Miracle-dz/UniFAR-FTUs huggingface dataset:
The rich lack close neighbours: the dependence of blue-straggler fraction on metallicity

Blue straggler stars (BSS) have been identified in star clusters and in field populations in our own Milky Way galaxy and in its satellite galaxies. They manifest as stars bluer and more luminous than the dominant old population, and usually have a spatial distribution that follows the old population. Their progenitors are likely to have been close binaries. We investigate trends of the BSS population in dwarf spheroidal galaxies (dSph) and in the bulge of the Milky Way and find an anti-correlation between the relative frequency of BSS and the metallicity of the parent population. The rate of occurrence of BSS in the metal-poor dwarf galaxies is approximately twice that found in the solar-metallicity bulge population. This trend of decreasing relative population of BSS with increasing metallicity mirrors that found for the close-binary fraction in the field population of the Milky Way. We argue that the dominant mode of BSS formation in low-density environments is likely to be mass transfer in close-binary systems. It then follows that the similarity between the trends for BSS in the dSph and field stars in our Galaxy supports the proposal that the small-scale fragmentation during star formation is driven by the same dominant physical process, despite the diversity in environments, plausibly gravitational instability of proto-stellar discs.
Shared Cross-Modal Trajectory Prediction for Autonomous Driving

Predicting future trajectories of traffic agents in highly interactive environments is an essential and challenging problem for the safe operation of autonomous driving systems. On the basis of the fact that self-driving vehicles are equipped with various types of sensors (e.g., LiDAR scanner, RGB camera, radar, etc.), we propose a Cross-Modal Embedding framework that aims to benefit from the use of multiple input modalities. At training time, our model learns to embed a set of complementary features in a shared latent space by jointly optimizing the objective functions across different types of input data. At test time, a single input modality (e.g., LiDAR data) is required to generate predictions from the input perspective (i.e., in the LiDAR space), while taking advantages from the model trained with multiple sensor modalities. An extensive evaluation is con-ducted to show the efficacy of the proposed framework using two benchmark driving datasets.
Evading Deepfake-Image Detectors with White- and Black-Box Attacks

It is now possible to synthesize highly realistic images of people who do not exist. Such content has, for example, been implicated in the creation of fraudulent socialmedia profiles responsible for dis-information campaigns. Significant efforts are, therefore, being deployed to detect synthetically-generated content. One popular forensic approach trains a neural network to distinguish real from synthetic content.We show that such forensic classifiers are vulnerable to a range of attacks that reduce the classifier to near- 0% accuracy. We develop five attack case studies on a state- of-the-art classifier that achieves an area under the ROC curve (AUC) of 0.95 on almost all existing image generators, when only trained on one generator. With full access to the classifier, we can flip the lowest bit of each pixel in an image to reduce the classifier’s AUC to 0.0005; perturb 1% of the image area to reduce the classifier’s AUC to 0.08; or add a single noise pattern in the synthesizer’s latent space to reduce the classifier’s AUC to 0.17. We also develop a black-box attack that, with no access to the target classifier, reduces the AUC to 0.22. These attacks reveal significant vulnerabilities of certain image-forensic classifiers.
Factorized Higher-Order CNNs With an Application to Spatio-Temporal Emotion Estimation

