Miracle-dz/UniFAR-FTUs huggingface dataset:
Unapparent virus infection and host fitness in three weedy grass species

1 The effects of pathogens on natural plant populations are poorly understood, in part because many cause few overt symptoms. I investigated the correlations between the incidence of barley yellow dwarf viruses (BYDVs) and total panicle length, a fitness proxy, in three apparently unaffected hosts, Bromus hordeaceus (Pooideae), Setaria viridis and Setaria lutescens (Panicoideae). 2 Whereas B. hordeaceus individuals did not differ in fitness with respect to infection, average fitness of infected S. viridis was approximately half that of uninfected individuals, and in one out of two years, infected S. lutescens had approximately 25% higher fitness. Variability among hosts appears to contribute more than variability among BYDV serotypes to the pattern of infection outcomes. 3 These correlations suggest that the relationship between BYDVs and these co‐occurring host species ranges from antagonism to mutualism. If so, BYDVs can act as agents of selection in natural populations in which the symptoms of infection are unapparent. 4 The results also demonstrate that infections can be unapparent due to large trait variance rather than small mean effects. Because high trait variances are widespread in many natural populations, unapparent infections may be common in wild plant communities.
Using Context in Neural Machine Translation Training Objectives

We present Neural Machine Translation (NMT) training using document-level metrics with batch-level documents. Previous sequence-objective approaches to NMT training focus exclusively on sentence-level metrics like sentence BLEU which do not correspond to the desired evaluation metric, typically document BLEU. Meanwhile research into document-level NMT training focuses on data or model architecture rather than training procedure. We find that each of these lines of research has a clear space in it for the other, and propose merging them with a scheme that allows a document-level evaluation metric to be used in the NMT training objective. We first sample pseudo-documents from sentence samples. We then approximate the expected document BLEU gradient with Monte Carlo sampling for use as a cost function in Minimum Risk Training (MRT). This two-level sampling procedure gives NMT performance gains over sequence MRT and maximum-likelihood training. We demonstrate that training is more robust for document-level metrics than with sequence metrics. We further demonstrate improvements on NMT with TER and Grammatical Error Correction (GEC) using GLEU, both metrics used at the document level for evaluations.
Supercritical carbon dioxide extraction of sea buckthorn (Hippophae rhamnoides L.) pomace

BACKGROUND: The goal of this work was to utilize the sea buckthorn pomace, which is the by-product of a sea buckthorn juice process. Pilot plant supercritical fluid extraction (SFE) experiments were performed in a 5 × 10−3 m3 volume high-pressure vessel. The effects of pressure and temperature on extraction yield and recoveries of biologically active components were studied using a 32 full factorial design. The pressure and temperature were varied over the ranges of 30–46 MPa and 313–353 K, respectively. The extract samples were analysed by TLC-densitometry, UV/VIS spectrofotometry and HPLC methods. RESULTS: The obtained yields changed between 142–164 g kg−1, according to the solvent power of the supercritical fluid. The recoveries of the different minor components were (g minor components kg−1 dried raw material): 2.50–4.25 sitosterol, 0.20–1.60 ursolic acid, 0.04–0.18 carotenoid, 0.35–0.42 total tocopherol. CONCLUSION: By evaluation the designed experiments 46 MPa and 333 K were chosen as the optimum conditions. Copyright © 2007 Society of Chemical Industry
Further results on weighted core inverse in a ring

The notion of the weighted core inverse in a ring with involution was introduced recently [Mosić et al. Comm. Algebra, 2018; 46(6); 2332–2345]. In this paper, we explore new characterizations of the weighted core inverse of sum and the difference between two weighted core invertible elements in a ring with involution under different conditions. Further, we discuss reverse order and mixed-type reverse order laws for the weighted core invertible elements in a ring.
Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking

The study of label noise in sound event recognition has recently gained attention with the advent of larger and noisier datasets. This work addresses the problem of missing labels, one of the big weaknesses of large audio datasets, and one of the most conspicuous issues for AudioSet. We propose a simple and model-agnostic method based on a teacher-student framework with loss masking to first identify the most critical missing label candidates, and then ignore their contribution during the learning process. We find that a simple optimisation of the training label set improves recognition performance without additional computation. We discover that most of the improvement comes from ignoring a critical tiny portion of the missing labels. We also show that the damage done by missing labels is larger as the training set gets smaller, yet it can still be observed even when training with massive amounts of audio. We believe these insights can generalize to other large-scale datasets.
Simultaneous supersingular reductions of CM elliptic curves

Abstract We study the simultaneous reductions at several supersingular primes of elliptic curves with complex multiplication. We show – under additional congruence assumptions on the CM order – that the reductions are surjective (and even become equidistributed) on the product of supersingular loci when the discriminant of the order becomes large. This variant of the equidistribution theorems of Duke and Cornut–Vatsal is an(other) application of the recent work of Einsiedler and Lindenstrauss on the classification of joinings of higher-rank diagonalizable actions.
The SAMI Galaxy Survey: Stellar Population Gradients of Central Galaxies

We examine the stellar population radial gradients (age, metallicity, and [α/Fe]) of 96 passive central galaxies up to ∼2 Re in the SAMI Galaxy Survey. The targeted groups have a halo mass range of . The main goal of this work is to determine whether central galaxies have different stellar population properties when compared to similarly massive satellite galaxies. For the whole sample, we find negative metallicity radial gradients, which show evidence of becoming shallower with increasing stellar mass. The age and [α/Fe] gradients are slightly positive and consistent with zero, respectively. The [α/Fe] gradients become more negative with increasing mass, while the age gradients do not show any significant trend with mass. We do not observe a significant difference between the stellar population gradients of central and satellite galaxies at fixed stellar mass. The mean metallicity gradients are for central galaxies and for satellites. The mean age and [α/Fe] gradients are consistent between central and satellite galaxies, within the uncertainties, with a mean value of for centrals and for satellites and for centrals and for satellites. The stellar population gradients of central and satellite galaxies show no difference as a function of halo mass. This evidence suggests that the inner regions of central passive galaxies form in a similar fashion to those of satellite passive galaxies, in agreement with a two-phase formation scenario.
A phylogenetic study of subgenus Polypompholyx: a parallel radiation of Utricularia (Lentibulariaceae) throughout Australasia

Phylogenetic relationships among 26 of the 37 recognised taxa of Utricularia subgenus Polypompholyx sensu MullerB however, our strict consensus tree shows an unresolved relationship between the sections Polypompholyx and Pleiochasia. Within the section Pleiochasia, we found two supported clades, generally differing in a more northern or southern distribution. Despite high levels of morphological heterogeneity and convergence, we found some clade-specific character homogeneity within these two clades, particularly that of growth and bladder-trap form, and floral structure. Bladder-trap form corresponds most strongly with terrestrial v. aquatic habits. The evolution of filiform corolla appendages corresponds with floralcolour,andispossiblyassociatedwithsexualmimicry,withthoseoftheuppercorollaarisingtwiceindependently. Furthermore,wefoundthatU.monanthosandU. novae-zelandiaeremainsynonymsofU.dichotoma,andthatU.simmonsii is not included in the subgenus Polypompholyx, but instead is allied with sections Stomoisia and Enskide of subgenus Bivalvaria.
Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees

The fragility of deep neural networks to adversarially-chosen inputs has motivated the need to revisit deep learning algorithms. Including adversarial examples during training is a popular defense mechanism against adversarial attacks. This mechanism can be formulated as a min-max optimization problem, where the adversary seeks to maximize the loss function using an iterative first-order algorithm while the learner attempts to minimize it. However, finding adversarial examples in this way causes excessive computational overhead during training. By interpreting the min-max problem as an optimal control problem, it has recently been shown that one can exploit the compositional structure of neural networks in the optimization problem to improve the training time significantly. In this paper, we provide the first convergence analysis of this adversarial training algorithm by combining techniques from robust optimal control and inexact oracle methods in optimization. Our analysis sheds light on how the hyperparameters of the algorithm affect the its stability and convergence. We support our insights with experiments on a robust classification problem.
Reinforcement Learning for Decentralized Stable Matching

When it comes to finding a match/partner in the real world, it is usually an independent and autonomous task performed by people/entities. For a person, a match can be several things such as a romantic partner, business partner, school, roommate, etc. Our purpose in this paper is to train autonomous agents to find suitable matches for themselves using reinforcement learning. We consider the decentralized two-sided stable matching problem, where an agent is allowed to have at most one partner at a time from the opposite set. Each agent receives some utility for being in a match with a member of the opposite set. We formulate the problem spatially as a grid world environment and having autonomous agents acting independently makes our environment very uncertain and dynamic. We run experiments with various instances of both complete and incomplete weighted preference lists for agents. Agents learn their policies separately, using separate training modules. Our goal is to train agents to find partners such that the outcome is a stable matching if one exists and also a matching with set-equality, meaning the outcome is approximately equally likable by agents from both the sets.
Measurement spaces

The question of what should be meant by a measurement is tackled from a mathematical perspective whose physical interpretation is that a measurement is a process via which a finite amount of classical information is generated. This motivates a mathematical definition of space of measurements that consists of a topological stably Gelfand quantale whose open sets represent measurable physical properties. It also accounts for the distinction between quantum and classical measurements, and for the emergence of "classical observers. " The latter have a relation to groupoid C*-algebras, and link naturally to Schwinger's notion of selective measurement.
Optimal Epidemic Suppression Under an ICU Constraint

How much and when should we limit economic and social activity to ensure that the health-care system is not overwhelmed during an epidemic? We study a setting where ICU resources are constrained while suppression is costly (e.g., limiting economic interaction). Providing a fully analytical solution we show that the common wisdom of "flattening the curve", where suppression measures are continuously taken to hold down the spread throughout the epidemic, is suboptimal. Instead, the optimal suppression is discountinuous. The epidemic should be left unregulated in a first phase and when the ICU constraint is approaching society should quickly lock down (a discontinuity). After the lockdown regulation should gradually be lifted, holding the rate of infected constant thus respecting the ICU resources while not unnecessarily limiting economic activity. In a final phase, regulation is lifted. We call this strategy "filling the box". * We wish to thank Tommy Andersson, Hannes Malmberg and Robert Ostling for valuable comments.
Multi-scale Transformer Language Models

We investigate multi-scale transformer language models that learn representations of text at multiple scales, and present three different architectures that have an inductive bias to handle the hierarchical nature of language. Experiments on large-scale language modeling benchmarks empirically demonstrate favorable likelihood vs memory footprint trade-offs, e.g. we show that it is possible to train a hierarchical variant with 30 layers that has 23% smaller memory footprint and better perplexity, compared to a vanilla transformer with less than half the number of layers, on the Toronto BookCorpus. We analyze the advantages of learned representations at multiple scales in terms of memory footprint, compute time, and perplexity, which are particularly appealing given the quadratic scaling of transformers' run time and memory usage with respect to sequence length.
COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19

