Reviewerly/exHarmony huggingface dataset:
Simulating open quantum dynamics on an NMR quantum processor using the Sz.-Nagy dilation algorithm

We experimentally implement the Sz.-Nagy dilation algorithm to simulate open quantum dynamics on a nuclear magnetic resonance quantum processor. The Sz.-Nagy algorithm enables the simulation of the dynamics of an $n$-qubit system using $n+1$ qubits. We experimentally simulate the action of three nonunitary processes, namely, a phase damping channel acting independently on two qubits, a two-qubit correlated amplitude damping channel, and a magnetic-field-gradient pulse acting on an ensemble of two coupled nuclear spin-$\frac{1}{2}$ particles. To evaluate the quality of the experimentally simulated quantum process, we perform convex-optimization-based full quantum process tomography to reconstruct the quantum process from the experimental data and compare it with the target quantum process to be simulated.
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems

A central issue addressed by the rapidly growing research area of eXplainable Artificial Intelligence (XAI) is to provide methods to give explanations for the behaviours of Machine Learning (ML) non-interpretable models after the training. Recently, it is becoming more and more evident that new directions to create better explanations should take into account what a good explanation is to a human user. This paper suggests taking advantage of developing an XAI framework that allows producing multiple explanations for the response of image a classification system in terms of potentially different middle-level input features. To this end, we propose an XAI framework able to construct explanations in terms of input features extracted by auto-encoders. We start from the hypothesis that some autoencoders, relying on standard data representation approaches, could extract more salient and understandable input properties, which we call here \textit{Middle-Level input Features} (MLFs), for a user with respect to raw low-level features. Furthermore, extracting different types of MLFs through different type of autoencoders, different types of explanations for the same ML system behaviour can be returned. We experimentally tested our method on two different image datasets and using three different types of MLFs. The results are encouraging. Although our novel approach was tested in the context of image classification, it can potentially be used on other data types to the extent that auto-encoders to extract humanly understandable representations can be applied.
Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services

Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods. However, they still face de-biasing challenges such as exposure bias and cold-start problem, where circulations of machine learning training on human interaction history leads algorithms to repeatedly suggest exposed items while ignoring less-active ones. Additional problems exist in multi-scenario platforms, e.g. appropriate data fusion from subsidiary scenarios, which we observe could be alleviated through graph structured data integration via message passing. In this paper, we present a multi-graph structured multi-scenario recommendation solution, which encapsulates interaction data across scenarios with multi-graph and obtains representation via graph learning. Extensive offline and online experiments on real-world datasets are conducted where the proposed method demonstrates an increase of 0.63% and 0.71% in CTR and Video Views per capita on new users over deployed set of baselines and outperforms regular method in increasing the number of outer-scenario videos by 25% and video watches by 116%, validating its superiority in activating cold videos and enriching target recommendation.
Navigation Control Method of Indoor Mobile Robot Based on Visual Servo

In order to improve the navigation control effect of indoor mobile robot, this paper studies the navigation control method of indoor mobile robot combined with visual servo technology. For the problem that the target may be out of the camera’s field of view during the servo process, a control algorithm with field-of-view constraints is proposed, which assigns a certain weight to the image feature error at each moment. Moreover, this paper realizes the servo process that the image features are always located in the field of view by setting the variable values involved in the weight value calculation method. Through the simulation test, it is verified that the indoor mobile robot navigation control method based on visual servo has certain effects.
Online knowledge distillation network for single image dehazing

Abstract Single image dehazing, as a key prerequisite of high-level computer vision tasks, catches more and more attentions. Traditional model-based methods recover haze-free images via atmospheric scattering model, which achieve favorable dehazing effect but endure artifacts, halos, and color distortion. By contrast, recent learning-based methods dehaze images by a model-free way, which achieve better color fidelity but tend to acquire under-dehazed results due to lacking of knowledge guiding. To combine these merits, we propose a novel online knowledge distillation network for single image dehazing named OKDNet. Specifically, the proposed OKDNet firstly preprocesses hazy images and acquires abundant shared features by a multiscale network constructed with attention guided residual dense blocks. After that, these features are sent to different branches to generate two preliminary dehazed images via supervision training: one branch acquires dehazed images via the atmospheric scattering model; another branch directly establishes the mapping relationship between hazy images and clear images, which dehazes images by a model-free way. To effectively fuse useful information from these two branches and acquire a better dehazed results, we propose an efficient feature aggregation block consisted of multiple parallel convolutions with different receptive. Moreover, we adopt a one-stage knowledge distillation strategy named online knowledge distillation to joint optimization of our OKDNet. The proposed OKDNet achieves superior performance compared with state-of-the-art methods on both synthetic and real-world images with fewer model parameters. Project website: https://github.com/lanyunwei/OKDNet .
Stream-Based Active Learning with Verification Latency in Non-stationary Environments

