A generative model for separating illumination and reflectance from images

It is well known that even slight changes in nonuniform illumination lead to a large image variability and are crucial for many visual tasks. This paper presents a new ICA related probabilistic model where the number of sources exceeds the number of sensors to perform an image segmentation and illumination removal, simultaneously. We model illumination and reflectance in log space by a generalized autoregressive process and Hidden Gaussian Markov random field, respectively. The model ability to deal with segmentation of illuminated images is compared with a Canny edge detector and homomorphic filtering. We apply the model to two problems: synthetic image segmentation and sea surface pollution detection from intensity images.

Constructing localized non-linear projection manifolds in a principled way:hierarchical generative topographic mapping

It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis (Bishop98a) in several directions: 1. We allow for em non-linear projection manifolds. The basic building block is the Generative Topographic Mapping. 2. We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. 3. Using tools from differential geometry we derive expressions for local directionalcurvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the parent visualization plot which are captured by a child model.We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set andapply our system to two more complex 12- and 19-dimensional data sets.

A hybrid generative/discriminative framework to train a semantic parser from an un-annotated corpus

We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HM-SVMs). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. The HM-SVMs combine the advantages of the hidden Markov models and the support vector machines. By employing a modified K-means clustering method, a small set of most representative sentences can be automatically selected from an un-annotated corpus. These sentences together with their abstract annotations are used to train an HVS model which could be subsequently applied on the whole corpus to generate semantic parsing results. The most confident semantic parsing results are selected to generate a fully-annotated corpus which is used to train the HM-SVMs. The proposed framework has been tested on the DARPA Communicator Data. Experimental results show that an improvement over the baseline HVS parser has been observed using the hybrid framework. When compared with the HM-SVMs trained from the fully-annotated corpus, the hybrid framework gave a comparable performance with only a small set of lightly annotated sentences. © 2008. Licensed under the Creative Commons.

A generative model for online depth fusion

We present a probabilistic, online, depth map fusion framework, whose generative model for the sensor measurement process accurately incorporates both long-range visibility constraints and a spatially varying, probabilistic outlier model. In addition, we propose an inference algorithm that updates the state variables of this model in linear time each frame. Our detailed evaluation compares our approach against several others, demonstrating and explaining the improvements that this model offers, as well as highlighting a problem with all current methods: systemic bias. © 2012 Springer-Verlag.

Visualisation of heterogeneous data with the generalised generative topographic mapping

Heterogeneous and incomplete datasets are common in many real-world visualisation applications. The probabilistic nature of the Generative Topographic Mapping (GTM), which was originally developed for complete continuous data, can be extended to model heterogeneous (i.e. containing both continuous and discrete values) and missing data. This paper describes and assesses the resulting model on both synthetic and real-world heterogeneous data with missing values.

Visualisation of heterogeneous data with simultaneous feature saliency using Generalised Generative Topographic Mapping

Most machine-learning algorithms are designed for datasets with features of a single type whereas very little attention has been given to datasets with mixed-type features. We recently proposed a model to handle mixed types with a probabilistic latent variable formalism. This proposed model describes the data by type-specific distributions that are conditionally independent given the latent space and is called generalised generative topographic mapping (GGTM). It has often been observed that visualisations of high-dimensional datasets can be poor in the presence of noisy features. In this paper we therefore propose to extend the GGTM to estimate feature saliency values (GGTMFS) as an integrated part of the parameter learning process with an expectation-maximisation (EM) algorithm. The efficacy of the proposed GGTMFS model is demonstrated both for synthetic and real datasets.

Of Marx and Makers: an Historical Perspective on Generative Justice

In Marxist frameworks “distributive justice” depends on extracting value through a centralized state. Many new social movements—peer to peer economy, maker activism, community agriculture, queer ecology, etc.—take the opposite approach, keeping value in its unalienated form and allowing it to freely circulate from the bottom up. Unlike Marxism, there is no general theory for bottom-up, unalienated value circulation. This paper examines the concept of “generative justice” through an historical contrast between Marx’s writings and the indigenous cultures that he drew upon. Marx erroneously concluded that while indigenous cultures had unalienated forms of production, only centralized value extraction could allow the productivity needed for a high quality of life. To the contrary, indigenous cultures now provide a robust model for the “gift economy” that underpins open source technological production, agroecology, and restorative approaches to civil rights. Expanding Marx’s concept of unalienated labor value to include unalienated ecological (nonhuman) value, as well as the domain of freedom in speech, sexual orientation, spirituality and other forms of “expressive” value, we arrive at an historically informed perspective for generative justice. 

