Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph representation learning for street networks

Streets networks provide an invaluable source of information about the different temporal and spatial patterns emerging in our cities. These streets are often represented as graphs where intersections are modelled as nodes and streets as links between them. Previous work has shown that raster representations of the original data can be created through a learning algorithm on low-dimensional representations of the street networks. In contrast, models that capture high-level urban network metrics can be trained through convolutional neural networks. However, the detailed topological data is lost through the rasterisation of the street network. The models need help to recover this information from the image alone, failing to capture complex street network features. This paper proposes a model capable of inferring good representations directly from the street network. Specifically, we use a variational autoencoder with graph convolutional layers and a decoder that outputs a probabilistic fully-connected graph to learn latent representations that encode both local network structure and the spatial distribution of nodes. We train the model on thousands of street network segments and use the learnt representations to generate synthetic street configurations. Finally, we proposed a possible application to classify the urban morphology of different network segments by investigating their common characteristics in the learnt space.
Asymptotic closure model for inertial particle transport in turbulent boundary layers

Transport equations for heavy inertial particles in turbulent boundary layers may be derived from an underlying phase-space probability density function (PDF) equation. These equations, however, are unclosed, and the standard closure approach is to use a quasi-Normal approximation (QNA) in which the fourth moments are approximated as behaving as if the velocities were Normally distributed. Except for particles with weak inertia, the QNA leads to large quantitative errors, and is not consistent with the known asymptotic predictions of Sikovsky (Flow Turbulence Combust, vol. 92, 2014, pp. 41-64) for the moments of the PDF in the viscous sublayer. We derive a new closure approximation based on an asymptotic solution to the transport equations in regions where the effect of particle inertia is significant. The new closure is consistent with the asymptotic predictions of Sikovsky, but applies even outside the viscous sublayer. Comparisons with direct numerical simulations (DNS) show that the new closure gives similar results to the QNA (with the QNA results in slightly better agreement with the DNS) when the viscous Stokes number is $St<10$, but for $St>10$ the new model is in far better agreement with the DNS than the QNA. While the predictions from the new model leave room for improvement, the results suggest that this new closure strategy is a very effective alternative to the traditional QNA approach, and the closure could be refined in future work.
Rotational hot Brownian motion

We establish an effective Markov theory for the rotational Brownian motion of hot nanobeads and nanorods. Compact analytical expressions for the effective temperature and friction are derived from the fluctuating hydrodynamic equations of motion. They are verified by comparison with recent measurements and with parallel molecular dynamics simulations over a wide temperature range. This provides unique insights into the physics of hot Brownian motion and an excellent starting point for further experimental tests and applications involving laser-heated nanobeads, nanorods and Janus particles.
Thermodynamics of classical integrable field theories

We investigate the thermodynamics of integrable classical field theories under the effect of a random initial configuration, motivated by the nonequilibrium evolution of quantum field theories. The approach to thermal equilibrium is analysed with numerical simulations and confronted with analytical results. The possibility of comparing the classical results with their quantum counterparts could be a way of inferring the behaviour of out-of-equilibrium quantum field theories during the thermalization process.
Going for GOAL: A Resource for Grounded Football Commentaries

Recent video+language datasets cover domains where the interaction is highly structured, such as instructional videos, or where the interaction is scripted, such as TV shows. Both of these properties can lead to spurious cues to be exploited by models rather than learning to ground language. In this paper, we present GrOunded footbAlL commentaries (GOAL), a novel dataset of football (or `soccer') highlights videos with transcribed live commentaries in English. As the course of a game is unpredictable, so are commentaries, which makes them a unique resource to investigate dynamic language grounding. We also provide state-of-the-art baselines for the following tasks: frame reordering, moment retrieval, live commentary retrieval and play-by-play live commentary generation. Results show that SOTA models perform reasonably well in most tasks. We discuss the implications of these results and suggest new tasks for which GOAL can be used. Our codebase is available at: https://gitlab.com/grounded-sport-convai/goal-baselines.
ParGAN: Learning Real Parametrizable Transformations

