Miracle-dz/UniFAR-FTUs huggingface dataset:
Equilibrium stability analysis of hyperbolic shallow water moment equations

In this paper, we analyze the stability of equilibrium manifolds of hyperbolic shallow water moment equations. Shallow water moment equations describe shallow flows for complex velocity profiles which vary in vertical direction and the models can be seen as extensions of the standard shallow water equations. Equilibrium stability is an important property of balance laws that determines the linear stability of solutions in the vicinity of equilibrium manifolds, and it is seen as a necessary condition for stable numerical solutions. After an analysis of the hyperbolic structure of the models, we identify three different stability manifolds based on three different limits of the right‐hand side friction term, which physically correspond to water‐at‐rest, constant‐velocity, and bottom‐at‐rest velocity profiles. The stability analysis then shows that the structural stability conditions are fulfilled for the water‐at‐rest equilibrium and the constant‐velocity equilibrium. However, the bottom‐at‐rest equilibrium can lead to instable modes depending on the velocity profile. Relaxation toward the respective equilibrium manifolds is investigated numerically for different models.
Alternative reproductive tactics and lifetime reproductive success in a polygynandrous mammal

The widespread occurrence of alternative reproductive tactics (ARTs) highlights the 1 diverse ways in which sexual selection can operate within a population. We studied ARTs in 2 Columbian ground squirrels ( Urocitellus columbianus ), evaluating paternity, lifetime 3 reproductive success, and life histories. Reproductively mature male Columbian ground squirrels 4 displayed either a territorial or satellite (non-territorial) tactic. Territorial males secured a higher 5 proportion of copulations, were more likely to mate at earlier positions in females’ mating 6 sequences and sired more offspring than satellite males. The tactic males adopted was largely a 7 function of weight, age, and experience, with larger, older and more experienced males 8 displaying the territorial tactic. At two years of age, males adopted the satellite tactic or deferred 9 breeding for the season. Two-year-old satellite males were heavier than males that deferred 10 breeding, but by age three there was no difference between their weights. Males that deferred 11 breeding either adopted the satellite or territorial tactic in the following year, with the lighter 12 males displaying the satellite tactic. The ARTs that males adopted at two and three years of age 13 led to alternative life history pathways. Males that deferred breeding and only adopted the 14 territorial tactic during their lifetime had twice the number of lifetime offspring compared to 15 males that adopted the satellite tactic and then switched to territorial behavior in following years, 16 although this difference was not statistically significant. Our findings show that the satellite 17 tactic results in lower reproductive output both within a single year, except at age two, and over 18 their lifetime. 19
Tidally perturbed pulsations in the pre-main sequence δ Scuti binary RS Cha

Context. Stellar components in binaries are subject to tidal forces which influence asteroseismic properties. Tidally pertubed pulsations have been reported for different objects but none of these are in their pre-main sequence phase of evolution. This makes RS Cha, consisting of two δ Scuti stars and with pulsational characteristics influenced by tidal effects, the first such object observed. Aims. We aim to investigate the pulsational properties of the eclipsing binary RS Cha in terms of the theory of tidally perturbed pulsations. Methods. Based on photometric time series obtained from the TESS satellite, we performed binary modelling using PHOEBE to interpret the binary light curve and to allow the investigation of the pulsations of both components in RS Cha. We modelled the detrended light curve with the superposition of linear modes. The frequencies were then interpreted as self excited modes perturbed by tidal forces. Results. We find evidence for tidally perturbed modes, which enables the identification of pulsation modes. RS Cha mainly exhibits dipole modes, while one prominent l = 2 or l = 3 mode is also inferred. The latter verifies previous results from spectroscopic time series. Conclusion. This work shows that RS Cha is an ideal candidate to test the theory of tidally perturbed pulsations within the framework of asteroseismic modelling. The identification of multiple pulsation modes using this theory is unprecedented and will be a keystone in the future of pre-main sequence asteroseismology. However, amplitude modulation caused by the changing light ratio during the orbital phase in an eclipsing binary also plays a significant role, which can complicate mode identification.
Traveling waves are hydrodynamically optimal for long-wavelength flagella

Swimming eukaryotic microorganisms such as spermatozoa, algae and ciliates self-propel in viscous fluids using travelling wave-like deformations of slender appendages called flagella. Waves are predominant because Purcell's scallop theorem precludes time-reversible kinematics for locomotion. Using the calculus of variations on a periodic long-wavelength model of flagellar swimming, we show that the planar flagellar kinematics maximising the time-averaged propulsive force for a fixed amount of energy dissipated in the surrounding fluid correspond for all times to waves travelling with constant speed, potentially on a curved centreline, with propulsion always in the direction opposite to the wave.
Learning Canonical Transformations

