Sail into one of Europe's greatest cities

A travel article about the university towns of Heidelberg and Tubingen in Germany. "DURING an extended visit to Heidelberg, Germany's oldest university town, Mark Twain was shocked to see students out and about at all hours of the day and night..."--publisher website

Pearl of a town may entrap you

A 1,000-word travel article about Broome in Western Australia. "BY THE end of a weekend in Broome, you are quite sure that this town is a fiction, an imagining. I am in a provincial comedy. On my way from the airport, I will be kidnapped, driven to the newspaper offices and told to write the local shipping news, or the pearler news, or the gas works news - the news of whichever resources boom is next..."--publisher website

Going sideways in New Zealand

A 1,000-word travel article about wineries near Queenstown, New Zealand. "GRANT Taylor holds a glass of pinot noir to his ear and says, "I listen to it." It's 11 in the morning. I steal a glance at my guide, Mike Stevens, an English ex-pat who's lived in Queenstown for nearly 20 years, and so is very nearly a local. He's brought me here and knows Taylor well. We are all quite sober..."---publisher website

Visual sea-floor mapping from low overlap imagery using bi-objective bundle adjustment and constrained motion

In most visual mapping applications suited to Autonomous Underwater Vehicles (AUVs), stereo visual odometry (VO) is rarely utilised as a pose estimator as imagery is typically of very low framerate due to energy conservation and data storage requirements. This adversely affects the robustness of a vision-based pose estimator and its ability to generate a smooth trajectory. This paper presents a novel VO pipeline for low-overlap imagery from an AUV that utilises constrained motion and integrates magnetometer data in a bi-objective bundle adjustment stage to achieve low-drift pose estimates over large trajectories. We analyse the performance of a standard stereo VO algorithm and compare the results to the modified vo algorithm. Results are demonstrated in a virtual environment in addition to low-overlap imagery gathered from an AUV. The modified VO algorithm shows significantly improved pose accuracy and performance over trajectories of more than 300m. In addition, dense 3D meshes generated from the visual odometry pipeline are presented as a qualitative output of the solution.

Micas in experimentally shocked gneiss

Biotites and muscovites from a gneiss have been experimentally shocked between 18 and 70 GPa using powder-propellant guns at NASA Johnson Space Center and at the California Institute of Technology. This study shows that shock in biotite and muscovite can produce homogeneous and devolatilized glasses within microseconds. Shock-deformed micas display fracturing, kinking, and complex extinction patterns over the entire pressure range investigated. However, these deformation features are not a sensitive pressure indicator. Localized melting of micas begins at 33 GPa and goes to completion at 70 GPa. Melted biotite and muscovite are optically opaque, but show extensive microvesiculation and flow when observed with the SEM. Electron diffraction confirms that biotite and muscovite have transformed to a glass. The distribution of vesicles in shock-vitrified mica shows escape of volatiles within the short duration of the shock experiment. Experimentally shocked biotite and muscovite undergo congruent melting. Compositions of the glasses are similar to the unshocked micas except for volatiles (H2O loss and K loss). These unusual glasses derived from mica may be quenched by rapid cooling conditions during the shock experiment. Based on these results, the extremely low H2O content of tektites may be reconciled with a terrestrial origin by impact. Release of volatiles in shock-melted micas affects the melting behavior of coexisting dry silicates during the short duration of the shock experiment. Transportation and escape of volatiles released from shock-melted micas may provide plausible mechanisms for the origin of protoatmospheres on terrestrial planets, hydrothermal activity on phyllosilicate-rich meteorite parent bodies, and fluid entrapment in meteorites.

A walk through Reykjavik

A 1,000-word travel article about Iceland

Loss of breast epithelial marker hCLCA2 promotes epithelial to mesenchymal transition and indicates higher risk of metastasis

