Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial spawning of European catfish, Silurus glanis L.: stimulation of ovulation using LHRH‐a, Ovaprim and carp pituitary extract

Ovulation was stimulated in four groups of European catfish, Silurus glanis L. , using injections of des-Gly10, [D-Ala6]-LHRH Ethylamide (20 μg kg–1) and pimozide (10 mg kg–1), Ovaprim (0.33 mL kg–1), and carp pituitary extract (4 mg kg–1, in one or two doses). A higher percentage of ovulating females (producing eggs of sufficient quality) was found after the LHRH-a and Ovaprim treatments (100% and 80%) in relation to fish treated with the pituitary extract (60% and 66.67%). The greatest weight of eggs was obtained in the case of repeated hypophysation and LHRH-a (1299.69 and 1298.57 g, respectively), and the smallest after single hypophysation (1144.08 g). After 60 h of incubation, the best quality of eggs was found in the group treated with Ovaprim (62.9% of live embryos) and the poorest in the two groups which underwent hypophysation (50.41% and 50.75%). No statistically significant effect by the ovulation stimulators on the characteristic qualitative and quantitative traits of obtained eggs was ascertained.
Enhanced Pulse Compression within Sign-Alternating Dispersion Waveguides

We show theoretically and numerically how to optimize sign-alternating dispersion waveguides for maximum nonlinear pulse compression, while leveraging the substantial increase in bandwidth-to-input peak power advantage of these structures. We find that the spectral phase can converge to a parabolic profile independent of uncompensated higher-order dispersion. The combination of an easy to compress phase spectrum, with low input power requirements, then makes sign-alternating dispersion a scheme for high-quality nonlinear pulse compression that does not require high powered lasers, which is beneficial for instance in integrated photonic circuits. We also show a new nonlinear compression regime and soliton shaping dynamic only seen in sign-alternating dispersion waveguides. Through an example SiN-based integrated waveguide, we show that the dynamic enables the attainment of compression to two optical cycles at a pulse energy of 100 pJ which surpasses the compression achieved using similar parameters for a current state-of-the-art SiN system.
Concept Learners for Generalizable Few-Shot Learning

Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance. The core of human cognition lies in the structured, reusable concepts that help us to rapidly adapt to new tasks and provide reasoning behind our decisions. However, existing meta-learning methods learn complex representations across prior labeled tasks without imposing any structure on the learned representations. Here we propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions. Instead of learning a joint unstructured metric space, COMET learns mappings of high-level concepts into semi-structured metric spaces, and effectively combines the outputs of independent concept learners. We evaluate our model on few-shot tasks from diverse domains, including a benchmark image classification dataset and a novel single-cell dataset from a biological domain developed in our work. COMET significantly outperforms strong meta-learning baselines, achieving $9$-$12\%$ average improvement on the most challenging $1$-shot learning tasks, while unlike existing methods also providing interpretations behind the model's predictions.
The Atacama Cosmology Telescope: arcminute-resolution maps of 18 000 square degrees of the microwave sky from ACT 2008–2018 data combined with Planck

This paper presents a maximum-likelihood algorithm for combining sky maps with disparate sky coverage, angular resolution and spatially varying anisotropic noise into a single map of the sky. We use this to merge hundreds of individual maps covering the 2008–2018 ACT observing seasons, resulting in by far the deepest ACT maps released so far. We also combine the maps with the full Planck maps, resulting in maps that have the best features of both Planck and ACT: Planck's nearly white noise on intermediate and large angular scales and ACT's high-resolution and sensitivity on small angular scales. The maps cover over 18 000 square degrees, nearly half the full sky, at 100, 150 and 220 GHz. They reveal 4 000 optically-confirmed clusters through the Sunyaev Zel'dovich effect (SZ) and 18 500 point source candidates at > 5σ, the largest single collection of SZ clusters and millimeter wave sources to date. The multi-frequency maps provide millimeter images of nearby galaxies and individual Milky Way nebulae, and even clear detections of several nearby stars. Other anticipated uses of these maps include, for example, thermal SZ and kinematic SZ cluster stacking, CMB cluster lensing and galactic dust science. The method itself has negligible bias. However, due to the preliminary nature of some of the component data sets, we caution that these maps should not be used for precision cosmological analysis. The maps are part of ACT DR5, and will be made available on \href{https://lambda.gsfc. nasa.gov/}{LAMBDA} no later than three months after the journal publication of this article, along with an interactive sky atlas.
Hardware Acceleration of Monte-Carlo Sampling for Energy Efficient Robust Robot Manipulation

