Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Sparse Voxel Fields

Photo-realistic free-viewpoint rendering of real-world scenes using classical computer graphics techniques is challenging, because it requires the difficult step of capturing detailed appearance and geometry models. Recent studies have demonstrated promising results by learning scene representations that implicitly encode both geometry and appearance without 3D supervision. However, existing approaches in practice often show blurry renderings caused by the limited network capacity or the difficulty in finding accurate intersections of camera rays with the scene geometry. Synthesizing high-resolution imagery from these representations often requires time-consuming optical ray marching. In this work, we introduce Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering. NSVF defines a set of voxel-bounded implicit fields organized in a sparse voxel octree to model local properties in each cell. We progressively learn the underlying voxel structures with a diffentiable ray-marching operation from only a set of posed RGB images. With the sparse voxel octree structure, rendering novel views can be accelerated by skipping the voxels containing no relevant scene content. Our method is over 10 times faster than the state-of-the-art (namely, NeRF) at inference time while achieving higher quality results. Furthermore, by utilizing an explicit sparse voxel representation, our method can easily be applied to scene editing and scene composition. We also demonstrate several challenging tasks, including multi-scene learning, free-viewpoint rendering of a moving human, and large-scale scene rendering.
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation

Scalable Vector Graphics (SVG) are ubiquitous in modern 2D interfaces due to their ability to scale to different resolutions. However, despite the success of deep learning-based models applied to rasterized images, the problem of vector graphics representation learning and generation remains largely unexplored. In this work, we propose a novel hierarchical generative network, called DeepSVG, for complex SVG icons generation and interpolation. Our architecture effectively disentangles high-level shapes from the low-level commands that encode the shape itself. The network directly predicts a set of shapes in a non-autoregressive fashion. We introduce the task of complex SVG icons generation by releasing a new large-scale dataset along with an open-source library for SVG manipulation. We demonstrate that our network learns to accurately reconstruct diverse vector graphics, and can serve as a powerful animation tool by performing interpolations and other latent space operations. Our code is available at this https URL.
On the Connection Between Stirling Numbers and Bessel Numbers

We present new proofs for some summation identities involving Stirling numbers of both first and second kind. The two main identities show a connection between Stirling numbers and Bessel numbers. Our method is based on solving a particular recurrence relation in two different ways and comparing the coefficients in the resulting polynomial expressions. We also briefly discuss a probabilistic setting where this recurrence relation occurs.
The engulfing property for sections of convex functions in the Heisenberg group and the associated quasi--metric

In this paper we investigate the property of engulfing for $H$-convex functions defined on the Heisenberg group ${\mathbb{H}}^n$. Starting from the horizontal sections introduced by Capogna and Maldonado, we consider a new notion of section, called ${\mathbb{H}}^n$-section, as well as a new condition of engulfing associated to the ${\mathbb{H}}^n$-sections, for an $H$-convex function defined in ${\mathbb{H}}^n. $ These sections, that arise as suitable unions of horizontal sections, are dimensionally larger; as a matter of fact, the ${\mathbb{H}}^n$-sections, with their engulfing property, will lead to the definition of a pseudo-metric in ${\mathbb{H}}^n$ in a way similar to Aimar, Forzani and Toledano in the Euclidean case. A key role is played by the property of round $H$-sections for an $H$-convex function, and by its connection with the engulfing properties.
LEGO – II. A 3 mm molecular line study covering 100 pc of one of the most actively star-forming portions within the Milky Way disc

The current generation of (sub)mm-telescopes has allowed molecular line emission to become a major tool for studying the physical, kinematic, and chemical properties of extragalactic systems, yet exploiting these observations requires a detailed understanding of where emission lines originate within the Milky Way. In this paper, we present 60 arcsec (∼3 pc) resolution observations of many 3 mm band molecular lines across a large map of the W49 massive star-forming region (∼100 pc × 100 pc at 11 kpc), which were taken as part of the ‘LEGO’ IRAM-30m large project. We find that the spatial extent or brightness of the molecular line transitions are not well correlated with their critical densities, highlighting abundance and optical depth must be considered when estimating line emission characteristics. We explore how the total emission and emission efficiency (i.e. line brightness per H2 column density) of the line emission vary as a function of molecular hydrogen column density and dust temperature. We find that there is not a single region of this parameter space responsible for the brightest and most efficiently emitting gas for all species. For example, we find that the HCN transition shows high emission efficiency at high column density (1022 cm−2) and moderate temperatures (35 K), whilst e.g. N2H+ emits most efficiently towards lower temperatures (1022 cm−2; <20 K). We determine $X_{\mathrm{CO} (1-0)} \sim 0.3 \times 10^{20} \, \mathrm{cm^{-2}\, (K\, km\, s^{-1})^{-1}}$, and $\alpha _{\mathrm{HCN} (1-0)} \sim 30\, \mathrm{M_\odot \, (K\, km\, s^{-1}\, pc^2)^{-1}}$, which both differ significantly from the commonly adopted values. In all, these results suggest caution should be taken when interpreting molecular line emission.
Atmospheric change alters performance of an invasive forest insect

