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Effective knowledge communication presupposes common ground (Clark & Brennan, 1991) that needs to be established and maintained. This is particularly difficult in remote communication as well as in non-interactive settings, because the speaker cannot use gestures or mimic and has to tailor his utterances to the addressee without receiving feedback. In these situations, the speaker may achieve mutual understanding for example by adopting the addressee’s perspective. We present a study conducted to test the impact of instructions that support and hinder individual problem solving and knowledge communication. We used a picture-sorting task requiring individual cognitive processes of feature search (Treisman & Gelade, 1980) in addition to referential communication. As our study focused on the design of utterances, all participants assumed the role of speaker. Participants were told that their descriptions would be recorded and then listened to later on by a participant in the role of addressee. Eight sets of pictures were used, which varied on two dimensions: the individual cognitive demands of detecting the relevant features (varied as between-subject factor) and the communicative demands (varied as within-subject factor). A further between-subject factor was the type of instructions: The participants received either a collaboration script as supporting instructions, or time pressure was applied to induce stress, or else they were given no additional instructions (control group). We used the speakers’ verbal utterances to examine the quality of the speakers’ descriptions. For both dimensions of difficulty, we found the expected effects. In the conditions with a collaboration script, there were fewer irrelevant features mentioned and fewer features were described with delay. In the conditions with time pressure, there were fewer irrelevant features described, but the number of correctly described pictures was impaired through the fact that relevant features were also neglected. Under time pressure, speakers tended to provide ambiguous descriptions regarding the frame of reference.
We present the measured correlation functions for pi+ pi-, pi- pi- and pi+ pi+ pairs in central S+Ag collisions at 200 GeV per nucleon. The Gamov function, which has been traditionally used to correct the correlation functions of charged pions for the Coulomb interaction, is found to be inconsistent with all measured correlation functions. Certain problems which have been dominating the systematic uncertainty of the correlation analysis are related to this inconsistency. It is demonstrated that a new Coulomb correction method, based exclusively on the measured correlation function for pi+ pi- pairs, may solve the problem.
The transverse momentum and rapidity distributions of negative hadrons and participant protons have been measured for central 32S+ 32S collisions at plab=200 GeV/c per nucleon. The proton mean rapidity shift < Delta y>~1.6 and mean transverse momentum <pT>~0.6 GeV/c are much higher than in pp or peripheral AA collisions and indicate an increase in the nuclear stopping power. All pT spectra exhibit similar source temperatures. Including previous results for K0s Lambda , and Lambda -bar, we account for all important contributions to particle production.
The NA35 experiment has collected a high statistics set of momentum analyzed negative hadrons near and forward of midrapidity for central collisions of 200A GeV/c 32S+S, Cu, Ag, and Au. Using momentum space correlations to study the size of the source of particle production, the transverse source radii are found to decrease by ~40% at midrapidity and ~20% at forward rapidity while the longitudinal radius RL is found to decrease by ~50% as pT increases over the interval 50<pT<600 MeV/c. Calculations using a microscopic phase space approach (relativistic quantum molecular dynamics) reproduce the observed trends of the data. PACS: 25.75.+r
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p–Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions: N.A.
Main outcomes and measures: Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.