Miracle-dz/UniFAR-FTUs huggingface dataset:
Shear flows and their suppression at large aspect ratio: Two-dimensional simulations of a growing convection zone

We investigate the onset and evolution of zonal flows in a growing convective layer when a stably-stratified fluid with a composition gradient is cooled from above. This configuration allows the study of zonal flows for a wide range of values of the Rayleigh number, $Ra$, and aspect ratio of the convection zone within a given simulation. We perform a series of 2D simulations using the Boussinesq approximation, with aspect ratio of the computational domain between $1$ and $5$, and Prandtl number $Pr = 0.1$, 0.5, 1, and $7$. We find that for square domains zonal flows appear when the aspect ratio of the convective layer is smaller than two, and the evolution of the system depends on the Prandtl number. For $Pr\leq 1$, the fluid experiences bursts of convective transport with negligible convective transport between bursts. The magnitude and frequency of the bursts are smaller at low $Pr$, which suggests that the bursting regime is stronger in a narrow range around $Pr=1$, as observed in previous studies of thermal convection. For $Pr=7$, the structure of the flow consists of tilted convective plumes, and the convective transport is sustained at all times. In wider domains, the aspect ratio of the convective zone is always much larger than two and zonal flows do not appear. These results confirm and extend to fluids with stable composition gradients previous findings on thermal convection. The fact that zonal flows can be avoided by using computational domains with large aspect ratios opens up the possibility of 2D studies of convective overshoot, layer formation and transport properties across diffusive interfaces.
Computing the Information Content of Trained Neural Networks

How much information does a learning algorithm extract from the training data and store in a neural network's weights? Too much, and the network would overfit to the training data. Too little, and the network would not fit to anything at all. Na\"ively, the amount of information the network stores should scale in proportion to the number of trainable weights. This raises the question: how can neural networks with vastly more weights than training data still generalise? A simple resolution to this conundrum is that the number of weights is usually a bad proxy for the actual amount of information stored. For instance, typical weight vectors may be highly compressible. Then another question occurs: is it possible to compute the actual amount of information stored? This paper derives both a consistent estimator and a closed-form upper bound on the information content of infinitely wide neural networks. The derivation is based on an identification between neural information content and the negative log probability of a Gaussian orthant. This identification yields bounds that analytically control the generalisation behaviour of the entire solution space of infinitely wide networks. The bounds have a simple dependence on both the network architecture and the training data. Corroborating the findings of Valle-P\'erez et al. (2019), who conducted a similar analysis using approximate Gaussian integration techniques, the bounds are found to be both non-vacuous and correlated with the empirical generalisation behaviour at finite width.
The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games

Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings. This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems. In this work, we carefully study the performance of PPO in cooperative multi-agent settings. We show that PPO-based multi-agent algorithms achieve surprisingly strong performance in four popular multi-agent testbeds: the particle-world environments, the StarCraft multi-agent challenge, Google Research Football, and the Hanabi challenge, with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures. Importantly, compared to competitive off-policy methods, PPO often achieves competitive or superior results in both final returns and sample efficiency. Finally, through ablation studies, we analyze implementation and hyperparameter factors that are critical to PPO's empirical performance, and give concrete practical suggestions regarding these factors. Our results show that when using these practices, simple PPO-based methods can be a strong baseline in cooperative multi-agent reinforcement learning. Source code is released at \url{https://github.com/marlbenchmark/on-policy}.
Canonical quantization on the half-line and in an interval based upon an alternative concept for the momentum in a space with boundaries

For a particle moving on a half-line or in an interval the operator $\hat p = - i \partial_x$ is not self-adjoint and thus does not qualify as the physical momentum. Consequently canonical quantization based on $\hat p$ fails. Based upon a new concept for a self-adjoint momentum operator $\hat p_R$, we show that canonical quantization can indeed be implemented on the half-line and on an interval. Both the Hamiltonian $\hat H$ and the momentum operator $\hat p_R$ are endowed with self-adjoint extension parameters that characterize the corresponding domains $D(\hat H)$ and $D(\hat p_R)$ in the Hilbert space. When one replaces Poisson brackets by commutators, one obtains meaningful results only if the corresponding operator domains are properly taken into account. The new concept for the momentum is used to describe the results of momentum measurements of a quantum mechanical particle that is reflected at impenetrable boundaries, either at the end of the half-line or at the two ends of an interval.
Overdensity of VVV galaxies behind the Galactic bulge

