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About 10,000 papers for “density mapping”

Density-based Isometric Mapping

Bardia Yousefi, Mélina Khansari, Ryan Trask et al. · arXiv (Cornell University) · 2024

The isometric mapping method employs the shortest path algorithm to estimate the Euclidean distance between points on High dimensional (HD) manifolds. This may not be sufficient for weakly uniformed HD data as it could lead to overestimating distances between far neighboring points, resulting in inconsistencies between the intrinsic (local) and extrinsic (global) distances during the projection. To address this issue, we modify the shortest path algorithm by adding a novel constraint inspired by the Parzen-Rosenblatt (PR) window, which helps to maintain the uniformity of the constructed shortest-path graph in Isomap. Multiple imaging datasets overall of 72,236 cases, 70,000 MINST data, 1596 from multiple Chest-XRay pneumonia datasets, and three NSCLC CT/PET datasets with a total of 640 lung cancer patients, were used to benchmark and validate PR-Isomap. 431 imaging biomarkers were extracted from each modality. Our results indicate that PR-Isomap projects HD attributes into a lower-dimensional (LD) space while preserving information, visualized by the MNIST dataset indicating the maintaining local and global distances. PR-Isomap achieved the highest comparative accuracies of 80.9% (STD:5.8) for pneumonia and 78.5% (STD:4.4), 88.4% (STD:1.4), and 61.4% (STD:11.4) for three NSCLC datasets, with a confidence interval of 95% for outcome prediction. Similarly, the multivariate Cox model showed higher overall survival, measured with c-statistics and log-likelihood test, of PR-Isomap compared to other dimensionality reduction methods. Kaplan Meier survival curve also signifies the notable ability of PR-Isomap to distinguish between high-risk and low-risk patients using multimodal imaging biomarkers preserving HD imaging characteristics for precision medicine.

Cited by 1Open access

Unit-Vector Density Mapping

Stephen J. Lavin, Randall S. Cerveny · The Cartographic Journal · 1987

AbstractAn automated mapping technique, termed unit-vector density mapping, is introduced as a new method of displaying continuous surface vector quantities. With this graphic procedure, the density of short standard-length line segments (unit-vectors) with, or without, arrowheads represents geographical magnitudes and unit-vector orientation shows direction or movement. This paper describes the plotting algorithms for producing unit-vector density maps and demonstrates the versatility of this technique in a number of geoscientific applications.

Cited by 9

Mapping of Population Density

Carsten M. Haaland, Michael T. Heath · Demography · 1974

The Bureau of the Census listing of geographical coordinates of centroids of all enumeration districts together with population counts from the U. S. 1970 Census of Population was used to contruct via computer five nationwide geographical grids of population density with sector dimensions of 0.01, 0.02, 0.04, 0.1, and 0.25 degrees of latitude and longitude. The entire population of a district was assigned to a grid sector if the coordinates of the district centroid fell within the boundaries of the sector. The sectors were then rank-ordered according to population density, and listings were made of sector population, population density, geographical location, cumulative population, area of sector, and cumulative area. The five sets of data were synthesized into single equations describing population as a function of density in one case and of area in another. From these data it was found, for example, that about 800,000 people live in 19 sectors of 0.01-degree dimensions with a population density of 100,000 people per square mile or greater (nearly all in Manhattan); about 10 million live in 183 sectors of 0.02-degree dimensions with a population density of 23,000 per square mile or greater; and about half of the total U. S. population, that is, about 100 million people, reside within about 0.6 percent of the area of the United States, that is, within 20,000 square miles.Four representative displays of population density are shown for the Northeast Corridor, including isometric views and a contour map.

Cited by 14Open access

Functional connectivity density mapping

Dardo Tomasi, Nora D. Volkow · Proceedings of the National Academy of Sciences · 2010

Brain networks with energy-efficient hubs might support the high cognitive performance of humans and a better understanding of their organization is likely of relevance for studying not only brain development and plasticity but also neuropsychiatric disorders. However, the distribution of hubs in the human brain is largely unknown due to the high computational demands of comprehensive analytical methods. Here we propose a 10(3) times faster method to map the distribution of the local functional connectivity density (lFCD) in the human brain. The robustness of this method was tested in 979 subjects from a large repository of MRI time series collected in resting conditions. Consistently across research sites, a region located in the posterior cingulate/ventral precuneus (BA 23/31) was the area with the highest lFCD, which suggest that this is the most prominent functional hub in the brain. In addition, regions located in the inferior parietal cortex (BA 18) and cuneus (BA 18) had high lFCD. The variability of this pattern across subjects was <36% and within subjects was 12%. The power scaling of the lFCD was consistent across research centers, suggesting that that brain networks have a "scale-free" organization.

