Patients' practices and experiences of using nebuliser therapy in the management of COPD at home.

How patients use their nebulisers at home is vital to ensure effective treatment and optimal health outcomes for patients with chronic obstructive pulmonary disease (COPD). The aim of the study was to identify the practicalities and problems associated with nebuliser use by patients with COPD at home, which may impact on the safety and effectiveness of therapy

Generalized Shortest Path Kernel on Graphs

We consider the problem of classifying graphs using graph kernels. We define a new graph kernel, called the generalized shortest path kernel, based on the number and length of shortest paths between nodes. For our example classification problem, we consider the task of classifying random graphs from two well-known families, by the number of clusters they contain. We verify empirically that the generalized shortest path kernel outperforms the original shortest path kernel on a number of datasets. We give a theoretical analysis for explaining our experimental results. In particular, we estimate distributions of the expected feature vectors for the shortest path kernel and the generalized shortest path kernel, and we show some evidence explaining why our graph kernel outperforms the shortest path kernel for our graph classification problem.Comment: Short version presented at Discovery Science 2015 in Banf

Robust beamforming with magnitude response constraints using iterative second-order cone programming

The problem of robust beamforming for antenna arrays with arbitrary geometry and magnitude response constraints is one of considerable importance. Due to the presence of the non-convex magnitude response constraints, conventional convex optimization techniques cannot be applied directly. A new approach based on iteratively linearizing the non-convex constraints is then proposed to reformulate the non-convex problem to a series of convex subproblems, each of which can be optimally solved using second-order cone programming (SOCP). Moreover, in order to obtain a more robust beamformer against array imperfections, the proposed method is further extended by optimizing its worst-case performance using again SOCP. Different from some conventional methods which are restricted to linear arrays, the proposed method is applicable to arbitrary array geometries since the weight vector, rather than its autocorrelation sequence, is used as the variable. Simulation results show that the performance of the proposed method is comparable to the optimal solution previously proposed for uniform linear arrays, and it also gives satisfactory results under different array specifications and geometries tested. © 2006 IEEE.published_or_final_versio

Frequency invariant uniform concentric circular arrays with directional elements

A new approach for designing frequency invariant (FI) uniform concentric circular arrays (UCCAs) with directional elements is proposed, and their applications to direction-of-arrival (DOA) estimation and adaptive beamforming are studied. By treating the sensors along the radial direction of the UCCA as linear subarrays and using appropriately designed beamformers, each subarray is transformed to a virtual element with appropriate directivity. Consequently, the whole UCCA can be viewed as a virtual uniform circular array (UCA) with desired element directivity for broadband processing. By extending the approach for designing FI-UCAs, the frequency dependency of the phase modes of the virtual UCA is compensated to facilitate broadband DOA and adaptive beamforming. Both the linear array beamformers (LABFs) and compensation filters can be designed separately using second- order cone programming (SOCP). Moreover, a new method to tackle the possible noise amplification problem in such large arrays by imposing additional norm constraints on the design of the compensation filters is proposed. The advantages of this decoupled approach are 1) the complicated design problem of large UCCAs can be decoupled into simpler problems of designing the LABFs and compensation filters, and 2) directional elements, which are frequently encountered, can be treated readily under the proposed framework. Numerical examples are provided to demonstrate the effectiveness and improvement of the proposed methods in DOA estimation, adaptive beamforming, and elevation control over the conventional FI-UCCA design method.published_or_final_versio

A new method for robust beamforming using iterative second-order cone programming

This paper addresses the problem of beamforming for antenna arrays in the presence of mismatches between the true and nominal steering vectors. A new method for robust beamforming is proposed by minimizing the array output power while controlling the array mainlobe response. Due to the presence of the non-convex response constraints, a new approach based on iteratively linearizing the non-convex constraints is proposed to reformulate the non-convex problem to a series of second-order cone programming (SOCP) subproblems, each of which can be optimally solved by well-established convex optimization techniques. Simulation results show that the proposed method offers better performance than conventional methods tested. © 2012 IEEE.published_or_final_versionThe 2012 IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, Korea, 20-23 May 2012. In IEEE International Symposium on Circuits and Systems Proceedings, 2012, p. 2569-257

