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The semicircular canals of the inner ear are involved in balance and velocity control. Being crucial to ensure efficient mobility, their morphology exhibits an evolutionary conservatism attributed to stabilizing selection. Release of selection in slow-moving animals has been argued to lead to morphological divergence and increased inter-individual variation. In its natural habitat, the house mouse Mus musculus moves in a tridimensional space where efficient balance is required. In contrast, laboratory mice in standard cages are severely restricted in their ability to move, which possibly reduces selection on the inner ear morphology. This effect was tested by comparing four groups of mice: several populations of wild mice trapped in commensal habitats in France; their second-generation laboratory offspring, to assess plastic effects related to breeding conditions; a standard laboratory strain (Swiss) that evolved for many generations in a regime of mobility reduction; and hybrids between wild offspring and Swiss mice. The morphology of the semicircular canals was quantified using a set of 3D landmarks and semi-landmarks analyzed using geometric morphometric protocols. Levels of inter-population, inter-individual (disparity) and intra-individual (asymmetry) variation were compared. All wild mice shared a similar inner ear morphology, in contrast to the important divergence of the Swiss strain. The release of selection in the laboratory strain obviously allowed for an important and rapid drift in the otherwise conserved structure. Shared traits between the inner ear of the lab strain and domestic pigs suggested a common response to mobility reduction in captivity. The lab-bred offspring of wild mice also differed from their wild relatives, suggesting plastic response related to maternal locomotory behavior, since inner ear morphology matures before birth in mammals. The signature observed in lab-bred wild mice and the lab strain was however not congruent, suggesting that plasticity did not participate to the divergence of the laboratory strain. However, contrary to the expectation, wild mice displayed slightly higher levels of inter-individual variation than laboratory mice, possibly due to the higher levels of genetic variance within and among wild populations compared to the lab strain. Differences in fluctuating asymmetry levels were detected, with the laboratory strain occasionally displaying higher asymmetry scores than its wild relatives. This suggests that there may indeed be a release of selection and/or a decrease in developmental stability in the laboratory strain., Comment: Data available as Supplementary files and a contribution in MorphoMuseuM

The rapid growth in artificial intelligence and modern communication systems demands innovative solutions for increased computational power and advanced signaling capabilities. Integrated photonics, leveraging the analog nature of electromagnetic waves at the chip scale, offers a promising complement to approaches based on digital electronics. To fully unlock their potential as analog processors, establishing a common technological base between conventional digital electronic systems and analog photonics is imperative to building next-generation computing and communications hardware. However, the absence of an efficient interface has critically challenged comprehensive demonstrations of analog advantage thus far, with the scalability, speed, and energy consumption as primary bottlenecks. Here, we address this challenge and demonstrate a general electro-optic digital-to-analog link (EO-DiAL) enabled by foundry-based lithium niobate nanophotonics. Using purely digital inputs, we achieve on-demand generation of (i) optical and (ii) electronic waveforms at information rates up to 186 Gbit/s. The former addresses the digital-to-analog electro-optic conversion challenge in photonic computing, showcasing high-fidelity MNIST encoding while consuming 0.058 pJ/bit. The latter enables a pulse-shaping-free microwave arbitrary waveform generation method with ultrabroadband tunable delay and gain. Our results pave the way for efficient and compact digital-to-analog conversion paradigms enabled by integrated photonics and underscore the transformative impact analog photonic hardware may have on various applications, such as computing, optical interconnects, and high-speed ranging.

Here we show a photonic computing accelerator utilizing a system-level thin-film lithium niobate circuit which overcomes this limitation. Leveraging the strong electro-optic (Pockels) effect and the scalability of this platform, we demonstrate photonic computation at speeds up to 1.36 TOPS while consuming 0.057 pJ/OP. Our system features more than 100 thin-film lithium niobate high-performance components working synergistically, surpassing state-of-the-art systems on this platform. We further demonstrate binary-classification, handwritten-digit classification, and image classification with remarkable accuracy, showcasing our system's capability of executing real algorithms. Finally, we investigate the opportunities offered by combining our system with a hybrid-integrated distributed feedback laser source and a heterogeneous-integrated modified uni-traveling carrier photodiode. Our results illustrate the promise of thin-film lithium niobate as a computational platform, addressing current bottlenecks in both electronic and photonic computation. Its unique properties of high-performance electro-optic weight encoding and conversion, wafer-scale scalability, and compatibility with integrated lasers and detectors, position thin-film lithium niobate photonics as a valuable complement to silicon photonics, with extensions to applications in ultrafast and power-efficient signal processing and ranging.

The peculiar Galactic globular cluster $\omega$ Centauri (NGC 5139) has drawn attention for its unique features - such as a high stellar mass and a broad distribution of chemical elements - that have led to the hypothesis that it might be the nuclear remnant of an ancient dwarf galaxy accreted by the Milky Way (MW), potentially bringing along its own globular cluster (GC) system. In this work, we adopt an innovative approach by examining the individual chemical abundances of Galactic GCs. Applying Gaussian Mixture Models to globular cluster stars, whose membership is based on Gaia EDR3, and whose chemical abundances are provided by APOGEE DR17, we depart from traditional kinematic-based procedures and search for GCs that are chemically compatible with $\omega$ Cen in a 8-dimensional space defined by [Fe/H], [Mg/Fe], [Si/Fe], [Ca/Fe], [C/Fe], [Al/Fe], [K/Fe], and [Mn/Fe]. Our analysis leads to the identification of six GCs - NGC 6752, NGC 6656, NGC 6809, NGC 6273, NGC 6205, and NGC 6254 - that exhibit strong chemical similarities with $\omega$ Cen, and which have metallicities that coincide with those of the two main peaks of $\omega$ Cen's metallicity distribution. The chemical patterns of these clusters lead to the exclusion that they formed in progenitor galaxies with chemical enrichment histories similar to those of the Large and Small Magellanic Clouds, Sagittarius, and Fornax. Once placed in kinematic spaces such as the energy - angular momentum plane, these GCs result scattered across an extended region, which is predicted by N-body simulations if their common progenitor was sufficiently massive compared to the MW. Our novel approach suggests a common origin for NGC 6752, NGC 6656, NGC 6809, NGC 6273, NGC 6205, NGC 6254 and $\omega$ Cen, indicating that Nephele, as we propose to call the progenitor in which these GCs formed, played a substantial role in the Galaxy's history., Comment: Accepted for publication on A&A

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Identifying significant community structures in networks with incomplete data is a challenging task, as the reliability of solutions diminishes with increasing levels of missing information. However, in many empirical contexts, some information about the uncertainty in the network measurements can be estimated. In this work, we extend the recently developed Flow Stability framework, originally designed for detecting communities in time-varying networks, to address the problem of community detection in weighted, directed networks with missing links. Our approach leverages known uncertainty levels in nodes' out-degrees to enhance the robustness of community detection. Through comparisons on synthetic networks and a real-world network of messaging channels on the Telegram platform, we demonstrate that our method delivers more reliable community structures, even when a significant portion of data is missing.

The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups -- low, middle, and high -- driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis also highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions., Comment: 35 total pages. 26 pages of main text with 6 figures and 9 pages of supplementary information with 7 figures

We prove a quenched version of the large deviation principle for Birkhoff-like sums along a sequence of random quantum measurements driven by an ergodic process. We apply the result to the study of entropy production in the two-time measurement framework.

The diagnosis of diabetic retinopathy, which relies on fundus images, faces challenges in achieving transparency and interpretability when using a global classification approach. However, segmentation-based databases are significantly more expensive to acquire and combining them is often problematic. This paper introduces a novel method, termed adversarial style conversion, to address the lack of standardization in annotation styles across diverse databases. By training a single architecture on combined databases, the model spontaneously modifies its segmentation style depending on the input, demonstrating the ability to convert among different labeling styles. The proposed methodology adds a linear probe to detect dataset origin based on encoder features and employs adversarial attacks to condition the model's segmentation style. Results indicate significant qualitative and quantitative through dataset combination, offering avenues for improved model generalization, uncertainty estimation and continuous interpolation between annotation styles. Our approach enables training a segmentation model with diverse databases while controlling and leveraging annotation styles for improved retinopathy diagnosis., Comment: preprint

Distributed approaches have many computational benefits, but they are vulnerable to attacks from a subset of devices transmitting incorrect information. This paper investigates Byzantine-resilient algorithms in a decentralized setting, where devices communicate directly with one another. We investigate the notion of breakdown point, and show an upper bound on the number of adversaries that decentralized algorithms can tolerate. We introduce $\mathrm{CG}^+$, an algorithm at the intersection of $\mathrm{ClippedGossip}$ and $\mathrm{NNA}$, two popular approaches for robust decentralized learning. $\mathrm{CG}^+$ meets our upper bound, and thus obtains optimal robustness guarantees, whereas neither of the existing two does. We provide experimental evidence for this gap by presenting an attack tailored to sparse graphs which breaks $\mathrm{NNA}$ but against which $\mathrm{CG}^+$ is robust.

Air ducts are integral to modern buildings but are challenging to access for inspection. Small quadrotor drones offer a potential solution, as they can navigate both horizontal and vertical sections and smoothly fly over debris. However, hovering inside air ducts is problematic due to the airflow generated by the rotors, which recirculates inside the duct and destabilizes the drone, whereas hovering is a key feature for many inspection missions. In this article, we map the aerodynamic forces that affect a hovering drone in a duct using a robotic setup and a force/torque sensor. Based on the collected aerodynamic data, we identify a recommended position for stable flight, which corresponds to the bottom third for a circular duct. We then develop a neural network-based positioning system that leverages low-cost time-of-flight sensors. By combining these aerodynamic insights and the data-driven positioning system, we show that a small quadrotor drone (here, 180 mm) can hover and fly inside small air ducts, starting with a diameter of 350 mm. These results open a new and promising application domain for drones., Comment: Video: https://youtu.be/BLQqoa7Zolw

Some metrological means, such as Shack-Hartmann, deflectometry sensors or fringe projection profilometry, measure the shape of an optical surface indirectly from slope measurements. Zonal shape reconstruction, a method to reconstruct shape with a high number of degrees of freedom, is used for all of these applications. It has risen in interest with the use of deflectometers for the acquisition of high resolution slope data for optical manufacturing, especially because shape reconstruction is limiting in terms of shape estimation error. Zonal reconstruction methods all rely on the choice of a data formation model, a basis on which the shape will be decomposed, and an estimator. In this paper, we first study the canonical Fried and Southwell models of the literature and analyze their limitations. We show that modeling the slope measurement by a point-wise derivative as they both do can induce a bias on the shape estimation, and that the bases on which the shape is decomposed are imposed because of this assumption. In the second part of this paper, we propose to build an unbiased model of the data formation, without constraints on the choice of the decomposition basis. We then compare these models to the canonical models of Fried and Southwell. Lastly, we perform a regularized MAP reconstruction, and compare the performance in terms of total shape error of this method to the state of the art for the Southwell and Fried models, first by simulation, then on experimental data. We demonstrate that the suggested method outperforms the canonical models in terms of total shape reconstruction error on a deflectometry measurement of the high-frequency content of a freeform mirror.

