Reviewerly/exHarmony huggingface dataset:
Texture Variation Adaptive Image Denoising With Nonlocal PCA

Image textures, as a kind of local variations, provide important information for the human visual system. Many image textures, especially the small-scale or stochastic textures, are rich in high-frequency variations, and are difficult to be preserved. Current state-of-the-art denoising algorithms typically adopt a nonlocal approach consisting of image patch grouping and group-wise denoising filtering. To achieve a better image denoising while preserving the variations in texture, we first adaptively group high correlated image patches with the same kinds of texture elements (texels) via an adaptive clustering method. This adaptive clustering method is applied in an over-clustering-and-iterative-merging approach, where its noise robustness is improved with a custom merging threshold relating to the noise level and cluster size. For texture-preserving denoising of each cluster, considering that the variations in texture are captured and wrapped in not only the between-dimension energy variations but also the within-dimension variations of PCA transform coefficients, we further propose a PCA-transform-domain variation adaptive filtering method to preserve the local variations in textures. Experiments on natural images show the superiority of the proposed transform-domain variation adaptive filtering to traditional PCA-based hard or soft threshold filtering. As a whole, the proposed denoising method achieves a favorable texture-preserving performance both quantitatively and visually, especially for irregular textures, which is further verified in camera raw image denoising.
Quantum algorithms with local particle-number conservation: Noise effects and error correction

Quantum circuits with local particle-number conservation restrict the quantum computation to a subspace of the Hilbert space of the qubit register. In a noiseless or fault-tolerant quantum computation, such quantities are preserved. In the presence of noise, however, the evolution's symmetry could be broken and nonvalid states could be sampled at the end of the computation. On the other hand, the restriction to a subspace in the ideal case suggests the possibility of more resource-efficient error-mitigation techniques for circuits preserving symmetries that are not possible for general circuits. Here, we analyze the probability of staying in such symmetry-preserved subspaces under noise, providing an exact formula for local depolarizing noise. We apply our findings to benchmark, under depolarizing noise, the symmetry robustness of the XY quantum alternating operator Ansatz, which has local particle-number conserving symmetries and is a special case of the quantum alternating operator Ansatz. We also analyze the influence of the choice of encoding the problem on the symmetry robustness of the algorithm and discuss a simple adaption of the bit-flip code to correct for symmetry-breaking errors with reduced resources.
Evidence Localization for Pathology Images Using Weakly Supervised Learning

Despite deep convolutional neural networks boost the performance of image classification and segmentation in digital pathology analysis, they are usually weak in interpretability for clinical applications or require heavy annotations to achieve object localization. To overcome this problem, we propose a weakly supervised learning-based approach that can effectively learn to localize the discriminative evidence for a diagnostic label from weakly labeled training data. Experimental results show that our proposed method can reliably pinpoint the location of cancerous evidence supporting the decision of interest, while still achieving a competitive performance on glimpse-level and slide-level histopathologic cancer detection tasks.
Circuit-Based Quantum Random Access Memory for Classical Data With Continuous Amplitudes

Loading data in a quantum device is required in several quantum computing applications. Without an efficient loading procedure, the cost to initialize the algorithms can dominate the overall computational cost. A circuit-based quantum random access memory named FF-QRAM can load <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -bit patterns with computational cost <inline-formula><tex-math notation="LaTeX">$O(CMn)$</tex-math></inline-formula> to load continuous data where <inline-formula><tex-math notation="LaTeX">$C$</tex-math></inline-formula> depends on the data distribution. In this article, we propose a strategy to load continuous data without post-selection with computational cost <inline-formula><tex-math notation="LaTeX">$O(Mn$</tex-math></inline-formula> ). The proposed method is based on the probabilistic quantum memory, a strategy to load binary data in quantum devices, and the FF-QRAM using standard quantum gates, and is suitable for noisy intermediate-scale quantum computers.
Nonlinear semiclassical dynamics of the unbalanced, open Dicke model

