Search

Principal stratification provides a robust causal inference framework for the adjustment of post-treatment variables when comparing the effects of a treatment in health and social sciences. In this paper, we introduce a novel Bayesian nonparametric model for principal stratification, leveraging the dependent Dirichlet process to flexibly model the distribution of potential outcomes. By incorporating confounders and potential outcomes for the post-treatment variable in the Bayesian mixture model for the final outcome, our approach improves the accuracy of missing data imputation and allows for the characterization of treatment effects across strata defined based on the values of the post-treatment variable. We assess the performance of our method through a Monte Carlo simulation study where we compare the proposed method with state-of-the-art Bayesian method in principal stratification. Finally, we leverage the proposed method to evaluate the principal causal effects of exposure to air pollution on social mobility in the US on strata defined by educational attainment.

Challenging neutron-capture cross-section measurements of small cross sections and samples with a very limited number of atoms require high-flux time-of-flight facilities. In turn, such facilities need innovative detection setups that are fast, have low sensitivity to neutrons, can quickly recover from the so-called $\gamma$-flash, and offer the highest possible detection sensitivity. In this paper, we present several steps toward such advanced systems. Specifically, we describe the performance of a high-sensitivity experimental setup at CERN n\_TOF EAR2. It consists of nine sTED detector modules in a compact cylindrical configuration, two conventional used large-volume C$_{6}$D$_{6}$ detectors, and one LaCl$_{3}$(Ce) detector. The performance of these detection systems is compared using $^{93}$Nb($n$,$\gamma$) data. We also developed a detailed \textsc{Geant4} Monte Carlo model of the experimental EAR2 setup, which allows for a better understanding of the detector features, including their efficiency determination. This Monte Carlo model has been used for further optimization, thus leading to a new conceptual design of a $\gamma$ detector array, STAR, based on a deuterated-stilbene crystal array. Finally, the suitability of deuterated-stilbene crystals for the future STAR array is investigaged experimentally utilizing a small stilbene-d12 prototype. The results suggest a similar or superior performance of STAR with respect to other setups based on liquid-scintillators, and allow for additional features such as neutron-gamma discrimination and a higher level of customization capability.

Many studies have predicted SocioEconomic Position (SEP) for aggregated spatial units such as villages using satellite data, but SEP prediction at the household level and other sources of imagery have not been yet explored. We assembled a dataset of 975 households in a semi-rural district in southern Mozambique, consisting of self-reported asset, expenditure, and income SEP data, as well as multimodal imagery including satellite images and a ground-based photograph survey of 11 household elements. We fine-tuned a convolutional neural network to extract feature vectors from the images, which we then used in regression analyzes to model household SEP using different sets of image types. The best prediction performance was found when modeling asset-based SEP using random forest models with all image types, while the performance for expenditure- and income-based SEP was lower. Using SHAP, we observed clear differences between the images with the largest positive and negative effects, as well as identified the most relevant household elements in the predictions. Finally, we fitted an additional reduced model using only the identified relevant household elements, which had an only slightly lower performance compared to models using all images. Our results show how ground-based household photographs allow to zoom in from an area-level to an individual household prediction while minimizing the data collection effort by using explainable machine learning. The developed workflow can be potentially integrated into routine household surveys, where the collected household imagery could be used for other purposes, such as refined asset characterization and environmental exposure assessment.

