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Timekeeping becomes an art with automatic luxury watches from TAG Heuer.
All around the world, Swiss watches and luxury watches are celebrated for their quality, craftsmanship and precision. And when it comes to Swiss watches, TAG Heuer timepieces know no equal. Possessing inventive, contemporary designs and cutting-edge technology, they are more than mere luxury timepieces or automatic watches – they are in a class apart.
Conceived and manufactured by leading experts, these luxury Swiss watches perfectly pair the finest materials with innovative technology. Classic materials, such as soft calfskin, quality alligator leather, noble solid gold and hardwearing stainless steel, are given a fresh, avant-garde edge through the addition of high-tech rubber or modern fabric. Redefining sports watches and luxury watches, TAG Heuer applies quality finishes, including fine brushing, black PVD finish and ruthenium treatment, for timepieces that are elegant, modern, beautiful and practical, all at once.
No less impressive, its watches for women are the pinnacle of discrete elegance. With its diamond watches, for example, TAG Heuer adds a hint of sparkle to the renowned men's watch models that are the foundation of the TAG Heuer name: the TAG Heuer Carrera, the Monaco, the Link, the Aquaracer and the TAG Heuer Formula 1 are also sophisticated ladies' timepieces that carry on the heritage of artistry and accuracy that have been the brand's calling card for over a century.
Of course, TAG Heuer is more than just sublime aesthetics. Its watches combine manual craftsmanship with the best high technology, for ladies' and men's watches that perpetuate a legacy while inspiring innovation and originality. Entirely modern yet always sophisticated, a TAG Heuer watch doesn't stop at gorgeous aesthetics. Indeed, its automatic watches boast high frequencies of between 21,600 to 360,000 vibrations per hour (as with the Calibre 360), ensuring impeccable mechanical precision. It therefore comes as little surprise that a number of its automatic watch movements bear the prestigious Official Swiss Chronometer Control (C.O.S.C.) certificate.
It's no wonder that TAG Heuer chronograph watches are admired as the peak of Swiss watch timekeeping accuracy. These luxury sport watches are the official timekeepers of the Monaco Grand Prix – one of the world's most esteemed racing events – while the brand is the official partner of both America's-Cup-winner Oracle Team USA and revered Formula 1 automotive manufacture McLaren. An ideal marriage of speed and timekeeping precision.
One thing is clear: TAG Heuer timepieces go beyond mere luxury or precision: they unite the best in Swiss craftsmanship while delving deep into the future. Visually sublime yet eternally reliable, perfectly crafted yet resolutely high-tech, modern yet timeless, TAG Heuer is redefining luxury watches for men and women.


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Damiani Apartments
This well positioned property will appeal to couples and families looking for comfortable accommodation with a friendly atmosphere.The complex has its own roof garden pool / sundeck and also a buffet style restaurant and a bar.
WI-FI internet access is available throughout the property.
All apartments have: bathroom/shower, WC, balcony/terrace, kitchenette, fridge, internet access, (certain amenities may be payable locally)
Prices are on self-catering basis.
Throughout the winter months the complex will not be offering 24 hr reception.
Cancellation Policy: Customers can cancel their reservation free of charge up to 2 days before arrival (local time). Value of 1 night is incurred for late cancellations or no shows.
Pre authorisation to the value of the first night, will be reserved from customers' credit card, 3 working days prior to arrival
Check-in 14:00
Check-out - 11:00
Taxes included
No pets are allowed
Availability & RatesTo view availability and prices please select dates and click 'Update Dates'
Apartment Facilities & Policies
- Breakfast not included
- Cable TV
- Credit Cards Accepted
- Free Internet Access
- Free Wifi
- Fridge/Freezer
- Kitchen
- Linen Included
- Outdoor Swimming Pool
- Swimming Pool
- Tea/Coffee Making Facilities
- Utensils
Cancellation PolicyThis property has a 1 day cancellation policy. Failure to cancel within this time will result in a cancellation charge equal to the first night of your stay.
Must-Read Apartment InfoThis property has specific Terms & Conditions.
Please note:
Cancellation policy: 24h advance notice
Payment upon arrival by credit card
Check-in 14:00 - 23:30
Check-out 03:00 - 11:00
Breakfast not included
Taxes included
No pets are allowed
DirectionsShow MapHide Map
Triq il Hgejjeg,
Directions from Airport: Follow signs to Valletta,pass through Msida and follow the road Number 1 towards Gozo, untill you are on the Salina coast road that goes past Bahar ic-Caghak, Madliena and Salina Keep driving driving untill you see the signs for Qawra or Bugibba.
Located Near
Megaliths, Medieval Dungeons and Calypso's Cave - The Maltese Islands are positively mythic. Narrow meandering streets of their towns and villages are crowded with Renaissance cathedrals and Baroque palaces. The countryside is dotted with the oldest known human structures in the world, the Islands have rightly been described as an open-air museum.
St Paul's Bay and its neighbours Bugibba and Qawra are Malta's largest, seaside resorts offering a variety of nightlife and leisure options.

subset: commoncrawl


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Major AI textbooks define the field as "the study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. John McCarthy, who coined the term in 1956, defines it as "the science and engineering of making intelligent machines.
Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or "strong AI") has not yet been achieved and is a long-term goal of some AI research.
AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, ontology, operations research, economics, control theory, probability, optimization and logic. AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.
Other names for the field have been proposed, such as computational intelligence, synthetic intelligence, intelligent systems, or computational rationality. These alternative names are sometimes used to set oneself apart from the part of AI dealing with symbols (considered outdated by many, see GOFAI) which is often associated with the term "AI" itself.
Thinking machines and artificial beings appear in Greek myths, such as Talos of Crete, the golden robots of Hephaestus and Pygmalion's Galatea. Human likenesses believed to have intelligence were built in every civilization, beginning with the sacred statues worshipped in Egypt and Greece, and including the machines of Yan Shi, Hero of Alexandria, Al-Jazari or Wolfgang von Kempelen. It was widely believed that artificial beings had been created by Geber, Judah Loew and Paracelsus. Stories of these creatures and their fates discuss many of the same hopes, fears and ethical concerns that are presented by artificial intelligence.
