Miracle-dz/UniFAR-FTUs huggingface dataset:
Differences in growth rates of juvenile Solea solea and Solea senegalensis in the Tagus estuary, Portugal

The growth patterns of juveniles of Solea solea and Solea senegalensis were compared, within the nursery areas of the Tagus estuary. Growth was estimated by modal progression analysis of length–frequency data, using the Bhattacharya method based on 1327 and 3255 individuals of S. solea and S. senegalensis, respectively. For the two species several cohorts of the same age-group were identified, being the number relative to S. senegalensis higher compared to S. solea. 0-group individuals of S. solea begin to occur within the nursery in May, while for S. senegalensis the recruitment is longer (from June to October). The number of individuals of the first cohort was higher relative to those belonging to the subsequent cohorts. The growth rate of S. solea juveniles varied from 0·01 mm d−1 to 1·51 mm d−1 and that of S. senegalensis was between 0·22 mm d−1 and 0·89 mm d−1. Although no significant differences were found when comparing growth rate values determined for the different cohorts, age-groups and species, it was noticed that the estimates determined for cohort I were generally higher at the beginning of the nursery colonization period. The differences in the seasonal variation of growth rate of S. solea and S. senegalensis reflected their spawning behaviour and time spent in the plankton and also their differential habitat use pattern.
The Relationship between Environmental Variables and Demographic Patterns of Hyalella azteca (Crustacea:Amphipoda)

Twenty-two populations of Hyalella azteca (Saussure) from freshwater habitats located across eastern, central, and southern Ontario were studied to investigate the relationship between the variability in demographic traits, and a gradient in environmental conditions. Environmental variables were chosen to reflect the buffering capacity of each habitat, and to describe additional physical and chemical attributes of both the water and sediment. Canonical correlation analysis of the data described a significant multivariate relationship between demographic and environmental variables (p = 0.041). Only the first pair of canonical variates was significant (p ≤ 0.01) and indicated increasing reproductive output of H. azteca in habitats of increasing sulphate, calcium hardness, sediment particle size, conductivity, alkalinity, seston, and the organic matter of the fine sediment. Reproductive output was described by the weight at sexual maturity, mean brood size, duration of the reproductive season, and the proportion of the population that was juvenile. Although variables related to the buffering capacity of the water were an important part of the environmental gradient, they were not the only variables important to the demography of H. azteca. Other ions, sediment grain size, seston, and the organic matter of the fine sediment were also important to the relationship described by canonical correlation. The data documented the level of demographic variability among populations of H. azteca in Ontario, and emphasized the relationships among habitat variables and their combined effect on demographic patterns.
Double exceptional links in a three-dimensional dissipative cold atomic gas

Peng He, Jia-Hao Fu, Dan-Wei Zhang, 3, ∗ and Shi-Liang Zhu 2, 3, † National Laboratory of Solid State Microstructures and School of Physics, Nanjing University, Nanjing 210093, China Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, GPETR Center for Quantum Precision Measurement and SPTE, South China Normal University, Guangzhou 510006, China Frontier Research Institute for Physics, South China Normal University, Guangzhou 510006, China (Dated: May 14, 2020)
That is a Known Lie: Detecting Previously Fact-Checked Claims

The recent proliferation of ”fake news” has triggered a number of responses, most notably the emergence of several manual fact-checking initiatives. As a result and over time, a large number of fact-checked claims have been accumulated, which increases the likelihood that a new claim in social media or a new statement by a politician might have already been fact-checked by some trusted fact-checking organization, as viral claims often come back after a while in social media, and politicians like to repeat their favorite statements, true or false, over and over again. As manual fact-checking is very time-consuming (and fully automatic fact-checking has credibility issues), it is important to try to save this effort and to avoid wasting time on claims that have already been fact-checked. Interestingly, despite the importance of the task, it has been largely ignored by the research community so far. Here, we aim to bridge this gap. In particular, we formulate the task and we discuss how it relates to, but also differs from, previous work. We further create a specialized dataset, which we release to the research community. Finally, we present learning-to-rank experiments that demonstrate sizable improvements over state-of-the-art retrieval and textual similarity approaches.
Systematically asymmetric: a comparison of H i profile asymmetries in real and simulated galaxies

