964 research outputs found
Regulation of Microtubule Dynamics by Protein: Interaction Networks at Microtubule Tips
Microtubules are cytoskeletal fi laments, which play essenti al roles in cell division, morphology,
migrati on and organizati on of intracellular organelles. Many of these functi ons are regulated by
the associati on of microtubule plus ends with a group of structurally diverse and unrelated proteins
- the microtubule plus-end tracking proteins (+TIPs). This thesis describes how +TIPs infl uence
microtubule dynamics, how the assembly of interacti on networks from a large number of +TIPs
at the relati vely small MT end is regulated both in space and ti me, and how this contributes to
the cel
The Horseshoe Estimator: Posterior Concentration around Nearly Black Vectors
We consider the horseshoe estimator due to Carvalho, Polson and Scott (2010)
for the multivariate normal mean model in the situation that the mean vector is
sparse in the nearly black sense. We assume the frequentist framework where the
data is generated according to a fixed mean vector. We show that if the number
of nonzero parameters of the mean vector is known, the horseshoe estimator
attains the minimax risk, possibly up to a multiplicative constant. We
provide conditions under which the horseshoe estimator combined with an
empirical Bayes estimate of the number of nonzero means still yields the
minimax risk. We furthermore prove an upper bound on the rate of contraction of
the posterior distribution around the horseshoe estimator, and a lower bound on
the posterior variance. These bounds indicate that the posterior distribution
of the horseshoe prior may be more informative than that of other one-component
priors, including the Lasso.Comment: This version differs from the final published version in pagination
and typographical detail; Available at
http://projecteuclid.org/euclid.ejs/141813426
Bayesian inverse problems with Gaussian priors
The posterior distribution in a nonparametric inverse problem is shown to
contract to the true parameter at a rate that depends on the smoothness of the
parameter, and the smoothness and scale of the prior. Correct combinations of
these characteristics lead to the minimax rate. The frequentist coverage of
credible sets is shown to depend on the combination of prior and true
parameter, with smoother priors leading to zero coverage and rougher priors to
conservative coverage. In the latter case credible sets are of the correct
order of magnitude. The results are numerically illustrated by the problem of
recovering a function from observation of a noisy version of its primitive.Comment: Published in at http://dx.doi.org/10.1214/11-AOS920 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Fryslân yn de Nederlânske geografyske tydskriften. In bibliografy oer de perioade 1876-1991 / Friesland in de Nederlandse geografische tijdschriften. Een bibliografie over de periode 1876-1991
Resampling-based confidence regions and multiple tests for a correlated random vector
We derive non-asymptotic confidence regions for the mean of a random vector
whose coordinates have an unknown dependence structure. The random vector is
supposed to be either Gaussian or to have a symmetric bounded distribution, and
we observe i.i.d copies of it. The confidence regions are built using a
data-dependent threshold based on a weighted bootstrap procedure. We consider
two approaches, the first based on a concentration approach and the second on a
direct boostrapped quantile approach. The first one allows to deal with a very
large class of resampling weights while our results for the second are
restricted to Rademacher weights. However, the second method seems more
accurate in practice. Our results are motivated by multiple testing problems,
and we show on simulations that our procedures are better than the Bonferroni
procedure (union bound) as soon as the observed vector has sufficiently
correlated coordinates.Comment: submitted to COL
Sequential Data-Adaptive Bandwidth Selection by Cross-Validation for Nonparametric Prediction
We consider the problem of bandwidth selection by cross-validation from a
sequential point of view in a nonparametric regression model. Having in mind
that in applications one often aims at estimation, prediction and change
detection simultaneously, we investigate that approach for sequential kernel
smoothers in order to base these tasks on a single statistic. We provide
uniform weak laws of large numbers and weak consistency results for the
cross-validated bandwidth. Extensions to weakly dependent error terms are
discussed as well. The errors may be {\alpha}-mixing or L2-near epoch
dependent, which guarantees that the uniform convergence of the cross
validation sum and the consistency of the cross-validated bandwidth hold true
for a large class of time series. The method is illustrated by analyzing
photovoltaic data.Comment: 26 page
Field systems and later prehistoric land use:New insights into land use detectability and palaeodemography in the Netherlands through LiDAR, automatic detection and traditional field data
This paper discusses how the use of AI (artificial intelligence) detected later prehistoric field systems provides a more reliable base for reconstructing palaeodemographic trends, using the Netherlands as a case study. Despite its long tradition of settlement excavations, models that could be used to reconstruct (changes in) prehistoric land use have been few and often relied on (insufficiently mapped) nodal data points such as settlements and barrows. We argue that prehistoric field systems of field plots beset on all sides by earthen banks—known as Celtic fields—are a more suitable (i.e. less nodal) proxy for reconstructing later prehistoric land use.For four 32.25 km2 case study areas in different geogenetic regions of the Netherlands, prehistoric land use surface areas are modelled based on conventional methods and the results are compared to the results we obtained by using AI-assisted detection of prehistoric field systems. The nationally available LiDAR data were used for automated detection. Geotiff DTM images were fed into an object detection algorithm (based on the YOLOv4 framework and trained with known Dutch sites), and resultant geospatial vectors were imported into GIS.Our analysis shows that AI-assisted detection of prehistoric embanked field systems on average leads to a factor 1.84 increase in known surface areas of Celtic fields. Modelling the numbers of occupants from this spatial coverage, yields population sizes of 37–135 persons for the case study regions (i.e. 1.15 to 4.19 p/km2). This range aligns well with previous estimates and offers a more robust and representative proxy for palaeodemographic reconstructions. Variations in land use coverage between the regions could be explained by differences in present-day land use and research intensity. Particularly the regionally different extent of forestlands and heathlands (ideal for the (a) preservation and (b) automated LiDAR detection of embanked field systems) explains minor variations between the four case study regions
Interactive spatial augmented reality in the Allard-Pierson museum: Exploration of cultural artifacts by simple finger pointing
Tranfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on LiDAR Data
When applying deep learning to remote sensing data in archaeological
research, a notable obstacle is the limited availability of suitable datasets
for training models. The application of transfer learning is frequently
employed to mitigate this drawback. However, there is still a need to explore
its effectiveness when applied across different archaeological datasets. This
paper compares the performance of various transfer learning configurations
using two semantic segmentation deep neural networks on two LiDAR datasets. The
experimental results indicate that transfer learning-based approaches in
archaeology can lead to performance improvements, although a systematic
enhancement has not yet been observed. We provide specific insights about the
validity of such techniques that can serve as a baseline for future works.Comment: Accepted to IEEE International Geoscience and Remote Sensing
Symposium 2023 (IGARSS 2023) @IEEE copyrigh
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