The document outlines an agenda for a math class that includes a warm-up on missed test questions, learning about functions and their domains and ranges, using the vertical line test to determine if a relation is a function, and interpreting graphs. It provides examples of mapping relations to check if they are functions and using the vertical line test. Students are given practice problems to determine if relations are functions and to graph relations and use the vertical line test.
Gilbert: Declarative Sparse Linear Algebra on Massively Parallel Dataflow Sys...Till Rohrmann
In recent years, the generated and collected data is increasing at an almost exponential rate. At the same time, the data’s value has been identified in terms of insights that can be provided. However, retrieving the value requires powerful analysis tools, since valuable insights are buried deep in large amounts of noise. Unfortunately, analytic capacities did not scale well with the growing data. Many existing tools run only on a single computer and are limited in terms of data size by its memory. A very promising solution to deal with large-scale data is scaling systems and exploiting parallelism.
In this presentation, we propose Gilbert, a distributed sparse linear algebra system, to decrease the imminent lack of analytic capacities. Gilbert offers a MATLAB-like programming language for linear algebra programs, which are automatically executed in parallel. Transparent parallelization is achieved by compiling the linear algebra operations first into an intermediate representation. This language-independent form enables high-level algebraic optimizations. Different optimization strategies are evaluated and the best one is chosen by a cost-based optimizer. The optimized result is then transformed into a suitable format for parallel execution. Gilbert generates execution plans for Apache Spark and Apache Flink, two massively parallel dataflow systems. Distributed matrices are represented by square blocks to guarantee a well-balanced trade-off between data parallelism and data granularity.
An exhaustive evaluation indicates that Gilbert is able to process varying amounts of data exceeding the memory of a single computer on clusters of different sizes. Two well known machine learning (ML) algorithms, namely PageRank and Gaussian non-negative matrix factorization (GNMF), are implemented with Gilbert. The performance of these algorithms is compared to optimized implementations based on Spark and Flink. Even though Gilbert is not as fast as the optimized algorithms, it simplifies the development process significantly due to its high-level programming abstraction.
F. Petroni, L. Querzoni, K. Daudjee, S. Kamali and G. Iacoboni:
"HDRF: Stream-Based Partitioning for Power-Law Graphs."
In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015.
Abstract: "Balanced graph partitioning is a fundamental problem that is receiving growing attention with the emergence of distributed graph-computing (DGC) frameworks. In these frameworks, the partitioning strategy plays an important role since it drives the communication cost and the workload balance among computing nodes, thereby affecting system performance.
However, existing solutions only partially exploit a key characteristic of natural graphs commonly found in the
real-world: their highly skewed power-law degree distributions.
In this paper, we propose High-Degree (are) Replicated First (HDRF), a novel streaming vertex-cut graph partitioning algorithm that effectively exploits skewed degree distributions by explicitly taking into account vertex degree in the placement decision. We analytically and experimentally evaluate HDRF on both synthetic and real-world graphs and show that it outperforms all existing algorithms in partitioning quality."
I am Nikita L. I am a Digital Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, University of Alberta, Canada. I have been helping students with their homework for the past 5 years. I solve assignments related to Digital Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Digital Signal Processing Assignments.
Gilbert: Declarative Sparse Linear Algebra on Massively Parallel Dataflow Sys...Till Rohrmann
In recent years, the generated and collected data is increasing at an almost exponential rate. At the same time, the data’s value has been identified in terms of insights that can be provided. However, retrieving the value requires powerful analysis tools, since valuable insights are buried deep in large amounts of noise. Unfortunately, analytic capacities did not scale well with the growing data. Many existing tools run only on a single computer and are limited in terms of data size by its memory. A very promising solution to deal with large-scale data is scaling systems and exploiting parallelism.
In this presentation, we propose Gilbert, a distributed sparse linear algebra system, to decrease the imminent lack of analytic capacities. Gilbert offers a MATLAB-like programming language for linear algebra programs, which are automatically executed in parallel. Transparent parallelization is achieved by compiling the linear algebra operations first into an intermediate representation. This language-independent form enables high-level algebraic optimizations. Different optimization strategies are evaluated and the best one is chosen by a cost-based optimizer. The optimized result is then transformed into a suitable format for parallel execution. Gilbert generates execution plans for Apache Spark and Apache Flink, two massively parallel dataflow systems. Distributed matrices are represented by square blocks to guarantee a well-balanced trade-off between data parallelism and data granularity.
