Comment and Reply Why eye movements and perceptual factors have to be controlled in studies on "representational momentum”

In order to study memory of the final position of a smoothly moving target, Hubbard (e.g., Hubbard & Bharucha, 1988) presented smooth stimulus motion and used motor responses. In contrast, Freyd (e.g., Freyd & Finke, 1984) presented implied stimulus motion and used the method of constant stimuli. The same forward error was observed in both paradigms. However, the processes underlying the error may be very different. When smooth stimulus motion is followed by smooth pursuit eye movements, the forward error is associated with asynchronous processing of retinal and extraretinal information. In the absence of eye movements, no forward displacement is observed with smooth motion. In contrast, implied motion produces a forward error even without eye movements, suggesting that observers extrapolate the next target step when successive target presentations are far apart. Finally, motor responses produce errors that are not observed with perceptual judgments, indicating that the motor system may compensate for neuronal latencie

On the structure of defects in the Fe7Mo6 μ\mu-Phase

Topologically close packed phases, among them the $\mu$-phase studied here, are commonly considered as being hard and brittle due to their close packed and complex structure. Nanoindentation enables plastic deformation and therefore investigation of the structure of mobile defects in the $\mu$-phase, which, in contrast to grown-in defects, has not been examined yet. High resolution transmission electron microscopy (HR-TEM) performed on samples deformed by nanoindentation revealed stacking faults which are likely induced by plastic deformation. These defects were compared to theoretically possible stacking faults within the $\mu$-phase building blocks, and in particular Laves phase layers. The experimentally observed stacking faults were found resulting from synchroshear assumed to be associated with deformation in the Laves-phase building blocks

LHCb RICH Online-Monitor and Data-Quality

The LHCb experiment at the LHC (CERN) has been optimised for high precision measurements of the beauty quark sector. Its main objective is to precisely determine and over-constrain the parameters of the CKM mixing matrix, and to search for further sources of CP violation and new physics beyond the Standard Model in rare B-decays. Efficient particle identification at high purities over a wide momentum range from around 1 to ~100GeV/c is vital to many LHCb analyses. Central to the LHCb particle identification strategy are two Ring Imaging CHerenkov (RICH) detectors which use Silica Aerogel and C4F10 and CF4 gas radiators. A rigorous quality control scheme is being developed to insure that the data recorded by the RICH detector meets the stringent requirements of the physics analyses. The talk summarises the LHCb RICH online monitoring and data-quality strategy. Multiple dedicated algorithms are deployed to detect any potential issue already during data-taking ranging from integrity checks, mis-alignments to changes in the refractive determined from changes in the radii of Cherenkov rings found using a Markov Chain approach. A further key ingredient is the online monitoring of the particle ID performance using multiple exclusively reconstructed decay channels where the particle identity can be determined from kinematic constraints. In addition, any re-calibration of the detector can be performed using a dedicated express stream covering a dedicated data-taking period. The same tests are performed during the reconstruction phase of the full statistics of the recorded data to verify the quality of the data before made available for physics analyses

The LHCb RICH detectors

The LHCb experiment at the Large Hadron Collider has been optimised for high precision measurements of the charm and beauty quark sector. The different particle species produced in the high-energy collision are identified using two Ring-Imaging Cherenkov detectors

Enterprise AI Canvas -- Integrating Artificial Intelligence into Business

Artificial Intelligence (AI) and Machine Learning have enormous potential to transform businesses and disrupt entire industry sectors. However, companies wishing to integrate algorithmic decisions into their face multiple challenges: They have to identify use-cases in which artificial intelligence can create value, as well as decisions that can be supported or executed automatically. Furthermore, the organization will need to be transformed to be able to integrate AI based systems into their human work-force. Furthermore, the more technical aspects of the underlying machine learning model have to be discussed in terms of how they impact the various units of a business: Where do the relevant data come from, which constraints have to be considered, how is the quality of the data and the prediction evaluated? The Enterprise AI canvas is designed to bring Data Scientist and business expert together to discuss and define all relevant aspects which need to be clarified in order to integrate AI based systems into a digital enterprise. It consists of two parts where part one focuses on the business view and organizational aspects, whereas part two focuses on the underlying machine learning model and the data it uses.Comment: Accepted at "Applied Artificial Intelligence UAAI

Three-dimensional characterization of damage in dual phase steels with deep learning

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Atomistic Simulations of Basal Dislocations Interacting with Mg17_{17}Al12_{12} Precipitates in Mg

The mechanical properties of Mg-Al alloys are greatly influenced by the complex intermetallic phase Mg$_{17}$Al$_{12}$, which is the most dominant precipitate found in this alloy system. The interaction of basal edge and 30$^\text{o}$ dislocations with Mg$_{17}$Al$_{12}$ precipitates is studied by molecular dynamics and statics simulations, varying the inter-precipitate spacing ($L$), and size ($D$), shape and orientation of the precipitates. The critical resolved shear stress $\tau_c$ to pass an array of precipitates follows the usual $\ln((1/D + 1/L)^{-1})$ proportionality. In all cases but the smallest precipitate, the dislocations pass the obstacles by depositing dislocation segments in the disordered interphase boundary rather than shearing the precipitate or leaving Orowan loops in the matrix around the precipitate. An absorbed dislocation increases the stress necessary for a second dislocation to pass the precipitate also by absorbing dislocation segments into the boundary. Replacing the precipitate with a void of identical size and shape decreases the critical passing stress and work hardening contribution while an artificially impenetrable Mg$_{17}$Al$_{12}$ precipitate increases both. These insights will help improve mesoscale models of hardening by incoherent particles.Comment: 13 pages with 9 figures and 2 tables. Supplementary materia