Since the early 1980s, the research community has developed ever more sophisticated algorithms for the problem of energy disaggregation, but despite decades of research, there is still a dearth of applications with demonstrated value. In this work, we explore a question that is highly pertinent to this research community: how good does energy disaggregation need to be in order to infer characte...

In this paper we develop a dynamic data-consistent way for estimating agricultural land use choices at a disaggregate level (district-level), using more aggregate data (regional-level). The disaggregation procedure requires two steps. The first step consists in specifying and estimating a dynamic model of land use at the regionallevel. In the second step, we disaggregate outcomes of the aggrega...

A dual-functional fluorescent chelator can specifically target and disassemble metal-associated Aβ aggregates and simultaneously self-monitor the disaggregation by fluorescence in brain homogenates of mice with Alzheimer's disease.

Cell survival under severe thermal stress requires the activity of the ClpB (Hsp104) AAA+ chaperone that solubilizes and reactivates aggregated proteins in concert with the DnaK (Hsp70) chaperone system. How protein disaggregation is achieved and whether survival is solely dependent on ClpB-mediated elimination of aggregates or also on reactivation of aggregated proteins has been unclear. We en...

how to cite this article: bhadbhade a, cheng dw. amyloid precursor protein processing in alzheimer’s disease. iranian journal of child neurology2012;6(1):1-5. alzheimer’s disease (ad) is a progressive neurodegenerative disorder and a leading cause of dementia. the ad is characterized by presence of intraneuronal tangles and extracellular plaques in the brain. the plaques are composed of dense a...

Economists and econometricians very often work with data which has been temporally disaggregated prior to use. Hence, the quality of the disaggregation clearly affects the quality of the analyses. Building on Chow and Lin’s (1971) disaggregation model this paper proposes a new estimation approach and a specification test which assesses the quality of the disaggregation model. An advantage of th...

Energy and sustainability issues raise a large number of problems that can be tackled using approaches from data mining and machine learning, but traction of such problems has been slow due to the lack of publicly available data. In this paper we present the Reference Energy Disaggregation Data Set (REDD), a freely available data set containing detailed power usage information from several home...

Recently, and with the growing development of big energy datasets, data-driven learning techniques began to represent a potential solution to the energy disaggregation problem outperforming engineered and hand-crafted models. However, most proposed deep disaggregation models are load-dependent in the sense that either expert knowledge or a hyper-parameter optimization stage is required prior to...