Miracle-dz/UniFAR-FTUs huggingface dataset:
Rate-Regularization and Generalization in VAEs

Variational autoencoders (VAEs) optimize an objective that comprises a reconstruction loss (the distortion) and a KL term (the rate). The rate is an upper bound on the mutual information, which is often interpreted as a regularizer that controls the degree of compression. We here examine whether inclusion of the rate term also improves generalization. We perform rate-distortion analyses in which we control the strength of the rate term, the network capacity, and the difficulty of the generalization problem. Lowering the strength of the rate term paradoxically improves generalization in most settings, and reducing the mutual information typically leads to underfitting. Moreover, we show that generalization performance continues to improve even after the mutual information saturates, indicating that the gap on the bound (i.e. the KL divergence relative to the inference marginal) affects generalization. This suggests that the standard spherical Gaussian prior is not an inductive bias that typically improves generalization, prompting further work to understand what choices of priors improve generalization in VAEs.
Comparisons Between Autonomous Acoustic Recordings and Avian Point Counts in Open Woodland Savanna

ABSTRACT In order to evaluate the possibility of using this method in the Brazilian Cerrado or other open vegetation areas, we compared the performance of point counts and autonomous recording units (ARU) for avian monitoring. From September to November 2012, we surveyed birds in 13 points established in six localities in central Brazil by using simultaneously ARU and point counts for species presence only comparisons. We identified a total of 84 species and found no significant differences between the number of species obtained by each method. Differing from what we initially expected, few records in point counts were obtained only by visual detection. The number of species registered by ARU corresponded to 90.4% of all observed species. About 92% of the recorded species sang at least once during point counts. Of all species recorded, 17% were not recorded by point counts and 10% were missed by ARU. Jaccard’s index of similarity was 68%. Our results indicate that the ARU can be an effective method to sample bird assemblages in open vegetation areas, such as the Brazilian Cerrado, being as efficient as the point count proceeding is. The disadvantages of ARU have already been highlighted in the literature (huge data volume to process, high costs to acquire the equipment, malfunctioning and loss, among others), but we must add that missing visual contacts with species must be considered depending of the study’s scope and the type of bird species in question. Nevertheless, the use of ARU can be a cost-effective method for long-term monitoring programs and also helps to quickly obtain the necessary data to characterize species assemblages associated with highly threatened ecosystems, such as the Brazilian Cerrado.
Buellia sharpiana (Physciaceae, Lichenized Ascomycetes), Another New Species from the Great Smoky Mountains of Eastern North America

ABSTRACT Buellia sharpiana is described as new to science based on material from a single locality at the summit of Mt. LeConte in Great Smoky Mountains National Park in the southern Appalachian Mountains of eastern North America. The species is characterized by its ascospore size, thallus morphology, medullary I reaction, and the production of xanthones. Comparison is made to two morphologically similar species, B. ocellata (Flörke ex Flot.) Körb. and B. jugorum (Arnold) Arnold.
Order parameter focalization and critical temperature enhancement in synthetic networks of superconducting islands

A generalization of the de Gennes–Alexander micronetworks theory is presented. In this framework, the phase transition of synthetic networks of superconducting islands is described by means of a Ginzburg–Landau approach adapted to the case of granular systems. The general implications of the theory are carefully explained. As a specific example, we demonstrate that star networks support the exponential localization of the order parameter accompanied by an enhancement of the critical temperature of the system. These findings contribute to clarify the physics of the phase transitions in synthetic networks of Josephson-coupled superconducting islands.
Kinematic modelling of clusters with Gaia: the death throes of the Hyades

The precision of the Gaia data offers a unique opportunity to study the internal velocity field of star clusters. We develop and validate a forward-modelling method for the internal motions of stars in a cluster. The model allows an anisotropic velocity dispersion matrix and linear velocity gradient describing rotation and shear, combines radial velocities available for a subset of stars, and accounts for contamination from background sources via a mixture model. We apply the method to Gaia DR2 data of the Hyades cluster and its tidal tails, dividing and comparing the kinematics of stars within and beyond 10 pc, which is roughly the tidal radius of the cluster. While the velocity dispersion for the cluster is nearly isotropic, the velocity ellipsoid for the tails is clearly elongated with the major axis pointing towards the Galactic centre. We find positive and negative expansion at $\approx 2\sigma$ significance in Galactic azimuthal and vertical direction for the cluster but no rotation. The tidal tails are stretching in a direction tilted from the Galactic centre while equally contracting as the cluster in Galactic vertical direction. The tails have a shear (A) of $16.90\pm 0.92$ m/s/pc and a vorticity (B) of $-6.48\pm1.15$ m/s/pc, values distinct from the local Oort constants.
Stellar Characterization of Keck HIRES Spectra with The Cannon

