Miracle-dz/UniFAR-FTUs huggingface dataset:
Tail asymptotics for the bivariate equi-skew Variance-Gamma distribution

We derive the asymptotic rate of decay to zero of the tail dependence of the bivariate skew Variance Gamma (VG) distribution under the equal-skewness condition, as an explicit regularly varying function. Our development is in terms of a slightly more general bivariate skew Generalized Hyperbolic (GH) distribution. Our initial reduction of the bivariate problem to a univariate one is motivated by our earlier study of tail dependence rate for the bivariate skew normal distribution
Constraining the formation of NGC 1052-DF2 from its unusual globular cluster population

The ultra-diffuse galaxy (UDG) NGC 1052-DF2 has a low dark matter content and hosts a very unusual globular cluster (GC) population, with a median luminosity ∼4 times higher than in most galaxies and containing about 5 per cent of the galaxy’s stars. We apply a theoretical model that predicts the initial cluster mass function as a function of the galactic environment to investigate the origin of DF2’s peculiar GC system. Using the GC mass function, the model constrains the star-forming conditions in the galaxy during the formation of its GCs, ∼9 Gyr ago. We predict that the GCs formed in an environment with very high gas surface density, $\Sigma _{\rm ISM}\gtrsim 10^3\rm \,{M_\odot}\rm \,{pc}^{-2}$, and strong centrifugal support, $\Omega \gtrsim 0.7\rm \,{Myr}^{-1}$, similar to nearby circumnuclear starbursts and the central region of the Milky Way. The extreme conditions required to form the observed GC population imply a very high cluster formation efficiency of ≈87 per cent, and contrast strongly with the current diffuse nature of the galaxy. Since a nuclear starburst would lead to the rapid in-spiral of the GCs and is ruled out by the absence of a nuclear star cluster, we propose that the GCs plausibly formed during a major merger at z ∼ 1.3. The merger remnant must have undergone significant expansion of its stellar (and perhaps also its dark matter) component to reach its low present surface brightness, leading to the interesting possibility that it was the formation of DF2’s extreme GC population that caused it to become a UDG. If true, this strong structural evolution would have important implications for understanding the origins of UDGs.
Whole-Body MPC and Online Gait Sequence Generation for Wheeled-Legged Robots

Our paper proposes a model predictive controller as a single-task formulation that simultaneously optimizes wheel and torso motions. This online joint velocity and ground reaction force optimization integrates a kinodynamic model of a wheeled quadrupedal robot. It defines the single rigid body dynamics along with the robot’s kinematics while treating the wheels as moving ground contacts. With this approach, we can accurately capture the robot’s rolling constraint and dynamics, enabling automatic discovery of hybrid maneuvers without needless motion heuristics. The formulation’s generality through the simultaneous optimization over the robot’s whole-body variables allows for a single set of parameters and makes online gait sequence adaptation possible. Aperiodic gait sequences are automatically found through kinematic leg utilities without the need for predefined contact and lift-off timings, reducing the cost of transport by up to 85 %. Our experiments demonstrate dynamic motions on a quadrupedal robot with non-steerable wheels in challenging indoor and outdoor environments. The paper’s findings contribute to evaluating a decomposed, i.e., sequential optimization of wheel and torso motion, and single-task motion planner with a novel quantity, the prediction error, which describes how well a receding horizon planner can predict the robot’s future state. To this end, we report an improvement of up to 71 % using our proposed single-task approach, making fast locomotion feasible and revealing wheeled-legged robots’ full potential.
Controlling the Interaction Between Generation and Inference in Semi-Supervised Variational Autoencoders Using Importance Weighting

Even though Variational Autoencoders (VAEs) are widely used for semi-supervised learning, the reason why they work remains unclear. In fact, the addition of the unsupervised objective is most often vaguely described as a regularization. The strength of this regularization is controlled by down-weighting the objective on the unlabeled part of the training set. Through an analysis of the objective of semi-supervised VAEs, we observe that they use the posterior of the learned generative model to guide the inference model in learning the partially observed latent variable. We show that given this observation, it is possible to gain finer control on the effect of the unsupervised objective on the training procedure. Using importance weighting, we derive two novel objectives that prioritize either one of the partially observed latent variable, or the unobserved latent variable. Experiments on the IMDB english sentiment analysis dataset and on the AG News topic classification dataset show the improvements brought by our prioritization mechanism and exhibit a behavior that is inline with our description of the inner working of Semi-Supervised VAEs.
On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness

We prove that classifiers with the ability to abstain are provably more powerful than those that cannot against an adversary that can perturb datapoints by arbitrary amounts in random directions. Specifically, we show that no matter how well-behaved the natural data is, any classifier that cannot abstain will be defeated by such an adversary. However, by allowing abstention, we give a parameterized algorithm with provably good performance against such an adversary when classes are reasonably well-separated and the data dimension is high. We further use a data-driven method to set our algorithm parameters to optimize over the accuracy vs. abstention trade-off with strong theoretical guarantees. Our theory has direct applications to the technique of contrastive learning, where we empirically demonstrate the ability of our algorithms to obtain high robust accuracy with only small amounts of abstention in both supervised and self-supervised settings.
Color Hom-Lie algebras, color Hom-Leibniz algebras and color omni-Hom-Lie algebras

Representations of color Hom-Lie algebras are reviewed, and it is shown that there exist a series of coboundary operators. We also introduce the notion of a color omni-Hom-Lie algebra associated to a vector space and an even invertible linear map. We show how regular color Hom-Lie algebra structures on a vector space can be characterized. Moreover, it is shown that the underlying algebraic structure of the color omni-Hom-Lie algebra is a color Hom-Leibniz algebra.
Particle scattering in analogue-gravity models

We investigate a simple toy model of particle scattering in an analogue-gravity model. The analogue-gravity medium is treated as a scalar field of phonons that obeys the Klein--Gordon equation and thus admits a Lorentz symmetry with respect to $c_\mathrm{s}$, the speed of sound in the medium. The particle from which the phonons are scattered is external to the system and does not obey the sonic Lorentz symmetry that the phonon field obeys. In-universe observers who use the exchange of sound to operationally measure distance and duration find that the external particle appears to be a sonically Lorentz-violating particle. By performing a sonic analogue to Compton scattering, in-universe observers can determine if they are in motion with respect to their medium. If in-universe observers were then to correctly postulate the dispersion relation of the external particle, their velocity with respect to the medium could be found.
Annotationsaurus: A Searchable Directory of Annotation Tools

Manual annotation of textual documents is a necessary task when constructing benchmark corpora for training and evaluating machine learning algorithms. We created a comprehensive directory of annotation tools that currently includes 93 tools. We analyzed the tools over a set of 31 features and implemented simple scripts and a Web application that filters the tools based on chosen criteria. We present two use cases using the directory and propose ideas for its maintenance. The directory, source codes for scripts, and link to the Web application are available at: this https URL
Collisional ionisation, recombination, and ionisation potential in two-fluid slow-mode shocks: Analytical and numerical results

Context. Shocks are a universal feature of warm plasma environments, such as the lower solar atmosphere and molecular clouds, which consist of both ionised and neutral species. Including partial ionisation leads to the existence of a finite width for shocks, where the ionised and neutral species decouple and recouple. As such, drift velocities exist within the shock that lead to frictional heating between the two species, in addition to adiabatic temperature changes across the shock. The local temperature enhancements within the shock alter the recombination and ionisation rates and hence change the composition of the plasma. Aims. We study the role of collisional ionisation and recombination in slow-mode partially ionised shocks. In particular, we incorporate the ionisation potential energy loss and analyse the consequences of having a non-conservative energy equation. Methods. A semi-analytical approach is used to determine the possible equilibrium shock jumps for a two-fluid model with ionisation, recombination, ionisation potential, and arbitrary heating. Two-fluid numerical simulations are performed using the (PIP) code. Results are compared to the magnetohydrodynamic (MHD) model and the semi-analytic solution. Results. Accounting for ionisation, recombination, and ionisation potential significantly alters the behaviour of shocks in both substructure and post-shock regions. In particular, for a given temperature, equilibrium can only exist for specific densities due to the radiative losses needing to be balanced by the heating function. A consequence of the ionisation potential is that a compressional shock will lead to a reduction in temperature in the post-shock region, rather than the increase seen for MHD. The numerical simulations pair well with the derived analytic model for shock velocities. Conclusion. Multi-fluid effects can lead to a significant departure from MHD results. The results in this paper are applicable to a wide range of partially ionised plasmas, including the solar chromosphere and molecular clouds.
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach

Despite the widely reported success of embedding-based machine learning methods on natural language processing tasks, the use of more easily interpreted engineered features remains common in fields such as cognitive impairment (CI) detection. Manually engineering features from noisy text is time and resource consuming, and can potentially result in features that do not enhance model performance. To combat this, we describe a new approach to feature engineering that leverages sequential machine learning models and domain knowledge to predict which features help enhance performance. We provide a concrete example of this method on a standard data set of CI speech and demonstrate that CI classification accuracy improves by 2.3% over a strong baseline when using features produced by this method. This demonstration provides an example of how this method can be used to assist classification in fields where interpretability is important, such as health care.
A Generalized Zero-Shot Framework for Emotion Recognition from Body Gestures

