Miracle-dz/UniFAR-FTUs huggingface dataset:
Lithium and beryllium in the Gaia-Enceladus galaxy

Data from Gaia DR2 and The Apache Point Observatory Galactic Evolution Experiment surveys revealed a relatively new component in the inner Galactic halo, which is likely the dynamical remnant of a disrupted dwarf galaxy named Gaia-Enceladus that collided with the Milky Way about 10 Gyr ago. This merging event offers an extraordinary opportunity to study chemical abundances of elements in a dwarf galaxy, since they are generally hampered in external galaxies. Here, we focus on 7Li and 9Be in dwarf stars that are out of reach even in Local Group galaxies. Searching in GALAH, Gaia-ESO survey and in literature, we found several existing 7Li abundance determinations of stars belonging to the Gaia-Enceladus galaxy. The 7Li abundances of stars at the low metallicity end overlap with those of the Galactic halo. These are effective extragalactic 7Li measurements, which suggest that the 7Li Spite plateau is universal, as is the cosmological 7Li problem. We found a 7Li-rich giant out of 101 stars, which suggests a small percentage similar to that of the Milky Way. We also collect 9Be abundance for a subsample of 25 Gaia-Enceladus stars from literature. Their abundances share the Galactic [Be/H] values at the low metallicity end but grow slower with [Fe/H] and show a reduced dispersion. This suggests that the scatter observed in the Milky Way could reflect the different 9Be evolution patterns of different stellar components that are mixed-up in the Galactic halo.
Ionization: a possible explanation for the difference of mean disk sizes in star-forming regions

Surveys of protoplanetary disks in star-forming regions of similar age revealed significant variations in average disk mass between some regions. For instance, disks in the Orion Nebular Cluster (ONC) and Corona Australis (CrA) are on average smaller than disks observed in Lupus, Taurus, Chamaeleon I or Ophiuchus. In contrast to previous models that study truncation of disks at a late stage of their evolution, we investigate whether disks may already be born with systematically smaller disk sizes in more massive star-forming regions as a consequence of enhanced ionization rates. Assuming various cosmic-ray ionization rates, we compute the resistivities for ambipolar diffusion and Ohmic dissipation with a chemical network, and perform 2D non-ideal magnetohydrodynamical protostellar collapse simulations. A higher ionization rate leads to stronger magnetic braking, and hence to the formation of smaller disks. Accounting for recent findings that protostars act as forges of cosmic rays and considering only mild attenuation during the collapse phase, we show that a high average cosmic-ray ionization rate in star-forming regions like the ONC or CrA can explain the detection of smaller disks in these regions. Our results show that on average a higher ionization rate leads to the formation of smaller disks. Therefore, smaller disks in regions of similar age can be the consequence of different levels of ionization, and may not exclusively be caused by disk truncation via external photoevaporation. We strongly encourage observations that allow measuring the cosmic-ray ionization degrees in different star-forming regions to test our hypothesis.
EMIGRATION AND SURVIVAL OF ORPHANED FEMALE DEER IN ILLINOIS

Emigration and survival were examined for 13 orphaned and 94 nonorphaned female white-tailed deer (Odocoileus virginianus) captured at < 10 months of age from free-ranging populations in Illinois. Emigration rates were higher (P = 0.026) for female orphans than for nonorphans. Nine of 13 (69%) orphans emigrated in spring through early summer. In contrast, only 35 of 94 (37%) nonorphans emigrated. Annual survival rates for all females from three Illinois study areas were 0.59, 0.71, and 0.80 for the east-central, west-central, and northern study areas, respectively. Possible stimuli for emigration among orphaned female white-tailed deer include pregnancy, low social position, and loss of maternal support.
Machine learning fund categorizations

