Reviewerly/exHarmony huggingface dataset:
Phase transitions and gaps in quantum random energy models

By using a previously established exact characterization of the ground state of random potential systems in the thermodynamic limit, we determine the ground and first excited energy levels of quantum random energy models, discrete and continuous. We rigorously establish the existence of a universal first order quantum phase transition, obeyed by both the ground and the first excited states. The presence of an exponentially vanishing minimal gap at the transition is general but, quite interestingly, the gap averaged over the realizations of the random potential is finite. This fact leaves still open the chance for some effective quantum annealing algorithm, not necessarily based on a quantum adiabatic scheme.
Revisiting the Isoperimetric Graph Partitioning Problem

Isoperimetric graph partitioning, which is also known as the Cheeger cut, is NP-hard in its original form. In the literature, multiple modifications to this problem have been proposed to obtain approximation algorithms for clustering applications. In the context of image segmentation, a heuristic continuous relaxation to this problem introduced by Leo Grady and Eric L. Schwartz has yielded good quality results. This algorithm is based on solving a linear system of equations involving the Laplacian of the image graph. Furthermore, the same algorithm applied to a maximum spanning tree (MST) of the image graph was shown to produce similar results at a much lesser computational cost. However, the data reduction step (i.e., considering an MST, a much sparser graph compared to the original graph) leading to a faster yet useful algorithm has not been analyzed. In this paper, we revisit the isoperimetric graph partitioning problem and rectify a few discrepancies in the simplifications of the heuristic continuous relaxation, leading to a better interpretation of what is really done by this algorithm. We then use the power watershed (PW) framework to show that is enough to solve the relaxed isoperimetric graph partitioning problem on the graph induced by the union of MSTs (UMST) with a seed constraint. The UMST has a lesser number of edges compared to the original graph, thus improving the speed of sparse matrix multiplication. Furthermore, given the interest of PW framework in solving the relaxed seeded isoperimetric partitioning problem, we discuss the links between the PW limit of the discrete isoperimetric graph partitioning and watershed cuts. We then illustrate with experiments, a detailed comparison of solutions to the relaxed seeded isoperimetric partitioning problem on the original graph with the ones on the UMST and an MST. Our study opens many research directions, which are discussed in the conclusion section.
Energy-Oriented Analysis of HPC Cluster Queues: Emerging Metrics for Sustainable Data Center

This work analyzes a very subtle kind of energy metrics for Data Centers (DCs), namely productivity metrics which affect the global energy efficiency assessment in a DC since they focus on the energy used for processing computing operations. By exploiting the available set of energy consumption data concerning operating systems in ENEA-DC, HPC-Cluster, the authors evaluated the energy consumed by different queues with several running applications. The queues energy waste has been calculated to provide an assessment for the ineffective use of computation-related energy load within the Cluster. This work shows an increment innovation beyond state-of-the-art for productivity metrics and useful work, and it will also help provide an invaluable insight into useful energy use and the use of enhanced sustainability metrics with the goal of driving a more sustainable DC. Additionally, sustainability concept in DC operations is driven by estimation of its indirect carbon emissions, which is shown in this work.
Domain and range for angelic and demonic compositions

We give finite axiomatizations for the varieties generated by representable domain--range algebras when the semigroup operation is interpreted as angelic or demonic composition, respectively.
Feature selection as Monte-Carlo Search in Growing Single Rooted Directed Acyclic Graph by Best Leaf Identification

Monte Carlo tree search (MCTS) has received considerable interest due to its spectacular success in the difficult problem of computer Go and also proved beneficial in a range of other domains. A major issue that has received little attention in the MCTS literature is the fact that, in most games, different actions can lead to the same state, that may lead to a high degree of redundancy in tree representation and unnecessary additional computational cost. We extend MCTS to single rooted directed acyclic graph (SR-DAG), and consider the Best Arm Identification (BAI) and the Best Leaf Identification (BLI) problem of an expanding SR-DAG of arbitrary depth. We propose algorithms that are (∊, σ)-correct in the fixed confidence setting, and prove an asymptotic upper bounds of sample complexity for our BAI algorithm. As a major application for our BLI algorithm, a novel approach for Feature Selection is proposed by representing the feature set space as a SR-DAG and repeatedly evaluating feature subsets until a candidate for the best leaf is returned, a proof of concept is shown on benchmark data sets.MSC codesKeywords:DAG, MCTSBAIBLIFeature Selection
Some improvements in Monte Carlo tree search algorithms for sudden death games

