Operator Precedence Languages: Their Automata-Theoretic and Logic Characterization

Operator precedence languages were introduced half a century ago by Robert Floyd to support deterministic and efficient parsing of context-free languages. Recently, we renewed our interest in this class of languages thanks to a few distinguishing properties that make them attractive for exploiting various modern technologies. Precisely, their local parsability enables parallel and incremental parsing, whereas their closure properties make them amenable to automatic verification techniques, including model checking. In this paper we provide a fairly complete theory of this class of languages: we introduce a class of automata with the same recognizing power as the generative power of their grammars; we provide a characterization of their sentences in terms of monadic second-order logic as has been done in previous literature for more restricted language classes such as regular, parenthesis, and input-driven ones; we investigate preserved and lost properties when extending the language sentences from finite length to infinite length ($omega$-languages). As a result, we obtain a class of languages that enjoys many of the nice properties of regular languages (closure and decidability properties, logic characterization) but is considerably larger than other families---typically parenthesis and input-driven ones---with the same properties, covering “almost” all deterministic languages

Multi-damage detection in composite space structures via deep learning

The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands of modern payloads and scientific instruments. Due to their large surface, these components are more susceptible to impacts from orbital debris compared to other satellite locations. However, the detection of debris-induced damages still proves challenging in large structures due to minimal alterations in the spacecraft global dynamics and calls for advanced structural health monitoring solutions. To address this issue, a data-driven methodology using Long Short-Term Memory (LSTM) networks is applied here to the case of damaged solar arrays. Finite element models of the solar panels are used to reproduce damage locations, which are selected based on the most critical risk areas in the structures. The modal parameters of the healthy and damaged arrays are extracted to build the governing equations of the flexible spacecraft. Standard attitude manoeuvres are simulated to generate two datasets, one including local accelerations and the other consisting of piezoelectric voltages, both measured in specific locations of the structure. The LSTM architecture is then trained by associating each sensed time series with the corresponding damage label. The performance of the deep learning approach is assessed, and a comparison is presented between the accuracy of the two distinct sets of sensors: accelerometers and piezoelectric patches. In both cases, the framework proved effective in promptly identifying the location of damaged elements within limited measured time samples

Parallel parsing made practical

The property of local parsability allows to parse inputs through inspecting only a bounded-length string around the current token. This in turn enables the construction of a scalable, data-parallel parsing algorithm, which is presented in this work. Such an algorithm is easily amenable to be automatically generated via a parser generator tool, which was realized, and is also presented in the following. Furthermore, to complete the framework of a parallel input analysis, a parallel scanner can also combined with the parser. To prove the practicality of a parallel lexing and parsing approach, we report the results of the adaptation of JSON and Lua to a form fit for parallel parsing (i.e. an operator-precedence grammar) through simple grammar changes and scanning transformations. The approach is validated with performance figures from both high performance and embedded multicore platforms, obtained analyzing real-world inputs as a test-bench. The results show that our approach matches or dominates the performances of production-grade LR parsers in sequential execution, and achieves significant speedups and good scaling on multi-core machines. The work is concluded by a broad and critical survey of the past work on parallel parsing and future directions on the integration with semantic analysis and incremental parsing

A structural study of hcp and liquid iron under shock compression up to 275 GPa

We combine nanosecond laser shock compression with \emph{in-situ} picosecond X-ray diffraction to provide structural data on iron up to 275 GPa. We constrain the extent of hcp-liquid coexistence, the onset of total melt, and the structure within the liquid phase. Our results indicate that iron, under shock compression, melts completely by 258(8) GPa. A coordination number analysis indicates that iron is a simple liquid at these pressure-temperature conditions. We also perform texture analysis between the ambient body-centered-cubic (bcc) $\alpha$, and the hexagonal-closed-packed (hcp) high-pressure $\epsilon-$phase. We rule out the Rong-Dunlop orientation relationship (OR) between the $\alpha$ and $\epsilon-$phases. However, we cannot distinguish between three other closely related ORs: Burger's, Mao-Bassett-Takahashi, and Potter's OR. The solid-liquid coexistence region is constrained from a melt onset pressure of 225(3) GPa from previously published sound speed measurements and full melt (246.5(1.8)-258(8) GPa) from X-ray diffraction measurements, with an associated maximum latent heat of melting of 623 J/g. This value is lower than recently reported theoretical estimates and suggests that the contribution to the earth's geodynamo energy budget from heat release due to freezing of the inner core is smaller than previously thought. Melt pressures for these nanosecond shock experiments are consistent with gas gun shock experiments that last for microseconds, indicating that the melt transition occurs rapidly

