How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks

Deep neural networks typically outperform more traditional machine learning models in their ability to classify complex data, and yet is not clear how the individual hidden layers of a deep network contribute to the overall classification performance. We thus introduce a Generalized Discrimination Value (GDV) that measures, in a non-invasive manner, how well different data classes separate in each given network layer. The GDV can be used for the automatic tuning of hyper-parameters, such as the width profile and the total depth of a network. Moreover, the layer-dependent GDV(L) provides new insights into the data transformations that self-organize during training: In the case of multi-layer perceptrons trained with error backpropagation, we find that classification of highly complex data sets requires a temporal {\em reduction} of class separability, marked by a characteristic 'energy barrier' in the initial part of the GDV(L) curve. Even more surprisingly, for a given data set, the GDV(L) is running through a fixed 'master curve', independently from the total number of network layers. Furthermore, applying the GDV to Deep Belief Networks reveals that also unsupervised training with the Contrastive Divergence method can systematically increase class separability over tens of layers, even though the system does not 'know' the desired class labels. These results indicate that the GDV may become a useful tool to open the black box of deep learning

A time series model of the occurrence of gastric dilatation-volvulus in a population of dogs

<p>Abstract</p> <p>Background</p> <p>Gastric dilatation-volvulus (GDV) is a life-threatening condition of mammals, with increased risk in large breed dogs. The study of its etiological factors is difficult due to the variety of possible living conditions. The association between meteorological events and the occurrence of GDV has been postulated but remains unclear. This study introduces the binary time series approach to the investigation of the possible meteorological risk factors for GDV. The data collected in a population of high-risk working dogs in Texas was used.</p> <p>Results</p> <p>Minimum and maximum daily atmospheric pressure on the day of GDV event and the maximum daily atmospheric pressure on the day before the GDV event were positively associated with the probability of GDV. All of the odds/multiplicative factors of a day being GDV day were interpreted conditionally on the past GDV occurrences. There was minimal difference between the binary and Poisson general linear models.</p> <p>Conclusion</p> <p>Time series modeling provided a novel method for evaluating the association between meteorological variables and GDV in a large population of dogs. Appropriate application of this method was enhanced by a common environment for the dogs and availability of meteorological data. The potential interaction between weather changes and patient risk factors for GDV deserves further investigation.</p

Are dogs that are fed from a raised bowl at an increased risk of gastric dilation volvulus compared with floor-fed dogs?

<p>There are only two studies that study the effect of raised feeders on the risk of Gastric Dilatation Volvulus (GDV) and their findings conflict. Only one study found a significant effect of feeder height, with large and giant breeds fed from a raised feeder being at an increased risk of GDV floor fed dogs. However, these authors found that, where the feeder was raised, the height of the feeder that increased the GDV risk was affected by the size of the dog. Large breed dogs were more likely to develop a GDV if fed from a bowl ≤ 1 foot tall, whereas giant breed dogs were more likely to develop a GDV if fed from a bowl &gt; 1 foot tall. No studies found that feeding from a raised feeder reduced the risk of GDV relative to feeding from the floor. Therefore, the safest option in the absence of further evidence is to advise that owners of ‘at risk’ dogs feed from a feeder on the floor. This may not reduce the risk of GDV, but there is no evidence to suggest that it will increase the risk. </p><br /> <img src="https://www.veterinaryevidence.org/rcvskmod/icons/oa-icon.jpg" alt="Open Access" /> <img src="https://www.veterinaryevidence.org/rcvskmod/icons/pr-icon.jpg" alt="Peer Reviewed" /

Causal relationships between the parameters of gas discharge visualization and phagocytosis

Background. Previously we have been shown that between parameters of GDV and principal neuroendocrine factors of adaptation exist strong canonical correlation. In the next study, we detected very strong (R=0,994) integral canonical correlation between the parameters of GDV and Immunity. This study, conducted in the same contingent, will analyze the relationships between GDV parameters, on the one hand, and Phagocytosis parameters, on the other. Material and Methods. We observed twice ten women and ten men aged 33-76 years without clinical diagnose. In the morning in basal conditions at first registered kirlianogram by the method of GDV by the device “GDV Chamber” (“Biotechprogress”, SPb, RF). Than we estimated the parameters of Phagocytic function of neutrophils. Results processed by method of canonical analysis, using the software package “Statistica 5.5”. Results. According to the value of the canonical correlation coefficient R with GDV parameters, the registered Phagocytosis parameters are arranged in the following order: activity (0,616), bactericidal capacity (0,493), completeness (0,489) and intensity (0,484) of Phagocytosis of E. coli; completeness (0,482), bactericidal capacity (0,448), activity (0,364) and intensity (0,338) of Phagocytosis of Staph. aureus. Coefficient of canonical correlation between parameters of GDV, on the one hand, and Phagocytosis, on the other hand, makes 0,847. Conclusion. The above data, taken together with the previous ones, state that between parameters of Neuroendocrine-Immune complex and GDV exist strong canonical correlation suggesting suitability of the latter method

The Effect of Experimental Gastric Dilatation-Volvulus on the Adenosine Triphosphate Content and Cellular Permeability of the Canine Gastric and Jejunal Mucosa

This study was designed to characterize changes in the content of adenosine triphosphate (ATP) and cellular permeability of gastric and jejunal mucosa during gastric dilatation-volvulus (GDV). Our hypothesis stated that experimental GDV would result in a decrease in ATP content and cellular integrity of the gastric and intestinal mucosa that may improve after decompression and derotation of the stomach. Fifteen medium-to-large mixed-breed male dogs were randomly divided into three groups: GDV dogs, Ischemia dogs and Control dogs. All dogs were maintained on gas anesthesia for 210 minutes. The GDV dogs had a GDV for 120 minutes with a 90-minute period of decompression, gastric derotation and reperfusion equaling 210 minutes. The Ischemia dogs had a GDV maintained for 210 minutes. The Control dogs had no gastric manipulation. Tissue samples were taken in all dogs from the fundus, pylorus and jejunum at 0 (baseline), 120 and 210 minutes. Quantification of mucosal ATP (ìg/ml) was accomplished using high performance liquid chromatography (HPLC). Mucosal cellular permeability was evaluated using Ussing chambers by measuring conductance over 180 minutes (ÄGt). A significant decrease in ATP below baseline occurred in the fundic mucosa in the Ischemia dogs. A significant decrease in ATP was seen in the jejunual mucosa in GDV and Ischemia dogs between baseline and 120 minutes; however, a return to baseline levels was seen in GDV dogs from 120 to 210 minutes. No significant change in Gt from baseline was seen in the fundic mucosa in any dogs. A significant increase in Gt was seen in the jejunal mucosa in the GDV and Ischemia dogs between baseline and 120 minutes. The increase in Gt continued from 120 to 210 minutes and was most profound in the GDV dogs. The decrease in fundic ATP did not coincide with permeability changes. Interestingly, the jejunum showed profound changes in both ATP concentration and permeability. It is the authors’ opinion that with the decrease in ATP concentration and loss of cellular integrity in the jejunal mucosa, the jejunum could be implicated as a contributor to complications associated with GDV

Reduction of atmospheric ammonia (NH3) and incidence of pulmonary lesions in mice kept in plenum chamber microenvironmental ventilation system

In the plenum chamber microenvironmental ventilation system (MEV) for laboratory animal housing, air exchanges are made directly inside animal cages. In this study we measured the daily levels of ammonia (NH3) in cages without beddingchanges and made comparative histopathologieal analyscs of mice born and kept in two different systems. Mice were kept under the MEV (n = 40, in five cages) and general diluting ventilation (GDV) (n= 32, in four cages) systems for nine days. In the MEV system. NH3 was not detected in the first three days, the highest concentration occurred on the seventh day (5.00 +/- 2.90ppm). On the ninth day, a level of 2.50 +/- 1.70 ppm was measured. In GDV. NH3 was detected from the first day, and the highest levels were observed on the third and fifth day (31.20 +/- 12.50 ppm), respectively. Front the fourth to the ninth day, the GDV system presented higher concentrations of NH3 than the MIEV system (p&lt; 0.05). Histopathologieal analyses of lungs of six female mice from each group were performed after keeping mice in the two systems for 56 days. In the score evaluation, the incidence ofehrunie focal pneumonia, catarrhal bronchitis, and interstitial pneumonia was significantly higher (p&lt; 0.05) in the GDV group. Using morphometry, it was observed that animals from the GDV system showed a significant increase (p&lt;005) in the volume fractions of the epithelium, when compared to thc MEV system (2450 +/- 5.60 um3/um2 and 19.70 +/- 4.90 um3/um2, respectively). An estimator of the numerical density of nuclei over 100 um of basement membrane was significantly higher (p&lt;0.05) in animals from the GDV system. when compared to animals from theMEV system (14.60 +/- 3.00 and 10.84 +/- 3.00, respectively). It was shown that animals kept in the MEV system presented better health condition than animals kept in the GDV system

GDV images: Current research and results

We use statistical analysis and machine learning to interpret the GDV coronas of fruits and human’s fingers in order to verify two hypotheses: (A) the GDV images contain useful information about the object/patient and (B) the human bioelectromagnetic field can be influenced by some outside factors. We performed several independent studies, three of which we here briefly describe: (a) recording coronas of berries of different grapevines, (b) detecting the influence of drinking the tap water from ordinary glass and energetic glass K2000, and (c) detecting the influence of natural energy source in Tunjice near Kamnik, Slovenia on the human bioelectromagnetic field. All three studies, as well as some other studies described elsewhere, gave significant results and therefore support both hypotheses

Gas Discharge Visualization: An Imaging and Modeling Tool for Medical Biometrics

The need for automated identification of a disease makes the issue of medical biometrics very current in our society. Not all biometric tools available provide real-time feedback. We introduce gas discharge visualization (GDV) technique as one of the biometric tools that have the potential to identify deviations from the normal functional state at early stages and in real time. GDV is a nonintrusive technique to capture the physiological and psychoemotional status of a person and the functional status of different organs and organ systems through the electrophotonic emissions of fingertips placed on the surface of an impulse analyzer. This paper first introduces biometrics and its different types and then specifically focuses on medical biometrics and the potential applications of GDV in medical biometrics. We also present our previous experience with GDV in the research regarding autism and the potential use of GDV in combination with computer science for the potential development of biological pattern/biomarker for different kinds of health abnormalities including cancer and mental diseases