Humans understand a set of canonical geometric transformations (such as translation and rotation) that support generalization by being untethered to any specific object. We explore inductive biases that help a neural network model learn these transformations in pixel space in a way that can generalize out-of-domain. Specifically, we find that high training set diversity is sufficient for the extrapolation of translation to unseen shapes and scales, and that an iterative training scheme achieves significant extrapolation of rotation in time.
Curiosity Based Reinforcement Learning on Robot Manufacturing Cell

This paper introduces a novel combination of scheduling control on a flexible robot manufacturing cell with curiosity based reinforcement learning. Reinforcement learning has proved to be highly successful in solving tasks like robotics and scheduling. But this requires hand tuning of rewards in problem domains like robotics and scheduling even where the solution is not obvious. To this end, we apply a curiosity based reinforcement learning, using intrinsic motivation as a form of reward, on a flexible robot manufacturing cell to alleviate this problem. Further, the learning agents are embedded into the transportation robots to enable a generalized learning solution that can be applied to a variety of environments. In the first approach, the curiosity based reinforcement learning is applied to a simple structured robot manufacturing cell. And in the second approach, the same algorithm is applied to a graph structured robot manufacturing cell. Results from the experiments show that the agents are able to solve both the environments with the ability to transfer the curiosity module directly from one environment to another. We conclude that curiosity based learning on scheduling tasks provide a viable alternative to the reward shaped reinforcement learning traditionally used.
Exploring Intermediate Representation for Monocular Vehicle Pose Estimation

We present a new learning-based framework to recover vehicle pose in SO(3) from a single RGB image. In contrast to previous works that map local appearance to observation angles, we explore a progressive approach by extracting meaningful Intermediate Geometrical Representations (IGRs) to estimate egocentric vehicle orientation. This approach features a deep model that transforms perceived intensities to IGRs, which are mapped to a 3D representation encoding object orientation in the camera coordinate system. Core problems are what IGRs to use and how to learn them more effectively. We answer the former question by designing IGRs based on an interpolated cuboid that derives from primitive 3D annotation readily. The latter question motivates us to incorporate geometry knowledge with a new loss function based on a projective invariant. This loss function allows unlabeled data to be used in the training stage to improve representation learning. Without additional labels, our system outperforms previous monocular RGB-based methods for joint vehicle detection and pose estimation on the KITTI benchmark, achieving performance even comparable to stereo methods. Code and pre-trained models are available at this HTTPS URL1.
Dynamic Occupancy Grid Mapping with Recurrent Neural Networks

Modeling and understanding the environment is an essential task for autonomous driving. In addition to the detection of objects, in complex traffic scenarios the motion of other road participants is of special interest. Therefore, we propose to use a recurrent neural network to predict a dynamic occupancy grid map, which divides the vehicle surrounding in cells, each containing the occupancy probability and a velocity estimate. During training, our network is fed with sequences of measurement grid maps, which encode the lidar measurements of a single time step. Due to the combination of convolutional and recurrent layers, our approach is capable to use spatial and temporal information for the robust detection of static and dynamic environment. In order to apply our approach with measurements from a moving ego-vehicle, we propose a method for ego-motion compensation that is applicable in neural network architectures with recurrent layers working on different resolutions. In our evaluations, we compare our approach with a state-of-the-art particle-based algorithm on a large publicly available dataset to demonstrate the improved accuracy of velocity estimates and the more robust separation of the environment in static and dynamic area. Additionally, we show that our proposed method for ego-motion compensation leads to comparable results in scenarios with stationary and with moving ego-vehicle.
An Interpretable Machine Learning Model for Deformation of Multi-Walled Carbon Nanotubes

We present a novel interpretable machine learning model to accurately predict complex rippling deformations of Multi-Walled Carbon Nanotubes(MWCNTs) made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitive for such large systems. To overcome this bottleneck, we have developed a machine learning model that consists of a novel dimensionality reduction technique and a deep neural network-based learning in the reduced dimension. The proposed nonlinear dimensionality reduction technique extends the functional principal component analysis to satisfy the constraint of deformation. Its novelty lies in designing a function space that satisfies the constraint exactly, which is crucial for efficient dimensionality reduction. Owing to the dimensionality reduction and several other strategies adopted in the present work, learning through deep neural networks is remarkably accurate. The proposed model accurately matches an atomistic-physics-based model while being orders of magnitude faster. It extracts universally dominant patterns of deformation in an unsupervised manner. These patterns are comprehensible and elucidate how the model predicts, yielding interpretability. The proposed model can form a basis for the exploration of machine learning toward the mechanics of one and two-dimensional materials.
Extremely deep 150 MHz source counts from the LoTSS Deep Fields

