Reviewerly/exHarmony huggingface dataset:
Statistical learning algorithms for dendritic neuron model artificial neural network based on sine cosine algorithm

Training of dendritic neuron model artificial neural networks is generally achieved by using nonlinear least square methods. The distribution of random error terms is ignored in training algorithms although error terms are random variables. Maximum likelihood estimators can be obtained for dendritic neuron model artificial neural networks by using some indefinite symmetric probability distributions. In this study, statistical learning algorithms are proposed for dendritic neuron model artificial neural networks. Maximum likelihood estimators for dendritic neuron model artificial neural networks are obtained by using Normal, Cauchy, Logistic, Gumbel and Laplace distributions. The Sine cosine algorithm is used for maximization of the likelihood function under error terms that have Normal, Cauchy, Logistic, Gumbel and Laplace distributions. The proposed learning algorithms are applied to Istanbul Stock Exchange time series data sets. At the end of the analysis of application results, the performance of the proposed method is statistically better than well-known deep and shallow artificial neural networks.
Twitter Hate Speech Detection: A Systematic Review of Methods, Taxonomy Analysis, Challenges, and Opportunities

Hate speech detection has substantially increased interest among researchers in the domain of natural language processing (NLP) and text mining. The number of studies on this topic has been growing dramatically. Thus, the purpose of this analysis is to develop a resource that consists of an outline of the approaches, methods, and techniques employed to address the issue of Twitter hate speech. This study can be used to aid researchers in the development of a more effective model for future studies. This review focused on studies published over the past eight years, i.e., from 2015 to 2022. This systematic search was carried out in December 2020 and updated in July 2022. Ninety-one articles published within the mentioned period met the set criteria and were selected for this review. From the evaluation of these works, it is clear that a perfect solution has yet to be found. To conclude, this paper focused on presenting an in-depth understanding of current perspectives and highlighted research opportunities to boost the quality of hate speech detection systems. In turn, this helps social networking services that seek to detect hate messages generated by users before they are posted, thus reducing the risk of targeted harassment.
Communication-Efficient Distributed Learning: An Overview

Distributed learning is envisioned as the bedrock of next-generation intelligent networks, where intelligent agents, such as mobile devices, robots, and sensors, exchange information with each other or a parameter server to train machine learning models collaboratively without uploading raw data to a central entity for centralized processing. By utilizing the computation/communication capability of individual agents, the distributed learning paradigm can mitigate the burden at central processors and help preserve data privacy of users. Despite its promising applications, a downside of distributed learning is its need for iterative information exchange over wireless channels, which may lead to high communication overhead unaffordable in many practical systems with limited radio resources such as energy and bandwidth. To overcome this communication bottleneck, there is an urgent need for the development of communication-efficient distributed learning algorithms capable of reducing the communication cost and achieving satisfactory learning/optimization performance simultaneously. In this paper, we present a comprehensive survey of prevailing methodologies for communication-efficient distributed learning, including reduction of the number of communications, compression and quantization of the exchanged information, radio resource management for efficient learning, and game-theoretic mechanisms incentivizing user participation. We also point out potential directions for future research to further enhance the communication efficiency of distributed learning in various scenarios.
Experimental multiparameter quantum metrology in adaptive regime

Relevant metrological scenarios involve the simultaneous estimation of multiple parameters. The fundamental ingredient to achieve quantum-enhanced performance is based on the use of appropriately tailored quantum probes. However, reaching the ultimate resolution allowed by physical laws requires nontrivial estimation strategies from both a theoretical and a practical point of view. A crucial tool for this purpose is the application of adaptive learning techniques. Indeed, adaptive strategies provide a flexible approach to obtain optimal parameter-independent performance and optimize convergence to the fundamental bounds with a limited amount of resources. Here, we combine on the same platform quantum-enhanced multiparameter estimation attaining the corresponding quantum limit and adaptive techniques. We demonstrate the simultaneous estimation of three optical phases in a programmable integrated photonic circuit, in the limited-resource regime. The obtained results show the possibility of successfully combining different fundamental methodologies towards transition to quantum sensors applications.
Engineering of Zeno Dynamics in Integrated Photonics

