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Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach. It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant.

The connection between digital literacy and the three core dimensions of psychological well-being is not yet well understood, and the evidence is controversial. We analyzed a sample of 2,314 individuals, aged 50 years and older, that participated in the English Longitudinal Study of Aging. Participants were clustered according to drivers of psychological well-being using Self-Organizing Maps. The resulting groups were subsequently studied separately using generalized estimating equations fitted on 2-year lagged repeated measures using three scales to capture the dimensions of well-being and Markov models. The clustering analysis suggested the existence of four different groups of participants. Statistical models found differences in the connection between internet use and psychological well-being depending on the group. The Markov models showed a clear association between internet use and the potential for transition among groups of the population characterized, among other things, by higher levels of psychological well-being.

Background The accurate provision of weather information holds immense significance to many disciplines. One example corresponds to the field of air traffic management, in which one basis for weather detection is set upon recordings from sparse weather stations on ground. The scarcity of data and their lack of precision poses significant challenges to achieve a detailed description of the atmosphere state at a certain moment in time. Methods In this article, we foster the use of physics-informed neural networks (PINNs), a type of machine learning (ML) architecture which embeds mathematically accurate physics models, to generate high-quality weather information subject to the regularization provided by the Navier-Stokes equations. Results The application of PINNs is oriented to the reconstruction of dense and precise wind and pressure fields in areas where only a few local measurements provided by weather stations are available. Our model does not only disclose and regularize such data, which are potentially corrupted by noise, but is also able to precisely compute wind and pressure in target areas. Conclusions The effect of time and spatial resolution over the capability of the PINN to accurately reconstruct fluid phenomena is thoroughly discussed through a parametric study, concluding that a proper tuning of the neural network’s loss function during training is of utmost importance.

Mammary cancer is the most frequently diagnosed neoplasia in women and non-spayed female dogs and is one of the leading causes of death in both species. Canines develop spontaneous mammary tumors that share a significant number of biological, clinical, pathological and molecular characteristics with human breast cancers. This review provides a detailed description of the histological, molecular and clinical aspects of mammary cancer in canines; it discusses risk factors and currently available diagnostic and treatment options, as well as remaining challenges and unanswered questions. The incidence of mammary tumors is highly variable and is impacted by biological, pathological, cultural and socioeconomic factors, including hormonal status, breed, advanced age, obesity and diet. Diagnosis is mainly based on histopathology, although several efforts have been made to establish a molecular classification of canine mammary tumors to widen the spectrum of treatment options, which today rely heavily on surgical removal of tumors. Lastly, standardization of clinical study protocols, development of canine-specific biological tools, establishment of adequate dog-specific disease biomarkers and identification of targets for the development of new therapies that could improve survival and have less adverse effects than chemotherapy are among the remaining challenges.

This critical and conceptual analysis problematizes the machismo binary and situates it in a third space framework. It explores Latinx males’ higher education experiences and how machismo influences academic decision-making. Situating Latinx males’ college-going in the third space provides a more nuanced portrayal of their academic decision-making across diverse postsecondary settings. In doing so, the Latinx Gender Identity Development Model for Latinx Males in the Third Space is proposed and connects machismo with other domains such as marianismo, caballerismo and familismo. The proposed framework is illustrated by applying it to Latinx males across critical dimensions that impact their decision-making in the higher education landscape.

Convective weather is a large source of disruption for air traffic management operations. Being able to predict thunderstorms the day before operations can help traffic managers plan around weather and improve air traffic flow management operations. In this paper, machine learning is applied on data from satellite storm observations and ensemble numerical weather prediction products to detect convective weather 36 h in advance. The learning task is formulated as a binary classification problem and a neural network is trained to predict the occurrence of storms. The neural network results are used to develop a probabilistic based convection indicator capable of outperforming existing convection indicators found in the literature. Lastly, applications of the neural network based indicator in an air traffic management setting are presented.

