Statistical mechanics of mean-field disordered systems: a Hamilton-Jacobi approach

The goal of this book is to present new mathematical techniques for studying the behaviour of mean-field systems with disordered interactions. We mostly focus on certain problems of statistical inference in high dimension, and on spin glasses. The techniques we present aim to determine the free energy of these systems, in the limit of large system size, by showing that they asymptotically satisfy a Hamilton-Jacobi equation. The first chapter is a general introduction to statistical mechanics, with a focus on the Curie-Weiss model. We give a brief introduction to convex analysis and large deviation principles in Chapter 2, and identify the limit free energy of the Curie-Weiss model using these tools. In Chapter 3, we define the notion of viscosity solution to a Hamilton-Jacobi equation, and use it to recover the limit free energy of the Curie-Weiss model. We discover technical challenges to applying the same method to generalized versions of the Curie-Weiss model, and develop a new selection principle based on convexity to overcome these. We then turn to statistical inference in Chapter 4, focusing on the problem of recovering a large symmetric rank-one matrix from a noisy observation, and we see that the tools developed in the previous chapter apply to this setting as well. Chapter 5 is preparatory work for a discussion of the more challenging case of spin glasses. The first half of this chapter is a self-contained introduction to Poisson point processes, including limit theorems on extreme values of independent and identically distributed random variables, which we believe to be of independent interest. We finally turn to the setting of spin glasses in Chapter 6. For the Sherrington-Kirkpatrick model, we show how to relate the Parisi formula with the Hamilton-Jacobi approach. We conclude with a more informal discussion on the status of current research for more challenging models.Comment: 368 page

Mutual information for the sparse stochastic block model

We consider the problem of recovering the community structure in the stochastic block model with two communities. We aim to describe the mutual information between the observed network and the actual community structure in the sparse regime, where the total number of nodes diverges while the average degree of a given node remains bounded. Our main contributions are a conjecture for the limit of this quantity, which we express in terms of a Hamilton-Jacobi equation posed over a space of probability measures, and a proof that this conjectured limit provides a lower bound for the asymptotic mutual information. The well-posedness of the Hamilton-Jacobi equation is obtained in our companion paper. In the case when links across communities are more likely than links within communities, the asymptotic mutual information is known to be given by a variational formula. We also show that our conjectured limit coincides with this formula in this case

An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data

The current gold standard for human activity recognition (HAR) is based on the use of cameras. However, the poor scalability of camera systems renders them impractical in pursuit of the goal of wider adoption of HAR in mobile computing contexts. Consequently, researchers instead rely on wearable sensors and in particular inertial sensors. A particularly prevalent wearable is the smart watch which due to its integrated inertial and optical sensing capabilities holds great potential for realising better HAR in a non-obtrusive way. This paper seeks to simplify the wearable approach to HAR through determining if the wrist-mounted optical sensor alone typically found in a smartwatch or similar device can be used as a useful source of data for activity recognition. The approach has the potential to eliminate the need for the inertial sensing element which would in turn reduce the cost of and complexity of smartwatches and fitness trackers. This could potentially commoditise the hardware requirements for HAR while retaining the functionality of both heart rate monitoring and activity capture all from a single optical sensor. Our approach relies on the adoption of machine vision for activity recognition based on suitably scaled plots of the optical signals. We take this approach so as to produce classifications that are easily explainable and interpretable by non-technical users. More specifically, images of photoplethysmography signal time series are used to retrain the penultimate layer of a convolutional neural network which has initially been trained on the ImageNet database. We then use the 2048 dimensional features from the penultimate layer as input to a support vector machine. Results from the experiment yielded an average classification accuracy of 92.3%. This result outperforms that of an optical and inertial sensor combined (78%) and illustrates the capability of HAR systems using...Comment: 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Scienc

A machine learning approach for banks classification and forecast

n this research, a classification model is developed for the banking sector using the machine earning technique GLMNET. In the first place, a clustering process was developed, where 3 clearly differentiated groups were found. Subsequently, a Fuzzy analysis was performed finding the probabilities of transition of the banks to each group found, finally, the GLMNET algorithm was implemented, the automatic classification of the banks according to their financial items, obtaining a result of 95% accuracy. © 2019 International Business Information Management Association (IBIMA)

Clima organizacional y desempeño laboral de los servidores públicos profesionales durante el periodo de pandemia Covid-19 de una UGEL, 2021

La investigación tuvo como objetivo general determinar la influencia del clima organizacional en el desempeño laboral de los servidores publicos durante el periodo de pandemia Covid-19 de una UGEL, 2021 es una investigación aplicada, con diseño no experimental correlacional causal de corte transversal. La población estuvo conformada por 59 servidores publicos profesionales de una UGEL; y la muestra es censal con la totalidad de la población. Las técnicas empleadas fueron la encuesta y la técnica documental. Se han empleado como instrumentos dos cuestionarios confiables y validados, procesándose la información mediante el software de estadística para ciencias sociales (SPSS v. 26). Los resultados fueron presentados en tablas y figuras estadísticas; y se obtuvo un coeficiente de confiabilidad de ,880 para la variable clima organizacional y de ,924 para la variable desempeño laboral. Se concluyó que existe una relación directa, significativa y moderada entre las variables durante el periodo de pandemia Covid-19 de una UGEL, 2021. (P=0.675). Asimismo, se corroboró que el nivel del clima organizacional y el desempeño de los trabajadores durante el periodo de pandemia Covid-19 de una UGEL, 2021 fue medio con un 59.3% y 49.2% respectivamente

