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Trapping refers to the event when a household falls into the area of poverty. Households that live or fall into the area of poverty are said to be in a poverty trap, where a poverty trap is a state of poverty from which it is difficult to escape without external help. Similarly, extreme poverty is considered as the most severe type of poverty, in which households experience severe deprivation of basic human needs. In this article, we consider an Omega risk process with deterministic growth and a multiplicative jump (collapse) structure to model the capital of a household. It is assumed that, when a household's capital level is above a certain capital barrier level that determines a household's eligibility for a capital cash transfer programme, its capital grows exponentially. As soon as its capital falls below the capital barrier level, the capital dynamics incorporate external support in the form of direct transfers (capital cash transfers) provided by donors or governments. Otherwise, once trapped, the capital grows only due to the capital cash transfers. Under this model, we first derive closed-form expressions for the trapping probability and then do the same for the probability of extreme poverty, which only depends on the current value of the capital given by some extreme poverty rate function. Numerical examples illustrate the role of capital cash transfers on poverty and extreme poverty dynamics., Comment: 47 pages, 14 figures

The symmedian point of a triangle enjoys several geometric and optimality properties, which also serve to define it. We develop a new dynamical coordinatization of the symmedian, which naturally generalizes to other ideal hyperbolic polygons beyond triangles. We prove that in general this point still satisfies analogous geometric and optimality properties to those of the symmedian, making it into a hyperbolic barycenter. We initiate a study of moduli spaces of ideal polygons with fixed hyperbolic barycenter, and of some additional optimality properties of this point for harmonic (and sufficiently regular) ideal polygons., Comment: Submitted -- not yet peer reviewed

The continuous ranked probability score (crps) is the most commonly used scoring rule in the evaluation of probabilistic forecasts for real-valued outcomes. To assess and rank forecasting methods, researchers compute the mean crps over given sets of forecast situations, based on the respective predictive distributions and outcomes. We propose a new, isotonicity-based decomposition of the mean crps into interpretable components that quantify miscalibration (MSC), discrimination ability (DSC), and uncertainty (UNC), respectively. In a detailed theoretical analysis, we compare the new approach to empirical decompositions proposed earlier, generalize to population versions, analyse their properties and relationships, and relate to a hierarchy of notions of calibration. The isotonicity-based decomposition guarantees the nonnegativity of the components and quantifies calibration in a sense that is stronger than for other types of decompositions, subject to the nondegeneracy of empirical decompositions. We illustrate the usage of the isotonicity-based decomposition in case studies from weather prediction and machine learning.

Transiting planets with orbital periods longer than 40 d are extremely rare among the 5000+ planets discovered so far. The lack of discoveries of this population poses a challenge to research into planetary demographics, formation, and evolution. Here, we present the detection and characterization of HD88986b, a potentially transiting sub-Neptune, possessing the longest orbital period among known transiting small planets (< 4 R$_{\oplus}$) with a precise mass measurement ($\sigma_M/M$ > 25%). Additionally, we identified the presence of a massive companion in a wider orbit around HD88986. Our analysis reveals that HD88986b, based on two potential single transits on sector 21 and sector 48 which are both consistent with the predicted transit time from the RV model, is potentially transiting. The joint analysis of RV and photometric data show that HD88986b has a radius of 2.49$\pm$0.18 R$_{\oplus}$, a mass of 17.2$^{+4.0}_{-3.8}$ M$_{\oplus}$, and it orbits every 146.05$^{+0.43}_{-0.40}$ d around a subgiant HD88986 which is one of the closest and brightest exoplanet host stars (G2V type, R=1.543 $\pm$0.065 R$_{\odot}$, V=$6.47\pm 0.01$ mag, distance=33.37$\pm$0.04 pc). The nature of the outer, massive companion is still to be confirmed; a joint analysis of RVs, Hipparcos, and Gaia astrometric data shows that with a 3$\sigma$ confidence interval, its semi-major axis is between 16.7 and 38.8 au and its mass is between 68 and 284 M$_{Jup}$. HD88986b's wide orbit suggests the planet did not undergo significant mass loss due to extreme-ultraviolet radiation from its host star. Therefore, it probably maintained its primordial composition, allowing us to probe its formation scenario. Furthermore, the cold nature of HD88986b (460$\pm$8 K), thanks to its long orbital period, will open up exciting opportunities for future studies of cold atmosphere composition characterization., Comment: 37 pages, accepted to be published in A&A

The nucleus of comet 67P/Churyumov-Gerasimenko exhibits a broad spectral reflectance feature around 3.2 $\mu$m, which is omnipresent in all spectra of the surface, and whose attribution has remained elusive since its discovery. Based on laboratory experiments, we have shown that most of this absorption feature is due to ammonium (NH4+) salts mixed with the dark surface material. The depth of the band is compatible with semi-volatile ammonium salts being a major reservoir of nitrogen in the comet, which could dominate over refractory organic matter and volatile species. These salts may thus represent the long-sought reservoir of nitrogen in comets, possibly bringing their nitrogen-to-carbon ratio in agreement with the solar value. Moreover, the reflectance spectra of several asteroids are compatible with the presence of NH4+ salts at their surfaces. The presence of such salts, and other NH4+-bearing compounds on asteroids, comets, and possibly in proto-stellar environments, suggests that NH4+ may be a tracer of the incorporation and transformation of nitrogen in ices, minerals and organics, at different phases of the formation of the Solar System.