This position paper explores the integration of Artificial Intelligence (AI) into force-controlled robotic tasks within the scope of advanced manufacturing, a cornerstone of Industry 4.0. AI's role in enhancing robotic manipulators - key drivers in the Fourth Industrial Revolution - is rapidly leading to significant innovations in smart manufacturing. The objective of this article is to frame these innovations in practical force-controlled applications - e.g. deburring, polishing, and assembly tasks like peg-in-hole (PiH) - highlighting their necessity for maintaining high-quality production standards. By reporting on recent AI-based methodologies, this article contrasts them and identifies current challenges to be addressed in future research. The analysis concludes with a perspective on future research directions, emphasizing the need for common performance metrics to validate AI techniques, integration of various enhancements for performance optimization, and the importance of validating them in relevant scenarios. These future directions aim to provide consistency with already adopted approaches, so as to be compatible with manufacturing standards, increasing the relevance of AI-driven methods in both academic and industrial contexts., Comment: In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, 392-399, 2024 , Porto, Portugal

We present recent laboratory results demonstrating high-contrast coronagraphy for the future space-based large IR/Optical/Ultraviolet telescope recommended by the Decadal Survey. The High-contrast Imager for Complex Aperture Telescopes (HiCAT) testbed aims to implement a system-level hardware demonstration for segmented aperture coronagraphs with wavefront control. The telescope hardware simulator employs a segmented deformable mirror with 37 hexagonal segments that can be controlled in piston, tip, and tilt. In addition, two continuous deformable mirrors are used for high-order wavefront sensing and control. The low-order sensing subsystem includes a dedicated tip-tilt stage, a coronagraphic target acquisition camera, and a Zernike wavefront sensor that is used to measure and correct low-order aberration drifts. We explore the performance of a segmented aperture coronagraph both in static operations (limited by natural drifts and instabilities) and in dynamic operations (in the presence of artificial wavefront drifts added to the deformable mirrors), and discuss the estimation and control strategies used to reach and maintain the dark-zone contrast using our low-order wavefront sensing and control. We summarize experimental results that quantify the performance of the testbed in terms of contrast, inner/outer working angle and bandpass, and analyze limiting factors., Comment: 17 pages, 14 figures, SPIE Astronomical Telescopes + Instrumentation, 2022, Montr\'eal, Qu\'ebec, Canada

We present the final results of the Apodized Pupil Lyot Coronagraph (APLC) on the High-contrast imager for Complex Aperture Telescopes (HiCAT) testbed, under NASA's Strategic Astrophysics Technology program. The HiCAT testbed was developed over the past decade to enable a system-level demonstration of coronagraphy for exoplanet direct imaging with the future Habitable Wolds Observatory. HiCAT includes an active, segmented telescope simulator, a coronagraph, and metrology systems (Low-order and Mid-Order Zernike Wavefront Sensors, and Phase Retrieval camera). These results correspond to an off-axis (un-obscured) configuration, as was envisioned in the 2020 Decadal Survey Recommendations. Narrowband and broadband dark holes are generated using two continuous deformable mirrors (DM) to control high order wavefront aberrations, and low-order drifts can be further stabilized using the LOWFS loop. The APLC apodizers, manufactured using carbon nanotubes, were optimized for broadband performance and include the calibrated geometric aperture. HiCAT is, to this date, the only testbed facility able to demonstrate high-contrast coronagraphy with a truly segmented aperture, as is required for the Habitable World Observatory, albeit limited to ambient conditions. Results presented here include $6\times 10^{-8}$ (90% CI) contrast in 9% bandpass in a 360 deg dark hole with inner and outer working angles of $4.4 \lambda/D_{pupil}$ and $11 \lambda/D_{pupil}$ . Narrowband contrast (3% bandpass) reaches $2.4\times 10^{-8}$ (90% confidence interval)., Comment: 17 pages, 16 figures, SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