Mary Shelley's Frankenstein, considers a key issue in the ethics of artificial intelligence: if a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human being? The idea also appears in modern science fiction: the film Artificial Intelligence: A.I. considers a machine in the form of a small boy which has been given the ability to feel human emotions, including, tragically, the capacity to suffer. This issue, now known as "robot rights", is currently being considered by, for example, California's Institute for the Future, although many critics believe that the discussion is premature.
Another issue explored by both science fiction writers and futurists is the impact of artificial intelligence on society. In fiction, AI has appeared as a servant (R2D2 in Star Wars), a comrade (Lt. Commander Data in Star Trek), an extension to human abilities (Ghost in the Shell), a conqueror (The Matrix), a dictator (With Folded Hands), an exterminator (Terminator, Battlestar Galactica) and a race (Asurans in "Stargate Atlantis"). Academic sources have considered such consequences as: a decreased demand for human labor; the enhancement of human ability or experience; and a need for redefinition of human identity and basic values.
Several futurists argue that artificial intelligence will transcend the limits of progress and fundamentally transform humanity. Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology with uncanny accuracy) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and that by 2045 artificial intelligence will reach a point where it is able to improve itself at a rate that far exceeds anything conceivable in the past, a scenario that science fiction writer Vernor Vinge named the "technological singularity". Edward Fredkin argues that "artificial intelligence is the next stage in evolution, an idea first proposed by Samuel Butler's Darwin Among the Machines (1863), and expanded upon by George Dyson in his book of the same name in 1998. Several futurists and science fiction writers have predicted that human beings and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, which has roots in Aldous Huxley and Robert Ettinger, is now associated with robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil. Transhumanism has been illustrated in fiction as well, for example on the manga Ghost in the Shell. Pamela McCorduck believes that these scenarios are expressions of an ancient human desire to, as she calls it, "forge the gods."
In the middle of the 20th century, a handful of scientists began a new approach to building intelligent machines, based on recent discoveries in neurology, a new mathematical theory of information, an understanding of control and stability called cybernetics, and above all, by the invention of the digital computer, a machine based on the abstract essence of mathematical reasoning.
The field of modern AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for many decades, especially John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, who founded AI laboratories at MIT, CMU and Stanford. They and their students wrote programs that were, to most people, simply astonishing: computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle 60s their research was heavily funded by the U.S. Department of Defense and they were optimistic about the future of the new field:
These predictions, and many like them, would not come true. They had failed to recognize the difficulty of some of the problems they faced. In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI. This was the first AI Winter.
In the early 80s, AI research was revived by the commercial success of expert systems (a form of AI program that simulated the knowledge and analytical skills of one or more human experts). By 1985 the market for AI had reached more than a billion dollars and governments around the world poured money back into the field. However, just a few years later, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, more lasting AI Winter began.
In the 90s and early 21st century AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence was adopted throughout the technology industry, providing the heavy lifting for logistics, data mining, medical diagnosis and many other areas. The success was due to several factors: the incredible power of computers today (see Moore's law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.
Artificial intelligence, by claiming to be able to recreate the capabilities of the human mind, is both a challenge and an inspiration for philosophy. Are there limits to how intelligent machines can be? Is there an essential difference between human intelligence and artificial intelligence? Can a machine have a mind and consciousness? A few of the most influential answers to these questions are given below.
While there is no universally accepted definition of intelligence, AI researchers have studied several traits that are considered essential.
For difficult problems, most of these algorithms can require enormous computational resources — most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem solving algorithms is a high priority for AI research.
It is not clear, however, that conscious human reasoning is any more efficient when faced with a difficult abstract problem. Cognitive scientists have demonstrated that human beings solve most of their problems using nonconscious reasoning, rather than the conscious, step-by-step deduction that early AI research was able to model. Embodied cognitive science argues that sensorimotor skills are essential to our problem solving abilities. It is hoped that sub-symbolic methods, like computational intelligence and situated AI, will be able to model these instinctive skills. The problem of unconscious problem solving, which forms part of our commonsense reasoning, is largely unsolved.
Knowledge representation and knowledge engineering are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); and many other, less well researched domains. A complete representation of "what exists" is an ontology (borrowing a word from traditional philosophy), of which the most general are called upper ontologies.
Among the most difficult problems in knowledge representation are:
Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future (they must have a representation of the state of the world and be able to make predictions about how their actions will change it) and be able to make choices that maximize the utility (or "value") of the available choices.
In some planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be. However, if this is not true, it must periodically check if the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty.
Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.
Important machine learning problems are:
The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.
Natural language processing gives machines the ability to read and understand the languages human beings speak. Many researchers hope that a sufficiently powerful natural language processing system would be able to acquire knowledge on its own, by reading the existing text available over the internet. Some straightforward applications of natural language processing include information retrieval (or text mining) and machine translation.
The field of robotics is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation and navigation, with sub-problems of localization (knowing where you are), mapping (learning what is around you) and motion planning (figuring out how to get there).
Machine perception is the ability to use input from sensors (such as cameras, microphones, sonar and others more exotic) to deduce aspects of the world. Computer vision is the ability to analyze visual input. A few selected subproblems are speech recognition, facial recognition and object recognition.
Emotion and social skills play two roles for an intelligent agent:
A sub-field of AI addresses creativity both theoretically (from a philosophical and psychological perspective) and practically (via specific implementations of systems that generate outputs that can be considered creative).
Most researchers hope that their work will eventually be incorporated into a machine with general intelligence (known as strong AI), combining all the skills above and exceeding human abilities at most or all of them. A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project.
Many of the problems above are considered AI-complete: to solve one problem, you must solve them all. For example, even a straightforward, specific task like machine translation requires that the machine follow the author's argument (reason), know what it's talking about (knowledge), and faithfully reproduce the author's intention (social intelligence). Machine translation, therefore, is believed to be AI-complete: it may require strong AI to be done as well as humans can do it.
Artificial intelligence is a young science and there is still no established unifying theory. The field is fragmented and research communities have grown around different approaches.
In the 40s and 50s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton and the Ratio Club in England.
During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or neural networks were abandoned or pushed into the background. By the 1980s, however, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems. Bottom-up, situated, behavior based or nouvelle AI: Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focussed on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 50s and reintroduced the use of control theory in AI. These approaches are also conceptually related to the embodied mind thesis. Computational Intelligence:Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle 1980s. These and other sub-symbolic approaches, such as fuzzy systems and evolutionary computation, are now studied collectively by the emerging discipline of computational intelligence. Formalisation: In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly scientific, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (like mathematics, economics or operations research). describe this movement as nothing less than a "revolution" and "the victory of the neats."
