Reviewerly/exHarmony huggingface dataset:
Automated Generation of Shuttling Sequences for a Linear Segmented Ion Trap Quantum Computer

A promising approach for scaling-up trapped-ion quantum computer platforms is by storing multiple trapped-ion qubit sets (&amp;apos;ion crystals&amp;apos;) in segmented microchip traps and to interconnect these via physical movement of the ions (&amp;apos;shuttling&amp;apos;). Already for realizing quantum circuits with moderate complexity, the design of suitable qubit assignments and shuttling schedules require automation. Here, we describe and test algorithms which address exactly these tasks. We describe an algorithm for fully automated generation of shuttling schedules, complying to constraints imposed by a given trap structure. Furthermore, we introduce different methods for initial qubit assignment and compare these for random circuit (of up to 20 qubits) and quantum Fourier transform-like circuits, and generalized Toffoli gates of up to 40 qubits each. We find that for quantum circuits which contain a fixed structure, advanced assignment algorithms can serve to reduce the shuttling overhead.
Quantum Circuit Compiler for a Shuttling-Based Trapped-Ion Quantum Computer

The increasing capabilities of quantum computing hardware and the challenge of realizing deep quantum circuits require fully automated and efficient tools for compiling quantum circuits. To express arbitrary circuits in a sequence of native gates specific to the quantum computer architecture, it is necessary to make algorithms portable across the landscape of quantum hardware providers. In this work, we present a compiler capable of transforming and optimizing a quantum circuit targeting a shuttling-based trapped-ion quantum processor. It consists of custom algorithms set on top of the quantum circuit framework Pytket. The performance was evaluated for a wide range of quantum circuits and the results show that the gate counts can be reduced by factors up to 5.1 compared to standard Pytket and up to 2.2 compared to standard Qiskit compilation.
Stochastic Adversarial Noise in the “Black Box” Optimization Problem

This paper is devoted to the study of the solution of a stochastic convex black box optimization problem. Where the black box problem means that the gradient-free oracle only returns the value of objective function, not its gradient. We consider non-smooth and smooth setting of the solution to the black box problem under adversarial stochastic noise. For two techniques creating gradient-free methods: smoothing schemes via $$L_1$$ and $$L_2$$ randomizations, we find the maximum allowable level of adversarial stochastic noise that guarantees convergence. Finally, we analyze the convergence behavior of the algorithms under the condition of a large value of noise level.
Subgroup Discovery Through Sharp Transitions Using Implicative Type Rules

Subgroup Discovery is a descriptive data mining technique for obtaining subgroups with unusual statistical characteristics with respect to a given target variable. In this paper, unlike existing approaches, we capture the data in the form of implicative-type fuzzy rules and propose an algorithm to determine sharp transitions in the consequent when there is a minimal change in the antecedent. Our study contained herein highlights the role and employability of fuzzy implication functions in such settings through illustrative examples with several real datasets.
HAE: A Hybrid Cryptographic Algorithm for Blockchain Medical Scenario Applications

The integration of cryptographic algorithms like Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) is pivotal in bolstering the core attributes of blockchain technology, especially in achieving decentralization, tamper resistance, and anonymization within the realm of medical applications. Despite their widespread utilization, the conventional AES and ECC face significant hurdles in security and efficiency when dealing with expansive medical data, posing a challenge to the effective preservation of patient privacy. In light of these challenges, this study introduces HAE (hybrid AES and ECC), an innovative hybrid cryptographic algorithm that ingeniously amalgamates the robustness of AES with the agility of ECC. HAE is designed to symmetrically encrypt original data with AES while employing ECC for the asymmetric encryption of the initial AES key. This strategy not only alleviates the complexities associated with AES key management but also enhances the algorithm’s security without compromising its efficiency. We provide an in-depth exposition of HAE’s deployment within a framework tailored for medical scenarios, offering empirical insights into its enhanced performance metrics. Our experimental outcomes underscore HAE’s exemplary security, time efficiency, and optimized resource consumption, affirming its potential as a breakthrough advancement for augmenting blockchain applications in the medical sector, heralding a new era of enhanced data security and privacy within this critical domain.
Community-Based Digital Contact Tracing of Emerging Infectious Diseases: Design and Implementation Study With Empirical COVID-19 Cases

