Miracle-dz/UniFAR-FTUs huggingface dataset:
The oldest monitoring site of the Alps revisited: accelerated increase in plant species richness on Piz Linard summit since 1835

Background: High-altitude ecosystems in the Alps have experienced severe environmental changes over the past decades, such as strong warming and increasing numbers of visitors and grazers. Few studies have followed the effects of such changes on the alpine flora over a period longer than a few decades. The summit of Piz Linard (3410 m, south-eastern Switzerland) is the oldest site in the Alps whose flora has been recorded, on average every 20 years since the Little Ice Age (1835). Aims: We re-surveyed the summit flora of Piz Linard to trace its floristic changes and identify their patterns and possible drivers. Methods: We mapped each species' highest location, distribution and abundance in the uppermost 30 m of the Piz Linard summit in 2011, and compared species composition and species’ altitudinal distribution over time. Results: Species richness increased at an accelerated rate since 1992 and rose from 12 to 16 species since the previous record in 2003. Most already present species increased in abundance and colonised new areas of the summit, while new arrivals mainly established at sites with already high species richness. Species appeared after 1992 differed from species already present previously by having had lower maximum altitudes elsewhere in south-eastern Switzerland. Conclusions: Temporal and spatial patterns of colonisations and former altitudinal ranges of species all point to climate warming as the principal driver of floristic change on Piz Linard.
Simulation of memristive synapses and neuromorphic computing on a quantum computer

One of the major approaches to neuromorphic computing is using memristors as analogue synapses. We propose unitary quantum gates that exhibit memristive behaviours, including Ohm's law, pinched hysteresis loop and synaptic plasticity. Hysteresis depending on the quantum phase and long-term plasticity that encodes the quantum state are observed. We also propose a three-layer neural network with the capability of universal quantum computing. Quantum state classification on the memristive neural network is demonstrated. Our results pave the way towards brain-inspired quantum computing. We obtain these results in numerical simulations and experiments on the superconducting quantum computer ibmq_vigo.
Spatially resolving the chemical composition of the planet building blocks

The inner regions of protoplanetary discs (from ∼0.1 to 10 au) are the expected birthplace of planets, especially telluric. In those high-temperature regions, solids can experience cyclical annealing, vapourisation, and recondensation. Hot and warm dusty grains emit mostly in the infrared domain, notably in N-band (8–13 μm). Studying their fine chemistry through mid-infrared spectro-interferometry with the new Very Large Telescope Interferometer (VLTI) instrument Multi AperTure mid-Infrared SpectroScopic Experiment (MATISSE), which can spatially resolve these regions, requires detailed dust chemistry models. Using radiative transfer, we derived infrared spectra of a fiducial static protoplanetary disc model with different inner-disc (<1 au) dust compositions. The latter were derived from condensation sequences computed at local thermodynamic equilibrium (LTE) for three initial C/O ratios: subsolar (C/O = 0.4), solar (C/O = 0.54), and supersolar (C/O = 1). The three scenarios return very different N-band spectra, especially when considering the presence of sub-micron-sized dust grains. MATISSE should be able to detect these differences and trace the associated sub-au-scale radial changes. We propose a first interpretation of N-band ‘inner-disc’ spectra obtained with the former VLTI instrument MID-infrared Interferometric instrument (MIDI) on three Herbig stars (HD 142527, HD 144432, HD 163296) and one T Tauri star (AS 209). Notably, we could associate a supersolar (‘carbon-rich’) composition for HD 142527 and a subsolar (‘oxygen-rich’) one for HD 1444432. We show that the inner-disc mineralogy can be very specific and not related to the dust composition derived from spatially unresolved mid-infrared spectroscopy. We highlight the need for including more complex chemistry when interpreting solid-state spectroscopic observations of the inner regions of discs, and for considering dynamical aspects for future studies.
ThriftyNets : Convolutional Neural Networks with Tiny Parameter Budget

