WCET Analysis of a Parallel 3D Multigrid Solver Executed on the MERASA Multi-Core

To meet performance requirements as well as constraints on cost and power consumption, future embedded systems will be designed with multi-core processors. However, the question of timing analysability is raised with these architectures. In the MERASA project, a WCET-aware multi-core processor has been designed with the appropriate system software. They both guarantee that the WCET of tasks running on different cores can be safely analyzed since their possible interactions can be bounded. Nevertheless, computing the WCET of a parallel application is still not straightforward and a high-level preliminary analysis of the communication and synchronization patterns must be performed. In this paper, we report on our experience in evaluating the WCET of a parallel 3D multigrid solver code and we propose lines for further research on this topic

parMERASA Multi-Core Execution of Parallelised Hard Real-Time Applications Supporting Analysability

International audienceEngineers who design hard real-time embedded systems express a need for several times the performance available today while keeping safety as major criterion. A breakthrough in performance is expected by parallelizing hard real-time applications and running them on an embedded multi-core processor, which enables combining the requirements for high-performance with timing-predictable execution. parMERASA will provide a timing analyzable system of parallel hard real-time applications running on a scalable multicore processor. parMERASA goes one step beyond mixed criticality demands: It targets future complex control algorithms by parallelizing hard real-time programs to run on predictable multi-/many-core processors. We aim to achieve a breakthrough in techniques for parallelization of industrial hard real-time programs, provide hard real-time support in system software, WCET analysis and verification tools for multi-cores, and techniques for predictable multi-core designs with up to 64 cores

Structural Basis for Type VI Secretion Effector Recognition by a Cognate Immunity Protein

The type VI secretion system (T6SS) has emerged as an important mediator of interbacterial interactions. A T6SS from Pseudomonas aeruginosa targets at least three effector proteins, type VI secretion exported 1–3 (Tse1–3), to recipient Gram-negative cells. The Tse2 protein is a cytoplasmic effector that acts as a potent inhibitor of target cell proliferation, thus providing a pronounced fitness advantage for P. aeruginosa donor cells. P. aeruginosa utilizes a dedicated immunity protein, type VI secretion immunity 2 (Tsi2), to protect against endogenous and intercellularly-transferred Tse2. Here we show that Tse2 delivered by the T6SS efficiently induces quiescence, not death, within recipient cells. We demonstrate that despite direct interaction of Tsi2 and Tse2 in the cytoplasm, Tsi2 is dispensable for targeting the toxin to the secretory apparatus. To gain insights into the molecular basis of Tse2 immunity, we solved the 1.00 Å X-ray crystal structure of Tsi2. The structure shows that Tsi2 assembles as a dimer that does not resemble previously characterized immunity or antitoxin proteins. A genetic screen for Tsi2 mutants deficient in Tse2 interaction revealed an acidic patch distal to the Tsi2 homodimer interface that mediates toxin interaction and immunity. Consistent with this finding, we observed that destabilization of the Tsi2 dimer does not impact Tse2 interaction. The molecular insights into Tsi2 structure and function garnered from this study shed light on the mechanisms of T6 effector secretion, and indicate that the Tse2–Tsi2 effector–immunity pair has features distinguishing it from previously characterized toxin–immunity and toxin–antitoxin systems

Cell-Surface Marker Signatures for the Isolation of Neural Stem Cells, Glia and Neurons Derived from Human Pluripotent Stem Cells

Neural induction of human pluripotent stem cells often yields heterogeneous cell populations that can hamper quantitative and comparative analyses. There is a need for improved differentiation and enrichment procedures that generate highly pure populations of neural stem cells (NSC), glia and neurons. One way to address this problem is to identify cell-surface signatures that enable the isolation of these cell types from heterogeneous cell populations by fluorescence activated cell sorting (FACS).We performed an unbiased FACS- and image-based immunophenotyping analysis using 190 antibodies to cell surface markers on naïve human embryonic stem cells (hESC) and cell derivatives from neural differentiation cultures. From this analysis we identified prospective cell surface signatures for the isolation of NSC, glia and neurons. We isolated a population of NSC that was CD184(+)/CD271(-)/CD44(-)/CD24(+) from neural induction cultures of hESC and human induced pluripotent stem cells (hiPSC). Sorted NSC could be propagated for many passages and could differentiate to mixed cultures of neurons and glia in vitro and in vivo. A population of neurons that was CD184(-)/CD44(-)/CD15(LOW)/CD24(+) and a population of glia that was CD184(+)/CD44(+) were subsequently purified from cultures of differentiating NSC. Purified neurons were viable, expressed mature and subtype-specific neuronal markers, and could fire action potentials. Purified glia were mitotic and could mature to GFAP-expressing astrocytes in vitro and in vivo.These findings illustrate the utility of immunophenotyping screens for the identification of cell surface signatures of neural cells derived from human pluripotent stem cells. These signatures can be used for isolating highly pure populations of viable NSC, glia and neurons by FACS. The methods described here will enable downstream studies that require consistent and defined neural cell populations

