Hotspots: Modelling capacity for vector-borne disease risk analysis in New Zealand: A case study of Ochlerotatus camptorhynchus incursions in New Zealand

This Hotspots case study of Oc. camptorhynchus in New Zealand forms part of the wider aims and objectives of the Hotspots project. The overall aims of the case study were: 1. To evaluate the performance of the Hotspots model as a risk analysis tool for Oc. camptorhynchus; 2. To use and learn from the experience of the various incursions of Oc. camptorhynchus in order to critically assess and improve the model; 3. To gain experience in using the model for risk analysis for Oc. camptorhynchus in particular, and in so doing, also develop experience applicable to risk analysis for other vectors of concern (Table 1); and, 4. To develop an experience and knowledge base as well as guidelines for future use of the model in its various applications related to biosecurity, surveillance and risk assessment and management

Image-based Recommendations on Styles and Substitutes

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201

Hotspots: Exotic mosquito risk profiles for New Zealand

This document reports the main findings of the first systematic, spatial analyses of risks to New Zealand associated with exotic mosquitoes of current public health concern

Detection scheme for space-time block codes wireless communications without channel state information

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/ICCS.2008.4737147In this paper, the new detection scheme proposed by Tarokh and Alamouti is investigated and expanded to two schemes of two transmit two receive antennas and four transmit one receive antennas. Previously, Tarokh and Alamouti, based on the simple transmit diversity scheme proposed by Alamouti, have provided two new detection methods to decode signals at the receiver without channel information. We expand their work to two and four transmit antennas with different number of receive antennas. We also demonstrate that the two methods are in fact the same with one being a special case of the other. The theory and detailed derivation of detection formulas for three cases, Tarokh and Alamouti work, two transmit two receive antennas and four transmit one receive antennas are presented.Peer reviewe

CNN Architectures for Large-Scale Audio Classification

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.Comment: Accepted for publication at ICASSP 2017 Changes: Added definitions of mAP, AUC, and d-prime. Updated mAP/AUC/d-prime numbers for Audio Set based on changes of latest Audio Set revision. Changed wording to fit 4 page limit with new addition

On the Expressivity and Applicability of Model Representation Formalisms

A number of first-order calculi employ an explicit model representation formalism for automated reasoning and for detecting satisfiability. Many of these formalisms can represent infinite Herbrand models. The first-order fragment of monadic, shallow, linear, Horn (MSLH) clauses, is such a formalism used in the approximation refinement calculus. Our first result is a finite model property for MSLH clause sets. Therefore, MSLH clause sets cannot represent models of clause sets with inherently infinite models. Through a translation to tree automata, we further show that this limitation also applies to the linear fragments of implicit generalizations, which is the formalism used in the model-evolution calculus, to atoms with disequality constraints, the formalisms used in the non-redundant clause learning calculus (NRCL), and to atoms with membership constraints, a formalism used for example in decision procedures for algebraic data types. Although these formalisms cannot represent models of clause sets with inherently infinite models, through an additional approximation step they can. This is our second main result. For clause sets including the definition of an equivalence relation with the help of an additional, novel approximation, called reflexive relation splitting, the approximation refinement calculus can automatically show satisfiability through the MSLH clause set formalism.Comment: 15 page

Network conduciveness with application to the graph-coloring and independent-set optimization transitions

We introduce the notion of a network's conduciveness, a probabilistically interpretable measure of how the network's structure allows it to be conducive to roaming agents, in certain conditions, from one portion of the network to another. We exemplify its use through an application to the two problems in combinatorial optimization that, given an undirected graph, ask that its so-called chromatic and independence numbers be found. Though NP-hard, when solved on sequences of expanding random graphs there appear marked transitions at which optimal solutions can be obtained substantially more easily than right before them. We demonstrate that these phenomena can be understood by resorting to the network that represents the solution space of the problems for each graph and examining its conduciveness between the non-optimal solutions and the optimal ones. At the said transitions, this network becomes strikingly more conducive in the direction of the optimal solutions than it was just before them, while at the same time becoming less conducive in the opposite direction. We believe that, besides becoming useful also in other areas in which network theory has a role to play, network conduciveness may become instrumental in helping clarify further issues related to NP-hardness that remain poorly understood

Imaging with Diffraction Tomography

The problem of cross sectional (tomographic) imaging bf objects with diffracting sources is addressed. Specifically the area of investigation is the effect of multiple scattering and attenuation phenomena in diffraction imaging. This work reviews the theory and limits of first order diffraction tomography and studies iterative techniques that can be used to improve the quality of tomographic imaging with diffracting sources. Conventional (straight-ray) tomographic algorithms are not valid when used with acoustic or microwave energy. Thus more sophisticated algorithms are needed; First order diffraction tomography uses a linearized version of the wave equation and gives an especially simple reconstruction algorithm. This work reviews first order approximations to the scattered field and studies the quality of the reconstructions when the assumptions behind these approximations are violated. It will be shown that the Born approximation is valid when the phase change across the object is less than it and the Rytov approximation is valid when the refractive index changes by less than two or three percent. Better reconstructions will be based on higher order approximations to the scattered field. This work describes two fixed point algorithms (the Born and the Rytov approximations) and an algebraic approach to more accurately calculate the scattered fields. The limits of each of these approaches is discussed and simulated results are shown. Finally a review of higher order inversion techniques is presented. Each of these techniques is reviewed and some of their limitations are discussed

Preliminary Geologic Interpretation of SAR Data, Yellowknife-Hearne Lake Area, N.W.T.

Airborne, narrow swath, C-band synthetic aperture radar (SAR) imagery, obtained from the Yellowknife-Hearne Lake area, essentially reflects the geomorphology or landforms of the region. These in turn can be readily related to specific lithologies, rock masses, structure and cultural features. Terrain analysis using textural and tonal (brightness) characteristics of the radar images along with drainage and lakeshore characteristics permitted definition of several lithologic classes: Granite terrain type-1, generally the brightest (lightest) area, has a "coarse" mottled signature, reflecting the hummocky surface characteristic of granites in this area. Metasedimentary terrain is typified by an intermediate tone, a thinly laminated texture reflecting bedding and angular shorelines of some lakes. Metavolcanic terrain is subordinate in area and lacks well-defined textural or tonal characteristics. It is most easily recognized as parallel ridges with little or no curvature. The city of Yellowknife is readily identifiable by its bright signature and rectangular pattern or texture. Lineaments, recognized by the alignment of rivers and shorelines, are greatly enhanced by bright radar reflections from northerly facing cliffs and radar shadow (zero signal return) of southerly facing cliffs. Several major structural lineaments in the area, known from aeromagnetic and geological maps (diabase dykes, faulted contacts, etc.) are readily apparent in the SAR imagery, as are numerous extensions and subsidiary lineaments. Circumstantial evidence suggests that post-Precambrian and neotectonic activity may be related to lineaments.Key words: synthetic aperture radar (SAR), Yellowknife-Hearne Lake area, terrain analysis, lineaments, neotectonicsMots clés: radar antenne synthttique (RAAS), zone Yellowknife-lac Hearne, analyse de terrain, linéaments, néotectoniqu