Anonymous Obstruction-free (n,k)(n,k)-Set Agreement with n−k+1n-k+1 Atomic Read/Write Registers

The $k$-set agreement problem is a generalization of the consensus problem. Namely, assuming each process proposes a value, each non-faulty process has to decide a value such that each decided value was proposed, and no more than $k$ different values are decided. This is a hard problem in the sense that it cannot be solved in asynchronous systems as soon as $k$ or more processes may crash. One way to circumvent this impossibility consists in weakening its termination property, requiring that a process terminates (decides) only if it executes alone during a long enough period. This is the well-known obstruction-freedom progress condition. Considering a system of $n$ {\it anonymous asynchronous} processes, which communicate through atomic {\it read/write registers only}, and where {\it any number of processes may crash}, this paper addresses and solves the challenging open problem of designing an obstruction-free $k$-set agreement algorithm with $(n-k+1)$ atomic registers only. From a shared memory cost point of view, this algorithm is the best algorithm known so far, thereby establishing a new upper bound on the number of registers needed to solve the problem (its gain is $(n-k)$ with respect to the previous upper bound). The algorithm is then extended to address the repeated version of $(n,k)$-set agreement. As it is optimal in the number of atomic read/write registers, this algorithm closes the gap on previously established lower/upper bounds for both the anonymous and non-anonymous versions of the repeated $(n,k)$-set agreement problem. Finally, for 1 \leq x\leq k \textless{} n, a generalization suited to $x$-obstruction-freedom is also described, which requires $(n-k+x)$ atomic registers only

Optimal byzantine resilient convergence in oblivious robot networks

Given a set of robots with arbitrary initial location and no agreement on a global coordinate system, convergence requires that all robots asymptotically approach the exact same, but unknown beforehand, location. Robots are oblivious-- they do not recall the past computations -- and are allowed to move in a one-dimensional space. Additionally, robots cannot communicate directly, instead they obtain system related information only via visual sensors. We draw a connection between the convergence problem in robot networks, and the distributed \emph{approximate agreement} problem (that requires correct processes to decide, for some constant $\epsilon$, values distance $\epsilon$ apart and within the range of initial proposed values). Surprisingly, even though specifications are similar, the convergence implementation in robot networks requires specific assumptions about synchrony and Byzantine resilience. In more details, we prove necessary and sufficient conditions for the convergence of mobile robots despite a subset of them being Byzantine (i.e. they can exhibit arbitrary behavior). Additionally, we propose a deterministic convergence algorithm for robot networks and analyze its correctness and complexity in various synchrony settings. The proposed algorithm tolerates f Byzantine robots for (2f+1)-sized robot networks in fully synchronous networks, (3f+1)-sized in semi-synchronous networks. These bounds are optimal for the class of cautious algorithms, which guarantee that correct robots always move inside the range of positions of the correct robots

Contribution the Failure Mode Analysis and Criticality Evaluation Method to the Rehabilitation of Cork Oak (Quercus suber) Forests in Forest Massif of Tlemcen (Algeria)

The controling of forest sustainability and preforest ecosystems in achieving stability of forest ecosystem require the identification of biophysical indicators, anthropological, and technological. The significant degradation of Quercus suber formations in forest massif of Tlemcen (Algeria) are imposed by both climatic factors, the fires, the overgrazing land, anthropogenic aggression as well as by ineffective management. The making of a reference matrix would make possibility the identification of probable hazards and risks. This study aimed to identify the understanding how the mode of operation of a system to identify failures and treat, and the create the intention of eliminating or minimizing the associated risks. This matrix will consist of relevant indicators which easy guide to estimate and following the understanding of the forest degradation process in Algeria. The FMECA method allowed identification of 20 main defective targets which be grouped into 3 categories namely: technical, ecological, organizational, and facilitate of remediation. Each error can be scored and action plans can be prioritized, allowing different with all forest sector players to better understand the degradation of this natural space in order to implement efficient and appropriate remediation plans. &nbsp

Design and Analysis of New Erbium Doped Fiber Amplifiers and Lasers

Erbium doped fiber amplifiers and lasers have a great impact on optical communication due to their ideal advantages for the compensation of light energy during transmission through the fiber optic systems. This has been the focus of many research groups. Replacing the bottleneck electronic amplifier by the optical amplifier, a great improvement is made for the optical communication systems. New configurations have been conceived and realized in this thesis. They have shown a great enhancement in amplifier and laser performance parameters. Several new configurations, for example double pass with filter which also supports multiple wavelengths, have been demonstrated and investigated. The design and performance parameters of the new configurations are thoroughly characterized, showing an improvement in gain, noise figure output power flatness efficiency and side mode suppression ratio. Enhancement of results has been experimentally demonstrated, where a higher gain (56dB) and flat output power (<0.7dB), a higher signal to noise ratio (77dB) and a higher efficiency (22%) are demonstrated

Automatic target recognition with deep metric learning.

