ATTac-2000: An Adaptive Autonomous Bidding Agent

The First Trading Agent Competition (TAC) was held from June 22nd to July 8th, 2000. TAC was designed to create a benchmark problem in the complex domain of e-marketplaces and to motivate researchers to apply unique approaches to a common task. This article describes ATTac-2000, the first-place finisher in TAC. ATTac-2000 uses a principled bidding strategy that includes several elements of adaptivity. In addition to the success at the competition, isolated empirical results are presented indicating the robustness and effectiveness of ATTac-2000's adaptive strategy

Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic

We consider the problem of using a heuristic policy to improve the value approximation by the Upper Confidence Bound applied in Trees (UCT) algorithm in non-adversarial settings such as planning with large-state space Markov Decision Processes. Current improvements to UCT focus on either changing the action selection formula at the internal nodes or the rollout policy at the leaf nodes of the search tree. In this work, we propose to add an auxiliary arm to each of the internal nodes, and always use the heuristic policy to roll out simulations at the auxiliary arms. The method aims to get fast convergence to optimal values at states where the heuristic policy is optimal, while retaining similar approximation as the original UCT in other states. We show that bootstrapping with the proposed method in the new algorithm, UCT-Aux, performs better compared to the original UCT algorithm and its variants in two benchmark experiment settings. We also examine conditions under which UCT-Aux works well.Comment: 16 pages, accepted for presentation at ECML'1

A randomised, controlled, double blind, non-inferiority trial of ultrasound-guided fascia iliaca block vs. spinal morphine for analgesia after primary hip arthroplasty

We performed a single centre, double blind, randomised, controlled, non-inferiority study comparing ultrasound-guided fascia iliaca block with spinal morphine for the primary outcome of 24-h postoperative morphine consumption in patients undergoing primary total hip arthroplasty under spinal anaesthesia with levobupivacaine. One hundred and eight patients were randomly allocated to receive either ultrasound-guided fascia iliaca block with 2 mg.kg−1 levobupivacaine (fascia iliaca group) or spinal morphine 100 μg plus a sham ultrasound-guided fascia iliaca block using saline (spinal morphine group). The pre-defined non-inferiority margin was a median difference between the groups of 10 mg in cumulative intravenous morphine use in the first 24 h postoperatively. Patients in the fascia iliaca group received 25 mg more intravenous morphine than patients in the spinal morphine group (95% CI 9.0–30.5 mg, p < 0.001). Ultrasound-guided fascia iliaca block was significantly worse than spinal morphine in the provision of analgesia in the first 24 h after total hip arthroplasty. No increase in side-effects was noted in the spinal morphine group but the study was not powered to investigate all secondary outcomes

Risk-Averse Matchings over Uncertain Graph Databases

A large number of applications such as querying sensor networks, and analyzing protein-protein interaction (PPI) networks, rely on mining uncertain graph and hypergraph databases. In this work we study the following problem: given an uncertain, weighted (hyper)graph, how can we efficiently find a (hyper)matching with high expected reward, and low risk? This problem naturally arises in the context of several important applications, such as online dating, kidney exchanges, and team formation. We introduce a novel formulation for finding matchings with maximum expected reward and bounded risk under a general model of uncertain weighted (hyper)graphs that we introduce in this work. Our model generalizes probabilistic models used in prior work, and captures both continuous and discrete probability distributions, thus allowing to handle privacy related applications that inject appropriately distributed noise to (hyper)edge weights. Given that our optimization problem is NP-hard, we turn our attention to designing efficient approximation algorithms. For the case of uncertain weighted graphs, we provide a $\frac{1}{3}$-approximation algorithm, and a $\frac{1}{5}$-approximation algorithm with near optimal run time. For the case of uncertain weighted hypergraphs, we provide a $\Omega(\frac{1}{k})$-approximation algorithm, where $k$ is the rank of the hypergraph (i.e., any hyperedge includes at most $k$ nodes), that runs in almost (modulo log factors) linear time. We complement our theoretical results by testing our approximation algorithms on a wide variety of synthetic experiments, where we observe in a controlled setting interesting findings on the trade-off between reward, and risk. We also provide an application of our formulation for providing recommendations of teams that are likely to collaborate, and have high impact.Comment: 25 page

