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Network scientists often use complex dynamic processes to describe network contagions, but tools for fitting contagion models typically assume simple dynamics. Here, we address this gap by developing a nonparametric method to reconstruct a network and dynamics from a series of node states, using a model that breaks the dichotomy between simple pairwise and complex neighborhood-based contagions. We then show that a network is more easily reconstructed when observed through the lens of complex contagions if it is dense or the dynamic saturates, and that simple contagions are better otherwise., Comment: 8 pages, 5 figures

We propose a novel soft-aided hard-decision decoding algorithm for general product-like codes. It achieves error correcting performance similar to that of a soft-decision turbo decoder for staircase and OFEC codes, while maintaining a low complexity., Comment: submitted to ECOC 2024

We formulate a unifying framework for unsupervised continual learning (UCL), which disentangles learning objectives that are specific to the present and the past data, encompassing stability, plasticity, and cross-task consolidation. The framework reveals that many existing UCL approaches overlook cross-task consolidation and try to balance plasticity and stability in a shared embedding space. This results in worse performance due to a lack of within-task data diversity and reduced effectiveness in learning the current task. Our method, Osiris, which explicitly optimizes all three objectives on separate embedding spaces, achieves state-of-the-art performance on all benchmarks, including two novel benchmarks proposed in this paper featuring semantically structured task sequences. Compared to standard benchmarks, these two structured benchmarks more closely resemble visual signals received by humans and animals when navigating real-world environments. Finally, we show some preliminary evidence that continual models can benefit from such realistic learning scenarios., Comment: CoLLAs 2024 (Oral)

We summarize the current best polychromatic (10 to 20 % bandwidth) contrast performance demonstrated in the laboratory by different starlight suppression approaches and systems designed to directly characterize exoplanets around nearby stars. We present results obtained by internal coronagraph and external starshade experimental testbeds using entrance apertures equivalent to off-axis or on-axis telescopes, either monolithic or segmented. For a given angular separation and spectral bandwidth, the performance of each starlight suppression system is characterized by the values of raw contrast (before image processing), off-axis (exoplanet) core throughput, and post-calibration contrast (the final 1 sigma detection limit of off-axis point sources, after image processing). To place the current laboratory results in the perspective of the future Habitable Worlds Observatory (HWO) mission, we simulate visible observations of a fiducial Earth/Sun twin system at 12 pc, assuming a 6m (inscribed diameter) collecting aperture and a realistic end-to-end optical throughput. The exposure times required for broadband exoearth detection (20% bandwidth around a wavelength of 0.55 microns) and visible spectroscopic observations (R=70) are then computed assuming various levels of starlight suppression performance, including the values currently demonstrated in the laboratory. Using spectroscopic exposure time as a simple metric, our results point to key starlight suppression system design performance improvements and trades to be conducted in support of HWO exoplanet science capabilities. These trades may be explored via numerical studies, lab experiments, as well as high contrast space-based observations and demonstrations., Comment: 63 pages, 28 pages, submitted to JATIS

This paper presents a semi-automatic approach to create a diachronic corpus of voices balanced for speaker's age, gender, and recording period, according to 32 categories (2 genders, 4 age ranges and 4 recording periods). Corpora were selected at French National Institute of Audiovisual (INA) to obtain at least 30 speakers per category (a total of 960 speakers; only 874 have be found yet). For each speaker, speech excerpts were extracted from audiovisual documents using an automatic pipeline consisting of speech detection, background music and overlapped speech removal and speaker diarization, used to present clean speaker segments to human annotators identifying target speakers. This pipeline proved highly effective, cutting down manual processing by a factor of ten. Evaluation of the quality of the automatic processing and of the final output is provided. It shows the automatic processing compare to up-to-date process, and that the output provides high quality speech for most of the selected excerpts. This method shows promise for creating large corpora of known target speakers., Comment: Keywords:, semi-automatic processing, corpus creation, diarization, speaker identification, gender-balanced, age-balanced, speaker corpus, diachrony

We study the expectation propagation (EP) algorithm for symbol detection in massive multiple-input multiple-output (MIMO) systems. The EP detector shows excellent performance but suffers from a high computational complexity due to the matrix inversion, required in each EP iteration to perform marginal inference on a Gaussian system. We propose an inversion-free variant of the EP algorithm by treating inference on the mean and variance as two separate and simpler subtasks: We study the preconditioned conjugate gradient algorithm for obtaining the mean, which can significantly reduce the complexity and increase stability by relying on the Jacobi preconditioner that proves to fit the EP characteristics very well. For the variance, we use a simple approximation based on linear regression of the Gram channel matrix. Numerical studies on the Rayleigh-fading channel and on a realistic 3GPP channel model reveal the efficiency of the proposed scheme, which offers an attractive performance-complexity tradeoff and even outperforms the original EP detector in high multi-user inference cases where the matrix inversion becomes numerically unstable., Comment: Accepted for presentation at Asilomar Conference on Signals, Systems, and Computers 2024

