Large Scale Data Streaming

Over the last few years, applications that require real-time processing of an huge amount of data are pushing the limits of traditional data processing infrastructure. Many applications in several domains such as telecommunications, large scale sensor networks, financial, online applications, computer network management, security and others, require real-time processing of continuos data flows: this kind of computation systems are usually called Data Stream Management Systems (DSMSs) or Stream Processing Engines (SPEs). Traditional Data Base Management Systems (DBMSs) implements the store than process paradigm; it means that: data require to be stored (persistently) and indexed before they could be processed and data processing is asynchronous in relation to their arrival. In DSMSs data streams are not stored but are rather processed on-the-fly using continuos queries: the query is constantly standing over the streaming data and results are continuously output. One of the most famous and used DSMS is called Storm. Storm is a powerful tool and has a simple programming model, but it does not provide a bulit-in implementation of stream-oriented operators: this is a strong limitation because the user is forced to write a case-specic implementation every time. The goal of the work described in this thesis is to build a distributed real-time computation system on top of Storm, called Enhanced Storm, that provides to the user built-in relation algebra and database-specic operators for streaming computation. Enhanced Storm maintains Storm fault-tolerance and scalability: in this way we supply to the user a generic, high performing and easy-to-use system. Enhanced Storm was developed at the Distributed System Laboratory(LSD) of the Universidad Politecnica de Madrid(UPM)[UPM]

R. F\"urth's 1933 paper "On certain relations between classical Statistics and Quantum Mechanics" ["\"Uber einige Beziehungen zwischen klassischer Statistik und Quantenmechanik", \textit{Zeitschrift f\"ur Physik,} \textbf{81} 143-162]

We present a translation of the 1933 paper by R. F\"urth in which a profound analogy between quantum fluctuations and Brownian motion is pointed out. This paper opened in some sense the way to the stochastic methods of quantization developed almost 30 years later by Edward Nelson and others.Comment: 23 pages, 4 figure

Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks

We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. Finally, we demonstrate the effectiveness of our framework on a full-size humanoid robot WALK-MAN

XBot: A Cross-Robot Software Framework for Real-Time Control

The widespread use of robotics in new application domains outside the industrial workplace settings requires robotic systems which demonstrate functionalities far beyond that of classical industrial robotic machines. The implementation of these capabilities inevitably increases the complexity of the robotic hardware, control a and software components. This chapter introduces the XBot software architecture for robotics, which is capable of Real-Time (RT) performance with minimum jitter at relatively high control frequency while demonstrating enhanced flexibility and abstraction features making it suitable for the control of robotic systems of diverse hardware embodiment and complexity. A key feature of the XBot is its cross-robot compatibility, which makes possible the use of the framework on different robots, without code modifications, based only on a set of configuration files. The design of the framework ensures easy interoperability and built-in integration with other existing software tools for robotics, such as ROS, YARP or OROCOS, thanks to a robot agnostic API called XBotInterface. The framework has been successfully used and validated as a software infrastructure for collaborative robotic arms as KUKA lbr iiwa/lwr 4+ and Franka Emika Panda, other than humanoid robots such as WALK-MAN and COMAN+, and quadruped centaur-like robots as CENTAURO

Higher Frequencies of Lymphocytes Expressing the Natural Killer Group 2D Receptor in Patients With Behçet Disease

Behçet disease (BD) is an inflammatory systemic disease with a fluctuating course, which can affect the skin, eyes, central nervous system, musculoskeletal, gastrointestinal, and vascular systems. No laboratory tests are currently available for the diagnosis of BD and monitoring disease activity. Moreover there is a lack of knowledge on BD pathogenesis. This study focused on circulating Natural Killer (NK), NKT and T cells evaluated as CD3neg CD56pos, CD3pos CD56pos, and CD3pos CD56neg. Peripheral blood mononuclear cells (PBMCs) were collected from 38 BD patients and 20 healthy controls (HC). The frequencies of NK, NKT, and T cells expressing CD16, CD69, NKG2D, Nkp30, Nkp46, and NKG2A were assessed by flow cytometry. Cytotoxic potential of NK cells was evaluated by flow cytometry as the percentage of cells expressing the degranulation marker CD107a after incubation with K562 cells. The levels of 27 cytokines were determined in plasma with a multiplex bead-based assay. Higher percentages of NK, NKT, and T cells expressing NKG2D were detected in PBMCs of BD patients than HC. ROC curve analysis showed that the evaluation of NKG2Dpos NK, NKT, and T cell percentages discriminated between BD patients and HC. Moreover, there was a positive correlation between the BD Current Activity Form (BDCAF) scores and the frequencies of NKG2Dpos NK and NKT cells. A higher frequency of NK cells expressing CD107a was induced in PBMCs from BD patients than HC after incubation with K562 cells. Concentrations of IL-5, IL-6, IL-10, IL-13, IP-10, and MIP-1β were higher in plasma of BD patients than HC. Monitoring the frequencies of NKG2Dpos lymphocytes could help the clinicians in BD patients management. In addition, the increased expression of NKG2D in BD patients is likely involved in disease pathogenesis