In recent years there have been significant advances in the study of many-body interactions between atoms and light confined to optical cavities. One model that has received widespread attention of late is the Dicke model, which under certain conditions exhibits a quantum phase transition to a state in which the atoms collectively emit light into the cavity mode, known as superradiance. We consider a generalization of this model that features independently controllable strengths of the corotating and counter-rotating terms of the interaction Hamiltonian. We study this system in the semiclassical (mean field) limit, i.e., neglecting the role of quantum fluctuations. Under this approximation, the model is described by a set of nonlinear differential equations, which determine the system's semiclassical evolution. By taking a dynamical systems approach, we perform a comprehensive analysis of these equations to reveal an abundance of complex dynamics. Examples of the phenomena that we observe are the emergence of superradiant oscillations arising due to Hopf bifurcations, and the appearance of a pair of chaotic attractors arising from period-doubling cascades, followed by their collision to form a single, larger chaotic attractor via a sequence of infinitely many homoclinic bifurcations. Moreover, we find that a flip of the collective spin can result in the sudden emergence of chaotic dynamics. Overall, we provide a comprehensive road map of the possible dynamics that arise in the unbalanced, open Dicke model in the form of a phase diagram in the plane of the two interaction strengths. Hence, we lay out the foundations to make further advances in the study of the fingerprint of semiclassical chaos when considering the master equation of the unbalanced Dicke model, thus, providing the possibility of studying a manifestation of quantum chaos in a specific, experimentally realizable system.11 MoreReceived 14 April 2020Accepted 2 July 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.033131Published 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 AreasAtoms, ions, & molecules in cavitiesCavity quantum electrodynamicsChaosPhase transitionsSuperradiance & subradianceTechniquesChaos & nonlinear dynamicsDicke modelSemiclassical methodsAtomic, Molecular & OpticalNonlinear Dynamics
A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese

Semantic parsing is an important NLP task. However, Vietnamese is a low-resource language in this research area. In this paper, we present the first public large-scale Text-to-SQL semantic parsing dataset for Vietnamese. We extend and evaluate two strong semantic parsing baselines EditSQL (Zhang et al., 2019) and IRNet (Guo et al., 2019) on our dataset. We compare the two baselines with key configurations and find that: automatic Vietnamese word segmentation improves the parsing results of both baselines; the normalized pointwise mutual information (NPMI) score (Bouma, 2009) is useful for schema linking; latent syntactic features extracted from a neural dependency parser for Vietnamese also improve the results; and the monolingual language model PhoBERT for Vietnamese (Nguyen and Nguyen, 2020) helps produce higher performances than the recent best multilingual language model XLM-R (Conneau et al., 2020).
Multiparticle Interference of Pairwise Distinguishable Photons

One of the central principles of quantum mechanics is that if there are multiple paths that lead to the same event and there is no way to distinguish between them, interference occurs. It is often assumed that distinguishing information in the preparation, evolution, or measurement of a system is sufficient to destroy interference. However, it is still possible for photons in distinguishable, separable states to interfere due to the indistinguishability of paths corresponding to possible exchange processes. Here we experimentally measure an interference signal that depends only on the multiparticle interference of four photons in a four-port interferometer despite pairs of them occupying distinguishable states.
Robust image watermarking based on generative adversarial network

Digital watermark embeds information bits into digital cover such as images and videos to prove the creator's ownership of his work. In this paper, we propose a robust image watermark algorithm based on a generative adversarial network. This model includes two modules, generator and adversary. Generator is mainly used to generate images embedded with watermark, and decode the image damaged by noise to obtain the watermark. Adversary is used to discriminate whether the image is embedded with watermark and damage the image by noise. Based on the model Hidden (hiding data with deep networks), we add a high-pass filter in front of the discriminator, making the watermark tend to be embedded in the mid-frequency region of the image. Since the human visual system pays more attention to the central area of the image, we give a higher weight to the image center region, and a lower weight to the edge region when calculating the loss between cover and embedded image. The watermarked image obtained by this scheme has a better visual performance. Experimental results show that the proposed architecture is more robust against noise interference compared with the state-of-art schemes.
Dilated-Scale-Aware Category-Attention ConvNet for Multi-Class Object Counting

Object counting aims to estimate the number of objects in images. The leading counting approaches focus on the single category counting task and achieve impressive performance. Note that there are multiple categories of objects in real scenes. Multi-class object counting expands the scope of application of object counting task. The multi-target detection task can achieve multi-class object counting in some scenarios. However, it requires the dataset annotated with bounding boxes. Compared with the point annotations in mainstream object counting issues, the coordinate box-level annotations are more difficult to obtain. In this paper, we propose a simple yet efficient counting network based on point-level annotations. Specifically, we first change the traditional output channel from one to the number of categories to achieve multiclass counting. Since all categories of objects use the same feature extractor in our proposed framework, their features will interfere mutually in the shared feature space. We further design a multi-mask structure to suppress harmful interaction among objects. Extensive experiments on the challenging benchmarks illustrate that the proposed method achieves state-of-the-art counting performance.
Average biased ReLU based CNN descriptor for improved face retrieval