Training deep neural networks with spatio-temporal (i.e., 3D) or multidimensional convolutions of higher-order is computationally challenging due to millions of unknown parameters across dozens of layers. To alleviate this, one approach is to apply low-rank tensor decompositions to convolution kernels in order to compress the network and reduce its number of parameters. Alternatively, new convolutional blocks, such as MobileNet, can be directly designed for efficiency. In this paper, we unify these two approaches by proposing a tensor factorization framework for efficient multidimensional (separable) convolutions of higher-order. Interestingly, the proposed framework enables a novel higher-order transduction, allowing to train a network on a given domain (e.g., 2D images or N-dimensional data in general) and using transduction to generalize to higher-order data such as videos (or (N+K)--dimensional data in general), capturing for instance temporal dynamics while preserving the learnt spatial information. We apply the proposed methodology, coined CP-Higher-Order Convolution (HO-CPConv), to spatio-temporal facial emotion analysis. Most existing facial affect models focus on static imagery and discard all temporal information. This is due to the above-mentioned burden of training 3D convolutional nets and the lack of large bodies of video data annotated by experts. We address both issues with our proposed framework. Initial training is first done on static imagery before using transduction to generalize to the temporal domain. We demonstrate superior performance on three challenging large scale affect estimation datasets, AffectNet, SEWA, and AFEW-VA.
Anisotropic and crystalline mean curvature flow of mean-convex sets

We consider a variational scheme for the anisotropic (including crystalline) mean curvature flow of sets with strictly positive anisotropic mean curvature. We show that such condition is preserved by the scheme, and we prove the strict convergence in BV of the time-integrated perimeters of the approximating evolutions, extending a recent result of De Philippis and Laux to the anisotropic setting. We also prove uniqueness of the flat flow obtained in the limit.
EPOS: Estimating 6D Pose of Objects With Symmetries

We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. The method is applicable to a broad range of objects, including challenging ones with global or partial symmetries. An object is represented by compact surface fragments which allow handling symmetries in a systematic manner. Correspondences between densely sampled pixels and the fragments are predicted using an encoder-decoder network. At each pixel, the network predicts: (i) the probability of each object's presence, (ii) the probability of the fragments given the object's presence, and (iii) the precise 3D location on each fragment. A data-dependent number of corresponding 3D locations is selected per pixel, and poses of possibly multiple object instances are estimated using a robust and efficient variant of the PnP-RANSAC algorithm. In the BOP Challenge 2019, the method outperforms all RGB and most RGB-D and D methods on the T-LESS and LM-O datasets. On the YCB-V dataset, it is superior to all competitors, with a large margin over the second-best RGB method. Source code is at: cmp.felk. cvut.cz/epos.
Hard X-ray properties of radio-selected blazars

Context. Hard X-ray properties of beamed active galactic nuclei have been published in the 105-month Swift/BAT catalog, but there have not been any studies carried out so far on a well-defined, radio-selected sample of low-peaked blazars in the hard X-ray band. Aims. Using the statistically complete MOJAVE-1 sample, we aim to determine the hard X-ray properties of radio-selected blazars, including the enigmatic group of gamma-ray-faint blazars. Additionally, we aim to determine the contribution of radio-selected low-peaked blazars to the diffuse cosmic X-ray background (CXB). Methods. We determined photon indices, fluxes, and luminosities in the range of 20 keV–100 keV of the X-ray spectra of blazars and other extragalactic jets from the MOJAVE-1 sample, derived from the 105-month Swift/BAT survey. We calculated log N–log S distributions and determined the luminosity functions. Results. The majority of the MOJAVE-1 blazars are found to be hard X-ray emitters albeit many at low count rates. The log N–log S distribution for the hard X-ray emission of radio-selected blazars is clearly non-Euclidean, in contrast to the radio flux density distribution. Approximately 0.2% of the CXB in the 20 keV–100 keV band can be resolved into MOJAVE-1 blazars. Conclusions. The peculiar log N–log S distribution disparity might be attributed to different evolutionary paths in the X-ray and radio bands, as tested by luminosity-function modeling. X-ray variability can be ruled out as the dominant contributor. Low-peaked blazars constitute an intrinsically different source population in terms of CXB contribution compared to similar studies of X-ray-selected blazars. The hard X-ray flux and spectral index can serve as a good proxy for the gamma-ray detection probability of individual sources. Future observations combining deep X-ray survey, for example, with eROSITA, and targeted gamma-ray observations with CTA can benefit strongly from the tight connection between these high-energy bands for the different blazar sub-classes.
Ferromagnetism of LaCoO$_3$ films

