Miracle-dz/UniFAR-FTUs huggingface dataset:
On the usefulness of a minimalistic model to study tree-grass biomass distributions along biogeographic gradients in the savanna biome

We present and analyze a model aiming at recovering as dynamical outcomes of tree-grass interactions the wide range of vegetation physiognomies observable in the savanna biome along rainfall gradients at regional/continental scales. The model is based on two ordinary differential equations (ODE), for woody and grass biomass. It is parameterized from literature and retains mathematical tractability, since we restricted it to the main processes, notably tree-grass asym-metric interactions (either facilitative or competitive) and the grass-fire feedback. We used a fully qualitative analysis to derive all possible long term dynamics and express them in a bifur-cation diagram in relation to mean annual rainfall and fire frequency. We delineated domains of monostability (forest, grassland, savanna), of bistability (e.g. forest-grassland or forest-savanna) and even tristability. Notably, we highlighted regions in which two savanna equilibria may be jointly stable (possibly in addition to forest or grassland). We verified that common knowledge about decreasing woody biomass with increasing fire frequency is recovered for all levels of rainfall, contrary to previous attempts using analogous ODE frameworks. Thus, this framework appears able to render more realistic and diversified outcomes than often thought of. Our model can help figure out the ongoing dynamics of savanna vegetation in large territories for which local data are sparse or absent. To explore the bifurcation diagram with different combinations of the model parameters, we have developed a user-friendly R-Shiny application freely available at : this https URL.
Primordial black holes confront LIGO/Virgo data: current situation

The LIGO and Virgo Interferometers have so far provided 11 gravitational-wave (GW) observations of black-hole binaries. Similar detections are bound to become very frequent in the near future. With the current and upcoming wealth of data, it is possible to confront specific formation models with observations. We investigate here whether current data are compatible with the hypothesis that LIGO/Virgo black holes are of primordial origin. We compute in detail the mass and spin distributions of primordial black holes (PBHs), their merger rates, the stochastic background of unresolved coalescences, and confront them with current data from the first two observational runs, also including the recently discovered GW190412. We compute the best-fit values for the parameters of the PBH mass distribution at formation that are compatible with current GW data. In all cases, the maximum fraction of PBHs in dark matter is constrained by these observations to be f PBH≈ few× 10−3. We discuss the predictions of the PBH scenario that can be directly tested as new data become available. In the most likely formation scenarios where PBHs are born with negligible spin, the fact that at least one of the components of GW190412 is moderately spinning is incompatible with a primordial origin for this event, unless accretion or hierarchical mergers are significant. In the absence of accretion, current non-GW constraints already exclude that LIGO/Virgo events are all of primordial origin, whereas in the presence of accretion the GW bounds on the PBH abundance are the most stringent ones in the relevant mass range. A strong phase of accretion during the cosmic history would favour mass ratios close to unity, and a redshift-dependent correlation between high masses, high spins and nearly-equal mass binaries, with the secondary component spinning faster than the primary. Finally, we highlight that accretion can play an important role to relax current constraints on the PBH abundance, which calls for a better modelling of the mass and angular momentum accretion rates at redshift 0z≲3.
Towards Transparency of TD-RL Robotic Systems with a Human Teacher

The high request for autonomous and flexible HRI implies the necessity of deploying Machine Learning (ML) mechanisms in the robot control. Indeed, the use of ML techniques, such as Reinforcement Learning (RL), makes the robot behaviour, during the learning process, not transparent to the observing user. In this work, we proposed an emotional model to improve the transparency in RL tasks for human-robot collaborative scenarios. The architecture we propose supports the RL algorithm with an emotional model able to both receive human feedback and exhibit emotional responses based on the learning process. The model is entirely based on the Temporal Difference (TD) error. The architecture was tested in an isolated laboratory with a simple setup. The results highlight that showing its internal state through an emotional response is enough to make a robot transparent to its human teacher. People also prefer to interact with a responsive robot because they are used to understand their intentions via emotions and social signals.
Handling Concept Drift for Predictions in Business Process Mining

Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift. Prediction quality of models can be largely influenced by this phenomenon. Therefore, concept drift is usually handled by retraining of the model. However, current research lacks a recommendation which data should be selected for the retraining of the machine learning model. Therefore, we systematically analyze different data selection strategies in this work. Subsequently, we instantiate our findings on a use case in process mining which is strongly affected by concept drift. We can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift handling. Furthermore, we depict the effects of the different data selection strategies.
Analysis in k-Space of Magnetization Dynamics Driven by Strong Terahertz Fields

Demagnetization in a thin film due to a terahertz pulse of magnetic field is investigated. Linearized Landau-Lifshitz-Gilbert (LLG) equation in the Fourier space to describe the magnetization dynamics is derived, and spin wave time evolution is studied. Finally, the demagnetization due to spin wave dynamics and recent experimental observations on similar magnetic system is compared. As a result, the marginal role of spin wave dynamics in loss of magnetization is established.
Design of a Privacy-Preserving Data Platform for Collaboration Against Human Trafficking

Case records on victims of human trafficking are highly sensitive, yet the ability to share such data is critical to evidence-based practice and policy development across government, business, and civil society. We present new methods to anonymize, publish, and explore such data, implemented as a pipeline generating three artifacts: (1) synthetic data mitigating the privacy risk that published attribute combinations might be linked to known individuals or groups; (2) aggregate data mitigating the utility risk that synthetic data might misrepresent statistics needed for official reporting; and (3) visual analytics interfaces to both datasets mitigating the accessibility risk that privacy mechanisms or analysis tools might not be understandable and usable by all stakeholders. We present our work as a design study motivated by the goal of transforming how the world's largest database of identified victims is made available for global collaboration against human trafficking.
List homomorphism problems for signed graphs

We consider homomorphisms of signed graphs from a computational perspective. In particular, we study the list homomorphism problem seeking a homomorphism of an input signed graph $(G,\sigma)$, equipped with lists $L(v) \subseteq V(H), v \in V(G)$, of allowed images, to a fixed target signed graph $(H,\pi)$. The complexity of the similar homomorphism problem without lists (corresponding to all lists being $L(v)=V(H)$) has been previously classified by Brewster and Siggers, but the list version remains open and appears difficult. We illustrate this difficulty by classifying the complexity of the problem when $H$ is a (reflexive or irreflexive) tree. The tools we develop will be useful for classifications of other classes of signed graphs, and we mention some follow-up research of this kind; the classifications are surprisingly complex.
Mathematical analysis of wave radiation by a step-like surface

This paper proposes, for wave propagating in a globally perturbed half plane with a perfectly conducting step-like surface, a sharp Sommerfeld radiation condition (SRC) for the first time, an analytic formula of the far-field pattern, and a high-accuracy numerical solver. We adopt the Wiener-Hopf method to compute the Green function for a cracked half plane, a background for the perturbed half plane. We rigorously show that the Green function asymptotically satisfies a universal-direction SRC (uSRC) and radiates purely outgoing at infinity. This helps to propose an implicit transparent boundary condition for the scattered wave, by either a cylindrical incident wave due to a line source or a plane incident wave. Then, a well-posedness theory is established via an associated variational formulation. The theory reveals that the scattered wave, post-subtracting a known wave field, satisfies the same uSRC so that its far-field pattern is accessible theoretically. For a plane-wave incidence, asymptotic analysis shows that merely subtracting reflected plane waves, due to non-uniform heights of the step-like surface at infinity, from the scattered wave in respective regions produces a discontinuous wave satisfying the uSRC as well. Numerically, we adopt a previously developed perfectly-matched-layer (PML) boundary-integral-equation method to solve the problem. Numerical results demonstrate that the PML truncation error decays exponentially fast as thickness or absorbing power of the PML increases, of which the convergence relies heavily on the Green function exponentially decaying in the PML.
When Pull Turns to Shove: A Mathematical Model For Opinion Dynamics

