Miracle-dz/UniFAR-FTUs huggingface dataset:
MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis

Identity documents recognition is an important sub-field of document analysis, which deals with tasks of robust document detection, type identification, text fields recognition, as well as identity fraud prevention and document authenticity validation given photos, scans, or video frames of an identity document capture. Significant amount of research has been published on this topic in recent years, however a chief difficulty for such research is scarcity of datasets, due to the subject matter being protected by security requirements. A few datasets of identity documents which are available lack diversity of document types, capturing conditions, or variability of document field values. In this paper, we present a dataset MIDV-2020 which consists of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock identity documents, each with unique text field values and unique artificially generated faces, with rich annotation. The dataset contains 72409 annotated images in total, making it the largest publicly available identity document dataset to the date of publication. We describe the structure of the dataset, its content and annotations, and present baseline experimental results to serve as a basis for future research. For the task of document location and identification content-independent, feature-based, and semantic segmentation-based methods were evaluated. For the task of document text field recognition, the Tesseract system was evaluated on field and character levels with grouping by field alphabets and document types. For the task of face detection, the performance of Multi Task Cascaded Convolutional Neural Networks-based method was evaluated separately for different types of image input modes. The baseline evaluations show that the existing methods of identity document analysis have a lot of room for improvement given modern challenges. We believe that the proposed dataset will prove invaluable for advancement of the field of document analysis and recognition.
Fast Margin Maximization via Dual Acceleration

We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (e.g., the exponential or logistic loss), which maximizes the classification margin on separable data at a rate of $\widetilde{\mathcal{O}}(1/t^2)$. This contrasts with a rate of $\mathcal{O}(1/\log(t))$ for standard gradient descent, and $\mathcal{O}(1/t)$ for normalized gradient descent. This momentum-based method is derived via the convex dual of the maximum-margin problem, and specifically by applying Nesterov acceleration to this dual, which manages to result in a simple and intuitive method in the primal. This dual view can also be used to derive a stochastic variant, which performs adaptive non-uniform sampling via the dual variables.
On independent domination of regular graphs

The domination number of a graph G $G$ , denoted γ ( G ) $\gamma (G)$ , is the minimum size of a dominating set of G $G$ , and the independent domination number of G $G$ , denoted i ( G ) $i(G)$ , is the minimum size of a dominating set of G $G$ that is also independent. Let k ≥ 4 $k\ge 4$ be an integer. Generalizing a result on cubic graphs by Lam, Shiu, and Sun, we prove that i ( G ) ≤ k − 1 2 k − 1 | V ( G ) | $i(G)\le \frac{k-1}{2k-1}|V(G)|$ for a connected k $k$ ‐regular graph G $G$ that is not K k , k ${K}_{k,k}$ , which is tight for k = 4 $k=4$ . This answers a question by Goddard et al. in the affirmative. We also show that i ( G ) γ ( G ) ≤ k 3 − 3 k 2 + 2 2 k 2 − 6 k + 2 $\frac{i(G)}{\gamma (G)}\le \frac{{k}^{3}-3{k}^{2}+2}{2{k}^{2}-6k+2}$ for a connected k $k$ ‐regular graph G $G$ that is not K k , k ${K}_{k,k}$ , strengthening upon a result of Knor, Škrekovski, and Tepeh. In addition, we prove that a graph G $G$ with maximum degree at most 4 satisfies i ( G ) ≤ 5 9 | V ( G ) | $i(G)\le \frac{5}{9}|V(G)|$ , which is also tight.
Prediction of tone detection thresholds in interaurally delayed noise based on interaural phase difference fluctuations