Constraints on dark matter to dark radiation conversion in the late universe with DES-Y1 and external data

84siWe study a class of decaying dark matter models as a possible resolution to the observed discrepancies between early- and late-time probes of the universe. This class of models, dubbed DDM, characterizes the evolution of comoving dark matter density with two extra parameters. We investigate how DDM affects key cosmological observables such as the CMB temperature and matter power spectra. Combining 3x2pt data from Year 1 of the Dark Energy Survey,Planck-2018 CMB temperature and polarization data, Supernova (SN) Type Ia data from Pantheon, and BAO data from BOSS DR12, MGS and 6dFGS, we place new constraints on the amount of dark matter that has decayed and the rate with which it converts to dark radiation. The fraction of the decayed dark matter in units of the current amount of dark matter, $zeta$, is constrained at 68% confidence level to be <0.32 for DES-Y1 3x2pt data, <0.030 for CMB+SN+BAO data, and <0.037 for the combined dataset. The probability that the DES and CMB+SN+BAO datasets are concordant increases from 4% for the $Lambda$CDM model to 8% (less tension) for DDM. Moreover, tension in $S_8=sigma_8sqrt{Omega_m/0.3}$ between DES-Y1 3x2pt and CMB+SN+BAO is reduced from 2.3$sigma$ to 1.9$sigma$. We find no reduction in the Hubble tension when the combined data is compared to distance-ladder measurements in the DDM model. The maximum-posterior goodness-of-fit statistics of DDM and $Lambda$CDM are comparable, indicating no preference for the DDM cosmology over $Lambda$CDM....partially_openopenChen, Angela; Huterer, Dragan; Lee, Sujeong; Ferté, Agnès; Weaverdyck, Noah; Alonso Alves, Otavio; Leonard, C. Danielle; MacCrann, Niall; Raveri, Marco; Porredon, Anna; Di Valentino, Eleonora; Muir, Jessica; Lemos, Pablo; Liddle, Andrew; Blazek, Jonathan; Campos, Andresa; Cawthon, Ross; Choi, Ami; Dodelson, Scott; Elvin-Poole, Jack; Gruen, Daniel; Ross, Ashley; Secco, Lucas F.; Sevilla, Ignacio; Sheldon, Erin; Troxel, Michael A.; Zuntz, Joe; Abbott, Tim; Aguena, Michel; Allam, Sahar; Annis, James; Avila, Santiago; Bertin, Emmanuel; Bhargava, Sunayana; Bridle, Sarah; Brooks, David; Carnero Rosell, Aurelio; Carrasco Kind, Matias; Carretero, Jorge; Costanzi, Matteo; Crocce, Martin; da Costa, Luiz; Elidaiana da Silva Pereira, Maria; Davis, Tamara; Doel, Peter; Eifler, Tim; Ferrero, Ismael; Fosalba, Pablo; Frieman, Josh; Garcia-Bellido, Juan; Gaztanaga, Enrique; Gerdes, David; Gruendl, Robert; Gschwend, Julia; Gutierrez, Gaston; Hinton, Samuel; Hollowood, Devon L.; Honscheid, Klaus; Hoyle, Ben; James, David; Jarvis, Mike; Kuehn, Kyler; Lahav, Ofer; Maia, Marcio; Marshall, Jennifer; Menanteau, Felipe; Miquel, Ramon; Morgan, Robert; Palmese, Antonella; Paz-Chinchon, Francisco; Plazas Malagón, Andrés; Roodman, Aaron; Sanchez, Eusebio; Scarpine, Vic; Schubnell, Michael; Serrano, Santiago; Smith, Mathew; Suchyta, Eric; Tarle, Gregory; Thomas, Daniel; To, Chun-Hao; Varga, Tamas Norbert; Weller, Jochen; Wilkinson, ReeseChen, Angela; Huterer, Dragan; Lee, Sujeong; Ferté, Agnès; Weaverdyck, Noah; Alonso Alves, Otavio; Leonard, C. Danielle; Maccrann, Niall; Raveri, Marco; Porredon, Anna; Di Valentino, Eleonora; Muir, Jessica; Lemos, Pablo; Liddle, Andrew; Blazek, Jonathan; Campos, Andresa; Cawthon, Ross; Choi, Ami; Dodelson, Scott; Elvin-Poole, Jack; Gruen, Daniel; Ross, Ashley; Secco, Lucas F.; Sevilla, Ignacio; Sheldon, Erin; Troxel, Michael A.; Zuntz, Joe; Abbott, Tim; Aguena, Michel; Allam, Sahar; Annis, James; Avila, Santiago; Bertin, Emmanuel; Bhargava, Sunayana; Bridle, Sarah; Brooks, David; Carnero Rosell, Aurelio; Carrasco Kind, Matias; Carretero, Jorge; Costanzi, Matteo; Crocce, Martin; da Costa, Luiz; Elidaiana da Silva Pereira, Maria; Davis, Tamara; Doel, Peter; Eifler, Tim; Ferrero, Ismael; Fosalba, Pablo; Frieman, Josh; Garcia-Bellido, Juan; Gaztanaga, Enrique; Gerdes, David; Gruendl, Robert; Gschwend, Julia; Gutierrez, Gaston; Hinton, Samuel; Hollowood, Devon L.; Honscheid, Klaus; Hoyle, Ben; James, David; Jarvis, Mike; Kuehn, Kyler; Lahav, Ofer; Maia, Marcio; Marshall, Jennifer; Menanteau, Felipe; Miquel, Ramon; Morgan, Robert; Palmese, Antonella; Paz-Chinchon, Francisco; Plazas Malagón, Andrés; Roodman, Aaron; Sanchez, Eusebio; Scarpine, Vic; Schubnell, Michael; Serrano, Santiago; Smith, Mathew; Suchyta, Eric; Tarle, Gregory; Thomas, Daniel; Chun-Hao, To; Varga, Tamas Norbert; Weller, Jochen; Wilkinson, Rees

