Reviewerly/exHarmony huggingface dataset:
Reverse annealing for nonnegative/binary matrix factorization

It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms. The quantum annealing algorithm performed best for quick, approximate answers, but performance rapidly plateaued. In this paper, we utilize reverse annealing instead of forward annealing in the quantum annealing subroutine for nonnegative/binary matrix factorization problems. After an initial global search with forward annealing, reverse annealing performs a series of local searches that refine existing solutions. The combination of forward and reverse annealing significantly improves performance compared to forward annealing alone for all but the shortest run times.
An Economical Robotic Arm for Playing Chess Using Visual Servoing

The proposed paper outlines the design of an economical robotic arm which is used to visualize the chess board and play with the opponent using visual servoing system. We have used the FaBLab RUC's mechanical design prototype proposed and have further used Solidworks software to design the 4 jointed gripper. The proposed methodology involves detecting the squares on the corners of the chessboard and further segmenting the images. This is followed by using convolutional neural networks to train and recognize the image in order to determine the movement of the chess pieces. To trace the manipulator, Kanade-Lucas-Tomasi method is used in the visual servoing system. An Arduino uses Gcode commands to interact with the robotic arm. Game Decisions are taken with the help of chess game engine the pieces on the board are moved accordingly. Thus a didactic robotic arm is developed for decision making and data processing, serving to be a good opponent in playing chess.
Flower: A Friendly Federated Learning Research Framework

Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud. However, FL is difficult to implement and deploy in practice, considering the heterogeneity in mobile devices, e.g., different programming languages, frameworks, and hardware accelerators. Although there are a few frameworks available to simulate FL algorithms (e.g., TensorFlow Federated), they do not support implementing FL workloads on mobile devices. Furthermore, these frameworks are designed to simulate FL in a server environment and hence do not allow experimentation in distributed mobile settings for a large number of clients. In this paper, we present Flower (https://flower.dev/), a FL framework which is both agnostic towards heterogeneous client environments and also scales to a large number of clients, including mobile and embedded devices. Flower's abstractions let developers port existing mobile workloads with little overhead, regardless of the programming language or ML framework used, while also allowing researchers flexibility to experiment with novel approaches to advance the state-of-the-art. We describe the design goals and implementation considerations of Flower and show our experiences in evaluating the performance of FL across clients with heterogeneous computational and communication capabilities.
MHSA-Net: Multihead Self-Attention Network for Occluded Person Re-Identification

This paper presents a novel person re-identification model, named Multi-Head Self-Attention Network (MHSA-Net), to prune unimportant information and capture key local information from person images. MHSA-Net contains two main novel components: Multi-Head Self-Attention Branch (MHSAB) and Attention Competition Mechanism (ACM). The MHSAB adaptively captures key local person information, and then produces effective diversity embeddings of an image for the person matching. The ACM further helps filter out attention noise and non-key information. Through extensive ablation studies, we verified that the Multi-Head Self-Attention Branch (MHSAB) and Attention Competition Mechanism (ACM) both contribute to the performance improvement of the MHSA-Net. Our MHSA-Net achieves competitive performance in the standard and occluded person Re-ID tasks.
Deep Anomaly Detection for CNC Machine Cutting Tool Using Spindle Current Signals

In recent years, industrial production has become more and more automated. Machine cutting tool as an important part of industrial production have a large impact on the production efficiency and costs of products. In a real manufacturing process, tool breakage often occurs in an instant without warning, which results a extremely unbalanced ratio of the tool breakage samples to the normal ones. In this case, the traditional supervised learning model can not fit the sample of tool breakage well, which results to inaccurate prediction of tool breakage. In this paper, we use the high precision Hall sensor to collect spindle current data of computer numerical control (CNC). Combining the anomaly detection and deep learning methods, we propose a simple and novel method called CNN-AD to solve the class-imbalance problem in tool breakage prediction. Compared with other prediction algorithms, the proposed method can converge faster and has better accuracy.
Design and Implementation of High-Performance ECC Processor with Unified Point Addition on Twisted Edwards Curve

