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A field theory for the distribution of transmission eigenvalues through a disordered medium is developed in details. The central equation of this theory is a self-consistent transport equation for a $2\times 2$ matrix radiance. This equation determines the transmission eigenvalue distribution not only in the diffusive regime but also in the quasiballistic regime, where the radiance is expected to be significantly anisotropic in the material bulk. We show that the matrix transport equation can be solved analytically in this regime. We also show that, in addition to the finite-width waveguide, our equation is able to predict the transmission eigenvalue distribution through an infinite slab., Comment: 25 pages, 7 figures

We present a field theory to characterize the mean distribution of transmission eigenvalues for coherent wave propagation through a disordered medium. Unlike the Dorokhov-Mello-Pereyra-Kumar (DMPK) theory, our approach does not rely on the isotropy hypothesis, which assumes uniform field intensity across scattering channels. As a result, it accurately predicts the transmission eigenvalue distribution both in the diffusive and quasiballistic regime. In addition, it proves to be more versatile than the DMPK theory, allowing for the incorporation of physical effects such as absorption or partial channel control with minimal adjustments to the formalism. Although these effects are commonly encountered in experiments on complex media, until now there has been no ab initio theory addressing them. Our predictions are numerically validated against transmission eigenvalue distributions computed from the microscopic wave equation., Comment: 6 pages, 3 figures

PDS 453 is a rare highly inclined disk where the stellar photosphere is seen at grazing incidence on the disk surface. Our goal is take advantage of this geometry to constrain the structure and composition of this disk, in particular the fact that it shows a 3.1 $\mu$m water ice band in absorption that can be related uniquely to the disk. We observed the system in polarized intensity with the VLT/SPHERE instrument, as well as in polarized light and total intensity using the HST/NICMOS camera. Infrared archival photometry and a spectrum showing the water ice band are used to model the spectral energy distribution under Mie scattering theory. Based on these data, we fit a model using the radiative transfer code MCFOST to retrieve the geometry and dust and ice content of the disk. PDS 453 has the typical morphology of a highly inclined system with two reflection nebulae where the disk partially attenuates the stellar light. The upper nebula is brighter than the lower nebula and shows a curved surface brightness profile in polarized intensity, indicating a ring-like structure. With an inclination of 80{\deg} estimated from models, the line-of-sight crosses the disk surface and a combination of absorption and scattering by ice-rich dust grains produces the water ice band. PDS 453 is seen highly inclined and is composed of a mixture of silicate dust and water ice. The radial structure of the disk includes a significant jump in density and scale height at a radius of 70 au in order to produce a ring-like image. The depth of the 3.1 $\mu$m water ice band depends on the amount of water ice, until it saturates when the optical thickness along the line-of-sight becomes too large. Therefore, quantifying the exact amount of water from absorption bands in edge-on disks requires a detailed analysis of the disk structure and tailored radiative transfer modeling., Comment: 11 pages, 11 figures

This paper presents SibylSat, a novel SAT-based method designed to efficiently solve totally-ordered HTN problems (TOHTN). In contrast to prevailing SAT-based HTN planners that employ a breadth-first search strategy, SibylSat adopts a greedy search approach, enabling it to identify promising decompositions for expansion. The selection process is facilitated by a heuristic derived from solving a relaxed problem, which is also expressed as a SAT problem. Our experimental evaluations demonstrate that SibylSat outperforms existing SAT-based TOHTN approaches in terms of both runtime and plan quality on most of the IPC benchmarks, while also solving a larger number of problems.

System call filtering is widely used to secure programs in multi-tenant environments, and to sandbox applications in modern desktop software deployment and package management systems. Filtering rules are hard to write and maintain manually, hence generating them automatically is essential. To that aim, analysis tools able to identify every system call that can legitimately be invoked by a program are needed. Existing static analysis works lack precision because of a high number of false positives, and/or assume the availability of program/libraries source code -- something unrealistic in many scenarios such as cloud production environments. We present B-Side, a static binary analysis tool able to identify a superset of the system calls that an x86-64 static/dynamic executable may invoke at runtime. B-Side assumes no access to program/libraries sources, and shows a good degree of precision by leveraging symbolic execution, combined with a heuristic to detect system call wrappers, which represent an important source of precision loss in existing works. B-Side also allows to statically detect phases of execution in a program in which different filtering rules can be applied. We validate B-Side and demonstrate its higher precision compared to state-of-the-art works: over a set of popular applications, B-Side's average $F_1$ score is 0.81, vs. 0.31 and 0.53 for competitors. Over 557 static and dynamically-compiled binaries taken from the Debian repositories, B-Side identifies an average of 43 system calls, vs. 271 and 95 for two state-of-the art competitors. We further evaluate the strictness of the phase-based filtering policies that can be obtained with B-Side., Comment: Accepted to appear in the 25th ACM/IFIP International Middleware Conference (Middleware'24)

Radially extended disk winds could be the key to unlocking how protoplanetary disks accrete and how planets form and migrate. A distinctive characteristic is their nested morphology of velocity and chemistry. Here we report JWST/NIRSpec spectro-imaging of four young stars with edge-on disks in the Taurus star-forming region that demonstrate the ubiquity of this structure. In each source, a fast collimated jet traced by [Fe II] is nested inside a hollow cavity within wider lower-velocity H2 and, in one case, also CO ro-vibrational (v=1-0) emission. Furthermore, in one of our sources, ALMA CO(2-1) emission, paired with our NIRSpec images, reveals the nested wind structure extends further outward. This nested wind morphology strongly supports theoretical predictions for wind-driven accretion and underscores the need for theoretical work to assess the role of winds in the formation and evolution of planetary systems, Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in Nature Astronomy and is available online at https://doi.org/10.1038/s41550-024-02385-7

