On the effect of the deployment setting on broadcasting in Euclidean radio networks

The paper studies broadcasting in radio networks whose stations are represented by points in the Euclidean plane (each station knows its own coordinates). In any given time step, a station can either receive or transmit. A message transmitted from station v is delivered to every station u at distance at most 1 from v, but u successfully hears the message if and only if v is the only station at distance at most 1 from u that transmitted in this time step. A designated source station has a message that should be disseminated throughout the network. All stations other than the source are initially idle and wake up upon the first time they hear the source message. It is shown in [17] that the time complexity of deterministic broadcasting algorithms depends on two parameters of the network, namely, its diameter (in hops) D and a lower bound d on the Euclidean distance between any two stations. The inverse of d is called the granularity of the network, denoted by g. Specifically, the authors of [17] present a deterministic broadcasting algorithm that works in time O (Dg) and prove that every broadcasting algorithm requires \(\varOmega \left( D \sqrt{g} \right) \) time. In this paper, we distinguish between the arbitrary deployment setting, originally studied in [17], in which stations can be placed everywhere in the plane, and the new grid deployment setting, in which stations are only allowed to be placed on a d-spaced grid. Does the latter (more restricted) setting provide any speedup in broadcasting time complexity? Although the O (Dg) broadcasting algorithm of [17] works under the (original) arbitrary deployment setting, it turns out that the \(\varOmega \left( D \sqrt{g} \right) \) lower bound remains valid under the grid deployment setting. Still, the above question is left unanswered. The current paper answers this question affirmatively by presenting a provable separation between the two deployment settings. We establish a tight lower bound on the time complexity of deterministic broadcasting algorithms under the arbitrary deployment setting proving that broadcasting cannot be completed in less than \(\varOmega (D g)\) time. For the grid deployment setting, we develop a deterministic broadcasting algorithm that runs in time \(O \left( D g^{5 / 6} \log g \right) \), thus breaking the linear dependency on g.     

Particle Size,Surface Coating,and PEGylation Influence the Biodistribution of Quantum Dots in Living Mice

This study evaluates the influence of particle size, PEGylation, and surface coating on the quantitative biodistribution of near‐infrared‐emitting quantum dots (QDs) in mice. Polymer‐ or peptide‐coated ⁶⁴Cu‐labeled QDs 2 or 12 nm in diameter, with or without polyethylene glycol (PEG) of molecular weight 2000, are studied by serial micropositron emission tomography imaging and region‐of‐interest analysis, as well as transmission electron microscopy and inductively coupled plasma mass spectrometry. PEGylation and peptide coating slow QD uptake into the organs of the reticuloendothelial system (RES), liver and spleen, by a factor of 6–9 and 2–3, respectively. Small particles are in part renally excreted. Peptide‐coated particles are cleared from liver faster than physical decay alone would suggest. Renal excretion of small QDs and slowing of RES clearance by PEGylation or peptide surface coating are encouraging steps toward the use of modified QDs for imaging living subjects.     

Estimating forest parameters using Landsat ETM+ spectral responses and monocultured plantation fieldwork measurements data

Forest parameters, such as mean diameter at breast height (DBH), mean stand height (H) or volume per hectare (V), are imperative for forest resources assessment. Traditional forest inventory that is usually based on fieldwork is often difficult, time-consuming, and expensive to conduct over large areas. Therefore, estimating forest parameters in large areas using a traditional inventory approach combined with satellite data analysis can improve the spatial estimates of forest inventory data, and hence be useful for sustainable forest management and natural resources assessment. However, extracting practical information from satellite imagery for such purpose is a challenging task mainly because of insufficient knowledge linking forest inventory data to satellite spectral response. Here, we present the use of a cost-free Landsat-7 Enhanced Thematic Mapper Plus (ETM+) in order to explore whether it is possible to combine all available optical bands from a specific sensor for improving forest parameter spatial estimates, based on fieldwork at Lahav and Kramim Forests, in the Israeli Northern Negev. A generic strategy, based on morphological structuring element, convex hall and spectral band linear combination algorithms, was developed in order to extract the mathematical dependencies between the forest inventory measurements and linear combination sets of Landsat-7 ETM+ spectral bands, which yields the highest possible correlation with the forest inventory measured data. Using the mathematical dependency functions, we then convert the entire Landsat-7 ETM+ scenes into forest inventory parameter values with sufficient accuracy and tolerance errors needed for sustainable forest management. The root mean square error obtained between the measured and the estimated values for Lahav Forest are 0.70 cm, 0.29 m, and 1.48 m³ ha^?1 for the mean DBH, H, and V, respectively, and for Kramim forest are 0.61 cm, 0.70 m, and 6.31 m³ ha^?1, respectively. Furthermore, the suggested strategy could also be applied with other satellites data sources.     