A deep learning strategy for on-orbit servicing via space robotic manipulator

Autonomous robotic systems are currently being addressed as a critical element in the development of present and future on-orbit operations. Modern missions are calling for systems capable of reproducing human’s decision-making process thus enhancing their performance. Generally, space manipulators are mounted on a floating spacecraft in a microgravity environment, consequently leading to a mutual influence between the robotic arms and the platform dynamics, thus making the motion planning and control design more challenging than those of terrestrial robots. Another aspect to be considered is that space robots are designed as lightweight systems, resulting in a significant dynamic coupling between their rigid motion and structural elasticity. These effects involve critical issues in modelling their dynamics and designing a suitable controller. In this context, Deep Neural Network (DNN) architectures and the related Deep Learning (DL) techniques have widely proved to have powerful capability in solving data-driven nonlinear modelling problems and they can hence represent a viable solution for space activities. The present paper deals with the design of a DNN controller for a space manipulator system, which has to follow a specific path for a typical on-orbit servicing mission. The goal is to provide proper control inputs autonomously adapting to the given desired trajectory. Structural flexibility and joint friction features are implemented in the dynamic model as well

A deep learning strategy for on‑orbit servicing via space robotic manipulator

Autonomous robotic systems are currently being addressed as a critical element in the development of present and future on-orbit operations. Modern missions are calling for systems capable of reproducing human’s decision-making process, thus enhancing their performance. Generally, space manipulators are mounted on a floating spacecraft in a microgravity environment, consequently leading to a mutual influence between the robotic arms and the platform dynamics, thus making the motion planning and control design more challenging than those of terrestrial robots. Another aspect to be considered is that space robots are designed as lightweight systems, resulting in a significant dynamic coupling between their rigid motion and structural elasticity. These effects involve critical issues in modelling their dynamics and designing a suitable controller. In this context, Deep Neural Network (DNN) architectures and the related Deep Learning (DL) techniques have widely proved to have powerful capability in solving data-driven nonlinear modelling problems and they can hence represent a viable solution for space activities. The present paper deals with the design of a DNN controller for a space manipulator system, which has to follow a specific path for a typical on-orbit servicing mission. The goal is to provide proper control inputs autonomously adapting to the given desired trajectory. Structural flexibility and joint friction features are implemented in the dynamic model as well

Detection of Autism Spectrum Disorder by a Fast Deep Neural Network

Autism spectrum disorder is a psychiatric illness that refers to a wide range of conditions caused by a biologically determined developmental disorder with onset of symptoms within the first three years of life. Autism can be diagnosed at any stage of life with problems beginning in childhood and continuing into adolescence and adulthood. Given the immense attraction gained by deep learning as one of the most successful paradigms for a plethora of real-world medical applications, in this paper we explore the possibility of using fast deep learning models for the detection of autism in children. To this end, random deep neural networks are one of the most important alternatives, in particular because they strike a good balance in the trade-off between accuracy and efficiency. We propose a deep neural architecture that employs the randomization of some parameters in a complex structure for the detection of autism spectrum disorder. The proposed approach is validated by using a threedimensional dataset consisting of body joint positions taken from videos of both suffering and sane children. To evaluate the classification performance of the proposed network, the latter is compared with a fully trainable, non-randomized version of the same model and with stateof-the-art binary classifiers applied to the same data. Numerical results show that the proposed method outperforms reference benchmarks in terms of accuracy and speed, demonstrating the inherent capabilities of the implemented system that makes use of such specific features

A Study on Structural Health Monitoring of a Large Space Antenna via Distributed Sensors and Deep Learning

Most modern Earth and Universe observation spacecraft are now equipped with large lightweight and flexible structures, such as antennas, telescopes, and extendable elements. The trend of hosting more complex and bigger appendages, essential for high-precision scientific applications, made orbiting satellites more susceptible to performance loss or degradation due to structural damages. In this scenario, Structural Health Monitoring strategies can be used to evaluate the health status of satellite substructures. However, in particular when analysing large appendages, traditional approaches may not be sufficient to identify local damages, as they will generally induce less observable changes in the system dynamics yet cause a relevant loss of payload data and information. This paper proposes a deep neural network to detect failures and investigate sensor sensitivity to damage classification for an orbiting satellite hosting a distributed network of accelerometers on a large mesh reflector antenna. The sensors-acquired time series are generated by using a fully coupled 3D simulator of the in-orbit attitude behaviour of a flexible satellite, whose appendages are modelled by using finite element techniques. The machine learning architecture is then trained and tested by using the sensors’ responses gathered in a composite scenario, including not only the complete failure of a structural element (structural break) but also an intermediate level of structural damage. The proposed deep learning framework and sensors configuration proved to accurately detect failures in the most critical area or the structure while opening new investigation possibilities regarding geometrical properties and sensor distribution