Reviewerly/exHarmony huggingface dataset:
Efficient choice of colored noise in the stochastic dynamics of open quantum systems

The Stochastic Liouville-von Neumann (SLN) equation describes the dynamics of an open quantum system reduced density matrix coupled to a non-Markovian harmonic environment. The interaction with the environment is represented by complex coloured noises which drive the system, and whose correlation functions are set by the properties of the environment. We present a number of schemes capable of generating coloured noises of this kind that are built on a noise amplitude reduction procedure [Imai et al, Chem. Phys. 446, 134 (2015)], including two analytically optimised schemes. In doing so, we pay close attention to the properties of the correlation functions in Fourier space, which we derive in full. For some schemes the method of Wiener filtering for deconvolutions leads to the realisation that weakening causality in one of the noise correlation functions improves numerical convergence considerably, allowing us to introduce a well controlled method for doing so. We compare the ability of these schemes, along with an alternative optimised scheme [Schmitz and Stockburger, Eur. Phys. J.: Spec. Top. 227, 1929 (2019)], to reduce the growth in the mean and variance of the trace of the reduced density matrix, and their ability to extend the region in which the dynamics is stable and well converged for a range of temperatures. By numerically optimising an additional noise scaling freedom, we identify the scheme which performs best for the parameters used, improving convergence by orders of magnitude and increasing the time accessible by simulation.
Recognition and Classification of Ornamental Fish Image Based on Machine Vision

With the gradual maturity of smart home technology, it requires a lot of knowledge and experience to judge the life state of ornamental fish artificially when raising them in a traditional aquarium in a smart home environment. Obviously not everyone can accurately judge the life state of ornamental fish. Accurate judgment, therefore, ornamental fish life state and timely feedback is essential for pet fish. Aiming at the actual need of intelligent feeding of traditional ornamental fish, an application of image recognition and classification of ornamental fish based on machine vision was designed. The fish classification model based on Convolutional Neural Network (CNN) is studied, and based on the model, the training of ornamental fish classification model is further realized by combining migration learning. The experiment used TensorFlow to train the network model. The experimental results show that the recognition accuracy of ornamental fish can reach 98.1% by using this method, and the corresponding knowledge base and rule base are constructed by combining with the expert system, which effectively solves the problem of insufficient knowledge and experience in artificial feeding of ornamental fish.
Quantum proxy signature with provable security

A quantum proxy signature scheme makes the proxy signer can generate a quantum signature on behalf of the original signer. Although many quantum proxy signature schemes have been proposed, none of them can be formally proved to be secure. There is not even security model for the quantum proxy signatures. Some quantum proxy signature schemes have been proved to be insecure against forgery attacks. In this paper, first, the formal definition and the corresponding security model for the quantum proxy signatures are proposed. Second, based on the Hadamard operator and the controlled NOT operation, a new quantum proxy signature scheme is proposed. The security of our quantum proxy signature scheme can be formally proved under security model. The security model of the quantum proxy signatures is helpful for analyzing and improving the security of the quantum proxy signature schemes. On the other hand, compared with the other quantum proxy signatures, the new one proposed in this paper is the first that can be formally proved to be secure under security model.
Learning and Understanding Different Categories of Sexism Using Convolutional Neural Network's Filters.

Sexism is very common in social media and makes the boundaries of free speech tighter for female users. Automatically flagging and removing sexist content requires niche identification and description of the categories. In this study, inspired by social science work, we propose three categories of sexism toward women as follows: “Indirect sexism”, “Sexual sexism” and “Physical sexism”. We build classifiers such as Convolutional Neural Network (CNN) to automatically detect different types of sexism and address problems of annotation. Even though inherent non-interpretability of CNN is a challenge for users who detect sexism, as the reason classifying a given speech instance with regard to sexism is difficult to glance from a CNN. However, recent research developed interpretable CNN filters for text data. In a CNN, filters followed by different activation patterns along with global max-pooling can help us tease apart the most important ngrams from the rest. In this paper, we interpret a CNN model trained to classify sexism in order to understand different categories of sexism by detecting semantic categories of ngrams and clustering them. Then, these ngrams in each category are used to improve the performance of the classification task. It is a preliminary work using machine learning and natural language techniques to learn the concept of sexism and distinguishes itself by looking at more precise categories of sexism in social media along with an in-depth investigation of CNN’s filters.
Effects of postselected von Neumann measurement on the properties of single-mode radiation fields*