Frequent observations to a quantum system modify its coherent evolution through the Zeno effect and Zeno dynamics. Generally, the measurement process destroys the evolution environment of the monitored system, making repeated observations remain a challenge. Here, using the quantum analogy experiments, we realize and engineer the Zeno effect and Zeno dynamics in optical waveguide arrays, where the optical modes correspond to distinct quantum states, and the temporal evolution is mapped into the spatial propagation. We propose a new, extensible experimental strategy for realizing an optical analog of stroboscopic measurements, which are performed by the build-in, on-demand segmented waveguide portions. The weak-to-strong stroboscopic measurements are realized, where the monitored system undergoes a transition from free evolution to optical Zeno freezing. Setting the measurements in the strong regime, the optical Zeno effect and optical Zeno dynamics are successfully generated, and their relationship is demonstrated in optics. We then propose a novel quantum Zeno slicing approach, which allows us to dynamically engineer the Hilbert space of the monitored system. This generic approach is verified by generating a series of Zeno subspaces with different measurement projectors, based on the quantum-optical analogy. The complexity of light dynamics is largely increased, providing full control of the propagation via steering Zeno dynamics. Our results pave the way for manipulation of quantum states by harnessing Zeno dynamics in integrated photonics.
Chatbot-based serious games: A useful tool for training medical students? A randomized controlled trial

Objectives Chatbots, conversational agents that walk medical students (MS) though a clinical case, are serious games that seem to be appreciated by MS. Their impact on MS’s performance in exams however was not yet evaluated. Chatprogress is a chatbot-based game developed at Paris Descartes University. It contains 8 pulmonology cases with step-by-step answers delivered with pedagogical comments. The CHATPROGRESS study aimed to evaluate the impact of Chatprogress on students’ success rate in their end-term exams. Methods We conducted a post-test randomized controlled trial held on all fourth-year MS at Paris Descartes University. All MS were asked to follow the University’s regular lectures, and half of them were randomly given access to Chatprogress. At the end of the term, medical students were evaluated on pulmonology, cardiology and critical care medicine. Main outcomes measures The primary aim was to evaluate an increase in scores in the pulmonology sub-test for students who had access to Chatprogress, compared to those who didn’t. Secondary aims were to evaluate an increase in scores in the overall test (Pulmonology, Cardiology and Critical care medicine test (PCC)) and to evaluate the correlation between access to Chatprogress and overall test score. Finally, students’ satisfaction was assessed using a survey. Results From 10/2018 to 06/2019, 171 students had access to Chatprogress (the Gamers) and among them, 104 ended up using it (the Users). Gamers and Users were compared to 255 Controls with no access to Chatprogress. Differences in scores on the pulmonology sub-test over the academic year were significantly higher among Gamers and Users vs Controls (mean score: 12.7/20 vs 12.0/20, p = 0.0104 and mean score: 12.7/20 vs 12.0/20, p = 0.0365 respectively). This significant difference was present as well in the overall PCC test scores: (mean score: 12.5/20 vs 12.1/20, p = 0.0285 and 12.6/20 vs 12.1/20, p = 0.0355 respectively). Although no significant correlation was found between the pulmonology sub-test’s scores and MS’s assiduity parameters (number of finished games among the 8 proposed to Users and number of times a User finished a game), there was a trend to a better correlation when users were evaluated on a subject covered by Chatprogress. MS were also found to be fans of this teaching tool, asking for more pedagogical comments even when they got the questions right. Conclusion This randomised controlled trial is the first to demonstrate a significant improvement in students’ results (in both the pulmonology subtest and the overall PCC exam) when they had access to Chatbots, and even more so when they actually used it.
Feature clustering-Assisted feature selection with differential evolution

Modern data collection technologies may produce thousands of or even more features in a single dataset. The high dimensionality of data poses a barrier to determining discriminating features due to the curse of dimensionality. Thanks to the global search ability, many population-based feature selection approaches have been proposed. However, very few studies pay attention on that a feature selection task has multiple optimal feature subsets. To search for multiple optimal feature subsets, we propose a feature clustering-assisted feature selection method. The proposed method employs the knowledge of correlation measures to group features. And, this correlation knowledge is embedded into the encoding method and the search process. A niching-based mutation operator is also used to explore the vicinity of a target individual. The aim is to find different feature subsets with very similar or the same classification performance. In addition, a modification operator is proposed aiming to increase the population diversity to improve the feature selection performance. The experiments on 16 datasets show that the proposed algorithm outperforms other popular feature selection methods in terms of classification accuracy and feature subset size.
Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators

Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders. However, the calculation of likelihood function at whole-brain scale is often intractable. Thus, likelihood-free algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas, ideally including the uncertainty. In this study, we introduce the simulation-based inference for the virtual epileptic patient model (SBI-VEP), enabling us to amortize the approximate posterior of the generative process from a low-dimensional representation of whole-brain epileptic patterns. The state-of-the-art deep learning algorithms for conditional density estimation are used to readily retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. We show that the SBI-VEP is able to efficiently estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones from sparse intracranial electroencephalography recordings. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for fast and reliable inference on brain disorders from neuroimaging modalities.
Pedestrian Detection and Tracking System Based on Deep-SORT, YOLOv5, and New Data Association Metrics

Pedestrian tracking and detection have become critical aspects of advanced driver assistance systems (ADASs), due to their academic and commercial potential. Their objective is to locate various pedestrians in videos and assign them unique identities. The data association task is problematic, particularly when dealing with inter-pedestrian occlusion. This occurs when multiple pedestrians cross paths or move too close together, making it difficult for the system to identify and track individual pedestrians. Inaccurate tracking can lead to false alarms, missed detections, and incorrect decisions. To overcome this challenge, our paper focuses on improving data association in our pedestrian detection system’s Deep-SORT tracking algorithm, which is solved as a linear optimization problem using a newly generated cost matrix. We introduce a set of new data association cost matrices that rely on metrics such as intersections, distances, and bounding boxes. To evaluate trackers in real time, we use YOLOv5 to identify pedestrians in images. We also perform experimental evaluations on the Multiple Object Tracking 17 (MOT17) challenge dataset. The proposed cost matrices demonstrate promising results, showing an improvement in most MOT performance metrics compared to the default intersection over union (IOU) data association cost matrix.
Quantum Machine Intelligence for 6G URLLC

Immersive and mission-critical data-driven applications, such as virtual or augmented reality, tactile Internet, industrial automation, and autonomous mobility, are creating unprecedented challenges for ultra-reliable and low-latency communication (URLLC) in the sixth generation (6G) networks. Machine intelligence approaches deep learning, reinforcement learning, and federated learning (FL), to provide new paradigms to ensure 6G URLLC on the stream of big data training. However, classical limitations of machine learning capabilities make it challenging to achieve stringent 6G URLLC requirements. In this article, we investigate the potential of variational quantum computing and quantum machine learning (QML) for 6G URLLC by utilizing the advantage of quantum resources, such as superposition, entanglement, and quantum parallelism. The underlying idea is to integrate quantum machine intelligence with 6G networks to ensure stringent 6G URLLC requirements. As an example, we demonstrate the quantum approximate optimization algorithm for NP-hard URLLC task offloading optimization problems. The variational quantum computation for QML is also adopted in wireless networks to enhance the learning rate of machine intelligence and ensure the learning optimality for mission-critical applications. Considering the security and privacy issues, as well as computational-resource overheads in FL, distributed quantum computation in blind and remote fashions is further investigated for quantum-assisted FL.
APSO: An A*-PSO Hybrid Algorithm for Mobile Robot Path Planning

Aiming at the problems of the A * algorithm in mobile robot path planning, such as multiple nodes, low path accuracy, long running time and difficult path initialization of particle swarm optimization, an APSO algorithm combining A * and PSO was proposed to calculate the optimal path.First, a redundant point removal strategy is adopted to preliminarily optimize the path planned by the A * algorithm and obtain the set of key nodes.Second, a stochastic inertia weight is proposed to improve the search ability of PSO.Third, a stochastic opposition-based learning strategy is proposed to further improve the search ability of PSO.Fourth, the global path is obtained by using the improved PSO to optimize the set of key nodes.Fifth, a motion time objective function that is more in line with the actual motion requirements of the mobile robot is used to evaluate the algorithm.The simulation results of path planning show that the path planned by APSO not only reduces the running time of the mobile robot by 17.35%, 14.84%, 15.31%, 15.21%, 18.97%, 15.70% compared with the A * algorithm in the six environment maps but also outperforms other path planning algorithms to varying degrees.Therefore, the proposed APSO is more in line with the actual movement of the mobile robot.
Two electrons interacting at a mesoscopic beam splitter