Accurate modeling of opinion dynamics has the potential to help us understand polarization and what makes effective political discourse possible or impossible. Here, we use differential equations to model the evolution of political opinions within a continuously distributed population. We utilize a network-free system of determining political influence and a local-attraction, distal-repulsion dynamic for reaction to perceived content. Our approach allows for the incorporation of intergroup bias such that messages from trusted in-group sources enjoy greater leeway than out-group ones. The framework we put forward can reproduce real-world political distributions and experimentally observed dynamics, and is amenable to further refinement as more data becomes available.
A comparison of the effects of two low‐calcium diets on egg production in the domestic fowl

1. Hens fed on a diet containing 0–05% calcium virtually ceased egg laying (production <4%) whereas those fed on a diet containing 0–5% Ca maintained production at between 20 and 30%. 2. After a return to a normal diet (3% Ca) egg weight, shell weight, ovarian characteristics and oviduct size were of a similar nature in the two groups, though the differences in egg weight, shell weight and shell calcium were significant at the 1 % level. 3. Bone weight and bone ash did not differ significantly between the groups but after a return to normal rations, bone weight and bone ash became higher in the group that had received the 0.05% Ca diet. 4. There were no clinical signs of bone demineralisation during the experiments.
SkeleDock: A Web Application for Scaffold Docking in PlayMolecule

SkeleDock is a scaffold docking algorithm which uses the structure of a protein-ligand complex as a template to model the binding mode of a chemically similar system. This algorithm was evaluated in the D3R Grand Challenge 4 pose prediction challenge, where it achieved competitive performance. Furthermore, we show that, if crystallized fragments of the target ligand are available, SkeleDock can outperform rDock docking software at predicting the binding mode. This article also addresses the capacity of this algorithm to model macrocycles and deal with scaffold hopping. SkeleDock can be accessed at https://playmolecule.org/SkeleDock/.
Climatology of Severe Local Storm Environments and Synoptic-Scale Features over North America in ERA5 Reanalysis and CAM6 Simulation

Severe local storm (SLS) activity is known to occur within specific thermodynamic and kinematic environments. These environments are commonly associated with key synoptic-scale features—including southerly Great Plains low-level jets, drylines, elevated mixed layers, and extratropical cyclones—that link the large-scale climate to SLS environments. This work analyzes spatiotemporal distributions of both extreme values of SLS environmental parameters and synoptic-scale features in the ERA5 reanalysis and in the Community Atmosphere Model, version 6 (CAM6), historical simulation during 1980–2014 over North America. Compared to radiosondes, ERA5 successfully reproduces SLS environments, with strong spatiotemporal correlations and low biases, especially over the Great Plains. Both ERA5 and CAM6 reproduce the climatology of SLS environments over the central United States as well as its strong seasonal and diurnal cycles. ERA5 and CAM6 also reproduce the climatological occurrence of the synoptic-scale features, with the distribution pattern similar to that of SLS environments. Compared to ERA5, CAM6 exhibits a high bias in convective available potential energy over the eastern United States primarily due to a high bias in surface moisture and, to a lesser extent, storm-relative helicity due to enhanced low-level winds. Composite analysis indicates consistent synoptic anomaly patterns favorable for significant SLS environments over much of the eastern half of the United States in both ERA5 and CAM6, though the pattern differs for the southeastern United States. Overall, our results indicate that both ERA5 and CAM6 are capable of reproducing SLS environments as well as the synoptic-scale features and transient events that generate them.
Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled Data

