Reviewerly/exHarmony huggingface dataset:
Choriented Maps: Visualizing SDG Data on Mobile Devices

Choropleth maps and graduated symbol maps are often used to visualize quantitative geographic data. However, as the number of classes grows, distinguishing between adjacent classes increasingly becomes challenging. To mitigate this issue, this work introduces two new visualization types: choriented maps (maps that use colour and orientation as variables to encode geographic information) and choriented mobile (an optimization of choriented maps for mobile devices). The maps were evaluated in a graphical perception study featuring the comparison of SDG (Sustainable Development Goal) data for several European countries. Choriented maps and choriented mobile visualizations resulted in comparable, sometimes better effectiveness and confidence scores than choropleth and graduated symbol maps. Choriented maps and choriented mobile visualizations also performed well regarding efficiency overall and performed worse only than graduated symbol maps. These results suggest that the use of colour and orientation as visual variables in combination can improve the selectivity of map symbols and user performance during the exploration of geographic data in some scenarios.
Projection-operator-based Newton method for the trajectory optimization of closed quantum systems

Quantum optimal control is an important technology that enables fast state preparation and gate design. In the absence of an analytic solution, most quantum optimal control methods rely on an iterative scheme to update the solution estimate. At present, the convergence rate of existing solvers is, at most, superlinear. This paper develops a general-purpose solver for quantum optimal control based on the projection operator Newton method for trajectory optimization, or PRONTO. Specifically, the proposed approach uses a projection operator to incorporate the Schr\"odinger equation directly into the cost function, which is then minimized using Newton's descent method. At each iteration, the descent direction is obtained by computing the analytic solution to a linear-quadratic trajectory-optimization problem. The resulting method guarantees monotonic convergence at every iteration and quadratic convergence in the proximity of the solution. The potential of PRONTO is showcased by solving the optimal state-to-state mapping problem for a qubit and providing comparisons to a state-of-the-art quantum optimal control method.
The concept of designing the main professional educational programs of future teachers

Introduction. The article is devoted to the description of approaches to the design of educational programs of the pedagogical profile of training implemented at the Minin University. The article substantiates the application of the modular structure of educational programs and describes the blocks of modules in the preparation of the future teacher. The idea of introducing modules of "universal training", based on the anthropological approach and providing the possibility of changing the educational trajectory after the 2nd year of study, is considered. The article presents an analysis of the methodological and practical results of the work of the team of teachers in the framework of the project "Modernization of educational activities: new logistics solutions" in 2016-2020 at Minin University. Materials and Methods. To solve the tasks set in the work, general scientific, psychological, pedagogical and practical methods of scientific research were used. The main methods of practical research are analysis, synthesis and formalization, pedagogical design, description of approaches to the design of the structure and content of educational programs for the training of future teachers. Results. The article presents the main conceptual approaches to the design of educational programs. The structure of the educational program of training of the future teacher is defined, including such blocks of modules as modules of universal training, modules of psychological and pedagogical training, modules of methodological and subject training, modules of specialization. The stages of design and management levels of the modular educational program of the future teacher are presented. Discussion and Conclusions. Conclusions are drawn that the presented approaches to the construction of educational programs allow us to implement the ideas of effective "entry into the profession", changing the direction or profile of training after the 2nd year. The main directions of further development of approaches to the design of educational programs are determined.
Cumulative generation of maximal entanglement between spectrally distinct qubits using squeezed light

We demonstrate how to create maximal entanglement between two qubits that are encoded in two spectrally distinct solid-state quantum emitters embedded in a waveguide interferometer. The optical probe is provided by readily accessible squeezed light, generated by parametric down-conversion. By continuously illuminating the emitters, the photon scattering and incremental path-erasure builds up entanglement. Our method does not require perfectly identical emitters, and accommodates spectral variations due to the fabrication process. Furthermore, for some line-width and energy ratios, the entanglement build-up can be significantly faster than for more similar emitters. It is also robust enough to create entanglement with a concurrence above 99\% in the event of scattering losses and detector inefficiencies, and can form the basis for practical entangled networks.
Receiver-device-independent quantum key distribution protocols