The "intelligent agent" paradigm became widely accepted during the 1990s. An intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success. The simplest intelligent agents are programs that solve specific problems. The most complicated intelligent agents are rational, thinking human beings. The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works — some agents are symbolic and logical, some are sub-symbolic neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents.
An agent architecture or cognitive architecture allows researchers to build more versatile and intelligent systems out of interacting intelligent agents in a multi-agent system. A system with both symbolic and sub-symbolic components is a hybrid intelligent system, and the study of such systems is artificial intelligence systems integration. A hierarchical control system provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling. Rodney Brooks' subsumption architecture was an early proposal for such a hierarchical system.
In the course of 50 years of research, AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.
Many problems in AI can be solved in theory by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. Many learning algorithms use search algorithms based on optimization.
Simple exhaustive searches are rarely sufficient for most real world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that eliminate choices that are unlikely to lead to the goal (called "pruning the search tree"). Heuristics supply the program with a "best guess" for what path the solution lies on.
A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.
Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Forms of evolutionary computation include swarm intelligence algorithms (such as ant colony or particle swarm optimization) and evolutionary algorithms (such as genetic algorithms and genetic programming).
Logic was introduced into AI research by John McCarthy in his 1958 Advice Taker proposal. The most important technical development was J. Alan Robinson's discovery of the resolution and unification algorithm for logical deduction in 1963. This procedure is simple, complete and entirely algorithmic, and can easily be performed by digital computers. However, a naive implementation of the algorithm quickly leads to a combinatorial explosion or an infinite loop. In 1974, Robert Kowalski suggested representing logical expressions as Horn clauses (statements in the form of rules: "if p then q"), which reduced logical deduction to backward chaining or forward chaining. This greatly alleviated (but did not eliminate) the problem.
Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning, and inductive logic programming is a method for learning. There are several different forms of logic used in AI research.
Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or uncertain information. Starting in the late 80s and early 90s, Judea Pearl and others championed the use of methods drawn from probability theory and economics to devise a number of powerful tools to solve these problems.
Bayesian networks are very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm), planning (using decision networks) and perception (using dynamic Bayesian networks).
Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models and Kalman filters).
A key concept from the science of economics is "utility": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design
The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do however also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems.
Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set.
When a new observation is received, that observation is classified based on previous experience. A classifier can be trained in various ways; there are many statistical and machine learning approaches.
A wide range of classifiers are available, each with its strengths and weaknesses. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Various empirical tests have been performed to compare classifier performance and to find the characteristics of data that determine classifier performance. Determining a suitable classifier for a given problem is however still more an art than science.
The most widely used classifiers are the neural network, kernel methods such as the support vector machine, k-nearest neighbor algorithm, Gaussian mixture model, naive Bayes classifier, and decision tree. The performance of these classifiers have been compared over a wide range of classification tasks in order to find data characteristics that determine classifier performance.
The study of artificial neural networks began in the decade before the field AI research was founded. In the 1960s Frank Rosenblatt developed an important early version, the perceptron. Paul Werbos developed the backpropagation algorithm for multilayer perceptrons in 1974, which led to a renaissance in neural network research and connectionism in general in the middle 1980s. The Hopfield net, a form of attractor network, was first described by John Hopfield in 1982.
Common network architectures which have been developed include the feedforward neural network, the radial basis network, the Kohonen self-organizing map and various recurrent neural networks. Neural networks are applied to the problem of learning, using such techniques as Hebbian learning, competitive learning and the relatively new field of Hierarchical Temporal Memory which simulates the architecture of the neocortex.
Control theory, the grandchild of cybernetics, has many important applications, especially in robotics.
AI researchers have developed several specialized languages for AI research:
AI applications are also often written in standard languages like C++ and languages designed for mathematics, such as Matlab and Lush.
Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed subject matter expert Turing tests. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results.
The broad classes of outcome for an AI test are:
For example, performance at checkers (draughts) is optimal, performance at chess is super-human and nearing strong super-human, and performance at many everyday tasks performed by humans is sub-human.
Artificial intelligence has successfully been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discovery and toys. Frequently, when a technique reaches mainstream use it is no longer considered artificial intelligence, sometimes described as the AI effect. It may also become integrated into artificial life.


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Moderators: Tonyblack, Jason, Toothy
AuntyVague wrote:That photo is just magnificent mc! But I feel for you dealing with that much snow.
You Queenslanders can keep your humidity! That was the one thing I found really tough about Vanuatu (which is roughly the same latitude as Cairns I think) it was so...damp. If I'd stayed any longer I would have looked like some horrible scaly monster because my skin was so irritated
Having said that the lack of humidity is a big part of our bushfire problem here in Victoria. Unfortunately a couple of 40c+ days crisps all the moisture out of grass and leaves where they stand. Because we had such a cool wet winter there is so much growth that is now tinder dry. We had been pretty lucky up until yesterday here in Gippsland to avoid the horrible condions in other parts of Southern Australia, but not so now. Our own town is quite safe, but several towns North East of us are under direct threat.
The heat has backed off, but the biggest problem now is the wind.
Users browsing this forum: Who's Wee Dug, Yahoo [Bot] and 7 guests


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Class-Based Affirmative Action Is Not the Answer
The reason affirmative action matters is not because of the possible educational benefits of diversity but because it raises a more fundamental question: do race-conscious admissions policies amount to unjustifiable discrimination against white people or are they an appropriate response to both past and present discrimination against black people? But even though racism against blacks and Latinos remains a real issue in American society (the idea that whites are also its victims is a joke the Supreme Court has never gotten), the fundamental inequalities in American life today—the rich getting richer while the poor get poorer—are not produced by discrimination and cannot be resolved by anti-discrimination. And affirmative action—whether class-based or race-based—is only a way of buttressing those inequalities. As is, indeed, the entire emphasis on education as the key to a more economically just society. In other words, the reason both affirmative action and education matter in the way they do is not because they solve the problem we actually have but because they conceal it. And the increasingly popular idea that we can fix affirmative action by making it work for poor people as well as or instead of for people of color is just another turn of the neoliberal screw. The problem is not unequal access to the elite; it's the very idea of the elite.