Context. The extragalactic vision we have through the Milky Way is very unclear. There is significant extinction of the optical emission from objects located in the region called the zone of avoidance (ZOA). NIR wavelengths are less affected by extinction, and therefore the infrared surveys in this zone are a potential source of astronomical discoveries. Nevertheless, these observations need to be compared with cosmological simulations in order to carry out high−accuracy studies. Aims. Our aim is to identify extragalactic sources in the ZOA, using infrared images of the VVV survey. We consider mock galaxy catalogues in order to interpret observational results. Methods. We studied a region of 1.636 square degrees corresponding to the VVV tile b204. Using SExtractor, we analysed photometric data generating a catalogue of extended sources in this area. In order to confirm these sources as galaxy candidates we visually inspected RGB images looking for typical galaxy features. Using 2MASX and GCMW catalogued sources we tested completeness and contamination of our catalogue and define suitable colour cuts to select galaxies. We also compared the observational results with those obtained from two semi-analytical models on Dark Matter simulations. One galaxy catalogue was constructed with the SAG semi-analytic model of galaxy formation, and the other one was constructed with the L-Galaxies semi-analytic model. Results. By adopting CLASS_STAR < 0.5, r1/2 > 0.7 arcsec and specific colour cuts (J − Ks > 0.97, J − H > 0 and H − Ks > 0) we generated an automatic catalogue of extended sources. After visual inspection we identified 624 sources with 10 < Ks < 17 as galaxy candidates. The contamination of the automatic catalogue is 28% when considering visually confirmed galaxies as reliable objects. The estimated completeness is 87% up to magnitude Ks = 13.5. We analysed the spatial distribution of galaxy candidates, finding a high concentration of galaxies in a small region of 15 arcmin radius. This region has three times higher density than similar areas in the tile. We compared the number of galaxies in this small area with the mean density values obtained from a suitable sample of galaxies from semi-analytic models finding that our results are consistent with an overdensity region. Conclusions. Using VVV near-infrared data and mock catalogues we detect new extragalactic sources that have not been identified by other catalogues. We demonstrate the potentiality of the VVV survey in finding and studying a large number of galaxy candidates and extragalactic structures obscured by the Milky Way.
Projected Rotational Velocities and Fundamental Properties of Low-mass Pre-main-sequence Stars in the Taurus–Auriga Star-forming Region

The projected stellar rotational velocity ( ) is critical for our understanding of processes related to the evolution of angular momentum in pre-main-sequence stars. We present measurements of high-resolution infrared and optical spectroscopy for 70 pre-main-sequence stars in the Taurus–Auriga star-forming region, in addition to effective temperatures measured from line-depth ratios, as well as stellar rotation periods determined from optical photometry. From the literature, we identified the stars in our sample that show evidence of residing in circumstellar disks or multiple systems. The comparison of infrared measurements calculated using two techniques shows a residual scatter of ∼1.8 km s−1, defining a typical error floor for the of pre-main-sequence stars from infrared spectra. A comparison of the distributions of stars with and without companions shows that binaries/multiples typically have a higher measured , which may be caused by contamination by companion lines, shorter disk lifetimes in binary systems, or tidal interactions in hierarchical triples. A comparison of optical and infrared values shows no significant difference regardless of whether the star has a disk or not, indicating that CO contamination from the disk does not impact measurements above the typical ∼1.8 km s−1 error floor of our measurements. Finally, we observe a lack of a correlation between the , presence of a disk, and H-R diagram position, which indicates a complex interplay between stellar rotation and evolution of pre-main-sequence stars.
Learning-Based Bias Correction for Time Difference of Arrival Ultra-Wideband Localization of Resource-Constrained Mobile Robots

Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization is a promising lightweight, low-cost solution that can scale to a large number of devices—making it especially suited for resource-constrained multi-robot applications. However, the localization accuracy of standard, commercially available UWB radios is often insufficient due to significant measurement bias and outliers. In this letter, we address these issues by proposing a robust UWB TDOA localization framework comprising of (i) learning-based bias correction and (ii) M-estimation-based robust filtering to handle outliers. The key properties of our approach are that (i) the learned biases generalize to different UWB anchor setups and (ii) the approach is computationally efficient enough to run on resource-constrained hardware. We demonstrate our approach on a Crazyflie nano-quadcopter. Experimental results show that the proposed localization framework, relying only on the onboard IMU and UWB, provides an average of 42.08% localization error reduction (in three different anchor setups) compared to the baseline approach without bias compensation. We also show autonomous trajectory tracking on a quadcopter using our UWB TDOA localization approach.
Pseudogauge dependence of quantum fluctuations of the energy in a hot relativistic gas of fermions

