Visual Odometry using Convolutional Neural Networks

Visual odometry is the process of tracking an agent\u27s motion over time using a visual sensor. The visual odometry problem has only been recently solved using traditional, non-machine learning techniques. Despite the success of neural networks at many related problems such as object recognition, feature detection, and optical flow, visual odometry still has not been solved with a deep learning technique. This paper attempts to implement several Convolutional Neural Networks to solve the visual odometry problem and compare slight variations in data preprocessing. The work presented is a step toward reaching a legitimate neural network solution

Evaluation of Candida spp. and Other Fungi in Feces from Dogs with Naturally Occurring Diabetes Mellitus

Diabetes mellitus is a common endocrinopathy in dogs and in most cases is analogous to type 1 diabetes mellitus (T1DM) in humans. Candida spp. is a common commensal fungi with higher prevalence and magnitude of growth in humans with T1DM. There is currently no published information about the fungal microbiome in diabetic dogs. Therefore, the objectives of this study were to (i) determine whether diabetic dogs were more likely to have Candida spp. or other types of fungi from feces compared to non-diabetic controls, and (ii) identify variables associated with fungi colonization. Fourteen diabetic dogs and 14 age, sex, and breed matched non-diabetic healthy control dogs were included in this prospective case–control study. Matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was used for fungal identification. Diabetic dogs had greater quantitative fungal growth compared to controls (p = 0.004). Moreover, female dogs were more likely to have fungi colonization than males (p = 0.02). All instances of Candida spp. and Aspergillus spp. colonization were exclusively identified in diabetic dogs. Serum fructosamine concentration was higher in diabetic dogs with fecal colonization of Candida spp. compared to diabetic dogs without growth (p = 0.03). Our results indicate that the fungal microbiome in feces is altered in diabetic dogs, which seem to favor an increased prevalence of Candida spp. and higher quantitative fungal growth. Moreover, female sex and glycemic control could affect the intestinal mycobiome

Current status and recommendations for the future of research, teaching, and testing in the biological sciences of radiation oncology: report of the American Society for Radiation Oncology Cancer Biology/Radiation Biology Task Force, executive summary.

In early 2011, a dialogue was initiated within the Board of Directors (BOD) of the American Society for Radiation Oncology (ASTRO) regarding the future of the basic sciences of the specialty, primarily focused on the current state and potential future direction of basic research within radiation oncology. After consideration of the complexity of the issues involved and the precise nature of the undertaking, in August 2011, the BOD empanelled a Cancer Biology/Radiation Biology Task Force (TF). The TF was charged with developing an accurate snapshot of the current state of basic (preclinical) research in radiation oncology from the perspective of relevance to the modern clinical practice of radiation oncology as well as the education of our trainees and attending physicians in the biological sciences. The TF was further charged with making suggestions as to critical areas of biological basic research investigation that might be most likely to maintain and build further the scientific foundation and vitality of radiation oncology as an independent and vibrant medical specialty. It was not within the scope of service of the TF to consider the quality of ongoing research efforts within the broader radiation oncology space, to presume to consider their future potential, or to discourage in any way the investigators committed to areas of interest other than those targeted. The TF charge specifically precluded consideration of research issues related to technology, physics, or clinical investigations. This document represents an Executive Summary of the Task Force report

Current status and recommendations for the future of research, teaching, and testing in the biological sciences of radiation oncology:Report of the american society for radiation oncology cancer biology/radiation biology task force, executive summary

In early 2011, a dialogue was initiated within the Board of Directors (BOD) of the American Society for Radiation Oncology (ASTRO) regarding the future of the basic sciences of the specialty, primarily focused on the current state and potential future direction of basic research within radiation oncology. After consideration of the complexity of the issues involved and the precise nature of the undertaking, in August 2011, the BOD empanelled a Cancer Biology/Radiation Biology Task Force (TF). The TF was charged with developing an accurate snapshot of the current state of basic (preclinical) research in radiation oncology from the perspective of relevance to the modern clinical practice of radiation oncology as well as the education of our trainees and attending physicians in the biological sciences. The TF was further charged with making suggestions as to critical areas of biological basic research investigation that might be most likely to maintain and build further the scientific foundation and vitality of radiation oncology as an independent and vibrant medical specialty. It was not within the scope of service of the TF to consider the quality of ongoing research efforts within the broader radiation oncology space, to presume to consider their future potential, or to discourage in any way the investigators committed to areas of interest other than those targeted. The TF charge specifically precluded consideration of research issues related to technology, physics, or clinical investigations. This document represents an Executive Summary of the Task Force report

Tabula nearly rasa: probing the linguistic knowledge of character-level neural language models trained on unsegmented text

Recurrent neural networks (RNNs) have reached striking performance in many natural language processing tasks. This has renewed interest in whether these generic sequence processing devices are inducing genuine linguistic knowledge. Nearly all current analytical studies, however, initialize the RNNs with a vocabulary of known words, and feed them tokenized input during training. We present a multi-lingual study of the linguistic knowledge encoded in RNNs trained as character-level language models, on input data with word boundaries removed. These networks face a tougher and more cognitively realistic task, having to discover any useful linguistic unit from scratch based on input statistics. The results show that our “near tabula rasa” RNNs are mostly able to solve morphological, syntactic and semantic tasks that intuitively presuppose word-level knowledge, and indeed they learned, to some extent, to track word boundaries. Our study opens the door to speculations about the necessity of an explicit, rigid word lexicon in language learning and usage