Spike Timing-Dependent Plasticity (STDP) is a promising substitute to backpropagation for local training of Spiking Neural Networks (SNNs) on neuromorphic hardware. STDP allows SNNs to address classification tasks by combining unsupervised STDP for feature extraction and supervised STDP for classification. Unsupervised STDP is usually employed with Winner-Takes-All (WTA) competition to learn distinct patterns. However, WTA for supervised STDP classification faces unbalanced competition challenges. In this paper, we propose a method to effectively implement WTA competition in a spiking classification layer employing first-spike coding and supervised STDP training. We introduce the Neuronal Competition Group (NCG), an architecture that improves classification capabilities by promoting the learning of various patterns per class. An NCG is a group of neurons mapped to a specific class, implementing intra-class WTA and a novel competition regulation mechanism based on two-compartment thresholds. We incorporate our proposed architecture into spiking classification layers trained with state-of-the-art supervised STDP rules. On top of two different unsupervised feature extractors, we obtain significant accuracy improvements on image recognition datasets such as CIFAR-10 and CIFAR-100. We show that our competition regulation mechanism is crucial for ensuring balanced competition and improved class separation.

Humanoid robotics faces significant challenges in achieving stable locomotion and recovering from falls in dynamic environments. Traditional methods, such as Model Predictive Control (MPC) and Key Frame Based (KFB) routines, either require extensive fine-tuning or lack real-time adaptability. This paper introduces FRASA, a Deep Reinforcement Learning (DRL) agent that integrates fall recovery and stand up strategies into a unified framework. Leveraging the Cross-Q algorithm, FRASA significantly reduces training time and offers a versatile recovery strategy that adapts to unpredictable disturbances. Comparative tests on Sigmaban humanoid robots demonstrate FRASA superior performance against the KFB method deployed in the RoboCup 2023 by the Rhoban Team, world champion of the KidSize League.

Establishing and maintaining 5G mmWave vehicular connectivity poses a significant challenge due to high user mobility that necessitates frequent triggering of beam switching procedures. Departing from reactive beam switching based on the user device channel state feedback, proactive beam switching prepares in advance for upcoming beam switching decisions by exploiting accurate channel state information (CSI) prediction. In this paper, we develop a framework for autonomous self-trained CSI prediction for mmWave vehicular users where a base station (gNB) collects and labels a dataset that it uses for training recurrent neural network (RNN)-based CSI prediction model. The proposed framework exploits the CSI feedback from vehicular users combined with overhearing the C-V2X cooperative awareness messages (CAMs) they broadcast. We implement and evaluate the proposed framework using deepMIMO dataset generation environment and demonstrate its capability to provide accurate CSI prediction for 5G mmWave vehicular users. CSI prediction model is trained and its capability to provide accurate CSI predictions from various input features are investigated., Comment: Accepted for publication at European Wireless 2024

We present JWST broadband images of the highly inclined protoplanetary disk SSTc2d J163131.2-242627 (Oph163131) from 2.0 to 21$\mu$m. The images show a remarkable evolution in disk structure with wavelength, quite different from previous JWST observations of other edge-on disks. At 2.0 and 4.4$\mu$m, Oph163131 shows two scattering surfaces separated by a dark lane, typical of highly inclined disks. Starting at 7.7$\mu$m however, 1) the two linear nebulosities flanking the dark lane disappear; 2) the brighter nebula tracing the disk upper surface transitions into a compact central source distinctly larger than the JWST PSF and whose intrinsic size increases with wavelength; and 3) patches of extended emission appear at low latitudes, and at surprisingly large radii nearly twice that of the scattered light seen with $HST$ and NIRCam, and of the gas. We interpret the compact central source as thermal emission from the star and the inner disk that is not seen directly, but which instead is able to progressively propagate to greater distances at longer wavelengths. The lack of sharp-edged structures in the extended patchy emission argues against the presence of shocks and suggests photoexcitation or stochastic heating of material smoothly flowing away from the star along the disk surface. Finally, the dark lane thickness decreases significantly between 0.6$\mu$m and 4.4$\mu$m which indicates that the surface layers of Oph163131 lack grains larger than 1$\mu$m., Comment: Accepted for publication in the Astrophysical Journal. 11 pages, 7 figures

Stars and planets form in regions of enhanced stellar density, subjecting protoplanetary discs to gravitational perturbations from neighbouring stars. Observations in the Taurus star-forming have uncovered evidence of at least three recent, star-disc encounters that have truncated discs (HV/DO Tau, RW Aurigae, UX Tau), raising questions about the frequency of such events. We aim to assess the probability of observing truncating star-disc encounters in Taurus. We generate a physically motivated dynamical model including binaries and spatial-kinematic substructure to follow the historical dynamical evolution and stellar encounters in the Taurus star forming region. We track the star-disc encounters and outer disc radius evolution over the lifetime of Taurus. A quarter of discs are truncated below 30 au by dynamical encounters, but this truncation mostly occurs in binaries over the course of a few orbital periods, on a time-scale $\lesssim 0.1$ Myr. Nonetheless, some truncating encounters still occur up to the present age of Taurus. Strongly truncating encounters (ejecting $\gtrsim 10$ percent of the disc mass) occur at a rate $\sim 10$ Myr$^{-1}$, sufficient to explain the encounter between HV and DO Tau $\sim 0.1$ Myr ago. If encounters that eject only $\sim 1$ percent of the disc mass are responsible for RW Aurigae and UX Tau, then they are also expected with encounter rate $\Gamma_\mathrm{enc} \sim 100{-}200$ Myr$^{-1}$. However, the observed sample of recent encounters is probably incomplete, since these examples occurred in systems that are not consistent with random drawing from the mass function. One more observed example would statistically imply additional physics, such as replenishment of the outer disc material., Comment: 20 pages, 20 figures, accepted for publication in A&A

