Holographic Generative Memory: Neurally Inspired One-Shot Learning with Memory Augmented Neural Networks

Humans quickly parse and categorize stimuli by combining perceptual information and previously learned knowledge. We are capable of learning new information quickly with only a few observations, and sometimes even a single observation. This one-shot learning (OSL) capability is still very difficult to realize in machine learning models. Novelty is commonly thought to be the primary driver for OSL. However, neuroscience literature shows that biological OSL mechanisms are guided by uncertainty, rather than novelty, motivating us to explore this idea for machine learning. In this work, we investigate OSL for neural networks using more robust compositional knowledge representations and a biologically inspired uncertainty mechanism to modulate the rate of learning. We introduce several new neural network models that combine Holographic Reduced Representation (HRR) and Variational Autoencoders. Extending these new models culminates in the Holographic Generative Memory (HGMEM) model. HGMEM is a novel unsupervised memory augmented neural network. It offers solutions to many of the practical drawbacks associated with HRRs while also providing storage, recall, and generation of latent compositional knowledge representations. Uncertainty is measured as a native part of HGMEM operation by applying trained probabilistic dropout to fully-connected layers. During training, the learning rate is modulated using these uncertainty measurements in a manner inspired by our motivating neuroscience mechanism for OSL. Model performance is demonstrated on several image datasets with experiments that reflect our theoretical approach

Convolutional Drift Networks for Video Classification

Analyzing spatio-temporal data like video is a challenging task that requires processing visual and temporal information effectively. Convolutional Neural Networks have shown promise as baseline fixed feature extractors through transfer learning, a technique that helps minimize the training cost on visual information. Temporal information is often handled using hand-crafted features or Recurrent Neural Networks, but this can be overly specific or prohibitively complex. Building a fully trainable system that can efficiently analyze spatio-temporal data without hand-crafted features or complex training is an open challenge. We present a new neural network architecture to address this challenge, the Convolutional Drift Network (CDN). Our CDN architecture combines the visual feature extraction power of deep Convolutional Neural Networks with the intrinsically efficient temporal processing provided by Reservoir Computing. In this introductory paper on the CDN, we provide a very simple baseline implementation tested on two egocentric (first-person) video activity datasets.We achieve video-level activity classification results on-par with state-of-the art methods. Notably, performance on this complex spatio-temporal task was produced by only training a single feed-forward layer in the CDN.Comment: Published in IEEE Rebooting Computin

Rhizobial peptidase HrrP cleaves host-encoded signaling peptides and mediates symbiotic compatibility

Legume–rhizobium pairs are often observed that produce symbiotic root nodules but fail to fix nitrogen. Using the Sinorhizobium meliloti and Medicago truncatula symbiotic system, we previously described several naturally occurring accessory plasmids capable of disrupting the late stages of nodule development while enhancing bacterial proliferation within the nodule. We report here that host range restriction peptidase (hrrP), a gene found on one of these plasmids, is capable of conferring both these properties. hrrP encodes an M16A family metallopeptidase whose catalytic activity is required for these symbiotic effects. The ability of hrrP to suppress nitrogen fixation is conditioned upon the genotypes of both the host plant and the hrrP-expressing rhizobial strain, suggesting its involvement in symbiotic communication. Purified HrrP protein is capable of degrading a range of nodule-specific cysteine-rich (NCR) peptides encoded by M. truncatula. NCR peptides are crucial signals used by M. truncatula for inducing and maintaining rhizobial differentiation within nodules, as demonstrated in the accompanying article [Horváth B, et al. (2015) Proc Natl Acad Sci USA, 10.1073/pnas.1500777112]. The expression pattern of hrrP and its effects on rhizobial morphology are consistent with the NCR peptide cleavage model. This work points to a symbiotic dialogue involving a complex ensemble of host-derived signaling peptides and bacterial modifier enzymes capable of adjusting signal strength, sometimes with exploitative outcomes.National Institutes of Health (U.S.) (GM31010

A framework for incorporating evolutionary genomics into biodiversity conservation and management

Evolutionary adaptation drives biodiversity. So far, however, evolutionary thinking has had limited impact on plans to counter the effects of climate change on biodiversity and associated ecosystem services. This is despite habitat fragmentation diminishing the ability of populations to mount evolutionary responses, via reductions in population size, reductions in gene flow and reductions in the heterogeneity of environments that populations occupy. Research on evolutionary adaptation to other challenges has benefitted enormously in recent years from genomic tools, but these have so far only been applied to the climate change issue in a piecemeal manner. Here, we explore how new genomic knowledge might be combined with evolutionary thinking in a decision framework aimed at reducing the long-term impacts of climate change on biodiversity and ecosystem services. This framework highlights the need to rethink local conservation and management efforts in biodiversity conservation. We take a dynamic view of biodiversity based on the recognition of continuously evolving lineages, and we highlight when and where new genomic approaches are justified. In general, and despite challenges in developing genomic tools for non-model organisms, genomics can help management decide when resources should be redirected to increasing gene flow and hybridisation across climate zones and facilitating in situ evolutionary change in large heterogeneous areas. It can also help inform when conservation priorities need to shift from maintaining genetically distinct populations and species to supporting processes of evolutionary change. We illustrate our argument with particular reference to Australia’s biodiversity.This paper arose out of a workshop funded through the Office of the Chief Executive Science Team at CSIRO and the Science Industry Endowment Fund