Administrative efficiency of IPS providers of health entities accredited in quality in Colombia

This study evaluated the efficiencies of the IPS health providers accredited in quality in Colombia. The normative framework associated with the Mandatory Quality System, the quality accreditation standards for the IPS and the Data Envelopment analysis models, related to the purely technical or administrative efficiency were used as theoretical support. As an epistemological conception, we worked with the logical positivism paradigm, with which the scientific verification and the logical analysis for the development of all the research were sought. The research type had an evaluative approach. As a population, 27 accredited IPS health service providers were taken, which lent their financial statements to the superintendence of health in 2015 and 2016. The inductive and deductive method was used. The information generated by the health superintendence and the Ministry of Health were used as primary sources. The DEA BCC-O model focused on the optimization of outputs was used as an analysis technique. As a result of this research, a method to evaluate the efficiencies of high-quality accredited IPSs in Colombia was provided. Likewise, it could be demonstrated with empirical evidence that the implementation of high-quality standards in the IPSs studied has a significant impact on administrative efficiency. The research showed that the best IPS accredited in Colombia was the Pablo VI Hospital in Bosa. © 2019 International Business Information Management Association (IBIMA)

Knowledge, Practices, and Challenges of Library Practitioners on Abstracting

Access is the primary concern of a library thus information retrieval tool like an abstract is as important as the Online Public Access Catalog. Abstracting then plays an important role in improving access to information that is needed by library users. Librarians then perform this task primarily to guide the users in deciding whether an information resource will be consulted or not. The study utilized the descriptive method of research using a survey questionnaire to determine the level of knowledge and practices on the parts and types of abstracts and the challenges on abstracts a document surrogate encountered by library practitioners in selected parts of Northern Luzon, Philippines. The study found out that the library practitioners exhibited a high knowledge and practice on abstracting which can be attributed to their undergraduate studies specifically in the core major subject, indexing and abstracting. However, they enumerated challenges such as: 1) lack of time to carry out their abstracting function; 2) lack of policies and procedures in abstracting; 3) lack of manpower to do the job; 4) lack of motivation to perform the task; and 5) abstract is not a concern of most faculty and students. On the other hand, abstracts continue to be useful to the academic and research community and that library practitioners must find time to create or innovate ways of abstracting important documents useful to their users

An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data

The current gold standard for human activity recognition (HAR) is based on the use of cameras. However, the poor scalability of camera systems renders them impractical in pursuit of the goal of wider adoption of HAR in mobile computing contexts. Consequently, researchers instead rely on wearable sensors and in particular inertial sensors. A particularly prevalent wearable is the smart watch which due to its integrated inertial and optical sensing capabilities holds great potential for realising better HAR in a non-obtrusive way. This paper seeks to simplify the wearable approach to HAR through determining if the wrist-mounted optical sensor alone typically found in a smartwatch or similar device can be used as a useful source of data for activity recognition. The approach has the potential to eliminate the need for the inertial sensing element which would in turn reduce the cost of and complexity of smartwatches and fitness trackers. This could potentially commoditise the hardware requirements for HAR while retaining the functionality of both heart rate monitoring and activity capture all from a single optical sensor. Our approach relies on the adoption of machine vision for activity recognition based on suitably scaled plots of the optical signals. We take this approach so as to produce classifications that are easily explainable and interpretable by non-technical users. More specifically, images of photoplethysmography signal time series are used to retrain the penultimate layer of a convolutional neural network which has initially been trained on the ImageNet database. We then use the 2048 dimensional features from the penultimate layer as input to a support vector machine. Results from the experiment yielded an average classification accuracy of 92.3%. This result outperforms that of an optical and inertial sensor combined (78%) and illustrates the capability of HAR systems using...Comment: 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Scienc

Nueva alternativa para incrementar el indice de procreo en bovinos de carne

El incremento del índice de procreo en bovinos de carne permite reducir los costos necesarios para obtener terneros mejorados genéticamente a ser destinado para en engorde y reemplazo de vientres. El objetivo de este proyecto de investigación fue encontrar la manera efectiva de incrementar el índice de procreo aumentando la tasa ovulatoria a través de la combinación de procedimientos mecánicos y hormonales para incrementar los índices reproductivos en el ganado bovino que pueda ser aplicado extensivamente a la ganadería en el Paraguay. Difundiendo ésta nueva técnica y su aplicación se aumentarán los índices de procreo en el ganado bovino de carne, y se deja una capacidad local instalada de infraestructura, equipamientos y RRHH capacitados.CONACYT – Consejo Nacional de Ciencia y TecnologíaPROCIENCI