VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Thus, we propose a full convolutional volumetric auto encoder that learns volumetric representation from noisy data by estimating the voxel occupancy grids. The proposed method outperforms prior work on challenging tasks like denoising and shape completion. We also show that the obtained deep embedding gives competitive performance when used for classification and promising results for shape interpolation

Reentrant ventricular arrhythmias in the late myocardial infarction period. 12. Spontaneous versus induced reentry and intramural versus epicardial circuits

One to 5 days after one-stage ligation of the left anterior descending coronary artery in dogs, reentrant excitation can be induced by programmed premature stimulation in the surviving electrophysiologically abnormal, thin epicardial layer overlying the infarct. In experiments in four dogs, reentrant excitation occurred “spontaneously” during a regular sinus or atria) rhythm. A tachycardia-dependent Wenckebach conduction sequence in a potentially reentrant pathway was the initiating mechanism for spontaneous reentrant tachycardias and was the basis for both manifest and concealed reentrant extrasystolic rhythms. In all dogs showing spontaneous reentry, reentrant excitation could also be induced by premature stimulation at cycle lengths much shorter than those associated with spontaneous reentry, and induced reentrant circuits were always different from those during spontaneous reentry. In two dogs, the reentrant circuit was located intramurally in close proximity to a patchy septal infarction.The study illustrates that irrespective of the anatomic localization of reentrant circuits (epicardial or intramural), their dimension (large or small) or their mechanism of initiation (programmed premature stimulation or “spontaneous”), reentrant excitation always occurred in a figure 8 configuration (or a modification thereof). The figure 8 model, rather than the ring model or the leading circle model, may be the common model of reentry in the mammalian heart

PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model

Improved adhesion for thermoplastic polymers using oxyfluorination

Industrial applications of thermoplastic polymers are often limited by their poor adhesion properties. In this work the effect of surface oxyfluorination on the adhesion properties was investigated for polyethylene (PE), polyoxymethylene (POM), polybutylene terephthalate (PBT) and polyamide 6 (PA6). The adhesive joint strength was quantified using lap-shear tests. These results were correlated with the changes in the chemical composition of the surface, determined by X-ray photoelectron spectroscopy (XPS), in the surface free energy, measured by the contact angle method, and in the topography, using white-light confocal microscopy. The adhesive strength is strongly improved for all four polymers, but the degree of this increase depends on the polymer type. The surface free energy shows a similar trend for all four polymers. A high surface free energy exceeding 50 mN/m was observed after oxy-fluorination, whereby the polar component was strongly predominant. Surface topography measurements show no significant increase of the surface roughness. So the effect of oxyfluorination results primarily in increased wettability and polarity, due to changes of the chemical composition of the surface. XPS measurements confirm the integration of fluorine and oxygen groups in the polymer chain, which correlates with the increased polarity

Deep Over-sampling Framework for Classifying Imbalanced Data

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to exploit the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings

Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p Peer reviewe

Transport control by coherent zonal flows in the core/edge transitional regime

3D Braginskii turbulence simulations show that the energy flux in the core/edge transition region of a tokamak is strongly modulated - locally and on average - by radially propagating, nearly coherent sinusoidal or solitary zonal flows. The flows are geodesic acoustic modes (GAM), which are primarily driven by the Stringer-Winsor term. The flow amplitude together with the average anomalous transport sensitively depend on the GAM frequency and on the magnetic curvature acting on the flows, which could be influenced in a real tokamak, e.g., by shaping the plasma cross section. The local modulation of the turbulence by the flows and the excitation of the flows are due to wave-kinetic effects, which have been studied for the first time in a turbulence simulation.Comment: 5 pages, 5 figures, submitted to PR

The Investigation of Structure Heterogeneous Joint Welds in Pipelines

Welding joints of dissimilar steels don’t withstand design life. One of the important causes of premature destructions can be the acceleration of steel structural degradation due to cyclic mechanical and thermal gradients. Two zones of tube from steel 12H18N9T, exhibiting the structural instability at early stages of the decomposition of a supersaturated solid austenite solution, were subjected to investigation. Methods of x-ray spectral and structure analysis, micro hardnessmetry were applied for the research. Made the following conclusions, inside and outside tube wall surfaces of hazardous zones in welding joint have different technological and resource characteristics. The microhardness very sensitive to changes of metal structure and can be regarded as integral characteristic of strength and ductility. The welding processes are responsible for the further fibering of tube wall structure, they impact to the characteristics of hot-resistance and long-term strength due to development of ring cracks in the welding joint of pipeline. The monitoring of microhardness and structural phase conversions can be used for control by changes of mechanical properties in result of post welding and reductive heat treatment of welding joints

“Am I my genes?”: Questions of identity among individuals confronting genetic disease

Purpose: To explore many questions raised by genetics concerning personal identities that have not been fully investigated. Methods: We interviewed in depth, for 2 hours each, 64 individuals who had or were at risk for Huntington disease, breast cancer, or alpha-1 antitrypsin deficiency. Results: These individuals struggled with several difficult issues of identity. They drew on a range of genotypes and phenotypes (e.g., family history alone; mutations, but no symptoms; or symptoms). They often felt that their predicament did not fit preexisting categories well (e.g., “sick,” “healthy,” “disabled,” “predisposed”), due in part to uncertainties involved (e.g., unclear prognoses, since mutations may not produce symptoms). Hence, individuals varied in how much genetics affected their identity, in what ways, and how negatively. Factors emerged related to disease, family history, and other sources of identity. These identities may, in turn, shape disclosure, coping, and other health decisions. Conclusions: Individuals struggle to construct a genetic identity. They view genetic information in highly subjective ways, varying widely in what aspects of genetic information they focus on and how. These data have important implications for education of providers (to assist patients with these issues), patients, and family members; and for research, to understand these issues more fully

Rap1 binding and a lipid-dependent helix in talin F1 domain promote integrin activation in tandem.

Rap1 GTPases bind effectors, such as RIAM, to enable talin1 to induce integrin activation. In addition, Rap1 binds directly to the talin1 F0 domain (F0); however, this interaction makes a limited contribution to integrin activation in CHO cells or platelets. Here, we show that talin1 F1 domain (F1) contains a previously undetected Rap1-binding site of similar affinity to that in F0. A structure-guided point mutant (R118E) in F1, which blocks Rap1 binding, abolishes the capacity of Rap1 to potentiate talin1-induced integrin activation. The capacity of F1 to mediate Rap1-dependent integrin activation depends on a unique loop in F1 that has a propensity to form a helix upon binding to membrane lipids. Basic membrane-facing residues of this helix are critical, as charge-reversal mutations led to dramatic suppression of talin1-dependent activation. Thus, a novel Rap1-binding site and a transient lipid-dependent helix in F1 work in tandem to enable a direct Rap1-talin1 interaction to cause integrin activation