Reviewerly/exHarmony huggingface dataset:
Cryptocurrency liquidity during the Russia–Ukraine war: the case of Bitcoin and Ethereum

Purpose This study examine the response of liquidity of Bitcoin and Ethereum to the Russia-Ukraine war in an event study context and investigate whether the war had a transitory or a permanent effect on cryptocurrency liquidity. Design/methodology/approach A event study was applied to hourly transactions on Bitcoin and Ethereum cryptocurrencies from 1/02/2022 to 31/03/2022. This is period is subdivided in two sample periods to capture transitory and permanent effects. The transitory effect is investigated over a window spanning -20 and +20 days. For a more extended post-event period, a linear regression model was applied to analyze the effects of other factors on the liquidity risk of BTC and ETH. Findings The findings reveal a significant but temporary impact of the Russia–Ukraine war on the liquidity of Bitcoin and Ethereum. Liquidity levels have increased within the first two days around the event day and then returned to the pre-event level after that. However, the response of BTC and ETH cryptocurrencies' liquidities to the Russian invasion of Ukraine is not uniform. Originality/value This is the first paper that assesses the liquidity level of two major cryptocurrencies (Bitcoin and Ethereum) in response to an extreme event: the Russia–Ukraine war. The hypothesis is that trading in the cryptocurrency market will increase due to market participants' goal of evading regulatory sanctions. Furthermore, market participants may also take advantage of cryptocurrencies' popularity as safe-haven assets.
Interpretable machine learning prediction of all-cause mortality

Unlike linear models which are traditionally used to study all-cause mortality, complex machine learning models can capture non-linear interrelations and provide opportunities to identify unexplored risk factors. Explainable artificial intelligence can improve prediction accuracy over linear models and reveal great insights into outcomes like mortality. This paper comprehensively analyzes all-cause mortality by explaining complex machine learning models.We propose the IMPACT framework that uses XAI technique to explain a state-of-the-art tree ensemble mortality prediction model. We apply IMPACT to understand all-cause mortality for 1-, 3-, 5-, and 10-year follow-up times within the NHANES dataset, which contains 47,261 samples and 151 features.We show that IMPACT models achieve higher accuracy than linear models and neural networks. Using IMPACT, we identify several overlooked risk factors and interaction effects. Furthermore, we identify relationships between laboratory features and mortality that may suggest adjusting established reference intervals. Finally, we develop highly accurate, efficient and interpretable mortality risk scores that can be used by medical professionals and individuals without medical expertise. We ensure generalizability by performing temporal validation of the mortality risk scores and external validation of important findings with the UK Biobank dataset.IMPACT's unique strength is the explainable prediction, which provides insights into the complex, non-linear relationships between mortality and features, while maintaining high accuracy. Our explainable risk scores could help individuals improve self-awareness of their health status and help clinicians identify patients with high risk. IMPACT takes a consequential step towards bringing contemporary developments in XAI to epidemiology.This study identifies characteristics that will make a person more likely to die sooner than expected based on life expectancy for the population. We developed a computer program and applied it to information obtained about the characteristics and medical history of people from the USA. We identified previously unidentified characteristics that impact how likely it is someone will die sooner than expected, for example the circumference of the arm. We also identified combinations of characteristics that interact to increase the likelihood of death sooner than expected. By adding a person’s characteristics to the program, the likelihood of death over the next 5 years can be calculated and characteristics identified that a person could modify to improve their health and reduce their chance of death during this period.
Scalable Randomized Benchmarking of Quantum Computers Using Mirror Circuits

The performance of quantum gates is often assessed using some form of randomized benchmarking. However, the existing methods become infeasible for more than approximately five qubits. Here we show how to use a simple and customizable class of circuits-randomized mirror circuits-to perform scalable, robust, and flexible randomized benchmarking of Clifford gates. We show that this technique approximately estimates the infidelity of an average many-qubit logic layer, and we use simulations of up to 225 qubits with physically realistic error rates in the range 0.1%-1% to demonstrate its scalability. We then use up to 16 physical qubits of a cloud quantum computing platform to demonstrate that our technique can reveal and quantify crosstalk errors in many-qubit circuits.
“Development and validation of the ICAP Technology Scale to measure how teachers integrate technology into learning activities”