En un ciclo de producción de tilapia, se realizó el cultivo de hongos setas, forrajes verdes y cucurbitáceas en un sistema acuapónico, con el objetivo de evaluar la eciencia en la remoción y aprovechamiento de desechos nitrogenados. Para el estudio se usó un sistema de recirculación de tres tinas circulares de 7 m3 , en las que se sembraron 64 tilapias por m3 . En la primera fase, se mantuvieron tres ciclos de cultivo de setas de 20 d, en dos sistemas: acuaponía y riego tradicional. Posteriormente, se realizaron seis ciclos de 12 d de crecimiento de cuatro forrajes verdes: maíz amarillo y blanco, trigo y sorgo, para luego realizar un ciclo de 45 d de pepino y sandía en bicultivo. La producción acumulada total de setas en el tratamiento de acuaponía durante los tres ciclos fue 16 % superior al obtenido con riego tradicional. La mayor producción de forraje en biomasa total fresca se obtuvo el trigo (47.55 kg); la mayor longitud de tallos (27 ± 2.20 cm) y eciencia de remoción de metabolitos nitrogenados en el maíz blanco. Las plantas de pepino y sandía produjeron frutos al segundo mes de cultivo, con rendimiento de 27.15 y 4.84 kg, respectivamente, las cuales fueron más ecientes en aprovechar los metabolitos que las setas y los forrajes. En el ciclo de producción producción de tilapia se puede cultivar diferentes vegetales y aprovechar los metabolitos nitrogenados con acuaponía.

This paper tries to tackle the modern challenge of practical steganalysis over large data by presenting a novel approach whose aim is to perform with perfect accuracy and in a completely automatic manner. The objective is to detect changes introduced by the steganographic process in those data objects, including signatures related to the tools being used. Our approach achieves this by first extracting reliable regularities by analyzing pairs of modified and unmodified data objects; then, combines these findings by creating general patterns present on data used for training. Finally, we construct a Naive Bayes model that is used to perform classification, and operates on attributes extracted using the aforementioned patterns. This technique has been be applied for different steganographic tools that operate in media files of several types. We are able to replicate or improve on a number or previously published results, but more importantly, we in addition present new steganalytic findings over a number of popular tools that had no previous known attacks.

We have compared the performance of different machine learning techniques for human activity recognition. Experiments were made using a benchmark dataset where each subject wore a device in the pocket and another on the wrist. The dataset comprises thirteen activities, including physical activities, common postures, working activities and leisure activities. We apply a methodology known as the activity recognition chain, a sequence of steps involving preprocessing, segmentation, feature extraction and classification for traditional machine learning methods; we also tested convolutional deep learning networks that operate on raw data instead of using computed features. Results show that combination of two sensors does not necessarily result in an improved accuracy. We have determined that best results are obtained by the extremely randomized trees approach, operating on precomputed features and on data obtained from the wrist sensor. Deep learning architectures did not produce competitive results with the tested architecture.

In recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the non-stationary nature and the likelihood of drastic structural or concept changes in the markets. Traditional systems are unable or slow to adapt to these changes. Ensemble-based systems are widely known for their good results predicting both cyclic and non-stationary data such as stock prices. In this work, we propose RCARF (Recurring Concepts Adaptive Random Forests), an ensemble tree-based online classifier that handles recurring concepts explicitly. The algorithm extends the capabilities of a version of Random Forest for evolving data streams, adding on top a mechanism to store and handle a shared collection of inactive trees, called concept history, which holds memories of the way market operators reacted in similar circumstances. This works in conjunction with a decision strategy that reacts to drift by replacing active trees with the best available alternative: either a previously stored tree from the concept history or a newly trained background tree. Both mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The experimental validation of the algorithm is based on the prediction of price movement directions one second ahead in the SPDR (Standard & Poor’s Depositary Receipts) S&P 500 Exchange-Traded Fund. RCARF is benchmarked against other popular methods from the incremental online machine learning literature and is able to achieve competitive results.

Se realizó un estudio prospectivo longitudinal en el Hospital “Dr. Ernesto Guevara de la Serna”, en el período comprendido de enero a diciembre del 2013, con un universo y muestra de 286 pacientes, que acudieron a la consulta de Medicina Natural y Tradicional de forma voluntaria, o que fueron remitidos por otras especialidades médicas con diagnóstico de obesidad y podían o no estar acompañados de otra patología (asma, diabetes mellitus, gota, hipertensión arterial, etc.). A estos pacientes se les realizó diagnóstico oriental, para posterior tratamiento con catgupuntura, la que se aplicó en los puntos escogidos al efecto. Del total de los pacientes (193 femeninas, 93 masculinos) evolucionaron de forma satisfactoria 178 y no así el resto. El grupo de edades que más acudió fue el de 26 a 35 años y este mismo grupo etario fue el que mejor respuesta al tratamiento tuvo, poniéndose de manifiesto la efectividad de la terapia aplicada, a la que se le conjugaron dietoterapia y ejercicios.

