Reviewerly/exHarmony huggingface dataset:
VF-Mask-Net: A Visual Field Noise Reduction Method Using Neural Networks

Visual Field (VF) measurements, crucial for diagnosing and treating glaucoma, often contain noise originating from both the instrument and subjects during the response process. This study proposes a neural network-based denoising method for VF data, obviating the need for ground truth labels or paired measurements. Using a mask-imposed VF as an input for the neural network, while the original VF serves as a training label, we evaluated performance metrics such as the accuracy, precision, and sensitivity of denoised VFs. Orthogonal experiments were also employed to assess the impact of mask number, mask structure, and replacement strategy on model accuracy. This study reveals that mask number, replacement strategy, and their interaction significantly affect the accuracy of the denoising model. Under recommended parameters, VF-Mask-Net effectively enhances the accuracy and precision of VF measurements. Furthermore, in deterioration detection tasks, denoised VFs display heightened sensitivity compared to their pre-denoising counterparts.
Research on the development of principles for designing elementary English speaking lessons using artificial intelligence chatbots

Abstract The present study was conducted with the aim of developing principles for designing elementary English speaking lessons using artificial intelligence chatbots. To achieve this, design and development research methods were applied, and initial design principles and detailed guidelines were developed through a review of relevant literature. Subsequently, the design principles were modified and refined through two rounds of expert validation and usability evaluation. The research results yielded a total of 10 principles for designing elementary English speaking lessons using artificial intelligence chatbots, including: 1) principle of media selection, 2) principle of creating a learning environment, 3) principle of content restructuring, 4) principle of stimulating and sustaining interest and motivation, 5) principle of providing guidance, 6) principle of scaffolded learning support, 7) principle of individualized feedback provision, 8) principle of fostering a learning environment that supports growth and development, 9) principle of communication and collaboration, and 10) principle of learning management. Additionally, a set of 24 detailed guidelines necessary for implementing each lesson design principle was developed. Based on the research findings, the principles for designing elementary English speaking lessons using artificial intelligence chatbots, as well as the theoretical and practical implications of the study, were discussed. Finally, the limitations of the research were identified, and suggestions for future research were proposed.
DRR: Global Context-Aware Neural Network Using Disease Relationship Reasoning and Attention-Based Feature Fusion

The prediction of future disease development based on past diagnosis records has gained significant attention due to the growing health awareness among individuals. Recent deep learning-based methods have successfully predicted disease development by establishing relationships for each diagnosis record and extracting features from a patient’s past diagnoses in chronological order. However, most of these models have ignored the connections between identified diseases and low-risk diseases, leading to bottlenecks and limitations. In addition, the extraction of temporal characteristics is also hindered by the problem of global feature forgetting. To address these issues, we propose a global context-aware net using disease relationship reasoning and attention-based feature fusion, abbreviated as DRR. Our model incorporates a disease relationship reasoning module that enhances the model’s attention to the relationship between confirmed diseases and low-risk diseases, thereby alleviating the current model’s bottlenecks. Moreover, we have established a global graph-based feature fusion module that integrates global graph-based features with temporal features, mitigating the issue of global feature forgetting. Extensive experiments were conducted on two publicly available datasets, and the experiments show that our method achieves advanced performance.
Data-driven criteria for quantum correlations

We build a machine learning model to detect correlations in a three-qubit system using a neural network trained in an unsupervised manner on randomly generated states. The network is forced to recognize separable states, and correlated states are detected as anomalies. Quite surprisingly, we find that the proposed detector performs much better at distinguishing a weaker form of quantum correlations, namely, the quantum discord, than entanglement. In fact, it has a tendency to grossly overestimate the set of entangled states even at the optimal threshold for entanglement detection, while it underestimates the set of discordant states to a much lesser extent. In order to illustrate the nature of states classified as quantum correlated, we construct a diagram containing various types of states---entangled, as well as separable, both discordant and nondiscordant. We find that the near-zero value of the recognition loss reproduces the shape of the nondiscordant separable states with high accuracy, especially considering the nontrivial shape of this set on the diagram. The network architecture is designed carefully: it preserves separability, and its output is equivariant with respect to qubit permutations. We show that the choice of architecture is important to get the highest detection accuracy, much better than for a baseline model that just utilizes a partial trace operation.
Verifiably exact solution of the electronic Schrödinger equation on quantum devices