Previous research investigating the use of technology in school has focused mainly on the frequency of use of digital tools during lessons rather than investigating how technology is integrated with respect to different kinds of learning activities. Since the impact of technology use on learning depends on how it is used and on what activities supported by technology are implemented in lessons, a measurement instrument assessing how technology is integrated into learning activities is necessary to investigate its impact on teaching and learning processes. According to the interactive, constructive, active, and passive (ICAP) framework, which distinguishes four different learning activities based on the level of students' cognitive engagement, we developed the 12-item ICAP Technology Scale (ICAP-TS) that accounts for all four dimensions of technology integration in lessons. We used confirmatory factor analysis to validate the four-factor structure of the ICAP-TS with a sample of 1059 upper-secondary school teachers from Switzerland. We also examined reliability using classical test theory and Rasch model analysis to assess the scale's psychometric characteristics. We then analyzed the associations between the ICAP-TS and a general use frequency measure of 12 educational technologies to test the criterion validity. The results confirmed the four-factor structure of the ICAP-TS and revealed good instrument accuracy. The most difficult items to endorse are those describing the integration of technology into interactive learning activities. Furthermore, all 12 items significantly correlated with the frequency of use of 12 educational technologies. We recommend the ICAP-TS as a short and reliable measurement scale for assessing how technology is integrated into lessons, considering different learning activities based on the ICAP theoretical model.
How the different explanation classes impact trust calibration: The case of clinical decision support systems

Machine learning has made rapid advances in safety-critical applications, such as traffic control, finance, and healthcare. With the criticality of decisions they support and the potential consequences of following their recommendations, it also became critical to provide users with explanations to interpret machine learning models in general, and black-box models in particular. However, despite the agreement on explainability as a necessity, there is little evidence on how recent advances in eXplainable Artificial Intelligence literature (XAI) can be applied in collaborative decision-making tasks, i.e., human decision-maker and an AI system working together, to contribute to the process of trust calibration effectively. This research conducts an empirical study to evaluate four XAI classes for their impact on trust calibration. We take clinical decision support systems as a case study and adopt a within-subject design followed by semi-structured interviews. We gave participants clinical scenarios and XAI interfaces as a basis for decision-making and rating tasks. Our study involved 41 medical practitioners who use clinical decision support systems frequently. We found that users perceive the contribution of explanations to trust calibration differently according to the XAI class and to whether XAI interface design fits their job constraints and scope. We revealed additional requirements on how explanations shall be instantiated and designed to help a better trust calibration. Finally, we build on our findings and present guidelines for designing XAI interfaces.
ProDiff

Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hinder their applications to text-to-speech deployment. Through the preliminary study on diffusion model parameterization, we find that previous gradient-based TTS models require hundreds or thousands of iterations to guarantee high sample quality, which poses a challenge for accelerating sampling. In this work, we propose ProDiff, on progressive fast diffusion model for high-quality text-to-speech. Unlike previous work estimating the gradient for data density, ProDiff parameterizes the denoising model by directly predicting clean data to avoid distinct quality degradation in accelerating sampling. To tackle the model convergence challenge with decreased diffusion iterations, ProDiff reduces the data variance in the target site via knowledge distillation. Specifically, the denoising model uses the generated mel-spectrogram from an N-step DDIM teacher as the training target and distills the behavior into a new model with N/2 steps. As such, it allows the TTS model to make sharp predictions and further reduces the sampling time by orders of magnitude. Our evaluation demonstrates that ProDiff needs only 2 iterations to synthesize high-fidelity mel-spectrograms, while it maintains sample quality and diversity competitive with state-of-the-art models using hundreds of steps. ProDiff enables a sampling speed of 24x faster than real-time on a single NVIDIA 2080Ti GPU, making diffusion models practically applicable to text-to-speech synthesis deployment for the first time. Our extensive ablation studies demonstrate that each design in ProDiff is effective, and we further show that ProDiff can be easily extended to the multi-speaker setting.
Observing ground-state properties of the Fermi-Hubbard model using a scalable algorithm on a quantum computer