In this article, we derive fully guaranteed error bounds for the energy of convex nonlinear mean-field models. These results apply in particular to Kohn-Sham equations with convex density functionals, which includes the reduced Hartree-Fock (rHF) model, as well as the Kohn-Sham model with exact exchange-density functional (which is unfortunately not explicit and therefore not usable in practice). We then decompose the obtained bounds into two parts, one depending on the chosen discretization and one depending on the number of iterations performed in the self-consistent algorithm used to solve the nonlinear eigenvalue problem, paving the way for adaptive refinement strategies. The accuracy of the bounds is demonstrated on a series of test cases, including a Silicon crystal and an Hydrogen Fluoride molecule simulated with the rHF model and discretized with planewaves. We also show that, although not anymore guaranteed, the error bounds remain very accurate for a Silicon crystal simulated with the Kohn-Sham model using nonconvex exchangecorrelation functionals of practical interest.

Motion camouflage can be a useful tactic for a pursuer attempting to conceal their true trajectory from their target. Many previous studies determine optimal trajectories subject to motion camouflage constraints, but these analyses do not address when it is optimal to use, nor do they account for the pursuer's inability to predict if and when the target will try to escape. We present an optimal control framework to determine when the pursuer should use motion camouflage amidst uncertainty in the target's escape attempt time. Focusing on the illustrative problem of a male hover fly pursuing a female hover fly for mating, we model the female fly's escape response as the result of a non-homogeneous Poisson point process with a biologically informed rate function, and we obtain and numerically solve two Hamilton-Jacobi-Bellman (HJB) PDEs which encode the pursuer's optimal trajectories. Our numerical experiments and statistics illustrate when it is optimal to use motion camouflage pursuit tactics under varying degrees of the target's visual acuity and tolerance to the pursuer's presence.

Terahertz pulses generated by two-color laser plasmas have reported peak field strengths exceeding MV/cm, and when illuminating metal nanotips the near-field enhancement at the tip apex should result in extremely high bunch charges and electron energies via sub-cycle cold field emission. Here, electron emission from tungsten nanotips driven by THz pulses generated by a long filament air-plasma are reported. Electron energies up to 1.1 keV and bunch charges up to 2x$10^5$ electrons per pulse were detected, well below values expected for peak field calculated via the time averaged Poynting vector. Investigations revealed a failure in the use of the time-averaged Poynting vector when applied to long filament THz pulses, due to spatio-temporal restructuring of the THz pulse in the focus. Accounting for this restructuring significantly reduces the field strength to approximately 160 ~kV/cm, consistent with the observed electron bunch charges, peak energies and their dependence on the tip position in the THz focus. Despite these findings, our results surpass previous THz plasma-driven electron generation by an order of magnitude in both electron energy and bunch charge and a path to increasing these by an additional order of magnitude by modification of the THz optics is proposed.

This paper provides an adaptation of branching bisimilarity to reactive systems with time-outs. Multiple equivalent definitions are procured, along with a modal characterisation and a proof of its congruence property for a standard process algebra with recursion. The last section presents a complete axiomatisation for guarded processes without infinite sequences of unobservable actions., Comment: An extended abstract of this paper appears in Proc. CONCUR'24, see https://doi.org/10.4230/LIPIcs.CONCUR.2024.36

Time-dependent response and correlation functions are studied in random quantum systems composed of infinitely many parts without mutual interaction and defined with statistically independent random matrices. The latter are taken within the three Wigner-Dyson universality classes. In these systems, the response functions are shown to be exactly given by statistical averages over the random-matrix ensemble. Analytical results are obtained for the time dependence of the mean response and correlation functions at zero and positive temperatures. At long times, the mean correlation functions are shown to have a power-law decay for GOE at positive temperatures, but for GUE and GSE at zero temperature. Otherwise, the decay is much faster in time. In relation to these power-law decays, the associated spectral densities have a dip around zero frequency. The diagrammatic method is developed to obtain higher-order response functions and the third-order response function is explicitly calculated. The response to impulsive perturbations is also considered. In addition, the quantum fluctuations of the correlation function in individual members of the ensemble are characterised in terms of their probability distribution, which is shown to change with the temperature.

The hydrodynamics of crystals with vacancies is developed on the basis of local-equilibrium thermodynamics, where the chemical potential of vacancies plays a key role together with a constraint relating the concentration of vacancies to the density of mass and the strain tensor. The microscopic foundations are established, leading to Green-Kubo and Einstein-Helfand formulas for the transport coefficients, including the vacancy conductivities and the coefficients of vacancy thermodiffusion. As a consequence of having introduced the chemical potential of vacancies, a relationship is obtained between the conductivities and the Fickian diffusion coefficients for the vacancies. The macroscopic equations are linearized around equilibrium to deduce the dispersion relations of the eight hydrodynamic modes. The theoretical predictions are confirmed by numerical simulations of the hard-sphere crystal with vacancies. The study explicitly shows that the eighth hydrodynamic mode of nonperfect monatomic crystals is indeed a mode of vacancy diffusion., Comment: 24 pages, 6 figures, 8 tables

