Reviewerly/exHarmony huggingface dataset:
Lithium quantification based on random forest with multi-source geoinformation in Coipasa salt flats, Bolivia

With the widespread use of Lithium (Li) batteries, there is an urgent demand to explore Li deposits. This study aims to apply efficient remote sensing methodologies with multi-resolution images of Sentinel-2, ASTER, and JILIN GP and soil geoinformation to locate Li by mapping its alterations and directly identifying the minerals that contain it. The principal component analysis (PCA) is applied to the satellite bands to gain an initial understanding and analysis of the temporal and spatial behaviour in the Coipasa salt flats in western Bolivia. Then, we employed the Random Forest (RF) method for defining repeatable mineral exploration targets and predictive modelling of mineral prospecting using geographical information system (GIS) tools. Predictive maps are created that contemplate the source, sediment transport, and chemical deposition processes crucial to produce this mineral. The predictive models used satellite bands and physical and chemical soil grid maps; the training data is extracted from sixteen soil and salt samples evaluated using the results of a laboratory test utilizing Atomic Absorption Spectrophotometry. The results demonstrate the high reliability of our predictive models: the values of the Area Under the Curve (AUC) of the Receiver Operating Characteristic Curve (ROC) are between 0.90 and 0.92. The results also show that the combination of PCA on satellite images and terrain information, such as physical, chemical, and morphological properties of the terrain, improves the model to predict the formation of Li ore within the Coipasa salt flats.
Utilizing optical neural network to establish high-performance OR and XOR logic gates

The optical–neural-network logic gates using unsupervised learning method and supervised learning method are investigated. The structures of the optical neurons using self-connection configuration and interconnection configuration are proposed. The performance of the AND, OR, NAND, NOR and XOR logic gates are analyzed. According to our simulation results, the bit error ratio (BER) of the optical neurons using the interconnection configuration is lower than that using self-connection configuration. For OR logic gate, the best performance is BER = 6.54%. For XOR logic gate, the best performance is BER < 4.89 × 10−5. The results show that the proposed optical structure can work for different logic gates by tuning the parameters of the couplers and the phase shifters.
MixGradient: A gradient-based re-weighting scheme with mixup for imbalanced data streams

A challenge for contemporary deep neural networks in real-world problems is learning from an imbalanced data stream, where data tends to be received chunk by chunk over time, and the prior class distribution is severely imbalanced. Although many sophisticated algorithms have been derived, most of them overlook the importance of gradient information. From this perspective, the difficulty of learning from imbalanced data streams lies in the fact that the gradient estimated on an uneven class distribution is not informative enough to reflect the critical pattern of each class. To this end, we propose to assign higher weights on the training samples whose gradients are close to the gradient of corresponding typical samples, thus highlighting the important samples in minority classes and suppressing the noisy samples in majority classes. Such an idea can be combined with Mixup, which exploits the interpolation information of data to further compensate for the information of sample space that the typical samples do not provide and expand the role of the proposed re-weighting scheme. Experiments on artificially induced long-tailed CIFAR data streams and long-tailed MiniPlaces data stream show that the resulting method, termed MixGradient, boosts the generalization performance of DNNs under different imbalance ratios and achieves up to 10% accuracy improvement.
Multimodal spectroscopy with chemometrics: Application to simulated forensic soil casework

A multimodal analytical sequence in combination with chemometrics was applied to a blinded case simulation to showcase its suitability for use within the context of forensic casework involving arid, sandy soils low in clay and organic matter. Using microspectrophotometry, attenuated total reflectance Fourier-transform infrared spectroscopy and X-ray diffraction, in combination with principal component analysis and linear discriminant analysis, an unknown “recovered” sample was able to be associated with a single reference soil and discriminated from the other soils. The spectroscopic techniques employed produced varying results, however, these results offered complementary information that was able to be interpreted collectively to discriminate between the reference soils and identify the source of the “recovered” soil with greater confidence. The use of chemometric methods to interpret the data provided an objective, statistical measure of the similarities and/or variation between samples that could be presented as weighting for evidence in court. Hence, this approach was deemed suitable for incorporation into forensic casework to provide discrimination between similar sandy soil samples in an objective manner.
A digital geometry on the tetrakis square tiling—Distance and coarsening

