Monitoring framework for stream-processing networks

Vu Thien Nga Nguyen, Raimund Kirner, and Frank Penczek, 'Monitoring framework for stream-processing networks'. Paper presented at the Workshop on Feedback-Directed Compiler Optimization for Multi-Core Architectures (FD-COMA 2012), Berlin, Germany. 21-23 January 2013.In this paper we present a monitoring framework that exploits special characteristics of stream-processing networks in order to reason the performance. The novelty of the framework is to trace the non-deterministic execution which is reflected in i) the dynamic mapping and scheduling of network components at the operating system level and ii) the dynamic message routing across the network at runtime. We evaluate the efficiency with an implementation for the coordination language S-Net, showing negligible overhead in most cases

Early error detection predicted by reduced pre-response control process: an ERP topographic mapping study

Advanced ERP topographic mapping techniques were used to study error monitoring functions in human adult participants, and test whether proactive attentional effects during the pre-response time period could later influence early error detection mechanisms (as measured by the ERN component) or not. Participants performed a speeded go/nogo task, and made a substantial number of false alarms that did not differ from correct hits as a function of behavioral speed or actual motor response. While errors clearly elicited an ERN component generated within the dACC following the onset of these incorrect responses, I also found that correct hits were associated with a different sequence of topographic events during the pre-response baseline time-period, relative to errors. A main topographic transition from occipital to posterior parietal regions (including primarily the precuneus) was evidenced for correct hits similar to 170-150 ms before the response, whereas this topographic change was markedly reduced for errors. The same topographic transition was found for correct hits that were eventually performed slower than either errors or fast (correct) hits, confirming the involvement of this distinctive posterior parietal activity in top-down attentional control rather than motor preparation. Control analyses further ensured that this pre-response topographic effect was not related to differences in stimulus processing. Furthermore, I found a reliable association between the magnitude of the ERN following errors and the duration of this differential precuneus activity during the pre-response baseline, suggesting a functional link between an anticipatory attentional control component subserved by the precuneus and early error detection mechanisms within the dACC. These results suggest reciprocal links between proactive attention control and decision making processes during error monitoring

Post-failure evolution analysis of a rainfall-triggered landslide by multi-temporal interferometry SAR approaches integrated with geotechnical analysis

Persistent Scatterers Interferometry (PSI) represents one of the most powerful techniques for Earth's surface deformation processes' monitoring, especially for long-term evolution phenomena. In this work, a dataset of 34 TerraSAR-X StripMap images (October 2013–October 2014) has been processed by two PSI techniques - Coherent Pixel Technique-Temporal Sublook Coherence (CPT-TSC) and Small Baseline Subset (SBAS) - in order to study the evolution of a slow-moving landslide which occurred on February 23, 2012 in the Papanice hamlet (Crotone municipality, southern Italy) and induced by a significant rainfall event (185 mm in three days). The mass movement caused structural damage (buildings' collapse), and destruction of utility lines (gas, water and electricity) and roads. The results showed analogous displacement rates (30–40 mm/yr along the Line of Sight – LOS-of the satellite) with respect to the pre-failure phase (2008–2010) analyzed in previous works. Both approaches allowed detect the landslide-affected area, however the higher density of targets identified by means of CPT-TSC enabled to analyze in detail the slope behavior in order to design possible mitigation interventions. For this aim, a slope stability analysis has been carried out, considering the comparison between groundwater oscillations and time-series of displacement. Hence, the crucial role of the interaction between rainfall and groundwater level has been inferred for the landslide triggering. In conclusion, we showed that the integration of geotechnical and remote sensing approaches can be seen as the best practice to support stakeholders to design remedial works.Peer ReviewedPostprint (author's final draft

Unmanned Aerial Vehicle (UAV) for monitoring soil erosion in Morocco

This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with a digital system camera (Panasonic) is employed. UAV surveys are conducted over different study sites with varying extents and flying heights in order to provide both very high resolution site-specific data and lower-resolution overviews, thus fully exploiting the large potential of the chosen UAV for multi-scale mapping purposes. Depending on the scale and area coverage, two different approaches for georeferencing are used, based on high-precision GCPs or the UAV’s log file with exterior orientation values respectively. The photogrammetric image processing enables the creation of Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimetre level. The created data products were used for quantifying gully and badland erosion in 2D and 3D as well as for the analysis of the surrounding areas and landscape development for larger extents

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

Abnormal proactive and reactive cognitive control during conflict processing in major depression

According to the Dual Mechanisms of Control framework, cognitive control consists of two complementary components: proactive control refers to anticipatory maintenance of goal-relevant information, whereas reactive control acts as a correction mechanism that is activated when a conflict occurs. Possibly, the well-known diminished inhibitory control in response to negative stimuli in Major Depressive Disorder (MDD) patients stems from a breakdown in proactive control, and/or anomalies in reactive cognitive control. In our study, MDD patients specifically showed increased response latencies when actively inhibiting a dominant response to a sad compared with a happy face. This condition was associated with a longer duration of a dominant ERP topography (800-900 ms poststimulus onset) and a stronger activity in the bilateral dorsal anterior cingulate cortex, reflecting abnormal reactive control when inhibiting attention to a negative stimulus. Moreover, MDD patients showed abnormalities in proactive cognitive control when preparing for the upcoming imperative stimulus (abnormal modulation of the contingent negative variation component), accompanied by more activity in brain regions belonging to the default mode network. All together, deficits to inhibit attention to negative information in MDD might originate from an abnormal use of both proactive resources and reactive control processes. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly

When the outcome is different than expected : subjective expectancy shapes reward prediction error at the FRN level

Converging evidence in human electrophysiology suggests that evaluative feedback provided during performance monitoring (PM) elicits two distinctive and successive ERP components: the feedback-related negativity (FRN) and the P3b. Whereas the FRN has previously been linked to reward prediction error (RPE), the P3b has been conceived as reflecting motivational or attentional processes following the early processing of the RPE, including action value updating. However, it remains unclear whether these two consecutive neurophysiological effects depend on the direction of the unexpectedness (better- or worse-than-expected outcomes; signed RPE) or instead only on the degree of unexpectedness irrespective of direction (i.e., unsigned RPE). To address this question, we devised an experiment in which we manipulated the objective reward probability and the subjective reward expectancy (via instructions) in a factorial within-subject design and explored amplitude changes of the FRN and the P3b. A 64-channel EEG was recorded while 32 participants performed a speeded go/no-go task in which evaluative feedback based on the reward probability either violated expectancy (thereby creating a RPE) or did not. This violation corresponded either to better- or worse-than-expected events. Results showed that the FRN was larger when RPE occurred than when it did not, but irrespective of the direction of this violation. Interestingly, in these two conditions, action value was updated for the positive feedback selectively, as shown by the P3b amplitude. These results obey a two-stage model of PM assuming that unsigned RPE is first rapidly detected (FRN level) before the positive feedback's value is updated selectively (P3b effect)

Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset