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Accurate sensor calibration is crucial for autonomous systems, yet its uncertainty quantification remains underexplored. We present the first approach to integrate uncertainty awareness into online extrinsic calibration, combining Monte Carlo Dropout with Conformal Prediction to generate prediction intervals with a guaranteed level of coverage. Our method proposes a framework to enhance existing calibration models with uncertainty quantification, compatible with various network architectures. Validated on KITTI (RGB Camera-LiDAR) and DSEC (Event Camera-LiDAR) datasets, we demonstrate effectiveness across different visual sensor types, measuring performance with adapted metrics to evaluate the efficiency and reliability of the intervals. By providing calibration parameters with quantifiable confidence measures, we offer insights into the reliability of calibration estimates, which can greatly improve the robustness of sensor fusion in dynamic environments and usefully serve the Computer Vision community., Comment: Accepted for publication at WACV 2025

This paper is devoted to the class of paraconvex functions and presents some of its fundamental properties, characterization, and examples that can be used for their recognition and optimization. Next, the convergence analysis of the projected subgradient methods with several step-sizes (i.e., constant, nonsummable, square-summable but not summable, geometrically decaying, and Scaled Polyak's step-sizes) to global minima for this class of functions is studied. In particular, the convergence rate of the proposed methods is investigated under paraconvexity and the H\"{o}lderian error bound condition, where the latter is an extension of the classical error bound condition. The preliminary numerical experiments on several robust low-rank matrix recovery problems (i.e., robust matrix completion, image inpainting, robust nonnegative matrix factorization, robust matrix compression) indicate promising behavior for these projected subgradient methods, validating our theoretical foundations., Comment: 38 pages, 62 figures

Block-Term Tensor Regression (BTTR) has proven to be a powerful tool for modeling complex, high-dimensional data by leveraging multilinear relationships, making it particularly well-suited for applications in healthcare and neuroscience. However, traditional implementations of BTTR rely on centralized datasets, which pose significant privacy risks and hinder collaboration across institutions. To address these challenges, we introduce Federated Block-Term Tensor Regression (FBTTR), an extension of BTTR designed for federated learning scenarios. FBTTR enables decentralized data analysis, allowing institutions to collaboratively build predictive models while preserving data privacy and complying with regulations. FBTTR represents a major step forward in applying tensor regression to federated learning environments. Its performance is evaluated in two case studies: finger movement decoding from Electrocorticography (ECoG) signals and heart disease prediction. In the first case study, using the BCI Competition IV dataset, FBTTR outperforms non-multilinear models, demonstrating superior accuracy in decoding finger movements. For the dataset, for subject 3, the thumb obtained a performance of 0.76 $\pm$ .05 compared to 0.71 $\pm$ 0.05 for centralised BTTR. In the second case study, FBTTR is applied to predict heart disease using real-world clinical datasets, outperforming both standard federated learning approaches and centralized BTTR models. In the Fed-Heart-Disease Dataset, an AUC-ROC was obtained of 0.872 $\pm$ 0.02 and an accuracy of 0.772 $\pm$ 0.02 compared to 0.812 $\pm$ 0.003 and 0.753 $\pm$ 0.007 for the centralized model.

We demonstrate that including the second viscosity of an electron gas in the hydrodynamic model allows for highly accurate modeling of the optical response of heavily doped semiconductors. In our setup, which improves resonance visibility compared to previous approaches, plasmon resonances become more distinct, allowing for detailed analysis of the underlying physics. With advanced fitting techniques based on a physics-informed cost function and a tailored optimization algorithm, we obtain close agreement between simulations and experimental data across different sample thicknesses. This enhanced resonance visibility, combined with our integrated approach, shows that key parameters such as doping level and effective electron mass can be retrieved from a single optical measurement. The spatial dispersion taken into account in the hydrodynamic framework is essential for accurately describing the optical response of plasmonic materials in this frequency range and is likely to become a standard modeling approach.