Differences between the interaural phase of a noise and a target tone improve detection thresholds. The maximum masking release is obtained for detecting an antiphasic tone (S$\pi$) in diotic noise (N0). It has been shown in several studies that this benefit gradually declines as an interaural delay is applied to the N0S$\pi$ complex. This decline has been attributed to the reduced interaural coherence of the noise. Here, we report detection thresholds for a 500 Hz tone in masking noise with up to 8 ms interaural delay and bandwidths from 25 to 1000 Hz. When reducing the noise bandwidth from 100 to 50 and 25 Hz, the masking release at 8 ms delay increases, as expected for increasing temporal coherence with decreasing bandwidth. For bandwidths of 100 to 1000 Hz, no significant difference was observed and detection thresholds with these noises have a delay dependence that is fully described by the temporal coherence imposed by the typical monaurally determined auditory filter bandwidth. A minimalistic binaural model is suggested based on interaural phase difference fluctuations without the assumption of delay lines.
Well-calibrated prediction intervals for regression problems

Over the last few decades, various methods have been proposed for estimating prediction intervals in regression settings, including Bayesian methods, ensemble methods, direct interval estimation methods and conformal prediction methods. An important issue is the calibration of these methods: the generated prediction intervals should have a predefined coverage level, without being overly conservative. In this work, we review the above four classes of methods from a conceptual and experimental point of view. Results on benchmark data sets from various domains highlight large fluctuations in performance from one data set to another. These observations can be attributed to the violation of certain assumptions that are inherent to some classes of methods. We illustrate how conformal prediction can be used as a general calibration procedure for methods that deliver poor results without a calibration step.
Model-independent Constraints on Clustering and Growth of Cosmic Structures from BOSS DR12 Galaxies in Harmonic Space

We present a new, model-independent measurement of the clustering amplitude of galaxies and the growth of cosmic large-scale structures from the Baryon Oscillation Spectroscopic Survey (BOSS) 12th data release. This is achieved by generalizing harmonic-space power spectra for galaxy clustering to measure separately the magnitudes of the density and the redshift-space distortion terms, respectively related to the clustering amplitude of structures, b σ 8(z), and their growth, f σ 8(z). We adopt a tomographic approach with 15 redshift bins in z ∈ [0.15, 0.67]. We restrict our analysis to strictly linear scales, implementing a redshift-dependent maximum multipole for each bin. The measurements do not appear to suffer from systematic effects, and they show excellent agreement with the theoretical predictions from the Planck cosmic microwave background analysis assuming a ΛCDM cosmology. Our results also agree with previous analyses by the BOSS collaboration. Furthermore, our method provides the community with a new tool for data analyses of the cosmic large-scale structure, complementary to state-of-the-art approaches in configuration or Fourier space. Among its merits, we list: it being more agnostic with respect to the underlying cosmological model; its roots in a well-defined and gauge-invariant observable; the possibility to account naturally for wide-angle effects and even relativistic corrections on ultra-large scales; and the capability to perform an almost arbitrarily fine redshift binning with little computational effort. These aspects are all the more relevant for the oncoming generation of cosmological experiments such as Euclid, the Dark Energy Spectroscopic Instrument, the Legacy Survey of Space and Time, and the SKA Project.
Joint parametric specification checking of conditional mean and volatility in time series models with martingale difference innovations

Using cumulative residual processes, we introduce powerful joint specification tests for conditional mean and variance functions in the context of nonlinear time series with martingale difference innovations. The main challenge comes from the fact that, the cumulative residual process no longer admits a distribution-free limit. To obtain a practical solution one either transforms the process to achieve a distribution-free limit or approximates the non-distribution free limit using numerical or re-sampling techniques. In this paper, the three solutions are considered and compared. The proposed tests have nontrivial power against a class of root-n local alternatives and are suitable when the conditioning set is infinite-dimensional, which allows including more general models such as ARMAX-GARCH with dependent innovations. Numerical results based on simulated and real data show that the powers of tests based on re-sampling or numerical approximation are in general slightly better than those based on martingale transformation.
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation

While agents trained by Reinforcement Learning (RL) can solve increasingly challenging tasks directly from visual observations, generalizing learned skills to novel environments remains very challenging. Extensive use of data augmentation is a promising technique for improving generalization in RL, but it is often found to decrease sample efficiency and can even lead to divergence. In this paper, we investigate causes of instability when using data augmentation in common off-policy RL algorithms. We identify two problems, both rooted in high-variance Q-targets. Based on our findings, we propose a simple yet effective technique for stabilizing this class of algorithms under augmentation. We perform extensive empirical evaluation of image-based RL using both ConvNets and Vision Transformers (ViT) on a family of benchmarks based on DeepMind Control Suite, as well as in robotic manipulation tasks. Our method greatly improves stability and sample efficiency of ConvNets under augmentation, and achieves generalization results competitive with state-of-the-art methods for image-based RL in environments with unseen visuals. We further show that our method scales to RL with ViT-based architectures, and that data augmentation may be especially important in this setting.
Anisotropic upper critical field, Seebeck and Nernst coefficient of Nb0.20Bi2Se3 superconductor

We present the magneto-transport and the thermoelectric (Seebeck and Nernst coefficient) studies of the Nb-doped Bi2Se3 superconductor. The angle-dependent magnetoresistance study highlights the anisotropy of upper critical field (Hc 2) for in-plane and out-of-plane magnetic field orientation with the anisotropy parameter Γ ∼ 1.3. The estimated value of the carrier concentration (∼1019 cm−3) for Nb0.2Bi2Se3 is one order larger than for Bi2Se3. Doping of Nb shows a significant decrease in the Seebeck coefficient value and the estimated Fermi temperature of the three-dimensional Fermi surface at the centre of Brillouin zone in the zero-temperature limit enhances by ∼4 times in comparison to pristine Bi2Se3. We have observed a large value (∼2.3 µV K−1T−1) of Nernst coefficient for Bi2Se3 at room temperature, which decreases with Nb doping (∼0.5 µV K−1 T−1).
MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting

Citation context analysis (CCA) is an important task in natural language processing that studies how and why scholars discuss each others’ work. Despite decades of study, computational methods for CCA have largely relied on overly-simplistic assumptions of how authors cite, which ignore several important phenomena. For instance, scholarly papers often contain rich discussions of cited work that span multiple sentences and express multiple intents concurrently. Yet, recent work in CCA is often approached as a single-sentence, single-label classification task, and thus many datasets used to develop modern computational approaches fail to capture this interesting discourse. To address this research gap, we highlight three understudied phenomena for CCA and release MULTICITE, a new dataset of 12.6K citation contexts from 1.2K computational linguistics papers that fully models these phenomena. Not only is it the largest collection of expert-annotated citation contexts to-date, MULTICITE contains multi-sentence, multi-label citation contexts annotated through-out entire full paper texts. We demonstrate how MULTICITE can enable the development of new computational methods on three important CCA tasks. We release our code and dataset at https://github.com/allenai/multicite.
Uncertainty quantification of a thrombosis model considering the clotting assay PFA‐100®

Mathematical models of thrombosis are currently used to study clinical scenarios of pathological thrombus formation. As these models become more complex to predict thrombus formation dynamics high computational cost must be alleviated and inherent uncertainties must be assessed. Evaluating model uncertainties allows to increase the confidence in model predictions and identify avenues of improvement for both thrombosis modeling and anti‐platelet therapies. In this work, an uncertainty quantification analysis of a multi‐constituent thrombosis model is performed considering a common assay for platelet function (PFA‐100®). The analysis is facilitated thanks to time‐evolving polynomial chaos expansions used as a parametric surrogate for the full thrombosis model considering two quantities of interest; namely, thrombus volume and occlusion percentage. The surrogate is thoroughly validated and provides a straightforward access to a global sensitivity analysis via computation of Sobol' coefficients. Six out of 15 parameters linked to thrombus consitution, vWF activity, and platelet adhesion dynamics were found to be most influential in the simulation variability considering only individual effects; while parameter interactions are highlighted when considering the total Sobol' indices. The influential parameters are related to thrombus constitution, vWF activity, and platelet to platelet adhesion dynamics. The surrogate model allowed to predict realistic PFA‐100® closure times of 300,000 virtual cases that followed the trends observed in clinical data. The current methodology could be used including common anti‐platelet therapies to identify scenarios that preserve the hematological balance.
Distributed Nonparametric Function Estimation: Optimal Rate of Convergence and Cost of Adaptation