Current methods for image-to-image translation produce compelling results, however, the applied transformation is difficult to control, since existing mechanisms are often limited and non-intuitive. We propose ParGAN, a generalization of the cycle-consistent GAN framework to learn image transformations with simple and intuitive controls. The proposed generator takes as input both an image and a parametrization of the transformation. We train this network to preserve the content of the input image while ensuring that the result is consistent with the given parametrization. Our approach does not require paired data and can learn transformations across several tasks and datasets. We show how, with disjoint image domains with no annotated parametrization, our framework can create smooth interpolations as well as learn multiple transformations simultaneously.
Frozen Gaussian Sampling for Scalar Wave Equations

In this article, we introduce the frozen Gaussian sampling (FGS) algorithm to solve the scalar wave equation in the high-frequency regime. The FGS algorithm is a Monte Carlo sampling strategy based on the frozen Gaussian approximation, which greatly reduces the computation workload in the wave propagation and reconstruction. In this work, we propose feasible and detailed procedures to implement the FGS algorithm to approximate scalar wave equations with Gaussian initial conditions and WKB initial conditions respectively. For both initial data cases, we rigorously analyze the error of applying this algorithm to wave equations of dimensionality $d \geq 3$. In Gaussian initial data cases, we prove that the sampling error due to the Monte Carlo method is independent of the typical wave number. We also derive a quantitative bound of the sampling error in WKB initial data cases. Finally, we validate the performance of the FGS and the theoretical estimates about the sampling error through various numerical examples, which include using the FGS to solve wave equations with both Gaussian and WKB initial data of dimensionality $d = 1, 2$, and $3$.
Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning

In this paper, we build on advances introduced by the Deep Q-Networks (DQN) approach to extend the multi-objective tabular Reinforcement Learning (RL) algorithm W-learning to large state spaces. W-learning algorithm can naturally solve the competition between multiple single policies in multi-objective environments. However, the tabular version does not scale well to environments with large state spaces. To address this issue, we replace underlying Q-tables with DQN, and propose an addition of W-Networks, as a replacement for tabular weights (W) representations. We evaluate the resulting Deep W-Networks (DWN) approach in two widely-accepted multi-objective RL benchmarks: deep sea treasure and multi-objective mountain car. We show that DWN solves the competition between multiple policies while outperforming the baseline in the form of a DQN solution. Additionally, we demonstrate that the proposed algorithm can find the Pareto front in both tested environments.
A STRONGLY LENSED MASSIVE ULTRACOMPACT QUIESCENT GALAXY AT z ∼ 2.4 IN THE COSMOS/UltraVISTA FIELD

We report the discovery of a massive ultracompact quiescent galaxy that has been strongly lensed into multiple images by a foreground galaxy at z = 0.960. This system was serendipitously discovered as a set of extremely Ks-bright high-redshift galaxies with red J − Ks colors using new data from the UltraVISTA YJHKs near-infrared survey. The system was also previously identified as an optically faint lens/source system using the COSMOS Advanced Camera for Surveys (ACS) imaging by Faure et al. Photometric redshifts for the three brightest images of the source galaxy determined from 27-band photometry place the source at z = 2.4 ± 0.1. We provide an updated lens model for the system that is a good fit to the positions and morphologies of the galaxies in the ACS image. The lens model implies that the magnification of the three brightest images is a factor of 4–5. We use the lens model, combined with the Ks-band image, to constrain the size and Sérsic profile of the galaxy. The best-fit model is an ultracompact galaxy (Re = 0.64+0.08 − 0.18 kpc, lensing-corrected), with a Sérsic profile that is intermediate between a disk and a bulge profile (n = 2.2+2.3 − 0.9), albeit with considerable uncertainties on the Sérsic profile. We present aperture photometry for the source galaxy images that have been corrected for flux contamination from the central lens. The best-fit stellar population model is a massive galaxy (log(Mstar/M☉) = 10.8+0.1 − 0.1, lensing-corrected) with an age of 1.0+1.0 − 0.4 Gyr, moderate dust extinction (Av = 0.8+0.5 − 0.6), and a low specific star formation rate (log(SSFR) <−11.0 yr−1). This is typical of massive “red-and-dead” galaxies at this redshift and confirms that this source is the first bona fide strongly lensed massive ultracompact quiescent galaxy to be discovered. We conclude with a discussion of the prospects of finding a larger sample of these galaxies.
Massive star population of the Virgo Cluster galaxy NGC4535

