50 research outputs found
TRADES: A new software to derive orbital parameters from observed transit times and radial velocities. Revisiting Kepler-11 and Kepler-9
Aims. With the purpose of determining the orbital parameters of exoplanetary
systems from observational data, we have developed a software, named TRADES
(TRAnsits and Dynamics of Exoplanetary Systems), to simultaneously fit observed
radial velocities and transit times data. Methods. We implemented a dynamical
simulator for N-body systems, which also fits the available data during the
orbital integration and determines the best combination of the orbital
parameters using grid search, minimization, genetic algorithms,
particle swarm optimization, and bootstrap analysis. Results. To validate
TRADES, we tested the code on a synthetic three-body system and on two real
systems discovered by the Kepler mission: Kepler-9 and Kepler-11. These systems
are good benchmarks to test multiple exoplanet systems showing transit time
variations (TTVs) due to the gravitational interaction among planets. We have
found that orbital parameters of Kepler-11 planets agree well with the values
proposed in the discovery paper and with a a recent work from the same authors.
We analyzed the first three quarters of Kepler-9 system and found parameters in
partial agreement with discovery paper. Analyzing transit times (T0s) covering
12 quarters of Kepler data, that we have found a new best-fit solution. This
solution outputs masses that are about 55% of the values proposed in the
discovery paper; this leads to a reduced semi-amplitude of the radial
velocities of about 12.80 m/s.Comment: 14 pages, 13 figures, 6 tables; accepted for publication in Astronomy
& Astrophysics, and corrected by the Language Edito
Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigation
Safety is essential for deploying Deep Reinforcement Learning (DRL)
algorithms in real-world scenarios. Recently, verification approaches have been
proposed to allow quantifying the number of violations of a DRL policy over
input-output relationships, called properties. However, such properties are
hard-coded and require task-level knowledge, making their application
intractable in challenging safety-critical tasks. To this end, we introduce the
Collection and Refinement of Online Properties (CROP) framework to design
properties at training time. CROP employs a cost signal to identify unsafe
interactions and use them to shape safety properties. Hence, we propose a
refinement strategy to combine properties that model similar unsafe
interactions. Our evaluation compares the benefits of computing the number of
violations using standard hard-coded properties and the ones generated with
CROP. We evaluate our approach in several robotic mapless navigation tasks and
demonstrate that the violation metric computed with CROP allows higher returns
and lower violations over previous Safe DRL approaches.Comment: Accepted at the 2023 IEEE International Conference on Robotics and
Automation (ICRA). Marzari and Marchesini contributed equall
Prediction of a novel type-I antiferromagnetic Weyl semimetal
Topological materials have been a main focus of studies in the past decade
due to their protected properties that can be exploited for the fabrication of
new devices. Among them, Weyl semimetals are a class of topological semimetals
with non-trivial linear band crossing close to the Fermi level. The existence
of such crossings requires the breaking of either time-reversal or inversion
symmetry and is responsible for the exotic physical properties.
In this work we identify the full-Heusler compound InMnTi, as a
promising, easy to synthesize, - and -breaking Weyl semimetal. This
material exhibits several features that are comparatively more intriguing with
respect to other known Weyl semimetals: the distance between two neighboring
nodes is large enough to observe a wide range of linear dispersions in the
bands, and only one kind of such node's pairs is present in the Brillouin zone.