Deep Neural Networks are state-of-the-art in a large number of challenges in machine learning. However, to reach the best performance they require a huge pool of parameters. Indeed, typical deep convolutional architectures present an increasing number of feature maps as we go deeper in the network, whereas spatial resolution of inputs is decreased through downsampling operations. This means that most of the parameters lay in the final layers, while a large portion of the computations are performed by a small fraction of the total parameters in the first layers. In an effort to use every parameter of a network at its maximum, we propose a new convolutional neural network architecture, called ThriftyNet. In ThriftyNet, only one convolutional layer is defined and used recursively, leading to a maximal parameter factorization. In complement, normalization, non-linearities, downsamplings and shortcut ensure sufficient expressivity of the model. ThriftyNet achieves competitive performance on a tiny parameters budget, exceeding 91% accuracy on CIFAR-10 with less than 40 k parameters in total, 74.3% on CIFAR-100 with less than 600 k parameters, and 67.1% On ImageNet ILSVRC 2012 with no more than 4.15 M parameters. However, the proposed method typically requires more computations than existing counterparts.
Non-Candida albicans Candida yeasts of the oral cavity

Candida species are prevalent in the oral cavity, in particular among elderly patients with reduced salivary flow and/or dental prostheses, and in immunosuppressed patients. The most frequently encountered species are Candida albicans. Non-Candida albicans Candida (NCAC) strains, however, are isolated in increasing numbers in medically compromised patients. These strains may cause systemic infections and are often resistant to commonly used antifungal agents such as fluconazole. Candida species may be capable of metabolizing ethanol to carcinogenic acetaldehyde and can thus progress oral and upper gastrointestinal tract cancer. Consequently, more focus should be placed on diagnosis and treatment of oral Candida infections, also on other Candida species than C. albicans. This review outlines the current knowledge about the NCAC yeasts with emphasis on oral medicine.
Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise

The feasibility of federated learning is highly constrained by the server-clients infrastructure in terms of network communication. Most newly launched smartphones and IoT devices are equipped with GPUs or sufficient computing hardware to run powerful AI models. However, in case of the original synchronous federated learning, client devices suffer waiting times and regular communication between clients and server is required. This implies more sensitivity to local model training times and irregular or missed updates, hence, less or limited scalability to large numbers of clients and convergence rates measured in real time will suffer. We propose a new algorithm for asynchronous federated learning which eliminates waiting times and reduces overall network communication - we provide rigorous theoretical analysis for strongly convex objective functions and provide simulation results. By adding Gaussian noise we show how our algorithm can be made differentially private -- new theorems show how the aggregated added Gaussian noise is significantly reduced.
Blow-up analysis of fast-slow PDEs with loss of hyperbolicity

We consider a fast-slow partial differential equation (PDE) with reaction-diffusion dynamics in the fast variable and the slow variable driven by a differential operator on a bounded domain. Assuming a transcritical normal form for the reaction term and viewing the slow variable as a dynamic bifurcation parameter, we analyze the passage through the fast subsystem bifurcation point. In particular, we employ a spectral Galerkin approximation and characterize the invariant manifolds for the finite-dimensional Galerkin approximation for each finite truncation using geometric desingularization via a blow-up analysis. In addition to the crucial approximation procedure, a key step is to make the domain dynamic as well during the blow-up analysis. Finally, we prove that our results extend to the infinite-dimensional problem, showing the convergence of the finite-dimensional manifolds to infinite-dimensional Banach manifolds for different parameter regimes near the bifurcation point. Within our analysis, we find that the PDEs appearing in entry and exit blow-up charts are quasi-linear free boundary value problems, while in the central/scaling chart we obtain a PDE, which is often encountered in classical reaction-diffusion problems exhibiting solutions with finite-time singularities. In summary, we establish a first full case of a geometric blow-up analysis for fast-slow PDEs with a non-hyperbolic point. Our methodological approach has the potential to deal with the loss of hyperbolicity for a wide variety of infinite-dimensional dynamical systems.
A Lojasiewicz inequality for ALE metrics

