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In Canada's northern regions, linear disturbances such as roads, seismic exploration lines, and pipelines pose a significant threat to the boreal woodland caribou population (Rangifer tarandus). To address the critical need for management of these disturbances, there is a strong emphasis on developing mapping approaches that accurately identify forest habitat fragmentation. The traditional approach is manually generating maps, which is time-consuming and lacks the capability for frequent updates. Instead, applying deep learning methods to multispectral satellite imagery offers a cost-effective solution for automated and regularly updated map production. Deep learning models have shown promise in extracting paved roads in urban environments when paired with high-resolution (<0.5m) imagery, but their effectiveness for general linear feature extraction in forested areas from lower resolution imagery remains underexplored. This research employs a deep convolutional neural network model based on the VGGNet16 architecture for semantic segmentation of lower resolution (10m) Sentinel-2 satellite imagery, creating precise multi-class linear disturbance maps. The model is trained using ground-truth label maps sourced from the freely available Alberta Institute of Biodiversity Monitoring Human Footprint dataset, specifically targeting the Boreal and Taiga Plains ecozones in Alberta, Canada. Despite challenges in segmenting lower resolution imagery, particularly for thin linear disturbances like seismic exploration lines that can exhibit a width of 1-3 pixels in Sentinel-2 imagery, our results demonstrate the effectiveness of the VGGNet model for accurate linear disturbance retrieval. By leveraging the freely available Sentinel-2 imagery, this work advances cost-effective automated mapping techniques for identifying and monitoring linear disturbance fragmentation.

This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements for trustworthy AI. Topics addressed include the role of AI at various operational layers of AVs, the implications of the EU's AI Act on AVs, and the need for new testing methodologies for Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). The study also provides a detailed analysis on the importance of cybersecurity audits, the need for explainability in AI decision-making processes and protocols for assessing the robustness and ethical behaviour of predictive systems in AVs. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology, highlighting the need for multidisciplinary expertise., Comment: 44 pages, 8 figures, submitted to a peer-review journal

In this paper, we assess the efficacy of ChatGPT (version Feb 2023), a large-scale language model, in solving probability problems typically presented in introductory computer engineering exams. Our study comprised a set of 23 probability exercises administered to students at Rey Juan Carlos University (URJC) in Madrid. The responses produced by ChatGPT were evaluated by a group of five statistics professors, who assessed them qualitatively and assigned grades based on the same criteria used for students. Our results indicate that ChatGPT surpasses the average student in terms of phrasing, organization, and logical reasoning. The model's performance remained consistent for both the Spanish and English versions of the exercises. However, ChatGPT encountered difficulties in executing basic numerical operations. Our experiments demonstrate that requesting ChatGPT to provide the solution in the form of an R script proved to be an effective approach for overcoming these limitations. In summary, our results indicate that ChatGPT surpasses the average student in solving probability problems commonly presented in introductory computer engineering exams. Nonetheless, the model exhibits limitations in reasoning around certain probability concepts. The model's ability to deliver high-quality explanations and illustrate solutions in any programming language, coupled with its performance in solving probability exercises, suggests that large language models have the potential to serve as learning assistants., Comment: 10 pages, 6 figures, 4 tables

We propose that spontaneous folding and molecular evolution of biopolymers are two universal aspects that must concur for life to happen. These aspects are fundamentally related to the chemical composition of biopolymers and crucially depend on the solvent in which they are embedded. We show that molecular information theory and energy landscape theory allow us to explore the limits that solvents impose on biopolymer existence. We consider 54 solvents, including water, alcohols, hydrocarbons, halogenated solvents, aromatic solvents, and low molecular weight substances made up of elements abundant in the universe, which may potentially take part in alternative biochemistries. We find that along with water, there are many solvents for which the liquid regime is compatible with biopolymer folding and evolution. We present a ranking of the solvents in terms of biopolymer compatibility. Many of these solvents have been found in molecular clouds or may be expected to occur in extrasolar planets., Comment: 25 pages, 6 figures

Microbes are often discussed in terms of dichotomies such as copiotrophic/oligotrophic and fast/slow-growing microbes, defined using the characterisation of microbial growth in isolated cultures. The dichotomies are usually qualitative and/or study-specific, sometimes precluding clear-cut results interpretation. We are able to interpret microbial dichotomies as life history strategies by combining ecology theory with Monod curves, a classical laboratory tool of bacterial physiology. Monod curves relate the specific growth rate of a microbe with the concentration of a limiting nutrient, and provide quantities that directly correspond to key ecological parameters in McArthur and Wilsons r/K selection theory, Tilmans resource competition and community structure theory and Grimes triangle of life strategies. The resulting model allows us to reconcile the copiotrophic/oligotrophic and fast/slow-growing dichotomies as different subsamples of a life history strategy triangle that also includes r/K strategists. We analyzed some ecological context by considering the known viable carbon sources for heterotrophic microbes in the framework of community structure theory. This partly explains the microbial diversity observed using metagenomics. In sum, ecology theory in combination with Monod curves can be a unifying quantitative framework for the study of natural microbial communities, calling for the integration of modern laboratory and field experiments.

