•Download as DOCX, PDF•

0 likes•9 views

If Manhattan distances were used in a clustering algorithm, what shape will the clusters take on? Solution Euclidean Distance: 1) A Clustering is performed for all spaces by providing the distance measure. 2) Distance measure means a distance between any two points in the space. 3) The common Euclidean distance(Square root of the sums of the squares of the differences between the coordinates of the points in each dimension) serves for all Euclidean spaces. 4) so some Euclidean distance includes the manhattan distance. Manhattan Distance: 1) Manhattan distance means sum of the magnitudes of the differences in each dimension amd the maximum magnitude of the difference in any dimension. 2) The manhattan distance function computes the distance between that would be traveled to get from one data point to the other if a grid-like path is followed. 3) The Manhattan distance between two items is the sum of the differences of their corresponding components. 4) The formula for this distance between a point X=(X1, X2, etc.) and a point Y=(Y1, Y2, etc.) is: d=|Xi-Yi| Xi and Yi are the values of the ith variable, at points X and Y respectively. 5)If Manhattan distances were used in a clustering algorithm, the shape the clusters take is that the arbitrary-shaped clusters. .

Report

Share

Report

Share

Distance

Distance is a numerical description of how far apart objects are. In mathematics, a distance function or metric describes distance in a generalized way and must satisfy specific rules. There are various ways to define and calculate distance between points, objects, and sets depending on the context, such as Euclidean distance, taxicab distance, or Hausdorff distance. Distance is an important concept in fields like physics, geometry, and graph theory.

Clustering-dendogram.pptx

Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Cluster

clustering tendency

This document discusses methods for determining clustering tendency in datasets. It describes generating clustered and regularly spaced data using the Neyman-Scott and simple sequential inhibition procedures. Three methods for detecting clustering tendency are outlined: tests based on structural graphs like minimum spanning trees, tests based on nearest neighbor distances like Hopkins and Cox-Lewis tests, and a sparse decomposition technique. The document provides details on how these methods work and their relative performance at detecting different patterns in datasets.

Statistical Measures of Location: Mathematical Formulas versus Geometric Appr...

Statistical Measures of Location: Mathematical Formulas versus Geometric Appr...BRNSS Publication Hub

This paper illustrates with an example of the comparison of the geometrical and the numerical approaches
of measures of location. A geometrical derivation of the most popular measure of location (mean) was
derived from a histogram by determining the centroid of a histogram. The numerical or mathematical
expression of the other measures of location, median and mode were derived from ogive and histogram,
respectively. Finally, the research establishes that the two approaches produce the same results.Introduction to mechanics

This document discusses concepts in mechanics including kinematics, dynamics, and statics. It defines key terms like reference frames, position vectors, displacement, average speed, average velocity, and instantaneous acceleration. It also provides examples of determining trajectory, displacement, velocity, and center of mass for systems of particles.

01_AJMS_18_19_RA.pdf

This document discusses different approaches to calculating measures of central tendency (location) from data. It compares the geometric approach of deriving formulas from histograms and ogives to the numerical/mathematical formula approach. Formulas for mean, median, and mode are presented and derived both geometrically and mathematically. The key findings are that the geometric approach can produce the same results as the mathematical formulas and that both approaches are useful for understanding measures of location.

01_AJMS_18_19_RA.pdf

This document discusses different approaches to calculating measures of central tendency (location) from data. It compares the geometric approach of deriving formulas from histograms and ogives to the numerical/mathematical formula approach. Formulas for mean, median, and mode are presented and derived both geometrically and mathematically. The key findings are that the geometric approach can produce the same results as the mathematical formulas and that both approaches are useful for understanding measures of location.

icarsn

This document describes rsnSieve, a MATLAB application that automatically selects resting state networks from fMRI data. It uses independent component analysis to decompose fMRI signals, then applies several processing steps like Pearson's index evaluation, k-means clustering, power spectral analysis to identify and select components corresponding to resting state networks. The selected networks are then used for further analysis.

Distance

Distance is a numerical description of how far apart objects are. In mathematics, a distance function or metric describes distance in a generalized way and must satisfy specific rules. There are various ways to define and calculate distance between points, objects, and sets depending on the context, such as Euclidean distance, taxicab distance, or Hausdorff distance. Distance is an important concept in fields like physics, geometry, and graph theory.

Clustering-dendogram.pptx

Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogramClustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Clustering and dendogram Cluster

clustering tendency

This document discusses methods for determining clustering tendency in datasets. It describes generating clustered and regularly spaced data using the Neyman-Scott and simple sequential inhibition procedures. Three methods for detecting clustering tendency are outlined: tests based on structural graphs like minimum spanning trees, tests based on nearest neighbor distances like Hopkins and Cox-Lewis tests, and a sparse decomposition technique. The document provides details on how these methods work and their relative performance at detecting different patterns in datasets.

Statistical Measures of Location: Mathematical Formulas versus Geometric Appr...

Statistical Measures of Location: Mathematical Formulas versus Geometric Appr...BRNSS Publication Hub

This paper illustrates with an example of the comparison of the geometrical and the numerical approaches
of measures of location. A geometrical derivation of the most popular measure of location (mean) was
derived from a histogram by determining the centroid of a histogram. The numerical or mathematical
expression of the other measures of location, median and mode were derived from ogive and histogram,
respectively. Finally, the research establishes that the two approaches produce the same results.Introduction to mechanics

This document discusses concepts in mechanics including kinematics, dynamics, and statics. It defines key terms like reference frames, position vectors, displacement, average speed, average velocity, and instantaneous acceleration. It also provides examples of determining trajectory, displacement, velocity, and center of mass for systems of particles.

01_AJMS_18_19_RA.pdf

This document discusses different approaches to calculating measures of central tendency (location) from data. It compares the geometric approach of deriving formulas from histograms and ogives to the numerical/mathematical formula approach. Formulas for mean, median, and mode are presented and derived both geometrically and mathematically. The key findings are that the geometric approach can produce the same results as the mathematical formulas and that both approaches are useful for understanding measures of location.

icarsn

This document describes rsnSieve, a MATLAB application that automatically selects resting state networks from fMRI data. It uses independent component analysis to decompose fMRI signals, then applies several processing steps like Pearson's index evaluation, k-means clustering, power spectral analysis to identify and select components corresponding to resting state networks. The selected networks are then used for further analysis.

Heuristic Function Influence to the Global Optimum Value in Shortest Path Pro...