The convolutional neural networks (CNN), including AlexNet, GoogleNet, VGGNet, etc. extract features for many computer vision problems which are very discriminative. The trained CNN model over one dataset performs reasonably well whereas on another dataset of similar type the hand-designed feature descriptor outperforms the same trained CNN model. The Rectified Linear Unit (ReLU) layer discards some values in order to introduce the non-linearity. In this paper, it is proposed that the discriminative ability of deep image representation using trained model can be improved by Average Biased ReLU (AB-ReLU) at the last few layers. Basically, AB-ReLU improves the discriminative ability in two ways: 1) it exploits some of the discriminative and discarded negative information of ReLU and 2) it also neglects the irrelevant and positive information used in ReLU. The VGGFace model trained in MatConvNet over the VGG-Face dataset is used as the feature descriptor for face retrieval over other face datasets. The proposed approach is tested over six challenging, unconstrained and robust face datasets (PubFig, LFW, PaSC, AR, FERET and ExtYale) and also on a large scale face dataset (PolyUNIR) in retrieval framework. It is observed that the AB-ReLU outperforms the ReLU when used with a pre-trained VGGFace model over the face datasets. The validation error by training the network after replacing all ReLUs with AB-ReLUs is also observed to be favorable over each dataset. The AB-ReLU even outperforms the state-of-the-art activation functions, such as Sigmoid, ReLU, Leaky ReLU and Flexible ReLU over all seven face datasets.
Distributed Quantum Computing and Network Control for Accelerated VQE

Interconnecting small quantum computers will be essential in the future for creating largescale, robust quantum computers.Methods for distributing monolithic quantum algorithms efficiently are, thus, needed.In this article, we consider an approach for distributing the accelerated variational quantum eigensolver algorithm over arbitrary sized-in terms of number of qubits-distributed quantum computers.We consider approaches for distributing qubit assignments of the Ansatz states required to estimate the expectation value of Hamiltonian operators in quantum chemistry in a parallelized computation and provide a systematic approach to generate distributed quantum circuits for distributed quantum computing.Moreover, we propose an architecture for a distributed quantum control system in the context of centralized and decentralized network control.
Dynamical Phases and Quantum Correlations in an Emitter-Waveguide System with Feedback

We investigate the creation and control of emergent collective behavior and quantum correlations using feedback in an emitter-waveguide system using a minimal model. Employing homodyne detection of photons emitted from a laser-driven emitter ensemble into the modes of a waveguide allows for the generation of intricate dynamical phases. In particular, we show the emergence of a time-crystal phase, the transition to which is controlled by the feedback strength. Feedback enables furthermore the control of many-body quantum correlations, which become manifest in spin squeezing in the emitter ensemble. Developing a theory for the dynamics of fluctuation operators we discuss how the feedback strength controls the squeezing and investigate its temporal dynamics and dependence on system size. The largely analytical results allow to quantify spin squeezing and fluctuations in the limit of large number of emitters, revealing critical scaling of the squeezing close to the transition to the time crystal. Our study corroborates the potential of integrated emitter-waveguide systems---which feature highly controllable photon emission channels---for the exploration of collective quantum phenomena and the generation of resources, such as squeezed states, for quantum enhanced metrology.
Monitoring People’s Emotions and Symptoms from Arabic Tweets during the COVID-19 Pandemic

Coronavirus-19 (COVID-19) started from Wuhan, China, in late December 2019. It swept most of the world’s countries with confirmed cases and deaths. The World Health Organization (WHO) declared the virus a pandemic on 11 March 2020 due to its widespread transmission. A public health crisis was declared in specific regions and nation-wide by governments all around the world. Citizens have gone through a wide range of emotions, such as fear of shortage of food, anger at the performance of governments and health authorities in facing the virus, sadness over the deaths of friends or relatives, etc. We present a monitoring system of citizens’ concerns using emotion detection in Twitter data. We also track public emotions and link these emotions with COVID-19 symptoms. We aim to show the effect of emotion monitoring on improving people’s daily health behavior and reduce the spread of negative emotions that affect the mental health of citizens. We collected and annotated 5.5 million tweets in the period from January to August 2020. A hybrid approach combined rule-based and neural network techniques to annotate the collected tweets. The rule-based technique was used to classify 300,000 tweets relying on Arabic emotion and COVID-19 symptom lexicons while the neural network was used to expand the sample tweets that were annotated using the rule-based technique. We used long short-term memory (LSTM) deep learning to classify all of the tweets into six emotion classes and two types (symptom and non-symptom tweets). The monitoring system shows that most of the tweets were posted in March 2020. The anger and fear emotions have the highest number of tweets and user interactions after the joy emotion. The results of user interaction monitoring show that people use likes and replies to interact with non-symptom tweets while they use re-tweets to propagate tweets that mention any of COVID-19 symptoms. Our study should help governments and decision-makers to dispel people’s fears and discover new symptoms associated with the symptoms that were declared by the WHO. It can also help in the understanding of people’s mental and emotional issues to address them before the impact of disease anxiety becomes harmful in itself.
Natural Language Video Localization: A Revisit in Span-based Question Answering Framework