Abstract We discuss quantum key distribution protocols and their security analysis, considering a receiver-device-independent (RDI) model. The sender’s (Alice’s) device is partially characterized, in the sense that we assume bounds on the overlaps of the prepared quantum states. The receiver’s (Bob’s) device requires no characterisation and can be represented as a black-box. Our protocols are therefore robust to any attack on Bob, such as blinding attacks. In particular, we show that a secret key can be established even when the quantum channel has arbitrarily low transmission by considering RDI protocols exploiting sufficiently many states. Finally, we discuss how the hypothesis of bounded overlaps can be naturally applied to practical devices.
Confidence polytopes for quantum process tomography

Abstract In the present work, we propose a generalization of the confidence polytopes approach for quantum state tomography (QST) to the case of quantum process tomography (QPT). Our approach allows obtaining a confidence region in the polytope form for a Choi matrix of an unknown quantum channel based on the measurement results of the corresponding QPT experiment. The method uses the improved version of the expression for confidence levels for the case of several positive operator-valued measures. We then demonstrate how confidence polytopes can be employed for calculating confidence intervals for affine functions of quantum states (Choi matrices), such as fidelities and observables mean values, which are used both in QST and QPT settings. As we propose, this problem can be efficiently solved using linear programming tools. We also study the performance and scalability of the developed approach on the basis of simulation and experimental data collected using IBM cloud quantum processor.
The UK COVID-19 contact tracing app as both an emerging technology and public health intervention: The need to consider promissory discourses

The UK's National Health Service (NHS) COVID-19 contact tracing app was announced to the British public on 12th April 2020. The UK government endorsed the app as a public health intervention that would improve public health, protect the NHS and 'save lives'. On 5th May 2020 the technology was released for trial on the Isle of Wight. However, the trial was halted in June 2020, reportedly due to technological issues. The app was later remodelled and launched to the public in September 2020. The rapid development, trial and discontinuation of the app over a short period of a few months meant that the mobilisation and effect of the discourses associated with the app could be traced relatively easily. In this paper we aimed to explore how these discourses were constructed in the media, and their effect on actors - in particular, those who developed and those who trialled the app. Promissory discourses were prevalent, the trajectory of which aligned with theories developed in the sociology of expectations. We describe this trajectory, and then interpret its implications in terms of infectious disease public health practices and responsibilities.
Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models

We describe CounterSynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations. Focusing on demographic attributes, we evaluate the quality of synthesised counterfactuals with voxel-based morphometry, classification and regression of the conditioning attributes, and the Fréchet inception distance. Examining downstream discriminative performance in the context of engineered demographic imbalance and confounding, we use UK Biobank and OASIS magnetic resonance imaging data to benchmark CounterSynth augmentation against current solutions to these problems. We achieve state-of-the-art improvements, both in overall fidelity and equity. The source code for CounterSynth is available at https://github.com/guilherme-pombo/CounterSynth.
Suppressed Crosstalk between Two-Junction Superconducting Qubits with Mode-Selective Exchange Coupling

Fixed-frequency qubits can suffer from always-on interactions that inhibit independent control. Here, we address this issue by experimentally demonstrating a superconducting architecture using qubits that comprise of two capacitively-shunted Josephson junctions connected in series. Historically known as tunable coupling qubits (TCQs), such two-junction qubits support two modes with distinct frequencies and spatial symmetries. By selectively coupling only one type of mode and using the other as our computational basis, we greatly suppress crosstalk between the data modes while permitting all-microwave two-qubit gates.
Attention based end to end Speech Recognition for Voice Search in Hindi and English

We describe here our work with automatic speech recognition (ASR) in the context of voice search functionality on the Flipkart e-Commerce platform. Starting with the deep learning architecture of Listen-Attend-Spell (LAS), we build upon and expand the model design and attention mechanisms to incorporate innovative approaches including multi-objective training, multi-pass training, and external rescoring using language models and phoneme based losses. We report a relative WER improvement of 15.7% on top of state-of-the-art LAS models using these modifications. Overall, we report an improvement of 36.9% over the phoneme-CTC system. The paper also provides an overview of different components that can be tuned in a LAS-based system.
Clustering and enhanced classification using a hybrid quantum autoencoder

Abstract Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify—and classically represent—their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states—which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.
A Generative Adversarial Network Fused with Dual-Attention Mechanism and Its Application in Multitarget Image Fine Segmentation

