary in order for National development to occur. In addition, human capital development teaches people how to utilize the advantages of diverse thinking styles (analytical and intuitive) so that they achieve the best holistic practical solutions. Human capital development and training are basically the same. This paper aims to examine the meaning of human capital development in relation to nation-building. The authors also took a cursory look at the concept of business education and its roles for sustainable development for nation-building. The study examined human capital investment as a catalyst for sustainable economic environment in Nigeria. The broad objective of the study is to analyze the effect of human capital investment on the Nigerian economy from 1986 to2017. The data used for the study were sourced from the central bank statistical bulletin and national bureau of Statistics. Ordinary Least Squares (OLS) techniques were used to analyze the data. The findings of the study reveal that there is a positive relationship between government expenditure on health and real gross domestic product. The adjusted coefficient of determination (R2) shows that 97.3% of variations in the real gross domestic product is being accounted for by government expenditure on education, government expenditure on health and gross capital formation while the remaining 2.7% is accounted for by variables not included in the model. The study suggests that Nigerian policymakers should pay more attention to the health sector and increase its yearly budgetary allocation to it. Nevertheless, the key to achieving best results lies not in ordinarily increasing particular budgetary allocation but rather in implementing a public expenditure and revenue and ensuring the usage of the allocated fund as transparently as possible.
Tight bounds for private communication over bosonic Gaussian channels based on teleportation simulation with optimal finite resources

Upper bounds for private communication over quantum channels can be derived by adopting channel simulation, protocol stretching, and relative entropy of entanglement. All these ingredients have led to single-letter upper bounds to the secret key capacity which can be directly computed over suitable resource states. For bosonic Gaussian channels, the tightest upper bounds have been derived by employing teleportation simulation over asymptotic resource states, namely the asymptotic Choi matrices of these channels. In this work, we adopt a different approach. We show that teleporting over an analytical class of finite-energy resource states allows us to closely approximate the ultimate bounds for increasing energy, so as to provide increasingly tight upper bounds to the secret-key capacity of one-mode phase-insensitive Gaussian channels. We then show that an optimization over the same class of resource states can be used to bound the maximum secret key rates that are achievable in a finite number of channel uses.
Adaptive Kernel Value Caching for SVM Training

Support Vector Machines (SVMs) can solve structured multi-output learning problems such as multi-label classification, multiclass classification and vector regression. SVM training is expensive especially for large and high dimensional datasets. The bottleneck of the SVM training often lies in the kernel value computation. In many real-world problems, the same kernel values are used in many iterations during the training, which makes the caching of kernel values potentially useful. The majority of the existing studies simply adopt the LRU (least recently used) replacement strategy for caching kernel values. However, as we analyze in this paper, the LRU strategy generally achieves high hit ratio near the final stage of the training, but does not work well in the whole training process. Therefore, we propose a new caching strategy called EFU (less frequently used) which replaces the less frequently used kernel values that enhances LFU (least frequently used). Our experimental results show that EFU often has 20\% higher hit ratio than LRU in the training with the Gaussian kernel. To further optimize the strategy, we propose a caching strategy called HCST (hybrid caching for the SVM training), which has a novel mechanism to automatically adapt the better caching strategy in the different stages of the training. We have integrated the caching strategy into ThunderSVM, a recent SVM library on many-core processors. Our experiments show that HCST adaptively achieves high hit ratios with little runtime overhead among different problems including multi-label classification, multiclass classification and regression problems. Compared with other existing caching strategies, HCST achieves 20\% more reduction in training time on average.
Optimal entangled coherent states in lossy quantum-enhanced metrology

We investigate an optimal distance of two components in an entangled coherent state for quantum phase estimation in lossy interferometry. The optimal distance is obtained by an economical point, representing the quantum Fisher information that we can extract per input energy. Maximizing the formula of the quantum Fisher information over an input mean photon number, we show that, as the losses of the interferometer increase, it can be more beneficial to prepare an initially entangled coherent state that is less entangled. This represents that the optimal distance of the two-mode components decreases with more loss in the interferometry. Under the constraint of the input mean photon number, we obtain that the optimal entangled coherent state is more robust than a separable coherent state, even in a high photon loss rate. The optimal entangled coherent state preserves quantum advantage over the standard interferometric limit of the separable coherent state. We also show that the corresponding optimal measurement requires correlation measurement bases.
A Study of Security Vulnerabilities and Software Weaknesses in Vehicles

