Reviewerly/exHarmony huggingface dataset:
Unravelling the global landscape of Bitcoin research: insights from bibliometric analysis

Bitcoin has been gaining increasing attention in academia and industry. This article investigates Bitcoin’s research status and evolution via bibliometrics using a dataset of 3,873 publications between 2012 and 2022 from the Web of Science Core Collection. The findings reveal a significant increase in research on Bitcoin since 2017, coinciding with the cryptocurrency bull market. The article identifies publication trends, influential contributors, collaboration networks, and topics evolution in Bitcoin research. The three Bitcoin research stages are conceptualisation and fundamentals of Bitcoin (2012–2016), cryptocurrency and market efficiency (2017–2018), and technical analysis, big data, data privacy, and the connection between Bitcoin and financial markets (2019–2022). The four prominent emerging areas for future Bitcoin research are decentralised finance (DeFi), non-fungible tokens (NFTs), clean energy and mining, and monetary policy. The article offers valuable insights for researchers, policymakers, and practitioners, facilitating a better understanding of the status quo of Bitcoin research.
Regularization and optimization in model-based clustering

Due to their conceptual simplicity, k-means algorithm variants have been extensively used for unsupervised cluster analysis. However, one main shortcoming of these algorithms is that they essentially fit a mixture of identical spherical Gaussians to data that vastly deviates from such a distribution. In comparison, general Gaussian Mixture Models (GMMs) can fit richer structures but require estimating a quadratic number of parameters per cluster to represent the covariance matrices. This poses two main issues: (i) the underlying optimization problems are challenging due to their larger number of local minima, and (ii) their solutions can overfit the data. In this work, we design search strategies that circumvent both issues. We develop more effective optimization algorithms for general GMMs, and we combine these algorithms with regularization strategies that avoid overfitting. Through extensive computational analyses, we observe that optimization or regularization in isolation does not substantially improve cluster recovery. However, combining these techniques permits a completely new level of performance previously unachieved by k-means algorithm variants, unraveling vastly different cluster structures. These results shed new light on the current status quo between GMM and k-means methods and suggest the more frequent use of general GMMs for data exploration. To facilitate such applications, we provide open-source code as well as Julia packages (UnsupervisedClustering.jl and RegularizedCovarianceMatrices.jl) implementing the proposed techniques.
Optimal Transport and Model Predictive Control-based Simultaneous Task Assignment and Trajectory Planning for Unmanned System Swarm

Abstract This paper presents a simultaneous task assignment and trajectory planning method for unmanned system swarm by using optimal transport and model predictive control (OT-MPC). Unlike the conventional hierarchical assignment and planning, the proposed approach addresses both the task assignment and trajectory planning subproblems concurrently. To be specific, a unified cost function is designed to solve task assignment and trajectory planning problem. Moreover, the multi-tasks are assigned by using optimal transport, which establishes an optimal mapping between tasks and unmanned system vehicles based on transportation cost. The trajectory planning is achieved by using model predictive control, which generates high-quality navigation trajectories considering obstacle avoidance. Finally, the proposed method is applied to the unmanned surface vehicles swarm. Numerical simulations and experiments were conducted to validate the effectiveness of the proposed method.
Mixed reality and digital twins for astronaut training

Astronaut training is a complex activity given its nature; the infrastructure and equipment required are very specific and expensive. Immersive technologies have the capability to simulate complete environments and allow users to interact with them. The effectiveness of immersive technologies for training and learning activities has already been addressed in various works. However, merely simulating scenarios is not enough; participants need to interact with vehicles, machines, and spacecraft systems to see how they affect the environment and surrounding objects. This study develops four scenarios across three modules, inspired by NASA and ESA astronaut training programs. The first module introduces a theoretical scenario visualized through mixed reality (MR) with topics from ESA’s training module. The second module presents two practical scenarios: an ISS emergency simulation involving Hohmann transfer and circular motion concepts, and a spacewalk for repair and maintenance tasks, in these scenarios, the digital twins of the ISS propulsion and navigation systems were developed as well as the digital twin of a space suit. The third module simulates a spaceship launch, utilizing 3D models from SpaceX’s Falcon Heavy and digital twins of the propulsion and navigation systems. During the interaction of the participants with the digital twins and the scenarios, the data generated is stored in a database so it can be analyzed and compared against the ISS dataset from the Jet Propulsion Laboratory (JPL) and the telemetry from SpaceX Falcon heavy launch. In the first phase of this work, the main goal was to check if digital twins can be implemented in conjunction with immersive technologies and therefore be suitable for training activities. We used a mean squared error metric between the digital twins and the physic object data, demonstrating that developed digital twins closely align with physical systems, validating their efficacy for astronaut training.
UoloNet: based on multi-tasking enhanced small target medical segmentation model

