Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

BLP-2LASSO for aggregate discrete choice models with rich covariates

We introduce the BLP-2LASSO model, which augments the classic BLP (Berry, Levinsohn, and Pakes, 1995) random-coefficients logit model to allow for data-driven selection among a high-dimensional set of control variables using the 'double-LASSO' procedure proposed by Belloni, Chernozhukov, and Hansen (2013). Economists often study consumers’ aggregate behaviour across markets choosing from a menu of differentiated products. In this analysis, local demographic characteristics can serve as controls for market-specific preference heterogeneity. Given rich demographic data, implementing these models requires specifying which variables to include in the analysis, an ad hoc process typically guided primarily by a researcher’s intuition. We propose a data-driven approach to estimate these models, applying penalized estimation algorithms from the recent literature in high-dimensional econometrics. Our application explores the effect of campaign spending on vote shares in data from Mexican elections

Unsupervised machine learning for detection of phase transitions in off-lattice systems II. Applications

We outline how principal component analysis (PCA) can be applied to particle configuration data to detect a variety of phase transitions in off-lattice systems, both in and out of equilibrium. Specifically, we discuss its application to study 1) the nonequilibrium random organization (RandOrg) model that exhibits a phase transition from quiescent to steady-state behavior as a function of density, 2) orientationally and positionally driven equilibrium phase transitions for hard ellipses, and 3) compositionally driven demixing transitions in the non-additive binary Widom-Rowlinson mixture

The analysis of facial beauty: an emerging area of research in pattern analysis

Much research presented recently supports the idea that the human perception of attractiveness is data-driven and largely irrespective of the perceiver. This suggests using pattern analysis techniques for beauty analysis. Several scientific papers on this subject are appearing in image processing, computer vision and pattern analysis contexts, or use techniques of these areas. In this paper, we will survey the recent studies on automatic analysis of facial beauty, and discuss research lines and practical application

An application of data-driven analysis in road tunnels monitoring

In order to comply with the minimum safety requirements imposed by the Directive 2004/54/EC it is of paramount mportance to correctly manage the operation and maintenance of road tunnels. This research describes how Artificial Intelligence techniques can play a supportive role both for maintenance operators in monitoring tunnels and for safety managers in operation. It is possible to extract relevant information from large volumes of data from sensor equipment in an efficient, fast, dynamic and adaptive way and make it immediately usable by those who manage machinery and servicesto aid quick decisions. Carrying out an analysis based on sensors in motorway tunnels, represents an important technological innovation, which would simplify tunnels management activities and therefore the detection of any possible deterioration, while keeping the risk within tolerance limits. The idea involves the creation of an algorithm for the detection of faults by acquiring data in real time from the sensors of tunnel sub-systems and using them to help identify the service state of the tunnel. The AI models are trained on a period of 6 months with one hour time series granularity measured on a road tunnel part of the Italian motorway systems. The verification has been done with reference to a number of recorded sensor faults

Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows

In machine learning, likelihood-free inference refers to the task of performing an analysis driven by data instead of an analytical expression. We discuss the application of Neural Spline Flows, a neural density estimation algorithm, to the likelihood-free inference problem of the measurement of neutrino oscillation parameters in Long Baseline neutrino experiments. A method adapted to physics parameter inference is developed and applied to the case of the disappearance muon neutrino analysis at the T2K experiment.Comment: 10 pages, 3 figure

Sleuth: A Quasi-Model-Independent Search Strategy for New Physics

How can we search for new physics when we only vaguely know what it should look like? How can we perform an unbiased yet data-driven search? If we see apparently anomalous events in our data, how can we quantify their "interestingness" a posteriori? We present an analysis strategy (Sleuth) that simultaneously addresses each of these questions, and we demonstrate its application to over thirty exclusive final states in data collected by D0 in Run I of the Fermilab Tevatron.Comment: 4 pages, presented at Moriond QCD 200