Postselected von Neumann measurement characterized by postselection and weak value has been found potential applications in quantum metrology and, solved plenty of fundamental problems in quantum theory. As an application of this new measurement technique in quantum optics, its effects on the features of single-mode radiation fields such as coherent state, squeezed vacuum state and Schrodinger cat sate are investigated by considering full-order effects of unitary evolution. The results show that the conditional probabilities of finding photons, second-order correlation functions, Q-factors and squeezing effects of those states after postselected measurement significantly changed compared with corresponding initial pointer states, respectively.
Where the really hard problems aren’t

Not all problem instances in combinatorial optimization are equally hard. One famous study “Where the Really Hard Problems Are” shows that for three decision problems and one optimization problem, computational costs can vary dramatically for equally sized instances. Moreover, runtimes could be predicted from an ‘order parameter’, which is a property of the problem instance itself. For the only optimization problem in the study, the asymmetric traveling salesman problem (ATSP), the proposed order parameter was the standard deviation in the probability distribution used for generating distance matrices. For greater standard deviations, most randomly generated instances turned out to be easily solved to optimality, whereas smaller standard deviations produced harder instances. In this replication study, we show these findings can be contested. Most likely, the difference in instance hardness stems from a roundoff error that was possibly overlooked. This gives rise to a sudden emergence of minimum-cost tours, a feature that is readily exploited by most branch and bound algorithms. This new contradiction renders the earlier proposed order parameter unsuitable and changes the perspective on the fundamentals of ATSP instance hardness for this kind of algorithm.
Experience in the Application of Cognitive Techniques in the Field of Physical Education and Sports

Improving the professional training of managers in the field of physical education and sports today requires the need to use foreign experience in their preparation, especially for the experience of using cognitive psychology techniques in professional and personal training.The study of international experience training of future managers of physical education and sports is of great importance for the implementation of positive trends in higher education in terms of the quality of their training and the formation of their professional competence.And this requires a critical analysis of the achievements of the educational systems of foreign countries and the adaptation of these achievements to national needs.The relevance of the study of world experience is due to the possibility of using the achievements and practices in the context of the establishment of a new paradigm of professional education for managers of physical education and sports, in which the cognitive-contextual goals of forming the professional competence of these specialists should become priorities.The importance of studying international experience in the application of cognitive teaching methods in the training of managers of physical education and sports is also due to insufficient information on relevant studies conducted in this area.Unfortunately, the study of the individual components of the cognitive-oriented education system in higher education institutions of the world space is characterized by the lack of a holistic view of its functioning in modern conditions.
Unanswerable Questions About Images and Texts

Questions about a text or an image that cannot be answered raise distinctive issues for an AI. This note discusses the problem of unanswerable questions in VQA (visual question answering), in QA (visual question answering), and in AI generally.
Learning Deep Graph Representations via Convolutional Neural Networks

Graph-structured data arise in many scenarios.A fundamental problem is to quantify the similarities of graphs for tasks such as classification.R-convolution graph kernels are positive-semidefinite functions that decompose graphs into substructures and compare them.One problem in the effective implementation of this idea is that the substructures are not independent, which leads to high-dimensional feature space.In addition, graph kernels cannot capture the high-order complex interactions between vertices.To mitigate these two problems, we propose a framework called DEEPMAP to learn deep representations for graph feature maps.The learned deep representation for a graph is a dense and low-dimensional vector that captures complex high-order interactions in a vertex neighborhood.DEEPMAP extends Convolutional Neural Networks (CNNs) to arbitrary graphs by generating aligned vertex sequences and building the receptive field for each vertex.We empirically validate DEEPMAP on various graph classification benchmarks and demonstrate that it achieves state-of-the-art performance.
Simulating the effect of weak measurements by a phase damping channel and determining different measures of bipartite correlations in nuclear magnetic resonance

Quantum discord is a measure based on local projective measurements which captures quantum correlations that may not be fully captured by entanglement. A change in the measurement process, achieved by replacing rank-one projectors with a weak positive operator-valued measure (POVM), allows one to define weak variants of quantum discord. In this work, we experimentally simulate the effect of a weak POVM on a nuclear magnetic resonance quantum information processor. The two-qubit system under investigation is part of a three-qubit system, where one of the qubits is used as an ancillary to implement the phase damping channel. The strength of the weak POVM is controlled by varying the strength of the phase damping channel. We experimentally observed two weak variants of quantum discord namely, super quantum discord and weak quantum discord, in two-qubit Werner and Bell-diagonal states. The resultant dynamics of the states is investigated as a function of the measurement strength.
iKarate: Karate Kata Guidance System