We study ferromagnetic ordering and microscopic inhomogeneity in tensile strained LaCoO_33 using numerical simulations. We argue that both phenomena originate from effective superexchange interactions between atoms in the high-spin (HS) state mediated by the intermediate-spin excitations. We derive a model of the HS excitation as a bare atomic state dressed by electron and electron-hole fluctuations on the neighbor atoms. We construct a series of approximations to account for electron correlation effects responsible for HS fluctuations and magnetic exchange. The obtained amplitudes and directional dependence of magnetic couplings between the “dressed” HS states show a qualitative agreement with experimental observations and provide a new physical picture of LaCoO_33 films.
A research on recognition of oral health care among some high school students

교신저자:류경자 우) 300-802 대전광역시 동구 가양2동 77-7 대전보건대학 치위생과 전화 : 042-670-9197 E-mail : ryukj@hit.ac. kr 접수일-2010년 8월 5일 수정일-2010년 10월 14일 게재확정일-2010년 10월 23일 ABSTRACT Objectives : This study aimed to research subjective recognition of oral health, oral disease prevention, and knowledge of oral health among high school students to provide basic data necessary to develop an oral health education program for helping them prevent oral diseases and improve oral health in the future. Methods : Respondents’ general characteristics and knowledge of oral health were estimated at frequency and percentage, and chi-square () test was carried out to make a comparison for subjective recognition of oral health and oral disease prevention by general characteristics. ANOVA was used to get scores concerning knowledge of oral health care by general characteristics, and Pearson correlation was used to analyze correlation between recognition of oral health, oral disease prevention, and knowledge of oral health. Results : The findings of the study were as follows: 1. The percentage of those who had visited a dental clinic (hospital) within the previous one year (p<0.01) and had a regular oral examination (p<0.05) was higher in female students than in male ones, and percentage of those who had experienced oral health education (p<0.01) and who had regular oral examination (p<0.001) was highest in 11th graders. The percentage of those who used oral health products (p<0.01) and had pit and fissure sealing (p<0.01) was significantly higher among low‐grade students, and the percentage of those who had scaling (p<0.01) was significantly higher among high‐grade students. 2. Knowledge of oral health management was higher in female students (12.6) than in male ones (11.2) (p<0.001). Conclusions : A systematic oral health education program is necessary to help high school students improve recognition, prevention, and knowledge concerning oral health care, and continuous attention and efforts must be given to activate continuous oral health care implemented at the elementary level by inducing secondary school students to go on with it. (J Korean Soc Dent Hygiene 2010;10(5):925-933)
A parameter survey of Sgr A* radiative models from GRMHD simulations with self-consistent electron heating

The Galactic centre black hole candidate Sgr A* is the best target for studies of low-luminosity accretion physics, including with near-infrared (NIR) and submillimetre wavelength long baseline interferometry experiments. Here, we compare images and spectra generated from a parameter survey of general relativistic MHD simulations to a set of radio to NIR observations of Sgr A*. Our models span the limits of weak and strong magnetization and use a range of sub-grid prescriptions for electron heating. We find two classes of scenarios can explain the broad shape of the submillimetre spectral peak and the highly variable NIR flaring emission. Weakly magnetized ‘disc-jet’ models where most of the emission is produced near the jet wall, consistent with past work, as well as strongly magnetized (magnetically arrested disc) models where hot electrons are present everywhere. Disc-jet models are strongly depolarized at submillimetre wavelengths as a result of strong Faraday rotation, inconsistent with observations of Sgr A*. We instead favour the strongly magnetized models, which provide a good description of the median and highly variable linear polarization signal. The same models can also explain the observed mean Faraday rotation measure and potentially the polarization signals seen recently in Sgr A* NIR flares.
Pose-Guided Visible Part Matching for Occluded Person ReID