We introduce a new functional inspired by Perelman's $\lambda$-functional adapted to the asymptotically locally Euclidean (ALE) setting and denoted $\lambda_{\operatorname{ALE}}$. Its expression includes a boundary term which turns out to be the ADM-mass. We prove that $\lambda_{\operatorname{ALE}}$ is defined and analytic on convenient neighborhoods of Ricci-flat ALE metrics and we show that it is monotonic along the Ricci flow. This for example lets us establish that small perturbations of integrable and stable Ricci-flat ALE metrics with nonnegative scalar curvature have nonnegative mass. We then introduce a general scheme of proof for a Lojasiewicz-Simon inequality on non-compact manifolds and prove that it applies to $\lambda_{\operatorname{ALE}}$ around Ricci-flat metrics. We moreover obtain an optimal weighted Lojasiewicz exponent for metrics with integrable Ricci-flat deformations.
Akt regulates L-type Ca2+ channel activity by modulating Cavα1 protein stability

The insulin IGF-1–PI3K–Akt signaling pathway has been suggested to improve cardiac inotropism and increase Ca2+ handling through the effects of the protein kinase Akt. However, the underlying molecular mechanisms remain largely unknown. In this study, we provide evidence for an unanticipated regulatory function of Akt controlling L-type Ca2+ channel (LTCC) protein density. The pore-forming channel subunit Cavα1 contains highly conserved PEST sequences (signals for rapid protein degradation), and in-frame deletion of these PEST sequences results in increased Cavα1 protein levels. Our findings show that Akt-dependent phosphorylation of Cavβ2, the LTCC chaperone for Cavα1, antagonizes Cavα1 protein degradation by preventing Cavα1 PEST sequence recognition, leading to increased LTCC density and the consequent modulation of Ca2+ channel function. This novel mechanism by which Akt modulates LTCC stability could profoundly influence cardiac myocyte Ca2+ entry, Ca2+ handling, and contractility.
FPT Algorithms for Finding Dense Subgraphs in c-Closed Graphs

Dense subgraph detection is a fundamental problem in network analysis for which few worst-case guarantees are known, motivating its study through the lens of fixed-parameter tractability. But for what parameter? Recent work has proposed parameterizing graphs by their degree of triadic closure, with a $c$-closed graph defined as one in which every vertex pair with at least $c$ common neighbors are themselves connected by an edge. The special case of enumerating all maximal cliques (and hence computing a maximum clique) of a $c$-closed graph is known to be fixed-parameter tractable with respect to $c$ (Fox et al., SICOMP 2020). In network analysis, sufficiently dense subgraphs are typically as notable and meaningful as cliques. We investigate the fixed-parameter tractability (with respect to $c$) of optimization and enumeration in $c$-closed graphs, for several notions of dense subgraphs. We focus on graph families that are the complements of the most well-studied notions of sparse graphs, including graphs with bounded degree, bounded treewidth, or bounded degeneracy, and provide fixed-parameter tractable enumeration and optimization algorithms for these families. To go beyond the special case of maximal cliques, we use a new combinatorial bound (generalizing the Moon-Moser theorem); new techniques for exploiting the $c$-closed condition; and more sophisticated enumeration algorithms.
The interaction between salinity stress and seed vigour during germination of soyabean seeds