Abstract There are various tessellations of the plane, including three regular and eight semi‐regular tilings. The square grid is self‐dual, and the hexagonal and triangular tilings are dual to each other. The semi‐regular tessellations are based on more than one type of regular tiles, while their dual tilings are based on a sole but not a regular tile. In various applications, including Geographical Information Systems, it is worth considering non‐regular grids instead of the most used square grid. In this article, we are interested in the dual of the semi‐regular truncated quadrille tiling, T(8,8,4), which is also known as the Khalimsky grid due to its connectedness structure. In our grid, which is called the tetrakis square or kisquadrille tiling, while it is denoted by D(8,8,4), we consider the right‐angled triangle regions of the usual two‐dimensional Khalimsky graph as tiles/pixels. We give an easy‐to‐use coordinate frame addressing the triangles of all the four different orientations. Neighbor relations are described mathematically based on this frame. Based on the shortest path algorithm, a closed formula is proven to compute the digital, that is, path‐based distance on this grid. Some properties of the distance function have also been studied. Hierarchical coarsening is a frequently used technique both in Geometric and Geographical Information Systems to rescale some parts of the map. The tetrakis square grid is apt for hierarchical coarsening, and thus, it can easily be used in image compression and multigrid and other related methods.
Mixed and Augmented Reality in Healthcare: When Will It Deliver Its Promises?

Medical innovations mean improvement to patients' safety and outcome. Sometimes modern technologies that are not designed for medicine turn out to have applications in medical practice. Augmented or mixed reality is a great example and countless applications in various medical fields have proved its usefulness. So, when will mixed reality be used routinely in medical practice and what is keeping this technology from becoming part of modern medicine? This chapter will highlight possible benefits and some challenges concerning the use of mixed reality in medical practice and education.
Characterization of AI Model Configurations for Model Reuse

With the widespread creation of artificial intelligence (AI) models in biosciences, bio-medical researchers are reusing trained AI models from other applications. This work is motivated by the need to characterize trained AI models for reuse based on metrics derived from optimization curves captured during model training. Such AI model characterizations can aid future model accuracy refinement, inform users about model hyper-parameter sensitivity, and assist in model reuse according to multi-purpose objectives. The challenges lie in understanding relationships between trained AI models and optimization curves, defining and validating quantitative AI model metrics, and disseminating metrics with trained AI models. We approach these challenges by analyzing optimization curves generated for image segmentation and classification tasks to assist in a multi-objective reuse of AI models.
Biological survival optimization algorithm with its engineering and neural network applications

This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. This algorithm consists of two important courses, escape phase and adjustment phase. Specifically, in the escape phase, each search agent is required to update its location using the best, the worst and a neighboring individual of the population. The adjustment phase is implemented using the simplex algorithm for search better location of the worst agent within a small region. The effectiveness of the BSO is validated on the CEC2017 benchmark problems, three classical engineering structural problems and neural network training models. Simulation comparison results considering both convergence and accuracy simultaneously show that BSO has competitive performance compared with other state-of-the-art optimization techniques.
Impact of Blended-Learning on Higher Education and English Language

New teaching-learning alternatives are needed, and they should be aligned with the technological reality in which we live. This study aimed to analyze the impact of Blended Learning on Higher Education and the English language. The methodology on which this article is based on [1], in which it is divided into the following stages: formulation of research questions, localization of literature, selection and evaluation of localized articles, analysis and synthesis of content and presentation of results. Several studies of the databases of indexed journals were reviewed. Twelve bibliographic sources were selected and processed into three categories: academic performance, development of English language skills, and acceptability; however, the number of studies on academic performance area is limited. The information obtained was analyzed and contrasted through a statistical analysis which determines that this educational model has a significant impact on the academic performance of higher education students.
Experimental Assessment of Photovoltaic Systems Using One-Axis Tracking and Positioning Strategies in Equatorial Regions

The performance of photovoltaic systems may be enhanced by using solar tracking and positioning strategies. In equatorial regions, one-axis tracking seems to be more advantageous than two-axes tracking, because the solar movement that implies similar energy productions with less mechanical elements for the one-axis tracking. Moreover, to increase the energy production of such systems, positioning strategies, that consist in put the FV panels in horizontal position when cloudy conditions, are suggested to be implemented. In this work, experimental results of the enhancement of a one-axis solar tracking system and a positioning strategy system are reported. For the positioning strategies, three algorithms were studied: one with a GHI threshold, one with a DNI/GHI threshold and one with a TA threshold. The test bench was implemented in Quito-Ecuador. Results show the big advantage to use one-axis solar tracking for PV systems, achieving enhancements between 6 to 37%, facing to a fixed system. Also, results suggest that positioning strategies do not mean big enhancements, with increases of 6% for cloudy conditions and becoming negative for sunny conditions. These results suggest that positioning strategies are interesting only for locations with high number of time with cloudy conditions.
State of ICTs as Support for the Educational Process in the Andean Region