Atmospheric change and species invasions are arguably two of the most important factors affecting the long‐term sustainability of natural ecosystems. We examined the independent and interactive effects of atmospheric carbon dioxide (CO2) and tropospheric ozone (O3) on the foliar quality of two host species and performance of an invasive folivorous insect. Trembling aspen (Populus tremuloides) and paper birch (Betula papyrifera) were grown at the Aspen FACE research site in northern Wisconsin, USA, under all combinations of ambient and elevated CO2 and O3. We measured the effects of elevated CO2 and O3 on aspen and birch phytochemistry and on the survivorship, development time, growth, and fecundity of the gypsy moth (Lymantria dispar). Elevated CO2 had little effect on, whereas elevated O3 altered, the composite phytochemical profiles of aspen and birch. Nutritional quality in aspen and birch leaves was marginally affected by elevated CO2 and reduced by elevated O3. Both gases increased concentrations of phenolic and structural compounds in aspen and birch. Elevated CO2 offset reduced foliar quality under elevated O3, but only in aspen, and to a greater extent later than earlier in spring. Elevated CO2 generally had beneficial effects on, while elevated O3 detrimentally affected, gypsy moth performance. Elevated CO2 ameliorated most of the reductions in gypsy moth performance under elevated O3. Our findings suggest that atmospheric change can alter foliar quality in gypsy moth hosts sufficiently to influence gypsy moth performance, but that these responses will depend on interactions among CO2, O3, and tree species. Our findings also contrast with those of earlier studies at Aspen FACE, indicating that foliar quality responses to environmental change are likely influenced by tree stand age and longevity of exposure to pollutants to the extent that they affect plant‐herbivore interactions differently over decadal time spans.
Radio Activity of Supermassive Black Holes with Extremely High Accretion Rates

Radio emission from the high- and super-Eddington accreting active galactic nuclei (AGNs) has various origins: a persistent jet, the magnetized corona, and the wind-like outflows. It is still unclear which is the leading mechanism responsible for the observed radio emission and how the radio emission is related to other characteristic parameters such as the Eddington ratio and black hole mass. In this paper, we present the 5 GHz Very Large Array (VLA) observational results of a sample of 25 extremely high Eddington accreting supermassive black holes (EESBHs, the Eddington ratio λEdd close to or above 1) in narrow-line Seyfert 1 galaxies, among which 22 sources are detected. Most of the EESBHs show a compact radio structure from a few hundred parsecs to 1 kpc scale. We estimated the lowest star formation rate surface density required for producing the observed radio emission and found that it is higher than the largest value previously detected in circumnuclear starburst galaxies, implying that the radio emission is from the AGN activity. Along with a comparison sample, we find an overall inverse –λEdd correlation ranging from sub- to super-Eddington ratios. The high-Eddington and mildly super-Eddington AGNs (−0.5 < log ) have a radio-to-X-ray luminosity ratio LR/LX ∼ 10−5–10−4 and a steep radio spectrum, supporting that the radio emission is from transient ejecta (outflows) of corona; however, the jet contribution cannot be entirely ruled out. Our highly super-Eddington sources (log ) have a flatter radio spectrum, along with its low radio luminosity: ; their radio emission is likely dominated by a magnetized corona, and a radiation-pressure-caused jet is also proposed in this paper.
Student interpretations of axioms in planar geometry

ABSTRACT This study investigates students’ development of metamathematical understanding of axioms. Based on four semesters of experiments teaching neutral, axiomatic geometry, often through guided reinvention, I identify five categories of student interpretations of their axiomatizing activity. Similar to previously observed patterns in student interpretations of definitions and proofs, the most problematic student interpretations of axioms resulted from focusing exclusively on the referents of mathematical theory, which precluded generalization through abstraction. As a result of engaging in axiomatizing, study participants constructed several quite sophisticated views of axiomatizing compatible with various aspects of modern mathematical practice—what I call stipulated and formal interpretations.
DYNAMICS OF A SUBTIDAL SEAGRASS LANDSCAPE: SEASONAL AND ANNUAL CHANGE IN RELATION TO WATER DEPTH