Current star formation models are based on the local structure of the interstellar medium (ISM), yet the details on how the small-scale physics propagates up to global galactic-scale properties are still under debate. To investigate this we use {\small VINTERGATAN}, a high-resolution (20 pc) cosmological zoom-in simulation of a Milky Way-like galaxy. We study how the velocity dispersion and density structure of the ISM on 50-100 pc scales evolve with redshift, and quantify their impact on the star formation efficiency per free-fall timescale, $\epsilon_{\rm ff}$. During starbursts the ISM can reach velocity dispersions as high as $\sim 50$ km s$^{-1}$ for the densest and coldest gas, most noticeable during the last major merger event ($1.3 < z < 1.5$). After a merger-dominated phase ($11$ to 0.3\% at $z<1$. The mass-weighted average shows a universal $\langle \epsilon_{\rm ff} \rangle \approx 1\%$, caused by an almost invariant virial parameter distribution in star forming clouds. Changes in their density and turbulence levels are coupled so the kinetic-to-gravitational energy ratio remains close to constant. Finally, we show that a \textit{theoretically} motivated instantaneous $\epsilon_{\rm ff}$ is intrinsically different to its \textit{observational} estimates adopting tracers of star formation e.g. H$\alpha$. Since the physics underlying star formation can be lost on short ($\sim$ 10 Myr) timescales, caution must be taken when constraining star formation models from observational estimates of $\epsilon_{\rm ff}$., Comment: 14 pages, 6 figures, comments welcome

Empirically, it has been observed that adding momentum to Stochastic Gradient Descent (SGD) accelerates the convergence of the algorithm. However, the literature has been rather pessimistic, even in the case of convex functions, about the possibility of theoretically proving this observation. We investigate the possibility of obtaining accelerated convergence of the Stochastic Nesterov Accelerated Gradient (SNAG), a momentum-based version of SGD, when minimizing a sum of functions in a convex setting. We demonstrate that the average correlation between gradients allows to verify the strong growth condition, which is the key ingredient to obtain acceleration with SNAG. Numerical experiments, both in linear regression and deep neural network optimization, confirm in practice our theoretical results.

Solving differential equations is one of the most computationally expensive problems in classical computing, occupying the vast majority of high-performance computing resources devoted towards practical applications in various fields of science and engineering. Despite recent progress made in the field of quantum computing and quantum algorithms, its end-to-end application towards practical realization still remains unattainable. In this article, we tackle one of the primary obstacles towards this ultimate objective, specifically the encoding of matrices derived via finite difference method solving Poisson partial differential equations in simple boundary-value problems. To that end, we propose a novel methodology called block-diagonalization, which provides a common decomposition form for our matrices, and similarly a common procedure for block-encoding these matrices inside a unitary operator of a quantum circuit. The depth of these circuits is double-logarithmic in the matrix size, which is an exponential improvement over existing quantum methods and a superexponential improvement over existing classical methods. These improvements come at the price of a constant multiplicative overhead on the number of qubits and the number of gates. Combined with quantum linear solver algorithms, we can utilize these quantum circuits to produce a quantum state representation of the solution to the Poisson partial differential equations and their boundary-value problems.

Networks are powerful tools for modeling interactions in complex systems. While traditional networks use scalar edge weights, many real-world systems involve multidimensional interactions. For example, in social networks, individuals often have multiple interconnected opinions that can affect different opinions of other individuals, which can be better characterized by matrices. We propose a novel, general framework for modeling such multidimensional interacting dynamics: matrix-weighted networks (MWNs). We present the mathematical foundations of MWNs and examine consensus dynamics and random walks within this context. Our results reveal that the coherence of MWNs gives rise to non-trivial steady states that generalize the notions of communities and structural balance in traditional networks., Comment: 14 pages, 8 figures

We consider de Finetti's stochastic control problem for a spectrally negative L\'evy process in an Omega model. In such a model, the (controlled) process is allowed to spend time under the critical level but is then subject to a level-dependent intensity of bankruptcy. First, before considering the control problem, we derive some analytical properties of the corresponding Omega scale functions. Second, we prove that exists a barrier strategy that is optimal for this control problem under a mild assumption on the L\'evy measure. Finally, we analyse numerically the impact of the bankruptcy rate function on the optimal strategy.

