Reviewerly/exHarmony huggingface dataset:
Deep Generative Storm Model for Dynamic Imaging

We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed generative framework represents the image time series as a smooth non-linear function of low-dimensional latent vectors that capture the cardiac and respiratory phases. The non-linear function is represented using a deep convolutional neural network (CNN). Unlike the popular CNN approaches that require extensive fully-sampled training data that is not available in this setting, the parameters of the CNN generator as well as the latent vectors are jointly estimated from the undersampled measurements using stochastic gradient descent. We penalize the norm of the gradient of the generator to encourage the learning of a smooth surface/manifold, while temporal gradients of the latent vectors are penalized to encourage the time series to be smooth. The main benefits of the proposed scheme are (a) the quite significant reduction in memory demand compared to the analysis based SToRM model, and (b) the spatial regularization brought in by the CNN model. We also introduce efficient progressive approaches to minimize the computational complexity of the algorithm.
Learning deep morphological networks with neural architecture search

Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. The combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space. Most non-linear operators are derivations of activation functions or pooling functions. Mathematical morphology is a branch of mathematics that provides non-linear operators for various image processing problems. This paper investigates the utility of integrating these operations into an end-to-end deep learning framework. DNNs are designed to acquire a realistic representation for a particular job. Morphological operators give topological descriptors that convey salient information about the shapes of objects depicted in images. We propose a method based on meta-learning to incorporate morphological operators into DNNs. The learned architecture demonstrates how our novel morphological operations significantly increase DNN performance on various tasks, including picture classification, edge detection, and semantic segmentation. Our codes are available at https://nao-morpho.github.io/.
Quantum simulations with multiphoton Fock states

Abstract Quantum simulations are becoming an essential tool for studying complex phenomena, e.g. quantum topology, quantum information transfer and relativistic wave equations, beyond the limitations of analytical computations and experimental observations. To date, the primary resources used in proof-of-principle experiments are collections of qubits, coherent states or multiple single-particle Fock states. Here we show a quantum simulation performed using genuine higher-order Fock states, with two or more indistinguishable particles occupying the same bosonic mode. This was implemented by interfering pairs of Fock states with up to five photons on an interferometer, and measuring the output states with photon-number-resolving detectors. Already this resource-efficient demonstration reveals topological matter, simulates non-linear systems and elucidates a perfect quantum transfer mechanism which can be used to transport Majorana fermions.
Improving Cross-Domain Hate Speech Detection by Reducing the False Positive Rate

Hate speech detection is an actively growing field of research with a variety of recently proposed approaches that allowed to push the state-of-the-art results. One of the challenges of such automated approaches – namely recent deep learning models – is a risk of false positives (i.e., false accusations), which may lead to over-blocking or removal of harmless social media content in applications with little moderator intervention. We evaluate deep learning models both under in-domain and cross-domain hate speech detection conditions, and introduce an SVM approach that allows to significantly improve the state-of-the-art results when combined with the deep learning models through a simple majority-voting ensemble. The improvement is mainly due to a reduction of the false positive rate.
Linguistic issues behind visual question answering

Abstract Answering a question that is grounded in an image is a crucial ability that requires understanding the question, the visual context, and their interaction at many linguistic levels: among others, semantics, syntax and pragmatics. As such, visually‐grounded questions have long been of interest to theoretical linguists and cognitive scientists. Moreover, they have inspired the first attempts to computationally model natural language understanding, where pioneering systems were faced with the highly challenging task—still unsolved—of jointly dealing with syntax, semantics and inference whilst understanding a visual context. Boosted by impressive advancements in machine learning, the task of answering visually‐grounded questions has experienced a renewed interest in recent years, to the point of becoming a research sub‐field at the intersection of computational linguistics and computer vision. In this paper, we review current approaches to the problem which encompass the development of datasets, models and frameworks. We conduct our investigation from the perspective of the theoretical linguists; we extract from pioneering computational linguistic work a list of desiderata that we use to review current computational achievements. We acknowledge that impressive progress has been made to reconcile the engineering with the theoretical view. At the same time, we claim that further research is needed to get to a unified approach which jointly encompasses all the underlying linguistic problems. We conclude the paper by sharing our own desiderata for the future.
Decentralized Cross-Network Identity Management for Blockchain Interoperation

