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Using lexical language models to detect borrowings in monolingual wordlists
(Public Library of Science, 2020-12-01)
Lexical borrowing, the transfer of words from one language to another, is one of the most frequent processes in language evolution. In order to detect borrowings, linguists make use of various strategies, combining evidence from various sources. Despite the increasing popularity of computational approaches in comparative linguistics, automated approaches to lexical borrowing detection are still in their infancy, disregarding many aspects of the evidence that is routinely considered by human experts. One example for this kind of evidence are phonological and phonotactic clues that are especially useful for the detection of recent borrowings that have not yet been adapted to the structure of their recipient languages. In this study, we test how these clues can be exploited in automated frameworks for borrowing detection. By modeling phonology and phonotactics with the support of Support Vector Machines, Markov models, and recurrent neural networks, we propose a framework for the supervised detection of borrowings in mono-lingual wordlists. Based on a substantially revised dataset in which lexical borrowings have been thoroughly annotated for 41 different languages from different families, featuring a large typological diversity, we use these models to conduct a series of experiments to investigate their performance in mono-lingual borrowing detection. While the general results appear largely unsatisfying at a first glance, further tests show that the performance of our models improves with increasing amounts of attested borrowings and in those cases where most borrowings were introduced by one donor language alone. Our results show that phonological and phonotactic clues derived from monolingual language data alone are often not sufficient to detect borrowings when using them in isolation. Based on our detailed findings, however, we express hope that they could prove to be useful in integrated approaches that take multi-lingual information into account.
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Application of semantic segmentation with few labels in the detection of water bodies from PeruSat-1 satellite’s images
(Copernicus Publications, 2020-11-06)
Abstract. Remote sensing is widely used to monitor earth surfaces with the main objective of extracting information from it. Such is the case of water surface, which is one of the most affected extensions when flood events occur, and its monitoring helps in the analysis of detecting such affected areas, considering that adequately defining water surfaces is one of the biggest problems that Peruvian authorities are concerned with. In this regard, semiautomatic mapping methods improve this monitoring, but this process remains a time-consuming task and into the subjectivity of the experts.In this work, we present a new approach for segmenting water surfaces from satellite images based on the application of convolutional neural networks. First, we explore the application of a U-Net model and then a transfer knowledge-based model. Our results show that both approaches are comparable when trained using an 680-labelled satellite image dataset; however, as the number of training samples is reduced, the performance of the transfer knowledge-based model, which combines high and very high image resolution characteristics, is improved.
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Mobiloscope: a technological solution for early mastitis detection in dairy cattle
(Institute of Electrical and Electronics Engineers Inc., 2021-12-23)
One of the most critical challenges in dairy farms is the Mastistis condition causing economic losses associated with milk production reduction and veterinary treatment expenses. Although it exists different methodologies for diagnosing animals with mastitis, these tests are usually indirect; others require laboratory analysis taking a lot of time to obtain the result, limiting its viability and monitoring in the field. To solve this problem, we propose a Mobiloscope, which is a portable, practical, effective, and low-cost diagnostic system for sub-clinical mastitis. Hence, this device provides an early detection in-situ and at a low cost to cover farmers' unsatisfied demand for having innovative tools that allow them to carry out better sub-clinical mastitis early detection. Our system comprises four components: (i) the holder for the electronic device and the screen to display the graphic interface; (ii) a part where the battery for the micro-computer will be housed; (iii) a dedicated part for the microscope and sample holder; and (iv) a holder for the light source. Despite the need to validate the prototype for commercial purposes, our prototype is able to estimate the number of somatic cells. Therefore, our mobiloscope could help the farmers to make an in-situ analysis of milk quality at a low-cost.
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An approach to growth delimitation of straight line segment classifiers based on a minimum bounding box
(MDPI, 2021-11-01)
Several supervised machine learning algorithms focused on binary classification for solving daily problems can be found in the literature. The straight-line segment classifier stands out for its low complexity and competitiveness, compared to well-knownconventional classifiers. This binary classifier is based on distances between points and two labeled sets of straight-line segments. Its training phase consists of finding the placement of labeled straight-line segment extremities (and consequently, their lengths) which gives the minimum mean square error. However, during the training phase, the straight-line segment lengths can grow significantly, giving a negative impact on the classification rate. Therefore, this paper proposes an approach for adjusting the placements of labeled straight-line segment extremities to build reliable classifiers in a constrained search space (tuned by a scale factor parameter) in order to restrict their lengths. Ten artificial and eight datasets from the UCI Machine Learning Repository were used to prove that our approach shows promising results, compared to other classifiers. We conclude that this classifier can be used in industry for decision-making problems, due to the straightforward interpretation and classification rates.
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A distributed N-FINDR cloud computing-based solution for endmembers extraction on large-scale hyperspectral remote sensing data
(MDPI, 2022-05-01)
In this work, we introduce a novel, distributed version of the N-FINDR endmember extraction algorithm, which is able to exploit computer cluster resources in order to efficiently process large volumes of hyperspectral data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for endmember extraction, which can be executed on cloud computing environments, allowing users to elastically administer processing power and storage space for adequately handling very large datasets. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, evaluating both accuracy and execution time, over the processing of different synthetic versions of the AVIRIS Cuprite hyperspectral dataset, with 3.1 Gb, 6.2 Gb, and 15.1Gb respectively, thus addressing the issue of dealing with large-scale hyperspectral data. As a further contribution of this work, we describe in detail how to extend the HyperCloud-RS framework by integrating other endmember extraction algorithms, thus enabling researchers to implement algorithms specifically designed for their own assessment.