New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. In particular, data-driven learning approaches (i.e., Machine Learning (ML)) have been a true revolution in the advancement of multiple technologies in various application domains. But at the same time there is growing concern about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. Although there are mechanisms in the adoption process to minimize these risks (e.g., safety regulations), these do not exclude the possibility of harm occurring, and if this happens, victims should be able to seek compensation. Liability regimes will therefore play a key role in ensuring basic protection for victims using or interacting with these systems. However, the same characteristics that make AI systems inherently risky, such as lack of causality, opacity, unpredictability or their self and continuous learning capabilities, may lead to considerable difficulties when it comes to proving causation. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties. Specifically, we address the cases of cleaning robots, delivery drones and robots in education. The outcome of the proposed analysis suggests the need to revise liability regimes to alleviate the burden of proof on victims in cases involving AI technologies., Comment: Paper published at the Journal of Artificial Intelligence Research

The GC content of prokaryotic genomes is species-specific and takes values from 16 to 77 percent. There are currently no accepted explanations for this diversity of selection for GC content. We analyzed the known correlations between GC content, genome size and amino acid cost in thousands of prokaryotes, together with new or recently compiled data on cell shape and volume, duplication times, motility, nutrient assimilation, sporulation, defense mechanisms and Gram staining. GC content integrates well with these traits into r/K selection theory when phenotypic plasticity is considered. High GC content prokaryotes are r-strategists with cheaper descendants thanks to a lower average amino acid metabolic cost and a smaller cell volume, colonize unstable environments thanks to flagella and a bacillus form and are generalists in terms of resource opportunism and the ability to defend themselves from various hazards. Low GC content prokaryotes are K-strategists specialized for stable environments that maintain homeostasis via a high-cost outer cell membrane and endospore formation as a response to nutrient deprivation and attain a higher nutrient-to-biomass yield. The lower proteome cost of high GC content prokaryotes is driven by the association between GC-rich codons and cheaper amino acids in the genetic code, while the correlation between GC content and genome size may be partly due to a shift in the functional repertoire of genomes driven by r/K selection. In all, we show that molecular diversity in the GC content of prokaryotes and the corresponding species diversity may be a consequence of ecological r/K selection, Comment: 15 pages, 6 figures

We propose an application of molecular information theory to analyze the folding of single domain proteins. We analyze results from various areas of protein science, such as sequence-based potentials, reduced amino acid alphabets, backbone configurational entropy, secondary structure content, residue burial layers, and mutational studies of protein stability changes. We found that the average information contained in the sequences of evolved proteins is very close to the average information needed to specify a fold ~2.2 $\pm$ 0.3 bits/(site operation). The effective alphabet size in evolved proteins equals the effective number of conformations of a residue in the compact unfolded state at around 5. We calculated an energy-to-information conversion efficiency upon folding of around 50%, lower than the theoretical limit of 70%, but much higher than human built macroscopic machines. We propose a simple mapping between molecular information theory and energy landscape theory and explore the connections between sequence evolution, configurational entropy and the energetics of protein folding., Comment: 33pages, 2 figures, plus supporting information

Linear impulsively controlled systems are suitable to describe a venue of real-life problems, going from disease treatment to aerospace guidance. The main characteristic of such systems is that they remain uncontrolled for certain periods of time. As a consequence, punctual equilibria characterizations outside the origin are no longer useful, and the whole concept of equilibrium and its natural extension, the controlled invariant sets, needs to be redefined. Also, an exact characterization of the admissible states, i.e., states such that their uncontrolled evolution between impulse times remain within a predefined set, is required. An approach to such tasks -- based on the Markov-Lukasz theorem -- is presented, providing a tractable and non-conservative characterization, emerging from polynomial positivity that has application to systems with rational eigenvalues. This is in turn the basis for obtaining a tractable approximation to the maximal admissible invariant sets. In this work, it is also demonstrated that, in order for the problem to have a solution, an invariant set (and moreover, an equilibrium set) must be contained within the target zone. To assess the proposal, the so-obtained impulsive invariant set is explicitly used in the formulation of a set-based model predictive controller, with application to zone tracking. In this context, specific MPC theory needs to be considered, as the target is not necessarily stable in the sense of Lyapunov. A zone MPC formulation is proposed, which is able to i) track an invariant set such that the uncontrolled propagation fulfills the zone constraint at all times and ii) converge asymptotically to the set of periodic orbits completely contained within the target zone.