Antibiotic Prescriptions and Prophylaxis in Italian Children. Is It Time to Change? Data from the ARPEC Project.

BACKGROUND: Antimicrobials are the most commonly prescribed drugs. Many studies have evaluated antibiotic prescriptions in the paediatric outpatient but few studies describing the real antibiotic consumption in Italian children's hospitals have been published. Point-prevalence survey (PPS) has been shown to be a simple, feasible and reliable standardized method for antimicrobials surveillance in children and neonates admitted to the hospital. In this paper, we presented data from a PPS on antimicrobial prescriptions carried out in 7 large Italian paediatric institutions. METHODS: A 1-day PPS on antibiotic use in hospitalized neonates and children was performed in Italy between October and December 2012 as part of the Antibiotic Resistance and Prescribing in European Children project (ARPEC). Seven institutions in seven Italian cities were involved. The survey included all admitted patients less than 18 years of age present in the ward at 8:00 am on the day of the survey, who had at least one on-going antibiotic prescription. For all patients data about age, weight, underlying disease, antimicrobial agent, dose and indication for treatment were collected. RESULTS: The PPS was performed in 61 wards within 7 Italian institutions. A total of 899 patients were eligible and 349 (38.9%) had an on-going prescription for one or more antibiotics, with variable rates among the hospitals (25.7% - 53.8%). We describe antibiotic prescriptions separately in neonates ( = 30 days to <18 years old). In the neonatal cohort, 62.8% received antibiotics for prophylaxis and only 37.2% on those on antibiotics were treated for infection. Penicillins and aminoglycosides were the most prescribed antibiotic classes. In the paediatric cohort, 64.4% of patients were receiving antibiotics for treatment of infections and 35.5% for prophylaxis. Third generation cephalosporins and penicillin plus inhibitors were the top two antibiotic classes. The main reason for prescribing antibiotic therapy in children was lower respiratory tract infections (LRTI), followed by febrile neutropenia/fever in oncologic patients, while, in neonates, sepsis was the most common indication for treatment. Focusing on prescriptions for LRTI, 43.3% of patients were treated with 3rd generation cephalosporins, followed by macrolides (26.9%), quinolones (16.4%) and carbapenems (14.9%) and 50.1% of LRTI cases were receiving more than one antibiotic. For neutropenic fever/fever in oncologic patients, the preferred antibiotics were penicillins with inhibitors (47.8%), followed by carbapenems (34.8%), aminoglycosides (26.1%) and glycopeptides (26.1%). Overall, the 60.9% of patients were treated with a combination therapy. CONCLUSIONS: Our study provides insight on the Italian situation in terms of antibiotic prescriptions in hospitalized neonates and children. An over-use of third generation cephalosporins both for prophylaxis and treatment was the most worrisome finding. A misuse and abuse of carbapenems and quinolones was also noted. Antibiotic stewardship programs should immediately identify feasible targets to monitor and modify the prescription patterns in children's hospital, also considering the continuous and alarming emergence of MDR bacteria