The X-ray Integral Field Unit (X-IFU) is the high-resolution X-ray spectrometer to fly on board the Athena Space Observatory of the European Space Agency (ESA). It is being developed by an international Consortium led by France, involving twelve ESA member states, plus the United States. It is a cryogenic instrument, involving state of the art technology, such as micro-calorimeters, to be read out by low noise electronics. As the instrument was undergoing its system requirement review (in 2022), a life cycle assessment (LCA) was performed to estimate the environmental impacts associated with the development of the sub-systems that were under the responsibility of the X-IFU Consortium. The assessment included the supply, manufacturing and testing of sub systems, as well as involved logistics and manpower. We find that the most significant environmental impacts arise from testing activities, which is related to energy consumption in clean rooms, office work, which is related to energy consumption in office buildings, and instrument manufacturing, which is related to the use of mineral and metal resources. Furthermore, business travels is another area of concern, despite the policy to reduced flying adopted by the Consortium. As the instrument is now being redesigned to fit within the new boundaries set by ESA, the LCA will be updated, with a focus on the hot spots identified in the first iteration. The new configuration, consolidated in 2023, is significantly different from the previously studied version and is marked by an increase of the perimeter of responsibility for the Consortium. This will need to be folded in the updated LCA, keeping the ambition to reduce the environmental footprint of X-IFU, while complying with its stringent requirements in terms of performance and risk management., Comment: 28 pages, 7 figures, accepted for publication in Experimental Astronomy

To satisfy the growing throughput demand of data-intensive applications, the performance of optical communication systems increased dramatically in recent years. With higher throughput, more advanced equalizers are crucial, to compensate for impairments caused by inter-symbol interference (ISI). The latest research shows that artificial neural network (ANN)-based equalizers are promising candidates to replace traditional algorithms for high-throughput communications. On the other hand, not only throughput but also flexibility is a main objective of beyond-5G and 6G communication systems. A platform that is able to satisfy the strict throughput and flexibility requirements of modern communication systems are field programmable gate arrays (FPGAs). Thus, in this work, we present a high-performance FPGA implementation of an ANN-based equalizer, which meets the throughput requirements of modern optical communication systems. Further, our architecture is highly flexible since it includes a variable degree of parallelism (DOP) and therefore can also be applied to low-cost or low-power applications which is demonstrated for a magnetic recording channel. The implementation is based on a cross-layer design approach featuring optimizations from the algorithm down to the hardware architecture, including a detailed quantization analysis. Moreover, we present a framework to reduce the latency of the ANN-based equalizer under given throughput constraints. As a result, the bit error ratio (BER) of our equalizer for the optical fiber channel is around four times lower than that of a conventional one, while the corresponding FPGA implementation achieves a throughput of more than 40 GBd, outperforming a high-performance graphics processing unit (GPU) by three orders of magnitude for a similar batch size., Comment: The article was submitted to the International Journal of Parallel Programming (IJPP) and is currently under review

Passive Wi-Fi-based radars (PWRs) are devices that enable the localization of targets using Wi-Fi signals of opportunity transmitted by an access point. Unlike active radars that optimize their transmitted waveform for localization, PWRs align with the 802.11 amendments. Specifically, during the channel sounding session preceding a multi-user multiple-input multiple-output downlink transmission, an access point isotropically transmits a null data packet (NDP) with a known preamble. From these known symbols, client user equipments derive their channel state information and transmit an unencrypted beamforming feedback (BFF) back to the access point. The BFF comprises the right singular matrix of the channel and the corresponding stream gain for each subcarrier, which allows the computation of a beamforming matrix at the access point. In a classical PWR processing, only the preamble symbols from the NDP are exploited during the channel sounding session. In this study, we investigate multiple target localization by a PWR exploiting hybrid information sources. On one hand, the joint angle-of-departure and angle-of-arrival evaluated from the NDP. On another hand, the line-of-sight angle-of-departures inferred from the BFFs. The processing steps at the PWR are defined and an optimal hybrid fusion rule is derived in the maximum likelihood framework. Monte-Carlo simulations assess the enhanced accuracy of the proposed combination method compared to classical PWR processing based solely on the NDP, and compare the localisation performance between client and non-client targets., Comment: 5 pages, 3 figures, under review for SPAWC24

We explore Riemannian optimization methods for Restricted-Open-shell Hartree-Fock (ROHF) and Complete Active Space Self-Consistent Field (CASSCF) methods. After showing that ROHF and CASSCF can be reformulated as optimization problems on so-called flag manifolds, we review Riemannian optimization basics and their application to these specific problems. We compare these methods to traditional ones and find robust convergence properties without fine-tuning of numerical parameters. Our study suggests Riemannian optimization as a valuable addition to orbital optimization for ROHF and CASSCF, warranting further investigation., Comment: 21 pages