We analyze the evolution of the radial profiles and the azimuthal variations of the stellar metallicities from the Vintergatan simulation of a Milky Way-like galaxy. We find that negative gradients exist as soon as the disk settles at high redshift, and are maintained throughout the long term evolution of the galaxy, including during major merger events. The inside-out growth of the disk and an overall outward radial migration tend to flatten these gradients in time. Major merger events only have a moderate and short-lived imprint on the [Fe/H] distributions with almost no radial dependence. The reason lies in the timescale for enrichment in Fe being significantly longer than the duration of the starbursts episodes, themselves slower than dynamical mixing during typical interactions. It results that signatures of major mergers become undetectable in [Fe/H] only a few Myr after pericenter passages. We note that considering other tracers like the warm interstellar medium, or monitoring the evolution of the metallicity gradient as a single value instead of a radial full profile could lead to different interpretations, and warn against an oversimplification of this complex problem., Comment: A&A accepted

Online object segmentation and tracking in Lidar point clouds enables autonomous agents to understand their surroundings and make safe decisions. Unfortunately, manual annotations for these tasks are prohibitively costly. We tackle this problem with the task of class-agnostic unsupervised online instance segmentation and tracking. To that end, we leverage an instance segmentation backbone and propose a new training recipe that enables the online tracking of objects. Our network is trained on pseudo-labels, eliminating the need for manual annotations. We conduct an evaluation using metrics adapted for temporal instance segmentation. Computing these metrics requires temporally-consistent instance labels. When unavailable, we construct these labels using the available 3D bounding boxes and semantic labels in the dataset. We compare our method against strong baselines and demonstrate its superiority across two different outdoor Lidar datasets.

Drainage in porous media can be broken down into two main mechanisms: a primary piston-like displacement of the interfaces through the bulk of pore bodies and throats, and a secondary slow flow through corners and films in the wake of the invasion front. In granular porous media, this secondary drainage mechanism unfolds in connected pathways of pendular structures, such as capillary bridges and liquid rings, formed between liquid clusters. To represent both mechanisms, we proposed a dynamic dual-network model for drainage, considering that a gas displaces a wetting liquid from quasi-2D granular porous media. For this model, dedicated analyses of the capillary bridge shapes and hydraulic conductivity were conducted so that the secondary drainage mechanism could be properly quantified at finite speeds. With the model, an investigation of the wetting-phase connectivity and flow during drainage was carried out, covering a broad range of flow conditions. Results indicate that the span of liquid-connected structures in the unsaturated region, as well as their ability to contribute to flow, varies significantly with Capillary and Bond numbers.

We tackle the challenging problem of source-free unsupervised domain adaptation (SFUDA) for 3D semantic segmentation. It amounts to performing domain adaptation on an unlabeled target domain without any access to source data; the available information is a model trained to achieve good performance on the source domain. A common issue with existing SFUDA approaches is that performance degrades after some training time, which is a by product of an under-constrained and ill-posed problem. We discuss two strategies to alleviate this issue. First, we propose a sensible way to regularize the learning problem. Second, we introduce a novel criterion based on agreement with a reference model. It is used (1) to stop the training when appropriate and (2) as validator to select hyperparameters without any knowledge on the target domain. Our contributions are easy to implement and readily amenable for all SFUDA methods, ensuring stable improvements over all baselines. We validate our findings on various 3D lidar settings, achieving state-of-the-art performance. The project repository (with code) is: github.com/valeoai/TTYD., Comment: Accepted to ECCV 2024. Project repository: github.com/valeoai/TTYD

Disentangling irreversible and reversible forces from random fluctuations is a challenging problem in the analysis of stochastic trajectories measured from real-world dynamical systems. We present an approach to approximate the dynamics of a stationary Langevin process as a discrete-state Markov process evolving over a graph-representation of phase-space, reconstructed from stochastic trajectories. Next, we utilise the analogy of the Helmholtz-Hodge decomposition of an edge-flow on a contractible simplicial complex with the associated decomposition of a stochastic process into its irreversible and reversible parts. This allows us to decompose our reconstructed flow and to differentiate between the irreversible currents and reversible gradient flows underlying the stochastic trajectories. We validate our approach on a range of solvable and nonlinear systems and apply it to derive insight into the dynamics of flickering red-blood cells and healthy and arrhythmic heartbeats. In particular, we capture the difference in irreversible circulating currents between healthy and passive cells and healthy and arrhythmic heartbeats. Our method breaks new ground at the interface of data-driven approaches to stochastic dynamics and graph signal processing, with the potential for further applications in the analysis of biological experiments and physiological recordings. Finally, it prompts future analysis of the convergence of the Helmholtz-Hodge decomposition in discrete and continuous spaces., Comment: 26 pages, 12 figures

This article presents a contribution to multi-criteria decision support intended for industrial decision-makers in order to determine the best compromise between design criteria when working on risky or innovative products. In (RENAUD et al. 2008) we used the OWA operator (Ordered Weighted Average), a well-known multi-criteria analysis technique introduced by (YAGER 1988). The interest of this aggregation method is, beyond its ease of use, its ability to evaluate a product according to a single scale. When using the OWA method, the choice of criterion weights and their values remains an important and delicate operation. Indeed, the weights are not fixed by criterion but according to a level of utility (FISHBURN 1967). The weights can, in addition, be determined using different methods. A traditional approach consists in estimating the weights from an a priori selection of the most representative samples by an expert. We propose a new approach based on applied D-Optimality to determine the best sample. The results of the two approaches are compared. Indeed, in some multi-criteria decision problems, it is difficult to choose the method that best describes the behavior of the decision maker. As said above, the first part of this paper presents an original method based on D-optimality to determine the most representative set of samples to determine the decision parameters. This method has been applied and validated in an OWA approach. It has been shown that the accuracy and reliability of the method have been improved. Since the OWA application has been improved, the other goal of this paper is to verify whether the real decision maker has an OWA-based behavior or not. To achieve this, the D-optimality approach is applied to the MAUT (Multi-Attribute Utility Theory) approach to find the best set of samples. The results of the two methods are compared. Both approaches show that, while OWA better simulates the decision maker's behavior, there is still a gap between its scores and that of OWA. The question here is how to improve the accuracy of the score estimated by OWA compared to those given by the expert. Subsequently, a hybrid approach is tested, such as a linear combination of the MAUT and OWA approaches. The results obtained with this combination were found to be more accurate than those of OWA. However, we also tested the CHOQUET discrete integral approach. In other words, it is possible to find a model capable of better describing the decision maker's behavior., Comment: in French language