Whether you're for affirmative action or against it, you can't understand the political importance of the debate you've taken a side in without understanding the economic context in which that debate has taken place. In 1978, the year in which the Court first allowed race to play a role in university admissions, the United States was already an economically unequal country, about the same as France and a little worse than Germany and the United Kingdom. Since then inequality has grown everywhere, but the U.S. has emerged as the uncontested champion; in 1978, the top decile of Americans earned about 33 percent of all income, in 2011 it was more like 48 percent.
And that inequality is reflected in college attendance. At the University of Michigan, for example, a school that's led the way in the fight for affirmative action, around 60 percent of entering students (in 2011) came from families with incomes of over $100,000; less than 20 percent came from families with incomes under $60,000. And the state's median household income was $48,669. Half of the state's households make less than that; fewer than 15 percent of the university's students come from those households.
So while the U.S. has gone from unequal to spectacularly unequal and while our elite colleges and universities have devoted themselves almost entirely to catering to the needs of the beneficiaries of this process, the debate about their social role has focused entirely on the question of whether true social justice consists of making sure that a suitable number of the rich kids you educate are rich kids of color or in refusing to take race into account, even if the rich kids you then wind up with are all white and Asian. The supporters of affirmative action, in other words, want the oligarchy that runs and owns the U.S. to be (in the old Jesse Jackson tradition) a rainbow oligarchy (yay Obama!). The opponents don't mind (might even prefer) something a little more vanilla, although with a substantial admixture of Asian (the new white: yay Bobby Jindal! Yay Nikki Haley!).
The debate around Fisher, however, has been a little different from previous iterations of the affirmative action struggle, mainly because the recent economic crisis and the so-called recovery have made the fact of economic inequality more visible. Which in turn has made both the advantage of going to truly elite schools and the exclusion of the poor (i.e. the majority of the American population) from them more obviously consequential. So the question of whether our elites are insufficiently composed of people whose parents are black and Hispanic has been supplemented by the question of whether they're also insufficiently composed of people whose parents are working class. Hence the call for racial diversity to be replaced by economic diversity, to move, as Bill Keller says in the New York Times, "from race to class."
But economic diversity is just as irrelevant to actual equality as racial diversity has been. Why? For one thing, insofar as diversity (which amounts here to enhancing the rich kids' education by exposing them to what one of the defendant's amicus briefs described as "difference"—i.e. poor kids who are good at math) really is the thing we care about, the gains in social justice will remain minimal. It's not just that economic diversity can hardly be understood along the same lines as racial diversity—black kids don't go to elite colleges hoping to become less black but poor kids are definitely looking to be less poor—it's that economic diversity is more or less in principle a kind of tokenism. It's imaginable that elite schools could have racially proportionate student bodies—at 10 percent African American students, the Harvard Class of 2016 is almost there. But it's completely unimaginable that that they could have economically proportionate ones. Right now almost half of Harvard students come from families making over $200,000 a year; if they took economic diversity seriously, they'd have to send nine tenths of those kids home.
The real problem with economic diversity is not that we can't get it. It's that it isn't worth getting. As a simple, albeit utopian, thought experiment shows: Suppose instead of trying to make our best colleges more diverse, we succeeded in making all our colleges as good as our best. Now everybody, black and white, rich and poor, has a great education; everybody's on track for those "financial services" jobs that have been and continue to be the favorite destination of young Ivy Leaguers. The only problem is if you look at the Bureau of Labor Statistics job projections for 2020, the services America needs most will not be financial. The fastest growing jobs are personal care aides and home health aides, and not only do they not require a Michigan or Texas B.A. They don't even require a high school diploma. And they pay accordingly: $19,640 for personal care, $20,560 for home health. Today, only about 20 percent of jobs require a four-year degree; in 2020, that number will grow, but only to 20.5 percent. In the real world, because most people can't go to selective colleges (that's the definition of selective), the ones who can are saved from those jobs. But those are the jobs there are and will be. So even if every student in America got the best possible education, the overwhelming majority of them would still end up doing work that didn't require and didn't reward it. We would have made the inequality of education go away but left inequality itself undiminished.
In fact, the debate over affirmative action has never had anything to do with reducing inequality; it's always been about justifying it. That's what it means to call affirmative action a class project. The economic function elite colleges perform is to separate the few winners from the great mass of losers in American life and the function of both racial and economic affirmative action is just to make sure that everyone believes those winners are chosen fairly. What the dispute's about, in other words, is what color the elite will be and whether or not a few more of them will come from working class families, not about how to diminish the gap between them and everybody else. So whoever wins, the vast majority loses. And this is true not only of affirmative action but of all the commitments to anti-discrimination that have come to occupy the center of American social justice. After all, the whole point of anti-discrimination is not to make people less poor but only to make sure that their poverty is not an effect of racism or sexism—to make sure that the average worker at, say, Wal-Mart, earns only 1/523rd as much as the CEO not because she's black or a woman but because she's only 1/523rd as valuable as the CEO.
In other words, anti-discrimination is a class project as well. Its ideal is not a world where the worker is a little less poor and the CEO a little less rich; it's a world where the worker deserves her poverty and the CEO deserves his wealth. Which isn't to say that anti-discrimination is, in itself, bad; it's to say instead that it's not in itself good, or at least not good enough. Unlinked to any project of redistribution, it functions less as the critique of inequality than as its good conscience.
Tue, 2013-07-02 10:24
Racial and economic diversity are, to use a mathematical term, NECESSARY but not SUFFICIENT conditions for a truly prosperous nation. The "affirmative action" we REALLY need is to cut tax loopholes for the very wealthy and for corporations, discourage outsourcing by removing the tax breaks for outsourcing (and maybe add back some smaller ones for INSOURCING), pass a really good minimum wage such as, maybe MORE than the POVERTY level? (perhaps keep it low for the people conservatives cite as not justifying more: teenagers living with, and supported by, their parent(s) or guardian(s)!), and go back to protecting unions. Maybe cut the personal taxes of hiring managers for hiring people from lesser known colleges and people with "obsolete" college degrees who cannot afford to retire completely.