Explicit expressions for quantum fluctuations of energy in subsystems of a hot relativistic gas of spin-1/2 particles are derived. The results depend on the form of the energy-momentum tensor used in the calculations, which is a feature described as pseudo-gauge dependence. However, for sufficiently large subsystems the results obtained in different pseudo-gauges converge and agree with the canonical-ensemble formula known from statistical physics. As different forms of the energymomentum tensor of a gas are a priori equivalent, our finding suggests that the concept of quantum fluctuations of energy in very small thermodynamic systems is pseudo-gauge dependent. On the practical side, the results of our calculations determine a scale of coarse graining for which the choice of the pseudo-gauge becomes irrelevant.
Follow Your Nose - Which Code Smells are Worth Chasing?

The common use case of code smells assumes causality: Identify a smell, remove it, and by doing so improve the code. We empirically investigate their fitness to this use. We present a list of properties that code smells should have if they indeed cause lower quality. We evaluated the smells in 31,687 Java files from 677 GitHub repositories, all the repositories with 200+ commits in 2019. We measured the influence of smells on four metrics for quality, productivity, and bug detection efficiency. Out of 151 code smells computed by the CheckStyle smell detector, less than 20% were found to be potentially causal, and only a handful are rather robust. The strongest smells deal with simplicity, defensive programming, and abstraction. Files without the potentially causal smells are 50% more likely to be of high quality. Unfortunately, most smells are not removed, and developers tend to remove the easy ones and not the effective ones.
Numérations cellulaires du lait de chèvre

Resume - Contrairement a ce qui est couramment pratique dans l'espece bovine, l'utilisation des numerations cellulaires comme methode permettant d'apprecier la qualite du lait fait l'objet dans l'espece caprine de nombreuses controverses; la nature des differentes populations cellulaires, les variations des numerations et leur incidence sur la qualite du lait sont successivement examinees. Les auteurs concluent sur la difficulte de definir des normes en l'absence d'une definition prealable de critere precis de qualite du lait. lait de chevre 1qualite 1cellule somatique 1numeration cellulaire Summary - Somatic cell counts ln goat's mllk. Contrary to cutrent practice for cow's milk, somatie cell counts for estimating goat's milk quality is still a subject of controversy. The influence on milk quality of the different soma tic cell populations and the variations in cell counts has been reviewed. The accuracy of enumeration methods has been discussed. In conclusion, it appears difficult to define precise regulations for soma tic cell counts in goat's milk before a general agreement has been reached on specifie criteria regarding milk quality.
Information Discrepancy in Strategic Learning

We initiate the study of the effects of non-transparency in decision rules on individuals' ability to improve in strategic learning settings. Inspired by real-life settings, such as loan approvals and college admissions, we remove the assumption typically made in the strategic learning literature, that the decision rule is fully known to individuals, and focus instead on settings where it is inaccessible. In their lack of knowledge, individuals try to infer this rule by learning from their peers (e.g., friends and acquaintances who previously applied for a loan), naturally forming groups in the population, each with possibly different type and level of information regarding the decision rule. We show that, in equilibrium, the principal's decision rule optimizing welfare across sub-populations may cause a strong negative externality: the true quality of some of the groups can actually deteriorate. On the positive side, we show that, in many natural cases, optimal improvement can be guaranteed simultaneously for all sub-populations. We further introduce a measure we term information overlap proxy, and demonstrate its usefulness in characterizing the disparity in improvements across sub-populations. Finally, we identify a natural condition under which improvement can be guaranteed for all sub-populations while maintaining high predictive accuracy. We complement our theoretical analysis with experiments on real-world datasets.
What Are We Really Testing in Mutation Testing for Machine Learning? A Critical Reflection

