Reviewerly/exHarmony huggingface dataset:
How does the cryptocurrency market affect the stock market performance in the MENA region?

Purpose This paper examines the impact of cryptocurrency market on the stock market performance in Middle East and North Africa (MENA) region. A comparative analysis is extended to distinguish this impact between Gulf countries and other economies in the region. Design/methodology/approach The analysis uses the information of cryptocurrencies and the stock market indices of the Gulf countries for the period 2014–2018 on a daily basis. Two strategies have been implemented to fulfill the goal of the study: first, the tests strategy, which is applied using the cointegration analysis and panel-specific forms of Granger causality; second, the regression strategy, which is applied mainly using the instrument variable with generalized method of moments (IV-GMM) method. Findings The results show that there is a significant relationship between the cryptocurrency market and the stock market performance in the MENA region. On the one hand, for the Gulf countries that claim full obedience to the Islamic Sharia rules, each 1% increase in the cryptocurrency returns reduces the stock market performance by 0.15%. On the other hand, for the non-Gulf (other MENA) countries that have flexibility in applying the Islamic Sharia rules or do not follow it, the stock market performance increases by 0.13%, for each 1% increase in the cryptocurrency returns. Originality/value The paper proposes two main contributions: First, the paper introduces the cryptocurrency returns as one of the determinants of the stock market performance in the MENA region. This impact is distinguished based on the degree of applying the Islamic Sharia rules and the vision of the government to the stock market. Second, the paper provides an empirical guideline for governments in the MENA region for efficient measures in their stock market, given the important expansion of the cryptocurrency market and the government type.
Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists

Digital pathology (DP) using whole slide imaging (WSI) is becoming a fundamental issue in pathology with recent advances and the rapid development of associated technologies. However, the available evidence on its diagnostic uses and practical advice for pathologists on implementing DP remains insufficient, particularly in light of the exponential growth of this industry. To inform DP implementation in Korea, we developed relevant and timely recommendations. We first performed a literature review of DP guidelines, recommendations, and position papers from major countries, as well as a review of relevant studies validating WSI. Based on that information, we prepared a draft. After several revisions, we released this draft to the public and the members of the Korean Society of Pathologists through our homepage and held an open forum for interested parties. Through that process, this final manuscript has been prepared. This recommendation contains an overview describing the background, objectives, scope of application, and basic terminology; guidelines and considerations for the hardware and software used in DP systems and the validation required for DP implementation; conclusions; and references and appendices, including literature on DP from major countries and WSI validation studies.
Imperceptible Misclassification Attack on Deep Learning Accelerator by Glitch Injection

The convergence of edge computing and deep learning empowers endpoint hardwares or edge devices to perform inferences locally with the help of deep neural network (DNN) accelerator. This trend of edge intelligence invites new attack vectors, which are methodologically different from the well-known software oriented deep learning attacks like the input of adversarial examples. Current studies of threats on DNN hardware focus mainly on model parameters interpolation. Such kind of manipulation is not stealthy as it will leave non-erasable traces or create conspicuous output patterns. In this paper, we present and investigate an imperceptible misclassification attack on DNN hardware by introducing infrequent instantaneous glitches into the clock signal. Comparing with falsifying model parameters by permanent faults, corruption of targeted intermediate results of convolution layer(s) by disrupting associated computations intermittently leaves no trace. We demonstrated our attack on nine state-of-the-art ImageNet models running on Xilinx FPGA based deep learning accelerator. With no knowledge about the models, our attack can achieve over 98% misclassification on 8 out of 9 models with only 10% glitches launched into the computation clock cycles. Given the model details and inputs, all the test images applied to ResNet50 can be successfully misclassified with no more than 1.7% glitch injection.
A Slow-I-Fast-P Architecture for Compressed Video Action Recognition

Compressed video action recognition has drawn growing attention for the storage and processing advantages of compressed videos over original raw videos. While the past few years have witnessed remarkable progress in this problem, most existing approaches rely on RGB frames from raw videos and require multi-step training. In this paper, we propose a novel Slow-I-Fast-P (SIFP) neural network model for compressed video action recognition. It consists of the slow I pathway receiving a sparse sampling I-frame clip and the fast P pathway receiving a dense sampling pseudo optical flow clip. An unsupervised estimation method and a new loss function are designed to generate pseudo optical flows in compressed videos. Our model eliminates the dependence on the traditional optical flows calculated from raw videos. The model is trained in an end-to-end way. The proposed method is evaluated on the challenging HMDB51 and UCF101 datasets. The extensive comparison results and ablation studies demonstrate the effectiveness and strength of the proposed method.
Concrete quantum cryptanalysis of binary elliptic curves