Given the surge in popularity of mutual funds (including exchange-traded funds (ETFs)) as a diversified financial investment, a vast variety of mutual funds from various investment management firms and diversification strategies have become available in the market. Identifying similar mutual funds among such a wide landscape of mutual funds has become more important than ever because of many applications ranging from sales and marketing to portfolio replication, portfolio diversification and tax loss harvesting. The current best method is data-vendor provided categorization which usually relies on curation by human experts with the help of available data. In this work, we establish that an industry wide well-regarded categorization system is learnable using machine learning and largely reproducible, and in turn constructing a truly data-driven categorization. We discuss the intellectual challenges in learning this man-made system, our results and their implications.
Fiducial displacements with improved accuracy for the global network of gravitational wave detectors

As sensitivities improve and more detectors are added to the global network of gravitational wave observatories, calibration accuracy and precision are becoming increasingly important. Photon calibrators, relying on power-modulated auxiliary laser beams reflecting from suspended interferometer optics, enable continuous calibration by generating displacement fiducials proportional to the modulated laser power. Developments in the propagation of laser power calibration via transfer standards to on-line power sensors monitoring the modulated laser power have enabled generation of length fiducials with improved accuracy. Estimated uncertainties are almost a factor of two smaller than the lowest values previously reported. This is partly due to improvements in methodology that have increased confidence in the results reported. Referencing the laser power calibration standards for each observatory to a single transfer standard enables reducing relative calibration errors between elements of the detector network. Efforts within the national metrology institute community to realize improved laser power sensor calibration accuracy are ongoing.
Probabilistic self-learning framework for Low-dose CT Denoising

PURPOSE Despite the indispensable role of X-ray computed tomography (CT) in diagnostic medicine, the associated harmful ionizing radiation dose is a major concern, as it may cause genetic diseases and cancer. Decreasing patients' exposure can reduce the radiation dose and hence the related risks, but it would inevitably induce higher quantum noise. Supervised deep learning techniques have been used to train deep neural networks for denoising low-dose CT (LDCT) images, but the success of such strategies requires massive sets of pixel-level paired LDCT and normal-dose CT (NDCT) images, which are rarely available in real clinical practice. Our purpose is to mitigate the data scarcity problem for deep learning-based LDCT denoising. METHODS To solve this problem, we devised a shift-invariant property-based neural network that uses only the LDCT images to characterize both the inherent pixel correlations and the noise distribution, shaping into our probabilistic self-learning (PSL) framework. The AAPM Low-dose CT Challenge dataset was used to train the network. Both simulated datasets and real dataset were employed to test the denoising performance as well as the model generalizability. The performance was compared to a conventional method (total variation (TV)-based), a popular self-learning method (noise2void (N2V)), and a well-known unsupervised learning method (CycleGAN) by using both qualitative visual inspection and quantitative metrics including peak signal-noise-ratio (PSNR), structural similarity index (SSIM) and contrast-to-noise-ratio (CNR). The standard deviations (STD) of selected flat regions were also calculated for comparison. RESULTS The PSL method can improve the averaged PSNR/SSIM values from 27.61/0.5939 (LDCT) to 30.50/0.6797. By contrast, the averaged PSNR/SSIM values were 31.49/0.7284 (TV), 29.43/0.6699 (N2V), and 29.79/0.6992 (CycleGAN). The averaged STDs of selected flat regions were calculated to be 132.3 HU (LDCT), 25.77 HU (TV), 19.95 HU (N2V), 75.06 HU (CycleGAN), 60.62 HU (PSL) and 57.28 HU (NDCT). As for the low-contrast lesion detectability quantification, the CNR were calculated to be 0.202 (LDCT), 0.356 (TV), 0.372 (N2V), 0.383 (CycleGAN), 0.399 (PSL), and 0.359 (NDCT). By visual inspection, we observed that the proposed PSL method can deliver a noise-suppressed and detail-preserved image, while the TV-based method would lead to the blocky artifact, the N2V method would produce over-smoothed structures and CT value biased effect, and the CycleGAN method would generate slightly noisy results with inaccurate CT values. We also verified the generalizability of the PSL method, which exhibited superior denoising performance among various testing datasets with different data distribution shifts. CONCLUSIONS A deep learning-based convolutional neural network can be trained without paired datasets. Qualitatively visual inspection showed the proposed PSL method can achieve superior denoising performance than all the competitors, despite that the employed quantitative metrics in terms of PSNR, SSIM and CNR did not always show consistently better values.
Stability of gene regulatory networks