Heuristic Function Influence to the Global Optimum Value in Shortest Path Pro...Universitas Pembangunan Panca Budi

Determination of the optimum route is often encountered in daily life. The purpose of the optimum route itself is to find the best trajectory of the two pairs of vertices contained in a map or graph. The search algorithm applied is A*. This algorithm has the evaluation function to assist the search. The function is called heuristic. Two methods which have been introduced as a step to obtain the value of heuristic function are by using Euclidean and Manhattan distance. Both of these methods create the optimum distance in shortest path problem, but these functions gain the different results. This research performs the development of the heuristic function using Euclidean, Manhattan, Euclidean Square and a new method to compare the results.A COMPARATIVE STUDY ON DISTANCE MEASURING APPROACHES FOR CLUSTERING

Clustering plays a vital role in the various areas of research like Data Mining, Image Retrieval, Bio-computing and many a lot. Distance measure plays an important role in clustering data points. Choosing the right distance measure for a given dataset is a biggest challenge. In this paper, we study various distance measures and their effect on different clustering. This paper surveys existing distance measures for clustering and present a comparison between them based on application domain, efficiency, benefits and drawbacks. This comparison helps the researchers to take quick decision about which distance measure to use for clustering. We conclude this work by identifying trends and challenges of research and development towards clustering.

DOMV No 4 PHYSICAL DYNAMIC MODEL TYPES (1).pdf

This document discusses three physical modeling techniques for dynamic analysis of structures:
1. The lumped-mass procedure simplifies structures by concentrating their mass at discrete points and defining displacements only at those points.
2. The generalized displacement model expresses the deflected shape of a structure as the sum of specified displacement patterns defined by shape functions.
3. The finite-element concept divides structures into elements and expresses displacements in terms of the displacements of nodal points where elements connect, using interpolation functions within each element. All three techniques aim to create a system of differential equations relating mass, damping, stiffness, and external forces.

Massive MIMO and Random Matrix

The document summarizes topics related to massive MIMO and random matrix theory (RMT). It discusses how massive MIMO uses a large number of antennas to focus signal energy into small regions. RMT provides mathematical results for analyzing random matrices arising in wireless communications, such as Gaussian, Wigner, and Wishart matrices. Wireless channel models and capacity formulations are also summarized. The document concludes that RMT is useful for characterizing fundamental limits of wireless channels and converging analysis of very large matrices.

overviewPCA

Principal component analysis (PCA) is a dimensionality reduction technique that identifies important contributing components in big data. It works by transforming the data to a new coordinate system such that the greatest variance by some projection of the data lies on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. Specifically, PCA analyzes the covariance matrix of the data to obtain its eigenvectors and eigenvalues. It then chooses principal components corresponding to the largest eigenvalues, which identify the directions with the most variance in the data.

K means clustering

K-means clustering is an algorithm that groups data points into k clusters based on their attributes and distances from initial cluster center points. It works by first randomly selecting k data points as initial centroids, then assigning all other points to the closest centroid and recalculating the centroids. This process repeats until the centroids are stable or a maximum number of iterations is reached. K-means clustering is widely used for machine learning applications like image segmentation and speech recognition due to its efficiency, but it is sensitive to initialization and assumes spherical clusters of similar size and density.

An_Accelerated_Nearest_Neighbor_Search_Method_for_the_K-Means_Clustering_Algo...

This document summarizes a research paper that proposes a new method to accelerate the nearest neighbor search step of the k-means clustering algorithm. The k-means algorithm is computationally expensive due to calculating distances between data points and cluster centers. The proposed method uses geometric relationships between data points and centers to reject centers that are unlikely to be the nearest neighbor, without decreasing clustering accuracy. Experimental results showed the method significantly reduced the number of distance computations required.

REU_paper

This document presents a novel algorithm for classifying signals (glitches) that arise in gravitational wave channels of the Laser Interferometer Gravitational-Wave Observatory (LIGO). The algorithm uses Kohonen Self Organizing Feature Maps and discrete wavelet transform coefficients to classify glitches based on their morphology and other parameters like signal-to-noise ratio and duration. This low-latency algorithm aims to help the LIGO detector characterization group identify and mitigate noise sources more quickly.

Paper id 21201483

1. The document presents a new approach for steganography detection using a combination of Fisher's linear discriminant function (FLD) and radial basis function neural network (RBF).
2. In the training phase, FLD is used to project high-dimensional image data onto a lower dimensional space, then an RBF network is trained to classify images as containing hidden data or not.
3. Experiments show the combined FLD-RBF method provides promising results for steganography detection compared to existing supervised methods, though extracting the hidden information remains challenging.

Fessant aknin oukhellou_midenet_2001:comparison_of_supervised_self_organizing...

The document compares using Euclidean distance versus Mahalanobis distance for supervised self-organizing maps in a rail defect classification application. It finds that using Mahalanobis distance, which accounts for variance differences across input dimensions, leads to better classification results, especially when using a partitioning approach that separates the multi-class problem into binary sub-problems. Specifically, the Mahalanobis distance improved classification rates from 92.8% to 94.1% for a global classifier, and from 90% to 95% for a partitioning classifier.

Three rotational invariant variants of the 3SOME algorithms

This document presents three variants of the Three Stage Optimal Memetic Exploration (3SOME) algorithm for handling non-separable fitness landscapes: Rotation Invariant Shrinking 3SOME (RIS-3SOME), Micro-Population Differential Evolution 3SOME (μDE-3SOME), and 3SOME with 1+1 Covariance Matrix Adaptation Evolution Strategy ((1+1)CMA-ES-3SOME). Experiments on BBOB benchmark functions show that all three variants improve upon the original 3SOME algorithm's performance on non-separable problems without degrading performance on separable problems. The simplest variant, RIS-3SOME, showed results comparable to the more complex variants.

Introduction fea

This lecture provides an introduction to finite element analysis (FEA). It discusses the basic concepts of FEA, including dividing a complex object into simple finite elements and using polynomial terms to describe field quantities within each element. The lecture covers the history and applications of FEA, as well as the basic procedure, which involves meshing a structure into elements, describing element behavior, assembling elements at nodes, solving the system of equations, and calculating results. It also reviews matrix algebra concepts needed for FEA. Finally, it presents the simple example of a spring element and spring system to demonstrate the finite element modeling process.

Paper id 21201488

This document proposes a new method to remove the dependence of fuzzy c-means clustering on random initialization. The conventional fuzzy c-means algorithm's performance is highly dependent on the randomly initialized membership values used to select initial centroids. The proposed method uses an algorithm by Yuan et al. to determine initial centroids without randomization. These centroids are then used as inputs to the conventional fuzzy c-means algorithm. The performance of the proposed method is compared to conventional fuzzy c-means using partition coefficient and clustering entropy validity indices. Results show the proposed method produces more consistent and better performance by removing the effect of random initialization.

The International Journal of Engineering and Science (The IJES)

This document summarizes a research paper that proposes a novel approach to improving the k-means clustering algorithm. The standard k-means algorithm is computationally expensive and produces results that depend heavily on the initial centroid selection. The proposed approach determines initial centroids systematically and uses a heuristic to efficiently assign data points to clusters. It improves both the accuracy and efficiency of k-means clustering by ensuring the entire process takes O(n2) time without sacrificing cluster quality.

The International Journal of Computational Science, Information Technology an...

The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.

