The effect of pulse stimulation on marine biota - Research in relation to ICES advice - Progress report on the effects on benthic invertebrates

In response to ecosystem related concerns about bottom trawling and particularly beam trawling that were raised by various scientists in the last decades of the previous century. Many studies were done in the 1970s and 1980s, but in spite of promising results commercial uptake was lacking. The development of pulse trawling was again taken up in the 1990s by a private company (Verburg:Holland Ltd.) in The Netherlands. Meanwhile questions about ecosystem effects of introducing pulse beam trawling in the Dutch flatfish fishery were raised by the European Scientific, Technical and Economic Committee for Fisheries (STECF) and the Inter: national Council for the Exploration of the Sea (ICES) and discussed at the meeting of the ICES Working Group on Fishing Technology and Fish Behaviour (WGFTFB) in 2006. These questions led to field strength measurements in situ onboard the commercial beam trawler, and research on the effects of pulse stimulation on cod (Gadus morhua L.), and elasmobranch fish

Resource allocation and health technology assessment in Australia: Views from the local level

Objectives: Several studies have shown that a key determinant of successful health technology assessment (HTA) uptake is a clear, fair, and consistent decision-making process for the approval and introduction of health technologies. The aim of this study was to gauge healthcare providers' and managers' perceptions of local level decision making and determine whether these processes offer a conducive environment for HTA. An Area Health Service (AHS) aimed to use the results of this study to help design a new process of technology assessment and decision making. Methods: An online survey was sent to all health service managers and healthcare providers working in one AHS in Sydney, Australia. Questions related to perceptions of current health technology decisions in participants' own institution/facility and opinions on key criteria for successful decision-making processes. Results: Less than a third of participants agreed with the statements that local decision-making processes were appropriate, easy to understand, evidence-based, fair, or consistently applied. Decisions were reportedly largely influenced by total cost considerations as well as by the central state health departments and the Area executive. Conclusions: Although there are renewed initiatives in HTA in Australia, there is a risk that such investments will not be productive unless policy makers also examine the decision-making contexts within which HTA can successfully be implemented. The results of this survey show that this is especially true at the local level and that any HTA initiative should be accompanied by efforts to improve decision-making processes. Copyright © 2009 Cambridge University Press

{DAFormer}: {I}mproving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a costly process, a model can instead be trained with more accessible synthetic data and adapted to real images without requiring their annotations. This process is studied in unsupervised domain adaptation (UDA). Even though a large number of methods propose new adaptation strategies, they are mostly based on outdated network architectures. As the influence of recent network architectures has not been systematically studied, we first benchmark different network architectures for UDA and then propose a novel UDA method, DAFormer, based on the benchmark results. The DAFormer network consists of a Transformer encoder and a multi-level context-aware feature fusion decoder. It is enabled by three simple but crucial training strategies to stabilize the training and to avoid overfitting DAFormer to the source domain: While the Rare Class Sampling on the source domain improves the quality of pseudo-labels by mitigating the confirmation bias of self-training towards common classes, the Thing-Class ImageNet Feature Distance and a learning rate warmup promote feature transfer from ImageNet pretraining. DAFormer significantly improves the state-of-the-art performance by 10.8 mIoU for GTA->Cityscapes and 5.4 mIoU for Synthia->Cityscapes and enables learning even difficult classes such as train, bus, and truck well. The implementation is available at https://github.com/lhoyer/DAFormer

Sound and Visual Representation Learning with Multiple Pretraining Tasks

Different self-supervised tasks (SSL) reveal different features from the data. The learned feature representations can exhibit different performance for each downstream task. In this light, this work aims to combine Multiple SSL tasks (Multi-SSL) that generalizes well for all downstream tasks. Specifically, for this study, we investigate binaural sounds and image data in isolation. For binaural sounds, we propose three SSL tasks namely, spatial alignment, temporal synchronization of foreground objects and binaural audio and temporal gap prediction. We investigate several approaches of Multi-SSL and give insights into the downstream task performance on video retrieval, spatial sound super resolution, and semantic prediction on the OmniAudio dataset. Our experiments on binaural sound representations demonstrate that Multi-SSL via incremental learning (IL) of SSL tasks outperforms single SSL task models and fully supervised models in the downstream task performance. As a check of applicability on other modality, we also formulate our Multi-SSL models for image representation learning and we use the recently proposed SSL tasks, MoCov2 and DenseCL. Here, Multi-SSL surpasses recent methods such as MoCov2, DenseCL and DetCo by 2.06%, 3.27% and 1.19% on VOC07 classification and +2.83, +1.56 and +1.61 AP on COCO detection. Code will be made publicly available

Behavior profiles in children with functional urinary incontinence before and after incontinence treatment

OBJECTIVE. The purpose of this work was to analyze prospectively the prevalence of behavioral disorders in children with urinary incontinence because of nonneuropathic bladder-sphincter dysfunction before and after treatment for incontinence. METHODS. A total of 202 children with nonneuropathic bladder-sphincter dysfunction were enrolled in the European Bladder Dysfunction Study, in branches for urge syndrome (branch 1) and dysfunctional voiding (branch 2); 188 filled out Achenbach's Child Behavior Checklist before treatment and 111 after treatment. Child Behavior Checklist scales for total behavior problems were used along with subscales for externalizing problems and internalizing problems. RESULTS. After European Bladder Dysfunction Study treatment, the total behavior problem score dropped from 19% to 11%, the same prevalence as in the normative population; in branch 1 the score dropped from 14% to 13%, and in branch 2 it dropped from 23% to 8%. The prevalence of externalizing problems dropped too, from 12% to 8%: in branch 1 it was unchanged at 10%, and in branch 2 it dropped from 14% to 7%. The decrease in prevalence of internalizing problems after treatment, from 16% to 14%, was not significant. CONCLUSION. More behavioral problems were found in dysfunctional voiding than in urge syndrome, but none of the abnormal scores related to the outcome of European Bladder Dysfunction Study treatment for incontinence. With such treatment, both the total behavior problem score and the score for externalizing problems returned to normal, but the score for internalizing problems did not change. The drops in prevalence are statistically significant only in dysfunctional voiding

HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection

Besides standard cameras, autonomous vehicles typically include multipleadditional sensors, such as lidars and radars, which help acquire richerinformation for perceiving the content of the driving scene. While severalrecent works focus on fusing certain pairs of sensors - such as camera andlidar or camera and radar - by using architectural components specific to theexamined setting, a generic and modular sensor fusion architecture is missingfrom the literature. In this work, we focus on 2D object detection, afundamental high-level task which is defined on the 2D image domain, andpropose HRFuser, a multi-resolution sensor fusion architecture that scalesstraightforwardly to an arbitrary number of input modalities. The design ofHRFuser is based on state-of-the-art high-resolution networks for image-onlydense prediction and incorporates a novel multi-window cross-attention block asthe means to perform fusion of multiple modalities at multiple resolutions.Even though cameras alone provide very informative features for 2D detection,we demonstrate via extensive experiments on the nuScenes and Seeing Through Fogdatasets that our model effectively leverages complementary features fromadditional modalities, substantially improving upon camera-only performance andconsistently outperforming state-of-the-art fusion methods for 2D detectionboth in normal and adverse conditions. The source code will be made publiclyavailable.<br