Letters from William Tudor to his father, William Tudor, approximately

Two letters in which Tudor carefully debates the merits of careers in law versus mercantilism, and discusses the business prospects of several young merchants, a journey Tudor took with his brother, Frederic, throughout New England, and the state of politics, including the election to Congress of James Otis, and Thomas Jefferson’s prospects for the presidency.

Deeds for properties in Boston [and Cambridge?], 1798-approximately 1945 (inclusive); approximately 1880-1920 (bulk)

This group of records contains deeds and related documents for a selection of properties owned by Harvard University in Boston and possibly Cambridge and other nearby communities through the mid 1940s. Documents include deeds, assignments of mortgages, receipts, correspondence, and other legal documents. Many of the documents record property transfers prior to Harvard's acquisition of the property, and often the documents do not fully identify Harvard's involvement with the property. The bulk of the documents date from the late 19th and early 20th centuries.

Die Ermöglichung des endlichen Seins nach Johannes Duns Scotus /

Includes index.

A.L.A. catalog, 1937-1941; an annotated list of approximately 4,000 titles

Mode of access: Internet.

Johannes Gabrieli und sein zeitalter.

v.1-2 text.--v.3 music.

Protection of roadless areas : hearing before the Subcommittee on Forests and Public Land Management of the Committee on Energy and Natural Resources, United States Senate, One Hundred Sixth Congress, first session, to receive testimony on the President's announcement to survey approximately 40 million acres for increased protection.

Shipping list no.: 2000-0219-P (pt. 1), 2000-0328-P (pt. 2), 2001-0124-P (pt. 3).

Approximately optimal experimental design for heteroscedastic Gaussian process models

This paper presents a greedy Bayesian experimental design criterion for heteroscedastic Gaussian process models. The criterion is based on the Fisher information and is optimal in the sense of minimizing parameter uncertainty for likelihood based estimators. We demonstrate the validity of the criterion under different noise regimes and present experimental results from a rabies simulator to demonstrate the effectiveness of the resulting approximately optimal designs.

K-Theoretic Characterization of C*-Algebras with Approximately Inner Flip

The author is supported by an NSERC PDF.

K-Theoretic Characterization of C*-Algebras with Approximately Inner Flip

The author is supported by an NSERC PDF.

Musical commonplace book : manuscript, [approximately 1790-approximately 1810]

Contains songs, partly from English operas, and instrumental music.

Ah' quest'amplesso, o cara: scena e duetto nella Cleopatra / manuscript, [approximately 1800]

del Sig:re Sebastiano Nasolini :

A Sex-Specific Association between a 15q25 Variant and Upper Aerodigestive Tract Cancers

Background: Sequence variants located at 15q25 have been associated with lung cancer and propensity to smoke. We recently reported an association between rs16969968 and risk of upper aerodigestive tract (UADT) cancers (oral cavity, oropharynx, hypopharynx, larynx, and esophagus) in women (OR = 1.24, P = 0.003) with little effect in men (OR = 1.04, P = 0.35). Methods: In a coordinated genotyping study within the International Head and Neck Cancer Epidemiology (INHANCE) consortium, we have sought to replicate these findings in an additional 4,604 cases and 6,239 controls from 10 independent UADT cancer case-control studies. Results: rs16969968 was again associated with UADT cancers in women (OR = 1.21, 95% CI = 1.08-1.36, P = 0.001) and a similar lack of observed effect in men [OR = 1.02, 95% CI = 0.95-1.09, P = 0.66; P-heterogeneity (P(het)) = 0.01]. In a pooled analysis of the original and current studies, totaling 8,572 UADT cancer cases and 11,558 controls, the association was observed among females (OR = 1.22, 95% CI = 1.12-1.34, P = 7 x 10(-6)) but not males (OR = 1.02, 95% CI = 0.97-1.08, P = 0.35; P(het) = 6 x 10(-4)). There was little evidence for a sex difference in the association between this variant and cigarettes smoked per day, with male and female rs16969968 variant carriers smoking approximately the same amount more in the 11,991 ever smokers in the pooled analysis of the 14 studies (P(het) = 0.86). Conclusions: This study has confirmed a sex difference in the association between the 15q25 variant rs16969968 and UADT cancers. Impact: Further research is warranted to elucidate the mechanisms underlying these observations. Cancer Epidemiol Biomarkers Prev; 20(4); 658-64. (C) 2011 AACR.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Geochemistry of highly acidic mine water following disposal into a natural lake with carbonate bedrock

Acid mine drainage (AMD) from the Zn-Pb(-Ag-Bi-Cu) deposit of Cerro de Pasco (Central Peru) and waste water from a Cu-extraction plant has been discharged since 1981 into Lake Yanamate, a natural lake with carbonate bedrock. The lake has developed a highly acidic pH of similar to 1. Mean lake water chemistry was characterized by 16,775 mg/L acidity as CaCO(3), 4330 mg/L Fe and 29,250 mg/L SO(4). Mean trace element concentrations were 86.8 mg/L Cu, 493 mg/L Zn, 2.9 mg/L Pb and 48 mg/L As, which did not differ greatly from the discharged AMD. Most elements showed increasing concentrations from the surface to the lake bottom at a maximal depth of 41 m (e.g. from 3581 to 5433 mg/L Fe and 25,609 to 35,959 mg/L SO(4)). The variations in the H and 0 isotope compositions and the element concentrations within the upper 10 m of the water column suggest mixing with recently discharged AMD, shallow groundwater and precipitation waters. Below 15 m a stagnant zone had developed. Gypsum (saturation index, SI similar to 0.25) and anglesite (SI similar to 0.1) were in equilibrium with lake water. Jarosite was oversaturated (SI similar to 1.7) in the upper part of the water column, resulting in downward settling and re-dissolution in the lower part of the water column (SI similar to -0.7). Accordingly, jarosite was only found in sediments from less than 7 m water depth. At the lake bottom, a layer of gel-like material (similar to 90 wt.% water) of pH similar to 1 with a total organic C content of up to 4.40 wet wt.% originated from the kerosene discharge of the Cu-extraction plant and had contaminant element concentrations similar to the lake water. Below the organic layer followed a layer of gypsum with pH 1.5, which overlaid the dissolving carbonate sediments of pH 5.3-7. In these two layers the contaminant elements were enriched compared to lake water in the sequence As < Pb approximate to Cu < Cd < Zn = Mn with increasing depth. This sequence of enrichment was explained by the following processes: (i) adsorption of As on Fe-hydroxides coating plant roots at low pH (up to 3326 mg/kg As), (ii) adsorption at increasing pH near the gypsum/calcite boundary (up to 1812 mg/kg Pb, 2531 mg/kg Cu. and 36 mg/kg Cd), and (iii) precipitation of carbonates (up to 5177 mg/kg Zn and 810 mg/kg Mn: all data corrected to a wet base). The infiltration rate was approximately equal to the discharge rate, thus gypsum and hydroxide precipitation had not resulted in complete clogging of the lake bedrocks. (C) 2010 Elsevier Ltd. All rights reserved.