The famous, yet unsolved, Fermi-Hubbard model for strongly-correlated electronic systems is a prominent target for quantum computers. However, accurately representing the Fermi-Hubbard ground state for large instances may be beyond the reach of near-term quantum hardware. Here we show experimentally that an efficient, low-depth variational quantum algorithm with few parameters can reproduce important qualitative features of medium-size instances of the Fermi-Hubbard model. We address 1 × 8 and 2 × 4 instances on 16 qubits on a superconducting quantum processor, substantially larger than previous work based on less scalable compression techniques, and going beyond the family of 1D Fermi-Hubbard instances, which are solvable classically. Consistent with predictions for the ground state, we observe the onset of the metal-insulator transition and Friedel oscillations in 1D, and antiferromagnetic order in both 1D and 2D. We use a variety of error-mitigation techniques, including symmetries of the Fermi-Hubbard model and a recently developed technique tailored to simulating fermionic systems. We also introduce a new variational optimisation algorithm based on iterative Bayesian updates of a local surrogate model.
SVD-GCN

With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance. However, most GCN-based methods rigorously stick to a common GCN learning paradigm and suffer from two limitations: (1) the limited scalability due to the high computational cost and slow training convergence; (2) the notorious over-smoothing issue which reduces performance as stacking graph convolution layers. We argue that the above limitations are due to the lack of a deep understanding of GCN-based methods. To this end, we first investigate what design makes GCN effective for recommendation. By simplifying LightGCN, we show the close connection between GCN-based and low-rank methods such as Singular Value Decomposition (SVD) and Matrix Factorization (MF), where stacking graph convolution layers is to learn a low-rank representation by emphasizing (suppressing) components with larger (smaller) singular values. Based on this observation, we replace the core design of GCN-based methods with a flexible truncated SVD and propose a simplified GCN learning paradigm dubbed SVD-GCN, which only exploits K-largest singular vectors for recommendation. To alleviate the over-smoothing issue, we propose a renormalization trick to adjust the singular value gap, resulting in significant improvement. Extensive experiments on three real-world datasets show that our proposed SVD-GCN not only significantly outperforms state-of-the-arts but also achieves over 100x and 10x speedups over LightGCN and MF, respectively.
Climate change impact on photovoltaic power potential in China based on CMIP6 models

China has the largest worldwide cumulative installed photovoltaic (PV) capacity, which is expected to be 1300 GW in 2050. Industrial production, population explosion and fossil fuel combustion would reduce the surface solar radiation that could be received by PV panels. However, it is still a problem to explore the integrated effects of socio-economic and air pollutant emissions on PV power potential in China. In this study, climate change impact on PV power potential in 2023-2100 were assessed using the Coupled Model Intercomparison Project Phase 6 (CMIP6) model, combining Shared Socio-economic Pathway (SSPs) and Representative Concentration Pathways (RCPs). The validation results with ground-based surface solar radiation measurements collected from 17 China Meteorological Administration (CMA) stations showed that the Meteorological Research Institute Earth System Model version 2-0 (MRI-ESM2-0) attained a better performance with mean correlation coefficients (R), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) of 0.85, 35.80 Wm-2 and 29.37 Wm-2, respectively. Then, the MRI-ESM2-0 model was selected to analyze the spatial and temporal variations in PV power potential. PV power potential decreased significantly in SSP585 ranging from 192.71 Wm-2 to 189.96 Wm-2 in 2023-2100 corresponding to the growing resource intensity and fossil fuel dependency. In contrast, if China continues on the path of sustainable and low-carbon development and keeps temperature rise to about 1.5 °C by 2100, PV power potential will increase by 1.36-5.90 Wm-2. Meanwhile, the effects of climatological factors on PV power potential were analyzed by Empirical Orthogonal Function (EOF) method. Results indicated that surface solar radiation had the highest contribution of >50 %, and the contribution of aerosols and cloud cover was about 20 %. This study is conducive to the full utilization of solar resources and has important implications for the future formulation of solar energy policy in China.
A novel improved whale optimization algorithm for optimization problems with multi-strategy and hybrid algorithm

Whale optimization algorithm (WOA), as an advanced optimization algorithm with simple structure, has been favored by various fields. However, there are some disadvantages of WOA, such as slow convergence speed, low precision and falling into local optimal value easily. In this paper, a novel improved whale optimization algorithm (IWOA) with multi-strategy and hybrid algorithm is proposed to overcome above shortcomings. Firstly, IWOA initializes the population by chaotic mapping to avoid the initial population distribution of WOA deviating from the optimal value. Secondly, IWOA combines the pheromone of the black widow algorithm and the opposition-based learning strategy to modify the population, which improves the convergence speed and the global performance of WOA respectively. Finally, the adaptive coefficients and the new update modes replace the original update modes, which makes the structure of WOA simpler and more accurate. In addition, the convergence of IWOA is also proved in this paper. On the one hand, to demonstrate the effectiveness of IWOA, 23 benchmark functions are used to test various performance of the algorithm. On the other hand, in order to prove the superiority of IWOA, the experimental results are compared and analyzed with other optimization algorithms. Simulation results show that IWOA proposed in this paper owns excellent performance in convergence speed, stability, accuracy and global performance, compared with other algorithms.
BreaST-Net: Multi-Class Classification of Breast Cancer from Histopathological Images Using Ensemble of Swin Transformers

