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Regression via classification (RvC) is a common method used for regression problems in deep learning, where the target variable belongs to a set of continuous values. By discretizing the target into a set of non-overlapping classes, it has been shown that training a classifier can improve neural network accuracy compared to using a standard regression approach. However, it is not clear how the set
In recent years, great progress has been made on the problem of 3D scene reconstruction using depth sensors. On a large scale, these reconstructions look impressive, but often many fine details are lacking due to limitations in the sensor resolution. In this paper we combine two well-known principles for recovery of 3D models, namely fusion of depth images with photometric stereo to enhance the de
Precipitation in clouds can form by either warm-rain or ice crystal processes, referred to as warm and cold formation pathways, respectively. Here, we investigate the warm and cold pathway contributions to surface precipitation in simulated continental convective storms. We analyze three contrasting convective storms that are cold-based, slightly warm-based and very warm-based. We apply tracer-tag
We consider the statistical sparse jump model, a recently developed, robust and interpretable regime switching model, to identify features that drive the return dynamics of the largest cryptocurrencies. The approach simultaneously performs feature selection, parameter estimation, and state classification. Our large number of candidate features comprises cryptocurrency, sentiment, and financial mar
Localization is an important enabling technology for many applications, such as wireless sensor networks, emergency rescue services, civil defense and transportation. Suppose that a room is equipped with several microphones (or sensors), and one person is making a sound while moving around in the room. Can one find microphone and sound source positions as well as reconstruct a room geometry? The a
The central problem of this thesis is locating several sources and simultaneously locating the positions of the sensors. The measurements captured by the sensors are time of arrival (TOA), time difference of arrival (TDOA), unsynchronized TDOA, or received signal strength indication (RSSI), all a variation of distance measurement between sensors and sources. Signals can be either sound or radio fo
The problem of estimating receiver-sender node positionsfrom measured receiver-sender distances is a key issue indifferent applications such as microphone array calibration, radioantenna array calibration, mapping and positioning using ultrawidebandand mapping and positioning using round-trip-timemeasurements between mobile phones and Wi-Fi-units. Thanks torecent research in this area we have an i
Indoor positioning for smartphone usershas received a lot of attention in recent years. Whilemany solutions have been developed, most rely on aneed for pre-deployment of infrastructure or collectingground truth data to train on. In this paper we see whatcan be done using existing WiFi-infrastructure andReceived Signal Strength from these to smartphones,not using any calibration of the signal envir
A fully automatic system for abdominal organ segmentation is presented. As a first step, an organ localization is obtained via a robust and efficient feature registration method where the center of the organ is estimated together with a region of interest surrounding the center. Then, a convolutional neural network performing voxelwise classification is applied. Two convolutional neural networks o
Dissolved organic carbon (DOC) loading is rarely estimated in tropical watersheds. This study quantifies DOC loading in the Rukarara River Watershed (RRW), a Rwandan tropical forest and agricultural watershed, and evaluates its relationship with hydrological factors, land use and land cover (LULC), and topography to better understand the impact of stream DOC export on watershed carbon budgets. The
Explanations of leaf size variation commonly focus on water availability, yet leaf size also varies with latitude and elevation in environments where water is not strongly limiting. We provide the first conclusive test of a prediction of leaf energy balance theory that may explain this pattern: large leaves are more vulnerable to night-time chilling, because their thick boundary layers impede conv
Dark matter direct detection experiments need to know the local phase space density of dark matter fdm(r,v,t) in order to derive dark matter particle properties. To date, calculations for fdm(r,v,t) have been based on simulations that model the dark matter alone. Here we include the influence of the baryonic matter. We find that a star/gas disc at high redshift (z∼1) causes merging satellites to b
Pairwise comparison of sound and radio signals can be used to estimate the distance between two units that send and receive signals. In a similar way it is possible to estimate differences of distances by correlating two received signals. There are essentially two groups of such methods, namely methods that are robust to noise and reverberation, but give limited precision and sub-sample refinement
Background: A crucial step in image fusion for intraoperative guidance during endovascular procedures is the registration of preoperative computed tomography angiography (CTA) with intraoperative Cone Beam CT (CBCT). Automatic tools for image registration facilitate the 3D image guidance workflow. However their performance is not always satisfactory. The aim of this study is to assess the accuracy
The ongoing ESA funded'SMOS + Vegetation' project combines a retrieval component that aims at further improving the SMOS VOD product with an assimilation component that aims at demonstrating the added value of this product in constraining simulated land surface fluxes of carbon dioxide. This contribution focuses on the project's modelling and assimilation component. We describe the construction of
This thesis consists of four self-contained papers in applied microeconomics. The first paper asks how a neighbors purchase of a specific good affects a households likelihood of purchase. I use an image classification algorithm to process a large set of aerial photos in order to infer household ownership of a visible durable good, specifically a trampoline. To estimate the neighborhood effect, I u
We study the one-dimensional version of the Rudin–Osher–Fatemi (ROF) denoising model and some related TV-minimization problems. A new proof of the equivalence between the ROF model and the so-called taut string algorithm is presented, and a fundamental estimate on the denoised signal in terms of the corrupted signal is derived. Based on duality and the projection theorem in Hilbert space, the proo
