Multiple path prediction for traffic scenes using LSTMs and mixture density models

This work presents an analysis of predicting multiple future paths of moving objects in traffic scenes by leveraging Long Short-Term Memory architectures (LSTMs) and Mixture Density Networks (MDNs) in a single-shot manner. Path prediction allows estimating the future positions of objects. This is useful in important applications such as security monitoring systems, Autonomous Driver Assistance Systems and assistive technologies. Normal approaches use observed positions (tracklets) of objects in video frames to predict their future paths as a sequence of position values. This can be treated as a time series. LSTMs have achieved good performance when dealing with time series. However, LSTMs have the limitation of only predicting a single path per tracklet. Path prediction is not a deterministic task and requires predicting with a level of uncertainty. Predicting multiple paths instead of a single one is therefore a more realistic manner of approaching this task. In this work, predicting a set of future paths with associated uncertainty was archived by combining LSTMs and MDNs. The evaluation was made on the KITTI and the CityFlow datasets on three type of objects, four prediction horizons and two different points of view (image coordinates and birds-eye vie

The application of antibodies in optical and electrochemical transduction processes

Chapter 1 relates the background information of the structure of antibodies, the nature of their interaction with antigen and their production as polyclonal antibodies. Examples of antibodies which have been produced against luminescent molecules are given and it is illustrated how the specific interaction of antibodies with luminescent complexes can be used to gain information on the structure, function and rotational dynamics of antibodies. The principle features of immunoassays are outlined with a focus on amperometric immunosensing including some areas of active research within amperometric immunosensing. This serves as a general introduction to Chapter 4; the development of an amperometric immunosensor based on single wall carbon nanotubes. Chapter 2 begins with a review of the fundamental chemical, photochemical and electrochemical properties of Ru(II) and Os(II) polypyridyl complexes. It is shown how their photophysical properties can be modulated by the interaction of the complexes with biomolecules such as proteins, nucleic acids and antibodies. The synthesis and characterisation of [Os(bpy)2dcbpy] and some related Os(II) and Ru(II) complexes is described. The production and characterisation of a [Os(bpy)2dcbpy]-thyroglobulin conjugate which was used as the immunogen is described, as well as the purification and characterisation of the resulting polyclonal antibody. Competition ELISA served to confirm the cross-reactivity of the antibody with the Os and Ru complexes synthesised. Chapter 3 describes the effect of antibody binding on the spectrochemical properties of the complexes. Changes in the emission spectra and lifetimes were examined. Association constants were derived from emission titrations. The extent that the antibody binding site protects the complexes from excited state deactivation via interaction with solvent was investigated. The possibility of energy transfer from [Ru(bpy)2dcbpy] to [Os(bpy)2dcbpy] when both were bound to the same antibody was investigated, as were the effects of antibody binding to a self-assembled layer of [Os(bpy)2(p2 p)2]2+. Chapter 4 begins with an introduction to the structure and properties of CNTs and outlines their application thus far in biosensing. The assembly of oxidatively shortened SWNTs onto Nafion/iron oxide coated pyrolytic graphite electrodes is described and characterised by both AFM and resonance Raman spectroscopy. The immunosensing strategy investigated involved the adsorption of anti-biotin antibody to the carbon nanotube surface. The presence of HRP-labelled biotin was determined via the reduction of hydrogen peroxide in the presence of the soluble mediator hydroquinone. A short investigation is also presented on the ability of HRP-modified SWNT forest electrodes to detect H2O2 produced by a mutant catalase negative E. coli bacteria which was co-immobilised with the HRP. Recommendations for future work arising from this thesis are given in Chapter 5

Characteristics of U.S. Manufacturing Companies Investing Abroad and their Choice of Production Locations

The purpose of this paper is to examine the relations among characteristics of U.S. firms, their tendency to invest abroad, and their choice of production locations. The larger the firm, and the higher its profitability, capital intensity, technological Intensity, and the skill level ofits labor force, the higher the probability that it was a foreign investor.Some of these factors were largely associated with the industry the firm was in but size, R&D, and profitability were characteristics of investing firms within individual industries.Despite its importance in determining the probability that a firm would invest abroad, size of firm appeared to have no relation to the importance of foreign investment; among firms that invested at all, large firms did not produce a higher proportion of their output abroad than small firms. The concentration of manufacturing abroad in a small number of corn-panies is largely a reflection of the concentration within the United States. The influence of size, we conclude, reflects economies of scale not in production but in investing.We found no evidence that, in general, low-wage U.S. firms tended to invest in low-wage countries or that R&D - intensive firms tended to operate more in countries with highly sophisticated or educated labor. In fact,investors in developing countries, and particularly those in some Southeast Asian countries, tended to be more R&D intensive than investors in developed countries. There was some indication that in industries other than machinery R&D - intensive firms were more inclined than others to license technology, while in the machinery industries, R&D - intensive firms tended to license less:to exploit their technological capital in foreign markets by producing there rather than by licensing.