We analyzed the massive star population of the Virgo Cluster galaxy NGC 4535 using archival Hubble Space Telescope Wide Field Planetary Camera 2 images in filters F555W and F814W, equivalent to Johnson V and Kron-Cousins I. We performed high precision point spread function fitting photometry of 24353 sources including 3762 candidate blue supergiants, 841 candidate yellow supergiants, and 370 candidate red supergiants. We estimated the ratio of blue to red supergiants as a decreasing function of galactocentric radius. Using Modules for Experiments in Stellar Astrophysics (MESA) isochrones at solar metallicity, we defined the luminosity function and estimated the star formation history of the galaxy over the last 60 Myr. We conducted a variability search in the V and I filters using three variability indexes: the median absolute deviation, the interquartile range, and the inverse von-Neumann ratio. This analysis yielded 120 new variable candidates with absolute magnitudes ranging from MV = −4 to −11 mag. We used the MESA evolutionary tracks at solar metallicity to classify the variables based on their absolute magnitude and their position on the color-magnitude diagram. Among the new candidate variable sources are eight candidate variable red supergiants, three candidate variable yellow supergiants and one candidate luminous blue variable, which we suggest for follow-up observations.
Macroeconomic performance of oil price shocks in Russia

Oil price fluctuations severely impact the economies of both oil-exporting and importing countries. High oil prices can benefit oil exporters by increasing foreign currency inflow; however, an economy can suffer from a weakening of the manufacturing sectors and experience a significant downtrend in the country's price competitiveness as the domestic currency appreciates. We investigate the oil price fluctuations from Q1, 2004 to Q3, 2021 and their impact on the Russian macroeconomic indicators, particularly industrial production, exchange rate, inflation and interest rates. We assess whether and how much the Russian macroeconomic variables have been responsive to the oil price fluctuations in recent years. The outcomes from VAR model confirm that the monetary channel is more responsive to oil price shocks than the fiscal one. Regarding fiscal channel of the oil price impact, industrial production is strongly pro-cyclical to oil price shocks. As for the monetary channel, higher oil price volatility is pressuring the Russian ruble, inflation and interest rates are substantially counter-cyclical to oil price shocks.
Quality Control in Weather Monitoring with Dynamic Linear Models

Decisions in agriculture are frequently based on weather. With an increase in the availability and affordability of off-the-shelf weather stations, farmers able to acquire localised weather information. However, with uncertainty in the sensor and installation quality, farmers are at risk of making poor decisions based on incorrect data. We present an automated approach to perform quality control on weather sensors. Our approach uses time-series modelling and data fusion with Bayesian principles to provide predictions with uncertainty quantification. These predictions and uncertainty are used to estimate the validity of a sensor observation. We test on temperature, wind, and humidity data and achieve error hit rates above 80% and false negative rates below 11%.
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information

Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation. Prior works on GSG-I have used deep reinforcement learning (DRL) to learn the best policy for the agent in such an environment without any need to store the huge number of state representations for GSG-I. However, the decision-making process of DRL methods is largely opaque, which results in a lack of trust in their predictions. To tackle this issue, we present an interpretable DRL method for GSG-I that generates visualization to explain the decisions taken by the DRL algorithm. We also show that this approach performs better and works well with a simpler training regimen compared to the existing method.
Sparse unitary operators and repeated entries in the process matrix