Despite the widespread use and success of machine-learning techniques for detecting phase transitions from data, their working principle and fundamental limits remain elusive. Here, we explain the inner workings and identify potential failure modes of these techniques by rooting popular machine-learning indicators of phase transitions in information-theoretic concepts. Using tools from information geometry, we prove that several machine-learning indicators of phase transitions approximate the square root of the system's (quantum) Fisher information from below -- a quantity that is known to indicate phase transitions but is often difficult to compute from data. We numerically demonstrate the quality of these bounds for phase transitions in classical and quantum systems., Comment: 7+11 pages, 2+3 figures

Machine learning has been successfully used to study phase transitions. One of the most popular approaches to identifying critical points from data without prior knowledge of the underlying phases is the learning-by-confusion scheme. As input, it requires system samples drawn from a grid of the parameter whose change is associated with potential phase transitions. Up to now, the scheme required training a distinct binary classifier for each possible splitting of the grid into two sides, resulting in a computational cost that scales linearly with the number of grid points. In this work, we propose and showcase an alternative implementation that only requires the training of a single multi-class classifier. Ideally, such multi-task learning eliminates the scaling with respect to the number of grid points. In applications to the Ising model and an image dataset generated with Stable Diffusion, we find significant speedups that closely correspond to the ideal case, with only minor deviations., Comment: 7 pages, 3 figures, Machine Learning and the Physical Sciences Workshop, NeurIPS 2023

Local variable selection aims to test for the effect of covariates on an outcome within specific regions. We outline a challenge that arises in the presence of non-linear effects and model misspecification. Specifically, for common semi-parametric methods even slight model misspecification can result in a high false positive rate, in a manner that is highly sensitive to the chosen basis functions. We propose a methodology based on orthogonal cut splines that avoids false positive inflation for any choice of knots, and achieves consistent local variable selection. Our approach offers simplicity, handles both continuous and categorical covariates, and provides theory for high-dimensional covariates and model misspecification. We discuss settings with either independent or dependent data. Our proposal allows including adjustment covariates that do not undergo selection, enhancing the model's flexibility. Our examples describe salary gaps associated with various discrimination factors at different ages, and the effects of covariates on functional data measuring brain activation at different times.

Halite (NaCl mineral) has exhibited the potential to preserve microorganisms for millions of years on Earth. This mineral was also identified on Mars and in meteorites. In this study, we investigated the potential of halite crystals to protect microbial life forms on the surface of an airless body (e.g., meteorite), for instance, during a lithopanspermia process (interplanetary travel step) in the early Solar System. To investigate the effect of the radiation of the young Sun on microorganisms, we performed extensive simulation experiments by employing a synchrotron facility. We focused on two exposure conditions: vacuum (low Earth orbit, 10^{-4}Pa) and vacuum-ultraviolet (VUV) radiation (range 57.6 - 124 nm, flux 7.14 W m^{-2}), with the latter representing an extreme scenario with high VUV fluxes comparable to the amount of radiation of a stellar superflare from the young Sun. The stellar VUV parameters were estimated by using the very well-studied solar analog of the young Sun, k^{1}Cet. To evaluate the protective effects of halite, we entrapped a halophilic archaeon (Haloferax volcanii) and a non-halophilic bacterium (Deinococcus radiodurans) in laboratory-grown halite. Control groups were cells entrapped in salt crystals (mixtures of different salts and NaCl) and non-trapped (naked) cells, respectively. All groups were exposed either to vacuum alone or to vacuum plus VUV. Our results demonstrate that halite can serve as protection against vacuum and VUV radiation, regardless of the type of microorganism. In addition, we found that the protection is higher than provided by crystals obtained from mixtures of salts. This extends the protective effects of halite documented in previous studies and reinforces the possibility to consider the crystals of this mineral as potential preservation structures in airless bodies or as vehicles for the interplanetary transfer of microorganisms.

We present the first large-scale 3D kinematic study of ~2000 spectroscopically-confirmed young stars (<20 Myr) in 18 star clusters and OB associations (hereafter groups) from the combination of Gaia astrometry and Gaia-ESO Survey spectroscopy. We measure 3D velocity dispersions for all groups, which range from 0.61 to 7.4 km/s (1D velocity dispersions of 0.35 to 4.3 km/s). We find the majority of groups have anisotropic velocity dispersions, suggesting they are not dynamically relaxed. From the 3D velocity dispersions, measured radii and estimates of total mass we estimate the virial state and find that all systems are super-virial when only the stellar mass is considered, but that some systems are sub-virial when the mass of the molecular cloud is taken into account. We observe an approximately linear correlation between the 3D velocity dispersion and the group mass, which would imply that the virial state of groups scales as the square root of the group mass. However, we do not observe a strong correlation between virial state and group mass. In agreement with their virial state we find that nearly all of the groups studied are in the process of expanding and that the expansion is anisotropic, implying that groups were not spherical prior to expansion. One group, Rho Oph, is found to be contracting and in a sub-virial state (when the mass of the surrounding molecular cloud is considered). This work provides a glimpse of the potential of the combination of Gaia and data from the next generation of spectroscopic surveys., Comment: 26 pages, 15 figures, accepted for publication in MNRAS

