Agora: A Knowledge Marketplace for Machine Learning

More and more data are becoming part of people\u27s lives. With the popularization of technologies like sensors, and the Internet of Things, data gathering is becoming possible and accessible for users. With these data in hand, users should be able to extract insights from them, and they want results as soon as possible. Average users have little or no experience in data analytics and machine learning and are not great observers who can collect enough data to build their own machine learning models. With large quantities of similar data being generated around the world and many machine learning models being used, it should be possible to use additional data and existing models to create accurate machine learning models for these users. This thesis proposes Agora, a Web-based marketplace where users can share their data and machine learning models with other users with small datasets and little experience. This thesis includes an overview of all the components that make up Agora, as well as details of two of its main components: Hephaestus and Sibyl. Hephaestus is a domain adaptation method for multi-feature regression models with seasonal adjustment, which can improve predictions for small datasets using information from additional datasets. Hephaestus works in the pre- and post- processing phases, making it possible to work with any standard machine learning algorithm. As a case study, we built predictive models using the proposed method to predict school energy consumption with only one month of data, improving accuracy to the same level as if 12 months of data were being used. Sibyl is a flexible, scalable and non-blocking machine learning as a service, which facilitates the creation of multiple predictive models and running them at the same time. As a case study, we implemented Sibyl equipped with three machine learning algorithms to show the flexibility of adding new algorithms. We also executed three models at the same time to demonstrate that they can run without interference from another model. The results obtained in this research demonstrates the concept of Agora. Users can share the same platform to provide or consume knowledge and create multiple concurrent machine learning models

Little-Parks oscillations near a persistent current loop

We investigate the Little-Parks oscillations caused by a persistent current loop set on the top edge of a mesoscopic superconducting thin-walled cylinder with a finite height. For a short cylinder the Little-Parks oscillations are approximately the same ones as the standard effect, as there is only one magnetic flux piercing the cylinder. For a tall cylinder the inhomogeneity of the magnetic field makes different magnetic fluxes pierce the cylinder at distinct heights and we show here that this produces two distinct Little-Parks oscillatory regimes according to the persistent current loop. We show that these two regimes, and also the transition between them, are observable in current measurements done in the superconducting cylinder. The two regimes stem from different behavior along the height, as seen in the order parameter, numerically obtained from the Ginzburg-Landau theory through the finite element methodComment: 13 pages, 12 figure

Paramagnetic excited vortex states in superconductors

We consider excited vortex states, which are vortex states left inside a superconductor once the external applied magnetic field is switched off and whose energy is lower than of the normal state. We show that this state is paramagnetic and develop here a general method to obtain its Gibbs free energy through conformal mapping. The solution for any number of vortices in any cross section geometry can be read off from the Schwarz - Christoffel mapping. The method is based on the first order equations used by A. Abrikosov to discover vortices.Comment: 14 pages, 7 figure

Computer simulations of dynamical properties of fluids: atomistic-continuum hybrid methods

Computational methods for the calculation of dynamical properties of fluids might consider the system as a continuum or as an assembly of molecules. Molecular dynamics (MD) simulation includes molecular resolution, whereas computational fluid dynamics (CFD) considers the fluid as a continuum. This work provides a review of hybrid methods MD/CFD recently proposed in the literature. Theoretical foundations, basic approaches of computational methods, and dynamical properties typically calculated by MD and CFD are first presented in order to appreciate the similarities and differences between these two methods. Then, methods for coupling MD and CFD, and applications of hybrid simulations MD/CFD, are presented.FAPESPCNP

Thought disorder measured as random speech structure classifies negative symptoms and schizophrenia diagnosis 6 months in advance