By effectively implementing the strategies for resource allocation, the capabilities, and reliability of non-terrestrial networks (NTN) can be enhanced. This leads to enhance spectrum utilization performance while minimizing the unmet system capacity, meeting quality of service (QoS) requirements and overall system optimization. In turn, a wide range of applications and services in various domains can be supported. However, allocating resources in a multi-constellation system with heterogeneous satellite links and highly dynamic user traffic demand pose challenges in ensuring sufficient and fair resource distribution. To mitigate these complexities and minimize the overhead, there is a growing shift towards utilizing artificial intelligence (AI) for its ability to handle such problems effectively. This calls for the development of an intelligent decision-making controller using AI to efficiently manage resources in this complex environment. In this context, real-world open datasets play a pivotal role in the development of AI models addressing radio control optimization problems. As a matter of fact, acquiring suitable datasets can be arduous. Therefore, this paper identifies pertinent real-world open datasets representing realistic traffic pattern, network performances and demand for fixed and dynamic user terminals, enabling a variety of uses cases. The aim of gathering and publishing the information of these datasets are to inspire and assist the research community in crafting the advance resource management solutions. In a nutshell, this paper establishes a solid foundation of commercially accessible data, with the potential to set benchmarks and accelerate the resolution of resource allocation optimization challenges., Comment: In the proceedings of IEEE Future Networks World Forum 13_15 November 2023, Baltimore, MD, USA

We observed the planet-hosting system PDS 70 with the James Webb Interferometer, JWST's Aperture Masking Interferometric (AMI) mode within NIRISS. Observing with the F480M filter centered at 4.8 $\mu$m, we simultaneously fit a geometric model to the outer disk and the two known planetary companions. We re-detect the protoplanets PDS 70 b and c at an SNR of 21 and 11, respectively. Our photometry of both PDS 70 b and c provide evidence for circumplanetary disk emission through fitting SED models to these new measurements and those found in the literature. We also newly detect emission within the disk gap at an SNR of $\sim$4, at a position angle of $207^{+11}_{-10}$ degrees, and an unconstrained separation within $\sim$200 mas. Follow-up observations will be needed to determine the nature of this emission. We place a 5$\sigma$ upper limit of $\Delta$mag = 7.56 on the contrast of the candidate PDS 70 d at 4.8 $\mu$m, which indicates that if the previously observed emission at shorter wavelengths is due to a planet, this putative planet has a different atmospheric composition than PDS 70 b or c. Finally, we place upper limits on emission from any additional planets in the disk gap. We find an azimuthally averaged 5$\sigma$ upper limit of $\Delta$mag $\approx$ 7.5 at separations greater than 125 mas. These are the deepest limits to date within $\sim$250 mas at 4.8 $\mu$m and the first space-based interferometric observations of this system., Comment: Submitted to ApJ

We propose an approach for estimating the latent knowledge embedded inside large language models (LLMs). We leverage the in-context learning (ICL) abilities of LLMs to estimate the extent to which an LLM knows the facts stored in a knowledge base. Our knowledge estimator avoids reliability concerns with previous prompting-based methods, is both conceptually simpler and easier to apply, and we demonstrate that it can surface more of the latent knowledge embedded in LLMs. We also investigate how different design choices affect the performance of ICL-based knowledge estimation. Using the proposed estimator, we perform a large-scale evaluation of the factual knowledge of a variety of open source LLMs, like OPT, Pythia, Llama(2), Mistral, Gemma, etc. over a large set of relations and facts from the Wikidata knowledge base. We observe differences in the factual knowledge between different model families and models of different sizes, that some relations are consistently better known than others but that models differ in the precise facts they know, and differences in the knowledge of base models and their finetuned counterparts.

This first VELOCE data release comprises 18,225 high-precision RV measurements of 258 bona fide classical Cepheids on both hemispheres collected mainly between 2010 and 2022, alongside 1161 additional observations of 164 other stars. The median per-observation RV uncertainty is 0.037 km/s, and some reach 0.002 km/s. Non-variable standard stars characterize RV zero-point stability and provide a base for future cross-calibrations. We determined zero-point differences between VELOCE and 31 literature data sets using template fitting and measured linear period changes of 146 Cepheids. Seventy six spectroscopic binary Cepheids and 14 candidates are identified using VELOCE data alone and are investigated in detail in a companion paper (VELOCE II). Several new insights into Cepheid pulsations were obtained, including: a) the most detailed description of the Hertzsprung progression by RVs; b) the identification of double-peaked bumps in the RV curve; c) clear evidence that virtually all Cepheids feature spectroscopic variability signals that lead to modulated RV variability. We identified 36 such stars, of which 4 also exhibit orbital motion. Linear radius variations depend strongly on pulsation period and a steep increase in slope of the $\Delta$R/p versus logP-relation is found near 10d, challenging the existence of a tight relation between Baade-Wesselink projection factors and pulsation periods. We investigated the accuracy of RV time series measurements, v$_\gamma$, and RV amplitudes published in Gaia's DR3 and determined an average offset of 0.65 \pm 0.11 km/s relative to VELOCE. We recommend adopting a single set of template correlation parameters for distinct classes of large-amplitude variable stars to avoid systematic offsets in v$_\gamma$ among stars belonging to the same class. Peak-to-peak amplitudes of Gaia RVs exhibit significant (16%) dispersion compared to VELOCE. [abridged], Comment: A&A in press, 46 pages, 35 figures, 20 tables. Some data only available via the CDS at publication. VELOCE DR1 data will be made public in FITS format via zenodo.org at DOI: 10.5281/zenodo.10793507 upon publication of the paper in A&A

