The effect of side chains on the partial specific volumes of poly(o-alkylphenyl methacrylate)s and polydiitaconates

Partial specific volumes ν of a series of poly(o-alkyl phenyl methacrylate)s and polydiitaconates were determined by density measurements, in toluene and tetrahydrofuran solutions, respectively. The effect of the size and nature of the side groups on partial specific volume is analyzed. Good agreement is found between the experimental ν values and those obtained theoretically from the group contribution method.     

Learning a Generative Model for Multi‐Step Human‐Object Interactions from Videos

Creating dynamic virtual environments consisting of humans interacting with objects is a fundamental problem in computer graphics. While it is well‐accepted that agent interactions play an essential role in synthesizing such scenes, most extant techniques exclusively focus on static scenes, leaving the dynamic component out. In this paper, we present a generative model to synthesize plausible multi‐step dynamic human‐object interactions. Generating multi‐step interactions is challenging since the space of such interactions is exponential in the number of objects, activities, and time steps. We propose to handle this combinatorial complexity by learning a lower dimensional space of plausible human‐object interactions. We use action plots to represent interactions as a sequence of discrete actions along with the participating objects and their states. To build action plots, we present an automatic method that uses state‐of‐the‐art computer vision techniques on RGB videos in order to detect individual objects and their states, extract the involved hands, and recognize the actions performed. The action plots are built from observing videos of everyday activities and are used to train a generative model based on a Recurrent Neural Network (RNN). The network learns the causal dependencies and constraints between individual actions and can be used to generate novel and diverse multi‐step human‐object interactions. Our representation and generative model allows new capabilities in a variety of applications such as interaction prediction, animation synthesis, and motion planning for a real robotic agent.     

Strategic adaptation of humans playing computer algorithms in a repeated constant-sum game

This paper examines strategic adaptation in participants’ behavior conditional on the type of their opponent. Participants played a constant-sum game for 100 rounds against each of three pattern-detecting computer algorithms designed to exploit regularities in human behavior such as imperfections in randomizing and the use of simple heuristics. Significant evidence is presented that human participants not only change their marginal probabilities of choosing actions, but also their conditional probabilities dependent on the recent history of play. A cognitive model incorporating pattern recognition is proposed that capture the shifts in strategic behavior of the participants better than the standard non-pattern detecting model employed in the literature, the Experience Weighted Attraction model (and by extension its nested models, reinforcement learning and fictitious play belief learning).     

Join-Reachability Problems in Directed Graphs

For a given collection \(\mathcal{G}\) of directed graphs we define the join-reachability graph of \(\mathcal{G}\) , denoted by \(\mathcal{J}(\mathcal{G})\) , as the directed graph that, for any pair of vertices u and v, contains a path from u to v if and only if such a path exists in all graphs of  \(\mathcal{G}\) . Our goal is to compute an efficient representation of  \(\mathcal{J}(\mathcal{G})\) . In particular, we consider two versions of this problem. In the explicit version we wish to construct the smallest join-reachability graph for  \(\mathcal{G}\) . In the implicit version we wish to build an efficient data structure, in terms of space and query time, such that we can report fast the set of vertices that reach a query vertex in all graphs of  \(\mathcal{G}\) . This problem is related to the well-studied reachability problem and is motivated by emerging applications of graph-structured databases and graph algorithms. We consider the construction of join-reachability structures for two graphs and develop techniques that can be applied to both the explicit and the implicit problems. First we present optimal and near-optimal structures for paths and trees. Then, based on these results, we provide efficient structures for planar graphs and general directed graphs.     

Constructing strongly convex approximate hulls with inaccurate primitives

The first half of this paper introducesEpsilon Geometry, a framework for the development of robust geometric algorithms using inaccurate primitives. Epsilon Geometry is based on a very general model of imprecise computations, which includes floating-point and rounded-integer arithmetic as special cases. The second half of the paper introduces the notion of a (–)-convex polygon, a polygon that remains convex even if its vertices are all arbitrarily displaced by a distance of of less, and proves some interesting properties of such polygons. In particular, we prove that for every point set there exists a (–)-convex polygonH such that every point is at most 4 away fromH. Using the tools of Epsilon Geometry, we develop robust algorithms for testing whether a polygon is (–)-convex, for testing whether a point is inside a (–)-convex polygon, and for computing a (–)-convex approximate hull for a set of points.     

Finding extrema with unary predicates

We consider the problem of determining the maximum and minimum elements of a setX={x₁...,x _n }, drawn from some finite universeU of real numbers, using only unary predicates of the inputs. It is shown that (n+ log¦U¦) unary predicate evaluations are necessary and sufficient, in the worst case. Results are applied to (i) the problem of determining approximate extrema of a set of real numbers, in the same model, and (ii) the multiparty broadcast communication complexity of determining the extrema of an arbitrary set of numbers held by distinct processors.     

PLATON: Peer-to-Peer load adjusting tree overlay networks

Peer-to-Peer systems supporting multi attribute and range queries use a number of techniques to partition the multi dimensional data space among participating peers. Load-balancing of data accross peer partitions is necessary in order to avoid the presence of network hotspots which may cause performance degradation or failures within the distributed environment. In this paper, we introduce a novel framework, PLATON, that preserves load balancing accross peer partitions when the multi-dimensional data space is dynamic, without requiring up-to-date global load information, e.g. information about the most loaded or least loaded peers in the network. A theoretical analysis on the upper bounds (ie. worst case) of the proposed algorithm is presented; its performance is evaluated in large-scale simulated networks and validated within in the PlanetLab emulation platform.     

Off lattice Monte Carlo simulations of AB hybrid dendritic star copolymers

The conformational properties of Hybrid Dendritic Star copolymers (HDS) which combine the characteristics of dendrimers with those of flexible polymers are studied, for the first time, by means of Off Lattice Monte Carlo simulations. Using the efficient Pivot algorithm we calculate the asphericity and the acylindricity of the whole molecule for various solvent conditions and different characteristics of dendritic and star chains. Moreover, the effects of the number and the length of star branches on the conformation of the dendritic part are also studied. By considering the HDS copolymers as ‘hairy spheres’ we have calculated the star monomer distribution profiles. The shapes of the profiles are compared with previous Monte Carlo results.     

Sampling Conditions for the Measurement of Nucleation Mode Particles in the Exhaust of a Diesel Vehicle

A novel porous tube diluter was characterized to define sampling parameters for repeatable measurements of nucleation-mode particles (NMPs) in the exhaust of a modern diesel passenger car at moderate engine load. This porous tube diluter permits the variation of sampling parameters independently and in a wide range. We investigated the sampling parameters: primary dilution temperature (PDT; 15–55°C), primary dilution ratio (PDR; 8–45), residence time (RT; 0.5–4.0 s), and relative humidity of primary dilution (PRH; 5–90%). Decreased PDT and increased PRH led to a growth of particle number and size in the nucleation mode. While a maximum number of NMPs was found at moderate PDRs between 20 and 30, a maximum volume of NMPs was achieved at PDRs below 20. Coagulation explains the number reduction of NMPs under sampling conditions of prolonged RT and decreased PDR. However, the size growth of the nucleation mode can only partly be attributed to coagulation, and thus growth due to organic compounds from the exhaust probably plays an important role. Sensitivity analysis was conducted as a function of PDR-PDT and revealed two optimal sampling conditions for repeatable NMPs in number or repeatable maximum NMPs in volume. In addition to the sampling parameters, exhaust line conditioning was found to affect NMPs strongly and thus needs to be controlled to minimize effects on the history of measurements.     

On Discrete Killing Vector Fields and Patterns on Surfaces

Symmetry is one of the most important properties of a shape, unifying form and function. It encodes semantic information on one hand, and affects the shape's aesthetic value on the other. Symmetry comes in many flavors, amongst the most interesting being intrinsic symmetry, which is defined only in terms of the intrinsic geometry of the shape. Continuous intrinsic symmetries can be represented using infinitesimal rigid transformations, which are given as tangent vector fields on the surface – known as Killing Vector Fields. As exact symmetries are quite rare, especially when considering noisy sampled surfaces, we propose a method for relaxing the exact symmetry constraint to allow for approximate symmetries and approximate Killing Vector Fields, and show how to discretize these concepts for generating such vector fields on a triangulated mesh. We discuss the properties of approximate Killing Vector Fields, and propose an application to utilize them for texture and geometry synthesis.