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Vector Control and the
"... showed welcome progress in the fight against the world’s most important vectorborne disease. In the last 10 years, the estimated incidence of malaria has fallen by 17 % globally, with malariaspecific mortality rates reduced by 25%. Central to these gains, especially in Africa, has been the massiv ..."
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Cited by 1 (0 self)
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the massive scaleup of chemical insecticide interventions against malaria mosquito vectors. Current malaria vector control relies almost exclusively on killing adult mosquitoes with chemical insecticides deployed as either insecticidetreated nets (ITNs) or indoor residual sprays (IRS). However
VECTOR CONTROL
"... Malaria vector control is primarily insecticide based. It relies on indoor residual spraying (IRS) of houses and the distribution of longlasting insecticidetreated bednets (LLINs). Only twelve insecticides from four different chemical classes (organochlorines, organophosphates, pyrethroids, and ca ..."
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Malaria vector control is primarily insecticide based. It relies on indoor residual spraying (IRS) of houses and the distribution of longlasting insecticidetreated bednets (LLINs). Only twelve insecticides from four different chemical classes (organochlorines, organophosphates, pyrethroids
The Vector Field Histogram  Fast Obstacle Avoidance For Mobile Robots
 IEEE JOURNAL OF ROBOTICS AND AUTOMATION
, 1991
"... A new realtime obstacle avoidance method for mobile robots has been developed and implemented. This method, named the vector field histogram(VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. The VFH method uses a ..."
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Cited by 471 (23 self)
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A new realtime obstacle avoidance method for mobile robots has been developed and implemented. This method, named the vector field histogram(VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. The VFH method uses
A Framework for Uplink Power Control in Cellular Radio Systems
 IEEE Journal on Selected Areas in Communications
, 1996
"... In cellular wireless communication systems, transmitted power is regulated to provide each user an acceptable connection by limiting the interference caused by other users. Several models have been considered including: (1) fixed base station assignment where the assignment of users to base stations ..."
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Cited by 633 (18 self)
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power control problem can be reduced to finding a vector p of users' transmitter powers satisfying p I(p) where the jth constraint p j I j (p) describes the interference that user j must overcome to achieve an acceptable connection. This work unifies results found for these systems
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
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Cited by 456 (46 self)
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In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set
The program dependence graph and its use in optimization
 ACM Transactions on Programming Languages and Systems
, 1987
"... In this paper we present an intermediate program representation, called the program dependence graph (PDG), that makes explicit both the data and control dependence5 for each operation in a program. Data dependences have been used to represent only the relevant data flow relationships of a program. ..."
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Cited by 990 (3 self)
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computationally related parts of the program, a single walk of these dependences is sufficient to perform many optimizations. The PDG allows transformations such as vectorization, that previously required special treatment of control dependence, to be performed in a manner that is uniform for both control
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 769 (29 self)
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propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length
Multicast Routing in Datagram Internetworks and Extended LANs
 ACM Transactions on Computer Systems
, 1990
"... Multicasting, the transmission of a packet to a group of hosts, is an important service for improving the efficiency and robustness of distributed systems and applications. Although multicast capability is available and widely used in local area networks, when those LANs are interconnected by store ..."
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Cited by 1075 (5 self)
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andforward routers, the multicast service is usually not offered across the resulting internetwork. To address this limitation, we specify extensions to two common internetwork routing algorithmsdistancevector routing and linkstate routingto support lowdelay datagram multicasting beyond a single LAN. We also
A ReExamination of Text Categorization Methods
, 1999
"... This paper reports a controlled study with statistical significance tests on five text categorization methods: the Support Vector Machines (SVM), a kNearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a NaiveBayes (NB) classifier. We f ..."
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Cited by 832 (24 self)
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This paper reports a controlled study with statistical significance tests on five text categorization methods: the Support Vector Machines (SVM), a kNearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a NaiveBayes (NB) classifier. We
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