Cluster Analysis: Measuring Similarity & Dissimilarity

Dissimilarity of Numeric Data: Minskowski Distance Euclidean
distance and Manhattan distance Proximity Measures for Categorical,
Ordinal Attributes, Ratio scaled variables; Dissimilarity for Attributes
of Mixed Types, Cosine Similarity, partitioning methods- k-means, kmedoids

Paper id 26201482

This document summarizes a research paper that designed and implemented sphere decoding (SD) for multiple-input multiple-output (MIMO) systems using an FPGA. It used Newton's iterative method to calculate the matrix inverse as part of the SD algorithm, which reduces complexity compared to direct matrix inversion. The authors implemented SD for a 2x2 MIMO system with 4-QAM modulation. Simulation results showed that Newton's method converged after 7 iterations, and SD successfully calculated the minimum Euclidean distance vector.

Analysis and algebra on differentiable manifolds

This chapter discusses integration on manifolds. It defines orientation of manifolds and orientation-preserving maps between manifolds. It presents Stokes' theorem and Green's theorem, which relate integrals over boundaries to integrals of differential forms. It introduces de Rham cohomology groups, which classify closed forms modulo exact forms. Examples are given of calculating the orientability and cohomology of manifolds like the cylinder, Möbius strip, and real projective plane.

Eucluidian and Non eucluidian space in Tensor analysis.Non Euclidian space

The document discusses Euclidean geometry and how it differs from non-Euclidean geometry. In Euclidean geometry, objects can be moved without deformation, compared using congruence and similarity, and parallel lines never meet. Non-Euclidean geometry describes spaces like spheres where parallel lines intersect and the sum of angles in a triangle is greater than 180 degrees. The document also introduces different coordinate systems used to describe geometric spaces, including Cartesian, cylindrical, and spherical coordinate systems. It discusses the properties of vectors in these systems and the concepts of covariant and contravariant vectors.

Project_report_BSS

This document summarizes a student project on blind source separation of audio signals. The student was able to recover three independent audio source signals from three mixtures with over 99.97% accuracy. Blind source separation is the separation of source signals from mixed signals without information about the sources or mixing process. It has applications in areas like speech processing. The student acknowledges help from advisors and friends. They provide background on blind source separation and describe their methodology, results, and references.

Identify which of the following is NOT a TCP-IP Attack- Question 5 opt.docx

Identify which of the following is NOT a TCP/IP Attack:
Question 5 options:
a) SQL Injection
b) Source Routing Attack
c) IP Spoofing Attack
d) SYN Flood Attack
The software-based access control that identifies data items that require different types of protection is:
Question 8 options:
a) encryption
b) need to know
c) internal security labeling
d) integrity checking
A point of presence system that analyzes network traffic to detect leaking data is:
Question 19 options:
a) a data loss prevention system
b) an intrusion detection system
c) an intrusion prevention system
d) a DMZ
Solution
5) a) SQL Injection
8) c) internal security labeling
19) a) a data loss prevention system
.

Identifying Matter- Physical Properties (Section 1-4 15- The elements.docx

Identifying Matter: Physical Properties (Section 1.4 15. The elements sulfur and bromine are shown in the photo- graph. Based on the photograph, describe as many proper tiles of each sample as you can. Are any properties the samme? Which properties are different? Sulfur and bromine. The sulfur is on the flat dish; the bro- mine is in a closed flask. ou see a crystal of the min-
Solution
Physical form -
Sulfur is in solid form whereas Bromine is a mixture of liquid and vapour form.
No similarity is present. This property is completely different
Color -
Sulfur is light yellow color whereas Bromine liquid and vapour both has dark orangish color.
No similarity is present. This property is completely different.
Volatility -
sulphur is not volatile and kept open in the atmosphere whereas bromine liquid is volatile and kept in a closed flask otherwise all Bromine liquid will convert to Bromine vapour and this vapour will dissipate in atmosphere.
No similarity is present. This property is completely different.
.

Heuristic Function Influence to the Global Optimum Value in Shortest Path Pro...

Heuristic Function Influence to the Global Optimum Value in Shortest Path Pro...Universitas Pembangunan Panca Budi

Determination of the optimum route is often encountered in daily life. The purpose of the optimum route itself is to find the best trajectory of the two pairs of vertices contained in a map or graph. The search algorithm applied is A*. This algorithm has the evaluation function to assist the search. The function is called heuristic. Two methods which have been introduced as a step to obtain the value of heuristic function are by using Euclidean and Manhattan distance. Both of these methods create the optimum distance in shortest path problem, but these functions gain the different results. This research performs the development of the heuristic function using Euclidean, Manhattan, Euclidean Square and a new method to compare the results.A COMPARATIVE STUDY ON DISTANCE MEASURING APPROACHES FOR CLUSTERING

Clustering plays a vital role in the various areas of research like Data Mining, Image Retrieval, Bio-computing and many a lot. Distance measure plays an important role in clustering data points. Choosing the right distance measure for a given dataset is a biggest challenge. In this paper, we study various distance measures and their effect on different clustering. This paper surveys existing distance measures for clustering and present a comparison between them based on application domain, efficiency, benefits and drawbacks. This comparison helps the researchers to take quick decision about which distance measure to use for clustering. We conclude this work by identifying trends and challenges of research and development towards clustering.

DOMV No 4 PHYSICAL DYNAMIC MODEL TYPES (1).pdf

This document discusses three physical modeling techniques for dynamic analysis of structures:
1. The lumped-mass procedure simplifies structures by concentrating their mass at discrete points and defining displacements only at those points.
2. The generalized displacement model expresses the deflected shape of a structure as the sum of specified displacement patterns defined by shape functions.
3. The finite-element concept divides structures into elements and expresses displacements in terms of the displacements of nodal points where elements connect, using interpolation functions within each element. All three techniques aim to create a system of differential equations relating mass, damping, stiffness, and external forces.

Massive MIMO and Random Matrix

The document summarizes topics related to massive MIMO and random matrix theory (RMT). It discusses how massive MIMO uses a large number of antennas to focus signal energy into small regions. RMT provides mathematical results for analyzing random matrices arising in wireless communications, such as Gaussian, Wigner, and Wishart matrices. Wireless channel models and capacity formulations are also summarized. The document concludes that RMT is useful for characterizing fundamental limits of wireless channels and converging analysis of very large matrices.

overviewPCA

Principal component analysis (PCA) is a dimensionality reduction technique that identifies important contributing components in big data. It works by transforming the data to a new coordinate system such that the greatest variance by some projection of the data lies on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. Specifically, PCA analyzes the covariance matrix of the data to obtain its eigenvectors and eigenvalues. It then chooses principal components corresponding to the largest eigenvalues, which identify the directions with the most variance in the data.

K means clustering

K-means clustering is an algorithm that groups data points into k clusters based on their attributes and distances from initial cluster center points. It works by first randomly selecting k data points as initial centroids, then assigning all other points to the closest centroid and recalculating the centroids. This process repeats until the centroids are stable or a maximum number of iterations is reached. K-means clustering is widely used for machine learning applications like image segmentation and speech recognition due to its efficiency, but it is sensitive to initialization and assumes spherical clusters of similar size and density.

