Ferdinand Enke Verlag, Stuttgart 1. I: XV, 8. 85 S., 2. Abb., geb. DM 1. 08.— - Klemm - 1. Angewandte Chemie. EBook (German): Brauer II also available now. Georg - Handbuch der Pr. Georg Brauer's three-volumed handbook deals with the synthesis of a large number. Publication » Handbuch der Pr. Edited by Georg Brauer.
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Reasons Why Even Microsoft Agrees the Windows Desktop is a Nightmare. Let’s be honest: The Windows desktop is a mess. Sure, it’s extremely powerful and has a huge software library, but it’s not a good experience for average people. It’s not even a good experience for geeks, although we tolerate it. Even Microsoft agrees about this. Reports quickly came back concerning application removal. Following the update, a number of programs no longer functioned properly with Windows 10, with instances of crashes and BSODs documented throughout various forums. Features & Benefits Cleanly uninstalls programs. Junk items left behind by programs you no longer use can build up over time taking up valuable disk space and clogging your Windows registry. Express Uninstaller keeps your PC. Today we are going to address a weird and annoying problem which has been faced by many Windows 8.1 users. We have received many emails from our. You may have noticed a problem when uninstalling some programs, if you have Ntfs in Windows XP, Vista or 7, you are likely to face this problem. When you try to uninstall ’em from Add/Remove Programs List, they show a. Microsoft’s Surface tablets with Windows RT don’t support any third- party desktop apps. They consider this a feature — users can’t install malware and other desktop junk, so the system will always be speedy and secure. Malware is Still Common. Malware may not affect geeks, but it certainly continues to affect average people. Securing Windows, keeping it secure, and avoiding unsafe programs is a complex process. There are over 5. One of the most controversial things about Windows 8 is the missing Start button and Start menu. After relying on the Start menu through one version of Windows after another, PC users have found themselves floundering around. It’s easy to have theoretical discussions about how malware could infect Mac computers, Android devices, and other systems. But Mac malware is extremely rare, and has generally been caused by a problem with the terrible Java plug- in. Macs are configured to only run executables from identified developers by default, whereas Windows will run everything. Android malware is talked about a lot, but Android malware is rare in the real world and is generally confined to users who disable security protections and install pirated apps. Google has also taken action, rolling out built- in antivirus- like app checking to all Android devices, even old ones running Android 2. Play Services. Whatever the reason, Windows malware is still common, while malware for other systems isn’t. We all know it — anyone who does tech support for average users has dealt with infected Windows computers. 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Manufacturers can customize the Refresh image, preventing bloatware from easily being removed. Finding a Desktop Program is Dangerous. Want to install a Windows desktop program? Well, you’ll have to head to your web browser and start searching. It’s up to you, the user, to know which programs are safe and which are dangerous. Even if you find a website for a reputable program, the advertisements on that page will often try to trick you into downloading fake installers full of adware. While it’s great to have the ability to leave the app store and get software that the platform’s owner hasn’t approved — as on Android — this is no excuse for not providing a good, secure software installation experience for typical users installing typical programs. Even Reputable Desktop Programs Try to Install Junk. Even if you do find an entirely reputable program, you’ll have to keep your eyes open while installing it. It will likely try to install adware, add browse toolbars, change your default search engine, or change your web browser’s home page. Even Microsoft’s own programs do this — when you install Skype for Windows desktop, it will attempt to modify your browser settings to use Bing, even if you’ve specially chosen another search engine and home page. With Microsoft setting such an example, it’s no surprise so many other software developers have followed suit. Geeks know how to avoid this stuff, but there’s a reason program installers continue to do this. It works and tricks many users, who end up with junk installed and settings changed. The Update Process is Confusing. On i. OS, Android, and Windows RT, software updates come from a single place — the app store. On Linux, software updates come from the package manager. On Mac OS X, typical users’ software updates likely come from the Mac App Store. On the Windows desktop, software updates come from. Users have to keep track of all these updaters and make sure their software is up- to- date. Most programs now have their act together and automatically update by default, but users who have old versions of Flash and Adobe Reader installed are vulnerable until they realize their software isn’t automatically updating. Even if every program updates properly, the