The scholarly networks of Albrecht von Haller and the Economic Society of Bern – a multi-level network analysis

Here, by the example of the transfer of cultivated plants in the context of the correspondence networks of Albrecht von Haller and the Economic Society, a multi-level network analysis is suggested. By a multi-level procedure, the chronological dynamics, the social structure, the spatial distribution and the functional networking are analyzed one after the other. These four levels of network analysis do not compete with each other but are mutually supporting. This aims at a deeper understanding of how these networks contributed to an international transfer of knowledge in the 18th century.

Recurrent antitopographic inhibition mediates competitive stimulus selection in an attention network

Topographically organized neurons represent multiple stimuli within complex visual scenes and compete for subsequent processing in higher visual centers. The underlying neural mechanisms of this process have long been elusive. We investigate an experimentally constrained model of a midbrain structure: the optic tectum and the reciprocally connected nucleus isthmi. We show that a recurrent antitopographic inhibition mediates the competitive stimulus selection between distant sensory inputs in this visual pathway. This recurrent antitopographic inhibition is fundamentally different from surround inhibition in that it projects on all locations of its input layer, except to the locus from which it receives input. At a larger scale, the model shows how a focal top-down input from a forebrain region, the arcopallial gaze field, biases the competitive stimulus selection via the combined activation of a local excitation and the recurrent antitopographic inhibition. Our findings reveal circuit mechanisms of competitive stimulus selection and should motivate a search for anatomical implementations of these mechanisms in a range of vertebrate attentional systems.

Analysis Methodology for Flow-level Evaluation of a Hybrid Mobile-Sensor Network

Our society uses a large diversity of co-existing wired and wireless networks in order to satisfy its communication needs. A cooper- ation between these networks can benefit performance, service availabil- ity and deployment ease, and leads to the emergence of hybrid networks. This position paper focuses on a hybrid mobile-sensor network identify- ing potential advantages and challenges of its use and defining feasible applications. The main value of the paper, however, is in the proposed analysis approach to evaluate the performance at the mobile network side given the mixed mobile-sensor traffic. The approach combines packet- level analysis with modelling of flow-level behaviour and can be applied for the study of various application scenarios. In this paper we consider two applications with distinct traffic models namely multimedia traffic and best-effort traffic.

Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition

Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.

Analysis of postprandial lipemia as a Cardiovascular Disease risk factor using genetic and clinical information: an Artificial Neural Network perspective

Clinical studies indicate that exaggerated postprandial lipemia is linked to the progression of atherosclerosis, leading cause of Cardiovascular Diseases (CVD). CVD is a multi-factorial disease with complex etiology and according to the literature postprandial Triglycerides (TG) can be used as an independent CVD risk factor. Aim of the current study is to construct an Artificial Neural Network (ANN) based system for the identification of the most important gene-gene and/or gene-environmental interactions that contribute to a fast or slow postprandial metabolism of TG in blood and consequently to investigate the causality of postprandial TG response. The design and development of the system is based on a dataset of 213 subjects who underwent a two meals fatty prandial protocol. For each of the subjects a total of 30 input variables corresponding to genetic variations, sex, age and fasting levels of clinical measurements were known. Those variables provide input to the system, which is based on the combined use of Parameter Decreasing Method (PDM) and an ANN. The system was able to identify the ten (10) most informative variables and achieve a mean accuracy equal to 85.21%.