Compact laser-plasma acceleration of fast ions has made great strides since its discovery over two decades ago, resulting in the current generation of high-energy ($\geq 100\,\rm MeV$) ultracold beams over ultrashort ($\leq 1\,\rm ps$) durations. To unlock broader applications of these beams, we need the ability to tailor the ion energy spectrum. Here, we present a scheme that achieves precisely this by accelerating protons in a "lighthouse" fashion, whereby the highest-energy component of the beam is emitted in a narrow cone, well separated from the lower-energy components. This is made possible by a two-stage interaction in which the rear surface of the target is first set into rapid motion before the main acceleration phase. This approach offers the additional advantages of leveraging a robust sheath acceleration process in standard micron-thick targets and being optically controllable., Comment: 9 pages, 6 figures

Document Understanding is an evolving field in Natural Language Processing (NLP). In particular, visual and spatial features are essential in addition to the raw text itself and hence, several multimodal models were developed in the field of Visual Document Understanding (VDU). However, while research is mainly focused on Key Information Extraction (KIE), Relation Extraction (RE) between identified entities is still under-studied. For instance, RE is crucial to regroup entities or obtain a comprehensive hierarchy of data in a document. In this paper, we present a model that, initialized from LayoutLMv3, can match or outperform the current state-of-the-art results in RE applied to Visually-Rich Documents (VRD) on FUNSD and CORD datasets, without any specific pre-training and with fewer parameters. We also report an extensive ablation study performed on FUNSD, highlighting the great impact of certain features and modelization choices on the performances., Comment: Accepted at the International Conference on Document Analysis and Recognition (ICDAR 2024)

Microquasars are Black Hole X-ray binaries (BHXB) which can eject material in the form of a bipolar jet, similarly to quasars, but at much smaller scales. Their high-energy emission comes from an accretion disk (~ 1 keV) and from a hot "corona" near the black hole that up-scatters photons from the disk in the hard X-ray domain (1--100 keV). A high-energy component above 150 keV has been detected in bright sources and its precise origin is still unknown: it could come either from Compton scattering of disk photons on coronal relativistic non-thermal electrons (a.k.a hybrid Comptonization), or from the synchrotron emission from the very base of the compact jet. The measurement of polarization above 150 keV can provide valuable insights into the processes at play as we expect higher polarization fraction due to synchrotron emission from the jets (up to 70 % with a very ordered magnetic field). We use the INTEGRAL/IBIS telescope to measure the soft gamma-ray polarization of the Crab Nebula and the BHXB Swift J1727.8-1613., Comment: 6 pages, 6 figures, HEPRO VIII conference (Paris, 2023), submitted in Proceeding of Science

We demonstrate a bipartition technique using a super-lattice architecture to access correlations between alternating planes of a mesoscopic array of spin-3 chromium atoms trapped in a 3D optical lattice. Using this method, we observe that out-of-equilibrium dynamics driven by long-range dipolar interactions lead to spin anti-correlations between the two spatially separated subsystems. Our bipartite measurements reveal a subtle interplay between the anisotropy of the 3D dipolar interactions and that of the lattice structure, without requiring single-site addressing. We compare our results to theoretical predictions based on a truncated cumulant expansion and a new cluster semi-classical method that we use to investigate correlations at the microscopic scale. Comparison with a high-temperature analytical model reveals quantum thermalization at a high negative spin temperature., Comment: Main article: 5 pages, 3 Figures Supp Mat: 4 pages, 3 Figures

We show how to transform a non-differentiable rasterizer into a differentiable one with minimal engineering efforts and no external dependencies (no Pytorch/Tensorflow). We rely on Stochastic Gradient Estimation, a technique that consists of rasterizing after randomly perturbing the scene's parameters such that their gradient can be stochastically estimated and descended. This method is simple and robust but does not scale in dimensionality (number of scene parameters). Our insight is that the number of parameters contributing to a given rasterized pixel is bounded. Estimating and averaging gradients on a per-pixel basis hence bounds the dimensionality of the underlying optimization problem and makes the method scalable. Furthermore, it is simple to track per-pixel contributing parameters by rasterizing ID- and UV-buffers, which are trivial additions to a rasterization engine if not already available. With these minor modifications, we obtain an in-engine optimizer for 3D assets with millions of geometry and texture parameters., Comment: Proceedings of 2024 ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games

Consider the global optimisation of a function $U$ defined on a finite set $V$ endowed with an irreducible and reversible Markov generator.By integration, we extend $U$ to the set $\mathcal{P}(V)$ of probability distributions on $V$ and we penalise it with a time-dependent generalised entropy functional.Endowing $\mathcal{P}(V)$ with a Maas' Wasserstein-type Riemannian structure, enables us to consider an associated time-inhomogeneous gradient descent algorithm.There are several ways to interpret this $\cP(V)$-valued dynamical system as the time-marginal laws of a time-inhomogeneous non-linear Markov process taking values in $V$, each of them allowing for interacting particle approximations.This procedure extends to the discrete framework the continuous state space swarm algorithm approach of Bolte, Miclo and Villeneuve \cite{Bolte}, but here we go further by considering more general generalised entropy functionals for which functional inequalities can be proven.Thus in the full generality of the above finite framework, we give conditions on the underlying time dependence ensuring the convergence of the algorithm toward laws supported by the set of global minima of $U$.Numerical simulations illustrate that one has to be careful about the choice of the time-inhomogeneous non-linear Markov process interpretation.

Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used to infer training data. Conventional defenses such as differential privacy and secure aggregation fall short in effectively safeguarding user privacy in DL, either sacrificing model utility or efficiency. We introduce Shatter, a novel DL approach in which nodes create virtual nodes (VNs) to disseminate chunks of their full model on their behalf. This enhances privacy by (i) preventing attackers from collecting full models from other nodes, and (ii) hiding the identity of the original node that produced a given model chunk. We theoretically prove the convergence of Shatter and provide a formal analysis demonstrating how Shatter reduces the efficacy of attacks compared to when exchanging full models between nodes. We evaluate the convergence and attack resilience of Shatter with existing DL algorithms, with heterogeneous datasets, and against three standard privacy attacks. Our evaluation shows that Shatter not only renders these privacy attacks infeasible when each node operates 16 VNs but also exhibits a positive impact on model utility compared to standard DL. In summary, Shatter enhances the privacy of DL while maintaining the utility and efficiency of the model., Comment: Accepted at PETS 2025

The longitudinal nonreciprocal charge transport (NCT) in crystalline materials is a highly non-trivial phenomenon, motivating the design of next generation two-terminal rectification devices (e.g., semiconductor diodes beyond PN junctions). The practical application of such devices is built upon crystalline materials whose longitudinal NCT occurs at room temperature and under low magnetic field. However, materials of this type are rather rare and elusive, and theory guiding the discovery of these materials is lacking. Here, we develop such a theory within the framework of semiclassical Boltzmann transport theory. By symmetry analysis, we classify the complete 122 magnetic point groups with respect to the longitudinal NCT phenomenon. The symmetry-adapted Hamiltonian analysis further uncovers a previously overlooked mechanism for this phenomenon. Our theory guides the first-principles prediction of longitudinal NCT in multiferroic \epsilon-Fe2O3 semiconductor that possibly occurs at room temperature, without the application of external magnetic field. These findings advance our fundamental understandings of longitudinal NCT in crystalline materials, and aid the corresponding materials discoveries., Comment: 6 pages, 2 figures

Azure Core workload insights have time-series data with different metric units. Faults or Anomalies are observed in these time-series data owing to faults observed with respect to metric name, resources region, dimensions, and its dimension value associated with the data. For Azure Core, an important task is to highlight faults or anomalies to the user on a dashboard that they can perceive easily. The number of anomalies reported should be highly significant and in a limited number, e.g., 5-20 anomalies reported per hour. The reported anomalies will have significant user perception and high reconstruction error in any time-series forecasting model. Hence, our task is to automatically identify 'high significant anomalies' and their associated information for user perception., Comment: Published in IAAI 2024, which is the Industrial track of AAAI 2024

The notion of Laplacian of a graph can be generalized to simplicial complexes and hypergraphs, and contains information on the topology of these structures. Even for a graph, the consideration of associated simplicial complexes is interesting to understand its shape. Whereas the Laplacian of a graph has a simple probabilistic interpretation as the generator of a continuous time Markov chain on the graph, things are not so direct when considering simplicial complexes. We define here new Markov chains on simplicial complexes. For a given order~$k$, the state space is the set of $k$-cycles that are chains of $k$-simplexes with null boundary. This new framework is a natural generalization of the canonical Markov chains on graphs. We show that the generator of our Markov chain is the upper Laplacian defined in the context of algebraic topology for discrete structure. We establish several key properties of this new process: in particular, when the number of vertices is finite, the Markov chain is positive recurrent. This result is not trivial, since the cycles can loop over themselves an unbounded number of times. We study the diffusive limits when the simplicial complexes under scrutiny are a sequence of ever refining triangulations of the flat torus. Using the analogy between singular and Hodge homologies, we express this limit as valued in the set of currents. The proof of tightness and the identification of the limiting martingale problem make use of the flat norm and carefully controls of the error terms in the convergence of the generator. Uniqueness of the solution to the martingale problem is left open. An application to hole detection is carried.

