Progressive Transient Photon Beams

In this work, we introduce a novel algorithm for transient rendering in participating media. Our method is consistent, robust and is able to generate animations of time‐resolved light transport featuring complex caustic light paths in media. We base our method on the observation that the spatial continuity provides an increased coverage of the temporal domain, and generalize photon beams to transient‐state. We extend stead‐state photon beam radiance estimates to include the temporal domain. Then, we develop a progressive variant of our approach which provably converges to the correct solution using finite memory by averaging independent realizations of the estimates with progressively reduced kernel bandwidths. We derive the optimal convergence rates accounting for space and time kernels, and demonstrate our method against previous consistent transient rendering methods for participating media.     

Understanding water disputes in Chile with text and data mining tools

ABSTRACT

This article provides a multidimensional study based on data and text mining of prosecuted disputes on water rights in Chile, and an analysis of the state’s capacity, particularly of the institutions related to water regulation. This study shows not only a substantial increase of legal disputes regarding water rights over the years (1981–2014), but also clear patterns in the geographic location of these conflicts, as well as in the types of legal actions, arguments and strategies used in their pursuit. Through a topic analysis, we find a growing diversification over time of the subjects contained in the legal claims, suggesting an increase in structure and complexity.     

SOM ensemble for unsupervised outlier analysis. Application to outlier identification in the Gaia astronomical survey

Gaia is an ESA cornerstone astronomical mission that will observe with unprecedented precision positions, distances, space motions, and many physical properties of more than one billion objects in our Galaxy and beyond. It will observe all objects in the sky in the visible magnitude range from 6 to 20, up to approximately 10⁹ sources. An international scientific consortium, the Gaia Data Processing and Analysis Consortium (Gaia DPAC), has organized itself in several coordination units, with the aim, among others, of addressing the work of classifying the observed astronomical sources, using both supervised and unsupervised classification algorithms. This work focuses on the analysis of classification outliers by means of unsupervised classification. We present a novel method to combine SOMs trained with independent features that are calculated from spectrophotometry. The method as described here can help to improve the models used for the supervised classification of astronomical sources. Furthermore, it allows for data exploration and knowledge discovery in huge astronomical databases such as the upcoming Gaia mission.     

Differential expression of ceruloplasmin isoforms in the cerebrospinal fluid of amyotrophic lateral sclerosis patients

Amyotrophic lateral sclerosis (ALS) a fatal degenerative disease that selectively affects motor neurons, likely results from a complex interplay among oxidative injury, excitotoxic stimulation, protein aggregation and genetic factors. Ceruloplasmin (Cp) protein is a ferroxidase that oxidizes toxic ferrous iron to its nontoxic ferric form, protecting the central nervous system (CNS) from iron deposition. Cp is thus considered as one of the main systems dedicated to the protection of the CNS from oxidative stress damage. We investigated Cp protein behaviour in the cerebrospinal fluid (CSF) of ALS patients of recent onset. An increased expression of Cp was observed in ALS (n = 16) compared to two control groups (healthy subjects, n = 11 and peripheral neuropathy patients, n = 10). 2‐DE analysis revealed a differential expression of Cp isoforms in ALS patients compared to controls. ALS samples showed an increase in the relative abundance of more basic Cp forms, corresponding to the nonsialylated proteins. Despite the increase in protein expression, ferroxidase activity evaluated in the CSF of ALS patients was comparable to that of the controls, indicating a Cp functional impairment. Ceruloplasmin isoforms profile may be proposed as disease feature that could provide insight into the molecular mechanisms of ALS pathogenesis.     

Model checking mobile stochastic logic

The Temporal Mobile Stochastic Logic (MoSL) has been introduced in previous work by the authors for formulating properties of systems specified in StoKlaim, a Markovian extension of Klaim. The main purpose of MoSL is to address key functional aspects of global computing such as distribution awareness, mobility, and security and their integration with performance and dependability guarantees. In this paper, we present MoSL⁺${\text{<small-caps>MoSL</small-caps>}}^{+}$, an extension of MoSL, which incorporates some basic features of the Modal Logic for MObility (MoMo), a logic specifically designed for dealing with resource management and mobility aspects of concurrent behaviours. We also show how MoSL⁺${\text{<small-caps>MoSL</small-caps>}}^{+}$ formulae can be model-checked against StoKlaim specifications. For this purpose, we show how existing state-based stochastic model-checkers, like e.g. the Markov Reward Model Checker (MRMC), can be exploited by using a front-end for StoKlaim that performs appropriate pre-processing of MoSL⁺${\text{<small-caps>MoSL</small-caps>}}^{+}$ formulae. The proposed approach is illustrated by modelling and verifying a sample system.     

Robust feedforward design for a two-degrees of freedom controller

In this paper we address the problem of designing the feedforward block of a two degrees of freedom controller in the case of single input single output (SISO) discrete-time linear systems with model uncertainty. In this work the design of the feedforward filter is formulated as a robust model matching problem. First, a closed form solution of the optimal noncausal filter, which minimizes the worst-case model matching error for each ω[0,2π], is obtained. Then, suitable rational stable approximations of the optimal solution are derived either by means of causal filters or, when a preview of the reference signal is available, by means of noncausal FIR filters. The efficiency of the proposed method is tested on a simulated example.     

The BonaFide C Analyzer: automatic loop-level characterization and coverage measurement

The advent of multicore technologies has increased the interest in parallelization techniques for existing sequential applications. These techniques include the need of detecting loops that are good candidates for parallelization, and classifying all variables of these loops according to their use, a task surprisingly hard to be carried out manually. In this paper, we introduce the BonaFide C Analyzer, an XML-based framework that combines static analysis of source code with profiling information to generate complete reports regarding all loops in a C application, including loop coverage, loop suitability for parallelization, a classification of all variables inside loops based on their accesses, and other hurdles that restrict the parallelization. This information allows to analyze how particular language constructs are used in real-world applications, and helps the programmer to parallelize the code. To show the features of the framework, we present the results of an in-depth loop characterization of C applications that are part of the SPEC CPU2006 benchmark suite. Our study shows that 47.72 % of loops present in the applications analyzed are potentially parallelizable with existent parallel programming models such as OpenMP, while an additional 37.7 % of loops could be run in parallel with the help of runtime speculative parallelization techniques.     

A Neuro-Fuzzy Approach for Automatic Detection of Breast Cancer Based on Raman Spectroscopy

The harmful presence of cancerous cells in the feminine breast brings as a result, breast cancer, illness that has spread widely lately, not only in Mexico, but in other parts of the planet. In this paper, we present a method of automatic breast cancer classification, in which a Raman signal is classified as coming from a biopsy of healthy tissue （class ω1） or biopsy of diseased tissue （class ω2）; to do so, we created patterns from Raman spectra accurately measuring each Raman peak to provide naturally reduced data to a classifier; we used ANFIS （adaptative neuro-fuzzy inference system） classifier and high rates of correct classification were obtained. This provides the specialists with important clinical tools for a rapid and efficient automatic detection of breast cancer. We consider that our approach can be applicable to other kinds of cancer, e.g., lung, prostate, and stomach.