3,099 research outputs found
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
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