We also show the presence of Fermi arcs stable across a wide range of chemical
potentials. Finally, the lack of contributions from trivial points to the
low-energy properties makes the materials a promising candidate for practical
devices
The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks
Deep Neural Networks are increasingly adopted in critical tasks that require
a high level of safety, e.g., autonomous driving. While state-of-the-art
verifiers can be employed to check whether a DNN is unsafe w.r.t. some given
property (i.e., whether there is at least one unsafe input configuration),
their yes/no output is not informative enough for other purposes, such as
shielding, model selection, or training improvements. In this paper, we
introduce the #DNN-Verification problem, which involves counting the number of
input configurations of a DNN that result in a violation of a particular safety
property. We analyze the complexity of this problem and propose a novel
approach that returns the exact count of violations. Due to the #P-completeness
of the problem, we also propose a randomized, approximate method that provides
a provable probabilistic bound of the correct count while significantly
reducing computational requirements. We present experimental results on a set
of safety-critical benchmarks that demonstrate the effectiveness of our
approximate method and evaluate the tightness of the bound.Comment: Accepted in the International Joint Conference on Artificial
Intelligence (IJCAI), 2023. [Marzari and Corsi contributed equally
Pulay forces in density-functional theory with extended Hubbard functionals: From nonorthogonalized to orthogonalized manifolds
We present a derivation of the exact expression for Pulay forces in
density-functional theory calculations augmented with extended Hubbard
functionals, and arising from the use of orthogonalized atomic orbitals as
projectors for the Hubbard manifold. The derivative of the inverse square root
of the orbital overlap matrix is obtained as a closed-form solution of the
associated Lyapunov (Sylvester) equation. The expression for the resulting
contribution to the forces is presented in the framework of ultrasoft
pseudopotentials and the projector-augmented-wave method, and using a plane
wave basis set. We have benchmarked the present implementation with respect to
finite differences of total energies for the case of NiO, finding excellent
agreement. Owing to the accuracy of Hubbard-corrected density-functional theory
calculations - provided the Hubbard parameters are computed for the manifold
under consideration - the present work paves the way for systematic studies of
solid-state and molecular transition-metal and rare-earth compounds.Comment: 16 pages, 1 figur
Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks
Deep Reinforcement Learning (DRL) is emerging as a promising approach to
generate adaptive behaviors for robotic platforms. However, a major drawback of
using DRL is the data-hungry training regime that requires millions of trial
and error attempts, which is impractical when running experiments on robotic
systems. Learning from Demonstrations (LfD) has been introduced to solve this
issue by cloning the behavior of expert demonstrations. However, LfD requires a
large number of demonstrations that are difficult to be acquired since
dedicated complex setups are required. To overcome these limitations, we
propose a multi-subtask reinforcement learning methodology where complex pick
and place tasks can be decomposed into low-level subtasks. These subtasks are
parametrized as expert networks and learned via DRL methods. Trained subtasks
are then combined by a high-level choreographer to accomplish the intended pick
and place task considering different initial configurations. As a testbed, we
use a pick and place robotic simulator to demonstrate our methodology and show
that our method outperforms a benchmark methodology based on LfD in terms of
sample-efficiency. We transfer the learned policy to the real robotic system
and demonstrate robust grasping using various geometric-shaped objects.Comment: This work has been accepted to the IEEE International Conference on
Advanced Robotics (ICAR) 202
Constrained Reinforcement Learning and Formal Verification for Safe Colonoscopy Navigation
The field of robotic Flexible Endoscopes (FEs) has progressed significantly,
offering a promising solution to reduce patient discomfort. However, the
limited autonomy of most robotic FEs results in non-intuitive and challenging
manoeuvres, constraining their application in clinical settings. While previous
studies have employed lumen tracking for autonomous navigation, they fail to
adapt to the presence of obstructions and sharp turns when the endoscope faces
the colon wall. In this work, we propose a Deep Reinforcement Learning
(DRL)-based navigation strategy that eliminates the need for lumen tracking.
However, the use of DRL methods poses safety risks as they do not account for
potential hazards associated with the actions taken. To ensure safety, we
exploit a Constrained Reinforcement Learning (CRL) method to restrict the
policy in a predefined safety regime. Moreover, we present a model selection
strategy that utilises Formal Verification (FV) to choose a policy that is
entirely safe before deployment. We validate our approach in a virtual
colonoscopy environment and report that out of the 300 trained policies, we
could identify three policies that are entirely safe. Our work demonstrates
that CRL, combined with model selection through FV, can improve the robustness
and safety of robotic behaviour in surgical applications.Comment: Accepted in the IEEE International Conference on Intelligent Robots
and Systems (IROS), 2023. [Corsi, Marzari and Pore contributed equally
Safe and Efficient Reinforcement Learning for Environmental Monitoring
This paper discusses the challenges of applying reinforcement techniques to real-world environmental monitoring problems and proposes innovative solutions to overcome them. In particular, we focus on safety, a fundamental problem in RL that arises when it is applied to domains involving humans or hazardous uncertain situations. We propose to use deep neural networks, formal verification, and online refinement of domain knowledge to improve the transparency and efficiency of the learning process, as well as the quality of the final policies. We present two case studies, specifically (i) autonomous water monitoring and (ii) smart control of air quality indoors. In particular, we discuss the challenges and solutions to these problems, addressing crucial issues such as anomaly detection and prevention, real-time control, and online learning. We believe that the proposed techniques can be used to overcome some limitations of RL, providing safe and efficient solutions to complex and urgent problems
Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients
Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk