Reviewerly/exHarmony huggingface dataset:
Quantum circuit design search

This article explores search strategies for the design of parameterized quantum circuits. We propose several optimization approaches including random search plus survival of the fittest, reinforcement learning both with classical and hybrid quantum classical controllers, and Bayesian optimization as decision makers to design a quantum circuit in an automated way for a specific task such as multi-labeled classification over a dataset. We introduce nontrivial circuit architectures that are arduous to be hand-designed and efficient in terms of trainability. In addition, we introduce reuploading of initial data into quantum circuits as an option to find more general designs. We numerically show that some of the suggested architectures for the Iris dataset accomplish better results compared to the established parameterized quantum circuit designs in the literature. In addition, we investigate the trainability of these structures on the unseen dataset Glass. We report meaningful advantages over the benchmarks for the classification of the Glass dataset which supports the fact that the suggested designs are inherently more trainable.
Support Directional Shifting Vector: A Direction Based Machine Learning Classifier

Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm. Doi: 10.28991/esj-2021-01306 Full Text: PDF
The Use of Artificial Neural Networks to Forecast the Behavior of Agent-Based Models of Pathophysiology: An Example Utilizing an Agent-Based Model of Sepsis

Introduction: Disease states are being characterized at finer and finer levels of resolution via biomarker or gene expression profiles, while at the same time. Machine learning (ML) is increasingly used to analyze and potentially classify or predict the behavior of biological systems based on such characterization. As ML applications are extremely data-intensive, given the relative sparsity of biomedical data sets ML training of artificial neural networks (ANNs) often require the use of synthetic training data. Agent-based models (ABMs) that incorporate known biological mechanisms and their associated stochastic properties are a potential means of generating synthetic data. Herein we present an example of ML used to train an artificial neural network (ANN) as a surrogate system used to predict the time evolution of an ABM focusing on the clinical condition of sepsis. Methods: The disease trajectories for clinical sepsis, in terms of temporal cytokine and phenotypic dynamics, can be interpreted as a random dynamical system. The Innate Immune Response Agent-based Model (IIRABM) is a well-established model that utilizes known cellular and molecular rules to simulate disease trajectories corresponding to clinical sepsis. We have utilized two distinct neural network architectures, Long Short-Term Memory and Multi-Layer Perceptron, to take a time sequence of five measurements of eleven IIRABM simulated serum cytokine concentrations as input and to return both the future cytokine trajectories as well as an aggregate metric representing the patient’s state of health. Results: The ANNs predicted model trajectories with the expected amount of error, due to stochasticity in the simulation, and recognizing that the mapping from a specific cytokine profile to a state-of-health is not unique. The Multi-Layer Perceptron neural network, generated predictions with a more accurate forecasted trajectory cone. Discussion: This work serves as a proof-of-concept for the use of ANNs to predict disease progression in sepsis as represented by an ABM. The findings demonstrate that multicellular systems with intrinsic stochasticity can be approximated with an ANN, but that forecasting a specific trajectory of the system requires sequential updating of the system state to provide a rolling forecast horizon.
Curriculum learning with Hindsight Experience Replay for sequential object manipulation tasks

Learning complex tasks from scratch is challenging and often impossible for humans as well as for artificial agents. Instead, a curriculum can be used, which decomposes a complex task – the target task – into a sequence of source tasks. Each source task is a simplified version of the next source task with increasing complexity. Learning then occurs gradually by training on each source task while using knowledge from the curriculum’s prior source tasks. In this study, we present a new algorithm that combines curriculum learning with Hindsight Experience Replay (HER), to learn sequential object manipulation tasks for multiple goals and sparse feedback. The algorithm exploits the recurrent structure inherent in many object manipulation tasks and implements the entire learning process in the original simulation without adjusting it to each source task. We test our algorithm on three challenging throwing tasks in simulation and show significant improvements compared to vanilla-HER.
Cross-domain authentication for 5G-enabled UAVs

While 5G facilitates high-speed Internet access and makes over-the-horizon control a reality for unmanned aerial vehicles (UAVs), there are also potential security and privacy considerations, for example, authentication among drones. The centralized authentication approaches not only suffer from a single point of failure, but they are also incapable of cross-domain authentication, which complicates the cooperation of drones from different domains. To address these challenges, we propose a blockchain-based solution to achieve cross-domain authentication for 5G-enabled UAVs. Our approach employs multiple signatures based on threshold sharing to build an identity federation for collaborative domains. Reliable communication between cross-domain devices is achieved by utilizing smart contract for authentication. Our performance evaluations show the effectiveness and efficiency of the proposed scheme.
Fusion of Centroid-Based Clustering With Graph Clustering: An Expectation-Maximization-Based Hybrid Clustering

