Search

Massive stars mainly form in close binaries, where their mutual interactions can profoundly alter their evolutionary paths. Evolved binaries consisting of a massive OB-type main-sequence star with a stripped helium star or a compact companion represent a crucial stage in the evolution towards double compact objects, whose mergers are (potentially) detectable via gravitational waves. The recent detection of X-ray quiet OB+black hole binaries and OB+stripped helium star binaries has set the stage for discovering more of these systems in the near future. In this work, based on 3670 detailed binary-evolution models and using empirical distributions of initial binary parameters, we compute the expected population of such evolved massive binaries in coeval stellar populations, including stars in star clusters and in galaxies with starburst activities, for ages up to 100 Myr. Our results are vividly illustrated in an animation that shows the evolution of these binaries in the color-magnitude diagram over time. We find that the number of OB+black hole binaries peaks around 10 Myr, and OB+neutron star binaries are most abundant at approximately 20 Myr. Both black holes and neutron stars can potentially be found in populations with ages up to 90 Myr. Additionally, we analyze the properties of such binaries at specific ages. We find that OB+helium stars and OB+black hole binaries are likely to be identifiable as single-lined spectroscopic binaries. Our research serves as a guide for future observational efforts to discover such binaries in young star clusters and starburst environments., Comment: 32 pages, 15 figures. Accepted for Publication in Astrophysical Journal Letters (ApJL)

This paper investigates the existence of a separation principle between model identification and control design in the context of model predictive control. First, we elucidate that the separation principle holds asymptotically in the number of data in a Fisherian setting, and universally in a Bayesian setting. Then, by formulating model predictive control within a Gaussian regression framework, we describe how the Bayesian separation principle can be used to derive explicit, uncertainty-aware expressions for the control cost and optimal input sequence, thereby bridging direct and indirect data-driven approaches., Comment: 13 pages, 1 figure

This report describes our proposed solution for the second AI Olympics competition held at IROS 2024. Our solution is based on a recent Model-Based Reinforcement Learning algorithm named MC-PILCO. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the tasks at hand.

In the following report, we describe the solution we propose for the AI Olympics competition held at IROS 2024. Our solution is based on a Model-free Deep Reinforcement Learning approach combined with an evolutionary strategy. We will briefly describe the algorithms that have been used and then provide details of the approach, Comment: Added Sec 9 after testing on real robot

Deep Neural Networks are well known for efficiently fitting training data, yet experiencing poor generalization capabilities whenever some kind of bias dominates over the actual task labels, resulting in models learning "shortcuts". In essence, such models are often prone to learn spurious correlations between data and labels. In this work, we tackle the problem of learning from biased data in the very realistic unsupervised scenario, i.e., when the bias is unknown. This is a much harder task as compared to the supervised case, where auxiliary, bias-related annotations, can be exploited in the learning process. This paper proposes a novel 2-stage learning pipeline featuring a data augmentation strategy able to regularize the training. First, biased/unbiased samples are identified by training over-biased models. Second, such subdivision (typically noisy) is exploited within a data augmentation framework, properly combining the original samples while learning mixing parameters, which has a regularization effect. Experiments on synthetic and realistic biased datasets show state-of-the-art classification accuracy, outperforming competing methods, ultimately proving robust performance on both biased and unbiased examples. Notably, being our training method totally agnostic to the level of bias, it also positively affects performance for any, even apparently unbiased, dataset, thus improving the model generalization regardless of the level of bias (or its absence) in the data.

In recent years, impressive performance of deep learning technology has been recognized in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). Since a large amount of annotated data is required in this technique, it poses a trenchant challenge to the issue of obtaining a high recognition rate through less labeled data. To overcome this problem, inspired by the contrastive learning, we proposed a novel framework named Batch Instance Discrimination and Feature Clustering (BIDFC). In this framework, different from that of the objective of general contrastive learning methods, embedding distance between samples should be moderate because of the high similarity between samples in the SAR images. Consequently, our flexible framework is equipped with adjustable distance between embedding, which we term as weakly contrastive learning. Technically, instance labels are assigned to the unlabeled data in per batch and random augmentation and training are performed few times on these augmented data. Meanwhile, a novel Dynamic-Weighted Variance loss (DWV loss) function is also posed to cluster the embedding of enhanced versions for each sample. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database indicate a 91.25% classification accuracy of our method fine-tuned on only 3.13% training data. Even though a linear evaluation is performed on the same training data, the accuracy can still reach 90.13%. We also verified the effectiveness of BIDFC in OpenSarShip database, indicating that our method can be generalized to other datasets. Our code is avaliable at: https://github.com/Wenlve-Zhou/BIDFC-master.

A class of globally scale-invariant scalar-tensor theories have been proposed to be invariant under a larger class of transformations that take the form of local Weyl transformations supplemented by a restriction that the conformal factor satisfies a covariant Klein-Gordon equation. The action of these theories indeed seems to be invariant under such transformations up to boundary terms, this property being referred to as "restricted Weyl symmetry". However, we find that corresponding equations of motion are not invariant under these transformations. This is a paradox, that is explained by realizing that the restriction condition on the conformal factor forces the "restricted Weyl transformation" to be a nonlocal transformation. For nonlocal transformations would-be boundary terms cannot in general be discarded from the action. Moreover, variations of trajectories cannot be assumed to vanish at boundaries of the action when deriving equations of motion. We illustrate both of these less known properties by considering a series of simple examples. Finally, we apply these observations to the case of globally scale-invariant scalar-tensor theories to demonstrate that "restricted Weyl transformations" are, in fact, not symmetries of the full system., Comment: 12 pages

The ability to achieve precise and smooth trajectory tracking is crucial for ensuring the successful execution of various tasks involving robotic manipulators. State-of-the-art techniques require accurate mathematical models of the robot dynamics, and robustness to model uncertainties is achieved by relying on precise bounds on the model mismatch. In this paper, we propose a novel adaptive robust feedback linearization scheme able to compensate for model uncertainties without any a-priori knowledge on them, and we provide a theoretical proof of convergence under mild assumptions. We evaluate the method on a simulated RR robot. First, we consider a nominal model with known model mismatch, which allows us to compare our strategy with state-of-the-art uncertainty-aware methods. Second, we implement the proposed control law in combination with a learned model, for which uncertainty bounds are not available. Results show that our method leads to performance comparable to uncertainty-aware methods while requiring less prior knowledge.

Multi-agent systems are increasingly widespread in a range of application domains, with optimization and learning underpinning many of the tasks that arise in this context. Different approaches have been proposed to enable the cooperative solution of these optimization and learning problems, including first- and second-order methods, and dual (or Lagrangian) methods, all of which rely on consensus and message-passing. In this article we discuss these algorithms through the lens of non-expansive operator theory, providing a unifying perspective. We highlight the insights that this viewpoint delivers, and discuss how it can spark future original research.

In this letter, we construct a supersymmetric model, obtained by deforming $\mathcal N=2$ AdS$_3$ supergravity through a chiral vector component of the torsion. Moreover, we study the existence of BPS states of such theory, by inspecting the presence of Killing spinors on a specific bosonic solution., Comment: 8 pages, 3 figures

The perspectives opened by the development of a low-energy antideuteron beam are here reviewed: precision measurements of the antideuteron properties; formation and spectroscopic analysis of antideuteronic atoms and antideuterium; the pioneering synthesis of heavier anti-elements in particle traps and at low energies. Some practical aspects of generating a low-energy antideuteron beam in the existing AD/ELENA facility are discussed.

Accurate dynamic models are crucial for many robotic applications. Traditional approaches to deriving these models are based on the application of Lagrangian or Newtonian mechanics. Although these methods provide a good insight into the physical behaviour of the system, they rely on the exact knowledge of parameters such as inertia, friction and joint flexibility. In addition, the system is often affected by uncertain and nonlinear effects, such as saturation and dead zones, which can be difficult to model. A popular alternative is the application of Machine Learning (ML) techniques - e.g., Neural Networks (NNs) - in the context of a "black-box" methodology. This paper reports on our experience with this approach for a real-life 6 degrees of freedom (DoF) manipulator. Specifically, we considered several NN architectures: single NN, multiple NNs, and cascade NN. We compared the performance of the system by using different policies for selecting the NN hyperparameters. Our experiments reveal that the best accuracy and performance are obtained by a cascade NN, in which we encode our prior physical knowledge about the dependencies between joints, complemented by an appropriate optimisation of the hyperparameters., Comment: 6 pages, 5 figures, submitted to CASE 2024

This paper contributes a formal framework for quantitative analysis of bounded sensor attacks on cyber-physical systems, using the formalism of differential dynamic logic. Given a precondition and postcondition of a system, we formalize two quantitative safety notions, quantitative forward and backward safety, which respectively express (1) how strong the strongest postcondition of the system is with respect to the specified postcondition, and (2) how strong the specified precondition is with respect to the weakest precondition of the system needed to ensure the specified postcondition holds. We introduce two notions, forward and backward robustness, to characterize the robustness of a system against sensor attacks as the loss of safety. To reason about robustness, we introduce two simulation distances, forward and backward simulation distances, which are defined based on the behavioral distances between the original system and the system with compromised sensors. Forward and backward distances, respectively, characterize upper bounds of the degree of forward and backward safety loss caused by the sensor attacks. We verify the two simulation distances by expressing them as modalities, i.e., formulas of differential dynamic logic, and develop an ad-hoc proof system to reason with such formulas. We showcase our formal notions and reasoning techniques on two non-trivial case studies: an autonomous vehicle that needs to avoid collision and a water tank system., Comment: Preprint submitted to Elsevier

We describe the construction of the slice fibration of a given one.