sheer mess of updaters is clunky, slow, and confusing in comparison to a centralized update process. Browser Plugins Open Security Holes. It’s no surprise that other modern platforms like i. OS, Android, Chrome OS, Windows RT, and Windows Phone don’t allow traditional browser plugins, or only allow Flash and build it into the system. Browser plugins provide a wealth of different ways for malicious web pages to exploit the browser and open the system to attack. Browser plugins are one of the most popular attack vectors because of how many users have out- of- date plugins and how many plugins, especially Java, seem to be designed without taking security seriously. Oracle’s Java plugin even tries to install the terrible Ask toolbar when installing security updates. That’s right — the security update process is also used to cram additional adware into users’ machines so unscrupulous companies like Oracle can make a quick buck. It’s no wonder that most Windows PCs have an out- of- date, vulnerable version of Java installed. Battery Life is Terrible. Windows PCs have bad battery life compared to Macs, IOS devices, and Android tablets, all of which Windows now competes with. Even Microsoft’s own Surface Pro 2 has bad battery life. Apple’s 1. 1- inch Mac. Book Air, which has very similar hardware to the Surface Pro 2, offers double its battery life when web browsing. Microsoft has been fond of blaming third- party hardware manufacturers for their poorly optimized drivers in the past, but there’s no longer any room to hide. The problem is clearly Windows. Why is this? No one really knows for sure. Perhaps Microsoft has kept on piling Windows component on top of Windows component and many older Windows components were never properly optimized. Windows Users Become Stuck on Old Windows Versions. Apple’s new OS X 1. Mavericks upgrade is completely free to all Mac users and supports Macs going back to 2. Apple has also announced their intention that all new releases of Mac OS X will be free. In 2. 00. 7, Microsoft had just shipped Windows Vista. Macs from the Windows Vista era are being upgraded to the latest version of the Mac operating system for free, while Windows PCs from the same era are probably still using Windows Vista. There’s no easy upgrade path for these people. They’re stuck using Windows Vista and maybe even the outdated Internet Explorer 9 if they haven’t installed a third- party web browser. Microsoft’s upgrade path is for these people to pay $1. Windows 8. 1 and go through a complicated process that’s actaully a clean install. Even users of Windows 8 devices will probably have to pay money to upgrade to Windows 9, while updates for other operating systems are completely free. If you’re a PC geek, a PC gamer, or someone who just requires specialized software that only runs on Windows, you probably use the Windows desktop and don’t want to switch. That’s fine, but it doesn’t mean the Windows desktop is actually a good experience. Much of the burden falls on average users, who have to struggle with malware, bloatware, adware bundled in installers, complex software installation processes, and out- of- date software. In return, all they get is the ability to use a web browser and some basic Office apps that they could use on almost any other platform without all the hassle. Microsoft would agree with this, touting Windows RT and their new “Windows 8- style” app platform as the solution. Why else would Microsoft, a “devices and services” company, position the Surface — a device without traditional Windows desktop programs — as their mass- market device recommended for average people? This isn’t necessarily an endorsement of Windows RT. If you’re tech support for your family members and it comes time for them to upgrade, you may want to get them off the Windows desktop and tell them to get a Mac or something else that’s simple. Better yet, if they get a Mac, you can tell them to visit the Apple Store for help instead of calling you. That’s another thing Windows PCs don’t offer — good manufacturer support. Image Credit: Blanca Stella Mejia on Flickr, Collin Andserson on Flickr, Luca Conti on Flickr. To Program In An Expressive And Powerful Language Python - The best free software for your12/8/2016 Anytime these objects are needed in a Python program. Python is an extremely powerful language that can be used to build highly. However Python is an extremely expressive language. Why is Python considered the beginner's learning programming language vs. Python 3 is the best version of the language yet: It is more powerful, convenient, consistent, and expressive than ever before. Now, leading Python programmer Mark Summerfield demonstrates how to write code that takes full. Python Success Stories . A big problem early in the process is identifying those. Computational chemists have developed many techniques to predict. These can be used to evaluate the likelihood that. If these computational techniques were good enough there would be no. But today's computer models cannot fully. Python for research and teaching economics. Python comes with a powerful interactive interpreter that. To Program In An Expressive And Powerful Language PythonsTo Program In An Expressive And Powerful Language Python DownloadWhat does “powerful” mean, when discussing programming languages? Python is a powerful language for writing general-purpose applications. This approximation still won't answer 'what is an expressive language'. Python is a powerful, expressive programming language that’s easy. Python for Kids brings Python to life. 20 reviews for Python online course. Python is a powerful and expressive language that has very simple syntax. If you're unsure of which programming language to learn, Python is a great c. Real molecules must. To save time and money on laboratory work, experimental chemists use. Process Improvements Needed. Much of the work on drug identification actually takes place through. An experimental chemist usually. In 2. 