This article extends the expectation-maximization (EM) formulation for the Gaussian mixture model (GMM) with a novel weighted dissimilarity loss. This extension results in the fusion of two different clustering methods, namely, centroid-based clustering and graph clustering in the same framework in order to leverage their advantages. The fusion of centroid-based clustering and graph clustering results in a simple ``soft'' asynchronous hybrid clustering method. The proposed algorithm may start as a pure centroid-based clustering algorithm (e.g., k-means), and as the time evolves, it may eventually and gradually turn into a pure graph clustering algorithm [e.g., basic greedy asynchronous distributed interference avoidance (GADIA) (Babadi and Tarokh, 2010)] as the algorithm converges and vice versa. The ``hard'' version of the proposed hybrid algorithm includes the standard Hopfield neural networks (and, thus, Bruck's Ln algorithm by (Bruck, 1990) and the Ising model in statistical mechanics), Babadi and Tarokh's basic GADIA in 2010, and the standard k-means (Steinhaus, 1956), (MacQueen, 1967) [i.e., the Lloyd algorithm (Lloyd, 1957, 1982)] as its special cases. We call the ``hard version'' of the proposed clustering as ``hybrid-nongreedy asynchronous clustering (H-NAC).'' We apply the H-NAC to various clustering problems using well-known benchmark datasets. The computer simulations confirm the superior performance of the H-NAC compared to the k-means clustering, k-GADIA, spectral clustering, and a very recent clustering algorithm structured graph learning (SGL) by Kang et al. (2021), which represents one of the state-of-the-art clustering algorithms.
Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

Abstract Modern tokamaks have achieved significant fusion production, but further progress towards steady-state operation has been stymied by a host of kinetic and MHD instabilities. Control and identification of these instabilities is often complicated, warranting the application of data-driven methods to complement and improve physical understanding. In particular, Alfvén eigenmodes are a class of ubiquitous mixed kinetic and MHD instabilities that are important to identify and control because they can lead to loss of confinement and potential damage to the walls of a plasma device. In the present work, we use reservoir computing networks to classify Alfvén eigenmodes in a large labeled database of DIII-D discharges, covering a broad range of operational parameter space. Despite the large parameter space, we show excellent classification and prediction performance, with an average hit rate of 91% and false alarm ratio of 7%, indicating promise for future implementation with additional diagnostic data and consolidation into a real-time control strategy.
Realizing a Downstream-Access Network Using Continuous-Variable Quantum Key Distribution

Quantum key distribution (QKD), which enables the secure distribution of symmetric keys between two legitimate parties, is highly useful in future network security. An access network that connects multiple end users with one network backbone can be combined with QKD to build security for end users in a scalable and cost-effective way. In this paper, we show that a downstream-access network can be implemented using continuous-variable (CV) QKD. The security analysis is conducted independently with other network nodes, which greatly relax the trustfulness requirement on the network nodes. The security is then obtained against other network parties even when the quantum signals are broadcast throughout the network. The passive beam splitter is sufficient to deterministically distribute the quantum signals, and standard CV QKD systems can be directly fitted in the downstream-access network, which makes it more applicable for practical implementations. Numerous simulation results are provided to demonstrate the performance of the CV QKD downstream-access network, where up to 64 end users are shown to be feasible to access the network. We provide the security-analysis framework for realizing CV QKD in the downstream-access network, which will boost the diversity for constructing practical CV QKD networks.
Research on computer interactive optimization design of power system based on genetic algorithm

With the rapid development of network, network technology has been applied to all aspects of real life and has been widely used in power system. There are many network resources in power system, which can strengthen the safe operation of power system and protect system data. However, in real life, there are still some problems in power system security, which have a great impact on power enterprises and people’s lives. In the long-term research and practice, genetic algorithm has shown positive significance in promoting the optimization design of computer interaction network in power system. Based on this, this paper analyzes the application of genetic algorithm in the optimization design of computer interaction network in power system, expounds the basic characteristics of genetic algorithm, and then constructs the optimization model of computer interaction in power system.
Generalized forgery attack to optical encryption systems