The average reward problem in the traditional Monte-Carlo tree search algorithms troubles the sudden death games for a long time, because of the average reward criterion will reduce the probability of a deterministic result. But this does not bother the non-sudden death games, such as Go, which can focus only on higher win rates rather than higher scores. In this work, we propose the miniMax-Monte-Carlo tree search with depth rewards to Outer-Open Gomoku (a variant of Gomoku) to discover a forced win/lose without any human knowledge, evaluation function, or pre-training. And it can solve not only the average reward problem but also the inaccurate win-rate problem in deep playout simulation in sudden death games. Finally, we propose a new integrated framework named BBQ (Big, Best, Quick win) MCTS for improving the performance of traditional MCTS.
Joint reconstruction and prediction\break of random dynamical systems under\break borrowing of strength

We propose a Bayesian nonparametric model based on Markov Chain Monte Carlo methods for the joint reconstruction and prediction of discrete time stochastic dynamical systems based on m-multiple time-series data, perturbed by additive dynamical noise. We introduce the Pairwise Dependent Geometric Stick-Breaking Reconstruction (PD-GSBR) model, which relies on the construction of an m-variate nonparametric prior over the space of densities supported over Rm. We are focusing on the case where at least one of the time-series has a sufficiently large sample size representation for an independent and accurate Geometric Stick-Breaking estimation, as defined in Merkatas et al. [Chaos 27, 063116 (2017)]. Our contention is that whenever the dynamical error processes perturbing the underlying dynamical systems share common characteristics, underrepresented data sets can benefit in terms of model estimation accuracy. The PD-GSBR estimation and prediction procedure is demonstrated specifically in the case of maps with polynomial nonlinearities of an arbitrary degree. Simulations based on synthetic time-series are presented.
Galilean Transformations of Kinetic Energy, Work, and Potential Energy

Work, kinetic energy (KE), and potential energy (PE) are key physics concepts, taught in essentially every introductory physics course along with the physical laws such as the work-kinetic energy theorem and the conservation of mechanical energy, which describe how the values of those quantities change during various processes. Quite appropriately for introductory courses, little attention is paid to how those concepts play out when implemented via observations in reference frames moving with respect to each other.
Visual Analysis of Collective Anomalies Using Faceted High-Order Correlation Graphs

Successfully detecting, analyzing, and reasoning about collective anomalies is important for many real-life application domains (e.g., intrusion detection, fraud analysis, software security). The primary challenges to achieving this goal include the overwhelming number of low-risk events and their multimodal relationships, the diversity of collective anomalies by various data and anomaly types, and the difficulty in incorporating the domain knowledge of experts. In this paper, we propose the novel concept of the faceted High-Order Correlation Graph (HOCG). Compared with previous, low-order correlation graphs, HOCG achieves better user interactivity, computational scalability, and domain generality through synthesizing heterogeneous types of objects, their anomalies, and the multimodal relationships, all in a single graph. We design elaborate visual metaphors, interaction models, and the coordinated multiple view based interface to allow users to fully unleash the visual analytics power of the HOCG. We conduct case studies for three application domains and collect feedback from domain experts who apply our method to these scenarios. The results demonstrate the effectiveness of the HOCG in the overview of point anomalies, the detection of collective anomalies, and the reasoning process of root cause analyses.
Web Search Skill Evaluation from Eye and Mouse Momentum

Conventionally, it has been pointed out that the eye movement when using the PC correlates with the movement of the mouse, and those are utilized as additional information on the usability improvement and other observed data. This study had analized the eye movement and the mouse movement data, and had examined the method to estimate the user’s skill.
Efficient Algorithms for Hierarchical Graph-Based Segmentation Relying on the Felzenszwalb–Huttenlocher Dissimilarity

Hierarchical image segmentation provides a region-oriented scale-space, i.e. a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. However, most image segmentation algorithms, among which a graph-based image segmentation method relying on a region merging criterion was proposed by Felzenszwalb–Huttenlocher in 2004, do not lead to a hierarchy. In order to cope with a demand for hierarchical segmentation, Guimarães et al. proposed in 2012 a method for hierarchizing the popular Felzenszwalb–Huttenlocher method, without providing an algorithm to compute the proposed hierarchy. This paper is devoted to providing a series of algorithms to compute the result of this hierarchical graph-based image segmentation method efficiently, based mainly on two ideas: optimal dissimilarity measuring and incremental update of the hierarchical structure. Experiments show that, for an image of size 321 × 481 pixels, the most efficient algorithm produces the result in half a second whereas the most naive one requires more than 4 h.
Improving the Robustness of Entangled States by Basis Transformation