Homeostasis of protein concentrations in cells is crucial for their proper functioning, and this requires concentrations (at their steady-state levels) to be stable to fluctuations. Since gene expression is regulated by proteins such as transcription factors (TFs), the full set of proteins within the cell constitutes a large system of interacting components. Here, we explore factors affecting the stability of this system by coupling the dynamics of mRNAs and protein concentrations in a growing cell. We find that it is possible for protein concentrations to become unstable if the regulation strengths or system size becomes too large, and that other global structural features of the networks can dramatically enhance the stability of the system. In particular, given the same number of proteins, TFs, number of interactions, and regulation strengths, a network that resembles a bipartite graph with a lower fraction of interactions that target TFs has a higher chance of being stable. By scrambling the $\textit{E. coli. }$ transcription network, we find that the randomized network with the same number of regulatory interactions is much more likely to be unstable than the real network. These findings suggest that constraints imposed by system stability could have played a role in shaping the existing regulatory network during the evolutionary process. We also find that contrary to what one might expect from random matrix theory and what has been argued in the literature, the degradation rate of mRNA does not affect whether the system is stable.
The impact of Fusarium avenaceum on lupin production on the Canadian prairies

Abstract Narrow-leaved lupin has the potential to become an important pulse crop for the Canadian Prairies because of its high protein content and its climatic adaptation. However, cultivation in the region is constrained by seedling blight and root rot caused by Fusarium avenaceum, an endemic fungal pathogen that reduces seedling establishment and crop yield. This pathogen attacks a broad range of crop and non-crop plants in the region. It varies in colony morphology, in the aggressiveness of individual isolates toward different crops, and in genetic makeup. However, genetically and phenotypically similar isolates can occur over a broad geographic range and diverse isolates can be found in the same field. Despite this diversity, the sexual stage has been observed only rarely in nature. The deleterious effects of the pathogen can be mitigated by optimizing seeding depth. Temperatures that are near optimum for plant growth also reduced the effect of the pathogen, but few studies showed a consistent relationship between seeding date and damage by the pathogen. Fungicidal seed treatments have been shown to reduce the incidence of seedling blight. Although there is some variation in resistance to the pathogen, no completely resistant cultivars have been found under western Canadian conditions. Development of management strategies to mitigate the effects of fusarium seedling blight and root rot on lupin crops will make cultivation of this crop a much more attractive option for western Canadian producers.
The Effects of Starvation and Subsequent Feeding on Survival and Growth of Fulton Channel Sockeye Salmon Fry (Oncorhynchus nerka)

In the laboratory 8 groups of Fulton River channel fry were starved for periods ranging from 1 to 7 weeks, then offered food ad libitum for 8 weeks. Fry withstood up to 4-weeks starvation with less than 10% mortality; beyond this point mortality increased sharply to 90% at 7-weeks starvation. At 5-weeks starvation both fry length and weight decreased significantly. Mortality continued when fish were offered food ad libitum after being starved. Among fish starved for 1 and 2 weeks, mortalities continued to be low, 0 and 6.6%, respectively; but, among fish starved from 3 to 5 weeks, mortalities rose considerably, ranging from 42 to 99%.
Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective

Many research domains use data elicited from ‘citizen scientists’ when a direct measure of a process is expensive or infeasible. However, participants may report incorrect estimates or classifications due to their lack of skill. We demonstrate how Bayesian hierarchical models can be used to learn about latent variables of interest, while accounting for the participants’ abilities. The model is described in the context of an ecological application that involves crowdsourced classifications of georeferenced coral‐reef images from the Great Barrier Reef, Australia. The latent variable of interest is the proportion of coral cover, which is a common indicator of coral reef health. The participants’ abilities are expressed in terms of sensitivity and specificity of a correctly classified set of points on the images. The model also incorporates a spatial component, which allows prediction of the latent variable in locations that have not been surveyed. We show that the model outperforms traditional weighted‐regression approaches used to account for uncertainty in citizen science data. Our approach produces more accurate regression coefficients and provides a better characterisation of the latent process of interest. This new method is implemented in the probabilistic programming language Stan and can be applied to a wide number of problems that rely on uncertain citizen science data.
Biology of Dineulophus phtorimaeae (Hymenoptera: Eulophidae) and Field Interaction with Pseudapanteles dignus (Hymenoptera: Braconidae), Larval Parasitoids of Tuta absoluta (Lepidoptera: Gelechiidae) in Tomato

ABSTRACT Some biological characteristics of the ectoparasitoid Dineulophus phtorimaeae (de Santis) (Hymenoptera: Eulophidae) and field interaction with the endoparasitoid Pseudapanteles dignus (Muesebeck), both larval parasitoids of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae), were examined. In addition, we completed the original description of D. phtorimaeae. Preimaginal developmental time of the ectoparasitoid was 11.17 ± 0.60 d for both sexes, and adult life span was 11.73 ± 0.92 d for females and 8.78 ± 0.93 d for males. The proportion of males to females was equal. In the field, hosts were parasitized at the third larval instar. On average, a female attacked ca. four hosts throughout her lifetime and deposited eggs in ≈50% of cases. The most successful female attacked six hosts, yielding a potential fecundity of ≈10 eggs. Host paralysis without parasitism was observed, suggesting stinging for host feeding. The daily oviposition curve is compatible with a synovigenic-type parasitoid. Our prediction stating that D. phtorimaeae would succeed when competing for hosts with P. dignus was correct, because the former species had greater parasitism rates. In the field, both T. absoluta parasitoids were able to coexist at leaf scale. The negative aspects of differences in feeding behavior, narrower host range (third instar), and lower fecundity of D. phtorimaeae would be compensated by its better attributes as natural enemy, in comparison with P. dignus, enabling coexistence.
Characterizing the Most Primitive Seed Ferns. I. A Reconstruction of Elkinsia polymorpha

Abundant gymnospermous remains occur in association with Elkinsia polymorpha in Upper Devonian deposits near Elkins, West Virginia, and these provide an opportunity to characterize and further refine our concepts of the most ancient seed ferns. The sporophyte of E. polymorpha has helically arranged dimorphic fronds, with large numbers of either preovulate cupules or synangiate prepollen organs borne terminally on the fertile fronds. Vegetative fronds are planar, with rachides that fork up to four times, and pinnate laterals that terminate in sphenopterid pinnules. Fertile fronds branch dichotomously throughout and are three-dimensional in the distal regions. Stems display a centrarch, three-ribbed protostele with prominent sclerenchyma of the sparganum type in the outer cortex. The frond rachis is vascularized by two C-shaped bundles. The hydrasperman preovulate cupules are as characterized in earlier studies. Prepollen organs consist of six to eight radially arranged sporangia that are fused at the base and free distally. Elkinsia polymorpha is the first seed fern to be reconstructed from Devonian sediments and the first preovulate seed fern to be characterized as a plant. As such, it represents an important evolutionary unit for ongoing phylogenetic studies of the origin of seed plants.
GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce

In this paper, we present GrokNet, a deployed image recognition system for commerce applications. GrokNet leverages a multi-task learning approach to train a single computer vision trunk. We achieve a 2.1x improvement in exact product match accuracy when compared to the previous state-of-the-art Facebook product recognition system. We achieve this by training on 7 datasets across several commerce verticals, using 80 categorical loss functions and 3 embedding losses. We share our experience of combining diverse sources with wide-ranging label semantics and image statistics, including learning from human annotations, user-generated tags, and noisy search engine interaction data. GrokNet has demonstrated gains in production applications and operates at Facebook scale.
Energy­efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter: A Panacea to Green

Cloud computing has observed significant interest due to the increasing service demands from organizations ofﬂoading computationally intensive tasks to datacenters. Meanwhile, datacenter infrastructure comprises hardware resources consuming a high amount of energy and increasing carbon emissions at a hazardous level. In Cloud data-center, Virtual Machine (VM) need to be allocated on various Physical Machines (PM) in order to minimize resource wastage and increase energy efficiency. Resource allocation problem is NP-hard, hence finding an exact solution is complicated especially for large-scale datacenters. In this context, this paper proposes an Energy-oriented Flower Pollination Algorithm (E-FPA) for VM allocation in Cloud datacenter environments. FPA is a Natured-Inspired optimization technique used in solving global and numerical optimization problems. A system framework was developed to enable energy-oriented allocation of various VMs on a PM. The allocation uses a strategy namely, Dynamic Switching Probability (DSP). The framework finds near optimal solution quickly and balances the exploration and exploitation of the global and local search. It considers a processor, storage, and memory constraints of a physical machine while prioritizing energy-oriented allocation for a set of virtual machines. Simulations are performed on MultiRecCloudSim utilizing planet workload. It is evident that the E-FPA outperforms state-of-the-art techniques in terms of energy consumption including Genetic Algorithm for Power-Aware (GAPA) by 21.8%, Order of Exchange Migration (OEM) ant colony system by 21.5%, and First Fit Decreasing (FFD) by 24.9%. This implies that, the dat-acenter performance and environmental sustainability has been improved significantly due reduction in energy consumption and as well carbon emission.
Effect of nitrogen, phosphorus and potash on growth, yield and quality of onion (Allium cepa L.) raised from onion sets

A field experiment was conducted during Kharif season of 2004-05 and 2005-06, to study the effect of nitrogen, phosphorus and potassium application raised from onion sets on growth, yield and quality characters. In a trial with cv. AGRI found Dark Red, different levels of nitrogen, phosphorus and potassium significantly affected the growth characters of onion, but yield characters was found non-significantly except total yield. The application of nitrogen significantly increased total yield of onion. Application of 150kg N + 80kg P 2 O 5 + 80 kg K 2 O per ha. was most appropriate combination of nutrients with respect to growth and yield of the rainy season onion crops raised from seedlings.
Offset analgesia and onset hyperalgesia with different stimulus ranges

Offset analgesia (OA), a large reduction in pain following a brief increase in intensity of an otherwise stable painful stimulus, has been established by a large body of research. But the opposite effect, onset hyperalgesia (OH), a disproportional hyperalgesic response following a briefly decreased intensity of a painful stimulus, has only been investigated in one previous study. The aim of this study was to induce OA and OH in healthy participants and explore the effects of different stimulus ranges on OA and OH. A total of 62 participants were tested in two identical experiments. OA and OH conditions included two different temperature deviations ({+/-}1{degrees}C/{+/-}2{degrees}C) from baseline and were compared to a constant temperature (control). OA was successfully elicited in three out of four conditions. A dose-response relationship was demonstrated between the increase of temperature and the resulting hypoalgesic response. OH was only elicited in one out of four conditions (OH2{degrees}C in experiment 1). Exploratory analysis showed that OA and OH responses were only weakly correlated. The asymmetry between pain responses following a brief temperature increase and decrease could be seen as evidence for different mechanisms involved in the pain responses to increasing and decreasing temperature. This asymmetry may also be explained by high temperatures in OA condition (+1{degrees}C/+2{degrees}C above baseline) that could be seen as salient "learning signals", which augment the response to following changes in temperature.