The current investigation aims to study the stochastic homogenization for a family of functionals with convex and nonstandard growth integrands defined on Orlicz-Sobolev's spaces. It focuses on the concept of stochastic two-scale convergence, which is a combinaison of both well-know two-scale convergence and stochastic two-scale convergence in the mean schemes. One fundamental in this topic is to extend the classical compactness results of the stochastic two-scale convergence method to this type of spaces., Comment: arXiv admin note: substantial text overlap with arXiv:2310.13202; text overlap with arXiv:1106.0409 by other authors

This study focuses on implementing a real-time control system for a particle accelerator facility that performs high energy physics experiments. A critical operating parameter in this facility is beam loss, which is the fraction of particles deviating from the accelerated proton beam into a cascade of secondary particles. Accelerators employ a large number of sensors to monitor beam loss. The data from these sensors is monitored by human operators who predict the relative contribution of different sub-systems to the beam loss. Using this information, they engage control interventions. In this paper, we present a controller to track this phenomenon in real-time using edge-Machine Learning (ML) and support control with low latency and high accuracy. We implemented this system on an Intel Arria 10 SoC. Optimizations at the algorithm, high-level synthesis, and interface levels to improve latency and resource usage are presented. Our design implements a neural network, which can predict the main source of beam loss (between two possible causes) at speeds up to 575 frames per second (fps) (average latency of 1.74 ms). The practical deployed system is required to operate at 320 fps, with a 3ms latency requirement, which has been met by our design successfully.

We present a scalable, bottom-up and intrinsically diverse data collection scheme that can be used for high-level reasoning with long and medium horizons and that has 2.2x higher throughput compared to traditional narrow top-down step-by-step collection. We collect realistic data by performing any user requests within the entirety of 3 office buildings and using multiple robot and human embodiments. With this data, we show that models trained on all embodiments perform better than ones trained on the robot data only, even when evaluated solely on robot episodes. We find that for a fixed collection budget it is beneficial to take advantage of cheaper human collection along with robot collection. We release a large and highly diverse (29,520 unique instructions) dataset dubbed RoboVQA containing 829,502 (video, text) pairs for robotics-focused visual question answering. We also demonstrate how evaluating real robot experiments with an intervention mechanism enables performing tasks to completion, making it deployable with human oversight even if imperfect while also providing a single performance metric. We demonstrate a single video-conditioned model named RoboVQA-VideoCoCa trained on our dataset that is capable of performing a variety of grounded high-level reasoning tasks in broad realistic settings with a cognitive intervention rate 46% lower than the zero-shot state of the art visual language model (VLM) baseline and is able to guide real robots through long-horizon tasks. The performance gap with zero-shot state-of-the-art models indicates that a lot of grounded data remains to be collected for real-world deployment, emphasizing the critical need for scalable data collection approaches. Finally, we show that video VLMs significantly outperform single-image VLMs with an average error rate reduction of 19% across all VQA tasks. Data and videos available at https://robovqa.github.io

In this note, we connect two different topics from linear algebra and numerical analysis: hypocoercivity of semi-dissipative matrices and strong stability for explicit Runge--Kutta schemes. Linear autonomous ODE systems with a non-coercive matrix are called hypocoercive if they still exhibit uniform exponential decay towards the steady state. Strong stability is a property of time-integration schemes for ODEs that preserve the temporal monotonicity of the discrete solutions. It is proved that explicit Runge--Kutta schemes are strongly stable with respect to semi-dissipative, asymptotically stable matrices if the hypocoercivity index is sufficiently small compared to the order of the scheme. Otherwise, the Runge--Kutta schemes are in general not strongly stable. As a corollary, explicit Runge--Kutta schemes of order $p\in 4\N$ with $s=p$ stages turn out to be \emph{not} strongly stable. This result was proved in \cite{AAJ23}, filling a gap left open in \cite{SunShu19}. Here, we present an alternative, direct proof.

The rapid development of superconducting quantum hardware is expected to run into significant I/O restrictions due to the need for large-scale error correction in a cryogenic environment. Classical data centers rely on fiber-optic interconnects to remove similar networking bottlenecks and to allow for reconfigurable, software-defined infrastructures. In the same spirit, ultra-cold electro-optic links have been proposed and used to generate qubit control signals, or to replace cryogenic readout electronics. So far, the latter suffered from either low efficiency, low bandwidth and the need for additional microwave drives, or breaking of Cooper pairs and qubit states. In this work we realize electro-optic microwave photonics at millikelvin temperatures to implement a radio-over-fiber qubit readout that does not require any active or passive cryogenic microwave equipment. We demonstrate all-optical single-shot-readout by means of the Jaynes-Cummings nonlinearity in a circulator-free readout scheme. Importantly, we do not observe any direct radiation impact on the qubit state as verified with high-fidelity quantum-non-demolition measurements despite the absence of shielding elements. This compatibility between superconducting circuits and telecom wavelength light is not only a prerequisite to establish modular quantum networks, it is also relevant for multiplexed readout of superconducting photon detectors and classical superconducting logic. Moreover, this experiment showcases the potential of electro-optic radiometry in harsh environments - an electronics-free sensing principle that extends into the THz regime with applications in radio astronomy, planetary missions and earth observation.