In the practical application of quantum entanglement, entangled particles usually need to be distributed to many distant parties or stored in different quantum memories. In these processes, entangled particles unavoidably interact with their surrounding environments, respectively. We here systematically investigate the entanglement-decay laws of cat-like states under independent Pauli noises with unbalanced probability distribution of three kinds of errors. We show that the robustness of cat-like entangled states is not only related to the overall noise strength and error distribution parameters, but also to the basis of qubits. Moreover, we find that whether a multi-qubit state is more robust in the computational basis or transversal basis depends on the initial entanglement and number of qubits of the state as well as the overall noise strength and error distribution parameters of the environment. However, which qubit basis is conductive to enhancing the robustness of two-qubit states is only dependent on the error distribution parameters. These results imply that one could improve the intrinsic robustness of entangled states by simply transforming the qubit basis at the right moment. This robustness-improving method does not introduce extra particles and works in a deterministic manner.
Comparing Knowledge-Based Reinforcement Learning to Neural Networks in a Strategy Game

The paper reports on an experiment, in which a Knowledge-Based Reinforcement Learning (KB-RL) method was compared to a Neural Network (NN) approach in solving a classical Artificial Intelligence (AI) task. In contrast to NNs, which require a substantial amount of data to learn a good policy, the KB-RL method seeks to encode human knowledge into the solution, considerably reducing the amount of data needed for a good policy. By means of Reinforcement Learning (RL), KB-RL learns to optimize the model and improves the output of the system. Furthermore, KB-RL offers the advantage of a clear explanation of the taken decisions as well as transparent reasoning behind the solution. The goal of the reported experiment was to examine the performance of the KB-RL method in contrast to the Neural Network and to explore the capabilities of KB-RL to deliver a strong solution for the AI tasks. The results show that, within the designed settings, KB-RL outperformed the NN, and was able to learn a better policy from the available amount of data. These results support the opinion that Artificial Intelligence can benefit from the discovery and study of alternative approaches, potentially extending the frontiers of AI.
Predicting the Usability of the Dice CAPTCHA via Artificial Neural Network

This paper introduces a new study of the CAPTCHA usability which analyses the predictability of the solution time, also called response time, to solve the Dice CAPTCHA. This is accomplished by proposing a new artificial neural network model for predicting the response time from known personal and demographic features of the users who solve the CAPTCHA: (i) age, (ii) device on which the CAPTCHA is solved, and (iii) Web use in years. The experiment involves a population of 197 Internet users, who is required to solve two types of Dice CAPTCHA on laptop or tablet computer. The data collected from the experiment is subject to the artificial neural network model which is trained and tested to predict the response time. The proposed analysis provides new results of usability of the Dice CAPTCHA and important suggestions for designing new CAPTCHAs which could be closer to an “ideal” CAPTCHA.
Temporal Limitations of the Standard Leaky Integrate and Fire Model

Itti and Koch’s Saliency Model has been used extensively to simulate fixation selection in a variety of tasks from visual search to simple reaction times. Although the Saliency Model has been tested for its spatial prediction of fixations in visual salience, it has not been well tested for their temporal accuracy. Visual tasks, like search, invariably result in a positively skewed distribution of saccadic reaction times over large numbers of samples, yet we show that the leaky integrate and fire (LIF) neuronal model included in the classic implementation of the model tends to produce a distribution shifted to shorter fixations (in comparison with human data). Further, while parameter optimization using a genetic algorithm and Nelder–Mead method does improve the fit of the resulting distribution, it is still unable to match temporal distributions of human responses in a visual task. Analysis of times for individual images reveal that the LIF algorithm produces initial fixation durations that are fixed instead of a sample from a distribution (as in the human case). Only by aggregating responses over many input images do they result in a distribution, although the form of this distribution still depends on the input images used to create it and not on internal model variability.
Characterization of multilevel quantum coherence without ideal measurements

Coherent superpositions are one of the hallmarks of quantum mechanics and are vital for any quantum mechanical device to outperform the classically achievable. Generically, superpositions are verified in interference experiments, but despite their longstanding central role we know very little about how to extract the number of coherently superposed amplitudes from a general interference pattern. A fundamental issue is that performing a phase-sensitive measurement is as challenging as creating a coherent superposition, so that assuming a perfectly implemented measurement for verification of quantum coherence is hard to justify. In order to overcome this issue, we construct a coherence certifier derived from simple statistical properties of an interference pattern, such that any imperfection in the measurement can never over-estimate the number of coherently superposed amplitudes. We numerically test how robust this measure is to under-estimating the coherence in the case of imperfect state preparation or measurement, and find it to be very resilient in both cases.
The Lifecycle of a User Transaction in a Hyperledger Fabric Blockchain Network Part 2: Order and Validate

