Reviewerly/exHarmony huggingface dataset:
Semantics for variational Quantum programming

We consider a programming language that can manipulate both classical and quantum information. Our language is type-safe and designed for variational quantum programming, which is a hybrid classical-quantum computational paradigm. The classical subsystem of the language is the Probabilistic FixPoint Calculus (PFPC), which is a lambda calculus with mixed-variance recursive types, term recursion and probabilistic choice. The quantum subsystem is a first-order linear type system that can manipulate quantum information. The two subsystems are related by mixed classical/quantum terms that specify how classical probabilistic effects are induced by quantum measurements, and conversely, how classical (probabilistic) programs can influence the quantum dynamics. We also describe a sound and computationally adequate denotational semantics for the language. Classical probabilistic effects are interpreted using a recently-described commutative probabilistic monad on DCPO. Quantum effects and resources are interpreted in a category of von Neumann algebras that we show is enriched over (continuous) domains. This strong sense of enrichment allows us to develop novel semantic methods that we use to interpret the relationship between the quantum and classical probabilistic effects. By doing so we provide a very detailed denotational analysis that relates domain-theoretic models of classical probabilistic programming to models of quantum programming.
Quality Assessment of DIBR-Synthesized Views Based on Sparsity of Difference of Closings and Difference of Gaussians

Images synthesized using depth-image-based-rendering (DIBR) techniques may suffer from complex structural distortions. The goal of the primary visual cortex and other parts of brain is to reduce redundancies of input visual signal in order to discover the intrinsic image structure, and thus create sparse image representation. Human visual system (HVS) treats images on several scales and several levels of resolution when perceiving the visual scene. With an attempt to emulate the properties of HVS, we have designed the no-reference model for the quality assessment of DIBR-synthesized views. To extract a higher-order structure of high curvature which corresponds to distortion of shapes to which the HVS is highly sensitive, we define a morphological oriented Difference of Closings (DoC) operator and use it at multiple scales and resolutions. DoC operator nonlinearly removes redundancies and extracts fine grained details, texture of an image local structure and contrast to which HVS is highly sensitive. We introduce a new feature based on sparsity of DoC band. To extract perceptually important low-order structural information (edges), we use the non-oriented Difference of Gaussians (DoG) operator at different scales and resolutions. Measure of sparsity is calculated for DoG bands to get scalar features. To model the relationship between the extracted features and subjective scores, the general regression neural network (GRNN) is used. Quality predictions by the proposed DoC-DoG-GRNN model show higher compatibility with perceptual quality scores in comparison to the tested state-of-the-art metrics when evaluated on four benchmark datasets with synthesized views, IRCCyN/IVC image/video dataset, MCL-3D stereoscopic image dataset and IST image dataset.
Parallel-path implementation of nonadiabatic geometric quantum gates in a decoherence-free subspace with nitrogen-vacancy centers

We propose a scheme for nonadiabatic noncyclic geometric quantum computation in a decoherence-free subspace (DFS). The physical system we used is a cavity quantum electrodynamics system consisting of nitrogen-vacancy (NV) centers in diamond and a whispering-gallery-mode (WGM) microresonator. This scheme not only has the feature of DFSs insensitive to decoherence, but also has the advantage of nonadiabatic noncyclic geometric quantum computation which has the built-in noise-resilience geometric feature and further enhances the robustness due to the ability beyond the limitation of the cyclic condition. An unconventional two-qubit phase gate in DFS has been realized in one step with our parallel protocol, which can largely shorten the computation time and the system error caused by decoherence. In addition, a scheme to realize a parallel process of two evolution paths is put forward to achieve the distribution quantum computation of two WGM microresonators containing a NV center in each. Our scheme may provide a promising way towards the practical realization of high-fidelity geometric quantum computation with the features of a DFS, geometric phase, and parallel implementation process.
Compositional data analysis tutorial.