Interoperation for data sharing between permissioned blockchain networks relies on networks' abilities to independently authenticate requests and validate proofs accompanying the data; these typically contain digital signatures. This requires counterparty networks to know the identities and certification chains of each other's members, establishing a common trust basis rooted in identity. But permissioned networks are ad hoc consortia of existing organizations, whose network affiliations may not be well-known or well-established even though their individual identities are. In this paper, we describe an architecture and set of protocols for distributed identity management across permissioned blockchain networks to establish a trust basis for data sharing. Networks wishing to interoperate can associate with one or more distributed identity registries that maintain credentials on shared ledgers managed by groups of reputed identity providers. A network's participants possess self-sovereign decentralized identities (DIDs) on these registries and can obtain privacy-preserving verifiable membership credentials. During interoperation, networks can securely and dynamically discover each others' latest membership lists and members' credentials. We implement a solution based on Hyperledger Indy and Aries, and demonstrate its viability and usefulness by linking a trade finance network with a trade logistics network, both built on Hyperledger Fabric. We also analyze the extensibility, security, and trustworthiness of our system.
Quantum-enhanced radiometry via approximate quantum error correction

Quantum sensing based on exotic quantum states is appealing for practical metrology applications and fundamental studies. However, these quantum states are vulnerable to noise and the resulting quantum enhancement is weakened in practice. Here, we experimentally demonstrate a quantum-enhanced sensing scheme with a bosonic probe, by exploring the large Hilbert space of the bosonic mode and developing both the approximate quantum error correction and the quantum jump tracking approaches. In a practical radiometry scenario, we attain a 5.3 dB enhancement of sensitivity, which reaches 9.1 × 10-4 Hz-1/2 when measuring the excitation population of a receiver mode. Our results demonstrate the potential of quantum sensing with near-term quantum technologies, not only shedding new light on the quantum advantage of sensing, but also stimulating further efforts on bosonic quantum technologies.
De Novo Identification and Visualization of Important Cell Populations for Classic Hodgkin Lymphoma Using Flow Cytometry and Machine Learning

Abstract Objectives Automated classification of flow cytometry data has the potential to reduce errors and accelerate flow cytometry interpretation. We desired a machine learning approach that is accurate, is intuitively easy to understand, and highlights the cells that are most important in the algorithm’s prediction for a given case. Methods We developed an ensemble of convolutional neural networks for classification and visualization of impactful cell populations in detecting classic Hodgkin lymphoma using two-dimensional (2D) histograms. Data from 977 and 245 clinical flow cytometry cases were used for training and testing, respectively. Seventy-eight nongated 2D histograms were created per flow cytometry file. Shapley additive explanation (SHAP) values were calculated to determine the most impactful 2D histograms and regions within histograms. SHAP values from all 78 histograms were then projected back to the original cell data for gating and visualization using standard flow cytometry software. Results The algorithm achieved 67.7% recall (sensitivity), 82.4% precision, and 0.92 area under the receiver operating characteristic. Visualization of the important cell populations for individual predictions demonstrated correlations with known biology. Conclusions The method presented enables model explainability while highlighting important cell populations in individual flow cytometry specimens, with potential applications in both diagnosis and discovery of previously overlooked key cell populations.
An evaluation of performance measures for arterial brain vessel segmentation

Abstract Background Arterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation. Methods To simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient. Results The distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation. Conclusions Average Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches.
Frame decompositions, sampling, and uncertainty principle inequalities

Gabor’s approach to signal analysis has been formalized and generalized in several ways. The approach in this chapter integrates signal reconstruction and the uncertainty principle. The signal reconstruction is in terms of frame decompositions and sampling theory, with an emphasis on local sampling and effective irregular sampling theorems and algorithms. The uncertainty principle is formulated in terms of the uncertainty principle for variances in the context of Gabor and wavelet systems; and is developed to include general weighted uncertainty principle inequalities. Examples include dissections of the information plane, real-time sampling, auditory models, stationary systems, the Plancherel-Pólya and Boas theory, aliasing, zero-crossings, entropy inequalities, uniqueness theory and logarithmic integrals, local uncertainty principle inequalities, and the role of Riesz transforms and Ap - spaces. The theory unifies some of these notions in the parlance of wavelets and coherent states. The methods are from harmonic and functional analysis.
Certification of quantum states with hidden structure of their bitstrings