I like your thought experiment about ALL colleges having equally excellent programs, but it's really the NAME of the college that matters, not the education quality. The REAL reason managers like to hire from Ivy League colleges is that they have a better chance of getting graduates with the same SOCIOPATHIC MORAL PHILOSOPHY as themselves, who see things as a member of the "right" class. Even the "token poor student" at Yale, for example, if he or she makes the effort to fit in and make good grades, exposes himself or herself to indoctrination in these "preppy" class attitudes; often, when they go home, they criticize their PARENTS as part of the "taker 47 % of losers" because they have forgotten where their roots are. So bosses want to hire Ivy League graduates as their future replacements when they retire, but they want to hire "losers" for actual WORK (really, how much WORK does a financial analyst do compared to a Teamster?) and then pay them so little that they are KEPT losers.
Another useful reform would be to REMOVE STOCK VOTING RIGHTS from stock that is held by an entity other than a natural person (and give back REAL voting rights to natural persons who own stocks indirectly through a mutual fund; allow these stockholders the OPTION of viewing live feed of shareholder meetings, with live interaction, including votes for directors, and remove the PROXY; and not actually count the votes at the meeting, but allow a couple of weeks for mailed-in ballots). This will remove the incentive for one corporation to "buy" up another, since it will only share in the profits, NOT the control, of the bought corporation. Ideally, corporations should not own other corporations anyway. A corporation is PROPERTY divided up into many pieces owned by many persons, and property should not own other property. In other words, corporate ENTITIES, not their employees, should be treated as slaves!
Of course, to get a Congress that does these things (eventually) will require a big reform: repeal Citizens United with a Constitutional amendment; allow political advertising ONLY to the extent of a corporation (such as Exxon) or a not-for-profit (such as the Red Cross) airing ads DIRECTLY relating to its core mission (Exxon could advertise on energy issues, the Red Cross could advertise to stop Congress from banning blood transfusions) with its ACTUAL NAME in the ad (not "Citizens for More Drilling"), and disallowing outside political donations COMPLETELY. My reasoning is that if a CEO or Treasurer donates CORPORATE funds to ANY candidate or political action group, he/she is EITHER spending shareholders' funds with NO expectation of improving corporate profits, thus breaking his/her FIDUCIARY duty, which is EMBEZZLING, OR is spending them to get favors that WILL improve corporate profits, which is, by definition, BRIBERY.
Tue, 2013-07-02 18:53
Wed, 2013-07-03 08:04
The problem we've always had with this 'best educational system in the world' is that it tends to slide towards the "pay to play" end of the spectrum unless a variety of active measures are taken to halt that slide.
Affirmative action is just one of those measures, but I have to point out that "meritocracy" is actually consistent with affirmative action.
Unless a large number of HR people and trainers have been lying to me for decades, if a white male is better qualified for a job than anyone else, you hire the white male. Think about that. All the whining about "blacks stole my job" or whatever the modern variant of that is, are complete bullsh*&t. They (whoever 'they' are) stole your job because they were as good or better than you.
Affirmative action is only supposed to be applied when two candidates are equally qualified. And yes, that means that you make the choice in that case in favor of the person who improves the diversity of your workforce.
Now, does that mean that you hire the priveleged child of a wealthy black couple (who has -- let us say -- never attended a public school in his life) over a young man raised in Appalachia, in a coal-mining town, who won a merit scholarship to the same university, worked part-time to meet expenses, and just happened to finish with exactly the same GPA, and a degree in the same field? Personally, I would have to say that the white child from Appalachia improves the diversity of that workforce more than does the black child of privilege.
Hope this helps.
Wed, 2013-07-03 16:00
Fri, 2013-07-05 17:12
Can't really disagree with this, except for the fairly obvious quibble that Affirmative action was never really about college, it was about making jobs available. For example, you could be a cop or a fireman or a city maintenance worker. It's actually been extremely successful at integrating those kind of jobs.
Which is of course a main reason why the anti-govt. arguments that rose to prominence in the 80s and still dominate our discourse got so much traction. Govt. was fine when white people got the benefits; it was a horrible crushing liberty devouring monster when black people got jobs with the police.
So yes, American higher ed is a rotten borough and Affirmative reactions is one of the obvious spots. But think about it more broadly and the picture changes. It's not unreasonable to suggest that a significant number of those upper class Americans at Harvard got their because of affirmative cation boosting their families chances.
IMHO the only possible solution to inequality in the US is a revived labor movement. Everything else is soft headed fantasy. The people who do the lousy work have to organize to take a bigger share of the pay.
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Removing unwanted hair has been one of the routine tasks of beauty care throughout
Removing unwanted hair has been one of the routine tasks of beauty care throughout written history – and probably before that – and there are a variety of methods that can be used. They range from traditional plucking to modern high-tech lasers. Which method, or methods, used to remove unwanted hair will depend upon a variety of factors, including where it is located and skin type. Each has its own advantages and disadvantages, but they don't have to be painful, messy or frightening.
There are two types of hair on the human body. The very fine, soft vellus hair, sometimes called peach fuzz, covers all of the body except the palms of the hands and soles of the feet. What is usually considered to be real hair is called terminal hair, and is on the head, but it also exists on the pubic region, under the arms, the forearms and the front of the legs; men also have terminal hair on the face, chest and sometimes the shoulders and back. During childhood, only the terminal hair on the head is active, but upon puberty the terminal hair follicles begin to change and to grow out thicker and darker as the melanin in the follicles begins to be produced. This secondary sexual characteristic hair, called androgenic hair, is often unwanted in many places.
There are also two types of hair removal: depilation removes the hair above the skin and epilation removes the entire hair, including the follicle. Neither method is really permanent: with depilation methods, such as shaving, the hair simply grows back; even when the follicle is removed, through tweezing for example, some can regenerate. This means that, whatever method of hair removal used, the process will be a lifelong task.
Shaving and depilatories are common ways of removing unwanted hair in most places. Both remove the hair that has already grown from the follicle, cutting it off at skin level through shaving or using chemicals to break down the keratin fibers in hair and wiping or scraping the weakened hairs off. Both are relatively inexpensive and painless (unless nicks occur), but they have a tendency to be messy and cause irritation. The process has to be repeated as the hair grows back.
