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Global climate models (GCMs) are challenged by difficulties in simulating cloud phase and cloud radiative effect over the Southern Ocean (SO). Some of the new-generation GCMs predict too much liquid and too little ice in mixed-phase clouds. This misrepresentation of cloud phase in GCMs results in weaker negative cloud feedback over the SO and a higher climate sensitivity. Based on a model comparis

Att odla ekologiskt istället för konventionellt bidrar till att öka den biologiska mångfalden. Den positiva effekten på biologiskmångfald är särskilt stor i odlingslandskap med få kvarvarande naturliga livsmiljöer som exempelvis naturbetesmarker. Samtidigt har sådana odlingslandskap ofta en hög jordbruksproduktion och därför är också kostnaderna i form av skördebortfall höga vid en övergång till e

Recent years have witnessed great interest in mass-participation running events (Hindley, 2020), and organisations such as parkrun and fitness programmes like Couch to 5K, have been instrumental in enabling participation for inexperienced runners. Concomitant with this has been a number of fictional works which centre on the 5K run. I contend that exploring fictional texts can offer a fresh take o

Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional multi-atlas methods, our proposed approach does not rely on label fusion on the voxel level. Inst

In this paper we study the problem of estimating receiver and sender positions using time-difference-of-arrival measurements. For this, we use a stratified, two-tiered approach. In the first step the problem is converted to a low-rank matrix estimation problem. We present new, efficient solvers for the minimal problems of this low-rank problem. These solvers are used in a hypothesis and test manne

Digital twins are taking a central role in the industry 4.0 narrative. However, they are still illusive. Many aspects of the digital-twins have yet to materialize. For example, to what degree will they be integrated into cloud and industry 4.0 systems as well as how and if they should augment their physical counterpart. Those choices are accompanied by challenging security aspects, many of which h

In this paper, we investigate the security of SNOW-V, demonstrating two guess-and-determine (GnD) attacks against the full version with complexities 2384 and 2378, respectively, and one distinguishing attack against a reduced variant with complexity 2303 . Our GnD attacks use enumeration with recursion to explore valid guessing paths, and try to truncate as many invalid guessing paths as possible

The decomposition of nonlinear chirp modes is a challenging task, typically requiring prior knowledge of the number of modes a signal contains. In this work, we present a greedy nonlinear chirp mode estimation (NCME) technique that forms the used decomposition basis from the signal itself, using an arctangent demodulation technique. The resulting decomposition is formed by considering the residual

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

Inference of CO2 surface fluxes using atmospheric CO2 observations in atmospheric inversions depends critically on accurate representation of atmospheric transport. Here we characterize regional-scale CO2 transport uncertainties due to uncertainties in meteorological fields using a mesoscale atmospheric model and an ensemble of simulations with flow-dependent transport errors. During a 1-month sum

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

The Learning with Errors problem (LWE) is one of the main candidates for post-quantum cryptography. At Asiacrypt 2017, coded-BKW with sieving, an algorithm combining the Blum-Kalai-Wasserman algorithm (BKW) with lattice sieving techniques, was proposed. In this paper, we improve that algorithm by using different reduction factors in different steps of the sieving part of the algorithm. In the Rege

In this paper we address the problem of resource allocation for alarm traffic in industrial Internet of Things networks using massive MIMO. We formulate the general problem of how to allocate pilot signals to alarm traffic such that delivery is guaranteed, while also minimising the number of pilots reserved for alarms, thus maximising the channel resources available for other traffic, such as indu

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

Learning a computationally efficient kernel from data is an important machine learning problem. The majority of kernels in the literature do not leverage the geometry of the data, and those that do are computationally infeasible for contemporary datasets. Recent advances in approximation techniques have expanded the applicability of the kernel methodology to scale linearly with the data size. Data

Statement of Purpose: Chronic lung diseases are one of the major health problems that cause death and disability. Approximately 65 million people suffer from chronic lung diseases, and the number of patients is predicted to increase worldwide 1 . Lung transplantation is the only available treatment option for patients at end-stage disease. However, there is a chronic shortage of donor organs, re

Statement of Purpose: Many chronic pulmonary diseases, including idiopathic pulmonary fibrosis (IPF), pulmonary hypertension (PH) and chronic obstructive pulmonary disease (COPD), are complex and poorly understood. While great progress has been made to elucidate the cellular and molecular pathways underlying these diseases, treatment options remain limited. The dynamic alterations in mechanical pr

The problem of estimating receiver or sender node positions from measured receiver-sender distances is a key issue in different applications such as microphone array calibration, radio antenna array calibration, mapping and positioning using UWB or using round-trip-time measurements between mobile phones and WiFi-units. In this paper we address the problem of optimally estimating a receiver positi

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