Reviewerly/exHarmony huggingface dataset:
Pulse based Variational Quantum Optimal Control for hybrid quantum computing

This work studies pulse based variational quantum algorithms (VQAs), which are designed to determine the ground state of a quantum mechanical system by combining classical and quantum hardware. In contrast to more standard gate based methods, pulse based methods aim to directly optimize the laser pulses interacting with the qubits, instead of using some parametrized gate based circuit. Using the mathematical formalism of optimal control, these laser pulses are optimized. This method has been used in quantum computing to optimize pulses for quantum gate implementations, but has only recently been proposed for full optimization in VQAs. Pulse based methods have several advantages over gate based methods such as faster state preparation, simpler implementation and more freedom in moving through the state space. Based on these ideas, we present the development of a novel adjoint based variational method. This method can be tailored towards and applied in neutral atom quantum computers. This method of pulse based variational quantum optimal control is able to approximate molecular ground states of simple molecules up to chemical accuracy and is able to compete with the gate based variational quantum eigensolver in terms of total number of quantum evaluations. The total evolution time <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math> and the form of the control Hamiltonian <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>H</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:math> are important factors in the convergence behavior to the ground state energy, both having influence on the quantum speed limit and the controllability of the system.
A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models

As seen in the COVID-19 pandemic, one of the most important measures is physical distance in viruses transmitted from person to person. According to the World Health Organization (WHO), it is mandatory to have a limited number of people in indoor spaces. Depending on the size of the indoors, the number of persons that can fit in that area varies. Then, the size of the indoor area should be measured and the maximum number of people should be calculated accordingly. Computers can be used to ensure the correct application of the capacity rule in indoors monitored by cameras. In this study, a method is proposed to measure the size of a prespecified region in the video and count the people there in real time. According to this method: (1) predetermining the borders of a region on the video, (2) identification and counting of people in this specified region, (3) it is aimed to estimate the size of the specified area and to find the maximum number of people it can take. For this purpose, the You Only Look Once (YOLO) object detection model was used. In addition, Microsoft COCO dataset pre-trained weights were used to identify and label persons. YOLO models were tested separately in the proposed method and their performances were analyzed. Mean average precision (mAP), frame per second (fps), and accuracy rate metrics were found for the detection of persons in the specified region. While the YOLO v3 model achieved the highest value in accuracy rate and mAP (both 0.50 and 0.75) metrics, the YOLO v5s model achieved the highest fps rate among non-Tiny models.
Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment. Despite the impressive progress in generating high-resolution, perceptually realistic images, it is not clear if modern GANs reliably learn the statistics that are meaningful to a downstream medical imaging application. In this work, the ability of a state-of-the-art GAN to learn the statistics of canonical stochastic image models (SIMs) that are relevant to objective assessment of image quality is investigated. It is shown that although the employed GAN successfully learned several basic first- and second-order statistics of the specific medical SIMs under consideration and generated images with high perceptual quality, it failed to correctly learn several per-image statistics pertinent to the these SIMs, highlighting the urgent need to assess medical image GANs in terms of objective measures of image quality.
Dynamical quantum phase transitions in a spinor Bose-Einstein condensate and criticality enhanced quantum sensing

Quantum phase transitions universally exist in the ground and excited states of quantum many-body systems, and they have a close relationship with the nonequilibrium dynamical phase transitions, which however are challenging to identify. In the system of spin-1 Bose-Einstein condensates, though dynamical phase transitions with correspondence to equilibrium phase transitions in the ground state and uppermost excited state have been probed, those taking place in intermediate excited states remain untouched in experiments thus far. Here we unravel that both the ground- and excited-state quantum phase transitions in spinor condensates can be diagnosed with dynamical phase transitions. A connection between equilibrium phase transitions and nonequilibrium behaviors of the system is disclosed in terms of the quantum Fisher information. We also demonstrate that near the critical points parameter estimation beyond the standard quantum limit can be implemented. This work not only advances the exploration of excited-state quantum phase transitions via a scheme that can immediately be applied to a broad class of few-mode quantum systems, but also provides a new perspective on the relationship between quantum criticality and quantum enhanced sensing.
Quantifying quantum coherence via Kirkwood-Dirac quasiprobability