Occluded person re-identification is a challenging task as the appearance varies substantially with various obstacles, especially in the crowd scenario. To address this issue, we propose a Pose-guided Visible Part Matching (PVPM) method that jointly learns the discriminative features with pose-guided attention and self-mines the part visibility in an end-to-end framework. Specifically, the proposed PVPM includes two key components: 1) pose-guided attention (PGA) method for part feature pooling that exploits more discriminative local features; 2) pose-guided visibility predictor (PVP) that estimates whether a part suffers the occlusion or not. As there are no ground truth training annotations for the occluded part, we turn to utilize the characteristic of part correspondence in positive pairs and self-mining the correspondence scores via graph matching. The generated correspondence scores are then utilized as pseudo-labels for visibility predictor (PVP). Experimental results on three reported occluded benchmarks show that the proposed method achieves competitive performance to state-of-the-art methods. The source codes are available at https://github.com/hh23333/PVPM
More Grounded Image Captioning by Distilling Image-Text Matching Model

Visual attention not only improves the performance of image captioners, but also serves as a visual interpretation to qualitatively measure the caption rationality and model transparency. Specifically, we expect that a captioner can fix its attentive gaze on the correct objects while generating the corresponding words. This ability is also known as grounded image captioning. However, the grounding accuracy of existing captioners is far from satisfactory.To improve the grounding accuracy while retaining the captioning quality, it is expensive to collect the word-region alignment as strong supervision.To this end, we propose a Part-of-Speech (POS) enhanced image-text matching model (SCAN): POS-SCAN, as the effective knowledge distillation for more grounded image captioning. The benefits are two-fold: 1) given a sentence and an image, POS-SCAN can ground the objects more accurately than SCAN; 2) POS-SCAN serves as a word-region alignment regularization for the captioner's visual attention module. By showing benchmark experimental results, we demonstrate that conventional image captioners equipped with POS-SCAN can significantly improve the grounding accuracy without strong supervision. Last but not the least, we explore the indispensable Self-Critical Sequence Training (SCST) in the context of grounded image captioning and show that the image-text matching score can serve as a reward for more grounded captioning.
Application of artiﬁcial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface di ﬀ usion

Kinetic Monte Carlo (KMC) is a powerful method for simulation of di ﬀ usion processes in various systems. The accuracy of the method, however, relies on the extent of details used for the parameterization of the model. Migration barriers are often used to describe di ﬀ usion on atomic scale, but the full set of these barriers may become easily unmanageable in materials with increased chemical complexity or a large number of defects. This work is a feasibility study for applying a machine learning approach for Cu surface di ﬀ usion. We train an artiﬁcial neural network on a subset of the large set of 2 26 barriers needed to correctly describe the surface di ﬀ usion in Cu. Our KMC simulations using the obtained barrier predictor show su ﬃ cient accuracy in modelling processes on the low-index surfaces and display the correct thermodynamical stability of these surfaces.
Time-Varying Volatility in Bitcoin Market and Information Flow at Minute-Level Frequency

In this article, we analyze the time series of minute price returns on the Bitcoin market through the statistical models of the generalized autoregressive conditional heteroscedasticity (GARCH) family. We combine an approach that uses historical values of returns and their volatilities—GARCH family of models, with a so-called Mixture of Distribution Hypothesis, which states that the dynamics of price returns are governed by the information flow about the market. Using time series of Bitcoin-related tweets, the Bitcoin trade volume, and the Bitcoin bid–ask spread, as external information signals, we test for improvement in volatility prediction of several GARCH model variants on a minute-level Bitcoin price time series. Statistical tests show that GARCH(1,1) and cGARCH(1,1) react the best to the addition of external signals to model the volatility process on out-of-sample data.
StyleRig: Rigging StyleGAN for 3D Control Over Portrait Images

StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face parameters that are interpretable in 3D, such as face pose, expressions, and scene illumination. Three-dimensional morphable face models (3DMMs) on the other hand offer control over the semantic parameters, but lack photorealism when rendered and only model the face interior, not other parts of a portrait image (hair, mouth interior, background). We present the first method to provide a face rig-like control over a pretrained and fixed StyleGAN via a 3DMM. A new rigging network, \textit{RigNet} is trained between the 3DMM's semantic parameters and StyleGAN's input. The network is trained in a self-supervised manner, without the need for manual annotations. At test time, our method generates portrait images with the photorealism of StyleGAN and provides explicit control over the 3D semantic parameters of the face.
Conditional Channel Gated Networks for Task-Aware Continual Learning

Convolutional Neural Networks experience catastrophic forgetting when optimized on a sequence of learning problems: as they meet the objective of the current training examples, their performance on previous tasks drops drastically. In this work, we introduce a novel framework to tackle this problem with conditional computation. We equip each convolutional layer with task-specific gating modules, selecting which filters to apply on the given input. This way, we achieve two appealing properties. Firstly, the execution patterns of the gates allow to identify and protect important filters, ensuring no loss in the performance of the model for previously learned tasks. Secondly, by using a sparsity objective, we can promote the selection of a limited set of kernels, allowing to retain sufficient model capacity to digest new tasks. Existing solutions require, at test time, awareness of the task to which each example belongs to. This knowledge, however, may not be available in many practical scenarios. Therefore, we additionally introduce a task classifier that predicts the task label of each example, to deal with settings in which a task oracle is not available. We validate our proposal on four continual learning datasets. Results show that our model consistently outperforms existing methods both in the presence and the absence of a task oracle. Notably, on Split SVHN and Imagenet-50 datasets, our model yields up to 23.98% and 17.42% improvement in accuracy w.r. t. competing methods.
Ghost problems from Pauli–Villars to fourth-order quantum gravity and their resolution

We review the history of the ghost problem in quantum field theory from the Pauli-Villars regulator theory to currently popular fourth-order derivative quantum gravity theories. While these theories all appear to have unitarity-violating ghost states with negative norm, we show that in fact these ghost states only appear because the theories are being formulated in the wrong Hilbert space. In these theories the Hamiltonians are not Hermitian but instead possess an antilinear symmetry. Consequently, one cannot use inner products that are built out of states and their Hermitian conjugates. Rather, one must use inner products built out of states and their conjugates with respect to the antilinear symmetry, and these latter inner products are positive. In this way one can build quantum theories of gravity in four spacetime dimensions that are unitary.
Revisiting the Sun’s Strong Differential Rotation along Radial Lines

Current state-of-the-art models of the solar convection zone consist of solutions to the Navier–Stokes equations in rotating, 3D spherical shells. Such models are highly sensitive to the choice of boundary conditions. Here we present two suites of simulations differing only in their outer thermal boundary condition, which is either one of fixed entropy (FE) or fixed flux (FF; corresponding to a fixed gradient in the entropy). We find that the resulting differential rotation is markedly different between the two sets. The FF simulations have strong differential rotation contrast and isocontours tilted along radial lines (in good agreement with the Sun’s interior rotation revealed by helioseismology), whereas the FE simulations have weaker contrast and contours tilted in the opposite sense. We examine in detail the force balances in our models and find that the poleward transport of heat by Busse columns drives a thermal wind responsible for the different rotation profiles. We conclude that the Sun’s strong differential rotation along radial lines may result from the solar emissivity being invariant with latitude (which is similar to the FF condition in our models) and the poleward transport of heat by Busse columns. In future work on convection in the solar context, we strongly advise modelers to use an FF outer boundary condition.
Dense Induced Subgraphs of Dense Bipartite Graphs

We prove that every bipartite graph of sufficiently large average degree has either a $K_{t,t}$-subgraph or an induced subgraph of average degree at least $t$ and girth at least $6$. We conjecture that "$6$" can be replaced by "$k$", which strengthens a conjecture of Thomassen. In support of this conjecture, we show that it holds for regular graphs.