Reviewerly/exHarmony huggingface dataset:
The Energy Footprint of Blockchain Consensus Mechanisms Beyond Proof-of-Work

Popular distributed ledger technology (DLT) systems using proof-of-work (PoW) for Sybil attack resistance have extreme energy requirements, drawing stern criticism from academia, businesses, and the media. DLT systems building on alternative consensus mechanisms, foremost proof-of-stake (PoS), aim to address this downside. In this paper, we take a first step towards comparing the energy requirements of such systems to understand whether they achieve this goal equally well. While multiple studies have been undertaken that analyze the energy demands of individual Blockchains, little comparative work has been done. We approach this research question by formalizing a basic consumption model for PoS blockchains. Applying this model to six archetypal blockchains generates three main findings: First, we confirm the concerns around the energy footprint of PoW by showing that Bitcoin's energy consumption exceeds the energy consumption of all PoS-based systems analyzed by at least three orders of magnitude. Second, we illustrate that there are significant differences in energy consumption among the PoSbased systems analyzed, with permissionless systems having an overall larger energy footprint. Third, we point out that the type of hardware that validators use has a considerable impact on whether PoS blockchains' energy consumption is comparable with or considerably larger than that of centralized, non-DLT systems.
Variational Quantum Gibbs State Preparation with a Truncated Taylor Series

The preparation of quantum Gibbs state is an essential part of quantum computation and has wide-ranging applications in various areas, including quantum simulation, quantum optimization, and quantum machine learning. In this paper, we propose variational hybrid quantum-classical algorithms for quantum Gibbs state preparation. We first utilize a truncated Taylor series to evaluate the free energy and choose the truncated free energy as the loss function. Our protocol then trains the parameterized quantum circuits to learn the desired quantum Gibbs state. Notably, this algorithm can be implemented on near-term quantum computers equipped with parameterized quantum circuits. By performing numerical experiments, we show that shallow parameterized circuits with only one additional qubit can be trained to prepare the Ising chain and spin chain Gibbs states with a fidelity higher than 95%. In particular, for the Ising chain model, we find that a simplified circuit ansatz with only one parameter and one additional qubit can be trained to realize a 99% fidelity in Gibbs state preparation at inverse temperatures larger than 2.
Validating Whole Slide Imaging Systems for Diagnostic Purposes in Pathology.

The original guideline, "Validating Whole Slide Imaging for Diagnostic Purposes in Pathology," was published in 2013 and included 12 guideline statements. The College of American Pathologists convened an expert panel to update the guideline following standards established by the National Academies of Medicine for developing trustworthy clinical practice guidelines.To assess evidence published since the release of the original guideline and provide updated recommendations for validating whole slide imaging (WSI) systems used for diagnostic purposes.An expert panel performed a systematic review of the literature. Frozen sections, anatomic pathology specimens (biopsies, curettings, and resections), and hematopathology cases were included. Cytology cases were excluded. Using the Grading of Recommendations Assessment, Development, and Evaluation approach, the panel reassessed and updated the original guideline recommendations.Three strong recommendations and 9 good practice statements are offered to assist laboratories with validating WSI digital pathology systems.Systematic review of literature following release of the 2013 guideline reaffirms the use of a validation set of at least 60 cases, establishing intraobserver diagnostic concordance between WSI and glass slides and the use of a 2-week washout period between modalities. Although all discordances between WSI and glass slide diagnoses discovered during validation need to be reconciled, laboratories should be particularly concerned if their overall WSI-glass slide concordance is less than 95%.
Perceptual Pyramid Adversarial Networks for Text-to-Image Synthesis

Generating photo-realistic images conditioned on semantic text descriptions is a challenging task in computer vision field. Due to the nature of hierarchical representations learned in CNN, it is intuitive to utilize richer convolutional features to improve text-to-image synthesis. In this paper, we propose Perceptual Pyramid Adversarial Network (PPAN) to directly synthesize multi-scale images conditioned on texts in an adversarial way. Specifically, we design one pyramid generator and three independent discriminators to synthesize and regularize multi-scale photo-realistic images in one feed-forward process. At each pyramid level, our method takes coarse-resolution features as input, synthesizes highresolution images, and uses convolutions for up-sampling to finer level. Furthermore, the generator adopts the perceptual loss to enforce semantic similarity between the synthesized image and the ground truth, while a multi-purpose discriminator encourages semantic consistency, image fidelity and class invariance. Experimental results show that our PPAN sets new records for text-to-image synthesis on two benchmark datasets: CUB (i.e., 4.38 Inception Score and .290 Visual-semantic Similarity) and Oxford-102 (i.e., 3.52 Inception Score and .297 Visual-semantic Similarity).
Bitcoin as a new asset class

Purpose Bitcoin is the best-known cryptocurrency which currently holds the largest market capitalisation and is regarded as a standard example of a cryptocurrency. There is, however, no consensus as to the nature of the Bitcoin. The purpose of this paper is to determine whether Bitcoin represents a new asset class by building on prior research. Design/methodology/approach The prior literature on asset classes is explored in detail and then applied to the Bitcoin. Four key criteria of asset classes are discussed, namely, investability, politico-economic profile, correlation of returns and risk-reward profile. Statistical techniques are used to inform the conclusions for the third and fourth criteria. Findings This research finds that the Bitcoin represents a distinct alternative investment and asset class. There are significant opportunities for investment. The politico-economic profile of the decentralised and consensus-based Bitcoin is dissimilar to other asset classes. The Bitcoin shares little or no correlation with other asset classes. Using Sharpe Ratios, it is shown that the Bitcoin provides risk-adjusted returns over and above most asset classes. Research limitations/implications The aim of this research is to present a normative exploration into the asset class nature of the Bitcoin and, as a result, the aim is not to create positivist generalisable conclusions. This paper does not address cryptocurrencies, other than Bitcoin and does not constitute a detailed manual on modern portfolio theory. Originality/value This research adds to finance paradigm research on the Bitcoin by including a developing country perspective on Bitcoin as an asset class as prior studies have concentrated on developed country settings. Further, this research introduces recent economic data (2014 to 2017) in the form of daily observations to enhance prior understanding. It is important to understand if the Bitcoin represents an alternative investment and new asset class as this may affect investment decisions.
Quantum metrology of solid-state single-photon sources using photon-number-resolving detectors

Quantum-light sources based on semiconductor quantum dots (QDs) are promising candidates for many applications in quantum photonics and quantum communication. Important emission characteristics of such emitters, namely the single-photon purity and photon indistinguishability, are usually assessed via time-correlated measurements using standard 'click' detectors in Hanbury Brown and Twiss or Hong-Ou-Mandel (HOM-) type configurations. In this work, we employ a state-of-the-art photon-number-resolving (PNR) detection system based on superconducting transition-edge sensors (TESs) to directly access the photon-number distribution of deterministically fabricated solid-state single-photon sources. Offering quantum efficiencies close to unity and high energy resolution, our TES-based two-channel detector system allows us to analyse the quantum optical properties of a QD-based non-classical light source. In particular, it enables the direct observation of the two-particle Fock-state resulting from interference of quantum mechanically indistinguishable photons in HOM-experiments. Additionally, comparative measurements reveal excellent quantitative agreement of the photon-indistinguishabilities obtained with PNR ((90 ± 7)%) and standard click ((90 ± 5)%) detectors. Our work thus demonstrates that TES-based detectors are perfectly suitable for the quantum metrology of non-classical light sources and higlights appealing prospects for the efficient implementation of quantum information tasks based on multi-photon states.
Fast De-anonymization of Social Networks with Structural Information

Ever since the social networks became the focus of a great number of researches, the privacy risks of published network data have also raised considerable concerns. To evaluate users’ privacy risks, researchers have developed methods to de-anonymize the networks and identify the same person in the different networks. However, the existing solutions either require high-quality seed mappings for cold start, or exhibit low accuracy without fully exploiting the structural information, and entail high computation expense. In this paper, we propose a fast and effective seedless network de-anonymization approach simply relying on structural information, named RoleMatch. RoleMatch equips with a new pairwise node similarity measure and an efficient node matching algorithm. Through testing RoleMatch with both real and synthesized social networks, which are anonymized by several popular anonymization algorithms, we demonstrate that the RoleMatch receives superior performance compared with existing de-anonymization algorithms.
System design of a single-shot reconfigurable null test using a spatial light modulator for freeform metrology

We report the simulation of an adaptive interferometric null test using a high-definition phase-only spatial light modulator (SLM) to measure form and mid spatial frequencies of a freeform mirror with a sag departure of 150 μm from its base sphere. A state-of-the-art commercial SLM is modeled as a reconfigurable phase computer generated hologram (CGH) that generates a nulling phase function with close to an order of magnitude higher amplitude than deformable mirrors. The theoretical uncertainty in form measurement arising from pixelation and phase quantization of the SLM is 50.62 nm RMS. The calibration requirements for hardware implementation are detailed.
Learning Generative Models across Incomparable Spaces.

Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases, we may want to only learn some aspects (e.g., cluster or manifold structure), while modifying others (e.g., style, orientation or dimension). In this work, we propose an approach to learn generative models across such incomparable spaces, and demonstrate how to steer the learned distribution towards target properties. A key component of our model is the Gromov-Wasserstein distance, a notion of discrepancy that compares distributions relationally rather than absolutely. While this framework subsumes current generative models in identically reproducing distributions, its inherent flexibility allows application to tasks in manifold learning, relational learning and cross-domain learning.
Branch-and-Cut-and-Price for Multi-Agent Pathfinding

There are currently two broad strategies for optimal Multi-agent Pathfinding (MAPF): (1) search-based methods, which model and solve MAPF directly, and (2) compilation-based solvers, which reduce MAPF to instances of well-known combinatorial problems, and thus, can benefit from advances in solver techniques. In this work, we present an optimal algorithm, BCP, that hybridizes both approaches using Branch-and-Cut-and-Price, a decomposition framework developed for mathematical optimization. We formalize BCP and compare it empirically against CBSH and CBSH-RM, two leading search-based solvers. Conclusive results on standard benchmarks indicate that its performance exceeds the state-of-the-art: solving more instances on smaller grids and scaling reliably to 100 or more agents on larger game maps.
MVStream: Multiview Data Stream Clustering

This article studies a new problem of data stream clustering, namely, multiview data stream (MVStream) clustering. Although many data stream clustering algorithms have been developed, they are restricted to the single-view streaming data, and clustering MVStreams still remains largely unsolved. In addition to the many issues encountered by the conventional single-view data stream clustering, such as capturing cluster evolution and discovering clusters of arbitrary shapes under the limited computational resources, the main challenge of MVStream clustering lies in integrating information from multiple views in a streaming manner and abstracting summary statistics from the integrated features simultaneously. In this article, we propose a novel MVStream clustering algorithm for the first time. The main idea is to design a multiview support vector domain description (MVSVDD) model, by which the information from multiple insufficient views can be integrated, and the outputting support vectors (SVs) are utilized to abstract the summary statistics of the historical multiview data objects. Based on the MVSVDD model, a new multiview cluster labeling method is designed, whereby clusters of arbitrary shapes can be discovered for each view. By tracking the cluster labels of SVs in each view, the cluster evolution associated with concept drift can be captured. Since the SVs occupy only a small portion of data objects, the proposed MVStream algorithm is quite efficient with the limited computational resources. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method.
Gas-dynamic phenomena caused by rock mass tremors and rock bursts

Similar to coal, rock and gas ejections, rock mass tremors and rock bursts are among the most serious natural hazards accompanying the underground extraction of coal. Gas-dynamic phenomena caused by rock mass tremors and rock bursts observed as transient states of air parameters in mining headings, are usually generated as a result of a change in the geometry of headings and the release of considerable amounts of gases. Particular significance is attributed to transient states caused by disasters, which are often accompanied by rapid incidents, presenting threats to the life and health of the underground crew. In Polish mining there are known examples of transient states of air parameters recorded during gas-dynamic phenomena, e.g. tremors and rock bursts. The paper presents the case studies of rapid seismic incidents to show how records in mine monitoring systems broaden the knowledge about the transient states of air parameters in mining headings generated because of them.
MixConv: Mixed Depthwise Convolutional Kernels

Depthwise convolution is becoming increasingly popular in modern efficient ConvNets, but its kernel size is often overlooked. In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency. Based on this observation, we propose a new mixed depthwise convolution (MixConv), which naturally mixes up multiple kernel sizes in a single convolution. As a simple drop-in replacement of vanilla depthwise convolution, our MixConv improves the accuracy and efficiency for existing MobileNets on both ImageNet classification and COCO object detection. To demonstrate the effectiveness of MixConv, we integrate it into AutoML search space and develop a new family of models, named as MixNets, which outperform previous mobile models including MobileNetV2 [20] (ImageNet top-1 accuracy +4.2%), ShuffleNetV2 [16] (+3.5%), MnasNet [26] (+1.3%), ProxylessNAS [2] (+2.2%), and FBNet [27] (+2.0%). In particular, our MixNet-L achieves a new state-of-the-art 78.9% ImageNet top-1 accuracy under typical mobile settings (<600M FLOPS). Code is at this https URL tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
UNQOVERing Stereotyping Biases via Underspecified Questions

Warning: This paper contains examples of stereotypes that are potentially offensive.While language embeddings have been shown to have stereotyping biases, how these biases affect downstream question answering (QA) models remains unexplored.We present UN-QOVER, a general framework to probe and quantify biases through underspecified questions.We show that a naïve use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors: positional dependence and question independence.We design a formalism that isolates the aforementioned errors.As case studies, we use this metric to analyze four important classes of stereotypes: gender, nationality, ethnicity, and religion.We probe five transformer-based QA models trained on two QA datasets, along with their underlying language models.Our broad study reveals that (1) all these models, with and without fine-tuning, have notable stereotyping biases in these classes; (2) larger models often have higher bias; and (3) the effect of fine-tuning on bias varies strongly with the dataset and the model size.
Rethinking Pre-training and Self-training

Pre-training is a dominant paradigm in computer vision. For example, supervised ImageNet pre-training is commonly used to initialize the backbones of object detection and segmentation models. He et al., however, show a surprising result that ImageNet pre-training has limited impact on COCO object detection. Here we investigate self-training as another method to utilize additional data on the same setup and contrast it against ImageNet pre-training. Our study reveals the generality and flexibility of self-training with three additional insights: 1) stronger data augmentation and more labeled data further diminish the value of pre-training, 2) unlike pre-training, self-training is always helpful when using stronger data augmentation, in both low-data and high-data regimes, and 3) in the case that pre-training is helpful, self-training improves upon pre-training. For example, on the COCO object detection dataset, pre-training benefits when we use one fifth of the labeled data, and hurts accuracy when we use all labeled data. Self-training, on the other hand, shows positive improvements from +1.3 to +3.4AP across all dataset sizes. In other words, self-training works well exactly on the same setup that pre-training does not work (using ImageNet to help COCO). On the PASCAL segmentation dataset, which is a much smaller dataset than COCO, though pre-training does help significantly, self-training improves upon the pre-trained model. On COCO object detection, we achieve 54.3AP, an improvement of +1.5AP over the strongest SpineNet model. On PASCAL segmentation, we achieve 90.5 mIOU, an improvement of +1.5% mIOU over the previous state-of-the-art result by DeepLabv3+.
Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory

We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the 8 cavities in the cryomodule. Subject matter experts (SME) are able to analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time (rather than post-mortem) identification of the offending cavity and classification of the fault type has been implemented. We discuss performance of the ML models during a recent physics run. Results show the cavity identification and fault classification models have accuracies of 84.9% and 78.2%, respectively.
Uncovering the Far-Right Online Ecosystem: An Analytical Framework and Research Agenda

Abstract Recent years have seen a substantial increase in far-right inspired attacks. In this context, the present article offers an analytical framework for the study of right-wing extremists’ multifaceted and fast-growing activity on the Internet. Specifically, we conceptualize the far-right online presence as a dynamic ecosystem, teasing out four major components that correspond to the different levels of analysis available to future research. We illustrate the benefits of this framework with key illustrative examples from the English-, French-, and German- speaking far-right, revealing the worrying size and breadth – but also heterogeneity – of today’s far-right online ecosystem.
Improving Real-Time Hand Gesture Recognition with Semantic Segmentation

Hand gesture recognition (HGR) takes a central role in human–computer interaction, covering a wide range of applications in the automotive sector, consumer electronics, home automation, and others. In recent years, accurate and efficient deep learning models have been proposed for real-time applications. However, the most accurate approaches tend to employ multiple modalities derived from RGB input frames, such as optical flow. This practice limits real-time performance due to intense extra computational cost. In this paper, we avoid the optical flow computation by proposing a real-time hand gesture recognition method based on RGB frames combined with hand segmentation masks. We employ a light-weight semantic segmentation method (FASSD-Net) to boost the accuracy of two efficient HGR methods: Temporal Segment Networks (TSN) and Temporal Shift Modules (TSM). We demonstrate the efficiency of the proposal on our IPN Hand dataset, which includes thirteen different gestures focused on interaction with touchless screens. The experimental results show that our approach significantly overcomes the accuracy of the original TSN and TSM algorithms by keeping real-time performance.
A Quantum Chaotic Image Cryptosystem and Its Application in IoT Secure Communication

In the Internet of Things environment, the secure transmission of digital images has attracted much attention. To improve the confidentiality, we propose an image cryptosystem adopting a quantum chaotic map and the certain security-enhanced mechanisms. Firstly, we use the good random characteristics of quantum chaotic sequences to enhance security performance. Then, we introduce a plaintext correlation mechanism and a diffusion-permutation-diffusion structure in the cryptosystem. Finally, we verify the cryptosystem on a common secure communication platform. The theoretical and statistical analysis results demonstrate that the cryptosystem has excellent performance and can resist various cryptographic attacks. Moreover, feasibility and effectiveness of the image cryptosystem are verified on the Internet of Things secure communication experimental platform. It proves that the proposed image cryptosystem is a preferred and promising secure communication technology solution.