Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining the orientation of the spin axes of extrasolar minor bodies 1I/2017 U1 (‘Oumuamua) and 2I/Borisov

Context. The orientation of the spin axis of a comet is defined by the values of its equatorial obliquity and its cometocentric longitude of the Sun at perihelion. These parameters can be computed from the components of the nongravitational force caused by outgassing if the cometary activity is well characterized. The trajectories of known interstellar bodies passing through the Solar System show nongravitational accelerations. Aims. The spin-axis orientation of 1I/2017 U1 (‘Oumuamua) remains to be determined; for 2I/Borisov, the already released results are mutually exclusive. In both cases, the values of the components of the nongravitational force are relatively well constrained. Here, we investigate – within the framework of the forced precession model of a nonspherical cometary nucleus – the orientation of the spin axes of ‘Oumuamua and 2I/Borisov using public orbit determinations that consider outgassing. Methods. We applied a Monte Carlo simulation using the covariance matrix method together with Monte Carlo random search techniques to compute the distributions of equatorial obliquities and cometocentric longitudes of the Sun at perihelion of ‘Oumuamua and 2I/Borisov from the values of the nongravitational parameters. Results. We find that the equatorial obliquity of ‘Oumuamua could be about 93°, if it has a very prolate (fusiform) shape, or close to 16°, if it is very oblate (disk-like). Different orbit determinations of 2I/Borisov gave obliquity values of 59° and 90°. The distributions of cometocentric longitudes were in general multimodal. Conclusions. Our calculations suggest that the most probable spin-axis direction of ‘Oumuamua in equatorial coordinates is (280°, +46°) if very prolate or (312°, −50°) if very oblate. Our analysis favors a prolate shape. For the orbit determinations of 2I/Borisov used here, we find most probable poles pointing near (275°, +65°) and (231°, +30°), respectively. Although our analysis favors an oblate shape for 2I/Borisov, a prolate one cannot be ruled out.
Multi-frequency temporal and spectral variability study of blazar PKS 1424−418

A study of blazar PKS 1424-418 was carried out using multi waveband data collected by Fermi-LAT, Swift-XRT, Swift-UVOT and SMARTS telescopes between MJD 56000 to MJD 56600 (14 Mar 2012 to 4 Nov 2013). Two flaring episodes were identified by analysing the gamma ray light curve. Simultaneous multi waveband Spectral Energy Distributions (SED) were obtained for those two flaring periods. A cross-correlation analysis of IR-Optical and $\gamma$-ray data suggested the origin of these emissions from the same region. We have set a lower limit for the Doppler factor using the highest energy photon observed from this source during the flaring periods, which should be $>$12. The broadband emission mechanism was studied by modelling the SED using leptonic emission mechanism.
Multiˆ2OIE: Multilingual Open Information Extraction based on Multi-Head Attention with BERT

In this paper, we propose Multi^2OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention. Our model is a sequence-labeling system with an efficient and effective argument extraction method. We use a query, key, and value setting inspired by the Multimodal Transformer to replace the previously used bidirectional long short-term memory architecture with multi-head attention. Multi^2OIE outperforms existing sequence-labeling systems with high computational efficiency on two benchmark evaluation datasets, Re-OIE2016 and CaRB. Additionally, we apply the proposed method to multilingual open IE using multilingual BERT. Experimental results on new benchmark datasets introduced for two languages (Spanish and Portuguese) demonstrate that our model outperforms other multilingual systems without training data for the target languages.
Dynamic Regions Graph Neural Networks for Spatio-Temporal Reasoning

Graph Neural Networks are perfectly suited to capture latent interactions occurring in the spatio-temporal domain. But when an explicit structure is not available, as in the visual domain, it is not obvious what atomic elements should be represented as nodes. They should depend on the context and the kinds of relations that we are interested in. We are focusing on modeling relations between instances by proposing a method that takes advantage of the locality assumption to create nodes that are clearly localised in space. Current works are using external object detectors or fixed regions to extract features corresponding to graph nodes, while we propose a module for generating the regions associated with each node dynamically, without explicit object-level supervision. Conditioned on the input, for each node we predict the location and size of a region and use them to pool node features using a differentiable mechanism. Constructing these localised, adaptive nodes makes our model biased towards object-centric representations and we show that it improves the modeling of visual interactions. By relying on a few localized nodes, our method learns to focus on salient regions leading to a more explainable model. Our model achieves superior results on video classification tasks involving instance interactions.
Learning a Deep Part-based Representation by Preserving Data Distribution