Breast cancer (BC) is one of the deadly forms of cancer, causing mortality worldwide in the female population. The standard imaging procedures for screening BC involve mammography and ultrasonography. However, these imaging procedures cannot differentiate subtypes of benign and malignant cancers. Here, histopathology images could provide better sensitivity toward benign and malignant cancer subtypes. Recently, vision transformers have been gaining attention in medical imaging due to their success in various computer vision tasks. Swin transformer (SwinT) is a variant of vision transformer that works on the concept of non-overlapping shifted windows and is a proven method for various vision detection tasks. Thus, in this study, we investigated the ability of an ensemble of SwinTs in the two-class classification of benign vs. malignant and eight-class classification of four benign and four malignant subtypes, using an openly available BreaKHis dataset containing 7909 histopathology images acquired at different zoom factors of 40×, 100×, 200×, and 400×. The ensemble of SwinTs (including tiny, small, base, and large) demonstrated an average test accuracy of 96.0% for the eight-class and 99.6% for the two-class classification, outperforming all the previous works. Thus, an ensemble of SwinTs could identify BC subtypes using histopathological images and may lead to pathologist relief.
An analysis of explainability methods for convolutional neural networks

Deep learning models have gained a reputation of high accuracy in many domains. Convolutional Neural Networks (CNN) are specialized towards image recognition and have high accuracy in classifying objects within images. However, CNNs are an example of a black box model, meaning that experts are unsure how they work internally to reach a classification decision. Without knowing the reasoning behind a decision, there is low confidence that CNNs will continue to make accurate decisions, so it is unsafe to use them in high-risk or safety–critical​ fields without first developing methods to explain their decisions. This paper is a survey and analysis of the available explainability methods for showing the reasoning behind CNN decisions.
Beyond explaining: Opportunities and challenges of XAI-based model improvement

Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. Despite the development of a multitude of methods to explain the decisions of black-box classifiers in recent years, these tools are seldomly used beyond visualization purposes. Only recently, researchers have started to employ explanations in practice to actually improve models. This paper offers a comprehensive overview over techniques that apply XAI practically to obtain better ML models, and systematically categorizes these approaches, comparing their respective strengths and weaknesses. We provide a theoretical perspective on these methods, and show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning, among others. We further discuss potential caveats and drawbacks of these methods. We conclude that while model improvement based on XAI can have significant beneficial effects even on complex and not easily quantifiable model properties, these methods need to be applied carefully, since their success can vary depending on a number of factors, such as the model and dataset used, or the employed explanation method.
Elite archives-driven particle swarm optimization for large scale numerical optimization and its engineering applications

Particle swarm optimization (PSO) is a very simple and effective metaheuristic algorithm. Search operators with similar behavior may lead to the loss of diversity in the search space. All particles in PSO have the same and single search strategy. Therefore, PSO may suffer from premature convergence in solving complex multimodal problems. To improve the global search ability of PSO, this paper reports an elite archives-driven particle swarm optimization (EAPSO). Note that, EAPSO only needs population size and terminal condition for performing the search task, which can distinguish EAPSO over the other reported variants of PSO. In addition, EAPSO has a clear structure, which first builds three types of elite archives to save three different hierarchical particles. Then, six learning strategies for updating the positions of particles are designed by reusing these particles of the three elite archives. To verify the performance of EAPSO, EAPSO is employed to solve CEC 2013 test suite with dimensions 30–100 and three constrained engineering problems. Experimental results show that EAPSO outperforms the compared seven powerful variants of PSO on more than half of test functions and offers highly competitive optimal solutions on the considered engineering problems. That is, experimental results support the validity of the improved strategies and prove the superiority of EAPSO in solving complex multimodal problems. The source code of EAPSO can be found by the following website: https://github.com/jsuzyy/EAPSO.