Aiming at the problem of insignificant target morphological features, inaccurate detection and unclear boundary of small-target regions, and multitarget boundary overlap in multitarget complex image segmentation, combining the image segmentation mechanism of generative adversarial network with the feature enhancement method of nonlocal attention, a generative adversarial network fused with attention mechanism (AM-GAN) is proposed. The generative network in the model is composed of residual network and nonlocal attention module, which use the feature extraction and multiscale fusion mechanism of residual network, as well as feature enhancement and global information fusion ability of nonlocal spatial-channel dual attention to enhance the target features in the detection area and improve the continuity and clarity of the segmentation boundary. The adversarial network is composed of fully convolutional networks, which penalizes the loss of information in small-target regions by judging the authenticity of prediction and label segmentation and improves the detection ability of the generative adversarial model for small targets and the accuracy of multitarget segmentation. AM-GAN can use the GAN’s inherent mechanism that reconstruct and repair high-resolution image, as well as the ability of nonlocal attention global receptive field to strengthen detail features, automatically learn to focus on target structures of different shapes and sizes, highlight salient features useful for specific tasks, reduce the loss of image detail features, improve the accuracy of small-target detection, and optimize the segmentation boundary of multitargets. Taking medical MRI abdominal image segmentation as a verification experiment, multitargets such as liver, left/right kidney, and spleen are selected for segmentation and abnormal tissue detection. In the case of small and unbalanced sample datasets, the class pixels’ accuracy reaches 87.37%, the intersection over union is 92.42%, and the average Dice coefficient is 93%. Compared with other methods in the experiment, the segmentation precision and accuracy are greatly improved. It shows that the proposed method has good applicability for solving typical multitarget image segmentation problems such as small-target feature detection, boundary overlap, and offset deformation.
Assessment of weak-coupling approximations on a driven two-level system under dissipation

The standard weak-coupling approximations associated to open quantum systems have been extensively used in the description of a two-level quantum system, qubit, subjected to relatively weak dissipation compared with the qubit frequency. However, recent progress in the experimental implementations of controlled quantum systems with increased levels of on-demand engineered dissipation has motivated precision studies in parameter regimes that question the validity of the approximations, especially in the presence of time-dependent drive fields. In this paper, we address the precision of weak-coupling approximations by studying a driven qubit through the numerically exact and non-perturbative method known as the stochastic Liouville-von Neumann equation with dissipation. By considering weak drive fields and a cold Ohmic environment with a high cutoff frequency, we use the Markovian Lindblad master equation as a point of comparison for the SLED method and study the influence of the bath-induced energy shift on the qubit dynamics. We also propose a metric that may be used in experiments to map the regime of validity of the Lindblad equation in predicting the steady state of the driven qubit. In addition, we study signatures of the well-known Mollow triplet and observe its meltdown owing to dissipation in an experimentally feasible parameter regime of circuit electrodynamics. Besides shedding light on the practical limitations of the Lindblad equation, we expect our results to inspire future experimental research on engineered open quantum systems, the accurate modeling of which may benefit from non-perturbative methods.
Impossibility to produce arbitrary non-Gaussian states using zero-mean Gaussian states and partial photon number resolving detection

Gaussian states and measurements collectively are not powerful-enough resources for quantum computing, as any Gaussian dynamics can be simulated efficiently, classically. However, it is known that any one non-Gaussian resource---a state, a unitary operation, or a measurement---together with Gaussian unitaries, makes for universal quantum resources. Photon number resolving (PNR) detection, a readily realizable non-Gaussian measurement, has been a popular tool to try and engineer non-Gaussian states for universal quantum processing. In this paper, we consider PNR detection of a subset of the modes of a zero-mean pure multimode Gaussian state as a means to herald a target non-Gaussian state on the undetected modes. This is motivated from the ease of scalable preparation of Gaussian states that have zero mean, using squeezed vacuum and passive linear optics. We calculate upper bounds on the fidelity between the actual heralded state and the target state. We find that this fidelity upper bound is $1/2$ when the target state is a multimode coherent cat-basis cluster state, a resource sufficient for universal quantum computing. This proves that there exist non-Gaussian states that are not producible by this method. Our fidelity upper bound is a simple expression that depends only on the target state represented in the photon-number basis, which could be applied to other non-Gaussian states of interest.
Motion = Video - Content: Towards Unsupervised Learning of Motion Representation from Videos