This paper analyzes and optimizes quantum circuits for computing discrete logarithms on binary elliptic curves, including reversible circuits for fixed-base-point scalar multiplication and the full stack of relevant subroutines. The main optimization target is the size of the quantum computer, i.e., the number of logical qubits required, as this appears to be the main obstacle to implementing Shor’s polynomial-time discrete-logarithm algorithm. The secondary optimization target is the number of logical Toffoli gates. For an elliptic curve over a field of 2n elements, this paper reduces the number of qubits to 7n + ⌊log2(n)⌋ + 9. At the same time this paper reduces the number of Toffoli gates to 48n3 + 8nlog2(3)+1 + 352n2 log2(n) + 512n2 + O(nlog2(3)) with double-and-add scalar multiplication, and a logarithmic factor smaller with fixed-window scalar multiplication. The number of CNOT gates is also O(n3). Exact gate counts are given for various sizes of elliptic curves currently used for cryptography.
A Novel Chaos-Based Image Encryption Scheme by Using Randomly DNA Encode and Plaintext Related Permutation

To ensure the security and privacy of digital image when its transmitting online or storing in the cloud, we proposed a novel chaos based image encryption scheme by using randomly DNA encode and plaintext related permutation. In our scheme, we first randomly encode plain image into a nucleotide sequence under the control by the piecewise linear chaotic map(PWLCM). After that, the plaintext related permutation would be done under the control sequence which generated by hyper chaotic Lorenz system (HCLS). Next, we make diffusion processing with key DNA sequence which is generated by another PWLCM system and also encoded randomly. Finally, we decode DNA sequence into cipher image matrix. In addition, we used many common security analysis methods to test our scheme, and the result compared with other works. The tests and comparison results are shown that our proposed image cryptosystem has excellent security performance to ensure the digital image security on communication.
Optimal synthesis of the Fredkin gate in a multilevel system

Abstract The optimal cost of a three-qubit Fredkin gate is 5 two-qubit entangling gates, and the overhead climbs to 8 when restricted to controlled-not (CNOT) gates. By harnessing higher-dimensional Hilbert spaces, we reduce the cost of a three-qubit Fredkin gate from 8 CNOTs to 5 nearest-neighbor CNOTs. We also present a construction of an n -control-qubit Fredkin gate with 2 n + 3 CNOTs and 2 n single-qudit operations. Finally, we design deterministic and non-deterministic three-qubit Fredkin gates in photonic architectures. The cost of a non-deterministic three-qubit Fredkin gate is further reduced to 4 nearest-neighbor CNOTs, and the success of such a gate is heralded by a single-photon detector. Our insights bridge the gap between the theoretical lower bound and the current best result for the n -qubit quantum computation.
A Blockchain-Powered Decentralized and Secure Computing Paradigm

Thanks to the advances in machine learning, data-driven analysis tools have become valuable solutions for various applications. However, there still remain essential challenges to develop effective data-driven methods because of the need to acquire a large amount of data and to have sufficient computing power to handle the data. In many instances these challenges are addressed by relying on a cloud computing vendor. However, although commercial cloud vendors provide valuable platforms for data analytics, they can suffer from a lack of transparency, security, and privacy-preservation. Furthermore, reliance on cloud servers prevents applying big data analytics in environments where the computing power is distributed. To address these challenges, a decentralized, secure, and privacy-preserving computing paradigm is proposed to enable an asynchronized cooperative computing process amongst distributed and untrustworthy computing nodes that may have limited computing power and computing intelligence. This paradigm is designed by exploring blockchain, decentralized learning, and homomorphic encryption techniques. The performance of the proposed paradigm is evaluated via different scenarios in the simulation section.
Two-Qubit Circuit Depth and the Monodromy Polytope

For a native gate set which includes all single-qubit gates, we apply results from symplectic geometry to analyze the spaces of two-qubit programs accessible within a fixed number of gates. These techniques yield an explicit description of this subspace as a convex polytope, presented by a family of linear inequalities themselves accessible via a finite calculation. We completely describe this family of inequalities in a variety of familiar example cases, and as a consequence we highlight a certain member of the ``<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">X</mml:mi><mml:mi mathvariant="normal">Y</mml:mi></mml:mrow></mml:math>--family'' for which this subspace is particularly large, i.e., for which many two-qubit programs admit expression as low-depth circuits.
Enhancing Underexposed Photos Using Perceptually Bidirectional Similarity

