Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Quality Assessment: Unifying Structure and Texture Similarity

Objective measures of image quality generally operate by comparing pixels of a “degraded” image to those of the original. Relative to human observers, these measures are overly sensitive to resampling of texture regions (e.g., replacing one patch of grass with another). Here, we develop the first full-reference image quality model with explicit tolerance to texture resampling. Using a convolutional neural network, we construct an injective and differentiable function that transforms images to multi-scale overcomplete representations. We demonstrate empirically that the spatial averages of the feature maps in this representation capture texture appearance, in that they provide a set of sufficient statistical constraints to synthesize a wide variety of texture patterns. We then describe an image quality method that combines correlations of these spatial averages (“texture similarity”) with correlations of the feature maps (“structure similarity”). The parameters of the proposed measure are jointly optimized to match human ratings of image quality, while minimizing the reported distances between subimages cropped from the same texture images. Experiments show that the optimized method explains human perceptual scores, both on conventional image quality databases, as well as on texture databases. The measure also offers competitive performance on related tasks such as texture classification and retrieval. Finally, we show that our method is relatively insensitive to geometric transformations (e.g., translation and dilation), without use of any specialized training or data augmentation. Code is available at https://github.com/dingkeyan93/DISTS.
Statistical relations between stellar spectral and luminosity classes and stellar effective temperature and surface gravity

We have determined new statistical relations to estimate the fundamental atmospheric parameters of effective temperature and surface gravity, using MK spectral classification, and vice versa. The relations were constructed based on the published calibration tables (for main sequence stars) and observational data from stellar spectral atlases (for giants and supergiants). These new relations were applied to field giants with known atmospheric parameters, and the results of the comparison of our estimations with available spectral classification have been quite satisfactory.
Experimental observation of magnetic dimers in diluted 
Yb:YAlO3

We present a comprehensive experimental investigation of Yb magnetic dimers in Yb$_{0.04}$Y$_{0.96}$AlO$_3$, an Yb-doped Yttrium Aluminum Perovskite (YAP) YAlO$_3$ by means of specific heat, magnetization and high-resolution inelastic neutron scattering (INS) measurements. In our sample, the Yb ions are randomly distributed over the lattice and $\sim 7$\% of Yb ions form quantum dimers due to nearest-neighbor antiferromagnetic coupling along the $c$-axis. At zero field, the dimer formation manifests itself in an appearance of an inelastic peak at $\Delta \approx 0.2$~meV in the INS spectrum and a Schottky-like anomaly in the specific heat. The structure factor of the INS peak exhibits a cosine modulation along the $L$ direction, in agreement with the $c$-axis nearest-neighbor intra-dimer coupling. A careful fitting of the low-temperature specific heat shows that the excited state is a degenerate triplet, which indicates a surprisingly small anisotropy of the effective Yb-Yb exchange interaction despite the low crystal symmetry and anisotropic magnetic dipole contribution, in agreement with previous reports for the Yb parent compound, YbAlO$_3$ [arXiv:1904.11513, arXiv:1902.04112], and in contrast to Yb$_2$Pt$_2$Pb [arXiv:1606.01309, arXiv:1907.01067]. The obtained results are precisely reproduced by analytical calculations for the Yb dimers.
Ahlfors regularity and fractal dimension of Smale spaces

Abstract We prove that, up to topological conjugacy, every Smale space admits an Ahlfors regular Bowen measure. Bowen’s construction of Markov partitions implies that Smale spaces are factors of topological Markov chains. The latter are equipped with Parry’s measure, which is Ahlfors regular. By extending Bowen’s construction, we create a tool for transferring the Ahlfors regularity of the Parry measure down to the Bowen measure of the Smale space. An essential part of our method uses a refined notion of approximation graphs over compact metric spaces. Moreover, we obtain new estimates for the Hausdorff, box-counting and Assouad dimensions of a large class of Smale spaces.
Spatially Attentive Output Layer for Image Classification

