Opportunity cost algorithms for reduction of I/O and interprocess communication overhead in a computing cluster

Computing clusters (CC) consisting of several connected machines, could provide a high-performance, multiuser, timesharing environment for executing parallel and sequential jobs. In order to achieve good performance in such an environment, it is necessary to assign processes to machines in a manner that ensures efficient allocation of resources among the jobs. The paper presents opportunity cost algorithms for online assignment of jobs to machines in a CC. These algorithms are designed to improve the overall CPU utilization of the cluster and to reduce the I/O and the interprocess communication (IPC) overhead. Our approach is based on known theoretical results on competitive algorithms. The main contribution of the paper is how to adapt this theory into working algorithms that can assign jobs to machines in a manner that guarantees near-optimal utilization of the CPU resource for jobs that perform I/O and IPC operations. The developed algorithms are easy to implement. We tested the algorithms by means of simulations and executions in a real system and show that they outperform existing methods for process allocation that are based on ad hoc heuristics.     

You said it before and you'll say it again: Expectations of consistency in communication.

Repeated reference creates strong expectations in addressees that a speaker will continue to use the same expression for the same object. The authors investigate the root reason for these expectations by comparing a cooperativeness-based account (Grice, 1975) with a simpler consistency-based account. In two eye-tracking experiments, the authors investigated the expectations underlying the effect of precedents on comprehension. The authors show that listeners expect speakers to be consistent in their use of expressions even when these expectations cannot be motivated by the assumption of cooperativeness. The authors conclude that though this phenomenon seems to be motivated by cooperativeness, listeners' expectation that speakers be consistent in their use of expressions is governed by a general expectation of consistency. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Modeling the Combining Site of the Human Asialoglycoprotein Receptor

A model for the carbohydrate recognition domain (CRD) and combining site of the human asialoglycoprotein (ASGP) receptor has been computed on the basis of the close sequence homology with the mannose-binding lectin (MBP), whose three-dimensional structure in complex with a ligand has been determined by crystallographic methods (Weis, W.I.; Drickamer, K.; Hendrickson, W.A. Nature 1992, 360: 127). Within the limitations of modeling methods, the model is compatible with data on ligand binding of the family C-type lectins, of which the MBP and the ASGP receptor are members. The model derived can serve as a guide for designing site-directed mutagenesis experiments in order to further elucidate the origins of specificity of the ASGP receptor toward galactose. In particular, the model focuses attention on the possible role of position 207 (MBP sequence numbering) in promoting galactose binding.     

Learning science via animated movies: Its effect on students’ thinking and motivation

Some researchers claim that animations may hinder students’ meaningful learning or evoke misunderstandings. In order to examine these assertions, our study investigated the effect of animated movies on students’ learning outcomes and motivation to learn. Applying the quantitative methodology, two pre- and post-questionnaires were administered: Science thinking skills and Motivation to learn science. Students’ overall achievement in science was examined by their report card scores. The research population (N = 1335) was divided into experimental (N = 926) and control (N = 409) groups from 11 elementary schools. Findings indicated that the use of animated movies promoted students’ explanation ability and their understanding of scientific concepts. Findings also indicated that students who studied science with the use of animated movies developed higher motivation to learn science, in terms of: self-efficacy, interest and enjoyment, connection to daily life, and importance to their future, compared to the control students. Following the definition of multimedia, the students who study with the use of animated movies, applied all three learning styles: visual, auditory and kinesthetic. The use of multimedia and the fact that the students were engaged in exploring new concepts, that were relevant to their daily life experiences, can explain the positive results.     

Guaranteeing the Diversity of Number Generators

A major problem in using iterative number generators of the form x_i=f(x_i−1) is that they can enter unexpectedly short cycles. This is hard to analyze when the generator is designed, hard to detect in real time when the generator is used, and can have devastating cryptanalytic implications. In this paper we define a measure of security, called sequence diversity, which generalizes the notion of cycle-length for noniterative generators. We then introduce the class of counter-assisted generators and show how to turn any iterative generator (even a bad one designed or seeded by an adversary) into a counter-assisted generator with a provably high diversity, without reducing the quality of generators which are already cryptographically strong.     

Real-time 2D to 3D video conversion

We present a real-time implementation of 2D to 3D video conversion using compressed video. In our method, compressed 2D video is analyzed by extracting motion vectors. Using the motion vector maps, depth maps are built for each frame and the frames are segmented to provide object-wise depth ordering. These data are then used to synthesize stereo pairs. 3D video synthesized in this fashion can be viewed using any stereoscopic display. In our implementation, anaglyph projection was selected as the 3D visualization method, because it is mostly suited to standard displays.

Combinatorial Optimization and Performance Analysis of a Multi-arm Cartesian Robotic Fruit Harvester—Extensions of Graph Coloring

A mobile melon robotic harvester consisting of multiple Cartesian manipulators, each with three degrees of freedom, is being developed. In order to design an optimal robot in terms of number of arms, manipulator capabilities, and robot speed, a method of allocating the fruits to be picked by each manipulator in a way that yields the maximum harvest has been developed. Such a method has already been devised for a multi-arm robot with 2DOF each. The maximum robotic harvesting problem was shown there to be an example of the maximum k-colorable subgraph problem (MKCSP) on an interval graph. However, for manipulators with 3DOF, the additional longitudinal motion results in variable intervals. To overcome this issue, we devise a new model based on the color-dependent interval graph (CDIG). This enables the harvest by multiple robotic arms to be modeled as a modified version of the MKCSP. Based on previous research, we develop a greedy algorithm that solves the problem in polynomial time, and prove its optimality using induction. As with the multi-arm 2DOF robot, when simulated numerous times on a field of randomly distributed fruits, the algorithm yields a nearly identical percentage of fruit harvested for given robot parameters. The results of the probabilistic analysis developed for the 2DOF robot was modified to yield a formula for the expected harvest ratio of the 3DOF robot. The significance of this method is that it enables selecting the most efficient actuators, number of manipulators, and robot forward velocity for maximal robotic fruit harvest.     

Sleeved co-clustering of lagged data

The paper focuses on mining clusters that are characterized by a lagged relationship between the data objects. We call such clusters lagged co-clusters. A lagged co-cluster of a matrix is a submatrix determined by a subset of rows and their corresponding lag over a subset of columns. Extracting such subsets may reveal an underlying governing regulatory mechanism. Such a regulatory mechanism is quite common in real-life settings. It appears in a variety of fields: meteorology, seismic activity, stock market behavior, neuronal brain activity, river flow, and navigation, but a limited list of examples. Mining such lagged co-clusters not only helps in understanding the relationship between objects in the domain, but assists in forecasting their future behavior. For most interesting variants of this problem, finding an optimal lagged co-cluster is NP-complete problem. We present a polynomial-time Monte-Carlo algorithm for mining lagged co-clusters. We prove that, with fixed probability, the algorithm mines a lagged co-cluster which encompasses the optimal lagged co-cluster by a maximum 2 ratio columns overhead and completely no rows overhead. Moreover, the algorithm handles noise, anti-correlations, missing values, and overlapping patterns. The algorithm is extensively evaluated using both artificial and real-world test environments. The first enable the evaluation of specific, isolated properties of the algorithm. The latter (river flow and topographic data) enable the evaluation of the algorithm to efficiently mine relevant and coherent lagged co-clusters in environments that are temporal, i.e., time reading data and non-temporal.