Adaptive FEM for parameter-errors in elliptic linear-quadratic parameter estimation problems

We consider an elliptic linear-quadratic parameter estimation problem with a finite number of parameters. A novel a priori bound for the parameter error is proved and, based on this bound, an adaptive finite element method driven by an a posteriori error estimator is presented. Unlike prior results in the literature, our estimator, which is composed of standard energy error residual estimators for the state equation and suitable co-state problems, reflects the faster convergence of the parameter error compared to the (co)-state variables. We show optimal convergence rates of our method; in particular and unlike prior works, we prove that the estimator decreases with a rate that is the sum of the best approximation rates of the state and co-state variables. Experiments confirm that our method matches the convergence rate of the parameter error

An optoacoustic field-programmable perceptron for recurrent neural networks

A critical feature in signal processing is the ability to interpret correlations in time series signals, such as speech. Machine learning systems process this contextual information by tracking internal states in recurrent neural networks (RNNs), but these can cause memory and processor bottlenecks in applications from edge devices to data centers, motivating research into new analog inference architectures. But whereas photonic accelerators, in particular, have demonstrated big leaps in uni-directional feedforward deep neural network (DNN) inference, the bi-directional architecture of RNNs presents a unique challenge: the need for a short-term memory that (i) programmably transforms optical waveforms with phase coherence , (ii) minimizes added noise, and (iii) enables programmable readily scales to large neuron counts. Here, we address this challenge by introducing an optoacoustic recurrent operator (OREO) that simultaneously meets (i,ii,iii). Specifically, we experimentally demonstrate an OREO that contextualizes and computes the information carried by a sequence of optical pulses via acoustic waves. We show that the acoustic waves act as a link between the different optical pulses, capturing the optical information and using it to manipulate the subsequent operations. Our approach can be controlled completely optically on a pulse-by-pulse basis, offering simple reconfigurability for a use case-specific optimization. We use this feature to demonstrate a recurrent drop-out, which excludes optical input pulses from the recurrent operation. We furthermore apply OREO as an acceptor to recognize up-to $27$ patterns in a sequence of optical pulses. Finally, we introduce a DNN architecture that uses the OREO as bi-directional perceptrons to enable new classes of DNNs in coherent optical signal processing

Goal-oriented adaptive finite element methods with optimal computational complexity

We consider a linear symmetric and elliptic PDE and a linear goal functional. We design and analyze a goal-oriented adaptive finite element method, which steers the adaptive mesh-refinement as well as the approximate solution of the arising linear systems by means of a contractive iterative solver like the optimally preconditioned conjugate gradient method or geometric multigrid. We prove linear convergence of the proposed adaptive algorithm with optimal algebraic rates. Unlike prior work, we do not only consider rates with respect to the number of degrees of freedom but even prove optimal complexity, i.e., optimal convergence rates with respect to the total computational cost

PREDICTING THE INDIVIDUAL MOOD LEVEL BASED ON DIARY DATA

Understanding mood changes of individuals with depressive disorders is crucial in order to guide personalized therapeutic interventions. Based on diary data, in which clients of an online depression treatment report their activities as free text, we categorize these activities and predict the mood level of clients. We apply a bag-of-words text-mining approach for activity categorization and explore recurrent neuronal networks to support this task. Using the identified activities, we develop partial ordered logit models with varying levels of heterogeneity among clients to predict their mood. We estimate the parameters of these models by employing Markov Chain Monte Carlo techniques and compare the models regarding their predictive performance. Therefore, by combining text-mining and Bayesian estimation techniques, we apply a two-stage analysis approach in order to reveal relationships between various activity categories and the individual mood level. Our findings indicate that the mood level is influenced negatively when participants report about sickness or rumination. Social activities have a positive influence on the mood. By understanding the influences of daily activities on the individual mood level, we hope to improve the efficacy of online behavior therapy, provide support in the context of clinical decision-making, and contribute to the development of personalized interventions

Perspectives in Microvascular Fluid Handling: Does the Distribution of Coagulation Factors in Human Myocardium Comply with Plasma Extravasation in Venular Coronary Segments?