Contact tracing for containing emerging infectious diseases such as COVID-19 is resource intensive and requires digital transformation to enable timely decision-making.This study demonstrates the design and implementation of digital contact tracing using multimodal health informatics to efficiently collect personal information and contain community outbreaks. The implementation of digital contact tracing was further illustrated by 3 empirical SARS-CoV-2 infection clusters.The implementation in Changhua, Taiwan, served as a demonstration of the multisectoral informatics and connectivity between electronic health systems needed for digital contact tracing. The framework incorporates traditional travel, occupation, contact, and cluster approaches and a dynamic contact process enabled by digital technology. A centralized registry system, accessible only to authorized health personnel, ensures privacy and data security. The efficiency of the digital contact tracing system was evaluated through a field study in Changhua.The digital contact tracing system integrates the immigration registry, communicable disease report system, and national health records to provide real-time information about travel, occupation, contact, and clusters for potential contacts and to facilitate a timely assessment of the risk of COVID-19 transmission. The digitalized system allows for informed decision-making regarding quarantine, isolation, and treatment, with a focus on personal privacy. In the first cluster infection, the system monitored 665 contacts and isolated 4 (0.6%) cases; none of the contacts (0/665, 0%) were infected during quarantine. The estimated reproduction number of 0.92 suggests an effective containment strategy for preventing community-acquired outbreak. The system was also used in a cluster investigation involving foreign workers, where none of the 462 contacts (0/462, 0%) tested positive for SARS-CoV-2.By integrating the multisectoral database, the contact tracing process can be digitalized to provide the information required for risk assessment and decision-making in a timely manner to contain a community-acquired outbreak when facing the outbreak of emerging infectious disease.
A Graphical Multi-Fidelity Gaussian Process Model, with Application to Emulation of Heavy-Ion Collisions

With advances in scientific computing and mathematical modeling, complex scientific phenomena such as galaxy formations and rocket propulsion can now be reliably simulated. Such simulations can however be very time-intensive, requiring millions of CPU hours to perform. One solution is multi-fidelity emulation, which uses data of different fidelities to train an efficient predictive model which emulates the expensive simulator. For complex scientific problems and with careful elicitation from scientists, such multi-fidelity data may often be linked by a directed acyclic graph (DAG) representing its scientific model dependencies. We thus propose a new Graphical Multi-fidelity Gaussian Process (GMGP) model, which embeds this DAG structure (capturing scientific dependencies) within a Gaussian process framework. We show that the GMGP has desirable modeling traits via two Markov properties, and admits a scalable algorithm for recursive computation of the posterior mean and variance along at each depth level of the DAG. We also present a novel experimental design methodology over the DAG given an experimental budget, and propose a nonlinear extension of the GMGP via deep Gaussian processes. The advantages of the GMGP are then demonstrated via a suite of numerical experiments and an application to emulation of heavy-ion collisions, which can be used to study the conditions of matter in the Universe shortly after the Big Bang. The proposed model has broader uses in data fusion applications with graphical structure, which we further discuss.
Three-dimensional path planning for a novel sediment sampler in ocean environment based on an improved mutation operator genetic algorithm

A stable and efficient path planning algorithm can improve the accuracy and efficiency of sediment sampling process. To determine a path that can guide the sampler to the preset landing point within the shortest traveling time, an improved mutation operator genetic algorithm (IMOGA) is proposed herein. First, an attitude-adjusting mechanism is introduced. Then, a three-dimensional ocean current model is developed. A hydrodynamic model of the sediment sampler is developed via CFD simulations, and an adaptive cost function for the path-planning mathematical model is proposed to improve the convergence problem. Finally, the problem is solved using a basic genetic algorithm and IMOGA separately. The comparison of the simulation results reveals that the IMOGA can achieve a better global optimal solution in less computing time than the basic genetic algorithm. The improved path planning algorithm can be used for sediment samplers and landers that deploy instruments at a designated point as well as for profiles that need to adjust their position.
Metaverse influence on transportation: A mission impossible?

