Vergleichende Untersuchung der endokrinen Ophthalmopathie mittels Ultrasonographie, Computertomographie und Fischbioassay

Bei 35 Patienten mit endokriner Ophthalmopathie (eO) wurde zu den Parametern der Schilddrüsenfunktion (T3, T4, TBI, TRH-Test), dem Szintigramm und der Bestimmung der Schilddrüsenantikörper ergänzend die Ultrasonographie (A-scan) und Computertomographie (CT) der Orbita, sowie der Nachweis exophthalmogener Serumaktivität im Fischbioassay durchgeführt. Charakteristische Sonogramme für eine eO fanden sich in 26 Fällen. Die CT ergab bei 24 von 33 Patienten die Verdickung der musculi recti mediales und/oder der musculi recti laterales, sowie bei 17 Patienten eine Verdichtung im Bereich der Orbitaspitze. Im retrobulbären Bindegewebe zeigte sich nach Kontrastmittelgabe keine signifikante Dichtezunahme. Mit beiden Verfahren zusammen waren nur bei 2 Patienten die Kriterien einer eO nicht erfüllt. Die exophthalmogene Serumaktivität wurde in der IgG-Fraktion im Fischbioassay nachgewiesen; die Trefferquote war mit 69% relativ hoch. Zur Diagnostik der eO kann jedoch der Fischbioassay nicht empfohlen werden

Towards endowing collaborative robots with fast learning for minimizing tutors’ demonstrations: what and when to do?

Programming by demonstration allows non-experts in robot programming to train the robots in an intuitive manner. However, this learning paradigm requires multiple demonstrations of the same task, which can be time-consuming and annoying for the human tutor. To overcome this limitation, we propose a fast learning system – based on neural dynamics – that permits collaborative robots to memorize sequential information from single task demonstrations by a human-tutor. Important, the learning system allows not only to memorize long sequences of sub-goals in a task but also the time interval between them. We implement this learning system in Sawyer (a collaborative robot from Rethink Robotics) and test it in a construction task, where the robot observes several human-tutors with different preferences on the sequential order to perform the task and different behavioral time scales. After learning, memory recall (of what and when to do a sub-task) allows the robot to instruct inexperienced human workers, in a particular human-centered task scenario.POFC - Programa Operacional Temático Factores de Competitividade(POCI-01-0247-FEDER-024541

Neural field model for measuring and reproducing time intervals

The continuous real-time motor interaction with our environment requires the capacity to measure and produce time intervals in a highly flexible manner. Recent neurophysiological evidence suggests that the neural computational principles supporting this capacity may be understood from a dynamical systems perspective: Inputs and initial conditions determine how a recurrent neural network evolves from a “resting state” to a state triggering the action. Here we test this hypothesis in a time measurement and time reproduction experiment using a model of a robust neural integrator based on the theoretical framework of dynamic neural fields. During measurement, the temporal accumulation of input leads to the evolution of a self-stabilized bump whose amplitude reflects elapsed time. During production, the stored information is used to reproduce on a trial-by-trial basis the time interval either by adjusting input strength or initial condition of the integrator. We discuss the impact of the results on our goal to endow autonomous robots with a human-like temporal cognition capacity for natural human-robot interactions.The work received financial support from FCT through the PhD fellowship PD/BD/128183/2016, the project ”Neurofield” (POCI-01-0145-FEDER-031393) and the research centre CMAT within the project UID/MAT/00013/2013

Climate Changes and Their Elevational Patterns in the Mountains of the World

Quantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world's water supply. Whilst the concept of elevation-dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation-dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded data sets (observations, reanalyses, and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency toward enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high versus low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded data sets (CRU, GISTEMP, GPCC, ERA5, and CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation-dependency of precipitation, especially in midlatitudes. Agreement on elevation-dependent changes between gridded data sets is weak for temperature but stronger for precipitation

Development of proglacial lakes and evaluation of related outburst susceptibility at the Adygine ice-debris complex, northern Tien Shan

The formation and development of glacial lakes in mountainous regions is one of the consequences of glacier recession. Such lakes may drain partially or completely when the stability of their dams is disturbed or as a consequence of impacts. We present a case study from the Central Asian mountain range of Tien Shan – a north-oriented tributary of the Adygine Valley, where the retreat of a polythermal glacier surrounded by permafrost has resulted in the formation of several generations of lakes. The aim of this study was to analyse the past development of different types of glacial lakes influenced by the same glacier, to project the site's future development, and to evaluate the outburst susceptibility of individual lakes with an outlook for expected future change. We addressed the problem using a combination of methods, namely bathymetric, geodetic and geophysical on-site surveys, satellite images and digital elevation model analysis, and modelling of glacier development. Based on this case of the glacial lakes being of varied age and type, we demonstrated the significance of glacier ice in lake development. Lake 3, which is in contact with the glacier terminus, has changed rapidly over the last decade, expanding both in area and depth and increasing its volume by more than 13 times (7800 to 106&thinsp;000&thinsp;m3). The hydrological connections and routing of glacier meltwater have proved to be an important factor as well, since most lakes in the region are drained by subsurface channels. As the site is at the boundary between continuous and discontinuous permafrost, the subsurface water flow is strongly governed by the distribution of non-frozen zones above, within, or beneath the perennially frozen ground. In the evaluation of lake outburst susceptibility, we have highlighted the importance of field data, which can provide crucial information on lake stability. In our case, an understanding of the hydrological system at the site, and its regime, helped to categorise Lake 2 as having low outburst susceptibility, while Lake 1 and Lake 3 were labelled as lakes with medium outburst susceptibility. Further development of the site will be driven mainly by rising air temperatures and increasingly negative glacier mass balance. All three climate model scenarios predicted a significant glacier areal decrease by 2050, specifically leaving 73.2&thinsp;% (A1B), 62.3&thinsp;% (A2), and 55.6&thinsp;% (B1) of the extent of the glacier in 2012. The glacier retreat will be accompanied by changes in glacier runoff, with the first peak expected around 2020, and the formation of additional lakes.</p

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

A dynamic neural field approach to natural and efficient human-robot collaboration

A major challenge in modern robotics is the design of autonomous robots that are able to cooperate with people in their daily tasks in a human-like way. We address the challenge of natural human-robot interactions by using the theoretical framework of dynamic neural fields (DNFs) to develop processing architectures that are based on neuro-cognitive mechanisms supporting human joint action. By explaining the emergence of self-stabilized activity in neuronal populations, dynamic field theory provides a systematic way to endow a robot with crucial cognitive functions such as working memory, prediction and decision making . The DNF architecture for joint action is organized as a large scale network of reciprocally connected neuronal populations that encode in their firing patterns specific motor behaviors, action goals, contextual cues and shared task knowledge. Ultimately, it implements a context-dependent mapping from observed actions of the human onto adequate complementary behaviors that takes into account the inferred goal of the co-actor. We present results of flexible and fluent human-robot cooperation in a task in which the team has to assemble a toy object from its components.The present research was conducted in the context of the fp6-IST2 EU-IP Project JAST (proj. nr. 003747) and partly financed by the FCT grants POCI/V.5/A0119/2005 and CONC-REEQ/17/2001. We would like to thank Luis Louro, Emanuel Sousa, Flora Ferreira, Eliana Costa e Silva, Rui Silva and Toni Machado for their assistance during the robotic experiment