Selecting an appropriate prior to compensate for information loss due to the measurement operator is a fundamental challenge in imaging inverse problems. Implicit priors based on denoising neural networks have become central to widely-used frameworks such as Plug-and-Play (PnP) algorithms. In this work, we introduce Fixed-points of Restoration (FiRe) priors as a new framework for expanding the notion of priors in PnP to general restoration models beyond traditional denoising models. The key insight behind FiRe is that natural images emerge as fixed points of the composition of a degradation operator with the corresponding restoration model. This enables us to derive an explicit formula for our implicit prior by quantifying invariance of images under this composite operation. Adopting this fixed-point perspective, we show how various restoration networks can effectively serve as priors for solving inverse problems. The FiRe framework further enables ensemble-like combinations of multiple restoration models as well as acquisition-informed restoration networks, all within a unified optimization approach. Experimental results validate the effectiveness of FiRe across various inverse problems, establishing a new paradigm for incorporating pretrained restoration models into PnP-like algorithms.

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended $\texttt{sbi}$, a PyTorch-based package that implements Bayesian SBI algorithms based on neural networks. The $\texttt{sbi}$ toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the $\texttt{sbi}$ toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data.

In this work we study the behavior of the forward-backward (FB) algorithm when the proximity operator is replaced by a sub-iterative procedure to approximate a Gaussian denoiser, in a Plug-and-Play (PnP) fashion. In particular, we consider both analysis and synthesis Gaussian denoisers within a dictionary framework, obtained by unrolling dual-FB iterations or FB iterations, respectively. We analyze the associated minimization problems as well as the asymptotic behavior of the resulting FB-PnP iterations. In particular, we show that the synthesis Gaussian denoising problem can be viewed as a proximity operator. For each case, analysis and synthesis, we show that the FB-PnP algorithms solve the same problem whether we use only one or an infinite number of sub-iteration to solve the denoising problem at each iteration. To this aim, we show that each "one sub-iteration" strategy within the FB-PnP can be interpreted as a primal-dual algorithm when a warm-restart strategy is used. We further present similar results when using a Moreau-Yosida smoothing of the global problem, for an arbitrary number of sub-iterations. Finally, we provide numerical simulations to illustrate our theoretical results. In particular we first consider a toy compressive sensing example, as well as an image restoration problem in a deep dictionary framework.

MXenes stand out from other 2D materials because they combine very good electrical conductivity with hydrophilicity, allowing cost-effective processing as thin films. Therefore, there is a high fundamental interest in unraveling the electronic transport mechanisms at stake in multilayers of the most conducting MXene, Ti$_3$C$_2$T$_x$. Although weak localization (WL) has been proposed as the dominating low-temperature (LT) transport mechanism in Ti$_3$C$_2$T$_x$ thin films, there have been few attempts to model it quantitatively. In this paper, the role of important structural parameters -- thickness, interflake coupling, defects -- on the dimensionality of the LT transport mechanisms in spin-coated Ti$_3$C$_2$T$_x$ thin films is investigated through LT and magnetic field dependent resistivity measurements. A dimensional crossover from 2D to 3D WL is clearly evidenced when the film thickness exceeds the dephasing length $l_\phi$, which is in the $50-100\,\mathrm{nm}$ range. 2D WL can be restored by weakening the coupling between adjacent flakes, the intrinsic thickness of which is lower than $l_\phi$, hence acting as parallel 2D conductors. Alternatively, $l_\phi$ can be reduced down to the $10\,\mathrm{nm}$ range by defects. Our results clearly emphasize the ability of WL quantitative study to give deep insights in the physics of electron transport in MXene thin films.

Traditionally, 3d indoor datasets have generally prioritized scale over ground-truth accuracy in order to obtain improved generalization. However, using these datasets to evaluate dense geometry tasks, such as depth rendering, can be problematic as the meshes of the dataset are often incomplete and may produce wrong ground truth to evaluate the details. In this paper, we propose SCRREAM, a dataset annotation framework that allows annotation of fully dense meshes of objects in the scene and registers camera poses on the real image sequence, which can produce accurate ground truth for both sparse 3D as well as dense 3D tasks. We show the details of the dataset annotation pipeline and showcase four possible variants of datasets that can be obtained from our framework with example scenes, such as indoor reconstruction and SLAM, scene editing & object removal, human reconstruction and 6d pose estimation. Recent pipelines for indoor reconstruction and SLAM serve as new benchmarks. In contrast to previous indoor dataset, our design allows to evaluate dense geometry tasks on eleven sample scenes against accurately rendered ground truth depth maps.

The present study investigates the behaviour of the far-field sound radiated by low Mach number tip clearance flow induced by placing a stationary cambered airfoil adjacent to a stationary wall. The tip clearance heights ranged from 14% to 30% of the incoming, undisturbed boundary layer thickness and the clearance heights based Reynolds numbers were between 2,600 and 16,000. The far-field sound measured using a microphone array was beamformed to reveal the dominant noise sources and how they behave when the flow Mach number, angle of attack and the clearance height were varied. The near-field behaviour was also examined through PIV measurements and surface pressure fluctuation measurements on the tip. The results show that the mid-to-high frequency noise generated by tip clearances is dominated by the leakage flow in the mid-chord and leading-edge regions, while a distinct low-frequency noise source with a different scaling behaviour exists close to the trailing-edge of the tip clearance. The origin of this low-frequency noise source is believed to be the tip separation vortex that resides close to the trailing-edge and induces significant turbulence levels in the region. The strength of this noise source decreases with clearance height which is consistent with a reduction in turbulence levels associated with the separation vortex. The magnitude of the mid-frequency clearance noise which scales with the sixth power of the Mach number, decreases with tip clearance height due to a reduction in the fluctuating pressure on the airfoil tip surface. The time-scale of this sound was independent of the flow velocity, implying that the source is non-compact. Smaller tip clearances were also found to generate louder high-frequency noise due to intense turbulence and pressure fluctuation levels concentrated near the leading-edge of the clearance., Comment: Submitted to Applied Acoustics Journal

This paper introduces AMMORE, a new dataset of 53,000 math open-response question-answer pairs from Rori, a learning platform used by students in several African countries and conducts two experiments to evaluate the use of large language models (LLM) for grading particularly challenging student answers. The AMMORE dataset enables various potential analyses and provides an important resource for researching student math acquisition in understudied, real-world, educational contexts. In experiment 1 we use a variety of LLM-driven approaches, including zero-shot, few-shot, and chain-of-thought prompting, to grade the 1% of student answers that a rule-based classifier fails to grade accurately. We find that the best-performing approach -- chain-of-thought prompting -- accurately scored 92% of these edge cases, effectively boosting the overall accuracy of the grading from 98.7% to 99.9%. In experiment 2, we aim to better understand the consequential validity of the improved grading accuracy, by passing grades generated by the best-performing LLM-based approach to a Bayesian Knowledge Tracing (BKT) model, which estimated student mastery of specific lessons. We find that relatively modest improvements in model accuracy at the individual question level can lead to significant changes in the estimation of student mastery. Where the rules-based classifier currently used to grade student, answers misclassified the mastery status of 6.9% of students across their completed lessons, using the LLM chain-of-thought approach this misclassification rate was reduced to 2.6% of students. Taken together, these findings suggest that LLMs could be a valuable tool for grading open-response questions in K-12 mathematics education, potentially enabling encouraging wider adoption of open-ended questions in formative assessment.

Nighttime camera-based depth estimation is a highly challenging task, especially for autonomous driving applications, where accurate depth perception is essential for ensuring safe navigation. We aim to improve the reliability of perception systems at night time, where models trained on daytime data often fail in the absence of precise but costly LiDAR sensors. In this work, we introduce Light Enhanced Depth (LED), a novel cost-effective approach that significantly improves depth estimation in low-light environments by harnessing a pattern projected by high definition headlights available in modern vehicles. LED leads to significant performance boosts across multiple depth-estimation architectures (encoder-decoder, Adabins, DepthFormer) both on synthetic and real datasets. Furthermore, increased performances beyond illuminated areas reveal a holistic enhancement in scene understanding. Finally, we release the Nighttime Synthetic Drive Dataset, a new synthetic and photo-realistic nighttime dataset, which comprises 49,990 comprehensively annotated images., Comment: Preprint. Code and dataset available on the project page : https://simondemoreau.github.io/LED/