The ranking of possible alternatives during the design or operation of an industrial process leads to the definition of a solution representing the best compromise between several contradictory objectives. The choice of solution is a decision that can be modeled using knowledge of the corresponding preferences. The decision and preference model used is that of flow balances. Munster cheese is used as an example. These are highly distinctive cheeses that are not suitable for uninformed consumers. If the decision-maker wishes to widen his consumer panel, he must modify the production line settings. In the event of a change in preferences due to an external influence, the decision-maker must adapt, but not too abruptly, to avoid both production line oscillations and consumer annoyance. To solve this problem, we propose a new technique: dynamic flow balances. This makes it possible to forecast production trends. Prospects are also proposed, such as the creation of a multi-criteria predictive control., Comment: in French language

In this work, we uncover patterns of usage mobile phone applications and information spread in response to perturbations caused by unprecedented events. We focus on categorizing patterns of response in both space and time and tracking their relaxation over time. To this end, we use the NetMob2023 Data Challenge dataset, which provides mobile phone applications traffic volume data for several cities in France at a spatial resolution of 100$m^2$ and a time resolution of 15 minutes for a time period ranging from March to May 2019. We analyze the spread of information before, during, and after the catastrophic Notre-Dame fire on April 15th and a bombing that took place in the city centre of Lyon on May 24th using volume of data uploaded and downloaded to different mobile applications as a proxy of information transfer dynamics. We identify different clusters of information transfer dynamics in response to the Notre-Dame fire within the city of Paris as well as in other major French cities. We find a clear pattern of significantly above-baseline usage of the application Twitter (currently known as X) in Paris that radially spreads from the area surrounding the Notre-Dame cathedral to the rest of the city. We detect a similar pattern in the city of Lyon in response to the bombing. Further, we present a null model of radial information spread and develop methods of tracking radial patterns over time. Overall, we illustrate novel analytical methods we devise, showing how they enable a new perspective on mobile phone user response to unplanned catastrophic events, giving insight into how information spreads during a catastrophe in both time and space.

Threshold cryptography has gained momentum in the last decades as a mechanism to protect long term secret keys. Rather than having a single secret key, this allows to distribute the ability to perform a cryptographic operation such as signing or encrypting. Threshold cryptographic operations are shared among different parties such that a threshold number of them must participate in order to run the operation. This makes the job of an attacker strictly more difficult in the sense that they would have to corrupt at least a threshold number of parties to breach the security. Most works in this field focus on asymmetric-key schemes that allow threshold signing or decrypting. We focus on the symmetric-key setting, allowing both threshold encryption and threshold decryption. Previous work relies on the presence of a trusted third party. Such a party may not exist in some use cases, and it represents a single point of failure. We propose to remove the requirement of a trusted third party by designing a dealer-free setup in which no entity can at any point obtain full knowledge of the secret keys. We implement a proof of concept of our construction in Python. We evaluate the proof of concept with timing metrics to compare to theoretical expectations and assess the cost in complexity of not relying on a trusted third party. While the setup phase suffers moderate additional cost, the encryption and decryption phases perform the same as the original algorithm., Comment: 13 pages, Submitted to IEEE Transactions on Information Forensics & Security

The transition matrix, frequently abbreviated as T-matrix, contains the complete information in a linear approximation of how a spatially localized object scatters an incident field. The T-matrix is used to study the scattering response of an isolated object and describes the optical response of complex photonic materials made from ensembles of individual objects. T-matrices of certain common structures, potentially, have been repeatedly calculated all over the world again and again. This is not necessary and constitutes a major challenge for various reasons. First, the resources spent on their computation represent an unsustainable financial and ecological burden. Second, with the onset of machine learning, data is the gold of our era, and it should be freely available to everybody to address novel scientific challenges. Finally, the possibility of reproducing simulations could tremendously improve if the considered T-matrices could be shared. To address these challenges, we found it important to agree on a common data format for T-matrices and to enable their collection from different sources and distribution. This document aims to develop the specifications for storing T-matrices and associated metadata. The specifications should allow maximum freedom to accommodate as many use cases as possible without introducing any ambiguity in the stored data. The common format will assist in setting up a public database of T-matrices., Comment: Submitted to the Journal of Quantitative Spectroscopy and Radiative Transfer

We discuss a series of results aimed at bringing saturation physics and gluon tomography into an era of precision. In particular, the NLO treatment of diffractive: 1) exclusive dijet, 2) exclusive longitudinally polarized light vector meson and 3) semi-inclusive single or double hadron photo- or electroproduction with large $p_T$, on a nucleon or a nuclei. Finally, we discuss the more complicated 4) exclusive transversely polarized light vector meson production, which starts at the next-to-leading power and therefore requires a beyond leading twist treatment. This new class of processes provides an access to precision physics of gluon saturation dynamics, with very promising future phenomenological studies at the EIC, or, at the LHC in $p A$ and $A A$ scattering, using Ultra Peripheral Collisions (UPC)., Comment: 6 pages, 2 figures, proceedings of the 31st International Workshop on Deep Inelastic Scattering (DIS2024), 8-12 April 2024, Grenoble, France

