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Archive for 七月 2010

Erfolgreich fern der Heimat

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Weiwei Cheng bei der PreisverleihungDoktorand des Marburger Fachbereichs Mathematik und Informatik erhält chinesische Auszeichnung

Weiwei Cheng, Doktorand am Marburger Fachbereich Mathematik und Informatik, hat den “Chinese Government Award for Outstanding Self-Financed Students Abroad” erhalten.

Chengs Forschungsschwerpunkt liegt im so genannten Präferenzlernen: Er entwickelt beispielsweise auf der Basis von gegebenen Daten und Beobachtungen über die bevorzugt besuchten Internetseiten einer Person Ranking-Modelle, mit deren Hilfe man dem Nutzer Alternativen zur Verfügung stellen kann. In seiner Ansprache, die Cheng bei der Preisverleihung in der chinesischen Botschaft in Berlin auch stellvertretend für die 36 weiteren Geehrten gab, dankte er seinem Marburger Mentor Professor Dr. Eyke Hüllermeier dafür, dass er “mich an das Forschungsfeld Maschinelles Lernen herangeführt hat”.

Hüllermeier lobt Fleiß und Ehrgeiz seines Doktoranden: “Er hat alles, was ein guter Wissenschaftler braucht, und kann seine Arbeiten sehr gut kommunizieren.”

Der mit 5000 Dollar dotierte Preis wird jährlich vom “China Scholarship Council” an chinesische Nachwuchswissenschaftler vergeben, die während ihres Graduiertenstudiums im Ausland überdurchschnittliche Leistungen erzielen und sich selbst finanzieren, also nicht von staatlicher Förderung abhängig sind.

Quelle: Pressestelle der Philipps-Universität Marburg
Update: Ein Artikel aus Oberhessische Presse


Written by Weiwei

21/07/2010 at 00:43

发表在 转贴

The Most Important Algorithms (in CS and Math)

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我接触的同僚之中,大约每个人心里都有自己最爱的几种算法。下面是Christoph Koutschan列出来的32类计算机与数学领域最为重要的算法(按字符顺序排列)。覆盖的面很广,评价很精准。

