Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds

A novel multi-scale operator for unorganized 3D point clouds is introduced. The Difference of Normals (DoN) provides a computationally efficient, multi-scale approach to processing large unorganized 3D point clouds. The application of DoN in the multi-scale filtering of two different real-world outdoor urban LIDAR scene datasets is quantitatively and qualitatively demonstrated. In both datasets the DoN operator is shown to segment large 3D point clouds into scale-salient clusters, such as cars, people, and lamp posts towards applications in semi-automatic annotation, and as a pre-processing step in automatic object recognition. The application of the operator to segmentation is evaluated on a large public dataset of outdoor LIDAR scenes with ground truth annotations.Comment: To be published in proceedings of 3DIMPVT 201

On the tensor convolution and the quantum separability problem

We consider the problem of separability: decide whether a Hermitian operator on a finite dimensional Hilbert tensor product is separable or entangled. We show that the tensor convolution defined for certain mappings on an almost arbitrary locally compact abelian group, give rise to formulation of an equivalent problem to the separability one.Comment: 13 pages, two sections adde

Individuals that are consistent in risk-taking benefit during collective foraging

It is well established that living in groups helps animals avoid predation and locate resources, but maintaining a group requires collective coordination, which can be difficult when individuals differ from one another. Personality variation (consistent behavioural differences within a population) is already known to be important in group interactions. Growing evidence suggests that individuals also differ in their consistency, i.e. differing in how variable they are over time, and theoretical models predict that this consistency can be beneficial in social contexts. We used three-spined sticklebacks (Gasterosteus aculeatus) to test whether the consistency in, as well as average levels of, risk taking behaviour (i.e. boldness) when individuals were tested alone affects social interactions when fish were retested in groups of 2 and 4. Behavioural consistency, independently of average levels of risk-taking, can be advantageous: more consistent individuals showed higher rates of initiating group movements as leaders, more behavioural coordination by joining others as followers, and greater food consumption. Our results have implications for both group decision making, as groups composed of consistent individuals are more cohesive, and personality traits, as social interactions can have functional consequences for consistency in behaviour and hence the evolution of personality variation

Deep roots: Improving CNN efficiency with hierarchical filter groups

We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root. This allows a significant reduction in computational cost and number of parameters compared to state-of-the-art deep CNNs, without compromising accuracy, by exploiting the sparsity of inter-layer filter dependencies. We validate our approach by using it to train more efficient variants of state-of-the-art CNN architectures, evaluated on the CIFAR10 and ILSVRC datasets. Our results show similar or higher accuracy than the baseline architectures with much less computation, as measured by CPU and GPU timings. For example, for ResNet 50, our model has 40% fewer parameters, 45% fewer floating point operations, and is 31% (12%) faster on a CPU (GPU). For the deeper ResNet 200 our model has 25% fewer floating point operations and 44% fewer parameters, while maintaining state-of-the-art accuracy. For GoogLeNet, our model has 7% fewer parameters and is 21% (16%) faster on a CPU (GPU).Microsoft Research PhD Scholarshi

Quantum Separability and Entanglement Detection via Entanglement-Witness Search and Global Optimization

We focus on determining the separability of an unknown bipartite quantum state $\rho$ by invoking a sufficiently large subset of all possible entanglement witnesses given the expected value of each element of a set of mutually orthogonal observables. We review the concept of an entanglement witness from the geometrical point of view and use this geometry to show that the set of separable states is not a polytope and to characterize the class of entanglement witnesses (observables) that detect entangled states on opposite sides of the set of separable states. All this serves to motivate a classical algorithm which, given the expected values of a subset of an orthogonal basis of observables of an otherwise unknown quantum state, searches for an entanglement witness in the span of the subset of observables. The idea of such an algorithm, which is an efficient reduction of the quantum separability problem to a global optimization problem, was introduced in PRA 70 060303(R), where it was shown to be an improvement on the naive approach for the quantum separability problem (exhaustive search for a decomposition of the given state into a convex combination of separable states). The last section of the paper discusses in more generality such algorithms, which, in our case, assume a subroutine that computes the global maximum of a real function of several variables. Despite this, we anticipate that such algorithms will perform sufficiently well on small instances that they will render a feasible test for separability in some cases of interest (e.g. in 3-by-3 dimensional systems)

