3,244 research outputs found
Byte The Bullet: Learning on Real-World Computing Architectures
No full textFast, effective, and reliable models: these are the desiderata of every theorist and practitioner. Machine Learning (ML) algorithms, proposed in the last decades, proved to be effective and reliable in solving complex real-world problems, but they are usually designed without taking into account the underlying computing architecture. On the contrary, the effort of contemplating the exploited computing device is often motivated by application-specific and real-world requirements, such as the need to accelerate the learning process with dedicated/distributed hardware, or to foster energy-sparing requirements of applications based on mobile standalone devices. The ESANN 2014 Byte The Bullet: Learning on Real-World Computing Architectures special session has pooled a compilation of the most recent proposals in this area, by encouraging submissions related to the development and the application of fast, effective, reliable techniques, which consider possibilities, potentialities and constraints of real-world computing architectures as basic cornerstones and motivations
Byte The Bullet: Learning on Real-World Computing Architectures
No full textFast, effective, and reliable models: these are the desiderata of every theorist and practitioner. Machine Learning (ML) algorithms, proposed in the last decades, proved to be effective and reliable in solving complex real-world problems, but they are usually designed without taking into account the underlying computing architecture. On the contrary, the effort of contemplating the exploited computing device is often motivated by application-specific and real-world requirements, such as the need to accelerate the learning process with dedicated/distributed hardware, or to foster energy-sparing requirements of applications based on mobile standalone devices. The ESANN 2014 Byte The Bullet: Learning on Real-World Computing Architectures special session has pooled a compilation of the most recent proposals in this area, by encouraging submissions related to the development and the application of fast, effective, reliable techniques, which consider possibilities, potentialities and constraints of real-world computing architectures as basic cornerstones and motivations
On the Hilbert function of general fat points in P1xP1
No full textWe study the bi-graded Hilbert function of ideals of general fat points with same multiplicity in P1×P1. Our first tool is the multiprojective-affine-projective method introduced by the second author in previous works with A.V. Geramita and A. Gimigliano where they solved the case of double points. In this way, we compute the Hilbert function when the smallest entry of the bi-degree is at most the multiplicity of the points. Our second tool is the differential Horace method introduced by J. Alexander and A. Hirschowitz to study the Hilbert function of sets of fat points in standard projective spaces. In this way, we compute the entire bi-graded Hilbert function in the case of triple points
On the real rank of monomials
No full textIn this paper we study the real rank of monomials and we give an upper bound for it. We show that the real and the complex ranks of a monomial coincide if and only if the least exponent is equal to one.</p
A imagem de Alessandro Baricco no Brasil
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Comunicação e Expressão, Programa de Pós-Graduação em Estudos da Tradução, Florianópolis, 2013.Com a intenção de delinear o modo pelo qual o escritor italiano Alessandro Baricco se inseriu no sistema literário brasileiro e os caminhos percorridos pelos seus livros traduzidos, esta dissertação dá voz às experiências tradutórias de seus tradutores. A inserção de Bariccono Brasil tem seu início em 1997, através de uma proposição da Profa. Dra. Roberta Barni à editora Iluminuras da tradução de Oceano Mare. A partir daí, outras sete obras foram publicadas no Brasil, sendo três delas traduzidas por Roberta Barni e as outras quatro por quatro tradutores diferentes. De um lado, considera-se o tradutor como figura principal namediação entre culturas, e, de outro, se analisa a realidade desta figuradentro do sistema literário, sua invisibilidade, seus limites e o exercíciode sua profissão. A pesquisa conta, ainda, com críticas e resenhas referentes ao autor italiano publicadas em jornais consagrados no Brasil, considerando estas como parte constituinte da imagem de Baricco refletida em território nacional. Abstract : Intending to delineate the way the Italian writer Alessandro Baricco has been inserted in the Brazilian literary system and the paths his translated books have followed, this thesis gives voice to the translating experiences of his translators. Baricco's insertion in Brazil began in 1997, through a personal project of Dr. Roberta Barni, with her translation of Oceano Mare. Since then, seven other of his works have been published in Brazil, three of which were translated by Roberta Barni and the other four by four different translators. On the one hand,the translator is considered as the main figure in mediation betweencultures and, on the other, this figure's reality is analyzed within theliterary system: its invisibility, its limits and its professional practice. Criticisms and reviews of this Italian author published in well established Brazilian newspapers are also considered, with the understanding that they are part of Baricco's image reflected here