Data stream classification is an important problem in the field of machine learning. Due to the non-stationary nature of the data where the underlying distribution changes over time (concept drift), the model needs to continuously adapt to new data statistics. Stream-based Active Learning (AL) approaches address this problem by interactively querying a human expert to provide new data labels for the most recent samples, within a limited budget. Existing AL strategies assume that labels are immediately available, while in a real-world scenario the expert requires time to provide a queried label (verification latency), and by the time the requested labels arrive they may not be relevant anymore. In this article, we investigate the influence of finite, time-variable, and unknown verification delay, in the presence of concept drift on AL approaches. We propose PRopagate (PR), a latency independent utility estimator which also predicts the requested, but not yet known, labels. Furthermore, we propose a drift-dependent dynamic budget strategy, which uses a variable distribution of the labelling budget over time, after a detected drift. Thorough experimental evaluation, with both synthetic and real-world non-stationary datasets, and different settings of verification latency and budget are conducted and analyzed. We empirically show that the proposed method consistently outperforms the state-of-the-art. Additionally, we demonstrate that with variable budget allocation in time, it is possible to boost the performance of AL strategies, without increasing the overall labeling budget.
Using FIWARE and blockchain in smart cities solutions

Nowadays, Blockchain has been widely used to store decentralized and secure transactions involving cryptocurrency, e.g., Bitcoin, Ethereum, etc. However, Blockchain can also store other types of information besides monetary transactions. On the other hand, innovative solutions for smart cities are concerned with how services and information can be safely stored and shared. For this reason, smart city systems can benefit from using Blockchain to integrate their data and services. These smart solutions also demand consistency and standardization across the industry. However, this Blockchain integration varies according to its implementation. FIWARE, a framework of an open-source platform for smart solutions, adopts NGSI Standards (Context Information Management (CIM); NGSI-LD API: Tech. Rep., CIM and ETSI Industry Specification Group (ISG), 2020) to enable the integration of components and provides the basis for interoperability and portability among smart solutions. Unfortunately, FIWARE does not support any integration with Blockchain technology. Hence, this paper proposes a set of new components to allow FIWARE to be integrated with Blockchain technology. With these proposed components, it is possible to support Blockchain technology with smart city applications via the FIWARE platform. For instance, we have designed and implemented a FIWARE Blockchain adapter to submit/listen to transactions from/to FIWARE Context Broker to/from any Blockchain implementation without human intervention. In addition, we present a global post-pandemic vaccination case study to evaluate the proposed approach in the Smart City context.
Multicenter, Multivendor Validation of an FDA-approved Algorithm for Mammography Triage

Artificial intelligence (AI)-based triage algorithms may improve cancer detection and expedite radiologist workflow. To this end, the performance of a commercial AI-based triage algorithm on screening mammograms was evaluated across breast densities and lesion types.This retrospective, IRB-exempt, multicenter, multivendor study examined 1255 screening 4-view mammograms (400 positive and 855 negative studies). Images were anonymized by providing institutions and analyzed by a commercially available AI algorithm (cmTriage, CureMetrix, La Jolla, CA) that performed retrospective triage at the study level by flagging exams as "suspicious" or not. Sensitivities and specificities with confidence intervals were derived from area under the curve (AUC) calculations.The algorithm demonstrated an AUC of 0.95 (95% CI: 0.94-0.96) for case identification. Area under the curve held across densities (0.95) and lesion types (masses: 0.94 [95% CI: 0.92-0.96] or microcalcifications: 0.97 [95% CI: 0.96-0.99]). The algorithm has a default sensitivity of 93% (95% CI: 95.6%-90.5%) with specificity of 76.3% (95% CI: 79.2%-73.4%). To evaluate real-world performance, a sensitivity of 86.9% (95% CI: 83.6%-90.2%) was tested, as observed for practicing radiologists by the Breast Cancer Surveillance Consortium (BCSC) study. The resulting specificity was 88.5% (95% CI: 86.4%-90.7%), similar to the BCSC specificity of 88.9%, indicating performance comparable to real-world results.When tested for lesion detection, an AI-based triage software can perform at the level of practicing radiologists. Drawing attention to suspicious exams may improve reader specificity and help streamline radiologist workflow, enabling faster turnaround times and improving care.
A Survey on the Integration of Blockchain and IoT: Challenges and Opportunities