We study a mid-order wavefront sensor (MOWFS) to address fine cophasing errors in exoplanet imaging with future large segmented aperture space telescopes. Observing Earth analogs around Sun-like stars requires contrasts down to $10^{-10}$ in visible light. One promising solution consists of producing a high-contrast dark zone in the image of an observed star. In a space observatory, this dark region will be altered by several effects, and among them, the small misalignments of the telescope mirror segments due to fine thermo-mechanical drifts. To correct for these errors in real time, we investigate a wavefront control loop based on a MOWFS with a Zernike sensor. Such a MOWFS was installed on the high-contrast imager for complex aperture telescopes (HiCAT) testbed in Baltimore in June 2023. The bench uses a 37-segment Iris-AO deformable mirror to mimic telescope segmentation and some wavefront control strategies to produce a dark zone with such an aperture. In this contribution, we first use the MOWFS to characterize the Iris-AO segment discretization steps. For the central segment, we find a minimal step of 125 $\pm$ 31 pm. This result will help us to assess the contribution of the Iris-AO DM on the contrast in HiCAT. We then determine the detection limits of the MOWFS, estimating wavefront error amplitudes of 119 and 102 pm for 10 s and 1 min exposure time with a SNR of 3. These values inform us about the measurement capabilities of our wavefront sensor on the testbed. These preliminary results will be useful to provide insights on metrology and stability for exo-Earth observations with the Habitable Worlds Observatory., Comment: 9 pages, 6 figures

As human space exploration evolves toward longer voyages farther from our home planet, in-situ resource utilization (ISRU) becomes increasingly important. Haptic teleoperations are one of the technologies by which such activities can be carried out remotely by humans, whose expertise is still necessary for complex activities. In order to perform precision tasks with effectiveness, the operator must experience ease of use and accuracy. The same features are demanded to reduce the complexity of the training procedures and the associated learning time for operators without a specific background in robotic teleoperations. Haptic teleoperation systems, that allow for a natural feeling of forces, need to cope with the trade-off between accurate movements and workspace extension. Clearly, both of them are required for typical ISRU tasks. In this work, we develop a new concept of operations and suitable human-robot interfaces to achieve sample collection and assembly with ease of use and accuracy. In the proposed operational concept, the teleoperation space is extended by executing automated trajectories, offline planned at the control station. In three different experimental scenarios, we validate the end-to-end system involving the control station and the robotic asset, by assessing the contribution of haptics to mission success, the system robustness to consistent delays, and the ease of training new operators.

A single aperture Canted-Cosine-Theta (CCT) quadrupole magnet, made of NbTi superconductors, has been developed for the final focus region of the FCC-ee study. The conductor layout is optimised to mitigate edge effects on one of the two sides of the magnet that typically lead to undesired higher-order multipoles. Experimental results of a prototype, including paraffin wax impregnation and cryogenic temperature measurements, are presented. The magnet exhibits no training behaviour, surpassing the nominal current during the initial ramp. Field quality is excellent, with higher-order multipoles below $1\times 10^{-4}$ units, consistent with simulations and room temperature tests. These findings confirm the potential of superconducting CCT magnets to offer compact solutions for applications demanding stringent field quality., Comment: The published paper had mistakes. The values in Table 2 for the Warm & Center measurements were wrongly reported. They have been corrected here

In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving complex continuous control tasks like locomotion and dexterous manipulation. However, this comes at the cost of an enormous amount of experience required for training, exacerbated by the sensitivity of learning efficiency and the policy performance to hyperparameter selection, which often requires numerous trials of time-consuming experiments. This work introduces a Population-Based Reinforcement Learning (PBRL) approach that exploits a GPU-accelerated physics simulator to enhance the exploration capabilities of RL by concurrently training multiple policies in parallel. The PBRL framework is applied to three state-of-the-art RL algorithms - PPO, SAC, and DDPG - dynamically adjusting hyperparameters based on the performance of learning agents. The experiments are performed on four challenging tasks in Isaac Gym - Anymal Terrain, Shadow Hand, Humanoid, Franka Nut Pick - by analyzing the effect of population size and mutation mechanisms for hyperparameters. The results demonstrate that PBRL agents outperform non-evolutionary baseline agents across tasks essential for humanoid robots, such as bipedal locomotion, manipulation, and grasping in unstructured environments. The trained agents are finally deployed in the real world for the Franka Nut Pick manipulation task. To our knowledge, this is the first sim-to-real attempt for successfully deploying PBRL agents on real hardware. Code and videos of the learned policies are available on our project website (https://sites.google.com/view/pbrl)., Comment: Submitted for publication to IEEE-RAS 23rd International Conference on Humanoid Robots