With the swift evolution of wireless technologies, the demand for the Internet of Things (IoT) security is rising immensely. Elliptic curve cryptography (ECC) provides an attractive solution to fulfill this demand. In recent years, Edwards curves have gained widespread acceptance in digital signatures and ECC due to their faster group operations and higher resistance against side-channel attacks (SCAs) than that of the Weierstrass form of elliptic curves. In this paper, we propose a high-speed, low-area, simple power analysis (SPA)-resistant field-programmable gate array (FPGA) implementation of ECC processor with unified point addition on a twisted Edwards curve, namely Edwards25519. Efficient hardware architectures for modular multiplication, modular inversion, unified point addition, and elliptic curve point multiplication (ECPM) are proposed. To reduce the computational complexity of ECPM, the ECPM scheme is designed in projective coordinates instead of affine coordinates. The proposed ECC processor performs 256-bit point multiplication over a prime field in 198,715 clock cycles and takes 1.9 ms with a throughput of 134.5 kbps, occupying only 6543 slices on Xilinx Virtex-7 FPGA platform. It supports high-speed public-key generation using fewer hardware resources without compromising the security level, which is a challenging requirement for IoT security.
Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities

Decentralized cryptocurrencies have gained a lot of attention over the last decade. Bitcoin was introduced as the first cryptocurrency to allow direct online payments without relying on centralized financial entities. The use of Bitcoin has vastly grown as a financial asset rather than just a tool for online payments. A lot of cryptocurrencies have been created since 2011 with Bitcoin dominating the cryptocurrencies' market. With plenty of cryptocurrencies being used as financial assets and with millions of trades being executed through different exchange services, cryptocurrencies are susceptible to trading problems and challenges similar to those traditionally encountered in the financial domain. Price and trend prediction, volatility prediction, portfolio construction and fraud detection are some examples related to trading. In addition, there are other challenges that are specific to the domain of cryptocurrencies such as mining, cybersecurity, anonymity and privacy. In this paper, we survey the application of artificial intelligence techniques to address these challenges for cryptocurrencies with their vast amount of daily transactions, trades and news that are beyond human capabilities to analyze and learn from. This paper discusses the recent research work done in this emerging area and compares them in terms of used techniques and datasets. It also highlights possible research gaps and some potential areas for improvement.
Multi-party Energy Management of Energy Hub: A Hybrid Approach with Stackelberg Game and Blockchain

In an integrated energy distribution system (IEDS), an energy hub has been introduced and deemed to be a suitable tool for managing and integrating multi-party energy forms. Due to different energies having diverse characteristics and being coupled with each other, it is difficult for an energy hub to implement the optimal scheduling of multiple energy sources. Therefore, an energy optimization management model is proposed based on the Stackelberg game, which considers the exergy conversion of multi-party energy sources in different operation modes. The problem is solved by the two-layer distributed optimization algorithm, in which the energy hub acts as the leader and is followed by the users. Furthermore, in order to alleviate the deception, malicious tampering, subpeption, and other secure risks in energy trading, blockchain is introduced into the energy hub and the concept of exergy coin (EC) is proposed. A credit-based blockchain framework and concurrent block building consensus process is explored to reduce the calculation cost and promote the exergy trading efficiency. Finally, the case study shows how the proposed method can effectively optimize energy scheduling and configure a more reasonable energy solution.
Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Background Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Methods Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P&lt;.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P&lt;.001). Conclusions DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.
Fundamental aspects of noise in analog-hardware neural networks