We present a proof of strong consistency of a Ziv-Merhav-type estimator of the cross entropy rate for pairs of hidden-Markov processes. Our proof strategy has two novel aspects: the focus on decoupling properties of the laws and the use of tools from the thermodynamic formalism., Comment: Short note prepared for the IEEE International Symposium on Information Theory 2024 on a special case of arXiv:2312.02098

Malaria remains a significant global health challenge, necessitating rapid and accurate diagnostic methods. While computer-aided diagnosis (CAD) tools utilizing deep learning have shown promise, their generalization to diverse clinical settings remains poorly assessed. This study evaluates the generalization capabilities of a CAD model for malaria diagnosis from thin blood smear images across four sites. We explore strategies to enhance generalization, including fine-tuning and incremental learning. Our results demonstrate that incorporating site-specific data significantly improves model performance, paving the way for broader clinical application., Comment: MICCAI 2024 AMAI Workshop, Accepted for presentation, Submitted Manuscript Version, 10 pages

We derive explicit restriction and continuation formulas between n-dimensional (Anti)-de Sitter spaces and the (n + 1)-dimensional Minkowskian ambient space for the codifferential and Laplace-de Rham operators acting on p-forms., Comment: 9 pages, v2: typos corrected

Synaptic plasticity is a key component of neuronal dynamics, describing the process by which the connections between neurons change in response to experiences. In this study, we extend a network model of $\theta$-neuron oscillators to include a realistic form of adaptive plasticity. In place of the less tractable spike-timing-dependent plasticity, we employ recently validated phase-difference-dependent plasticity rules, which adjust coupling strengths based on the relative phases of $\theta$-neuron oscillators. We investigate two approaches for implementing this plasticity: pairwise coupling strength updates and global coupling strength updates. A mean-field approximation of the system is derived and we investigate its validity through comparison with the $\theta$-neuron simulations across various stability states. The synchrony of the system is examined using the Kuramoto order parameter. A bifurcation analysis, by means of numerical continuation and the calculation of maximal Lyapunov exponents, reveals interesting phenomena, including bistability and evidence of period-doubling and boundary crisis routes to chaos, that would otherwise not exist in the absence of adaptive coupling.

In this article, we consider the problem of community detection in signed networks. We propose SignedLouvain, an adaptation of the Louvain method to maximise signed modularity, efficiently taking advantage of the structure induced by signed relations. We begin by identifying the inherent limitations of applying the standard Louvain algorithm to signed networks, before introducing a novel variant specifically engineered to overcome these challenges. Through extensive experiments on real-world datasets, we demonstrate that the proposed method not only maintains the speed and scalability of its predecessor but also significantly enhances accuracy in detecting communities within signed networks.

Exclusive diffractive meson production represents a golden channel for investigating gluonic saturation inside nucleons and nuclei. In this letter, we settle a systematic framework to deal with beyond leading power corrections at small-$x$, including the saturation regime, and obtain the $\gamma^{*} \rightarrow M (\rho, \phi, \omega)$ impact factor with both incoming photon and outgoing meson carrying arbitrary polarizations. This is of particular interest since the saturation scale at modern colliders, although entering a perturbative regime, is not large enough to prevents higher-twist effects to be sizable., Comment: 8 pages, 2 figures

We develop a framework combining the higher-twist formalism of exclusive processes in the $s$ channel with the semi-classical effective description of small-$x$ physics in the $t$ channel. We apply it to transversely polarized light vector meson production, $\gamma^{*} p \rightarrow V (\rho, \varphi ,\omega) \; p$, which starts at the next-to-leading power and for which a purely collinear treatment leads to end-point singularities. The result is obtained in the most general kinematics, including both forward and non-forward cases by preserving the full impact parameter dependence in the non-perturbative correlators, in both momentum and coordinate space representations. A systematic expansion of the Wilson lines in terms of Reggeized gluon fields is performed in order to obtain the results in the weak-field BFKL approximation. These new results will allow for investigating the dilute-to-dense regime transition of QCD for a wide class of observables., Comment: 73 pages, 12 Figures

Remote sensing data are increasingly available and frequently used to produce forest attributes maps. The sampling strategy of the calibration plots may directly affect predictions and map qualities. The aim of this manuscript is to evaluate models transferability at different spatial scales according to the sampling efforts and the calibration domain of these models. Forest inventory plots from locals and regionals networks were used to calibrate randomForest (RF) models for stand basal area predictions. Auxiliary data from ALS flights and a Sentinel-2 image were used. Model transferability was assessed by comparing models developed over a given area and applied elsewhere. Performances were measured in terms of precision (RMSE and bias), coefficient of determination (R2) and the proportion of extrapolated predictions. Regional networks were also thinned to evaluate the effect of sampling efforts on models' performances. Local models showed large bias and extrapolation issues when applied elsewhere. Local issues of regional models were also observed, raising transferability and extrapolation concerns. An increase in sampling efforts was shown to reduce extrapolation issues. The outcoming results of this study underline the importance of considering models' validity domain while producing forest attribute maps, since their transferability is of crucial importance from a forest management perspective.