This article presents techniques for dealing with a form of dependency in data arising when numerical data sum to a constant for individual cases, that is, "compositional" or "ipsative" data. Examples are percentages that sum to 100, and hours in a day that sum to 24. Ipsative scales fell out of fashion in psychology during the 1960s and 1970s due to a lack of methods for analyzing them. However, ipsative scales have merits, and compositional data commonly occur in psychological research. Moreover, as we demonstrate, sometimes converting data to a compositional form yields insights not otherwise accessible. Fortunately, there are sound methods for analyzing compositional data. We seek to enable researchers to analyze compositional data by presenting appropriate techniques and illustrating their application to real data. First, we elaborate the technical details of compositional data and discuss both established and new approaches to their analysis. We then present applications of these methods to real social science data-sets (data and code using R are available in a supplementary document). We conclude with a discussion of the state of the art in compositional data analysis and remaining unsolved problems. A brief guide to available software resources is provided in the first section of the supplementary document. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Swarm Deep Reinforcement Learning for Robotic Manipulation

Deep reinforcement learning scheme, which combines both deep learning and reinforcement learning, enables robots to learn from exploration and flexibly performance in a range of different operational tasks under highly dynamic and complex environments encountered in daily life. However, robotic manipulation still face many serious threats due to inadequate data sharing between robots and concerns about data privacy and security. To privacy-protect the data of all owners, we propose a swarm reinforcement learning method, a decentralized deep reinforcement learning technology based on block chain. Specifically, each robotic agent controls the robot using actor-critic strategy optimization algorithm, and shares their learning experience (i.e. loss function gradient) through the blockchain network, and passes on a mature strategy model parameters to other agents. Experimental results indicate that our swarm reinforcement learning method can improve the learning process of several agents, and the more agents there are, the faster the learning speed will be.
Bayes-Probabilistic-Based Fusion Method for Image Inpainting

Image inpainting removes unwanted objects from the image, signifying the original image restoration. Even though several techniques are introduced for image inpainting, but still, there are several challenging issues associated with the conventional methods regarding data loss, which are effectively handled based on the proposed approach. In this paper, we propose an effective hybrid image inpainting method that is termed as ALGDKH, which is the hybridization of Ant Lion–Gray Wolf Optimizer (ALG)-based Markov random field (MRF) modeling, deep learning, [Formula: see text]-nearest neighbors (KNN) and the harmonic functions. The crack input image is forwarded as an input to Markov random field modeling to obtain image inpainting, where the MRF energy is minimized based on the ALG. Then, the same crack image is subjected to the Whale–MBO-based DCNN, KNN with Bhattacharya distance and Bi-harmonic function modules to obtain the inpainting results. Finally, the results from the proposed ALG-based Markov random field modeling, Whale–MBO-based DCNN, KNN with Bhattacharya distance and Bi-harmonic function modules are fused through Bayes-probabilistic fusion for the final inpainting results. The proposed method produces the maximal PSNR of 38.14[Formula: see text]dB, maximal SDME of 75.70[Formula: see text]dB and the maximal SSIM of 0.983.
JOT: A Variational Signal Decomposition Into Jump, Oscillation and Trend

We propose a two stages signal decomposition method which efficiently separates a given signal into Jump, Oscillation and Trend. While there have been numerous advances in signal processing in past few decades, they mainly aim to analyze the signal in terms of oscillating (underlying frequencies) or non-oscillating (underlying trend) features. Both traditional Time-Frequency analysis methods, like Short Time Fourier Transform, wavelet, and advanced ones, like Synchrosqueezing wavelet, Hilbert Huang Transform or IMFogram, can fail when abrupt changes and jump discontinuities appear in the signal. We present a variational framework separating piece-wise constant jump features as well as smooth trends and oscillating features of a given signal. In the first stage, a three component signal decomposition is applied, using sparsity promoting regularization, and Sobolev spaces of negative differentiability to model oscillations. In the second stage, components are refined using residuals of other components. The proposed method finds big and small jumps, is stable against high level of noise, is independent from the choice of basis functions, and does not have different level of decompositions which can be affected by large discontinuities. This variational framework is free from training in network-based approaches, and can be used for generating training data. The optimization problem is efficiently solved by an alternating minimization strategy. Applied as pre-processing for time-frequency analysis and Synchrosqueezing, it allows for improvements in results showing much clearer separation without artifacts. The proposed method is tested against synthetic data, where the ground truth is known, and real world data.
Digital Face Manipulation in Biometric Systems