Unsupervised dimensionality reduction is one of the commonly used techniques in the field of high dimensional data recognition problems. The deep autoencoder network which constrains the weights to be non-negative, can learn a low dimensional part-based representation of data. On the other hand, the inherent structure of the each data cluster can be described by the distribution of the intraclass samples. Then one hopes to learn a new low dimensional representation which can preserve the intrinsic structure embedded in the original high dimensional data space perfectly. In this paper, by preserving the data distribution, a deep part-based representation can be learned, and the novel algorithm is called Distribution Preserving Network Embedding (DPNE). In DPNE, we first need to estimate the distribution of the original high dimensional data using the $k$-nearest neighbor kernel density estimation, and then we seek a part-based representation which respects the above distribution. The experimental results on the real-world data sets show that the proposed algorithm has good performance in terms of cluster accuracy and AMI. It turns out that the manifold structure in the raw data can be well preserved in the low dimensional feature space.
Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing

In this paper, we consider data acquired by multimodal sensors capturing complementary aspects and features of a measured phenomenon. We focus on a scenario in which the measurements share mutual sources of variability but might also be contaminated by other measurement-specific sources such as interferences or noise. Our approach combines manifold learning, which is a class of nonlinear data-driven dimension reduction methods, with the well-known Riemannian geometry of symmetric and positive-definite (SPD) matrices. Manifold learning typically includes the spectral analysis of a kernel built from the measurements. Here, we take a different approach, utilizing the Riemannian geometry of the kernels. In particular, we study the way the spectrum of the kernels changes along geodesic paths on the manifold of SPD matrices. We show that this change enables us, in a purely unsupervised manner, to derive a compact, yet informative, description of the relations between the measurements, in terms of their underlying components. Based on this result, we present new algorithms for extracting the common latent components and for identifying common and measurement-specific components.
Novel Parameter Estimation and Radar Detection Approaches for Multiple Point-Like Targets: Designs and Comparisons

In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets. The first strategy, which appears here for the first time, jointly exploits the maximum likelihood approach and Bayesian learning to estimate targets’ parameters including their positions in terms of range bins. The second strategy relies on the intuition that for high signal-to-interference-plus-noise ratio values, the energy of data containing target components projected onto the nominal steering direction should be higher than the energy of data affected by interference only. The adaptivity with respect to the interference covariance matrix is also considered exploiting a training data set collected in the proximity of the window under test. Finally, another important innovation aspect concerns the adaptive estimation of the unknown number of targets by means of the model order selection rules.
Strongly lensed candidates from the HSC transient survey

We present a lensed quasar search based on the time variability of lens systems in the HSC transient survey. Starting from 101,353 variable objects with $\textit{i}$-band difference images in the HSC transient survey, we use a time-variability-based lens search method measuring the spatial extendedness in difference images to select potential lensed quasar candidates. We adopt conservative constraints in this variability selection and obtain 83,657 variable objects as possible lens candidates. We then run CHITAH, a lens search algorithm based on the image configuration, on those 83,657 variable objects, and 2,130 variable objects are identified as lensed objects. We visually inspect the 2,130 variable objects, and seven of them are our final candidates of lensed quasars. Additionally, we find one lensed galaxy candidate as a serendipitous discovery. Among the eight final lensed candidates, one is the only known quadruply lensed quasar in the survey field, HSCJ095921+020638. None of the other seven lensed candidates has been previously classified as a lens nor a lensed candidate. Three of the five final candidates with available Hubble Space Telescope (HST) images, including HSCJ095921+020638, show clues of lensed feature in the HST images. A tightening of variability selection criteria might result in the loss of possible lensed quasar candidates, especially the lensed quasars with faint brightness or narrow separation, without efficiently eliminating the non-lensed objects; CHITAH is therefore important as an advanced examination to improve the lens search efficiency through the object configuration. The recovery of HSCJ095921+020638 proves the feasibility of the time-variability-based lens search method, and this lens search method can be used in other cadenced imaging surveys, such as the upcoming Rubin Observatory Legacy Survey of Space and Time.
Video based real-time positional tracker

We propose a system that uses video as the input to track the position of objects relative to their surrounding environment in real-time. The neural network employed is trained on a 100% synthetic dataset coming from our own automated generator. The positional tracker relies on a range of 1 to n video cameras placed around an arena of choice. The system returns the positions of the tracked objects relative to the broader world by understanding the overlapping matrices formed by the cameras and therefore these can be extrapolated into real world coordinates. In most cases, we achieve a higher update rate and positioning precision than any of the existing GPS-based systems, in particular for indoor objects or those occluded from clear sky.
Lensing rates of gravitational wave signals displaying beat patterns detectable by (B-)DECIGO

