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TIM having the vision of "to render the change is to design the future" acts to insure the coordination between the public and private sector organizations and exporters and decision-makers.
TIM working with the objective of being a solution center for the export issues establishes policies towards the development of the export in addition to representation duty.
Umbrella organization of Turkey's export
Turkish Exporters Assembly (TIM) as the umbrella organization of the exporting firms around 83,000 was established in 1993. It continues its studies on legitimate basis as the professional organization of the firms involved in foreign trade. TIM organized as a general secretary organization as being the upper organization of the associations of exporters is represented by the Chairman.
The primary reason of existence of TIM is to determine the issues related to the export, conduct improvement studies, and to insure the transfer of the problems of the exporters to the highest competent authorities.
Representative of Exporters
TIM attempts considerably to negotiate with the upper management authorities from the international fairs in order to expand the export vision. The renovations and developments related to the world markets are followed closely; strategies are developed to insure the effective involvement of Turkish companies in international studies; and coordination is provided.
In this framework, a considerable part of the studies is towards he academic activities conducted with the universities and export sectors, the prepared reports are shared with the business sphere.
The Board of Directors of TIM conducts studies related to the improvement of exports, prepares reports, and gives opinions and advices.
Upper Organization to 61 exporter associations
Each of the exporter firms should be a member to a association in the sector and region. The exporter firms around 83 thousand acting in 27 separate sectors in which export records are maintained conduct foreign trade transactions on 61 exporter associations organized in region and sector-wide. Such 61 associations are collected under 13 general secretaryships. Turkish Exporters Assembly is the upper organization of 13 general secretaryship and exporter associations.
TIM's managerial organs
TIM's headquarters is in Istanbul. The organs of TIM are General Assembly, Council of Sectors, Board of Directors, Board of Advisors, and General Secretaryship. TIM representing exporters in domestic and abroad is established on the structure of the General Assembly consisted of the managements of the exporter associations.
Democratic Management
TIM represents all exporters in all levels through the most democratic elections. It establishes and operates the required mechanisms for the involvement of the members in the decision processes.
It provides contact and coordination between the exporters' associations and sectors.
EXPORTER ASSOCIATIONS
The subjects including Informing the exporter firms of the developments in the foreign trade, statistics and reporting studies, organizing mutual exchange committees with various countries are included in the job definitions of the exporter associations.
It provides online service to the members of the exporter associations thanks to its strong technological infrastructure.
Producer cells in the countrywide
Exporter Associations are established by sector and regional classifications to conduct studies related to the foreign trade, to provide occupational ethic and solidarity between the exporters. Exporter Associations established in Turkey-wide in 11 different cities are organized in 27 different sectors.
Managerial structure of the Associations
The associations of exporters are managed by the board of director elected in the General Assemblies convened once in four years. The Board of Directors consists of an exporter firm's representative, a chairman, and ten permanent members. As the exporters are required to be member to the association related to the profession, the exporter firm being a member to one of the associations will be entitled to export the goods under the area of the other associations without being a member in the relevant association.
Effective service and well-built coordination
Exporter associations conduct studies in a wide framework from the protection of the rights of the exporter firms in legal sense through being explorer in the new market development efforts.
Lobby Activities
International relations are of great importance to expand the vision of the exports for Turkey. Strategies are developed in order to achieve active involvement of Turkish firms in the international activities, and coordination is provided between the exporters.
TIM claiming that export based commercial relations is the cornerstone of a world of peace serves for the establishment of world peace through the projects and establishment of relationships at international levels.
Defender of the exporter rights
TIM as the upper organization and the representative of the exporters is the pursuer of their rights in each area. Intensive lobby activity is conducted for the solution of the problems of the producer exporters in intellectual property and industrial ownership rights in the triangle of design, production and marketing triangle at international level. TIM conducts studies such as informing the decision-makers in domestic about the benefits of the exporters and making suggestions on the increase of the value added to the economy of the country.
Worldwide representative of the brand "Turkey"
It searches for solutions through negotiations with the authorities from the state presidency to commercial attaché in the subjects including the promotion of the Turkey which has become a brand itself through its strong production structure and sturdy steps in each area, development of new markets, and elimination of the problems experienced in the current markets.
500 BILLION US DOLLAR EXPORT IN YEAR 2023
The Republic of Turkey plans to reach 500 billion dollar for exports and 1 trillion dollar for foreign trade volume in 2023 as the hundredth year of its foundation.
Turkish economy which shall increase export level to quintuple in thirteen years re-designs its strategy in order to produce high value added products.
To be among Top 10 economies in 2023
Turkey as the 17th largest economy in the world aims at being among the Top 10 economies in the globe in 2023 as the 100th year of its foundation. Turkey plans, designs, and works for increasing exports to 500 billion US Dollar and foreign trade volume to 1 trillion dollar.
Correct Strategy, high performance
Turkey has established "2023 Turkey Export Strategy" according to this target. As the first step of the Project conducted in the sectoral dimensions, it is aimed to determine Turkey's export strategy till 2023 in macro level, adapting it for execution, and performance management. Strategy map and the framework of performance program are drawn as the output of the project through which the export vision is evaluated in three five-year periods.
Investing in high value added areas
We, as TIM, aim at being explorer through the data network established by us by promoting the innovation-based studies, for the exporter firms investing in design and R&D continuously.
Statistical Studies
The country reports based on economic, social and political analysis about the target markets at the base of the foreign trade strategies of Turkey are prepared and publicized.
Any and all legal information related to export may be accessed by consulting with TIM and its experienced legal team.
Databank of the export records
According to the recorded figures of the exporter associations , the export of Turkey related to 27 sectors are analyzed monthly, yearly and comparatively under the main groups "agriculture", "manufacturing" and "mining". Annual export reports are prepared in the nature of the data source of Turkish exports.
The statistical progress in exports are followed immediately and publicized in electronic environment.
R&D INNOVATION
Reports to improve exports
TIM prepares country reports based on economic, social and political analysis about the target markets in the basis of the foreign trade strategies of Turkey.
In addition, macroeconomic outlook reports are prepared monthly quarterly, and annually in order to monitor the macro economic developments such as foreign trade and inflation.
FOEREIGN TRADE CONSULTANCY
Multi-dimensional support is provided to our exporter firms in order to provide information about Brand/ license laws, representation contracts, consumer laws, customs laws and similar regulations.
Guideway to Exports
TIM Foreign Trade Consultancy Service provides information to exporter firms on foreign trade laws and the solutions of the disputes arisen from international trade and investments. It provides consultancy service in the areas including the collection of export values, logistics and transportation law, insurance laws, banking regulations, legal, administrative, ad financial aspects in the Foreign Trade Organization and the European Union Regulations.
In order to play trade in accordance with the applicable rules
TIM aims at providing information on the subjects which may be required by the exporters and increasing legal knowledge of the firms about export through the support of the legal firms as the solution partners of TIM as being the umbrella organization in exports defined as the locomotive for Turkish economy.


