Causal networks for climate model evaluation and constrained projections

Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections. Algorithms to assess causal relationships in data sets have seen increasing applications in climate science in recent years. Here, the authors show that these techniques can help to systematically evaluate the performance of climate models and, as a result, to constrain uncertainties in future climate change projections

Occlusion Coherence: Detecting and Localizing Occluded Faces

The presence of occluders significantly impacts object recognition accuracy. However, occlusion is typically treated as an unstructured source of noise and explicit models for occluders have lagged behind those for object appearance and shape. In this paper we describe a hierarchical deformable part model for face detection and landmark localization that explicitly models part occlusion. The proposed model structure makes it possible to augment positive training data with large numbers of synthetically occluded instances. This allows us to easily incorporate the statistics of occlusion patterns in a discriminatively trained model. We test the model on several benchmarks for landmark localization and detection including challenging new data sets featuring significant occlusion. We find that the addition of an explicit occlusion model yields a detection system that outperforms existing approaches for occluded instances while maintaining competitive accuracy in detection and landmark localization for unoccluded instances

Contrast sensitivity of insect motion detectors to natural images

How do animals regulate self-movement despite large variation in the luminance contrast of the environment? Insects are capable of regulating flight speed based on the velocity of image motion, but the mechanisms for this are unclear. The Hassenstein–Reichardt correlator model and elaborations can accurately predict responses of motion detecting neurons under many conditions but fail to explain the apparent lack of spatial pattern and contrast dependence observed in freely flying bees and flies. To investigate this apparent discrepancy, we recorded intracellularly from horizontal-sensitive (HS) motion detecting neurons in the hoverfly while displaying moving images of natural environments. Contrary to results obtained with grating patterns, we show these neurons encode the velocity of natural images largely independently of the particular image used despite a threefold range of contrast. This invariance in response to natural images is observed in both strongly and minimally motion-adapted neurons but is sensitive to artificial manipulations in contrast. Current models of these cells account for some, but not all, of the observed insensitivity to image contrast. We conclude that fly visual processing may be matched to commonalities between natural scenes, enabling accurate estimates of velocity largely independent of the particular scene

Acoustic event detection for multiple overlapping similar sources

Many current paradigms for acoustic event detection (AED) are not adapted to the organic variability of natural sounds, and/or they assume a limit on the number of simultaneous sources: often only one source, or one source of each type, may be active. These aspects are highly undesirable for applications such as bird population monitoring. We introduce a simple method modelling the onsets, durations and offsets of acoustic events to avoid intrinsic limits on polyphony or on inter-event temporal patterns. We evaluate the method in a case study with over 3000 zebra finch calls. In comparison against a HMM-based method we find it more accurate at recovering acoustic events, and more robust for estimating calling rates.Comment: Accepted for WASPAA 201

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed

A Review of the Monitoring of Market Power The Possible Roles of TSOs in Monitoring for Market Power Issues in Congested Transmission Systems

The paper surveys the literature and publicly available information on market power monitoring in electricity wholesale markets. After briefly reviewing definitions, strategies and methods of mitigating market power we examine the various methods of detecting market power that have been employed by academics and market monitors/regulators. These techniques include structural and behavioural indices and analysis as well as various simulation approaches. The applications of these tools range from spot market mitigation and congestion management through to long-term market design assessment and merger decisions. Various market-power monitoring units already track market behaviour and produce indices. Our survey shows that these units collect a large amount of data from various market participants and we identify the crucial role of the transmission system operators with their access to dispatch and system information. Easily accessible and comprehensive data supports effective market power monitoring and facilitates market design evaluation. The discretion required for effective market monitoring is facilitated by institutional independence.Electricity, liberalisation, market power, regulation