Seeds of six soyabean (Glycine max (L.) Merr. ) cultivars were germinated between paper towels moistened with a range of solutions of both NaCI (32.2 to 421.1 mmolal NaCI) and polyethylene glycol (PEG, 5-30% w/v). The solutions of NaCI and PEG used had the same water potentials within the range -0.23 to -1.9 MPa. After 8d the ungerminated seeds were transferred to towels moistened with deionised water for 7d. Cultivars differed in their response to salinity and failure of seeds to recover from high salinity when transferred to deionised water, revealed the toxicity of NaCl. In contrast, the increase in germination during the recovery period after exposure to PEG suggested that PEG was not toxic. At the same external water potential, the lower mean germination time (MGT) and higher final germination in NaCl than in PEG could be explained by the more rapid imbibition in NaCl solutions and achievement of a moisture content that allowed germination. The influence of seed vigour on the response of soyabean to stress was examined using seeds having high percentage germination but a range of seed vigour, produced by rapid ageing at 15% moisture content (MC) and 45°C for up to 9h. The germination of aged seeds having reduced vigour fell with increased salinity (330, 421 mmolal NaCI) but there was no significant interaction between salinity and seed ageing. Transfer of unaged and aged seeds to 0 MPa after exposure to NaCI resulted in a greater increase in the germination of unaged seeds. The negative correlation of seed leachate conductivity and MGT with the increase percentage germination after transfer to 0 MPa suggested that seed vigour influences the ability of seeds to recover from saline conditions.
Social Learning and the Accuracy-Risk Trade-off in the Wisdom of the Crowd

How do we design and deploy crowdsourced prediction platforms for real-world applications where risk is an important dimension of prediction performance? To answer this question, we conducted a large online Wisdom of the Crowd study where participants predicted the prices of real financial assets (e.g. S&P 500). We observe a Pareto frontier between accuracy of prediction and risk, and find that this trade-off is mediated by social learning i.e. as social learning is increasingly leveraged, it leads to lower accuracy but also lower risk. We also observe that social learning leads to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote. Our results have implications for the design of crowdsourced prediction platforms: for example, they suggest that the performance of the crowd should be more comprehensively characterized by using both accuracy and risk (as is standard in financial and statistical forecasting), in contrast to prior work where risk of prediction has been overlooked.
Effect of elevated CO2 on rhizosphere carbon flow and soil microbial processes

Direct effects of increased above‐ground CO2 concentration on soil microbial processes are unlikely, due to the high pCO2 of the soil atmosphere in most terrestrial ecosystems. However, below‐ ground microbial processes are likely to be affected through altered plant inputs at elevated CO2. A major component of plant input is derived from litter fall and root turnover. Inputs also derive from rhizodeposition (loss of C‐compounds from active root systems) which may account for up to 40% of photoassimilate. This input fuels the activity of complex microbial communities around roots. These communities are centrally important not only to plant–microbe interactions and consequent effects on plant growth, but also, through their high relative activity and abundance, to microbially mediated processes in soil generally. This review focuses on approaches to measure C‐flow from roots, in particular, as affected by increased atmospheric CO2 concentration. The available evidence for impacts on microbial communities inhabiting this niche, which constitutes an interface for possible perturbations on terrestrial ecosystems through the influence of environmental change, will also be discussed. While methodologies for measuring effects of increased CO2 concentration on plant growth, physiology and C‐partitioning are abundant and widely reported, there is relatively little information on plant‐mediated effects on soil microbial communities and processes. Importantly, many studies have also neglected to recognize that any secondary effects on microbial communities may have profound effects on plant parameters measured in relation to environmental change. We critically review approaches which have been used to measure rhizodeposition under conditions of increased atmospheric CO2 concentration, and then consider evidence for changes in microbial communities and processes, and the methodologies which have been recently developed, and are appropriate to study such changes.
Algebraic approach to Rump’s results on relations between braces and pre-lie algebras

In 2014, Wolfgang Rump showed that there exists a correspondence between left nilpotent right [Formula: see text]-braces and pre-Lie algebras. This correspondence, established using a geometric approach related to flat affine manifolds and affine torsors, works locally. In this paper, we explain Rump’s correspondence using only algebraic formulae. An algebraic interpretation of the correspondence works for fields of sufficiently large prime characteristic as well as for fields of characteristic zero.
Large Intelligent Surface Assisted Non-Orthogonal Multiple Access: Performance Analysis

