Reinforcement Learning via AIXI Approximation

This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motivate the design of practical algorithms. We answer this hitherto open question in the affirmative, by providing the first computationally feasible approximation to the AIXI agent. To develop our approximation, we introduce a Monte Carlo Tree Search algorithm along with an agent-specific extension of the Context Tree Weighting algorithm. Empirically, we present a set of encouraging results on a number of stochastic, unknown, and partially observable domains.Comment: 8 LaTeX pages, 1 figur

Structural Color 3D Printing By Shrinking Photonic Crystals

The rings, spots and stripes found on some butterflies, Pachyrhynchus weevils, and many chameleons are notable examples of natural organisms employing photonic crystals to produce colorful patterns. Despite advances in nanotechnology, we still lack the ability to print arbitrary colors and shapes in all three dimensions at this microscopic length scale. Commercial nanoscale 3D printers based on two-photon polymerization are incapable of patterning photonic crystal structures with the requisite ~300 nm lattice constant to achieve photonic stopbands/ bandgaps in the visible spectrum and generate colors. Here, we introduce a means to produce 3D-printed photonic crystals with a 5x reduction in lattice constants (periodicity as small as 280 nm), achieving sub-100-nm features with a full range of colors. The reliability of this process enables us to engineer the bandstructures of woodpile photonic crystals that match experiments, showing that observed colors can be attributed to either slow light modes or stopbands. With these lattice structures as 3D color volumetric elements (voxels), we printed 3D microscopic scale objects, including the first multi-color microscopic model of the Eiffel Tower measuring only 39-microns tall with a color pixel size of 1.45 microns. The technology to print 3D structures in color at the microscopic scale promises the direct patterning and integration of spectrally selective devices, such as photonic crystal-based color filters, onto free-form optical elements and curved surfaces

The UN Sustainable Development Goals (SDGs): Contributions from the Humanities

It is widely agreed that achieving the United Nations Sustainable Development Goals requires the insights, knowledge and comparative perspectives of Humanities disciplines. The UNESCO Hangzhou Declaration of 2013 highlighted the importance of culture as ‘an enabler and driver of sustainable development’. Yet the Humanities have so far featured relatively little in work on the SDGs

An Empirical Evaluation of Deep Learning on Highway Driving

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio

Antibodies to Enteroviruses in Cerebrospinal Fluid of Patients with Acute Flaccid Myelitis.

Acute flaccid myelitis (AFM) has caused motor paralysis in >560 children in the United States since 2014. The temporal association of enterovirus (EV) outbreaks with increases in AFM cases and reports of fever, respiratory, or gastrointestinal illness prior to AFM in >90% of cases suggest a role for infectious agents. Cerebrospinal fluid (CSF) from 14 AFM and 5 non-AFM patients with central nervous system (CNS) diseases in 2018 were investigated by viral-capture high-throughput sequencing (VirCapSeq-VERT system). These CSF and serum samples, as well as multiple controls, were tested for antibodies to human EVs using peptide microarrays. EV RNA was confirmed in CSF from only 1 adult AFM case and 1 non-AFM case. In contrast, antibodies to EV peptides were present in CSF of 11 of 14 AFM patients (79%), significantly higher than controls, including non-AFM patients (1/5 [20%]), children with Kawasaki disease (0/10), and adults with non-AFM CNS diseases (2/11 [18%]) (P = 0.023, 0.0001, and 0.0028, respectively). Six of 14 CSF samples (43%) and 8 of 11 sera (73%) from AFM patients were immunoreactive to an EV-D68-specific peptide, whereas the three control groups were not immunoreactive in either CSF (0/5, 0/10, and 0/11; P = 0.008, 0.0003, and 0.035, respectively) or sera (0/2, 0/8, and 0/5; P = 0.139, 0.002, and 0.009, respectively).IMPORTANCE The presence in cerebrospinal fluid of antibodies to EV peptides at higher levels than non-AFM controls supports the plausibility of a link between EV infection and AFM that warrants further investigation and has the potential to lead to strategies for diagnosis and prevention of disease