00. 0, Astra. Zeneca wanted to improve this process so that experimental. Pierre Bruneau, a Principal Scientist at Astra. Zeneca, had worked on this. Zeneca, which merged to form Astra. Zeneca. He developed a. H2. X, named after the allied navigation systems used. H2. X was based on an in- house molecular property. Drone. This system used a Perl script which computed some of. Perl, csh, or a domain specific. Python Chosen. H2. X using Drone was a successful experiment and it was used by many. In 2. 00. 1 Astra. Zeneca decided to develop it further and brought. Andrew Dalke as a consultant, to improve the back- end code by making. Andrew, a well- known. Python in computational chemistry and biology, convinced. Python was the appropriate language for the next. Py. Drone. Python was chosen for this work because it is one of the best languages. Many other powerful and expressive high. Perl, Lisp, Scheme, Ruby, CAML, and. Haskell. Of all these, Python is one of the few that is based on. Yet Python was also designed to solve. The result is a. language that scales well from small scripts written by a chemist to. Python's Error Handling Improves Robustness. The first iteration of Py. Drone refactored the existing Perl code into. Python. Refactoring the Perl code without moving to. Python would have produced comparable architectural results, but. Python's explicit error handling and stronger type checking helped. The current version of Py. Drone uses about 2. When an. external program works correctly, then the output is easy to parse into. However, these programs don't always work. To compensate for. Py. Drone developers wrote tests to anticipate as many error. From experience dealing with this issue first in Perl (Drone) and then. Python (Py. Drone), we found that Python is better at catching many. This is a result of the way in which the two languages. For example, Perl's I/O routines. A conscientious programmer will always add those, but they. In contrast to. this, Python functions are noisy and almost always raise an exception. After rewriting in Python, we initially thought this noisy behavior was. Python kept raising exceptions and stopping where the. Perl code had kept on going. However, we soon found that nearly. Python was helping us find. One example of an error case that Python uncovered for us was caused by. In Perl, strings and numbers are converted automatically, for. Unfortunately, Perl. As a result. of this, Drone was treating . Python's stronger typing uncovered the. Another way in which Python helped us to improve our code was by its. We. found this very useful in helping us understand the source of a problem. Andrew is in the United States and Pierre is in France. Some molecular properties depend on other properties. The older. Drone code maintained these dependencies manually with a set of 'if'. In this approach. To solve this problem in Python, we developed a simple rule base which. Python dictionary. 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As it turned out, for performance reasons, the eval() implementation in. Python accepts only built- in dictionary types and not an emulated. Nevertheless, the. Results. Py. Drone took about 3 months of development time, another 3 months of. QA, and 3 weeks of documentation time to produce about 5,6. Python code. Overall Py. Drone has been a wonderful success for Astra. Zeneca. As a. result of using Python, we were able to quickly and easily develop a. The biggest problem we've had with Python is that relatively few people. Astra. Zeneca use it for development. The IT group prefers either Perl. Java (architecture people) so we occasionally get. Scott. received his Ph. D. After studying. Ecole Nationale Sup. After several years acting as a. Medicinal Chemist, Pierre now heads the local physical chemistry and. French lab, while maintaining an interest in. Visitors Guide - Visit Fort Wayne by Visit Fort Wayne. Visit. Fort. Wayne. Visitors Guide for 2. 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Locations get a free peanuts pumpkin stencil, character stickers and take a photo with the peanuts crew at all these locations! Big Data Initiative @ CSAOverview. Today is the era of Big Data. The need to analyze vast amounts of data, being generated in different fields ranging from medicine to financial markets and from transportation to environmental modeling, is emerging as the next big challenge and opportunity. The primary objective of the Big Data Initiative is to help build a strong academic and research ecosystem that allows India to address this challenge and take a leadership position in this critical area. Yogesh Simmhan. Scalable Graph Processing in a Connected World& Vianney Koelman. Bits, bytes, barrels: Data assimilation challenges in the oil & gas industry. Thursday, May 2. 