Distributed minimax estimation and distributed adaptive estimation under communication constraints for Gaussian sequence model and white noise model are studied. The minimax rate of convergence for distributed estimation over a given Besov class, which serves as a benchmark for the cost of adaptation, is established. We then quantify the exact communication cost for adaptation and construct an optimally adaptive procedure for distributed estimation over a range of Besov classes. The results demonstrate significant differences between nonparametric function estimation in the distributed setting and the conventional centralized setting. For global estimation, adaptation in general cannot be achieved for free in the distributed setting. The new technical tools to obtain the exact characterization for the cost of adaptation can be of independent interest.
The Role of Binance in Bitcoin Volatility Transmission

ABSTRACT We analyse high-frequency realized volatility dynamics and spillovers between centralized crypto exchanges that offer spot and derivative contracts for bitcoin against the US dollar or the stable coin tether. The tether-margined perpetual contract on Binance is clearly the main source of volatility, continuously transmitting strong flows to all other instruments and receiving very little volatility from other sources. We also find that crypto exchanges exhibit much higher interconnectedness when traditional Western stock markets are open. Especially during the US time zone, volatility outflows from Binance are much higher than at other times, and Bitcoin traders are more attentive and reactive to prevailing market conditions. Our results highlight that market regulators should pay more attention to the tether-margined derivatives products available on most self-regulated exchanges, most importantly on Binance.
Hemispheric sunspot numbers 1874–2020

Context. Previous studies show significant north–south asymmetries for various features and indicators of solar activity. These findings suggest some decoupling between the two hemispheres over the solar cycle evolution, which is in agreement with dynamo theories. For the most important solar activity index, the sunspot numbers, so far only limited data are available for the two hemispheres independently. Aims. The aim of this study is to create a continuous series of daily and monthly hemispheric sunspot numbers (HSNs) from 1874 to 2020, which will be continuously expanded in the future with the HSNs provided by SILSO. Methods. Based on the available daily measurements of hemispheric sunspot areas from 1874 to 2016 from Greenwich Royal Observatory and National Oceanic and Atmospheric Administration, we derive the relative fractions of the northern and southern activity. These fractions are applied to the international sunspot number (ISN) to derive the HSNs. This method and obtained data are validated against published HSNs for the period 1945–2004 and those provided by SILSO for 1992 to 2016. Results. We provide a continuous data series and catalogue of daily, monthly mean, and 13-month smoothed monthly mean HSNs for the time range 1874–2020 –fully covering solar cycles 12 to 24– that are consistent with the newly calibrated ISN (Clette et al., 2014). Validation of the reconstructed HSNs against the direct data available since 1945 reveals a high level of consistency, with Pearson correlation coefficients of r = 0.94 (0.97) for the daily (monthly mean) data. The cumulative hemispheric asymmetries for cycles 12–24 give a mean value of 16%, with no obvious pattern in north–south predominance over the cycle evolution. The strongest asymmetry occurs for cycle no. 19, in which the northern hemisphere shows a cumulated predominance of 42%. The phase shift between the peaks of solar activity in the two hemispheres may be up to 28 months, with a mean absolute value over cycles 12–24 of 16.8 months. The phase shifts reveal an overall asymmetry of the northern hemisphere reaching its cycle maximum earlier (in 10 out of 13 cases), with a mean signed phase shift of −7.6 months. Relating the ISN and HSN peak growth rates during the cycle rise phase with the cycle amplitude reveals higher correlations when considering the two hemispheres individually, with r ≈ 0.9. Conclusions. Our findings provide further evidence that to some degree the solar cycle evolves independently in the two hemispheres, and demonstrate that empirical solar cycle prediction methods can be improved by investigating the solar cycle dynamics in terms of the HSN evolution.
Regressing Location on Text for Probabilistic Geocoding