To accurately interpret the observed properties of exoplanets, it is necessary to first obtain a detailed understanding of host star properties. However, physical models that analyze stellar properties on a per-star basis can become computationally intractable for sufficiently large samples. Furthermore, these models are limited by the wavelength coverage of available spectra. We combine previously derived spectral properties from the Spectroscopic Properties of Cool Stars (SPOCS) catalog with generative modeling using The Cannon to produce a model capable of deriving stellar parameters ( , Teff, and ) and 15 elemental abundances (C, N, O, Na, Mg, Al, Si, Ca, Ti, V, Cr, Mn, Fe, Ni, and Y) for stellar spectra observed with Keck Observatory’s High Resolution Echelle Spectrometer (HIRES). We demonstrate the high accuracy and precision of our model, which takes just ∼3 s to classify each star, through cross-validation with pre-labeled spectra from the SPOCS sample. Our trained model, which takes continuum-normalized template spectra as its inputs, is publicly available at https://github.com/malenarice/keckspec. Finally, we interpolate our spectra and employ the same modeling scheme to recover labels for 477 stars using archival stellar spectra obtained prior to Keck’s 2004 detector upgrade, demonstrating that our interpolated model can successfully predict stellar labels for different spectrographs that have (1) sufficiently similar systematics and (2) a wavelength range that substantially overlaps with that of the post-2004 HIRES spectra.
Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

Since the past few decades, human trajectory forecasting has been a field of active research owing to its numerous real-world applications: evacuation situation analysis, deployment of intelligent transport systems, traffic operations, to name a few. In this work, we cast the problem of human trajectory forecasting as learning a representation of human social interactions. Early works handcrafted this representation based on domain knowledge. However, social interactions in crowded environments are not only diverse but often subtle. Recently, deep learning methods have outperformed their handcrafted counterparts, as they learn about human-human interactions in a more generic data-driven fashion. In this work, we present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two domain-knowledge inspired data-driven methods to effectively capture these social interactions. To objectively compare the performance of these interaction-based forecasting models, we develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting. We propose novel performance metrics that evaluate the ability of a model to output socially acceptable trajectories. Experiments on TrajNet++ validate the need for our proposed metrics, and our method outperforms competitive baselines on both real-world and synthetic datasets.
RAPTOR II: Polarized radiative transfer in curved spacetime

Accreting supermassive black holes are sources of polarized radiation that propagates through highly curved spacetime before reaching the observer. In order to help interpret observations of such polarized emission, accurate and efficient numerical schemes for polarized radiative transfer in curved spacetime are needed. In this manuscript we extend our publicly available radiative transfer code RAPTOR to include polarization. We provide a brief review of different codes and methods for covariant polarized radiative transfer available in the literature and existing codes, and present an efficient new scheme. For the spacetime-propagation aspect of the computation, we develop a compact, Lorentz-invariant representation of a polarized ray. For the plasma-propagation aspect of the computation, we perform a formal analysis of the stiffness of the polarized radiative-transfer equation with respect to our explicit integrator, and develop a hybrid integration scheme that switches to an implicit integrator in case of stiffness, in order to solve the equation with optimal speed and accuracy for all possible values of the local optical/Faraday thickness of the plasma. We perform a comprehensive code verification by solving a number of well-known test problems using RAPTOR and comparing its output to exact solutions. We also demonstrate convergence with existing polarized radiative-transfer codes in the context of complex astrophysical problems. RAPTOR is capable of performing polarized radiative transfer in arbitrary, highly curved spacetimes. This capability is crucial for interpreting polarized observations of accreting black holes, which can yield information about the magnetic-field configuration in such accretion flows. The efficient formalism implemented in RAPTOR is computationally light and conceptually simple. The code is publicly available.
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation

A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handle situations with new users or new items. Recently, some works introduce the meta-optimization idea into the recommendation scenarios, i.e. predicting the user preference by only a few of past interacted items. The core idea is learning a global sharing initialization parameter for all users and then learning the local parameters for each user separately. However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users. In this paper, we design two memory matrices that can store task-specific memories and feature-specific memories. Specifically, the feature-specific memories are used to guide the model with personalized parameter initialization, while the task-specific memories are used to guide the model fast predicting the user preference. And we adopt a meta-optimization approach for optimizing the proposed method. We test the model on two widely used recommendation datasets and consider four cold-start situations. The experimental results show the effectiveness of the proposed methods.
Molecular gas in the central region of NGC 7213