An exhaustive evaluation indicates that Gilbert is able to process varying amounts of data exceeding the memory of a single computer on clusters of different sizes. Two well known machine learning (ML) algorithms, namely PageRank and Gaussian non-negative matrix factorization (GNMF), are implemented with Gilbert. The performance of these algorithms is compared to optimized implementations based on Spark and Flink. Even though Gilbert is not as fast as the optimized algorithms, it simplifies the development process significantly due to its high-level programming abstraction.
F. Petroni, L. Querzoni, K. Daudjee, S. Kamali and G. Iacoboni:
"HDRF: Stream-Based Partitioning for Power-Law Graphs."
In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015.
Abstract: "Balanced graph partitioning is a fundamental problem that is receiving growing attention with the emergence of distributed graph-computing (DGC) frameworks. In these frameworks, the partitioning strategy plays an important role since it drives the communication cost and the workload balance among computing nodes, thereby affecting system performance.
However, existing solutions only partially exploit a key characteristic of natural graphs commonly found in the
real-world: their highly skewed power-law degree distributions.
In this paper, we propose High-Degree (are) Replicated First (HDRF), a novel streaming vertex-cut graph partitioning algorithm that effectively exploits skewed degree distributions by explicitly taking into account vertex degree in the placement decision. We analytically and experimentally evaluate HDRF on both synthetic and real-world graphs and show that it outperforms all existing algorithms in partitioning quality."
I am Nikita L. I am a Digital Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, University of Alberta, Canada. I have been helping students with their homework for the past 5 years. I solve assignments related to Digital Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Digital Signal Processing Assignments.
In this talk we discuss the connections between (Supervised) Learning and Mathematical Optimization. Topics include iterative algorithm, search directions and stepsizes. The talk has been held at the Computer Science, Machine Learning and Statistics Meetup Hamburg.
SINGLE‐PHASE TO THREE‐PHASE DRIVE SYSTEM USING TWO PARALLEL SINGLE‐PHASE RECT...ijiert bestjournal
Now a days digital image processing is rapid emerging field with fast growing
applications in sciences and engineering technologies. Digital image processing has broad
spectrum of applications such as remote sensing, medical processing, radar, sonar,
robotics, sport field and automated processes [1-2]. Edge detection techniques are
employed for the detecting the edges of the primitive picture. Earlier some primitive
methods were used for the image processing. H. C. Andrew et.al. gave the method of
digital image restoration [3-5], A. K. Jain and et.al put forwarded the partial difference
equations and finite differences in image processing [6]. Image process, image models and
estimation regarding the edge detection has been flourished during last decade [7-9]. Most
modules in practical vision system depend, directly or indirectly, on the performance of an
edge detector and digital image processing.
I am Martin J. I am a Digital Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, Arizona University, USA. I have been helping students with their homework for the past 6 years. I solve assignments related to Digital Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Digital Signal Processing Assignments.
Designed to shows the animated view of working of Karnaugh map and logical simulation to the obtained minimized expression of K Map.
The coding of the project is mainly done in OpenGL and C++
In this talk we discuss the connections between (Supervised) Learning and Mathematical Optimization. Topics include iterative algorithm, search directions and stepsizes. The talk has been held at the Computer Science, Machine Learning and Statistics Meetup Hamburg.
SINGLE‐PHASE TO THREE‐PHASE DRIVE SYSTEM USING TWO PARALLEL SINGLE‐PHASE RECT...ijiert bestjournal
Now a days digital image processing is rapid emerging field with fast growing
applications in sciences and engineering technologies. Digital image processing has broad
spectrum of applications such as remote sensing, medical processing, radar, sonar,
robotics, sport field and automated processes [1-2]. Edge detection techniques are
employed for the detecting the edges of the primitive picture. Earlier some primitive
methods were used for the image processing. H. C. Andrew et.al. gave the method of
digital image restoration [3-5], A. K. Jain and et.al put forwarded the partial difference
equations and finite differences in image processing [6]. Image process, image models and
estimation regarding the edge detection has been flourished during last decade [7-9]. Most
modules in practical vision system depend, directly or indirectly, on the performance of an
edge detector and digital image processing.
I am Martin J. I am a Digital Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, Arizona University, USA. I have been helping students with their homework for the past 6 years. I solve assignments related to Digital Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Digital Signal Processing Assignments.
Designed to shows the animated view of working of Karnaugh map and logical simulation to the obtained minimized expression of K Map.
The coding of the project is mainly done in OpenGL and C++
Wave File Features Extraction using Reduced LBP IJECEIAES
In this work, we present a novel approach for extracting features of a digital wave file. This approach will be presented, implemented and tested. A signature or a key to any wave file will be created. This signature will be reduced to minimize the efforts of digital signal processing applications. Hence, the features array can be used as key to recover a wave file from a database consisting of several wave files using reduced Local binary patterns (RLBP). Experimental results are presented and show that The proposed RLBP method is at least 3 times faster than CSLBP method, which mean that the proposed method is more efficient.
Mining at scale with latent factor models for matrix completionFabio Petroni, PhD
PhD Thesis
F. Petroni:
"Mining at scale with latent factor models for matrix completion."
Sapienza University of Rome, 2016.
Abstract: "Predicting which relationships are likely to occur between real-world objects is a key task for several applications. For instance, recommender systems aim at predicting the existence of unknown relationships between users and items, and exploit this information to provide personalized suggestions for items to be of use to a specific user. Matrix completion techniques aim at solving this task, identifying and leveraging the latent factors that triggered the the creation of known relationships to infer missing ones.
This problem, however, is made challenging by the size of today’s datasets. One way to handle such large-scale data, in a reasonable amount of time, is to distribute the matrix completion procedure over a cluster of commodity machines. However, current approaches lack of efficiency and scalability, since, for instance, they do not minimize the communication or ensure a balance workload in the cluster.
A further aspect of matrix completion techniques we investigate is how to improve their prediction performance. This can be done, for instance, considering the context in which relationships have been captured. However, incorporating generic contextual information within a matrix completion algorithm is a challenging task.
In the first part of this thesis, we study distributed matrix completion solutions, and address the above issues by examining input slicing techniques based on graph partitioning algorithms. In the second part of the thesis, we focus on context-aware matrix completion techniques, providing solutions that can work both (i) when the revealed entries in the matrix have multiple values and (ii) all the same value."
ENG3104 Engineering Simulations and Computations Semester 2, 2.docxYASHU40
ENG3104 Engineering Simulations and Computations Semester 2, 2015
Assessment: Assignment 3
Due: 23 October 2015
Marks: 300
Value: 30%
1 (worth 40 marks)
1.1 Introduction
To assess how useful the wind power could be as an energy source, use the file ass2data.xls to
calculate the total energy available in the wind for each year of data.
1.2 Requirements
For this assessment item, you must produce MATLAB code which:
1. Calculates the total energy for each of the years.
2. Reports to the Command Window the energy for each year.
3. Briefly discusses whether there is any trend in the results for annual energy production.
4. Has appropriate comments throughout.
You must also calculate the total energy for the first four hours of power data (i.e. over
the first five data entries) by hand to verify your code; submit this working in a pdf file.
Your MATLAB code must test (verify) whether the computed value of energy is the same as
calculated by hand.
1.3 Assessment Criteria
Your code will be assessed using the following scheme. Note that you are marked based on how
well you perform for each category, so the correct answer determined in a basic way will receive
half marks and the correct answer determined using an excellent method/code will receive full
marks.
Quality of the code 5 marks
Quality of header(s) and comments 5 marks
Quality of calculation of the energy for each year 15 marks
Quality of reporting 5 marks
Quality of discussion 5 marks
Quality of verification based on hand calculations 5 marks
1
ENG3104 Engineering Simulations and Computations Semester 2, 2015
2 (worth 65 marks)
2.1 Introduction
For the wind turbines to operate effectively, they must turn to face into the wind. This could
create large stresses in the structure if the wind changes direction quickly while the wind speed
is high. You are to assess if this is likely to happen using the data in ass2data.xls.
2.2 Requirements
For this assessment item, you must produce MATLAB code which:
1. Calculates the instantaneous rate of change of wind direction using:
(a) backward differences
(b) forward differences
(c) central differences
2. Plots the three sets of derivatives as functions of time.
3. Produces scatter plots of maximum wind gust as functions of each of the derivatives.
4. Displays a message in the Command Window with a brief discussion of the scatter plots.
Discuss which of the derivatives should be used to compare with the wind gust and why.
Discuss whether you think the wind changes direction too quickly while the wind speed
is high and why.
5. Has appropriate comments throughout.
You must also use a backward difference, forward difference and central difference by hand to
determine the rate of change of wind direction for the twelfth data entry; submit this working
in a pdf file. Your MATLAB code must test (verify) whether these values are the same as
computed by the code for the three differences.
2.3 Assessment Criteria
Your code will ...
This paper presents a design and implementation of FPGA based Bose, Chaudhuri and Hocquenghem (BCH) codes for wireless communication applications. The codes are written in VHDL (Very High Speed Hardware Description Language). Here BCH decoder (15, 5, and 3) is implemented and discussed. And decoder uses serial input and serial output architecture. BCH code forms a large class of powerful random error correcting cyclic codes. BCH operates over algebraic structure called finite fields and they are binary multiple error correcting codes. BCH decoder is implemented by syndrome calculation circuit, the BMA (Berlekamp-Massey algorithm) and Chien search circuit. The codecs are implemented over cyclone FPGA device.
Welcome to the Program Your Destiny course. In this course, we will be learning the technology of personal transformation, neuroassociative conditioning (NAC) as pioneered by Tony Robbins. NAC is used to deprogram negative neuroassociations that are causing approach avoidance and instead reprogram yourself with positive neuroassociations that lead to being approach automatic. In doing so, you change your destiny, moving towards unlocking the hypersocial self within, the true self free from fear and operating from a place of personal power and love.
2. Warm-Up:
5 Most Missed from Test 35
Vers. 1;
39%
4
4
V.2;
34%
10. The final part of your landscaping budget went to
building a fish pond and stocking it with lobsters. While
shipping the lobsters, 30% of them died. Now you only
have 14 lobsters left. How many lobsters did you order?
A. 15 B. 20 C. 25 D. 30 E. 40
1. A 32 in. now TV costs $325 after a 13% discount.
What was the cost of the TV before the discount?
A. $358.76 B. $362.49 C. $367.85 D. $373.56 E. $388.76
3. 3
V2
22%
2. A 45 in. TV costs $ 550 after a 60% markup. What is
the wholesale price of the TV?
A. $880.00 B. $220.00 C. $330.00 D. $392.86 E. $343.75
2
V.2;
22%
8. For a science project you used 98 toothpicks, or 28%
of all the toothpicks you own. How many toothpicks do
you have left? A. 350 B. 327 C. 252 D. 197 E. 282
1
V.3;
20%
9. Your uncle Mario is hired to plant the new flowers
and trees. His wage is $6.25/hr. and the planting took 32
hours. What percent of your $8000 budget did you pay
Uncle Mario? A) 2.5% B) .25% C) 12% D) 112.5% E) 25%
5. The Geometry Theorem:
Through any two points in a plane,
there can be only one line.
(Every equation has only one line, every line
has only one equation)
11. The Cost of Renting a Backhoe
y =20x + 50
$50 rental fee plus $20 per hour.
The Process
B. Which one is the independent? The dependent?
Cost, # of hours
12. What is a Function?
A function is a relation that assigns each x-value only one y-value.
What does that mean? It means, in order for the relation to be
considered a function, each domain (x) value can point or be
associated with only one range (y) value.
There are two ways to see if a relation is a function:
1. Mappings
2. Vertical Line Test
……….
14. Mapping to Determine a Function
Create a mapping of the following relation and state
whether or not it is a function.
{(-1,2) ; (1, 2) ; (5, 3) ; (6, 8)}
Notice that even though there
are two 2’s in the range, you
only list the 2 once.
-1
1
5
6
2
3
8
This relation is a function
because each x-value maps to
only one y-value. It is still a
function if two x-values go to
the same y-value.
15. Examples of the Vertical Line Test
function
function
Not a function
Not a function
……….
16. Interpreting the Graph
H
e
i
g
h
t
H
e
i
g
h
t
P
e
o
p
l
e
A. Cost of a laptop, past 10 years B. Drag racing before hitting tree
C. World Population, last 700 years D. Person's height during lifetime
E. 3-point shot F. Running up, then down a hill
C
o
s
t
S
p
e
e
d
S
p
e
e
d
17. 3. Graph the following relations and use the vertical line test to see
if the relation is a function. Connect the pairs in the given order.
a. {(-3,-3) ; (0, 6) ; (3, -3)}
b. {(0,6) ; (3, 3) ; (0, 0)}
2. Use a mapping to see if the following relations are functions:
a. {(-4,-1) ; (-2, 2) ; (3, 1) ; (4, 2)}
b. {(0,-6) ; (1, 2) ; (7, -4) ; (1, 4)}
18. Answers:
1a. Domain: {-4, -2, 3, 4} Range: {-2, 2, 1}
1b. Domain: {0, 1, 7} Range: {-6, 2, -4, 4}
3a. 3b.
2a. 2b.
Function Not a
Function
-4
-2
3
4
-1
2
1
0
1
7
-6
2
-4
4
Not a
Function