Kirkwood-Dirac (KD) quasiprobability is a quantum analog of phase space probability of classical statistical mechanics, allowing negative or/and nonreal values. It gives an informationally complete representation of a quantum state. Recent works have revealed the important roles played by the KD quasiprobability in the broad fields of quantum science and quantum technology. In the present work, we use the KD quasiprobability to access the quantum coherence in a quantum state. We show that the ${l}_{1}$ norm of the imaginary part of the KD quasiprobability over an incoherent reference basis and a second basis, maximized over all possible choices of the latter, can be used to quantify quantum coherence, satisfying certain desirable properties. It is upper bounded by the quantum uncertainty, i.e., the quantum standard deviation, of the incoherent basis in the state. It gives a lower bound to the ${l}_{1}$ norm quantum coherence, and for a single qubit, they are identical. We discuss the measurement of the KD coherence based on the measurement of the KD quasiprobability and an optimization procedure in hybrid quantum-classical schemes, and suggest statistical interpretations. We also discuss its relevance in the physics of linear response regime.
A spectral method for assessing and combining multiple data visualizations

Dimension reduction is an indispensable part of modern data science, and many algorithms have been developed. However, different algorithms have their own strengths and weaknesses, making it important to evaluate their relative performance, and to leverage and combine their individual strengths. This paper proposes a spectral method for assessing and combining multiple visualizations of a given dataset produced by diverse algorithms. The proposed method provides a quantitative measure - the visualization eigenscore - of the relative performance of the visualizations for preserving the structure around each data point. It also generates a consensus visualization, having improved quality over individual visualizations in capturing the underlying structure. Our approach is flexible and works as a wrapper around any visualizations. We analyze multiple real-world datasets to demonstrate the effectiveness of the method. We also provide theoretical justifications based on a general statistical framework, yielding several fundamental principles along with practical guidance.
Adaptive temporal feature modeling for visual tracking via cross-channel learning

Convolution Neural Networks (CNNs) based trackers achieve excellent tracking performance on tracking accuracy and speed. Feature extraction from the target template and search regions is a key part in visual tracking. Recently, existing feature subnetworks combine CNNs with channel attention for feature extraction. However, some existing feature subnetworks do not make the best of the target location dependencies, which result the target location dependency information lost in extracted target features. In this work, we design a novel feature extraction subnetwork with local temporal adaptive modules to obtain location sensitive importance maps, which effectively capture the diverse motion information and highlight the target location information. The target location dependency information is fully utilized to obtain more accurate target location information of the target template and search region in feature extraction subnetwork. The feature extraction subnetwork also fully exploits the local temporal semantics. Furthermore, we learn an interactive module in the template branch, which further captures the non-linear cross-channel interaction and channel-wise dependencies by combining every channel and its k neighbors. The template branch further utilizes cross-channel interactions for capturing the channel dependencies. The interactive module only increases a little extra computational burden. Comparing with other attention modules for visual tracking, this interactive module is lightweight. We propose a novel tracking framework, which mainly includes the designed feature extraction subnetwork and the interactive learning module. We evaluate the proposed tracker on GOT-10k, UAV123, DTB70, NFS, OTB-100, VOT2018, LaSOT and VOT-RGBT2019 benchmarks against advanced trackers, achieving leading performance with 60 FPS tracking speed.
Superconducting cavity qubit with tens of milliseconds single-photon coherence time

Storing quantum information for an extended period of time is essential for running quantum algorithms with low errors. Currently, superconducting quantum memories have coherence times of a few milliseconds, and surpassing this performance has remained an outstanding challenge. In this work, we report a qubit encoded in a novel superconducting cavity with a coherence time of 34 ms, an improvement of over an order of magnitude compared to previous demonstrations. We use this long-lived quantum memory to store a Schr\"odinger cat state with a record size of 1024 photons, indicating the cavity's potential for bosonic quantum error correction.
Entanglement and quantum correlation measures for quantum multipartite mixed states