The abstract of a scientific paper distills the contents of the paper into a short paragraph. In the biomedical literature, it is customary to structure an abstract into discourse categories like BACKGROUND, OBJECTIVE, METHOD, RESULT, and CONCLUSION, but this segmentation is uncommon in other fields like computer science. Explicit categories could be helpful for more granular, that is, discourse-level search and recommendation. The sparsity of labeled data makes it challenging to construct supervised machine learning solutions for automatic discourse-level segmentation of abstracts in non-bio domains. In this paper, we address this problem using transfer learning. We define three discourse categories -- BACKGROUND, TECHNIQUE, and OBSERVATION -- for an abstract because these three categories are most common. We train a deep neural network on structured abstracts from PubMed, then fine-tune it on a small hand-labeled corpus of computer science papers. We observe an accuracy of 75% on the test corpus of computer science papers. We also perform an ablation study to highlight the roles of the different parts of the model. Our method appears to be a promising solution to the automatic segmentation of abstracts, where the labeled data is sparse.
Effects of climate on earlywood vessel formation of Quercus robur and Q. pyrenaica at a site in the northwestern Iberian Peninsula

We analysed climate–growth relationships for two ring-porous oak species (Quercus robur L. and Q. pyrenaica Willd.) at one site in northwestern Spain. Increment cores from 12 living trees per species were taken for ring and earlywood vessel measurements, considering three width variables (for earlywood, latewood, and the whole ring) and six anatomical variables (based on vessel area distribution, number of vessels, and conductivity) in the earlywood. We used standard dendrochronological procedures and compared each mean series with meteorological records of temperature, precipitation, and estimates of soil water content. Earlywood width was negatively related to precipitation in the previous late season for Q. robur, but it was controlled by the previous spring temperature for Q. pyrenaica. These responses were highly dependent on vessel number and probably related to carbohydrate storage dynamics but differed between species. Vessel size showed a stronger relationship to climate and was mainly coupled to.
Fatty acid composition of heavy pig back fat in relationship to some animal factors

Abstarct The influence of genetic type, lean meat percentage and sex on fatty acids (FA) composition of back fat of heavy pigs, reared for the production of San Daniele dry cured ham, was studied. One-hundred pigs of four genetic types - Large White or Duroc x (Landrace x Large White), GOLAND and DANBRED - were considered. They were randomly chosen during the slaughtering of 21 lots of heavy animals, in groups of four to eight subjects, balanced by sex, female and castrated males, and carcass leanness, U and R classes following the European grading system. Principal components (PCs) analysis showed that 94.3% of lot-diets’ FA variability was accounted for a five PCs model. The diet effect on the lard composition was weighted using the PCs scores as covariates in a tri-factorial (genotype, carcass leanness, sex) covariance design. Diet had a great effect on lard composition, indeed every examined back fat FA co-varied with the extracted PCs. On the other hand, sex effect never reached a significance threshold, as well as the interactions between factors. Genetic type influenced stearic acid and MUFA content and n6-PUFA to n3-PUFA ratio of back fat while leanness mainly influenced PUFA, the fatter class showing a significantly lower content of linoleic acid (13.2 vs. 11.9% total lipids in U vs. R class respectively).
Large reservoirs as ecological barriers to downstream movements of Neotropical migratory fish

Most large rivers in South America are fragmented by large dams, and a common management strategy to mitigate impacts has been construction of fish passes. Recent studies, however, indicate that downstream passage of adults and young fish is nil or minimal. Better understanding of this phenomenon is needed if fishways are to provide any tangible conservation value in South America. We propose, in this article, that large reservoirs impose a different kind of barrier to migrating fish: impoundments create a diffuse gradient of hydraulic/limnological conditions that affects fish behaviour and functions as an extensive environmental filter that discourages downstream movements. To develop this idea, we characterize the barriers created by dams and reservoirs by describing their distinct nature, the effects on fish migration and potential solutions. We show, for example, that dams generally prevent upstream movements, whereas reservoirs impede mainly downstream movements. In this context, we explain how fish passes, in some instances, can partially mitigate fragmentation caused by dams, but there is no technical solution to solve the barrier effect of reservoirs. In addition, we present a body of empirical evidence that supports the theory that large reservoirs are important barriers to fish migration in South America, we offer an overview of the size of reservoirs to show that impoundments typically have large dimensions, and we discuss the significance of this theory for other regions. Based on current and proposed river regulation scenarios, we conclude that conservation of Neotropical migratory fish will be much more complicated than previously believed.
From String Breaking to Quarkonium Spectrum