We examine different measures of asymmetry for galaxy HI velocity profiles. We introduce the channel-by-channel asymmetry and the velocity-of-equality statistics to quantify profile asymmetries. Using a sample of simulated galaxies, we examine how these and the standard lopsidedness morphometric statistic depend on a variety of observational effects including the viewing angle and inclination. We find that our newly introduced channel-by-channel asymmetry is less sensitive to the effects of viewing angle and inclination than other morphometrics. Applying our statistics to the WHISP HI galaxy sample, we also find that the channel-by-channel asymmetry, is a better indicator of visually-classified asymmetric profiles. In addition, we find that the lopsidedness-velocity of equality space can be used to identify profiles with deep central dips without visual inspection.
Progressive growing of self-organized hierarchical representations for exploration

Designing agent that can autonomously discover and learn a diversity of structures and skills in unknown changing environments is key for lifelong machine learning. A central challenge is how to learn incrementally representations in order to progressively build a map of the discovered structures and re-use it to further explore. To address this challenge, we identify and target several key functionalities. First, we aim to build lasting representations and avoid catastrophic forgetting throughout the exploration process. Secondly we aim to learn a diversity of representations allowing to discover a "diversity of diversity" of structures (and associated skills) in complex high-dimensional environments. Thirdly, we target representations that can structure the agent discoveries in a coarse-to-fine manner. Finally, we target the reuse of such representations to drive exploration toward an "interesting" type of diversity, for instance leveraging human guidance. Current approaches in state representation learning rely generally on monolithic architectures which do not enable all these functionalities. Therefore, we present a novel technique to progressively construct a Hierarchy of Observation Latent Models for Exploration Stratification, called HOLMES. This technique couples the use of a dynamic modular model architecture for representation learning with intrinsically-motivated goal exploration processes (IMGEPs). The paper shows results in the domain of automated discovery of diverse self-organized patterns, considering as testbed the experimental framework from Reinke et al. (2019).
Red-Blue Point Separation for Points on a Circle

Given a set R of red points and a set B of blue points in the plane, the Red-Blue point separation problem asks if there are at most k lines that separate R from B, that is, each cell induced by the lines of the solution is either empty or monochromatic (containing points of only one color). A common variant of the problem is when the lines are required to be axis-parallel. The problem is known to be NP-complete for both scenarios, and W[1]-hard parameterized by k in the former setting and FPT in the latter. We demonstrate a polynomial-time algorithm for the special case when the points lie on a circle. Further, we also demonstrate the W-hardness of a related problem in the axis-parallel setting, where the question is if there are p horizontal and q vertical lines that separate R from B. The hardness here is shown in the parameter p.
On the subvarieties with nonsingular real loci of a real algebraic variety

Let $X$ be a smooth projective real algebraic variety. We give new positive and negative results on the problem of approximating a submanifold of the real locus of $X$ by real loci of subvarieties of $X$, as well as on the problem of determining the subgroups of the Chow groups of $X$ generated by subvarieties with nonsingular real loci, or with empty real loci.
Temperature Independence of the Annual Cycle of Standard Metabolism in the Pumpkinseed

Abstract Standard metabolic rate of the pumpkinseed Lepomis gibbosus was estimated from measurements of routine oxygen consumption and random activity at acclimation temperatures of 8–32 C and within each season of the year. Routine and standard metabolic rates increased from winter (January-March) minima to spring (April-June) maxima, independently of acclimation temperature. For example, standard metabolic rate of a 100-g fish increased from 1.8, 3.0, and 4.8 mg O2˙hour−1 at 8, 16, and 24 C, respectively, during winter, to 3.5, 5.6, and 9.2 mg O2˙hour−1 at the same temperatures, respectively, during spring. The annual energy requirement for standard metabolism, derived from seasonal rate-temperature curves and a simulated annual temperature cycle at 44°N latitude, is 5.01 × 105 joules. The annual cycle is anticipatory in that the increase in metabolic rate during spring begins independently of the vernal rise in environmental temperature. Depression of standard metabolism during fall and winter suggests.
Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations

Personalized recommendations are the backbone machine learning (ML) algorithm that powers several important application domains (e.g., ads, e-commerce, etc) serviced from cloud datacenters. Sparse embedding layers are a crucial building block in designing recommendations yet little attention has been paid in properly accelerating this important ML algorithm. This paper first provides a detailed workload characterization on personalized recommendations and identifies two significant performance limiters: memory-intensive embedding layers and compute-intensive multi-layer perceptron (MLP) layers. We then present Centaur, a chiplet-based hybrid sparse-dense accelerator that addresses both the memory throughput challenges of embedding layers and the compute limitations of MLP layers. We implement and demonstrate our proposal on an Intel HARPv2, a package-integrated $\mathrm{C}\mathrm{P}\mathrm{U}+$FPGA device, which shows a $ 1.7-17.2\times$ performance speedup and $1.7-19.5\times \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}$-efficiency improvement than conventional approaches.
Signaling through JAM-1 and αvβ3 is required for the angiogenic action of bFGF: dissociation of the JAM-1 and αvβ3 complex

Growth factor–induced neovascularization has received a great deal of attention because it is fundamental to the growth and metastasis of solid tumors. This multistep process requires extensive signaling through growth factor receptors and integrins. Among the integrins involved in this process, integrin αvβ3 is specific to basic fibroblast growth factor (bFGF)–induced angiogenesis. Here we show that junctional adhesion molecule 1/A (JAM-1/A) and αvβ3 form a complex in the absence of bFGF. JAM-1, which is normally localized at the cell-cell junctions of quiescent endothelial cells, redistributes to the cell surface on bFGF treatment. Blockage of the extracellular domain of JAM-1 inhibits bFGF-induced endothelial cell morphology, proliferation, and angiogenesis. Additionally, mutation in the JAM-1 cytoplasmic domain blocks bFGF-induced mitogen-activated protein (MAP) kinase activation and ablates its ability to induce endothelial cell tube formation, suggesting that signaling through JAM-1 is key to bFGF-induced signaling. Immunoprecipitation analysis suggests that bFGF signaling dissociates the JAM-1/ αvβ3 complex, allowing for signaling through JAM-1 and αvβ3. In addition, blockage of either JAM-1 or αvβ3 inhibits bFGF-induced MAP kinase activation. Thus, our results suggest that signaling through JAM-1 and αvβ3 is necessary for bFGF-induced angiogenesis.
Byzantine-Robust Decentralized Stochastic Optimization

In this paper, we consider the Byzantine-robust stochastic optimization problem defined over a decentralized network, where the agents collaboratively minimize the summation of expectations of stochastic local cost functions, but some of the agents are unreliable. Due to data corruptions, equipment failures or cyber-attacks, these Byzantine agents can send faulty values to their neighbors and bias the optimization process. Our key idea to handle the Byzantine attacks is to formulate a total variation (TV) norm-penalized approximation of the Byzantine-free problem, where the penalty term forces the local models of regular agents to be close, but also allows the existence of outliers from the Byzantine agents. A stochastic subgra-dient method is applied to solve the penalized problem. We prove that the proposed method converges to a near-optimal solution of the Byzantine-free problem under mild assumptions, and the gap is determined by the number of Byzantine agents and the network topology. Numerical experiments corroborate the theoretical analysis, as well as demonstrate the robustness of proposed method to Byzantine attacks and its superior performance over existing methods.
Smart To-Do: Automatic Generation of To-Do Items from Emails

