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.     

Modelling and prediction of malaria vector distribution in Bangladesh from remote-sensing data

Epidemic malaria cases and satellite-based vegetation health (VH) indices were investigated to be used as predictors of malaria vector activities in Bangladesh. The VH indices were derived from radiances, measured by the Advanced Very High Resolution Radiometer (AVHRR) on National Oceanic and Atmospheric Administration (NOAA) afternoon polar orbiting satellites. Two indices characterizing moisture and thermal conditions were investigated using correlation and regression analysis applied to the number of malaria cases recorded in the entire Bangladesh region and three administrative divisions (Chittagong, Sylhet and Dhaka) during 1992–2001. It is shown that during the cooler months (November to March), when mosquitoes are less active, the correlation between number of malaria cases and two investigated indices was near zero. From April, when the mosquito activity season starts, the correlation increased, reaching a maximum value of 0.5–0.8 by the middle of the high season (June to July), reducing thereafter to zero by the beginning of the cool season in November. Following these results, regressional equations for the number of malaria cases as a function of VH indices were built and tested independently. They showed that, in the main malaria administrative division (Chittagong) and the entire Bangladesh region, the regressional equations can be used for early prediction of malaria development.     

Staff perceptions of emotional distress in paitents with burn trauma.

Objective: To compare staff and patient perceptions of patients' emotional distress after acute burn trauma. Design: Staff ratings of patients' psychological states on 6 dimensions of emotional functioning were compared with patient self-report. Results: Staff as a whole and by discipline tended to overestimate depression and underestimate optimism in patients. Moreover, the more experienced (more than 2 years of burn care experience) nurses and occupational/physical therapists were less accurate in estimating depression and optimism than their less experienced counterparts. Conclusion: Consistent with results from spinal cord injury research, findings from the current study indicate a distinct tendency on the part of burn care staff to overestimate their patients' emotional distress and underestimate their positive outlook, supporting the notion that staff may be imposing a "requirement for mourning" on their patients. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Translation and Run-Time Validation of Loop Transformations

This paper presents new approaches to the validation of loop optimizations that compilers use to obtain the highest performance from modern architectures. Rather than verify the compiler, the approach of translation validationperforms a validation check after every run of the compiler, producing a formal proof that the produced target code is a correct implementation of the source code. As part of an active and ongoing research project on translation validation, we have previously described approaches for validating optimizations that preserve the loop structure of the code and have presented a simulation-based general technique for validating such optimizations. In this paper, for more aggressive optimizations that alter the loop structure of the code—such as distribution, fusion, tiling, and interchange—we present a set of permutation ruleswhich establish that the transformed code satisfies all the implied data dependencies necessary for the validity of the considered transformation. We describe the extensions to our tool voc-64 which are required to validate these structure-modifying optimizations. This paper also discusses preliminary work on run-time validation of speculative loop optimizations. This involves using run-time tests to ensure the correctness of loop optimizations whose correctness cannot be guaranteed at compile time. Unlike compiler validation, run-time validation must not only determine when an optimization has generated incorrect code, but also recover from the optimization without aborting the program or producing an incorrect result. This technique has been applied to several loop optimizations, including loop interchange and loop tiling, and appears to be quite promising. This research was supported in part by NSF grant CCR-0098299, ONR grant N00014-99-1-0131, and the John von Neumann Minerva Center for Verification of Reactive Systems.     

Linkage problem, distribution estimation, and Bayesian networks

This paper proposes an algorithm that uses an estimation of the joint distribution of promising solutions in order to generate new candidate solutions. The algorithm is settled into the context of genetic and evolutionary computation and the algorithms based on the estimation of distributions. The proposed algorithm is called the Bayesian Optimization Algorithm (BOA). To estimate the distribution of promising solutions, the techniques for modeling multivariate data by Bayesian networks are used. The BOA identifies, reproduces, and mixes building blocks up to a specified order. It is independent of the ordering of the variables in strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm, but it is not essential. First experiments were done with additively decomposable problems with both nonoverlapping as well as overlapping building blocks. The proposed algorithm is able to solve all but one of the tested problems in linear or close to linear time with respect to the problem size. Except for the maximal order of interactions to be covered, the algorithm does not use any prior knowledge about the problem. The BOA represents a step toward alleviating the problem of identifying and mixing building blocks correctly to obtain good solutions for problems with very limited domain information.     

PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance

We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the generation of strings of symbols, that have been used in the context of speech and handwriting recognition, and bioinformatics. Recent work on learning PDFAs from random examples has used the KL-divergence as the error measure; here we use the variation distance. We build on recent work by Clark and Thollard, and show that the use of the variation distance allows simplifications to be made to the algorithms, and also a strengthening of the results; in particular that using the variation distance, we obtain polynomial sample size bounds that are independent of the expected length of strings.     

The role of spatial location in remembering and forgetting peripheral words.

This study examined whether spatial location mediates intentional forgetting of peripherally presented words. Using an item-method directed forgetting paradigm, words were presented in peripheral locations at study. A recognition test presented all words at either the same or a different location relative to study. Results showed that while recognition of Remember words was unaffected by test location, when Forget words were presented in the same location at test as at study, recognition accuracy was significantly greater than when presented in a different location. Experiment 2 showed that the speed to localize a previously studied word was faster when it was presented in the same rather than a different study-test location but that the magnitude of this spatial priming was unaffected by memory instruction. We suggest that the location of peripherally presented words is represented in memory and can aid the retrieval of poorly encoded words. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Kernel machines for current status data

Machine Learning - In survival analysis, estimating the failure time distribution is an important and difficult task, since usually the data is subject to censoring. Specifically, in this paper we...     

Maximizing Reward in a Non-Stationary Mobile Robot Environment

The ability of a robot to improve its performance on a task can be critical, especially in poorly known and non-stationary environments where the best action or strategy is dependent upon the current state of the environment. In such systems, a good estimate of the current state of the environment is key to establishing high performance, however quantified. In this paper, we present an approach to state estimation in poorly known and non-stationary mobile robot environments, focusing on its application to a mine collection scenario, where performance is quantified using reward maximization. The approach is based on the use of augmented Markov models (AMMs), a sub-class of semi-Markov processes. We have developed an algorithm for incrementally constructing arbitrary-order AMMs on-line. It is used to capture the interaction dynamics between a robot and its environment in terms of behavior sequences executed during the performance of a task. For the purposes of reward maximization in a non-stationary environment, multiple AMMs monitor events at different timescales and provide statistics used to select the AMM likely to have a good estimate of the environmental state. AMMs with redundant or outdated information are discarded, while attempting to maintain sufficient data to reduce conformation to noise. This approach has been successfully implemented on a mobile robot performing a mine collection task. In the context of this task, we first present experimental results validating our reward maximization performance criterion. We then incorporate our algorithm for state estimation using multiple AMMs, allowing the robot to select appropriate actions based on the estimated state of the environment. The approach is tested first with a physical robot, in a non-stationary environment with an abrupt change, then with a simulation, in a gradually shifting environment.