Abstract Biometric technologies, in particular facerecognition, are employed in many personal, commercial, and governmental identity management systems around the world. The processing of digitally manipulated face images within a face recognition system may lead to false decisions and thus decrease the reliability of the decision system. This necessitates the development of manipulation detection modules which can be seamlessly integrated into the processing chain of face recognition systems. This chapter discusses the impact of face image manipulation on face recognition technologies. To this end, the basic processes and key components of biometric systems are briefly introduced with particular emphasis on facial recognition. Additionally, face manipulation detection scenarios and concepts of how to integrate detection methods to face recognition systems are discussed. In an experimental evaluation, it is shown that different types of face manipulation, i.e. retouching, face morphing, and swapping, can significantly affect the biometric performance of face recognition systems and hence impair their security. Eventually, this chapter provides an outlook on issues and challenges that face manipulation poses to face recognition technologies.
Ship detection based on medium-low resolution remote sensing data and super-resolved feature representation

Existing methods enhance medium-low resolution remote sensing ship detection by feeding super-resolved images to the detectors. Although these methods marginally improve the detection accuracy, the correlation between image super-resolution (SR) and ship detection is under-exploited. In this paper, we propose a simple but effective ship detection method called ShipSR-Det, in which both the output image and the intermediate features of the SR module are fed to the detection module. Using the super-resolved feature representation, the potential benefit introduced by image SR can be fully used for ship detection. We apply our method to the SSD and Faster-RCNN detectors and develop ShipSR-SSD and ShipSR-Faster-RCNN, respectively. Extensive ablation studies validate the effectiveness and generality of our method. Moreover, we compare ShipSR-Faster-RCNN with several state-of-the-art ship detection methods. Comparative results on the HRSC2016, DOTA and NWPU VHR-10 datasets demonstrate the superior performance of our proposed method.
Distance Education: Imbalance between Possibilities and Threats

Purpose : To quantify the opportunities and threats arising from the spontaneous expansion of distance learning in the wake of the pandemic and measures to counteract its spread. Research methods : ex-pert analysis, fuzzy logic, comparative analysis. Results and conclusions . The analysis of three groups of experts’ opinions: employers, university professors and students showed not-high values of revealed possibilities, connected with distance learning. Among them the average level of assessment reached: the increase of the level of computer literacy and the possibility of learning without disconnecting from the main activity in any geographical location. The latter opportunity is given more importance by employers than by teachers and students. Many threats of distance learning are evaluated as having a very high and high importance. The highest level of threat of reduction of the level of practical knowledge is marked by all expert groups. Almost the same level of significance was assigned by all expert groups to the negative impact of long-term computer use on students’ health. Opinions of expert groups differ with regard to other threats. Employers are the most cautious about them, they single out several other significant threats connected with the learning process – decrease of emotional component of the educational process and teachers’ lack of mastery of computer technologies which does not provide transfer of the necessary amount of information to students. Teachers and students assess these threats as less significant. The threats noted actualize the need to: strengthen the practical orientation of training in both distance and face-to-face format, expanding the competence of teaching staff in the field of computer technology, developing a system to counteract the negative impact of long-term use of computers on the health of students.
Estimating Network Structures using Model Selection

This chapter continues discussing the estimation of pairwise Markov random fields—undirected network models in which edges indicate the strength of conditional associations—introduced in Chapter 6. While Chapter 6 was concerned with the interpretation and saturated estimation (i.e., network structures estimated with all edges included) of such models, this chapter is concerned with unsaturated estimation and model search strategies: how to select which edges should be included in the network model. The chapter discusses four methods of estimating the model structure: thresholding (removing edges that do not meet some criterion), pruning (thresholding followed by re-estimation of non-zero edge-weights), extensive model search strategies (searching through the model space for an optimal model), and finally regularization (penalized likelihood estimation resulting in a sparse model). The chapter ends with recommendations for which estimation strategy should be used in which setting.
A Survey on Optical Character Recognition for Handwritten Devanagari Script Using Deep Learning

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An All-MRR-Based Photonic Spiking Neural Network for Spike Sequence Learning

Photonic spiking neural networks (SNN) have the advantages of high power efficiency, high bandwidth and low delay, but limitations are encountered in large-scale integration. The silicon photonics platform is a promising candidate for realizing large-scale photonic SNN because it is compatible with the current mature CMOS platforms. Here, we present an architecture of photonic SNN which consists of photonic neuron, photonic spike timing dependent plasticity (STDP) and weight configuration that are all based on silicon micro-ring resonators (MRRs), via taking advantage of the nonlinear effects in silicon. The photonic spiking neuron based on the add-drop MRR is proposed, and a system-level computational model of all-MRR-based photonic SNN is presented. The proposed architecture could exploit the properties of small area, high integration and flexible structure of MRR, but also faces challenges caused by the high sensitivity of MRR. The spike sequence learning problem is addressed based on the proposed all-MRR-based photonic SNN architecture via adopting supervised training algorithms. We show the importance of algorithms when hardware devices are limited.
VeilGraph: incremental graph stream processing

