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..

Trapdoors for hard lattices and new cryptographic constructions

Abstract: We show how to construct a variety of "trapdoor" cryptographic tools assuming the worst-case hardness of standard lattice problems (such as approximating the length of the shortest nonzero vector to within certain polynomial factors). Our contributions include a new notion of trapdoor function with preimage sampling, simple and efficient "hash-and-sign" digital signature schemes, and identity-based encryption. A core technical component of our constructions is an efficient algorithm that, given a basis of an arbitrary lattice, samples lattice points from a discrete Gaussian probability distribution whose standard deviation is essentially the length of the longest Gram-Schmidt vector of the basis. A crucial security property is that the output distribution of the algorithm is oblivious to the particular geometry of the given basis.

Telerobotics, Automation, and Human Supervisory Control

Abstract: Theory and models of supervisory control - frameworks and fragments supervisory control of anthropomorphic teleoperators for space, undersea, and other applications supervisory control in transportation, process, and other automated systems social implications of telerobotics, automation, and supervisory control.

Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

Abstract: In this paper we present a new methodology of class discovery and clustering validation tailored to the task of analyzing gene expression data. The method can best be thought of as an analysis approach, to guide and assist in the use of any of a wide range of available clustering algorithms. We call the new methodology consensus clustering, and in conjunction with resampling techniques, it provides for a method to represent the consensus across multiple runs of a clustering algorithm and to assess the stability of the discovered clusters. The method can also be used to represent the consensus over multiple runs of a clustering algorithm with random restart (such as K-means, model-based Bayesian clustering, SOM, etc.), so as to account for its sensitivity to the initial conditions. Finally, it provides for a visualization tool to inspect cluster number, membership, and boundaries. We present the results of our experiments on both simulated data and real gene expression data aimed at evaluating the effectiveness of the methodology in discovering biologically meaningful clusters.

Oxidized redox state of glutathione in the endoplasmic reticulum

Abstract: The redox state of the endoplasmic reticulum (ER) was measured with the peptide N-Acetyl-Asn-Tyr-Thr-Cys-NH2. The peptide diffused across cellular membranes; some became glycosylated and thus trapped within the secretory pathway, and its cysteine residue underwent reversible thiol-disulfide exchanges with the surrounding redox buffer. Glycosylated peptides from cells were disulfide-linked to glutathione, indicating that glutathione is the major redox buffer in the secretory pathway. The redox state of the secretory pathway was more oxidative than that of the cytosol; the ratio of reduced glutathione to the disulfide form (GSH/GSSG) within the secretory pathway ranged from 1:1 to 3:1, whereas the overall cellular GSH/GSSG ratio ranged from 30:1 to 100:1. Cytosolic glutathione was also transported into the lumen of microsomes in a cell-free system. Although how the ER maintains an oxidative environment is not known, these results suggest that the demonstrated preferential transport of GSSG compared to GSH into the ER lumen may contribute to this redox compartmentation.

A Bayesian computer vision system for modeling human interactions

Abstract: We describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task. The system deals in particularly with detecting when interactions between people occur and classifying the type of interaction. Examples of interesting interaction behaviors include following another person, altering one's path to meet another, and so forth. Our system combines top-down with bottom-up information in a closed feedback loop, with both components employing a statistical Bayesian approach. We propose and compare two different state-based learning architectures, namely, HMMs and CHMMs for modeling behaviors and interactions. Finally, a synthetic "Alife-style" training system is used to develop flexible prior models for recognizing human interactions. We demonstrate the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.