This position paper briefly discusses nudging, its use by autonomous agents, potential risks and ethical considerations while creating such systems. Instead of taking a normative approach, which guides all situations, the paper proposes a risk-driven questions-and-answer approach. The paper takes the position that this is a pragmatic method, that is transparent about beneficial intentions, foreseeable risks, and mitigations. Given the uncertainty in AI and autonomous agent capabilities, we believe that such pragmatic methods offer a plausibly safe path, without sacrificing flexibility in domain and technology., Comment: 14 pages (incl. 3p pages of references), accepted for publication in EUMAS 2024

The Tarantula Nebula in the Large Magellanic Cloud is known for its high star formation activity. At its center lies the young massive star cluster R136, providing a significant amount of the energy that makes the nebula shine so brightly at many wavelengths. Recently, young massive star clusters have been suggested to also efficiently produce high-energy cosmic rays, potentially beyond PeV energies. Here, we report the detection of very-high-energy $\gamma$-ray emission from the direction of R136 with the High Energy Stereoscopic System, achieved through a multicomponent, likelihood-based modeling of the data. This supports the hypothesis that R136 is indeed a very powerful cosmic-ray accelerator. Moreover, from the same analysis, we provide an updated measurement of the $\gamma$-ray emission from 30 Dor C, the only superbubble detected at TeV energies presently. The $\gamma$-ray luminosity above $0.5\,\mathrm{TeV}$ of both sources is $(2-3)\times 10^{35}\,\mathrm{erg}\,\mathrm{s}^{-1}$. This exceeds by more than a factor of 2 the luminosity of HESS J1646$-$458, which is associated with the most massive young star cluster in the Milky Way, Westerlund 1. Furthermore, the $\gamma$-ray emission from each source is extended with a significance of $>3\sigma$ and a Gaussian width of about $30\,\mathrm{pc}$. For 30 Dor C, a connection between the $\gamma$-ray emission and the nonthermal X-ray emission appears likely. Different interpretations of the $\gamma$-ray signal from R136 are discussed., Comment: 10+11 pages, 4+6 figures. Corresponding authors: L. Mohrmann, N. Komin

Annotating lidar point clouds for autonomous driving is a notoriously expensive and time-consuming task. In this work, we show that the quality of recent self-supervised lidar scan representations allows a great reduction of the annotation cost. Our method has two main steps. First, we show that self-supervised representations allow a simple and direct selection of highly informative lidar scans to annotate: training a network on these selected scans leads to much better results than a random selection of scans and, more interestingly, to results on par with selections made by SOTA active learning methods. In a second step, we leverage the same self-supervised representations to cluster points in our selected scans. Asking the annotator to classify each cluster, with a single click per cluster, then permits us to close the gap with fully-annotated training sets, while only requiring one thousandth of the point labels.

The propagation of a wave in a medium is generally affected when the medium is moving with respect to the observer. Because plasma equilibria often involve plasma flows, for instance in astrophysics or in magnetic confinement nuclear fusion devices, understanding the effect of motion on plasma waves is important. Meanwhile, the presence of a background magnetic field in a plasma makes it anisotropic. To address this problem, we derive here ray tracing equations for the trajectory of rays propagating in a moving anisotropic medium. The proposed approach is to use an effective dispersion relation for the moving medium as seen from the laboratory, obtained by performing a Lorentz transformation of the dispersion relation known for the medium at rest. This formalism is illustrated by considering the standard ordinary and extraordinary modes in a magnetized plasma at rest. Although we work here at lowest order in the geometrical optics approximation, this method is a first step towards higher order expansions, as required for instance to capture polarization effects.

This work introduces a novel augmentation method that increases the diversity of a train set to improve the generalization abilities of a 6D pose estimation network. For this purpose, a Neural Radiance Field is trained from synthetic images and exploited to generate an augmented set. Our method enriches the initial set by enabling the synthesis of images with (i) unseen viewpoints, (ii) rich illumination conditions through appearance extrapolation, and (iii) randomized textures. We validate our augmentation method on the challenging use-case of spacecraft pose estimation and show that it significantly improves the pose estimation generalization capabilities. On the SPEED+ dataset, our method reduces the error on the pose by 50% on both target domains.

Most of the analyses concerning signed networks have focused on the balance theory, hence identifying frustration with undirected, triadic motifs having an odd number of negative edges; much less attention has been paid to their directed counterparts. To fill this gap, we focus on signed, directed connections, with the aim of exploring the notion of frustration in such a context. When dealing with signed, directed edges, frustration is a multi-faceted concept, admitting different definitions at different scales: if we limit ourselves to consider cycles of length two, frustration is related to reciprocity, i.e. the tendency of edges to admit the presence of partners pointing in the opposite direction. As the reciprocity of signed networks is still poorly understood, we adopt a principled approach for its study, defining quantities and introducing models to consistently capture empirical patterns of the kind. In order to quantify the tendency of empirical networks to form either mutualistic or antagonistic cycles of length two, we extend the Exponential Random Graphs framework to binary, directed, signed networks with global and local constraints and, then, compare the empirical abundance of the aforementioned patterns with the one expected under each model. We find that the (directed extension of the) balance theory is not capable of providing a consistent explanation of the patterns characterising the directed, signed networks considered in this work. Although part of the ambiguities can be solved by adopting a coarser definition of balance, our results call for a different theory, accounting for the directionality of edges in a coherent manner. In any case, the evidence that the empirical, signed networks can be highly reciprocated leads us to recommend to explicitly account for the role played by bidirectional dyads in determining frustration at higher levels (e.g. the triadic one)., Comment: 35 pages, 9 figures, 4 tables

