Dimensionality Reduction Mappings

A wealth of powerful dimensionality reduction methods has been established which can be used for data visualization and preprocessing. These are accompanied by formal evaluation schemes, which allow a quantitative evaluation along general principles and which even lead to further visualization schemes based on these objectives. Most methods, however, provide a mapping of a priorly given finite set of points only, requiring additional steps for out-of-sample extensions. We propose a general view on dimensionality reduction based on the concept of cost functions, and, based on this general principle, extend dimensionality reduction to explicit mappings of the data manifold. This offers simple out-of-sample extensions. Further, it opens a way towards a theory of data visualization taking the perspective of its generalization ability to new data points. We demonstrate the approach based on a simple global linear mapping as well as prototype-based local linear mappings.

Single-Player and Two-Player Buttons & Scissors Games

We study the computational complexity of the Buttons \& Scissors game and obtain sharp thresholds with respect to several parameters. Specifically we show that the game is NP-complete for $C = 2$ colors but polytime solvable for $C = 1$. Similarly the game is NP-complete if every color is used by at most $F = 4$ buttons but polytime solvable for $F \leq 3$. We also consider restrictions on the board size, cut directions, and cut sizes. Finally, we introduce several natural two-player versions of the game and show that they are PSPACE-complete.Comment: 21 pages, 15 figures. Presented at JCDCG2 2015, Kyoto University, Kyoto, Japan, September 14 - 16, 201

Scaling laws for the movement of people between locations in a large city

Large scale simulations of the movements of people in a ``virtual'' city and their analyses are used to generate new insights into understanding the dynamic processes that depend on the interactions between people. Models, based on these interactions, can be used in optimizing traffic flow, slowing the spread of infectious diseases or predicting the change in cell phone usage in a disaster. We analyzed cumulative and aggregated data generated from the simulated movements of 1.6 million individuals in a computer (pseudo agent-based) model during a typical day in Portland, Oregon. This city is mapped into a graph with $181,206$ nodes representing physical locations such as buildings. Connecting edges model individual's flow between nodes. Edge weights are constructed from the daily traffic of individuals moving between locations. The number of edges leaving a node (out-degree), the edge weights (out-traffic), and the edge-weights per location (total out-traffic) are fitted well by power law distributions. The power law distributions also fit subgraphs based on work, school, and social/recreational activities. The resulting weighted graph is a ``small world'' and has scaling laws consistent with an underlying hierarchical structure. We also explore the time evolution of the largest connected component and the distribution of the component sizes. We observe a strong linear correlation between the out-degree and total out-traffic distributions and significant levels of clustering. We discuss how these network features can be used to characterize social networks and their relationship to dynamic processes.Comment: 18 pages, 10 figure

Binary Black Hole Waveform Extraction at Null Infinity

In this work, we present a work in progress towards an efficient and economical computational module which interfaces between Cauchy and characteristic evolution codes. Our goal is to provide a standardized waveform extraction tool for the numerical relativity community which will allow CCE to be readily applied to a generic Cauchy code. The tool provides a means of unambiguous comparison between the waveforms generated by evolution codes based upon different formulations of the Einstein equations and different numerical approximation.Comment: 11 pages, 7 figure

Data Mining Methods Applied to a Digital Forensics Task for Supervised Machine Learning

Digital forensics research includes several stages. Once we have collected the data the last goal is to obtain a model in order to predict the output with unseen data. We focus on supervised machine learning techniques. This chapter performs an experimental study on a forensics data task for multi-class classification including several types of methods such as decision trees, bayes classifiers, based on rules, artificial neural networks and based on nearest neighbors. The classifiers have been evaluated with two performance measures: accuracy and Cohen’s kappa. The followed experimental design has been a 4-fold cross validation with thirty repetitions for non-deterministic algorithms in order to obtain reliable results, averaging the results from 120 runs. A statistical analysis has been conducted in order to compare each pair of algorithms by means of t-tests using both the accuracy and Cohen’s kappa metrics

Crystallization and preliminary X-ray analysis of neoagarobiose hydrolase from Saccharophagus degradans 2-40

Many agarolytic bacteria degrade agar polysaccharide into the disaccharide unit neoagarobiose [O-3,6-anhydro-α-L-galactopyranosyl-(1→3)-D-galactose] using various β-agarases. Neoagarobiose hydrolase is an enzyme that acts on the α-1,3 linkage in neoagarobiose to yield D-galactose and 3,6-anhydro-L-galactose. This activity is essential in both the metabolism of agar by agarolytic bacteria and the production of fermentable sugars from agar biomass for bioenergy production. Neoagarobiose hydrolase from the marine bacterium Saccharophagus degradans 2-40 was overexpressed in Escherichia coli and crystallized in the monoclinic space group C2, with unit-cell parameters a = 129.83, b = 76.81, c = 90.11 Å, β = 101.86°. The crystals diffracted to 1.98 Å resolution and possibly contains two molecules in the asymmetric unit

Complete phenomenological gravitational waveforms from spinning coalescing binaries

The quest for gravitational waves from coalescing binaries is customarily performed by the LIGO-Virgo collaboration via matched filtering, which requires a detailed knowledge of the signal. Complete analytical coalescence waveforms are currently available only for the non-precessing binary systems. In this paper we introduce complete phenomenological waveforms for the dominant quadrupolar mode of generically spinning systems. These waveforms are constructed by bridging the gap between the analytically known inspiral phase, described by spin Taylor (T4) approximants in the restricted waveform approximation, and the ring-down phase through a phenomenological intermediate phase, calibrated by comparison with specific, numerically generated waveforms, describing equal mass systems with dimension-less spin magnitudes equal to 0.6. The overlap integral between numerical and phenomenological waveforms ranges between 0.95 and 0.99.Comment: Proceeding for the GWDAW-14 conference. Added reference in v

Searching for surprise

Inspired by the notion of surprise for unconventional discovery in computational creativity, we introduce a general search algorithm we name surprise search. Surprise search is grounded in the divergent search paradigm and is fabricated within the principles of metaheuristic (evolutionary) search. The algorithm mimics the self-surprise cognitive process of creativity and equips computational creators with the ability to search for outcomes that deviate from the algorithm’s expected behavior. The predictive model of expected outcomes is based on historical trails of where the search has been and some local information about the search space. We showcase the basic steps of the algorithm via a problem solving (maze navigation) and a generative art task. What distinguishes surprise search from other forms of divergent search, such as the search for novelty, is its ability to diverge not from earlier and seen outcomes but rather from predicted and unseen points in the creative domain considered.This work has been supported in part by the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).peer-reviewe