An_Accelerated_Nearest_Neighbor_Search_Method_for_the_K-Means_Clustering_Algo...

This document summarizes a research paper that proposes a new method to accelerate the nearest neighbor search step of the k-means clustering algorithm. The k-means algorithm is computationally expensive due to calculating distances between data points and cluster centers. The proposed method uses geometric relationships between data points and centers to reject centers that are unlikely to be the nearest neighbor, without decreasing clustering accuracy. Experimental results showed the method significantly reduced the number of distance computations required.

REU_paper

This document presents a novel algorithm for classifying signals (glitches) that arise in gravitational wave channels of the Laser Interferometer Gravitational-Wave Observatory (LIGO). The algorithm uses Kohonen Self Organizing Feature Maps and discrete wavelet transform coefficients to classify glitches based on their morphology and other parameters like signal-to-noise ratio and duration. This low-latency algorithm aims to help the LIGO detector characterization group identify and mitigate noise sources more quickly.

Paper id 21201483

1. The document presents a new approach for steganography detection using a combination of Fisher's linear discriminant function (FLD) and radial basis function neural network (RBF).
2. In the training phase, FLD is used to project high-dimensional image data onto a lower dimensional space, then an RBF network is trained to classify images as containing hidden data or not.
3. Experiments show the combined FLD-RBF method provides promising results for steganography detection compared to existing supervised methods, though extracting the hidden information remains challenging.

Fessant aknin oukhellou_midenet_2001:comparison_of_supervised_self_organizing...

The document compares using Euclidean distance versus Mahalanobis distance for supervised self-organizing maps in a rail defect classification application. It finds that using Mahalanobis distance, which accounts for variance differences across input dimensions, leads to better classification results, especially when using a partitioning approach that separates the multi-class problem into binary sub-problems. Specifically, the Mahalanobis distance improved classification rates from 92.8% to 94.1% for a global classifier, and from 90% to 95% for a partitioning classifier.

Three rotational invariant variants of the 3SOME algorithms

This document presents three variants of the Three Stage Optimal Memetic Exploration (3SOME) algorithm for handling non-separable fitness landscapes: Rotation Invariant Shrinking 3SOME (RIS-3SOME), Micro-Population Differential Evolution 3SOME (μDE-3SOME), and 3SOME with 1+1 Covariance Matrix Adaptation Evolution Strategy ((1+1)CMA-ES-3SOME). Experiments on BBOB benchmark functions show that all three variants improve upon the original 3SOME algorithm's performance on non-separable problems without degrading performance on separable problems. The simplest variant, RIS-3SOME, showed results comparable to the more complex variants.

Introduction fea

This lecture provides an introduction to finite element analysis (FEA). It discusses the basic concepts of FEA, including dividing a complex object into simple finite elements and using polynomial terms to describe field quantities within each element. The lecture covers the history and applications of FEA, as well as the basic procedure, which involves meshing a structure into elements, describing element behavior, assembling elements at nodes, solving the system of equations, and calculating results. It also reviews matrix algebra concepts needed for FEA. Finally, it presents the simple example of a spring element and spring system to demonstrate the finite element modeling process.

Paper id 21201488

This document proposes a new method to remove the dependence of fuzzy c-means clustering on random initialization. The conventional fuzzy c-means algorithm's performance is highly dependent on the randomly initialized membership values used to select initial centroids. The proposed method uses an algorithm by Yuan et al. to determine initial centroids without randomization. These centroids are then used as inputs to the conventional fuzzy c-means algorithm. The performance of the proposed method is compared to conventional fuzzy c-means using partition coefficient and clustering entropy validity indices. Results show the proposed method produces more consistent and better performance by removing the effect of random initialization.

The International Journal of Engineering and Science (The IJES)

This document summarizes a research paper that proposes a novel approach to improving the k-means clustering algorithm. The standard k-means algorithm is computationally expensive and produces results that depend heavily on the initial centroid selection. The proposed approach determines initial centroids systematically and uses a heuristic to efficiently assign data points to clusters. It improves both the accuracy and efficiency of k-means clustering by ensuring the entire process takes O(n2) time without sacrificing cluster quality.

The International Journal of Computational Science, Information Technology an...

The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.

Cluster Analysis: Measuring Similarity & Dissimilarity

Dissimilarity of Numeric Data: Minskowski Distance Euclidean
distance and Manhattan distance Proximity Measures for Categorical,
Ordinal Attributes, Ratio scaled variables; Dissimilarity for Attributes
of Mixed Types, Cosine Similarity, partitioning methods- k-means, kmedoids

Paper id 26201482

This document summarizes a research paper that designed and implemented sphere decoding (SD) for multiple-input multiple-output (MIMO) systems using an FPGA. It used Newton's iterative method to calculate the matrix inverse as part of the SD algorithm, which reduces complexity compared to direct matrix inversion. The authors implemented SD for a 2x2 MIMO system with 4-QAM modulation. Simulation results showed that Newton's method converged after 7 iterations, and SD successfully calculated the minimum Euclidean distance vector.

Analysis and algebra on differentiable manifolds

This chapter discusses integration on manifolds. It defines orientation of manifolds and orientation-preserving maps between manifolds. It presents Stokes' theorem and Green's theorem, which relate integrals over boundaries to integrals of differential forms. It introduces de Rham cohomology groups, which classify closed forms modulo exact forms. Examples are given of calculating the orientability and cohomology of manifolds like the cylinder, Möbius strip, and real projective plane.

Eucluidian and Non eucluidian space in Tensor analysis.Non Euclidian space

The document discusses Euclidean geometry and how it differs from non-Euclidean geometry. In Euclidean geometry, objects can be moved without deformation, compared using congruence and similarity, and parallel lines never meet. Non-Euclidean geometry describes spaces like spheres where parallel lines intersect and the sum of angles in a triangle is greater than 180 degrees. The document also introduces different coordinate systems used to describe geometric spaces, including Cartesian, cylindrical, and spherical coordinate systems. It discusses the properties of vectors in these systems and the concepts of covariant and contravariant vectors.

Project_report_BSS

This document summarizes a student project on blind source separation of audio signals. The student was able to recover three independent audio source signals from three mixtures with over 99.97% accuracy. Blind source separation is the separation of source signals from mixed signals without information about the sources or mixing process. It has applications in areas like speech processing. The student acknowledges help from advisors and friends. They provide background on blind source separation and describe their methodology, results, and references.

Heuristic Function Influence to the Global Optimum Value in Shortest Path Pro...

Heuristic Function Influence to the Global Optimum Value in Shortest Path Pro...