In this paper we present the European Low Frequency Survey (ELFS), a project that will enable foregrounds-free measurements of primordial $B$-mode polarization to a level 10$^{-3}$ by measuring the Galactic and extra-Galactic emissions in the 5--120\,GHz frequency window. Indeed, the main difficulty in measuring the B-mode polarization comes not just from its sheer faintness, but from the fact that many other objects in the Universe also emit polarized microwaves, which mask the faint CMB signal. The first stage of this project will be carried out in synergy with the Simons Array (SA) collaboration, installing a 5.5--11 GHz coherent receiver at the focus of one of the three 3.5\,m SA telescopes in Atacama, Chile ("ELFS on SA"). The receiver will be equipped with a fully digital back-end based on the latest Xilinx RF System-on-Chip devices that will provide frequency resolution of 1\,MHz across the whole observing band, allowing us to clean the scientific signal from unwanted radio frequency interference, particularly from low-Earth orbit satellite mega-constellations. This paper reviews the scientific motivation for ELFS and its instrumental characteristics, and provides an update on the development of ELFS on SA., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

It has long been understood that dilute samples of chiral molecules such as rarefied gases should exhibit Rayleigh optical activity. We extend the existing theory by accounting for molecular dynamics and correlations, thus obtaining a more general theory of Rayleigh-Brillouin optical activity applicable to dense samples such as neat liquids.

Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants ($\text{n}=267$; $\text{contrasts}=6$), we first generated participant-wise soft ground truth (GT) by averaging the binary segmentations across all 6 contrasts. These soft GT, along with aggressive data augmentation and a regression-based loss function, were used to train a U-Net model for spinal cord segmentation. We evaluated our model against state-of-the-art methods and performed ablation studies involving different loss functions and domain generalization methods. Our results show that using the soft segmentations along with a regression loss function reduces CSA variability ($p < 0.05$, Wilcoxon signed-rank test). The proposed spinal cord segmentation model generalizes better than the state-of-the-art methods amongst unseen datasets, vendors, contrasts, and pathologies (compression, lesions), while accounting for partial volume effects., Comment: Revision Submitted to Medical Image Analysis

Low-distortional metric embeddings are a crucial component in the modern algorithmic toolkit. In an online metric embedding, points arrive sequentially and the goal is to embed them into a simple space irrevocably, while minimizing the distortion. Our first result is a deterministic online embedding of a general metric into Euclidean space with distortion $O(\log n)\cdot\min\{\sqrt{\log\Phi},\sqrt{n}\}$ (or, $O(d)\cdot\min\{\sqrt{\log\Phi},\sqrt{n}\}$ if the metric has doubling dimension $d$), solving a conjecture by Newman and Rabinovich (2020), and quadratically improving the dependence on the aspect ratio $\Phi$ from Indyk et al.\ (2010). Our second result is a stochastic embedding of a metric space into trees with expected distortion $O(d\cdot \log\Phi)$, generalizing previous results (Indyk et al.\ (2010), Bartal et al.\ (2020)). Next, we study the \emph{online minimum-weight perfect matching} problem, where a sequence of $2n$ metric points arrive in pairs, and one has to maintain a perfect matching at all times. We allow recourse (as otherwise the order of arrival determines the matching). The goal is to return a perfect matching that approximates the \emph{minimum-weight} perfect matching at all times, while minimizing the recourse. Our third result is a randomized algorithm with competitive ratio $O(d\cdot \log \Phi)$ and recourse $O(\log \Phi)$ against an oblivious adversary, this result is obtained via our new stochastic online embedding. Our fourth result is a deterministic algorithm against an adaptive adversary, using $O(\log^2 n)$ recourse, that maintains a matching of weight at most $O(\log n)$ times the weight of the MST, i.e., a matching of lightness $O(\log n)$. We complement our upper bounds with a strategy for an oblivious adversary that, with recourse $r$, establishes a lower bound of $\Omega(\frac{\log n}{r \log r})$ for both competitive ratio and lightness., Comment: A preliminary version of this paper appeared in the Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA24)

The prediction of crystal properties plays a crucial role in the crystal design process. Current methods for predicting crystal properties focus on modeling crystal structures using graph neural networks (GNNs). Although GNNs are powerful, accurately modeling the complex interactions between atoms and molecules within a crystal remains a challenge. Surprisingly, predicting crystal properties from crystal text descriptions is understudied, despite the rich information and expressiveness that text data offer. One of the main reasons is the lack of publicly available data for this task. In this paper, we develop and make public a benchmark dataset (called TextEdge) that contains text descriptions of crystal structures with their properties. We then propose LLM-Prop, a method that leverages the general-purpose learning capabilities of large language models (LLMs) to predict the physical and electronic properties of crystals from their text descriptions. LLM-Prop outperforms the current state-of-the-art GNN-based crystal property predictor by about 4% in predicting band gap, 3% in classifying whether the band gap is direct or indirect, and 66% in predicting unit cell volume. LLM-Prop also outperforms a finetuned MatBERT, a domain-specific pre-trained BERT model, despite having 3 times fewer parameters. Our empirical results may highlight the current inability of GNNs to capture information pertaining to space group symmetry and Wyckoff sites for accurate crystal property prediction., Comment: Code for LLM-Prop can be found at: https://github.com/vertaix/LLM-Prop

