@article{3320,
abstract = {Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints. This monograph introduces the reader to the most popular classes of structured models in computer vision. Our focus is discrete undirected graphical models which we cover in detail together with a description of algorithms for both probabilistic inference and maximum a posteriori inference. We discuss separately recently successful techniques for prediction in general structured models. In the second part of this monograph we describe methods for parameter learning where we distinguish the classic maximum likelihood based methods from the more recent prediction-based parameter learning methods. We highlight developments to enhance current models and discuss kernelized models and latent variable models. To make the monograph more practical and to provide links to further study we provide examples of successful application of many methods in the computer vision literature.},
author = {Nowozin, Sebastian and Lampert, Christoph},
journal = {Foundations and Trends in Computer Graphics and Vision},
number = {3-4},
pages = {185 -- 365},
publisher = {now},
title = {{Structured learning and prediction in computer vision}},
doi = {10.1561/0600000033},
volume = {6},
year = {2011},
}
@inproceedings{3325,
abstract = {We introduce streaming data string transducers that map input data strings to output data strings in a single left-to-right pass in linear time. Data strings are (unbounded) sequences of data values, tagged with symbols from a finite set, over a potentially infinite data do- main that supports only the operations of equality and ordering. The transducer uses a finite set of states, a finite set of variables ranging over the data domain, and a finite set of variables ranging over data strings. At every step, it can make decisions based on the next in- put symbol, updating its state, remembering the input data value in its data variables, and updating data-string variables by concatenat- ing data-string variables and new symbols formed from data vari- ables, while avoiding duplication. We establish that the problems of checking functional equivalence of two streaming transducers, and of checking whether a streaming transducer satisfies pre/post verification conditions specified by streaming acceptors over in- put/output data-strings, are in PSPACE. We identify a class of imperative and a class of functional pro- grams, manipulating lists of data items, which can be effectively translated to streaming data-string transducers. The imperative pro- grams dynamically modify a singly-linked heap by changing next- pointers of heap-nodes and by adding new nodes. The main re- striction specifies how the next-pointers can be used for traversal. We also identify an expressively equivalent fragment of functional programs that traverse a list using syntactically restricted recursive calls. Our results lead to algorithms for assertion checking and for checking functional equivalence of two programs, written possibly in different programming styles, for commonly used routines such as insert, delete, and reverse.},
author = {Alur, Rajeev and Cerny, Pavol},
location = {Texas, USA},
number = {1},
pages = {599 -- 610},
publisher = {ACM},
title = {{Streaming transducers for algorithmic verification of single pass list processing programs}},
doi = {10.1145/1926385.1926454},
volume = {46},
year = {2011},
}
@article{3382,
abstract = {Dynamic tactile sensing is a fundamental ability to recognize materials and objects. However, while humans are born with partially developed dynamic tactile sensing and quickly master this skill, today's robots remain in their infancy. The development of such a sense requires not only better sensors but the right algorithms to deal with these sensors' data as well. For example, when classifying a material based on touch, the data are noisy, high-dimensional, and contain irrelevant signals as well as essential ones. Few classification methods from machine learning can deal with such problems. In this paper, we propose an efficient approach to infer suitable lower dimensional representations of the tactile data. In order to classify materials based on only the sense of touch, these representations are autonomously discovered using visual information of the surfaces during training. However, accurately pairing vision and tactile samples in real-robot applications is a difficult problem. The proposed approach, therefore, works with weak pairings between the modalities. Experiments show that the resulting approach is very robust and yields significantly higher classification performance based on only dynamic tactile sensing.},
author = {Kroemer, Oliver and Lampert, Christoph and Peters, Jan},
journal = {IEEE Transactions on Robotics},
number = {3},
pages = {545 -- 557},
publisher = {IEEE},
title = {{Learning dynamic tactile sensing with robust vision based training}},
doi = {10.1109/TRO.2011.2121130},
volume = {27},
year = {2011},
}
@article{3387,
abstract = {Background: Supertree methods combine overlapping input trees into a larger supertree. Here, I consider split-based supertree methods that first extract the split information of the input trees and subsequently combine this split information into a phylogeny. Well known split-based supertree methods are matrix representation with parsimony and matrix representation with compatibility. Combining input trees on the same taxon set, as in the consensus setting, is a well-studied task and it is thus desirable to generalize consensus methods to supertree methods. Results: Here, three variants of majority-rule (MR) supertrees that generalize majority-rule consensus trees are investigated. I provide simple formulas for computing the respective score for bifurcating input- and supertrees. These score computations, together with a heuristic tree search minmizing the scores, were implemented in the python program PluMiST (Plus- and Minus SuperTrees) available from http://www.cibiv.at/software/ plumist. The different MR methods were tested by simulation and on real data sets. The search heuristic was successful in combining compatible input trees. When combining incompatible input trees, especially one variant, MR(-) supertrees, performed well. Conclusions: The presented framework allows for an efficient score computation of three majority-rule supertree variants and input trees. I combined the score computation with a heuristic search over the supertree space. The implementation was tested by simulation and on real data sets and showed promising results. Especially the MR(-) variant seems to be a reasonable score for supertree reconstruction. Generalizing these computations to multifurcating trees is an open problem, which may be tackled using this framework.},
author = {Kupczok, Anne},
journal = {BMC Evolutionary Biology},
number = {205},
publisher = {BioMed Central},
title = {{Split based computation of majority rule supertrees}},
doi = {10.1186/1471-2148-11-205},
volume = {11},
year = {2011},
}
@article{3394,
abstract = {Random genetic drift shifts clines in space, alters their width, and distorts their shape. Such random fluctuations complicate inferences from cline width and position. Notably, the effect of genetic drift on the expected shape of the cline is opposite to the naive (but quite common) misinterpretation of classic results on the expected cline. While random drift on average broadens the overall cline in expected allele frequency, it narrows the width of any particular cline. The opposing effects arise because locally, drift drives alleles to fixation—but fluctuations in position widen the expected cline. The effect of genetic drift can be predicted from standardized variance in allele frequencies, averaged across the habitat: 〈F〉. A cline maintained by spatially varying selection (step change) is expected to be narrower by a factor of relative to the cline in the absence of drift. The expected cline is broader by the inverse of this factor. In a tension zone maintained by underdominance, the expected cline width is narrower by about 1 – 〈F〉relative to the width in the absence of drift. Individual clines can differ substantially from the expectation, and we give quantitative predictions for the variance in cline position and width. The predictions apply to clines in almost one-dimensional circumstances such as hybrid zones in rivers, deep valleys, or along a coast line and give a guide to what patterns to expect in two dimensions.},
author = {Polechova, Jitka and Barton, Nicholas H},
journal = {Genetics},
number = {1},
pages = {227 -- 235},
publisher = {Genetics Society of America},
title = {{Genetic drift widens the expected cline but narrows the expected cline width}},
doi = {10.1534/genetics.111.129817},
volume = {189},
year = {2011},
}