The coherent nature of gravitational wave emanating from a compact binary system makes it possible to detect some interference patterns in two (or more) signals registered simultaneously by the detector. Gravitational lensing effect can be used to bend trajectories of gravitational waves, which might reach the detector at the same time. Once this happens, a beat pattern may form, and can be used to obtain the luminosity distance of the source, the lens mass, and cosmological parameters such as the Hubble constant. Crucial question is how many such kind of events could be detected. In this work, we study this issue for the future space-borne detectors: DECIGO and its downscale version, B-DECIGO. It is found out that there can be a few tens to a few hundreds of lensed gravitational wave events with the beat pattern observed by DECIGO and B-DECIGO per year, depending on the evolution scenario leading to the formation of double compact objects. In particular, black hole-black hole binaries are dominating population of lensed sources in which beat patterns may reveal. However, DECIGO could also register a considerable amount of lensed signals from binary neutron stars, which might be accompanied by electromagnetic counterparts. Our results suggest that, in the future, lensed gravitational wave signal with the beat pattern could play an important role in cosmology and astrophysics.
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy

Event cameras are novel vision sensors that sample, in an asynchronous fashion, brightness increments with low latency and high temporal resolution. The resulting streams of events are of high value by themselves, especially for high speed motion estimation. However, a growing body of work has also focused on the reconstruction of intensity frames from the events, as this allows bridging the gap with the existing literature on appearance- and frame-based computer vision. Recent work has mostly approached this problem using neural networks trained with synthetic, ground-truth data. In this work we approach, for the first time, the intensity reconstruction problem from a self-supervised learning perspective. Our method, which leverages the knowledge of the inner workings of event cameras, combines estimated optical flow and the event-based photometric constancy to train neural networks without the need for any ground-truth or synthetic data. Results across multiple datasets show that the performance of the proposed self-supervised approach is in line with the state-of-the-art. Additionally, we propose a novel, lightweight neural network for optical flow estimation that achieves high speed inference with only a minor drop in performance.
Does food availability affect growth and survival of males and females differently in a promiscuous small mammal, Tamiasciurus hudsonicus?

In dimorphic mammals, males grow faster than females, but often suffer higher mortality during periods of resource shortage. We compared growth and survival of males and females in a promiscuous small mammal with a relatively small degree of dimorphism (male/female body mass ratios ranged from 1.05 to 1.11). We compared two geographically distinct populations over 4 years during which natural food levels varied considerably. We also experimentally supplemented and reduced food levels
Review on Effect of Stress on Production and Reproduction of Dairy Cattle

A man can create stress as the result of confinement and an ambitious vision and carelessness.Many people create stress to fulfill their interest of improving production and reproduction, which carries out unconsciously through manipulating animal which is even impossible to adapt to our environment. Reduction in synthesis of milk and susceptibility of dairy cows more to illness is the result of stress, which can affects further directly and/or indirectly metabolic and physiological acclimation. Therefore, this review aims to review effect of stress on production and reproduction of dairy cattle. Stress affects the reproductive performance of both male and females. In male’s quantity and quality of the sperm and females fertility percentage, fertility and embryo quality declines. Moreover, the productive performance of cattle like milk production and growth of cattle is also affected by stress factors.Hence, managing effect of stress factors is aimed at alleviating rather than eliminating the challenges on animals production and providing shelters, insulation in the form of bedding, proper feeding, and reduction of dry matter daily intake, reduction of rumen fermentation activity, reduction of physical activity and direct cooling of animal are needed to alter stress. Furthermore, creating awareness on animal handling techniques, continuing research and development endeavors are needed.
Applications of Encoding Methods for False Alarm Elimination in Physiological Monitors: A Comparative Analysis

Physiological Monitoring Systems (PMS) are widely evolving to deliver better patient outcomes. In specific, the elimination of false alarms in patient monitors is essential for clinicians to patients who needs attention. In this work, we explore how patient specific variations can be minimized by expressing the input feature vectors in terms of patient independent basis vectors. We used Locality-constrained Linear Coding (LLC) and Fisher Vector Encoding (FVE) for mapping the input features to a patient independent feature space. We obtained a sensitivity of 97.54%, specificity of 96.93%, and overall classification accuracy of 97.41% with LLC-linSVM, whereas with FVE-linSVM we obtained a sensitivity of 98.06%, specificity of 97.62%, and overall classification accuracy of 97.96%. Further we explored the concept of fusing the LLC and FVE mapped features to emulate, looking at the original feature vectors from two perspectives in an effort to improve the performance. We obtained an overall classification accuracy of 98.16%, sensitivity of 98.55%, and specificity of 97.84% with LLC-FVE-linSVM which achieved a performance improvement of 1.01% absolute for classification accuracy, 3.15% absolute for sensitivity and 0.60% absolute for specificity with respect to the baseline system. The multiparameter intelligent monitoring in intensive care II (MIMIC-II) dataset were used for the experiments performed in this work. Keywords— PMS, Patient Monitors, SVM, LLC, FVE, MIMIC II Database
Fearing the Robot Apocalypse: Correlates of AI Anxiety