Since Satoshi Nakamoto first introduced the blockchain as an open-source project for secure financial transactions, it has attracted the scientific community’s interest, paving the way for addressing problems in domains other than cryptocurrencies, one of them being the Internet of Things (IoT). However, to demonstrate this potential, a clear understanding of blockchain technology and its suitability to meet the IoT’s underlying security requirements is required. To accomplish this goal, this study intends to provide a coherent and comprehensive survey on blockchain integration with IoT identifying the limitations and benefits of Blockchain in IoT applications. The survey presented an overview of blockchain and IoT and illustrated their principal architecture. The state-of-the-art blockchain efforts in different IoT domains are also reviewed. Then presents the current challenges of integrating blockchain and IoT and looks at the recently proposed solutions. Finally, the potential future research directions for integrating blockchain and IoT is discussed. Based on the study’s findings, it is hoped that this survey will serve as a reference and source of motivation for future research directions in this area.
Mobile service robots for the operating room wing: balancing cost and performance by optimizing robotic fleet size and composition

Integrating fleets of mobile service robots into the operating room wing (OR wing) has the potential to help overcome staff shortages and reduce the amount of dull or unhealthy tasks for humans. However, the OR wing has been little studied in this regard and the requirements for realizing this vision have not yet been fully identified. This includes fundamental aspects such as fleet size and composition, which we have now studied comprehensively for the first time.Using simulation, 150 different scenarios with varying fleet compositions, robot speeds and workloads were studied for a setup based on a real-life OR wing. The simulation included battery recharging cycles and queueing due to shared resources.For all simulated scenarios we report results regarding total duration of execution, average task response times and fleet utilization. The relationship between these performance measures and global scenario parameters-such as fleet size, fleet composition, robot velocity and the number of operating rooms to be served-is visualized.Our simulation-based studies have proven to be a valuable tool for individualized dimensioning of mobile robotic fleets, based on realistic workflows and environmental models. Thereby, important implications for future developments of mobile robots have been identified and a basis of decision-making regarding fleet size, fleet composition, robot capabilities and robot velocities can be provided. Due to costs, space limitations and safety requirements, these aspects must be carefully considered to successfully integrate mobile robotic technology into real-world OR wing environments.
Low-Resource Adversarial Domain Adaptation for Cross-modality Nucleus Detection

Due to domain shifts, deep cell/nucleus detection models trained on one microscopy image dataset might not be applicable to other datasets acquired with different imaging modalities. Unsupervised domain adaptation (UDA) based on generative adversarial networks (GANs) has recently been exploited to close domain gaps and has achieved excellent nucleus detection performance. However, current GAN-based UDA model training often requires a large amount of unannotated target data, which may be prohibitively expensive to obtain in real practice. Additionally, these methods have significant performance degradation when using limited target training data. In this paper, we study a more realistic yet challenging UDA scenario, where (unannotated) target training data is very scarce, a low-resource case rarely explored for nucleus detection in previous work. Specifically, we augment a dual GAN network by leveraging a task-specific model to supplement the target-domain discriminator and facilitate generator learning with limited data. The task model is constrained by cross-domain prediction consistency to encourage semantic content preservation for image-to-image translation. Next, we incorporate a stochastic, differentiable data augmentation module into the task-augmented GAN network to further improve model training by alleviating discriminator overfitting. This data augmentation module is a plug-and-play component, requiring no modification of network architectures or loss functions. We evaluate the proposed low-resource UDA method for nucleus detection on multiple public cross-modality microscopy image datasets. With a single training image in the target domain, our method significantly outperforms recent state-of-the-art UDA approaches and delivers very competitive or superior performance over fully supervised models trained with real labeled target data.
Federated Fuzzy Neural Network With Evolutionary Rule Learning

Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local data are non-independent and identically distributed (non-IID). In this paper, we propose a federated fuzzy neural network (FedFNN) with evolutionary rule learning (ERL) to cope with non-IID issues as well as data uncertainties. The FedFNN maintains a global set of rules in a server and a personalized subset of these rules for each local client. ERL is inspired by the theory of biological evolution; it encourages rule variations while activating superior rules and deactivating inferior rules for local clients with non-IID data. Specifically, ERL consists of two stages in an iterative procedure: a rule cooperation stage that updates global rules by aggregating local rules based on their activation statuses and a rule evolution stage that evolves the global rules and updates the activation statuses of the local rules. This procedure improves both the generalization and personalization of the FedFNN for dealing with non-IID issues and data uncertainties. Extensive experiments conducted on a range of datasets demonstrate the superiority of the FedFNN over state-of-the-art methods.
Iterative correction method of a retrace error in interferometry