The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Most COVID-19 patients suffer from lung infection, so one important diagnostic method is to screen chest radiography images, e.g., X-Ray or CT images. However, such examinations are time-consuming and labor-intensive, leading to limited diagnostic efficiency. To solve this issue, AI-based technologies, such as deep learning, have been used recently as effective computer-aided means to improve diagnostic efficiency. However, one practical and critical difficulty is the limited availability of annotated COVID-19 data, due to the prohibitive annotation costs and urgent work of doctors to fight against the pandemic. This makes the learning of deep diagnosis models very challenging. To address this, motivated by that typical pneumonia has similar characteristics with COVID-19 and many pneumonia datasets are publicly available, we propose to conduct domain knowledge adaptation from typical pneumonia to COVID-19. There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19. To address them, we propose a new deep domain adaptation method for COVID-19 diagnosis, namely COVID-DA. Specifically, we alleviate the domain discrepancy via feature adversarial adaptation and handle the task difference issue via a novel classifier separation scheme. In this way, COVID-DA is able to diagnose COVID-19 effectively with only a small number of COVID-19 annotations. Extensive experiments verify the effectiveness of COVID-DA and its great potential for real-world applications.
On the Convergence Rate of Projected Gradient Descent for a Back-Projection based Objective

Ill-posed linear inverse problems appear in many fields of imaging science and engineering, and are typically addressed by solving optimization problems, which are composed of fidelity and prior terms or constraints. Traditionally, the research has been focused on different prior models, while the least squares (LS) objective has been the common choice for the fidelity term. However, recently a few works have considered a back-projection (BP) based fidelity term as an alternative to the LS, and demonstrated excellent reconstruction results for popular inverse problems. These prior works have also empirically shown that using the BP term, rather than the LS term, requires fewer iterations of plain and accelerated proximal gradient algorithms. In the current paper, we examine the convergence rate of the BP objective for the projected gradient descent (PGD) algorithm and identify an inherent source for its faster convergence compared to the LS objective. Numerical experiments with both $\ell_1$-norm and GAN-based priors corroborate our theoretical results for PGD. We also draw the connection to the observed behavior for proximal methods.
Exclusive tensor meson photoproduction

We study tensor meson photoproduction outside of the resonance region, at beam energies of a few GeVs. We build a model based on Regge theory that includes the leading vector and axial exchanges. We consider two determinations of the unknown helicity couplings and fit to the recent ${a}_{2}$ photoproduction data from CLAS. Both choices give a similar description of the ${a}_{2}$ cross section but result in different predictions for the parity asymmetries and the ${f}_{2}$ photoproduction cross section. We conclude that new measurements of ${f}_{2}$ photoproduction in the forward region are needed to pin down the correct production mechanism. We also extend our predictions to the 8.5 GeV beam energy, where current experiments are running.
Faces in random great hypersphere tessellations

The concept of typical and weighted typical spherical faces for tessellations of the $d$-dimensional unit sphere, generated by $n$ independent random great hyperspheres distributed according to a non-degenerate directional distribution, is introduced and studied. Probabilistic interpretations for such spherical faces are given and their directional distributions are determined. Explicit formulas for the expected $f$-vector, the expected spherical Quermas integrals and the expected spherical intrinsic volumes are found in the isotropic case. Their limiting behaviour as $n\to\infty$ is discussed and compared to the corresponding notions and results in the Euclidean case. The expected statistical dimension and a problem related to intersection probabilities of spherical random polytopes is investigated.
Are Emojis Emotional? A Study to Understand the Association between Emojis and Emotions

Given the growing ubiquity of emojis in language, there is a need for methods and resources that shed light on their meaning and communicative role. One conspicuous aspect of emojis is their use to convey affect in ways that may otherwise be non-trivial to achieve. In this paper, we seek to explore the connection between emojis and emotions by means of a new dataset consisting of human-solicited association ratings. We additionally conduct experiments to assess to what extent such associations can be inferred from existing data, such that similar associations can be predicted for a larger set of emojis. Our experiments show that this succeeds when high-quality word-level information is available.
Iron stress-induced redox reactions in bean roots