The nonlinear response of a beam splitter to the coincident arrival of interacting particles enables numerous applications in quantum engineering and metrology. Yet, it poses considerable challenges to control interactions on the individual particle level. Here, we probe the coincidence correlations at a mesoscopic constriction between individual ballistic electrons in a system with unscreened Coulomb interactions and introduce concepts to quantify the associated parametric nonlinearity. The full counting statistics of joint detection allows us to explore the interaction-mediated energy exchange. We observe an increase from 50% up to 70% in coincidence counts between statistically indistinguishable on-demand sources and a correlation signature consistent with the independent tomography of the electron emission. Analytical modelling and numerical simulations underpin the consistency of the experimental results with Coulomb interactions between two electrons counterpropagating in a quadratic saddle potential. Coulomb repulsion energy and beam splitter dispersion define a figure of merit, which in this experiment is demonstrated to be sufficiently large to enable future applications, such as single-shot in-flight detection and quantum logic gates.
The Potential Use of ChatGPT for Debugging and Bug Fixing

ChatGPT is a cutting-edge language model that has been making waves in the field of natural language processing. However, its capabilities extend far beyond language-based applications. ChatGPT can also be used as a powerful tool for debugging software code. As software applications become increasingly complex, the need for efficient and accurate debugging tools has become more pressing. ChatGPT's ability to analyze and understand code makes it a promising solution to this challenge. Debugging is a critical part of the software development process. Bugs, or errors in code, can have serious consequences for the functionality and security of software applications. Identifying and fixing bugs can be a time-consuming and labor-intensive process, requiring the expertise of experienced developers. ChatGPT has the potential to streamline this process and make it more accessible to a wider range of developers, regardless of their experience level. In this article, we will explore the capabilities of ChatGPT as a debugging tool, the advantages and limitations of using it, and best practices for integrating it into the software development workflow.
Optimal and Variational Multiparameter Quantum Metrology and Vector-Field Sensing

We study multiparameter sensing of two-dimensional (2D) and three-dimensional (3D) vector fields within the Bayesian framework for SU(2) quantum interferometry. We establish a method to determine the optimal quantum sensor, which establishes the fundamental limit on the precision of simultaneously estimating multiple parameters with an N-atom sensor. Keeping current experimental platforms in mind, we present sensors that have limited entanglement capabilities and yet significantly outperform sensors that operate without entanglement and approach the optimal quantum sensor in terms of performance. Furthermore, we show how these sensors can be implemented on current programmable quantum sensors with variational quantum circuits by minimizing a metrological cost function. The resulting circuits prepare tailored entangled states and perform measurements in an appropriate entangled basis to realize the best-possible quantum sensor given the native entangling resources available on a given sensor platform. Notable examples include a 2D and 3D quantum "compass" and a 2D sensor that provides a scalable improvement over unentangled sensors. Our results on optimal and variational multiparameter quantum metrology are useful for advancing precision measurements in fundamental science and ensuring the stability of quantum computers, which can be achieved through the incorporation of optimal quantum sensors in a quantum feedback loop.1 MoreReceived 15 February 2023Accepted 17 April 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020333Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum metrologyQuantum parameter estimationQuantum sensingPhysical SystemsTrapped ionsAtomic, Molecular & OpticalQuantum Information
The influence of management creativity on the optimality of management decisions over time: An innovative aspect

The study aimed at establishing the close relationship between the creativity of managers and the time spent making managerial decisions at the enterprise level. A linear least squares regression model determined the coefficient of dependence of time spent on management decision-making and creativity. The research used a quantitative approach and a sociological survey of heads and managers of enterprises of various sizes and organizational and legal forms. The authors compiled models of the formation of creative management in the enterprise based on motivational factors and the process of forming management decisions on the basis of management creativity. In the study, and for the first time, a scale of the creativity level and a scale of managers' motivation were proposed. The results of conducted sociological research and the use of the regression model of the factors taken into account show that the average level of creativity of managers involved in decision-making and their motivation coefficient has the greatest Influence on the time spent on managerial decisions. The research has theoretical and practical significance, and managers can use its results in practice to save time and improve the efficiency of management decision-making and staff motivation.&#x0D; &#x0D; &#x0D; &#x0D;
Perspective on unconventional computing using magnetic skyrmions

Learning and pattern recognition inevitably requires memory of previous events, a feature that conventional CMOS hardware needs to artificially simulate. Dynamical systems naturally provide the memory, complexity, and nonlinearity needed for a plethora of different unconventional computing approaches. In this perspective article, we focus on the unconventional computing concept of reservoir computing and provide an overview of key physical reservoir works reported. We focus on the promising platform of magnetic structures and, in particular, skyrmions, which potentially allow for low-power applications. Moreover, we discuss skyrmion-based implementations of Brownian computing, which has recently been combined with reservoir computing. This computing paradigm leverages the thermal fluctuations present in many skyrmion systems. Finally, we provide an outlook on the most important challenges in this field.