Showing papers by "Massachusetts Institute of Technology published in 1995"

Dynamic Programming and Optimal Control

Abstract: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization. The treatment focuses on basic unifying themes, and conceptual foundations. It illustrates the versatility, power, and generality of the method with many examples and applications from engineering, operations research, and other fields. It also addresses extensively the practical application of the methodology, possibly through the use of approximations, and provides an extensive treatment of the far-reaching methodology of Neuro-Dynamic Programming/Reinforcement Learning.

Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results

Abstract: Genetic studies are under way for many complex traits, spurred by the recent feasibility of whole genome scans. Clear guidelines for the interpretation of linkage results are needed to avoid a flood of false positive claims. At the same time, an overly cautious approach runs the risk of causing true hints of linkage to be missed. We address this problem by proposing specific standards designed to maintain rigor while also promoting communication.

Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming

Abstract: We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2-satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least.87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds the solution to a nonlinear programming relaxation. This relaxation can be interpreted both as a semidefinite program and as an eigenvalue minimization problem. The best previously known approximation algorithms for these problems had performance guarantees of 1/2 for MAX CUT and 3/4 or MAX 2SAT. Slight extensions of our analysis lead to a.79607-approximation algorithm for the maximum directed cut problem (MAX DICUT) and a.758-approximation algorithm for MAX SAT, where the best previously known approximation algorithms had performance guarantees of 1/4 and 3/4, respectively. Our algorithm gives the first substantial progress in approximating MAX CUT in nearly twenty years, and represents the first use of semidefinite programming in the design of approximation algorithms.

Bose-Einstein condensation in a gas of sodium atoms.

Abstract: We have observed Bose-Einstein condensation of sodium atoms. The atoms were trapped in a novel trap that employed both magnetic and optical forces. Evaporative cooling increased the phase-space density by 6 orders of magnitude within seven seconds. Condensates contained up to 5\ifmmode\times\else\texttimes\fi{}${10}^{5}$ atoms at densities exceeding ${10}^{14}$ ${\mathrm{cm}}^{\ensuremath{-}3}$. The striking signature of Bose condensation was the sudden appearance of a bimodal velocity distribution below the critical temperature of \ensuremath{\sim}2\ensuremath{\mu}K. The distribution consisted of an isotropic thermal distribution and an elliptical core attributed to the expansion of a dense condensate.

Large magnetoresistance at room temperature in ferromagnetic thin film tunnel junctions.

Abstract: Ferromagnetic-insulator-ferromagnetic tunneling has been measured in CoFe $/$A${\mathrm{l}}_{2}$${\mathrm{O}}_{3}$ $/$Co or NiFe junctions. At 295, 77, and 4.2 K the fractional change in junction resistance with magnetic field, $\ensuremath{\Delta}R/R$, is 11.8%, 20%, and 24%, respectively. The value at 4.2 K is consistent with Julliere's model based on the spin polarization of the conduction electrons of the magnetic films. $\ensuremath{\Delta}R/R$ changes little with a small voltage bias, whereas it decreases significantly at higher bias $(g0.1\mathrm{V})$, in qualitative agreement with Slonczewski's model. These junctions have potential use as low-power field sensors and memory elements.

Social information filtering: algorithms for automating “word of mouth”

Abstract: This paper describes a technique for making personalized recommendations from any type of database to a user based on similarities between the interest profile of that user and those of other users. In particular, we discuss the implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists. Ringo's database of users and artists grows dynamically as more people use the system and enter more information. Four different algorithms for making recommendations by using social information filtering were tested and compared. We present quantitative and qualitative results obtained from the use of Ringo by more than 2000 people.

An Internal Model for Sensorimotor Integration

Abstract: On the basis of computational studies it has been proposed that the central nervous system internally simulates the dynamic behavior of the motor system in planning, control, and learning; the existence and use of such an internal model is still under debate. A sensorimotor integration task was investigated in which participants estimated the location of one of their hands at the end of movements made in the dark and under externally imposed forces. The temporal propagation of errors in this task was analyzed within the theoretical framework of optimal state estimation. These results provide direct support for the existence of an internal model.

Robust text-independent speaker identification using Gaussian mixture speaker models

Abstract: This paper introduces and motivates the use of Gaussian mixture models (GMM) for robust text-independent speaker identification. The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modeling speaker identity. The focus of this work is on applications which require high identification rates using short utterance from unconstrained conversational speech and robustness to degradations produced by transmission over a telephone channel. A complete experimental evaluation of the Gaussian mixture speaker model is conducted on a 49 speaker, conversational telephone speech database. The experiments examine algorithmic issues (initialization, variance limiting, model order selection), spectral variability robustness techniques, large population performance, and comparisons to other speaker modeling techniques (uni-modal Gaussian, VQ codebook, tied Gaussian mixture, and radial basis functions). The Gaussian mixture speaker model attains 96.8% identification accuracy using 5 second clean speech utterances and 80.8% accuracy using 15 second telephone speech utterances with a 49 speaker population and is shown to outperform the other speaker modeling techniques on an identical 16 speaker telephone speech task. >

Automatic Lag Selection in Covariance Matrix Estimation

Abstract: We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, we prove that our procedure is asymptotically equivalent to one that is optimal under a mean squared error loss function. Monte Carlo simulations suggest that our procedure performs tolerably well, although it does result in size distortions.

Mechanisms and genes of cellular suicide

Abstract: Apoptosis is a morphologically distinct form of programmed cell death that plays a major role during development, homeostasis, and in many diseases including cancer, acquired immunodeficiency syndrome, and neurodegenerative disorders. Apoptosis occurs through the activation of a cell-intrinsic suicide program. The basic machinery to carry out apoptosis appears to be present in essentially all mammalian cells at all times, but the activation of the suicide program is regulated by many different signals that originate from both the intracellular and the extracellular milieu. Genetic studies in the nematode Caenorhabditis elegans and in the fruit fly Drosophila melanogaster have led to the isolation of genes that are specifically required for the induction of programmed cell death. At least some components of the apoptotic program have been conserved among worms, insects, and vertebrates.

Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging

Abstract: The borders of human visual areas V1, V2, VP, V3, and V4 were precisely and noninvasively determined. Functional magnetic resonance images were recorded during phase-encoded retinal stimulation. This volume data set was then sampled with a cortical surface reconstruction, making it possible to calculate the local visual field sign (mirror image versus non-mirror image representation). This method automatically and objectively outlines area borders because adjacent areas often have the opposite field sign. Cortical magnification factor curves for striate and extrastriate cortical areas were determined, which showed that human visual areas have a greater emphasis on the center-of-gaze than their counterparts in monkeys. Retinotopically organized visual areas in humans extend anteriorly to overlap several areas previously shown to be activated by written words.

The rician distribution of noisy mri data

Abstract: The image intensity in magnetic resonance magnitude images in the presence of noise is shown to be governed by a Rician distribution. Low signal intensities (SNR < 2) are therefore biased due to the noise. It is shown how the underlying noise can be estimated from the images and a simple correction scheme is provided to reduce the bias. The noise characteristics in phase images are also studied and shown to be very different from those of the magnitude images. Common to both, however, is that the noise distributions are nearly Gaussian for SNR larger than two.

Series elastic actuators

Abstract: It is traditional to make the interface between an actuator and its load as stiff as possible. Despite this tradition, reducing interface stiffness offers a number of advantages, including greater shock tolerance, lower reflected inertia, more accurate and stable force control, less inadvertent damage to the environment, and the capacity for energy storage. As a trade-off, reducing interface stiffness also lowers zero motion force bandwidth. In this paper, the authors propose that for natural tasks, zero motion force bandwidth isn't everything, and incorporating series elasticity as a purposeful element within the actuator is a good idea. The authors use the term elasticity instead of compliance to indicate the presence of a passive mechanical spring in the actuator. After a discussion of the trade-offs inherent in series elastic actuators, the authors present a control system for their use under general force or impedance control. The authors conclude with test results from a revolute series-elastic actuator meant for the arms of the MIT humanoid robot Cog and for a small planetary rover.

Heteronuclear decoupling in rotating solids

Abstract: A simple two pulse phase modulation (TPPM) scheme greatly reduces the residual linewidths arising from insufficient proton decoupling power in double resonance magic angle spinning (MAS) experiments. Optimization of pulse lengths and phases in the sequence produces substantial improvements in both the resolution and sensitivity of dilute spins (e.g., 13C) over a broad range of spinning speeds at high magnetic field. The theoretical complications introduced by large homo‐ and heteronuclear interactions among the spins, as well as the amplitude modulation imposed by MAS, are explored analytically and numerically. To our knowledge, this method is the first phase‐switched sequence to exhibit improvement over continuous‐wave (cw) decoupling in a strongly coupled homogeneous spin system undergoing sample spinning.