We study and analyze the fundamental aspects of noise propagation in recurrent as well as deep, multilayer networks. The motivation of our study is neural networks in analog hardware; yet, the methodology provides insight into networks in general. Considering noisy linear nodes, we investigate the signal-to-noise ratio at the network's outputs, which determines the upper limit of computational precision. We consider additive and multiplicative noise, which can be purely local as well as correlated across populations of neurons. This covers the chief internal-perturbations of hardware networks, and noise amplitudes were obtained from a physically implemented neural network. Analytically derived descriptions agree exceptionally well with numerical data, enabling clear identification of the components critical for management and mitigation of noise. We find that analog neural networks are surprisingly robust, in particular, against noisy neurons. Their uncorrelated perturbations are almost fully suppressed, while correlated noise can accumulate. Our work identifies notoriously sensitive points while highlighting a surprising robustness of such computational systems.
Mean-field inference methods for neural networks

Abstract Machine learning algorithms relying on deep neural networks recently allowed a great leap forward in artificial intelligence. Despite the popularity of their applications, the efficiency of these algorithms remains largely unexplained from a theoretical point of view. The mathematical description of learning problems involves very large collections of interacting random variables, difficult to handle analytically as well as numerically. This complexity is precisely the object of study of statistical physics. Its mission, originally pointed toward natural systems, is to understand how macroscopic behaviors arise from microscopic laws. Mean-field methods are one type of approximation strategy developed in this view. We review a selection of classical mean-field methods and recent progress relevant for inference in neural networks. In particular, we remind the principles of derivations of high-temperature expansions, the replica method and message passing algorithms, highlighting their equivalences and complementarities. We also provide references for past and current directions of research on neural networks relying on mean-field methods.
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder

Unsupervised anomaly detection (AD) is a major topic in the field of Cyber-Physical Production Systems (CPPSs).A closely related concern is dimensionality reduction (DR) which is: 1) often used as a preprocessing step in an AD solution, 2) a sort of AD, if a measure of observation conformity to the learned data manifold is provided.We argue that the two aspects can be complementary in a CPPS anomaly detection solution.In this work, we focus on the nonlinear autoencoder (AE) as a DR/AD approach.The contribution of this work is: 1) we examine the suitability of AE reconstruction error as an AD decision criterion in CPPS data.2) we analyze its relation to a potential second-phase AD approach in the AE latent space 3) we evaluate the performance of the approach on three real-world datasets.Moreover, the approach outperforms state-of-the-art techniques, alongside a relatively simple and straightforward application.
A Corpus for Modeling User and Language Effects in Argumentation on Online Debating

Existing argumentation datasets have succeeded in allowing researchers to develop computational methods for analyzing the content, structure and linguistic features of argumentative text.They have been much less successful in fostering studies of the effect of "user" traits -characteristics and beliefs of the participants -on the debate/argument outcome as this type of user information is generally not available.This paper presents a dataset of 78, 376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles.We also complete an example study using the dataset to analyze the effect of selected user traits on the debate outcome in comparison to the linguistic features typically employed in studies of this kind.
Quantum wave–particle superposition in a delayed-choice experiment

Wave-particle duality epitomizes the counterintuitive character of quantum physics. A striking illustration is the quantum delay-choice experiment, which is based on Wheeler's classic delayed-choice gedanken experiment, but with the addition of a quantum-controlled device enabling wave-to-particle transitions. Here we realize a quantum delayed-choice experiment in which we control the wave and the particle states of photons and in particular the phase between them, thus directly establishing the created quantum nature of the wave-particle. We generate three-photon entangled states and inject one photon into a Mach--Zehnder interferometer embedded in a 187-m-long two-photon Hong-Ou-Mandel interferometer. The third photon is sent 141m away from the interferometers and remotely prepares a two-photon quantum gate according to independent active choices under Einstein locality conditions. We have realized transitions between wave and particle states in both classical and quantum scenarios, and therefore tests of the complementarity principle that go fundamentally beyond earlier implementations.
Linking Gaussian process regression with data-driven manifold embeddings for nonlinear data fusion