The paper describes the second part of the happy path of a user transaction through a Hyperledger Fabric Blockchain Network. The full cycle of a user transaction originates from the Client Application (an actor outside the Hyperledger Ledger Network but inside an organization that is part of a set of organizations that jointly run the Hyperledger Fabric Blockchain Network). In the process of making the Hyperledger Fabric Blockchain network knowledge explicit, essential parts of FBM were applied in cooperation with the developers of the Fabric Blockchain platform.
Mobile Robot Obstacle Avoidance Based on Deep Reinforcement Learning

Abstract Obstacle avoidance is one of the core problems in the field of mobile robot autonomous navigation. This paper aims to solve the obstacle avoidance problem using Deep Reinforcement Learning. In previous work, various mathematical models have been developed to plan collision-free paths for such robots. In contrast, our method enables the robot to learn by itself from its experiences, and then fit a mathematical model by updating the parameters of a neural network. The derived mathematical model is capable of choosing an action directly according to the input sensor data for the mobile robot. In this paper, we develop an obstacle avoidance framework based on deep reinforcement learning. A 3D simulator is designed as well to provide the training and testing environments. In addition, we developed and compared obstacle avoidance methods based on different Deep Reinforcement Learning strategies, such as Deep Q-Network (DQN), Double Deep Q-Network (DDQN) and DDQN with Prioritized Experience Replay (DDQN-PER) using our simulator.
The economical design of a hand-gesture and bluetooth controlled wheel-chair by integrating indigenous components: mobility aid for the disabled

Millions of people around the world are handicapped and most of the amputees belong to a war struck third world regions. Any amputee who is unable to walk needs prosthetics which are costly or wheel chairs for movement. Manual wheel chairs are not easy to use. Most of the people cannot afford high quality motorized expensive wheel chairs. Keeping all this in mind, a very economical hand gesture-controlled wheel chair design is proposed. This wheel chair reads the signal sent from control glove and can also be switched to mobile application control where needed. The overall implementation design has simplicity and robustness which are dire features of wheelchair applications.
Co-optimization of ethnic-pattern segmentation based on hierarchical patch matching

The segmentation of ethnic patterns is vital for digital analyses of ethnic cultures. Although existing image segmentation methods can properly segment natural images, they cannot preserve the main structure of the image. They also require many user interactions to segment ethnic patterns. This paper presents a co-optimization method that segments ethnic patterns using hierarchical patch matching. Exploiting the repetitive characteristics of ethnic patterns, the method first automatically detects all similar patterns using a global patch match. Second, the relative orientation between similar patterns is estimated by a local patch match, and an accurate dense correspondence is constructed by a constrained patch match. Finally, the pre-segmentation of ethnic patterns is co-optimized to preserve their main structures. Our method can segment all similar ethnic patterns into separate elements with dense correspondence. Besides reducing the user interaction and improving the segmentation accuracy, the proposed method improves the quality of ethnic pattern digital analysis such as vectorization. Experiments demonstrated the validity of our method.
Head-up Display (HUD) Requirements Posed by Aspects of Human Visual System

HUD is a key cutting edge technology for automobile display systems and is expected to become increasingly popular in the near future. Before starting mass-production, we should carefully consider (1) effects of user diversity (e.g., skill, age, and color vision deficiency, etc.) and (2) effects of HUD on the human visual system (e.g., high dynamic range of background illumination, stereopsis conflict created between background still and/or moving information (actual) and moving information with driver (artificial), and human-vision characteristics of resource distribution for different direction motions, etc.). In this paper, we first describe (1) a primitive analysis of the effects of HUD on the human visual system from the aspects of background illumination and color vision deficiency and (2) a summary of our previous work on the visibility of moving information. Our key finding is the necessity of a diffuser and an attenuation filter for separating real background information from the artificial information.
A framework for designing an immersive language learning environment integrated with educational robots and IoT-based toys

In view of the benefits of and success in acquiring multiple languages in an immersive learning environment while young, this research aims to create an immersive language learning environment for young children to acquire multiple languages utilizing robots and IoT (Internet of Things) -based toys. This paper presents the results from the first two stages of this project aiming to develop a design framework to guide the development of such an immersive environment. Our extensive review of the relevant literature indicates that the framework should, at least, consist of five main pedagogical considerations: language input, activity design, interaction design, toy design and robot design. In each of the five dimensions, a number of key factors should also be addressed in creating an effective learning environment. The development of the design framework is to serve as a road map providing design principles and guidelines for educators and researcher to create an immersive learning environment.