En un ciclo de producción de tilapia, se realizó el cultivo de hongos setas, forrajes verdes y cucurbitáceas en un sistema acuapónico, con el objetivo de evaluar la eficiencia en la remoción y aprovechamiento de desechos nitro- genados. Para el estudio se usó un sistema de recirculación de tres tinas circulares de 7 m3, en las que se sembraron 64 tilapias por m3. En la primera fase, se mantuvieron tres ciclos de cultivo de setas de 20 d, en dos sistemas: acuaponía y riego tradicional. Posteriormente, se realizaron seis ciclos de 12 d de crecimiento de cuatro forrajes verdes: maíz amarillo y blanco, trigo y sorgo, para luego realizar un ciclo de 45 d de pepino y sandía en bicultivo. La producción acumulada total de setas en el tratamiento de acuaponía durante los tres ciclos fue 16 % superior al obtenido con riego tradicional. La mayor producción de forraje en biomasa total fresca se obtuvo el trigo (47.55 kg); la mayor longitud de tallos (27 ± 2.20 cm) y eficiencia de remoción de metabolitos nitrogenados en el maíz blanco. Las plantas de pepino y sandía produjeron frutos al segundo mes de cultivo, con rendimiento de 27.15 y 4.84 kg, respectivamente, las cuales fueron más eficientes en aprovechar los metabolitos que las setas y los forrajes. En el ciclo de producción producción de tilapia se puede cultivar diferentes vegetales y aprovechar los metabolitos nitrogenados con acuaponía.

El propósito central de este artículo es adelantar un proyecto político y normativo parael establecimiento internacional de los derechos sexuales como derechos humanos.Debido a la organización social del sexo y del género en nuestras sociedades con-temporáneas, consideramos que son urgentes las demandas por diseñar y establecernormas para proteger la diferencia sexual y auspiciar la afirmación de la diversidadsexual. Por un lado, identificamos la necesidad de establecer algunos derechos sexualesnegativos capaces de proteger la integridad sexual de ciertos individuos que histórica-mente han sido marginados y de algunos grupos que suelen ser blanco de la violenciaheterosexista. Por el otro, consideramos necesario promover derechos sexuales positivosque afirmen la diversidad sexual y auspicien vidas sexuales llenas de placer. Sostenemos que para la justificación y aceptación de los derechos sexuales negati-vos sólo se requiere una política y una ética de la tolerancia. En cambio los derechossexuales positivos exigen la adopción de un paradigma político y ético diferente, basa-do en el reconocimiento. El proyecto político de los derechos sexuales positivos busca, enúltima instancia, destruir la hegemonía masculina sobre la práctica y el discurso de lasexualidad, y pretende asimismo descentrar la heterosexualidad. Pese a los límites delos movimientos sociales basados en la identidad sexual, su impacto social y culturalha sido profundo. Por ello opinamos que los derechos sexuales negativos son hoy unproyecto más que factible. Más aún, hemos desarrollado la idea de que la posibilidadde concretar el proyecto de los derechos sexuales positivos resulta de la combinacióncreativa de dos fuerzas. Primero, los potenciales emancipatorios de los movimientossociales basados en la identidad sexual que han permitido mantener un cuestionamientoconstante de la actual organización social de la sexualidad. Segundo, las transforma-ciones profundas en teoría social y filosofía que nos lleva a pensar y experimentar lasexualidad (y sus identidades) de manera no esencialista, descentrada, relacional,interactiva y fluida. Finalmente, esto abre la posibilidad de desestabilizar los efectosque las relaciones de poder tienen sobre el sexo, la sexualidad y la identidad sexual.

Derechos reproductivos ha pasado de ser un enunciado descriptivo para convertirse en un área temática con legitimidad de estudio. Sin embargo, su transformación en campo cognoscitivo dependerá de la dinámica que se genere por su relevancia social y política, por su aceptación en comunidades académicas y de las intervenciones conceptuales de los estudiosos. Con base en una reconstrucción histórico-crítica del enunciado, de las dimensiones y las relaciones constitutivas implicadas en su uso contemporáneo, este trabajo elabora una diagramación conceptual y una propuesta analítica, como una contribución a la fundación del campo cognoscitivo.