With the advent of new generation low-frequency telescopes, such as the LOw Frequency ARray (LOFAR), and improved calibration techniques, we have now started to unveil the subgigahertz radio sky with unprecedented depth and sensitivity. The LOFAR Two Meter Sky Survey (LoTSS) is an ongoing project in which the whole northern radio sky will be observed at 150 MHz with a sensitivity better than 100 μJy beam−1 at a resolution of 6′′. Additionally, deeper observations are planned to cover smaller areas with higher sensitivity. The Lockman Hole, the Boötes, and the Elais-N1 regions are among the most well known northern extra-galactic fields and the deepest of the LoTSS Deep Fields so far. We exploited these deep observations to derive the deepest radio source counts at 150 MHz to date. Our counts are in broad agreement with those from the literature and show the well known upturn at ≤1 mJy, mainly associated with the emergence of the star-forming galaxy population. More interestingly, our counts show, for the first time a very pronounced drop around S ~ 2 mJy, which results in a prominent “bump” at sub-mJy flux densities. Such a feature was not observed in previous counts’ determinations (neither at 150 MHz nor at a higher frequency). While sample variance can play a role in explaining the observed discrepancies, we believe this is mostly the result of a careful analysis aimed at deblending confused sources and removing spurious sources and artifacts from the radio catalogs. This “drop and bump” feature cannot be reproduced by any of the existing state-of-the-art evolutionary models, and it appears to be associated with a deficiency of active galactic nuclei (AGN) at an intermediate redshift (1 < z < 2) and an excess of low-redshift (z < 1) galaxies and/or AGN.
Learn2Sing: Target Speaker Singing Voice Synthesis by Learning from a Singing Teacher

Singing voice synthesis has been paid rising attention with the rapid development of speech synthesis area. In general, a studio-level singing corpus is usually necessary to produce a natural singing voice from lyrics and music-related transcription. However, such a corpus is difficult to collect since it’s hard for many of us to sing like a professional singer. In this paper, we propose an approach – Learn2Sing that only needs a singing teacher to generate the target speakers’ singing voice without their singing voice data. In our approach, a teacher’s singing corpus and speech from multiple target speakers are trained in a frame-level auto-regressive acoustic model where singing and speaking share the common speaker embedding and style tag embedding. Meanwhile, since there is no music-related transcription for the target speaker, we use log-scale fundamental frequency (LF0) as an auxiliary feature as the inputs of the acoustic model for building a unified input representation. In order to enable the target speaker to sing without singing reference audio in the inference stage, a duration model and an LF0 prediction model are also trained. Particularly, we employ domain adversarial training (DAT) in the acoustic model, which aims to enhance the singing performance of target speakers by disentangling style from acoustic features of singing and speaking data. Our experiments indicate that the proposed approach is capable of synthesizing singing voice for target speaker given only their speech samples.
Colliding stellar wind modelling of the X-ray emission from WR 140

We modelled the Chandra and RXTE X-ray spectra of the massive binary WR 140 in the framework of the standard colliding stellar wind (CSW) picture. Models with partial electron heating at the shock fronts are a better representation of the X-ray data than those with complete temperature equalization. Emission measure of the X-ray plasma in the CSW region exhibits a considerable decrease at orbital phases near periastron. This is equivalent to variable effective mass-loss rates over the binary orbit. At orbital phases near periastron, a considerable X-ray absorption in excess to that from the stellar winds in WR 140 is present. The standard CSW model provides line profiles that in general do not match well the observed line profiles of the strong line features in the X-ray spectrum of WR 140. The variable effective mass-loss rate could be understood qualitatively in CSW picture of clumpy stellar winds where clumps are efficiently dissolved in the CSW region near apastron but not at periastron. However, future development of CSW models with non-spherically-symmetric stellar winds might be needed to get a better correspondence between theory and observations.
Domain Adaptation based Interpretable Image Emotion Recognition using Facial Expression Recognition

A domain adaptation technique has been proposed in this paper to identify the emotions in generic images containing facial&non-facial objects and non-human components. It addresses the challenge of the insufficient availability of pre-trained models and well-annotated datasets for image emotion recognition (IER). It starts with proposing a facial emotion recognition (FER) system and then moves on to adapting it for image emotion recognition. First, a deep-learning-based FER system has been proposed that classifies a given facial image into discrete emotion classes. Further, an image recognition system has been proposed that adapts the proposed FER system to recognize the emotions portrayed by images using domain adaptation. It classifies the generic images into 'happy,' 'sad,' 'hate,' and 'anger' classes. A novel interpretability approach, Divide and Conquer based Shap (DnCShap), has also been proposed to interpret the highly relevant visual features for emotion recognition. The proposed system's architecture has been decided through ablation studies, and the experiments are conducted on four FER and four IER datasets. The proposed IER system has shown an emotion classification accuracy of 59.61% for the IAPSa dataset, 57.83% for the ArtPhoto dataset, 67.93% for the FI dataset, and 55.13% for the EMOTIC dataset. The important visual features leading to a particular emotion class have been identified, and the embedding plots for various emotion classes have been analyzed to explain the proposed system's predictions.
Variation of Dust Properties with Cosmic Time Implied by Radiative Torque Disruption