Most convolutional neural networks (CNNs) for image classification use a global average pooling (GAP) followed by a fully-connected (FC) layer for output logits. However, this spatial aggregation procedure inherently restricts the utilization of location-specific information at the output layer, although this spatial information can be beneficial for classification. In this paper, we propose a novel spatial output layer on top of the existing convolutional feature maps to explicitly exploit the location-specific output information. In specific, given the spatial feature maps, we replace the previous GAP-FC layer with a spatially attentive output layer (SAOL) by employing a attention mask on spatial logits. The proposed location-specific attention selectively aggregates spatial logits within a target region, which leads to not only the performance improvement but also spatially interpretable outputs. Moreover, the proposed SAOL also permits to fully exploit location-specific self-supervision as well as self-distillation to enhance the generalization ability during training. The proposed SAOL with self-supervision and self-distillation can be easily plugged into existing CNNs. Experimental results on various classification tasks with representative architectures show consistent performance improvements by SAOL at almost the same computational cost.
Do stellar-mass and super-massive black holes have similar dining habits?

Over the years, numerous attempts have been made to connect the phenomenology and physics of mass accretion onto stellar-mass and super-massive black holes in a scale-invariant fashion. In this paper, we explore this connection at the radiatively efficient (and non-jetted) end of accretion modes by comparing the relationship between the luminosity of the accretion disc and corona in the two source classes. Motivated by the apparently tight relationship between these two quantities in active galactic nuclei (AGNs), we analyse 458 RXTE-PCA archival observations of the X-ray binary (XRB) GX 339–4, using this object as an exemplar for the properties of XRBs in general. We focus on the soft and soft-intermediate states, which have been suggested to be analogous to radiatively efficient AGNs. The observed scatter in the logLdisc − logLcoronarelationship of GX 339–4 is high (∼0.43 dex) and significantly larger than in a representative sample of radiatively efficient, non- or weakly jetted AGNs (∼0.30 dex). At first glance, this would appear contrary to the hypothesis that the systems simply scale with mass. On the other hand, we also find that GX 339–4 and our AGN sample show different accretion rate and power-law index distributions, with the latter in particular being broader in GX 339–4 (dispersion of ∼0.16 cf. ∼0.08 for AGN). GX 339–4 also shows an overall softer slope, with a mean value of ∼2.20 as opposed to ∼2.07 for the AGN sample. Remarkably, once similarly broad Γ andṁdistributions are selected, the AGN sample overlaps nicely with GX 339–4 observations in the mass-normalised logLdisc − logLcoronaplane, with a scatter of ∼0.30 − 0.33 dex in both cases. This indicates that a mass-scaling of properties might hold after all, with our results being consistent with the disc-corona systems in AGNs and XRBs exhibiting the same physical processes, albeit under different conditions for instance in terms of temperature, optical depth and/or electron energy distribution in the corona, heating-cooling balance, coronal geometry and/or black hole spin.
Momentum-kick model application to high multiplicity pp collisions at $\sqrt{s}=13\,\mathrm{TeV}$ at the LHC

In this study, the momentum-kick model is used to understand the ridge behaviours in dihadron $\Delta\eta$--$\Delta\varphi$ correlations recently reported by the LHC in high-multiplicity proton-proton (pp) collisions. The kick stand model is based on a momentum kick by leading jets to partons in the medium close to the leading jets. The medium where partons move freely is assumed in the model regardless of collision systems. This helps us apply the method to small systems like pp collisions in a simple way. Also, the momentum transfer is purely kinematic and this provides us a strong way to approach the ridge behaviour analytically. There are already several results with this approach in high-energy heavy-ion collisions from the STAR and PHENIX at RHIC and from the CMS at LHC. The momentum-kick model is extended to the recent ridge results in high-multiplicity pp collisions with the ATLAS and CMS at LHC. The medium property in high-multiplicity pp collisions is diagnosed with the result of the model.
Local-Global Video-Text Interactions for Temporal Grounding

This paper addresses the problem of text-to-video temporal grounding, which aims to identify the time interval in a video semantically relevant to a text query. We tackle this problem using a novel regression-based model that learns to extract a collection of mid-level features for semantic phrases in a text query, which corresponds to important semantic entities described in the query (e.g., actors, objects, and actions), and reflect bi-modal interactions between the linguistic features of the query and the visual features of the video in multiple levels. The proposed method effectively predicts the target time interval by exploiting contextual information from local to global during bi-modal interactions. Through in-depth ablation studies, we find out that incorporating both local and global context in video and text interactions is crucial to the accurate grounding. Our experiment shows that the proposed method outperforms the state of the arts on Charades-STA and ActivityNet Captions datasets by large margins, 7.44\% and 4.61\% points at Recall@tIoU=0.5 metric, respectively.
Neuromorphic Eye-in-Hand Visual Servoing

Robotic vision plays a major role in factory automation to service robot applications. However, the traditional use of frame-based cameras sets a limitation on continuous visual feedback due to their low sampling rate, poor performance in low light conditions and redundant data in real-time image processing, especially in the case of high-speed tasks. Neuromorphic event-based vision is a recent technology that gives human-like vision capabilities such as observing the dynamic changes asynchronously at a high temporal resolution ( $1~\mu s$ ) with low latency and wide dynamic range. In this paper, for the first time, we present a purely event-based visual servoing method using a neuromorphic camera in an eye-in-hand configuration for the grasping pipeline of a robotic manipulator. We devise three surface layers of active events to directly process the incoming stream of events from relative motion. A purely event-based approach is used to detect corner features, localize them robustly using heatmaps and generate virtual features for tracking and grasp alignment. Based on the visual feedback, the motion of the robot is controlled to make the temporal upcoming event features converge to the desired event in Spatio-temporal space. The controller switches its operation such that it explores the workspace, reaches the target object and achieves a stable grasp. The event-based visual servoing (EBVS) method is comprehensively studied and validated experimentally using a commercial robot manipulator in an eye-in-hand configuration for both static and dynamic targets. Experimental results show superior performance of the EBVS method over frame-based vision, especially in high-speed operations and poor lighting conditions. As such, EBVS overcomes the issues of motion blur, lighting and exposure timing that exist in conventional frame-based visual servoing methods.
Measuring Distances to Low-luminosity Galaxies Using Surface Brightness Fluctuations

We present an in-depth study of surface brightness fluctuations (SBFs) in low-luminosity stellar systems. Using the MIST models, we compute theoretical predictions for absolute SBF magnitudes in the LSST, HST ACS/WFC, and proposed Roman Space Telescope filter systems. We compare our calculations to observed SBF–color relations of systems that span a wide range of age and metallicity. Consistent with previous studies, we find that single-age population models show excellent agreement with observations of low-mass galaxies with 0.5 ≲ g − i ≲ 0.9. For bluer galaxies, the observed relation is better fit by models with composite stellar populations. To study SBF recovery from low-luminosity systems, we perform detailed image simulations in which we inject fully populated model galaxies into deep ground-based images from real observations. Our simulations show that LSST will provide data of sufficient quality and depth to measure SBF magnitudes with precisions of ∼0.2–0.5 mag in ultra-faint and low-mass classical (M ⋆ ≤ 107 M ⊙) dwarf galaxies out to ∼4 Mpc and ∼25 Mpc, respectively, within the first few years of its deep-wide-fast survey. Many significant practical challenges and systematic uncertainties remain, including an irreducible “sampling scatter” in the SBFs of ultra-faint dwarfs due to their undersampled stellar mass functions. We nonetheless conclude that SBFs in the new generation of wide-field imaging surveys have the potential to play a critical role in the efficient confirmation and characterization of dwarf galaxies in the nearby universe.
The quantum Witten–Kontsevich series and
one-part double Hurwitz numbers

