Effective Actions for the SU(2) Confinement-Deconfinement Phase Transition

We compare different Polyakov loop actions yielding effective descriptions of finite-temperature SU(2) Yang-Mills theory on the lattice. The actions are motivated by a simultaneous strong-coupling and character expansion obeying center symmetry and include both Ising and Ginzburg-Landau type models. To keep things simple we limit ourselves to nearest-neighbor interactions. Some truncations involving the most relevant characters are studied within a novel mean-field approximation. Using inverse Monte-Carlo techniques based on exact geometrical Schwinger-Dyson equations we determine the effective couplings of the Polyakov loop actions. Monte-Carlo simulations of these actions reveal that the mean-field analysis is a fairly good guide to the physics involved. Our Polyakov loop actions reproduce standard Yang-Mills observables well up to limitations due to the nearest-neighbor approximation.Comment: 14 pages, 10 figures, v2: typos correcte

The Latin Music Database

In this paper we present the Latin Music Database, a novel database of Latin musical recordings which has been developed for automatic music genre classification, but can also be used in other music information retrieval tasks. The method for assigning genres to the musical recordings is based on human expert perception and therefore capture their tacit knowledge in the genre labeling process. We also present the ethnomusicology of the genres available in the database as it might provide important information for the analysis of the results of any experiment that employs the database

Automatic Genre Classification of Latin Music Using Ensemble of Classifiers

This paper presents a novel approach to the task of automatic music genre classification which is based on ensemble learning. Feature vectors are extracted from three 30-second music segments from the beginning, middle and end of each music piece. Individual classifiers are trained to account for each music segment. During classification, the output provided by each classifier is combined with the aim of improving music genre classification accuracy. Experiments carried out on a dataset containing 600 music samples from two Latin genres (Tango and Salsa) have shown that for the task of automatic music genre classification, the features extracted from the middle and end music segments provide better results than using the beginning music segment. Furthermore, the proposed ensemble method provides better accuracy than using single classifiers and any individual segment

3D tomography of cells in micro-channels

We combine confocal imaging, microfluidics and image analysis to record 3D-images of cells in flow. This enables us to recover the full 3D representation of several hundred living cells per minute. Whereas 3D confocal imaging has thus far been limited to steady specimen, we overcome this restriction and present a method to access the 3D shape of moving objects. The key of our principle is a tilted arrangement of the micro-channel with respect to the focal plane of the microscope. This forces cells to traverse the focal plane in an inclined manner. As a consequence, individual layers of passing cells are recorded which can then be assembled to obtain the volumetric representation. The full 3D information allows for a detailed comparisons with theoretical and numerical predictions unfeasible with e.g.\ 2D imaging. Our technique is exemplified by studying flowing red blood cells in a micro-channel reflecting the conditions prevailing in the microvasculature. We observe two very different types of shapes: `croissants' and `slippers'. Additionally, we perform 3D numerical simulations of our experiment to confirm the observations. Since 3D confocal imaging of cells in flow has not yet been realized, we see high potential in the field of flow cytometry where cell classification thus far mostly relies on 1D scattering and fluorescence signals

Artificial neural networks for 3D cell shape recognition from confocal images

We present a dual-stage neural network architecture for analyzing fine shape details from microscopy recordings in 3D. The system, tested on red blood cells, uses training data from both healthy donors and patients with a congenital blood disease. Characteristic shape features are revealed from the spherical harmonics spectrum of each cell and are automatically processed to create a reproducible and unbiased shape recognition and classification for diagnostic and theragnostic use.Comment: 17 pages, 8 figure

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

An overview of memory: some issues on structures and organization in the legal domain

Lawyers often need to look for previous similar legal cases when analysing new ones. The more previous cases, the more time is spent. Classical search engines execute termbased retrieval, which may miss relevant documents as well as fetch several irrelevant ones, causing lack of useful information and waste of time. Ideally, retrieval should be meaning-based. Humans beings are able to do e cient searches due to their knowledge. Therefore, semantic search requires knowledge. This paper presents a semantic search engine. Along the paper, several issues concerning specially knowledge representation and memory are discussed. A formalism based on models of comprehension is introduced, as well as its motivation. Examples of representation of sentences in natural language from the Legal Domain are provided. The search engine and its architecture, based on domain knowledge, are brie y commented. The main goal is to give legal o ces the opportunity to save time by providing a more suitable document retrieval.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Query expansion and noise treatment for information retrieval

Most of the search engines available over the Web are based on mathematical approaches | classical techniques in the Information Retrieval area. Thereby, they are suitable for the retrieval of documents containing some or all the terms of a query, though not to retrieve the documents containing the meaning those terms were intended to express. This paper presents some advantages obtained from query expansion with WordNet and noise treatment with knowledge on top of Paraconsistent Logic. Both methods are semantically driven, allowing the retrieval of documents which do not contain any term of the original query. Noise treatment results from the combination of a smooth term comparison with knowledge about term authentication based on behaviors of features in the collection. Although query expansion recurs for every query, noise treatment is part of the indexing mechanism, causing no overhead in queries. The domain is retrieval of ontologies represented in Resource Description Framework.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI