Resistencia adhesiva de dos agentes de fijación a base de resinas

Los agentes de fijación a base de resina autoacondicionantes - autoadhesivos (SE/SA) fueron desarrollados con el objetivo de simplificar la técnica de cementado a un solo paso, ahorrando tiempo y errores por parte del operador. El objetivo de este trabajo fue evaluar si dicha simplificación influye negativamente sobre los valores adhesivos tal como sucediera en los sistemas adhesivos a esmalte y dentina autoacondicionantes. Se confeccionaron 20 probetas de dentina en las cuales se cementaron probetas de resina compuesta: 10 muestras fueron cementadas con un cemento de grabado total (TE) y 10 con un cemento SE/SA y conservadas a 37 ºC y 100 % de humedad hasta su ensayo mecánico. Los resultados observados muestran una disminución en la resistencia adhesiva de los cementos SE/SA. Esto se explica por la menor traba micro mecánica generada por la presencia del barro dentinario y por el menor coeficiente de penetración de los monómeros modificados en estos cementos comparados con los cementos de resinas convencionales.The self-etching - self-adhesive (SE/SA) fixing agents based on resin were developed in order to simplify the cement technique to one step, saving time and avoiding mistakes from the operator. The goal of this study is to evaluate if such simplification can influence negatively on the adhesive values as it may happen in the case of the self-etching techniques on enamel and dentin. 20 dentin probes were made. Composite resin probes were cemented: 10 samples were cemented with total-etch cement (TE) and 10 samples with SE/SA cement. They were stored at 37ºC and 100% humidity until they were mechanically tested. The observational results show a decrease on the SE/ SA cements adhesive resistance. This is explained by the minor micromechanical bond generated by the presence of smear layer and the minor penetration coefficient of the modified monomers on these cements compared with the conventional resin cements.Fil: Peña, José. Universidad Nacional de Cuyo. Facultad de Odontologí

Multiplicative Auditory Spatial Receptive Fields Created by a Hierarchy of Population Codes

A multiplicative combination of tuning to interaural time difference (ITD) and interaural level difference (ILD) contributes to the generation of spatially selective auditory neurons in the owl's midbrain. Previous analyses of multiplicative responses in the owl have not taken into consideration the frequency-dependence of ITD and ILD cues that occur under natural listening conditions. Here, we present a model for the responses of ITD- and ILD-sensitive neurons in the barn owl's inferior colliculus which satisfies constraints raised by experimental data on frequency convergence, multiplicative interaction of ITD and ILD, and response properties of afferent neurons. We propose that multiplication between ITD- and ILD-dependent signals occurs only within frequency channels and that frequency integration occurs using a linear-threshold mechanism. The model reproduces the experimentally observed nonlinear responses to ITD and ILD in the inferior colliculus, with greater accuracy than previous models. We show that linear-threshold frequency integration allows the system to represent multiple sound sources with natural sound localization cues, whereas multiplicative frequency integration does not. Nonlinear responses in the owl's inferior colliculus can thus be generated using a combination of cellular and network mechanisms, showing that multiple elements of previous theories can be combined in a single system

Cross-Correlation in the Auditory Coincidence Detectors of Owls

Interaural time difference (ITD) plays a central role in many auditory functions, most importantly in sound localization. The classic model for how ITD is computed was put forth by Jeffress (1948). One of the predictions of the Jeffress model is that the neurons that compute ITD should behave as cross-correlators. Whereas cross-correlation-like properties of the ITD-computing neurons have been reported, attempts to show that the shape of the ITD response function is determined by the spectral tuning of the neuron, a core prediction of cross-correlation, have been unsuccessful. Using reverse correlation analysis, we demonstrate in the barn owl that the relationship between the spectral tuning and the ITD response of the ITD-computing neurons is that predicted by cross-correlation. Moreover, we show that a model of coincidence detector responses derived from responses to binaurally uncorrelated noise is consistent with binaural interaction based on cross-correlation. These results are thus consistent with one of the key tenets of the Jeffress model. Our work sets forth both the methodology to answer whether cross-correlation describes coincidence detector responses and a demonstration that in the barn owl, the result is that expected by theory

On the number of terms in the middle of almost split sequences over cycle-finite artin algebras

We prove that the number of terms in the middle of an almost split sequence in the module category of a cycle-finite artin algebra is bounded by 5

Walk entropies on graphs

Entropies based on walks on graphs and on their line-graphs are defined. They are based on the summation over diagonal and off-diagonal elements of the thermal Green’s function of a graph also known as the communicability. The walk entropies are strongly related to the walk regularity of graphs and line-graphs. They are not biased by the graph size and have significantly better correlation with the inverse participation ratio of the eigenmodes of the adjacency matrix than other graph entropies. The temperature dependence of the walk entropies is also discussed. In particular, the walk entropy of graphs is shown to be non-monotonic for regular but non-walk-regular graphs in contrast to non-regular graphs

Performance Evaluation of cuDNN Convolution Algorithms on NVIDIA Volta GPUs

Convolutional neural networks (CNNs) have recently attracted considerable attention due to their outstanding accuracy in applications, such as image recognition and natural language processing. While one advantage of the CNNs over other types of neural networks is their reduced computational cost, faster execution is still desired for both training and inference. Since convolution operations pose most of the execution time, multiple algorithms were and are being developed with the aim of accelerating this type of operations. However, due to the wide range of convolution parameter configurations used in the CNNs and the possible data type representations, it is not straightforward to assess in advance which of the available algorithms will be the best performing in each particular case. In this paper, we present a performance evaluation of the convolution algorithms provided by the cuDNN, the library used by most deep learning frameworks for their GPU operations. In our analysis, we leverage the convolution parameter configurations from widely used the CNNs and discuss which algorithms are better suited depending on the convolution parameters for both 32 and 16-bit floating-point (FP) data representations. Our results show that the filter size and the number of inputs are the most significant parameters when selecting a GPU convolution algorithm for 32-bit FP data. For 16-bit FP, leveraging specialized arithmetic units (NVIDIA Tensor Cores) is key to obtain the best performance.This work was supported by the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie under Grant 749516, and in part by the Spanish Juan de la Cierva under Grant IJCI-2017-33511Peer ReviewedPostprint (published version