Vibrations of a chain of Xe atoms in a groove of carbon nanotube bundle

We present a lattice dynamics study of the vibrations of a linear chain of Xe adsorbates in groove positions of a bundle of carbon nanotubes. The characteristic phonon frequencies are calculated and the adsorbate polarization vectors discussed. Comparison of the present results with the ones previously published shows that the adsorbate vibrations cannot be treated as completely decoupled from the vibrations of carbon nanotubes and that a significant hybridization between the adsorbate and the tube modes occurs for phonons of large wavelengths.Comment: 3 PS figure

Genome-to-genome analysis highlights the effect of the human innate and adaptive immune systems on the hepatitis C virus

Outcomes of hepatitis C virus (HCV) infection and treatment depend on viral and host genetic factors. Here we use human genome-wide genotyping arrays and new whole-genome HCV viral sequencing technologies to perform a systematic genome-to-genome study of 542 individuals who were chronically infected with HCV, predominantly genotype 3. We show that both alleles of genes encoding human leukocyte antigen molecules and genes encoding components of the interferon lambda innate immune system drive viral polymorphism. Additionally, we show that IFNL4 genotypes determine HCV viral load through a mechanism dependent on a specific amino acid residue in the HCV NS5A protein. These findings highlight the interplay between the innate immune system and the viral genome in HCV control

VLASS tidal disruption events with optical flares I: the sample and a comparison to optically-selected TDEs

In this work, we use the Jansky VLA Sky Survey (VLASS) to compile the first sample of six radio-selected tidal disruption events (TDEs) with transient optical counterparts. While we still lack the statistics to do detailed population studies of radio-selected TDEs, we use these events to suggest trends in host galaxy and optical light curve properties that may correlate with the presence of radio emission, and hence can inform optically-selected TDE radio follow-up campaigns. We find that radio-selected TDEs tend to have faint and cool optical flares, as well as host galaxies with low SMBH masses. Our radio-selected TDEs also tend to have more energetic, larger radio emitting regions than radio-detected, optically-selected TDEs. We consider possible explanations for these trends, including by invoking super-Eddington accretion and enhanced circumnuclear media. Finally, we constrain the radio-emitting TDE rate to be $\gtrsim 10$ Gpc$^{-3}$ yr$^{-1}$.Comment: 26 pages, 5 tables, 11 figures, submitted to Ap

A self-consistent, multi-variate method for the determination of gas phase rate coefficients, applied to reactions of atmospheric VOCs and the hydroxyl radical

Gas-phase rate coefficients are fundamental to understanding atmospheric chemistry, yet experimental data are not available for the oxidation reactions of many of the thousands of volatile organic compounds (VOCs) observed in the troposphere. Here a new experimental method is reported for the simultaneous study of reactions between multiple different VOCs and OH, the most important daytime atmospheric radical oxidant. This technique is based upon established relative rate concepts but has the advantage of a much higher throughput of target VOCs. By evaluating multiple VOCs in each experiment, and through measurement of the depletion in each VOC after reaction with OH, the OH + VOC reaction rate coefficients can be derived. Results from experiments conducted under controlled laboratory conditions were in good agreement with the available literature for the reaction of nineteen VOCs, prepared in synthetic gas mixtures, with OH. This approach was used to determine a rate coefficient for the reaction of OH with 2,3-dimethylpent-1-ene for the first time; k = 5.7 (±0.3) × 10–11–cm3 molecule−1 s−1. In addition, a further seven VOCs had only two, or fewer, individual OH rate coefficient measurements available in the literature. The results from this work were in good agreement with those measurements. A similar dataset, at an elevated temperature of 323 (±10) K, was used to determine new OH rate coefficients for twelve aromatic, five alkane, five alkene and three monoterpene VOC + OH reactions. In OH relative reactivity experiments that used ambient air at the University of York, a large number of different VOCs were observed, of which 23 were positively identified. 19 OH rate coefficients were derived from these ambient air samples, including ten reactions for which data was previously unavailable at the elevated reaction temperature of T = 323 (±10) K

eXtreme Adaptive Optics Planet Imager: overview and status

As adaptive optics (AO) matures, it becomes possible to envision AO systems oriented towards specific important scientific goals rather than general-purpose systems. One such goal for the next decade is the direct imaging detection of extrasolar planets. An "extreme" adaptive optics (ExAO) system optimized for extrasolar planet detection will have very high actuator counts and rapid update rates - designed for observations of bright stars - and will require exquisite internal calibration at the nanometer level. In addition to extrasolar planet detection, such a system will be capable of characterizing dust disks around young or mature stars, outflows from evolved stars, and high Strehl ratio imaging even at visible wavelengths. The NSF Center for Adaptive Optics has carried out a detailed conceptual design study for such an instrument, dubbed the eXtreme Adaptive Optics Planet Imager or XAOPI. XAOPI is a 4096-actuator AO system, notionally for the Keck telescope, capable of achieving contrast ratios >10^7 at angular separations of 0.2-1". ExAO system performance analysis is quite different than conventional AO systems - the spatial and temporal frequency content of wavefront error sources is as critical as their magnitude. We present here an overview of the XAOPI project, and an error budget highlighting the key areas determining achievable contrast. The most challenging requirement is for residual static errors to be less than 2 nm over the controlled range of spatial frequencies. If this can be achieved, direct imaging of extrasolar planets will be feasible within this decade