Large Deviations Analysis for Distributed Algorithms in an Ergodic Markovian Environment

We provide a large deviations analysis of deadlock phenomena occurring in distributed systems sharing common resources. In our model transition probabilities of resource allocation and deallocation are time and space dependent. The process is driven by an ergodic Markov chain and is reflected on the boundary of the d-dimensional cube. In the large resource limit, we prove Freidlin-Wentzell estimates, we study the asymptotic of the deadlock time and we show that the quasi-potential is a viscosity solution of a Hamilton-Jacobi equation with a Neumann boundary condition. We give a complete analysis of the colliding 2-stacks problem and show an example where the system has a stable attractor which is a limit cycle

On Robustness of Discrete Time Optimal Filters

A new result on stability of an optimal nonlinear filter for a Markov chain with respect to small perturbations on every step is established. An exponential recurrence of the signal is assumed

Observability and nonlinear filtering

This paper develops a connection between the asymptotic stability of nonlinear filters and a notion of observability. We consider a general class of hidden Markov models in continuous time with compact signal state space, and call such a model observable if no two initial measures of the signal process give rise to the same law of the observation process. We demonstrate that observability implies stability of the filter, i.e., the filtered estimates become insensitive to the initial measure at large times. For the special case where the signal is a finite-state Markov process and the observations are of the white noise type, a complete (necessary and sufficient) characterization of filter stability is obtained in terms of a slightly weaker detectability condition. In addition to observability, the role of controllability in filter stability is explored. Finally, the results are partially extended to non-compact signal state spaces

Large deviations for polling systems

Related INRIA Research report available at : http://hal.inria.fr/docs/00/07/27/62/PDF/RR-3892.pdfInternational audienceWe aim at presenting in short the technical report, which states a sample path large deviation principle for a resealed process n-1 Qnt, where Qt represents the joint number of clients at time t in a single server 1-limited polling system with Markovian routing. The main goal is to identify the rate function. A so-called empirical generator is introduced, which consists of Q t and of two empirical measures associated with S t the position of the server at time t. The analysis relies on a suitable change of measure and on a representation of fluid limits for polling systems. Finally, the rate function is solution of a meaningful convex program

A first order finite similitude approach to scaled aseismic structures

For many decades the designs of earthquake-resistant (aseismic) structures have been influenced by scaled experiments, underpinned by the theory of dimensional analysis. Although scaled experiments still play an important role, they are recognised to suffer shortcomings, which are particularly severe when scaling ratios are pronounced. The issue is one of scale effects and the inability of dimensional analysis to offer any solution in their presence. This paper is concerned with a new theory for the analysis of aseismic structures that is founded on the metaphysical concept of space scaling, where beams, substructures or buildings etc. are contracted through the mechanism of space contraction. Although space contraction is evidently practically impossible the theory describes the effects of such a process on the underpinning governing mechanics involved. Unlike dimensional analysis the approach which is termed finite similitude embraces scale effects and accounts for them by linking experiments at more than one scale. It is demonstrated in this work how it is possible to reconstruct full-scale behaviour by means of two scaled experiments of a selected beam, column and multi-storey structure when subjected to dynamic loading conditions.</p

The value of KRAS mutation testing with CEA for the diagnosis of pancreatic mucinous cysts

BACKGROUND AND AIMS: Pancreatic cyst fluid (PCF) CEA has been shown to be the most accurate preoperative test for detection of cystic mucinous neoplasms (CMNs). This study aimed to assess the added value of PCF KRAS mutational analysis to CEA for diagnosis of CMNs. PATIENTS AND METHODS: This is a retrospective study of prospectively collected endoscopic ultrasonography (EUS) fine-needle aspiration (FNA) data. KRAS mutation was determined by direct sequencing or equivalent methods. Cysts were classified histologically (surgical cohort) or by clinical (EUS or FNA) findings (clinical cohort). Performance characteristics of KRAS, CEA and their combination for detection of a cystic mucinous neoplasm (CMN) and malignancy were calculated. RESULTS: The study cohort consisted of 943 patients: 147 in the surgical cohort and 796 in the clinical cohort. Overall, KRAS and CEA each had high specificity (100 % and 93.2 %), but low sensitivity (48.3 % and 56.3 %) for the diagnosis of a CMN. The positivity of KRAS or CEA increased the diagnostic accuracy (80.8 %) and AUC (0.84) significantly compared to KRAS (65.3 % and 0.74) or CEA (65.8 % and 0.74) alone, but only in the clinical cohort (P < 0.0001 for both). KRAS mutation was significantly more frequent in malignant CMNs compared to histologically confirmed non-malignant CMNs (73 % vs. 37 %, P = 0.001). The negative predictive value of KRAS mutation was 77.6 % in differentiating non-malignant cysts. CONCLUSIONS: The detection of a KRAS mutation in PCF is a highly specific test for mucinous cysts. It outperforms CEA for sensitivity in mucinous cyst diagnosis, but the data does not support its routine use

