This document describes a sample assignment to simulate and optimize the range of a simplified trebuchet model. The model predicts projectile range based on the fulcrum position and launch angle. It defines variables for the launch angle and fulcrum position, evaluates the projectile range for combinations of these variables using a treb function, and plots the range as a surface over the variable values. The overall goal is to illustrate how optimization of design variables can maximize the projectile range for a trebuchet.
Meta learned Confidence for Few-shot LearningKIMMINHA3
This was presented Meta learned Confidence for Few-shot Learning on CVPR in 2020.
Few-shot learning is an important challenge under data scarcity.
When there is a lot of unlabeled data and data scarcity,
a) leveraging nearest neighbor graph
b) using predicted soft or hard labels on unlabeled samples to update the class prototype.
the model confidence may be unreliable, which may lead to incorrect predictions.
Meta learned Confidence for Few-shot LearningKIMMINHA3
This was presented Meta learned Confidence for Few-shot Learning on CVPR in 2020.
Few-shot learning is an important challenge under data scarcity.
When there is a lot of unlabeled data and data scarcity,
a) leveraging nearest neighbor graph
b) using predicted soft or hard labels on unlabeled samples to update the class prototype.
the model confidence may be unreliable, which may lead to incorrect predictions.
Interactive fault localization leveraging simple user feedback - by Liang GongLiang Gong
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Video Key-Frame Extraction using Unsupervised Clustering and Mutual ComparisonCSCJournals
Key-frame extraction is one of the important steps in semantic concept based video indexing and retrieval and accuracy of video concept detection highly depends on the effectiveness of keyframe extraction method. Therefore, extracting key-frames efficiently and effectively from video shots is considered to be a very challenging research problem in video retrieval systems. One of many approaches to extract key-frames from a shot is to make use of unsupervised clustering. Depending on the salient content of the shot and results of clustering, key-frames can be extracted. But usually, because of the visual complexity and/or the content of the video shot, we tend to get near duplicate or repetitive key-frames having the same semantic content in the output and hence accuracy of key-frame extraction decreases. In an attempt to improve accuracy, we proposed a novel key-frame extraction method based on unsupervised clustering and mutual comparison where we assigned 70% weightage to color component (HSV histogram) and 30% to texture (GLCM), while computing a combined frame similarity index used for clustering. We suggested a mutual comparison of the key-frames extracted from the output of the clustering where each key-frame is compared with every other to remove near duplicate keyframes. The proposed algorithm is both computationally simple and able to detect non-redundant and unique key-frames for the shot and as a result improving concept detection rate. The efficiency and effectiveness are validated by open database videos.
Interactive fault localization leveraging simple user feedback - by Liang GongLiang Gong
Many fault localization methods have been proposed in the literature. These methods take in a set of program execution profiles and output a list of suspicious program elements. The list of program elements ranked by their suspiciousness is then presented to developers for manual inspection. Currently, the suspicious elements are ranked in a batch process where developers' inspection efforts are rarely utilized for ranking. The inaccuracy and static nature of existing fault localization methods prompt us to incorporate user feedback to improve the accuracy of the existing methods. In this paper, we propose an interactive fault localization framework that leverages simple user feedback. Our framework only needs users to label the statements examined as faulty or clean, which does not require additional effort than conventional non-interactive methods. After users label suspicious program elements as faulty or clean, our framework incorporates such information and re-orders the rest of the suspicious program elements, aiming to expose truly faulty elements earlier. We have integrated our solution with three well-known fault localization methods: Ochiai, Tarantula, and Jaccard. The evaluation on five Unix programs and the Siemens test suite shows that our solution achieves significant improvements on fault localization accuracy.
Accessing non static members from the mainTutors On Net
In this presentation we will discuss about the ways of accessing nonstatic
members and calling non-static functions from the static main
method which is the entry point of any Java class
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Review : A Probabilistic U-Net for Segmentation of Ambiguous Images
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
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EuroSTAR Software Testing Conference 2010 presentation on From Model Driven Testing to Test Driven Modelling by Darius Silingas. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
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Single object range detection
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Trebuchet Range Simulation and Optimization
This sample assignment shows a Model of a simple trebuchet that predicts projectile range based on
fulcrum position & launch angle.
plot_treb.m
% Plot the projectile range for a simplified trebuchet over a
range of
% design variable values. Illustrates the nonsmoothness in
objective
% function that results from discrete events in the
simulation.
%
% Model corresponds to an example video created for ME
149 (Engineering
% System Design Optimization), a graduate course taught
in the mechanical
% engineering department at Tufts University. The video
can be viewed at:
%
% http://www.youtube.com/watch?v=3QUJNEzloDo
%
% Author: James T. Allison, Ph.D.
clear
% Define points for full-factorial numerical sampling
x1 = pi/2:0.1/2:pi; % theta_r
x2 = 0.6:0.005:0.99; % l1
range = zeros(length(x1),length(x2));
for i=1:length(x1)
for j=1:length(x2)
% evaluate projectile range
info@assignmentpedia.com
2. range(i,j) = treb([x1(i),x2(j)]);
end
end
% Plot range as a function of release angle and fulcrum
position
figure(2);
surf(x2,x1,range);
xlabel('l1 (m)')
ylabel('theta_r (rad)')
zlabel('range (m)')
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