Achieving greater anonymity in network latency

Each lnternet communication leaves trails that can be followed back to the user. Notably, anonymous communication schemes are purposed to hide users＇ identity as to personal, source, destination location and content information. Notably, no network capability is in existence to completely negate anonymity leakage in network latency[~l, thus, the minimization of anonymity leakage in network latency becomes critically salient. The purpose of this paper is to investigate network latency anonymity leaks, and propose practical techniques for their reduction. In this direction, the author investigate the following technical question： What implementation techniques can be configured to truly reduce anonymity leaks using deployable systems and exploiting the popular Tor security strategies.     

Magnetic quadrupole doublet focusing system for high energy ions

A high energy focused ion beam microprobe using a doublet arrangement of short magnetic quadrupole lenses was used to focus 1-3 MeV protons to spot sizes of 1x1 microm2 and 1-4.5 MeV carbon and silicon ion beams to spot sizes of 1.5x1.5 microm2. The results presented clearly demonstrate that this simple doublet configuration can provide high energy microbeams for microanalysis and microfabrication applications.     

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.     

One View of the Future

Abstract

Floods, droughts, water scarcity, and water contamination are some among many water problems that are present today and will be even more noticeable in the future. In the past, many different tools have been used for simulation and optimization of complex water resources systems in order to provide an improved basis for decision making. The continuing evolution of information technology (hardware and software) creates a good environment for the transition to new tools. Application of the systems approach to water resources planning, management, and operations has been established as one of the most important advances made in the field of water resources engineering. Based on the lessons learned, this contribution provides my personal view on the tools to be used in the future. Two paradigm shifts are discussed. The first one is focusing on the complexity of the water resources domain and the complexity of the modelling tools in an environment characterised by continuous rapid technological development. The second one deals with water-related data availability and natural variability of domain variables in time and space affecting the uncertainty of water resources decision making.     

The Forgiving Graph: a distributed data structure for low stretch under adversarial attack

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that, over a sequence of rounds, an adversary either inserts a node with arbitrary connections or deletes an arbitrary node from the network. The network responds to each such change by quick “repairs,” which consist of adding or deleting a small number of edges. These repairs essentially preserve closeness of nodes after adversarial deletions, without increasing node degrees by too much, in the following sense. At any point in the algorithm, nodes v and w whose distance would have been ? in the graph formed by considering only the adversarial insertions (not the adversarial deletions), will be at distance at most ? log n in the actual graph, where n is the total number of vertices seen so far. Similarly, at any point, a node v whose degree would have been d in the graph with adversarial insertions only, will have degree at most 3d in the actual graph. Our distributed data structure, which we call the Forgiving Graph, has low latency and bandwidth requirements. The Forgiving Graph improves on the Forgiving Tree distributed data structure from Hayes et?al. (2008) in the following ways: 1) it ensures low stretch over all pairs of nodes, while the Forgiving Tree only ensures low diameter increase; 2) it handles both node insertions and deletions, while the Forgiving Tree only handles deletions; 3) it requires only a very simple and minimal initialization phase, while the Forgiving Tree initially requires construction of a spanning tree of the network.     

Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion

This paper proposes an approach for decentralized fault detection and diagnosis in process monitoring sensor networks. The sensor network is decomposed into multiple, potentially overlapping, blocks using the Sparse Principal Component Analysis algorithm. Local predictions are generated at each block using Support Vector Machine classifiers. The local predictions are then fused via a Maximum Entropy algorithm. Empirical studies on the benchmark Tennessee Eastman Process data demonstrated that the proposed decentralized approach achieves accuracy comparable to that of the fully centralized approach, while offering benefits in terms of fault tolerance, reusability, and scalability.     

Impaired Higher Order Implicit Sequence Learning on the Verbal Version of the Serial Reaction Time Task in Patients With Parkinson's Disease.

Although neuroimaging studies have strongly implicated basal ganglia involvement in implicit sequence learning, serial reaction time (SRT) studies with Parkinson's disease (PD) patients have yielded mixed results. The present research sought to examine the ability of people with PD to implicitly learn sequences with different sequential structures and to objectively assess explicit knowledge. A version of the SRT task that reduces motor demands was used to compare 19 patients with PD but not dementia and 37 matched controls. PD patients showed less implicit sequence-specific learning for both sequences and reduced response time improvement over sequential trials for the more complex sequence. A closer examination revealed that the deficit involved higher order sequential associations as well as the learning of pairwise information. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Mathematical Model for the Calculation of the Adhesion of a Flame Sprayed Coating of Aluminum on S 235 JR Steel

In this work the method of response surfaces was used for the purpose of drawing up a mathematical model for the calculation (forecasting) of the adhesion of a flame sprayed coating of powdered aluminum on S 235 JR steel. Experimental work was conducted according to a fractional central-composite design at three levels. The adhesion of the coatings made with combinations of input parameters of the procedure according to the experiment plan mentioned is measured in line with the EN 582 standard. A mathematical model was derived describing the dependence of the experimentally obtained adhesion values on the input parameters of the procedure. The possibility of analyzing the influence of the parameters on the adhesion for a certain combination of input parameters has been presented with the use of perturbation plots.     

Multifunctional Nanochemistry: Ambient,Electroless, Template-Based Synthesis and Characterization of Segmented Bimetallic Pd/Au and Pd/Pt Nanowires as High-Performance Electrocatalysts and Nanomotors

Segmented noble-metal nanowires (NWs) represent an exciting, multifunctional, one-dimensional, structural architecture with a variety of potential applications. However, the widespread use of electrodeposition in the preparation of these systems has limited their potential to be produced on a large scale, since this protocol is costly and requires complex processes and caustic reaction media. Given the inherent limitations of electrodeposition, we report, for the first time, an ambient, surfactantless, template-based approach that is not only sustainable but also efficient for the reliable production of Pd/Pt and Pd/Au segmented NWs, possessing two spatially separated, chemically distinctive, but elementally pure, axial subunits. Our simple two-step synthetic approach allows for direct and predictable control over the relative segment lengths in these nanomaterials. Moreover, thorough structural characterization of these as-prepared samples confirms that our segmented NWs maintain high-quality, crystalline, elementally pure subunits with a well-defined interface between the constituent metals. In the context of preparing segmented NWs as multifunctional nanostructures, we demonstrate that these as-prepared NWs achieve high levels of performance when employed as both electrocatalysts and nanomotors.