We study the quantum Witten-Kontsevich series introduced by Buryak, Dubrovin, Guere and Rossi in \cite{buryak2016integrable} as the logarithm of a quantum tau function for the quantum KdV hierarchy. This series depends on a genus parameter $\epsilon$ and a quantum parameter $\hbar$. When $\hbar=0$, this series restricts to the Witten-Kontsevich generating series for intersection numbers of psi classes on moduli spaces of stable curves. We establish a link between the $\epsilon=0$ part of the quantum Witten-Kontsevich series and one-part double Hurwitz numbers. These numbers count the number non-equivalent holomorphic maps from a Riemann surface of genus $g$ to $\mathbb{P}^{1}$ with a prescribe ramification profile over $0$, a complete ramification over $\infty$ and a given number of simple ramifications elsewhere. Goulden, Jackson and Vakil proved in \cite{goulden2005towards} that these numbers have the property to be polynomial in the orders of ramification over $0$. We prove that the coefficients of these polynomials are the coefficients of the quantum Witten-Kontsevich series. We also present some partial results about the full quantum Witten-Kontsevich power series.
Continual Reinforcement Learning with Multi-Timescale Replay

In this paper, we propose a multi-timescale replay (MTR) buffer for improving continual learning in RL agents faced with environments that are changing continuously over time at timescales that are unknown to the agent. The basic MTR buffer comprises a cascade of sub-buffers that accumulate experiences at different timescales, enabling the agent to improve the trade-off between adaptation to new data and retention of old knowledge. We also combine the MTR framework with invariant risk minimization, with the idea of encouraging the agent to learn a policy that is robust across the various environments it encounters over time. The MTR methods are evaluated in three different continual learning settings on two continuous control tasks and, in many cases, show improvement over the baselines.
Painting a portrait of the Galactic disc with its stellar clusters

Context.The large astrometric and photometric survey performed by theGaiamission allows for a panoptic view of the Galactic disc and its stellar cluster population. Hundreds of stellar clusters were only discovered after the latestGaiadata release (DR2) and have yet to be characterised.Aims. Here we make use of the deep and homogeneousGaiaphotometry down toG= 18 to estimate the distance, age, and interstellar reddening for about 2000 stellar clusters identified withGaiaDR2 astrometry. We use these objects to study the structure and evolution of the Galactic disc.Methods. We relied on a set of objects with well-determined parameters in the literature to train an artificial neural network to estimate parameters from theGaiaphotometry of cluster members and their mean parallax.Results. We obtain reliable parameters for 1867 clusters. Our catalogue confirms the relative lack of old stellar clusters in the inner disc (with a few notable exceptions). We also quantify and discuss the variation of scale height with cluster age, and we detect the Galactic warp in the distribution of old clusters.Conclusions. This work results in a large and homogeneous cluster catalogue, allowing one to trace the structure of the disc out to distances of ∼4 kpc. However, the present sample is still unable to trace the outer spiral arm of the Milky Way, which indicates that the outer disc cluster census might still be incomplete.
2MASS J04435686+3723033 B: A Young Companion at the Substellar Boundary with Potential Membership in the β Pictoris Moving Group

We present a detailed characterization of 2MASS J04435750+3723031, a low-mass companion orbiting the young M2 star 2MASS J04435686+3723033 at 7. ″6 (550 au) with potential membership in the 23 Myr β Pictoris moving group (βPMG). Using near-infrared (NIR) spectroscopy of the companion from IRTF/SpeX, we have found a spectral type of M6 ± 1 and indications of youth through age-sensitive absorption lines and a low surface gravity index (VL-G). A young age is supported by Hα emission and lithium absorption in the host. We reevaluate the membership of this system and find that it is a marginally consistent kinematic match to the βPMG using Gaia parallaxes and new radial velocities for the host and companion. If this system does belong to the βPMG, it would be a kinematic outlier and the companion would be overluminous compared to other similar ultracool objects like PZ Tel B; this would suggest that 2M0443+3723 B could be a close binary (≈52+52 MJup if equal-flux, compared with 99 ± 5 MJup if single), and would make it the sixth substellar companion in this group. To test this hypothesis, we acquired NIR adaptive optics images with Keck II/NIRC2, but they do not resolve the companion to be a binary down to the diffraction limit of ∼3 au. If 2M0443+3723 AB does not belong to any moving group, then its age is more uncertain. In this case it is still young (≲30 Myr), and the implied mass of the companion would be between ∼30 and 110 MJup.
Light-Front Holography and Supersymmetric Conformal Algebra: A Novel Approach to Hadron Spectroscopy, Structure, and Dynamics