6, 2. It seems to me that these days lots of calculations are done on the GPU. Obviously graphics are done there, but using CUDA and the like, AI, hashing algorithms (think. Manocha / Fast BVH Construction on GPUs. Abstract: As the number of cores on a chip increase and key applications become even more data-intensive, memory systems in modern processors have to deal with. Modern enterprise applications represent an emergent application arena that requires the processing of queries and computations over massive amounts of data. Code Samples for Education. There are many CUDA code samples available online, but not many of them are useful for teaching specific concepts in an easy to consume. PM, followed by tea/coffee . Partha Talukdar. From Big Text to Big Knowledge. Thursday March 3. PM (followed by tea/coffee) . Vijay Natarajan. Symmetry in Scientific Data : An Approach to. Feature- Directed Visualization& Dr. Mayur Thakur. Leveraging Big Data Analytics for Compliance in Financial. Thursday, February 2. PM, followed by tea/coffee . Anurag Agrawal. The Role of Big Data in Public Health and Medicine. Thursday August 2. PM (followed by tea/coffee) . Rajeev Rastogi. Machine Learning@Amazon& S Anand. Visualizing Big Data. Thursday, October 2. PM, followed by tea/coffee . Mayur Datar. Machine learning challenges in E- commerce& Prof. Shubhabrata Das. Selected Problems in Sports Analytics. Thursday, November 2. PM, followed by tea/coffee . Ravindran Kannan. Foundations of Data Science. Tuesday February 2. PM, followed by tea/coffee . Ramesh Hariharan. Using Data to Understand Biological Systems. Thursday March 2. PM, followed by tea/coffee . Arnab Bhattacharyya. Spectral Graph Theory and Graph Partitioning& Prof. Rajesh Sundaresan. Belief Propagation for Large- Scale Optimization on Graphs. Friday April 1. 8, 2. PM, followed by tea/coffee . Chiranjib Bhattacharyya. Learning from Big Data: Using Statistics to tame the Complexity& Prof. Jayant Haritsa. Big Data, Small Testing? Chandra Murthy. Role of Sparse Signal Recovery in Big Data Analytics& Prof. Narahari. Mechanism Design for Strategic Networks, Crowds, and Markets. Tuesday June 1. 0, 2. PM, followed by tea/coffee . Uday Bondhugula. Scalable Programming Technologies and Architectures for Big Data& Prof. Viswanadham. Big Data Based Decision Making in Manufacturing Supply Chains. Thursday July 3, 2. PM, followed by tea/coffee . For details of how to sponsor a Fellowship, please feel free to contact us. If you are interested in supporting us in other ways, please feel free to contact us. Narahari, Vijay Natarajan, and Chandan Saha; as well as Professor Chandra Murthy from the ECE Department and Professor Partha Pratim Talukdar from the SERC Department. Yogesh Simmhan. Assistant Professor, CDS, IISc. Scalable Graph Processing in a Connected World. Abstract. Graph datasets exemplify the complexity and scaling challenges posed by Big Data. While linked data from the web and social networks were at the vanguard of network data analysis, the Internet of Things is generating property and temporal graphs that are feature- rich and generated continuously from billions of devices. Managing and processing such graphs running into billions of vertices and edges, and thousands of properties and temporal snapshots pose unique requirements: distributed programming abstractions that can operate of the graph structure and properties, across time; execution and query runtimes that can use distributed resources such as elastic Clouds; and new algorithms that can derive meaningful analytics. This talk will review these Big Data challenges related to graph datasets and offer a glimpse into related research. Previously, he was a Research Assistant Professor at the University of Southern California, Los Angeles and Associate Director of the USC Center for Energy Informatics. His research explores abstractions, algorithms and applications on distributed systems. These span Cloud Computing, Distributed Graph Processing Platforms and Elastic Stream Processing to support emerging Big Data and Internet of Things (Io. T) applications. He has won the IEEE/ACM Supercomputing HPC Storage Challenge and IEEE TCSC SCALE Challenge Awards, and has been funded by NSF, DARPA and Deit. Y. He is a Senior Member of IEEE and ACM, Associate Editor of IEEE Transactions on Cloud Computing and a member of the IEEE Future Directions Initiative on Big Data. Vianney Koelman. Vice President Computational R& D at Shell. Bits, bytes, barrels: Data assimilation challenges in the oil & gas industry. Abstract. An overview and outlook will be given of the Computational Research and Development conducted from Shell's Technology Center in Bangalore. The talk will focus on examples where data- driven modeling and data assimilation challenges pose fundamental challenges. A spectrum of methods involving advanced analytics, statistical analysis, compressive modeling, and machine learning - often involving open innovation collaborations with academia - are at the center of this research. Speaker Bio. Following a Ph. D in physics (Eindhoven University of Technology, 1. Vianney started with Shell in Research and