Perception is dependent on the ability to separate stimuli from different objects and causes in order to perform inference and further processing. We have models of how the human brain can perform such causal inference for simple binary stimuli (e.g., auditory and visual), but the complexity of the models increases dramatically with more than two stimuli. To characterize human perception with more complex stimuli, we developed a Bayesian inference model that takes into account a potentially unlimited number of stimulus sources: it is general enough to factor in any discrete sequential cues from any modality. Because the model employs a non-parametric prior, increased signal complexity does not necessitate the addition of more parameters. The model not only predicts the number of possible sources, but also specifies the source with which each signal is associated. As a test case, we demonstrate that such a model can explain several phenomena in the auditory stream perception literature, that it provides an excellent fit to experimental data, and that it makes novel predictions that we experimentally confirm. These findings have implications not just for human auditory temporal perception, but for a wide range of perceptual phenomena across unisensory and multisensory stimuli.

Risk management is a vital activity to ensure employee safety in construction projects. Various documents provide important supporting evidence, including details of previous incidents, consequences and mitigation strategies. Potential hazards may depend on a complex set of project-specific attributes, including activities undertaken, location, equipment used, etc. However, finding evidence about previous projects with similar attributes can be problematic, since information about risks and mitigations is usually hidden within and may be dispersed across a range of different free text documents. Automatic named entity recognition (NER), which identifies mentions of concepts in free text documents, is the first stage in structuring knowledge contained within them. While developing NER methods generally relies on annotated corpora, we are not aware of any such corpus targeted at concepts relevant to construction safety. In response, we have designed a novel named entity annotation scheme and associated guidelines for this domain, which covers hazards, consequences, mitigation strategies and project attributes. Four health and safety experts used the guidelines to annotate a total of 600 sentences from accident reports; an average inter-annotator agreement rate of 0.79 F-Score shows that our work constitutes an important first step towards developing tools for detailed semantic analysis of construction safety documents.

UNLABELLED: ADAPT is an online database providing comprehensive mappings between Affymetrix probes and RefSeq and Ensembl transcripts. ADAPT was designed to help interpret microarray experiments by providing a means to explore the many-to-many relationships that exist between probes, probesets, transcripts and genes. AVAILABILITY: ADAPT can be queried via the web at http://bioinformatics.picr.man.ac.uk/adapt