This commentary examines the implications of metaverse technology on transportation services in the real world. While metaverse technology has successfully replicated various activities, including fashion, banking, tourism, and retail, in the virtual world, its practical application in transportation in the real world faces significant challenges. This paper questions whether the physical involvement required in transportation, the limited interest in metaverse technology, and the need for immersiveness present significant limitations for extending transportation into the metaverse. The paper concludes that although it may be challenging to integrate metaverse technology into transportation systems, it presents considerable possibilities and opportunities that require substantial investment in the design of the digital twin. However, the practical activity of transportation presents significant challenges. This paper aims to initiate a conversation around metaverse technology and transportation to challenge existing norms and offer a practical and meaningful call to action for the transport research community. This paper contributes to the ongoing discourse on integrating metaverse technology into transportation systems.
Fixed-Wing UAV Path Planning and Collision Avoidance using Nonlinear Model Predictive Control and Sensor-based Cloud Detection

This article proposes a Nonlinear Model Predictive Control (NMPC) method for path planning and obstacle avoidance of a fixed-wing Unmanned Aerial Vehicle (UAV) coupled to an online monocular electro-optical cloud detection. A Nonlinear Programming (NLP) problem is solved at each discrete-time iteration of the NMPC using an inhouse Sequential Quadratic Programming (SQP) method. The proposed NMPC method enables real-time path planning while avoiding clouds, whose distance and extent are determined using electro-optical sensors and computer vision methods. Cloud detection and avoidance is intended to allow UAVs to operate under visual flight rules (VFR) to reduce the risk of collision with other air traffic. The mission task here consists of airborne reconnaissance of targets that can be defined by the user and updated online. The user interacts with the simulation by setting reconnaissance targets for the UAV at anytime during the flight.
Trends in using deep learning algorithms in biomedical prediction systems

In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.
New Ideas In Recognition of Cancer and Neutrosophic Super Hypergraph as Hyper Tool on Super Toot

In this scientific research, new setting is introduced to study dominating, resolving, Coloring, Eulerian (Hamiltonian) neutrosophic path, n-Eulerian (Hamiltonian) Neutrosophic path, zero forcing number, zero forcing neutrosophicnumber, independent Number, independent neutrosophic-number, clique number, clique neutrosophic-number, Matching number, matching neutrosophic-number, girth, neutrosophic girth, 1-zero-forcing number, 1-zero-forcing neutrosophicnumber, failed 1-zero-forcing number, failed 1-zero-forcing neutrosophic-number, global-offensive alliance, t-offensive alliance, t-defensive alliance, t-powerful alliance, and global-powerful alliance in SuperHyperGraph and Neutrosophic Super Hypergraph. Some Classes of SuperHyperGraph and Neutrosophic SuperHyperGraph are cases of study. Some results are applied in family of SuperHyperGraph and Neutrosophic SuperHyperGraph. A basic familiarity with Super Hypergraphs theory, and Neutrosophic SuperHyperGraph theory are proposed
An Experimental Study to Mitigate Environmental Impacts by Transforming Waste Plastic Bags into Paving Blocks and Roof Tiles

The world’s plastic bag problem adversely impacts the environment daily. Plastic bags decompose after years, and some may take centuries, leading to pollution. Society relies on plastic bags for every task, which causes many problems for humans and aquatic life. Focusing on Sustainable Development Goal 12 (SDG 12), this research used waste plastic bags melted in a boilery to develop plastocrete to cast plastocrete paving blocks and roofing tiles. Compressive and split tensile strength tests were performed on plastocrete paving blocks, while a thermal insulation test was performed on roofing tiles in the Concrete Lab of our Department. The compressive strength test on plastocrete showed that it can easily replace concrete pavement blocks after giving good compressive strengths compared with concrete blocks. Being very low in tensile strength, plastocrete is not recommended for flexure members. The thermal insulation test results indicate that using plastic bags as roofing tiles decreases thermal conductivity compared with a controlled reinforced slab. Hence, it is concluded that plastocrete will help reduce pollution in the terrestrial and aquatic environment and can be an effective waste disposal solution. The plastocrete-led paving blocks and roofing tiles not only help economically but also help preserve nature and the environment from land pollution.
Bayesian model comparison for simulation-based inference