Quantum computers have the potential for a significant speedup of molecular computations. However, existing algorithms have limitations; quantum phase estimation (QPE) is intractable on current hardware while variational quantum eigensolvers (VQE) are dependent upon approximate wave functions without guaranteed convergence. In this paper we present an algorithm that yields verifiably exact solutions of the many-electron Schr\"odinger equation. Rather than solve the Schr\"odinger equation directly, we solve its contraction over all electrons except two, known as the contracted Schr\"odinger equation (CSE). The CSE generates a wave-function Ansatz, constructed from an iterative product of nonunitary two-body transformations, whose energy gradient with respect to the two-body operator of the current iteration vanishes if and only if the CSE is satisfied. Because the CSE implies the Schr\"odinger equation, the two-electron Ansatz provides a verifiably exact Ansatz for solving the many-electron Schr\"odinger equation. The exactness property contrasts with that of Ans\"atze built from the product of unitary two-body transformations where the gradient---the residual of the anti-Hermitian part of the CSE (ACSE)---can vanish without implying a solution of the Schr\"odinger equation. We demonstrate the algorithm on both simulators and noisy quantum computers with ${\mathrm{H}}_{2}$ dissociation and the rectangle-to-square transition in ${\mathrm{H}}_{4}$.
Optimized convergence of stochastic gradient descent by weighted averaging

Under mild assumptions stochastic gradient methods asymptotically achieve an optimal rate of convergence if the arithmetic mean of all iterates is returned as an approximate optimal solution. However, in the absence of stochastic noise, the arithmetic mean of all iterates converges considerably slower to the optimal solution than the iterates themselves. And also in the presence of noise, when a termination of the stochastic gradient method after a finite number of steps is considered, the arithmetic mean is not necessarily the best possible approximation to the unknown optimal solution. This paper aims at identifying optimal strategies in a particularly simple case, the minimization of a strongly convex function with i. i. d. noise terms and termination after a finite number of steps. Explicit formulas for the stochastic error and the optimality error are derived in dependence of certain parameters of the SGD method. The aim was to choose parameters such that both stochastic error and optimality error are reduced compared to arithmetic averaging. This aim could not be achieved; however, by allowing a slight increase of the stochastic error it was possible to select the parameters such that a significant reduction of the optimality error could be achieved. This reduction of the optimality error has a strong effect on the approximate solution generated by the stochastic gradient method in case that only a moderate number of iterations is used or when the initial error is large. The numerical examples confirm the theoretical results and suggest that a generalization to non-quadratic objective functions may be possible.
Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.
Entity Management Using RDA and Wikibase: A Case Study at the National Library of Greece

The force driving the implementation of Linked Data (LD) technologies in libraries is the aspiration for widespread interoperability of library data and for advanced information services. Toward these goals, libraries change their data models, update their communication formats, and develop prototype applications that use new models and formats and enable entity-based cataloguing. The National Library of Greece (NLG) has been actively adapting its policies, workflows, and tools to make its data LD-ready. Within this context, NLG has developed -in cooperation with Open Knowledge Greece- an entity management prototype using RDA/RDF in a Wikibase instance. The vision is to build a shared entity management system used by all memory institutions in Greece to describe entities of Greek interest. Data is to be stored as a knowledge graph that can be exported in RDF serializations, and in legacy MARC format. This paper first examines related efforts from other National Libraries, and then outlines the steps involved in the publication of the NLG Authority File as linked data; namely dataset selection, modeling, data curation, mapping, and publication. The development of the NLG entity management prototype is also presented. The paper concludes with challenges, lessons learned, and next steps.
An attack on free speech? Examining content moderation, (de-), and (re-) platforming on American right-wing alternative social media

Contemporary research on social media looks different than it did in the late 2010s, with users facing a high-choice social media environment as new platforms emerge. Subsequently, alt-right sites have experienced a rise in users—sometimes those who have experienced content moderation by traditional social media sites. As such, scholars have investigated the impact of this content moderation (e.g. de-platforming) on users and the content posted on new alt-right platforms. This work seeks to expand extant research through analyzing a survey of Gab, Parler (now defunct), Truth Social, and Rumble users ( N = 427) who have experienced content moderation on other social media sites. While we find that those temporarily or permanently banned from traditional sites are unlikely to leave the platform altogether for a right-wing alternative social media (RWASM) site, there are active users on these sites worth studying.
Gold and Arsenic in Pyrite and Marcasite: Hydrothermal Experiment and Implications to Natural Ore-Stage Sulfides