Algorithms based on Monte-Carlo sampling have been widely adapted in robotics and other areas of engineering due to their performance robustness. However, these sampling-based approaches have high computational requirements, making them unsuitable for real-time applications with tight energy constraints. In this paper, we investigate 6 degree-of-freedom (6DoF) pose estimation for robot manipulation using this method, which uses rendering combined with sequential Monte-Carlo sampling. While potentially very accurate, the significant computational complexity of the algorithm makes it less attractive for mobile robots, where runtime and energy consumption are tightly constrained. To address these challenges, we develop a novel hardware implementation of Monte-Carlo sampling on an FPGA with lower computational complexity and memory usage, while achieving high parallelism and modularization. Our results show 12X–21X improvements in energy efficiency over low-power and high-end GPU implementations, respectively. Moreover, we achieve real time performance without compromising accuracy.
Non-relational Databases on FPGAs: Survey, Design Decisions, Challenges

Non-relational database systems (NRDS) such as graph and key-value have gained attention in various trending business and analytical application domains. However, while CPU performance scaling becomes increasingly more difficult, field-programmable gate arrays (FPGA)- accelerated NRDS have not been systematically studied yet. This survey describes and categorizes the inherent differences and non-trivial tradeoffs of relevant NRDS classes (i.e., graph, document, key-value, and wide-column) as well as their commonalities in the context of common design decisions when building such a system with FPGAs. In particular, we highlight accelerator tasks, FPGA placement, accelerator design patterns, and justification for using FPGAs in different system contexts. We close with open research and engineering challenges to outline the future of FPGA-accelerated NRDS.
Hatching Asynchrony in European Starlings (Sturnus vulgaris)

In many species of birds, the onset of incubation occurs prior to the completion of the clutch, causing eggs within a clutch to hatch asynchronously. Because nestlings that hatch later within a clutch are smaller and less able to compete with their siblings, they often suffer greater mortality than earlier hatched nestlings. While incubation prior to clutch completion has significant fitness consequences for both parents and offspring, little is known about the patterns of incubation during the laying stage that contribute to hatching asynchrony. Here, utilizing data collected by automated data loggers, we used a nonlinear mixed-model to construct growth curves that depict the application of heat to nests by parents. The use of growth curves allows us to examine variation between individuals with respect to changes in incubation behavior across the laying period and compare these curves to observed hatching patterns within starlings. While the use of growth curves is not a novel technique, their use here to model incubation behavior represents a unique application. Accordingly, we discuss their potential use in future studies, either as a conceptual tool when considering support for various explanations of
The occurrence of southern blight on Allium hookeri caused by Sclerotium rolfsii in Korea

Abstract In July 2014, an occurrence of southern blight was observed on hooker chives (Allium hookeri) grown in Hamyang, South Korea. The typical symptoms included blighting and rotting, and severely infected plants eventually died. White mycelial mats spread over the infected hooker chives bulbs and sclerotia formed near the soil surface. DNA sequencing and phylogenetic analysis of the internal transcribed spacer rRNA gene region confirmed that the isolates were S. rolfsii. Koch’s postulates were supported by pathogenicity tests conducted on healthy plants. Based on the morphological characteristics, pathogenicity tests on host plants, and molecular identification, the causal fungus was identified as S. rolfsii Saccardo. This is the first report of southern blight caused by S. rolfsii on hooker chives in South Korea.
Central velocity dispersion catalogue of LAMOST-DR7 galaxies

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) is a major facility to carry out spectroscopic surveys for cosmology and galaxy evolution studies. The seventh data release of the LAMOST ExtraGAlactic Survey (LEGAS) is currently available and including redshifts of 193\,361 galaxies. These sources are spread over $\sim 11\,500$ deg$^2$ of the sky, largely overlapping with other imaging (SDSS, HSC) and spectroscopic (BOSS) surveys. The estimated depth of the galaxy sample, $r\sim17.8$, the high signal-to-noise ratio, and the spectral resolution $R=1800$, make the LAMOST spectra suitable for galaxy velocity dispersion measurements, which are invaluable to study the structure and formation of galaxies and to determine their central dark matter (DM) content. We present the first estimates of central velocity dispersion of $\sim86\,000$ galaxies in LAMOST footprint. We have used a wrap-up procedure to perform the spectral fitting using \textsc{pPXF}, and derive velocity dispersion measurements. Statistical errors are also assessed by comparing LAMOST velocity dispersion estimates with the ones of SDSS and BOSS over a common sample of $\sim51\,000$ galaxies. The two datasets show a good agreement, within the statistical errors, in particular when velocity dispersion values are corrected to 1 effective radius aperture. We also present a preliminary Mass-$\sigma$ relation and find consistency with previous analyses based on local galaxy samples. These first results suggest that LAMOST spectra are suitable for galaxy velocity dispersion measurements to complement the available catalogs of galaxy internal kinematics in the northern hemisphere. We plan to expand this analysis to next LAMOST data releases.
Distribution of fish, crustacea and zooplankton at different distances from mangrove prop roots within a semi-isolated lagoon

