Coalitional tracking

We propose a novel tracking method that uses a network of independent particle filter trackers whose interactions are modeled using coalitional game theory. Our tracking method is general, it maintains pixel level accuracy, and can negotiate surface deformations and occlusions. We tested our method on a substantial video set featuring non-trivial motion from over 40 objects in both the infrared and visual spectra. The coalitional tracker demonstrated fault tolerant behavior that exceeds by far the performance of single particle filter trackers. Our method represents a shift from the typical tracking paradigms and may find application in demanding imaging problems across the electromagnetic spectrum.     

On labeling in graph visualization

When visualizing graphs, it is essential to communicate the meaning of each graph object via text or graphical labels. Automatic placement of labels in a graph is an NP-Hard problem, for which efficient heuristic solutions have been recently developed. In this paper, we describe a general framework for modeling, drawing, editing, and automatic placement of labels respecting user constraints. In addition, we present the interface and the basic engine of the Graph Editor Toolkit - a family of portable graph visualization libraries designed for integration into graphical user interface application programs. This toolkit produces a high quality automated placement of labels in a graph using our framework. A brief survey of automatic label placement algorithms is also presented. Finally we describe extensions to certain existing automatic label placement algorithms, allowing their integration into this visualization tool.     

Benefits of the use of impairment constraint routing in optical networks

In transparent optical networks, the optical signal accumulates the effects of all physical impairments present along the path it traverses. The conventional selection of signal paths based on e.g. shortest path routing without considering the signal quality and its association with the physical impairments does not always provide the optimum solution in terms of network performance such as blocking and resource utilization. This paper proposes an impairment constraint based routing algorithm to achieve an optimal combination of physical and networking performance taking into account all physical linear impairments including noise, chromatic and polarization mode dispersion, crosstalk and filter concatenation effects in an integrated approach. The performance of a typical metropolitan area network is examined and the improvement achieved when using the proposed approach compared to the conventional shortest path routing is demonstrated.     

Controlled Drug Release and Chemotherapy Response in a Novel Acoustofluidic 3D Tumor Platform

Overcoming transport barriers to delivery of therapeutic agents in tumors remains a major challenge. Focused ultrasound (FUS), in combination with modern nanomedicine drug formulations, offers the ability to maximize drug transport to tumor tissue while minimizing toxicity to normal tissue. This potential remains unfulfilled due to the limitations of current approaches in accurately assessing and quantifying how FUS modulates drug transport in solid tumors. A novel acoustofluidic platform is developed by integrating a physiologically relevant 3D microfluidic device and a FUS system with a closed‐loop controller to study drug transport and assess the response of cancer cells to chemotherapy in real time using live cell microscopy. FUS‐induced heating triggers local release of the chemotherapeutic agent doxorubicin from a liposomal carrier and results in higher cellular drug uptake in the FUS focal region. This differential drug uptake induces locally confined DNA damage and glioblastoma cell death in the 3D environment. The capabilities of acoustofluidics for accurate control of drug release and monitoring of localized cell response are demonstrated in a 3D in vitro tumor mode. This has important implications for developing novel strategies to deliver therapeutic agents directly to the tumor tissue while sparing healthy tissue.     

Dynamic action recognition based on dynemes and Extreme Learning Machine

In this paper, we propose a novel method that performs dynamic action classification by exploiting the effectiveness of the Extreme Learning Machine (ELM) algorithm for single hidden layer feedforward neural networks training. It involves data grouping and ELM based data projection in multiple levels. Given a test action instance, a neural network is trained by using labeled action instances forming the groups that reside to the test sample’s neighborhood. The action instances involved in this procedure are, subsequently, mapped to a new feature space, determined by the trained network outputs. This procedure is performed multiple times, which are determined by the test action instance at hand, until only a single class is retained. Experimental results denote the effectiveness of the dynamic classification approach, compared to the static one, as well as the effectiveness of the ELM in the proposed dynamic classification setting.     