We present a multi-wavelength study (from X-ray to mm) of the nearby low-luminosity active galactic nucleus NGC 7213. We combine the information from the different bands to characterise the source in terms of contribution from the AGN and the host-galaxy interstellar medium. This approach allows us to provide a coherent picture of the role of the AGN and its impact, if any, on the star formation and molecular gas properties of the host galaxy. We focused our study on archival ALMA Cycle 1 observations, where the CO(2–1) emission line has been used as a tracer of the molecular gas. Using the 3DBAROLO code on ALMA data, we performed the modelling of the molecular gas kinematics traced by the CO(2–1) emission, finding a rotationally dominated pattern. The molecular gas mass of the host galaxy was estimated from the integrated CO(2–1) emission line obtained with APEX data, assuming an αCO conversion factor. Had we used the ALMA data, we would have underestimated the gas masses by a factor ∼3, given the filtering out of the large-scale emission in interferometric observations. We also performed a complete X-ray spectral analysis on archival observations, revealing a relatively faint and unobscured AGN. The AGN proved to be too faint to significantly affect the properties of the host galaxy, such as star formation activity and molecular gas kinematics and distribution.
The UV Luminosity Function of Protocluster Galaxies at z ∼ 4: The Bright-end Excess and the Enhanced Star Formation Rate Density

We report the rest-frame ultraviolet luminosity function of g-dropout galaxies in 177 protocluster candidates (PC UVLF) at z ∼ 4 selected in the Hyper Suprime-Cam Subaru Strategic Program. Comparing it with the UVLF of field galaxies at the same redshift, we find that the PC UVLF shows a significant excess toward the bright end. This excess cannot be explained by the contribution of only active galactic nuclei, and we also find that this excess is more significant in higher density regions. Assuming that all protocluster members are located on the star formation main sequence, the PC UVLF can be converted into a stellar mass function. Consequently, our protocluster members are inferred to have a 2.8 times more massive characteristic stellar mass than that of the field Lyman break galaxies at the same redshift. This study, for the first time, clearly shows that the enhancement in star formation or stellar mass in overdense regions can generally be seen as early as at z ∼ 4. We also estimate the star formation rate density (SFRD) in protocluster regions as ≃6%–20% of the cosmic SFRD, based on the measured PC UVLF after correction for the selection incompleteness in our protocluster sample. This high value suggests that protoclusters make a nonnegligible contribution to the cosmic SFRD at z ∼ 4, as previously suggested by simulations. Our results suggest that protoclusters are essential components for galaxy evolution at z ∼ 4.
Analysis of Quantitative Trait Loci for Protein and Lipid Contents in Soybean Seeds Using Recombinant Inbred Lines

Protein content in the seed is quantitatively inherited and controlled by polygene. The quality of seed protein content has been studied extensively, however, information on its quantity is still limited. In order to analyze the genetic basis of these traits, recombinant inbred lines (RILs) derived from a cross between Glycine max (L.) Merrill variety Misuzudaizu and variety Moshidou Gong 503 were planted in two environments and evaluated for seed protein content. The broad sense heritability of the traits ranged from 0.74 to 0.79 in our RIL population. Single-factor analysis of variance, interval mapping and composite interval mapping were used to detect significant associations between traits and genetic markers. A total of 10 QTLs, which were significant in at least one environment were identified. Each QTL explained the total phenotypic variation for protein content in the range from 3.4% to 29.7%. Among all the detected QTLs, three of them were detected in both environments. QTLs identified in this study were mapped in the soybean linkage map. The results obtained in our study may serve as a base for analyzing the genetic control of protein content and may eventually enable to change the seed constituents.
Strichartz estimates and local regularity for the elastic wave equation with singular potentials

We obtain weighted $L^2$ estimates for the elastic wave equation perturbed by singular potentials including the inverse-square potential. We then deduce the Strichartz estimates under the sole ellipticity condition for the Lame operator $-\Delta^\ast$. This improves upon the previous result in \cite{BFRVV} which relies on a stronger condition to guarantee the self-adjointness of $-\Delta^\ast$. Furthermore, by establishing local energy estimates for the elastic wave equation we also prove that the solution has local regularity.
CO-to-H2 conversion and spectral column density in molecular clouds: the variability of the XCO factor