Tweezing is often used to shape eyebrows or pluck out stray hairs on the cheek or chin. It can be slightly painful and is not practical for larger areas. Epilators, which are simply automated hair-pluckers, can cover more surface area and are often used on the legs and under the arms in lieu of shaving. Waxing, either strip waxing (with a paper or cloth backing) and hot waxing (usually a little more comfortable) also remove the entire hair, can be used over larger areas and is often used for bikini lines. Sugaring is similar to waxing, but uses a mixture of sugar, lemon juice and water. Threading is an ancient technique that is still used, and involves rolling a thin string across the treatment area, allowing the hairs to be trapped and pulled out. All of these methods can be a little painful and can also cause soreness, rashes and ingrown hairs.
These epilation methods will give a respite of about two to three weeks before they have to be repeated. There are two reasons for this: Some of the follicles will regenerate and begin producing hair again, while other follicles weren't actually removed at all. At any given time, about 15% of terminal hairs are going through a resting period, called catagen, during which many of the hairs would have fallen out anyway. As the resting follicles return to their active, anagen, stage they begin to grow hair again.
Instead of pulling out hairs, other epilation methods simply kills the follicle in place. These treatments make it less likely that the follicles will regenerate, but it is not 100% effective. For this reason, while electrolysis and laser hair removal are sometimes called "permanent" hair removal, they are really in the nature of hair thinning. After these methods have been used on an area, follow-up maintenance treatment is required to take care of the few hairs that do come back.
Electrolysis was developed about 150 years ago and uses a thin metal wire that goes to the base of the follicle in the pore (the wire doesn't break the skin) and uses a very small electrical charge to kill the hair at its root. It is a long, slow, labor-intensive method of hair removal. While there are some home electrolysis kits, this technique is most often performed at a hair removal clinic.
Since the mid-1990s, when laser hair removal first became commercially available, it has replaced electrolysis for the most part. Laser treatments are faster, painless and very effective (although, as with all hair removal, not fully "permanent"), but it does have drawbacks. A laser emits a narrow beam of light focused to a particular wavelength to heat up the melanin cells (that gives hair its color), which kills the follicle. However, the laser only works on eumelanin, which produces brown or black hair, and not the pheomelanin present in blond and red hair; laser hair removal also does not work on white hair (which has no melanin at all) or grey hair (in which the melanin has stopped being produced). Furthermore, laser treatment only works when hair is in the anagen growth stage. Side effects, particularly some discomfort during the treatment, hyperpigmentation (dark spots), hypopigmentation (white spots), acne flares and swelling can occur.
To be effective, a series of laser treatments have to be performed. On average, a minimum of seven treatments, with a resting period of up to six weeks between them, are required to remove hair from an area. Once the initial course has been performed, however, the maintenance can be performed at home using personal laser kits. These home devices are carefully calibrated to avoid accidental burning, but that means that they are usually not considered being as effective as clinical treatments for first-time laser hair removal. They can be very useful for touch-ups, to keeping legs and underarms smooth.


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Shoe Storage Solutions
Shoes. We've all got 'em. The perfect place to store them? That's another story. Whether your collection of boots is spilling into your bedroom or your sneaker habit is interfering with your life, we have the shoe storage solution to bring some peace and order to your closet, bedroom or entryway. Choose from our selection of stacking bamboo shoe racks, hanging shoe organizers and boot racks to find the perfect solution for your collection of precious pairs!


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Aug 21, 2010
Over The Edge: abusing "toy" hardware in new (wet) ways
On a recent trip to Niagara Falls, I was fascinated again with this huge, impressive natural feature. And thinking about the foolhardy souls that over the years have tried to survive going over the falls in a barrel, I did what was natural for me - thought about the NXT doing it. Of course, getting permission to throw something over Niagara Falls is unlikely to happen... and it's dangerous, and retrieving it would be extremely difficult. But I could certainly do some test runs... so was born the "NXT in a barrel".
I used an NXT slid into a 4" PVC pipe segment, sealed tightly at either end with removable closeout plugs. To keep it from bouncing around inside, I used a hot-wire cutter to shape foam "bulkheads" to support and lock the NXT into place within the tube, and attached a 50' line to retrieve it after. Then I went to a small local dam, started it, sealed it tightly, took a deep breath... and threw it over.
The NXT logged two sensors 30 times a second, a 3-axis accelerometer from Mindsensors, and the LEGO sound sensor. Above the dam it floated tranquilly, quietly, and approached the edge. During the fall, the sound level went up only a little, but the accelerometer clearly registered a freefall as it went over the edge. Then there's a sharp acceleration as the pipe hits the rocky bottom end-on, and the noise level suddenly spikes up, as water crashes down on top of the PVC pipe and the unit is tossed and turned at the sloped, rocky base of the falls. Then suddenly it quiets down, bouncing in the tailrace in the fast-moving water below the waterfall.
All in all, this gave me yet another chance to "live vicariously" and understand something that I have no (safe) way of experiencing myself. Could I scale this up? Certainly (& I'm planning to, in a couple of different ways). Could this be used as a teaching tool? Absolutely, in so many ways. Total cost beyond the NXT was maybe $15 total for the plumbing, and programming was very easy.
Niagara Falls it's not... but it's as close as most of us are likely to get. Now excuse me, I've got to find a bigger waterfall...
8 comments:
- 222Doc said...
LOL Brian taking it to limit, hey Niagara! I can see it soon on the News, man in custody for sending a unmaned unknown devise over the falls today. Home land security is investigating this device as the HLS robot recovers it from the deep.
- Aug 21, 2010, 2:34:00 AM
- Brian Davis said...
Which is exactly why I *DIDN'T* do this at Niagara, and why I *WILL NOT* encourage people to do this at Niagara. Safety first... which includes following local laws. :)
- Aug 21, 2010, 3:00:00 AM
- I_Fixed_It_44 said...
Wow,Brian Your Really Pushing the Limit.0Next You Should Make A Submerine
- Aug 21, 2010, 9:47:00 AM
- FLL Team got robot? said...
I knew as soon as I saw the picture of the pipe bobbing in the water who posted this article. Scrolling further down only confirmed it. You crack me up Brian.
- Aug 21, 2010, 10:31:00 AM
- Martijn Hellemans "Dino_Martino" said...
O my... what ways of MINDSTORMS-(ab)use haven't you tried?
Were do you keep getting these ideas? =)
No video? such a shame...
although I'm not sure I can handle the picture of an NXT going over the edge...Horror:D
Good job!
- Aug 22, 2010, 2:17:00 AM
- Jordan Gray said...