Cited by 639Open access

Medium-density QTL mapping results.

Michael A. Seidman (267825), Tina W. Chew (267827), Alan R. Schenkel (267830) et al. · Figshare · 2015

&lt;p&gt;Using a proprietary allele-specific primer extension based assay, the Illumina GoldenGate technology, the QTL peak was narrowed to a point at 35.8 Mb (panel A). Near this well-defined peak are four genes of particular interest (panel B, colored circles at top of graph, from left to right): &lt;i&gt;Ptges&lt;/i&gt;, coding for mPGES-1 (prostaglandin E&lt;sub&gt;2&lt;/sub&gt; synthase 1, green); &lt;i&gt;Ptges2&lt;/i&gt;, coding for PGES-2 (prostaglandin E&lt;sub&gt;2&lt;/sub&gt; synthase 2, blue); &lt;i&gt;Hc&lt;/i&gt;, coding for hemolytic complement component C5 (black); and &lt;i&gt;Ptgs1&lt;/i&gt;, coding for the protein commonly known as COX-1 (red); these four genes are also indicated in the lower row of genes identified in &lt;a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004316#pone-0004316-g003" target="_blank"&gt;Figure 3B&lt;/a&gt;. The LOD score = 3.0 threshhold for significance is inidcated by a horizontal grey line in both panels.&lt;/p&gt;

Cited by 0Open access

Low-density QTL mapping results.

Michael A. Seidman (267825), Tina W. Chew (267827), Alan R. Schenkel (267830) et al. · Figshare · 2015

&lt;p&gt;The low-density microsatellite mapping study revealed a single locus (panel A) breaking genome-wide association cutoffs. Panel B shows a close-up view of that locus, at 38.2 Mb (NCBI map coordinates), indicated by an arrow, as well as a number of nearby genes identified in databases as relevant to inflammation, immunity, or hematologic function.&lt;/p&gt;

Cited by 0Open access

Mapping cone photopigment optical density

Ann E. Elsner, Stephen A. Burns, Robert H. Webb · Journal of the Optical Society of America A · 1993

The distribution of cone photopigment across the retina affects the amount of light captured by cones at each retinal location. Cone photopigment optical density is measured in two ways, with reflectometry and/or with color matching. Color matching measures a higher optical density than does reflectometry. Control experiments confirm that large-field color matches measure photopigment optical density toward their outer edge. There is qualitative agreement as to photopigment distribution from both techniques near the fovea. Beyond 1 deg, color matching indicates little decrease in photopigment with increasing eccentricity, whereas retinal densitometry shows a steep decline in photopigment. The decrease in perifoveal optical density measured with reflectometry is attributed to the decrease in cone coverage from fovea to perifovea as rods and interphotoreceptor spaces increase. Differences among subjects in photopigment distribution near the fovea, measured with both techniques, reflect differences in the specialization of the foveal center for cone length and/or photopigment concentration per cone, which are factors influencing results from both techniques.

Cited by 67

Density-Wavefunction Mapping in Degenerate Current-Density-Functional Theory

Laestadius, Andre, Tellgren, Erik I. · arXiv (Cornell University) · 2018

We show that the particle density, $ρ(\mathbf{r})$, and the paramagnetic current density, $\mathbf{j}^{p}(\mathbf{r})$, are not sufficient to determine the set of degenerate ground-state wave functions. This is a general feature of degenerate systems where the degenerate states have different angular momenta. We provide a general strategy for constructing Hamiltonians that share the same ground state density, yet differ in degree of degeneracy. We then provide a fully analytical example for a noninteracting system subject to electrostatic potentials and uniform magnetic fields. Moreover, we prove that when $(ρ,\mathbf{j}^p)$ is ensemble $(v,\mathbf{A})$-representable by a mixed state formed from $r$ degenerate ground states, then any Hamiltonian $H(v',\mathbf{A}')$ that shares this ground state density pair must have at least $r$ degenerate ground states in common with $H(v,\mathbf{A})$. Thus, any set of Hamiltonians that shares a ground-state density pair $(ρ,\mathbf{j}^p)$ by necessity has at least have one joint ground state.