  1. A* search algorithm
    Graph search algorithm that finds a path from a given initial node to a given goal node. It employs a heuristic estimate that ranks each node by an estimate of the best route that goes through that node. It visits the nodes in order of this heuristic estimate. The A* algorithm is therefore an example of best-first search.
  2. Beam Search
    Beam search is a search algorithm that is an optimization of best-first search. Like best-first search, it uses a heuristic function to evaluate the promise of each node it examines. Beam search, however, only unfolds the first m most promising nodes at each depth, where m is a fixed number, the beam width.
  3. Binary search
    Technique for finding a particular value in a linear array, by ruling out half of the data at each step.
  4. Branch and bound
    A general algorithmic method for finding optimal solutions of various optimization problems, especially in discrete and combinatorial optimization.
  5. Buchberger’s algorithm
    In computational algebraic geometry and computational commutative algebra, Buchberger’s algorithm is a method of transforming a given set of generators for a polynomial ideal into a Gröbner basis with respect to some monomial order. One can view it as a generalization of the Euclidean algorithm for univariate gcd computation and of Gaussian elimination for linear systems.
  6. Data compression
    Data compression or source coding is the process of encoding information using fewer bits (or other information-bearing units) than an unencoded representation would use through use of specific encoding schemes.
  7. Diffie-Hellman key exchange
    Cryptographic protocol which allows two parties that have no prior knowledge of each other to jointly establish a shared secret key over an insecure communications channel. This key can then be used to encrypt subsequent communications using a symmetric key cipher.
  8. Dijkstra’s algorithm
    Algorithm that solves the single-source shortest path problem for a directed graph with nonnegative edge weights.
  9. Discrete differentiation
    I.e., the formula f'(x) = (f(x+h) – f(x-h)) / 2h.
  10. Dynamic programming
    Dynamic programming is a method for reducing the runtime of algorithms exhibiting the properties of overlapping subproblems and optimal substructure, described below.
  11. Euclidean algorithm
    Algorithm to determine the greatest common divisor (gcd) of two integers. It is one of the oldest algorithms known, since it appeared in Euclid’s Elements around 300 BC. The algorithm does not require factoring the two integers.
  12. Expectation-maximization algorithm (EM-Training)
    In statistical computing, an expectation-maximization (EM) algorithm is an algorithm for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. EM alternates between performing an expectation step, which computes the expected value of the latent variables, and a maximization step, which computes the maximum likelihood estimates of the parameters given the data and setting the latent variables to their expectation.
  13. Fast Fourier transform (FFT)
    Efficient algorithm to compute the discrete Fourier transform (DFT) and its inverse. FFTs are of great importance to a wide variety of applications, from digital signal processing to solving partial differential equations to algorithms for quickly multiplying large integers.
  14. Gradient descent
    Gradient descent is an optimization algorithm that approaches a local minimum of a function by taking steps proportional to the negative of the gradient (or the approximate gradient) of the function at the current point. If instead one takes steps proportional to the gradient, one approaches a local maximum of that function; the procedure is then known as gradient ascent.
  15. Hashing
    A function for summarizing or probabilistically identifying data. Typically this means one applies a mathematical formula to the data, producing a string which is probably more or less unique to that data. The string is much shorter than the original data, but can be used to uniquely identify it.
  16. Heaps (heap sort)
    In computer science a heap is a specialized tree-based data structure. Heaps are favourite data structures for many applications: Heap sort, selection algorithms (finding the min, max or both of them, median or even any kth element in sublinear time), graph algorithms.
  17. Karatsuba multiplication
    For systems that need to multiply numbers in the range of several thousand digits, such as computer algebra systems and bignum libraries, long multiplication is too slow. These systems employ Karatsuba multiplication, which was discovered in 1962.
  18. LLL algorithm
    The Lenstra-Lenstra-Lovasz lattice reduction (LLL) algorithm is an algorithm which, given a lattice basis as input, outputs a basis with short, nearly orthogonal vectors. The LLL algorithm has found numerous applications in cryptanalysis of public-key encryption schemes: knapsack cryptosystems, RSA with particular
    settings, and so forth.
  19. Maximum flow
    The maximum flow problem is finding a legal flow through a flow network that is maximal. Sometimes it is defined as finding the value of such a flow. The maximum flow problem can be seen as special case of more complex network flow problems. The maximal flow is related to the cuts in a network by the Max-flow min-cut theorem. The Ford-Fulkerson algorithm computes the maximum flow in a flow network.
  20. Merge sort
    A sorting algorithm for rearranging lists (or any other data structure that can only be accessed sequentially, e.g. file streams) into a specified order.
  21. Newton’s method
    Efficient algorithm for finding approximations to the zeros (or roots) of a real-valued function. Newton’s method is also a well-known algorithm for finding roots of equations in one or more dimensions. It can also be used to find local maxima and local minima of functions.
  22. Q-learning
    Q-learning is a reinforcement learning technique that works by learning an action-value function that gives the expected utility of taking a given action in a given state and following a fixed policy thereafter. A strength with Q-learning is that it is able to compare the expected utility of the available actions without requiring a model of the environment.
  23. Quadratic sieve
    The quadratic sieve algorithm (QS) is a modern integer factorization algorithm and, in practice, the second fastest method known (after the number field sieve, NFS). It is still the fastest for integers under 110 decimal digits or so, and is considerably simpler than the number field sieve.
  24. RANSAC
    RANSAC is an abbreviation for “RANdom SAmple Consensus”. It is an algorithm to estimate parameters of a mathematical model from a set of observed data which contains “outliers”. A basic assumption is that the data consists of “inliers”, i. e., data points which can be explained by some set of model parameters, and “outliers” which are data points that do not fit the model.
  25. RSA
    Algorithm for public-key encryption. It was the first algorithm known to be suitable for signing as well as encryption. RSA is still widely used in electronic commerce protocols, and is believed to be secure given sufficiently long keys.
  26. Schönhage-Strassen algorithm
    In mathematics, the Schönhage-Strassen algorithm is an asymptotically fast method for multiplication of large integer numbers. The run-time is O(N log(N) log(log(N))). The algorithm uses Fast Fourier Transforms in rings.
  27. Simplex algorithm
    In mathematical optimization theory, the simplex algorithm a popular technique for numerical solution of the linear programming problem. A linear programming problem consists of a collection of linear inequalities on a number of real variables and a fixed linear functional which is to be maximized (or minimized).
  28. Singular value decomposition (SVD)
    In linear algebra, SVD is an important factorization of a rectangular real or complex matrix, with several applications in signal processing and statistics, e.g., computing the pseudoinverse of a matrix (to solve the least squares problem), solving overdetermined linear systems, matrix approximation, numerical weather prediction.
  29. Solving a system of linear equations
    Systems of linear equations belong to the oldest problems in mathematics and they have many applications, such as in digital signal processing, estimation, forecasting and generally in linear programming and in the approximation of non-linear problems in numerical analysis. An efficient way to solve systems of linear equations is given by the Gauss-Jordan elimination or by the Cholesky decomposition.
  30. Strukturtensor
    In pattern recognition: Computes a measure for every pixel which tells you if this pixel is located in a homogenous region, if it belongs to an edge, or if it is a vertex.
  31. Union-find
    Given a set of elements, it is often useful to partition them into a number of separate, nonoverlapping groups. A disjoint-set data structure is a data structure that keeps track of such a partitioning. A union-find algorithm is an
    algorithm that performs two useful operations on such a data structure:
    Find: Determine which group a particular element is in.
    Union: Combine or merge two groups into a single group.
  32. Viterbi algorithm
    Dynamic programming algorithm for finding the most likely sequence of hidden states – known as the Viterbi path – that result in a sequence of observed events, especially in the context of hidden Markov models.
Daniel Lemire在看到这个名单之后,列出了他心中的Top 5:
  • Binary search is the first non-trivial algorithm I remember learning.
  • The Fast Fourier transform (FFT) is an amazing algorithm. Combined with the convolution theorem, it lets you do magic.
  • While hashing is not an algorithm, it is one of the most powerful and useful idea in Computer Science. It takes minutes to explain it, but years to master.
  • Merge sort is the most elegant sorting algorithm. You can explain it in three sentences to anyone.
  • While not an algorithm per se, the Singular Value Decomposition (SVD) is the most important Linear Algebra concept I don’t remember learning as an undergraduate. (And yes, I went to a good school. And yes, I was an A student.) It can help you invert singular matrices and do other similar magic.