Abstract In recent years, UNET (Ronneberger et al. 2015) and its derivative models have been widely used in medical image segmentation with more superficial structures and excellent segmentation results. Due to the lack of modeling for the overall characteristics of the target, the division tasks of minor marks will produce some discrete noise points, resulting in a decline in model accuracy and application effects. We propose a multi-tasking medical image analysis model UoloNet, a YOLO-based (Redmon et al. 2016; Shafiee et al. 2017) object detection branch is added based on UNET. The shared learning of the two tasks through semantic segmentation and object detection has promoted the model’s mastery of the overall characteristics of the target. In the reasoning stage, merging the two functions of object detection and semantic segmentation can effectively remove discrete noise points in the division and enhance the accuracy of semantic segmentation. In the future, the object detection task will be the problem of excessive convergence of semantic segmentation tasks. The model uses CIOU (Zheng et al. 2020) losses instead of IOU losses in YOLO, which further improves the model’s overall accuracy. The effectiveness of the proposed model is verified both in the MRI dataset SEHPI, which we posted and in the public dataset LITS (Christ 2017).
Quantum two-block group algebra codes

We consider quantum two-block group algebra (2BGA) codes, a family of smallest lifted-product (LP) codes. These codes are related to generalized-bicycle codes, except a cyclic group is replaced with an arbitrary finite group, generally non-Abelian. As special cases, 2BGA codes include a subset of square-matrix LP codes over Abelian groups, including quasicyclic codes, and all square-matrix hypergraph-product codes constructed from a pair of classical group codes. We establish criteria for permutation equivalence of 2BGA codes and give bounds for their parameters, both explicit and in relation to other quantum and classical codes. We also enumerate the optimal parameters of all inequivalent binary connected 2BGA codes with stabilizer generator weights $W\ensuremath{\le}8$, of length $n\ensuremath{\le}100$ for Abelian groups, and $n\ensuremath{\le}200$ for non-Abelian groups.
Pyrite geochemistry reveals the key controlling factors of large gold deposit formation in Jiaodong Peninsula: A comparative study

With an estimated gold reserve of more than 5000 t, the Jiaodong Peninsula is incontestably the largest gold ore belt in China. Dozens of gold deposits developed in the area with variable deposit types, grades, and reserves, and the key controlling factors for these differences are not clear. In this study, the representative Jiuqu (JQ), Canzhuang (CZ), and Sanshandao Northern Sea (SNS) gold deposits located in the NW portion of the Jiaodong district have been comparatively investigated using pyrite geochemistry, to constrain the mineralization sources and grade differences of each deposit. The pyrite from ore samples in JQ, CZ, and SNS deposits display euhedral to sub-euhedral geometries and do not show obvious zonation. The SNS pyrites present higher values for most trace elements (e.g., Cu, As, Ni) than the JQ and CZ pyrites. Moreover, the SNS deposit has the highest δ34S value (+11.70 ‰), followed by the CZ deposit (+10.43 ‰) and the JQ deposit (+7.67 ‰). This is positively correlated to their ore grade in order. The presence of relatively high contents of Cu, As, Ni as well as δ34S values in the SNS deposit pyrites may suggest that more mantle materials have been contributed to the gold ores. It can be argued that these high values are associated with high oxygen fugacity in the subducting and stagnant slabs related to the lithospheric mantle metasomatism, which is responsible for the δ34S enrichment and formation of Au and S reservoirs, serving as the key controlling factor for the formation of large gold deposits in Jiaodong. This study enables us to better describe the evolution of the mineralizing fluids and understand the mechanisms of ore genesis in gold mineralization systems by using pyrite geochemistry.
Monthly Global Solar Radiation Model Based on Artificial Neural Network, Temperature Data and Geographical and Topographical Parameters: A Case Study in Spain