Transition between epithelial and mesenchymal states is a feature of both normal development and tumor progression. We report that expression of chloride channel accessory protein hCLCA2 is a characteristic of epithelial differentiation in the immortalized MCF10A and HMLE models, while induction of epithelial-to-mesenchymal transition by cell dilution, TGFβ or mesenchymal transcription factors sharply reduces hCLCA2 levels. Attenuation of hCLCA2 expression by lentiviral small hairpin RNA caused cell overgrowth and focus formation, enhanced migration and invasion, and increased mammosphere formation in methylcellulose. These changes were accompanied by downregulation of E-cadherin and upregulation of mesenchymal markers such as vimentin and fibronectin. Moreover, hCLCA2 expression is greatly downregulated in breast cancer cells with a mesenchymal or claudin-low profile. These observations suggest that loss of hCLCA2 may promote metastasis. We find that higher-than-median expression of hCLCA2 is associated with a one-third lower rate of metastasis over an 18-year period among breast cancer patients compared with lower-than-median (n=344, unfiltered for subtype). Thus, hCLCA2 is required for epithelial differentiation, and its loss during tumor progression contributes to metastasis. Overexpression of hCLCA2 has been reported to inhibit cell proliferation and is accompanied by increases in chloride current at the plasma membrane and reduced intracellular pH (pHi). We found that knockdown cells have sharply reduced chloride current and higher pHi, both characteristics of tumor cells. These results suggest a mechanism for the effects on differentiation. Loss of hCLCA2 may allow escape from pHi homeostatic mechanisms, permitting the higher intracellular and lower extracellular pH that are characteristic of aggressive tumor cells.

An evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles with tactical and kinematic constraints

This paper presents a novel evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles (UAVs) with tactical and kinematic constraints. A genetic algorithm (GA) is modified and extended for path planning. Two GAs are seeded at the initial and final positions with a common objective to minimise their distance apart under given UAV constraints. This is accomplished by the synchronous optimisation of subsequent control vectors. The proposed evolutionary computation approach is called synchronous genetic algorithm (SGA). The sequence of control vectors generated by the SGA constitutes to a near-optimal path plan. The resulting path plan exhibits no discontinuity when transitioning from curve to straight trajectories. Experiments and results show that the paths generated by the SGA are within 2% of the optimal solution. Such a path planner when implemented on a hardware accelerator, such as field programmable gate array chips, can be used in the UAV as on-board replanner, as well as in ground station systems for assisting in high precision planning and modelling of mission scenarios.

A multi-scaled approach for simulating chemical reaction systems

In this paper we give an overview of some very recent work, as well as presenting a new approach, on the stochastic simulation of multi-scaled systems involving chemical reactions. In many biological systems (such as genetic regulation and cellular dynamics) there is a mix between small numbers of key regulatory proteins, and medium and large numbers of molecules. In addition, it is important to be able to follow the trajectories of individual molecules by taking proper account of the randomness inherent in such a system. We describe different types of simulation techniques (including the stochastic simulation algorithm, Poisson Runge-Kutta methods and the balanced Euler method) for treating simulations in the three different reaction regimes: slow, medium and fast. We then review some recent techniques on the treatment of coupled slow and fast reactions for stochastic chemical kinetics and present a new approach which couples the three regimes mentioned above. We then apply this approach to a biologically inspired problem involving the expression and activity of LacZ and LacY proteins in E coli, and conclude with a discussion on the significance of this work. (C) 2004 Elsevier Ltd. All rights reserved.

Numerical methods for strong solutions of stochastic differential equations : an overview

This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations. We give a brief survey of the area focusing on a number of application areas where approximations to strong solutions are important, with a particular focus on computational biology applications, and give the necessary analytical tools for understanding some of the important concepts associated with stochastic processes. We present the stochastic Taylor series expansion as the fundamental mechanism for constructing effective numerical methods, give general results that relate local and global order of convergence and mention the Magnus expansion as a mechanism for designing methods that preserve the underlying structure of the problem. We also present various classes of explicit and implicit methods for strong solutions, based on the underlying structure of the problem. Finally, we discuss implementation issues relating to maintaining the Brownian path, efficient simulation of stochastic integrals and variable-step-size implementations based on various types of control.

STALKERBOT : learning to navigate dynamic human environments by following people

Service robots that operate in human environments will accomplish tasks most efficiently and least disruptively if they have the capability to mimic and understand the motion patterns of the people in their workspace. This work demonstrates how a robot can create a humancentric navigational map online, and that this map re ects changes in the environment that trigger altered motion patterns of people. An RGBD sensor mounted on the robot is used to detect and track people moving through the environment. The trajectories are clustered online and organised into a tree-like probabilistic data structure which can be used to detect anomalous trajectories. A costmap is reverse engineered from the clustered trajectories that can then inform the robot's onboard planning process. Results show that the resultant paths taken by the robot mimic expected human behaviour and can allow the robot to respond to altered human motion behaviours in the environment.