We report the H, C, and N isotopic compositions of microscale (0.2 to 2$\mu$m) organic matter in samples of asteroid Ryugu and the Orgueil CI carbonaceous chondrite. Three regolith particles of asteroid Ryugu, returned by the Hayabusa2 spacecraft, and several fragments of Orgueil were analyzed by NanoSIMS isotopic imaging. The isotopic distributions of the Ryugu samples from two different collection spots are closely similar to each other and to the Orgueil samples, strengthening the proposed Ryugu-CI chondrite connection. Most individual sub-$\mu$m organic grains have isotopic compositions within error of bulk values, but 2-8% of them are outliers exhibiting large isotopic enrichments or depletions in D, $^{15}$N, and/or $^{13}$C. The H, C and N isotopic compositions of the outliers are not correlated with each other: while some C-rich grains are both D- and $^{15}$N-enriched, many are enriched or depleted in one or the other system. This most likely points to a diversity in isotopic fractionation pathways and thus diversity in the local formation environments for the individual outlier grains. The observation of a relatively small population of isotopic outlier grains can be explained either by escape from nebular and/or parent body homogenization of carbonaceous precursor material or addition of later isotopic outlier grains. The strong chemical similarity of isotopically typical and isotopically outlying grains, as reflected by synchrotron x-ray absorption spectra, suggests a genetic connection and thus favors the former, homogenization scenario. However, the fact that even the least altered meteorites show the same pattern of a small population of outliers on top of a larger population of homogenized grains indicates that some or most of the homogenization occurred prior to accretion of the macromolecular organic grains into asteroidal parent bodies., Comment: Accepted for publication in Earth and Planetary Science Letters. 8 figures (plus 3 supplementary figs), two tables

Why do neurons communicate through spikes? By definition, spikes are all-or-none neural events which occur at continuous times. In other words, spikes are on one side binary, existing or not without further details, and on the other can occur at any asynchronous time, without the need for a centralized clock. This stands in stark contrast to the analog representation of values and the discretized timing classically used in digital processing and at the base of modern-day neural networks. As neural systems almost systematically use this so-called event-based representation in the living world, a better understanding of this phenomenon remains a fundamental challenge in neurobiology in order to better interpret the profusion of recorded data. With the growing need for intelligent embedded systems, it also emerges as a new computing paradigm to enable the efficient operation of a new class of sensors and event-based computers, called neuromorphic, which could enable significant gains in computation time and energy consumption -- a major societal issue in the era of the digital economy and global warming. In this review paper, we provide evidence from biology, theory and engineering that the precise timing of spikes plays a crucial role in our understanding of the efficiency of neural networks.

We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide two sizes of models, containing 2B and 9B parameters, and provide pre-trained and instruction tuned variants for both. Our models achieve comparable performance to similarly-sized Gemma baselines despite being trained on fewer tokens.

Communication between connected objects in the Internet of Things (IoT) often requires secure and reliable authentication mechanisms to verify identities of entities and prevent unauthorized access to sensitive data and resources. Unlike other domains, IoT offers several advantages and opportunities, such as the ability to collect real-time data through numerous sensors. These data contains valuable information about the environment and other objects that, if used, can significantly enhance authentication processes. In this paper, we propose a novel idea to building opportunistic sensor-based authentication factors by leveraging existing IoT sensors in a system of systems approach. The objective is to highlight the promising prospects of opportunistic authentication factors in enhancing IoT security. We claim that sensors can be utilized to create additional authentication factors, thereby reinforcing existing object-to-object authentication mechanisms. By integrating these opportunistic sensor-based authentication factors into multi-factor authentication schemes, IoT security can be substantially improved. We demonstrate the feasibility and effectivenness of our idea through illustrative experiments in a parking entry scenario, involving both mobile robots and cars, achieving high identification accuracy. We highlight the potential of this novel method to improve IoT security and suggest future research directions for formalizing and comparing our approach with existing techniques.

The present study explores combining machine learning (ML) algorithms with standard optical diagnostics (such as time-integrated emission spectroscopy and imaging) to accurately predict operating conditions and assess the emission uniformity of a cylindrical surface Dielectric Barrier Discharge (SDBD). It is demonstrated that ML can be complementary with these optical diagnostics and identify peculiarities associated with the discharge emission pattern at different high voltage waveforms (AC and pulsed) and amplitudes. By employing unsupervised (Principal Component Analysis (PCA)) and supervised (Multilayer Perceptron (MLP) neural networks) algorithms, the applied voltage waveform and amplitude are categorised and predicted based on correlations/differences identified within large amounts of corresponding data. PCA allowed us to effectively classify the voltage waveforms and amplitudes applied to the SDBD through a transformation of the spectroscopic/imaging data into principal components (PCs) and their projection to a two-dimensional PC space. Furthermore, an accurate prediction of the voltage amplitude is achieved using the MLP which is trained with PCA-preprocessed data. A particularly interesting aspect of this concept involves examining the uniformity of the emission pattern of the discharge. This is achieved by analysing spectroscopic data recorded at four different regions around the SDBD surface using the two ML-based techniques. These discoveries are instrumental in enhancing plasma-induced processes. They open up new avenues for real-time control, monitoring, and optimization of plasma-based applications across diverse fields such as flow control for the present SDBD.