The limited availability of accurate experimental data in wide ranges of pressure, temperature, and composition is the main constraining factor for the proper development and assessment of thermodynamic models and equations of state. In the particular case of carbon capture and storage (CCS) processes, there is a clear need for data sets related to the (carbon dioxide + oxygen) mixtures that this work aims to address. This work provides new experimental ($p$, $\rho$, $T$) data for three binary ($CO_{2}$ + $O_{2}$) mixtures with mole fractions of oxygen $x$($O_{2}$) = (0.05, 0.10, and 0.20) mol/mol, in the temperature range $T$ = (250 to 375) K and pressure range $p$ = (0.5 to 13) MPa. The measurements were performed with a high-precision single-sinker densimeter with magnetic suspension coupling. The density data were obtained with estimated expanded relative uncertainties of 0.02% for the highest densities and up to 0.3% for the lowest ones.The results were compared to the corresponding results calculated by the current reference equations of state for this kind of mixtures, namely the EOS-CG (combustion gases) and the GERG-2008 equation of state, respectively. The EOS-CG yields better estimations in density than the GERG-2008 equation of state. The results from the EOS-GC model show no systematic temperature dependence. For the GERG-2008 model, however, this criterion is significantly less fulfilled., Comment: arXiv admin note: substantial text overlap with arXiv:2409.06312

This work aims to address the technical aspects related to the thermodynamic characterization of natural gas mixtures blended with hydrogen for the introduction of alternative energy sources within the Power-to-Gas framework. For that purpose, new experimental speed of sound data are presented in the pressure range between (0.1 up to 13) MPa and at temperatures of (260, 273.16, 300, 325, and 350) K for two mixtures qualified as primary calibration standards: a 11 component synthetic natural gas mixture (11 M), and another low-calorific $H_{2}$-enriched natural gas mixture with a nominal molar percentage $x_{H_{2}}$ = 3%. Measurements have been gathered using a spherical acoustic resonator with an experimental expanded ($k$ = 2) uncertainty better than 200 parts in $10^{6}$ (0.02%) in the speed of sound. The heat capacity ratio as perfect-gas $\gamma_{pg}$, the molar heat capacity as perfect-gas $C_{p,m}^{pg}$, and the second $\beta_{a}$ and third $\gamma_{a}$ acoustic virial coefficients are derived from the speed of sound values. All the results are compared with the reference mixture models for natural gas-like mixtures, the AGA8-DC92 EoS and the GERG-2008 EoS, with special attention to the impact of hydrogen on those properties. Data are found to be mostly consistent within the model uncertainty in the 11 M synthetic mixture as expected, but for the hydrogen-enriched mixture in the limit of the model uncertainty at the highest measuring pressures.

This work aims to address the technical concerns related to the thermodynamic characterization of gas mixtures blended with hydrogen for the implementation of hydrogen as a new energy vector. For this purpose, new experimental speed of sound measurements have been done in gaseous and supercritical phases of two binary mixtures of nitrogen and hydrogen using the most accurate technique available, i.e., the spherical acoustic resonator, yielding an experimental expanded ($k$ = 2) uncertainty of only 220 parts in $10^{6}$ (0.022%). The measurements cover the pressure range between (0.5 and 20) MPa, the temperature range between (260 and 350) K, and the composition range with a nominal mole percentage of hydrogen of (5 and 10) mol%, respectively. From the speed of sound data sets, thermophysical properties that are relevant for the characterization of the mixture, namely the second $\beta_{a}$ and third $\gamma_{a}$ acoustic virial coefficients, are derived. These results are thoroughly compared and discussed with the established reference mixture models valid for mixtures of nitrogen and hydrogen, such as the AGA8-DC92 EoS, the GERG-2008 EoS, and the recently developed adaptation of the GERG-2008 EoS, here denoted GERG-$H_{2}$_improved EoS. Special attention has been given to the effect of hydrogen concentration on those properties, showing that only the GERG-$H_{2}$_improved EoS is consistent with the data sets within the experimental uncertainty in most measuring conditions.

The accurate knowledge of the thermophysical and thermodynamic properties of pure hydrogen and hydrogen mixtures plays an important role in the design and operation of many processes involved in hydrogen production, transport, storage, and use. These data are needed for the development of theoretical models necessary for the introduction of hydrogen as a promising energy carrier in the near future. A literature survey on both the available experimental data and the theoretical models associated with the thermodynamic properties of hydrogen mixtures, within the operational ranges of industrial interest for composition, temperature, and pressure, is presented in this work. Considering the available experimental data and the requirements for the design and operation of hydrogen systems, the most relevant gaps in temperature, pressure and composition are identified.

Modern machine learning relies nearly exclusively on dedicated electronic hardware accelerators. Photonic approaches, with low consumption and high operation speed, are increasingly considered for inference but, to date, remain mostly limited to relatively basic tasks. Simultaneously, the problem of training deep and complex neural networks, overwhelmingly performed through backpropagation, remains a significant limitation to the size and, consequently, the performance of current architectures and a major compute and energy bottleneck. Here, we experimentally implement a versatile and scalable training algorithm, called direct feedback alignment, on a hybrid electronic-photonic platform. An optical processing unit performs large-scale random matrix multiplications, which is the central operation of this algorithm, at speeds up to 1500 TeraOps. We perform optical training of one of the most recent deep learning architectures, including Transformers, with more than 1B parameters, and obtain good performances on both language and vision tasks. We study the compute scaling of our hybrid optical approach, and demonstrate a potential advantage for ultra-deep and wide neural networks, thus opening a promising route to sustain the exponential growth of modern artificial intelligence beyond traditional von Neumann approaches., Comment: 19 pages, 4 figures

JINet is a web browser-based platform intended to democratise access to advanced clinical and genomic data analysis software. It hosts numerous data analysis applications that are run in the safety of each User's web browser, without the data ever leaving their machine. JINet promotes collaboration, standardisation and reproducibility by sharing scripts rather than data and creating a self-sustaining community around it in which Users and data analysis tools developers interact thanks to JINets interoperability primitives., Comment: 13 pages, 6 figures, 1 table

Porous media are ubiquitous in the fields of energy storage and conversion, catalysis, biomechanics, hydrogeology, and other chemical engineering processes. These media possess high surface-to-volume ratios and their complex channels can restrict and direct the flow. This makes them key components of multiple equipment despite the challenges in selecting design parameters for specific applications. Pore-resolved CFD reveals the effects of their structure at the microscopic scale, but is currently limited by high computing costs and the performance of mesh generation algorithms. This work introduces a RBF-based representation of solids in a massively parallel immersed-boundary framework, enabling both the usage of non-conformal grids and dynamic mesh adaptation. We verify it using the method of manufactured solutions. We validate it using pressure drop measurements through porous silicone monoliths digitized by X-ray computed microtomography for Reynolds numbers up to 30, using grids of 200 M cells distributed over 8 k cores. The reliable model is then used to highlight that pore network structure is the main factor describing pressure evolution and that preferential channels are present at this scale of the porous media. This work opens the door to design and optimize processes by linking microscopic flow to macroscopic properties through the usage of physics-based digital twins of complex porous media., Comment: 26 pages, 18 figures

MISTRAL is the new Faint Object Spectroscopic Camera mounted at the folded Cassegrain focus of the 1.93m telescope of Haute-Provence Observatory. We describe the design and components of the instrument and give some details about its operation. We emphasise in particular the various observing modes and the performances of the detector. A short description is also given about the working environment. Various types of objects, including stars, nebulae, comets, novae, galaxies have been observed during various test phases to evaluate the performances of the instrument. The instrument covers the range of 4000 to 8000A with the blue setting, or from 6000 to 10000A with the red setting, at an average spectral resolution of 700. Its peak efficiency is about 22% at 6000A. In spectroscopy, a limiting magnitude of 19.5 can be achieved for a point source in one hour with a signal to noise of 3 in the continuum (and better if emission lines are present). In imaging mode, limiting magnitudes of 20-21 can be obtained in 10-20mn (with average seing conditions of 2.5 arcsec at OHP). The instrument is very users-friendly and can be put into operations in less than 15mn (rapid change-over from the other instrument in use) if required by the science (like for Gamma-Rays Bursts). Some first scientific results are described for various types of objects, and in particular for the follow-up of GRBs. While some further improvements are still under way, in particular to ease the switch from blue to red setting and add more grisms or filters, MISTRAL is ready for the follow-up of transients and other variable objects, in the soon-to-come era of e.g. the SVOM satellite and of the Rubin telescope., Comment: Accepted in A&A