In this paper, we study the stochastic homogenization for a family of functionals with convex and nonstandard growth integrands defined on Orlicz-Sobolev's spaces. One fundamental in this topic is to extend the classical compactness results of the two-scale convergence method in the mean to this type of spaces. This problematic relies on the notion of dynamical system which is our basic tool., Comment: 33 pages. arXiv admin note: text overlap with arXiv:1106.0409 by other authors

Differential Privacy (DP) has been tailored to address the unique challenges of text-to-text privatization. However, text-to-text privatization is known for degrading the performance of language models when trained on perturbed text. Employing a series of interpretation techniques on the internal representations extracted from BERT trained on perturbed pre-text, we intend to disentangle at the linguistic level the distortion induced by differential privacy. Experimental results from a representational similarity analysis indicate that the overall similarity of internal representations is substantially reduced. Using probing tasks to unpack this dissimilarity, we find evidence that text-to-text privatization affects the linguistic competence across several formalisms, encoding localized properties of words while falling short at encoding the contextual relationships between spans of words.

Electrons photoemitted by extreme ultraviolet attosecond pulses derive spatially from the first few atomic surface layers and energetically from the valence band and highest atomic orbitals. As a result, it is possible to probe the emission dynamics from a narrow two-dimensional region in the presence of optical fields as well as obtain elemental specific information. However, combining this with spatially-resolved imaging is a long-standing challenge because of the large inherent spectral width of attosecond pulses as well as the difficulty of making them at high repetition rates. Here we demonstrate an attosecond interferometry experiment on a zinc oxide (ZnO) surface using spatially and energetically resolved photoelectrons. We combine photoemission electron microscopy with near-infrared pump - extreme ultraviolet probe laser spectroscopy and resolve the instantaneous phase of an infrared field with high spatial resolution. Our results show how the core level states with low binding energy of ZnO are well suited to perform spatially resolved attosecond interferometry experiments. We observe a distinct phase shift of the attosecond beat signal across the laser focus which we attribute to wavefront differences between the pump and the probe fields at the surface. Our work demonstrates a clear pathway for attosecond interferometry with high spatial resolution at atomic scale surface regions opening up for a detailed understanding of nanometric light-matter interaction.

This document presents a summary of the 2023 Terrestrial Very-Long-Baseline Atom Interferometry Workshop hosted by CERN. The workshop brought together experts from around the world to discuss the exciting developments in large-scale atom interferometer (AI) prototypes and their potential for detecting ultralight dark matter and gravitational waves. The primary objective of the workshop was to lay the groundwork for an international TVLBAI proto-collaboration. This collaboration aims to unite researchers from different institutions to strategize and secure funding for terrestrial large-scale AI projects. The ultimate goal is to create a roadmap detailing the design and technology choices for one or more km-scale detectors, which will be operational in the mid-2030s. The key sections of this report present the physics case and technical challenges, together with a comprehensive overview of the discussions at the workshop together with the main conclusions., Comment: Summary of the Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at CERN: https://indico.cern.ch/event/1208783/

This study focuses on modelling galaxy cluster gas profiles via a semi-parametric nodal approach. While traditional methods like the generalised Navarro-Frenk-White (gNFW) often encounter parameter degeneracy, our flexible node-based method precisely defines a cluster gas pressure profile. Using Planck space telescope data from the Coma region, our model, focused on the pressure-radius relationship, showcases enhanced flexibility over the gNFW. Bayesian analyses indicated an optimal five-node structure for the Coma cluster pressure profile., Comment: 6 pages, 5 figures

Reward specification is a notoriously difficult problem in reinforcement learning, requiring extensive expert supervision to design robust reward functions. Imitation learning (IL) methods attempt to circumvent these problems by utilizing expert demonstrations but typically require a large number of in-domain expert demonstrations. Inspired by advances in the field of Video-and-Language Models (VLMs), we present RoboCLIP, an online imitation learning method that uses a single demonstration (overcoming the large data requirement) in the form of a video demonstration or a textual description of the task to generate rewards without manual reward function design. Additionally, RoboCLIP can also utilize out-of-domain demonstrations, like videos of humans solving the task for reward generation, circumventing the need to have the same demonstration and deployment domains. RoboCLIP utilizes pretrained VLMs without any finetuning for reward generation. Reinforcement learning agents trained with RoboCLIP rewards demonstrate 2-3 times higher zero-shot performance than competing imitation learning methods on downstream robot manipulation tasks, doing so using only one video/text demonstration.