Entanglement, and quantum correlation, are precious resources for quantum technologies implementation based on quantum information science, such as quantum communication, quantum computing, and quantum interferometry. Nevertheless, to our best knowledge, a directly or numerically computable measure for the entanglement of multipartite mixed states is still lacking. In this work, (i) we derive a measure of the degree of quantum correlation for mixed multipartite states. The latter possesses a closed-form expression valid in the general case unlike, to our best knowledge, all other known measures of quantum correlation. (ii) We further propose an entanglement measure, derived from this quantum correlation measure using a novel regularization procedure for the density matrix. Therefore, a comparison of the proposed measures, of quantum correlation and entanglement, allows one to distinguish between quantum correlation detached from entanglement and the one induced by entanglement and, hence, to identify separable but non-classical states. We have tested our quantum correlation and entanglement measures, on states well-known in literature: a general Bell diagonal state and the Werner states, which are easily tractable with our regularization procedure, and we have verified the accordance between our measures and the expected results for these states. Finally, we validate the two measures in two cases of multipartite states. The first is a generalization to three qubits of the Werner state, the second is a one-parameter three qubits mixed state interpolating between a bi-separable state and a genuine multipartite state, passing through a fully separable state.
Artificial Intelligence and the Indian Media Industry: the Future is Now

The Indian media industry is currently undergoing a significant transformation with the advent of Artificial Intelligence (AI). AI has the potential to revolutionize the way news stories are produced and consumed, providing new opportunities for innovation and growth. However, it is also important to consider the challenges that come with implementing AI in the Indian media industry. This research aims to provide a comprehensive examination of the future of the Indian media industry in the age of AI. The study will analyse the current state of AI in the Indian media industry, focusing on its impact on efficiency, cost-effectiveness, personalization, multilingual news production, real-time news generation, and combat against misinformation. Additionally, the research will also explore the challenges that come with the adoption and implementation of AI in the Indian media industry, such as job losses, ethical concerns, lack of understanding, data, and infrastructure. The study will use both primary and secondary data sources, including interviews with industry experts, case studies of media organizations that have implemented AI, and a review of relevant literature. The research will provide valuable insights for media organizations, policymakers, and researchers on the opportunities and challenges of AI in the Indian media industry, and how it is shaping the future of the industry.
Generalized image outpainting with U-transformer

In this paper, we develop a novel transformer-based generative adversarial neural network called U-Transformer for generalized image outpainting problems. Different from most present image outpainting methods conducting horizontal extrapolation, our generalized image outpainting could extrapolate visual context all-side around a given image with plausible structure and details even for complicated scenery, building, and art images. Specifically, we design a generator as an encoder-to-decoder structure embedded with the popular Swin Transformer blocks. As such, our novel neural network can better cope with image long-range dependencies which are crucially important for generalized image outpainting. We propose additionally a U-shaped structure and multi-view Temporal Spatial Predictor (TSP) module to reinforce image self-reconstruction as well as unknown-part prediction smoothly and realistically. By adjusting the predicting step in the TSP module in the testing stage, we can generate arbitrary outpainting size given the input sub-image. We experimentally demonstrate that our proposed method could produce visually appealing results for generalized image outpainting against the state-of-the-art image outpainting approaches.
Augmented reality for industrial services provision: the factors influencing a successful adoption in manufacturing companies

Purpose This paper presents a model aiming to identify the factors influencing the adoption of augmented reality (AR) for industrial services. Design/methodology/approach The study combines a literature analysis with an empirical study conducted exploring how five large industrial companies are introducing AR for supporting the provision of technical assistance and industrial services to their installed base. Findings The authors identify four categories (task, workforce, context and technology) that combine 18 factors that manufacturing companies should consider when introducing AR technology to support industrial services. Originality/value This paper systematises the fragmented literature on technology adoption and in particular those works related to the factors affecting the adoption of AR in industrial services. Based on literature and empirical evidence, the authors propose a novel framework that can help companies in the selection of AR solution based on their specific applications and situations. This study therefore contributes also to the existing literature on the adoption of I4.0 and digital technologies in industrial services.
Digital studying in times of COVID-19: teacher- and student-related aspects of learning success in german higher education

