Anthropogenic noise affects vocal interactions

Animal communication plays a crucial role in many species, and it involves a sender producing a signal and a receiver responding to that signal. The shape of a signal is determined by selection pressures acting upon it. One factor that exerts selection on acoustic signals is the acoustic environment through which the signal is transmitted. Recent experimental studies clearly show that senders adjust their signals in response to increased levels of anthropogenic noise. However, to understand how noise affects the whole process of communication, it is vital to know how noise affects the receiver’s response during vocal interactions. Therefore, we experimentally manipulated ambient noise levels to expose male European robins (Erithacus rubecula) to two playback treatments consisting of the same song: one with noise and another one without noise. We found that males responding to a conspecific in a noise polluted environment increased minimum frequency and decreased song complexity and song duration. Thus, we show that the whole process of communication is affected by noise, not just the behaviour of the sender.

The impact of a simulated intervention on attitudes of undergraduate nursing and medical students towards end of life care provision

Background: The concerns of undergraduate nursing and medical students’ regarding end of life care are well documented. Many report feelings of emotional distress, anxiety and a lack of preparation to provide care to patients at end of life and their families. Evidence suggests that increased exposure to patients who are dying and their families can improve attitudes toward end of life care. In the absence of such clinical exposure, simulation provides experiential learning with outcomes comparable to that of clinical practice. The aim of this study was therefore to assess the impact of a simulated intervention on the attitudes of undergraduate nursing and medical students towards end of life care.
Methods: A pilot quasi-experimental, pretest-posttest design. Attitudes towards end of life care were measured using the Frommelt Attitudes Towards Care of the Dying Part B Scale which was administered pre and post a simulated clinical scenario. 19 undergraduate nursing and medical students were recruited from one large Higher Education Institution in the United Kingdom.
Results: The results of this pilot study confirm that a simulated end of life care intervention has a positive impact on the attitudes of undergraduate nursing and medical students towards end of life care (p < 0.001).
Conclusions: Active, experiential learning in the form of simulation teaching helps improve attitudes of undergraduate nursing and medical students towards end of life. In the absence of clinical exposure, simulation is a viable alternative to help prepare students for their professional role regarding end of life care.

Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Background and aims: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging.

Materials and methods: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: ‘semi-structured’ and ‘unstructured’. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry.

Results: The best result of 99.4% accuracy – which included only one semi-structured report predicted as unstructured – was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured.

Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

Learning to read with a critical eye: cultivating discerning readers of media reports with a science component.

It is important for young people to be able to read science-related media reports with discernment. ‘Getting Newswise’ was a research project designed to enable science and English teachers, working collaboratively, to equip pupils through the curriculum with critical reading skills appropriate for science news. Phase one of the study found that science and English teachers respond differently to science news articles and eight categories of critical response were identified. These findings informed phase two, in which classroom activities were devised whereby pupils examined, evaluated and responded to science-related news reports. Science-English collaboration had positive outcomes for pupil understanding

Enlivening, Invigorating & Enhancing Science:This session explores learning from a range of Primary Science Teaching Trust projects focused on invigorating the experience of children, teachers and whole school communities with primary science.

This session will provide you with opportunity to find out what is being achieved and explore the implications for your own practice.

Efficient Learning of Bayesian Networks with Bounded Tree-width

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods \cite{korhonen2exact, nie2014advances} tackle the problem by using $k$-trees to learn the optimal Bayesian network with tree-width up to $k$. Finding the best $k$-tree, however, is computationally intractable. In this paper, we propose a sampling method to efficiently find representative $k$-trees by introducing an informative score function to characterize the quality of a $k$-tree. To further improve the quality of the $k$-trees, we propose a probabilistic hill climbing approach that locally refines the sampled $k$-trees. The proposed algorithm can efficiently learn a quality Bayesian network with tree-width at most $k$. Experimental results demonstrate that our approach is more computationally efficient than the exact methods with comparable accuracy, and outperforms most existing approximate methods.

Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables

We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance.