Reviewerly/exHarmony huggingface dataset:
E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI

Traditionally, Artificial Intelligence (AI) models are trained on the central cloud with data collected from end devices. This leads to high communication cost, long response time, and privacy concerns. Recently Edge-empowered AI, namely, Edge AI, has been proposed to support AI model learning and deployment at the network edge closer to the data sources. Existing research, including federated learning adopts a centralized architecture for model learning, where a central server aggregates the model updates from the clients/workers. The centralized architecture has drawbacks, such as performance bottleneck, poor scalability, and single point of failure. In this article, we propose a novel decentralized model learning approach, namely, E-Tree, which makes use of a well-designed tree structure imposed on the edge devices. The tree structure and the locations and orders of the aggregation on the tree are optimally designed to improve the training convergency and model accuracy. In particular, we design an efficient device clustering algorithm, named by K-Means and average accuracy, for E-Tree by taking into account the data distribution on the devices as well as the network distance. Evaluation results show that E-Tree significantly outperforms the benchmark approaches, such as federated learning and gossip learning under nonindependently and identically distributed (Non-i.i.d.) data in terms of model accuracy and convergency.
Epitaxial superconductor-semiconductor two-dimensional systems for superconducting quantum circuits

Qubits on solid state devices could potentially provide the rapid control necessary for developing scalable quantum information processors. Materials innovation and design breakthroughs have increased functionality and coherence of qubits substantially over the past two decades. Here, we show by improving interface between InAs as a semiconductor and Al as a superconductor, one can reliably fabricate voltage-controlled Josephson junction field effect transistor (JJ-FET) that can be used as tunable qubits, resonators, and coupler switches. We find that bandgap engineering is crucial in realizing a two-dimensional electron gas near the surface. In addition, we show how the coupling between the semiconductor layer and the superconducting contacts can affect qubit properties. We present the anharmonicity and coupling strengths from one and two-photon absorption in a quantum two level system fabricated with a JJ-FET.
Fixed Depth Hamiltonian Simulation via Cartan Decomposition

Simulating quantum dynamics on classical computers is challenging for large systems due to the significant memory requirements. Simulation on quantum computers is a promising alternative, but fully optimizing quantum circuits to minimize limited quantum resources remains an open problem. We tackle this problem presenting a constructive algorithm, based on Cartan decomposition of the Lie algebra generated by the Hamiltonian, that generates quantum circuits with time-independent depth. We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model, where a O(n^2)-gate circuits naturally emerge. Compared to product formulas with significantly larger gate counts, our algorithm drastically improves simulation precision. In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
Findings of the Shared Task on Offensive Language Identification in Tamil, Malayalam, and Kannada

Detecting offensive language in social media in local languages is critical for moderating user-generated content. Thus, the field of offensive language identification in under-resourced Tamil, Malayalam and Kannada languages are essential. As the user-generated content is more code-mixed and not well studied for under-resourced languages, it is imperative to create resources and conduct benchmarking studies to encourage research in under-resourced Dravidian languages. We created a shared task on offensive language detection in Dravidian languages. We summarize here the dataset for this challenge which are openly available at https://competitions.codalab.org/competitions/27654, and present an overview of the methods and the results of the competing systems.
Upper Bounds on Device-Independent Quantum Key Distribution

Quantum key distribution (QKD) is a method that distributes a secret key to a sender and a receiver by the transmission of quantum particles (e.g., photons). Device-independent quantum key distribution (DIQKD) is a version of QKD with a stronger notion of security, in that the sender and receiver base their protocol only on the statistics of input and outputs of their devices as inspired by Bell's theorem. We study the rate at which DIQKD can be carried out for a given bipartite quantum state distributed between the sender and receiver or a quantum channel connecting them. We provide upper bounds on the achievable rate going beyond upper bounds possible for QKD. In particular, we construct states and channels where the QKD rate is significant while the DIQKD rate is negligible. This gap is illustrated for a practical case arising when using standard postprocessing techniques for entangled two-qubit states.
A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata

With the rapid development of cloud computing and container technology, more and more applications are deployed to the cloud, and the scale of cloud platform is expanding.Due to the large number of container instances running in the platform, complex dependency relationship, fast version iteration and other characteristics, the update of business can often cause the change of the whole cloud resource environment, which triggers the repetitive scheduling problem of related tasks and affects stability of the business.In this paper, we propose a self-adapting task scheduling algorithm (ADATSA) using learning automata to solve these problems.Firstly, we design a learning automata model and objective function for the system on task scheduling problem.Then, we realize an effective reward-penalty mechanism for scheduling actions in combination with the idle state of resources and the running state of tasks in the current environment.Meanwhile, the environment is modeled by cluster, node and task, and the probability of action selected is optimized by scheduling execution, thus enhancing the adaptability to the cloud environment of the scheduling and accelerating convergence.Finally, we construct a framework of task load monitoring with buffer queue to achieve dynamic scheduling based on priority.The experimental part verifies the effectiveness of proposed algorithm with different angles such as resource imbalance degree, resource residual degree and QoS.Compared with other learning automata scheduling models such as LAEAS, non-automata technology based algorithms such as PSOS and K8S scheduling engine, ADATSA shows the better performance of environment adaptability, resource optimization efficiency and QoS in dynamic scheduling.The theoretical analysis was consistent with the experimental results.
A lossless compression method for multi-component medical images based on big data mining

Abstract In disease diagnosis, medical image plays an important part. Its lossless compression is pretty critical, which directly determines the requirement of local storage space and communication bandwidth of remote medical systems, so as to help the diagnosis and treatment of patients. There are two extraordinary properties related to medical images: lossless and similarity. How to take advantage of these two properties to reduce the information needed to represent an image is the key point of compression. In this paper, we employ the big data mining to set up the image codebook. That is, to find the basic components of images. We propose a soft compression algorithm for multi-component medical images, which can exactly reflect the fundamental structure of images. A general representation framework for image compression is also put forward and the results indicate that our developed soft compression algorithm can outperform the popular benchmarks PNG and JPEG2000 in terms of compression ratio.
Virtual Reality Usability and Accessibility for Cultural Heritage Practices: Challenges Mapping and Recommendations

In recent years, virtual reality (VR) is at its maturity level for real practical exercises amongst many fields of studies, especially in the virtual walkthrough exploration system of cultural heritage (CH). However, this study remains scattered and limited. This work presents a systematic review that maps out the usability and accessibility issues that are challenging in using VR in CH. We identified 45 challenges that are mapped into five problem groups: system design, development process, technology, assessment process and knowledge transfer. This mapping is then used to propose 58 recommendations to improve the usability and accessibility of VR in CH that are categorized in three different recommendation groups namely, discovery and planning, design and development, and finally the assessment factors. This analysis identified the persistence in certain accessibility and usability problems such as there is a limit in navigating the view and space that constraint the users’ free movement and the navigation control is not ideal with the keyboard arrow button. This work is important because it provides an overview of usability and accessibility based challenges that are faced in applying, developing, deploying and assessing VR in the usage of digitalizing CH and proposed a great number of constructive recommendations to guide future studies. The main contribution of this paper is the mapping of usability and accessibility challenges into categories and the development of recommendations based on the identified problems.
Adoption of online education channel during the COVID-19 pandemic and associated economic lockdown: an empirical study from push–pull-mooring framework

The purpose of the research was to study how the onset of COVID-19 pandemic fuelled shift towards digitalization in the education ecosystem. The study consisted of responses from 316 engineering students collected during the COVID-19 pandemic physical lockdown in India from March to May 2020. The collected data was analysed using structural equation modelling to develop a model for online education in the study context based upon the push–pull-mooring framework. Hayes' model was used to analyze the effect of the multiple mediators to understand the relationship between the antecedents (Platform knowledge scope, alternative media attractiveness and normative environmental pressure) and switching behaviour for performance. This research contributed to the push–pull-mooring framework theory in the context of a crisis event.
Reducing the Depth of Linear Reversible Quantum Circuits

In quantum computing the decoherence time of the qubits determines the computation time available, and this time is very limited when using current hardware. In this article, we minimize the execution time (the depth) for a class of circuits referred to as linear reversible circuits, which has many applications in quantum computing (e.g., stabilizer circuits, “CNOT+T” circuits, etc.). We propose a practical formulation of a divide-and-conquer algorithm that produces quantum circuits that are twice as shallow as those produced by existing algorithms. We improve the theoretical upper bound of the depth in the worst case for some range of qubits. We also propose greedy algorithms based on cost minimization to find more optimal circuits for small or simple operators. Overall, we manage to consistently reduce the total depth of a class of reversible functions, with up to 92% savings in an ancilla-free case and up to 99% when ancillary qubits are available.
Quantum Algorithms for Solving Ordinary Differential Equations via Classical Integration Methods

Identifying computational tasks suitable for (future) quantum computers is an active field of research. Here we explore utilizing quantum computers for the purpose of solving differential equations. We consider two approaches: (i) basis encoding and fixed-point arithmetic on a digital quantum computer, and (ii) representing and solving high-order Runge-Kutta methods as optimization problems on quantum annealers. As realizations applied to two-dimensional linear ordinary differential equations, we devise and simulate corresponding digital quantum circuits, and implement and run a 6<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi /><mml:mrow class="MJX-TeXAtom-ORD"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">t</mml:mi><mml:mi mathvariant="normal">h</mml:mi></mml:mrow></mml:mrow></mml:msup></mml:math> order Gauss-Legendre collocation method on a D-Wave 2000Q system, showing good agreement with the reference solution. We find that the quantum annealing approach exhibits the largest potential for high-order implicit integration methods. As promising future scenario, the digital arithmetic method could be employed as an "oracle" within quantum search algorithms for inverse problems.
Secure and practical multiparty quantum digital signatures

Quantum digital signatures (QDSs) promise information-theoretic security against repudiation and forgery of messages. Compared with currently existing three-party QDS protocols, multiparty protocols have unique advantages in the practical case of more than two receivers when sending a mass message. However, complex security analysis, numerous quantum channels and low data utilization efficiency make it intractable to expand three-party to multiparty scenario. Here, based on six-state non-orthogonal encoding protocol, we propose an effective multiparty QDS framework to overcome these difficulties. The number of quantum channels in our protocol only linearly depends on the number of users. The post-matching method is introduced to enhance data utilization efficiency and make it linearly scale with the probability of detection events even for five-party scenario. Our work compensates for the absence of practical multiparty protocols, which paves the way for future QDS networks.
Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by deep learning

We predict the emergence of extreme events in a parametrically driven nonlinear dynamical system using three Deep Learning models, namely Multi-Layer Perceptron, Convolutional Neural Network, and Long Short-Term Memory. The Deep Learning models are trained using the training set and are allowed to predict the test set data. After prediction, the time series of the actual and the predicted values are plotted one over the other to visualize the performance of the models. Upon evaluating the Root-Mean-Square Error value between predicted and the actual values of all three models, we find that the Long Short-Term Memory model can serve as the best model to forecast the chaotic time series and to predict the emergence of extreme events in the considered system.
Multi-Sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking

This paper provides a scalable, multi-sensor measurement adaptive track initiation technique for labeled random finite set filters. A naive construction of the multi-sensor measurement adaptive birth set distribution leads to an exponential number of newborn components in the number of sensors. A truncation criterion is established for a labeled multi-Bernoulli random finite set birth density. The proposed truncation criterion is shown to have a bounded L1 error in the generalized labeled multi-Bernoulli posterior density. This criterion is used to construct a Gibbs sampler that produces a truncated measurement-generated labeled multi-Bernoulli birth distribution with quadratic complexity in the number of sensors. A closed-form solution of the conditional sampling distribution assuming linear Gaussian likelihoods is provided, alongside an approximate solution using Monte Carlo importance sampling. Multiple simulation results are provided to verify the efficacy of the truncation criterion, as well as the reduction in complexity.
Interacting defects generate stochastic fluctuations in superconducting qubits

Superconducting quantum computers have long been plagued by defects causing unreliable performance. Here, the authors confirm the fundamental mechanism behind unwanted time fluctuations: `hot' defects kick `colder' ones, which, in turn, randomly jolt the qubits in a device. This domino effect results in a jitter that prevents scientists from conducting ideal quantum operations. Will it be possible to eliminate this jitter? The authors show here that the underlying physics is now well understood, a key step towards remedying the corresponding errors.
A method for finding anomalous astronomical light curves and their analogues

