Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of interstock (‘Salustiano’ orange) on growth, leaf mineral composition and water relations of one year old citrus under saline conditions

Summary We investigated the effects of salinity on the growth, mineral composition (leaf, stem and root) and water relations in one year old plants of ‘Cleopatra’ mandarin ( Citrus reticulata Blanco) (CM), ‘Salustiano’ orange ( Citrus sinensis (L.) Osbeck) grafted on ‘Cleopatra’ mandarin (CM/SAO), ‘Valencia Late’ orange ( Citrus sinensis (L) Osbeck) on ‘Cleopatra’ mandarin (CM/VLO) and ‘Valencia Late’ grafted on CM/SO (CM/SAO/VLO). Plants were grown in sand culture under greenhouse conditions and supplied with nutrient solution containing either 5 (control) or 50.mM NaCl. After ten weeks of salt treatment, the CM/SAO/VLO leaf growth was not affected, while CM/SAO showed the greatest leaf growth reduction. Root growth was less affected than leaf growth by salt treatment, and was similar for all four combinations. In the salt treatment, the leaf chloride concentration of CM/SAO/VLO was the lowest compared with the other plants and leaf Na concentration of CM/SAO/VLO was lower than CM/VLO, suggesting that the interstock limited Cl and Na uptake and/or transport from roots to leaves. The high leaf growth reduction observed in CM/SAO could be related to a decrease in turgor, due to a decrease in leaf water potential caused by salt treatment not being compensated by a reduction in leaf osmotic potential.
Estimating load points of a motor-pump system using pressure and inverter drive data

We propose a novel method for the estimation of rotor position, speed, and torque of a motor-pump system consisting of a progressive cavity pump (PCP) driven by an induction motor which operates under V/f open-loop control. We compute the speed and rotor position of the PCP by applying a phase locked loop to the pressure signal at the pressure side of the pump. An extended Kalman filter is used to estimate the torque of the PCP based on the speed, effective value of the stator current of the induction motor and a nonlinear motor model. Furthermore, we derive a tractable condition under which the convergence of the observer is guaranteed. We use a laboratory experiment to verify our results.
Dehn functions of finitely presented metabelian groups

Abstract In this paper, we compute an upper bound for the Dehn function of a finitely presented metabelian group. In addition, we prove that the same upper bound works for the relative Dehn function of a finitely generated metabelian group. We also show that every wreath product of a free abelian group of finite rank with a finitely generated abelian group can be embedded into a metabelian group with exponential Dehn function.
Are Adaptive Face Recognition Systems still Necessary? Experiments on the APE Dataset

In the last five years, deep learning methods, in particular CNN, have attracted considerable attention in the field of face-based recognition, achieving impressive results. Despite this progress, it is not yet clear precisely to what extent deep features are able to follow all the intra-class variations that the face can present over time. In this paper we investigate the performance the performance improvement of face recognition systems by adopting self updating strategies of the face templates. For that purpose, we evaluate the performance of a well-known deep-learning face representation, namely, FaceNet, on a dataset that we generated explicitly conceived to embed intra-class variations of users on a large time span of captures: the APhotoEveryday (APE) dataset11https://github.com/PRALabBiometrics/APhotoEverydayDB. Moreover, we compare these deep features with handcrafted features extracted using the BSIF algorithm. In both cases, we evaluate various template update strategies, in order to detect the most useful for such kind of features. Experimental results show the effectiveness of “optimized” self-update methods with respect to systems without update or random selection of templates.
Learning to Recombine and Resample Data for Compositional Generalization

Flexible neural models outperform grammar- and automaton-based counterparts on a variety of sequence modeling tasks. However, neural models perform poorly in settings requiring compositional generalization beyond the training data -- particularly to rare or unseen subsequences. Past work has found symbolic scaffolding (e.g. grammars or automata) essential in these settings. Here we present a family of learned data augmentation schemes that support a large category of compositional generalizations without appeal to latent symbolic structure. Our approach to data augmentation has two components: recombination of original training examples via a prototype-based generative model and resampling of generated examples to encourage extrapolation. Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems---instruction following (SCAN) and morphological analysis (Sigmorphon 2018)---where our approach enables learning of new constructions and tenses from as few as eight initial examples.
Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms

