Tree-Based Adaptive Spatial Detection for Adaptive Modulated MIMO Systems

Adaptive Multiple-Input Multiple-Output (MIMO) systems achieve a much higher information rate than conventional fixed schemes due to their ability to adapt their configurations according to the wireless communications environment. However, current adaptive MIMO detection schemes exhibit either low performance (and hence low spectral efficiency) or huge computational
complexity. In particular, whilst deterministic Sphere Decoder (SD) detection schemes are well established for static MIMO systems, exhibiting deterministic parallel structure, low computational complexity and quasi-ML detection performance, there are no corresponding adaptive schemes. This paper solves
this problem, describing a hybrid tree based adaptive modulation detection scheme. Fixed Complexity Sphere Decoding (FSD) and Real-Values FSD (RFSD) are modified and combined into a hybrid scheme exploited at low and medium SNR to provide the highest possible information rate with quasi-ML Bit Error
Rate (BER) performance, while Reduced Complexity RFSD, BChase and Decision Feedback (DFE) schemes are exploited in the high SNR regions. This algorithm provides the facility to balance the detection complexity with BER performance with compatible information rate in dynamic, adaptive MIMO communications
environments.

VQ tree search system based on bit level systolic arrays

A bit-level systolic array system for performing a binary tree vector quantization (VQ) codebook search is described. This is based on a highly regular VLSI building block circuit. The system in question exhibits a very high data rate suitable for a range of real-time applications. A technique is described which reduces the storage requirements of such a system by 50%, with a corresponding decrease in hardware complexity.

A bit-level systolic architecture for implementing a VQ tree search

A bit-level systolic array system for performing a binary tree Vector Quantization codebook search is described. This consists of a linear chain of regular VLSI building blocks and exhibits data rates suitable for a wide range of real-time applications. A technique is described which reduces the computation required at each node in the binary tree to that of a single inner product operation. This method applies to all the common distortion measures (including the Euclidean distance, the Weighted Euclidean distance and the Itakura-Saito distortion measure) and significantly reduces the hardware required to implement the tree search system. © 1990 Kluwer Academic Publishers.

Tea tree oil (5%) body wash versus standard care (Johnson's Baby Softwash) to prevent colonization with methicillin-resistant Staphylococcus aureus in critically ill adults::a randomized controlled trial

OBJECTIVES: To determine whether the daily use of 5% tea tree oil (TTO) body wash (Novabac 5% Skin Wash) compared with standard care [Johnson's Baby Softwash (JBS)] had a lower incidence of methicillin-resistant Staphylococcus aureus (MRSA) colonization.

PATIENTS: The study setting was two intensive care units (ICUs; mixed medical, surgical and trauma) in Northern Ireland between October 2007 and July 2009. The study population comprised 391 patients who were randomized to JBS or TTO body wash.

METHODS: This was a Phase 2/3, prospective, open-label, randomized, controlled trial. Trial registration: ISRCTN65190967. The primary outcome was new MRSA colonization during ICU stay. Secondary outcomes included the incidence of MRSA bacteraemia and maximum increase in sequential organ failure assessment score.

RESULTS: A total of 445 patients were randomized to the study. After randomization, 54 patients were withdrawn; 30 because of a positive MRSA screen at study entry, 11 due to lack of consent, 11 were inappropriately randomized and 2 had adverse reactions. Thirty-nine (10%) patients developed new MRSA colonization (JBS n?=?22, 11.2%; TTO body wash n?=?17, 8.7%). The difference in percentage colonized (2.5%, 95% CI -?8.95 to 3.94; P?=?0.50) was not significant. The mean maximum increase in sequential organ failure assessment score was not significant (JBS 1.44, SD 1.92; TTO body wash 1.28, SD 1.79; P?=?0.85) and no study patients developed MRSA bacteraemia.

CONCLUSIONS: Compared with JBS, TTO body wash cannot be recommended as an effective means of reducing MRSA colonization.

Alternate wetting and drying irrigation for rice in Bangladesh: Is it sustainable and has plant breeding something to offer?