In applications of Bayesian procedures, even when the prior law is carefully specified, it may be delicate to elicit the prior hyperparameters so that it is often tempting to fix them from the data, usually by their maximum likelihood estimates (MMLE), obtaining a so-called empirical Bayes posterior distribution. Although questionable, this is a common practice; but theoretical properties seem mostly only available on a case-by-case basis. In this paper we provide general properties for parametric models. First, we study the limit behavior of the MMLE and prove results in quite general settings, while also conceptualizing the frequentist context as an unexplored case of maximum likelihood estimation under model misspecification. We cover both identifiable models, illustrating applications to sparse regression, and non-identifiable models - specifically, overfitted mixture models. Finally, we prove higher order merging results. In regular cases, the empirical Bayes posterior is shown to be a fast approximation to the Bayesian posterior distribution of the researcher who, within the given class of priors, has the most information about the true model's parameters. This is a faster approximation than classic Bernstein-von Mises results. Given the class of priors, our work provides formal contents to common beliefs on this popular practice.

There is currently a renewed interest in the Bayesian predictive approach to statistics. This paper offers a review on foundational concepts and focuses on predictive modeling, which by directly reasoning on prediction, bypasses inferential models or may characterize them. We detail predictive characterizations in exchangeable and partially exchangeable settings, for a large variety of data structures, and hint at new directions. The underlying concept is that Bayesian predictive rules are probabilistic learning rules, formalizing through conditional probability how we learn on future events given the available information. This concept has implications in any statistical problem and in inference, from classic contexts to less explored challenges, such as providing Bayesian uncertainty quantification to predictive algorithms in data science, as we show in the last part of the paper. The paper gives a historical overview, but also includes a few new results, presents some recent developments and poses some open questions.

Isomers close to the doubly magic nucleus $^{78}$Ni ($Z=28$, $N=50$) provide essential information on the shell evolution and shape coexistence far from stability. The existence of a long-lived isomeric state in $^{76}$Cu has been debated for a long time. We have performed high-precision mass measurements of $^{76}$Cu with the JYFLTRAP double Penning trap mass spectrometer at the Ion Guide Isotope Separator On-Line facility and confirm the existence of such a isomeric state with an excitation energy $E_x=64.8(25)$ keV. Based on the ratio of detected ground- and isomeric-state ions as a function of time, we show that the isomer is the shorter-living state previously considered as the ground state of $^{76}$Cu. The result can potentially change the conclusions made in previous works related to the spin-parity and charge radius of the $^{76}$Cu ground state. Additionally, the new $^{76}$Cu$(n,\gamma)$ reaction $Q$-value has an impact on the astrophysical rapid neutron-capture process., Comment: 8 pages, 4 figures

One of the critical aspects for the accurate determination of neutron capture cross sections when combining time-of-flight and total energy detector techniques is the characterization and control of systematic uncertainties associated to the measuring devices. In this work we explore the most conspicuous effects associated to harsh count rate conditions: dead-time and pile-up effects. Both effects, when not properly treated, can lead to large systematic uncertainties and bias in the determination of neutron cross sections. In the majority of neutron capture measurements carried out at the CERN n\_TOF facility, the detectors of choice are the C$_{6}$D$_{6}$ liquid-based either in form of large-volume cells or recently commissioned sTED detector array, consisting of much smaller-volume modules. To account for the aforementioned effects, we introduce a Monte Carlo model for these detectors mimicking harsh count rate conditions similar to those happening at the CERN n\_TOF 20~m fligth path vertical measuring station. The model parameters are extracted by comparison with the experimental data taken at the same facility during 2022 experimental campaign. We propose a novel methodology to consider both, dead-time and pile-up effects simultaneously for these fast detectors and check the applicability to experimental data from $^{197}$Au($n$,$\gamma$), including the saturated 4.9~eV resonance which is an important component of normalization for neutron cross section measurements.