Understanding and removing bias from the decisions made by machine learning models is essential to avoid discrimination against unprivileged groups. Despite recent progress in algorithmic fairness, there is still no clear answer as to which bias-mitigation approaches are most effective. Evaluation strategies are typically use-case specific, rely on data with unclear bias, and employ a fixed policy to convert model outputs to decision outcomes. To address these problems, we performed a systematic comparison of a number of popular fairness algorithms applicable to supervised classification. Our study is the most comprehensive of its kind. It utilizes three real and four synthetic datasets, and two different ways of converting model outputs to decisions. It considers fairness, predictive-performance, calibration quality, and speed of 28 different modelling pipelines, corresponding to both fairness-unaware and fairness-aware algorithms. We found that fairness-unaware algorithms typically fail to produce adequately fair models and that the simplest algorithms are not necessarily the fairest ones. We also found that fairness-aware algorithms can induce fairness without material drops in predictive power. Finally, we found that dataset idiosyncracies (e.g., degree of intrinsic unfairness, nature of correlations) do affect the performance of fairness-aware approaches. Our results allow the practitioner to narrow down the approach(es) they would like to adopt without having to know in advance their fairness requirements.
Deep SVBRDF Estimation on Real Materials

Recent work has demonstrated that deep learning approaches can successfully be used to recover accurate estimates of the spatially-varying BRDF (SVBRDF) of a surface from as little as a single image. Closer inspection reveals, however, that most approaches in the literature are trained purely on synthetic data, which, while diverse and realistic, is often not representative of the richness of the real world. In this paper, we show that training such networks exclusively on synthetic data is insufficient to achieve adequate results when tested on real data. Our analysis leverages a new dataset of real materials obtained with a novel portable multi-light capture apparatus. Through an extensive series of experiments and with the use of a novel deep learning architecture, we explore two strategies for improving results on real data: finetuning, and a per-material optimization procedure. We show that adapting network weights to real data is of critical importance, resulting in an approach which significantly outperforms previous methods for SVBRDF estimation on real materials. Dataset and code are available at https://lvsn.github. io/ real-svbrdf.
Ribbonlength and crossing number for folded ribbon knots

We study Kauffman’s model of folded ribbon knots: knots made of a thin strip of paper folded flat in the plane. The ribbonlength is the length to width ratio of such a folded ribbon knot. We show for any knot or link type that there exist constants [Formula: see text] such that the ribbonlength is bounded above by [Formula: see text], and also by [Formula: see text]. We use a different method for each bound. The constant [Formula: see text] is quite small in comparison to [Formula: see text], and the first bound is lower than the second for knots and links with [Formula: see text] 12,748.
Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations

A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings. To do this, it is critical to ensure that our evaluation protocols are correct, and benchmarks are reliable. In this work, we set forth to design a set of experiments to understand an important but often ignored problem in visually grounded language generation: given that humans have different utilities and visual attention, how will the sample variance in multi-reference datasets affect the models' performance? Empirically, we study several multi-reference datasets and corresponding vision-and-language tasks. We show that it is of paramount importance to report variance in experiments; that human-generated references could vary drastically in different datasets/tasks, revealing the nature of each task; that metric-wise, CIDEr has shown systematically larger variances than others. Our evaluations on reference-per-instance shed light on the design of reliable datasets in the future.
Considerations of optimal litter size in mammals