Smart coaching in martial arts is one of the recent research areas in Human Motion Analysis. Numerous moves are performed incorrectly during the performance. In this paper we offer a system that will record the Players' movements using IR (Infrared) camera sensor, store the data in a database, pre-process the data, classify the data using F-DTW (Fast Dynamic Time Warping) and then show the users an accurate report that contains every movement the player had done, their mistakes and how to improve their performance the next time. This approach focuses on the first seven movements of Karate Kata 1 (Hein Shodan). The system has reached an accuracy of 91.07% in classifying the moves and one common mistake for each move.
Monogamy Inequality in Terms of Entanglement Measures Based on Distance for Pure Multiqubit States

Using very general arguments, we prove that any entanglement measures based on distance must be maximal on pure states. Furthermore, we show that Bures measure of entanglement and geometric measure of entanglement satisfy the monogamy inequality on all pure multiqubit states. Finally, using the power of Bures measure of entanglement and geometric measure of entanglement, we present a class of tight monogamy relations for pure states of multiqubit systems.
Comparison of Hybrid Recurrent Neural Networks for Univariate Time Series Forecasting

The work presented in this paper aims to improve the accuracy of forecasting models in univariate time series, for this it is experimented with different hybrid models of two and four layers based on recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). It is experimented with two time series corresponding to downward thermal infrared and all sky insolation incident on a horizontal surface obtained from NASA’s repository. In the first time series, the results achieved by the two-layer hybrid models (LSTM + GRU and GRU + LSTM) outperformed the results achieved by the non-hybrid models (LSTM + LSTM and GRU + GRU); while only two of six four-layer hybrid models (GRU + LSTM + GRU + LSTM and LSTM + LSTM + GRU + GRU) outperformed non-hybrid models (LSTM + LSTM + LSTM + LSTM and GRU + GRU + GRU + GRU). In the second time series, only one model (LSTM + GRU) of two hybrid models outperformed the two non-hybrid models (LSTM + LSTM and GRU + GRU); while the four-layer hybrid models, none could exceed the results of the non-hybrid models.
Context-dependent model for spam detection on social networks

Social media platforms are getting an important communication medium in our daily life, and their increasing popularity makes them an ideal platform for spammers to spread spam messages, known as spam problems. Moreover, messages on social media are vague and messy, so a good representation of the text may be the first step to address spam problem. While traditional weighting methods suffer from both high dimensionality and high sparsity problems, traditional word embedding methods suffer from context independence and out of vocabulary problems. To overcome these problems, in this study, we propose a novel architecture based on a context-dependent representation of text using the BERT model. The model was tested using the Twitter dataset, and experimental results show that the proposed method outperforms traditional weighting methods, traditional word embedding based methods as well as the existing state of the art methods used to detect spam on the twitter platform.
On Gaze Deployment to Audio-Visual Cues of Social Interactions

Attention supports our urge to forage on social cues.Under certain circumstances, we spend the majority of time scrutinising people, markedly their eyes and faces, and spotting persons that are talking.To account for such behaviour, this article develops a computational model for the deployment of gaze within a multimodal landscape, namely a conversational scene.Gaze dynamics is derived in a principled way by reformulating attention deployment as a stochastic foraging problem.Model simulation experiments on a publicly available dataset of eye-tracked subjects are presented.Results show that the simulated scan paths exhibit similar trends of eye movements of human observers watching and listening to conversational clips in a free-viewing condition.
Weight Hierarchies of a Class of Linear Codes Related to Non-Degenerate Quadratic Forms

In this paper, we discuss the generalized Hamming weights of a class of linear codes associated with non-degenerate quadratic forms. In order to do so, we study the quadratic forms over subspaces of finite field and obtain some interesting results about subspaces and their dual spaces. On this basis, we solve all the generalized Hamming weights of these linear codes.
Accurate, efficient and scalable training of Graph Neural Networks