In previous works, the attacking methods to optical encryption systems are mainly investigated from the aspect of cryptanalysis. An alternative forgery attack is proposed recently for an encrypted computational ghost imaging (CGI) system by confusing the receiver with forged keys and fake decrypted images. But the limitation is that it only works for binary single-pixel intensity values. We propose a different forgery attacking algorithm that works for grayscale single-pixel intensity values in a general CGI system. In addition, we transfer the concept of forgery attack to the well-known double random phase encoding (DRPE) system and propose a corresponding iterative attacking algorithm. Furthermore, we propose several metrics to evaluate the imperceptibility level of the forged keys. We prove that a forgery attack can be implemented not only in a particular situation but also for general optical encryption systems.
Potential reduction in transmission of COVID-19 by digital contact tracing systems: a modelling study

Abstract Background. Digital tools are being developed to support contact tracing as part of the global effort to control the spread of COVID-19. These include smartphone apps, Bluetooth-based proximity detection, location tracking and automatic exposure notification features. Evidence on the effectiveness of alternative approaches to digital contact tracing is so far limited. Methods. We use an age-structured branching process model of the transmission of COVID-19 in different settings to estimate the potential of manual contact tracing and digital tracing systems to help control the epidemic. We investigate the effect of the uptake rate and proportion of contacts recorded by the digital system on key model outputs: the effective reproduction number, the mean outbreak size after 30 days and the probability of elimination. Results. Effective manual contact tracing can reduce the effective reproduction number from 2.4 to around 1.5. The addition of a digital tracing system with a high uptake rate over 75% could further reduce the effective reproduction number to around 1.1. Fully automated digital tracing without manual contact tracing is predicted to be much less effective. Conclusions. For digital tracing systems to make a significant contribution to the control of COVID-19, they need be designed in close conjunction with public health agencies to support and complement manual contact tracing by trained professionals.
Scale development for analyzing the fit of real and virtual world integration: an example of Pokémon Go

Purpose Augmented reality (AR) has become a trend, and the effects of Pokémon Go, the most popular online and mobile game, have been explored in many studies. However, few studies have developed questionnaires of fit to investigate the relationship between the fit and the integration of the game's virtual world and reality. The paper intends to integrate the models of stimulus-organism-response (S-O-R) and information systems success with cognitive fit theory to explore the fit and reactions of users in the integration of real and virtual worlds. Design/methodology/approach Following MacKenzie's scale development, two surveys were conducted. The first survey was conducted to perform a scale development of fit. The second survey was collected from 315 Pokémon Go players to validate the fit scale and it was analyzed via structural equation modeling. Findings The results show that scale development of fit has good reliability and validity. Furthermore, game information quality, game system quality and virtual (Pokémon) characteristics have significantly positive effects on cognitive and emotional fit. Cognitive and emotional fit have significant positive effects on user satisfaction, and user satisfaction has significant positive effects on continued intention to play. The results suggest that maintaining the quality of the game and improving the virtual interface will provide a better fit between the real and virtual worlds, enhancing user satisfaction with the fit as well as their intention for continued use. Originality/value Although fit has been widely studied in various contexts, the application of AR has been rarely discussed. This study develops a scale of fit and takes Pokémon Go as the subject to validate the fit measurement and discuss players' cognition and feelings regarding the game. The authors measure user reactions to different stimuli and explore cognitive and emotional fit as well as the integration of virtual worlds and reality. In sum, to the best of the authors’ knowledge, this is one of the earliest studies to explore and develop a cognitive and emotional fit scale for future researchers and practitioners.
Adaptive time-delayed photonic reservoir computing based on Kalman-filter training

We propose an adaptive time-delayed photonic reservoir computing (RC) structure by utilizing the Kalman filter (KF) algorithm as training approach. Two benchmark tasks, namely the Santa Fe time-series prediction and the nonlinear channel equalization, are adopted to evaluate the performance of the proposed RC structure. The simulation results indicate that with the contribution of adaptive KF training, the prediction and equalization performance for the benchmark tasks can be significantly enhanced, with respect to the conventional RC using a training approach based on the least-squares (LS). Moreover, by introducing a complex mask derived from a bandwidth and complexity enhanced chaotic signal into the proposed RC, the performance of prediction and equalization can be further improved. In addition, it is demonstrated that the proposed RC system can provide a better equalization performance for the parameter-variant wireless channel equalization task, compared with the conventional RC based on LS training. The work presents a potential way to realize adaptive photonic computing.
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking

Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors, which are mainly caused by the phonological or visual similarity.Recently, pre-trained language models (PLMs) promote the progress of CSC task.However, there exists a gap between the learned knowledge of PLMs and the goal of CSC task.PLMs focus on the semantics in text and tend to correct the erroneous characters to semantically proper or commonly used ones, but these aren't the ground-truth corrections.To address this issue, we propose an Error-driven COntrastive Probability Optimization (ECOPO) framework for CSC task.ECOPO refines the knowledge representations of PLMs, and guides the model to avoid predicting these common characters through an error-driven way.Particularly, ECOPO is model-agnostic and it can be combined with existing CSC methods to achieve better performance.Extensive experiments and detailed analyses on SIGHAN datasets demonstrate that ECOPO is simple yet effective.
Graph Enhanced Contrastive Learning for Radiology Findings Summarization

The impression section of a radiology report summarizes the most prominent observation from the findings section and is the most important section for radiologists to communicate to physicians.Summarizing findings is timeconsuming and can be prone to error for inexperienced radiologists, and thus automatic impression generation has attracted substantial attention.With the encoder-decoder framework, most previous studies explore incorporating extra knowledge (e.g., static pre-defined clinical ontologies or extra background information).Yet, they encode such knowledge by a separate encoder to treat it as an extra input to their models, which is limited in leveraging their relations with the original findings.To address the limitation, we propose a unified framework for exploiting both extra knowledge and the original findings in an integrated way so that the critical information (i.e., key words and their relations) can be extracted in an appropriate way to facilitate impression generation.In detail, for each input findings, it is encoded by a text encoder, and a graph is constructed through its entities and dependency tree.Then, a graph encoder (e.g., graph neural networks (GNNs)) is adopted to model relation information in the constructed graph.Finally, to emphasize the key words in the findings, contrastive learning is introduced to map positive samples (constructed by masking non-key words) closer and push apart negative ones (constructed by masking key words).The experimental results on OpenI and MIMIC-CXR confirm the effectiveness of our proposed method.
Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation

Scene Graph Generation (SGG) aims to build a structured representation of a scene using objects and pairwise relationships, which benefits downstream tasks. However, current SGG methods usually suffer from sub-optimal scene graph generation because of the long-tailed distribution of training data. To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation. Specifically, RTPB uses a distributed-based prior bias to improve models' detecting ability on less frequent relationships during training, thus improving the model generalizability on tail categories. In addition, to further explore the contextual information of objects and relationships, we design a contextual encoding backbone network, termed as Dual Transformer (DTrans). We perform extensive experiments on a very popular benchmark, VG150, to demonstrate the effectiveness of our method for the unbiased scene graph generation. In specific, our RTPB achieves an improvement of over 10% under the mean recall when applied to current SGG methods. Furthermore, DTrans with RTPB outperforms nearly all state-of-the-art methods with a large margin. Code is available at https://github.com/ChCh1999/RTPB
Undersampled Multi-Contrast MRI Reconstruction Based on Double-Domain Generative Adversarial Network

Multi-contrast magnetic resonance imaging can provide comprehensive information for clinical diagnosis. However, multi-contrast imaging suffers from long acquisition time, which makes it inhibitive for daily clinical practice. Subsampling k-space is one of the main methods to speed up scan time. Missing k-space samples will lead to inevitable serious artifacts and noise. Considering the assumption that different contrast modalities share some mutual information, it may be possible to exploit this redundancy to accelerate multi-contrast imaging acquisition. Recently, generative adversarial network shows superior performance in image reconstruction and synthesis. Some studies based on k-space reconstruction also exhibit superior performance over conventional state-of-art method. In this study, we propose a cross-domain two-stage generative adversarial network for multi-contrast images reconstruction based on prior full-sampled contrast and undersampled information. The new approach integrates reconstruction and synthesis, which estimates and completes the missing k-space and then refines in image space. It takes one fully-sampled contrast modality data and highly undersampled data from several other modalities as input, and outputs high quality images for each contrast simultaneously. The network is trained and tested on a public brain dataset from healthy subjects. Quantitative comparisons against baseline clearly indicate that the proposed method can effectively reconstruct undersampled images. Even under high acceleration, the network still can recover texture details and reduce artifacts.