Health Monitoring for Aircraft Systems using Decision Trees and Genetic Evolution

Reducing unscheduled maintenance is important for aircraft operators. There are significant costs if flights must be delayed or cancelled, for example, if spares are not available and have to be shipped across the world. This thesis describes three methods of aircraft health condition monitoring and prediction; one for system monitoring, one for forecasting and one combining the two other methods for a complete monitoring and prediction process. Together, the three methods allow organizations to forecast possible failures. The first two use decision trees for decision-making and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have several advantages: the generated code is quickly and easily processed, it can be altered by human experts without much work, it is readable by humans, and it requires few resources for learning and evaluation. The readability and the ability to modify the results are especially important; special knowledge can be gained and errors produced by the automated code generation can be removed. A large number of data sets is needed for meaningful predictions. This thesis uses two data sources: first, data from existing aircraft sensors, and second, sound and vibration data from additionally installed sensors. It draws on methods from the field of big data and machine learning to analyse and prepare the data sets for the prediction process

Timing Analysable Synchronisation Techniques for Parallel Programs on Embedded Multi-Cores

The thesis on hand provides hardware-software co-design of timing analysable synchronisation techniques in embedded shared-memory multi-core processors. In hardware, an augmented memory controller including the logic to support consistent and atomic Read-Modify-Write (RMW) primitives for a predictable shared-memory multi-core processor has been developed. The techniques introduced with the augmented memory controller are also applicable to further (future) shared-memory multi-core platforms. On top of these RMW primitives, timing analysable software synchronisation techniques are provided. On the one hand, hard real-time (HRT) capable, worst-case efficient, lock-based synchronisation techniques employing busy-waiting (spinning) and blocking (suspending) are introduced. On the other hand, worst-case efficient barrier implementations for progress coordination of HRT threads are presented. The implemented hardware and software techniques are analysed in detail with an open-source static worst-case execution time (WCET) analysis tool, which supports the analysis of multithreaded parallel programs on shared-memory multi-cores. The static timing analysis includes worst-case memory latencies for the concurrent access of threads to a shared global memory, and allows for analysing multithreaded programs by introducing worst-case waiting times at synchronisation points. Furthermore, two benchmarking parallel programs, a parallel matrix multiplication and an Integer Fast Fourier Transformation, have been implemented and analysed using the proposed synchronisation techniques. The analyses have shown that busy-waiting spin locks are preferable over locking techniques with suspension for multithreaded parallel programs on shared-memory multi-core processors. Also, the static timing analyses indicate that pessimism in the WCET estimates could be further reduced by providing a technique that prioritises frequent normal load and store operations over infrequent RMW operations on synchronisation variables. The optimisation technique, the split-phase synchronisation technique, has been implemented in the augmented memory controller, and allows for splitting RMW operations into a load and modification phase and a store phase while maintaining atomicity and data consistency under a weak consistency model. The WCET analyses of parallel benchmark programs with the split-phase synchronisation technique applied shows an additional improvement of WCET guarantees. The remainder of this thesis introduces an approach towards a novel parallelisation method for HRT programs using parallel design patterns. The pattern-based parallelisation process integrates the developed HRT capable synchronisation techniques as synchronisation idioms. The proposed parallelisation approach is envisioned to be further evolved and integrated in a software engineering approach that limits the possible variability in the parallelisation