This study examines the relationship between individuals’ beliefs about AI (Artificial Intelligence) and levels of anxiety with respect to their technology readiness level. In this cross-sectional study, we surveyed 65 students at a southwestern US college. Using partial least squares analysis, we found that technology readiness contributors were significantly and positively related to only one AI anxiety factor: socio-technical illiteracy. In contrast, all four links between technology readiness inhibitors and AI anxiety factors were significant with medium effect sizes. Technology readiness inhibitors are positively related to learning, fears of job replacement, socio-technical illiteracy, and particular AI configurations. Thus, we conclude that AI anxiety runs through a spectrum. It is influenced by real, practical consequences of immediate effects of increased automatization but also by popular representations and discussions of the negative consequences of artificial general intelligence and killer robots and addressing technology readiness is unlikely to mitigate effects of AI anxiety.
Impact of Phytogenic Feed Additive on Some Hatching Parameters of Broiler Breeders Eggs

This study was established to investigate the influence of phytogenic supplementation on fertility and hatchability percentages for Broiler breeder flock. A total of 500 females and 55 males Ross 308 broiler breeders were allotted into two groups; the first received one cm of Activo@ * (specific formulation of essential oils) per liter of drinking water for five successive days each month throughout the experimental period (six months of production), while the second was a control one. 64,320 eggs were used for investigating fertility and hatchability traits throughout three production stages; the first one extended from the 6th to the 13th week, the second was extended from the 14th to the 21st week and the third stage extended from the 22nd till the 29th week of laying. Phytogenic supplementation significantly improved fertility and hatchability percentages (scientific and commercial) as well as the weight of hatched chick. Uunhatched and culled percentages were significantly declined especially those of early stages of production. Meanwhile there was none significant influence on embryonic mortality, piped live and dead percentages. Supplementation of essential oils to broiler breeders' flocks during early stages of production is recommended.
On the role and formation of covalently bound flavin cofactors

1 Laboratory of Biochemistry, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, The Netherlands 2 Manchester Interdisciplinary Biocentre, Faculty of Life Sciences, University of Manchester, UK 3 Molecular Biology Division, Department of Veterans Affairs Medical Center, San Francisco, CA, USA 4 Department of Biochemistry & Biophysics, University of California, San Francisco, CA, USA
Ratio of Drug/carrier as Dominant Factor in Determining Size of Doxorubicin-Loaded Beta-1,3- Glucan Nanoparticles: An Artificial Neural Networks Study

Size of nanoparticles is an important parameter in determining many of their properties. In this work, nanoparticles of β-1,3-glucan containing doxorubicin (Dox) in conjugated and unconjugated forms (Con-Dox-Glu and Un-Dox-Glu, respectively) were prepared. Then, artificial neural networks (ANNs) were used to find the effect of different formulation/processing parameters on their particle size, which was measured using dynamic light scattering (DLS). The parameters included ratio of Dox/Carrier as well as concentrations of polyethyleneimine (PEI), NaOH and succinic anhydride (Sa). To do so, fifty samples having different values of the four parameters were prepared and their particle size was measured. The data were divided randomly into training, test and unseen data. The ANN model demonstrated that in both conjugated and unconjugated forms, Dox/Carrier ratio is the dominant factor determining the particle size. Also, concentration of PEI showed to be important in determining particle size of unconjugated form of the nanoparticles. The remaining parameters indicated no considerable effect on the particle size
Fertility after Separation: Second Births in Higher Order Unions in Germany

This paper uses recent data from the German family panel (pairfam) to examine the fertility behavior after separation. More specifically, we focus on the transition to the second child and compare the behavior of respondents in ongoing partnerships (couples who are still partnered with the mother/father of their first child) with those who have experienced family dissolution after the first birth. The investigation reveals strong gender differences in postseparation fertility behavior. We also find large regional differences. Eastern Germans had much lower second birth rates than western Germans. However, they were more prone than western Germans to have their second child with a parent who was not the father or the mother of their firstborn child. This result is in line with descriptive findings on the diversity of family structures in eastern Germany