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Net Metering (NM) policies has been widely used as a mechanism to allow the inclusion of distributed generation (DG) resources in the energy system, especially the adoption of solar photovoltaic systems by small consumers like households and small business. The main objective of a NM policy is to permit consumers connected to the utility grid to offset the consumption by inputting self-generation surplus into the network. The regulatory and policy framework is key to determine the economics of DG (investment costs and benefits), as consequence to determine the adoption potential. We use individual data of consumers in Brazil, obtained from ANEEL's Consumers Satisfaction Index, to calibrate a Bass Diffusion model integrated with the project viability model. The results show that with a reasonable 3.6% of capital cost decreasing per year, in 15 years (circa 2030) the PV project will be viable for 50% of the consumers. We finish the note with Net Metering policy recommendations. [ABSTRACT FROM AUTHOR]

The attribution states of both ?healthy? and ?ill? groups share the common propensity to reflectively define their respective social identities. The desiderata of a group social identity of ?healthy? occurs within their group interaction with the ?unhealthy.? This juxtaposition of bipolar social identities of ?healthy? and ?unhealthy? promotes the reification of each group. The ostensible nature of illness may force and propel this process of social group identity reification. The physical symptomogical manifestation of human wasting of HIV seropositive individual places the mark of ?infected? and ?contagious? on the victim of HIV. This physical, psychological, and social harm forments a defense response of HIV victim both levels of individual and group social identity.The Covalent Security Attribution Model offers a cognitive ?deep structure? model based social roles (Heiss, 1981) that is well suited to the tenets of structural equation modeling. Hofer, Horn, and Eber (1997, p. 249) assert the more parsimonious structure of a model (one with less parameters) provides for more viable and critical test of theoretical proposition (Bentler & Mooijaart, 1989). Moreover, the CSA model presents an empirical test of the identification of the external and internal states that affect the perceptions of the two groups. [ABSTRACT FROM AUTHOR]

Many computer vision algorithms depend on configuration settings that are typically hand-tuned in the course of evaluating the algorithm for a particular data set. While such parameter tuning is often presented as being incidental to the algorithm, correctly setting these parameter choices is frequently critical to realizing a method’s full potential. Compounding matters, these parameters often must be re-tuned when the algorithm is applied to a new problem domain, and the tuning process itself often depends on personal experience and intuition in ways that are hard to quantify or describe. Since the performance of a given technique depends on both the fundamental quality of the algorithm and the details of its tuning, it is sometimes difficult to know whether a given technique is genuinely better, or simply better tuned. In this work, we propose a meta-modeling approach to support automated hyperparameter optimization, with the goal of providing practical tools that replace hand-tuning with a reproducible and unbiased optimization process. Our approach is to expose the underlying expression graph of how a performance metric (e.g. classification accuracy on validation examples) is computed from hyperparameters that govern not only how individual processing steps are applied, but even which processing steps are included. A hyperparameter optimization algorithm transforms this graph into a program for optimizing that performance metric. Our approach yields state of the art results on three disparate computer vision problems: a face-matching verification task (LFW), a face identification task (PubFig83) and an object recognition task (CIFAR-10), using a single broad class of feed-forward vision architectures., Engineering and Applied Sciences

One of the goals of the ICML workshop on representation and learning is to establish benchmark scores for a new data set of labeled facial expressions. This paper presents the performance of a "Null" model consisting of convolutions with random weights, PCA, pooling, normalization, and a linear readout. Our approach focused on hyperparameter optimization rather than novel model components. On the Facial Expression Recognition Challenge held by the Kaggle website, our hyperparameter optimization approach achieved a score of 60% accuracy on the test data. This paper also introduces a new ensemble construction variant that combines hyperparameter optimization with the construction of ensembles. This algorithm constructed an ensemble of four models that scored 65.5% accuracy. These scores rank 12th and 5th respectively among the 56 challenge participants. It is worth noting that our approach was developed prior to the release of the data set, and applied without modification; our strong competition performance suggests that the TPE hyperparameter optimization algorithm and domain expertise encoded in our Null model can generalize to new image classification data sets., Engineering and Applied Sciences

The rapidly evolving landscape of multicore architectures makes the construction of efficient libraries a daunting task. A family of methods known collectively as “auto-tuning” has emerged to address this challenge. Two major approaches to auto-tuning are empirical and model-based: empirical autotuning is a generic but slow approach that works by measuring runtimes of candidate implementations, model-based auto-tuning predicts those runtimes using simplified abstractions designed by hand. We show that machine learning methods for non-linear regression can be used to estimate timing models from data, capturing the best of both approaches. A statistically-derived model offers the speed of a model-based approach, with the generality and simplicity of empirical auto-tuning. We validate our approach using the filterbank correlation kernel described in Pinto and Cox [2012], where we find that 0.1 seconds of hill climbing on the regression model (“predictive auto-tuning”) can achieve almost the same speed-up as is brought by minutes of empirical auto-tuning. Our approach is not specific to filterbank correlation, nor even to GPU kernel auto-tuning, and can be applied to almost any templated-code optimization problem, spanning a wide variety of problem types, kernel types, and platforms., Engineering and Applied Sciences, Molecular and Cellular Biology

In this paper, we describe the first results of using the robot Probo as a facilitator in Social Story Intervention for children with autism spectrum disorders (ASD). Four preschoolers diagnosed with ASD participated in this research. For each of them, a specific social skill deficit was identified, like Sharing toys, Saying Thank you and SayingHello, and an individualized Social Story was developed. The stories were told by both, the therapist and the robot,in different intervention phases. Afterwards, an experimental task was created where the child needed to exercise the ability targeted by the story. The results of this study showed that the participant needed a decreased level of prompt to perform the targeted behavior, when the story was told by the robot compared to the intervention with the human storyteller. Therefore, this preliminary study created great expectancies about the potential of Robot Assisted Therapy as an added value for ASD interventions [ABSTRACT FROM AUTHOR]