Solar energy plays an essential role in the current energy context to achieve sustainable development while supplying energy needs, creating jobs, and protecting the environment. Many solar radiation models have provided valid estimates at many different locations, using appropriate input variables for specific climatic conditions, but predictions are less accurate on a regional scale. Since radiometric weather stations are relatively dispersed, even in the most developed countries, it is interesting to develop indirect models based on measurements that are common in secondary network stations. This paper develops a monthly global solar radiation model based on a simple neural network structure, using temperature, geographical, and topographical data from 105 meteorological stations, representative of the whole of peninsular Spain. A hierarchical clustering procedure was employed to select the data used to train and validate the model. To avoid functional dependencies between parameters and variables, which hinder the generality of the model, all input and output variables are dimensionless. The estimates fit the 1260 monthly data with RRMSE values of about 6%, which improves results obtained previously, using regression models, and proves that simplicity is compatible with the generality and accuracy of a model, even in large regions with very varied characteristics.
Dissolved oxygen prediction using regularized extreme learning machine with clustering mechanism in a black bass aquaculture pond

Dissolved oxygen (DO) is an important indicator of aquaculture water quality. The prediction accuracy of DO content is the key role in managing and controlling aquaculture water quality. However, potential trends of DO under various conditions (such as weather) are always overlooked. This study aims to develop a novel DO forecasting model using the optimized regularized extreme learning machine (RELM) with factor extraction operation and K-medoids clustering strategy in a black bass aquaculture pond. We adopt the leave-one-out cross (LOO) error validation to obtain the optimal regularization parameter of RELM and enhance the forecasting accuracy. We further adjust the activation function to accelerate the RELM. Next, we divide the time series into day and night segments, and construct the clustering mechanism with the K-medoids method to extract the different patterns of data streams under various weather conditions. The experiments on 14 days’ data from a real-world aquaculture pond demonstrate the efficiency and accuracy of our proposed DO prediction model. We believe that our research will facilitate the development of a forecasting tool for warning hypoxia in the near future, which combines intelligent prediction models and real-time data.
Scattering expansion for localization in one dimension

We present a perturbative approach to disordered systems in one spatial dimension that accesses the full range of phase disorder and clarifies the connection between localization and phase information. We consider a long chain of identically disordered scatterers and expand in the reflection strength of any individual scatterer. As an example application, we show analytically that in a discrete-time quantum walk, the localization length can depend nonmonotonically on the strength of phase disorder (whereas expanding in weak disorder yields monotonic decrease). More generally, we obtain to all orders in the expansion a particular nonseparable form for the joint probability distribution of the transmission coefficient logarithm and reflection phase. Furthermore, we show that for weak local reflection strength, a version of the scaling theory of localization holds: the joint distribution is determined by just three parameters.
Integrability Breaking and Bound States in Google’s Decorated XXZ Circuits

Recent quantum simulation by Google [Nature 612, 240 (2022)] has demonstrated the formation of bound states of interacting photons in a quantum-circuit version of the XXZ spin chain. While such bound states are protected by integrability in a one-dimensional chain, the experiment found the bound states to be unexpectedly robust when integrability was broken by decorating the circuit with additional qubits, at least for small numbers of qubits (≤24) within the experimental capability. Here we scrutinize this result by state-of-the-art classical simulations, which greatly exceed the experimental system sizes and provide a benchmark for future studies in larger circuits. We find that the bound states consisting of a finite number of photons are indeed robust in the nonintegrable regime, even after scaling to the infinite-time and infinite-system size limit. Moreover, we show that such systems possess unusual spectral properties, with level statistics that deviates from the random matrix theory expectation. On the other hand, for low but finite density of photons, we find a much faster onset of thermalization and significantly weaker signatures of bound states, suggesting that anomalous dynamics may only be a property of dilute systems with zero density of photons in the thermodynamic limit. The robustness of the bound states is also influenced by the number of decoration qubits and, to a lesser degree, by the regularity of their spatial arrangement.9 MoreReceived 6 July 2023Accepted 20 December 2023DOI:https://doi.org/10.1103/PRXQuantum.5.010316Published 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 AreasEigenstate thermalizationQuantum circuitsQuantum simulationPhysical SystemsFloquet systemsQuantum spin modelsTechniquesExact diagonalizationMatrix product statesRandom matrix theoryQuantum Information, Science & TechnologyStatistical Physics & Thermodynamics
It's Not a Feature, It's a Bug: Fault-Tolerant Model Mining from Noisy Data

The mining of models from data finds widespread use in industry. There exists a variety of model inference methods for perfectly deterministic behaviour, however, in practice, the provided data often contains noise due to faults such as message loss or environmental factors that many of the inference algorithms have problems dealing with. We present a novel model mining approach using Partial Max-SAT solving to infer the best possible automaton from a set of noisy execution traces. This approach enables us to ignore the minimal number of presumably faulty observations to allow the construction of a deterministic automaton. No pre-processing of the data is required. The method's performance as well as a number of considerations for practical use are evaluated, including three industrial use cases, for which we inferred the correct models.
Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners?