Although automatic emotion recognition from facial expressions and speech has made remarkable progress, emotion recognition from body gestures has not been thoroughly explored. People often use a variety of body language to express emotions, and it is difficult to enumerate all emotional body gestures and collect enough samples for each category. Therefore, recognizing new emotional body gestures is critical for better understanding human emotions. However, the existing methods fail to accurately determine which emotional state a new body gesture belongs to. In order to solve this problem, we introduce a Generalized Zero-Shot Learning (GZSL) framework, which consists of three branches to infer the emotional state of the new body gestures with only their semantic descriptions. The first branch is a Prototype-Based Detector (PBD) which is used to determine whether an sample belongs to a seen body gesture category and obtain the prediction results of the samples from the seen categories. The second branch is a Stacked AutoEncoder (StAE) with manifold regularization, which utilizes semantic representations to predict samples from unseen categories. Note that both of the above branches are for body gesture recognition. We further add an emotion classifier with a softmax layer as the third branch in order to better learn the feature representations for this emotion classification task. The input features for these three branches are learned by a shared feature extraction network, i.e., a Bidirectional Long Short-Term Memory Networks (BLSTM) with a self-attention module. We treat these three branches as subtasks and use multi-task learning strategies for joint training. The performance of our framework on an emotion recognition dataset is significantly superior to the traditional method of emotion classification and state-of-the-art zero-shot learning methods.
Soft gamma-ray selected giant radio galaxies: an update

We present an update on the sample of soft gamma-ray selected giant radio galaxies (GRGs) extracted from INTEGRAL/IBIS and Swift/BAT surveys; it includes 8 new sources and one candidate object. In the new sample all, but one source, display FR II radio morphologies; the only exception is B21144+35B which is an FR I. The objects belong to both type 1 and 2 AGN optical classes and have redshifts in the range 0.06-0.35, while the radio sizes span from 0.7 to 1 Mpc. In this study, we present for the first time two objects that were never discussed as GRGs before and propose a new candidate GRG. We confirm the correlation between the X-ray luminosity and the radio core luminosity found for other soft gamma-ray selected GRGs and expected for AGNs powered by efficient accretion. We also corroborate previous results that indicate that the luminosity of the radio lobes is relatively low compared with the nuclear X-ray emission. This supports the idea that the nucleus of these GRGs is now more powerful than in the past, consistent with a restarting activity scenario
Corruption Is Not All Bad: Incorporating Discourse Structure Into Pre-Training via Corruption for Essay Scoring

Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation. However, the performance of parsers is not always adequate, especially when they are used on noisy texts, such as student essays. In this paper, we propose an unsupervised pre-training approach to capture discourse structure of essays in terms of coherence and cohesion that does not require any discourse parser or annotation. We introduce several types of token, sentence and paragraph-level corruption techniques for our proposed pre-training approach and augment masked language modeling pre-training with our pre-training method to leverage both contextualized and discourse information. Our proposed unsupervised approach achieves a new state-of-the-art result on the task of essay Organization scoring.
DES Y1 results: Splitting growth and geometry to test 
ΛCDM

We analyze Dark Energy Survey (DES) data to constrain a cosmological model where a subset of parameters -- focusing on $\Omega_m$ -- are split into versions associated with structure growth (e.g. $\Omega_m^{\rm grow}$) and expansion history (e.g. $\Omega_m^{\rm geo}$). Once the parameters have been specified for the $\Lambda$CDM cosmological model, which includes general relativity as a theory of gravity, it uniquely predicts the evolution of both geometry (distances) and the growth of structure over cosmic time. Any inconsistency between measurements of geometry and growth could therefore indicate a breakdown of that model. Our growth-geometry split approach therefore serves as both a (largely) model-independent test for beyond-$\Lambda$CDM physics, and as a means to characterize how DES observables provide cosmological information. We analyze the same multi-probe DES data as arXiv:1811.02375 : DES Year 1 (Y1) galaxy clustering and weak lensing, which are sensitive to both growth and geometry, as well as Y1 BAO and Y3 supernovae, which probe geometry. We additionally include external geometric information from BOSS DR12 BAO and a compressed Planck 2015 likelihood, and external growth information from BOSS DR12 RSD. We find no significant disagreement with $\Omega_m^{\rm grow}=\Omega_m^{\rm geo}$. When DES and external data are analyzed separately, degeneracies with neutrino mass and intrinsic alignments limit our ability to measure $\Omega_m^{\rm grow}$, but combining DES with external data allows us to constrain both growth and geometric quantities. We also consider a parameterization where we split both $\Omega_m$ and $w$, but find that even our most constraining data combination is unable to separately constrain $\Omega_m^{\rm grow}$ and $w^{\rm grow}$. Relative to $\Lambda$CDM, splitting growth and geometry weakens bounds on $\sigma_8$ but does not alter constraints on $h$.
Robust Two-Stream Multi-Feature Network for Driver Drowsiness Detection