Monte Carlo Simulation of a NC Gauge Theory on The Fuzzy Sphere

We find using Monte Carlo simulation the phase structure of noncommutative U(1) gauge theory in two dimensions with the fuzzy sphere S^2_N as a non-perturbative regulator. There are three phases of the model. i) A matrix phase where the theory is essentially SU(N) Yang-Mills reduced to zero dimension . ii) A weak coupling fuzzy sphere phase with constant specific heat and iii) A strong coupling fuzzy sphere phase with non-constant specific heat. The order prameter distinguishing the matrix phase from the sphere phase is the radius of the fuzzy sphere. The three phases meet at a triple point. We also give the theoretical one-loop and 1/N expansion predictions for the transition lines which are in good agreement with the numerical data. A Monte Carlo measurement of the triple point is also given

Comment on ``Can Disorder Induce a Finite Thermal Conductivity in 1D Lattices?''

In a recent paper [Phys. Rev. Lett. 86, 63 (2001)], Li et al have reported that the nonequilibrium heat conducting steady state of a disordered harmonic chain is not unique. In this comment we point out that for a large class of stochastic heat baths the uniqueness of the steady state can be proved, and therefore the findings of Li et al could be either due to their use of deterministic heat baths or insufficient equilibration times in the simulations. We give a simple example where the uniquness of the steady state can be explicitly demonstrated.Comment: 1 page, 1 figure, accepted for publication in Phys. Rev. Let

From Group Recommendations to Group Formation

There has been significant recent interest in the area of group recommendations, where, given groups of users of a recommender system, one wants to recommend top-k items to a group that maximize the satisfaction of the group members, according to a chosen semantics of group satisfaction. Examples semantics of satisfaction of a recommended itemset to a group include the so-called least misery (LM) and aggregate voting (AV). We consider the complementary problem of how to form groups such that the users in the formed groups are most satisfied with the suggested top-k recommendations. We assume that the recommendations will be generated according to one of the two group recommendation semantics - LM or AV. Rather than assuming groups are given, or rely on ad hoc group formation dynamics, our framework allows a strategic approach for forming groups of users in order to maximize satisfaction. We show that the problem is NP-hard to solve optimally under both semantics. Furthermore, we develop two efficient algorithms for group formation under LM and show that they achieve bounded absolute error. We develop efficient heuristic algorithms for group formation under AV. We validate our results and demonstrate the scalability and effectiveness of our group formation algorithms on two large real data sets.Comment: 14 pages, 22 figure

Micro-Ramp Flow Control for Oblique Shock Interactions: Comparisons of Computational and Experimental Data

Computational fluid dynamics was used to study the effectiveness of micro-ramp vortex generators to control oblique shock boundary layer interactions. Simulations were based on experiments previously conducted in the 15 x 15 cm supersonic wind tunnel at NASA Glenn Research Center. Four micro-ramp geometries were tested at Mach 2.0 varying the height, chord length, and spanwise spacing between micro-ramps. The overall flow field was examined. Additionally, key parameters such as boundary-layer displacement thickness, momentum thickness and incompressible shape factor were also examined. The computational results predicted the effects of the micro-ramps well, including the trends for the impact that the devices had on the shock boundary layer interaction. However, computing the shock boundary layer interaction itself proved to be problematic since the calculations predicted more pronounced adverse effects on the boundary layer due to the shock than were seen in the experiment

Fur-mites of the family Atopomelidae (Acari: Astigmata) parasitic on Philippine mammals: systematics, phylogeny, and host-parasite relationships

http://deepblue.lib.umich.edu/bitstream/2027.42/56439/1/MP196.pd

Transfer Learning for Multi-language Twitter Election Classification

Both politicians and citizens are increasingly embracing social media as a means to disseminate information and comment on various topics, particularly during significant political events, such as elections. Such commentary during elections is also of interest to social scientists and pollsters. To facilitate the study of social media during elections, there is a need to automatically identify posts that are topically related to those elections. However, current studies have focused on elections within English-speaking regions, and hence the resultant election content classifiers are only applicable for elections in countries where the predominant language is English. On the other hand, as social media is becoming more prevalent worldwide, there is an increasing need for election classifiers that can be generalised across different languages, without building a training dataset for each election. In this paper, based upon transfer learning, we study the development of effective and reusable election classifiers for use on social media across multiple languages. We combine transfer learning with different classifiers such as Support Vector Machines (SVM) and state-of-the-art Convolutional Neural Networks (CNN), which make use of word embedding representations for each social media post. We generalise the learned classifier models for cross-language classification by using a linear translation approach to map the word embedding vectors from one language into another. Experiments conducted over two election datasets in different languages show that without using any training data from the target language, linear translations outperform a classical transfer learning approach, namely Transfer Component Analysis (TCA), by 80% in recall and 25% in F1 measure