Development in the Netherlands, and subsequently worked most of his career in various Petroleum Engineering and Oil and Gas Leadership roles in the Middle East, Europe, Africa and the Americas. Currently he combines his position as Chief Scientist with Vice President of Computational Technologies, based at Shell Technology Centre Bangalore, India. Throughout his career Vianney has focused on making technology contribute to the business. His work in Shell ranges from applied subsurface formation evaluation to fundamental computational Research and Development. The innovative methods he designed for complex micro- scale flow simulations led to Shell. Currently Vianney and his teams are working on computational technologies ranging from catalyst technology optimization using molecular simulations, to data assimilation using machine learning and compressive modeling. Partha Talukdar. Assistant Professor, CDS and CSA, IISc. From Big Text to Big Knowledge. Abstract. Knowledge harvesting from Web- scale text datasets has emerged as an important and active research area over the last few years, resulting in the automatic construction of large knowledge graphs (KGs) consisting of millions of entities and relationships among them. This has the potential to revolutionize Artificial Intelligence and intelligent decision making by removing the knowledge bottleneck which has plagued systems in these areas all along. In this talk, I shall provide an overview of research in this exciting and emerging area. Before that, he was a Postdoctoral Fellow in the Machine Learning Department at Carnegie Mellon University, working with Tom Mitchell on the NELL project. Partha received his Ph. D (2. 01. 0) in CIS from the University of Pennsylvania, working under the supervision of Fernando Pereira, Zack Ives, and Mark Liberman. Partha is broadly interested in Machine Learning, Natural Language Processing, and Cognitive Neuroscience, with particular interest in large- scale learning and inference. He is a co- author of the book on Graph- based Semi- Supervised Learning published by Morgan Claypool Publishers. Vijay Natarajan. Associate Professor, CSA, IISc. Symmetry in Scientific Data: An Approach to Feature- Directed Visualization. Abstract. Several natural and man- made objects exhibit symmetry in different forms, both in. The study of symmetry plays an. In this talk, I will introduce the problem of symmetry. The. goal is to identify regions of interest within the domain of a scalar field that. I will present algorithms to detect symmetry and discuss applications to. His research interests include scientific. Mayur Thakur. Data Analytics Group, Goldman Sachs. Leveraging Big Data Analytics for Compliance in Financial Institutions. Abstract. It is critical for a financial institution to comply with government. The cost of non- compliance can result in criminal indictment, multi- billion dollar fines and loss of. Employees of Compliance departments are responsible for implementation of proper. This discussion will focus on. Compliance leverages large quantities of data to establish monitoring controls. In particular. we will discuss specific business problems and show how they map into problems in natural language. No prior knowledge of finance will be assumed. Prior to joining the firm, Mayur worked at. Google, where he designed search algorithms for more than seven years. Previously, he was an assistant. University of Missouri. Mayur earned a Ph. D in Computer Science from the. University of Rochester in 2. BTech in Computer Science and Engineering from the Indian Institute of. Technology, Delhi, in 1. Mayur Datar. Principal Data Scientist, Flipkart. Machine learning challenges in E- commerce. Abstract. In this talk, we will look at what it means to be doing Big Data research in industry. Why is Big Data critical for success of companies and also examples when it is not needed. We will look at a selection of problems from the e- commerce space that are well served by standard machine learning constructs. We will take a breadth first view of around a dozen problems from e- commerce, some of which are applicable to the larger area of consumer internet. Time permitting, we will also discuss the new emerging field of Deep learning (aka Deep Neural Networks) and their applicability to Big Data. Prior to Flipkart, he was a Sr. Staff Research Scientist with Google where he worked for almost 1. Google products through his research and execution. His research interests are in datamining, algorithms, machine learning and computer science theory. Prior to joining Google, Mayur obtained his doctorate degree in computer science from Stanford university and a Bachelor of Technology degree from I. I. T. He was awarded the President of India, Gold Medal for being the most outstanding student of his graduating batch from I. Data parallelism - Wikipedia, the free encyclopedia. Sequential vs. Data Parallel job execution. Data parallelism is a form of parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism. A data parallel job on an array of 'n' elements can be divided equally among all the processors. Let us assume we want to sum all the elements of the given array and the time for a single addition operation is Ta time units. In the case of sequential execution, the time taken by the process will