Recent advancements in deep learning techniques have spurred considerable interest in their application to hyperspectral imagery processing. This paper provides a comprehensive review of the latest developments in this field, focusing on methodologies, challenges, and emerging trends. Deep learning architectures such as Convolutional Neural Networks (CNNs), Autoencoders, Deep Belief Networks (DBNs), Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs) are examined for their suitability in processing hyperspectral data. Key challenges, including limited training data and computational constraints, are identified, along with strategies such as data augmentation and noise reduction using GANs. The paper discusses the efficacy of different network architectures, highlighting the advantages of lightweight CNN models and 1D CNNs for onboard processing. Moreover, the potential of hardware accelerators, particularly Field Programmable Gate Arrays (FPGAs), for enhancing processing efficiency is explored. The review concludes with insights into ongoing research trends, including the integration of deep learning techniques into Earth observation missions such as the CHIME mission, and emphasizes the need for further exploration and refinement of deep learning methodologies to address the evolving demands of hyperspectral image processing., Comment: Hyperspectral, Deep Learning, Neural Networks, Image Processing, Classification, Segmentation, Hardware Accelerators, CHIME Mission

Topological defects are ubiquitous, they manifest in a wide variety of systems such as liquid crystals, magnets or superconductors. The recent quest for nonabelian anyons in condensed matter physics stimulates the interest for topological defects since they can be hosted in vortices in quantum magnets or topological superconductors. In addition to these vortex defects, in this study we propose to investigate edge dislocations in 2D magnets as new building blocks for topological physics since they can be described as vortices in the structural phase field. Here we demonstrate the existence of higher order topological dislocations within the higher order moir\'e pattern of the van der Waals 2D magnet CrCl3 deposited on Au(111). Surprizingly, these higher order dislocations arise from ordinary simple edge dislocations in the atomic lattice of CrCl3. We provide a theoretical framework explaining the higher order dislocations as vortex with a winding Chern number of 2. We expect that these original defects could stabilize some anyons either in a 2D quantum magnet or within a 2D superconductor coupled to it.

We study the Lowest Landau Level equation set on simply and doubly-periodic domains (in other words, rectangles and strips with appropriate boundary conditions). To begin with, we study well-posedness and establish the existence of stationary solutions. Then we investigate the linear stability of the lattice solution and prove it is stable for the (hexagonal) Abrikosov lattice, but unstable for rectangular lattices.

Despite the importance of scientific software for research, it is often not formally recognized and rewarded. This is especially true for foundation libraries, which are used by the software packages visible to the users, being ``hidden'' themselves. The funders and other organizations need to understand the complex network of computer programs that the modern research relies upon. In this work we used CZ Software Mentions Dataset to map the dependencies of the software used in biomedical papers and find the packages critical to the software ecosystems. We propose the centrality metrics for the network of software dependencies, analyze three ecosystems (PyPi, CRAN, Bioconductor) and determine the packages with the highest centrality.

We report the observation of the melting of a vortex lattice in a fast rotating quasi-two dimensional Bose gas, under the influence of thermal fluctuations. We image the vortex lattice after a time-of-flight expansion, for increasing rotation frequency at constant atom number and temperature. We detect the vortex positions and study the order of the lattice using the pair correlation function and the orientational correlation function. We evidence the melting transition by an abrupt change in the decay of orientational correlations, associated to a proliferation of dislocations. Our findings are consistent with the hexatic to liquid transition in the KTHNY scenario for two-dimensional melting.

The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. Weak tumour/non-tumour annotations served as labels for training a Resnet18 neural network, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm. Our model identified specific features of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). Despite no specific features of cHCC-CCA being recognized, the identification of HCC and iCCA tiles within a slide could facilitate the diagnosis of primary liver cancers, particularly cHCC-CCA. Method and results: 166 PLC biopsies were divided into training, internal and external validation sets: 90, 29 and 47 samples. Two liver pathologists reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI). After annotating the tumour/non-tumour areas, 256x256 pixel tiles were extracted from the WSIs and used to train a ResNet18. The network was used to extract new tile features. An unsupervised clustering algorithm was then applied to the new tile features. In a two-cluster model, Clusters 0 and 1 contained mainly HCC and iCCA histological features. The diagnostic agreement between the pathological diagnosis and the model predictions in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a highly variable proportion of tiles from each cluster (Cluster 0: 5-97%; Cluster 1: 2-94%)., Comment: https://www.sciencedirect.com/science/article/pii/S2589555924000090

Antiferromagnets have attracted significant attention in the field of magnonics, as promising candidates for ultralow-energy carriers for information transfer for future computing. The role of crystalline orientation distribution on magnon transport has received very little attention. In multiferroics such as BiFeO$_3$ the coupling between antiferromagnetic and polar order imposes yet another boundary condition on spin transport. Thus, understanding the fundamentals of spin transport in such systems requires a single domain, a single crystal. We show that through Lanthanum(La) substitution, a single ferroelectric domain can be engineered with a stable, single-variant spin cycloid, controllable by an electric field. The spin transport in such a single domain displays a strong anisotropy, arising from the underlying spin cycloid lattice. Our work shows a pathway to understand the fundamental origins of spin transport in such a single domain multiferroic., Comment: 15 pages, 9 figure

