Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal

Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data. The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of travel, through buildings and outdoor regions that collectively exhibit large variations in visual appearance, self-similarity, and connectivity. We compare pretrained visual encoders that enable precomputation of visual embeddings to achieve a throughput of tens of thousands of transitions per second at training time on a commodity desktop computer, allowing agents to learn from millions of trajectories of experience in a matter of hours. We propose multiple forms of computationally efficient stochastic augmentation to enable the learned policy to generalise beyond these precomputed embeddings, and demonstrate successful deployment of the learned policy on the real robot without fine tuning, despite environmental appearance differences at test time. The dataset and code required to reproduce these results and apply the technique to other datasets and robots is made publicly available at rl-navigation.github.io/deployable

The Development and Characterization of a First Generation Carbon Nanotube X-ray Based Microbeam Radiation Therapy System

Microbeam radiation therapy (MRT) is a new type of cancer treatment currently being studied at scattered synchrotron sites throughout the world. It has been shown to be capable of ablating aggressive brain tumors in rats while almost completely sparing the surrounding normal tissue. This promising technique has yet to find its way to the clinic, however, because the radiobiological mechanisms behind its efficacy are still largely unknown. This is partly due to the lack of a compact device that could facilitate more large scale research. The challenges inherent to creating a compact device lie within the structure of MRT, which uses parallel arrays of ultra high-dose, orthovoltage, microplanar beams on the order of 100μm thick and separated by four to ten times their width. Because of focal spot limitations, current commercial orthovoltage devices are simply not capable of creating such arrays at dose rates high enough for effective treatment while maintaining the microbeam pattern necessary to retain the high therapeutic ratio of the technique. Therefore, the development of a compact MRT device using carbon nanotube (CNT) cathode based X–ray technology is presented here. CNT cathodes have been shown to be capable of creating novel focal spot arrays on a single anode while being robust enough for long-term use in X-ray tubes. Using these cathodes, an X–ray tube with a single focal line has been created for the delivery of MRT dose distributions in radiobiological studies on small animals. In this work, the development process and final design of this specialized device will be detailed, along with the optimization and stabilization of its use for small animal studies. In addition, a detailed characterization of its final capabilities will be given; including a comprehensive measurement of its X–ray focal line dimensions, a description and evaluation of its collimator alignment and microbeam dimensions, and a full-scale phantom-based quantification of its dosimetric output. Finally, future project directions will be described briefly along with plans for a second generation device. Based on the results of this work, it is the author’s belief that compact CNT MRT devices have definite commercialization potential for radiobiological research.Doctor of Philosoph

Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework

We propose a self-supervised method to learn feature representations from videos. A standard approach in traditional self-supervised methods uses positive-negative data pairs to train with contrastive learning strategy. In such a case, different modalities of the same video are treated as positives and video clips from a different video are treated as negatives. Because the spatio-temporal information is important for video representation, we extend the negative samples by introducing intra-negative samples, which are transformed from the same anchor video by breaking temporal relations in video clips. With the proposed Inter-Intra Contrastive (IIC) framework, we can train spatio-temporal convolutional networks to learn video representations. There are many flexible options in our IIC framework and we conduct experiments by using several different configurations. Evaluations are conducted on video retrieval and video recognition tasks using the learned video representation. Our proposed IIC outperforms current state-of-the-art results by a large margin, such as 16.7% and 9.5% points improvements in top-1 accuracy on UCF101 and HMDB51 datasets for video retrieval, respectively. For video recognition, improvements can also be obtained on these two benchmark datasets. Code is available at https://github.com/BestJuly/Inter-intra-video-contrastive-learning.Comment: Accepted by ACMMM 2020. Our project page is at https://bestjuly.github.io/Inter-intra-video-contrastive-learning

Lactation and neonatal nutrition: defining and refining the critical questions.

This paper resulted from a conference entitled "Lactation and Milk: Defining and refining the critical questions" held at the University of Colorado School of Medicine from January 18-20, 2012. The mission of the conference was to identify unresolved questions and set future goals for research into human milk composition, mammary development and lactation. We first outline the unanswered questions regarding the composition of human milk (Section I) and the mechanisms by which milk components affect neonatal development, growth and health and recommend models for future research. Emerging questions about how milk components affect cognitive development and behavioral phenotype of the offspring are presented in Section II. In Section III we outline the important unanswered questions about regulation of mammary gland development, the heritability of defects, the effects of maternal nutrition, disease, metabolic status, and therapeutic drugs upon the subsequent lactation. Questions surrounding breastfeeding practice are also highlighted. In Section IV we describe the specific nutritional challenges faced by three different populations, namely preterm infants, infants born to obese mothers who may or may not have gestational diabetes, and infants born to undernourished mothers. The recognition that multidisciplinary training is critical to advancing the field led us to formulate specific training recommendations in Section V. Our recommendations for research emphasis are summarized in Section VI. In sum, we present a roadmap for multidisciplinary research into all aspects of human lactation, milk and its role in infant nutrition for the next decade and beyond

Nanotube x-ray for cancer therapy: a compact microbeam radiation therapy system for brain tumor treatment

Microbeam radiation therapy (MRT) is a promising preclinical modality for cancer treatment, with remarkable preferential tumoricidal effects, that is, tumor eradication without damaging normal tissue functions. Significant lifespan extension has been demonstrated in brain tumor-bearing small animals treated with MRT. So far, MRT experiments can only be performed in a few synchrotron facilities around the world. Limited access to MRT facilities prevents this enormously promising radiotherapy technology from reaching the broader biomedical research community and hinders its potential clinical translation. We recently demonstrated, for the first time, the feasibility of generating microbeam radiation in a laboratory environment using a carbon nanotube x-ray source array and performed initial small animal studies with various brain tumor models. This new nanotechnology-enabled microbeam delivery method, although still in its infancy, has shown promise for achieving comparable therapeutic effects to synchrotron MRT and has offered a potential pathway for clinical translation

Physiologically gated microbeam radiation using a field emission x-ray source array

Microbeam radiation therapy (MRT) uses narrow planes of high dose radiation beams to treat cancerous tumors. This experimental therapy method based on synchrotron radiation has been shown to spare normal tissue at up to 1000 Gy of peak entrance dose while still being effective in tumor eradication and extending the lifetime of tumor-bearing small animal models. Motion during treatment can lead to significant movement of microbeam positions resulting in broader beam width and lower peak to valley dose ratio (PVDR), which reduces the effectiveness of MRT. Recently, the authors have demonstrated the feasibility of generating microbeam radiation for small animal treatment using a carbon nanotube (CNT) x-ray source array. The purpose of this study is to incorporate physiological gating to the CNT microbeam irradiator to minimize motion-induced microbeam blurring

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Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201