PAC Learning of One-Dimensional Patterns

Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We consider the problem of PAC-learning the concept class of geometric patterns where the target geometric pattern is a configuration ofk points on the real line. Each instance is a configuration ofn points on the real line, where it is labeled according to whether or not it visually resembles the target pattern. To capture the notion of visual resemblance we use the Hausdorff metric. Informally, two geometric patternsP andQ resemble each other under the Hausdorff metric if every point on one pattern is close to some point on the other pattern. We relate the concept class of geometric patterns to the landmark matching problem and then present a polynomial-time algorithm that PAC-learns the class of one-dimensional geometric patterns. We also present some experimental results on how our algorithm performs.     

Multi‐Unit Bayesian Auction with Demand or Budget Constraints

We consider the problem of revenue maximization on multi‐unit auctions where items are distinguished by their relative values; any pair of items has the same ratio of values to all buyers. As is common in the study of revenue maximizing problems, we assume that buyers' valuations are drawn from public known distributions and they have additive valuations for multiple items. Our problem is well motivated by sponsored search auctions, which made money for Google and Yahoo! in practice. In this auction, each advertiser bids an amount b_i to compete for ad slots on a web page. The value of each ad slot corresponds to its click‐through‐rate, and each buyer has her own per‐click valuations, which is her private information. Obviously, a strategic bidder may bid an amount that is different with her true valuation to improve her utility. Our goal is to design truthful mechanisms avoiding this misreporting. We develop the optimal (with maximum revenue) truthful auction for a relaxed demand model (where each buyer i wants at most d_i items) and a sharp demand model (where buyer i wants exactly d_i items). We also find an auction that always guarantees at least half of the revenue of the optimal auction when the buyers are budget constrained. Moreover, all of the auctions we design can be computed efficiently, that is, in polynomial time.     

Pricing ad slots with consecutive multi-unit demand

We consider the optimal pricing problem for a model of the rich media advertisement market, that has other related applications. Our model differs from traditional position auctions in that we consider buyers whose demand might be multiple consecutive slots, which is motivated by modeling buyers who may require these to display a large size ad. We study three major pricing mechanisms, the Bayesian pricing model, the maximum revenue market equilibrium model and an envy-free solution model. Under the Bayesian model, we design a polynomial-time computable truthful mechanism that optimizes the revenue. For the market equilibrium paradigm, we find a polynomial-time algorithm to obtain the maximum revenue market equilibrium solution. In the envy-free setting, an optimal solution is presented for the case where the buyers have the same demand for the number of consecutive slots. We present results of a simulation that compares the revenues from the above schemes.     

Generalization in the XCSF classifier system: analysis, improvement, and extension

We analyze generalization in XCSF and introduce three improvements. We begin by showing that the types of generalizations evolved by XCSF can be influenced by the input range. To explain these results we present a theoretical analysis of the convergence of classifier weights in XCSF which highlights a broader issue. In XCSF, because of the mathematical properties of the Widrow-Hoff update, the convergence of classifier weights in a given subspace can be slow when the spread of the eigenvalues of the autocorrelation matrix associated with each classifier is large. As a major consequence, the system's accuracy pressure may act before classifier weights are adequately updated, so that XCSF may evolve piecewise constant approximations, instead of the intended, and more efficient, piecewise linear ones. We propose three different ways to update classifier weights in XCSF so as to increase the generalization capabilities of XCSF: one based on a condition-based normalization of the inputs, one based on linear least squares, and one based on the recursive version of linear least squares. Through a series of experiments we show that while all three approaches significantly improve XCSF, least squares approaches appear to be best performing and most robust. Finally we show how XCSF can be extended to include polynomial approximations.     

Voltage sensitivity of the osteoclast calcium receptor

We demonstrated previously that osteoclasts possess a divalent cation-sensitive "receptor", the Ca2+ receptor. Activation of the Ca2+ receptor by the surrogate cation Ni2+ was shown to elicit an increase in cytosolic [Ca2+] to a peak value followed by an exponential decline. In the present study we examined the influence of surface membrane voltage on the kinetics of Ca2+ receptor inactivation. The K+ ionophore, valinomycin was applied to intercept the declining phase of the cytosolic [Ca2+] transient elicited by application of between 50 microM- and 5 mM-[Ni2+]. This resulted in a sustained elevation of cytosolic [Ca2+] or even a 'hump' followed by a gradual decline. Such a kinetic alteration persisted in a Ca(2+)-free solution, but was abolished in high extracellular [K+] (105 mM). Thus, we demonstrate for the first time to our knowledge, a modulatory effect of membrane potential on the function of the osteoclast Ca2+ receptor.     

The effect of physician-controlled health insurance. U.S. v. Oregon State Medical Society

The trial record in an antitrust case against the Oregon State Medical Society, finally decided in 1952, was examined to reconstruct the behavior of a competitive market for health insurance coverage. Health insurers, called "hospital associations," were found to have engaged individually in cost-control efforts similar to, but possibly more aggressive than, today's utilization review under professional sponsorship. The subsequent disappearance of these insurer-initiated cost controls in Oregon is traced to the medical society's organization of a competing Blue Shield plan as a model of insurer conduct and to a simultaneous boycott by physicians of the hospital associations as long as they persisted in questioning doctors' practices. Some modern parallels are noted, and the advantages of fostering privately sponsored cost-control efforts are suggested.     

SNR estimation in time-varying fading channels

Signal-to-noise ratio (SNR) estimation is considered for phase-shift keying communication systems in time-varying fading channels. Both data-aided (DA) estimation and nondata-aided (NDA) estimation are addressed. The time-varying fading channel is modeled as a polynomial-in-time. Inherent estimation accuracy limitations are examined via the Cramer-Rao lower bound, where it is shown that the effect of the channel's time variation on SNR estimation is negligible. A novel maximum-likelihood (ML) SNR estimator is derived for the time-varying channel model. In DA scenarios, where the estimator has a simple closed-form solution, the exact performance is evaluated both with correct and incorrect (i.e., mismatched) polynomial order. In NDA estimation, the unknown data symbols are modeled as random, and the marginal likelihood is used. The expectation-maximization algorithm is proposed to iteratively maximize this likelihood function. Simulation results show that the resulting estimator offers statistical efficiency over a wider range of scenarios than previously published methods.     

ATSC DTV Receiver Implementation

Receivers of broadcast digital television (DTV) service operate in an often difficult environment of electrical interference, multiple other TV signals in close frequency proximity, multipath, wide dynamic range input signals, and uncertain antenna choice and installation. Receivers must demodulate and decode the signal and optimize its processing for different display technologies-a process that can include format conversion between progressive and interlaced scanning and different screen pixel counts. Data that supports the new services enabled by digital transmission must be decoded and processed. Receiver designers must implement the required functions cost-effectively even as they strive to provide performance and feature differentiation from their competitors' products. This paper describes all of the DTV receiver functions and references the associated standards. Emphasis is given to the difficult areas of signal reception and demodulation and to sections of the receiver that enable attractive and recognizable consumer features.