Why is this the "Ultimate Presentation Leader" for the successful Saltwater Sport Angler?
Stealthy Hybrid Leaders: start with a thin Nickel Titanium (NiTi) wire leader by Aquateko which is; Knottable, Toothproof, Kink resistant, Non-corrosive, and features a Static Shock absorption. It is then spliced to a length of Seaguar Premier Fluorocarbon Shock leader, strength matched to the wire, and provides a Dynamic Shock resistance. Next there is an matched Fluorocarbon Invisaswivel, also by Aquateko. The Invisaswivel can be placed anywhere inline within the length of the Fluorocarbon Shock Leader by the angler for his/her desired preference and fishing technique. This Hybrid design makes for a very Lightweight, and a Highly Sensitive leader giving you the advantage of Stealth and Finesse.
NOTE: "Smoker Lengths" 45 lb – 75 lb are not in production at this time. Anticipated availability May 2017.


Today, DJI released the latest drone to the Phantom series: the DJI Phantom 4. DJI took the world by storm with affordable drones for filming and photography, but ever since, they have had other companies hot on their tail in an effort to dominate the market. The Phantom 4 has taken a giant leap forward in consumer drone technology, bringing with it AI and advanced autopilot systems.
For drone enthusiasts, taking to the skies always comes with anticipation. Drones have been known for 'fly aways' and crashing them is all too easy. DJI have taken this onboard, implanting five cameras into the drone to observe both around and beneath the unit. Its AI systems help avoid collisions by stopping and hovering or swerving out the way to keep your drone safe.
The additional onboard cameras also reduce the reliability on GPS signals, increasing accuracy in both indoor and obscured areas.
Intelligent tracking systems have been introduced to allow you to hand over the controls to the drone itself. Tap on your smartphone's screen where you want to go and the drone will fly there safely. You can even select objects for it to track and follow (with no need for the tracked object to be wearing a beacon).
Battery life is another big thing, with the original DJI Phantom model only lasting up to 10 minutes in the sky. Well, the Phantom 4 will last an impressive 28 minutes before you run out of power.
The build is also more aerodynamic and robust, giving greater speeds and allowing you to perform more daring feats. The body is actually built with magnesium, reducing vibrations caused by the previous plastic design. The gimbal also holds the camera with two arms instead of one, further stabilizing the camera for perfectly still shots. The propellers are positioned higher this time, keeping them out of your shots.
The camera has been improved, shooting 4K at 30 frames per second, as well as 1080p at 120 fps. This is great for slow motion and action shots.
The Phantom 4 is flexible and brings with it multiple flight modes. In Position mode, you can use satellite and Vision Positioning, TapFly, ActiveTrack and Intelligent Flight Modes. Sport mode adds extra agility and speed, reaching 45mph (72kph) while maintaining satellite and Vision Positioning systems. Atti holds the Phantom's altitude and level but switches off satellite positioning, making it ideal for smooth filmmaking movements.
The DJI Phantom 4 is available now on the DJI website for $1,399.
Will Nicholls is a professional wildlife photographer and film-maker from the UK. He is the founder of Nature TTL, a nature photography blog filled with tutorials, inspirational features and kit reviews. You can download his free eBook: 10 Top Tips to INSTANTLY Improve Your Nature Photos.
It looks as if I may have to delve into a little drone photography. 3x battery power makes it more enticing!
The drone industry is just getting started too!
I fully expect a review from Anthony Thurston.
His dreams of indoor drone boudoir work may finally come true.


Treesaw carried out tree pruning, tree removals, stump removals and stump treatment within the grounds of Kirkstall Abbey in March 2015. The work was required to address overhanging branches restricting light from nearby lamp columns reaching the highway and to reduce the risk from trees requiring urgent remedial works. A truck mounted crane was used to dismantle some of the trees and transport the arising material to a designated landing area.
Treesaw liaised with Leeds City Council Highways, Network Management to ensure that appropriate traffic management systems of work were in place. These included the work being undertaken at night between 8.00pm and 5.00am to reduce disruption to road users on the A65, a contraflow and a two way traffic light system.
Particular attention was given to carrying out surveys for the presence of bat roosts before any work commenced. This was done with a preliminary scoping survey and again by the climbing arborist before work to each tree commenced.


text:
Tighter controls on entry into Gibraltar as virus spreads
STOCK PHOTO. Pic: Pixabay/leo2014
By Gabriella Peralta Share
Non-residents who have visited coronavirus risk countries in the previous 14 days will be asked not to enter Gibraltar unless they have a place where they can self-isolate, the Gibraltar Government told the Chronicle.
All passengers arriving at Gibraltar International Airport are being asked by Border and Coastguard Agency officers
whether they have visited any of the 16 countries on the list.
"Non-residents meeting this criteria who do not have the means to self-isolate will be asked not to enter Gibraltar," a
government spokesman said.
"BCA is asking every person who lands at Gibraltar International Airport if they have been to a listed country in the last
The checks are not as exhaustive at the border to avoid queues, but information signs have been installed to advice
people of their legal duty to report.
"BCA are not stopping every individual who enters Gibraltar via the land frontier, as this would create significant delays
at the frontier," the spokesman added.
The Chronicle understands that a number of visiting tourists have been turned back at the border in recent days.
Any Gibraltar residents who have visited the risk countries are being asked to self-isolate for 14 days, being the
incubation period for the virus.
Current countries of concern include China, Hong Kong, Japan, Macau, Malaysia, Republic of Korea, Singapore, Taiwan,
Thailand, Iran, Northern Italy (regions of Aosta Valley, Liguria, Lombardy, Piedmont, Emilia-Romagna, Friuli-Venezia
Giulia, Trentino-Alto Adige/Südtirol and Veneto), Cambodia, Laos, Myanmar, Vietnam and Tenerife.