Abstract In the wake of COVID-19, study conditions in Europe have changed dramatically. To limit contact between students and teachers, since March 2020 teaching has largely taken place digitally (remotely via digital means) and in private. Because the success of digital learning likely relies on many factors beyond good digital infrastructure conditions, this article focuses on which aspects, at both the teacher and the student levels, promote digital learning success. The large-scale student survey “Studying in Times of the Corona Pandemic” conducted at German universities and universities of applied sciences in the summer semester of 2020 offers data on how COVID-19 has affected several aspects of university studying in Germany. Here, we consider this data within the theoretical framework “theory of transactional distance” introduced by Moore (in: Moore (ed) Handbook of distance education, Routledge, 2018), according to which the success of digital teaching is influenced by dialogue, structure, and learner autonomy. Based on various regression analyses, our results show that several (digital) framework conditions must be created on both the teacher and student levels to achieve sufficient digital learning success. In this sense, our findings provide guidance on which aspects institutions of higher education should focus on when developing or updating their digitalization strategies. In accordance with collaborative learning approaches a key factor for learning success appears to be enabling peer-to-peer interactions. This finding supports our prediction that the possibility of engaging in interactive learning activities is crucial for students’ learning experience, as it might reduce the perception of transactional distance and allow for social exchange. The strongest predictor of students’ learning success turned out to be the (perceived) digital competencies of the teachers. This finding clearly emphasizes that teachers must be qualified to address the very specific challenges of teaching in digital contexts and indicates that universities may need to implement more teacher qualification programs.
Key functions in BIM-based AR platforms

The integration of Augmented Reality and Building Information Modelling is a promising area of research; however, fragmentation in literature hinders the development of mature BIM-based AR platforms. This paper aims to minimise the fragmentation in the literature by identifying the key functions that represent the essential capabilities of BIM-AR platforms. A systematic literature review is employed to identify, categorise, and discuss the key functions. The outcome of this paper identifies six key functions: positioning (P), interaction (I), visualisation (V), collaboration (C), automation (A), and integration (T). These key functions act as the foundation for an evaluation framework that can assist practitioners, developers, and researchers with assessing the requirements of the targeted application area, and hence be better informed on the appropriate devices, software, and techniques to use. Finally, this paper emphasises the importance of industrial-academic collaboration in BIM-AR research and suggests prospects for automation through the application of artificial intelligence.
Role of Artificial Intelligence in Future of Education

Purpose: The purpose of this study is to examine that Globalization has radically altered human society in the previous 150 years. With the internet of things, energy, and the cyber-physical systems governed by it coming to an end, conventional education faces an immense challenge. That will associate this tension with internet usage and reward students and teachers alike. It can be claimed that future education is entirely built on the internet of things, energy, and the cyber-physical systems ruled by it. As these systems end, traditional education confronts a massive challenge. This moves increases students' screening time, which influences their mental health. Theoretical Framework: The paper speculates on the near future of research in Artificial Intelligence and Education (AIED), on the basis of three uses of models of educational processes with also evaluating literature available. Design/Methodology/Approach: The classification algorithms SVM, Naive Bayes, and Random Forest benefit from 5-fold Cross-Validation with 206 students from Delhi NCR and outside. Researcher is finding for how are the ages distributed? How many students got mental health care? So, what did they do? Their ages? How many meals did they eat? After the COVID-19 virus spread in Delhi, India, the study looked at factors that led to an increased mental health burden for undergraduate students in the city. The dataset is constructed by combining data from several domains such as age, time, medium meals etc. Thus, researcher pre-processed the data and classified it into four categories based on their location within the Delhi NCR and outside the NCR. The suggested model is evaluated using a K field fold cross-validation test. Findings: The findings have shown that practical implications of technology will positively impact education in the future, but it may also have severe implications. Teachers and students should grasp this chance to encourage greatness and break down the hurdles that keep many children and schools from reaching it. As a result, all countries must develop a more technologically advanced education system in the future. Research, Practical and social implications: The study in advances in technology will have major distractions in the workforce as automation might replace more than fifty percent of jobs. It is crucial to teach students skills to thrive in digital workplace, engage positively with technology to explore its full potential. The contribution of this study about AI systems are technically feasible for instructor-learner interaction. It is important to foster AI literacy in students to break the barrier of misconceptions and make way for imagination, innovation with new perspectives in society. Originality/Value: The value of the study is to educational institutions and related organizations seeking for role of artificial intelligence in education.