Hydrothermal synthesis experiments were performed in order to quantify the states of Au and As in pyrite and marcasite. The experiments were performed at 350 °C/500 bar and 490 °C/1000 bar (pyrite–pyrrhotite buffer, C(NaCl) = 15 and 35 wt.%). The synthesis products were studied by EPMA, LA-ICP-MS, and EBSD. The EPMA was applied for simultaneous determinations of Au, As, Fe, and S, with a Au detection limit of 45–48 ppm (3σ). The analyses were performed along profiles across zonal grains. The concentrations of As and Au up to 5 wt.% and 8000 ppm, respectively, were determined in pyrite and up to 6 wt.% and 1300 ppm in marcasite. In pyrite, the Au concentration decreases with fluid salinity and temperature increases. Strong positive Au–As correlation and strong negative Au–Fe and As–S correlation were identified in pyrite. Comparison of the correlations with theoretical lines implies Au–As clustering. The cluster stoichiometry is inferred to be [AuAs10]. Most probably, As in pyrite presents in the form of clusters and in the As→S solid solution. Incorporation of Au in As-rich pyrite can be controlled by the reductive deposition mechanism. In marcasite, the concentrations of Au are not correlated with the As content. The [AuAs10] clusters enrich the {210}, {113}, and {111} pyrite faces, where the former exhibits the highest affinity to Au and As. The affinity of {110} and {100} forms to Au and As is lower. Implication of the experimental results to data for natural auriferous pyrite shows that the increase of Au content at C(As) &gt; 0.5–1 wt.% is caused by the incorporation of the Au-As clusters, but not because of the formation of Au→Fe solid solution. Therefore, the concentration of “invisible” gold in pyrite is dictated solely by the hydrothermal fluid chemistry and subsequent ore transformations.
A Majorization-Minimization Algorithm for Neuroimage Registration

.Intensity-based image registration is critical for neuroimaging tasks, such as 3D reconstruction, times-series alignment, and common coordinate mapping. The gradient-based optimization methods commonly used to solve this problem require a careful selection of step-length. This limitation imposes substantial time and computational costs. Here we propose a gradient-independent rigid-motion registration algorithm based on the majorization-minimization (MM) principle. Each iteration of our intensity-based MM algorithm reduces to a simple point-set rigid registration problem with a closed form solution that avoids the step-length issue altogether. The details of the algorithm are presented, and an error bound for its more practical truncated form is derived. The performance of the MM algorithm is shown to be more effective than gradient descent on simulated images and Nissl stained coronal slices of mouse brain. We also compare and contrast the similarities and differences between the MM algorithm and another gradient-free registration algorithm called the block-matching method. Finally, extensions of this algorithm to more complex problems are discussed.KeywordsMM algorithmimage registrationneuroimagingMSC codes92C5565K10
DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing

Abstract In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric refraction, equipment malfunctions, and other factors, resulting in diminished data quality. Traditional radar blockage correction methods, such as employing approximate radial information interpolation and supplementing missing data, often fail to effectively exploit potential patterns in massive radar data, for the large volume of data precludes a thorough analysis and understanding of the inherent complex patterns and dependencies through simple interpolation or supplementation techniques. Fortunately, edge computing possesses certain data processing capabilities and cloud center boasts substantial computational power, which together can collaboratively offer timely computation and storage for the correction of radar beam blockage. To this end, an edge-cloud collaborative driven deep learning model named DenMerD is proposed in this paper, which includes dense connection module and merge distribution (MD) unit. Compared to existing models such as RC-FCN, DenseNet, and VGG, this model greatly improves key performance metrics, with 30.7 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> improvement in Critical Success Index (CSI), 30.1 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> improvement in Probability of Detection (POD), and 3.1 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> improvement in False Alarm Rate (FAR). It also performs well in the Structure Similarity Index Measure (SSIM) metrics compared to its counterparts. These findings underscore the efficacy of the design in improving feature propagation and beam blockage accuracy, and also highlights the potential and value of mobile edge computing in processing large-scale meteorological data.
Optimization of functional diagnostic test: the effect of kernel method as an estimator of ROC curve