Intelligent features in email service applications aim to increase productivity by helping people organize their folders, compose their emails and respond to pending tasks. In this work, we explore a new application, Smart-To-Do, that helps users with task management over emails. We introduce a new task and dataset for automatically generating To-Do items from emails where the sender has promised to perform an action. We design a two-stage process leveraging recent advances in neural text generation and sequence-to-sequence learning, obtaining BLEU and ROUGE scores of 0.23 and 0.63 for this task. To the best of our knowledge, this is the first work to address the problem of composing To-Do items from emails.
Inference on Achieved Signal Noise Ratio

We describe a procedure to perform approximate inference on the achieved signal-noise ratio of the Markowitz Portfolio under Gaussian i.i. d. returns. The procedure relies on a statistic similar to the Sharpe Ratio Information Criterion. Testing indicates the procedure is somewhat conservative, but otherwise works well for reasonable values of sample and asset universe sizes. We adapt the procedure to deal with generalizations of the portfolio optimization problem.
Two Dimensions for Organizing Immersive Analytics: Toward a Taxonomy for Facet and Position

As immersive analytics continues to grow as a discipline, so too should its underlying methodological support. Taxonomies play an important role for information visualization and human computer interaction. They provide an organization of the techniques used in a particular domain that better enable researchers to describe their work, discover existing methods, and identify gaps in the literature. Existing taxonomies in related fields do not capture or describe the unique paradigms employed in immersive analytics. We conceptualize a taxonomy that organizes immersive analytics according to two dimensions: spatial and visual presentation. Each intersection of this taxonomy represents a unique design paradigm which, when thoroughly explored, can aid in the design and research of new immersive analytic applications.
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction

Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities. However, effective aggregation of relevant information in the document remains a challenging research question. Existing approaches construct static document-level graphs based on syntactic trees, co-references or heuristics from the unstructured text to model the dependencies. Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. We further develop a refinement strategy, which enables the model to incrementally aggregate relevant information for multi-hop reasoning. Specifically, our model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results, and also yields new state-of-the-art results on the CDR and GDA dataset. Furthermore, extensive analyses show that the model is able to discover more accurate inter-sentence relations.
Contribution to knowledge of the leafroller fauna (Lepidoptera: Tortricidae) of Turkey

A total of 75 species of leafrollers of the family Tortricidae belonging to 41 genera were found in northern and Inner Anatolia between 1999 and 2003. Cochylidia moguntiana (Rössler, 1864) is newly recorded for Turkey, and the first detailed records of Aphelia viburniana (Fabricius, 1787), Epiblema turbidana (Treitschke, 1835), Eucosma conterminana (Guenée, 1845) and Cochylidia rupicola (Curtis,1834) are communicated.
A NUMERICAL SIMULATION OF THE COVID-19 EPIDEMIC IN ARGENTINA USING THE SEIR MODEL

A pandemic caused by a new corona virus has spread worldwide, affecting Argentina. We implement an SEIR model to analyze the disease evolution in Buenos Aires and neighboring cities.The model parameters are calibrated using the number of casualties officially reported. Since infinite solutions honor the data, we show different cases. In all of them the reproduction ratio R0 decreasesafter early lockdown, but then raises, probably due to an increase in the community circulation of the virus. Therefore it is mandatory to reverse this growing trend in R0 by applying control strategiesto avoid a high number of infectious and dead individuals. The model provides an effective procedure to estimate epidemic parameters (fatality rate, transmission probability, infection and incubationperiods) and monitor control measures during the epidemic evolution.
DAugNet: Unsupervised, Multisource, Multitarget, and Life-Long Domain Adaptation for Semantic Segmentation of Satellite Images