In chronic psychotic patients, word graph analysis shows potential as complementary psychiatric assessment. This analysis relies mostly on connectedness, a structural feature of speech that is anti-correlated with negative symptoms. Here we aimed to verify whether speech disorganization during the first clinical contact, as measured by graph connectedness, can correctly classify negative symptoms and the schizophrenia diagnosis 6 months in advance. Positive and negative syndrome scale scores and memory reports were collected from 21 patients undergoing first clinical contact for recent-onset psychosis, followed for 6 months to establish diagnosis, and compared to 21 well-matched healthy subjects. Each report was represented as a word-trajectory graph. Connectedness was measured by number of edges, number of nodes in the largest connected component and number of nodes in the largest strongly connected component. Similarities to random graphs were estimated. All connectedness attributes were combined into a single Disorganization Index weighted by the correlation with the positive and negative syndrome scale negative subscale, and used for classifications. Random-like connectedness was more prevalent among schizophrenia patients (64 × 5% in Control group, p = 0.0002). Connectedness from two kinds of memory reports (dream and negative image) explained 88% of negative symptoms variance (p < 0.0001). The Disorganization Index classified low vs. high severity of negative symptoms with 100% accuracy (area under the receiver operating characteristic curve = 1), and schizophrenia diagnosis with 91.67% accuracy (area under the receiver operating characteristic curve = 0.85). The index was validated in an independent cohort of chronic psychotic patients and controls (N = 60) (85% accuracy). Thus, speech disorganization during the first clinical contact correlates tightly with negative symptoms, and is quite discriminative of the schizophrenia diagnosis

MLaaS: Machine Learning as a Service

The demand for knowledge extraction has been increasing. With the growing amount of data being generated by global data sources (e.g., social media and mobile apps) and the popularization of context-specific data (e.g., the Internet of Things), companies and researchers need to connect all these data and extract valuable information. Machine learning has been gaining much attention in data mining, leveraging the birth of new solutions. This paper proposes an architecture to create a flexible and scalable machine learning as a service. An open source solution was implemented and presented. As a case study, a forecast of electricity demand was generated using real-world sensor and weather data by running different algorithms at the same time

Graph theory applied to speech: insights on cognitive deficit diagnosis and dream research

In the past ten years, graph theory has been widely employed in the study of natural and technological phenomena. The representation of the relationships among the units of a network allow for a quantitative analysis of its overall structure, beyond what can be understood by considering only a few units. Here we discuss the application of graph theory to psychiatric diagnosis of psychoses and dementias. The aim is to quantify the flow of thoughts of psychiatric patients, as expressed by verbal reports of dream or waking events. This flow of thoughts is hard to measure but is at the roots of psychiatry as well as psychoanalysis. To this end, speech graphs were initially designed with nodes representing lexemes and edges representing the temporal sequence between consecutive words, leading to directed multigraphs. In a subsequent study, individual words were considered as nodes and their temporal sequence as edges; this simplification allowed for the automatization of the process, effected by the free software Speech Graphs. Using this approach, one can calculate local and global attributes that characterize the network structure, such as the total number of nodes and edges, the number of nodes present in the largest connected and the largest strongly connected components, measures of recurrence such as loops of 1, 2, and 3 nodes, parallel and repeated edges, and global measures such as the average degree, density, diameter, average shortest path, and clustering coefficient. Using these network attributes we were able to automatically sort schizophrenia and bipolar patients undergoing psychosis, and also to separate these psychotic patients from subjects without psychosis, with more than 90% sensitivity and specificity. In addition to the use of the method for strictly clinical purposes, we found that differences in the content of the verbal reports correspond to structural differences at the graph level. When reporting a dream, healthy subjects without psychosis and psychotic subjects with bipolar disorder produced more complex graphs than when reporting waking activities of the previous day; this difference was not observed in psychotic subjects with schizophrenia, which produced equally poor reports irrespective of the content. As a consequence, graphs of dream reports were more efficient for the differential diagnosis of psychosis than graphs of daily reports. Based on these results we can conclude that graphs from dream reports are more informative about mental states, echoing the psychoanalytic notion that dreams are a privileged window into thought.2019-07-3

Drying shrinkage behavior of metakaolin-based and bamboo fiber reinforced geopolymers

This Brazil-USA collaborative research uses bamboo cultivated in the Amazon region and metakaolin attained from calcined Amazonian kaolin. The durability of sustainable geopolymer materials is studied by means of the drying shrinkage aging behavior. Scanning electron microscopy and energy dispersive x-ray fluorescence were used to investigate the microstructure of the composite materials. X-ray diffraction was used to confirm the formation of geopolymer. The water treated geopolymer matrix (GP) samples dried at room conditions for the periods of 3-7-14-21-28-56-112 days showed very close and increasing weight and length changes. The GP reinforced with bamboo fiber (GPBF) treated samples weight and length changes increased from the 3-day sample up to the 21-day, then it dropped down to the 112-day. The GP water treated samples dried at room conditions for the aging periods showed increasing flexural strength (MOR) and modulus of elasticity (E). The GPBF treated samples MOR were higher and very close to each other