ELAN as flexible annotation framework for sound and image processing detectors

Annotation of digital recordings in humanities research still is, to a largeextend, a process that is performed manually. This paper describes the firstpattern recognition based software components developed in the AVATecH projectand their integration in the annotation tool ELAN. AVATecH (AdvancingVideo/Audio Technology in Humanities Research) is a project that involves twoMax Planck Institutes (Max Planck Institute for Psycholinguistics, Nijmegen,Max Planck Institute for Social Anthropology, Halle) and two FraunhoferInstitutes (Fraunhofer-Institut für Intelligente Analyse- undInformationssysteme IAIS, Sankt Augustin, Fraunhofer Heinrich-Hertz-Institute,Berlin) and that aims to develop and implement audio and video technology forsemi-automatic annotation of heterogeneous media collections as they occur inmultimedia based research. The highly diverse nature of the digital recordingsstored in the archives of both Max Planck Institutes, poses a huge challenge tomost of the existing pattern recognition solutions and is a motivation to makesuch technology available to researchers in the humanities

Uso do resíduo da produção de Beddingia siricidicola para a produção de inoculante de Trichoderma viride.

Organizado por Patricia Póvoa de Mattos, Celso Garcia Auer, Rejane Stumpf Sberze, Katia Regina Pichelli e Paulo César Botosso

nanoTRON: a Picasso module for MLP-based classification of super-resolution data

Motivation: Classification of images is an essential task in higher-level analysis of biological data. By bypassing the diffraction limit of light, super-resolution microscopy opened up a new way to look at molecular details using light microscopy, producing large amounts of data with exquisite spatial detail. Statistical exploration of data usually needs initial classification, which is up to now often performed manually. Results: We introduce nanoTRON, an interactive open-source tool, which allows super-resolution data classification based on image recognition. It extends the software package Picasso with the first deep learning tool with a graphic user interface

Co-evolution of RDF Datasets

Linking Data initiatives have fostered the publication of large number of RDF datasets in the Linked Open Data (LOD) cloud, as well as the development of query processing infrastructures to access these data in a federated fashion. However, different experimental studies have shown that availability of LOD datasets cannot be always ensured, being RDF data replication required for envisioning reliable federated query frameworks. Albeit enhancing data availability, RDF data replication requires synchronization and conflict resolution when replicas and source datasets are allowed to change data over time, i.e., co-evolution management needs to be provided to ensure consistency. In this paper, we tackle the problem of RDF data co-evolution and devise an approach for conflict resolution during co-evolution of RDF datasets. Our proposed approach is property-oriented and allows for exploiting semantics about RDF properties during co-evolution management. The quality of our approach is empirically evaluated in different scenarios on the DBpedia-live dataset. Experimental results suggest that proposed proposed techniques have a positive impact on the quality of data in source datasets and replicas.Comment: 18 pages, 4 figures, Accepted in ICWE, 201

MRI in vertebral artery dissection (multiple letters) [9]

published_or_final_versio

Magnetization reversal times in the 2D Ising model

We present a theoretical framework which is generally applicable to the study of time scales of activated processes in systems with Brownian type dynamics. This framework is applied to a prototype system: magnetization reversal times in the 2D Ising model. Direct simulation results for the magnetization reversal times, spanning more than five orders of magnitude, are compared with theoretical predictions; the two agree in most cases within 20%.Comment: 9 pages, 8 figure

Incentivizing Exploration with Heterogeneous Value of Money

Recently, Frazier et al. proposed a natural model for crowdsourced exploration of different a priori unknown options: a principal is interested in the long-term welfare of a population of agents who arrive one by one in a multi-armed bandit setting. However, each agent is myopic, so in order to incentivize him to explore options with better long-term prospects, the principal must offer the agent money. Frazier et al. showed that a simple class of policies called time-expanded are optimal in the worst case, and characterized their budget-reward tradeoff. The previous work assumed that all agents are equally and uniformly susceptible to financial incentives. In reality, agents may have different utility for money. We therefore extend the model of Frazier et al. to allow agents that have heterogeneous and non-linear utilities for money. The principal is informed of the agent's tradeoff via a signal that could be more or less informative. Our main result is to show that a convex program can be used to derive a signal-dependent time-expanded policy which achieves the best possible Lagrangian reward in the worst case. The worst-case guarantee is matched by so-called "Diamonds in the Rough" instances; the proof that the guarantees match is based on showing that two different convex programs have the same optimal solution for these specific instances. These results also extend to the budgeted case as in Frazier et al. We also show that the optimal policy is monotone with respect to information, i.e., the approximation ratio of the optimal policy improves as the signals become more informative.Comment: WINE 201

Universality in the morphology and mechanics of coarsening amyloid fibril networks

Peptide hydrogels have important applications as biomaterials and in nanotechnology, but utilization often depends on their mechanical properties for which we currently have no predictive capability. Here we use a peptide model to simulate the formation of percolating amyloid fibril networks and couple these to the elastic network theory to determine their mechanical properties. We find that the time variation of network length scales can be collapsed onto master curves by using a time scaling function that depends on the peptide interaction anisotropy. The same scaling applies to network mechanics, revealing a nonmonotonic dependence of the shear modulus with time. Our structure-function relationship between the peptide building blocks, network morphology, and network mechanical properties can aid in the design of amyloid fibril networks with tailored mechanical properties