Written by Weiwei

17/07/2010 at 14:10

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The Expendables

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I will definitely go to watch this movie of action legends.
check the trailer

Written by Weiwei

15/07/2010 at 23:03

发表在 娱乐

Stay Hungry. Stay Foolish.

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“Stay Hungry. Stay Foolish.”(保持饥饿,保持愚蠢)是一句励志格言,出自一九七四年版的The Whole Earth Catalog。The Whole Earth Catalog是一份类似于邮购杂志的刊物,上面刊登着各色各类的商品及其价格;不同于一般的邮购杂志,The Whole Earth Catalog立志于挖掘和展现立意新颖、形式嬉皮的产品。在六七十年代,她在美国青年人当中有着相当的影响力,被称作那个时代的圣物之一。现在,经常有人把她叫作“40年前纸质的Google”。“Stay Hungry. Stay Foolish.”就是写在一九七四年版The Whole Earth Catalog封底的临别赠言。二零零五年,苹果CEO斯蒂文·乔布斯在斯坦福大学毕业典礼上发表演讲。在那段传奇演讲的最后,乔布斯引用了这句话。



这种对自己的高估是普遍存在的。心理学有一个专门的名词,叫做“虚幻的优越性”(illusory superiority)。


“Stay Hungry. Stay Foolish.”


Written by Weiwei

03/07/2010 at 22:04

发表在 杂话

The key word is learning, of course! Again.

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It is the word cloud extracted from the titles of accepted papers at ECMLPKDD 2010. Click the image for an enlarged picture. It is also available at This year ECMLPKDD has accepted 120 papers with an acceptance rate of 18%.

You can check a similar word cloud extracted from ICML 2010 at my previous post. That makes a good comparison.

Written by Weiwei

02/07/2010 at 14:46

发表在 学术