GENERATIVE LEADERSHIP

This article presents the results of a research project that studied leadership from the standpoint of the personal conceptions that influence the behavior of local government leaders, as well as those conceptions desired to generate the social transformation processes required in communities. Qualitative methodology was used. Categories of analysis were created based on Pearson’s (1992) model of psychological archetypes. A relevant finding was the limited advance shown by interviewees regarding self-knowledge and a fragmented vision between the observer and the observee, which hinders their ability to take on the challenges that current reality demands from them.

iGrooving: A Generative Music Mobile Application for Runners

iGrooving is a generative music mobile application specifically designed for runners. The application’s foundation is a step-counter that is programmed using the iPhone’s built-in accelerometer. The runner’s steps generate the tempo of the performance by mapping each step to trigger a kick-drum sound file. Additionally, different sound files are triggered at specific step counts to generate the musical performance, allowing the runner a level of compositional autonomy. The sonic elements are chosen to promote a meditative aspect of running. iGrooving is conceived as a biofeedback-stimulated musical instrument and an environment for creating generative music processes with everyday technologies, inspiring us to rethink our everyday notions of musical performance as a shared experience. Isolation, dynamic changes, and music generation are detailed to show how iGrooving facilitates novel methods for music composition, performance and audience participation.

Reti Generative Avversarie nel contesto di servizi di rete sociale: un caso di studio

L’intelligenza artificiale è senza dubbio uno degli argomenti attualmente più in voga nel mondo dell’informatica, sempre in costante evoluzione ed espansione in nuovi settori. In questa elaborato progettuale viene combinato l’argomento sopracitato con il mondo dei social network, che ormai sono parte integrante della quotidianità di tutti. Viene infatti analizzato lo stato dell’arte attuale delle reti neurali, in particolare delle reti generative avversarie, e vengono esaminate le principali tipologie di social network. Su questa base, infatti, verrà realizzato un sistema di rete sociale completo nel quale una GAN sarà proprio la protagonista, sfruttando le più interessanti tecnologie attualmente disponibili. Il sistema sarà disponibile sia come applicativo per dispositivi mobile che come sito web e introdurrà elementi di gamification per aumentare l’interazione con l’utente.

Generative Neural Networks for image iper-resolution and improvement of Optical Character Recognition's performances

The usage of Optical Character Recognition’s (OCR, systems is a widely spread technology into the world of Computer Vision and Machine Learning. It is a topic that interest many field, for example the automotive, where becomes a specialized task known as License Plate Recognition, useful for many application from the automation of toll road to intelligent payments. However, OCR systems need to be very accurate and generalizable in order to be able to extract the text of license plates under high variable conditions, from the type of camera used for acquisition to light changes. Such variables compromise the quality of digitalized real scenes causing the presence of noise and degradation of various type, which can be minimized with the application of modern approaches for image iper resolution and noise reduction. Oneclass of them is known as Generative Neural Networks, which are very strong ally for the solution of this popular problem.

TEORIA SINTÁTICA: DE UMA PERSPECTIVA DE -ISMOS PARA UMAPERSPECTIVA DE PROGRAMAS

This paper tries to show that the developments in linguistic sciences are better viewed as stages in a single research program, rather than different ideological -isms. The first part contains an overview of the structuralistas' beliefs about the universality and equivalence of human languages, and their search for syntactic universals. In the second part, we will see that the generative program, in its turn, tries to answer why language is a universal faculty in the human species and addresses questions about its form, its development and its use. In the second part, we will see that the paper gives a brief glimpse of the tentative answers the program has been giving to each of these issues.

As relativas que são fáceis na aquisição do português brasileiro

The acquisition of Portuguese by two Brazilian children (aged 2;0 -5;0) is discussed in an attempt to describe and explain the first relative clauses produced in naturalistic, observational studies, according to the framework of generative syntax theory. The results show that at around 3;0: a) the child starts to deal with relative clauses as modifiers of N; b) cleft sentences appear before relative clauses, and c) the first relatives confirm the prevalence of the vernacular strategy of relativization in Brazilian Portuguese identified by other studies based on adult data.

A evolução da noção de parâmetros

This paper presents an overview of the concept of parameter in the Principles and Parameters theory, showing that a) in the first stage parameters were conceived as variation associated to the Principles and b) in the second stage as properties of the lexicon, and more specifically as properties of functional categories. The latter view has also developed from a substantive conception of functional categories to a more formal abstract characterization of functional heads. The paper also discusses parameters related to different levels of representation.