Continuous control Deep Reinforcement Learning (RL) approaches are known to suffer from estimation biases, leading to suboptimal policies. This paper introduces innovative methods in RL, focusing on addressing and exploiting estimation biases in Actor-Critic methods for continuous control tasks, using Deep Double Q-Learning. We design a Bias Exploiting (BE) mechanism to dynamically select the most advantageous estimation bias during training of the RL agent. Most State-of-the-art Deep RL algorithms can be equipped with the BE mechanism, without hindering performance or computational complexity. Our extensive experiments across various continuous control tasks demonstrate the effectiveness of our approaches. We show that RL algorithms equipped with this method can match or surpass their counterparts, particularly in environments where estimation biases significantly impact learning. The results underline the importance of bias exploitation in improving policy learning in RL.

A powerful and robust control system is a crucial, often neglected, pillar of any modern, complex physics experiment that requires the management of a multitude of different devices and their precise time synchronisation. The AEgIS collaboration presents CIRCUS, a novel, autonomous control system optimised for time-critical experiments such as those at CERN's Antiproton Decelerator and, more broadly, in atomic and quantum physics research. Its setup is based on Sinara/ARTIQ and TALOS, integrating the ALPACA analysis pipeline, the last two developed entirely in AEgIS. It is suitable for strict synchronicity requirements and repeatable, automated operation of experiments, culminating in autonomous parameter optimisation via feedback from real-time data analysis. CIRCUS has been successfully deployed and tested in AEgIS; being experiment-agnostic and released open-source, other experiments can leverage its capabilities.

Circumbinary disks are found in a variety of astrophysical scenarios, spanning binary star formation to accreting supermassive black hole binaries. The interaction with a circumbinary disk can yield opposite effects on the binary orbit leading to circularization, or exciting the eccentricity, widening the orbit or shrinking it and facilitating mergers. We present a new formalism for the long-term evolution of the disk-binary interaction based on the results of recent suites of hydrodynamic simulations, which resolve the complex geometry of the gas in the vicinity of the binary and fully account for the gravitational and accretion forces. We release a python package, \texttt{spindler}, that implements our model. We show that, unless the mass reservoir feeding the disk is comparable to the mass of the binary, accretion onto the binary depletes the disk mass before inducing a significant change in orbital separation or mass ratio. This finding implies that, in most scenarios, interaction with a circumbinary disk is not an efficient mechanism to shrink the orbit of the binary. However, as long as the mass of the disk is at least a few percent of the mass of the binary, the interaction can excite the eccentricity up to an equilibrium value, and induce a statistical correlation between mass ratio and eccentricity. We consider the applicability of our model to a variety of astrophysical scenarios: during star formation, in evolved stellar binaries, triples and in supermassive black hole binaries. We discuss the theoretical and observational implications of our predictions., Comment: 19 pages, 12 figures, 5 tables. Accepted in A&A. Comments welcome. Spindler available as a Python package on Zenodo: https://doi.org/10.5281/zenodo.10529060, and on github https://github.com/ruggero-valli/spindler. C-based version on https://github.com/ruggero-valli/spindler-c

The manipulation of microscopic objects requires precise and controllable forces and torques. Recent advances have led to the use of critical Casimir forces as a powerful tool, which can be finely tuned through the temperature of the environment and the chemical properties of the involved objects. For example, these forces have been used to self-organize ensembles of particles and to counteract stiction caused by Casimir-Liftshitz forces. However, until now, the potential of critical Casimir torques has been largely unexplored. Here, we demonstrate that critical Casimir torques can efficiently control the alignment of microscopic objects on nanopatterned substrates. We show experimentally and corroborate with theoretical calculations and Monte Carlo simulations that circular patterns on a substrate can stabilize the position and orientation of microscopic disks. By making the patterns elliptical, such microdisks can be subject to a torque which flips them upright while simultaneously allowing for more accurate control of the microdisk position. More complex patterns can selectively trap 2D-chiral particles and generate particle motion similar to non-equilibrium Brownian ratchets. These findings provide new opportunities for nanotechnological applications requiring precise positioning and orientation of microscopic objects.