Although remarkable progress has been made, existing methods for enhancing underexposed photos tend to produce visually unpleasing results due to the existence of visual artifacts (e.g., color distortion, loss of details and uneven exposure). We observed that this is because they fail to ensure the perceptual consistency of visual information between the source underexposed image and its enhanced output. To obtain high-quality results free of these artifacts, we present a novel underexposed photo enhancement approach that is able to maintain the perceptual consistency. We achieve this by proposing an effective criterion, referred to as perceptually bidirectional similarity, which explicitly describes how to ensure the perceptual consistency. Particularly, we adopt the Retinex theory and cast the enhancement problem as a constrained illumination estimation optimization, where we formulate perceptually bidirectional similarity as constraints on illumination and solve for the illumination which can recover the desired artifact-free enhancement results. In addition, we describe a video enhancement framework that adopts the presented illumination estimation for handling underexposed videos. To this end, a probabilistic approach is introduced to propagate illuminations of sampled keyframes to the entire video by tackling a Bayesian Maximum A Posteriori problem. Extensive experiments demonstrate the superiority of our method over the state-of-the-art methods.
Exponential separation between shallow quantum circuits and unbounded fan-in shallow classical circuits

Recently, Bravyi, Gosset, and Konig (Science, 2018) exhibited a search problem called the 2D Hidden Linear Function (2D HLF) problem that can be solved exactly by a constant-depth quantum circuit using bounded fan-in gates (or QNC^0 circuits), but cannot be solved by any constant-depth classical circuit using bounded fan-in AND, OR, and NOT gates (or NC^0 circuits). In other words, they exhibited a search problem in QNC^0 that is not in NC^0.
Quantum computation is the unique reversible circuit model for which bits are balls

Abstract The computational efficiency of quantum mechanics can be characterized in terms of the qubit circuit model, which is defined by a few simple properties: each computational gate is a reversible transformation in a connected matrix group; single wires carry quantum bits, i.e. states of a three-dimensional Bloch ball; states on two or more wires are uniquely determined by local measurement statistics and their correlations. In this paper, we ask whether other types of computation are possible if we relax one of those characteristics (and keep all others), namely, if we allow wires to be described by d -dimensional Bloch balls, where d is different from three. Theories of this kind have previously been proposed as possible generalizations of quantum physics, and it has been conjectured that some of them allow for interesting multipartite reversible transformations that cannot be realized within quantum theory. However, here we show that all such potential beyond-quantum models of computation are trivial: if d is not three, then the set of reversible transformations consists entirely of single-bit gates, and not even classical computation is possible. In this sense, qubit quantum computation is an island in theoryspace.
Is a Single Vector Enough?: Exploring Node Polysemy for Network Embedding

Networks have been widely used as the data structure for abstracting real-world systems as well as organizing the relations among entities. Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction. Existing network embedding models comprehensively integrate all information of each node, such as links and attributes, towards a single embedding vector to represent the node's general role in the network. However, a real-world entity could be multifaceted, where it connects to different neighborhoods due to different motives or self-characteristics that are not necessarily correlated. For example, in a movie recommender system, a user may love comedies or horror movies simultaneously, but it is not likely that these two types of movies are mutually close in the embedding space, nor the user embedding vector could be sufficiently close to them at the same time. In this paper, we propose a polysemous embedding approach for modeling multiple facets of nodes, as motivated by the phenomenon of word polysemy in language modeling. Each facet of a node is mapped to an embedding vector, while we also maintain an association degree between each pair of node and facet. The proposed method is adaptive to various existing embedding models, without significantly complicating the optimization process. We also discuss how to engage embedding vectors of different facets for inference tasks including classification and link prediction. Experiments on real-world datasets help comprehensively evaluate the performance of the proposed method.
LESS is More