The 16 Myr-old A0V star HD 156623 in the Scorpius--Centaurus association hosts a high-fractional-luminosity debris disk, recently resolved in scattered light for the first time by the Gemini Planet Imager (GPI) in polarized intensity. We present new analysis of the GPI H-band polarimetric detection of the HD 156623 debris disk, with particular interest in its unique morphology. This debris disk lacks a visible inner clearing, unlike the majority of low-inclination disks in the GPI sample and in Sco-Cen, and it is known to contain CO gas, positioning it as a candidate ``hybrid'' or ``shielded'' disk. We use radiative transfer models to constrain the geometric parameters of the disk based on scattered light data and thermal models to constrain the unresolved inner radius based on the system's spectral energy distribution (SED). We also compute a measurement of the polarized scattering phase function, adding to the existing sample of empirical phase function measurements. We find that HD 156623's debris disk inner radius is constrained to less than 26.6 AU from scattered light imagery and less than 13.4 AU from SED modeling at a 99.7% confidence interval, and suggest that gas drag may play a role in retaining sub-blowout size dust grains so close to the star., Comment: 18 pages, 10 figures. Accepted for publication in AJ

Attention-based models such as Transformers and recurrent models like state space models (SSMs) have emerged as successful methods for autoregressive sequence modeling. Although both enable parallel training, none enable parallel generation due to their autoregressiveness. We propose the variational SSM (VSSM), a variational autoencoder (VAE) where both the encoder and decoder are SSMs. Since sampling the latent variables and decoding them with the SSM can be parallelized, both training and generation can be conducted in parallel. Moreover, the decoder recurrence allows generation to be resumed without reprocessing the whole sequence. Finally, we propose the autoregressive VSSM that can be conditioned on a partial realization of the sequence, as is common in language generation tasks. Interestingly, the autoregressive VSSM still enables parallel generation. We highlight on toy problems (MNIST, CIFAR) the empirical gains in speed-up and show that it competes with traditional models in terms of generation quality (Transformer, Mamba SSM)., Comment: 4 pages, 11 pages total, 3 figures

This article is the continuation of \cite{ohlmann2021illposedness} where we exhibited the ill-posedness of a quasi-linear wave equation in dimension $2$ for initial data in $H^{7/4}(\ln H^{7/4})$. Here, we look at modifications of the equation and show that the blow-up phenomenon still occurs. First, we study another equation with the same characteristics but a different underlying ODE. Later, we study an equation where a $x_2$ dependency is introduced. The latter case constitutes the main contribution of this paper, as we are able to show that the solution still behaves pathologically without having an explicit formula for either the characteristics or the values of the solution. Finally, we study the case where a perturbation of the initial data is introduced.

In this work, we tackle the problems of efficiency and scalability for predictive coding networks in machine learning. To do so, we first propose a library called PCX, whose focus lies on performance and simplicity, and provides a user-friendly, deep-learning oriented interface. Second, we use PCX to implement a large set of benchmarks for the community to use for their experiments. As most works propose their own tasks and architectures, do not compare one against each other, and focus on small-scale tasks, a simple and fast open-source library adopted by the whole community would address all of these concerns. Third, we perform extensive benchmarks using multiple algorithms, setting new state-of-the-art results in multiple tasks and datasets, as well as highlighting limitations inherent to PC that should be addressed. Thanks to the efficiency of PCX, we are able to analyze larger architectures than commonly used, providing baselines to galvanize community efforts towards one of the main open problems in the field: scalability. The code for PCX is available at \textit{https://github.com/liukidar/pcax}., Comment: 33 pages, 25 figures

We present a new method for multimodal conditional 3D face geometry generation that allows user-friendly control over the output identity and expression via a number of different conditioning signals. Within a single model, we demonstrate 3D faces generated from artistic sketches, 2D face landmarks, Canny edges, FLAME face model parameters, portrait photos, or text prompts. Our approach is based on a diffusion process that generates 3D geometry in a 2D parameterized UV domain. Geometry generation passes each conditioning signal through a set of cross-attention layers (IP-Adapter), one set for each user-defined conditioning signal. The result is an easy-to-use 3D face generation tool that produces high resolution geometry with fine-grain user control.

We consider Abelson's model for opinion dynamics, where individuals interact if they are connected via a fixed network. Our aim is to develop a continuous description for these dynamics that captures well the global dynamics when the number of individuals grows to infinity. We aim at having a simple continuum description on which computations and comparison with the discrete dynamics are feasible. The difficulty lies in the network of connections being described through the labeling of the individuals: this makes the system not amenable to standard mean-field limits. We overcome this difficulty by introducing an evolving label-free network description. We compare the discrete model considered, and the continuum model proposed in the case of consensus dynamics, and carry out simulations for a system with three communities. Moreover, we propose extensions of the model to incorporate phenomena like creation and destruction of connections (dynamical networks), noise, and directional networks.

This study proposes a novel approach based on reinforcement learning (RL) to enhance the sorting efficiency of scrap metal using delta robots and a Pick-and-Place (PaP) process, widely used in the industry. We use three classical model-free RL algorithms (TD3, SAC and PPO) to reduce the time to sort metal scraps. We learn the release position and speed needed to throw an object in a bin instead of moving to the exact bin location, as with the classical PaP technique. Our contribution is threefold. First, we provide a new simulation environment for learning RL-based Pick-and-Throw (PaT) strategies for parallel grippers. Second, we use RL algorithms for learning this task in this environment resulting in 89% accuracy while speeding up the throughput by 51% in simulation. Third, we evaluate the performances of RL algorithms and compare them to a PaP and a state-of-the-art PaT method both in simulation and reality, learning only from simulation with domain randomisation and without fine tuning in reality to transfer our policies. This work shows the benefits of RL-based PaT compared to PaP or classical optimization PaT techniques used in the industry.