Although semi-isolated mangrove lagoons are common in the Caribbean, few studies have surveyed organisms of multiple trophic levels and taxa in these lagoons, which are characterized by a lack of adjacent seagrass and coral-reef habitats. In this study, visual surveys, minnow traps and plankton tows, which were deployed at abutting mangrove prop roots and on macro-algal beds 5 and 15 m away from the prop roots, were used to study assemblages of fish and their potential prey in a semi-isolated lagoon located on Utila, Honduras during the dry season. Assemblages of fish and macro-crustacea differed between the three distances from prop roots, while zooplankton abundances were highly variable and did not follow any distinct distribution patterns. Daytime visual surveys found that large lutjanid (snapper) juveniles, tetraodontid (pufferfish), and some species of brachyurans were more abundant near prop roots. Small lutjanid juveniles were also significantly more numerous near prop roots, but their potential prey, copepods, showed no such difference in abundance. However, 24-hour minnow trap catches found that mean fish abundance (although low) did not differ between near-mangrove transects and transects located in algal beds away from prop roots. Whereas it is well known that mangrove–seagrass habitats play a vital role for fish in open systems, low abundances of organisms in algal beds, particularly in the day time, in this study indicate that algal beds may not be as important to fish in a semi-isolated mangrove lagoon.
Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons

Gradient-based hyperparameter optimization is an attractive way to perform meta-learning across a distribution of tasks, or improve the performance of an optimizer on a single task. However, this approach has been unpopular for tasks requiring long horizons (many gradient steps), due to memory scaling and gradient degradation issues. A common workaround is to learn hyperparameters online or split the horizon into smaller chunks. However, this introduces greediness which comes with a large performance drop, since the best local hyperparameters can make for poor global solutions. In this work, we enable non-greediness over long horizons with a two-fold solution. First, we share hyperparameters that are contiguous in time, and show that this drastically mitigates gradient degradation issues. Then, we derive a forward-mode differentiation algorithm for the popular momentum-based SGD optimizer, which allows for a memory cost that is constant with horizon size. When put together, these solutions allow us to learn hyperparameters without any prior knowledge. Compared to the baseline of hand-tuned off-the-shelf hyperparameters, our method compares favorably on simple datasets like SVHN. On CIFAR-10 we match the baseline performance, and demonstrate for the first time that learning rate, momentum and weight decay schedules can be learned with gradients on a dataset of this size. Code is available at this https URL
A test of the mechanisms behind avian generalized individuals–area relationships

Aim Questions related to abundances of organisms are central to ecological research. A priori, a scale independent estimation of abundances would be expected. However, we find estimates of numbers of bird individuals from all over the world to increase less than proportionately with increasing plot size. At the whole assemblage level, the pattern holds across biogeographical regions and habitats. The slope of the interspecific and, for the majority of species, the intraspecific individuals–area relationship is also significantly shallower than 1. The question arises as to which mechanisms cause these patterns. Location Global. Methods At the assemblage, interspecific and intraspecific levels, we tested three mechanisms that could be responsible for these patterns by comparing the slope of the individuals–plot area relationship for subsets of a database compiled from the literature. Spatial autocorrelation was controlled for. Results There was no evidence for an influence of plot area choice in order to sample a constant number of individuals. Evidence for higher survey efficiency was available only with increasing number of visits at the intraspecific level. Evidence for influences of habitat heterogeneity was present at the assemblage, interspecific and intraspecific levels. This mechanism can work only if small plots are delimited non-randomly in homogeneous habitat. Main conclusions Avian population size estimates without indication of the area over which they were obtained are of substantially less value than those coupled with that information. Ecologists planning to compare avian abundances between plots varying in some other factor of interest should minimize variations in their areas and/or account for them in data analyses. Population viability analyses, regional and global population size estimates, site prioritization and the scaling of ecosystem and species energy utilization need to address the plot area effect on assemblage and individual species abundances.
Bot-Match: Social Bot Detection with Recursive Nearest Neighbors Search

Social bots have emerged over the last decade, initially creating a nuisance while more recently used to intimidate journalists, sway electoral events, and aggravate existing social fissures. This social threat has spawned a bot detection algorithms race in which detection algorithms evolve in an attempt to keep up with increasingly sophisticated bot accounts. This cat and mouse cycle has illuminated the limitations of supervised machine learning algorithms, where researchers attempt to use yesterday's data to predict tomorrow's bots. This gap means that researchers, journalists, and analysts daily identify malicious bot accounts that are undetected by state of the art supervised bot detection algorithms. These analysts often desire to find similar bot accounts without labeling/training a new model, where similarity can be defined by content, network position, or both. A similarity based algorithm could complement existing supervised and unsupervised methods and fill this gap. To this end, we present the Bot-Match methodology in which we evaluate social media embeddings that enable a semi-supervised recursive nearest neighbors search to map an emerging social cybersecurity threat given one or more seed accounts.
Automatic Image Labelling at Pixel Level