Crack random forest for arbitrary large datasets
No full textRandom Forests (RF) of tree classifiers are a state-of-the-art method for classification purposes. RF show limited hyperparameter sensitivity, have high numerical robustness, possess native capacity of dealing with numerical and categorical features, and are quite effective in many real world problems with respect to other state-of-the-art techniques. In this work we show how to crack RF in order to be able to train them on arbitrary large datasets. In particular, we extend ReForeSt, an Apache Spark-based RF implementation. The new version of ReForeSt computation automatically adapts to two methodologies to distribute the data and the computation on the available machines and automatically chooses the one able to provide the result in less time. The new ReForeSt also supports Random Rotations, a quite recent randomization technique which can bust the accuracy of the original RF. We perform an extensive experimental evaluation between ReForeSt and MLlib by taking advantage of the Google Cloud Platform1. We test the performances and the scalability of ReForeSt and MLlib on several real world datasets. Results confirm that ReForeSt outperforms MLlib both in terms of memory and computational efficiency, and classification performances. ReForeSt is publicly available via GitHub2
Simple continuous optimal regions of the space of data
No full textThe definition of efficient programs for both maintenance and optimization is a struggling task in many industrial sectors. In this context, data analysis can significantly improve the state-of-the-art techniques, employed, for instance, to determine if a particular component or product is showing an anomalous behavior with respect to a defined nominal state. In fact, through the analysis of data collected on field, it is possible to detect optimal operating regions and to detect anomalies in advance. In this context, we propose a multi-purpose algorithm for unsupervised or semi-supervised learning in order to determine a simple continuous region of points. This region can be adopted in order to describe a component or a product nominal behavior and can be used in order to detect anomalies which are outside it. Such a region can be defined adopting a finite ensemble of thresholds, whose value can be physically interpreted. In order to show the effectiveness of our approach, the proposed method has been tested in an Anomaly Detection problem concerning Predictive Maintenance, exploiting data coming from a naval vessel, characterized by a combined diesel-electric and gas propulsion plant
Tangential varieties of Segre–Veronese surfaces are never defective
No full textWe compute the dimensions of all the secant varieties to the tangential varieties of all Segre–Veronese surfaces. We exploit the typical approach of computing the Hilbert function of special 0-dimensional schemes on projective plane by using a new degeneration technique.Peer ReviewedPreprin
Selecting the hypothesis space for improving the generalization ability of support vector machines
No full textThe Structural Risk Minimization framework has been recently proposed as a practical method for model selection in Support Vector Machines (SVMs). The main idea is to effectively measure the complexity of the hypothesis space, as defined by the set of possible classifiers, and to use this quantity as a penalty term for guiding the model selection process. Unfortunately, the conventional SVM formulation defines a hypothesis space centered at the origin, which can cause undesired effects on the selection of the optimal classifier. We propose here a more flexible SVM formulation, which addresses this drawback, and describe a practical method for selecting more effective hypothesis spaces, leading to the improvement of the generalization ability of the final classifier
Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency
No full textMobile devices are resource-limited systems that provide a large number of services and features. Smartphones, for example, implement advanced functionalities and services for the final user, in addition to conventional communication capabilities. Machine Learning algorithms can help in providing such advanced functionalities, but mobile systems suffer from issues related to their resource-limited nature such as, for example, limited battery capacity and processing power and, therefore, even simple pattern recognition activities can become too demanding, in this respect. We propose here a method to design a Human Activity Recognition algorithm, which takes into account the fact that only limited resources are available for its execution. In particular, we restrict the hypothesis space of possible recognition models by applying some advanced concepts from Statistical Learning Theory, so as to force the selection of models with good generalization ability but low computational complexity. Then, the learned model can be effectively implemented on a mobile and resource-limited device: the experiments, carried out on a current-generation smartphone, show the benefits of the proposed approach in terms of both model accuracy and battery duration
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