In this paper, we conduct an empirical study with the purpose of identifying common security vulnerabilities discovered in vehicles. The vulnerability information is gathered for 60 vehicle OEMs (Original Equipment Manufacturers) and common vehicle components from the National Vulnerability Database (NVD). Each vulnerability (CVE) is analyzed with respect to its software weakness type (CWE) and severity score (CVSS). 44 unique CVEs were found in NVD and analyzed. The analysis results show that about 50% of the vulnerabilities fall into the medium severity category, and the three most common software weaknesses reported are protection mechanism failure, buffer errors, and information disclosure.
Economic and Legal Regulation of Educational Processes in the Field of Natural Sciences and Humanities

Among the most important issues that need to be addressed in order to improve higher professional education is the issue of proper and complete regulation of relations in the educational system of the Russian Federation at the legislative level. Only by ensuring proper legal regulation of higher professional education is it possible to achieve the main goal defined by the Constitution of the Russian Federation, namely, to ensure and protect the right of a citizen of the Russian Federation to receive a decent and desirable education. Legal regulation of higher professional provides a purposeful activity to streamline the existing and emerging social relations in the context of modernization of the HPE. Analysis of the state of legal regulation involves the analysis of sources regulating the legal relationship. As in other spheres of public life, the system of higher education has both international legal and national legal sources. This is explained by the fact that the system of law includes large interacting blocks ‒ a block of domestic (national) law and a block of those universally recognized principles and norms of international law that are included in the relevant system of law.
A practical primer on processing semantic property norm data

Semantic property listing tasks require participants to generate short propositions (e.g., $${<}{} barks {>}$$ , $${<}{} has\,\, fur {>}$$ ) for a specific concept (e.g., DOG). This task is the cornerstone of the creation of semantic property norms which are essential for modeling, stimuli creation, and understanding similarity between concepts. Despite the wide applicability of semantic property norms for a large variety of concepts across different groups of people, the methodological aspects of the property listing task have received less attention, even though the procedure and processing of the data can substantially affect the nature and quality of the measures derived from them. The goal of this paper is to provide a practical primer on how to collect and process semantic property norms. We will discuss the key methods to elicit semantic properties and compare different methods to derive meaningful representations from them. This will cover the role of instructions and test context, property preprocessing (e.g., lemmatization), property weighting, and relationship encoding using ontologies. With these choices in mind, we propose and demonstrate a processing pipeline that transparently documents these steps, resulting in improved comparability across different studies. The impact of these choices will be demonstrated using intrinsic (e.g., reliability, number of properties) and extrinsic measures (e.g., categorization, semantic similarity, lexical processing). This practical primer will offer potential solutions to several long-standing problems and allow researchers to develop new property listing norms overcoming the constraints of previous studies.
Definitions and Security of Quantum Electronic Voting

Recent advances indicate that quantum computers will soon be reality. Motivated by this ever more realistic threat for existing classical cryptographic protocols, researchers have developed several schemes to resist "quantum attacks". In particular, for electronic voting, several e-voting schemes relying on properties of quantum mechanics have been proposed. However, each of these proposals comes with a different and often not well-articulated corruption model, has different objectives, and is accompanied by security claims which are never formalized and are at best justified only against specific attacks. To address this, we propose the first formal security definitions for quantum e-voting protocols. With these at hand, we systematize and evaluate the security of previously-proposed quantum e-voting protocols; we examine the claims of these works concerning privacy, correctness and verifiability, and if they are correctly attributed to the proposed protocols. In all non-trivial cases, we identify specific quantum attacks that violate these properties. We argue that the cause of these failures lies in the absence of formal security models and references to the existing cryptographic literature.