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Graph embedding on mass spectrometry- and sequencing-based biomedical data
(BioMed Central Ltd, 2024-12-01)
Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein–protein interaction networks and predicting novel drug functions.
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Exploratory analysis of metabolic changes using mass spectrometry data and graph embeddings
(Nature Research, 2024-12-01)
Mass spectrometry (MS)-based metabolomics analysis is a powerful tool, but it comes with its own set of challenges. The MS workflow involves multiple steps before its interpretation in what is denominate data mining. Data mining consists of a two-step process. First, the MS data is ordered, arranged, and presented for filtering before being analyzed. Second, the filtered and reduced data are analyzed using statistics to remove further variability. This holds true particularly for MS-based untargeted metabolomics studies, which focused on understanding fold changes in metabolic networks. Since the task of filtering and identifying changes from a large dataset is challenging, automated techniques for mining untargeted MS-based metabolomic data are needed. The traditional statistics-based approach tends to overfilter raw data, which may result in the removal of relevant data and lead to the identification of fewer metabolomic changes. This limitation of the traditional approach underscores the need for a new method. In this work, we present a novel deep learning approach using node embeddings (powered by GNNs), edge embeddings, and anomaly detection algorithm to analyze the data generated by mass spectrometry (MS)-based metabolomics called GEMNA (Graph Embedding-based Metabolomics Network Analysis), for example for an untargeted volatile study on Mentos candy, the data clusters produced by GEMNA were better than the ones used traditional tools, i.e., GEMNA has, vs. the traditional approach has.
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A deep learning approach to distance map generation applied to automatic fiber diameter computation from digital micrographs
(Multidisciplinary Digital Publishing Institute (MDPI), 2024-09-01)
Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of (Formula presented.) and a mean square error (MSE) of (Formula presented.), demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing.
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A Comparison Study Between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous Navigation in Dynamic Scenarios
(Multidisciplinary Digital Publishing Institute (MDPI), 2023-12-01)
At the beginning of a project or research that involves the issue of autonomous navigation of mobile robots, a decision must be made about working with traditional control algorithms or algorithms based on artificial intelligence. This decision is not usually easy, as the computational capacity of the robot, the availability of information through its sensory systems and the characteristics of the environment must be taken into consideration. For this reason, this work focuses on a review of different autonomous-navigation algorithms applied to mobile robots, from which the most suitable ones have been identified for the cases in which the robot must navigate in dynamic environments. Based on the identified algorithms, a comparison of these traditional and DRL-based algorithms was made, using a robotic platform to evaluate their performance, identify their advantages and disadvantages and provide a recommendation for their use, according to the development requirements of the robot. The algorithms selected were DWA, TEB, CADRL and SAC, and the results show that—according to the application and the robot’s characteristics—it is recommended to use each of them, based on different conditions.
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GeoNeXt: Efficient landslide mapping using a pre-trained ConvNeXt V2 encoder with a PSA-ASPP decoder
(Elsevier BV, 2025-12-01)
Landslides constitute one of the most destructive geological hazards worldwide, causing thousands of casualties and billions in economic losses annually. To mitigate these risks, accurate and efficient pixel-wise mapping of landslides for automatic semantic segmentation is of paramount importance. While recent advances in deep learning, particularly with transformer architectures and large pre-trained models like the Segment Anything Model (SAM), have shown promise, their application to landslide mapping is often hindered by high computational costs, prompt dependency, and challenges with data imbalance. To address these limitations, we propose GeoNeXt, a novel semantic segmentation architecture for intelligent landslide recognition. It combines a scalable, pre-trained ConvNeXt V2 encoder with a decoder that utilizes Pyramid Squeeze Attention (PSA) and Atrous Spatial Pyramid Pooling (ASPP) to capture multi-scale features. Through domain adaptation on the large-scale CAS landslide dataset, we refined the encoder's general pre-trained features to learn robust, landslide-specific features. GeoNeXt exhibited zero-shot transferability, achieving 74–78 % F1 and 64–66 % mIoU across three distinct test datasets from diverse regions, which were entirely excluded from the training process. Ablation studies on decoder variants validated the PSA-ASPP synergy, achieving a superior F1 of 90.39 % and mIoU of 83.18 % on the CAS dataset. Comparative analysis confirmed that GeoNeXt outperformed SAM-based methods, achieving F1 scores of 94.25 %, 86.43 %, and 92.27 % (mIoU: 89.51 %, 78.21 %, 86.02 %) on the Bijie, Landslide4Sense, and GVLM datasets, respectively, with 10× fewer parameters than SAM-based methods and lower computational demands. We showed that modernized convolutions, paired with strategic training, were a viable alternative to resource-intensive transformers. This efficiency facilitated their use in operational intelligent landslide recognition and geohazard monitoring systems.
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