A COMPARATIVE STUDY ON DISTANCE MEASURING APPROACHES FOR CLUSTERING

A COMPARATIVE STUDY ON DISTANCE MEASURING APPROACHES FOR CLUSTERING

DOMV No 4 PHYSICAL DYNAMIC MODEL TYPES (1).pdf

DOMV No 4 PHYSICAL DYNAMIC MODEL TYPES (1).pdf

Massive MIMO and Random Matrix

Massive MIMO and Random Matrix

overviewPCA

overviewPCA

K means clustering

K means clustering

An_Accelerated_Nearest_Neighbor_Search_Method_for_the_K-Means_Clustering_Algo...

An_Accelerated_Nearest_Neighbor_Search_Method_for_the_K-Means_Clustering_Algo...

REU_paper

REU_paper

Paper id 21201483

Paper id 21201483

Fessant aknin oukhellou_midenet_2001:comparison_of_supervised_self_organizing...

Fessant aknin oukhellou_midenet_2001:comparison_of_supervised_self_organizing...

Three rotational invariant variants of the 3SOME algorithms

Three rotational invariant variants of the 3SOME algorithms

Introduction fea

Introduction fea

Paper id 21201488

Paper id 21201488

The International Journal of Engineering and Science (The IJES)

The International Journal of Engineering and Science (The IJES)

The International Journal of Computational Science, Information Technology an...

The International Journal of Computational Science, Information Technology an...

Cluster Analysis: Measuring Similarity & Dissimilarity

Cluster Analysis: Measuring Similarity & Dissimilarity

Paper id 26201482

Paper id 26201482

Analysis and algebra on differentiable manifolds

Analysis and algebra on differentiable manifolds

Eucluidian and Non eucluidian space in Tensor analysis.Non Euclidian space

Eucluidian and Non eucluidian space in Tensor analysis.Non Euclidian space

Project_report_BSS

Project_report_BSS

Identify which of the following is NOT a TCP-IP Attack- Question 5 opt.docx

Identify which of the following is NOT a TCP/IP Attack:
Question 5 options:
a) SQL Injection
b) Source Routing Attack
c) IP Spoofing Attack
d) SYN Flood Attack
The software-based access control that identifies data items that require different types of protection is:
Question 8 options:
a) encryption
b) need to know
c) internal security labeling
d) integrity checking
A point of presence system that analyzes network traffic to detect leaking data is:
Question 19 options:
a) a data loss prevention system
b) an intrusion detection system
c) an intrusion prevention system
d) a DMZ
Solution
5) a) SQL Injection
8) c) internal security labeling
19) a) a data loss prevention system
.

Identifying Matter- Physical Properties (Section 1-4 15- The elements.docx

Identifying Matter: Physical Properties (Section 1.4 15. The elements sulfur and bromine are shown in the photo- graph. Based on the photograph, describe as many proper tiles of each sample as you can. Are any properties the samme? Which properties are different? Sulfur and bromine. The sulfur is on the flat dish; the bro- mine is in a closed flask. ou see a crystal of the min-
Solution
Physical form -
Sulfur is in solid form whereas Bromine is a mixture of liquid and vapour form.
No similarity is present. This property is completely different
Color -
Sulfur is light yellow color whereas Bromine liquid and vapour both has dark orangish color.
No similarity is present. This property is completely different.
Volatility -
sulphur is not volatile and kept open in the atmosphere whereas bromine liquid is volatile and kept in a closed flask otherwise all Bromine liquid will convert to Bromine vapour and this vapour will dissipate in atmosphere.
No similarity is present. This property is completely different.
.

Identifying aclds and bases by their reaction with water Kri Some chem.docx

Identifying aclds and bases by their reaction with water Kri Some chemical compounds are listed in the first column of the table below. Each compound is soluble in water. Imagine that a few tenths of a mole of each compound is dissolved in a liter of water. The important chemical species that would be present in this solution are written in the second column of the table. Use the checkboxes to classify each compound. type of compound (check all that apply) important species present when dissolved in water compound strong weak strong weak acid acid basebase ionic mol KCI K CI, H,0 NH, HNO ?? NH OH NH, H20 H,o\'. C,H,0
Solution
The species that give off their H+ readily are strong acids and those who dissociate partially are called weak acids.
The species that give off OH- ions readily are strong bases and those who dissociate partially are weak bases.
The ionic compounds have ionic bonds between the ions , whereas molecular compounds do not.
So here :
KCl = ionc ,
NH3 = molecular , weak base
HNO2 = molecular , weak acid
HCl = molecular , strong acid
.

Identify the two primary sources of stockholders equity- and which sou.docx

Identify the two primary sources of stockholders equity, and which source would be considered to be internally generated?
Solution
Stockholders equity is built on two parts
Paid Up capital: When initially shares are issued in an IPO to the public the stockholder\'s equity has two parts i.e. face value of shares and the additional paid up capital i.e. price at which share was sold in the IPO less the face value.
Retained Earnings: Retained earnings form the second component of the Stockholder\'s equity. Each year a part of Net Income might be paid off by the company as dividend to preferred and common share holders. The amount left after paying dividends is called retained earning and is added to stock holder\'s equity.
Retianed earnings are the internally generated source of stockholder\'s equity becasue it comes from the earnings of the organization which are generated by selling products and not by accessing capital markets.
.

If you had a system- that had large amounts of daily data inflows- tha.docx

If you had a system, that had large amounts of daily data inflows, that you were running
queries on yearly data, how would you position Flume,Hive and Impala for this system
Solution
As we have system that have large amount of daily data inflows, and we also running queries on yearly data, in this case its important to collect and analyze the data. This collection of logs of the data is being done by Flume and Hive. This is why Flume and hive lots of importance in the system where we are dealing with large amount of data. We required to collect the logs because of following reasons:
1. In error scenario, if we have proper logs then its easy to go to the root cause of the problem.
2. These logs provides the proper picture of the system, its in\'s and out\'s of the system.
Flume is basically a opne-source software used for log collection. Its also can be used for montioring the log files, by installing it to various host machines. Hive integration can be done with Flume to get more features.
Impala is also used for data analytic\'s and its also open-source to use.
.

If two points are in a common reference frame- then a They are not in.docx

If two points are in a common reference frame, then
a They are not in motion relative to each other.
They are not accelerating relative to each other.
They are in the same gravitational field.
They must be the same point.
Solution
the motion has to be described with respect to a reference frame
Now in the given scenario if both the particles are in the same frame of reference, it may be possible that both of them are moving with respect to the frame of reference.
With the given options, it is most likely that they are not moving relative to each other. (a)
any information about the nature of reference frame could have been more useful.
.

Identify at least two types of system architectures that work well in.docx

Identify at least two types of system architectures that work well in a hierarchical arrangement.
Solution
Two types of system architectures that work well in a hierarchical arrangement are
1) Centralized Architecture
=> Hierarchy is built into its system by default. All clients are connect to a centralized server, defaulting in a hierarchical setup. The applications within the server/mainframe would need to have a command structure whereby the clients would be given distributed access to the varying components of the application. This command component could simply be a protocol, but nevertheless, would sit above the rest of the structure creating an application hierarchy, as well as hardware.
2) Client/Server Architecture
=> The client/server setup where system is splitted into clients and servers. Thus server is on top distributing data as per requests from the clients below. There is a single server or a cluster of servers which services multiple clients, creating an intrinsic hierarchical architecture.
.