Large intelligent surface (LIS) has recently emerged as a potential enabling technology for 6G networks, offering extended coverage and enhanced energy and spectral efficiency. In this work, motivated by its promising potentials, we investigate the error rate performance of LIS-assisted non-orthogonal multiple access (NOMA) networks. Specifically, we consider a downlink NOMA system, in which data transmission between a base station (BS) and $L$ NOMA users is assisted by an LIS comprising $M$ reflective elements. First, we derive the probability density function of the end-to-end wireless fading channels between the BS and NOMA users. Then, by leveraging the obtained results, we derive an approximate expression for the pairwise error probability (PEP) of NOMA users under the assumption of imperfect successive interference cancellation. Furthermore, accurate expressions for the PEP for $M = 1$ and large $M$ values ($M > 10$) are presented in closed-form. To gain further insights into the system performance, an asymptotic expression for the PEP in high signal-to-noise ratio regime, the achievable diversity order, and a tight union bound on the bit error rate are provided. Finally, numerical and simulation results are presented to validate the derived mathematical results.
One-Shot Learning for Language Modelling

Humans can infer a great deal about the meaning of a word, using the syntax and semantics of surrounding words even if it is their first time reading or hearing it. We can also generalise the learned concept of the word to new tasks. Despite great progress in achieving human-level performance in certain tasks (Silver et al., 2016), learning from one or few examples remains a key challenge in machine learning, and has not thoroughly been explored in Natural Language Processing (NLP). In this work we tackle the problem of oneshot learning for an NLP task by employing ideas from recent developments in machine learning: embeddings, attention mechanisms (softmax) and similarity measures (cosine, Euclidean, Poincare, and Minkowski). We adapt the framework suggested in matching networks (Vinyals et al., 2016), and explore the effectiveness of the aforementioned methods in one, two and three-shot learning problems on the task of predicting missing word explored in (Vinyals et al., 2016) by using the WikiText-2 dataset. Our work contributes in two ways: Our first contribution is that we explore the effectiveness of different distance metrics on k-shot learning, and show that there is no single best distance metric for k-shot learning, which challenges common belief. We found that the performance of a distance metric depends on the number of shots used during training. The second contribution of our work is that we establish a benchmark for one, two, and three-shot learning on a language task with a publicly available dataset that can be used to benchmark against in future research.
Microsatellite markers for the large blue butterflies Maculinea nausithous and Maculinea alcon (Lepidoptera: Lycaenidae) and their amplification in other Maculinea species

We developed microsatellite markers for Maculinea nausithous and Maculinea alcon, two of five species of endangered large blue butterflies found in Europe. Two separate microsatellite libraries were constructed. Eleven markers were developed for M. nausithous and one for M. alcon . The primers were tested on both species as well as on the three other European Maculinea species. The number of alleles per locus ranged from two to 14. These markers will be useful tools for population genetic studies of Maculinea species.
FAUST I. The hot corino at the heart of the prototypical Class I protostar L1551 IRS5

The study of hot corinos in solar-like protostars has been so far mostly limited to the Class 0 phase, hampering our understanding of their origin and evolution. In addition, recent evidence suggests that planet formation starts already during Class I phase, which therefore represents a crucial step in the future planetary system chemical composition. Hence, the study of hot corinos in Class I protostars has become of paramount importance. Here, we report the discovery of a hot corino towards the prototypical Class I protostar L1551 IRS5, obtained within the ALMA (Atacama Large Millimeter/submillimeter Array) Large Program FAUST (Fifty AU STudy of the chemistry in the disc/envelope system of solar-like protostars). We detected several lines from methanol and its isotopologues (13CH3OH and CH2DOH), methyl formate, and ethanol. Lines are bright towards the north component of the IRS5 binary system, and a possible second hot corino may be associated with the south component. The methanol lines' non-LTE analysis constrains the gas temperature (∼100 K), density (≥1.5 × 108 cm−3), and emitting size (∼10 au in radius). All CH3OH and 13CH3OH lines are optically thick, preventing a reliable measure of the deuteration. The methyl formate and ethanol relative abundances are compatible with those measured in Class 0 hot corinos. Thus, based on this work, little chemical evolution from Class 0 to I hot corinos occurs.