redpajama_set_name: RedPajamaCommonCrawl


My project is sketched in the Pop Art, Noir, and Minimalist Aesthetic art movements. I chose these because I believe that they show the most amount of variety between each aesthetic.
Pop Art Aesthetic works really well for this design as it came about during the 70's when the Iron Man armor was originally conceptualized in the comic book pages. This involves bright and stark colors that contrast and clash with each other. They are separated by large and defined outlines of black. Even the shading is refined and follows an organized path around each protruding piece of the armor. This aesthetic was not compelling in my final project because it does not resemble metal or armor at all. The sleek and strict shapes do not allow for a very large margin of error either.
The Noir Aesthetic is really cool in my opinion and allows for many re imaginings of today's classic designs. The darkness and rough nature of the black and white tells a story through its shapes and rough structure. This is very similar to the steam punk design that I finished with but it avoids color and relies more on organic shapes. This is similar to shadows or cloth in the wind.
The Minimalist Aesthetic is similar to the Pop Art rendition in its colors but the primary feature of this design is the lack of curves. These curves are replaced with minimal lines that just barely allow for the finished product to be recognizable. Without shading and coloring, the design would be even harder to distinguish. Minimalism does benefit the overall metallic feel behind the armor especially in comparison to the Pop Art aesthetic. The primary difference is the sharp lines that I hope to achieve in my final project.
SteamPunk Aesthetic. This is an extra sketch that I added to this series that just represents my design intentions for this project. This is the combination of the Marvel Cinematic Universe inspired Iron Man design in combination with the steampunk aesthetic in order to create an original and more interesting design. It is certainly more detailed and more rough even in comparison to the noir aesthetic. This was my final aesthetic intent because I find it to be the most visually intriguing. I hope to capture the details found in this conceptualized image.


