Reviewerly/exHarmony huggingface dataset:
Stable, low-error, and calibration-free polarization encoder for free-space quantum communication

Polarization-encoded free-space quantum communication requires a quantum state source featuring fast modulation, long-term stability, and a low intrinsic error rate. Here we present a polarization encoder that, contrary to previous solutions, generates predetermined polarization states with a fixed reference frame in free-space. The proposed device does not require calibration either at the transmitter or at the receiver and achieves long-term stability. A proof-of-concept experiment is also reported, demonstrating a quantum bit error rate lower than 0.2% for several hours without any active recalibration.
Solar image denoising with convolutional neural networks

The topology and dynamics of the solar chromosphere are greatly affected by the presence of magnetic fields. The magnetic field can be inferred by analyzing polarimetric observations of spectral lines. Polarimetric signals induced by chromospheric magnetic fields are, however, particularly weak, and in most cases very close to the detection limit of current instrumentation. Because of this, there are only few observational studies that have successfully reconstructed the three components of the magnetic field vector in the chromosphere. Traditionally, the signal-to-noise ratio of observations has been improved by performing time-averages or spatial averages, but in both cases, some information is lost. More advanced techniques, like principal-component-analysis, have also been employed to take advantage of the sparsity of the observations in the spectral direction. In the present study, we propose to use the spatial coherence of the observations to reduce the noise using deep-learning techniques. We design a neural network that is capable of recovering weak signals under a complex noise corruption (including instrumental artifacts and non-linear post-processing). The training of the network is carried out without a priori knowledge of the clean signals, or an explicit statistical characterization of the noise or other corruption. We only use the same observations as our generative model. The performance of this method is demonstrated on both, synthetic experiments and real data. We show examples of the improvement in typical signals obtained in current telescopes such as the Swedish 1-meter Solar Telescope. The presented method can recover weak signals equally well no matter on what spectral line or spectral sampling is used. It is especially suitable for cases when the wavelength sampling is scarce.
MGCN

Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information. Recent methods like Graph Convolutional Network (GCN) try to consider node attributes (if available) besides node relations and learn node embeddings for unsupervised and semi-supervised tasks on graphs. On the other hand, multi-layer graph analysis has been received attention recently. However, the existing methods for multi-layer graph embedding cannot incorporate all available information (like node attributes). Moreover, most of them consider either type of nodes or type of edges, and they do not treat within and between layer edges differently. In this paper, we propose a method called MGCN that utilizes the GCN for multi-layer graphs. MGCN embeds nodes of multi-layer graphs using both within and between layers relations and nodes attributes. We evaluate our method on the semi-supervised node classification task. Experimental results demonstrate the superiority of the proposed method to other multi-layer and single-layer competitors and also show the positive effect of using cross-layer edges.
A Simple Language Model for Task-Oriented Dialogue

Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response. While such decomposition might suggest a dedicated model for each sub-task, we find a simple, unified approach leads to state-of-the-art performance on the MultiWOZ dataset. SimpleTOD is a simple approach to task-oriented dialogue that uses a single causal language model trained on all sub-tasks recast as a single sequence prediction problem. This allows SimpleTOD to fully leverage transfer learning from pre-trained, open domain, causal language models such as GPT-2. SimpleTOD improves over the prior state-of-the-art by 0.49 points in joint goal accuracy for dialogue state tracking. More impressively, SimpleTOD also improves the main metrics used to evaluate action decisions and response generation in an end-to-end setting for task-oriented dialog systems: inform rate by 8.1 points, success rate by 9.7 points, and combined score by 7.2 points.
An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things

