Mitsubishi

About: Mitsubishi is a company organization based out in Tokyo, Japan. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 53115 authors who have published 54821 publications receiving 870150 citations. The organization is also known as: Mitsubishi Group of Companies & Mitsubishi Companies.

Rapid object detection using a boosted cascade of simple features

Abstract: This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

The complete genome sequence of the Gram-positive bacterium Bacillus subtilis

Abstract: Bacillus subtilis is the best-characterized member of the Gram-positive bacteria. Its genome of 4,214,810 base pairs comprises 4,100 protein-coding genes. Of these protein-coding genes, 53% are represented once, while a quarter of the genome corresponds to several gene families that have been greatly expanded by gene duplication, the largest family containing 77 putative ATP-binding transport proteins. In addition, a large proportion of the genetic capacity is devoted to the utilization of a variety of carbon sources, including many plant-derived molecules. The identification of five signal peptidase genes, as well as several genes for components of the secretion apparatus, is important given the capacity of Bacillus strains to secrete large amounts of industrially important enzymes. Many of the genes are involved in the synthesis of secondary metabolites, including antibiotics, that are more typically associated with Streptomyces species. The genome contains at least ten prophages or remnants of prophages, indicating that bacteriophage infection has played an important evolutionary role in horizontal gene transfer, in particular in the propagation of bacterial pathogenesis.

Suppression of basal autophagy in neural cells causes neurodegenerative disease in mice

Abstract: Autophagy is an intracellular bulk degradation process through which a portion of the cytoplasm is delivered to lysosomes to be degraded. Although the primary role of autophagy in many organisms is in adaptation to starvation, autophagy is also thought to be important for normal turnover of cytoplasmic contents, particularly in quiescent cells such as neurons. Autophagy may have a protective role against the development of a number of neurodegenerative diseases. Here we report that loss of autophagy causes neurodegeneration even in the absence of any disease-associated mutant proteins. Mice deficient for Atg5 (autophagy-related 5) specifically in neural cells develop progressive deficits in motor function that are accompanied by the accumulation of cytoplasmic inclusion bodies in neurons. In Atg5-/- cells, diffuse, abnormal intracellular proteins accumulate, and then form aggregates and inclusions. These results suggest that the continuous clearance of diffuse cytosolic proteins through basal autophagy is important for preventing the accumulation of abnormal proteins, which can disrupt neural function and ultimately lead to neurodegeneration.

Constructing free-energy approximations and generalized belief propagation algorithms

Abstract: Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems that is exact when the factor graph is a tree, but only approximate when the factor graph has cycles. We show that BP fixed points correspond to the stationary points of the Bethe approximation of the free energy for a factor graph. We explain how to obtain region-based free energy approximations that improve the Bethe approximation, and corresponding generalized belief propagation (GBP) algorithms. We emphasize the conditions a free energy approximation must satisfy in order to be a "valid" or "maxent-normal" approximation. We describe the relationship between four different methods that can be used to generate valid approximations: the "Bethe method", the "junction graph method", the "cluster variation method", and the "region graph method". Finally, we explain how to tell whether a region-based approximation, and its corresponding GBP algorithm, is likely to be accurate, and describe empirical results showing that GBP can significantly outperform BP.