process to well-known, timing analysable structures by embracing programmer and timing analyser knowledge in forms of design patterns, algorithmic skeletons, and idioms.Die vorliegende Dissertation behandelt Hardware-Software-Co-Design von zeitlich vorhersagbaren Synchronisierungstechniken für eingebettete Mehrkernprozessoren mit gemeinsamem Speicher. Dazu wurde der Speicher-Controller eines echtzeitfähigen eingebetteten Mehrkernprozessors um die Hardware-Logik zur Behandlung von atomaren und konsistenten Read-Modify-Write (RMW) Primitiven erweitert. Die entwickelten Techniken lassen sich auch in weiteren (zukünftigen) Mehrkernprozessoren verwenden. Darauf aufbauend werden zeitlich analysierbare, worst-case effiziente Software-Synchronisationstechniken bereitgestellt. Einerseits werden Synchronisationstechniken mit Busy-Waiting (Spinning) und Sperren (Aussetzung) und andererseits Barrieren zur Fortschrittskoordination von HRT (harte Echtzeit) Kontrollfäden vorgestellt. Die implementierten Hardware- und Software-Techniken werden im Detail mit einem open-source, statischen worst-case execution time (WCET) Analyse-Tool, das die Analyse von mehrfädigen parallelen Programmen auf Mehrkernprozessoren mit gemeinsamen Speicher unterstützt, evaluiert. Das benutzte WCET Analyse-Tool verwendet worst-case Speicherlatenzen zur Behandlung von konkurrierenden Zugriffen mehrerer Programmfäden auf einen gemeinsamen Hauptspeicher. Es ermöglicht die Analyse von mehrfädigen Programmen durch die Einführung von worst-case Wartezeiten an Synchronisationspunkten. Außerdem wurden zwei parallele Benchmark-Programme, eine parallele Matrixmultiplikation und eine mehrfädige (Ganzzahl) Fast Fourier Transformation, entwickelt und unter Verwendung der vorgeschlagenen Synchronisationstechniken evaluiert. Die Evaluierungen ergeben, dass für die echtzeitfähige Ausführung von mehrfädigen Programmen auf Mehrkernprozessoren mit gemeinsamen Speicher Busy-Waiting Spinlocks gegenüber den Synchronisationstechniken, die einen aktiven Kontrollfaden aussetzen, vorzuziehen sind. Außerdem zeigen die vorgenommenen Echtzeitanalysen, dass der Pessimismus in den WCET Abschätzungen weiter reduziert werden kann, wenn eine Technik eingesetzt wird, die sehr häufige normale Lade- und Schreibzugriffe gegenüber selteneren RMW Operationen auf Synchronisationsvariablen priorisiert. Die Optimierungstechnik ist im erweiterten Speichercontroller implementiert und erlaubt das Aufspalten von RMW Operationen unter Aufrechterhaltung der Atomarität und Datenkonsistenz unter einem schwachen Konsistenz-Modell. Die erzielten Ergebnisse mit der Optimierungstechnik zeigen eine weitere Verbesserung der WCET Garantien. Am Schluß dieser Dissertation wird ein Ansatz für eine neuartige Methode zur Parallelisierung von HRT-Programmen mit parallelem Entwurfsmuster vorgestellt. Dieses Parallelisierungsverfahren beinhaltet die entwickelten HRT-fähigen Synchronisierungstechniken als Synchronisations-Idiome. Das vorgeschlagene Parallelisierungsverfahren wird zukünftig weiterentwickelt, um die mögliche Variabilität des Parallelisierungsprozesses noch weiter auf bekannte, zeitlich analysierbare Strukturen zu begrenzen. Dazu wird Wissen von Software-Entwicklern und Echtzeit-Analysten in Form von Entwurfsmustern, algorithmischen Programmskeletten und Idiomen gesammelt und aufgeführt

Decision Trees and Genetic Algorithms for Condition Monitoring - Forecasting of Aircraft Air Conditioning

Unscheduled maintenance of aircraft can cause significant costs. The machine needs to be repaired before it can operate again. Thus it is desirable to have concepts and methods to prevent unscheduled maintenance. This paper proposes a method for forecasting the condition of aircraft air conditioning system based on observed past data. Forecasting is done in a point by point way, by iterating the algorithm. The proposed method uses decision trees to find and learn patterns in past data and use these patterns to select the best forecasting method to forecast future data points. Forecasting a data point is based on selecting the best applicable approximation method. The selection is done by calculating different features/attributes of the time series and then evaluating the decision tree. A genetic algorithm is used to find the best feature set for the given problem to increase the forecasting performance. The experiments show a good forecasting ability even when the function is disturbed by noise.PeerReviewe