In statistical modelling with Gaussian process regression, it has been shown that combining (few) high-fidelity data with (many) low-fidelity data can enhance prediction accuracy, compared to prediction based on the few high-fidelity data only. Such information fusion techniques for multi-fidelity data commonly approach the high-fidelity model fh(t) as a function of two variables (t, s), and then use fl(t) as the s data. More generally, the high-fidelity model can be written as a function of several variables (t, s1, s2….); the low-fidelity model fl and, say, some of its derivatives can then be substituted for these variables. In this paper, we will explore mathematical algorithms for multi-fidelity information fusion that use such an approach towards improving the representation of the high-fidelity function with only a few training data points. Given that fh may not be a simple function-and sometimes not even a function-of fl, we demonstrate that using additional functions of t, such as derivatives or shifts of fl, can drastically improve the approximation of fh through Gaussian processes. We also point out a connection with 'embedology' techniques from topology and dynamical systems. Our illustrative examples range from instructive caricatures to computational biology models, such as Hodgkin-Huxley neural oscillations.
Unscented Kalman Filters for Riemannian State-Space Systems

Unscented Kalman filters (UKFs) have become popular in the research community. Most UKFs work only with Euclidean systems, but in many scenarios it is advantageous to consider systems with state-variables taking values on Riemannian manifolds. However, we can still find some gaps in the literature's theory of UKFs for Riemannian systems: for instance, the literature has not yet developed first, Riemannian extensions of some fundamental concepts of the UKF theory (e.g., extensions of σ-representation, unscented transformation, additive UKF, augmented UKF, additive-noise system), second, proofs of some steps in their UKFs for Riemannian systems (e.g., proof of sigma points parameterization by vectors, state correction equations, noise statistics inclusion), and third, relations between their UKFs for Riemannian systems. In this paper, we attempt to develop a theory capable of filling these gaps. Among other results, we propose Riemannian extensions of the main concepts in the UKF theory (including closed forms), justify all steps of the proposed UKFs, and provide a framework able to relate UKFs for particular manifolds among themselves and with UKFs for Euclidean spaces. Compared with UKFs for Riemannian manifolds of the literature, the proposed filters are more consistent, formally principled, and general. An example of satellite attitude tracking illustrates the proposed theory.
Generation of arbitrary all-photonic graph states from quantum emitters

Abstract We present protocols to generate arbitrary photonic graph states from quantum emitters that are in principle deterministic. We focus primarily on two-dimensional cluster states of arbitrary size due to their importance for measurement-based quantum computing. Our protocols for these and many other types of two-dimensional graph states require a linear array of emitters in which each emitter can be controllably pumped, rotated about certain axes, and entangled with its nearest neighbors. We show that an error on one emitter produces a localized region of errors in the resulting graph state, where the size of the region is determined by the coordination number of the graph. We describe how these protocols can be implemented for different types of emitters, including trapped ions, quantum dots, and nitrogen-vacancy centers in diamond.
Demonstration of Quantum Image Edge Extration Enhancement Through Improved Sobel Operator

Quantum image processing is a system where both quantum computing and image processings are key.Now it is required to investigate how to apply image processing concepts such as image edge detection and improved Sobel operator, to quantum image processing.Just as traditional image processing helps to image manipulation, our efforts in quantum image processing contribute to the further development of quantum algorithm and quantum information theory.Edge detection is an important problem in traditional image processing, and image edge detection algorithm can filter image edge features and retain important attributes.We proposed a quantum edge detection algorithm, which employs improved Sobel operator to improve the performance, so as to solve the problem of unsatisfactory traditional image edge detection methods.In practical application, the amount of image data to be processed increases sharply, and the computing power of classical computer becomes a limitation.Quantum information processing can effectively accelerate many classical problems by virtue of quantum mechanical characteristics, such as quantum superposition, entanglement, parallelism.Our proposed method uses the novel enhanced quantum representation to store quantum images, which stores all the pixels in the image in a superimposed state, realizing parallel computation.The improved eight-direction Sobel operator is used to calculate the gray gradient, and the quantum circuit is designed to realize quantum edge detection.Simulation results have shown that our algorithm can extract edge with dimension 2 n × 2 n when the computational complexity is O(n 2 + 2 q+3 ).The improved algorithm can detect more edge details and has strong adaptability.