Large Language Models (LLMs) zero-shot and few-shot performance are subject to memorization and data contamination, complicating the assessment of their validity. In literary tasks, the performance of LLMs is often correlated to the degree of book memorization. In this work, we carry out a realistic evaluation of LLMs for quotation attribution in novels, taking the instruction fined-tuned version of Llama3 as an example. We design a task-specific memorization measure and use it to show that Llama3's ability to perform quotation attribution is positively correlated to the novel degree of memorization. However, Llama3 still performs impressively well on books it has not memorized nor seen. Data and code will be made publicly available., Comment: Paper under review

Humans naturally attribute utterances of direct speech to their speaker in literary works. When attributing quotes, we process contextual information but also access mental representations of characters that we build and revise throughout the narrative. Recent methods to automatically attribute such utterances have explored simulating human logic with deterministic rules or learning new implicit rules with neural networks when processing contextual information. However, these systems inherently lack \textit{character} representations, which often leads to errors in more challenging examples of attribution: anaphoric and implicit quotes. In this work, we propose to augment a popular quotation attribution system, BookNLP, with character embeddings that encode global stylistic information of characters derived from an off-the-shelf stylometric model, Universal Authorship Representation (UAR). We create DramaCV (Code and data can be found at https://github.com/deezer/character_embeddings_qa ), a corpus of English drama plays from the 15th to 20th century that we automatically annotate for Authorship Verification of fictional characters utterances, and release two versions of UAR trained on DramaCV, that are tailored for literary characters analysis. Then, through an extensive evaluation on 28 novels, we show that combining BookNLP's contextual information with our proposed global character embeddings improves the identification of speakers for anaphoric and implicit quotes, reaching state-of-the-art performance., Comment: EMNLP 2024 (Findings)

In this paper, we examine 3 important issues in the practical use of state-of-the-art facial landmark detectors and show how a combination of specific architectural modifications can directly improve their accuracy and temporal stability. First, many facial landmark detectors require face normalization as a preprocessing step, which is accomplished by a separately-trained neural network that crops and resizes the face in the input image. There is no guarantee that this pre-trained network performs the optimal face normalization for landmark detection. We instead analyze the use of a spatial transformer network that is trained alongside the landmark detector in an unsupervised manner, and jointly learn optimal face normalization and landmark detection. Second, we show that modifying the output head of the landmark predictor to infer landmarks in a canonical 3D space can further improve accuracy. To convert the predicted 3D landmarks into screen-space, we additionally predict the camera intrinsics and head pose from the input image. As a side benefit, this allows to predict the 3D face shape from a given image only using 2D landmarks as supervision, which is useful in determining landmark visibility among other things. Finally, when training a landmark detector on multiple datasets at the same time, annotation inconsistencies across datasets forces the network to produce a suboptimal average. We propose to add a semantic correction network to address this issue. This additional lightweight neural network is trained alongside the landmark detector, without requiring any additional supervision. While the insights of this paper can be applied to most common landmark detectors, we specifically target a recently-proposed continuous 2D landmark detector to demonstrate how each of our additions leads to meaningful improvements over the state-of-the-art on standard benchmarks., Comment: 12 pages, 13 figures

We propose a new model of scalar field dark matter interacting with dark energy. Adopting a fluid description of the dark matter field in the regime of rapid oscillations, we find that the equation of state for dark matter is non-zero and even becomes increasingly negative at late times during dark energy domination. Furthermore, the speed of sound of dark matter is non-vanishing at all length scales, and a non-adiabatic pressure contribution arises. The results indicate that there are still unexplored possible interactions within the dark sector that lead to novel background effects and can impact structure formation processes., Comment: 20 pages, 3 figures

GG Tau is one of the most studied multiple young stellar systems: GG Tau A is a hierarchical triple surrounded by a massive disc and its companion, GG Tau B, is also a binary. Despite numerous observational attempts, an understanding of the geometry of the GG Tau A system is still elusive. We provide new astrometric measures of the system and we run a set of hydrodynamical simulations with two representative orbits to test how they impact a disc composed of dust and gas. We test the dynamical evolution of the two scenarios on short and long timescales. We obtain synthetic flux emission from our simulations and we compare them with 1300 $\mu$m ALMA dust continuum emission and 1.67 $\mu$m SPHERE dust scattering images to infer the most likely orbital arrangement. We extend the analysis of the binary orbital parameters using six new epochs from archival data, showing that the current measurements alone are not capable of breaking the degeneracy between families of coplanar and misaligned orbits. We found that the time-scale for the onset of the disc eccentricity growth, $\tau_{ecc}$, is a fundamental time-scale for the morphology of the system. Results from numerical simulations show that the best match between is obtained with the misaligned configuration ($\Delta\theta= 30^\circ$) on timescales shorter than $\tau_{ecc}$. The results exhibit an almost circular cavity and dust ring. However, for both scenarios, the cavity size and its eccentricity quickly grow for timescales longer than $\tau_{ecc}$ and the models do not reproduce the observed morphology anymore. This implies that either the age of the system is shorter than $\tau_{ecc}$ or that the disc eccentricity growth is not triggered or dissipated. This finding raises questions on the future evolution of the GG Tau A system and, more in general, on the time evolution of eccentric binaries and their circumbinary discs., Comment: 14 pages, 12 figures

