California Institute of Technology

About: California Institute of Technology is a education organization based out in Pasadena, California, United States. It is known for research contribution in the topics: Galaxy & Population. The organization has 57649 authors who have published 146691 publications receiving 8620287 citations. The organization is also known as: Caltech & Cal Tech.

The Multidimensional Wisdom of Crowds

Abstract: Distributing labeling tasks among hundreds or thousands of annotators is an increasingly important method for annotating large datasets. We present a method for estimating the underlying value (e.g. the class) of each image from (noisy) annotations provided by multiple annotators. Our method is based on a model of the image formation and annotation process. Each image has different characteristics that are represented in an abstract Euclidean space. Each annotator is modeled as a multidimensional entity with variables representing competence, expertise and bias. This allows the model to discover and represent groups of annotators that have different sets of skills and knowledge, as well as groups of images that differ qualitatively. We find that our model predicts ground truth labels on both synthetic and real data more accurately than state of the art methods. Experiments also show that our model, starting from a set of binary labels, may discover rich information, such as different "schools of thought" amongst the annotators, and can group together images belonging to separate categories.

Neutron star dynamos and the origins of pulsar magnetism

Abstract: Neutron star convection is a transient phenomenon and has an extremely high magnetic Reynolds number In this sense, a neutron star dynamo is the quintessential fast dynamo The convective motions are only mildly turbulent on scales larger than the approximately 100 cm neutrino mean free path, but the turbulence is well developed on smaller scales Several fundamental issues in the theory of fast dynamos are raised in the study of a neutron star dynamo, in particular the possibility of dynamo action in mirror-symmetric turbulence It is argued that in any high magnetic Reynolds number dynamo, most of the magnetic energy becomes concentrated in thin flux ropes when the field pressure exceeds the turbulent pressure at the smallest scale of turbulence In addition, the possibilities for dynamo action during the various (pre-collapse) stages of convective motion that occur in the evolution of a massive star are examined, and the properties of white dwarf and neutron star progenitors are contrasted

The capacity of the Hopfield associative memory

Abstract: Techniques from coding theory are applied to study rigorously the capacity of the Hopfield associative memory. Such a memory stores n -tuple of \pm 1 's. The components change depending on a hard-limited version of linear functions of all other components. With symmetric connections between components, a stable state is ultimately reached. By building up the connection matrix as a sum-of-outer products of m fundamental memories, one hopes to be able to recover a certain one of the m memories by using an initial n -tuple probe vector less than a Hamming distance n/2 away from the fundamental memory. If m fundamental memories are chosen at random, the maximum asympotic value of m in order that most of the m original memories are exactly recoverable is n/(2 \log n) . With the added restriction that every one of the m fundamental memories be recoverable exactly, m can be no more than n/(4 \log n) asymptotically as n approaches infinity. Extensions are also considered, in particular to capacity under quantization of the outer-product connection matrix. This quantized memory capacity problem is closely related to the capacity of the quantized Gaussian channel.