Recent advances in understanding mammalian reproductive patterns mirror general developments in phylogenetic, physiological, and ecological research. We provide an overview of the diverse approaches used in studying mammalian litter size and discuss recent advances in evolutionary ecological theory and experimental data concerning reproductive patterns. Extensively documented natural history studies and breeding data from work in applied zoology and animal husbandry constitute a rich source of information for comparative studies. Such studies have helped identify fundamental differences in reproductive "strategies" depending on whether taxa produce altricial or precocial young and whether a given reproductive effort results in a single offspring or a litter. On the other hand, research on ecological and physiological correlates of litter size is providing new insights about reproductive energetics, the relationship between resource availability and litter size, and optimality in varying environments in general. Continuing experimental approaches are broadening our understanding of the mechanistic bases of patterns of mammalian litter size, but such studies, especially those involving manipulation of litter size, must carefully consider the potential biases introduced by experimentally induced non-congruence between preand postnatal litter sizes. For example, without accounting for the effects of placental hormones on mammogenesis and lactation, postnatal manipulations of litter size might be based on faulty assumptions implicit in such manipulations. To conclude, despite substantial advances in these diverse approaches, our degree of understanding of optimal litter size in mammals remains unclear because of inconsistencies in data often used in comparative studies and because of physiological and ecological constraints on litter size adjustments that hinder or bias experimental approaches. These areas should be addressed in future studies.
BGM: Building a Dynamic Guidance Map without Visual Images for Trajectory Prediction

Visual images usually contain the informative context of the environment, thereby helping to predict agents' behaviors. However, they hardly impose the dynamic effects on agents' actual behaviors due to the respectively fixed semantics. To solve this problem, we propose a deterministic model named BGM to construct a guidance map to represent the dynamic semantics, which circumvents to use visual images for each agent to reflect the difference of activities in different periods. We first record all agents' activities in the scene within a period close to the current to construct a guidance map and then feed it to a Context CNN to obtain their context features. We adopt a Historical Trajectory Encoder to extract the trajectory features and then combine them with the context feature as the input of the social energy based trajectory decoder, thus obtaining the prediction that meets the social rules. Experiments demonstrate that BGM achieves state-of-the-art prediction accuracy on the two widely used ETH and UCY datasets and handles more complex scenarios.
Production of monoclonal antibodies to potato virus Y Helper component-protease and their use for strain differentiation

Ten monoclonal antibodies (MAbs) and a rabbit antiserum containing polyclonal antibodies (PAbs) were prepared against helper component-protease (HC-Pro) purified from plants infected with a non-aphid-transmissible strain of potato virus Y (PVY-O NAT). All MAbs reacted with PVY HC-Pro in two enzyme-linked immunosorbent assay (ELISA) protocols, and seven were useful for Western blot analysis. Six MAbs and the PAbs were compared in direct antigen binding ELISA for reactivity with 22 isolates of PVY, potato virus A (PVA), tobacco etch virus (TEV), and pepper mottle virus (PepMoV). None of the MAbs or the PAbs reacted with PVA or TEV HC-Pro. The immunoreactivity of one MAb was similar to the PAbs, reacting with all 22 PVY isolates and with PepMoV, but failing to react with PVA and TEV HC-Pro. Another MAb reacted with all of the common strains of PVY (PVY O and a stipplestreak strain of PVY (PVY C ) but not with any of the tobacco veinal necrosis strains (PVY N ). The other MAbs could be used to differentiate a subgroup within the PVY O and PVY N strain groups. These MAbs proved useful for identifying strains of PVY
Development of EST-SSR and genomic-SSR in Chinese fir