Notorious places: image, reputation, stigma: the role of newspapers in area reputations for social housing estates

This paper reviews work in several disciplines to distinguish between image, reputation and stigma. It also shows that there has been little research on the process by which area reputations are established and sustained through transmission processes. This paper reports on research into the portrayal of two social housing estates in the printed media over an extended period of time (14 years). It was found that negative and mixed coverage of the estates dominated, with the amount of positive coverage being very small. By examining the way in which dominant themes were used by newspapers in respect of each estate, questions are raised about the mode of operation of the press and the communities' collective right to challenge this. By identifying the way regeneration stories are covered and the nature of the content of positive stories, lessons are drawn for programmes of area transformation. The need for social regeneration activities is identified as an important ingredient for changing deprived-area reputations

Untangling CP Violation and the Mass Hierarchy in Long Baseline Experiments

In the overlap region, for the normal and inverted hierarchies, of the neutrino-antineutrino bi-probability space for $\nu_\mu \to \nu_e$ appearance, we derive a simple identity between the solutions in the ($\sin^2 2\theta_{13}$, $\sin \delta$) plane for the different hierarchies. The parameter $\sin^2 2\theta_{13}$ sets the scale of the $\nu_\mu \to \nu_e$ appearance probabilities at the atmospheric $\delta m^2_{atm} \approx 2.4 \times 10^{-3}$ eV$^2$ whereas $\sin \delta$ controls the amount of CP violation in the lepton sector. The identity between the solutions is that the difference in the values of $\sin \delta$ for the two hierarchies equals twice the value of $\sqrt{\sin^2 2\theta_{13}}$ divided by the {\it critical} value of $\sqrt{\sin^2 2\theta_{13}}$. We apply this identity to the two proposed long baseline experiments, T2K and NO$\nu$A, and we show how it can be used to provide a simple understanding of when and why fake solutions are excluded when two or more experiments are combined. The identity demonstrates the true complimentarity of T2K and NO$\nu$A.Comment: 15 pages, Latex, 4 postscript figures. Submitted to New Journal of Physics, ``Focus on Neutrino Physics'' issu

Living IoT: A Flying Wireless Platform on Live Insects

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang, In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201

Abstract Learning Frameworks for Synthesis

We develop abstract learning frameworks (ALFs) for synthesis that embody the principles of CEGIS (counter-example based inductive synthesis) strategies that have become widely applicable in recent years. Our framework defines a general abstract framework of iterative learning, based on a hypothesis space that captures the synthesized objects, a sample space that forms the space on which induction is performed, and a concept space that abstractly defines the semantics of the learning process. We show that a variety of synthesis algorithms in current literature can be embedded in this general framework. While studying these embeddings, we also generalize some of the synthesis problems these instances are of, resulting in new ways of looking at synthesis problems using learning. We also investigate convergence issues for the general framework, and exhibit three recipes for convergence in finite time. The first two recipes generalize current techniques for convergence used by existing synthesis engines. The third technique is a more involved technique of which we know of no existing instantiation, and we instantiate it to concrete synthesis problems

Novel therapies for children with acute myeloid leukaemia

Significant improvements in survival for children with acute myeloid leukaemia (AML) have been made over the past three decades, with overall survival rates now approximately 60-70%. However, these gains can be largely attributed to more intensive use of conventional cytotoxics made possible by advances in supportive care, and although over 90% of children achieve remission with frontline therapy, approximately one third in current protocols relapse. Furthermore, late effects of therapy cause significant morbidity for many survivors. Novel therapies are therefore desperately needed. Early-phase paediatric trials of several new agents such as clofarabine, sorafenib and gemtuzumab ozogamicin have shown encouraging results in recent years. Due to the relatively low incidence of AML in childhood, the success of paediatric early-phase clinical trials is largely dependent upon collaborative clinical trial design by international cooperative study groups. Successfully incorporating novel therapies into frontline therapy remains a challenge, but the potential for significant improvement in the duration and quality of survival for children with AML is high