Flares in Biopsy-Proven Giant Cell Arteritis in Northern Italy: Characteristics and Predictors in a Long-Term Follow-Up Study

This study evaluated the frequency, timing, and characteristics of flares in a large cohort of Italian patients with biopsy-proven giant cell arteritis (GCA) and to identify factors at diagnosis able to predict the occurrence of flares. We evaluated 157 patients with biopsy-proven transmural GCA diagnosed and followed at the Rheumatology Unit of Reggio Emilia Hospital (Italy) for whom sufficient information was available from the time of diagnosis until at least 4 years of follow-up. Fifty-seven patients (36.5%) experienced ≥1 flares. Fifty-one (46.4%) of the 110 total flares (88 relapses and 22 recurrences) were experienced during the first 2 years after diagnosis. The majority of relapses occurred with doses of prednisone ≤ 10 mg/day (82.9%), whereas only 3.4% of relapses occurred for doses ≥ 25 mg/day. Polymyalgia rheumatica (46.5%) and cranial symptoms (41.9%) were the most frequent manifestations at the time of the first relapse. Cumulative prednisone dose during the first year and total cumulative prednisone dose were significantly higher in flaring patients compared with those without flares (7.8 ± 2.4 vs 6.7 ± 2.4 g, P = 0.02; 15.5 ± 8.9 vs 10.0 ± 9.2 g, P = 0.0001, respectively). The total duration of prednisone treatment was longer in flaring patients (58 ± 44 vs 30 ± 30 months, P = 0.0001).Patients with disease flares had at diagnosis more frequently systemic manifestations (P = 0.02) and fever ≥ 38°C (P = 0.02), significantly lower hemoglobin levels (P = 0.05), more frequent presence at temporal artery biopsy (TAB) specimens of giant cells (P = 0.04) and intraluminal acute thrombosis (P = 0.007), and more moderate/severe arterial inflammation (P = 0.009) compared with those without flares. In the multivariate model fever ≥ 38 °C (hazard ratio 2.14; 95% confidence interval, 1.06-4.32, P = 0.03) and the severity of inflammatory infiltrate (moderate/severe versus mild) (hazard ratio 5.41; 95% confidence interval, 1.64-17.87, P = 0.006) were significantly associated with an increased risk of flares. In conclusion, a flaring course is common in GCA and it is associated with prolonged GC requirements. Fever at diagnosis and severity of inflammation at TAB appear to predict the development of disease flares

Corrigendum: The Walk-Man Robot Software Architecture

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Increased expression of interleukin-22 in patients with giant cell arteritis

GCA is characterized by arterial remodelling driven by inflammation. IL-22 is an attractive cytokine which acts at the crosstalk between immune and stromal cells. We hypothesized that IL-22 might be induced in GCA and might be involved in disease pathogenesis

XBotCore: A Real-Time Cross-Robot Software Platform

Muratore L, Laurenzi A, Hoffman EM, Rocchi A, Caldwell DG, Tsagarakis NG. XBotCore: A Real-Time Cross-Robot Software Platform. In: IEEE International Conference on Robotic Computing, IRC17. 2017

Heat release by controlled continuous-time Markov jump processes

We derive the equations governing the protocols minimizing the heat released by a continuous-time Markov jump process on a one-dimensional countable state space during a transition between assigned initial and final probability distributions in a finite time horizon. In particular, we identify the hypotheses on the transition rates under which the optimal control strategy and the probability distribution of the Markov jump problem obey a system of differential equations of Hamilton-Bellman-Jacobi-type. As the state-space mesh tends to zero, these equations converge to those satisfied by the diffusion process minimizing the heat released in the Langevin formulation of the same problem. We also show that in full analogy with the continuum case, heat minimization is equivalent to entropy production minimization. Thus, our results may be interpreted as a refined version of the second law of thermodynamics.Comment: final version, section 2.1 revised, 26 pages, 3 figure