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Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Un

Millimeter-wave cellular communication requires beamforming procedures that enable alignment of the transmitter and receiver beams as the user equipment (UE) moves. For efficient beam tracking it is advantageous to classify users according to their traffic and mobility patterns. Research to date has demonstrated efficient ways of machine learning based UE classification. Although different machine

Over the last couple of years both cameras, audio and radio sensors have become cheaper and more common in our everyday lives. Such sensors can be used to create maps of where the sensors are positioned and the appearance of the surroundings. For sound and radio, the process of estimating the sender and receiver positions from time of arrival (TOA) or time-difference of arrival (TDOA) measurements

Atherosclerotic cardiovascular disease (ACVD) is the leading cause of death worldwide. This study aimed to investigate the association between diet and lifestyle factors, beyond traditional risk factors, and the risk of incident ACVD. The Malmö Diet and Cancer study included 30,446 middle-aged individuals. Baseline examinations including a dietary assessment, questionnaire and interviews, were per

The learning with errors (LWE) problem is one of the main mathematical foundations of post-quantum cryptography. One of the main groups of algorithms for solving LWE is the Blum–Kalai–Wasserman (BKW) algorithm. This paper presents new improvements of BKW-style algorithms for solving LWE instances. We target minimum concrete complexity, and we introduce a new reduction step where we partially reduc

In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor navigation using unmanned aerial vehicles (UAVs). We focus on cases where additional information from an onboard IMU is available and thus provides a partial extrinsic calibration through the gravitational vector. The solvers are designed for a partially calibrated camera, for a

In this paper, we present a side-channel attack on a first-order masked implementation of IND-CCA secure Saber KEM. We show how to recover both the session key and the long-term secret key from 24 traces using a deep neural network created at the profiling stage. The proposed message recovery approach learns a higher-order model directly, without explicitly extracting random masks at each executio

Beamforming technology plays a significant role in source localization and quantification. As traditional delay-and-sum beamformers generally yield low spatial resolution, as well as suffer from the occurrence of spurious sources, different forms of deconvolution methods have been proposed in the literature. In this work, we propose two approaches based on a sparse reconstruction framework combine

This paper investigates the stabilization of underactuated vehicles moving in a three-dimensional vector space. The vehicle's model is established on the matrix Lie group SE(3), which describes the configuration of rigid bodies globally and uniquely. We focus on the kinematic model of the underactuated vehicle, which features an underactuation form that has no sway and heave velocity. To compensat

This paper is a comprehensive review of in-cycle closed-loop combustion controllers to achieve higher indicated efficiencies. Closed-loop combustion control reduces the effect of external disturbances and system uncertainties, which permit tighter safety margins and robust operation with a reduced calibration effort. The paper combines different components of previous investigations by the authors

In this paper we target the color transfer estimation problem, when we have pixel-to-pixel correspondences. We present a feature-based method, that robustly fits color transforms to data containing gross outliers. Our solution is based on an optimal inlier maximization algorithm that maximizes the number of inliers in polynomial time. We introduce a simple feature detector and descriptor based on

In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can

Objective: Investigate all-cause and cause-specific late mortality after childhood acute lymphoblastic leukemia (ALL) in a population-based Nordic cohort. Methods: From the cancer registries of Denmark, Finland, and Sweden, we identified 3765 five-year survivors of ALL, diagnosed before age 20 during 1971–2008. For each survivor, up to five matched comparison subjects were randomly selected from t

AIM/HYPOTHESIS: Five subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian individuals with young-onset type 2 diabetes.METHODS: We applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the

In recent years, climate change and the corresponding expected extreme weather conditions have been widelyrecognized as potential problems. The construction industry is taking various actions to achieve sustainabledevelopment, implement energy conservation strategies, and provide climate change mitigation. In addition tomitigation, it is crucial to adapt to climate change, and to investigate the p

This thesis proposes approaches and models to increase the energy saving of the User Equipment (UE) in Long-Term Evolution (LTE) and 5G. The focus is mainly on Discontinuous Reception (DRX), the UE energy saving mechanism that was first introduced in LTE and will play an important role in 5G too.In this thesis, we take two main approaches. The first approach is based on joint optimization of DRX a

Purpose: Off-target binding in the skull and meninges is observed in some subjects undergoing tau positron emission tomography (PET) and could potentially differ between men and women. In this study we elucidate sex differences in tau off-target binding using three different tau PET tracers. Methods: 541 cognitively unimpaired amyloid-β negative participants underwent tau PET using [18F]flortaucip

Atmospheric inversions are used to derive constraints on the net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relat

Background: Gun violence is a global health problem. Population-based research on firearm-related injuries has been relatively limited considering the burden of disease. The aim of this study was to analyze nationwide epidemiological trends of firearm injuries. Methods: This is a retrospective nationwide epidemiological study including all patients with firearm injuries from the Swedish Trauma Reg

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