No abstract

Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a single-channel blind source separation problem, aims to decompose the mains which records the whole house electricity consumption into appliance-wise readings. This problem is difficult because it is inherently unidentifiable. Recent approaches have shown that the identifiability problem could be reduced by introducing domain knowledge into the model. Deep neural networks have been shown to be a promising approach for these problems, but sliding windows are necessary to handle the long sequences which arise in signal processing problems, which raises issues about how to combine predictions from different sliding windows. In this paper, we propose sequence-to-point learning, where the input is a window of the mains and the output is a single point of the target appliance. We use convolutional neural networks to train the model. Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem. We applied the proposed neural network approaches to real-world household energy data, and show that the methods achieve state-of-the-art performance, improving two standard error measures by 84% and 92%.

We discuss a framework for the organization of learning systems in the mammalian brain , in which the hippocampus and related areas form a memory system complementary to learning mechanisms in neocortex and other areas. The hippocampal system stores new episodes and "replays" them to the neocortical system , interleaved with ongoing experience, allowing generalization as cortical memories form. The data to account for include: 1) neurophysiological findings concerning represen.tations in hippocampal areas, 2) behavioral evidence demonstrating a spatial role for hippocampus , 3) and effects of surgical and pharmacologi. cal manipulations on neuronal firing in hippocampal regions in behaving animals. We hypothesize that the hippocampal memory system consists of three major modules: 1) an invertible encoder subsystem supported by the pathways between neocortex and entorhinal cortex, which provides a stable, compressed, invertible encoding in entorhinal cortex (EC) of cortical activity patterns , 2) a memory separation, storage, and retrieval subsystem, supported by pathways between EC, dentate gyrus and area CA3, including the CA3 recurrent collaterals , which facilitates encoding and storage in CA3 of individual EC patterns, and retrieval of those CA3 encodings, in a manner that minimizes interference, and 3) a memory decoding subsystem, supported by the Shaffer collaterals from area CA 1 to area CA3 and the bi.directional pathways between EC and CA3, which provides the means by which a retrieved CA3 coding of an EC pattern can reinstate that pattern on Ec. This model has shown that 1) there is a trade-off between the need for information-preserving, structure. extracting encoding of cortical traces and the need for effective storage and recall of arbitrary traces, 2) long. term depression of synaptic strength in the pathways subject to long. term potentiation is crucial in preserving information, 3) area CA1 must be able to exploit correlations in EC patterns in the direct perforant path synapses.

Schizophrenia is a highly heritable psychotic disorder. It has been suggested that deficits of the established state arise from abnormal interactions between brain regions. We sought to examine whether such connectivity abnormalities would be present in subjects at high genetic risk for the disorder. Functional connectivity analysis was carried out on functional MRI images from 21 controls and 69 high risk subjects performing the Hayling sentence completion task; 27 high risk subjects reported isolated psychotic symptoms, the remaining high risk subjects and controls did not. There were no significant differences in task performance between the groups. Based on previous findings we hypothesized: (i) state-related differences in connectivity between dorsolateral prefrontal cortex and lateral temporal lobe; (ii) genetically mediated reductions in a medial prefrontal-thalamic-cerebellar network; and (iii) increased prefrontal-parietal connectivity in high risk subjects (to a greater extent in those with isolated psychotic symptoms). Connectivity analysis was performed in two ways: with and without variance associated with task effects modelled and removed from the data. We did not find evidence to support our first hypothesis with either analysis method. However, consistent with hypothesis (ii), decreased connectivity between right medial prefrontal regions and contralateral cerebellum was found. This was only statistically significant in the analysis with task effects modelled and removed from the data. Finally, consistent with hypothesis (iii), increased connectivity between the left parietal and left prefrontal regions in high risk subjects was found in both analyses. These results, all in a situation uncontaminated by the effects of anti-psychotic medication, performance differences and prolonged illness, suggest there are abnormalities in functional connectivity over and above those attributable to task effects in high risk subjects. These connectivity abnormalities may underlie the diverse deficits seen in the established condition and the more subtle deficits seen in close relatives of those with the disorder.