This paper is concerned with a modified entropy method to establish the large-time convergence towards the (unique) steady state, for kinetic Fokker-Planck equations with non-quadratic confinement potentials in whole space. We extend previous approaches by analyzing Lyapunov functionals with non-constant weight matrices in the dissipation functional (a generalized Fisher information). We establish exponential convergence in a weighted $H^1$-norm with rates that become sharp in the case of quadratic potentials. In the defective case for quadratic potentials, i.e. when the drift matrix has non-trivial Jordan blocks, the weighted $L^2$-distance between a Fokker-Planck-solution and the steady state has always a sharp decay estimate of the order $\mathcal O\big( (1+t)e^{-t\nu/2}\big)$, with $\nu$ the friction parameter. The presented method also gives new hypoelliptic regularization results for kinetic Fokker-Planck equations (from a weighted $L^2$-space to a weighted $H^1$-space).

The problem of the expansion and assimilation of a cryogenic fuel pellet injected into a hot plasma is considered. Due to the transparency of the plasmoid to ambient particles, it is found that electrons reach a `quasi-equilibrium' (QE) which is characterised by a steady-state on the fastest collisional timescale. The simplified electron kinetic equation of the quasi-equilibrium state is solved. Taking a velocity moment of the electron kinetic equation permits a fluid closure, yielding an evolution equation for the parameters describing the QE distribution function. In contrast to the Braginskii equations, the closure does not require that electrons have a short mean free path compared to the size of density perturbations and permits an anisotropic and highly non-Maxwellian distribution function. Since the QE electron distribution function accounts for both trapped and passing electrons, the self-consistent electric potential that causes the expansion can be properly described, in contrast to earlier models of pellet plasmoid expansion with an unbounded potential. The plasmoid expansion is simulated using both a Vlasov model and a cold fluid model for the ions. During the expansion plasmoid ions and electrons obtain a nearly equal amount of energy; as hot ambient electrons provide this energy in the form of collisional heating of plasmoid electrons, the expansion of a pellet plasmoid is expected to be a potent mechanism for the transfer of energy from electrons to ions on a timescale shorter than that of ion-electron thermalisation.

Artificial intelligence (AI) methods have become critical in scientific applications to help accelerate scientific discovery. Large language models (LLMs) are being considered as a promising approach to address some of the challenging problems because of their superior generalization capabilities across domains. The effectiveness of the models and the accuracy of the applications is contingent upon their efficient execution on the underlying hardware infrastructure. Specialized AI accelerator hardware systems have recently become available for accelerating AI applications. However, the comparative performance of these AI accelerators on large language models has not been previously studied. In this paper, we systematically study LLMs on multiple AI accelerators and GPUs and evaluate their performance characteristics for these models. We evaluate these systems with (i) a micro-benchmark using a core transformer block, (ii) a GPT- 2 model, and (iii) an LLM-driven science use case, GenSLM. We present our findings and analyses of the models' performance to better understand the intrinsic capabilities of AI accelerators. Furthermore, our analysis takes into account key factors such as sequence lengths, scaling behavior, sparsity, and sensitivity to gradient accumulation steps.

Predictive flame spread models based on temperature dependent pyrolysis rates require numerous material properties as input parameters. These parameters are typically derived by optimisation and inverse modelling using data from bench scale experiments such as the cone calorimeter. The estimated parameters are then transferred to flame spread simulations, where self-sustained propagation is expected. A fundamental requirement for this transfer is that the simulation model used in the optimisation is sufficiently sensitive to the input parameters that are important to flame spread. Otherwise, the estimated parameters will have an increased associated uncertainty that will be transferred to the flame spread simulation. This is investigated here using a variance-based global sensitivity analysis method, the Sobol indices. The sensitivities of a cone calorimeter and a horizontal flame spread simulation to 15 effective properties of polymethyl methacrylate are compared. The results show significant differences between the setups: the cone calorimeter is dominated by strong interaction effects between two temperature dependent specific heat values, whereas the flame spread is influenced by several parameters. Furthermore, the importance of some parameters for the cone calorimeter is found to be time-varying, suggesting that single-value cost functions may not be sufficient to account for all sensitive parameters during optimisation.

Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using prediction intervals calibrated to an ad-hoc operating point, making evaluation and comparison across different studies relatively difficult. Our work leverages: (1) the concept of operating characteristics curves and (2) the notion of a gain over a null reference, to derive a novel operating point agnostic assessment methodology for prediction intervals. The paper defines the Uncertainty Characteristics Curve and demonstrates its utility in selected scenarios. We argue that the proposed method addresses the current need for comprehensive assessment of prediction intervals and thus represents a valuable addition to the uncertainty quantification toolbox., Comment: Published at Workshop on Distribution-Free Uncertainty Quantification, International Conference on Machine Learning (ICML), July 2022. arXiv admin note: substantial text overlap with arXiv:2106.00858