Iron stress-induced and constitutive redox activity of bean (Phaseolus vulgaris L. cv. Delinel) roots was measured on intact plants using FeEDTA and ferricyanide as electron acceptors. The presence of the translation inhibitor cycloheximide caused a decrease in the reduction of both oxidants. However, a differential decline in the reduction rates of FeEDTA and ferricyanide was observed, suggesting enzyme heterogeneity. In the presence of the H + -ATPase inhibitor vanadate, the reduction of FeEDTA was nearly completely suppressed in both Fe-deficient (-Fe) and Fe-sufficient (+Fe) plants, providing evidence for an involvement of plasma membrane-bound ATPase activity in the regulation of the reduction process
On the biology of the olive leaf moth Palpita unionalis Hb. (Lep., Pyralidae)

The incubation period of eggs of Palpita unionalis ranged between 3 days (at 30° C) and 12 days (at 15° C). The mortality of eggs depended on the temperature. Larvae passed through 6 instars. The duration of the last instar was almost double the duration of the first. The shortest larval period (15.6 days) was obtained at 30° C. At 15° and 20° C, the mean durations of pupal stage were 31.2 and 17.1 days for males and 23.4 and 15.7 days for females. The most favourable relative humidity for moth emergence was 65%, at which 90% of normal moths emerged. Copulation took place at mid-night, 24 hours after emergence, and lasted for 70 minutes on the average. Under laboratory conditions (27.5° C and 66.5% RH), the mean pre-oviposition period of fertilized females was 2–4 days and oviposition period was 4–7 days. The total number of eggs laid per fertilized female averaged 414 with a maximum of 531 eggs per day, the highest number being laid during the first day. Unfertilized females laid approximately half the above number of eggs. Terminal rows of leaves were preferred by moths for egg laying and more than 60% of the eggs were laid singly. The longevity of moths was much affected by feeding but was slightly affected by mating. The sex ratio was 1:1. Under laboratory conditions, 9 generations per year were recorded. The shortest (26.2 days) and the longest (66.2 days) were recorded during July and November, respectively. A hymenopterous parasite, most probably Apanteles syleptae Ferr. was observed attacking larvae of this pest and was abundant from June to August. Zusammenfassung Zur Biologie des Olivenblattzunslers Palpita unionalis Hb. (Lep., Pyralidae) Die Eientwicklung von P. unionalis dauerte 3 Tage bei 30° C und 12 Tage bei 15° C. Die Eimortalitat war von der Temperatur abhangig. Die Larven traten in 6 Stadien auf. Die Entwicklung der L6 war fast doppelt so lang wie die L1. Die kurzeste Gesamtlarvenzeit (15,6 Tage) wurde bei 30° C festgestellt. Bei 15 und 20° C betrug die mittlere Puppendauer 31,2 und 17,1 Tage fur ♂ sowie 23,4 und 15,7 Tage fur ♀ Puppen. Die optimale Luftfeuchtigkeit fur das Schlupfen der Falter lag bei 65%. Die Kopulation fand nachts statt, 24 Stunden nach dem Falterschlupfen, und dauerte i. M. 70 Minuten. Unter Laborbedingungen betrug die mittlere Preovipositionsperiode 2 bis 4 Tage, die Ovipositionsperiode 4–7 Tage. Im Mittel legte ein ♀ 414 Eier ab mit einem Maximum von 166 Eiern pro Tag. Die meisten Eier wurden am ersten Tag abgelegt. Unbefruchtete ♀♀ legten nur etwa halb so viele Eier wie befruchtete ab. Zur Eiablage wurden die oberen Blattlagen bevorzugt. Etwa 60% der Eier wurden im einzelnen abgelegt. Die Lebensdauer der Falter wurde nur wenig durch die Begattung, aber stark durch die Imaginalernahrung beeinflust. Im Labor kam es zur Entwicklung von 9 Generationen im Jahr. Die kurzeste Generationsdauer (26,2 Tage) wurde im Juli, die langste (66,2 Tage) im November registriert. Als Parasit trat eine Apanteles-Art auf, sehr wahrscheinlich A. syleptae Ferr. , welche die Larven parasitierte und zwischen Juni und August am haufigsten war.