In interferometer measurements, the inconsistency of the optical range through which the reference and test lights pass introduces a retrace error in the phase measurement. In this study, we propose an iterative retrace error correction method in interferometry. A black-box model is established based on the linear and squared relationships between the retrace error and the tilt of the testing surface. The error correction phase is obtained using the least-squares method; thereafter, the global tilt is determined to iteratively correct the retrace error. The root mean square (RMS) of the residuals was &gt; 3.2 × 10 −5 λ, &gt;6.4 × 10 −3 λ, and &gt;1.4 × 10 −3 λ in the simulation, experimentally computed retrace error correction in the planar measurement, and spherical measurement, respectively, proving that the retrace error can be effectively corrected.
InviCloak

In today's web ecosystem, a website that uses a Content Delivery Network (CDN) shares its Transport Layer Security (TLS) private key or session key with the CDN. In this paper, we present the design and implementation of InviCloak, a system that protects the confidentiality and integrity of a user and a website's private communications without changing TLS or upgrading a CDN. InviCloak builds a lightweight but secure and practical key distribution mechanism using the existing DNS infrastructure to distribute a new public key associated with a website's domain name. A web client and a website can use the new key pair to build an encryption channel inside TLS. InviCloak accommodates the current web ecosystem. A website can deploy InviCloak unilaterally without a client's involvement to prevent a passive attacker inside a CDN from eavesdropping on their communications. If a client also installs InviCloak's browser extension, the client and the website can achieve end-to-end confidential and untampered communications in the presence of an active attacker inside a CDN. Our evaluation shows that InviCloak increases the median page load times (PLTs) of realistic web pages from 2.0s to 2.1s, which is smaller than the median PLTs (2.8s) of a state-of-the-art TEE-based solution.
DeepOrientation: convolutional neural network for fringe pattern orientation map estimation

Fringe pattern based measurement techniques are the state-of-the-art in full-field optical metrology. They are crucial both in macroscale, e.g., fringe projection profilometry, and microscale, e.g., label-free quantitative phase microscopy. Accurate estimation of the local fringe orientation map can significantly facilitate the measurement process in various ways, e.g., fringe filtering (denoising), fringe pattern boundary padding, fringe skeletoning (contouring/following/tracking), local fringe spatial frequency (fringe period) estimation, and fringe pattern phase demodulation. Considering all of that, the accurate, robust, and preferably automatic estimation of local fringe orientation map is of high importance. In this paper we propose a novel numerical solution for local fringe orientation map estimation based on convolutional neural network and deep learning called DeepOrientation. Numerical simulations and experimental results corroborate the effectiveness of the proposed DeepOrientation comparing it with a representative of the classical approach to orientation estimation called combined plane fitting/gradient method. The example proving the effectiveness of DeepOrientation in fringe pattern analysis, which we present in this paper, is the application of DeepOrientation for guiding the phase demodulation process in Hilbert spiral transform. In particular, living HeLa cells quantitative phase imaging outcomes verify the method as an important asset in label-free microscopy.
Characterization of anomalous diffusion in one-dimensional quantum walks

Abstract Quantum walks are known to propagate quadratically faster than their classical counterparts and are used to model dynamics in various quantum systems. The spread of the quantum walk in position space shows anomalous diffusion behavior. By controlling the action of quantum coin operation on the corresponding coin degree of freedom of the walker, one can demonstrate control over the diffusion behavior. In this work, we report different forms of coin operations on quantum walks exhibiting anomalous diffusion behavior. Homogeneous and accelerated quantum walks display superdiffusive behavior, whereas uncorrelated static and dynamic disorders in the evolution induce strong and weak localization of the particle indicating subdiffusive and normal diffusive behavior. The role played by the interference effects in the spreading of the walker has remained elusive and our aim in this work is to present the interplay between quantum coherence and mean squared displacement of the walker. We employ two reliable measures of coherence for conclusively establishing the role of quantum interference as the driving force behind the anomalous diffusive behavior in the dynamics of quantum walks.
Detecting Einstein-Podolsky-Rosen steering via correlation matrices

Einstein-Podolsky-Rosen (EPR) steering is a kind of quantum nonlocal correlation characterizing the ability of one party to remotely affect another party's state by local measurements. It is regarded as an essential resource for one-sided device-independent quantum information processing. Although identification of EPR steerable states has been widely studied, it remains a hard issue under arbitrary local measurements. In this paper, we propose a steerability criterion for arbitrary dimensional bipartite systems via correlation matrices of local observables. In particular, we apply the steering criterion to three classes of local measurements which are of special significance: local orthogonal observables, mutually unbiased measurements, and general symmetric informationally complete measurements. We obtain some useful criteria that are operational and experimentally testable. We further illustrate the criteria through several examples, compare them with some steering criteria in the literature, and exhibit their power and advantages in certain cases.