The domain adaptation of satellite images has recently gained increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for adapting the model from one domain to another. However, such single-source and single-target setting prevents the methods from being scalable solutions since, nowadays, multiple sources and target domains having different data distributions are usually available. Besides, the continuous proliferation of satellite images necessitates the classifiers to adapt to continuously increasing data. We propose a novel approach, coined DAugNet, for unsupervised, multisource, multitarget, and life-long domain adaptation of satellite images. It consists of a classifier and a data augmentor. The data augmentor, which is a shallow network, is able to perform style transfer between multiple satellite images in an unsupervised manner, even when new data are added over time. In each training iteration, it provides the classifier with diversified data, which makes the classifier robust to large data distribution difference between the domains. Our extensive experiments prove that DAugNet significantly better generalizes to new geographic locations than the existing approaches.
The effect of fertilizer application on farming of the seaweed Undaria pinnatifida (Laminariales, Phaeophyta)

A slow‐release ammonium phosphate fertilizer coated with porous plastic was tested on Undaria pinnatifida (Harvey) Suringar as a possible solution to the nutrient deficiency in seawater that causes quality and yield deterioration in seaweed farming. The yield of U. pinnatifida within the fertilized area was 17–40% greater than that of the control area (unfertilized area). In addition, two harvests were possible per season and the quality of harvested U. pinnatifida was also better than that outside the fertilizer diffusion area. The released NH4‐N did not increase the concentration of NH4‐N outside the farming area. Therefore, this fertilizer increased yield and improved quality without causing water pollution.
Time-uniform Chernoff bounds via nonnegative supermartingales

We develop a class of exponential bounds for the probability that a martingale sequence crosses a time-dependent linear threshold. Our key insight is that it is both natural and fruitful to formulate exponential concentration inequalities in this way. We illustrate this point by presenting a single assumption and theorem that together unify and strengthen many tail bounds for martingales, including classical inequalities (1960-80) by Bernstein, Bennett, Hoeffding, and Freedman; contemporary inequalities (1980-2000) by Shorack and Wellner, Pinelis, Blackwell, van de Geer, and de la Pe\~na; and several modern inequalities (post-2000) by Khan, Tropp, Bercu and Touati, Delyon, and others. In each of these cases, we give the strongest and most general statements to date, quantifying the time-uniform concentration of scalar, matrix, and Banach-space-valued martingales, under a variety of nonparametric assumptions in discrete and continuous time. In doing so, we bridge the gap between existing line-crossing inequalities, the sequential probability ratio test, the Cram\'er-Chernoff method, self-normalized processes, and other parts of the literature.
Nonlinear dynamics of the fishbone-induced alpha transport on ITER

The fishbone-induced transport of alpha particles is computed for the ITER 15 MA baseline scenario (ITER Physics Expert Group on Energy Drive 1999 Nucl. Fusion 39 2471) using the non-linear hybrid Kinetic-MHD code XTOR-K. Two limit cases have been studied, in order to analyse the characteristic regimes of the fishbone instability: the weak kinetic drive limit (Berk et al 1999 Phys. Plasmas 6 3102) and the strong kinetic drive limit (Zonca et al 2015 New J. Phys. 17 013052). In both those regimes, characteristic features of the n = m = 1 fishbone instability are recovered, such as a strong up/down-chirping of the mode frequency, associated with a resonant transport of trapped and passing alpha particles. The effects of the n = m = 0 sheared poloidal and toroidal plasma rotation are taken into account in the simulations. The shear is not negligible, which implies that the fishbone mode frequency has a radial dependency, impacting the wave-particle resonance condition. Phase space hole and clump structures are observed in both non-linear regimes, centered around the precessional and passing resonances. These structures remains attached to the resonances as the different mode frequencies chirp up and down. In the non-linear phase, the transport of individual resonant trapped particles is identified to be linked to mode-particle synchronization. On this basis, a partial mechanism for the non-linear coupling between particle transport and mode dominant down-chirping is proposed. The overall transport of alpha particles through the q = 1 surface is of order 2–5\% of the initial population between the simulations. The loss of alpha power is found to be directly equal to the loss of alpha particles.