The crop management practice of alternate wetting and drying (AWD) is being promoted by IRRI and the national research and extension program in Bangladesh and other parts of the world as a water-saving irrigation practice that reduces the environmental impact of dry season rice production through decreased water usage, and potentially increases yield. Evidence is growing that AWD will dramatically reduce the concentration of arsenic in harvested rice grains conferring a third major advantage over permanently flooded dry season rice production. AWD may also increase the concentration of essential dietary micronutrients in the grain. However, three crucial aspects of AWD irrigation require further investigation. First, why is yield generally altered in AWD? Second, is AWD sustainable economically (viability of farmers' livelihoods) and environmentally (aquifer water table heights) over long-term use? Third, are current cultivars optimized for this irrigation system? This paper describes a multidisciplinary research project that could be conceived which would answer these questions by combining advanced soil biogeochemistry with crop physiology, genomics, and systems biology. The description attempts to show how the breakthroughs in next generation sequencing could be exploited to better utilize local collections of germplasm and identify the molecular mechanisms underlying biological adaptation to the environment within the context of soil chemistry and plant physiology.

Patterns of synchrony in natterjack toad breeding activity and reproductive success at local and regional scales

Empirical studies of the spatiotemporal dynamics of populations are required to better understand natural fluctuations in abundance and reproductive success, and to better target conservation and monitoring programmes. In particular, spatial synchrony in amphibian populations remains little studied. We used data from a comprehensive three year study of natterjack toad Bufo calamita populations breeding at 36 ponds to assess whether there was spatial synchrony in the toad breeding activity (start and length of breeding season, total number of egg strings) and reproductive success (premetamorphic survival and production of metamorphs). We defined a novel approach to assess the importance of short-term synchrony at both local and regional scales. The approach employs similarity indices and quantifies the interaction between the temporal and spatial components of populations using mixed effects models. There was no synchrony in the toad breeding activity and reproductive success at the local scale, suggesting that populations function as individual clusters independent of each other. Regional synchrony was apparent in the commencement and duration of the breeding season and in the number of egg strings laid (indicative of female population size). Regional synchrony in both rainfall and temperature are likely to explain the patterns observed (e.g. Moran effect). There was no evidence supporting regional synchrony in reproductive success, most likely due to spatial variability in the environmental conditions at the breeding ponds, and to differences in local population fitness (e.g. fecundity). The small scale asynchronous dynamics and regional synchronous dynamics in the number of breeding females indicate that it is best to monitor several populations within a subset of regions. Importantly, variations in the toad breeding activity and reproductive success are not synchronous, and it is thus important to consider them both when assessing the conservation status of pond-breeding amphibians. © 2012 The Authors. Ecography © 2012 Nordic Society Oikos.

The forgiving tree: a self-healing distributed data structure

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that the following process continues for up to n rounds where n is the total number of nodes initially in the network: the adversary deletesan arbitrary node from the network, then the network responds by quickly adding a small number of new edges.

We present a distributed data structure that ensures two key properties. First, the diameter of the network is never more than O(log Delta) times its original diameter, where Delta is the maximum degree of the network initially. We note that for many peer-to-peer systems, Delta is polylogarithmic, so the diameter increase would be a O(loglog n) multiplicative factor. Second, the degree of any node never increases by more than 3 over its original degree. Our data structure is fully distributed, has O(1) latency per round and requires each node to send and receive O(1) messages per round. The data structure requires an initial setup phase that has latency equal to the diameter of the original network, and requires, with high probability, each node v to send O(log n) messages along every edge incident to v. Our approach is orthogonal and complementary to traditional topology-based approaches to defending against attack.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

A tree augmented classifier based on Extreme Imprecise Dirichlet Model

We present TANC, a TAN classifier (tree-augmented naive) based on imprecise probabilities. TANC models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM). A first contribution of this paper is the experimental comparison between EDM and the global Imprecise Dirichlet Model using the naive credal classifier (NCC), with the aim of showing that EDM is a sensible approximation of the global IDM. TANC is able to deal with missing data in a conservative manner by considering all possible completions (without assuming them to be missing-at-random), but avoiding an exponential increase of the computational time. By experiments on real data sets, we show that TANC is more reliable than the Bayesian TAN and that it provides better performance compared to previous TANs based on imprecise probabilities. Yet, TANC is sometimes outperformed by NCC because the learned TAN structures are too complex; this calls for novel algorithms for learning the TAN structures, better suited for an imprecise probability classifier.

Bayesian network data imputation with application to survival tree analysis

Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation–maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.