In order to develop SSR primers of Cunninghamia lanceolata( Lamb.) Hook based on EST-SSR and genomic SSR,292 non-repeated Chinese fir EST-sequences were assembled by removing low-quality and redundant fragments depend upon 444 ESTs sequences from NCBI Public database,and 143 genome-sequences were assembled by removing redundant fragments in 1 142 genome sequences. All those non-redundant sequences of Cunninghamia lanceolata( Lamb.) Hook was searched for mono-to hexa-nucleotide simple sequence repeats( SSR or microsatellite) with software MISA,and the type,size and frequency of these SSRs were determined. Totally,There were 109 SSRs to be picked out among EST-sequences,the distribution density was 964. 58 SSR / Mbp; and 39 SSRs were found in genome sequences, accounting for the higher density of 1 037. 24 SSR / Mbp. In both Genomic and EST sequence library,the Hexanucleotides repeats,especially AT-rich repeat,was the most repeated type,and the AGC / CTG was the most trinucleotides repeats,The 37 and 95 pairs of SSR primers were designed based on EST library and Genomic by Primer 3. 0,respectively. Ten EST-SSR primers and eight pairs of gSSR primers were selected randomly to perform PCR amplification with 12 individuals. There were four pairs of primers showed polymorphism both in EST-SSR and gSSR,with polymorphic rate of 40% and 50%,respectively. Twenty-five polymorphic alleles were amplified by 8 SSR primer pairs,which showed an average 3. 125 polymorphism alleles for each primer,the average effective alleles( Na) 2. 399 5,average PIC 0. 519 1, average Hot 0. 307 4. There were more polymorphism alleles from gSSR markers than EST-SSR among groups. More allelic loci were amplified with four pairs of primer from gSSR than the four pairs of primer from EST-SSR,and the former had higher PIC value also.
ESTIMATION OF THE SPECTRAL MEASURE FROM CONVEX COMBINATIONS OF REGULARLY VARYING RANDOM VECTORS

The extremal dependence structure of a regularly varying random vector X is fully described by its limiting spectral measure. In this paper, we investigate how to recover characteristics of the measure, such as extremal coefficients, from the extremal behaviour of convex combinations of components of X. Our considerations result in a class of new estimators of moments of the corresponding combinations for the spectral vector. We show asymp-totic normality by means of a functional limit theorem and, focusing on the estimation of extremal coefficients, we verify that the minimal asymptotic variance can be achieved by a plug-in estimator.
Nonlinear Model Predictive Control of Permanent Magnet Synchronous Generators in DC Microgrids

A new strategy is proposed to control interior permanent magnet generators in dc systems interfaced through an active rectifier. The controller design is based on the decomposition of the system dynamics into slow and fast modes using singular perturbation theory. An inner current controller is developed based on output regulation techniques and an outer voltage controller is proposed using Nonlinear Model Predictive Control (NMPC). The NMPC regulates the dc bus voltage and minimizes the ac side losses. Simulation results are then presented for a PMSG in aircraft power system applications.
Prioritized Level Replay

Simulated environments with procedurally generated content have become popular benchmarks for testing systematic generalization of reinforcement learning agents. Every level in such an environment is algorithmically created, thereby exhibiting a unique configuration of underlying factors of variation, such as layout, positions of entities, asset appearances, or even the rules governing environment transitions. Fixed sets of training levels can be determined to aid comparison and reproducibility, and test levels can be held out to evaluate the generalization and robustness of agents. While prior work samples training levels in a direct way (e.g.~uniformly) for the agent to learn from, we investigate the hypothesis that different levels provide different learning progress for an agent at specific times during training. We introduce Prioritized Level Replay, a general framework for estimating the future learning potential of a level given the current state of the agent's policy. We find that temporal-difference (TD) errors, while previously used to selectively sample past transitions, also prove effective for scoring a level's future learning potential in generating entire episodes that an agent would experience when replaying it. We report significantly improved sample-efficiency and generalization on the majority of Procgen Benchmark environments as well as two challenging MiniGrid environments. Lastly, we present a qualitative analysis showing that Prioritized Level Replay induces an implicit curriculum, taking the agent gradually from easier to harder levels.
On the Bieri-Neumann-Strebel-Renz $\Sigma$-invariants of the Bestvina-Brady groups