Higher education institutions in the Andean region have shown their progress in the adoption of ICTs for education. The objective of this study is to understand how this adoption has been carried out, and what shortcomings can be identified in order to correct them. The proliferation of information and communication technologies in the field of education has posed great challenges to HEIs, so that adopting and reducing the ICT access gap, as well as its correct use among the different actors of the educational community, has become a pending task for the institutional authorities of HEIs and for local and national governments. The work focused on the use of mixed paradigm as a reference. As part of a broader study, the present work focused on determining the ICT use gap between teachers and students in this group of HEIs. To analyze the gap in ICT use, the study was based on the information resulting from a survey of professors and students, as well as on a documentary review of the public information of the HEIs under study. The results show a generalized use of LMS systems by the region's HEIs, however, there is a lack of use of the capabilities of these platforms, especially due to the lack of training and support of their professors and students. Likewise, the use of various tools and social networks has been spreading in the IES. On the other hand, academic-administrative management support systems (SIS) are mostly developed in-house or commercially but without a holistic vision of integration and process management; only a few HEIs have decided to adopt academic ERPs as a definitive and integrated solution to institutional educational processes.
Bibliometric Mapping of Scientific Literature Located in Scopus on Teaching Digital Competence in Higher Education

By using the PRISMA methodology, a systematic literature review (SLR) was carried out, supported by the meta-analysis (MA) on teaching digital competence (TDC) in universities, in the Scopus scientific base. The research is delimited through Eric’s thesauri and research questions related to the meta-analysis of the data of the scientific productions located were established, as follows: 1. In which years are there a greater number of publications related to the topic? 2. To which journals do the works belong? 3. Who are the most outstanding authors? 4. What affiliation do the publications have? 5. In which countries do the investigations come from? 6. What kind of documents are they? 7. In which areas is the subject investigated? 8. What is the predominant language? 9. Which are the most relevant works according to the number of citations?. It is discovered that worldwide authors and entities from Spain are more common in the theme, most of the selected works are in Spanish. Since 2019, the number of publications related to the subject increases, this may be since during the pandemic period many universities adopted virtual education in an emerging way; related relevant areas are social sciences and computer sciences. The study constitutes the starting point to establish future research in other scientific databases with the inclusion of educational data mining and business intelligence.
CTIS-GAN: computed tomography imaging spectrometry based on a generative adversarial network

Computed tomography imaging spectrometry (CTIS) is a snapshot hyperspectral imaging technique that can obtain a three-dimensional (2D spatial + 1D spectral) data cube of the scene captured within a single exposure. The CTIS inversion problem is typically highly ill-posed and is usually solved by time-consuming iterative algorithms. This work aims to take the full advantage of the recent advances in deep-learning algorithms to dramatically reduce the computational cost. For this purpose, a generative adversarial network is developed and integrated with self-attention, which cleverly exploits the clearly utilizable features of zero-order diffraction of CTIS. The proposed network is able to reconstruct a CTIS data cube (containing 31 spectral bands) in milliseconds with a higher quality than traditional methods and the state-of-the-art (SOTA). Simulation studies based on real image data sets confirmed the robustness and efficiency of the method. In numerical experiments with 1000 samples, the average reconstruction time for a single data cube was ∼16ms. The robustness of the method against noise is also confirmed by numerical experiments with different levels of Gaussian noise. The CTIS generative adversarial network framework can be easily extended to solve CTIS problems with larger spatial and spectral dimensions, or migrated to other compressed spectral imaging modalities.
Improving multi-scale attention networks: Bayesian optimization for segmenting medical images

Current deep learning-based image segmentation methods are notable for their use of large number of parameters and extensive computational resources in training. There is a persistent need for more efficient flexible systems without compromising on precision. This work proposes a novel model that combines the best of deep learning and probabilistic machine learning to segment a wide variety of medical image datasets with state-of-the-art accuracy and limited resources. The approach benefits from the introduction of new diverse attention modules that serve multiple purposes including capturing of relevant information at different scales. These proposed attention modules are generic and can potentially be used with other architectures to boost performance. In addition, Bayesian optimization is employed to tune multi-scale weight hyperparameters of the model. The architecture combined with one of the proposed novel attention modules and tuned hyperparameters achieves the best results in segmenting ISIC 2017, LUNGS, NERVE, Skin Lesion and CHEST datasets. Finally, the explainability of the network is analysed by visualizing the feature map learned from the attention modules.
Persistent dynamics in coupled non-degenerate parametric oscillators: pump saturation prevents mode competition

