Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings

Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream applications in robotics. Existing approaches either compute dense keypoint embeddings in a single forward pass, meaning the model is trained to track everything at once, or allocate their full capacity to a sparse predefined set of points, trading generality for accuracy. In this paper we explore a middle ground based on the observation that the number of relevant points at a given time are typically relatively few, e.g. grasp points on a target object. Our main contribution is a novel architecture, inspired by few-shot task adaptation, which allows a sparse-style network to condition on a keypoint embedding that indicates which point to track. Our central finding is that this approach provides the generality of dense-embedding models, while offering accuracy significantly closer to sparse-keypoint approaches. We present results illustrating this capacity vs. accuracy trade-off, and demonstrate the ability to zero-shot transfer to new object instances (within-class) using a real-robot pick-and-place task

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

Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks

Deep neural networks (DNNs) are powerful learning machines that have enabled breakthroughs in several domains. In this work, we introduce a new retrospective loss to improve the training of deep neural network models by utilizing the prior experience available in past model states during training. Minimizing the retrospective loss, along with the task-specific loss, pushes the parameter state at the current training step towards the optimal parameter state while pulling it away from the parameter state at a previous training step. Although a simple idea, we analyze the method as well as to conduct comprehensive sets of experiments across domains - images, speech, text, and graphs - to show that the proposed loss results in improved performance across input domains, tasks, and architectures.Comment: Accepted at KDD 2020; The first two authors contributed equall

Vector-based navigation using grid-like representations in artificial agents

Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex. Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space and is critical for integrating self-motion (path integration) and planning direct trajectories to goals (vector-based navigation). Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types12. We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments—optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation, demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments

A first generation compact microbeam radiation therapy system based on carbon nanotube X-ray technology

We have developed a compact microbeam radiation therapy device using carbon nanotube cathodes to create a linear array of narrow focal line segments on a tungsten anode and a custom collimator assembly to select a slice of the resulting wedge-shaped radiation pattern. Effective focal line width was measured to be 131 μm, resulting in a microbeam width of ∼300 μm. The instantaneous dose rate was projected to be 2 Gy/s at full-power. Peak to valley dose ratio was measured to be >17 when a 1.4 mm microbeam separation was employed. Finally, multiple microbeams were delivered to a mouse with beam paths verified through histology

Treating Brain Tumor with Microbeam Radiation Generated by a Compact Carbon-Nanotube-Based Irradiator: Initial Radiation Efficacy Study

Microbeam radiation treatment (MRT) using synchrotron radiation has shown great promise in the treatment of brain tumors, with a demonstrated ability to eradicate the tumor while sparing normal tissue in small animal models. With the goal of expediting the advancement of MRT research beyond the limited number of synchrotron facilities in the world, we recently developed a compact laboratory-scale microbeam irradiator using carbon nanotube (CNT) field emission-based X-ray source array technology. The focus of this study is to evaluate the effects of the microbeam radiation generated by this compact irradiator in terms of tumor control and normal tissue damage in a mouse brain tumor model. Mice with U87MG human glioblastoma were treated with sham irradiation, low-dose MRT, high-dose MRT or 10 Gy broad-beam radiation treatment (BRT). The microbeams were 280 µm wide and spaced at 900 µm center-to-center with peak dose at either 48 Gy (low-dose MRT) or 72 Gy (high-dose MRT). Survival studies showed that the mice treated with both MRT protocols had a significantly extended life span compared to the untreated control group (31.4 and 48.5% of life extension for low- and high-dose MRT, respectively) and had similar survival to the BRT group. Immunostaining on MRT mice demonstrated much higher DNA damage and apoptosis level in tumor tissue compared to the normal brain tissue. Apoptosis in normal tissue was significantly lower in the low-dose MRT group compared to that in the BRT group at 48 h postirradiation. Interestingly, there was a significantly higher level of cell proliferation in the MRT-treated normal tissue compared to that in the BRT-treated mice, indicating rapid normal tissue repairing process after MRT. Microbeam radiation exposure on normal brain tissue causes little apoptosis and no macrophage infiltration at 30 days after exposure. This study is the first biological assessment on MRT effects using the compact CNT-based irradiator. It provides an alternative technology that can enable widespread MRT research on mechanistic studies using a preclinical model, as well as further translational research towards clinical applications

Image-guided microbeam irradiation to brain tumour bearing mice using a carbon nanotube x-ray source array

Microbeam radiation therapy (MRT) is a promising experimental and preclinical radiotherapy method for cancer treatment. Synchrotron based MRT experiments have shown that spatially fractionated microbeam radiation has the unique capability of preferentially eradicating tumour cells while sparing normal tissue in brain tumour bearing animal models. We recently demonstrated the feasibility of generating orthovoltage microbeam radiation with an adjustable microbeam width using a carbon nanotube based X-ray source array. Here we report the preliminary results from our efforts in developing an image guidance procedure for the targeted delivery of the narrow microbeams to the small tumour region in the mouse brain. Magnetic resonance imaging was used for tumour identification, and on-board X-ray radiography was used for imaging of landmarks without contrast agents. The two images were aligned using 2D rigid body image registration to determine the relative position of the tumour with respect to a landmark. The targeting accuracy and consistency were evaluated by first irradiating a group of mice inoculated with U87 human glioma brain tumours using the present protocol and then determining the locations of the microbeam radiation tracks using γ-H2AX immunofluorescence staining. The histology results showed that among 14 mice irradiated, 11 received the prescribed number of microbeams on the targeted tumour, with an average localization accuracy of 454 μm measured directly from the histology (537 μm if measured from the registered histological images). Two mice received one of the three prescribed microbeams on the tumour site. One mouse was excluded from the analysis due to tissue staining errors