I can't figure out how to register in the NXTstep forums... When I click the Register link (http://thenxtstep.com/smf/index.php?action=register), it just tells me "An Error Has Occurred!
Sorry, registration is currently disabled."
When/How can I make an account?
- Aug 22, 2010, 9:51:00 AM
- AS said...
Very cool thing you did there.
I don't think I fully understand your graph though. You plot 'x fore/aft' and 'Net Accel'. I assume this data is somehow computed from the 3 axis accelerometer data, but could you give some more insight in what they exactly are and how they are computed?
- Aug 22, 2010, 6:21:00 PM
- Brian Davis said...
Martijn: Actually, the picture of Annie "on the brink" is a single frame from a video, but it really doesn't show much, as it's hard to follow a white cylinder as it bounces unpredictably down white water. As to what I haven't done as yet... I'm not out of ideas yet :).
AS: You're completely correct, it's a 3-axis accelerometer. To get the net acceleration (dark blue dataset), I take the data from each axis and square it, add it together, and take the square root: Net = sqrt( xaccel^2 + Yaccel^2 + Zaccel^2). That will be a measure of the total acceleration on the robot, regardless of how it happens to be oriented at the time. The "fore/aft" trace is the data from just one axis of the accelerometer - the one that was directed along the long axis of the cylinder. For this particular run, the cylinder went over almost exactly "bow first", and hit the bottom end-on, so the acceleration along that one axis was very distinctive. That didn't happen for every run (I did about 6), but for this run it was a very "clean" signal.
By looking closely at the 3-axis data, you can see when the cylinder rolls as well, but for such short runs and a well-balanced cylinder, that usually doesn't show much. Longer runs in a wave pool (OK, my in a pool making waves) need to be done next.
- Aug 22, 2010, 11:48:00 PM


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SPARTA, Ky. – Kyle Busch, driving for Joe Gibbs Racing, dominated the competition en route to capturing the inaugural NASCAR Sprint Cup Series race at the Kentucky Speedway Saturday.
A hot, muggy weekend of racing on the Ohio River was dampened by two serious accidents on the water during the Madison Regatta Sunday.
In the final outcome, Miss Madison's Oh Boy! Oberto was declared the 2011 Lucas Oil Indiana Governor's Cup champion. It was a bittersweet victory. The winning hydroplane was a broken battler and winning driver Steve David, a bruised warrior, learned of his victory in a hospital emergency room.
Communities in close regional proximity to the Kentucky Speedway need to begin bracing for the economic impact of next July's NASCAR Sprint Cup race.
That was the message expressed by Kentucky Speedway General Manager Mark Simendinger during a media day at the track last week. Ticket sales are already "way up" over previous years, he said, and the Cup race is still eight months away.
An exhaustive construction effort is underway at Kentucky Speedway to get ready for next July's inaugural NASCAR Sprint Cup Series event at the track.
Speedway Motorsports, Inc. Chairman and Chief Executive Officer Bruton Smith announced in August that the Gallatin County facility had been awarded a July 9 date on the 2011 Sprint Cup schedule. SMI owns the track that has hosted the Indy Racing League and NASCAR's Nationwide and trucks series since 2001.
The Hoegh Dubai, carrying the H1 Unlimited Hydroplane fleet, was to arrive in Doha, Qatar on Thursday, Nov. 4 (Friday, Nov. 5 in Qatar), according to the Web site www.h1unlimited.com.
The hydroplanes are in the Middle East for the Qatar Marine Sports Federation's 2010 Oryx Cup UIM World Championship. The event will be held November 18-20 on Doha Bay.
Madison's city-owned Unlimited hydroplane U-1 Oh Boy! Oberto, driven by Steve David, capped a near-perfect weekend by winning the 60th running of the Indiana Governor's Cup Race over the July 4th weekend.
With a Sprint Cup race car as a backdrop and surrounded by NASCAR and state government dignitaries, Speedway Motorsports Inc. Chairman and Chief Executive Officer Bruton Smith announced that Kentucky Speedway will play host to its inaugural 400-mile NASCAR Sprint Cup Series race on Saturday, July 9, 2011.
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The News-Democrat is your source for local news, sports, events, and information in Carroll County, KY and the surrounding area.


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Moderators: Tonyblack, Jason, Toothy
Gwishini Sanda (ghost house)
The movie follows a man who buys a (well, haunted) house and has to deal with the ghost living in it. Later on, through utterly cinematic contrivances, he sort of gets to turn the tables. In the last third of the movie, theres an evil company that wants to build a hotel that comes in and has really nothing to do with the rest of the movie, other than to finish the plot. That's probably the biggest problem with this horror/comedy.
Tonyblack wrote:Well I ordered the DVD of The Hobbit and have now watched it. I loved it and look forward to the next episode.
Ghost wrote:I found a pretty good documentary series whilst mooching about called Oddities I'm enjoying it a lot despite some of it's really obvious staging but what I'm really doing is window shopping I love the weird and unusual and a lot of the customers who come in are a damn site weirder than the object them selfs
Who's Wee Dug wrote:I thought it was rather good, I'm glad he made it into three films.
michelanCello wrote:Who's Wee Dug wrote:I thought it was rather good, I'm glad he made it into three films.
Me too! I sooo want to buy the DVD but it's still new and awfully expensive, will have to wait a year or two
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Mississippi State football coach Dan Mullen likes the situation he has at quarterback.
With a proven leader in junior Tyler Russell starting and a decent role player in redshirt freshman Dak Prescott, Mullen appears to have a pair of signal callers he can depend upon.
On Wednesday, it was announced by the Allstate Sugar Bowl that Russell was placed on the Manning Award Watch List.
"He's not only the leader of the offense, but also in the meeting room and the leader of his position group with the work ethic he's puts in and how he prepares," Mullen said on Wednesday's Southeastern Conference Coaches teleconference.
Mullen said that Russell is a good influence on Prescott.
"Dak get to see (Russell), learn from that and see how he does it," Mullen said. "They feed off each other's success.
"When Dak throws a touchdown pass, there's no one more excited than Tyler. When you get guys who want to win and do whatever it takes to win the game, they know it takes contributions from everybody on the entire roster. That excitement really helps and for Dak to see how Tyler prepares really helps him grow, helps him develop and helps him focus on what his role is from week to week."