The combined molecular adjuvant CASAC enhances the CD8+ T cell response to a tumor-associated self-antigen in aged, immunosenescent mice

BACKGROUND: Ineffective induction of T cell mediated immunity in older individuals remains a persistent challenge for vaccine development. Thus, there is a need for more efficient and sophisticated adjuvants that will complement novel vaccine strategies for the elderly. To this end, we have investigated a previously optimized, combined molecular adjuvant, CASAC (Combined Adjuvant for Synergistic Activation of Cellular immunity), incorporating two complementary Toll-like receptor agonists, CpG and polyI:C, a class-II epitope, and interferon (IFN)-γ in aged mice. FINDINGS: In aged mice with typical features of immunosenescence, antigen specific CD8+ T cell responses were stimulated after serial vaccinations with CASAC or Complete/Incomplete Freund's Adjuvant (CFA/IFA) and a class I epitope, deriving either from ovalbumin (SIINFEKL, SIL) or the melanoma-associated self-antigen, tyrosinase-related protein-2 (SVYDFFVWL, SVL). Pentamer analysis revealed that aged, CASAC/SIL-vaccinated animals had substantially higher frequencies of H-2K(b)/SIL-specific CD8+ T cells compared to the CFA/IFA-vaccinated groups. Similarly, higher frequencies of H-2K(b)/SVL-pentamer+ and IFN-γ+ CD8+ T cells were detected in the aged, CASAC + SVL-vaccinated mice than in their CFA/IFA-vaccinated counterparts. In both antigen settings, CASAC promoted significantly better functional CD8+ T cell activity. CONCLUSION: These studies demonstrate that functional CD8+ T cells, specific for both foreign and tumour-associated self-antigens, can be effectively induced in aged immunosenescent mice using the novel multi-factorial adjuvant CASAC

Refining Architectures of Deep Convolutional Neural Networks

© 2016 IEEE. Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks [11, 27]. However, a question of paramount importance is somewhat unanswered in deep learning research - is the selected CNN optimal for the dataset in terms of accuracy and model size? In this paper, we intend to answer this question and introduce a novel strategy that alters the architecture of a given CNN for a specified dataset, to potentially enhance the original accuracy while possibly reducing the model size. We use two operations for architecture refinement, viz. stretching and symmetrical splitting. Stretching increases the number of hidden units (nodes) in a given CNN layer, while a symmetrical split of say K between two layers separates the input and output channels into K equal groups, and connects only the corresponding input-output channel groups. Our procedure starts with a pre-trained CNN for a given dataset, and optimally decides the stretch and split factors across the network to refine the architecture. We empirically demonstrate the necessity of the two operations. We evaluate our approach on two natural scenes attributes datasets, SUN Attributes [16] and CAMIT-NSAD [20], with architectures of GoogleNet and VGG-11, that are quite contrasting in their construction. We justify our choice of datasets, and show that they are interestingly distinct from each other, and together pose a challenge to our architectural refinement algorithm. Our results substantiate the usefulness of the proposed method

A Spatially Resolved `Inside-out' Outburst of IP Pegasi

We present a comprehensive photometric dataset taken over the entire outburst of the eclipsing dwarf nova IP Peg in September/October 1997. Analysis of the lightcurves taken over the long rise to the peak-of-outburst shows conclusively that the outburst started near the centre of the disc and moved outwards. This is the first dataset that spatially resolves such an outburst. The dataset is consistent with the idea that long rise times are indicative of such `inside-out' outbursts. We show how the thickness and the radius of the disc, along with the mass transfer rate change over the whole outburst. In addition, we show evidence of the secondary and the irradiation thereof. We discuss the possibility of spiral shocks in the disc; however we find no conclusive evidence of their existence in this dataset.Comment: 8 pages, 8 figures, to be appear in MNRA

Evidence for multiple impurity bands in sodium-doped silicon MOSFETs

We report measurements of the temperature-dependent conductivity in a silicon metal-oxide-semiconductor field-effect transistor that contains sodium impurities in the oxide layer. We explain the variation of conductivity in terms of Coulomb interactions that are partially screened by the proximity of the metal gate. The study of the conductivity exponential prefactor and the localization length as a function of gate voltage have allowed us to determine the electronic density of states and has provided arguments for the presence of two distinct bands and a soft gap at low temperature.Comment: 4 pages; 5 figures; Published in PRB Rapid-Communication