University of São Paulo

About: University of São Paulo is a education organization based out in São Paulo, Brazil. It is known for research contribution in the topics: Population & Health care. The organization has 136513 authors who have published 272320 publications receiving 5127869 citations. The organization is also known as: USP & Universidade de São Paulo.

How many trees in a random forest

Abstract: Random Forest is a computationally efficient technique that can operate quickly over large datasets. It has been used in many recent research projects and real-world applications in diverse domains. However, the associated literature provides almost no directions about how many trees should be used to compose a Random Forest. The research reported here analyzes whether there is an optimal number of trees within a Random Forest, i.e., a threshold from which increasing the number of trees would bring no significant performance gain, and would only increase the computational cost. Our main conclusions are: as the number of trees grows, it does not always mean the performance of the forest is significantly better than previous forests (fewer trees), and doubling the number of trees is worthless. It is also possible to state there is a threshold beyond which there is no significant gain, unless a huge computational environment is available. In addition, it was found an experimental relationship for the AUC gain when doubling the number of trees in any forest. Furthermore, as the number of trees grows, the full set of attributes tend to be used within a Random Forest, which may not be interesting in the biomedical domain. Additionally, datasets' density-based metrics proposed here probably capture some aspects of the VC dimension on decision trees and low-density datasets may require large capacity machines whilst the opposite also seems to be true.

Melanopsin Is Required for Non-Image-Forming Photic Responses in Blind Mice

Abstract: Although mice lacking rod and cone photoreceptors are blind, they retain many eye-mediated responses to light, possibly through photosensitive retinal ganglion cells. These cells express melanopsin, a photopigment that confers this photosensitivity. Mice lacking melanopsin still retain nonvisual photoreception, suggesting that rods and cones could operate in this capacity. We observed that mice with both outer-retinal degeneration and a deficiency in melanopsin exhibited complete loss of photoentrainment of the circadian oscillator, pupillary light responses, photic suppression of arylalkylamine-N-acetyltransferase transcript, and acute suppression of locomotor activity by light. This indicates the importance of both nonvisual and classical visual photoreceptor systems for nonvisual photic responses in mammals.

Performance of the ALICE experiment at the CERN LHC

Abstract: ALICE is the heavy-ion experiment at the CERN Large Hadron Collider. The experiment continuously took data during the first physics campaign of the machine from fall 2009 until early 2013, using proton and lead-ion beams. In this paper we describe the running environment and the data handling procedures, and discuss the performance of the ALICE detectors and analysis methods for various physics observables.

A Survey of Evolutionary Algorithms for Clustering

Abstract: This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the paper. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or nonoriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer, or real encodings, centroid-based, medoid-based, label-based, tree-based, or graph-based representations, among others. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.