Elements of Effective Communication

Communication is central to the success of human beings and organizations. The ability to effectively communicate at work, home and in life is probably one of the most important sets of skills a person needs. Effective communication is not just a business skill-it is a life skill and the most important source of personal power at work, family and social situations. Communication is the process of understanding and being understood through ideas, facts, thoughts and emotions. Good communication is determined not by how well we say things but by how well we have been understood. (www.careerindia.com) Communication is a process by which message is conveyed to someone or a group of people. If the message is conveyed clearly and unambiguously, and is received by the receiver in the same way as intended by the sender, then communication is said to be effective. If the message reaches the receiver in a distorted form or somehow fails to create meaning or understanding, the communicator should realize that his/her communication has been affected by barriers. So we can say that communication becomes successful only if the receiver understands what the sender is trying to convey. The feedback received by the sender from the receiver allows the sender to determine how the message was interpreted and, if necessary, whether there is an opportunity to modify future messages. A careful communicator will remember that “to effectively communicate, we must realize that we are all different in the way we perceive the world and use this understanding as a guide to our communication with other.” An effective communicator anticipates the unlimited ways a message can be understood or misunderstood. Thus the communicator must not only take care of his/her message but also he/she has to keep his/her audience’s background in mind to ensure his/her communication receives desired feedback and his/her communication goal is achieved. In this context, John Powells’s observations sound sensible: “Communication works for those who work at it.” You can be sure of your communication skills when you get the ability to act and react quickly at a subconscious level

Genome-wide association study of phytic acid in wheat grain unravels markers for improving biofortification

Biofortification of cereal grains offers a lasting solution to combat micronutrient deficiency in developing countries where it poses developmental risks to children. Breeding efforts thus far have been directed toward increasing the grain concentrations of iron (Fe) and zinc (Zn) ions. Phytic acid (PA) chelates these metal ions, reducing their bioavailability in the digestive tract. We present a high-throughput assay for quantification of PA and its application in screening a breeding population. After extraction in 96-well megatiter plates, PA content was determined from the phosphate released after treatment with a commercially available phytase enzyme. In a set of 330 breeding lines of wheat grown in the field over 3 years as part of a Harvest Plus breeding program for high grain Fe and Zn, our assay unraveled variation for PA that ranged from 0.90 to 1.72% with a mean of 1.24%. PA content was not associated with grain yield. High yielding lines were further screened for low molar PA/Fe and PA/Zn ratios for increased metal ion bioavailability, demonstrating the utility of our assay. Genome-wide association study revealed 21 genetic associations, six of which were consistent across years. Five of these associations mapped to chromosomes 1A, 2A, 2D, 5A, and 7D. Additivity over four of these haplotypes accounted for an ∼10% reduction in PA. Our study demonstrates it is possible to scale up assays to directly select for low grain PA in forward breeding programs

Real-Time Object Detection for Smart Vehicles

This paper presents an efficient shape-based object detection method based on Distance Transforms and describes its use for real-time vision on-board vehicles. The method uses a template hierarchy to capture the variety of object shapes# efficient hierarchies can be generated offline for given shape distributions using stochastic optimization techniques (i.e. simulated annealing) . Online, matching involves a simultaneous coarse-to-fine approach over the shape hierarchy and over the transformation parameters. Very large speedup factors are typically obtained when comparing this approach with the equivalent brute-force formulation# we have measured gains of several orders of magnitudes. We present experimental results on the real-time detection of traffic signs and pedestrians from a moving vehicle. Because of the highly time sensitive nature of these vision tasks, we also discuss some hardwarespecific implementations of the proposed method as far as SIMD parallelism is concerned

Multi-stage Sampling with Boosting Cascades for Pedestrian Detection in Images and Videos

Many works address the problem of object detection by means of machine learning with boosted classifiers. They exploit sliding window search, spanning the whole image: the patches, at all possible positions and sizes, are sent to the classifier. Several methods have been proposed to speed up the search (adding complementary features or using specialized hardware). In this paper we propose a statisticalbased search approach for object detection which uses a Monte Carlo sampling approach for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multi-stage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifier (i.e. its response). For videos, this approach is plugged in a Bayesian-recursive framework which exploits the temporal coherency of the pedestrians. Several tests on both still images and videos on common datasets are provided in order to demonstrate therelevant speedup and the increased localization accuracy with respect to sliding window strategy using a pedestrian classifier based on covariance descriptors and a cascade of Logitboost classifiers

Multi-stage Sampling with Boosting Cascades for Pedestrian Detection in Images and Videos

Many works address the problem of object detection by means of machine learning with boosted classifiers. They exploit sliding window search, spanning the whole image: the patches, at all possible positions and sizes, are sent to the classifier. Several methods have been proposed to speed up the search (adding complementary features or using specialized hardware). In this paper we propose a statisticalbased search approach for object detection which uses a Monte Carlo sampling approach for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multi-stage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifier (i.e. its response). For videos, this approach is plugged in a Bayesian-recursive framework which exploits the temporal coherency of the pedestrians. Several tests on both still images and videos on common datasets are provided in order to demonstrate therelevant speedup and the increased localization accuracy with respect to sliding window strategy using a pedestrian classifier based on covariance descriptors and a cascade of Logitboost classifiers