Graph Neural Networks (GNNs) are powerful deep learning models to generate node embeddings on graphs. When applying deep GNNs on large graphs, it is still challenging to perform training in an efficient and scalable way. We propose a novel parallel training framework. Through sampling small subgraphs as minibatches, we reduce training workload by orders of magnitude compared with state-of-the-art minibatch methods. We then parallelize the key computation steps on tightly-coupled shared memory systems. For graph sampling, we exploit parallelism within and across sampler instances, and propose an efficient data structure supporting concurrent accesses from samplers. The parallel sampler theoretically achieves near-linear speedup with respect to number of processing units. For feature propagation within subgraphs, we improve cache utilization and reduce DRAM traffic by data partitioning. Our partitioning is a 2-approximation strategy for minimizing the communication cost compared to the optimal. We further develop a runtime scheduler to reorder the training operations and adjust the minibatch subgraphs to improve parallel performance. Finally, we generalize the above parallelization strategies to support multiple types of GNN models and graph samplers. The proposed training outperforms the state-of-the-art in scalability, efficiency and accuracy simultaneously. On a 40-core Xeon platform, we achieve 60× speedup (with AVX) in the sampling step and 20× speedup in the feature propagation step, compared to the serial implementation. Our algorithm enables fast training of deeper GNNs, as demonstrated by orders of magnitude speedup compared to the Tensorflow implementation. We open-source our code at https://github.com/GraphSAINT/GraphSAINT
“What you say and how you say it” matters: An experimental evidence of the role of synchronicity, modality, and message valence during smartphone-mediated communication

Nowadays, smartphone-Mediated Communication (SMC) has become a popular form of social interactions. The present experimental study manipulated three aspects of messaging in a WhatsApp chat as a form of SMC: synchronicity (immediate vs. time-lagged response), modality (with or without emojis), and valence (empathic accurate vs. empathic inaccurate response). The aim of this study was to investigate whether these three aspects had an impact on perceived social support, interpersonal trust, and personality attribution of the communication partner. The partial mediation of perceived social presence (the evaluation of the communication partner's accessibility) and subjective social presence (the perception of being concordant with him/her) was also examined. Participants were 160 young adults, balanced in gender. They were randomly assigned to different the experimental conditions where they engaged in a manipulated WhatsApp chat with a fictitious same-gender communication partner. Post-questionnaire data were analyzed using Structural Equation Modeling. Message valence (empathic accurate response) and modality (with emojis) significantly predicted higher levels of both forms of social presence. Synchronicity (immediate response) predicted higher levels of perceived but not subjective social presence. Social presence, in turn, was positively associated with social support, while subjective, but not perceived social presence, was positively associated with personality attribution. Neither perceived nor subjective social presence were related to interpersonal trust. Our results show that both what is said and how it is said impact the experience of interpersonal relations in SMC.
Structure-Preserving Imitation Learning With Delayed Reward: An Evaluation Within the RoboCup Soccer 2D Simulation Environment

We describe and evaluate a neural network-based architecture aimed to imitate and improve the performance of a fully autonomous soccer team in RoboCup Soccer 2D Simulation environment. The approach utilizes deep Q-network architecture for action determination and a deep neural network for parameter learning. The proposed solution is shown to be feasible for replacing a selected behavioral module in a well-established RoboCup base team, Gliders2d, in which behavioral modules have been evolved with human experts in the loop. Furthermore, we introduce an additional performance-correlated signal (a delayed reward signal), enabling a search for local maxima during a training phase. The extension is compared against a known benchmark. Finally, we investigate the extent to which preserving the structure of expert-designed behaviors affects the performance of a neural network-based solution.
DeepConsensus: Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction

Deep neural networks have achieved remarkable success in various challenging tasks. However, the black-box nature of such networks is not acceptable to critical applications, such as healthcare. In particular, the existence of adversarial examples and their overgeneralization to irrelevant, out-ofdistribution inputs with high confidence makes it difficult, if not impossible, to explain decisions by such networks. In this paper, we analyze the underlying mechanism of generalization of deep neural networks and propose an (n, k) consensus algorithm which is insensitive to adversarial examples and can reliably reject out- of-distribution samples. Furthermore, the consensus algorithm is able to improve classification accuracy by using multiple trained deep neural networks. To handle the complexity of deep neural networks, we cluster linear approximations of individual models and identify highly correlated clusters among different models to capture feature importance robustly, resulting in improved interpretability. Motivated by the importance of building accurate and interpretable prediction models for healthcare, our experimental results on an ICU dataset show the effectiveness of our algorithm in enhancing both the prediction accuracy and the interpretability of deep neural network models on one-year patient mortality prediction. In particular, while the proposed method maintains similar interpretability as conventional shallow models such as logistic regression, it improves the prediction accuracy significantly.