We present a preliminary computation of potentials between two static quarks in $n_f=2$ QCD with O(a) improved Wilson fermions. We explore different smearing choices (HYP, HYP2 and APE) and their effect on the signal to noise ratio in the computed static potentials. This is a part of a larger effort concerning, at first, a precise computation of the QCD string breaking parameters and their subsequent utilization for the recent approach based on Born-Oppenheimer approximation (Bicudo et al. 2020 \cite{Bicudo:2019ymo}) to study quarkonium resonances and bound states.
Schema-Guided Natural Language Generation

Neural network based approaches to data-to-text natural language generation (NLG) have gained popularity in recent years, with the goal of generating a natural language prompt that accurately realizes an input meaning representation. To facilitate the training of neural network models, researchers created large datasets of paired utterances and their meaning representations. However, the creation of such datasets is an arduous task and they mostly consist of simple meaning representations composed of slot and value tokens to be realized. These representations do not include any contextual information that an NLG system can use when trying to generalize, such as domain information and descriptions of slots and values. In this paper, we present the novel task of Schema-Guided Natural Language Generation (SG-NLG). Here, the goal is still to generate a natural language prompt, but in SG-NLG, the input MRs are paired with rich schemata providing contextual information. To generate a dataset for SG-NLG we re-purpose an existing dataset for another task: dialog state tracking, which includes a large and rich schema spanning multiple different attributes, including information about the domain, user intent, and slot descriptions. We train different state-of-the-art models for neural natural language generation on this dataset and show that in many cases, including rich schema information allows our models to produce higher quality outputs both in terms of semantics and diversity. We also conduct experiments comparing model performance on seen versus unseen domains, and present a human evaluation demonstrating high ratings for overall output quality.
Activity of retinoic acid receptor‐γ selectively binding retinoids alone and in combination with interferon‐γ in breast cancer cell lines

Retinoids modulate several cell functions and especially inhibit the growth of a wide variety of cells including breast cancer. Retinoic acid receptor‐γ (RAR‐γ) has been shown to mediate the antiproliferative activity of retinoids. To further test this hypothesis we examined the effects of different RAR‐γ selectively binding retinoids (CD2325, CD2247, CD666 and CD437) on breast cancer cell lines. With exception of CD2247, all retinoids inhibited proliferation of MCF‐7, SKBR‐3, T47D and ZR‐75‐1 breast cancer cell lines, similar to the natural compound all‐trans retinoic acid (ATRA). In addition, all 4 compounds were able to act synergistically with interferon‐γ (IFN‐γ) in all breast cancer cell lines including the retinoid‐resistant BT‐20 and 734‐B lines. In functional transactivation assays we demonstrated that only in the MCF‐7 cell line, TPA‐mediated AP‐1 activity was suppressed only by ATRA and CD2325, whereas in SKBR‐3, another RA‐sensitive breast cancer cell line, it was not. The synergistic antiproliferative activity involving retinoids and IFN‐γ could not be explained by an enhanced anti‐AP‐1 activity. No correlation was found between expression of RARs and cellular retinoic acid binding proteins (CRABPs) and antiproliferative effects of the retinoids. RAR‐γ selectively binding retinoids are potent inhibitors of breast cancer cell proliferation, alone and in combination with IFN‐γ. For this reason and because of a possible low toxicity, as compared with retinoic acid, we speculate that these RAR‐γ selective binding retinoids might be of clinical importance. Int. J. Cancer 71:497‐503, 1997. © 1997 Wiley‐Liss Inc.