The coherent dynamics in networks of coupled oscillators is of great interest in wave-physics since the coupling produces various dynamical effects, such as coherent energy exchange (beats) between the oscillators. However, it is common wisdom that these coherent dynamics are transients that quickly decay in active oscillators (e.g. lasers) since pump saturation causes mode competition that results, for homogeneous gain, in the prevalence of the single winning mode. We observe that pump saturation in coupled parametric oscillators counter-intuitively encourages the multi-mode dynamics of beating and indefinitely preserves it, despite the existence of mode competition. We explore in detail the coherent dynamics of a pair of coupled parametric oscillators with a shared pump and arbitrary coupling in a radio frequency (RF) experiment, as well as in simulation. Specifically, we realize two parametric oscillators as different frequency-modes of a single RF cavity and couple them arbitrarily using a digital high-bandwidth FPGA. We observe persistent coherent beats that are maintained at any pump level, even high above the threshold. The simulation highlights how the interplay of pump depletion between the two oscillators prevents them from synchronizing, even when the oscillation is deeply saturated.
Parallel unambiguous generalized phase-shifting and T-spline fitting algorithms for optical micro-structured surface 3D topography metrology

3D topography metrology of optical micro-structured surfaces is critical for controlled manufacturing and evaluation of optical properties. Coherence scanning interferometry technology has significant advantages for measuring optical micro-structured surfaces. However, the current research faces difficulties of designing high accuracy and efficient phase shifting, and characterization algorithms for optical micro-structured surface 3D topography metrology. In this paper, parallel unambiguous generalized phase-shifting and T-spline fitting algorithms are proposed. To avoid phase ambiguity and improve the accuracy of the phase-shifting algorithm, the zero-order fringe is determined by the iterative envelope fitting with Newton’s method, and the accurate zero optical path difference is determined by a generalized phase-shifting algorithm. In particular, the calculation procedures of the multithreading iterative envelope fitting with Newton’s method and generalized phase shifting are optimized with the graphics processing unit-Compute Unified Device Architecture kernel function. Additionally, to fit the base form of optical micro-structured surfaces and characterize the surface texture and roughness, an effective T-spline fitting algorithm is proposed by optimizing the preimage of the T-mesh with image quadtree decomposition. Experimental results show that the surface reconstruction of optical micro-structured surfaces using the proposed algorithm is more accurate, and the efficiency is 10 times higher than that of current algorithms; the time of the surface reconstruction is less than 1 s. Compared with the current B-spline method, the accuracy of roughness characterization using the proposed T-spline algorithm is improved by more than 10%.
FaxMatch: Multi‐Curriculum Pseudo‐Labeling for semi‐supervised medical image classification

Abstract Background Semi‐supervised learning (SSL) can effectively use information from unlabeled data to improve model performance, which has great significance in medical imaging tasks. Pseudo‐labeling is a classical SSL method that uses a model to predict unlabeled samples and selects the prediction with the highest confidence level as the pseudo‐labels and then uses the generated pseudo‐labels to train the model. Most of the current pseudo‐label‐based SSL algorithms use predefined fixed thresholds for all classes to select unlabeled data. Purpose However, data imbalance is a common problem in medical image tasks, where the use of fixed threshold to generate pseudo‐labels ignores different classes of learning status and learning difficulties. The aim of this study is to develop an algorithm to solve this problem. Methods In this work, we propose Multi‐Curriculum Pseudo‐Labeling (MCPL), which evaluates the learning status of the model for each class at each epoch and automatically adjusts the thresholds for each class. We apply MCPL to FixMatch and propose a new SSL framework for medical image classification, which we call the improved algorithm FaxMatch. To mitigate the impact of incorrect pseudo‐labels on the model, we use label smoothing (LS) strategy to generate soft labels (SL) for pseudo‐labels. Results We have conducted extensive experiments to evaluate our method on two public benchmark medical image classification datasets: the ISIC 2018 skin lesion analysis and COVID‐CT datasets. Experimental results show that our method outperforms fully supervised baseline, which uses only labeled data to train the model. Moreover, our method also outperforms other state‐of‐the‐art methods. Conclusions We propose MCPL and construct a semi‐supervised medical image classification framework to reduce the reliance of the model on a large number of labeled images and reduce the manual workload of labeling medical image data.