The spatial heterogeneity of a subtidal marine landscape and the areal extent of both monospecific and mixed patches of seagrass species were studied in Tampa Bay, Florida, USA. Specifically, we examined the temporal dynamics of seagrass distribution and its relationship to water depth and the serial replacement of one species by another. The ∼5-ha landscape was mapped at 1-m intervals in the spring and fall of 1994 and 1995. The landscape consisted of monospecific and mixed patches of seagrass (47%) and bare sediment (53%). Halodule wrightii was the most abundant seagrass (∼74%), while Thalassia testudinum was second most common (20%), and mixed patches of H. wrightii and T. testudinum composed the remaining 6%. There was an overall increase in seagrass of 14% from spring 1994 to fall 1995. The majority of change occurred along the margins of existing seagrass patches (i.e., H. wrightii invading bare sediment). Typically, “new” patches were the result of the transition of one seagrass species to another .
Accelerating Deep Learning Applications in Space

Computing at the edge offers intriguing possibilities for the development of autonomy and artificial intelligence. The advancements in autonomous technologies and the resurgence of computer vision have led to a rise in demand for fast and reliable deep learning applications. In recent years, the industry has introduced devices with impressive processing power to perform various object detection tasks. However, with real-time detection, devices are constrained in memory, computational capacity, and power, which may compromise the overall performance. This could be solved either by optimizing the object detector or modifying the images. In this paper, we investigate the performance of CNN-based object detectors on constrained devices when applying different image compression techniques. We examine the capabilities of a NVIDIA Jetson Nano; a low-power, high-performance computer, with an integrated GPU, small enough to fit on-board a CubeSat. We take a closer look at the Single Shot MultiBox Detector (SSD) and Region-based Fully Convolutional Network (R-FCN) that are pre-trained on DOTA - a Large Scale Dataset for Object Detection in Aerial Images. The performance is measured in terms of inference time, memory consumption, and accuracy. By applying image compression techniques, we are able to optimize performance. The two techniques applied, lossless compression and image scaling, improves speed and memory consumption with no or little change in accuracy. The image scaling technique achieves a 100% runnable dataset and we suggest combining both techniques in order to optimize the speed/memory/accuracy trade-off.
Robotic reverberation mapping of the broad-line radio galaxy 3C 120

MH and ERC acknowledge support from the South African National Research Foundation. KH acknowledges support from STFC grant ST/R000824/1. MH and SMC were supported in part by the Space Telescope Science Institute (STScI) Director’s Discretionary Research Fund (DDRF). Research by AJB was supported by NSF grants AST1412693 and AST-1907290. Research by DJS is supported by NSF grants AST-1821967, 1821987, 1813708, 1813466, and 1908972. TT acknowledges support by the NSF through grants AST-1412315 and AST-1907208 and by the Packard Foundation through a Packard Fellowship.
Finite-temperature density-matrix renormalization group method for electron-phonon systems: Thermodynamics and Holstein-polaron spectral functions

This work introduces an efficient numerical tool to calculate thermodynamic and spectral properties for electron-phonon problems at finite temperature in one dimension. Combining a finite-temperature density-matrix renormalization group method with local basis optimization allows one to access system sizes out of reach for other wave-function based approaches. The authors demonstrate the method's capabilities by computing several spectral functions of the Holstein polaron problem. The image shows the electron spectral function for two different system sizes ($L$=21, 101).
SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.
Inferring incubation period distribution of COVID-19 based on SEAIR Model

To reduce the biases of traditional survey-based methods, this paper proposes an epidemic model-based approach to inference the incubation period distribution of COVID-19 utilizing the publicly reported confirmed case number. We construct an epidemic model, namely SEAIR, and take advantage of the dynamic transmission process depicted by SEAIR to estimate the onset probability in each day of exposed individuals in eight impacted countries. Based on these estimations, the general incubation probability distribution of COVID-19 has been revealed. The proposed method can avoid several biases of traditional survey-based methods. However, due to the mathematical-model-based nature of this method, the inference results are somewhat sensitive to the setting of parameters. Therefore, this method should be practiced reasonably on the basis of a certain understanding of the studied epidemic.
Dwarfs in the Milky Way halo outer rim: first infall or backsplash satellites?

Leo T is a gas-rich dwarf located at |$414\, {\rm kpc}$| (1.4R_vir) distance from the Milky Way (MW) and it is currently assumed to be on its first approach. Here, we present an analysis of orbits calculated backwards in time for the dwarf with our new code delorean, exploring a range of systematic uncertainties, e.g. MW virial mass and accretion, M31 potential, and cosmic expansion. We discover that orbits with tangential velocities in the Galactic standard-of-rest frame lower than |$| \vec{u}_{\rm t}^{\rm GSR}| \le 63^{+47}_{-39}\, {\rm km}\, {\rm s}^{\rm -1}$| result in backsplash solutions, i.e. orbits that entered and left the MW dark matter halo in the past, and that velocities above |$| \vec{u}_{\rm t}^{\rm GSR}| \ge 21^{+33}_{-21}\, {\rm km}\, {\rm s}^{\rm -1}$| result in wide-orbit backsplash solutions with a minimum pericentre range of |$D_{\rm min} \ge 38^{+26}_{-16}\, {\rm kpc}$|⁠, which would allow this satellite to survive gas stripping and tidal disruption. Moreover, new proper motion estimates overlap with our orbital solution regions. We applied our method to other distant MW satellites, finding a range of gas stripped backsplash solutions for the gasless Cetus and Eridanus II, providing a possible explanation for their lack of cold gas, while only first infall solutions are found for the H i-rich Phoenix I. We also find that the cosmic expansion can delay their first pericentre passage when compared to the non-expanding scenario. This study explores the provenance of these distant dwarfs and provides constraints on the environmental and internal processes that shaped their evolution and current properties.
Analysis of the Genetic Structure of Sclerotinia sclerotiorum (Lib.) de Bary Populations from Different Regions and Host Plants by Random Amplified Polymorphic DNA Markers