Background: Heterogeneity of vascular permeability has been suggested for the coronary system. Whereas arteriolar and capillary segments are tight, plasma proteins pass readily into the interstitial space at venular sites. Fittingly, lymphatic fluid is able to coagulate. However, heart tissue contains high concentrations of tissue factor, presumably enabling bleeding to be stopped immediately in this vital organ. The distribution of pro- and anti-coagulatively active factors in human heart tissue has now been determined in relation to the types of microvessels. Methods and Results: Samples of healthy explanted hearts and dilated cardiomyopathic hearts were immunohistochemically stained. Albumin was found throughout the interstitial space. Tissue factor was packed tightly around arterioles and capillaries, whereas the tissue surrounding venules and small veins was practically free of this starter of coagulation. Thrombomodulin was present at the luminal surface of all vessel segments and especially at venular endothelial cell junctions. Its product, the anticoagulant protein C, appeared only at discrete extravascular sites, mainly next to capillaries. These distribution patterns were basically identical in the healthy and diseased hearts, suggesting a general principle. Conclusions: Venular extravasation of plasma proteins probably would not bring prothrombin into intimate contact with tissue factor, avoiding interstitial coagulation in the absence of injury. Generation of activated protein C via thrombomodulin is favored in the vicinity of venular gaps, should thrombin occur inside coronary vessels. This regionalization of distribution supports the proposed physiological heterogeneity of the vascular barrier and complies with the passage of plasma proteins into the lymphatic system of the heart. Copyright (C) 2010 S. Karger AG, Base

PlexinA3 restricts spinal exit points and branching of trunk motor nerves in embryonic zebrafish

The pioneering primary motor axons in the zebrafish trunk are guided by multiple cues along their pathways. Plexins are receptor components for semaphorins that influence motor axon growth and path finding. We cloned plexinA3 in zebrafish and localized plexinA3 mRNA in primary motor neurons during axon outgrowth. Antisense morpholino knock-down led to substantial errors in motor axon growth. Errors comprised aberrant branching of primary motor nerves as well as additional exit points of axons from the spinal cord. Excessively branched and supernumerary nerves were found in both ventral and dorsal pathways of motor axons. The trunk environment and several other types of axons, including trigeminal axons, were not detectably affected by plexinA3 knock-down. RNA overexpression rescued all morpholino effects. Synergistic effects of combined morpholino injections indicate interactions of plexinA3 with semaphorin3A homologs. Thus, plexinA3 is a crucial receptor for axon guidance cues in primary motor neurons

Simulating empathy for the virtual human Max

Boukricha H, Becker C, Wachsmuth I. Simulating empathy for the virtual human Max. In: Reichardt D, Levi P, eds. Proceedings of the 2nd Workshop on Emotion and Computing - Current Research and Future Impact. 2007: 23-28.Addressing user’s emotions in human-computer interaction significantly enhances the believability and lifelikeness of virtual humans. Emotion recognition and interpretation is realized in our approach by integrating empathy as a designated process within the agent’s cognitive architecture. In this paper we describe this empathy process which comprises of two interconnected components: a belief-desire-intention (BDI) based cognitive component and an affective component based on the emotion simulation system of the virtual human Max. The application and a preliminary evaluation of this empathy system are reported on in the context of a 3D competitive card game scenario

One million indents, a hardness (and modulus) story

Advances in nanomechanical testing have progressed to a point where high-speed mapping and large data sets have become achievable. An Edisonian approach to indentation spacing and rate determines the experimental parameters that are then applied to a modern Damascene steel. One million indents were then performed over a period of less than 6 days thereby mapping out an area of 1mm x 1mm with a spacing of 1µm. To make sense of the data, artificial intelligence algorithms are used to provide an analysis of the hardness and modulus data. Please click Additional Files below to see the full abstract