Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor

Using supporting backchannel (BC) cues can make human-computer interaction more social. BCs provide a feedback from the listener to the speaker indicating to the speaker that he is still listened to. BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues. In this work, we only considered acoustic cues. We are proposing an approach towards detecting BC opportunities based on acoustic input features like power and pitch. While other works in the field rely on the use of a hand-written rule set or specialized features, we made use of artificial neural networks. They are capable of deriving higher order features from input features themselves. In our setup, we first used a fully connected feed-forward network to establish an updated baseline in comparison to our previously proposed setup. We also extended this setup by the use of Long Short-Term Memory (LSTM) networks which have shown to outperform feed-forward based setups on various tasks. Our best system achieved an F1-Score of 0.37 using power and pitch features. Adding linguistic information using word2vec, the score increased to 0.39

Second-order QCD corrections to event shape distributions in deep inelastic scattering

We compute the next-to-next-to-leading order (NNLO) QCD corrections to event shape distributions and their mean values in deep inelastic lepton–nucleon scattering. The magnitude and shape of the corrections varies considerably between different variables. The corrections reduce the renormalization and factorization scale uncertainty of the predictions. Using a dispersive model to describe non-perturbative power corrections, we compare the NNLO QCD predictions with data from the H1 and ZEUS experiments. The newly derived corrections improve the theory description of the distributions and of their mean values

NNLO QCD corrections to event orientation in e+e- annihilation

We present a new implementation of the NNLO QCD corrections to three-jet final states and related event-shape observables in electron–positron annihilation. Our implementation is based on the antenna subtraction method, and is performed in the NNLOjet framework. The calculation improves upon earlier results by taking into account the full kinematical information on the initial state momenta, thereby allowing the event orientation to be computed to NNLO accuracy. We find the event-orientation distributions at LEP and SLC to be very robust under higher order QCD corrections

UBVRI photometric comparison sequences for symbiotic stars

We present accurate UBVRI photometric comparison sequences around 20 symbiotic stars. The sequences extend over wide brightness and color ranges, and are suited to cover quiescence as well as outburst phases. The sequences are intended to assist both present time photometry as well as measurement of photographic plates from historical archives. The types of variability presented by symbiotic stars are reviewed. Individual notes on the known photometric behaviour of the program stars are provided.Comment: in press in Astron.Astrophys.Supp

Induction maintenance concept for HAART as initial treatment in HIV infected infants

<p>Abstract</p> <p>Background</p> <p>Early initiated antiretroviral therapy (ART) in HIV infected infants leads to improved long-term viral suppression and survival. Guidelines recommend initiating therapy with a triple ART consisting of two nucleoside reverse transcriptase inhibitors (NRTIs) and either one additional non-nucleoside reverse transcriptase inhibitor (NNRTI) or a protease inhibitor (PI). Compared to older children and adults, viral relapse is seen more frequently in infants receiving triple ART. We now address the possibility of a more potent ART with a quadruple induction and triple maintenance therapy.</p> <p>Methods</p> <p>We examine the longitudinal course in four HIV infected infants, who were referred from other centers and could not be recruited to multicentre trials. We introduced ART initially consisting of two NRTIs, one NNRTI and one PI and later discontinued the PI at the age of 12 months maintaining a triple regime consisting of two NRTIs and one NNRTI.</p> <p>Results</p> <p>Provided that therapy adherence was maintained we observed an effective sustained decline of viral load and significant CD4 cell reconstitution even after switching to a triple regime. No drug associated toxicity was seen.</p> <p>Conclusion</p> <p>We suggest that a four drug therapy might be a possible initial therapy option in HIV infected infants, at least in those with a high viral load, followed by a maintenance triple regime after 12 months of therapy.</p

Computed Tomography Imaging in Simulated Ongoing Cardiopulmonary Resuscitation: No Need to Switch Off the Chest Compression Device during Image Acquisition

Computed tomography (CT) represents the current standard for imaging of patients with acute life-threatening diseases. As some patients present with circulatory arrest, they require cardiopulmonary resuscitation. Automated chest compression devices are used to continue resuscitation during CT examinations, but tend to cause motion artifacts degrading diagnostic evaluation of the chest. The aim was to investigate and evaluate a CT protocol for motion-free imaging of thoracic structures during ongoing mechanical resuscitation. The standard CT trauma protocol and a CT protocol with ECG triggering using a simulated ECG were applied in an experimental setup to examine a compressible thorax phantom during resuscitation with two different compression devices. Twenty-eight phantom examinations were performed, 14 with AutoPulse and 14 with corpuls cpr. With each device, seven CT examinations were carried out with ECG triggering and seven without. Image quality improved significantly applying the ECG-triggered protocol (p < 0.001), which allowed almost artifact-free chest evaluation. With the investigated protocol, radiation exposure was 5.09% higher (15.51 mSv vs. 14.76 mSv), and average reconstruction time of CT scans increased from 45 to 76 s. Image acquisition using the proposed CT protocol prevents thoracic motion artifacts and facilitates diagnosis of acute life-threatening conditions during continuous automated chest compression

Deep learning for accurately recognizing common causes of shoulder pain on radiographs

Objective: Training a convolutional neural network (CNN) to detect the most common causes of shoulder pain on plain radiographs and to assess its potential value in serving as an assistive device to physicians. Materials and methods: We used a CNN of the ResNet-50 architecture which was trained on 2700 shoulder radiographs from clinical practice of multiple institutions. All radiographs were reviewed and labeled for six findings: proximal humeral fractures, joint dislocation, periarticular calcification, osteoarthritis, osteosynthesis, and joint endoprosthesis. The trained model was then evaluated on a separate test dataset, which was previously annotated by three independent expert radiologists. Both the training and the test datasets included radiographs of highly variable image quality to reflect the clinical situation and to foster robustness of the CNN. Performance of the model was evaluated using receiver operating characteristic (ROC) curves, the thereof derived AUC as well as sensitivity and specificity. Results: The developed CNN demonstrated a high accuracy with an area under the curve (AUC) of 0.871 for detecting fractures, 0.896 for joint dislocation, 0.945 for osteoarthritis, and 0.800 for periarticular calcifications. It also detected osteosynthesis and endoprosthesis with near perfect accuracy (AUC 0.998 and 1.0, respectively). Sensitivity and specificity were 0.75 and 0.86 for fractures, 0.95 and 0.65 for joint dislocation, 0.90 and 0.86 for osteoarthrosis, and 0.60 and 0.89 for calcification. Conclusion: CNNs have the potential to serve as an assistive device by providing clinicians a means to prioritize worklists or providing additional safety in situations of increased workload

Development of an Electro-Optical Longitudinal Bunch Profile Monitor at KARA Towards a Beam Diagnostics Tool for FCC-ee

The Karlsruhe Research Accelerator (KARA) at KIT features an electro-optical (EO) near-field diagnostics setup to conduct turn-by-turn longitudinal bunch profile measurements in the storage ring using electro-optical spectral decoding (EOSD). Within the Future Circular Collider Innovation Study (FCCIS) an EO monitor using the same technique is being conceived to measure the longitudinal profile and center-of-charge of the bunches in the future electron-positron collider FCC-ee. This contribution provides an overview of the EO near-field diagnostics at KARA and discusses the development and its challenges towards an effective beam diagnostics concept for the FCC-ee