Analyzing the Galactic plane CO survey with the Nobeyama 45-m telescope, we compared the spectral column density (SCD) of H$_2$ calculated for $^{12}$CO line using the current conversion factor $X_{\rm CO}$ to that for $^{13}$CO line under LTE in M16 and W43 regions. Here, SCD is defined by $dN_{\rm H_2}/dv$ with $N_{\rm H_2}$ and $v$ being the column density and radial velocity, respectively. It is found that the $X_{\rm CO}$ method significantly under-estimates the H$_2$ density in a cloud or region, where SCD exceeds a critical value ($ \sim 3\times 10^{21}\ [{\rm H_2 \ cm^{-2} \ (km \ s^{-1})^{-1}}]$), but over-estimates in lower SCD regions. We point out that the actual CO-to-H$_2$ conversion factor varies with the H$_2$ column density or with the CO-line intensity: It increases in the inner and opaque parts of molecular clouds, whereas it decreases in the low-density envelopes. However, in so far as the current $X_{\rm CO}$ is used combined with the integrated $^{12}$CO intensity averaged over an entire cloud, it yields a consistent value with that calculated using the $^{13}$CO intensity by LTE. Based on the analysis, we propose a new CO-to-\Htwo conversion relation, $N_{\rm H_2}^* = \int X_{\rm CO}^*(T_{\rm B}) T_{\rm B} dv$, where $X_{\rm CO}^*=(T_{\rm B}/T_{\rm B}^*)^\beta X_{\rm CO}$ is the modified spectral conversion factor as a function of the brightness temperature, $T_{\rm B}$, of the ${12}$CO ($J=1-0$) line, and $\beta\sim 1-2$ and $T_{\rm B}^*=12-16$ K are empirical constants obtained by fitting to the observed data. The formula corrects for the over/under estimation of the column density at low/high-CO line intensities, and is applicable to molecular clouds with $T_{\rm B} \ge 1$ K (rms noise in the data) from envelope to cores at sub-parsec scales (resolution). (Full resolution copy available at this http URL)
Errors in Leaf Area Measurement with an Automatic Area Meter due to Leaf Chlorophyll in Crop Plants

Abstract This study revealed that an area meter with a semi-automatic feeding mechanism underestimated the area of yellowish leaves and wavy leaves in crop plants. It is recommended that area measurements should be made using photocopied leaves and leaf segments. The degree of underestimation should be checked when chlorophyll meter (SPAD) values are less than 30.
Completing the 3CR Chandra Snapshot Survey: Extragalactic Radio Sources at High Redshift

We present the analysis of nine radio sources belonging to the Third Cambridge Revised catalog (3CR) observed with Chandra during Cycle 20 in the redshift range between 1.5 and 2.5. This study completes the 3CR Chandra Snapshot Survey thus guaranteeing the X-ray coverage of all 3CR sources identified to date. This sample lists two compact steep spectrum sources, four radio galaxies, and three quasars. We detected X-ray emission from all nuclei, with the only exception of 3C 326.1 and 3C 454.1 and from radio lobes in six out of nine sources at a level of confidence larger than ∼5σ. We measured X-ray fluxes and luminosities for all nuclei and lobes in the soft (0.5–1 keV), medium (1–2 keV), and hard (2–7 keV) X-ray bands. Since the discovered X-ray extended emission is spatially coincident with the radio structure in all cases, its origin could be due to inverse Compton (IC) scattering of the cosmic microwave background (CMB) occurring in radio lobes.
On the Remarkable Superconductivity of FeSe and Its Close Cousins

Emergent electronic phenomena in iron-based superconductors have been at the forefront of condensed matter physics for more than a decade. Much has been learned about the origins and intertwined roles of ordered phases, including nematicity, magnetism, and superconductivity, in this fascinating class of materials. In recent years, focus has been centered on the peculiar and highly unusual properties of FeSe and its close cousins. This family of materials has attracted considerable attention due to the discovery of unexpected superconducting gap structures, a wide range of superconducting critical temperatures, and evidence for nontrivial band topology, including associated spin-helical surface states and vortex-induced Majorana bound states. Here, we review superconductivity in iron chalcogenide superconductors, including bulk FeSe, doped bulk FeSe, FeTe1−xSex, intercalated FeSe materials, and monolayer FeSe and FeTe1−xSex on SrTiO3. We focus on the superconducting properties, including a survey of the relevant experimental studies, and a discussion of the different proposed theoretical pairing scenarios. In the last part of the paper, we review the growing recent evidence for nontrivial topological effects in FeSe-related materials, focusing again on interesting implications for superconductivity.
The benefits of peer transparency in safe workplace operation post pandemic lockdown

The benefits of different levels of engagement with test, trace and isolate procedures are investigated for a pandemic in which there is little population immunity, in terms of productivity and public health. Simple mathematical modelling is used in the context of a single, relatively closed workplace such as a factory or back-office where, in normal operation, each worker has lengthy interactions with a fixed set of colleagues. A discrete-time SEIR model on a fixed interaction graph is simulated with parameters that are motivated by the recent COVID-19 pandemic in the UK during a post-peak phase, including a small risk of viral infection from outside the working environment. Two kinds of worker are assumed, transparents who regularly test, share their results with colleagues and isolate as soon as a contact tests positive for the disease, and opaques who do none of these. Moreover, the simulations are constructed as a ‘playable model’ in which the transparency level, disease parameters and mean interaction degree can be varied by the user. The model is also analysed in the continuum limit. All simulations point to the double benefit of transparency in both maximizing productivity and minimizing overall infection rates. Based on these findings, public policy implications are discussed for how to incentivise this mutually beneficial behaviour in different kinds of workplace, and simple recommendations are made.