We present a quantitative analysis of the properties of galaxies and structures evolving in universes dominated by different modified gravitational models, including two variants of the f(R)-gravity (F) and two of the Dvali-Gabdadze-Poratti (N) braneworld model, which respectively feature the chameleon and Vainshtein screening mechanisms. Using the Simulation HYdrodynamics BeyONd Einstein (SHYBONE) cosmological hydrodynamical full-physics simulations suite, we study the departures in the properties of galaxies residing in different environments with respect to the standard model (GR). Using two different criteria to compare, we find that structures formed within modified gravity tend to show a denser gas density profile than their GR counterparts. Within the different modified gravity models, N1 and F5 gravity models show greater departures from the standard model, with gas density profiles $\rho_{\rm IGM} \geq 30\%$ denser in the outskirts for the N1 model, and in the inner parts for the F5 model. Additionally, we find that haloes evolving in MG universes show, in general, larger quenched fractions than GR, reaching up to $20\%$ larger quenching fractions in F5 regardless of the stellar mass of the galaxy. With respect to the other models, F6, N1 and N5 show slightly larger quenched fractions, but no strong differences can be found. These results directly impact the colour distribution of galaxies, making them in MG models redder and older than their GR counterparts. Like GR, once the environment starts to play a role, galaxies rapidly get quenched and the differences between models vanish., Comment: 17 pages, 12 figures, submitted to MNRAS

We consider initial value problems for $\varepsilon^2\,\varphi''+a(x)\,\varphi=0$ in the highly oscillatory regime, i.e., with $a(x)>0$ and $0<\varepsilon\ll 1$. We discuss their efficient numerical integration on coarse grids, but still yielding accurate solutions. The $\mathcal{O}(h^2)$ one-step method from [2] is based on an analytic WKB-preprocessing of the equation. Here we extend this method to $\mathcal{O}(h^3)$ accuracy., Comment: 4 pages, 1 figure

We discuss the numerical solution of initial value problems for $\varepsilon^2\,\varphi''+a(x)\,\varphi=0$ in the highly oscillatory regime, i.e., with $a(x)>0$ and $0<\varepsilon\ll 1$. We analyze and implement an approximate solution based on the well-known WKB-ansatz. The resulting approximation error is of magnitude $\mathcal{O}(\varepsilon^{N})$ where $N$ refers to the truncation order of the underlying asymptotic series. When the optimal truncation order $N_{opt}$ is chosen, the error behaves like $\mathcal{O}(\varepsilon^{-2}\exp(-c\varepsilon^{-1}))$ with some $c>0$., Comment: 4 pages, 2 figures

Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI models should be shared. Open-sourcing in AI typically refers to making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling external oversight, accelerating progress, and decentralizing control over AI development and use. However, it also presents a growing potential for misuse and unintended consequences. This paper offers an examination of the risks and benefits of open-sourcing highly capable foundation models. While open-sourcing has historically provided substantial net benefits for most software and AI development processes, we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits. In such a case, highly capable foundation models should not be open-sourced, at least not initially. Alternative strategies, including non-open-source model sharing options, are explored. The paper concludes with recommendations for developers, standard-setting bodies, and governments for establishing safe and responsible model sharing practices and preserving open-source benefits where safe., Comment: Official release at https://www.governance.ai/research-paper/open-sourcing-highly-capable-foundation-models

We present the requirements, design and evaluation of the cryogenic continuously rotating half-wave plate (CHWP) for the Simons Observatory (SO). SO is a cosmic microwave background (CMB) polarization experiment at Parque Astron\'{o}mico Atacama in northern Chile that covers a wide range of angular scales using both small (0.42 m) and large (6 m) aperture telescopes. In particular, the small aperture telescopes (SATs) focus on large angular scales for primordial B-mode polarization. To this end, the SATs employ a CHWP to modulate the polarization of the incident light at 8~Hz, suppressing atmospheric $1/f$ noise and mitigating systematic uncertainties that would otherwise arise due to the differential response of detectors sensitive to orthogonal polarizations. The CHWP consists of a 505 mm diameter achromatic sapphire HWP and a cryogenic rotation mechanism, both of which are cooled down to $\sim$50 K to reduce detector thermal loading. Under normal operation the HWP is suspended by a superconducting magnetic bearing and rotates with a constant 2 Hz frequency, controlled by an electromagnetic synchronous motor. The rotation angle is detected through an angular encoder with a noise level of 0.07$\mu\mathrm{rad}\sqrt{\mathrm{s}}$. During a cooldown, the rotor is held in place by a grip-and-release mechanism that serves as both an alignment device and a thermal path. In this paper we provide an overview of the SO SAT CHWP: its requirements, hardware design, and laboratory performance., Comment: 19 pages, 21 figures, submitted to RSI

A photoelectron, emitted due to the absorption of light quanta as described by the photoelectric effect, is often characterized experimentally by a classical quantity, its momentum. However, since the photoelectron is a quantum object, its rigorous characterization requires the reconstruction of the complete quantum state, the photoelectron's density matrix. Here, we use quantum state tomography to fully characterize photoelectrons emitted from helium and argon atoms upon absorption of ultrashort, extreme ultraviolet light pulses. While in helium we measure a pure photoelectronic state, in argon, spin-orbit interaction induces entanglement between the ion and the photoelectron, leading to a reduced purity of the photoelectron state. Our work shows how state tomography gives new insights into the fundamental quantum aspects of light-induced electronic processes in matter, bridging the fields of photoelectron spectroscopy and quantum information, and offering new spectroscopic possibilities for quantum technology.