Efficient Coordination Mechanisms for Unrelated Machine Scheduling

We present three new coordination mechanisms for scheduling n selfish jobs on m unrelated machines. A coordination mechanism aims to mitigate the impact of selfishness of jobs on the efficiency of schedules by defining a local scheduling policy on each machine. The scheduling policies induce a game among the jobs and each job prefers to be scheduled on a machine so that its completion time is minimum given the assignments of the other jobs. We consider the maximum completion time among all jobs as the measure of the efficiency of schedules. The approximation ratio of a coordination mechanism quantifies the efficiency of pure Nash equilibria (price of anarchy) of the induced game. Our mechanisms are deterministic, local, and preemptive in the sense that the scheduling policy does not necessarily process the jobs in an uninterrupted way and may introduce some idle time. Our first coordination mechanism has approximation ratio Θ(logm) and always guarantees that the induced game has pure Nash equilibria to which the system converges in at most n rounds. This result improves a bound of O(log² m) due to Azar, Jain, and Mirrokni and, similarly to their mechanism, our mechanism uses a global ordering of the jobs according to their distinct IDs. Next we study the intriguing scenario where jobs are anonymous, i.e., they have no IDs. In this case, coordination mechanisms can only distinguish between jobs that have different load characteristics. Our second mechanism handles anonymous jobs and has approximation ratio $O(\frac{\log m}{\log\log m})$ although the game induced is not a potential game and, hence, the existence of pure Nash equilibria is not guaranteed by potential function arguments. However, it provides evidence that the known lower bounds for non-preemptive coordination mechanisms could be beaten using preemptive scheduling policies. Our third coordination mechanism also handles anonymous jobs and has a nice “cost-revealing” potential function. We use this potential function in order, not only to prove the existence of equilibria, but also to upper-bound the price of stability of the induced game by O(logm) and the price of anarchy by O(log² m). Our third coordination mechanism is the first that handles anonymous jobs and simultaneously guarantees that the induced game is a potential game and has bounded price of anarchy. In order to obtain the above bounds, our coordination mechanisms use m as a parameter. Slight variations of these mechanisms in which this information is not necessary achieve approximation ratios of O(m ^? ), for any constant ?>0.     

A PATH PLANNING METHOD BASED ON CELLULAR AUTOMATA FOR COOPERATIVE ROBOTS

A Cellular Automaton-based technique suitable for solving the path planning problem in a distributed robot team is outlined. Real-time path planning is a challenging task that has many applications in the fields of artificial intelligence, moving robots, virtual reality, and agent behavior simulation. The problem refers to finding a collision-free path for autonomous robots between two specified positions in a configuration area. The complexity of the problem increases in systems of multiple robots. More specifically, some distance should be covered by each robot in an unknown environment, avoiding obstacles found on its route to the destination. On the other hand, all robots must adjust their actions in order to keep their initial team formation immutable. Two different formations were tested in order to study the efficiency and the flexibility of the proposed method. Using different formations, the proposed technique could find applications to image processing tasks, swarm intelligence, etc. Furthermore, the presented Cellular Automaton (CA) method was implemented and tested in a real system using three autonomous mobile minirobots called E-pucks. Experimental results indicate that accurate collision-free paths could be created with low computational cost. Additionally, cooperation tasks could be achieved using minimal hardware resources, even in systems with low-cost robots.     

On stochasticity and turbulence in the federal funds market

This paper uses various tests from statistics and dynamical systems theory to support stochastic models to explain the behaviour of the federal funds rate. In particular, a fractional Brownian motion is supported by the power spectrum of the series, the structure function test and the Hurst test. A coloured noise model is supported by the signal differentiation test and the space-time separation test. Evidence against the turbulent behaviour hypothesis is also provided.     

Optical properties of aerosol over a South European urban environment

Aerosol optical depths (AODs) and single scattering albedos (SSAs) were derived from in situ aircraft measurements of scattering and absorption coefficients at 550 nm. These values were compared to the AODs and SSAs derived from the sun photometer (CIMEL) data of the Aerosol Robotic Network (AERONET) site, in Thessaloniki, Greece, between 18 and 21 July 2006. The aircraft-obtained AODs were lower than the corresponding columnar values. However, aircraft SSAs were found to be in good agreement with the columnar values retrieved from the CIMEL. Aircraft aerosol size distributions measured by means of an optical particle counter (OPC) were also in good agreement with the respective distributions derived from the AERONET site. Filter samples were collected on board the aircraft at different altitudes, to provide detailed information on the chemical composition of tropospheric aerosol. The concentrations of identified chemical species were used to calculate particle refractive indices (RIs) for comparison with the AERONET calculated values.