be n*Ta time units as it sums up all the elements of an array. On the other hand, if we execute this job as a data parallel job on 4 processors the time taken would reduce to (n/4)*Ta + Merging overhead time units. Parallel execution results in a speedup of 4 over sequential execution. One important thing to note is that the locality of data references plays an important part in evaluating the performance of a data parallel programming model. Locality of data depends on the memory accesses performed by the program as well as the size of the cache. History. Solomon machine, also called a vector processor wanted to expedite the math performance by working on a large data array(operating on multiple data in consecutive time steps). Concurrency of data was also exploited by operating on multiple data points at the same time using a single instruction. These generation of processors were called Array Processors. In some situations, a single execution thread controls operations on all pieces of data. In others, different threads control the operation, but they execute the same code. For instance, consider matrix multiplication and addition in a sequential manner as discussed in the example. Example. The pseudo- code for multiplication calculates the dot product of two matrices A, B and stores the result into the output matrix C. If the following programs were executed sequentially, the time taken to calculate the result would be of the O(n. Parallelization of the matrix multiplication code is achieved by using Open. MP. An Open. MP directive, . For multiplication, we can divide matrix A and B into blocks along rows and columns respectively. This allows us to calculate every element in matrix C individually thereby making the task parallel. Keeping the execution time low is the priority but as the matrix size increases, we are faced with other constraints like complexity of such a system and its associated costs. Therefore, constraining the number of processors in the system, we can still apply the same principle and divide the data into bigger chunks to calculate the product of two matrices. Since the two processors work in parallel, the job of performing array addition would take one half the time of performing the same operation in serial using one CPU alone. The program expressed in pseudocode below. Most real programs fall somewhere on a continuum between task parallelism and data parallelism. Steps to parallelization. This is called Mixed data and task parallelism. Mixed parallelism requires sophisticated scheduling algorithms and software support. It is the best kind of parallelism when communication is slow and number of processors is large. Mixed data and task parallelism has many applications. It is particularly used in the following applications: Mixed data and task parallelism finds applications in the global climate modeling. Large data parallel computations are performed by creating grids of data representing earth. The data is divided among different sub- circuits and parallelism is achieved with orchestration from the tasks. Data parallel programming environments. It defines the semantics of library functions to allow users to write portable message passing programs in C, C++ and Fortran. Open Multi Processing. Sciences imply data parallelism for simulating models like molecular dynamics. Driving forces in signal processing for data parallelism are video encoding, image and graphics processing, wireless communications . Fundamentals of Parallel Architecture. Boca Raton, FL: CRC Press. ISBN 9. 78- 1- 4. Journal of Computational Physics. IEEE Transactions on Computers. Paper Writing Service You Can Trust - Essay. Erudite. com. About Us. We value excellent academic writing and strive to deliver outstanding customer service each and every time you place an order. SMU BBA SEM 5 WINTER 2015 ASSIGNMENTS. BBA501 & PRODUCTION AND OPERATIONS MANAGEMENT 1 Write short notes on: Scope of production. Controlling function of operations. Review the exam schedule under 'Exam Information and Policies' before registering for classes to make sure there are no conflicts. You may click on the name of each. Research at Saint Mary's. About Research at SMU; SMU Research Achievements; Find a Researcher; Strategic Research Plan; Research Resources. Student Research Opportunities. Professor Kofele-Kale was a visiting professor of law at SMU for the spring term 1988, on leave from the faculty of the University of Tennessee School of Law. We value excellent academic writing and strive to deliver outstanding customer service each and every time you place an order. We write essays, research papers, term. The BCA-SMU-WDA Advanced Management Programme on Productivity and Leadership Development is a flagship programme tailored for the built environment industry. Writing Conferences & Events. The NewPages Big List of Writing Conferences and Events features writing conferences, book and literary festivals, writing workshops and. The Southern Methodist University football scandal (also known as Ponygate) was an incident in which the football program at Southern Methodist University was. We write essays, research papers, term papers, course works, reviews, theses and more, so our main mission is to help you succeed academically. 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