Mutation testing is a well-established technique for assessing a test suite's quality by injecting artificial faults into production code. In recent years, mutation testing has been extended to machine learning (ML) systems, and deep learning (DL) in particular; researchers have proposed approaches, tools, and statistically sound heuristics to determine whether mutants in DL systems are killed or not. However, as we will argue in this work, questions can be raised to what extent currently used mutation testing techniques in DL are actually in line with the classical interpretation of mutation testing. We observe that ML model development resembles a test-driven development (TDD) process, in which a training algorithm ('programmer') generates a model (program) that fits the data points (test data) to labels (implicit assertions), up to a certain threshold. However, considering proposed mutation testing techniques for ML systems under this TDD metaphor, in current approaches, the distinction between production and test code is blurry, and the realism of mutation operators can be challenged. We also consider the fundamental hypotheses underlying classical mutation testing: the competent programmer hypothesis and coupling effect hypothesis. As we will illustrate, these hypotheses do not trivially translate to ML system development, and more conscious and explicit scoping and concept mapping will be needed to truly draw parallels. Based on our observations, we propose several action points for better alignment of mutation testing techniques for ML with paradigms and vocabularies of classical mutation testing.
Pricing high-dimensional Bermudan options with hierarchical tensor formats

An efficient compression technique based on hierarchical tensors for popular option pricing methods is presented. It is shown that the"curse of dimensionality"can be alleviated for the computation of Bermudan option prices with the Monte Carlo least-squares approach as well as the dual martingale method, both using high-dimensional tensorized polynomial expansions. This discretization allows for a simple and computationally cheap evaluation of conditional expectations. Complexity estimates are provided as well as a description of the optimization procedures in the tensor train format. Numerical experiments illustrate the favourable accuracy of the proposed methods. The dynamical programming method yields results comparable to recent Neural Network based methods.
Double Coverage with Machine-Learned Advice

We study the fundamental online k-server problem in a learning-augmented setting. While in the traditional online model, an algorithm has no information about the request sequence, we assume that there is given some advice (e.g. machine-learned predictions) on an algorithm's decision. There is, however, no guarantee on the quality of the prediction and it might be far from being correct. Our main result is a learning-augmented variation of the well-known Double Coverage algorithm for k-server on the line (Chrobak et al., SIDMA 1991) in which we integrate predictions as well as our trust into their quality. We give an error-dependent competitive ratio, which is a function of a user-defined confidence parameter, and which interpolates smoothly between an optimal consistency, the performance in case that all predictions are correct, and the best-possible robustness regardless of the prediction quality. When given good predictions, we improve upon known lower bounds for online algorithms without advice. We further show that our algorithm achieves for any k an almost optimal consistency-robustness tradeoff, within a class of deterministic algorithms respecting local and memoryless properties. Our algorithm outperforms a previously proposed (more general) learning-augmented algorithm. It is remarkable that the previous algorithm crucially exploits memory, whereas our algorithm is memoryless. Finally, we demonstrate in experiments the practicability and the superior performance of our algorithm on real-world data.
The tidal evolution of dark matter substructure – II. The impact of artificial disruption on subhalo mass functions and radial profiles

Several recent studies have indicated that artificial subhalo disruption (the spontaneous, non-physical disintegration of a subhalo) remains prevalent in state-of-the-art dark matter (DM)-only cosmological simulations. In order to quantify the impact of disruption on the inferred subhalo demographics, we augment the semi-analytical SatGen dynamical subhalo evolution model with an improved treatment of tidal stripping that is calibrated using the Dynamical Aspects of SubHaloes database of idealized high-resolution simulations of subhalo evolution, which are free from artificial disruption. We also develop a model of artificial disruption that reproduces the statistical properties of disruption in the Bolshoi simulation. Using this framework, we predict subhalo mass functions (SHMFs), number density profiles, and substructure mass fractions and study how these quantities are impacted by artificial disruption and mass resolution limits. We find that artificial disruption affects these quantities at the $10{-}20{{\ \rm per\ cent}}$ level, ameliorating previous concerns that it may suppress the SHMF by as much as a factor of 2. We demonstrate that semi-analytical substructure modelling must include orbit integration in order to properly account for splashback haloes, which make up roughly half of the subhalo population. We show that the resolution limit of N-body simulations, rather than artificial disruption, is the primary cause of the radial bias in subhalo number density found in DM-only simulations. Hence, we conclude that the mass resolution remains the primary limitation of using such simulations to study subhaloes. Our model provides a fast, flexible, and accurate alternative to studying substructure statistics in the absence of both numerical resolution limits and artificial disruption.