Drowsiness driving is a major cause of traffic accidents and thus numerous previous researches have focused on driver drowsiness detection. Many drive relevant factors have been taken into consideration for fatigue detection and can lead to high precision, but there are still several serious constraints, such as most existing models are environmentally susceptible. In this paper, fatigue detection is considered as temporal action detection problem instead of image classification. The proposed detection system can be divided into four parts: (1) Localize the key patches of the detected driver picture which are critical for fatigue detection and calculate the corresponding optical flow. (2) Contrast Limited Adaptive Histogram Equalization (CLAHE) is used in our system to reduce the impact of different light conditions. (3) Three individual two-stream networks combined with attention mechanism are designed for each feature to extract temporal information. (4) The outputs of the three sub-networks will be concatenated and sent to the fully-connected network, which judges the status of the driver. The drowsiness detection system is trained and evaluated on the famous Nation Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset and we obtain an accuracy of 94.46%, which outperforms most existing fatigue detection models.
Shape-Texture Debiased Neural Network Training

Shape and texture are two prominent and complementary cues for recognizing objects. Nonetheless, Convolutional Neural Networks are often biased towards either texture or shape, depending on the training dataset. Our ablation shows that such bias degenerates model performance. Motivated by this observation, we develop a simple algorithm for shape-texture debiased learning. To prevent models from exclusively attending on a single cue in representation learning, we augment training data with images with conflicting shape and texture information (e.g., an image of chimpanzee shape but with lemon texture) and, most importantly, provide the corresponding supervisions from shape and texture simultaneously. Experiments show that our method successfully improves model performance on several image recognition benchmarks and adversarial robustness. For example, by training on ImageNet, it helps ResNet-152 achieve substantial improvements on ImageNet (+1.2%), ImageNet-A (+5.2%), ImageNet-C (+8.3%) and Stylized-ImageNet (+11.1%), and on defending against FGSM adversarial attacker on ImageNet (+14.4%). Our method also claims to be compatible to other advanced data augmentation strategies, e.g., Mixup and CutMix. The code is available here: https://github.com/LiYingwei/ShapeTextureDebiasedTraining.
Foliage Projective Cover,Effective Tool for Describing Structure of Plant Communities

Plant community coverage was often used as one of factors to describe the structure and importance of plant community.Usually it was estimated by sighting estimation in the field,and this procedure affected the accuracy of measurement of plant community.A useful concept,Foliage Projective Cover (FPC) that means the percentage of the land covered by foliage,was introduced,it had some ecological significances and was suitable for describing horizontal character of plant community.Using simple tool made by us,FPCs were measured at two sites in Xinjiang area.Through ecophysiological model of plant community developed using Stella model tool,we simulated the FPCs at those two sites.Compared with the measured data,we thought that FPC was a useful and an effective method and theory to describe the horizontal structure of plant community in arid area,and it may be used for measuring FPC for all plant communities.
High Area/Energy Efficiency RRAM CNN Accelerator with Kernel-Reordering Weight Mapping Scheme Based on Pattern Pruning

Resistive Random Access Memory (RRAM) is an emerging device for processing-in-memory (PIM) architecture to accelerate convolutional neural network (CNN). However, due to the highly coupled crossbar structure in the RRAM array, it is difficult to exploit the sparsity of the network in RRAM-based CNN accelerator. To optimize the weight mapping of sparse network in the RRAM array and achieve high area and energy efficiency, we propose a novel weight mapping scheme and corresponding RRAM-based CNN accelerator architecture based on pattern pruning and Operation Unit(OU) mechanism. Experimental results show that our work can achieve 4.16x-5.20x crossbar area efficiency, 1.98x-2.15x energy efficiency, and 1.15x-1.35x performance speedup in comparison with the traditional weight mapping method.
Generalized Rescaled P\'olya urn and its statistical applications.

We introduce the Generalized Rescaled Polya (GRP) urn. In particular, the GRP urn provides three different generative models for a chi-squared test of goodness of fit for the long-term probabilities of correlated data, generated by means of a reinforcement mechanism. Beside this statistical application, we point out that the GRP urn is a simple variant of the standard Eggenberger-Polya urn, that, with suitable choices of the parameters, shows "local" reinforcement, almost sure convergence of the empirical mean to a deterministic limit and different asymptotic behaviours of the predictive mean.