Identify and prioritize information assets- Identify and prioritize th.docx

Identify and prioritize information assets. Identify and prioritize threats to information assets. Choose a company (any company that interests you) to use as your company (You may also use your personal system as your company). You will assume the role of the Chief Security Officer of the company. You will identify all of your security assets. You will then identify and prioritize the threats to those assets. See NIST Special Publication 800-26 for guidance in doing the self-assessment.
Solution
In information security, computer security and network security an Asset is any data, device, or other component of the environment that supports information-related activities. Assets generally include hardware (e.g. servers and switches), software (e.g. mission critical applications and support systems) and confidential information.Assets should be protected from illicit access, use, disclosure, alteration, destruction, and/or theft, resulting in loss to the organization.
.

If the Android platform you are developing for does not support fragme.docx

If the Android platform you are developing for does not support fragments, how would you go about providing fragment-like processing in you Android Application ?
Hints: Would you have multiply activities? Which UI elements (controls/widgets) would you use?
Solution
A Fragment represents a behavior or a portion of user interface in an Activity.. One big reason to stick with the SupportFragment for a while is that you do not have access to the ChildFragmentManager until API17.
Instead of creating fragments we can use by custom views for mutliple display. Letâ€™s implementation of fragments in views. First, weâ€™ll have the notion of a Container, which can show an item and also handle back presses.
The activity assumes thereâ€™s always a container and merely delegates the work to it.
The list is also quite trivial.
Now, the meat of the work: loading different XML layouts based on resource qualifiers.
Using custom views works great, but we would like to isolate business logic into dedicated controllers. we call those parameters. This makes the code much more readable and facilitates testing. MyDetailView could look something like this
Make your fragments shells of themselves. Pull view code up into custom view classes, and push business logic down into a presenter that knows how to interact with the custom views. Then, your fragment is nearly empty, just inflating custom views that connect themselves with presenters:
At that point you can eliminate the fragment
.

E13-7 On January 1- 2010- the stockholders-' equity section of Nunez C.docx

E13-7 On January 1, 2010, the stockholders\' equity section of Nunez Corporation shows: Common stock ($5 par value) $1,500,000; paid-in capital in excess of par value $1,000,000; and re tained earnings $1,200,000. During the year, the following treasury stock transactions occurred. Mar. 1 Purchased 50,000 shares for cash at $15 per share. July 1 Sold 10,000 treasury shares for cash at $17 per share. Sept. I Sold 8,000 treasury shares for cash at $14 per share. Instructions (a) Journalize the treasury stock transactions.
Solution
Journalize the treasury stock transactions. (For multiple debit/credit entries, list amounts from largest to smallest eg 10, 5, 3, 2.)
Date Account/Description
Mar.1
Dr Treasury stock 750,000 (50,000 x $15)
Cr Cash 750,000
July 1
Dr Cash 170,000
Cr Treasury stock 150,000 (10,000 at COST of $15)
Cr Paid-in cap. from treasury stock 20,000
At this point your Paid-in cap. from treasury stock stands at $20,000 (credit).
Sept. 1
Dr Cash 112,000 (8,000 x $14)
Dr Paid-in cap. from treasury stock 8,000
Cr Treasury stock 120,000 (8,000 at COST of $15)
.

E 4- Elijah Samuels and Tony Winslow agreed to form a partnership- Sam.docx

E 4. Elijah Samuels and Tony Winslow agreed to form a partnership. Samuels contributed $200,000 in cash, and Winslow contributed assets with a fair market value of $400,000. The partnership, in its initial year, reported net income of $120,000. Calculate the distribution of the first year\'s income to the partners under each of the following conditions: 1. Samuels and Winslow failed to include stated ratios in the partnership agreement. 2. Samuels and Winslow agreed to share income and losses in a 3:2 ratio. 3. Samuels and Winslow agreed to share income and losses in the ratio of their original investments. 4. Samuels and Winslow agreed to share income and losses by allowing 10 percent interest on original investments and sharing any remainder equally.
Solution
1. When there is no stated ratio then profit distributed equally
Share of Samuels = 120000x1/2= 60000
Share of winslow= 120000x1/2 = 60000
2. Share of Samuels=Â Â 120000x 3/5 = 72000
Share of winslow= 120000x2/5 = 48000
3. Value of investments= Samuels (200000) + Winslow (400000)= 600000
Share of samuels = 120000x200000/600000 = 40000
Share of winslow = 120000x 400000/600000= 80000
4. Income after interest on investments = 120000- 600000x10%= 60000
Share of samuels = 60000x1/2 = 30000
Share of winslow = 60000x1/2 =30000
.

During October- Wichita Light Company experiences the following transa.docx

During October, Wichita Light Company experiences the following transactions in establishing a petty cash fund.
Journalize the entries in October that pertain to the petty cash fund. (Credit account titles are automatically indented when amount is entered. Do not indent manually.)
Date
Account Titles and Explanation
Debit
Credit
Oct. 1
Oct. 31
During October, Wichita Light Company experiences the following transactions in establishing a petty cash fund.Journalize the entries in October that pertain to the petty cash fund. (Credit account titles are automatically indented when amount is entered. Do not indent manually.)
Solution
Petty Cash Fund
Business generally keeps small amounts of cash to meet small obligations such as emergency supplies, small delivery charges etc. This is called petty cash fund. The person who is responsible for maintaining the petty cash fund, documentation of disbursements made, and reconciling the petty cash is called
.

During a total lunar eclipse- in which the moon passes entirely into t.docx

During a total lunar eclipse, in which the moon passes entirely into the shadow of the earth as cast by the sun, the moon is not completely dark, but is often a deep red in color. The shade of red varies widely from one total eclipse to the other. Why?
1. During the sunset, the Sun transfers some red light to the moon.
2. The earth acts like a giant water drop because of its oceans, and the moon winds up
3. None of the worldâ€™s sacred holy books discuss this situation, so it must be forever a total mystery.
4. Low frequencies pass more easily through the long grazing path through the Earthâ€™s atmosphere to be refracted finally onto the moon.
5. High frequencies pass more easily through the long grazing path through the Earthâ€™s atmosphere to be refracted finally onto the moon.
Solution
4) Some light from the sun passes through Earth\'s atmosphere and is bent toward the moon. Low frequencies (red is having low frequency and highest wavelength) pass more easily through the long grazing path through the Earthâ€™s atmosphere to be refracted finally onto the moon.
.