Non-Equilibrium Statistical Physics of Currents in Queuing Networks

We consider a stable open queuing network as a steady non-equilibrium system of interacting particles. The network is completely specified by its underlying graphical structure, type of interaction at each node, and the Markovian transition rates between nodes. For such systems, we ask the question ``What is the most likely way for large currents to accumulate over time in a network ?'', where time is large compared to the system correlation time scale. We identify two interesting regimes. In the first regime, in which the accumulation of currents over time exceeds the expected value by a small to moderate amount (moderate large deviation), we find that the large-deviation distribution of currents is universal (independent of the interaction details), and there is no long-time and averaged over time accumulation of particles (condensation) at any nodes. In the second regime, in which the accumulation of currents over time exceeds the expected value by a large amount (severe large deviation), we find that the large-deviation current distribution is sensitive to interaction details, and there is a long-time accumulation of particles (condensation) at some nodes. The transition between the two regimes can be described as a dynamical second order phase transition. We illustrate these ideas using the simple, yet non-trivial, example of a single node with feedback.Comment: 26 pages, 5 figure

Batch and continuous removal of heavy metals from industrial effluents using microbial consortia

Bio-removal of heavy metals, using microbial biomass, increasingly attracting scientific attention due to their significant role in purification of different types of wastewaters making it reusable. Heavy metals were reported to have a significant hazardous effect on human health, and while the conventional methods of removal were found to be insufficient; microbial biosorption was found to be the most suitable alternative. In this work, an immobilized microbial consortium was generated using Statistical Design of Experiment (DOE) as a robust method to screen the efficiency of the microbial isolates in heavy metal removal process. This is the first report of applying Statistical DOE to screen the efficacy of microbial isolates to remove heavy metals instead of screening normal variables. A mixture of bacterial biomass and fungal spores was used both in batch and continuous modes to remove Chromium and Iron ions from industrial effluents. Bakery yeast was applied as a positive control, and all the obtained biosorbent isolates showed more significant efficiency in heavy metal removal. In batch mode, the immobilized biomass was enclosed in a hanged tea bag-like cellulose membrane to facilitate the separation of the biosorbent from the treated solutions, which is one of the main challenges in applying microbial biosorption at large scale. The continuous flow removal was performed using fixed bed mini-bioreactor, and the process was optimized in terms of pH (6) and flow rates (1 ml/min) using Response Surface Methodology. The most potential biosorbent microbes were identified and characterized. The generated microbial consortia and process succeeded in the total removal of Chromium ions and more than half of Iron ions both from standard solutions and industrial effluents

Dronedarone in high-risk permanent atrial fibrillation

BACKGROUND: Dronedarone restores sinus rhythm and reduces hospitalization or death in intermittent atrial fibrillation. It also lowers heart rate and blood pressure and has antiadrenergic and potential ventricular antiarrhythmic effects. We hypothesized that dronedarone would reduce major vascular events in high-risk permanent atrial fibrillation. METHODS: We assigned patients who were at least 65 years of age with at least a 6-month history of permanent atrial fibrillation and risk factors for major vascular events to receive dronedarone or placebo. The first coprimary outcome was stroke, myocardial infarction, systemic embolism, or death from cardiovascular causes. The second coprimary outcome was unplanned hospitalization for a cardiovascular cause or death. RESULTS: After the enrollment of 3236 patients, the study was stopped for safety reasons. The first coprimary outcome occurred in 43 patients receiving dronedarone and 19 receiving placebo (hazard ratio, 2.29; 95% confidence interval [CI], 1.34 to 3.94; P = 0.002). There were 21 deaths from cardiovascular causes in the dronedarone group and 10 in the placebo group (hazard ratio, 2.11; 95% CI, 1.00 to 4.49; P = 0.046), including death from arrhythmia in 13 patients and 4 patients, respectively (hazard ratio, 3.26; 95% CI, 1.06 to 10.00; P = 0.03). Stroke occurred in 23 patients in the dronedarone group and 10 in the placebo group (hazard ratio, 2.32; 95% CI, 1.11 to 4.88; P = 0.02). Hospitalization for heart failure occurred in 43 patients in the dronedarone group and 24 in the placebo group (hazard ratio, 1.81; 95% CI, 1.10 to 2.99; P = 0.02). CONCLUSIONS: Dronedarone increased rates of heart failure, stroke, and death from cardiovascular causes in patients with permanent atrial fibrillation who were at risk for major vascular events. Our data show that this drug should not be used in such patients. (Funded by Sanofi-Aventis; PALLAS ClinicalTrials.gov number, NCT01151137.) Copyright © 2011 Massachusetts Medical Society. All rights reserved.published_or_final_versio