We show that quantum processes of low rank are inexorably connected to the dimensions of the environmental state space that the system is in contact with. We also show that the process matrix representing such low rank processes will have only a few distinct entries which goes as O(r^2) (r is the rank) and hence the number of independent measurements needed to characterize the process is greatly reduced.
On the Application of Efficient Neural Mapping to Real-Time Indoor Localisation for Unmanned Ground Vehicles

Global localisation from visual data is a challenging problem applicable to many robotics domains. Prior works have shown that neural networks can be trained to map images of an environment to absolute camera pose within that environment, learning an implicit neural mapping in the process. In this work we evaluate the applicability of such an approach to real-world robotics scenarios, demonstrating that by constraining the problem to 2-dimensions and significantly increasing the quantity of training data, a compact model capable of real-time inference on embedded platforms can be used to achieve localisation accuracy of several centimetres. We deploy our trained model onboard a UGV platform, demonstrating its effectiveness in a waypoint navigation task, wherein it is able to localise with a mean accuracy of 9cm at a rate of 6fps running on the UGV onboard CPU, 35fps on an embedded GPU, or 220fps on a desktop GPU. Along with this work we will release a novel localisation dataset comprising simulated and real environments, each with training samples numbering in the tens of thousands.
BASS XXXIX: Swift-BAT AGN with changing-look optical spectra

Changing-look (CL) AGN are unique probes of accretion onto supermassive black holes (SMBHs), especially when simultaneous observations in complementary wavebands allow investigations into the properties of their accretion flows. We present the results of a search for CL behaviour in 412 Swift-BAT detected AGN with multiple epochs of optical spectroscopy from the BAT AGN Spectroscopic Survey (BASS). 125 of these AGN also have 14-195 keV ultra-hard X-ray light-curves from Swift-BAT which are contemporaneous with the epochs of optical spectroscopy. Eight CL events are presented for the first time, where the appearance or disappearance of broad Balmer line emission leads to a change in the observed Seyfert type classification. Combining with known events from the literature, 21 AGN from BASS are now known to display CL behaviour. Nine CL events have 14-195 keV data available, and five of these CL events can be associated with significant changes in their 14-195 keV flux from BAT. The ultra-hard X-ray flux is less affected by obscuration and so these changes in the 14-195 keV band suggest that the majority of our CL events are not due to changes in line-of-sight obscuration. We derive a CL rate of 0.7-6.2 per cent on 10-25 year time-scales, and show that many transitions happen within at most a few years. Our results motivate further multi-wavelength observations with higher cadence to better understand the variability physics of accretion onto SMBHs.
Graph and distributed extensions of the Douglas-Rachford method

In this paper, we propose several graph-based extensions of the Douglas–Rachford splitting (DRS) method to solve monotone inclusion problems involving the sum of N maximal monotone operators. Our construction is based on a two-layer architecture that we refer to as bilevel graphs, to which we associate a generalization of the DRS algorithm that presents the prescribed structure. The resulting schemes can be understood as unconditionally stable frugal resolvent splitting methods with a minimal lifting in the sense of Ryu [Math Program 182(1):233–273, 2020], as well as instances of the (degenerate) Preconditioned Proximal Point method, which provides robust convergence guarantees. We further describe how the graph-based extensions of the DRS method can be leveraged to design new fully distributed protocols. Applications to a congested optimal transport problem and to distributed Support Vector Machines show interesting connections with the underlying graph topology and highly competitive performances with state-of-the-art distributed optimization approaches.
Two Candidates for Dual AGN in Dwarf-dwarf Galaxy Mergers