Abstract Comparison of appropriate models to describe observational data is a fundamental task of science. The Bayesian model evidence, or marginal likelihood, is a computationally challenging, yet crucial, quantity to estimate to perform Bayesian model comparison. We introduce a methodology to compute the Bayesian model evidence in simulation-based inference (SBI) scenarios (often called likelihood-free inference). In particular, we leverage the recently proposed learned harmonic mean estimator and exploit the fact that it is decoupled from the method used to generate posterior samples, i.e. it requires posterior samples only, which may be generated by any approach. This flexibility, which is lacking in many alternative methods for computing the model evidence, allows us to develop SBI model comparison techniques for the three main neural density estimation approaches, including neural posterior estimation, neural likelihood estimation, and neural ratio estimation. We demonstrate and validate our SBI evidence calculation techniques on a range of inference problems, including a gravitational wave example. Moreover, we further validate the accuracy of the learned harmonic mean estimator, implemented in the harmonic software, in likelihood-based settings. These results highlight the potential of harmonic as a sampler-agnostic method to estimate the model evidence in both likelihood-based and simulation-based scenarios.
Impact of school lockdown on access to online instruction during the war in Ukraine

Abstract Quarantine measures in connection to the COVID‐19 pandemic challenged the education system in Ukraine, like the education systems of other countries. This manifested over the course of three years in an increased need to develop distance learning online. Also, teacher ability to use digital tools, such as information communication technology (ICT), for instruction online is crucial in wartime circumstances in Ukraine. We describe the effects of school lockdown on teachers in Ukraine and how this affected teacher abilities to use distance instruction online in wartime circumstances. Drawing on a large‐scale survey of 54,254 educators conducted in January–February 2022, we report on challenges and needs identified by teachers in Ukraine. The survey collected responses from teachers and other categories of educators. Our analysis of survey findings focus on key challenges and needs of teachers and schools during the COVID‐19 quarantine. Respondents identified the following challenges: (1) lacking access to ICT equipment; (2) lacking access to stable high‐speed internet; (3) an insufficient level of knowledge and skills in using ICTs ( digital competence ). The study on which this article reports outlined possibilities for the professional development of teachers in Ukraine, complemented with a teacher self‐assessment of digital competence. The findings demonstrate that methods for organising online education in wartime circumstances in Ukraine benefit from the active interaction of all participants in the education process, their adaptability to changing learning conditions, continued communication about needs, notably teacher abilities to use ICT, and the ICT capacity of institutions.
Credit Card Financial Fraud Detection Using Deep Learning

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Retrieval for Extremely Long Queries and Documents with RPRS: a Highly Efficient and Effective Transformer-based Re-Ranker

Retrieval with extremely long queries and documents is a well-known and challenging task in information retrieval and is commonly known as Query-by-Document (QBD) retrieval. Specifically designed Transformer models that can handle long input sequences have not shown high effectiveness in QBD tasks in previous work. We propose a R e-Ranker based on the novel P roportional R elevance S core (RPRS) to compute the relevance score between a query and the top-k candidate documents. Our extensive evaluation shows RPRS obtains significantly better results than the state-of-the-art models on five different datasets. Furthermore, RPRS is highly efficient since all documents can be pre-processed, embedded, and indexed before query time which gives our re-ranker the advantage of having a complexity of O ( N ) where N is the total number of sentences in the query and candidate documents. Furthermore, our method solves the problem of the low-resource training in QBD retrieval tasks as it does not need large amounts of training data, and has only three parameters with a limited range that can be optimized with a grid search even if a small amount of labeled data is available. Our detailed analysis shows that RPRS benefits from covering the full length of candidate documents and queries.
Enhancing rooftop solar energy potential evaluation in high-density cities: A Deep Learning and GIS based approach

The accurate evaluation of rooftop solar potential can help with optimal photovoltaic system deployment in high-density cities and renewable energy policy creation. However, it has been a persistent challenge due to the complex building shading effect and diversity rooftop obstacles. In our study, we proposed a Deep Learning and Geographic Information System (GIS) based approach for accurate evaluation of rooftop solar energy potential on a city scale considering the combined effects of shading and obstacles. Regarding the shading effect, we used building footprint data enriched with height information to evaluate the shading effect between buildings. Regarding the obstacle effect, DeepLab-v3 Convolutional Neural Network model was adopted to identify the obstacles on the roof using high-resolution satellite images. By combining the above results and setting the solar radiation parameters and photovoltaic (PV) system efficiency, we obtained the spatial distribution of the rooftop PV power generation potential. This approach was applied in Shanghai, China, except for Chongming Island. The estimated annual rooftop solar radiation potential of Shanghai was 257,204 GWh, with a corresponding estimated annual PV power generation of 49,753 GWh. The combined effect of shading and obstacles resulted in a reduction of 25.6% in the estimation of Shanghai’s annual solar radiation potential.