Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method which incorporates appliance usage patterns (AUPs) to improve performance of active load identi- fication and forecasting. In the first stage, the AUPs of a given residence were learnt using a spectral decomposition based standard NILM algorithm. Then, learnt AUPs were utilized to bias the priori probabilities of the appliances through a specifically constructed fuzzy system. The AUPs contain likelihood measures for each appliance to be active at the present instant based on the recent activity/inactivity of appliances and the time of day. Hence, the priori probabilities determined through the AUPs increase the active load identification accuracy of the NILM algorithm. The proposed method was successfully tested for two standard databases containing real household measurements in USA and Germany. The proposed method demonstrates an improvement in active load estimation when applied to the aforementioned databases as the proposed method augments the smart meter readings with the behavioral trends obtained from AUPs. Furthermore, a residential power consumption forecasting mechanism, which can predict the total active power demand of an aggregated set of houses, five minutes ahead of real time, was successfully formulated and implemented utilizing the proposed AUP based technique

Scalable multimodal convolutional networks for brain tumour segmentation

Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture structured and sparse so that the final architecture becomes scalable to the number of modalities. We evaluate the scalable architecture for brain tumour segmentation and give evidence of its regularisation effect compared to the conventional concatenation approach.Comment: Paper accepted at MICCAI 201

Spectroscopic and Computational Comparisons of Thiolate-Ligated Ferric Nonheme Complexes to Cysteine Dioxygenase: Second-Sphere Effects on Substrate (Analogue) Positioning

Parallel spectroscopic and computational studies of iron(III) cysteine dioxygenase (CDO) and synthetic models are presented. The synthetic complexes utilize the ligand tris(4,5-diphenyl-1-methylimidazol-2-yl)phosphine (Ph2TIP), which mimics the facial three-histidine triad of CDO and other thiol dioxygenases. In addition to the previously reported [FeII(CysOEt)(Ph2TIP)]BPh4 (1; CysOEt is the ethyl ester of anionic l-cysteine), the formation and crystallographic characterization of [FeII(2-MTS)(Ph2TIP)]BPh4 (2) is reported, where the methyl 2-thiosalicylate anion (2-MTS) resembles the substrate of 3-mercaptopropionate dioxygenase (MDO). One-electron chemical oxidation of 1 and 2 yields ferric species that bind cyanide and azide anions, which have been used as spectroscopic probes of O2 binding in prior studies of FeIII-CDO. The six-coordinate FeIII-CN and FeIII-N3 adducts are examined with UV–vis absorption, electron paramagnetic resonance (EPR), and resonance Raman (rRaman) spectroscopies. In addition, UV–vis and rRaman studies of cysteine- and cyanide-bound FeIII-CDO are reported for both the wild-type (WT) enzyme and C93G variant, which lacks the Cys-Tyr cross-link that is present in the second coordination sphere of the WT active site. Density functional theory (DFT) and ab initio calculations are employed to provide geometric and electronic structure descriptions of the synthetic and enzymatic FeIII adducts. In particular, it is shown that the complete active space self-consistent field (CASSCF) method, in tandem with n-electron valence state second-order perturbation theory (NEVPT2), is capable of elucidating the structural basis of subtle shifts in EPR g values for low-spin FeIII species. Synopsis The geometric and electronic structures of thiolate-ligated FeIII complexes of relevance to the active sites of thiol dioxygenases have been elucidated with spectroscopic and computational methods. Data collected for the synthetic models are compared to those previously obtained for the analogous enzymatic species, and newly collected resonance Raman spectra of Cys- and CN-bound FeIII-CDO are presented. The combined enzymatic/synthetic approach reveals that second-sphere residues perturb the positions of substrate (analogues) coordinated to the nonheme iron site of CDO

Implications of Electronics Constraints for Solid-State Quantum Error Correction and Quantum Circuit Failure Probability

In this paper we present the impact of classical electronics constraints on a solid-state quantum dot logical qubit architecture. Constraints due to routing density, bandwidth allocation, signal timing, and thermally aware placement of classical supporting electronics significantly affect the quantum error correction circuit's error rate. We analyze one level of a quantum error correction circuit using nine data qubits in a Bacon-Shor code configured as a quantum memory. A hypothetical silicon double quantum dot quantum bit (qubit) is used as the fundamental element. A pessimistic estimate of the error probability of the quantum circuit is calculated using the total number of gates and idle time using a provably optimal schedule for the circuit operations obtained with an integer program methodology. The micro-architecture analysis provides insight about the different ways the electronics impact the circuit performance (e.g., extra idle time in the schedule), which can significantly limit the ultimate performance of any quantum circuit and therefore is a critical foundation for any future larger scale architecture analysis.Comment: 10 pages, 7 figures, 3 table

City refuse compost and sodium dodecyl sulphate as modifiers of diazinon leaching in soil

Spanish "Comisi6n Interminterial de Ciencia y Tecnologia" (Projet AMB94-0688). Consejo Superior de Investigaciones Científicas (CSIC.Peer reviewe

Real-time decoding of covert attention in higher-order visual areas

Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devices for communication. Until now this has only been demonstrated in primary motor and sensory brain regions, using surgical implants or non-invasive neuroimaging techniques. Here, we provide proof-of-principle for the use of higher-order brain regions involved in complex cognitive processes such as attention. Using realtime fMRI, we implemented an online ‘winner-takes-all approach’ with quadrant-specific parameter estimates, to achieve single-block classification of brain activations. These were linked to the covert allocation of attention to real-world images presented at 4-quadrant locations. Accuracies in three target regions were significantly above chance, with individual decoding accuracies reaching upto 70%. By utilising higher order mental processes, ‘cognitive BCIs’ access varied and therefore more versatile information, potentially providing a platform for communication in patients who are unable to speak or move due to brain injury

Groundwater quality and antibiotic resistance of coliform bacteria in Wilgamuwa, an area of Chronic Kidney Disease of unknown etiology (CKDu) in Sri Lanka

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