Inclusive e$^+$e$^-$ production has been studied with HADES in $\pi^-$ + p, $\pi^-$ + C and $\pi^- + \mathrm{CH}_2$ reactions, using the GSI pion beam at $\sqrt{s_{\pi p}}$ = 1.49 GeV. Invariant mass and transverse momentum distributions have been measured and reveal contributions from Dalitz decays of $\pi^0$, $\eta$ mesons and baryon resonances. The transverse momentum distributions are very sensitive to the underlying kinematics of the various processes. The baryon contribution exhibits a deviation up to a factor seven from the QED reference expected for the dielectron decay of a hypothetical point-like baryon with the production cross section constrained from the inverse $\gamma$ n$\rightarrow \pi^-$ p reaction. The enhancement is attributed to a strong four-momentum squared dependence of the time-like electromagnetic transition form factors as suggested by Vector Meson Dominance (VMD). Two versions of the VMD, that differ in the photon-baryon coupling, have been applied in simulations and compared to data. VMD1 (or two-component VMD) assumes a coupling via the $\rho$ meson and a direct coupling of the photon, while in VMD2 (or strict VMD) the coupling is only mediated via the $\rho$ meson. The VMD2 model, frequently used in transport calculations for dilepton decays, is found to overestimate the measured dielectron yields, while a good description of the data can be obtained with the VMD1 model assuming no phase difference between the two amplitudes. Similar descriptions have also been obtained using a time-like baryon transition form factor model where the pion cloud plays the major role., Comment: (HADES collaboration)

Conducting safety simulations in various simulators, such as the Gazebo simulator, became a very popular means of testing vehicles against potential safety risks (i.e. crashes). However, this was not the case with security testing. Performing security testing in a simulator is very difficult because security attacks are performed on a different abstraction level. In addition, the attacks themselves are becoming more sophisticated, which directly contributes to the difficulty of executing them in a simulator. In this paper, we attempt to tackle the aforementioned gap by investigating possible attacks that can be simulated, and then performing their simulations. The presented approach shows that attacks targeting the LiDAR and GPS components of unmanned aerial vehicles can be simulated. This is achieved by exploiting vulnerabilities of the ROS and MAVLink protocol and injecting malicious processes into an application. As a result, messages with arbitrary values can be spoofed to the corresponding topics, which allows attackers to update relevant parameters and cause a potential crash of a vehicle. This was tested in multiple scenarios, thereby proving that it is indeed possible to simulate certain attack types, such as spoofing and jamming.

The goal of this work is to find the sharp rate of convergence to equilibrium under the quadratic Fisher information functional for solutions to Fokker-Planck equations governed by a constant drift term and a constant, yet possibly degenerate, diffusion matrix. A key ingredient in our investigation is a recent work of Arnold, Signorello, and Schmeiser, where the $L^2$-propagator norm of such Fokker-Planck equations was shown to be identical to the propagator norm of a finite dimensional ODE which is determined by matrices that are intimately connected to those appearing in the associated Fokker-Planck equations.

Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language. In this paper we introduce TensorBank, a petabyte scale tensor lakehouse capable of streaming tensors from Cloud Object Store (COS) to GPU memory at wire speed based on complex relational queries. We use Hierarchical Statistical Indices (HSI) for query acceleration. Our architecture allows to directly address tensors on block level using HTTP range reads. Once in GPU memory, data can be transformed using PyTorch transforms. We provide a generic PyTorch dataset type with a corresponding dataset factory translating relational queries and requested transformations as an instance. By making use of the HSI, irrelevant blocks can be skipped without reading them as those indices contain statistics on their content at different hierarchical resolution levels. This is an opinionated architecture powered by open standards and making heavy use of open-source technology. Although, hardened for production use using geospatial-temporal data, this architecture generalizes to other use case like computer vision, computational neuroscience, biological sequence analysis and more.

For cosmic microwave background (CMB) polarization observations, calibration of detector polarization angles is essential. We have developed a fully remote controlled calibration system with a sparse wire grid that reflects linearly polarized light along the wire direction. The new feature is a remote-controlled system for regular calibration, which has not been possible in sparse wire grid calibrators in past experiments. The remote control can be achieved by two electric linear actuators that load or unload the sparse wire grid into a position centered on the optical axis of a telescope between the calibration time and CMB observation. Furthermore, the sparse wire grid can be rotated by a motor. A rotary encoder and a gravity sensor are installed on the sparse wire grid to monitor the wire direction. They allow us to achieve detector angle calibration with expected systematic error of $0.08^{\circ}$. The calibration system will be installed in small-aperture telescopes at Simons Observatory.

The dispersion scan (d-scan) technique is extended to measurement of the timedependent polarization state of ultrashort laser pulses. In the simplest implementation for linearly polarized ultrashort pulses, the d-scan technique records the second harmonic generation (SHG) spectrum as a function of a known spectral phase manipulation. By applying this method to two orthogonally polarized projections of an arbitrary polarized electric field and by measuring the spectrum at an intermediate angle, we can reconstruct the evolution over time of the polarization state. We demonstrate the method by measuring a polarization gate generated from 6 fs pulses with a combination of waveplates. The measurements are compared to simulations, showing an excellent agreement.