Abstract The application of the traditional single frame character image super-resolution reconstruction method has some problems, such as noise can not be removed completely and anti-interference performance is poor. A new method for the super-resolution reconstruction of single frame character image based on wavelet neural network is proposed. The structure and interface of image acquisition unit of solid state image sensor are designed. Combined with pinhole imaging model and camera self-calibration, image acquisition of Internet of Things is completed. An image degradation model was established to simulate the degradation process of ideal high-resolution image to low-resolution image. Wavelet threshold denoising method is used to remove the noise in a single frame character image and improve the anti-interference performance of the method. The wavelet neural network reflection model is used to reconstruct the single frame feature image and improve the resolution of the image. The experimental results show that the blur degree of the reconstructed image is always less than 5%. In the whole experiment, the accuracy of this method can be maintained at 80% ~ 90%. The image detail retention rate of the research method is relatively stable. With the increase of the number of experimental images, the retention rate of image details remains between 80% and 95%, indicating that the method is effective in practical application.
Interference between independent photonic integrated devices for quantum key distribution

Advances in quantum computing are a rapidly growing threat towards modern cryptography. Quantum key distribution (QKD) provides long-term security without assuming the computational power of an adversary. However, inconsistencies between theory and experiment have raised questions in terms of real-world security, while large and power-hungry commercial systems have slowed wide-scale adoption. Measurement-device-independent QKD (MDI-QKD) provides a method of sharing secret keys that removes all possible detector side-channel attacks which drastically improves security claims. In this Letter, we experimentally demonstrate a key step required to perform MDI-QKD with scalable integrated devices. We show Hong–Ou–Mandel interference between weak coherent states carved from two independent indium phosphide transmitters at 431 MHz with a visibility of 46.5±0.8%. This Letter demonstrates the feasibility of using integrated devices to lower a major barrier towards the adoption of QKD in metropolitan networks.
Dressed states from gauge invariance

A bstract The dressed state formalism enables us to define the infrared finite S -matrix for QED. In the formalism, asymptotic charged states are dressed by clouds of photons. The dressed asymptotic states are originally obtained by solving the dynamics of the asymptotic Hamiltonian in the far past or future region. However, there was an argument that the obtained dressed states are not gauge invariant. We resolve the problem by imposing a correct gauge invariant condition. We show that the dressed states can be obtained just by requiring the gauge invariance of asymptotic states. In other words, Gauss’s law naturally leads to proper asymptotic states for the infrared finite S -matrix. We also discuss the relation between the dressed state formalism and the asymptotic symmetry for QED.
Voice Conversion for Whispered Speech Synthesis

We present an approach to synthesize whisper by applying a handcrafted signal processing recipe and Voice Conversion (VC) techniques to convert normally phonated speech to whispered speech. We investigate using Gaussian Mixture Models (GMM) and Deep Neural Networks (DNN) to model the mapping between acoustic features of normal speech and those of whispered speech. We evaluate naturalness and speaker similarity of the converted whisper on an internal corpus and on the publicly available wTIMIT corpus. We show that applying VC techniques is significantly better than using rule-based signal processing methods and it achieves results that are indistinguishable from copy-synthesis of natural whisper recordings. We investigate the ability of the DNN model to generalize on unseen speakers, when trained with data from multiple speakers. We show that excluding the target speaker from the training set has little or no impact on the perceived naturalness and speaker similarity of the converted whisper. The proposed DNN method is used in the newly released Whisper Mode of Amazon Alexa.
Part-Aware Fine-Grained Object Categorization Using Weakly Supervised Part Detection Network

Fine-grained object categorization aims for distinguishing objects of subordinate categories that belong to the same entry-level object category. It is a rapidly developing subfield in multimedia content analysis. The task is challenging due to the facts that (1) training images with ground-truth labels are difficult to obtain, and (2) variations among different subordinate categories are subtle. It is well established that characterizing features of different subordinate categories are located on local parts of object instances. However, manually annotating object parts requires expertise, which is also difficult to generalize to new fine-grained categorization tasks. In this work, we propose a Weakly Supervised Part Detection Network (PartNet) that is able to detect discriminative local parts for the use of fine-grained categorization. A vanilla PartNet builds on top of a base subnetwork two parallel streams of upper network layers, which respectively compute scores of classification probabilities (over subordinate categories) and detection probabilities (over a specified number of discriminative part detectors) for local regions of interest (RoIs). The image-level prediction is obtained by aggregating element-wise products of these region-level probabilities, and meanwhile diverse part detectors can be learned in an end-to-end fashion under the image-level supervision. To generate a diverse set of RoIs as inputs of PartNet, we propose a simple Discretized Part Proposals module (DPP) that directly targets for proposing candidates of discriminative local parts, with no bridging via object-level proposals. Experiments on benchmark datasets of CUB-200-2011, Oxford Flower 102 and Oxford-IIIT Pet show the efficacy of our proposed method for both discriminative part detection and fine-grained categorization. In particular, we achieve the new state-of-the-art performance on CUB-200-2011 and Oxford-IIIT Pet datasets when ground-truth part annotations are not available.
Quantum circuit design for objective function maximization in gate-model quantum computers

