Boundary State in an Integrable Quantum Field Theory Out of Equilibrium

We study a quantum quench of the mass and the interaction in the Sinh-Gordon model starting from a large initial mass and zero initial coupling. Our focus is on the determination of the expansion of the initial state in terms of post-quench excitations. We argue that the large energy profile of the involved excitations can be relevant for the late time behaviour of the system and common regularization schemes are unreliable. We therefore proceed in determining the initial state by first principles expanding it in a systematic and controllable fashion on the basis of the asymptotic states. Our results show that, for the special limit of pre-quench parameters we consider, it assumes a squeezed state form that has been shown to evolve so as to exhibit the equilibrium behaviour predicted by the Generalized Gibbs Ensemble. © 2014 The Authors

Detecting performance degradation in cloud systems using LSTM autoencoders

Cloud computing technology is on the rise as it provides an easy to scale environment for Internet users in terms of computational resources. At the same time, cloud providers manage this demand for computational power by offering a pay per use model for virtualized resources. Yet, it is a challenging issue to administer the variety of different cloud applications and ensure high performance by identifying failures and errors on runtime. Distributed applications are error-prone, and creating a platform to support minimum hardware and software failures is a key challenge. In this work, we focus on anomaly detection of data storage systems, and we propose a solution for detecting performance degradation of cloud deployed systems in real time. We use Long Short-term Memory (LSTM) Autoencoders for learning the normal representations and reconstruct the input sequences. Then, we used the reconstructed errors of the LSTM Autoencoders on unseen time series data to detect abnormal behaviours. We used state-of-the-art benchmarks such as TPCx-IoT and YCSB to evaluate the performance of HBase and MongoDB systems. Our experimental analysis shows the ability of the proposed approach to detect abnormal behaviours in cloud systems

Autonomous inspection and repair of aircraft composite structures

This paper deals with the development of an innovative approach for inspection and repair of damage in aeronautical composites that took place in the first two years of the H2020 CompInnova project which. The aim is a newly designed robotic platform for autonomous inspection using combined infrared thermography (IRT) and phased array (PA) non-destructive investigation for damage detection and characterization, while integrated with laser repair capabilities. This will affect the increasing societal need for safer aircraft in the lowest possible cost, while new and effective techniques of inspection are needed because of the rapidly expanding use of composites in the aerospace industry

Real time anomaly detection of NoSQL systems based on resource usage monitoring

Today, with the emergence of the industry revolu- tion systems such as Industry 4.0, Internet of Things and big data frameworks pose new challenges in terms of storage and processing of real time data. As systems scale in humongous sizes, a crucial task is to administer the variety of different sub-systems and applications to ensure high performance. This is directly related with the identification and elimination of system failures and errors, while the system runs. In particular, database systems, may experience abnormalities related with decreased throughput or increased resource usage, that in turn affects system performance. In this work, we focus on NoSQL database systems, that are ideal for storing sensor data in the concept of Industry 4.0. This typically includes a variety of applications and workloads that are difficult to online monitor, thus making anomaly detection a challenging task. Creating a robust platform to serve such infrastructures with minimum hardware or software failures is a key challenge. In this work, we propose RADAR, an anomaly detection system that works on real-time. RADR is a data-driven decision-making system for NoSQL systems by providing process information extraction during resource monitoring and by associating resource usage with the top processes, to identify anomalous cases. In this work, we focus on anomalies such as hardware failures or software bugs that could lead to abnormal application runs, without necessarily stopping system functionality e.g. due to a system crash, but by affecting its performance e.g. decreased database system throughput. Although, different patterns may occur through time, we focus on periodic running workloads (e.g. monitoring daily usage) that are very common for NoSQL systems, and Internet of Things scenarios where data streams are forwarded to the Cloud for storage and processing. We apply various machine learning algorithms such as autoregressive integrated moving average (ARIMA), seasonal ARIMA and long-short-term memory recurrent neural networks. We experimentally analyse our solution to demonstrate the benefits of supporting online erroneous state identification and characterisation for modern applications

An architecture for the acceleration of a hybrid leaky integrate and fire SNN on the convey HC-2ex FPGA-based processor

Summarization: Neuromorphic computing is expanding by leaps and bounds through custom integrated circuits (digital and analog), and large scale platforms developed by industry or government-funded projects (e.g. TrueNorth and BrainScaleS, respectively). Whereas the trend is for massive parallelism and neuromorphic computation in order to solve problems, such as those that may appear in machine learning and deep learning algorithms, there is substantial work on brain-like highly accurate neuromorphic computing in order to model the human brain. In such a form of computing, spiking neural networks (SNN) such as the Hodgkin and Huxley model are mapped to various technologies, including FPGAs. In this work, we present a highly efficient FPGA-based architecture for the detailed hybrid Leaky Integrate and Fire SNN that can simulate generic characteristics of neurons of the cerebral cortex. This architecture supports arbitrary, sparse O(n2) interconnection of neurons without need to re-compile the design, and plasticity rules, yielding on a four-FPGA Convey 2ex hybrid computer a speedup of 923x for a non-trivial data set on 240 neurons vs. the same model in the software simulator BRAIN on a Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz, i.e. the reference state-of-the-art software. Although the reference, official software is single core, the speedup demonstrates that the application scales well among multiple FPGAs, whereas this would not be the case in general-purpose computers due to the arbitrary interconnect requirements. The FPGA-based approach leads to highly detailed models of parts of the human brain up to a few hundred neurons vs. a dozen or fewer neurons on the reference system.Presented on