During 2011- Arthur Corportation reported a net income of $3-059-000-.docx

During 2011, Arthur Corportation reported a net income of $3,059,000. On January 1, Arthur had 2,800,000 shares of common stock outstanding. The company issued an additional 1,680,000 shares of common stock on October 1. In 2011, the company had a simple capital structure. During 2012, there were no transactions involving common stock, and the company reported net income of $4,032,000.
1. Determine the weighted-average number of common shares outstanding each year.
2. Compute earnings per shre for each year.
Solution
Answer :
1. weighted-average number of common shares outstanding each year.
Note :
= (3/4 * 2,800,000) +(1/4 *1,680,000) = 2,100,000 + 420,000 = 2,520,000
In year 2012 new shares are not purchased hence (Opening balance * 1) = 4,480,000
2. earnings per shre for each year
Note :
earnings per share = Net Income / Weighted Average No of outstanding shares
.

Driving School has 4 learning centres- The network of each learning ce.docx

Driving School has 4 learning centres. The network of each learning centre is a LAN of its own. Network Cheap is contracted to install network connections between the learning centres. Network Cheap proposed to use RIP version 2. As the CEO of Driving School, will you accept this proposal? If you do not accept the proposal, what would you use for the routing protocol? Give your reasons.
Solution
IGRPâ€”Interior Gateway Routing Protocol , EIGRPâ€”Enhanced Interior Gateway Routing Protocol or OSPF V2â€”Open Shortest Path First came to a mind but RIP V2â€”Routing Information Protocol is a good choice
The characteristics of RIPv2 follow:
Distance-vector protocol.
Uses UDP port 520.
Classless protocol (support for CIDR).
Supports VLSMs.
Metric is router hop count.
Maximum hop count is 15; infinite (unreachable) routes have a metric of 16.
Periodic route updates sent every 30 seconds to multicast address 224.0.0.9.
25 routes per RIP message (24 if you use authentication).
Supports authentication.
Implements split horizon with poison reverse.
Implements triggered updates.
Subnet mask included in route entry.
Administrative distance for RIPv2 is 120.
Used in small, flat networks or at the edge of larger networks.
IGRP Converges slowly infact slower than RIP and for IGRP , OSPF or EIGRP all routers must be from Cisco Systems ( big constraint)
.

Donnie Hilfiger has two classes of stock authorized- S1 par preferred.docx

Donnie Hilfiger has two classes of stock authorized: S1 par preferred and S0.01 par value common. As of the beginning of 2015, 200 shares of preferred stock and 3,900 shares of common stock have been issued. The following transactions affect stockholders\' equity during 2015: March 1 Issues 1,000 shares of common stock for $41 per share. May 15 Repurchases 600 shares of treasury stock for S34 per share. July 10 Reissues 100 shares of treasury stock purchased on May 15 for $39 per share. October 15 Issues 100 shares of preferred stock for $44 per share. December 1 Declares a cash dividend on both common and preferred stock of SO.55 per share to all stockholders of record on December 15. (Hint Dividends are not paid on treasury stock.) December 31 Pays the cash dividends declared on December 1. Donnie Hilfiger has the following beginning balances in its stockholders\' equity accounts on January 1, 2015: Preferred Stock, S200; Common Stock, S39; Additional Paid-in Capital, $75,000; and Retained Earnings, $30,000. Net income for the year ended December 31, 2015, is $10,700. Taking into consideration all the transactions during 2015, respond to the following for Donnie Hilfiger: 4 .56 points Prepare the statement of stockholders\' equity for the year ended December 31, 2015. (Amounts to be deducted should be indicated by a minus sign.)
Solution
Par Value = 1000*0.01 = 10
Additional Paid in Capital = 40990
Par Value = 100
Additional Paid in Capital = 4300
.

Doris Washington recently assumed her new position as HR Director at t.docx

Doris Washington recently assumed her new position as HR Director at the XYZ Company, a software development firm with approximately 350 employees. Her staff includes a seasoned generalist as well as a benefits clerk. During her first 90 days on the job, Doris has noted the following issues that seem to be embedded in XYZ\'s culture: 1) managers create their own interview questions; 2) for some employees, their compensation is simply determined by managers, not based on training, experience, geographic region, and so on; and, 3) the company seems to draw new employees from only two sources, one is the university from which the President of the company graduated.
While Doris has been contemplating these trends, the CEO has asked her to attend a strategic planning meeting about opening the company\'s first location on in Toronto, Canada in approximately nine months. She needs to provide a recommendation to the CEO that can be used appropriately.
Instructions:
Solution
1.
Four potential issues faced by Doris as a HR director are listed below:
2.
Key issue: The Company does not follow a standard process for hiring the employees as managers of the company uses their own set of interview questions.
This would be one of the most difficult problems for the company, because uneven hiring process might result in hiring of incompetent employees. In future, such employees would not be able to contribute towards the company
.

Doppler Effect- True or False John is listening to a horn- He knows th.docx

Doppler Effect: True or False John is listening to a horn. He knows the frequency of the horn is 300 Hz when both he and the horn are at rest. If he hears a pitch of 330 Hz, there are clearly several possibilities. For each of the following statements, indicate whether it is correct or incorrect. Both can be moving and have different speeds, Both can be moving and have the same speed John and the horn are both moving in the same direction but John is behind and moving faster than the horn. Both can be moving, in opposite directions. John is moving towards the horn at rest. The distance between John and the horn is increasing with time. Submit Answer Tries 0/10
Solution
QUESTION:
A) John is moving towards the horn at rest.
B) Both can be moving and have the same speed.
C) Both can be moving and have different speeds.
D) Both can be moving, in opposite directions.
E) The distance between John and the horn is increasing with time.
F) Both cannot be moving in the same direction.
ANSWER:
A) True, when John moves towards the horn, it is the same as if he stood still and the horn moved towards him.
B) True, if they are moving toward each other at the right speed, John would hear more waves per unit time, thus a higher frequency.
C) True, the horn could be getting closer to John.
D) True, same as B, they can approach each other to achieve this effect.
E) False, it must be decreasing to hear a higher frequency.
F) False, they could be. If both are moving and the horn is catching up to John, this effect could be achieved.
.

DQ #4 Explain the US Government Thrift Savings Plans-SolutionThe US Go.docx

DQ #4 Explain the US Government Thrift Savings Plans.
Solution
The US Government Thrift Savings Plans is a defined-contribution plan designed to give federal employees the same retirement savings related benefits which will be equal to that of workers in the private sector enjoy with 401(k) plans. Contributions to these plans are automatically deducted from each paycheck of federal employees.
The thrift savings plan offers six different funds (government security fund, fixed-income fund, common stock fund, small cap stock fund, international stock fund and a life cycle fund) in which federal employees can invest.
Because the thrift savings plan is based on tax-deferred contributions, any contributions made into it will not be taxed until the money is withdrawn, which can be deferred until retirement.
Other Benefits of these US Government Thrift Savings Plans includes 1) agency matching contributions 2) agency automatic contributions 3) catch up contributions and 4) low expense ratios.
.