Self-Organization of CdSe Nanocrystallites into Three-Dimensional Quantum Dot Superlattices

Abstract: The self-organization of CdSe nanocrystallites into three-dimensional semiconductor quantum dot superlattices (colloidal crystals) is demonstrated. The size and spacing of the dots within the superlattice are controlled with near atomic precision. This control is a result of synthetic advances that provide CdSe nanocrystallites that are monodisperse within the limit of atomic roughness. The methodology is not limited to semiconductor quantum dots but provides general procedures for the preparation and characterization of ordered structures of nanocrystallites from a variety of materials.

The Tyranny of Numbers: Confronting the Statistical Realities of the East Asian Growth Experience

Abstract: This paper documents the fundamental role played by factor accumulation in explaining the extraordinary postwar growth of Hong Kong, Singapore, South Korea, and Taiwan. Participation rates, educational levels, and (excepting Hong Kong) investment rates have risen rapidly in all four economies. In addition, in most cases there has been a large intersectoral transfer of labor into manufacturing, which has helped fuel growth in that sector. Once one accounts for the dramatic rise in factor inputs, one arrives at estimated total factor productivity growth rates that are closely approximated by the historical performance of many of the OECD and Latin American economies. While the growth of output and manufacturing exports in the newly industrializing countries of East Asia is virtually unprecedented, the growth of total factor productivity in these economies is not.

The resource‐based view of the firm: Ten years after

Abstract: The article reflects on the diffusion of the ‘resource-based view of the firm’ into academic and practitioner thought. The contributions of many people are noted. In closing, I offer some speculations about the future use of these ideas.

Intelligence without reason.

Abstract: Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligence has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation.

Embryonic lethality and liver degeneration in mice lacking the RelA component of NF-kappa B.

Abstract: NF-κB, which consists of two polypeptides, p50 (Mr 50K) and p65/RelA (Mr 65K), is thought to be a key regulator of genes involved in responses to infection, inflammation and stress1. Indeed, although developmentally normal, mice deficient in p50 display functional defects in immune responses2. Here we describe the generation of mice deficient in the RelA subunit of NF-κB. Disruption of the relA locus leads to embryonic lethality at 15–16 days of gestation, concomitant with a massive degeneration of the liver by programmed cell death or apoptosis. Embryonic fibroblasts from RelA-deficient mice are defective in the tumour necrosis factor (TNF)-mediated induction of messenger RNAs for IκBα and granulocyte/macrophage colony stimulating factor (GM-CSF), although basal levels of these transcripts are unaltered. These results indicate that RelA controls inducible, but not basal, transcription in NF-κB-regulated pathways.

On a confusion about a function of consciousness.

Abstract: Consciousness is a mongrel concept: there are a number of very different "consciousnesses." Phenomenal consciousness is experience; the phenomenally conscious aspect of a state is what it is like to be in that state. The mark of access-consciousness, by contrast, is availability for use in reasoning and rationally guiding speech and action. These concepts are often partly or totally conflated, with bad results. This target article uses as an example a form of reasoning about a function of "consciousness" based on the phenomenon of blindsight. Some information about stimuli in the blind field is represented in the brains of blindsight patients, as shown by their correct "guesses," but they cannot harness this information in the service of action, and this is said to show that a function of phenomenal consciousness is somehow to enable information represented in the brain to guide action. But stimuli in the blind field are BOTH access-unconscious and phenomenally unconscious. The fallacy is: an obvious function of the machinery of access-consciousness is illicitly transferred to phenomenal consciousness.

Somatic Generation of Antibody Diversity1

Abstract: Publisher Summary This chapter reviews the somatic generation of antibody diversity. It was shown that a typical antibody molecule is composed of two identical light chains and two identical heavy chains. It had also been found that each of these two types of chain exhibits great sequence variability in the amino terminal region between one antibody molecule and the next and no sequence variability in the carboxyl terminal regions. Use of restriction enzymes and recombinant DNA methods allowed resolution of a long-standing and central issue in immunology, the genetic origins of antibody diversity. It turned out that an organism does not inherit even a single complete gene for antibody polypeptide chains. Rather, the genetic information is transmitted in germline as no more than several hundred gene segments. Through a series of specialized somatic recombination occurring specifically during the differentiation of B lymphocytes, these gene segments are assembled into tens of thousands of complete genes. In the immune system, organisms have exploited two major processes for modification of DNA, recombination and mutation, as a means to diversify somatically the limited amount of inherited genetic information to cope with the vastly diverse antigen universe. Somatic diversification allows the individual organism to generate a virtually limitless number of lymphocyte variants.