We study the Bieri-Neumann-Strebel-Renz invariants and we prove the following criterion: for groups $H$ and $K$ of type $FP_n$ such that $[H,H] \subseteq K \subseteq H$ and a character $\chi : K \to \mathbb{R}$ with $\chi([H,H]) = 0$ we have $[\chi] \in \Sigma^n(K, \mathbb{Z})$ if and only if $[\mu] \in \Sigma^n(H, \mathbb{Z})$ for every character $\mu : H \to \mathbb{R}$ that extends $\chi$. The same holds for the homotopical invariants $\Sigma^n(-)$ when $K$ and $H$ are groups of type $F_n$. We use these criteria to complete the description of the $\Sigma$-invariants of the Bieri-Stallings groups $G_m$ and more generally to describe the $\Sigma$-invariants of the Bestvina-Brady groups. We also show that the "only if" direction of such criterion holds if we assume only that $K$ is a subnormal subgroup of $H$, where both groups are of type $FP_n$. We apply this last result to wreath products.
Chiral hinge magnons in second-order topological magnon insulators

When interacting spins in condensed matter order ferromagnetically, their ground state wave function is topologically trivial. Nonetheless, in two dimensions, the ferromagnetic state can support spin excitations with nontrivial topology, an exotic state known as topological magnon insulator (TMI). Here, we theoretically unveil and numerically confirm a novel ferromagnetic state in three dimensions dubbed second-order TMI, whose hallmarks are excitations at its hinges, where facets intersect. Since ferromagnetism naturally comes with broken time-reversal symmetry, the hinge magnons are chiral, rendering backscattering impossible. Hence, they trace out a three-dimensional path about the sample unimpeded by defects and are topologically protected by the spectral gap. They are remarkably robust against disorder and simultaneously highly tunable by atomic-level engineering of the sample termination. Our findings empower magnonics with the tools of higher-order topology, a promising route to combine low-energy information transfer free of Joule heating with three-dimensional vertical integration.
Ensembling geophysical models with Bayesian Neural Networks

Ensembles of geophysical models improve projection accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bayesian Neural Networks, which infers spatiotemporally varying model weights and bias while accounting for heteroscedastic uncertainties in the observations. This produces more accurate and uncertainty-aware projections without sacrificing interpretability. Applied to the prediction of total column ozone from an ensemble of 15 chemistry-climate models, we find that the Bayesian neural network ensemble (BayNNE) outperforms existing ensembling methods, achieving a 49.4% reduction in RMSE for temporal extrapolation, and a 67.4% reduction in RMSE for polar data voids, compared to a weighted mean. Uncertainty is also well-characterized, with 90.6% of the data points in our extrapolation validation dataset lying within 2 standard deviations and 98.5% within 3 standard deviations.
Two-dimensional Fourier Continuation and applications

This paper presents a "two-dimensional Fourier Continuation" method (2D-FC) for construction of bi-periodic extensions of smooth non-periodic functions defined over general two-dimensional smooth domains. The approach can be directly generalized to domains of any given dimensionality, and even to non-smooth domains, but such generalizations are not considered here. The 2D-FC extensions are produced in a two-step procedure. In the first step the one-dimensional Fourier Continuation method is applied along a discrete set of outward boundary-normal directions to produce, along such directions, continuations that vanish outside a narrow interval beyond the boundary. Thus, the first step of the algorithm produces "blending-to-zero along normals" for the given function values. In the second step, the extended function values are evaluated on an underlying Cartesian grid by means of an efficient, high-order boundary-normal interpolation scheme. A Fourier Continuation expansion of the given function can then be obtained by a direct application of the two-dimensional FFT algorithm. Algorithms of arbitrarily high order of accuracy can be obtained by this method. The usefulness and performance of the proposed two-dimensional Fourier Continuation method are illustrated with applications to the Poisson equation and the time-domain wave equation within a bounded domain. As part of these examples the novel "Fourier Forwarding" solver is introduced which, propagating plane waves as they would in free space and relying on certain boundary corrections, can solve the time-domain wave equation and other hyperbolic partial differential equations within general domains at computing costs that grow sublinearly with the size of the spatial discretization.