ABSTRACT Our understanding of the Universe has profited from deliberate targeted studies of known phenomena, as well as from serendipitous unexpected discoveries, such as the discovery of a complex variability pattern in the direction of KIC 8462852 (Boyajian’s star). Upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time will explore the parameter space of astrophysical transients at all time-scales, and offer the opportunity to discover even more extreme examples of unexpected phenomena. We investigate strategies to identify novel objects and to contextualize them within large time-series data sets in order to facilitate the discovery of new classes of objects as well as the physical interpretation of their anomalous nature. We develop a method that combines tree-based and manifold-learning algorithms for anomaly detection in order to perform two tasks: 1) identify and rank anomalous objects in a time-domain data set; and 2) group those anomalies according to their similarity in order to identify analogues. We achieve the latter by combining an anomaly score from a tree-based method with a dimensionality manifold-learning reduction strategy. Clustering in the reduced space allows for the successful identification of anomalies and analogues. We also assess the impact of pre-processing and feature engineering schemes and investigate the astrophysical nature of the objects that our models identify as anomalous by augmenting the Kepler data with Gaia colour and luminosity information. We find that multiple models, used in combination, are a promising strategy to identify novel light curves and light curve families.
Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images

The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign and malignant breast ultrasound (BUS) tumor images, making rapid breast tumor screening possible.The classification model was evaluated with a different dataset of 100 BUS tumor images (50 benign cases and 50 malignant cases), which was not used in network training. Evaluation indicators include accuracy, sensitivity, specificity, and area under curve (AUC) value. The results in the Fus2Net model had an accuracy of 92%, the sensitivity reached 95.65%, the specificity reached 88.89%, and the AUC value reached 0.97 for classifying BUS tumor images.The experiment compared the existing CNN-categorized architecture, and the Fus2Net architecture we customed has more advantages in a comprehensive performance. The obtained results demonstrated that the Fus2Net classification method we proposed can better assist radiologists in the diagnosis of benign and malignant BUS tumor images.The existing public datasets are small and the amount of data suffer from the balance issue. In this paper, we provide a relatively larger dataset with a total of 1052 ultrasound images, including 696 benign images and 356 malignant images, which were collected from a local hospital. We proposed a novel CNN named Fus2Net for the benign and malignant classification of BUS tumor images and it contains two self-designed feature extraction modules. To evaluate how the classifier generalizes on the experimental dataset, we employed the training set (646 benign cases and 306 malignant cases) for tenfold cross-validation. Meanwhile, to solve the balance of the dataset, the training data were augmented before being fed into the Fus2Net. In the experiment, we used hyperparameter fine-tuning and regularization technology to make the Fus2Net convergence.
Realization of High-Fidelity Controlled-Phase Gates in Extensible Superconducting Qubits Design with a Tunable Coupler

High-fidelity two-qubit gates are essential for the realization of large-scale quantum computation and simulation. Tunable coupler design is used to reduce the problem of parasitic coupling and frequency crowding in many-qubit systems and thus thought to be advantageous. Here we design an extensible 5-qubit system in which center transmon qubit can couple to every four near-neighboring qubits via a capacitive tunable coupler and experimentally demonstrate high-fidelity controlled-phase (CZ) gate by manipulating central qubit and one near-neighboring qubit. Speckle purity benchmarking and cross entropy benchmarking are used to assess the purity fidelity and the fidelity of the CZ gate. The average purity fidelity of the CZ gate is 99.69±0.04% and the average fidelity of the CZ gate is 99.65±0.04%, which means that the control error is about 0.04%. Our work is helpful for resolving many challenges in implementation of large-scale quantum systems.
Probing nonclassical light fields with energetic witnesses in waveguide quantum electrodynamics

We propose an operational scenario to characterize the nature of energy exchanges between two coupled but otherwise isolated quantum systems. Defining work as the component stemming from effective unitary interactions performed by each system on one another, the remnant is stored in the correlations and generally prevents full energy extraction by local operations. Focusing on the case of a qubit coupled to the light field of a waveguide, we establish a bound relating work exchange and local energy extraction when the light statistics is coherent, and that gets violated in the presence of a quantum light pulse. Our results provide operational, energy-based witnesses to probe nonclassical resources.Received 15 February 2021Accepted 4 August 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.L032073Published 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 AreasEffects of atomic coherence on light propagationOpen quantum systems & decoherenceQuantum measurementsQuantum opticsQuantum thermodynamicsScattering theorySpontaneous emissionTechniquesMarkovian processesMaster equationNon-Markovian processesGeneral PhysicsQuantum InformationAtomic, Molecular & OpticalStatistical Physics