Predicting Future Instance Segmentation by Forecasting Convolutional Features

Anticipating future events is an important prerequisite towards intelligent behavior. Video forecasting has been studied as a proxy task towards this goal. Recent work has shown that to predict semantic segmentation of future frames, forecasting at the semantic level is more effective than forecasting RGB frames and then segmenting these. In this paper we consider the more challenging problem of future instance segmentation, which additionally segments out individual objects. To deal with a varying number of output labels per image, we develop a predictive model in the space of fixed-sized convolutional features of the Mask R-CNN instance segmentation model. We apply the "detection head'" of Mask R-CNN on the predicted features to produce the instance segmentation of future frames. Experiments show that this approach significantly improves over strong baselines based on optical flow and repurposed instance segmentation architectures

Robust Recursive Steering Vector Estimation and Adaptive Beamforming under Sensor Uncertainties

published_or_final_versio

Detailing the phonetic environment: a sociophonetic study of the London-Bengali community

The present study investigated variability in the production of English by London Bengali adults who speak the Sylheti dialect of Bengali. Speakers had been resident in London for differing lengths of time. They were recorded producing /l/, /r/ and the eleven monophthongal vowels in English. Phonetically trained listeners rated speakers' productions of /l/ and /r/ in terms of manner and place. F1 and F2 formant frequency values, and duration were measured for all monophthongal vowels. The results demonstrated that older speakers (first-generation immigrants) tended to use Sylhetilike variants when speaking English, whilst second-generation speakers tended to use native English-like variants. These findings will be used to inform studies of the role of phonetic input in child language acquisition from the London Bengali community

Serotonergic innervation of the amygdala is increased in autism spectrum disorder and decreased in Williams syndrome.

BackgroundWilliams syndrome (WS) and autism spectrum disorder (ASD) are neurodevelopmental disorders that demonstrate overlapping genetic associations, dichotomous sociobehavioral phenotypes, and dichotomous pathological differences in neuronal distribution in key social brain areas, including the prefrontal cortex and the amygdala. The serotonergic system is critical to many processes underlying neurodevelopment and is additionally an important neuromodulator associated with behavioral variation. The amygdala is heavily innervated by serotonergic projections, suggesting that the serotonergic system is a significant mediator of neuronal activity. Disruptions to the serotonergic system, and atypical structure and function of the amygdala, are implicated in both WS and ASD.MethodsWe quantified the serotonergic axon density in the four major subdivisions of the amygdala in the postmortem brains of individuals diagnosed with ASD and WS and neurotypical (NT) brains.ResultsWe found opposing directions of change in serotonergic innervation in the two disorders, with ASD displaying an increase in serotonergic axons compared to NT and WS displaying a decrease. Significant differences (p < 0.05) were observed between WS and ASD data sets across multiple amygdala nuclei.LimitationsThis study is limited by the availability of human postmortem tissue. Small sample size is an unavoidable limitation of most postmortem human brain research and particularly postmortem research in rare disorders.ConclusionsDifferential alterations to serotonergic innervation of the amygdala may contribute to differences in sociobehavioral phenotype in WS and ASD. These findings will inform future work identifying targets for future therapeutics in these and other disorders characterized by atypical social behavior

Relax, Don't RAN Translate It

The (GGGGCC)n repeat expansion in C9orf72, which is the most common cause of frontotemporal dementia and amyotrophic lateral sclerosis, is translated through repeat-associated non-AUG (RAN) translation. In this issue of Neuron, Cheng et al. (2019) report that the helicase DDX3X, which unwinds (or relaxes) RNA, suppresses RAN translation and toxicity