We give an overview of recent progress into the infrared structure of QCD based on the gauge/gravity correspondence and light-front quantization, where the color confining interaction for mesons and baryons is determined by an underlying superconformal algebraic structure. This new approach to hadron physics gives remarkable connections and predictions across the entire mass spectrum of hadrons and also describes the infrared behavior of the strong coupling. More recently, an extensive study of form factors, polarized and unpolarized parton distributions and the sea quark contribution to the nucleon has been carried out by extending the holographic formalism to incorporate the nonperturbative structure of Veneziano amplitudes. Contribution to the report "Strong QCD from Hadron Structure Experiments" Jefferson Lab Workshop, 5-9 November, 2019
Building a Multi-domain Neural Machine Translation Model using Knowledge Distillation

Lack of specialized data makes building a multi-domain neural machine translation tool challenging. Although emerging literature dealing with low resource languages starts to show promising results, most state-of-the-art models used millions of sentences. Today, the majority of multi-domain adaptation techniques are based on complex and sophisticated architectures that are not adapted for real-world applications. So far, no scalable method is performing better than the simple yet effective mixed-finetuning, i.e finetuning a generic model with a mix of all specialized data and generic data. In this paper, we propose a new training pipeline where knowledge distillation and multiple specialized teachers allow us to efficiently finetune a model without adding new costs at inference time. Our experiments demonstrated that our training pipeline allows improving the performance of multi-domain translation over finetuning in configurations with 2, 3, and 4 domains by up to 2 points in BLEU.
Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm

A popular method for anomaly detection is to use the generator of an adversarial network to formulate anomaly score over reconstruction loss of input. Due to the rare occurrence of anomalies, optimizing such networks can be a cumbersome task. Another possible approach is to use both generator and discriminator for anomaly detection. However, attributed to the involvement of adversarial training, this model is often unstable in a way that the performance fluctuates drastically with each training step. In this study, we propose a framework that effectively generates stable results across a wide range of training steps and allows us to use both the generator and the discriminator of an adversarial model for efficient and robust anomaly detection. Our approach transforms the fundamental role of a discriminator from identifying real and fake data to distinguishing between good and bad quality reconstructions. To this end, we prepare training examples for the good quality reconstruction by employing the current generator, whereas poor quality examples are obtained by utilizing an old state of the same generator. This way, the discriminator learns to detect subtle distortions that often appear in reconstructions of the anomaly inputs. Extensive experiments performed on Caltech-256 and MNIST image datasets for novelty detection show superior results. Furthermore, on UCSD Ped2 video dataset for anomaly detection, our model achieves a frame-level AUC of 98.1%, surpassing recent state-of-the-art methods
Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation

Maintaining a consistent personality in conversations is quite natural for human beings, but is still a non-trivial task for machines. The persona-based dialogue generation task is thus introduced to tackle the personality-inconsistent problem by incorporating explicit persona text into dialogue generation models. Despite the success of existing persona-based models on generating human-like responses, their one-stage decoding framework can hardly avoid the generation of inconsistent persona words. In this work, we introduce a three-stage framework that employs a generate-delete-rewrite mechanism to delete inconsistent words from a generated response prototype and further rewrite it to a personality-consistent one. We carry out evaluations by both human and automatic metrics. Experiments on the Persona-Chat dataset show that our approach achieves good performance.