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Title: A Many-Objective Evolutionary Algorithm Based on Decomposition and Local Dominance
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Subjects: Neural and Evolutionary Computing (cs.NE)

Many-objective evolutionary algorithms (MOEAs), especially the decomposition-based MOEAs, have attracted wide attention in recent years. Recent studies show that a well designed combination of the decomposition method and the domination method can improve the performance ,i.e., convergence and diversity, of a MOEA. In this paper, a novel way of combining the decomposition method and the domination method is proposed. More precisely, a set of weight vectors is employed to decompose a given many-objective optimization problem(MaOP), and a hybrid method of the penalty-based boundary intersection function and dominance is proposed to compare local solutions within a subpopulation defined by a weight vector. A MOEA based on the hybrid method is implemented and tested on problems chosen from two famous test suites, i.e., DTLZ and WFG. The experimental results show that our algorithm is very competitive in dealing with MaOPs. Subsequently, our algorithm is extended to solve constraint MaOPs, and the constrained version of our algorithm also shows good performance in terms of convergence and diversity. These reveals that using dominance locally and combining it with the decomposition method can effectively improve the performance of a MOEA.

Title: Emergent Democracy
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Subjects: Physics and Society (physics.soc-ph) ; Computers and Society (cs.CY); Networking and Internet Architecture (cs.NI)

This essay argues that a new form of democracy - an "Emergent Democracy" - will develop as a result of the use of Internet communication tools and platforms such as blogs. The essay explores a variety of tools available and explores the history of democracy, modern experiments with democracy and how these tools might support democracy. The essay also explores concerns as these new tools emerge. These issues include concerns such as privacy and the societally negative use of these tools by corporations, totalitarian regimes and terrorists.

Title: Modeling and Analysis of Cascading Failures in Interdependent Cyber-Physical Systems
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Subjects: Physics and Society (physics.soc-ph) ; Systems and Control (cs.SY)

Integrated cyber-physical systems (CPSs), such as the smart grid, are increasingly becoming the underpinning technology for major industries. A major concern regarding such systems are the seemingly unexpected large-scale failures, which are often attributed to a small initial shock getting escalated due to intricate dependencies within and across the individual counterparts of the system. In this paper, we develop a novel interdependent system model to capture this phenomenon, also known as cascading failures. Our framework consists of two networks that have inherently different characteristics governing their intra-dependency: i) a cyber-network where a node is functional as long as it belongs to the largest connected (i.e., giant) component; and ii) a physical network where nodes are given an initial flow and a capacity, and failure of a node results with redistribution of its flow to the remaining nodes, upon which further failures might take place due to overloading (i.e., the flow of a node exceeding its capacity). Furthermore, it is assumed that these two networks are inter-dependent. For simplicity, we consider a one-to-one interdependency model where every node in the cyber-network is dependent upon and supports a single node in the physical network, and vice versa. We provide a thorough analysis of the dynamics of cascading failures in this interdependent system initiated with a random attack. The system robustness is quantified as the surviving fraction of nodes at the end of cascading failures, and is derived in terms of all network parameters involved (e.g., degree distribution, load/capacity distribution, failure size, etc.). Analytic results are supported through an extensive numerical study. Among other things, these results demonstrate the ability of our model to capture the unexpected nature of large-scale failures and provide insights on improving system robustness.

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/ The charred remains of Walter Huang's Tesla Model X.
S. Engleman / NTSB
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about a March crash in Mountain View, California, that claimed the life of engineer Walter Huang. The Model X had its Autopilot driver assistance system engaged, and, according to the NTSB, the car "began a left steering movement" seven seconds before the crash that put it on a collision course with a concrete lane divider. Then, in the last three seconds before the crash, "the Tesla’s speed increased from 62mph to 70.8mph, with no precrash braking or evasive steering movement detected."

This isn't the only recent case where Autopilot steered a Tesla vehicle directly into a stationary object—though thankfully the others didn't get anyone killed. Back in January, firefighters in Culver City, California, said that a Tesla with Autopilot engaged had Shop Online Manchester Great Sale Sale Online Line Printed Stretch Fabric Athletic Shoes BLACK Outlet Best Store To Get hQSMC4RDY
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at 60mph in the suburbs of Salt Lake City.

A natural reaction to these incidents is to assume that there must be something seriously wrong with Tesla's Autopilot system. After all, you might expect that avoiding collisions with large, stationary objects like fire engines and concrete lane dividers would be one of the most basic functions of a car's automatic emergency braking technology.

But while there's obviously room for improvement, the reality is that the behavior of Tesla's driver assistance technology here isn't that different from that of competing systems from other carmakers. As surprising as it might seem, most of the driver-assistance systems on the roads today are simply not designed to prevent a crash in this kind of situation.

Sam Abuelsamid, an industry analyst at Navigant and former automotive engineer, tells Ars that it's "pretty much universal" that "vehicles are programmed to ignore stationary objects at higher speeds."