In the drug discovery process, where experiments can be costly and time-consuming, computational models that predict drug-target interactions are valuable tools to accelerate the development of new therapeutic agents. Estimating the uncertainty inherent in these neural network predictions provides valuable information that facilitates optimal decision-making when risk assessment is crucial. However, such models can be poorly calibrated, which results in unreliable uncertainty estimates that do not reflect the true predictive uncertainty. In this study, we compare different metrics, including accuracy and calibration scores, used for model hyperparameter tuning to investigate which model selection strategy achieves well-calibrated models. Furthermore, we propose to use a computationally efficient Bayesian uncertainty estimation method named Bayesian Linear Probing (BLP), which generates Hamiltonian Monte Carlo (HMC) trajectories to obtain samples for the parameters of a Bayesian Logistic Regression fitted to the hidden layer of the baseline neural network. We report that BLP improves model calibration and achieves the performance of common uncertainty quantification methods by combining the benefits of uncertainty estimation and probability calibration methods. Finally, we show that combining post hoc calibration method with well-performing uncertainty quantification approaches can boost model accuracy and calibration.

Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a labeled source domain to perform well on an unlabeled target domain with some data distribution shift. While many methods have been proposed in the literature, fair and realistic evaluation remains an open question, particularly due to methodological difficulties in selecting hyperparameters in the unsupervised setting. With SKADA-Bench, we propose a framework to evaluate DA methods and present a fair evaluation of existing shallow algorithms, including reweighting, mapping, and subspace alignment. Realistic hyperparameter selection is performed with nested cross-validation and various unsupervised model selection scores, on both simulated datasets with controlled shifts and real-world datasets across diverse modalities, such as images, text, biomedical, and tabular data with specific feature extraction. Our benchmark highlights the importance of realistic validation and provides practical guidance for real-life applications, with key insights into the choice and impact of model selection approaches. SKADA-Bench is open-source, reproducible, and can be easily extended with novel DA methods, datasets, and model selection criteria without requiring re-evaluating competitors. SKADA-Bench is available on GitHub at https://github.com/scikit-adaptation/skada-bench.

This study develops a robust movie recommendation system using various machine learning techniques, including Non- Negative Matrix Factorization (NMF), Truncated Singular Value Decomposition (SVD), and K-Means clustering. The primary objective is to enhance user experience by providing personalized movie recommendations. The research encompasses data preprocessing, model training, and evaluation, highlighting the efficacy of the employed methods. Results indicate that the proposed system achieves high accuracy and relevance in recommendations, making significant contributions to the field of recommendations systems., Comment: Accepted by 2024 4th International Symposium on Computer Technology and Information Science, IEEE

We study the positive logic FO+ on finite words, and its fragments, pursuing and refining the work initiated in [Kuperberg 2023]. First, we transpose notorious logic equivalences into positive first-order logic: FO+ is equivalent to LTL+ , and its two-variable fragment FO2+ with (resp. without) successor available is equivalent to UTL+ with (resp. without) the "next" operator X available. This shows that despite previous negative results, the class of FO+-definable languages exhibits some form of robustness. We then exhibit an example of an FO-definable monotone language on one predicate, that is not FO+-definable, refining the example from [Kuperberg 2023] with 3 predicates. Moreover, we show that such a counter-example cannot be FO2-definable.

Physiological signal analysis often involves identifying events crucial to understanding biological dynamics. Traditional methods rely on handcrafted procedures or supervised learning, presenting challenges such as expert dependence, lack of robustness, and the need for extensive labeled data. Data-driven methods like Convolutional Dictionary Learning (CDL) offer an alternative but tend to produce spurious detections. This work introduces UNHaP (Unmix Noise from Hawkes Processes), a novel approach addressing the joint learning of temporal structures in events and the removal of spurious detections. Leveraging marked Hawkes processes, UNHaP distinguishes between events of interest and spurious ones. By treating the event detection output as a mixture of structured and unstructured events, UNHaP efficiently unmixes these processes and estimates their parameters. This approach significantly enhances the understanding of event distributions while minimizing false detection rates.