Neural network based glucose - insulin metabolism models for children with Type 1 diabetes

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

Unprecedented Trapping of Difluorooctamolybdate Anions within an α-Polonium Type Coordination Network

New fluorinated hybrid solids [Mo2F2O5(tr2pr)] (1), [Co3(tr2pr)2(MoO4)2F2]·7H2O (2), and [Co3(H2O)2(tr2pr)3(Mo8O26F2)]·3H2O (3) (tr2pr = 1,3-bis(1,2,4-triazol-4-yl)propane) were prepared from the reaction systems consisting of Co(OAc)2/CoF2 and MoO3/(NH4)6Mo7O24, as CoII and MoVI sources, in water (2) or in aqueous HF (1, 3) employing mild hydrothermal conditions. The tr2pr ligand serves as a conformationally flexible tetradentate donor. In complex 1, the octahedrally coordinated Mo atoms are linked in the discrete corner-sharing {Mo2(μ2-O)F2O4N4} unit in which a pair of tr-heterocycles (tr = 1,2,4-triazole) is arranged in cis-positions opposite to “molybdenyl” oxygen atoms. The anti−anti conformation type of tr2pr facilitates the tight zigzag chain packing motif. The crystal structure of the mixed-anion complex salt 2 consists of trinuclear [Co3(μ3-MoO4)2(μ2-F)2] units self-assembling in CoII-undulating chains (Co···Co 3.0709(15) and 3.3596(7) Å), which are cross-linked by tr2pr in layers. In 3, containing condensed oxyfluoromolybdate species, linear centrosymmetric [Co3(μ2-tr)6]6+ SBUs are organized at distances of 10.72−12.45 Å in an α-Po-like network using bitopic tr-linkers. The octahedral {N6} and {N3O3} environments of the central and peripheral cobalt atoms, respectively, are filled by triazole N atoms, water molecules, and coordinating [Mo8O26F2]6− anions. Acting as a tetradentate O-donor, each difluorooctamolybdate anion anchors four [Co3(μ2-tr)6]6+ units through their peripheral Co-sites, which consequently leads to a novel type of a two-nodal 4,10-c net with the Schläfli symbol {32.43.5}{34.420.516.65}. The 2D and 3D coordination networks of 2 and 3, respectively, are characterized by significant overall antiferromagnetic exchange interactions (J/k) between the CoII spin centers on the order of −8 and −4 K. The [Mo8O26F2]6− anion is investigated in detail by quantum chemical calculations.

Simulator Network project report: a tool for improvement of teaching materials and targeted resource usage in Skills Labs

During the last decade, medical education in the German-speaking world has been striving to become more practice-oriented. This is currently being achieved in many schools through the implementation of simulation-based instruction in Skills Labs. Simulators are thus an essential part of this type of medical training, and their acquisition and operation by a Skills Lab require a large outlay of resources. Therefore, the Practical Skills Committee of the Medical Education Society (GMA) introduced a new project, which aims to improve the flow of information between the Skills Labs and enable a transparent assessment of the simulators via an online database (the Simulator Network).

The mentalizing network orchestrates the impact of parochial altruism on social norm enforcement.

Parochial altruism - a preference for altruistic behavior towards ingroup members and mistrust or hostility towards outgroup members--is a pervasive feature in human society and strongly shapes the enforcement of social norms. Since the uniqueness of human society critically depends on the enforcement of norms, the understanding of the neural circuitry of the impact of parochial altruism on social norm enforcement is key, but unexplored. To fill this gap, we measured brain activity with functional magnetic resonance imaging (fMRI) while subjects had the opportunity to punish ingroup members and outgroup members for violating social norms. Findings revealed that subjects' strong punishment of defecting outgroup members is associated with increased activity in a functionally connected network involved in sanction-related decisions (right orbitofrontal gyrus, right lateral prefrontal cortex, right dorsal caudatus). Moreover, the stronger the connectivity in this network, the more outgroup members are punished. In contrast, the much weaker punishment of ingroup members who committed the very same norm violation is associated with increased activity and connectivity in the mentalizing-network (dorsomedial prefrontal cortex, bilateral temporo-parietal junction), as if subjects tried to understand or justify ingroup members' behavior. Finally, connectivity analyses between the two networks suggest that the mentalizing-network modulates punishment by affecting the activity in the right orbitofrontal gyrus and right lateral prefrontal cortex, notably in the same areas showing enhanced activity and connectivity whenever third-parties strongly punished defecting outgroup members.