text: m and fminusub. I highly doubt it will do you any good though, but since you're not telling me what the problem is I can't solve it for you. Introduction [pdf] Matlab Code. These 2 lines of code are a little dense conceptually but nothing too fancy, so let's embark on this adventure to demystify PCA! First, as usual, we need a good example. Math 30650 Spring, 2009 USING MATLAB TO PLOT TRAJECTORIES There is a MATLAB program, pplane, written by John Polking at Rice University, to do plots of trajectories of an autonomous system of 2 ODE. 561 These numbers correspond to the 59. Analyse Matlab-PCA et reconstruction de données multidimensionnelles j'ai un grand ensemble de données multidimensionnelles(132 dimensions). An earlier post to this blog demonstrated linear unmixing variables using the PCA with MATLAB. In particular it allows us to identify the principal directions in which the data varies. Singular Value Decomposition and Principal Component Analysis Rasmus Elsborg Madsen, Lars Kai Hansen and Ole Winther February 2004 Introduction This note is intended as a brief introduction to singular value decomposition (SVD) and principal component analysis (PCA). There is nothing special about PCA on RNAseq counts. PCR: Principal Component Regression Optimal number of PC's Calculate Crossvalidation RMSE for different # PC's n RMSECV y i ( )2 PLS: Partial Least Squares Regression X PLS T p cols n-rows a col a 1 a 2 a a MLR y Phase 1 n-rows a 1 a 2 a a b 1 b 0 b p Y k cols n-rows Phase 2 a 1 k cols Phase 3 PLS: Partial Least Squares Regression. PCA is a useful statistical technique that has found application in Þelds such as face recognition and image compression, and is a common technique for Þnding patterns in data of high dimension. Create Principal Component Analysis (PCA) plot of microarray data Export the gene labels and indices to the MATLAB PDF Documentation; Support. edu Abstract Over the last ten years, face recognition has become a specialized applications area within the field. com); 2) to get some insight into the bilinear factor models Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression, focusing on the. There are several on-line resources that can help mainly with. The toolbox is freely available via Internet from the Milano Chemometrics and QSAR Research Group website. Course Handouts : ImageProcessing2010 C# Project (Zip File) Convolution Filters Project and Matlab Code(VZip File) Image Processing and Computer Vision Book. R and Matlab Relevant Sofware: fda package in R 3/184. This example shows how to use Principal Components Analysis (PCA) to fit a linear regression. –Note that, when the kernel function is the dot-product, the kernel PCA solution reduces to the snapshot PCA solution –However, unlike in snapshot PCA, here will be unable to find the. store large probability density functions. In the second section, we present basic geometric methods and template matching. Analyze Quality of Life in U. It is typical to standardize all the variables to zero mean and unit standard deviation prior to performing PCA, to eliminate the influence of different measurement scales and. Perform a weighted principal components analysis and interpret the results. 2 Matplotlib 1. Principal Component Analysis (PCA). For extracting only the first k components we can use probabilistic PCA (PPCA) [Verbeek 2002] based on sensible principal components analysis [S. It aims to be useful for both beginners. there are many better methods for dimensionality reduction PCA: 97,5 % correctness 0 500 1000 1500 2000 2500 3000 3500 4000-1500-1000-500 0 500 1000 1500 Figure:Visualization of 2-D projection onto Eigenfaces showing linear separability Autoencoder: 99,8 % correctness-30. In other words, a set of vectors (X1,X2, … , Xp) is linearly independent if the expression ∑αiXi = 0 → αi = 0 for all values. Probability Proportion Define the threshold between success and failure •Rapid onset of a clinically relevant effect: PCA should fall within 24 hours after start of therapy, or earlier, to 20% or lower. Substituting k. Principal Component Analysis • This transform is known as PCA – The features are the principal components • They are orthogonal to each other • And produce orthogonal (white) weights – Major tool in statistics • Removes dependencies from multivariate data • Also known as the KLT – Karhunen-Loeve transform. Chapter 52 The PRINCOMP Procedure Overview The PRINCOMP procedure performs principal component analysis. Consider all projections of the p-dimensional space onto 1 dimension. Start Matlab You can access it from CSE lab but it's more easy to go to engineering lab and use Matlab there. The number of princi-pal components will be less than or equal to the total number of variables in the original dataset. g, by using this modified PCA matlab script (ppca. This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). The goal of this paper is to dispel the magic behind this black box. Work with your files from anywhere, share with others. How to calculate the Principal Component Analysis for reuse on more data in scikit-learn. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters. Examples of its many applications include data compression, image processing, visual-. pdf extension. Save MATLAB plots as JPG, PDF, PNG saveas in MATLAB. Autoencoders and Principal Components Analysis (PCA) One of the purposes of machine learning is to automatically learn how to use data, without writing code by hand. These 2 lines of code are a little dense conceptually but nothing too fancy, so let's embark on this adventure to demystify PCA! First, as usual, we need a good example. The methods have been implemented with Matlab (R2009a). Here we discuss both PCA on covariance and correlation matrix for one's own consideration of whether to use which structure that is most suitable to him/her. 599 Fisher + PCA (D'=128) 128D mAP = 0. Principal component analysis (PCA) [38] is a widely used statistical procedure on mass-spectrometry data for dimension reduction and clustering visualization. They cover the basics that are needed in order to carry out multivariate analysis (MVA). eigenfaces_test; ellipse_grid, a library which computes a grid of points over the interior of an ellipse in 2D. Unfortunately, state-of-the-art approaches for robust PCA do not scale beyond small-to-medium sized datasets. Keywords: Eigen Core, Face Recognition, LDA, PCA, Histogram Equalization, Matching, Matlab 1 SUMMARY OF THE PAPER This paper presents the face recognition system using a LDA, PCA, Eigen Core Methods. Is there a simpler way of visualizing the data (which a priori is a collection of points in Rm, where mmight be large)? For. As input you can use raw data, a correlation matrix, a covariance matrix, or a sums of squares and. The algorithm allows a few eigenvectorsand eigenvalues to be extracted from large collections of high dimensional data. Lecture 5: Dimensionality reduction (PCA) g The curse of dimensionality g Dimensionality reduction n Feature selection Vs. Component Analysis (PCA) for fault detection and diagnosis (FDD) in a real plant. Contribute to David-Guo/face_recognize development by creating an account on GitHub. This MATLAB function or [] = wmspca(X,LEVEL,WNAME,'mode',EXTMODE,NPC) returns a simplified version X_SIM of the input matrix X obtained from the wavelet-based multiscale principal component analysis (PCA). Principal Component Analysis • This transform is known as PCA – The features are the principal components • They are orthogonal to each other • And produce orthogonal (white) weights – Major tool in statistics • Removes dependencies from multivariate data • Also known as the KLT – Karhunen-Loeve transform. We will always assume that we have. At each location i,. 0, iterated_power='auto', random_state=None) [source] ¶ Principal component analysis (PCA) Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional. , 2005) is especially suitable for data from experiments where the studied response is non-linear. pca() in Matlab) internally use SVD. It is computationally very efficient in space and time. mat into your MATLAB environment. Athens, Georgia 30602. An assignment in which dimension reduction is applied with PCA, clustering with k-means algorithm and classification with Naive Bayes Classifier. Lecture #7: Understanding and Using Principal Component Analysis (PCA) Tim Roughgarden & Gregory Valiant April 18, 2016 1 A Toy Example The following toy example gives a sense of the problem solved by principal component analysis (PCA) and many of the reasons why you might want to apply it to a data set | to. Principal Component Analysis (PCA) is a useful technique for exploratory data analysis, allowing you to better visualize the variation present in a dataset with many variables. • PCA is a useful statistical technique that has found application in:. This module is devoted to various method of clustering: principal component analysis, self-organizing maps, network-based clustering and hierarchical clustering. How to calculate the Principal Component Analysis for reuse on more data in scikit-learn. The literature has an abundance of algorithms for solving this problem, where most of these algorithms could be grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation,. Principal Component Analysis (PCA) is unsupervised learning technique and it is used to reduce the dimension of the data with minimum loss of information. In other words, a set of vectors (X1,X2, … , Xp) is linearly independent if the expression ∑αiXi = 0 → αi = 0 for all values. PCA is predominantly used as a dimensionality reduction technique in domains like facial recognition, computer vision and image compression. Lyngby, Denmark 6th March 2002 Abstract This note aims at giving a brief introduction to the field of statistical shape. for x¯ as in (1). Introduction to Matlab + PCA and PLS – LIFE/KU - 2 - Introduction Two titles = two aims: 1) to get a quick introduction to the computer program Matlab (see www. Principal component analysis (PCA) [38] is a widely used statistical procedure on mass-spectrometry data for dimension reduction and clustering visualization. We collected most searched pages list related with matlab codes for pca fingerprint recognition pdf and more about it. Robust Principal Component Analysis Yuxin Chen Princeton University, Spring 2017. Generalized Principal Component Analysis: Projection of Saturated Model Parameters Andrew J. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of. andrewsplot(X) creates an Andrews plot of the multivariate data in the matrix X. Someone who learns just the built-in functions will be well-prepared to use MATLAB, but would not under-stand basic programming concepts. As there are good, numerically stable algorithms for calculating SVD, many implementations of PCA (e. MATLAB also provides add-on toolboxes which are basically a collection of special purpose functions, which extends special environment to solve problems included in a particular class of applications. We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of a data matrix, or more com-ponents at once, respectively. Orange Data Mining Toolbox. eNote 4 INDHOLD 2 Indhold 4 PCR, Principal Component Regression in R 1 Do PCA on the X-matrix. How to use PCA in MATLAB Cheng-Hsuan Li. Principal Component Analysis The central idea of principal component analysis (PCA) is to reduce the dimensionality of a data set consisting of a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. ) This problem can be e ciently solved via the singular value decomposition (SVD) and enjoys a number. PCA is a statistical method that uses orthogonal transformations to turn a potentially correlated set of data into a linearly uncorrelated set of data which contain principal components. Principal Component Analysis (PCA) and Factor Analysis 4. Graesser March 14, 2016 Introduction Principal Component Analysis (PCA) is a popular technique for reducing the size of a dataset. In the second section, we present basic geometric methods and template matching. include principal component analysis, factor analysis, and projection pursuit. PCA transforms the feature from original space to a new feature space. Principal component analysis (PCA): PCA allows us to map data In practice, it is usually a convention to perform PCA on correlation matrix due to its scaling convenience. In this paper a quantitative analysis of PCA, ICA, LDA and SVM is performed. In this tutorial, you'll discover PCA in R. Rokhlin, Szlan and Tygert. This project explains Image fusion based on PCA Method. Problem set / handout (Zip Archive 253kB Sep29 16) Data set as. Add-ons Extend Functionality Use various add-ons available within Orange to mine data from external data sources, perform natural language processing and text mining, conduct network analysis, infer frequent itemset and do association rules mining. (b) The reconstructed data using top-1 PCA data must lie on a line and therefore cannot accurately represent the orig inal data. Wolfe Street, Baltimore, MD 21205 USA Han Liu [email protected] In a GC-MS (Gas Chromatography-Mass spectrometry) analysis, it is common to obtain in a single analysis 600,000 digital values whose size amounts to. Before applying the technique it can be useful to first inspect the swap curve over a period time and make qualitative observations. principal component analysis matlab demo. Fisher k=64 4096D mAP = 0. Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but poorly understood. org 2 University of Zagreb, FER, Unska 3/XII, Zagreb, Croatia. MAT File 121kB Sep29 16) Two solutions sets as. Work with your files from anywhere, share with others. There is no way for PCA to give you a 73 by 436 score matrix. [email protected] MATLAB provides a number of features that help you in documenting and sharing your work. Exit Matlab if you do not use it. Principal component analysis (PCA) is a well-known tech-nique initially designed to reduce the dimensionality of a typically huge data set while keeping most of its variance (Pearson 1901; Hotelling 1933). In this lecture we provide a fully worked out example that illustrates how to do so with MATLAB. Principal Component Analysis The file petrology_data. Features of MATLAB. the PCA are called active observations. The bene ts of this dimensionality reduction include providing a simpler representation of the data, reduction in memory, and faster classi cation. Be able to carry out a Principal Component Analysis factor/analysis using the psych package in R. A Matlab software routine to perform Principal Component Analysis using Covariance, Correlation or Comedian as the criterion. Autoencoders and Principal Components Analysis (PCA) One of the purposes of machine learning is to automatically learn how to use data, without writing code by hand. I have calculated Hotelling's T2 statistic for detection of outliers in PCA analysis in Matlab. The first input argument for the function CalcFreq is the string containing the text, the second input argument is the length of the words to be counted, and the third argument is the fragment length. 3: (a) The data in X space does not 'live' in a lower dimensional linear manifold. First principal component: 𝑧1≡𝑎1𝑇𝑥= 𝑎𝑖1𝑥𝑖 𝑝 𝑖=1 Where vector 𝑎1=𝑎11,𝑎21,…,𝑎𝑝1 st. Principal Component Analysis in Excel. My final project is how to recognize human face with PCA by using MATLAB. In particular it allows us to identify the principal directions in which the data varies. We introduce the follow-ing notations for PCA. Given a set of data on n dimensions, PCA aims to flnd a linear subspace of dimension d lower than n such that the data points lie mainly on this linear subspace (See Figure 1. En este flujo de trabajo, debe pasar los datos de entrenamiento, que pueden ser de un tamaño considerable. pcaMethods A collection of PCA methods. This set of the novel algorithms is implemented as a toolbox In Matlab. The reduction factor i. For extracting only the first k components we can use probabilistic PCA (PPCA) [Verbeek 2002] based on sensible principal components analysis [S. Amir Hassan Pathan Faculty of Engineering, Sciences and Technology, IQRA University Karachi, Pakistan Email: khan. Roweis 1997], e. Portions of the data sets are. This module is devoted to various method of clustering: principal component analysis, self-organizing maps, network-based clustering and hierarchical clustering. In this section, we introduce the state-space and transfer function representations of dynamic systems. The system is implemented based on Eigenfaces, PCA and ANN. edu Amit Singer Princeton University [email protected] >> sqrt([1,4;9. Robust Principal Component Analysis with Complex Noise natural idea is to use MoG to model noise since MoG is a universal approximator to any continuous distri-butions (Bishop,2006). EFA cannot actually be performed in SPSS (despite the name of menu item used to perform PCA). Fast Steerable Principal Component Analysis Zhizhen Zhao, Yoel Shkolnisky, and Amit Singer Abstract—Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2-D images as large as a few hundred pixels in each direction. Bayesian Compressive Sensing (BCS) is a Bayesian framework for solving the inverse problem of compressive sensing (CS). The MATLAB ® function plot displays a graph of the relationship between two variables. Principal components analysis is an important and well-studied subject in statistics and signal processing. But often we only need the first two or three principal components to visualize the data. Experience in such software is now a necessisity in today's high tech world. This is achieved by transforming to a new set of variables,. Im trying to get a frequency plot, or PDF probability density function plot for my biometrics project in. How to use PCA in MATLAB Cheng-Hsuan Li. It's something along these lines, but I can't quite manage it. Non-linear PCA (NLPCA) (Scholz et al. In this lecture we provide a fully worked out example that illustrates how to do so with MATLAB. Invented in 1901 by Karl Pearson the method is mostly used today as a tool in exploratory data analysis and dimension reduction, but also for making predictive models in machine learning. The bene ts of this dimensionality reduction include providing a simpler representation of the data, reduction in memory, and faster classi cation. tex trend; Latex2html; HP poster LAN protocols PDF; Column layout in HTML and Latex; Newsgroup traffic. Robust PCA for Anomaly Detection in Cyber Networks Randy Paffenroth, Kathleen Kay, and Les Servi Abstract—This paper uses network packet capture data to demonstrate how Robust Principal Component Analysis (RPCA) can be used in a new way to detect anoma-lies which serve as cyber-network attack indicators. Matlab can also approximate implement integrals. However, PCA will do so more directly, and will require only an eigenvector calculation (easily done with the eigfunction in Matlab), and does not need to resort to EM. Þ AbstractŠWe propose an image denoising method that ex-. The basic BCS algorithm adopts the relevance vector machine (RVM) [Tipping & Faul, 2003], and later it is extended by marginalizing the noise variance (see the multi-task CS paper below) with improved robustness. This biometric system has real time application as used in attendance systems. ICA was performed on face images in the FERET database under two different architectures,. Matlab PCA 人脸识别. Proch´azka Institute of Chemical Technology, Prague Department of Computing and Control Engineering Abstract Principal component analysis (PCA) is one of the statistical techniques fre-quently used in signal processing to the data dimension reduction or to the data decorrelation. The first input argument for the function CalcFreq is the string containing the text, the second input argument is the length of the words to be counted, and the third argument is the fragment length. Linear Discriminant Analysis Matlab. IMAGE PROCESSING AND ANALYSIS USING LANDSAT ETM+ IMAGERY FOR LITHOLOGICAL MAPPING AT FAWAKHIR, CENTRAL EASTERN DESERT OF EGYPT Reda Amer, PhD Student Timothy Kusky, Paul C. Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) Rodrigo B. Here are some of the questions we aim to answer by way of this technique: 1. 3 Approximate it using di erent d-s, monitor R2. The PCA toolbox for MATLAB is a collection of modules for calculating Principal Component Analysis, Cluster Analysis, and Multidimensional Scaling for unsupervised analysis of multivariate datasets. Select a Web Site. , and EDWARD D. A version of L-BFGS-B 3. The native C compiler with Matlab did not compile this program. Principal Component Analysis (PCA) is the general name for a technique which uses sophis- ticated underlying mathematical principles to transforms a number of possibly correlated variables into a smaller number of variables called principal components. Canonical Correlation & Principal Components Analysis Aaron French and Sally Chess Canonical Correlation Canonical Correlation is one of the most general of the multivariate techniques. 1 PCA Let's fist see what PCA is when we do not worry about kernels and feature spaces. to these high-order statistics. Here, we introduce an algorithm that efficiently and accurately performs principal com-. These models may be derived either from physical laws or experimental data. Machine learning study guides tailored to CS 229 by Afshine Amidi and Shervine Amidi. subset where elements are extricated by PCA , while matching and recognition is done utilizing SVM classifier and SURF Technique. Linear, Ridge Regression, and Principal Component Analysis Example The number of active physicians in a Standard Metropolitan Statistical Area (SMSA), denoted by Y, is expected to be related to total population (X 1, measured in thousands), land area (X 2, measured in square miles), and total personal income (X 3, measured in millions of dollars). If you run into a problem, ple. % We then transpose that result so that each vector is a column. Matlab units fig file; My chess games; Book collection; Matlab GUI in GUIDE; DNA analysis; Volume Managers survey; ASCII table; comp. com); 2) to get some insight into the bilinear factor models Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression, focusing on the. It can be run both under interactive sessions and as a batch job. Principal Component Analysis (PCA) Steps in PCA: #4 Transforming data set to the new basis F = ETA where: •F is the transformed data set •ET is the transpose of the E matrix containing the eigenvectors •A is the adjusted data set Note that the dimensions of the new dataset, F, are less than the data set A To recover A from F: (E T)-1F. Principal component analysis is implemented by the Matlab function princomp, in the Statistics toolbox. The experimental results show that SVM gives better accuracy (i. Principal component analysis, particularly in the form of singular value decomposition, is a useful technique for a number of applications, including the analysis of two-way tables, evaluation of experimental design, em- pirical fitting of functions, and regression. Robust Principal Component Analysis Yuxin Chen Princeton University, Spring 2017. Improved Neural Network Performance Using Principal Component Analysis on Matlab International Journal of The Computer, the Internet and Management Vol. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Transactions on Neural Networks, Vol. pdf] - Read File Online. Here, our desired outcome of the principal component analysis is to project a feature space (our dataset. It started out as a matrix programming language where linear algebra programming was simple. This MATLAB code implements Binary PCA, and mixtures and HMM's with Binary PCA components. Add the PCA-Based Anomaly Detection module. Thanks Anjuli for A2A. The face recognition algorithms used here are Principal Component Analysis(PCA), Multilinear Principal Component Analysis (MPCA) and Linear Discriminant Analysis(LDA). When we started the course with linear regression, we saw that we could represent complicated functions if we engineered features (or basis functions) by hand. e implicitly p erform PCA in a p ossibly high{dimensional space F whic h is nonlinearly related to input space. A second post explained the use of the principal component analysis (PCA) to decipher the statistically independent contribution of the source rocks to the sediment compositions in the Santa Maria Basin, NW Argentine Andes. Lecture 5: Dimensionality reduction (PCA) g The curse of dimensionality g Dimensionality reduction n Feature selection Vs. Recently popularized randomized methods for principal component analysis (PCA). You will learn how to predict new individuals and variables coordinates using PCA. This is particularly recommended when variables are measured in different scales (e. Feng, "A Probabilistic Collaborative Representation based Approach for Pattern Classification," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016). Some other open source tools you may want to consider include:. Cities Using PCA. Non-linear PCA (NLPCA) (Scholz et al. This is a collection of modules for calculating Principal Component Analysis, as well as Cluster Analysis and Multidimensional Scaling, which are two other well-known multivariate methods for unsupervised data exploration. Karhunen-Loève Transform (Principal Components Analysis - PCA) Key Idea: Model points in feature space by their deviation from the global mean in the primary directions of variation in feature space • Defines a new, smaller feature space, often with more discriminating information Directions of variation are computed from. Specifically a 1 ×J row vector xT sup,can be projected into the PCA space using Eq. COEFF = pcacov(V) performs principal components analysis on the p-by-p covariance matrix V and returns the principal component coefficients, also known as loadings. Our developer communicates with me every day, and is a very powerful coder. 𝑣𝑎 [𝑧 ] is a maximum. Principal-component analysis(PCA) is a useful technique that can be used to reduce the dimensionality of large data sets, such as those from microarrays. Substituting k. Each principal component is a eigen-classifier Other step of their procedure: Retrain classifiers, regularizing towards subspace U Principal component analysis (PCA) / Case studies 22 This is a neat application of PCA which is more abstract than the previous ones. Data standardization. example mapcaplot( data , labels ) labels the data points in the PCA plots using labels , instead of the row numbers. Generalized Principal Component Analysis: Projection of Saturated Model Parameters Andrew J. depend on computational resource 3. As there are good, numerically stable algorithms for calculating SVD, many implementations of PCA (e. In this paper a quantitative analysis of PCA, ICA, LDA and SVM is performed. Improved Neural Network Performance Using Principal Component Analysis on Matlab International Journal of The Computer, the Internet and Management Vol. I am a student and now working for my final project. sions to it (Glorfeld 1995). Homework 4: PCA and Regression Due: Friday, Mar 25, 11:59pm 1 Principal Components Analysis (PCA) Loadthe le PCA. Applications of Principal Component Analysis. Is there a simpler way of visualizing the data (which a priori is a collection of points in Rm, where mmight be large)? For. Stegmann and David Delgado Gomez⁄ Informatics and Mathematical Modelling, Technical University of Denmark Richard Petersens Plads, Building 321, DK-2800 Kgs. The MATLAB ® function plot displays a graph of the relationship between two variables. 2 as an example of a two-dimensional projection found by PCA). 