Cited by 0Open access

High-density Mapping Toy or Tool?

Helmut Pürerfellner · European Journal of Arrhythmia & Electrophysiology · 2018

High-density (HD) mapping with new multipolar HD-mapping catheters has changed the way we look at cardiac arrhythmias, especially atrial tachycardia and ventricular arrhythmia. Some small studies have shown a good feasibility and promising results. Scar definition with small and closely spaced electrodes still remains unstandardised. Randomised controlled studies are needed to prove the potential benefits of this new technology.

Cited by 1Open access

Anisotropic density estimation for photon mapping

Fujun Luan, Li-Fan Wu, Kun Xu · Computational Visual Media · 2015

Photon mapping is a widely used technique for global illumination rendering. In the density estimation step of photon mapping, the indirect radiance at a shading point is estimated through a filtering process using nearby stored photons; an isotropic filtering kernel is usually used. However, using an isotropic kernel is not always the optimal choice, especially for cases when eye paths intersect with surfaces with anisotropic BRDFs. In this paper, we propose an anisotropic filtering kernel for density estimation to handle such anisotropic eye paths. The anisotropic filtering kernel is derived from the recently introduced anisotropic spherical Gaussian representation of BRDFs. Compared to conventional photon mapping, our method is able to reduce rendering errors with negligible additional cost when rendering scenes containing anisotropic BRDFs.

Cited by 2Open access

Evaluating quantitative proton‐density‐mapping methods

Aviv Mezer, Ariel Rokem, Shai Berman et al. · Human Brain Mapping · 2016

Quantitative magnetic resonance imaging (qMRI) aims to quantify tissue parameters by eliminating instrumental bias. We describe qMRI theory, simulations, and software designed to estimate proton density (PD), the apparent local concentration of water protons in the living human brain. First, we show that, in the absence of noise, multichannel coil data contain enough information to separate PD and coil sensitivity, a limiting instrumental bias. Second, we show that, in the presence of noise, regularization by a constraint on the relationship between T1 and PD produces accurate coil sensitivity and PD maps. The ability to measure PD quantitatively has applications in the analysis of in-vivo human brain tissue and enables multisite comparisons between individuals and across instruments. Hum Brain Mapp 37:3623-3635, 2016. © 2016 Wiley Periodicals, Inc.

Cited by 92

Bibliometrics Analysis and Density-Equalizing Mapping

Fiona Geaney, Cristian Scutaru, Clare Kelly et al. · 2016

The objective of this paper is to provide a detailed evaluation of type 2 diabetes mellitus research output from 1951-2012, using large-scale data analysis, bibliometric indicators and density-equalizing mapping. Data were retrieved from the Science Citation Index Expanded database, one of the seven curated databases within Web of Science. Using Boolean operators &amp;quot;OR&amp;quot;, &amp;quot;AND &amp;quot; and &amp;quot;NOT&amp;quot;, a search strategy was developed to estimate the total number of published items. Only studies with an English abstract were eligible. Type 1 diabetes and gestational diabetes items were excluded. Specific software devel-oped for the database analysed the data. Information including titles, authors ’ affiliations and publication years were extracted from all files and exported to excel. Density-equaliz-ing mapping was conducted as described by Groenberg-Kloft et al, 2008. A total of 24,783 items were published and cited 476,002 times. The greatest number of outputs were pub-lished in 2010 (n=2,139). The United States contributed 28.8 % to the overall output, fol-lowed by the United Kingdom (8.2%) and Japan (7.7%). Bilateral cooperation was most common between the United States and United Kingdom (n=237). Harvard University pro-duced 2 % of all publications, followed by the University of California (1.1%). The leading journals were Diabetes, Diabetologia and Diabetes Care and they contributed 9.3%, 7.3% and 4.0 % of the research yield, respectively. In conclusion, the volume of research is ris-ing in parallel with the increasing global burden of disease due to type 2 diabetes mellitus. Bibliometrics analysis provides useful information to scientists and funding agencies involved in the development and implementation of research strategies to address global health issues.

Cited by 0