Abstract The rapid development of quantum computing technologies already made it possible to manipulate a collective state of several dozens of qubits, which poses a strong demand on efficient methods for characterization and verification of large-scale quantum states. Here, we propose a numerically cheap procedure to distinguish quantum states which is based on a limited number of projective measurements in at least two different bases and computing inter-scale dissimilarities of the resulting bit-string patterns via coarse-graining. The information one obtains through this procedure can be viewed as a ‘hash function’ of quantum state—a simple set of numbers which is specific for a concrete wave function and can be used for certification. We show that it is enough to characterize quantum states with different structure of entanglement, including the chaotic quantum states. Our approach can also be employed to detect phase transitions in quantum magnetic systems.
Computational overhead of locality reduction in binary optimization problems

Recently, there has been considerable interest in solving optimization problems by mapping these onto a binary representation, sparked mostly by the use of quantum annealing machines. Such binary representation is reminiscent of a discrete physical two-state system, such as the Ising model. As such, physics-inspired techniques -- commonly used in fundamental physics studies -- are ideally suited to solve optimization problems in a binary format. While binary representations can be often found for paradigmatic optimization problems, these typically result in k-local higher-order unconstrained binary optimization cost functions. In this work, we discuss the effects of locality reduction needed for the majority of the currently available quantum and quantum-inspired solvers that can only accommodate 2-local (quadratic) cost functions. General locality reduction approaches require the introduction of ancillary variables which cause an overhead over the native problem. Using a parallel tempering Monte Carlo solver on Microsoft Azure Quantum, as well as k-local binary problems with planted solutions, we show that post reduction to a corresponding 2-local representation the problems become considerably harder to solve. We further quantify the increase in computational hardness introduced by the reduction algorithm by measuring the variation of number of variables, statistics of the coefficient values, and the population annealing entropic family size. Our results demonstrate the importance of avoiding locality reduction when solving optimization problems.
Multi-Directional Scene Text Detection Based on Improved YOLOv3

To address the problem of low detection rate caused by the close alignment and multi-directional position of text words in practical application and the need to improve the detection speed of the algorithm, this paper proposes a multi-directional text detection algorithm based on improved YOLOv3, and applies it to natural text detection. To detect text in multiple directions, this paper introduces a method of box definition based on sliding vertices. Then, a new rotating box loss function MD-Closs based on CIOU is proposed to improve the detection accuracy. In addition, a step-by-step NMS method is used to further reduce the amount of calculation. Experimental results show that on the ICDAR 2015 data set, the accuracy rate is 86.2%, the recall rate is 81.9%, and the timeliness is 21.3 fps, which shows that the proposed algorithm has a good detection effect on text detection in natural scenes.
ReLoC Reloaded: A Mechanized Relational Logic for Fine-Grained Concurrency and Logical Atomicity

We present a new version of ReLoC: a relational separation logic for proving refinements of programs with higher-order state, fine-grained concurrency, polymorphism and recursive types. The core of ReLoC is its refinement judgment $e \precsim e' : \tau$, which states that a program $e$ refines a program $e'$ at type $\tau$. ReLoC provides type-directed structural rules and symbolic execution rules in separation-logic style for manipulating the judgment, whereas in prior work on refinements for languages with higher-order state and concurrency, such proofs were carried out by unfolding the judgment into its definition in the model. ReLoC's abstract proof rules make it simpler to carry out refinement proofs, and enable us to generalize the notion of logically atomic specifications to the relational case, which we call logically atomic relational specifications. We build ReLoC on top of the Iris framework for separation logic in Coq, allowing us to leverage features of Iris to prove soundness of ReLoC, and to carry out refinement proofs in ReLoC. We implement tactics for interactive proofs in ReLoC, allowing us to mechanize several case studies in Coq, and thereby demonstrate the practicality of ReLoC. ReLoC Reloaded extends ReLoC (LICS'18) with various technical improvements, a new Coq mechanization, and support for Iris's prophecy variables. The latter allows us to carry out refinement proofs that involve reasoning about the program's future. We also expand ReLoC's notion of logically atomic relational specifications with a new flavor based on the HOCAP pattern by Svendsen et al.
Knowledge-Grounded Dialogue Flow Management for Social Robots and Conversational Agents