1 The covariance matrix and principal com-ponent analysis Suppose S is an m ndata matrix, in which the rst dimension is the space-like dimension and the second is the time-like dimension. There are several on-line resources that can help mainly with. i and principal component Y j is Because reduction of dimensionality, that is, focussing on a few principal components versus many variables, is a goal of principal components analysis, several criteria have been proposed for determining how many PCs should be investigated and how many should be ignored. m is a function that chooses one out of C (C is the number of classes) subspaces estimated by dataproc func cpca. of dimensions/features. the BP Neural Network will be the recognition process. This gives the 1×L vector of factor. % We then apply our transform A to each column. m is an auxiliary function that does basic PCA decomposition. You can perform principal components analysis directly on a covariance or correlation matrix, but without constructing residuals, by using pcacov. test error) - FSS has a unique set of. PCA is intimately related to the singular value decomposition (SVD) since the principal components of a data set, whose arithmetic mean is zero,. Specifically they will give details of how to do and view the results of principal components analysis (PCA), discriminant. , 2012) 240pp. This module is devoted to various method of clustering: principal component analysis, self-organizing maps, network-based clustering and hierarchical clustering. A PCA-based Similarity Measure for Multivariate Time Series∗ Kiyoung Yang and Cyrus Shahabi Computer Science Department University of Southern California. will be given in Section 2. In other words, a set of vectors (X1,X2, … , Xp) is linearly independent if the expression ∑αiXi = 0 → αi = 0 for all values. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of. I have calculated Hotelling's T2 statistic for detection of outliers in PCA analysis in Matlab. Research in MATLAB Introduction to Machine & Deep Learning Scaling MATLAB for your Organisation and Beyond Demo Stations Big Data with MATLAB Deep Learning with MATLAB Predictive Maintenance with MATLAB and Simulink Deploying Video Processing Algorithms to Hardware Using MATLAB and ThingSpeak to Explore the Internet of Things. 2% accuracy on validation set, and 1. You can apply any of the arithmetic operators to arrays of type single and MATLAB returns an answer of type single. Principal component analysis tries to find the first principal component which would explain most of the variance in the dataset. This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). This is a collection of modules for calculating Principal Component Analysis, as well as Cluster Analysis and Multidimensional Scaling, which are two other well-known multivariate methods for unsupervised data exploration. Platte RTG Seminar, 08/29/2016. Independent component analysis (ICA) is a recently developed method in which the goal is to fin d a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Principal Component Analysis (PCA) is a classic among the many methods of multivariate data analysis. When we started the course with linear regression, we saw that we could represent complicated functions if we engineered features (or basis functions) by hand. Success is borderline, C. Apply Dimensionality Reduction technique using Principal Component Analysis (PCA) on customer dataset except on dependent variable and reduce it to two dimensions. References. The third principal component axis has the third largest variability, which is significantly smaller than the variability along the second principal component axis. PDF | Matlab program for Face Recognition problem using PCA. This is a collection of modules for calculating Principal Component Analysis, as well as Cluster Analysis and Multidimensional Scaling, which are two other well-known multivariate methods for unsupervised data exploration. Hand Gesture recognition and classification by Discriminant and Principal Component Analysis using Machine Learning techniques Sauvik Das Gupta, Souvik Kundu, Rick Pandey ESL Kolkata, West Bengal, India Rahul Ghosh, Rajesh Bag, Abhishek Mallik ESL Kolkata, West Bengal, India Abstract—This paper deals with the recognition of different. 2 Matlab implmentation of the Black and Anandan dense optical flow method. We can define the PCA transformation by using eigenvectors of the empirical covariance matrix (2). Overview Linear discriminant analysis (LDA) is one of the oldest mechanical classification systems, dating back to statistical pioneer Ronald Fisher, whose original 1936 paper on the subject, The Use of Multiple Measurements in Taxonomic Problems, can be found online (for example, here). pdf; Matlab implementation of the. mat into your MATLAB environment. sparse approximation to the th principal component. This biometric system has real time application as used in attendance systems. Principal components analysis (PCA, for short) is a variable-reduction technique that shares many similarities to exploratory factor analysis. Principal Component Analysis reduces the dimensionality of data by replacing several correlated variables with a new set of variables that are linear combinations of the original variables. We will always assume that we have. It can be applied in many types of general machine learning scenarios. Probabilistic Principal Component Analysis 2 1 Introduction Principal component analysis (PCA) (Jolliffe 1986) is a well-established technique for dimension-ality reduction, and a chapter on the subject may be found in numerous texts on multivariate analysis. Linear Discriminant Analysis Matlab. The toolbox is freely available via Internet from the Milano Chemometrics and QSAR Research Group website. Indeed, let v0,v1, ,vN 1 be the eigen- vectors of (2) that correspond to the eigenvalues l0,. First principal component: 𝑧1≡𝑎1𝑇𝑥= 𝑎𝑖1𝑥𝑖 𝑝 𝑖=1 Where vector 𝑎1=𝑎11,𝑎21,…,𝑎𝑝1 st. mlx and pdf from live editor. , MSTB-A, MSTB-B, and the ICS lab), and student licenses are fairly inexpensive ($100) and even free for UCI students on personal machines; see here for instructions. The main purposes of a principal component analysis are the analysis of data to identify patterns and finding patterns to reduce the dimensions of the dataset with minimal loss of information. How to calculate the Principal Component Analysis for reuse on more data in scikit-learn. Loading Unsubscribe from Cheng-Hsuan Li? How To Convert pdf to word without software - Duration: 9:04. Keywords: Eigen Core, Face Recognition, LDA, PCA, Histogram Equalization, Matching, Matlab 1 SUMMARY OF THE PAPER This paper presents the face recognition system using a LDA, PCA, Eigen Core Methods. Select a cell within the data set, then on the XLMiner ribbon, from the Data Analysis tab, select Transform - Principal Components to open the Principal Components Analysis - Step1 of 3 dialog. BM3D Image Denoising with Shape-Adaptive Principal Component Analysis Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian Department of Signal Processing, Tampere University of Technology P. m in Matlab 7 runs at a half to a quarter the speed of that in Matlab 6. Let us work on such a small data and you can download it from here ( the file is zipped and google AI monkeys are converting. An earlier post to this blog demonstrated linear unmixing variables using the PCA with MATLAB. Though, one may find many syn-onyms for PCA, such as the Hotellling transforma-Figure 1. pca() in Matlab) internally use SVD. While principal component analysis (PCA) is often used to reduce the size of data, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. 2 as an example of a two-dimensional projection found by PCA). This section reviews only the introductory concepts of principal component analysis (PCA) for extracting information from large data sets, specifically focusing on using images as the data source. Lecture #7: Understanding and Using Principal Component Analysis (PCA) Tim Roughgarden & Gregory Valiant April 18, 2016 1 A Toy Example The following toy example gives a sense of the problem solved by principal component analysis (PCA) and many of the reasons why you might want to apply it to a data set | to. Department of Pathology, Medical College of Ohio, Toledo, OH 43699 ABSTRACT Multidimensional scaling (MDS) and principal component analysis (PCA) were applied to bacterial taxonomy. g: kilograms, kilometers, centimeters, …); otherwise, the PCA outputs obtained will be severely affected. MATLAB provides a number of features that help you in documenting and sharing your work. Each principal component is a linear transformation of the entire original data set. Recently popularized randomized methods for principal component analysis (PCA). SVD and PCA SVD –non-square matrix decomposition, left transform and right transform, with scaling in between SVD –as an image decomposition, linear combination of outer-product basis PCA –eigen values indicate amount of info/energy in each dimension, PCA –basis are eigen vectors to the covariance matrix Many applications. A growing number of TDE techniques require an approximate but robust and fast method to initialize solving for TDE. 14 PCA and K-Means Decipher Genome 317 To calculate the tables, students use the CalcFreq. MATLAB also provides add-on toolboxes which are basically a collection of special purpose functions, which extends special environment to solve problems included in a particular class of applications. 3 Functional principal component analysis Before reviewing FPCA, it is necessary to revisit multivariate PCA that is used to reduce dimensionality for multivariate data. depend on the problem 2. Abdullah, Azizah A. They are very easy to use. Principal Component Analysis (PCA) is used for dimensionality reduction and for feature extraction. PCR: Principal Component Regression Optimal number of PC's Calculate Crossvalidation RMSE for different # PC's n RMSECV y i ( )2 PLS: Partial Least Squares Regression X PLS T p cols n-rows a col a 1 a 2 a a MLR y Phase 1 n-rows a 1 a 2 a a b 1 b 0 b p Y k cols n-rows Phase 2 a 1 k cols Phase 3 PLS: Partial Least Squares Regression. For example, a Gaussian is a special case of MoG and a Laplacian can be expressed as a scaled MoG (Andrews & Mallows,1974). The Rand index lies between 0 and 1.