: The genetic diversity and genetic structure of a population of isolates of Sclerotinia sclerotiorum (Lib.) de Bary from different regions and host plants were investigated using the random amplified polymorphic DNA (RAPD) method with 20 random decamer primer pairs in order to provide some information on the phylogenetic taxa and breeding for resistance to sclerotinia stem rot. A minimum of three and a maximum of 15 unambiguously amplified bands were generated, furnishing a total of 170 bands ranging in size from 100 to 3 200 bp, corresponding to an average of 8.5 bands per primer pair. One hundred and four of these 170 bands (61.2%) were polymorphic, the percentage of polymorphic bands for each primer pair ranging from 0.0% to 86.7%. The genetic relationships among the isolates, based on the results of RAPD analysis, were examined. The genetic similarity of all selected isolates was quite high. At the species level, the genetic diversity estimated by Nei's gene diversity (h) was 0.197 and Shannon's index of diversity (I) was 0.300. The unweighted pair-group mean analysis (UPGMA) cluster analysis showed that most isolates from the same regions were grouped in the same cluster or a close cluster. The population of isolates from Hefei (Anhui Province, China) was more uniform and relatively distant to other populations. The Canadian population collected from carrot (Daucus carota var. sativa DC.) was relatively close to the Polish population collected from oilseed rape (Brassica napus L.) plants. There was no relationship between isolates from the same host plants. An analysis of molecular variance (AMOVA) revealed that the percentage of variance attributable to variation among and within populations was 50.62% and 49.38%, respectively. When accessions from China, Europe, and Canada were treated as three separate groups, the variance components among groups, among populations within groups, and within populations were −0.96%, 51.48%, and 49.47%, respectively. The genetic differentiations among and within populations were highly significant (P < 0.001). Similarly, the coefficient of gene differentiation (Gst) in total populations calculated by population genetic analysis was 0.229 4, which indicated that the genetic variation among populations was 22.94%. The gene flow (Nm) was 1.68, which indicated that the gene permutation and interaction among populations was relatively high. (Managing editor: Wei WANG)
The asymptotic tails of limit distributions of continuous-time Markov chains

This paper investigates tail asymptotics of stationary distributions and quasi-stationary distributions (QSDs) of continuous-time Markov chains on subsets of the non-negative integers. Based on the so-called flux-balance equation, we establish identities for stationary measures and QSDs, which we use to derive tail asymptotics. In particular, for continuous-time Markov chains with asymptotic power law transition rates, tail asymptotics for stationary distributions and QSDs are classified into three types using three easily computable parameters: (i) super-exponential distributions, (ii) exponential-tailed distributions, and (iii) sub-exponential distributions. Our approach to establish tail asymptotics of stationary distributions is different from the classical semimartingale approach, and we do not impose ergodicity or moment bound conditions. In particular, the results also hold for explosive Markov chains, for which multiple stationary distributions may exist. Furthermore, our results on tail asymptotics of QSDs seem new. We apply our results to biochemical reaction networks, a general single-cell stochastic gene expression model, an extended class of branching processes, and stochastic population processes with bursty reproduction, none of which are birth–death processes. Our approach, together with the identities, easily extends to discrete-time Markov chains.
MetAL: Active Semi-Supervised Learning on Graphs via Meta Learning

The objective of active learning (AL) is to train classification models with less number of labeled instances by selecting only the most informative instances for labeling. The AL algorithms designed for other data types such as images and text do not perform well on graph-structured data. Although a few heuristics-based AL algorithms have been proposed for graphs, a principled approach is lacking. In this paper, we propose MetAL, an AL approach that selects unlabeled instances that directly improve the future performance of a classification model. For a semi-supervised learning problem, we formulate the AL task as a bilevel optimization problem. Based on recent work in meta-learning, we use the meta-gradients to approximate the impact of retraining the model with any unlabeled instance on the model performance. Using multiple graph datasets belonging to different domains, we demonstrate that MetAL efficiently outperforms existing state-of-the-art AL algorithms.