An Automatic Target Recognizer (ATR) is a real or near-real time understanding system where its input (images, signals) are obtained from sensors and its output is the detected and recognized target. ATR is an important task in many civilian and military computer vision applications. The used sensors, such as infrared (IR) imagery, enlarge our knowledge of the surrounding environment, especially at night as they provide continuous surveillance. However, ATR based on IR faces major challenges such as meteorological conditions, scale and viewpoint invariance. In this thesis, we propose solutions that are based on Deep Metric Learning (DML). DML is a technique that has been recently proposed to learn a transformation to a representation space (embedding space) in end-to-end manner based on convolutional neural networks. We explore three distinct approaches. The first one, is based on optimizing a loss function based on a set of triplets [47]. The second one is based on a method that aims to capture the explicit distributions of the different classes in the transformation space [45]. The third method aims to learn a compact hyper-spherical embedding based on Von Mises-Fisher distribution [64]. For these methods, we propose strategies to select and update the constraints to reduce the intra-class variations and increase the inter-class variations. To validate, analyze and compare the three different DML approaches, we use a large real benchmark data that contain multiple target classes of military and civilian vehicles. These targets are captured at different viewing angles, different ranges, and different times of the day. We validate the effectiveness of these methods by evaluating their classification performance as well as analyzing the compactness of their learned features. We show that the three considered methods can learn models that achieve their objectives

Characterization of Adipocyte Mechanical Properties and Stretch Control of Mechanosignaling in Adipocytes

Obesity has reached global epidemic levels in recent decades and is the leading risk factor for type-2 diabetes (T2D). Adipose tissue behaves as a link between obesity and T2D, with dysfunctional extracellular matrix (ECM) remodeling, adipokine secretion and lipid metabolism leading to insulin resistance. The extracellular mechanophysical milieus is understood to regulate adipocyte differentiation and function through activation of mechanosensory machinery and related pathways. Therefore, exploring the adipocytic response to mechanical loading can provide great pathophysiological insight into obesity and T2D. Adipose tissue is exposed to various compound forces due to weight-bearing and movement, and adipocyte stiffness influences stress distribution and deformation across the tissue. Our findings with atomic force microscopy (AFM) confirmed that differentiated adipocytes display higher stiffness than preadipocytes, and further showed that adipocyte stiffness is governed by cytoskeletal contractility modulated by Rho/RhoA Kinase (Rho/ROCK) mechanosensor. We also demonstrated that cytoskeletal rearrangements made to accommodate for lipid droplets during adipogenesis is associated with diminished force transmission at cell-substrate sites, as illustrated by traction force microscopy (TFM). Next, we investigated the role of adipocyte mechanosensors in modulating insulin signaling and found that cyclic stretch loading induced basal AKT activation and enhanced GLUT4 surface translocation. Impeding both Rho/ROCK and focal adhesion kinase (FAK) mechanosensor activity abrogated the stretch-activation of AKT, implying their regulatory role in the metabolic response of adipocytes to mechanical loading. In the final study, we found that cyclic stretch loading significantly altered adipokine expression and increased key adipogenic/lipogenic gene expression. Recent research has established transcriptional co-activator Yes-associated protein (YAP) as a molecular switch regulating adipose tissue fibrosis, lipid metabolism and adipokine secretion. Immunofluorescence and immunoblotting data showed that YAP is transiently activated by cyclic stretch loading. While YAP silencing did not affect AKT activation by stretch, YAP was required for the stretch-induced dephosphorylation of AMPK. These findings suggest that YAP may be responsible for metabolically shifting adipocytes towards a more anabolic state at the expense of catabolic activity when exposed to cyclic stretch loading

RoboCast: Asynchronous Communication in Robot Networks

This paper introduces the \emph{RoboCast} communication abstraction. The RoboCast allows a swarm of non oblivious, anonymous robots that are only endowed with visibility sensors and do not share a common coordinate system, to asynchronously exchange information. We propose a generic framework that covers a large class of asynchronous communication algorithms and show how our framework can be used to implement fundamental building blocks in robot networks such as gathering or stigmergy. In more details, we propose a RoboCast algorithm that allows robots to broadcast their local coordinate systems to each others. Our algorithm is further refined with a local collision avoidance scheme. Then, using the RoboCast primitive, we propose algorithms for deterministic asynchronous gathering and binary information exchange