In this work, we introduce a quantum-based quantitative phase microscopy technique using a phase gradient approach that is inherently background resistant and does not rely on interferometry or scanning. Here, a transparent sample is illuminated by both photons of a position-momentum entangled pair with one photon setup for position measurement in the near-field (NF) of the sample and its partner for momentum measurement in the far-field (FF). By virtue of the spatial correlation property inherent to the entanglement, both the position and momentum information of the photons can thus be obtained simultaneously. The phase profile of the sample is then deduced through a phase gradient measurement obtained by measuring the centroid shift of the photons' in the FF momentum plane for each NF position. We show that the technique, while achieving an imaging resolution of 2.76\,$\mu$m, is phase accurate to at least $\lambda/30$ and phase sensitive to $\lambda/100$ at a wavelength of 810\,nm. In addition, through the temporal correlation between the photon pairs, our technique shows resilience to strong dynamic background lights, which can prove difficult to account for in classical phase imaging techniques. We believe this work marks a significant advancement in the capabilities of quantum phase microscopy and quantum imaging in general, it showcases imaging and phase resolutions approaching those attainable with classical phase microscopes. This advancement brings quantum imaging closer to practical real-world applications, heralding new possibilities in the field.

Many modern spatio-temporal data sets, in sociology, epidemiology or seismology, for example, exhibit self-exciting characteristics, triggering and clustering behaviors both at the same time, that a suitable Hawkes space-time process can accurately capture. This paper aims to develop a fast and flexible parametric inference technique to recover the parameters of the kernel functions involved in the intensity function of a space-time Hawkes process based on such data. Our statistical approach combines three key ingredients: 1) kernels with finite support are considered, 2) the space-time domain is appropriately discretized, and 3) (approximate) precomputations are used. The inference technique we propose then consists of a $\ell_2$ gradient-based solver that is fast and statistically accurate. In addition to describing the algorithmic aspects, numerical experiments have been carried out on synthetic and real spatio-temporal data, providing solid empirical evidence of the relevance of the proposed methodology.

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

We report a theoretical and experimental investigation of fiber Fabry-Perot cavities aimed at enhancing Kerr frequency comb generation. The modulation instability (MI) power threshold is derived from the linear stability analysis of a generalized Lugiato-Lefever equation. By combining this analysis with the concepts of power enhancement factor (PEF) and optimal coupling, we predict the ideal manufacturing parameters of fiber Fabry-Perot (FFP) cavities for the MI Kerr frequency comb generation. Our findings reveal a distinction between the optimal coupling for modulation instability and that of the cold cavity. Consequently, mirror reflectivity must be adjusted to suit the specific application. We verified the predictions of our theory by measuring the MI power threshold as a function of detuning for three different cavities.

Despite the increasing interest in enhancing perception systems for autonomous vehicles, the online calibration between event cameras and LiDAR - two sensors pivotal in capturing comprehensive environmental information - remains unexplored. We introduce MULi-Ev, the first online, deep learning-based framework tailored for the extrinsic calibration of event cameras with LiDAR. This advancement is instrumental for the seamless integration of LiDAR and event cameras, enabling dynamic, real-time calibration adjustments that are essential for maintaining optimal sensor alignment amidst varying operational conditions. Rigorously evaluated against the real-world scenarios presented in the DSEC dataset, MULi-Ev not only achieves substantial improvements in calibration accuracy but also sets a new standard for integrating LiDAR with event cameras in mobile platforms. Our findings reveal the potential of MULi-Ev to bolster the safety, reliability, and overall performance of event-based perception systems in autonomous driving, marking a significant step forward in their real-world deployment and effectiveness., Comment: Accepted at CVPR 2024 Workshop on Autonomous Driving. Copyright 2024 IEEE

Dissipative environments are ubiquitous in nature, from microscopic swimmers in low-Reynolds-number fluids to macroscopic animals in frictional media. In this study, motivated by various behaviours of {\it Caenorhabditis elegans} during swimming and crawling locomotion, we consider a mathematical model of a slender elastic locomotor with an internal rhythmic neural pattern generator. By analysing the dynamical systems of the model using a Poincar\'e section, we found that local neuromechanical adjustments to the environment can create robust undulatory locomotion. This progressive behaviour emerges as a global stable periodic orbit in a broad range of parameter regions. Further, by controlling the mechanosensation, we were able to design the dynamical systems to manoeuvre with progressive, reverse, and turning motions as well as apparently random, complex behaviours, as experimentally observed in {\it C. elegans}. The mechanisms found in this study, together with our methodologies with the dynamical systems viewpoint, are useful for deciphering complex animal adaptive behaviours and also designing adaptive robots for a wide range of dissipative environments., Comment: 13 pages, 9 figures