Gate-model quantum computers provide an experimentally implementable architecture for near-term quantum computations. To design a reduced quantum circuit that can simulate a high-complexity reference quantum circuit, an optimization should be taken on the number of input quantum states, on the unitary operations of the quantum circuit, and on the number of output measurement rounds. Besides the optimization of the physical layout of the hardware layer, the quantum computer should also solve difficult computational problems very efficiently. To yield a desired output system, a particular objective function associated with the computational problem fed into the quantum computer should be maximized. The reduced gate structure should be able to produce the maximized value of the objective function. These parallel requirements must be satisfied simultaneously, which makes the optimization difficult. Here, we demonstrate a method for designing quantum circuits for gate-model quantum computers and define the Quantum Triple Annealing Minimization (QTAM) algorithm. The aim of QTAM is to determine an optimal reduced topology for the quantum circuits in the hardware layer at the maximization of the objective function of an arbitrary computational problem.
Measuring nonclassicality with silicon photomultipliers

Detector stochastic deviations from an ideal response can hamper the measurement of quantum properties of light, especially in the mesoscopic regime where photon-number resolution is required. We demonstrate that, by proper analysis of the output signal, nonclassicality of twin-beam states can be detected and exploited with commercial and cost-effective silicon-based photon-number-resolving detectors.
Improved adaptive neuro-fuzzy inference system based on modified glowworm swarm and differential evolution optimization algorithm for medical diagnosis

Medical diagnosis has seen a tremendous advancement in the recent years due to the advent of modern and hybrid techniques that aid in screening and management of the disease. This paper figures a predictive model for detecting neurodegenerative diseases like glaucoma, Parkinson's disease and carcinogenic diseases like breast cancer. The proposed approach focuses on enhancing the efficiency of adaptive neuro-fuzzy inference system (ANFIS) using a modified glowworm swarm optimization algorithm (M-GSO). This algorithm is a global optimization wrapper approach that simulates the collective behavior of glowworms in nature during food search. However, it still suffers from being trapped in local minima. Hence in order to improve glowworm swarm optimization algorithm, differential evolution (DE) algorithm is utilized to enhance the behavior of glowworms. The proposed (DE-GSO-ANFIS) approach estimates suitable prediction parameters of ANFIS by employing DE-GSO algorithm. The outcomes of the proposed model are compared with traditional ANFIS model, genetic algorithm-ANFIS (GA-ANFIS), particle swarm optimization-ANFIS (PSO-ANFIS), lion optimization algorithm-ANFIS (LOA-ANFIS), differential evolution-ANFIS (DE-ANFIS) and glowworm swarm optimization (GSO). Experimental results depict better performance and superiority of the DE-GSO-ANFIS over the similar methods in predicting medical disorders.
Answering Questions on COVID-19 in Real-Time

The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it.One reason why the fight is difficult is due to the lack of information and knowledge.In this work, we outline our effort to contribute to shrinking this knowledge vacuum by creating COVIDASK, 1 a question answering (QA) system that combines biomedical text mining and QA techniques to provide answers to questions in real-time.Our system also leverages information retrieval (IR) approaches to provide entity-level answers that are complementary to QA models.Evaluation of COVIDASK is carried out by using a manually created dataset called COVID-19 Questions which is based on information from various sources, including the CDC and the WHO.We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19, but for future pandemics as well.
User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

Abstract Collaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.
Research – A blockchain of knowledge?