Russell has completed 105-of-182 pass attempts for 1,382 yards and 12 touchdowns this season, while Prescott has performed well as a short-yardage quarterback with 18 rushing attempts for 54 yards and two touchdowns. He has completed 4-of-8 pass attempts for 54 yards and another score.
The two Bulldogs lead their team into action Saturday at Davis Wade Stadium against Middle Tennessee. The homecoming matchup kicks off at 6 p.m. on ESPN2.
Wild, wild West
Defending national champion Alabama is at the top of the Bowl Championship Series standings, while LSU, last year's national runner-up, is rated at No. 6.
The chances that the Southeastern Conference Western Division will be represented once again in the BCS National Championship game are pretty good.
Mullen has been keeping track of the situation.
"It's the toughest division in all of college football when you look at the depth and quality of teams that are on this side of the league," Mullen said. "Even the teams that have down years are pretty good teams. Arkansas has been a top 10 team at a point during the season. They have slipped, but have potential and still play like a top 10 team on film.
"Alabama, obviously the defending national champion, and LSU, the defending national runner-up, are two teams have really kept their game at that level. There's a lot of depth and talent in this part of the league and I can see what people look to that game (Alabama and LSU), because they played for the national championship last year and Alabama and LSU are two of the top teams in the country this year."
Green honored again
The midseason recognition keeps coming for MSU players.
Along with Russell's news on Wednesday, senior tight end Marcus Green was chosen the John Mackey Tight End of the Week, the Nassau County Sports Commission announced. Green is the only player thus far this season to earn the weekly national honor twice.
Green had a career-high six receptions for 71 yards and tied a career high with two touchdowns in the Bulldogs' 41-31 victory over Tennessee last Saturday.
On Monday, Green was selected to the midseason watch list for the 2012 John Mackey Award, given annually to the nation's top tight end. The 2012 recipient of "The Mackey" will be announced at the Home Depot ESPNU College Football Awards Red Carpet Show, to be aired live on ESPNU on Thursday, Dec. 6, 2012 at 6:30 p.m.
Alcorn back on schedule
The Bulldogs will play in-state opponent Alcorn State for the fourth time in five years at Davis Wade Stadium on Sept. 7, 2013.
MSU athletic director Scott Stricklin announced the addition of the game to the 2013 schedule on Wednesday.
"It remains important to us to schedule teams from the state of Mississippi," Stricklin said in a press release. "We've played Jackson State and Alcorn State and have Southern Miss on the schedule in coming years. We feel it benefits Mississippi State University, as well as our opponents and the state of Mississippi."
The Bulldogs announced last week a non-conference matchup in 2013 with Oklahoma State in the Texas Kick Off, which will be played at Houston's Reliant Stadium on August 31.View more articles in:


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2007 Angel Barbie® Doll
Designed by: Bill Greening
Release Date: 7/1/2007
Product Code: L3550
Meet 2007 Angel Barbie® doll, a celestial beauty from the heavenly world. Created by designer Bill Greening, this ethereal messenger represents peace, harmony, beauty and joy. Dressed in a lovely pink gown with beautiful attached wings, this divine doll comes with a golden harp, too. Talk about a heavenly vision!
For ages 6 and over.
Production doll may vary from the photo shown above. Mattel reserves the right to modify the fashion/fabrics, sculpt, hair color/style, and accessories. Doll cannot stand alone as shown.
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Meet Dream Wedding Contestants Aireal & Matt
In case you haven't heard all the buzzing yet, TheKnot.com is throwing the ultimate dream wedding on the most romantic day of the year — Valentine's Day! One lucky couple is going to win a fabulous and free New York City wedding planned by us editors at TheKnot.com and celeb wedding planner, Amy Shey Jacobs. The wedding includes everything from a designer gown and wedding bands, to luxe flowers and decor, to much much more! Each detail of the wedding will be voted on by you — Knotties across the country — so we are introducing you to some of our favorite couples!
Here's another couple we are loving!
Meet Aireal & Matt:
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Matt had a super unique and adorable proposal for Aireal. Since music is her love – second to Matt of course, he wanted to write her a song but didn't think he could compose or preform it. He decided to contact the lead singer of her favorite band to see if they would write music for his lyrics. The band actually agreed! Within three weeks they had taken Matt's words, put them to music, sang, and recorded the song. The band added extra instrumentals to the end of the song to set up his proposal. How sweet is that?
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Watch more about their story:
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They don't build houses like this anymore. That's why folks in Lake Forest, Ill. are happy to see the Lake County School House restored to its former glory.
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The harsh Midwestern winter caused Casey and crew to get a bit creative on this project. In their effort to complete the home before the snow began to fall in late 2003, they completely encapsulated the house in tarps and heated the space between the house and plastic with two torpedo heaters. This method kept both the substrate and paint at 50º F, effectively allowing Town & Country to extend its exterior painting season long enough to finish the job. The end result is something both he and the entire community can take pride in.
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The son of Star Trek legend Leonard Nimoy, Adam Nimoy raised over $600,000 through Kickstarter to make a documentary that will focus on the pop icon and his legacy, having received little interest from film studios.
Adam Nimoy is a TV director and film professor, so the production, titled "For the Love of…
Billy Joel, the 66-year-old music legend married his 34-year-old pregnant bride, Alexis Roderick in a ceremony on July 4th in a surprise wedding on Long Island, New York.
The newlyweds have been dating since 2009 and expect their first child together, a daughter, later this summer.
Never mind that Alexis i…
Amy Schumer is really having her moment right now, and she is continuing to do so with the offer of a "disappointing lap dance" that she will give to raise money for The National Multiple Sclerosis Society while also promoting her new film Trainwreck to hit theaters on July 17th.
Schumer is giving…
First daughter Malia Obama, and incidentally also one of our style icons, was spotted in Brooklyn on Friday, shooting an episode of Lena Dunham's television series 'Girls.'
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Alanz wrote:Then we wait for the expected Freezing weather to come, is there no end, I thought it was supposed to be getting warmer, what happend to Global warming?
I've always found the term 'Climate Change' myself, since it seems more accurate.
I hope everyone stays safe and dry. We only experienced high winds here and, as usual, bits of the neighbour's fence ended up in our garden. Surprisingly, we didn't appear to lose any roof slates this time.


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