Motion, according to its definition in physics, is the change in position with respect to time, regardless of the specific moving object and background. In this paper, we aim to learn appearance-independent motion representation in an unsupervised manner. The main idea is to separate motion from videos while leaving objects and background as content. Specifically, we design an encoder-decoder model which consists of a content encoder, a motion encoder and a video generator. To train the model, we leverage a one-step cycle-consistency in reconstruction within the same video and a two-step cycle-consistency in generation across different videos as self-supervised signals, and use adversarial training to remove the content representation from the motion representation. We demonstrate that the proposed framework can be used for conditional video generation and fine-grained action recognition.
Solution of interval-valued matrix games using intuitionistic fuzzy optimisation technique: an effective approach

In most of the cases in the real world scenario, there are many situations where the payoffs of a matrix game may vary within specific ranges and may be considered as interval numbers. In this paper, we developed an effective solution concept of the interval-valued matrix game using intuitionistic fuzzy optimisation technique (IFOT) with nonlinear membership and non-membership functions. In this proposed approach, we first convert the formulated game problem into a deterministic model by splitting the single interval type objective function into multi-objective functions. Then, IFOT with nonlinear membership and non-membership functions is used to solve the reduced model. Finally, we consider one numerical example to illustrate the methodology. A comparative study implies the fact that the proposed approach gives a better result and is more effective than the results obtained previously by the existing methods.
Vector Trajectory Method for Obstacle Avoidance Constrained Planetary Landing Trajectory Optimization

Considering the requirement of avoiding obstacles in different planetary landing missions, this article presents a generalized method dealing with the obstacle avoidance constrained landing trajectory optimization problem with vectorized strategy, i.e., vector trajectory method (VTM). By introducing a trajectory direction constraint with vectorized description, the character of the obstacle avoidance trajectory can be more specifically depicted. Moreover, by satisfying proper trajectory direction constraint, the lander’s particular performances, such as the observation to the target or probability of a safe flight, can be improved. To this end, the VTM and relevant theorem are first developed, revealing the relationship between the two aspects of obstacle avoidance constraint (i.e., distance constraint and trajectory direction constraint). Then, both aspects of constraints are uniformly transformed as the auxiliary angle magnitude constraint with the vectorized strategy, which significantly simplifies the solving procedure of obstacle avoidance constrained trajectory optimization problem. Finally, the proposed VTM is illustrated in detail through the examples with the planetary landing background of atmospheric entry and powered descent landing.
Blockchain Security

Blockchain is a technology of Bitcoin which has developed many applications in finance, trade, telecommunication, and manufacturing. This technology was launched in 2008. It was the brainchild of a bunch of people who are popularly known by the pseudonym Satoshi Naskamoto. Bitcoin has developed a popular cryptocurrency and has covered as a virtual money source for market capitalization. Blockchain is a chain of blocks as appears by the name but has a deeper significance. The context of the words “block” and “chain” refers to digital information (the “block”) stored in a public database (the “chain”). Taking note of the distributed database and its drawback like unreliability, intermittent or non-existent nodes lead to data conflicts. Being able to deal with conflicts is the fundamental difference between standard distributed databases and blockchains. Resolution of Conflict, verifying the transaction and transaction auditability, i.e., consensus, is a core function of blockchains.
TRC: Trust Region Conditional Value at Risk for Safe Reinforcement Learning

As safety is of paramount importance in robotics, reinforcement learning that reflects safety, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safe RL</i> , has been studied extensively. In safe RL, we aim to find a policy which maximizes the desired return while satisfying the defined safety constraints. There are various types of constraints, among which constraints on conditional value at risk (CVaR) effectively lower the probability of failures caused by high costs since CVaR is a conditional expectation obtained above a certain percentile. In this paper, we propose a trust region-based safe RL method with CVaR constraints, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TRC</i> . We first derive the upper bound on CVaR and then approximate the upper bound in a differentiable form in a trust region. Using this approximation, a subproblem to get policy gradients is formulated, and policies are trained by iteratively solving the subproblem. TRC is evaluated through safe navigation tasks in simulations with various robots and a sim-to-real environment with a Jackal robot from Clearpath. Compared to other safe RL methods, the performance is improved by 1.93 times while the constraints are satisfied in all experiments.