High-order multiple (triple and beyond) systems are relatively common. Their interaction with circumstellar and circumbinary material can have a large impact on the formation and evolution of planetary systems and depends on their orbital properties. GG\,Tau and UX\,Tau are two pre-main sequence high-order multiple systems in which the tightest pair has a projected separation of $\approx5$--20\,au. Characterizing precisely their orbits is crucial to establish their long-term stability, to predict the dynamics and evolution of circumstellar matter, and to evaluate the potential for planet formation in such systems. We combine existing astrometric measurements with previously unpublished high-resolution observations of the GG\,Tau\,Ab and UX\,Tau\,B pairs and perform Keplerian orbital fits. For GG\,Tau\,Ab the data presented here represent the first detection of orbital motion. For both systems they yield dramatic increases in orbital coverage ($\gtrsim60\%$ and $\approx100\%$ for UX\,Tau\,B and GG\,Tau\,Ab, for orbital periods of $\approx32$ and $\approx8$\,yr, respectively) and allow us to obtain well-constrained orbital fits, including dynamical masses with $\lesssim10\%$ and $\lesssim7\%$ random and systematic uncertainties. We find that both GG\,Tau\,A and UX\,Tau\,A--B likely form stable hierarchical systems, although one possible deprojection solution for GG\,Tau is strongly misaligned and could experience von Zeipel-Lidov-Kozai oscillations. We further find that the UX\,Tau\,B orbit is much more eccentric than the GG\,Tau\,Ab one, possibly explaining the lack of circumstellar material in the former. The newly-determined orbits revive the question of the dynamical fate of gas and dust in these two hierarchical systems and should spur new dedicated simulations to assess the long-term evolution of the systems and the dynamical perturbations imposed by the close binaries they host., Comment: Accepted for publication in Astronomy & Astrophysics

This paper deals with the numerical simulation of the Gross-Pitaevskii (GP) equation, for which a well-known feature is the appearance of quantized vortices with core size of the order of a small parameter $\varepsilon$. Without a magnetic field and with suitable initial conditions, these vortices interact, in the singular limit $\varepsilon\to0$, through an explicit Hamiltonian dynamics. Using this analytical framework, we develop and analyze a numerical strategy based on the reduced-order Hamiltonian system to efficiently simulate the infinite-dimensional GP equation for small, but finite, $\varepsilon$. This method allows us to avoid numerical stability issues in solving the GP equation, where small values of $\varepsilon$ typically require very fine meshes and time steps. We also provide a mathematical justification of our method in terms of rigorous error estimates of the error in the supercurrent, together with numerical illustrations.

Magnetic gels with embedded micro/nano-sized magnetic particles in crosslinked polymer networks can be actuated by external magnetic fields, with changes in their internal microscopic structures and macroscopic mechanical properties. We investigate the responses of such magnetic gels to an external magnetic field, by means of coarse-grained molecular dynamics simulations. We find that the dynamics of magnetic particles are determined by the interplay of between magnetic dipole-dipole interactions, polymer elasticity and thermal fluctuations. The corresponding microscopic structures formed by the magnetic particles such as elongated chains can be controlled by the external magnetic field. Furthermore, the magnetic gels can exhibit reinforced macroscopic mechanical properties, where the elastic modulus increases algebraically with the magnetic moments of the particles in the form of $\propto(m-m_{\mathrm{c}})^{2}$ when magnetic chains are formed. This simulation work can not only serve as a tool for studying the microscopic and the macroscopic responses of the magnetic gels, but also facilitate future fabrications and practical controls of magnetic composites with desired physical properties.

Designing a humanoid locomotion controller is challenging and classically split up in sub-problems. Footstep planning is one of those, where the sequence of footsteps is defined. Even in simpler environments, finding a minimal sequence, or even a feasible sequence, yields a complex optimization problem. In the literature, this problem is usually addressed by search-based algorithms (e.g. variants of A*). However, such approaches are either computationally expensive or rely on hand-crafted tuning of several parameters. In this work, at first, we propose an efficient footstep planning method to navigate in local environments with obstacles, based on state-of-the art Deep Reinforcement Learning (DRL) techniques, with very low computational requirements for on-line inference. Our approach is heuristic-free and relies on a continuous set of actions to generate feasible footsteps. In contrast, other methods necessitate the selection of a relevant discrete set of actions. Second, we propose a forecasting method, allowing to quickly estimate the number of footsteps required to reach different candidates of local targets. This approach relies on inherent computations made by the actor-critic DRL architecture. We demonstrate the validity of our approach with simulation results, and by a deployment on a kid-size humanoid robot during the RoboCup 2023 competition.

As we learn more about the multi-scale interstellar medium (ISM) of our Galaxy, we develop a greater understanding for the complex relationships between the large-scale diffuse gas and dust in Giant Molecular Clouds (GMCs), how it moves, how it is affected by the nearby massive stars, and which portions of those GMCs eventually collapse into star forming regions. The complex interactions of those gas, dust and stellar populations form what has come to be known as the ecology of our Galaxy. Because we are deeply embedded in the plane of our Galaxy, it takes up a significant fraction of the sky, with complex dust lanes scattered throughout the optically recognisable bands of the Milky Way. These bands become bright at (sub-)millimetre wavelengths, where we can study dust thermal emission and the chemical and kinematic signatures of the gas. To properly study such large-scale environments, requires deep, large area surveys that are not possible with current facilities. Moreover, where stars form, so too do planetary systems, growing from the dust and gas in circumstellar discs, to planets and planetesimal belts. Understanding the evolution of these belts requires deep imaging capable of studying belts around young stellar objects to Kuiper belt analogues around the nearest stars. Here we present a plan for observing the Galactic Plane and circumstellar environments to quantify the physical structure, the magnetic fields, the dynamics, chemistry, star formation, and planetary system evolution of the galaxy in which we live with AtLAST; a concept for a new, 50m single-dish sub-mm telescope with a large field of view which is the only type of facility that will allow us to observe our Galaxy deeply and widely enough to make a leap forward in our understanding of our local ecology., Comment: 27 pages, submitted to Open Research Europe as part of the AtLAST collection: https://open-research-europe.ec.europa.eu/collections/atlast/about