The performance of deep networks for semantic image segmentation largely depends on the availability of large-scale training images which are labelled at the pixel level. Typically, such pixel-level image labellings are obtained manually by a labour-intensive process. To alleviate the burden of manual image labelling, we propose an interesting learning approach to generate pixel-level image labellings automatically. A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from a source domain, and such GFN then transfers such segmentation knowledge to generate coarse object masks in the target domain. Such coarse object masks are treated as pseudo labels and they are further integrated to optimize/refine the GFN iteratively in the target domain. Our experiments on six image sets have demonstrated that our proposed approach can generate fine-grained object masks (i.e., pixel-level object labellings), whose quality is very comparable to the manually-labelled ones. Our proposed approach can also achieve better performance on semantic image segmentation than most existing weakly-supervised approaches.
Studying star-forming processes at core and clump scales: the case of the young stellar object G29.862−0.0044

Aims. To advance our knowledge of star formation, in addition to statistical studies and large surveys of young stellar objects (YSOs), it is important to carry out detailed studies towards particular objects. Given that massive molecular clumps fragment into cores where star formation takes place, these kinds of studies should be done on different spatial scales. The aim of this work is to investigate the star-forming processes at core and clump scales. Methods. Using near-infrared (NIR) data obtained with NIRI at the Gemini-North telescope, data of the complex molecular species CH3OCHO and CH3CN obtained from the Atacama Large Millimeter Array (ALMA) database, observations of HCN, HNC, HCO+, and C2H carried out with the Atacama Submillimeter Telescope Experiment (ASTE), and CO data from public surveys observed with the James Clerck Maxwell Telescope (JCMT), we perform a deep study of the YSO G29.862−0.0044 (YSO-G29) at core and clump spatial scales. Results. The NIR emission shows that YSO-G29 is composed of two nebulosities separated by a dark lane, suggesting a scenario consistent with a typical disc plus jets system, albeit in this case highly asymmetric. The northern nebulosity is open, diffuse, and is divided into two branches, while the southern one is smaller and sharper. They are likely produced by the scattered light in cavities carved out by jets or winds on an infalling envelope of material, which also presents line emission of H2 S(1) 1–0 and 2–1, and [FeII]. The presence of the complex molecular species observed with ALMA confirms that we are mapping a hot molecular core. The CH3CN emission concentrates at the position of the dark lane and appears slightly elongated from southwest to northeast in agreement with the inclination of the system as observed in the NIR. The morphology of the CH3OCHO emission is more complex and extends along some filaments and concentrates in knots and clumps, mainly southwards of the dark lane, suggesting that the southern jet is encountering a dense region. The northern jet is able to flow more freely, generating the more extended features as seen in the NIR. This is in agreement with the redshifted molecular outflow traced by the 12CO J = 3–2 line extending towards the northwest and the lack of a blueshifted outflow. This configuration can be explained by considering that G29-YSO is located at the furthest edge of the molecular clump along the line of sight, which is consistent with the position of the source in the cloud mapped in the C18O J = 3–2 line. The detection of HCN, HNC, HCO+, and C2H allowed us to characterise the dense gas at clump scales, yielding results that are in agreement with the presence of a high-mass protostellar object.
The Relationship between Side of Onset and Cerebral Regional Homogeneity in Parkinson's Disease: A Resting-State fMRI Study

Objective Motor symptoms are usually asymmetric in Parkinson's disease (PD), and asymmetry in PD may involve widespread brain areas. We sought to evaluate the effect of asymmetry on the whole brain spontaneous activity using the measure regional homogeneity (ReHo) through resting-state functional MRI. Methods We recruited 30 PD patients with left onset (LPD), 27 with right side (RPD), and 32 controls with satisfactory data. Their demographic, clinical, and neuropsychological information were obtained. Resting-state functional MRI was performed, and ReHo was used to determine the brain activity. ANCOVA was utilized to analyze between-group differences in ReHo and the associations between abnormal ReHo, and various clinical and neuropsychological variables were explored by Spearman's correlation. Results LPD patients had higher ReHo in the right temporal pole than the controls. RPD patients had increased ReHo in the right temporal pole and decreased ReHo in the primary motor cortex and premotor area, compared with the controls. Directly comparing LPD and RPD patients did not show a significant difference in ReHo. ReHo of the right temporal pole was significantly correlated with depression and anxiety in RPD patients. Conclusions Both LPD and RPD have increased brain activity synchronization in the right temporal pole, and only RPD has decreased brain activity synchronization in the right frontal motor areas. The changed brain activity in the right temporal pole may play a compensatory role for depression and anxiety in PD, and the altered cerebral function in the right frontal motor area in RPD may represent the reorganization of the motor system in RPD.