Motivated by contradicting reports in the literature, we have investigated the structural stability of tungsten nanoparticles using density functional theory calculations. The comparison of BCC, FCC, A15, disordered, and icosahedral configurations unequivocally shows that BCC is the energetically most stable structure when the number of atoms is greater than 40. A disordered structure is more stable for smaller sizes. This result conflicts with an earlier theoretical study on transition metal nanoparticles, based on a semi-empirical modeling of nanoparticles energetics [D. Tom{\'a}nek et al., Phys. Rev. B \textbf{28}, 665 (1983)]. Examining this latter work in the light of our results suggests that an erroneous description of clusters geometry is the source of the discrepancy. Finally, we improve the accuracy of the semi-empirical model proposed in this work, which will be useful to calculate nanoparticle energies for larger sizes., Comment: 8 pages, 4 figures

On any denumerable product of probability spaces, we extend the discrete Malliavin structure for conditionally independent random variables. As a consequence, we obtain the chaos decomposition for functionals of conditionally independent random variables. We also show how to derive some concentration results in that framework. The Malliavin-Stein method yields Berry-Esseen bounds for U-Statistics of such random variables. It leads to quantitative statements of conditional limit theorems: Lyapunov's central limit theorem, De Jong's limit theorem for multilinear forms. The latter is related to the fourth moment phenomenon. The final application consists of obtaining the rates of normal approximation for subhypergraph counts in random exchangeable hypergraphs including the Erd{\"o}s-R{\'e}nyi hypergraph model. The estimator of subhypergraph counts is an example of homogeneous sums for which we derive a new decomposition that extends the Hoeffding decomposition.

Lyman break galaxies (LBGs) are promising probes for clustering measurements at high redshift, $z>2$, a region only covered so far by Lyman-$\alpha$ forest measurements. In this paper, we investigate the feasibility of selecting LBGs by exploiting the existence of a strong deficit of flux shortward of the Lyman limit, due to various absorption processes along the line of sight. The target selection relies on deep imaging data from the HSC and CLAUDS surveys in the $g,r,z$ and $u$ bands, respectively, with median depths reaching 27 AB in all bands. The selections were validated by several dedicated spectroscopic observation campaigns with DESI. Visual inspection of spectra has enabled us to develop an automated spectroscopic typing and redshift estimation algorithm specific to LBGs. Based on these data and tools, we assess the efficiency and purity of target selections optimised for different purposes. Selections providing a wide redshift coverage retain $57\%$ of the observed targets after spectroscopic confirmation with DESI, and provide an efficiency for LBGs of $83\pm3\%$, for a purity of the selected LBG sample of $90\pm2\%$. This would deliver a confirmed LBG density of $\sim 620$ deg$^{-2}$ in the range $2.3

We present strong constraints on the spacetime variation of the fine-structure constant $\alpha$ using the Dark Energy Spectroscopic Instrument (DESI). In this pilot work, we utilize $\sim110,000$ galaxies with strong and narrow O III $\lambda\lambda$4959,5007 emission lines to measure the relative variation $\Delta\alpha/\alpha$ in space and time. The O III doublet is arguably the best choice for this purpose owing to its wide wavelength separation between the two lines and its strong emission in many galaxies. Our galaxy sample spans a redshift range of $0

Mid-infrared emission features probe the properties of ionized gas, and hot or warm molecular gas. The Orion Bar is a frequently studied photodissociation region (PDR) containing large amounts of gas under these conditions, and was observed with the MIRI IFU aboard JWST as part of the "PDRs4All" program. The resulting IR spectroscopic images of high angular resolution (0.2") reveal a rich observational inventory of mid-IR emission lines, and spatially resolve the substructure of the PDR, with a mosaic cutting perpendicularly across the ionization front and three dissociation fronts. We extracted five spectra that represent the ionized, atomic, and molecular gas layers, and measured the most prominent gas emission lines. An initial analysis summarizes the physical conditions of the gas and the potential of these data. We identified around 100 lines, report an additional 18 lines that remain unidentified, and measured the line intensities and central wavelengths. The H I recombination lines originating from the ionized gas layer bordering the PDR, have intensity ratios that are well matched by emissivity coefficients from H recombination theory, but deviate up to 10% due contamination by He I lines. We report the observed emission lines of various ionization stages of Ne, P, S, Cl, Ar, Fe, and Ni, and show how certain line ratios vary between the five regions. We observe the pure-rotational H$_2$ lines in the vibrational ground state from 0-0 S(1) to 0-0 S(8), and in the first vibrationally excited state from 1-1 S(5) to 1-1 S(9). We derive H$_2$ excitation diagrams, and approximate the excitation with one thermal (~700 K) component representative of an average gas temperature, and one non-thermal component (~2700 K) probing the effect of UV pumping. We compare these results to an existing model for the Orion Bar PDR and highlight the differences with the observations., Comment: 26 pages, 12 figures, 3 tables. Submitted to A&A, under review (1st revision)