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This study aims to analyse and compare the challenges and opportunities that refugees and asylum seekers experience in Sweden and South Africa along with the strategies they develop in relation to higher education. As both countries have undertaken a more restrictive refugee and asylum policy, this study hopes to contribute to the debate of inclusion and its challenges. The data-collection entails

The EU's Common Agricultural Policy (CAP) has had limited success in mitigating agriculture's environmental degradation. In this paper we simulate the impacts of the 2013 “greening” reform on biodiversity and ecosystem services in environmentally contrasting landscapes. We do this by integrating an agent-based model of structural change with spatial ecological production functions, and show that t

This paper presents a neural network based semantic plane detection method utilizing polygon representations. The method can for example be used to solve room layout estimations tasks and is built on, combines and further develops several different modules from previous research. The network takes an RGB image and estimates a wireframe as well as a feature space using an hourglass backbone. From t

We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation of safety-critical scenarios. The simulator learns from sequences of real-world driving sensor data and enables reconfigurations and renderings of new, unseen scenarios. In this work, we use our simulator to test the res

Dust storms have been an increasing concern in the Middle East. Severe drought events and human activities, particularly water and land management, have led to emerging dust sources in the last few decades. This study combines spatio-temporal maps of land susceptibility to dust storm incidence with agricultural land use change data to examine the link between farming patterns and dust storm genera

Interpretability has become a crucial component in the Machine Learning (ML) domain. This is particularly important in the context of medical and health applications, where the underlying reasons behind how an ML model makes a certain decision are as important as the decision itself for the experts. However, interpreting an ML model based on limited local data may potentially lead to inaccurate co

The linear inverse problem associated with the standard model for hyperspectral image recovery from CASSI measurements is considered. This is formulated as the minimization of an objective function which is the sum of a total variation regularizer and a least squares loss function. Standard first-order iterative minimization algorithms, such as ISTA, FISTA and TwIST, require as input the value of

This study analyses seven sufficiency-oriented SMEs in the French fashion sector, categorizing their strategies into efficiency, shift and sufficiency. It reveals the tension in balancing sustainability with financial viability, highlighting the systemic challenges posed by consumer preference for fast fashion.

roduction of biomass for bioenergy on marginal land has been promoted to decrease the use of non-renewable energy without competing with food production. Previous estimates of marginal land and abandoned farmland in Sweden available for bioenergy production are based on general statistics and assumptions but lack in-depth analyses of potential alternative land-use values. Easily accessible public

Machine learning techniques often lack formal correctness guarantees, evidenced by the widespread adversarial examples that plague most deep-learning applications. This lack of formal guarantees resulted in several research efforts that aim at verifying Deep Neural Networks (DNNs), with a particular focus on safety-critical applications. However, formal verification techniques still face major sca

In this paper we present the differentiable log-Mel spectrogram (DMEL) for audio classification. DMEL uses a Gaussian window, with a window length that can be jointly optimized with the neural network. DMEL is used as the input layer in different neural networks and evaluated on standard audio datasets. We show that DMEL achieves a higher average test accuracy for sub-optimal initial choices of th

Time-difference-of-arrival (TDOA) estimation from GCC-PHAT is not always as straight forward as finding the maximum peak. This work views the GCC output as an image, with time on the vertical axis and TDOA horizontally, to explore if image-to-image machine learning methods can make a more robust filter. The Structure from Sound Database provides audio recorded with a distributed microphone setup a

The human brain performs tasks with an outstanding energy efficiency, i.e., with approximately 20 Watts. The state-of-the-art Artificial/Deep Neural Networks (ANN/DNN), on the other hand, have recently been shown to consume massive amounts of energy. The training of these ANNs/DNNs is done almost exclusively based on the back-propagation algorithm, which is known to be biologically implausible. Th

The identification of carotid atheromatous plaques associated to a higher neurologic risk may be important in therapeutic decision making for asymptomatic patients and symptomatic patients with 50%-70% stenosis. The introduction of high-definition ultrasonography (HDU) and computer-assisted image analysis provides the possibility of a standard, objective and detailed characterization of the struct