Physically-based simulation is a powerful approach for 3D facial animation as the resulting deformations are governed by physical constraints, allowing to easily resolve self-collisions, respond to external forces and perform realistic anatomy edits. Today's methods are data-driven, where the actuations for finite elements are inferred from captured skin geometry. Unfortunately, these approaches have not been widely adopted due to the complexity of initializing the material space and learning the deformation model for each character separately, which often requires a skilled artist followed by lengthy network training. In this work, we aim to make physics-based facial animation more accessible by proposing a generalized physical face model that we learn from a large 3D face dataset. Once trained, our model can be quickly fit to any unseen identity and produce a ready-to-animate physical face model automatically. Fitting is as easy as providing a single 3D face scan, or even a single face image. After fitting, we offer intuitive animation controls, as well as the ability to retarget animations across characters. All the while, the resulting animations allow for physical effects like collision avoidance, gravity, paralysis, bone reshaping and more.

Driven or self-propelling particles moving in viscoelastic fluids recently emerge as novel class of active systems showing a complex yet rich set of phenomena due to the non-Newtonian nature of the dispersing medium. Here we investigate the one-dimensional growth of clusters made of active colloidal shakers, which are realized by oscillating magnetic rotors dispersed within a viscoelastic fluid and at different concentration of the dissolved polymer. These magnetic particles when actuated by an oscillating field display a flow profile similar to that of a shaker force dipole, i.e. without any net propulsion. We design a protocol to assemble clusters of colloidal shakers and induce their controlled expansion into elongated zigzag structures. We observe a power law growth of the mean chain length and use theoretical arguments to explain the measured $1/3$ exponent. These arguments agree well with both experiments and particle based numerical simulations.

In 2023, Rhoban Football Club reached the first place of the KidSize soccer competition for the fifth time, and received the best humanoid award. This paper presents and reviews important points in robots architecture and workflow, with hindsights from the competition.

We report the emergence of large zigzag bands in a population of reversibly actuated magnetic rotors that behave as active shakers, namely squirmers that shake the fluid around them without moving. The shakers collectively organize into dynamic structures displaying self-similar growth, and generate topological defects in form of cusps that connect vortices of rolling particles with alternating chirality. By combining experimental analysis with particle-based simulation, we show that the special flow field created by the shakers is the only ingredient needed to reproduce the observed spatiotemporal pattern. We unveil a self-organization scenario in a collection of driven particles in an viscoelastic medium emerging from the reduced particle degrees of freedom, as here the frozen orientational motion of the shakers.

Spatially-resolved images of debris disks are necessary to determine disk morphological properties and the scattering phase function (SPF) which quantifies the brightness of scattered light as a function of phase angle. Current high-contrast imaging instruments have successfully resolved several dozens of debris disks around other stars, but few studies have investigated trends in the scattered-light, resolved population of debris disks in a uniform and consistent manner. We have combined Karhunen-Loeve Image Projection (KLIP) with radiative-transfer disk forward modeling in order to obtain the highest quality image reductions and constrain disk morphological properties of eight debris disks imaged by the Gemini Planet Imager at H-band with a consistent and uniformly-applied approach. In describing the scattering properties of our models, we assume a common SPF informed from solar system dust scattering measurements and apply it to all systems. We identify a diverse range of dust density properties among the sample, including critical radius, radial width, and vertical width. We also identify radially narrow and vertically extended disks that may have resulted from substellar companion perturbations, along with a tentative positive trend in disk eccentricity with relative disk width. We also find that using a common SPF can achieve reasonable model fits for disks that are axisymmetric and asymmetric when fitting models to each side of the disk independently, suggesting that scattering behavior from debris disks may be similar to Solar System dust., Comment: 23+5 pages, 12+6 figures, 15 pages of Online Supplemental Material included; Accepted for publication in MNRAS

Policy-gradient algorithms are effective reinforcement learning methods for solving control problems with continuous state and action spaces. To compute near-optimal policies, it is essential in practice to include exploration terms in the learning objective. Although the effectiveness of these terms is usually justified by an intrinsic need to explore environments, we propose a novel analysis and distinguish two different implications of these techniques. First, they make it possible to smooth the learning objective and to eliminate local optima while preserving the global maximum. Second, they modify the gradient estimates, increasing the probability that the stochastic parameter update eventually provides an optimal policy. In light of these effects, we discuss and illustrate empirically exploration strategies based on entropy bonuses, highlighting their limitations and opening avenues for future works in the design and analysis of such strategies.

Recent approaches to automatically detect the speaker of an utterance of direct speech often disregard general information about characters in favor of local information found in the context, such as surrounding mentions of entities. In this work, we explore stylistic representations of characters built by encoding their quotes with off-the-shelf pretrained Authorship Verification models in a large corpus of English novels (the Project Dialogism Novel Corpus). Results suggest that the combination of stylistic and topical information captured in some of these models accurately distinguish characters among each other, but does not necessarily improve over semantic-only models when attributing quotes. However, these results vary across novels and more investigation of stylometric models particularly tailored for literary texts and the study of characters should be conducted., Comment: Accepted at EACL 2024's workshop LaTeCH-CLfL