A framework for operator – workstation interaction in Industry 4.0

We draw on cognitive and behavioural theories and on the artificial intelligence literature in order to propose a framework of future operator – workstation interaction in the ‘Industry 4.0’ era. We name the proposed framework ‘Operator – Workstation Interaction 4.0’. The latter’s capabilities permit an adaptive, ongoing interaction that aims to improve operator safety, performance, well-being, and satisfaction as well as the factory’s production measures. The framework is composed of three subsystems: (1) the observation subsystem which observes the operator and the processes occurring in the workstation, (2) the analysis subsystem which generates understanding and implications of the observations output, (3) the reaction subsystem which determines if and how to respond. The paper describes these elements and illustrate them using an example of a fatigued worker. The contributions, implications, and limitations of the proposed framework are discussed, and future research directions are presented.     

HOW TO SWINDLE RABIN

a computational short-cut is shown, which can compromise the security of Rabin's digital signature system.     

Detecting unknown computer worm activity via support vector machines and active learning

To detect the presence of unknown worms, we propose a technique based on computer measurements extracted from the operating system. We designed a series of experiments to test the new technique by employing several computer configurations and background application activities. In the course of the experiments, 323 computer features were monitored. Four feature-ranking measures were used to reduce the number of features required for classification. We applied support vector machines to the resulting feature subsets. In addition, we used active learning as a selective sampling method to increase the performance of the classifier and improve its robustness in the presence of misleading instances in the data. Our results indicate a mean detection accuracy in excess of 90?%, and an accuracy above 94?% for specific unknown worms using just 20 features, while maintaining a low false-positive rate when the active learning approach is applied.     

Least squares solutions of the HJB equation with neural network value-function approximators.

In this paper, we present an empirical study of iterative least squares minimization of the Hamilton-Jacobi-Bellman (HJB) residual with a neural network (NN) approximation of the value function. Although the nonlinearities in the optimal control problem and NN approximator preclude theoretical guarantees and raise concerns of numerical instabilities, we present two simple methods for promoting convergence, the effectiveness of which is presented in a series of experiments. The first method involves the gradual increase of the horizon time scale, with a corresponding gradual increase in value function complexity. The second method involves the assumption of stochastic dynamics which introduces a regularizing second derivative term to the HJB equation. A gradual reduction of this term provides further stabilization of the convergence. We demonstrate the solution of several problems, including the 4-D inverted-pendulum system with bounded control. Our approach requires no initial stabilizing policy or any restrictive assumptions on the plant or cost function, only knowledge of the plant dynamics. In the Appendix, we provide the equations for first- and second-order differential backpropagation.     

A new cohesive zone model for mixed mode interface fracture in bimaterials

A new two-dimensional cohesive zone model which is suitable for the prediction of mixed mode interface fracture in bimaterials is presented. The model accounts for the well known fact that the interfacial fracture toughness is not a constant, but a function of the mode mixity. Within the framework of this model, the cohesive energy and the cohesive strength are not chosen to be constant, but rather functions of the mode mixity. A polynomial cohesive zone model is derived in light of analytical and experimental observations of interface cracks. The validity of the new cohesive law is examined by analyzing double cantilever beam and Brazilian disk specimens. The methodology to determine the parameters of the model is outlined and a failure criterion for a pair of ceramic clays is suggested.     

Adaptive versus Non-Adaptive Security of Multi-Party Protocols

Security analysis of multi-party cryptographic protocols distinguishes between two types of adversarial settings: In the non-adaptive setting the set of corrupted parties is chosen in advance, before the interaction begins. In the adaptive setting the adversary chooses who to corrupt during the course of the computation. We study the relations between adaptive security (i.e., security in the adaptive setting) and nonadaptive security, according to two definitions and in several models of computation.