This perspective proposes that, by virtue of its sophisticated trust and consensus finding mechanisms, blockchain has the clear potential to substantially upgrade the processes and organization traditionally underpinning academic science and commercial technology development comprising funding, project delivery, generation of intellectual property, documentation and publication. For supporting this hypothesis, striking analogies between the concepts underlying blockchain technology with research are identified, and applied to the generation of verified knowledge in science and technology development. It is then elaborated how a blockchain-enabled token economy can efficiently and transparently incentivize and coordinate an integrative and community-inclusive participatory approach to fuel crowdsourcing of collective intelligence for contributing ideas, work, infrastructure, funding, data, validation, management, assessment, governance, arbitration and exploitation of projects. Quality, credibility and direction of projects are optimized by demanding collateral “skin-in-the-game” from contributors based on blockchain-enabled staking, reputation systems and prediction markets. This way research progress emerges as a chain of community generated and independently vetted blocks of scientific knowledge; these new blocks are concatenated with the state-of-the-art according to transparent consensus mechanisms.
Serverless Computing-Where Are We Now, and Where Are We Heading?

Serverless systems are becoming more popular and being adopted by a large number of companies. Serverless computing is a new paradigm that provides a platform to efficiently develop and deploy applications to the market without having to manage any underlying infrastructure.<sup>1,2</sup> However, serverless solutions introduce several new issues that companies need to tackle. In this article, we introduce serverless technology and its evolution, discuss its benefits and issues, and describe future challenges that researchers should address.
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection

Academia and industry have developed machine learning and natural language processing models to detect online hate speech automatically.However, most of these existing methods adopt a supervised approach that heavily depends on labeled datasets for training.This results in the methods' poor detection performance of the hate speech class as the training datasets are highly imbalanced.In this paper, we propose HateGAN, a deep generative reinforcement learning model, which addresses the challenge of imbalance class by augmenting the dataset with hateful tweets.We conduct extensive experiments to augment two commonly-used hate speech detection datasets with the HateGAN generated tweets.Our experiment results show that HateGAN improves the detection performance of the hate speech class regardless of the classifiers and datasets used in the detection task.Specifically, we observe an average 5% improvement for the hate class F1 scores across all state-of-the-art hate speech classifiers.We also conduct case studies to empirically examine the HateGAN generated hate speeches and show that the generated tweets are diverse, coherent, and relevant to hate speech detection.
Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identification

The objective of unsupervised person re-identification (Re-ID) is to learn discriminative features without labor-intensive identity annotations. State-of-the-art unsupervised Re-ID methods assign pseudo labels to unlabeled images in the target domain and learn from these noisy pseudo labels. Recently introduced Mean Teacher Model is a promising way to mitigate the label noise. However, during the training, self-ensembled teacher-student networks quickly converge to a consensus which leads to a local minimum. We explore the possibility of using an asymmetric structure inside neural network to address this problem. First, asymmetric branches are proposed to extract features in different manners, which enhances the feature diversity in appearance signatures. Then, our proposed cross-branch supervision allows one branch to get supervision from the other branch, which transfers distinct knowledge and enhances the weight diversity between teacher and student networks. Extensive experiments show that our proposed method can significantly surpass the performance of previous work on both unsupervised domain adaptation and fully unsupervised Re-ID tasks.
Protocols for Creating and Distilling Multipartite GHZ States With Bell Pairs

The distribution of high-quality Greenberger-Horne-Zeilinger (GHZ) states is at the heart of many quantum communication tasks, ranging from extending the baseline of telescopes to secret sharing. They also play an important role in error-correction architectures for distributed quantum computation, where Bell pairs can be leveraged to create an entangled network of quantum computers. We investigate the creation and distillation of GHZ states out of non-perfect Bell pairs over quantum networks. In particular, we introduce a heuristic dynamic programming algorithm to optimize over a large class of protocols that create and purify GHZ states. All protocols considered use a common framework based on measurements of non-local stabilizer operators of the target state (i.e., the GHZ state), where each non-local measurement consumes another (non-perfect) entangled state as a resource. The new protocols outperform previous proposals for scenarios without decoherence and local gate noise. Furthermore, the algorithms can be applied for finding protocols for any number of parties and any number of entangled pairs involved.