Isomers close to doubly-magic $^{78}_{28}$Ni$_{50}$ provide essential information on the shell evolution and shape coexistence near the ${Z=28}$ and ${N=50}$ double shell closure. We report the excitation energy measurement of the $1/2^{+}$ isomer in $^{79}_{30}$Zn$_{49}$ through independent high-precision mass measurements with the JYFLTRAP double Penning trap and with the ISOLTRAP Multi-Reflection Time-of-Flight Mass Spectrometer. We unambiguously place the $1/2^{+}$ isomer at 942(10) keV, slightly below the $5/2^+$ state at 983(3) keV. With the use of state-of-the-art shell-model diagonalizations, complemented with Discrete Non Orthogonal shell-model calculations which are used here the first time to interpret shape coexistence, we find low-lying deformed intruder states, similar to other ${N=49}$ isotones. The $1/2^{+}$ isomer is interpreted as the band-head of a low-lying deformed structure akin to a predicted low-lying deformed band in $^{80}$Zn, and points to shape coexistence in $^{79,80}$Zn similar to the one observed in $^{78}$Ni. The results make a strong case for confirming the claim of shape coexistence in this key region of the nuclear chart., Comment: 10 pages, three figures, two tables. Accepted to Phys. Rev. Lett

The time varying reproduction number R is a critical variable for situational awareness during infectious disease outbreaks, but delays between infection and reporting hinder its accurate estimation in real time. We propose a nowcasting method for improving the timeliness and accuracy of R estimates, based on comparisons of successive versions of surveillance databases. The method was validated against COVID-19 surveillance data collected in Italy over an 18-month period. Compared to traditional methods, the nowcasted reproduction number reduced the estimation delay from 13 to 8 days, while maintaining a better accuracy. Moreover, it allowed anticipating the detection of periods of epidemic growth by between 6 and 23 days. The method offers a simple and generally applicable tool to improve situational awareness during an epidemic outbreak, allowing for informed public health response planning.

For exchangeable data, mixture models are an extremely useful tool for density estimation due to their attractive balance between smoothness and flexibility. When additional covariate information is present, mixture models can be extended for flexible regression by modeling the mixture parameters, namely the weights and atoms, as functions of the covariates. These types of models are interpretable and highly flexible, allowing non only the mean but the whole density of the response to change with the covariates, which is also known as density regression. This article reviews Bayesian covariate-dependent mixture models and highlights which data types can be accommodated by the different models along with the methodological and applied areas where they have been used. In addition to being highly flexible, these models are also numerous; we focus on nonparametric constructions and broadly organize them into three categories: 1) joint models of the responses and covariates, 2) conditional models with single-weights and covariate-dependent atoms, and 3) conditional models with covariate-dependent weights. The diversity and variety of the available models in the literature raises the question of how to choose among them for the application at hand. We attempt to shed light on this question through a careful analysis of the predictive equations for the conditional mean and density function as well as predictive comparisons in three simulated data examples.

We study saturated packings produced according to random sequential adsorption (RSA) protocol built of identical rectangles deposited on a flat, continuous plane. An aspect ratio of rectangles is defined as the length-to-width ratio, $f=l/w$. The rectangles have a fixed unit area (i.e., $l \times w=1$), and therefore, their shape is defined by the value of $f$ ($l=\sqrt{f}$ and $w=1/\sqrt{f}$). The rectangles are allowed to align either vertically or horizontally with equal probability. The particles are deposited on a flat square substrate of side length $L$ (measured in units of particle length, $L \in [20, 1000]$) and periodic boundary conditions are applied along both directions. The finite-size scaling effects are characterized by a scaled anisotropy defined as $\alpha = l/L = \sqrt{f}/L$. We showed that the properties of such packings strongly depend on the value of aspect ratio $f$ and the most significant scaling effects are observed for relatively long rectangles when $l\ge L/2$ (i.e. $\alpha \ge 0.5$). It is especially visible for the mean packing fraction as a function of the scaled anisotropy $\alpha$. The kinetics of packing growth for low to moderate rectangle anisotropy is to be governed by $\ln t/t$ law, where $t$ is proportional to the number of RSA iterations, which is the same as in the case of RSA of parallel squares. We also analyzed global orientational ordering in such packings and properties of domains consisting of a set of neighboring rectangles of the same orientation, and the probability that such domain forms a percolation., Comment: 14 pages, 9 figures