Robots need models of human behavior for both inferring human goals and preferences, and predicting what people will do. A common model is the Boltzmann noisily-rational decision model, which assumes people approximately optimize a reward function and choose trajectories in proportion to their exponentiated reward. While this model has been successful in a variety of robotics domains, its roots lie in econometrics, and in modeling decisions among different discrete options, each with its own utility or reward. In contrast, human trajectories lie in a continuous space, with continuous-valued features that influence the reward function. We propose that it is time to rethink the Boltzmann model, and design it from the ground up to operate over such trajectory spaces. We introduce a model that explicitly accounts for distances between trajectories, rather than only their rewards. Rather than each trajectory affecting the decision independently, similar trajectories now affect the decision together. We start by showing that our model better explains human behavior in a user study. We then analyze the implications this has for robot inference, first in toy environments where we have ground truth and find more accurate inference, and finally for a 7DOF robot arm learning from user demonstrations.
Animo

We present Animo, a smartwatch app that enables people to share and view each other's biosignals. We designed and engineered Animo to explore new ground for smartwatch-based biosignals social computing systems: identifying opportunities where these systems can support lightweight and mood-centric interactions. In our work we develop, explore, and evaluate several innovative features designed for dyadic communication of heart rate. We discuss the results of a two-week study (N=34), including new communication patterns participants engaged in, and outline the design landscape for communicating with biosignals on smartwatches.
Domain adaptation with conditional distribution matching and generalized label shift

Adversarial learning has demonstrated good performance in the unsupervised domain adaptation setting, by learning domain-invariant representations that perform well on the source domain. However, recent work has underlined limitations of existing methods in the presence of mismatched label distributions between the source and target domains. In this paper, we extend a recent upper-bound on the performance of adversarial domain adaptation to multi-class classification and more general discriminators. We then propose generalized label shift (GLS) as a way to improve robustness against mismatched label distributions. GLS states that, conditioned on the label, there exists a representation of the input that is invariant between the source and target domains. Under GLS, we provide theoretical guarantees on the transfer performance of any classifier. We also devise necessary and sufficient conditions for GLS to hold. The conditions are based on the estimation of the relative class weights between domains and on an appropriate reweighting of samples. Guided by our theoretical insights, we modify three widely used algorithms, JAN, DANN and CDAN and evaluate their performance on standard domain adaptation tasks where our method outperforms the base versions. We also demonstrate significant gains on artificially created tasks with large divergences between their source and target label distributions.
Enhancing Performance of a Deep Neural Network: A Comparative Analysis of Optimization Algorithms

Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It’s a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing the best optimizer while modelling their deep neural network.
URIE: Universal Image Enhancement for Visual Recognition in the Wild

Despite the great advances in visual recognition, it has been witnessed that recognition models trained on clean images of common datasets are not robust against distorted images in the real world. To tackle this issue, we present a Universal and Recognition-friendly Image Enhancement network, dubbed URIE, which is attached in front of existing recognition models and enhances distorted input to improve their performance without retraining them. URIE is universal in that it aims to handle various factors of image degradation and to be incorporated with any arbitrary recognition models. Also, it is recognition-friendly since it is optimized to improve the robustness of following recognition models, instead of perceptual quality of output image. Our experiments demonstrate that URIE can handle various and latent image distortions and improve the performance of existing models for five diverse recognition tasks where input images are degraded.
Scalable Arrays of Micro-Penning Traps for Quantum Computing and Simulation

We propose the use of two-dimensional Penning trap arrays as a scalable platform for quantum simulation and quantum computing with trapped atomic ions. This approach involves placing arrays of microstructured electrodes defining static electric quadrupole sites in a magnetic field, with single ions trapped at each site and coupled to neighbors via the Coulomb interaction. We solve for the normal modes of ion motion in such arrays and derive a generalized multi-ion invariance theorem for stable motion even in the presence of trap imperfections. We use these techniques to investigate the feasibility of quantum simulation and quantum computation in fixed ion lattices. In homogeneous arrays, we show that sufficiently dense arrays are achievable, with axial, magnetron, and cyclotron motions exhibiting interion dipolar coupling with rates significantly higher than expected decoherence. With the addition of laser fields, these can realize tunable-range interacting spin Hamiltonians. We also show how local control of potentials allows isolation of small numbers of ions in a fixed array and can be used to implement high-fidelity gates. The use of static trapping fields means that our approach is not limited by power requirements as the system size increases, removing a major challenge for scaling which is present in standard radio-frequency traps. Thus, the architecture and methods provided here appear to open a path for trapped-ion quantum computing to reach fault-tolerant scale devices.3 MoreReceived 12 February 2019Revised 2 April 2020Accepted 4 June 2020DOI:https://doi.org/10.1103/PhysRevX.10.031027Published 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 AreasPenning trapsQuantum simulationAtomic, Molecular & OpticalQuantum Information