Dust properties within a galaxy are known to change from the diffuse medium to dense clouds due to increased local gas density. However, the question of whether dust properties change with redshift remains elusive. In this paper, using the fact that the mean radiation intensity of the interstellar medium (ISM) of star-forming galaxies increases with redshift, we show that dust properties should change due to increasing efficiency of rotational disruption by radiative torques, an effect named radiative torque disruption (RATD). We first show that because of RATD, the size distribution of interstellar dust varies with redshift, for instance, dust grains become smaller at higher z. We model the extinction curves and find that the curve becomes steeper with increasing redshift. The ratio of total-to-selective extinction, RV, decreases with redshift and achieves low values of RV ∼ 1.5–2.5 for grains having a composite structure. We also find that dust properties change with the local gas density because of RATD, but the change is dominated by the radiation field for the diffuse ISM. The low values of RV implied by RATD of interstellar dust could reproduce anomalous dust extinction observed toward supernovae Ia and Small Magellanic Cloud-like extinction curves with a steep far-UV rise toward high-z galaxies. Fluctuations in RV due to interstellar turbulence and varying radiation intensity may resolve the tension in measurements of the Hubble constant using supernovae Ia. We finally discuss the implications of evolving dust properties for high-z astrophysics.
A statistical machine learning approach for benchmarking in the presence of complex contextual factors and peer groups

The ability to compare between individuals or organisations fairly is important for the development of robust and meaningful quantitative benchmarks. To make fair comparisons, contextual factors must be taken into account, and comparisons should only be made between similar organisations such as peer groups. Previous benchmarking methods have used linear regression to adjust for contextual factors, however linear regression is known to be sub-optimal when nonlinear relationships exist between the comparative measure and covariates. In this paper we propose a random forest model for benchmarking that can adjust for these potential nonlinear relationships, and validate the approach in a case-study of high noise data. We provide new visualisations and numerical summaries of the fitted models and comparative measures to facilitate interpretation by both analysts and non-technical audiences. Comparisons can be made across the cohort or within peer groups, and bootstrapping provides a means of estimating uncertainty in both adjusted measures and rankings. We conclude that random forest models can facilitate fair comparisons between organisations for quantitative measures including in cases on complex contextual factor relationships, and that the models and outputs are readily interpreted by stakeholders.
Spatio-Temporal Analysis of Facial Actions using Lifecycle-Aware Capsule Networks

Most state-of-the-art approaches for Facial Action Unit (AU) detection rely upon evaluating facial expressions from static frames, encoding a snapshot of heightened facial activity. In real-world interactions, however, facial expressions are usually more subtle and evolve in a temporal manner requiring AU detection models to learn spatial as well as temporal information. In this paper, we focus on both spatial and spatio-temporal features encoding the temporal evolution of facial AU activation. For this purpose, we propose the Action Unit Lifecycle-Aware Capsule Network (AULA-Caps) that performs AU detection using both frame and sequence-level features. While at the frame-level the capsule layers of AULA-Caps learn spatial feature primitives to determine AU activations, at the sequence-level, it learns temporal dependencies between contiguous frames by focusing on relevant spatio-temporal segments in the sequence. The learnt feature capsules are routed together such that the model learns to selectively focus more on spatial or spatio-temporal information depending upon the AU lifecycle. The proposed model is evaluated on the commonly used BP4D and GFT benchmark datasets obtaining state-of-the-art results on both the datasets.
It\^{o}'s Formula for Noncommutative $C^2$ Functions of Free It\^{o} Processes with Respect to Circular Brownian Motion.

In a recent paper, the author introduced a rich class $NC^k(\mathbb{R})$ of "noncommutative $C^k$" functions $\mathbb{R} \to \mathbb{C}$ whose operator functional calculus is $k$-times differentiable and has derivatives expressible in terms of multiple operator integrals (MOIs). In this paper, we explore a connection between free stochastic calculus and the theory of MOIs by proving a "functional free Itō formula" for noncommutative $C^2$ functions of self-adjoint free Itō processes with respect to circular Brownian motion. To do this, we extend P. Biane and R. Speicher's theory of free stochastic calculus to allow free Itō processes involving circular Brownian motion and its adjoint -- instead of only semicircular Brownian motion -- and then reinterpret the quantities in the resultant Itō-type formulas as MOIs. As motivation, we also prove a "functional Itō formula" for $C^2$ functions of Itō processes with respect to $n \times n$ matrix Brownian motion.