Identify which of the following is NOT a TCP-IP Attack- Question 5 opt.docx

Identify which of the following is NOT a TCP-IP Attack- Question 5 opt.docx

Identifying Matter- Physical Properties (Section 1-4 15- The elements.docx

Identifying Matter- Physical Properties (Section 1-4 15- The elements.docx

Identifying aclds and bases by their reaction with water Kri Some chem.docx

Identifying aclds and bases by their reaction with water Kri Some chem.docx

Identify the two primary sources of stockholders equity- and which sou.docx

Identify the two primary sources of stockholders equity- and which sou.docx

If you had a system- that had large amounts of daily data inflows- tha.docx

If you had a system- that had large amounts of daily data inflows- tha.docx

If two points are in a common reference frame- then a They are not in.docx

If two points are in a common reference frame- then a They are not in.docx

Identify at least two types of system architectures that work well in.docx

Identify at least two types of system architectures that work well in.docx

Identify and prioritize information assets- Identify and prioritize th.docx

Identify and prioritize information assets- Identify and prioritize th.docx

If the Android platform you are developing for does not support fragme.docx

If the Android platform you are developing for does not support fragme.docx

E13-7 On January 1- 2010- the stockholders-' equity section of Nunez C.docx

E13-7 On January 1- 2010- the stockholders-' equity section of Nunez C.docx

E 4- Elijah Samuels and Tony Winslow agreed to form a partnership- Sam.docx

E 4- Elijah Samuels and Tony Winslow agreed to form a partnership- Sam.docx

During October- Wichita Light Company experiences the following transa.docx

During October- Wichita Light Company experiences the following transa.docx

During a total lunar eclipse- in which the moon passes entirely into t.docx

During a total lunar eclipse- in which the moon passes entirely into t.docx

During 2011- Arthur Corportation reported a net income of $3-059-000-.docx

During 2011- Arthur Corportation reported a net income of $3-059-000-.docx

Driving School has 4 learning centres- The network of each learning ce.docx

Driving School has 4 learning centres- The network of each learning ce.docx

Donnie Hilfiger has two classes of stock authorized- S1 par preferred.docx

Donnie Hilfiger has two classes of stock authorized- S1 par preferred.docx

Doris Washington recently assumed her new position as HR Director at t.docx

Doris Washington recently assumed her new position as HR Director at t.docx

Doppler Effect- True or False John is listening to a horn- He knows th.docx

Doppler Effect- True or False John is listening to a horn- He knows th.docx

DQ #4 Explain the US Government Thrift Savings Plans-SolutionThe US Go.docx

DQ #4 Explain the US Government Thrift Savings Plans-SolutionThe US Go.docx

The History of Stoke Newington Street Names

Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com

How to Fix the Import Error in the Odoo 17

An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.

Advantages and Disadvantages of CMS from an SEO Perspective

Advantages and Disadvantages of CMS from an SEO Perspective

June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...

Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202

MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...

In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.

A Independência da América Espanhola LAPBOOK.pdf

Lapbook sobre independência da América Espanhola.

What is the purpose of studying mathematics.pptx

Students often ask about what the purpose is for their learning. This PowerPoint highlights some really important reasons to study Mathematics.

Lapbook sobre os Regimes Totalitários.pdf

Lapbook sobre o Totalitarismo.

How to Build a Module in Odoo 17 Using the Scaffold Method

Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.

clinical examination of hip joint (1).pdf

described clinical examination all orthopeadic conditions .

Introduction to AI for Nonprofits with Tapp Network

Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.

A Strategic Approach: GenAI in Education

Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.

A Survey of Techniques for Maximizing LLM Performance.pptx

A Survey of Techniques for Maximizing LLM Performance

How to Add Chatter in the odoo 17 ERP Module

In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.

PCOS corelations and management through Ayurveda.

This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.

Main Java[All of the Base Concepts}.docx

This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.

Assignment_4_ArianaBusciglio Marvel(1).docx

Market Analysis Marvel entertainment.

Biological Screening of Herbal Drugs in detailed.

Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines

The History of Stoke Newington Street Names

The History of Stoke Newington Street Names

How to Fix the Import Error in the Odoo 17

How to Fix the Import Error in the Odoo 17

Advantages and Disadvantages of CMS from an SEO Perspective

Advantages and Disadvantages of CMS from an SEO Perspective

June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...

June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...

MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...

MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...

A Independência da América Espanhola LAPBOOK.pdf

A Independência da América Espanhola LAPBOOK.pdf

MARY JANE WILSON, A “BOA MÃE” .

MARY JANE WILSON, A “BOA MÃE” .

What is the purpose of studying mathematics.pptx

What is the purpose of studying mathematics.pptx

Lapbook sobre os Regimes Totalitários.pdf

Lapbook sobre os Regimes Totalitários.pdf

How to Build a Module in Odoo 17 Using the Scaffold Method

How to Build a Module in Odoo 17 Using the Scaffold Method

clinical examination of hip joint (1).pdf

clinical examination of hip joint (1).pdf

Introduction to AI for Nonprofits with Tapp Network

Introduction to AI for Nonprofits with Tapp Network

A Strategic Approach: GenAI in Education

A Strategic Approach: GenAI in Education

The basics of sentences session 5pptx.pptx

The basics of sentences session 5pptx.pptx

A Survey of Techniques for Maximizing LLM Performance.pptx

A Survey of Techniques for Maximizing LLM Performance.pptx

How to Add Chatter in the odoo 17 ERP Module

How to Add Chatter in the odoo 17 ERP Module

PCOS corelations and management through Ayurveda.

PCOS corelations and management through Ayurveda.

Main Java[All of the Base Concepts}.docx

Main Java[All of the Base Concepts}.docx

Assignment_4_ArianaBusciglio Marvel(1).docx

Assignment_4_ArianaBusciglio Marvel(1).docx

Biological Screening of Herbal Drugs in detailed.

Biological Screening of Herbal Drugs in detailed.

- 1. If Manhattan distances were used in a clustering algorithm, what shape will the clusters take on? Solution Euclidean Distance: 1) A Clustering is performed for all spaces by providing the distance measure. 2) Distance measure means a distance between any two points in the space. 3) The common Euclidean distance(Square root of the sums of the squares of the differences between the coordinates of the points in each dimension) serves for all Euclidean spaces. 4) so some Euclidean distance includes the manhattan distance. Manhattan Distance: 1) Manhattan distance means sum of the magnitudes of the differences in each dimension amd the maximum magnitude of the difference in any dimension. 2) The manhattan distance function computes the distance between that would be traveled to get from one data point to the other if a grid-like path is followed. 3) The Manhattan distance between two items is the sum of the differences of their corresponding components. 4) The formula for this distance between a point X=(X1, X2, etc.) and a point Y=(Y1, Y2, etc.) is: d=|Xi-Yi| Xi and Yi are the values of the ith variable, at points X and Y respectively. 5)If Manhattan distances were used in a clustering algorithm, the shape the clusters take is that the arbitrary-shaped clusters.