Text data are an important source of detailed information about social and political events. Automated systems parse large volumes of text data to infer or extract structured information that describes actors, actions, dates, times, and locations. One of these sub-tasks is geocoding: predicting the geographic coordinates associated with events or locations described by a given text. I present an end-to-end probabilistic model for geocoding text data. Additionally, I collect a novel data set for evaluating the performance of geocoding systems. I compare the model-based solution, called ELECTRo-map, to the current state-of-the-art open source system for geocoding texts for event data. Finally, I discuss the benefits of end-to-end model-based geocoding, including principled uncertainty estimation and the ability of these models to leverage contextual information.
EmoGen: Quantifiable Emotion Generation and Analysis for Experimental Psychology

3D facial modelling and animation in computer vision and graphics traditionally require either digital artist's skill or complex pipelines with objective-function-based solvers to fit models to motion capture. This inaccessibility of quality modelling to a non-expert is an impediment to effective quantitative study of facial stimuli in experimental psychology. The EmoGen methodology we present in this paper solves the issue democratising facial modelling technology. EmoGen is a robust and configurable framework letting anyone author arbitrary quantifiable facial expressions in 3D through a user-guided genetic algorithm search. Beyond sample generation, our methodology is made complete with techniques to analyse distributions of these expressions in a principled way. This paper covers the technical aspects of expression generation, specifically our production-quality facial blendshape model, automatic corrective mechanisms of implausible facial configurations in the absence of artist's supervision and the genetic algorithm implementation employed in the model space search. Further, we provide a comparative evaluation of ways to quantify generated facial expressions in the blendshape and geometric domains and compare them theoretically and empirically. The purpose of this analysis is 1. to define a similarity cost function to simulate model space search for convergence and parameter dependence assessment of the genetic algorithm and 2. to inform the best practices in the data distribution analysis for experimental psychology.
Small scale effects in the observable power spectrum at large angular scales

In this paper we show how effects from small scales can enter the angular-redshift power spectrum Cℓ (z,z'). In particular, we show that spectroscopic surveys with high redshift resolution are already affected on large angular scales, i.e. at low multipoles, by features from small scales. When considering the angular power spectrum with spectroscopic redshift resolution, it is therefore important to account for non-linearities relevant on small scales, even at low multipoles. This may also motivate the use of the correlation function in relatively wide redshift bins, which is not affected by non-linearities on large scales, instead of the angular power spectrum. The extent to which small-scale effects become visible on large scales, which is more relevant for bin auto-correlations than for cross-correlations, is quantified in detail.
Improving Human Motion Prediction Through Continual Learning

Human motion prediction is an essential component for enabling closer human-robot collaboration. The task of accurately predicting human motion is non-trivial. It is compounded by the variability of human motion, both at a skeletal level due to the varying size of humans and at a motion level due to individual movement's idiosyncrasies. These variables make it challenging for learning algorithms to obtain a general representation that is robust to the diverse spatio-temporal patterns of human motion. In this work, we propose a modular sequence learning approach that allows end-to-end training while also having the flexibility of being fine-tuned. Our approach relies on the diversity of training samples to first learn a robust representation, which can then be fine-tuned in a continual learning setup to predict the motion of new subjects. We evaluated the proposed approach by comparing its performance against state-of-the-art baselines. The results suggest that our approach outperforms other methods over all the evaluated temporal horizons, using a small amount of data for fine-tuning. The improved performance of our approach opens up the possibility of using continual learning for personalized and reliable motion prediction.