The growth of the decentralized finance (DeFi) ecosystem built on blockchain technology and smart contracts has led to an increased demand for secure and reliable smart contract development. However, attacks targeting smart contracts are increasing, causing an estimated $6.45 billion in financial losses. Researchers have proposed various automated security tools to detect vulnerabilities, but their real-world impact remains uncertain.
Research on Privacy Protection in Federated Learning Combining Distillation Defense and Blockchain

Traditional federated learning addresses the data security issues arising from the need to centralize client datasets on a central server for model training. However, this approach still poses privacy protection risks. For instance, central servers cannot verify privacy leaks resulting from poisoning attacks by malicious clients. Additionally, adversarial sample attacks can infer specific samples from the original data by testing the local models on client devices. This paper proposes a federated learning privacy protection method combining distillation defense technology with blockchain architecture. The method utilizes distillation defense technology to reduce the sensitivity of client devices participating in federated learning to perturbations and enhance their ability to resist adversarial sample attacks locally. This not only reduces communication overhead and improves learning efficiency but also enhances the model’s generalization ability. Furthermore, the method leverages the “decentralized” nature of blockchain architecture as a trusted record-keeping mechanism to audit information interactions among clients and shared model parameters. This addresses privacy leakage issues resulting from poisoning attacks by some clients during the model construction process. Simulation experiment results demonstrate that the proposed method, compared with traditional federated learning, ensures model convergence, detects malicious clients, and improves the participation level of highly reputable clients. Moreover, by reducing the sensitivity of local clients to perturbations, it enhances their ability to effectively resist adversarial sample attacks.
Whole Slide Image Understanding in Pathology: What Is the Salient Scale of Analysis?

Background: In recent years, there has been increasing research in the applications of Artificial Intelligence in the medical industry. Digital pathology has seen great success in introducing the use of technology in the digitisation and analysis of pathology slides to ease the burden of work on pathologists. Digitised pathology slides, otherwise known as whole slide images, can be analysed by pathologists with the same methods used to analyse traditional glass slides. Methods: The digitisation of pathology slides has also led to the possibility of using these whole slide images to train machine learning models to detect tumours. Patch-based methods are common in the analysis of whole slide images as these images are too large to be processed using normal machine learning methods. However, there is little work exploring the effect that the size of the patches has on the analysis. A patch-based whole slide image analysis method was implemented and then used to evaluate and compare the accuracy of the analysis using patches of different sizes. In addition, two different patch sampling methods are used to test if the optimal patch size is the same for both methods, as well as a downsampling method where whole slide images of low resolution images are used to train an analysis model. Results: It was discovered that the most successful method uses a patch size of 256 × 256 pixels with the informed sampling method, using the location of tumour regions to sample a balanced dataset. Conclusion: Future work on batch-based analysis of whole slide images in pathology should take into account our findings when designing new models.
Learning Output Relevant Features by Joint Autoencoder

Classical regression is a supervised task that uses target output to guide the modeling. Generally, the original input contains both output relevant and irrelevant information, whereas the latter may decrease the predictive performance to a certain extent. Without prior information about what information contributes to the prediction, the predictive performance of regression models can be improved based on such output relevant information as input. Thus, a joint autoencoder (JAE) combining the supervised and unsupervised mechanisms is proposed to learn output relevant features from the original input. In this way, both predictive and reconstructive performance are considered to learn the essential characteristics and avoid poor generalized performance on untrained instances. Meanwhile, hierarchy representations can be learned by successive JAEs with a local parameter embedding strategy, which is presented to preserve the predictive performance in this structure. In the experiments, the predictive performance and robustness of the proposal are verified on nine datasets with different sample sizes.
EDSUCh: A robust ensemble data summarization method for effective medical diagnosis

Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data. Data summarization can create a concise version of the original data that can be used for effective diagnosis. In this paper, we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns. To the best of our knowledge, there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis. The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used. Therefore, the medical diagnosis becomes more effective, and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.