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This paper is concerned with model fitting in the presence of noise and outliers. Previously it has been shown that the number of outliers can be minimized with polynomial complexity in the number of measurements. This paper improves on these results in two ways. First, it is shown that for a large class of problems, the statistically more desirable truncated L-2-norm can be optimized with the sam
In this paper we study the problem of estimating receiver and sender positions from time-difference-of-arrival measurements, assuming an unknown constant time-difference-of-arrival offset. This problem is relevant for example for repetitive sound events. In this paper it is shown that there are three minimal cases to the problem. One of these (the five receiver, five sender problem) is of particul
The fact that deep learning based algorithms used for digital pathology tend to overfit to the site of the training data is well-known. Since an algorithm that does not generalize is not very useful, we have in this work studied how different data augmentation techniques can reduce this problem but also how data from different sites can be normalized to each other. For both of these approaches we
Objectives Depression is a common illness with substantial economic consequences for society and a great burden for affected individuals. About 30% of patients with depression do not respond to repeated treatments. Psychiatric comorbidity is known to affect duration, recurrence and treatment outcome of depression. However, there is a lack of knowledge on the extent to which psychiatric comorbidity
The problem of monotone smoothing splines with bounds is formulated as a constrained minimization problem of the calculus of variations. Existence and uniqueness of solutions of this problem is proved, as well as the equivalence of it to a finite dimensional but nonlinear optimization problem. A new algorithm for computing the solution which is a spline curve, using a branch and bound technique, i
BackgroundThe primary aim of specialised palliative care (SPC) is to improve the quality of life (QoL) for patients with a high symptom burden from a life-threatening disease. This randomised study aimed to assess the QoL impact of early integration of SPC alongside tumour-specific palliative treatment in patients with gastrointestinal (GI) cancers.MethodsWe randomly assigned ambulatory patients w
The diurnal cycle of precipitation is a fundamental mode of climatic variability. Based on an hourly 0.1° × 0.1° gauge-satellite merged precipitation dataset, which is provided by the China Meteorological Data Service Center, this study used the K-means clustering algorithm to map the diurnal cycles of precipitation in summer over China. The obtained maps can objectively delineate irregular region
In this work, we study under which circumstances it is appropriate to use simplified models for range determination using radar. Typically, pulsed radar systems result in the backscattered, demodulated, and matched signal having a chirp signal structure, with the frequency rate being related to the range to the reflecting target and the relative velocity of the transmitter and reflector. Far from
This work is concerned with determining optimal microphone placements that allow for an accurate location estimate of the sound sources, taking into account the expected signal structure of voiced speech, as well as the expected location areas and the typical range of the fundamental frequencies of the speakers. To determine preferable microphone placements, we propose a scheme that minimizes a th
Background: Totally implantable venous access devices (TIVADs) have improved the quality of life for seriously ill and cancer patients. These devices represent a convenient option when long-term venous access is indicated. The Subclavian and Internal Jugular Veins are the vessels of choice for catheterization [1]. However, if it is not possible to catheterize them, an alternative vein should be so
In this paper, we extend the generalized SPICE estimator to the multichannel case, illustrating the resulting performance gain for direction of arrival estimation. The resulting estimator is found to offer improved estimation performance and robustness to the presence of correlated sources.
We compare classic scalar temporal difference learning with three new distributional algorithms for playing the game of 5-in-a-row using deep neural networks: distributional temporal difference learning with constant learning rate, and two distributional temporal difference algorithms with adaptive learning rate. All these algorithms are applicable to any two-player deterministic zero sum game and
We present a model for generating 3d articulated pedestrian locomotion in urban scenarios, with synthesis capabilities informed by the 3d scene semantics and geometry. We reformulate pedestrian trajectory forecasting as a structured reinforcement learning (RL) problem. This allows us to naturally combine prior knowledge on collision avoidance, 3d human motion capture and the motion of pedestrians
Over the last couple of years audio and radio sensors have become cheaper and more common in our everyday life. Such sensors can be used to form a network, from which one can obtain distance measures by correlating the different received signals. One example of such distance measures is time-difference of arrival measurements (TDoA), which can be used to estimate the positions of the senders and r
3D human body reconstruction from monocular images has wide applications in our life, such as movie, animation, Virtual/Augmented Reality, medical research and so on. Due to the high freedom of human body in real scene and the ambiguity of inferring 3D objects from 2D images, it is a challenging task to accurately recover 3D human body models from images. In this thesis, we explore the methods for
A wide range of medical examinations are using analysis of images from different types of equipment. Using artificial intelligence, the assessments could be done automatically. This can have multiple benefits for the healthcare; reduce workload for medical doctors, decrease variations in diagnoses and cut waiting times for the patient as well as improve the performance. The aim of this thesis has
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
The Amazon forest is increasingly vulnerable to dieback and encroachment of grasslands and agricultural fields. Threats to these forested ecosystems include drying, deforestation, and fire, but feedbacks among these make it difficult to determine their relative importance. Here, we reconstruct the central and western Amazon tree cover response to aridity and fire in the mid-Holocene—a time of less