3D facial animation is often produced by manipulating facial deformation models (or rigs), that are traditionally parameterized by expression controls. A key component that is usually overlooked is expression 'style', as in, how a particular expression is performed. Although it is common to define a semantic basis of expressions that characters can perform, most characters perform each expression in their own style. To date, style is usually entangled with the expression, and it is not possible to transfer the style of one character to another when considering facial animation. We present a new face model, based on a data-driven implicit neural physics model, that can be driven by both expression and style separately. At the core, we present a framework for learning implicit physics-based actuations for multiple subjects simultaneously, trained on a few arbitrary performance capture sequences from a small set of identities. Once trained, our method allows generalized physics-based facial animation for any of the trained identities, extending to unseen performances. Furthermore, it grants control over the animation style, enabling style transfer from one character to another or blending styles of different characters. Lastly, as a physics-based model, it is capable of synthesizing physical effects, such as collision handling, setting our method apart from conventional approaches., Comment: Accepted to SIGGRAPH ASIA 2023. Project page: https://studios.disneyresearch.com/2023/11/29/an-implicit-physical-face-model-driven-by-expression-and-style/ Video: https://www.youtube.com/watch?v=-qM_XUv-JhA&t

Active soft bodies can affect their shape through an internal actuation mechanism that induces a deformation. Similar to recent work, this paper utilizes a differentiable, quasi-static, and physics-based simulation layer to optimize for actuation signals parameterized by neural networks. Our key contribution is a general and implicit formulation to control active soft bodies by defining a function that enables a continuous mapping from a spatial point in the material space to the actuation value. This property allows us to capture the signal's dominant frequencies, making the method discretization agnostic and widely applicable. We extend our implicit model to mandible kinematics for the particular case of facial animation and show that we can reliably reproduce facial expressions captured with high-quality capture systems. We apply the method to volumetric soft bodies, human poses, and facial expressions, demonstrating artist-friendly properties, such as simple control over the latent space and resolution invariance at test time., Comment: Accepted to SIGGRAPH 2022. Project page: https://studios.disneyresearch.com/2022/07/24/implicit-neural-representation-for-physics-driven-actuated-soft-bodies/ Video: https://www.youtube.com/watch?v=9EERe_CTazk

In the quest of new exotic phases of matter due to the interplay of various interactions, iridates hosting a spin-orbit entangled $j_{\mathrm{eff}}=1/2$ ground state have been in the spotlight in recent years. Also in view of parallels with the low-energy physics of high-temperature superconducting cuprates, the validity of a single- or few-band picture in terms of the $j_{\mathrm{eff}}$ states is key. However, in particular for its structurally simple member Ba$_2$IrO$_4$, such a systematic construction and subsequent analysis of minimal low-energy models are still missing. Here we show by means of a combination of different ab initio techniques with dynamical mean-field theory that a three-band model in terms of Ir-$j_{\mathrm{eff}}$ states fully retains the low-energy physics of the system as compared to a full Ir-$5d$ model. Providing a detailed study of the three-band model in terms of spin-orbit coupling, Hund's coupling and Coulomb interactions, we map out a rich phase diagram and identify a region of effective one-band metal-insulator transition relevant to Ba$_2$IrO$_4$. Compared to available angle-resolved photoemission spectra, we find good agreement of salient aspects of the calculated spectral function and identify features which require the inclusion of non-local fluctuations. In a broader context, we envisage the three- and five-band models developed in this study to be relevant for the study of doped Ba$_2$IrO$_4$ and to clarify further the similarities and differences with cuprates., Comment: 14+1 pages, 13+2 figures, 3+1 tables; comments are welcome

The elastic and transport coefficients of a perfect face-centered cubic crystal of hard spheres are computed from the poles of the dynamic structure factor and of the spectral functions of transverse momentum density fluctuations. For such crystals, the relevant coefficients are the three isothermal elastic constants $(C_{11}^T,C_{12}^T,C_{44}^T)$, the heat conductivity $\kappa$, and the three viscosities $(\eta_{11},\eta_{12},\eta_{44})$ (in Voigt's notations), which are directly computed using molecular dynamics simulations. The elastic and transport coefficients are then compared to the values of the same coefficients obtained with the method of Helfand moments, showing good agreement and providing strong support for the microscopic hydrodynamic theory of perfect crystals based on the local-equilibrium approach., Comment: 21 pages, 11 figures, 11 tables

Coordinate based implicit neural representations have gained rapid popularity in recent years as they have been successfully used in image, geometry and scene modeling tasks. In this work, we present a novel use case for such implicit representations in the context of learning anatomically constrained face models. Actor specific anatomically constrained face models are the state of the art in both facial performance capture and performance retargeting. Despite their practical success, these anatomical models are slow to evaluate and often require extensive data capture to be built. We propose the anatomical implicit face model; an ensemble of implicit neural networks that jointly learn to model the facial anatomy and the skin surface with high-fidelity, and can readily be used as a drop in replacement to conventional blendshape models. Given an arbitrary set of skin surface meshes of an actor and only a neutral shape with estimated skull and jaw bones, our method can recover a dense anatomical substructure which constrains every point on the facial surface. We demonstrate the usefulness of our approach in several tasks ranging from shape fitting, shape editing, and performance retargeting.

An increasingly common approach for creating photo-realistic digital avatars is through the use of volumetric neural fields. The original neural radiance field (NeRF) allowed for impressive novel view synthesis of static heads when trained on a set of multi-view images, and follow up methods showed that these neural representations can be extended to dynamic avatars. Recently, new variants also surpassed the usual drawback of baked-in illumination in neural representations, showing that static neural avatars can be relit in any environment. In this work we simultaneously tackle both the motion and illumination problem, proposing a new method for relightable and animatable neural heads. Our method builds on a proven dynamic avatar approach based on a mixture of volumetric primitives, combined with a recently-proposed lightweight hardware setup for relightable neural fields, and includes a novel architecture that allows relighting dynamic neural avatars performing unseen expressions in any environment, even with nearfield illumination and viewpoints.