IDES: An Internet Distance Estimation Service for Large Networks

The responsiveness of networked applications is limited by communications delays, making network distance an important parameter in optimizing the choice of communications peers. Since accurate global snapshots are difficult and expensive to gather and maintain, it is desirable to use sampling techniques in the Internet to predict unknown network distances from a set of partially observed measurements. This paper makes three contributions. First, we present a model for representing and predicting distances in large-scale networks by matrix factorization which can model suboptimal and asymmetric routing policies, an improvement on previous approaches. Second, we describe two algorithms-singular value decomposition and non-negative matrix factorization-for representing a matrix of network distances as the product of two smaller matrices. Third, based on our model and algorithms, we have designed and implemented a scalable system-Internet Distance Estimation Service (IDES)-that predicts large numbers of network distances from limited samples of Internet measurements. Extensive simulations on real-world data sets show that IDES leads to more accurate, efficient and robust predictions of latencies in large-scale networks than existing approaches     

On the solution of linear-programming problems with free (unrestricted) variables

This paper reviews the way free (unrestricted) variables are handled in the formulation and solution of linear-programming problems, and how such variables are processed in computerbased mathematical programming systems. Key theorems are proved that demonstrate the role of free variables in an optimal solution.     

Model dependency of multivariate autoregressive spectral analysis

A combination of simulations and experimental data analysis has been used to demonstrate that, because cardiovascular control represents a complex linking of input and output parameters, interpreting the variability of individual parameters such as heart rate and arterial pressure virtually requires the use of techniques that quantify control by relating these inputs and outputs. Transfer functions represent appropriate techniques for this purpose. Further, despite the complexities of in vivo physiological control, many of the control elements can be well characterized by only taking into account single inputs and outputs and using a bivariate AR model. However, occasionally when two control systems have a strong and simultaneous influence on a single output parameter, such as arterial pressure and respiratory activity on RR interval, an expansion of the model to the general multivariate case may be required for a complete interpretation. Finally, although not fully demonstrated here, because of the closed-loop nature of cardiovascular control it is likely that algorithms that include causality to account for this characteristic, such as the AR formulation, will most accurately identify the transfer relations     

Using digital but physical surrogates to mediate awareness, communication and privacy in media spaces

Digital but physical surrogates are tangible representations of remote people (typically members of small intimate teams), positioned within an office and under digital control. Surrogates selectively collect and present awareness information about the people they represent. They also react to people's explicit and implicit physical actions: a person's explicit acts include grasping and moving them, while their implicit acts include how they move towards or away from the surrogate. By responding appropriately to these physical actions of people, surrogates can control the communication capabilities of a media space in a natural way. Surrogates also balance awareness and privacy by limiting and abstracting how activities are portrayed, and by offering different levels of salience to its users. The combination of all these attributes means that surrogates can make it easy for intimate collaborators to move smoothly from awareness of each other to casual interaction while mitigating privacy and distraction concerns.Exploring different surrogate designs and how they work together can be straightforward if a good infrastructure is in place. We use anawareness server based on a distributed model-view-controller architecture, which automatically captures, stores and distributes events. We also package surrogates as physical widgets orphidgets with a well-defined interface; this makes it easy for a programmer to plug a surrogate into the awareness server as a controller (to generate awareness events), or view (to display events that others have produced), or both. Because surrogate design, implementation and use is still a new discipline, we also present several issues and next steps.     

Dynamic effects of self-efficacy on smoking lapse and relapse.

Self-efficacy (SE) is thought to be critical to success in smoking cessation both as an individual difference and as a dynamic process after a quit attempt. In this study, 214 smokers used palm-top computers to record day-to-day variations in SE during 4 weeks after quitting. SE remained at high and stable levels prior to a 1st lapse but decreased and became more variable thereafter. The authors used event history models with time-varying covariates to assess the effect of daily SE on lapse and relapse risk. Daily SE measures predicted an initial lapse on the subsequent day. However, this relationship was accounted for by stable baseline differences in SE (assessed by questionnaire), rather than by day-to-day dynamics in SE. Progression from 1st lapse to relapse was also examined. In this instance, daily SE predicted subsequent relapse risk, even when baseline SE and concurrent smoking were accounted for, suggesting the importance of SE dynamics for this stage of the relapse process. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Simulated dipeptide recognition by vancomycin

The antimicrobial activity of vancomycin and related glycopeptide antibiotics is due to stereospecific recognition of polypeptide components in bacterial cell walls. To better understand how these antibiotics recognize polypeptide determinants, we have developed dynamic models of the complexes formed by the vancomycin aglycon and two different dipeptide ligands, Ac-D-ala-D-ala and Ac-D-ala-gly. Molecular dynamics simulations of the two complexes, initially conditioned with distance constraints derived from two-dimensional nuclear magnetic resonance (NMR) studies, are conformationally stable and propagate in a manner consistent with the NMR-derived constraints after the constraints are removed. Free energy calculations accurately predict the relative binding affinity of these two complexes and help validate the simulation models for detailed structural analysis. Although the two ligands adopt similar conformations when bound to the antibiotic, there are clear differences in the configuration of intermolecular hydrogen bonds, the overall shape of the antibiotic, and other structural features of the two complexes. This analysis illustrates how complex structural and dynamic factors interrelate and contribute to differences in binding affinity.