One of the crucial ingredients for the improvement of stellar models is the accurate knowledge of neutron capture cross-sections for the different isotopes involved in the $s$-,$r$- and $i$- processes. These measurements can shed light on existing discrepancies between observed and predicted isotopic abundances and help to constrain the physical conditions where these reactions take place along different stages of stellar evolution.In the particular case of the radioactive $^{94}$Nb, the $^{94}$Nb($n$,$\gamma$) cross-section could play a role in the determination of the $s$-process production of $^{94}$Mo in AGB stars, which presently cannot be reproduced by state-of-the-art stellar models. There are no previous $^{94}$Nb($n$,$\gamma$) experimental data for the resolved and unresolved resonance regions mainly due to the difficulties in producing high-quality samples and also due to limitations in conventional detection systems commonly used in time-of-flight experiments.Motivated by this situation, a first measurement of the $^{94}$Nb($n$,$\gamma$) reaction was carried out at CERN n\_TOF, thereby exploiting the high luminosity of the EAR2 area in combination with a new detection system of small-volume C6D6-detectors and a high quality $^{94}$Nb-sample. The latter was based on hyper-pure $^{93}$Nb material activated at the high-flux reactor of ILL-Grenoble. An innovative ring-configuration detection system in close geometry around the capture sample allowed us to significantly enhance the signal-to-background ratio. This set-up was supplemented with two conventional C$_{6}$D$_{6}$ detectors and a high-resolution LaCl$_{3}$(Ce)-detector, which will be employed for addressing reliably systematic effects and uncertainties.At the current status of the data analysis, 18 resonance in $^{94}$Nb+$n$ have been observed for the first time in the neutron energy range from thermal up to 10 keV., Comment: NPA-X conference proceeding

Wavefront sensing and control (WFSC) will play a key role in improving the stability of future large segmented space telescopes while relaxing the thermo-mechanical constraints on the observatory structure. Coupled with a coronagraph to reject the light of an observed bright star, WFSC enables the generation and stabilisation of a dark hole (DH) in the star image to perform planet observations. While WFSC traditionally relies on a single wavefront sensor (WFS) input to measure wavefront errors, the next generation of instruments will require several WFSs to address aberrations with different sets of spatial and temporal frequency contents. The multiple measurements produced in such a way will then have to be combined and converted to commands for deformable mirrors (DMs) to modify the wavefront subsequently. We asynchronously operate a loop controlling the high-order modes digging a DH and a control loop that uses the rejected light by a Lyot coronagraph with a Zernike wavefront sensor to stabilize the low-order aberrations. Using the HiCAT testbed with a segmented telescope aperture, we implement concurrent operations and quantify the expected cross-talk between the two controllers. We then present experiments that alternate high-order and low-order control loops to identify and estimate their respective contributions. We show an efficient combination of the high-order and low-order control loops, keeping a DH contrast better than 5 x 10-8 over a 30 min experiment and stability improvement by a factor of 1.5. In particular, we show a contrast gain of 1.5 at separations close to the DH inner working angle, thanks to the low-order controller contribution. Concurrently digging a DH and using the light rejected by a Lyot coronagraph to stabilize the wavefront is a promising path towards exoplanet imaging and spectroscopy with future large space observatories.