Massachusetts Institute of Technology

About: Massachusetts Institute of Technology is a education organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Population & Laser. The organization has 116795 authors who have published 268000 publications receiving 18272025 citations. The organization is also known as: MIT & M.I.T..

In Situ Formation of an Oxygen-Evolving Catalyst in Neutral Water Containing Phosphate and Co2+

Abstract: The utilization of solar energy on a large scale requires its storage. In natural photosynthesis, energy from sunlight is used to rearrange the bonds of water to oxygen and hydrogen equivalents. The realization of artificial systems that perform "water splitting" requires catalysts that produce oxygen from water without the need for excessive driving potentials. Here we report such a catalyst that forms upon the oxidative polarization of an inert indium tin oxide electrode in phosphate-buffered water containing cobalt (II) ions. A variety of analytical techniques indicates the presence of phosphate in an approximate 1:2 ratio with cobalt in this material. The pH dependence of the catalytic activity also implicates the hydrogen phosphate ion as the proton acceptor in the oxygen-producing reaction. This catalyst not only forms in situ from earth-abundant materials but also operates in neutral water under ambient conditions.

The entity-relationship model: toward a unified view of data

Abstract: A data model, called the entity-relationship model, is proposed. This model incorporates some of the important semantic information about the real world. A special diagrammatic technique is introduced as a tool for database design. An example of database design and description using the model and the diagrammatic technique is given. Some implications for data integrity, information retrieval, and data manipulation are discussed.The entity-relationship model can be used as a basis for unification of different views of data: the network model, the relational model, and the entity set model. Semantic ambiguities in these models are analyzed. Possible ways to derive their views of data from the entity-relationship model are presented.

Somatic generation of antibody diversity

Abstract: In the genome of a germ-line cell, the genetic information for an immunoglobulin polypeptide chain is contained in multiple gene segments scattered along a chromosome. During the development of bone marrow-derived lymphocytes, these gene segments are assembled by recombination which leads to the formation of a complete gene. In addition, mutations are somatically introduced at a high rate into the amino-terminal region. Both somatic recombination and mutation contribute greatly to an increase in the diversity of antibody synthesized by a single organism.

The Strength of Weak Learnability

Abstract: This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distribution-free (PAC) learning model. A concept class is learnable (or strongly learnable) if, given access to a source of examples of the unknown concept, the learner with high probability is able to output an hypothesis that is correct on all but an arbitrarily small fraction of the instances. The concept class is weakly learnable if the learner can produce an hypothesis that performs only slightly better than random guessing. In this paper, it is shown that these two notions of learnability are equivalent. A method is described for converting a weak learning algorithm into one that achieves arbitrarily high accuracy. This construction may have practical applications as a tool for efficiently converting a mediocre learning algorithm into one that performs extremely well. In addition, the construction has some interesting theoretical consequences, including a set of general upper bounds on the complexity of any strong learning algorithm as a function of the allowed error e.