To understand why these systems behave like this, it's helpful to keep in mind how they evolved. About 20 years ago, carmakers started offering adaptive cruise control systems on their high-end cars. Most of these systems used radar (a few early systems used lasers) to detect the position and speed of the car ahead of it and maintain a safe following distance.

Backbone.sync is the function that Backbone calls every time it attempts to read or save a model to the server. By default, it uses jQuery.ajax to make a RESTful JSON request and returns a jqXHR . You can override it in order to use a different persistence strategy, such as WebSockets, XML transport, or Local Storage.

The method signature of Backbone.sync is sync(method, model, [options])

With the default implementation, when Backbone.sync sends up a request to save a model, its attributes will be passed, serialized as JSON, and sent in the HTTP body with content-type application/json . When returning a JSON response, send down the attributes of the model that have been changed by the server, and need to be updated on the client. When responding to a "read" request from a collection ( Discount New Styles Marc Jacobs small Recruit Nomad bag Sale Fake Sale Deals Really BPxQIVIPk
), send down an array of model attribute objects.

Whenever a model or collection begins a sync with the server, a "request" event is emitted. If the request completes successfully you'll get a "sync" event, and an "error" event if not.

The sync function may be overridden globally as Backbone.sync , or at a finer-grained level, by adding a sync function to a Backbone collection or to an individual model.

The default sync handler maps CRUD to REST like so:

As an example, a Rails 4 handler responding to an "update" call from Backbone might look like this:

One more tip for integrating Rails versions prior to 3.1 is to disable the default namespacing for to_json calls on models by setting ActiveRecord::Base.include_root_in_json = false

ajax Backbone.ajax = function(request) { ... }; If you want to use a custom AJAX function, or your endpoint doesn't support the Outlet With Credit Card Discount 2018 Unisex Pants for Women On Sale Black polyester 2017 XXS IT 36 TwinSet Outlet Best Seller vdHZBiTPHW
API and you need to tweak things, you can do so by setting Backbone.ajax .

emulateHTTP Backbone.emulateHTTP = true If you want to work with a legacy web server that doesn't support Backbone's default REST/HTTP approach, you may choose to turn on Backbone.emulateHTTP . Setting this option will fake PUT , PATCH and DELETE requests with a HTTP POST , setting the X-HTTP-Method-Override header with the true method. If emulateJSON is also on, the true method will be passed as an additional _method parameter.

emulateJSON Backbone.emulateJSON = true If you're working with a legacy web server that can't handle requests encoded as application/json , setting Backbone.emulateJSON = true; will cause the JSON to be serialized under a model parameter, and the request to be made with a application/x-www-form-urlencoded MIME type, as if from an HTML form.

Title: Advanced iterative procedures for solving the implicit Colebrook equation for fluid flow friction
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Subjects: Computational Engineering, Finance, and Science (cs.CE) ; Numerical Analysis (cs.NA)

Empirical Colebrook equation from 1939 is still accepted as an informal standard to calculate friction factor during the turbulent flow through pipes from smooth with almost negligible relative roughness to the very rough inner surface. The Colebrook equation contains flow friction factor in implicit logarithmic form where it is, aside of itself, a function of the Reynolds number Re and the relative roughness of inner pipe surface. To evaluate the error introduced by many available explicit approximations to the Colebrook equation, it is necessary to determinate value of the friction factor from the Colebrook equation as accurate as possible. The most accurate way to achieve that is using some kind of iterative methods. Usually classical approach also known as simple fixed point method requires up to 8 iterations to achieve the high level of accuracy, but does not require derivatives of the Colebrook function as here presented accelerated Householder approach (3rd order, 2nd order: Halley and Schroder method and 1st order: Newton-Raphson) which needs only 3 to 7 iteration and three-point iterative methods which needs only 1 to 4 iteration to achieve the same high level of accuracy. Strategies how to find derivatives of the Colebrook function in symbolic form, how to avoid use of the derivatives (Secant method) and how to choose optimal starting point for the iterative procedure are shown. Householder approach to the Colebrook equations expressed through the Lambert W-function is also analyzed. One approximation to the Colebrook equation based on the analysis from the paper with the error of no more than 0.0617% is shown.

Title: Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition
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Comments: 16 pages, 3 figures
Subjects: Computer Vision and Pattern Recognition (cs.CV)

Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning. Bilinear pooling based models have been shown to be effective at fine-grained recognition, while most previous approaches neglect the fact that inter-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other. In this paper, we present a novel model to address these issues. First, a cross-layer bilinear pooling approach is proposed to capture the inter-layer part feature relations, which results in superior performance compared with other bilinear pooling based approaches. Second, we propose a novel hierarchical bilinear pooling framework to integrate multiple cross-layer bilinear features to enhance their representation capability. Our formulation is intuitive, efficient and achieves state-of-the-art results on the widely used fine-grained recognition datasets.

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