Recent VLMs, pre-trained on large amounts of image-text pairs to align both modalities, have opened the way to open-vocabulary semantic segmentation. Given an arbitrary set of textual queries, image regions are assigned the closest query in feature space. However, the usual setup expects the user to list all possible visual concepts that may occur in the image, typically all classes of benchmark datasets, that act as negatives to each other. We consider here the more challenging scenario of segmenting a single concept, given a textual prompt and nothing else. To achieve good results, besides contrasting with the generic 'background' text, we study different ways to generate query-specific test-time contrastive textual concepts, which leverage either the distribution of text in the VLM's training set or crafted LLM prompts. We show the relevance of our approach using a new, specific metric.

The change in direction of the wavevector and group velocity experienced by a wave refracted at the interface of an anisotropic medium in uniform linear motion are determined analytically. These transmission conditions, which are shown to be consistent with generalized Snell's law written in the laboratory frame, are then used to examine the effect of motion on waves incident on a magnetized plasma. For an incident wave in the plane perpendicular to the magnetic field the motion is observed to lead to non negligible deviation of the low-frequency X-mode, as well as to non-symmetrical total reflection angles. These effects are shown to be further complicated when the magnetic field is in the plane formed by the incident wavevector and the medium's velocity, as the anisotropy now competes with the motion-induced drag. Although obtained in simplified configurations, these results suggest that accounting for motion when modeling plasma waves trajectory could be important under certain conditions, calling for a more detailed quantification of the effect of motion in actual diagnostics and plasma control schemes., Comment: 21 pages, 9 figures

We study Kirby problems 1.92(E)-(G), which, roughly speaking, ask for which compact oriented $3$-manifold $M$ the Kauffman bracket skein module $\mathcal{S}(M)$ has torsion as a $\mathbb{Z}[A^{\pm 1}]$-module. We give new criteria for the presence of torsion in terms of how large the $SL_2(\mathbb{C})$-character variety of $M$ is. This gives many counterexamples to question 1.92(G)-(i) in Kirby's list. For manifolds with incompressible tori, we give new effective criteria for the presence of torsion, revisiting the work of Przytycki and Veve. We also show that $\mathcal{S}(\mathbb{R P}^3# L(p,1))$ has torsion when $p$ is even. Finally, we show that for $M$ an oriented Seifert manifold, closed or with boundary, $\mathcal{S}(M)$ has torsion if and only if $M$ admits a $2$-sided non-boundary parallel essential surface., Comment: 37 pages, 16 figures. Comments welcome!

Collective movement inspired by animal groups promises inherited benefits for robot swarms, such as enhanced sensing and efficiency. However, while animals move in groups using only their local senses, robots often obey central control or use direct communication, introducing systemic weaknesses to the swarm. In the hope of addressing such vulnerabilities, developing bio-inspired decentralized swarms has been a major focus in recent decades. Yet, creating robots that move efficiently together using only local sensory information remains an extraordinary challenge. In this work, we present a decentralized, purely vision-based swarm of terrestrial robots. Within this novel framework robots achieve collisionless, polarized motion exclusively through minimal visual interactions, computing everything on board based on their individual camera streams, making central processing or direct communication obsolete. With agent-based simulations, we further show that using this model, even with a strictly limited field of view and within confined spaces, ordered group motion can emerge, while also highlighting key limitations. Our results offer a multitude of practical applications from hybrid societies coordinating collective movement without any common communication protocol, to advanced, decentralized vision-based robot swarms capable of diverse tasks in ever-changing environments.

Mixed-anion compounds, which incorporate multiple types of anions into materials, displays tailored crystal structures and physical/chemical properties, garnering immense interests in various applications such as batteries, catalysis, photovoltaics, and thermoelectrics. However, detailed studies regarding correlations between crystal structure, chemical bonding, and thermal/vibrational properties are rare for these compounds, which limits the exploration of mixed-anion compounds for associated thermal applications. In this work, we investigate the lattice dynamics and thermal transport properties of the metal chalcohalides, CuBiSCl2. A high-purity polycrystalline CuBiSCl2 sample, successfully synthesized via modified solid-state synthetic method, exhibits a low lattice thermal conductivity of 0.9-0.6 W m-1 K-1 from 300 to 573 K. By combining various experimental techniques including 3D electron diffraction with theoretical calculations, we elucidate the origin of low lattice thermal conductivity in CuBiSCl2. The stereo-chemical activity of the 6s2 lone pair of Bi3+ favors an asymmetric environment with neighboring anions involving both short and long bond lengths. This particularity often implies weak bonding, low structure dimensionality, and strong anharmonicity, leading to low lattice thermal conductivity. In addition, the strong two-fold linear S-Cu-S coordination with weak Cu -- Cl interactions induces large anisotropic vibration of Cu or structural disorder, which enables strong phonon-phonon scattering and decreases lattice thermal conductivity. The investigations into lattice dynamics and thermal transport properties of CuBiSCl2 broadens the scope of the existing mixed-anion compounds suitable for the associated thermal applications, offering a new avenue for the search of low thermal conductivity materials in low-cost mixed-anion compounds.