Dual active galactic nuclei (DAGN) are important for understanding galaxy-merger-triggered fueling of black holes and the hierarchical growth of structures. The least explored type of DAGN are those associated with mergers of two dwarf galaxies. According to observations and cosmological simulations, dwarf galaxies are the most abundant type of galaxies in the early universe and the galaxy-merger rate is dominated by dwarfs. However, these mergers are generally too distant to be directly observed, and low-redshift dwarf-dwarf merger-related DAGN are notoriously hard to find. In this paper, we present the first results of our large-scale search for this elusive type of object and the first two candidates for DAGN in dwarf-dwarf mergers. Both objects exhibit tidal features (tails and bridges) characteristic of galaxy mergers/interactions. One object is apparently in a late-stage merger with an AGN separation of <5 kpc, while the second is in an early-stage merger with interacting galaxies having established a tidal bridge. Both objects have dual, luminous X-ray sources that are most likely due to actively accreting massive black holes. Also, both objects have infrared counterparts, with colors consistent with being AGN. Follow-up observations will provide us a glimpse into key processes that govern the earliest phases of growth of galaxies, their central black holes, and merger-induced star formation.
SimOn: A Simple Framework for Online Temporal Action Localization

Online Temporal Action Localization (On-TAL) aims to immediately provide action instances from untrimmed streaming videos. The model is not allowed to utilize future frames and any processing techniques to modify past predictions, making On-TAL much more challenging. In this paper, we propose a simple yet effective framework, termed SimOn, that learns to predict action instances using the popular Transformer architecture in an end-to-end manner. Speciﬁcally, the model takes the current frame feature as a query and a set of past context information as keys and values of the Transformer. Different from the prior work that uses a set of outputs of the model as past contexts, we leverage the past visual context and the learnable context embedding for the current query. Experimental results on the THUMOS14 and ActivityNet1.3 datasets show that our model remarkably outperforms the previous methods, achieving a new state-of-the-art On-TAL performance. In addition, the evaluation for Online Detection of Action Start (ODAS) demonstrates the effectiveness and robustness of our method in the online setting. The code is available at https://github.com/TuanTNG/SimOn .
Algebraic Properties of Quantum Reference Frames: Does Time Fluctuate?

Quantum reference frames are expected to differ from classical reference frames because they have to implement typical quantum features such as fluctuations and correlations. Here, we show that fluctuations and correlations of reference variables, in particular of time, are restricted by their very nature of being used for reference. Mathematically, this property is implemented by imposing constraints on the system to make sure that reference variables are not physical degrees of freedom. These constraints not only relate physical degrees of freedom to reference variables in order to describe their behavior, they also restrict quantum fluctuations of reference variables and their correlations with system degrees of freedom. We introduce the notion of “almost-positive” states as a suitable mathematical method. An explicit application of their properties to examples of recent interest in quantum reference frames reveals previously unrecognized restrictions on possible frame–system interactions. While currently discussed clock models rely on assumptions that, as shown here, make them consistent as quantum reference frames, relaxing these assumptions will expose the models to new restrictions that appear to be rather strong. Almost-positive states also shed some light on a recent debate about the consistency of relational quantum mechanics.
Spin-wave-induced correction to the conductivity of ferromagnets

We calculate the correction to the conductivity of a disordered ferromagnetic metal due to spin-wave-mediated electron--electron interactions. This correction is the generalization of the Altshuler-Aronov correction to spin-wave-mediated interactions. We derive a general expression for the conductivity correction to lowest order in the spin-wave-mediated interaction and for the limit that the exchange splitting $\Delta$ is much smaller than the Fermi energy. For a "clean" ferromagnet with $\Delta\tau_{\rm el}/\hbar \gg 1$, with $\tau_{\rm el}$ the mean time for impurity scattering, we find a correction $\delta \sigma \propto -T^{5/2}$ at temperatures $T$ above the spin wave gap. In the opposite, "dirty" limit, $\Delta\tau_{\rm el}/\hbar \ll 1$, the correction is a non-monotonous function of temperature.