Locally weighted learning model predictivek control for elastic joint robots

This paper proposes an efficient and online learning control system that uses the successful Model Predictive Control (MPC) method in a model based locally weighted learning framework. The new approach named Locally Weighted Learning Model Predictive Control (LWL-MPC) has been proposed as a solution to learn to control complex and nonlinear Elastic Joint Robots (EJR). Elastic Joint Robots are generally difficult to learn to control due to their elastic properties preventing standard model learning techniques from being used, such as learning computed torque control. This paper demonstrates the capability of LWL-MPC to perform online and incremental learning while controlling the joint positions of a real three Degree of Freedom (DoF) EJR. An experiment on a real EJR is presented and LWL-MPC is shown to successfully learn to control the system to follow two different figure of eight trajectories.

SAIVT-QUT@TRECVid 2012 : interactive surveillance event detection

In this paper, we propose an approach which attempts to solve the problem of surveillance event detection, assuming that we know the definition of the events. To facilitate the discussion, we first define two concepts. The event of interest refers to the event that the user requests the system to detect; and the background activities are any other events in the video corpus. This is an unsolved problem due to many factors as listed below: 1) Occlusions and clustering: The surveillance scenes which are of significant interest at locations such as airports, railway stations, shopping centers are often crowded, where occlusions and clustering of people are frequently encountered. This significantly affects the feature extraction step, and for instance, trajectories generated by object tracking algorithms are usually not robust under such a situation. 2) The requirement for real time detection: The system should process the video fast enough in both of the feature extraction and the detection step to facilitate real time operation. 3) Massive size of the training data set: Suppose there is an event that lasts for 1 minute in a video with a frame rate of 25fps, the number of frames for this events is 60X25 = 1500. If we want to have a training data set with many positive instances of the event, the video is likely to be very large in size (i.e. hundreds of thousands of frames or more). How to handle such a large data set is a problem frequently encountered in this application. 4) Difficulty in separating the event of interest from background activities: The events of interest often co-exist with a set of background activities. Temporal groundtruth typically very ambiguous, as it does not distinguish the event of interest from a wide range of co-existing background activities. However, it is not practical to annotate the locations of the events in large amounts of video data. This problem becomes more serious in the detection of multi-agent interactions, since the location of these events can often not be constrained to within a bounding box. 5) Challenges in determining the temporal boundaries of the events: An event can occur at any arbitrary time with an arbitrary duration. The temporal segmentation of events is difficult and ambiguous, and also affected by other factors such as occlusions.

Embedding human expert cognition into autonomous UAS trajectory planning

This paper presents a new approach for the inclusion of human expert cognition into autonomous trajectory planning for unmanned aerial systems (UASs) operating in low-altitude environments. During typical UAS operations, multiple objectives may exist; therefore, the use of multicriteria decision aid techniques can potentially allow for convergence to trajectory solutions which better reflect overall mission requirements. In that context, additive multiattribute value theory has been applied to optimize trajectories with respect to multiple objectives. A graphical user interface was developed to allow for knowledge capture from a human decision maker (HDM) through simulated decision scenarios. The expert decision data gathered are converted into value functions and corresponding criteria weightings using utility additive theory. The inclusion of preferences elicited from HDM data within an automated decision system allows for the generation of trajectories which more closely represent the candidate HDM decision preferences. This approach has been demonstrated in this paper through simulation using a fixed-wing UAS operating in low-altitude environments.

Efficient articulated trajectory reconstruction using dynamic programming and filters

This paper considers the problem of reconstructing the motion of a 3D articulated tree from 2D point correspondences subject to some temporal prior. Hitherto, smooth motion has been encouraged using a trajectory basis, yielding a hard combinatorial problem with time complexity growing exponentially in the number of frames. Branch and bound strategies have previously attempted to curb this complexity whilst maintaining global optimality. However, they provide no guarantee of being more efficient than exhaustive search. Inspired by recent work which reconstructs general trajectories using compact high-pass filters, we develop a dynamic programming approach which scales linearly in the number of frames, leveraging the intrinsically local nature of filter interactions. Extension to affine projection enables reconstruction without estimating cameras.