Natural Language Video Localization (NLVL) aims to locate a target moment from an untrimmed video that semantically corresponds to a text query. Existing approaches mainly solve the NLVL problem from the perspective of computer vision by formulating it as ranking, anchor, or regression tasks. These methods suffer from large performance degradation when localizing on long videos. In this work, we address the NLVL from a new perspective, i.e., span-based question answering (QA), by treating the input video as a text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework (named VSLBase), to address NLVL. VSLNet tackles the differences between NLVL and span-based QA through a simple yet effective query-guided highlighting (QGH) strategy. QGH guides VSLNet to search for the matching video span within a highlighted region. To address the performance degradation on long videos, we further extend VSLNet to VSLNet-L by applying a multi-scale split-and-concatenation strategy. VSLNet-L first splits the untrimmed video into short clip segments; then, it predicts which clip segment contains the target moment and suppresses the importance of other segments. Finally, the clip segments are concatenated, with different confidences, to locate the target moment accurately. Extensive experiments on three benchmark datasets show that the proposed VSLNet and VSLNet-L outperform the state-of-the-art methods; VSLNet-L addresses the issue of performance degradation on long videos. Our study suggests that the span-based QA framework is an effective strategy to solve the NLVL problem.
Simple quantum key distribution using a stable transmitter-receiver scheme

Quantum key distribution (QKD) is a technology that allows secure key exchange between two distant users. A widespread adoption of QKD requires the development of simple, low-cost, and stable systems. However, implementation of the current QKD requires a complex self-alignment process during the initial stage and an additional hardware to compensate the environmental disturbances. In this study, we present the implementation of a simple QKD with the help of a stable transmitter-receiver scheme, which simplifies the self-alignment and is robust enough to withstand environmental disturbances. In case of the stability test, the implementation system is able to remain stable for 48 h and exhibits an average quantum bit error rate of less than 1% without any feedback control. The scheme is also tested over a fiber spool, obtaining a stable and secure finite key rate of 7.32k bits per second over a fiber spool extending up to 75 km. The demonstrated long-term stability and obtained secure key rate prove that our method of implementation is a promising alternative for practical QKD systems, in particular, for CubeSat platform and satellite applications.
The Effect of Motion Direction and Eccentricity on Vection, VR Sickness and Head Movements in Virtual Reality

Virtual Reality (VR) experienced through head-mounted displays often leads to vection, discomfort and sway in the user. This study investigated the effect of motion direction and eccentricity on these three phenomena using optic flow patterns displayed using the Valve Index. Visual motion stimuli were presented in the centre, periphery or far periphery and moved either in depth (back and forth) or laterally (left and right). Overall vection was stronger for motion in depth compared to lateral motion. Additionally, eccentricity primarily affected stimuli moving in depth with stronger vection for more peripherally presented motion patterns compared to more central ones. Motion direction affected the various aspects of VR sickness differently and modulated the effect of eccentricity on VR sickness. For stimuli moving in depth far peripheral presentation caused more discomfort, whereas for lateral motion the central stimuli caused more discomfort. Stimuli moving in depth led to more head movements in the anterior-posterior direction when the entire visual field was stimulated. Observers demonstrated more head movements in the anterior-posterior direction compared to the medio-lateral direction throughout the entire experiment independent of motion direction or eccentricity of the presented moving stimulus. Head movements were elicited on the same plane as the moving stimulus only for stimuli moving in depth covering the entire visual field. A correlation showed a positive relationship between dizziness and vection duration and between general discomfort and sway. Identifying where in the visual field motion presented to an individual causes the least amount of VR sickness without losing vection and presence can guide development for Virtual Reality games, training and treatment programmes.
Universal quantum computing using single-particle discrete-time quantum walk

Quantum walk has been regarded as a primitive to universal quantum computation. By using the operations required to describe the single particle discrete-time quantum walk on a position space we demonstrate the realization of the universal set of quantum gates on two- and three-qubit systems. The idea is to utilize the effective Hilbert space of the single qubit and the position space on which it evolves in order to realize multi-qubit states and universal set of quantum gates on them. Realization of many non-trivial gates and engineering arbitrary states is simpler in the proposed quantum walk model when compared to the circuit based model of computation. We will also discuss the scalability of the model and some propositions for using lesser number of qubits in realizing larger qubit systems.