Abstract Graphs are found in a plethora of domains, including online social networks, the World Wide Web and the study of epidemics, to name a few. With the advent of greater volumes of information and the need for continuously updated results under temporal constraints, it is necessary to explore alternative approaches that further enable performance improvements. In the scope of stream processing over graphs, we research the trade-offs between result accuracy and the speedup of approximate computation techniques. The relationships between the frequency of graph algorithm execution, the update rate and the type of update play an important role in applying these techniques. Herein we present V eil G raph , through which we conducted our research. We showcase an innovative model for approximate graph processing implemented in . We analyse the feasibility of our model and evaluate it with the case study of the PageRank algorithm, the most famous measure of vertex centrality used to rank websites in search engine results. Our experiments show that V eil G raph can often reduce latency closely to half (speedup of 2.0×), while achieving result quality above 95% when compared to results of the traditional version of PageRank executing in with (i.e. without any summarization or approximation techniques). In some cases, depending on the workload, speedups against reach up to 3.0x (i.e. yielding a reduction of up to 66% in latency). We have found V eil G raph implementation on Flink to be scalable, as it is able to improve performance up to 10X speedups, when more resources are employed (16 workers), achieving better speedups with scale for larger graphs, which are the most relevant.
Tri-wavelength simultaneous ESPI for 3D micro-deformation field measurement

Electronic speckle pattern interferometry (ESPI), a well-established technique for micro-deformation measurement, can be used to determine both in-plane and out-of-plane displacement components. Although many works in ESPI have been reported for three-dimensional (3D) displacement measurement, few works have focused on the simultaneous measurement of 3D deformation fields. Here we present an ESPI system that consists of three sub-interferometers for simultaneous measurement of all three displacement components and in-plane strain fields. A 3CCD color camera, a specially designed shifting stage, and three lasers with optimal wavelengths are used in this system. The lasers and 3CCD camera provide independent interferograms with different color signals, while the shifting stage allows the sub-interferometers to achieve simultaneous phase shifting. The results of color separation and experimental measurement demonstrate the utility of the system.
A Survey on Healthy Food Decision Influences Through Technological Innovations

It is well known that unhealthy food consumption plays a significant role in dietary and lifestyle-related diseases. Therefore, it is important for researchers to examine methods that may encourage the consumer to consider healthier dietary and lifestyle habits as diseases such as obesity, heart disease, and high blood pressure remain a worldwide issue. One promising approach to influencing healthy dietary and lifestyle habits is food recommendation models that recommend food to users based on various factors such as health effects, nutrition, preferences, and daily habits. Unfortunately, much of this work has focused on individual factors such as taste preferences and often neglects to understand other factors that influence our choices. Additionally, the evaluation of technological approaches often lacks user studies in the context of intended use. In this systematic review of food choice technology, we focus on the factors that may influence food choices and how technology can play a role in supporting those choices. We also describe existing work, approaches, trends, and issues in current food choice technology and give advice for future work areas in this space.
Structural–Temporal embedding of large-scale dynamic networks with parallel implementation

Due to the widespread network data in the real world, network analysis has attracted increasing attention in recent years. In complex systems such as social networks, entities and their mutual relations can be respectively represented by nodes and edges composing a network. Because occurrences of entities and relations in these systems are often dynamic over time, their networks are called temporal networks describing the process of dynamic connection of nodes in the networks. Dynamic network embedding aims to embed nodes in a temporal network into a low-dimensional semantic space, such that the network structures and evolution patterns can be preserved as much as possible in the latent space. Most existing methods capture structural similarities (relations) of strongly-connected nodes based on their historical neighborhood information, they ignore the structural similarities of weakly-connected nodes that may also represent relations and include no explicit temporal information in node embeddings for capturing periodic dependency of events. To address these issues, we propose a novel temporal network embedding model by extending the structure similarity to cover both strong connections and weak connections among nodes, and including the temporal information in node embeddings. To improve the training efficiency of our model, we present a parallel training strategy to quickly acquire node embeddings. Extensive experiments on several real-world temporal networks demonstrate that our model significantly outperforms the state-of-the-arts in traditional tasks, including link prediction and node classification.