We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Motivation There is a strong demand in the genomic community to develop effective algorithms to reliably identify genomic variants. Indel detection using next-gen data is difficult and identification of long structural variations is extremely challenging. Results We present Pindel, a pattern growth approach, to detect breakpoints of large deletions and medium-sized insertions from paired-end short reads. We use both simulated reads and real data to demonstrate the efficiency of the computer program and accuracy of the results. Availability The binary code and a short user manual can be freely downloaded from http://www.ebi.ac.uk/ approximately kye/pindel/. Contact k.ye@lumc.nl; zn1@sanger.ac.uk.

/pdf/pindel-a-pattern-growth-approach-to-detect-break-points-of-n66j6n0f0o.pdf

Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads

Experimental Psychology Its Scope and Method. IV. Learning and Memory. By Jean-Francois Le Ny, G. De Montpellier, G. Oleron and Cesar Flores. Translated by Louise Elkington. Edited by P. Fraisse and Jean Piaget. Pp. viii + 376. (Routledge and Kegan Paul: London, April 1970.) 80s.

Learning and Memory

Convolutional neural networks (CNNs) have gained remarkable success on many image classification tasks in recent years. However, the performance of CNNs highly relies upon their architectures. For the most state-of-the-art CNNs, their architectures are often manually designed with expertise in both CNNs and the investigated problems. Therefore, it is difficult for users, who have no extended expertise in CNNs, to design optimal CNN architectures for their own image classification problems of interest. In this article, we propose an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks. The most merit of the proposed algorithm remains in its “automatic” characteristic that users do not need domain knowledge of CNNs when using the proposed algorithm, while they can still obtain a promising CNN architecture for the given images. The proposed algorithm is validated on widely used benchmark image classification datasets, compared to the state-of-the-art peer competitors covering eight manually designed CNNs, seven automatic + manually tuning, and five automatic CNN architecture design algorithms. The experimental results indicate the proposed algorithm outperforms the existing automatic CNN architecture design algorithms in terms of classification accuracy, parameter numbers, and consumed computational resources. The proposed algorithm also shows the very comparable classification accuracy to the best one from manually designed and automatic + manually tuning CNNs, while consuming fewer computational resources.

Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification

In the popular computer game of Tetris, the player is given a sequence of tetromino pieces and must pack them into a rectangular gameboard initially occupied by a given configuration of filled squares; any completely filled row of the gameboard is cleared and all filled squares above it drop by one row. We prove that in the offline version of Tetris, it is -complete to maximize the number of cleared rows, maximize the number of tetrises (quadruples of rows simultaneously filled and cleared), minimize the maximum height of an occupied square, or maximize the number of pieces placed before the game ends. We furthermore show the extreme inapproximability of the first and last of these objectives to within a factor of p1-e, when given a sequence of p pieces, and the inapproximability of the third objective to within a factor of 2-e, for any e>0. Our results hold under several variations on the rules of Tetris, including different models of rotation, limitations on player agility, and restricted piece sets.

Tetris is hard, even to approximate

When Genetic Programming is used to evolve decision trees for data classification, search spaces tend to become extremely large. We present several methods using techniques from the field of machine learning to refine and thereby reduce the search space sizes for decision tree evolvers. We will show that these refinement methods improve the classification performance of our algorithms.

/pdf/genetic-programming-for-data-classification-partitioning-the-1vjvz4syi3.pdf

Genetic Programming for data classification: partitioning the search space

Theory of genetic algorithms

A lanthanide complex, named CLaNP (caged lanthanide NMR probe) has been developed for the characterisation of proteins by par- amagnetic NMR spectroscopy. The probe consists of a lanthanide chelated by a derivative of DTPA (diethylene- triaminepentaacetic acid) with two thiol reactive functional groups. The CLaNP molecule is attached to a pro- tein by two engineered, surface-ex- posed, Cys residues in a bidentate manner. This drastically limits the dy- namics of the metal relative to the pro- tein and enables measurements of pseudocontact shifts. NMR spectrosco- py experiments on a diamagnetic con- trol and the crystal structure of the probe-protein complex demonstrate that the protein structure is not affect- ed by probe attachment. The probe is able to induce pseudocontact shifts to at least 40 a from the metal and causes residual dipolar couplings due to align- ment at a high magnetic field. The mol- ecule exists in several isomeric forms with different paramagnetic tensors; this provides a fast way to obtain long- range distance restraints.

/pdf/a-caged-lanthanide-complex-as-a-paramagnetic-shift-agent-for-3c6fztmqw7.pdf

A Caged Lanthanide Complex as a Paramagnetic Shift Agent for Protein NMR

Narrative reports and criminal records are stored digitally across individual police departments, enabling the collection of this data to compile a nation-wide database of criminals and the crimes they committed. The compilation of this data through the last years presents new possibilities of analyzing criminal activity through time. Augmenting the traditional, more socially oriented, approach of behavioral study of these criminals and traditional statistics, data mining methods like clustering and prediction enable police forces to get a clearer picture of criminal careers. This allows officers to recognize crucial spots in changing criminal behaviour and deploy resources to prevent these careers from unfolding. Four important factors play a role in the analysis of criminal careers: crime nature, frequency, duration and severity. We describe a tool that extracts these from the database and creates digital profiles for all offenders. It compares all individuals on these profiles by a new distance measure and clusters them accordingly. This method yields a visual clustering of these criminal careers and enables the identification of classes of criminals. The proposed method allows for several user-defined parameters.

/pdf/data-mining-approaches-to-criminal-career-analysis-37qsa4s25w.pdf

Walter A. Kosters

Papers

Tetris is hard, even to approximate

Genetic Programming for data classification: partitioning the search space

Theory of genetic algorithms

A Caged Lanthanide Complex as a Paramagnetic Shift Agent for Protein NMR

Data Mining Approaches to Criminal Career Analysis