The evolved stages of massive stars are poorly understood, but invaluable constraints can be derived from spatially resolved observations of nearby red supergiants, such as Betelgeuse. Atacama Large Millimeter/submillimeter Array (ALMA) observations of Betelgeuse showing a dipolar velocity field have been interpreted as evidence for a projected rotation rate of about $5\, \mathrm{km\, s^{-1}}$. This is 2 orders of magnitude larger than predicted by single-star evolution, which led to suggestions that Betelgeuse is a binary merger. We propose instead that large-scale convective motions can mimic rotation, especially if they are only partially resolved. We support this claim with 3D CO5BOLD simulations of nonrotating red supergiants that we postprocessed to predict ALMA images and SiO spectra. We show that our synthetic radial velocity maps have a 90% chance of being falsely interpreted as evidence for a projected rotation rate of $\mathrm{2\, km\, s^{-1}}$ or larger for our fiducial simulation. We conclude that we need at least another ALMA observation to firmly establish whether Betelgeuse is indeed rapidly rotating. Such observations would also provide insight into the role of angular momentum and binary interaction in the late evolutionary stages. The data will further probe the structure and complex physical processes in the atmospheres of red supergiants, which are immediate progenitors of supernovae and are believed to be essential in the formation of gravitational-wave sources., Comment: 14 pages, 10 figures, 2 tables. Published in ApJL. Comments welcome. Animations accessible to general readers and supplementary materials can be found at Zenodo: doi: 10.5281/zenodo.10199936 . Postprocessing packages and Jupiter notebook to reproduce the figures will be available in the same link soon

This document describes BinCodex, a common format for the output of binary population synthesis (BPS) codes agreed upon by the members of the LISA Synthetic UCB Catalogue Group. The goal of the format is to provide a common reference framework to describe the evolution of a single, isolated binary system or a population of isolated binaries.

The Toffoli gate is the essential ingredient for reversible computing, an energy efficient classical computational paradigm that evades the energy dissipation resulting from Landauer's principle. In this paper we analyze different setups to realize a magnetic implementation of the Toffoli gate using three interacting classical spins, each one embodying one of the three bits needed for the Toffoli gate. In our scheme, different control-spins configurations produce an effective field capable of conditionally flipping the target spin. We study what are the experimental requirements for the realization of our scheme, focusing on the degree of local control, the ability to dynamically switch the spin-spin interactions, and the required single-spin anisotropies to make the classical spin stable, showing that these are compatible with current technology.

Learning the inverse dynamics of robots directly from data, adopting a black-box approach, is interesting for several real-world scenarios where limited knowledge about the system is available. In this paper, we propose a black-box model based on Gaussian Process (GP) Regression for the identification of the inverse dynamics of robotic manipulators. The proposed model relies on a novel multidimensional kernel, called \textit{Lagrangian Inspired Polynomial} (\kernelInitials{}) kernel. The \kernelInitials{} kernel is based on two main ideas. First, instead of directly modeling the inverse dynamics components, we model as GPs the kinetic and potential energy of the system. The GP prior on the inverse dynamics components is derived from those on the energies by applying the properties of GPs under linear operators. Second, as regards the energy prior definition, we prove a polynomial structure of the kinetic and potential energy, and we derive a polynomial kernel that encodes this property. As a consequence, the proposed model allows also to estimate the kinetic and potential energy without requiring any label on these quantities. Results on simulation and on two real robotic manipulators, namely a 7 DOF Franka Emika Panda, and a 6 DOF MELFA RV4FL, show that the proposed model outperforms state-of-the-art black-box estimators based both on Gaussian Processes and Neural Networks in terms of accuracy, generality and data efficiency. The experiments on the MELFA robot also demonstrate that our approach achieves performance comparable to fine-tuned model-based estimators, despite requiring less prior information.

A composite likelihood is an inference function derived by multiplying a set of likelihood components. This approach provides a flexible framework for drawing inference when the likelihood function of a statistical model is computationally intractable. While composite likelihood has computational advantages, it can still be demanding when dealing with numerous likelihood components and a large sample size. This paper tackles this challenge by employing an approximation of the conventional composite likelihood estimator, which is derived from an optimization procedure relying on stochastic gradients. This novel estimator is shown to be asymptotically normally distributed around the true parameter. In particular, based on the relative divergent rate of the sample size and the number of iterations of the optimization, the variance of the limiting distribution is shown to compound for two sources of uncertainty: the sampling variability of the data and the optimization noise, with the latter depending on the sampling distribution used to construct the stochastic gradients. The advantages of the proposed framework are illustrated through simulation studies on two working examples: an Ising model for binary data and a gamma frailty model for count data. Finally, a real-data application is presented, showing its effectiveness in a large-scale mental health survey.