The article proposes a system for knowledge-based conversation designed for Social Robots and other conversational agents. The proposed system relies on an Ontology for the description of all concepts that may be relevant conversation topics, as well as their mutual relationships. The article focuses on the algorithm for Dialogue Management that selects the most appropriate conversation topic depending on the user input. Moreover, it discusses strategies to ensure a conversation flow that captures, as more coherently as possible, the user intention to drive the conversation in specific directions while avoiding purely reactive responses to what the user says. To measure the quality of the conversation, the article reports the tests performed with 100 recruited participants, comparing five conversational agents: (i) an agent addressing dialogue flow management based only on the detection of keywords in the speech, (ii) an agent based both on the detection of keywords and the Content Classification feature of Google Cloud Natural Language, (iii) an agent that picks conversation topics randomly, (iv) a human pretending to be a chatbot, and (v) one of the most famous chatbots worldwide: Replika. The subjective perception of the participants is measured both with the SASSI (Subjective Assessment of Speech System Interfaces) tool, as well as with a custom survey for measuring the subjective perception of coherence.
An Empirical Study on Innovation of College Blended Teaching under Big Data Analysis

With the advent of the information age, the way people obtain information has changed profoundly. The wave of informationization in higher education has also come with it, and the teaching mode, teaching content, and teaching form are constantly innovated. How to organically integrate information technology into education teaching in order to care for learners’ learning experience and promote the cultivation of new talents is an issue that current educational technology researchers need to pay great attention to. This paper first builds a complete blended teaching model of public English for higher education, but its application effect needs to be further examined. This paper is an investigation in the background of the current era to build a blended teaching model. Based on the continuous development of the era, the ideology and application technology of this field will keep upgrading, so the teaching model also needs to be changed and updated according to the characteristics of the development of the era. The investigation of mixed teaching modes is not permanent. The investigation of the mixed teaching mode is not permanent. At present, only a few courses apply the blended teaching mode. On the basis of the continuous updating of teaching concepts and the latest technologies, it is foreseen that the focus of subsequent investigations will be on the individualized development of the blended teaching mode.
Characterizing correlation within multipartite quantum systems via local randomized measurements

Given a quantum system on many qubits split into a few different parties, how many total correlations are there between these parties? Such a quantity, aimed to measure the deviation of the global quantum state from an uncorrelated state with the same local statistics, plays an important role in understanding multipartite correlations within complex networks of quantum states. Yet, the experimental access of this quantity remains challenging as it tends to be nonlinear, and hence often requires tomography which becomes quickly intractable as dimensions of relevant quantum systems scale. Here, we introduce a much more experimentally accessible quantifier of total correlations, which can be estimated using only single-qubit measurements. It requires far fewer measurements than state tomography, and obviates the need to coherently interfere multiple copies of a given state. Thus we provide a tool for proving multipartite correlations that can be applied to near-term quantum devices.
Security and Privacy for Mobile IoT Applications Using Blockchain

Internet of Things (IoT) applications are becoming more integrated into our society and daily lives, although many of them can expose the user to threats against their privacy. Therefore, we find that it is crucial to address the privacy requirements of most of such applications and develop solutions that implement, as far as possible, privacy by design in order to mitigate relevant threats. While in the literature we may find innovative proposals to enhance the privacy of IoT applications, many of those only focus on the edge layer. On the other hand, privacy by design approaches are required throughout the whole system (e.g., at the cloud layer), in order to guarantee robust solutions to privacy in IoT. With this in mind, we propose an architecture that leverages the properties of blockchain, integrated with other technologies, to address security and privacy in the context of IoT applications. The main focus of our proposal is to enhance the privacy of the users and their data, using the anonymisation properties of blockchain to implement user-controlled privacy. We consider an IoT application with mobility for smart vehicles as our usage case, which allows us to implement and experimentally evaluate the proposed architecture and mechanisms as a proof of concept. In this application, data related to the user's identity and location needs to be shared with security and privacy. Our proposal was implemented and experimentally validated in light of fundamental privacy and security requirements, as well as its performance. We found it to be a viable approach to security and privacy in IoT environments.