Letizia: an agent that assists web browsing

Abstract: Letizia is a user interface agent that assists a user browsing the World Wide Web. As the user operates a conventional Web browser such as Netscape, the agent tracks user behavior and attempts to anticipate items of interest by doing concurrent, autonomous exploration of links from the user's current position. The agent automates a browsing strategy consisting of a best-first search augmented by heuristics inferring user interest from browsing behavior.

Optical Coherence Tomography of the Human Retina

Abstract: Objective: To demonstrate optical coherence tomography for high-resolution, noninvasive imaging of the human retina. Optical coherence tomography is a new imaging technique analogous to ultrasound B scan that can provide cross-sectional images of the retina with micrometer-scale resolution. Design: Survey optical coherence tomographic examination of the retina, including the macula and optic nerve head in normal human subjects. Setting: Research laboratory. Participants: Convenience sample of normal human subjects. Main Outcome Measures: Correlation of optical coherence retinal tomographs with known normal retinal anatomy. Results: Optical coherence tomographs can discriminate the cross-sectional morphologic features of the fovea and optic disc, the layered structure of the retina, and normal anatomic variations in retinal and retinal nerve fiber layer thicknesses with 10-??m depth resolution. Conclusion: Optical coherence tomography is a potentially useful technique for high depth resolution, cross-sectional examination of the fundus.

Regularization theory and neural networks architectures

Abstract: We had previously shown that regularization principles lead to approximation schemes that are equivalent to networks with one layer of hidden units, called regularization networks . In particular, standard smoothness functionals lead to a subclass of regularization networks, the well known radial basis functions approximation schemes. This paper shows that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models and some of the neural networks. In particular, we introduce new classes of smoothness functionals that lead to different classes of basis functions. Additive splines as well as some tensor product splines can be obtained from appropriate classes of smoothness functionals. Furthermore, the same generalization that extends radial basis functions (RBF) to hyper basis functions (HBF) also leads from additive models to ridge approximation models, containing as special cases Breiman's hinge functions, some forms of projection pursuit regression, and several types of neural networks. We propose to use the term generalized regularization networks for this broad class of approximation schemes that follow from an extension of regularization. In the probabilistic interpretation of regularization, the different classes of basis functions correspond to different classes of prior probabilities on the approximating function spaces, and therefore to different types of smoothness assumptions. In summary, different multilayer networks with one hidden layer, which we collectively call generalized regularization networks, correspond to different classes of priors and associated smoothness functionals in a classical regularization principle. Three broad classes are (1) radial basis functions that can be generalized to hyper basis functions, (2) some tensor product splines, and (3) additive splines that can be generalized to schemes of the type of ridge approximation, hinge functions, and several perceptron-like neural networks with one hidden layer.

Factorial Hidden Markov Models

Abstract: Hidden Markov models (HMMs) have proven to be one of the most widely used tools for learning probabilistic models of time series data. In an HMM, information about the past is conveyed through a single discrete variable—the hidden state. We discuss a generalization of HMMs in which this state is factored into multiple state variables and is therefore represented in a distributed manner. We describe an exact algorithm for inferring the posterior probabilities of the hidden state variables given the observations, and relate it to the forward–backward algorithm for HMMs and to algorithms for more general graphical models. Due to the combinatorial nature of the hidden state representation, this exact algorithm is intractable. As in other intractable systems, approximate inference can be carried out using Gibbs sampling or variational methods. Within the variational framework, we present a structured approximation in which the the state variables are decoupled, yielding a tractable algorithm for learning the parameters of the model. Empirical comparisons suggest that these approximations are efficient and provide accurate alternatives to the exact methods. Finally, we use the structured approximation to model Bach‘s chorales and show that factorial HMMs can capture statistical structure in this data set which an unconstrained HMM cannot.

Techniques for data hiding

Abstract: Data hiding is the process of embedding data into image and audio signals. The process is constrained by the quantity of data, the need for invariance of the data under conditions where the `host' signal is subject to distortions, e.g., compression, and the degree to which the data must be immune to interception, modification, or removal. We explore both traditional and novel techniques for addressing the data hiding process and evaluate these techniques in light of three applications: copyright protecting, tamper-proofing, and augmentation data embedding.