Clayville Rural Life Center and Museum - Publications Series II - Research Report #05: Inns and Taverns in the Midwest - Typical Functions, Forms, and Layouts

Part I of this report explores the functions of typical inns and taverns. It established the bases for setting up a typical inventory of furniture, cookware, eating utensils, and all the other likely necessary and not-so-necessary items which would be found in a combined farm home and inn. Part II presents the results of field work on the form and layout of surviving structures in Illinois. It has several sections on the architectural content and the probable use of the space inside the inn. 109p.National Endowment for the Humanitiespublished or submitted for publicationnot peer reviewe

Introduction

The introduction to the book 'Music and/as Process

Traffic State Estimation via a Particle Filter with Compressive Sensing and Historical Traffic Data

In this paper we look at the problem of estimating traffic states within segments of road using a particle filter and traffic measurements at the segment boundaries. When there are missing measurements the estimation accuracy can decrease. We propose two methods of solving this problem by estimating the missing measurements by assuming the current measurements will approach the mean of the historical measurements from a suitable time period. The proposed solutions come in the form of an l1 norm minimisation and a relevance vector machine type optimisation. Test scenarios involving simulated and real data verify that an accurate estimate of the traffic measurements can be achieved. These estimated missing measurements can then be used to help to improve traffic state estimation accuracy of the particle filter without a significant increase in computation time. For the real data used this can be up to a 23.44% improvement in RMSE values

Nonperturbative Vertices in Supersymmetric Quantum Electrodynamics

We derive the complete set of supersymmetric Ward identities involving only two- and three- point proper vertices in supersymmetric QED. We also present the most general form of the proper vertices consistent with both the supersymmetric and U(1) gauge Ward identities. These vertices are the supersymmetric equivalent of the non supersymmetric Ball-Chiu vertices.Comment: seventeen pages late

Tests of a Semi-Analytical Case 1 and Gelbstoff Case 2 SeaWiFS Algorithm with a Global Data Set

A semi-analytical algorithm was tested with a total of 733 points of either unpackaged or packaged-pigment data, with corresponding algorithm parameters for each data type. The 'unpackaged' type consisted of data sets that were generally consistent with the Case 1 CZCS algorithm and other well calibrated data sets. The 'packaged' type consisted of data sets apparently containing somewhat more packaged pigments, requiring modification of the absorption parameters of the model consistent with the CalCOFI study area. This resulted in two equally divided data sets. A more thorough scrutiny of these and other data sets using a semianalytical model requires improved knowledge of the phytoplankton and gelbstoff of the specific environment studied. Since the semi-analytical algorithm is dependent upon 4 spectral channels including the 412 nm channel, while most other algorithms are not, a means of testing data sets for consistency was sought. A numerical filter was developed to classify data sets into the above classes. The filter uses reflectance ratios, which can be determined from space. The sensitivity of such numerical filters to measurement resulting from atmospheric correction and sensor noise errors requires further study. The semi-analytical algorithm performed superbly on each of the data sets after classification, resulting in RMS1 errors of 0.107 and 0.121, respectively, for the unpackaged and packaged data-set classes, with little bias and slopes near 1.0. In combination, the RMS1 performance was 0.114. While these numbers appear rather sterling, one must bear in mind what mis-classification does to the results. Using an average or compromise parameterization on the modified global data set yielded an RMS1 error of 0.171, while using the unpackaged parameterization on the global evaluation data set yielded an RMS1 error of 0.284. So, without classification, the algorithm performs better globally using the average parameters than it does using the unpackaged parameters. Finally, the effects of even more extreme pigment packaging must be examined in order to improve algorithm performance at high latitudes. Note, however, that the North Sea and Mississippi River plume studies contributed data to the packaged and unpackaged classess, respectively, with little effect on algorithm performance. This suggests that gelbstoff-rich Case 2 waters do not seriously degrade performance of the semi-analytical algorithm

Online Vehicle Logo Recognition Using Cauchy Prior Logistic Regression

Vehicle logo recognition is an important part of vehicle identification in intelligent transportation systems. State-of-the-art vehicle logo recognition approaches typically consider training models on large datasets. However, there might only be a small training dataset to start with and more images can be obtained during the real-time applications. This paper proposes an online image recognition framework which provides solutions for both small and large datasets. Using this recognition framework, models are built efficiently using a weight updating scheme. Another novelty of this work is that the Cauchy prior logistic regression with conjugate gradient descent is proposed to deal with the multinomial classification tasks. The Cauchy prior results in a quicker convergence speed for the weight updating process which could decrease the computational cost for both online and offline methods. By testing with a publicly available dataset, the Cauchy prior logistic regression deceases the classification time by 59%. An accuracy of up to 98.80% is achieved when the proposed framework is applied

Energetic and Environmental Constraints on the Community Structure of Benthic Microbial Mats in Lake Fryxell, Antarctica.

Ecological communities are regulated by the flow of energy through environments. Energy flow is typically limited by access to photosynthetically active radiation (PAR) and oxygen concentration (O2). The microbial mats growing on the bottom of Lake Fryxell, Antarctica, have well-defined environmental gradients in PAR and (O2). We analyzed the metagenomes of layers from these microbial mats to test the extent to which access to oxygen and light controls community structure. We found variation in the diversity and relative abundances of Archaea, Bacteria and Eukaryotes across three (O2) and PAR conditions: high (O2) and maximum PAR, variable (O2) with lower maximum PAR, and low (O2) and maximum PAR. We found distinct communities structured by the optimization of energy use on a millimeter-scale across these conditions. In mat layers where (O2) was saturated, PAR structured the community. In contrast, (O2) positively correlated with diversity and affected the distribution of dominant populations across the three habitats, suggesting that meter-scale diversity is structured by energy availability. Microbial communities changed across covarying gradients of PAR and (O2). The comprehensive metagenomic analysis suggests that the benthic microbial communities in Lake Fryxell are structured by energy flow across both meter- and millimeter-scales

Online Vehicle Logo Recognition Using Cauchy Prior Logistic Regression

Vehicle logo recognition is an important part of vehicle identification in intelligent transportation systems. State-of-the-art vehicle logo recognition approaches typically consider training models on large datasets. However, there might only be a small training dataset to start with and more images can be obtained during the real-time applications. This paper proposes an online image recognition framework which provides solutions for both small and large datasets. Using this recognition framework, models are built efficiently using a weight updating scheme. Another novelty of this work is that the Cauchy prior logistic regression with conjugate gradient descent is proposed to deal with the multinomial classification tasks. The Cauchy prior results in a quicker convergence speed for the weight updating process which could decrease the computational cost for both online and offline methods. By testing with a publicly available dataset, the Cauchy prior logistic regression deceases the classification time by 59%. An accuracy of up to 98.80% is achieved when the proposed framework is applied

Vehicle Logo Recognition by Spatial-SIFT Combined with Logistic Regression

An efficient recognition framework requires both good feature representation and effective classification methods. This paper proposes such a framework based on a spatial Scale Invariant Feature Transform (SIFT) combined with a logistic regression classifier. The performance of the proposed framework is compared to that of state-of-the-art methods based on the Histogram of Orientation Gradients, SIFT features, Support Vector Machine and K-Nearest Neighbours classifiers. By testing with the largest vehicle logo data-set, it is shown that the proposed framework can achieve a classification accuracy of 99.93%, the best among all studied methods. Moreover, the proposed framework shows robustness when noise is added in both training and testing images