text:
Q: Spring Boot - use H2 for unit testing AND for quick development (with a Spring profile) How can I use H2 both for the normal Unit Testing AND during (the first part of) development? I could start the application with a specific Spring Profile so that the application-h2.properties would be selected.
I have two projects: for simplicity I used exactly the same POM files.
When I create a simple Spring Boot Application -- immediately inserting/reading db stuff, then all works fine.
Important dependencies are:
<dependency>
  <groupId>org.springframework.boot</groupId>
  <artifactId>spring-boot-starter-test</artifactId>
  <scope>test</scope>
</dependency>
<dependency>
  <groupId>com.h2database</groupId>
  <artifactId>h2</artifactId>
  <scope>runtime</scope>
  <!--<scope>test</scope>-->
</dependency>

When I have an Angular / Spring Boot application, again with the same Maven dependencies, thus with much web stuff, then I keep on getting this error:

IllegalStateException: Cannot load driver class: org.h2.Driver

UPDATE: When looking for the maven dependency tree, in both projects I get the dependency:
[INFO] +- com.h2database:h2:jar:1.4.197:compile

UPDATE: Even when I put the h2 dependency with scope 'runtime', unit testing still works with h2 AND the driver is not found
[INFO] +- com.h2database:h2:jar:1.4.197:runtime

redpajama_set_name: RedPajamaStackExchange


Based on handwritten parts of unknown orchestration, with addition of harp part written by conductor Angel Šurev in 1982.
Manuscript with no markings, ca.1841.
It is one of the few ballets of that era which has survived.


Much like your website or brochures, PowerPoint presentations are typically the first piece of information you are providing prospects or partners. And, much like your website, these presentations need significant investment if you wish to impress your audience. The good news is, you probably own a license of PowerPoint and therefore feel empowered to create a compelling piece of marketing yourself. And, unless you are a design firm, this in only partly true. You see owning a software does not make you proficient in its use. Not that you can't handle the software, but you could probably use a little help.
PowerPoint designers like ourselves offer both custom presentations or custom templates. The latter is a PowerPoint file which will help create high-end presentations on your own time. They will help you keep your company's look and feel consistent in every presentation.
The PowerPoint template will be the framework to each and every one of your presentations and will help you create all the types of slides you may need. Here is a list of do's and don'ts to look into when creating a PowerPoint template.
While respecting your guidelines, ensure that there is a good contrast between the text and the background. If your dominant color is dark, try to use light fonts and vice versa. Chances are, if your guideline were professionally designed, this had been taken care of by the designer in the first place. This is very important, as you will be presenting this on projectors and screens, which don't necessarily have the best contrast settings.
Make sure the font size is big enough to be read from the back of a room when projected.
Try your template on an existing presentation. The template should have a master slide for each type of slide you currently use. We recommend a minimum of 8 master slides, but you may need many more than that.
Make sure your template includes media placeholders, which will minimize your margin of error when importing visuals.
The don'ts start with the list above, make sure your brand guidelines are respected, fonts are used correctly, your logo is displayed in a proper manner. Make sure your template looks compelling, but is also readable on a screen, from the back of a room.
Avoid templates that have ready built visuals, such as photos and graphics, instead request visual placeholders so that you can customize the images and videos in your slides.
Some presentation designers offer templates that only have one master slide. They may have done a stellar job at designing the slide, if you have to build the content yourself this will not be of much help to you. Make sure you have a master slide for each content slide you currently work with.
Make sure master slides are consistent and that titles, content and logos are aligned throughout the masters.
There you have it, a few tips on creating PowerPoint templates. There is much room for interpretation because each project will be different, but overall make sure the slide match your company image, are consistent and easy to use. If you don't have the budget for a custom template, there are some stock PowerPoint templates out there, however, these are unlikely to match your full identity, while being simple to use.
If you have the budget for it, we recommend getting a fully customized presentation. A customized presentation, not only has a template adapted to your brand, but all of the assets are built specifically for your brand as well. This is the most powerful visual asset you will have, whatever the event you are presenting for.


text:
Jamie Callender named new adviser of The College Republicans
Caitlin Restelli
The College Republicans student organization welcomed a new faculty adviser.
The organization approached Jamie Callender, a political science instructor, earlier this semester because they wanted someone who was very active.
"I was really happy to be asked to help out," Callender said.
In previous years, the College Republicans had many democratic faculty advisers, which surprised Callender.
"It would help if your faculty adviser would be the same as the organization they are supporting," Callender said.
Callender was an Ohio House Representative from 1997 to 2004 and is preparing to run for a seat in the senate in 2012. The districts have not been decided, but Callender said he thinks he will represent Lake and Geauga County.
Callender has been an attorney partner at Buckley King, a mid-size national law firm, since 2007.
He came to Kent State University in 2005 and instructs an American Politics course as well as a State Government course in the spring. In the fall, Callender is an instructor for the Columbus Program for Intergovernmental Issues, which allows Kent State students to spend a semester in Columbus, where they take classes and intern.
Greg Allison, media chairman for the College Republicans, attended the Columbus program last fall.
"I know him well enough to know that he will help our group out and that he'll help us grow," Allison, political science major, said.
Callender sees the position as his chance to mentor the members as they create a mission and fulfill their goals.
"I think the role of a faculty advisor is to sit back and watch and allow the students to develop and really shape their one organization," Callender said.
Andrew Polz, president of College Republicans, is very pleased to have Callender on board.
"He has a great deal of experience, and I think he could help us more than anybody we've had in years," Polz, senior integrated social studies major, said.
Polz said he is looking forward to working with Callender more this semester.
"He is a gentleman; he's very great guy, nice to work with," Polz said. "It's nice to have somebody that is very much for the republican cause."
Contact Caitlin Restelli at [email protected].


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Delegations from the Colombian government and leftist FARC rebel group met again in Havana, Cuba, Friday for the start of the newest round of ongoing peace talks.
Negotiations continue to revolve around the long-contested issue of illegal drug production and consumption, the third topic in a six-item agenda.
At the end of the previous cycle last month, both sides expressed their hope of promptly concluding the discussion on drug production, which has traditionally served as a major source of financing for the guerrilla organization.
Discussions have reportedly been divided into three parts: crop substitution, abuse prevention, and production and sales. The latter is the most controversial of the three points, since it implicates the FARC in drug trafficking, an issue that has exacerbated Colombia's long-standing armed conflict.
The two factions began discussing the issue in late 2013, after reaching a historic deal on the subject of political participation for a demobilized guerrilla.
The peace talks, which began in November 2012, are currently entering their 23 cycle.
The FARC has been fighting the Colombian state since its formation in 1964 in what has become the oldest internal armed conflict in the world. An estimated six-million Colombians are direct victims of the fighting between rebels, the Colombian military and state-aligned paramilitary groups.
The rebel group is currently negotiating a political way out of the conflict with the government of President Juan Manuel Santos.
Three previous attempts at peace talks between the government and rebels failed, but the Cuba talks have gone uninterrupted since their inception, despite continued hostilities between rebel and public security forces back in Colombia.