Technical development over the last few decades has resulted in the emergence of complex data, in many cases functional data (FD). This type of data can emerge in many medical studies which are geared towards detecting diseases, predicting their course or evaluating the response to a therapy, to name a few. Thus, it is very useful to have statistical methods enabling us to evaluate diagnostic tests based on functional biomarkers. In fact, a diagnostic test that uses functional variables as biomarkers has been developed recently. Their authors proposed a functional version of ROC analysis, resulting in an empirical estimate of the functional ROC curve. In order to improve this methodology, the present paper proposes a procedure to obtain a smooth version of non-parametric estimator of the ROC curve. In addition, a comprehensive simulation study lets to investigate the discriminatory and predictive abilities of the resulting functional diagnostic test. Two examples with real medical data illustrate the approach developed: one deals with gene expression levels for tumoural/normal samples of prostate cancer; the other dataset is about white matter structures in the brain in multiple sclerosis patients.
The use of a large language model to create plain language summaries of evidence reviews in healthcare: A feasibility study

Abstract Introduction Plain language summaries (PLSs) make complex healthcare evidence accessible to patients and the public. Large language models (LLMs) may assist in generating accurate, readable PLSs. This study explored using the LLM Claude 2 to create PLSs of evidence reviews from the Agency for Healthcare Research and Quality (AHRQ) Effective Health Care Program. Methods We selected 10 evidence reviews published from 2021 to 2023, representing a range of methods and topics. We iteratively developed a prompt to guide Claude 2 in creating PLSs which included specifications for plain language, reading level, length, organizational structure, active voice, and inclusive language. PLSs were assessed for adherence to prompt specifications, comprehensiveness, accuracy, readability, and cultural sensitivity. Results All PLSs met the word count. We judged one PLS as fully comprehensive; seven mostly comprehensive. We judged two PLSs as fully capturing the PICO elements; five with minor PICO errors. We judged three PLSs as accurately reporting the results; and four with minor result errors. We judged three PLSs as having major result errors for incorrectly reporting total participants. Five PLSs met the target 6th to 8th grade reading level. Passive voice use averaged 16%. All PLSs used inclusive language. Conclusions LLMs show promise for assisting in PLS creation but likely require human input to ensure accuracy, comprehensiveness, and the appropriate nuances of interpretation. Iterative prompt refinement may improve results and address the needs of specific reviews and audiences. As text‐only summaries, the AI‐generated PLSs could not meet all consumer communication criteria, such as textual design and visual representations. Further testing should include consumer reviewers and explore how to best leverage LLM support in drafting PLS text for complex evidence reviews.
Quran reciter identification using NASNetLarge

Abstract Speaker identification has significant advantages for the field of human–computer interaction. Recently, many scholars have made contributions in this field and successfully created deep learning models for automatic speaker identification systems. However, most of the speech signal processing work is limited to English-only applications, despite numerous challenges with Arabic speech, particularly with the recitation of the Holy Quran, which is the Islamic holy book. In the light of these considerations, this study proposes a model for identifying the reciter of the Holy Quran using a dataset of 11,000 audio samples extracted from 20 Quran reciters. To enable feeding the audio samples' visual representation to the pre-trained models, the audio samples are converted from their original audio representation to visual representation using the Mel-Frequency Cepstrum Coefficients. Six pre-trained deep learning models are evaluated separately in the proposed model. The results from the test dataset reveal that the NASNetLarge model achieved the highest accuracy rate of 98.50% among the pre-trained models used in this study.
The role of hyperparameters in machine learning models and how to tune them

Abstract Hyperparameters critically influence how well machine learning models perform on unseen, out-of-sample data. Systematically comparing the performance of different hyperparameter settings will often go a long way in building confidence about a model's performance. However, analyzing 64 machine learning related manuscripts published in three leading political science journals (APSR, PA, and PSRM) between 2016 and 2021, we find that only 13 publications (20.31 percent) report the hyperparameters and also how they tuned them in either the paper or the appendix. We illustrate the dangers of cursory attention to model and tuning transparency in comparing machine learning models’ capability to predict electoral violence from tweets. The tuning of hyperparameters and their documentation should become a standard component of robustness checks for machine learning models.