Abstract
This paper proposes a two-stage stochastic optimization model to support an aggregator of prosumers in the definition of bids for the day-ahead energy and secondary reserve markets. The aggregator optimizes the prosumers' flexibility with the objective of minimizing the net cost of buying and selling energy and secondary reserve in both day-ahead and real-time market stages. The uncertainties of the renewable generation, consumption, outdoor temperature, prosumers' preferences, and house occupancy are modeled through a set of scenarios. For a case study of 1000 prosumers, the results show that the proposed bidding strategy reduces the costs of both aggregator and prosumers by 40% compared to a bidding strategy typically used by retailers.

Abstract
We present a portable and low-cost system for interrogation of long-period fiber gratings (LPFGs) costing around a 30th of the price of a typical setup using an optical spectrum analyzer and a broadband light source. The unit is capable of performing real-time monitoring or as a stand-alone data-logger. The proposed technique uses three thermally modulated fiber-coupled laser diodes, sweeping a few nanometers around their central wavelength. The light signal is then modulated by the LPFG and its intensity is acquired by a single photo-detector. Through curve-fitting algorithms the sensor transmission spectrum is reconstructed. Testing and validation were accomplished by inducing variations in the spectral features of an LPFG through changes either in external air temperature from 22 to 425 degrees C or in refractive index (RI) of the surrounding medium from 1.3000 to 1.4240. A dynamic resolution between 3.5 and 1.9 degrees C was achieved, in temperatures from 125 to 325 degrees C. In RI measurements, maximum wavelength and optical power deviations of 2.75 nm and 2.86 dB, respectively, were obtained in the range from 1530 to 1570 nm. The worse RI resolution obtained was 3.47x10(-3). The interrogation platform was then applied in the detection of iron corrosion, expressing wavelength peak values within 1.12 nm from the real value in the region between 1530 and 1570 nm.

Abstract
Image segmentation is widely used in image processing, particularly when there is one or few objects of interest. The segmentation of multi-spectral remote sensing images is more challenging due to the large number and diversity of the objects of interest, and the difficulty in having ground truth to tune the segmentation algorithm parameters and to evaluate the results produced. The Synthetic Image TEsting Framework (SITEF) is a tool to address these issues. As the shape and location of the objects in a synthetic image are known, it provides references to be used for quantitative evaluation of the segmentation results. This paper presents SITEF with an experiment to evaluate the segmentation of a SENTINEL-2 image using the software SNAP.

Abstract
Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. However, while a global change in transcription is recognized as a defining feature of cancer, the contribution of alternative promoters still remains largely unexplored. Here, we infer active promoters using RNA-seq data from 18,468 cancer and normal samples, demonstrating that alternative promoters are a major contributor to context-specific regulation of transcription. We find that promoters are deregulated across tissues, cancer types, and patients, affecting known cancer genes and novel candidates. For genes with independently regulated promoters, we demonstrate that promoter activity provides a more accurate predictor of patient survival than gene expression. Our study suggests that a dynamic landscape of active promoters shapes the cancer transcriptome, opening new diagnostic avenues and opportunities to further explore the interplay of regulatory mechanisms with transcriptional aberrations in cancer.

Abstract
Hybrid computation harbours discrete and continuous dynamics in the form of an entangled mixture, inherently present in various natural phenomena and in applications ranging from control theory to microbiology. The emergent behaviours bear signs of both computational and physical processes, and thus present difficulties not only in their analysis, but also in describing them adequately in a structural, well-founded way. In order to tackle these issues and, more generally, to investigate hybridness as a dedicated computational phenomenon, we introduce a while-language for hybrid computation inspired by the fine-grain call-by-value paradigm. We equip it with operational and computationally adequate denotational semantics. The latter crucially relies on a hybrid monad supporting an (Elgot) iteration operator that we developed elsewhere. As an intermediate step, we introduce a more lightweight duration semantics furnished with analogous results and based on a new duration monad that we introduce as a lightweight counterpart to the hybrid monad.

Abstract
IEEE 802.11 Wireless Networks are getting more and more popular at university campuses, enterprises, shopping centers, airports and in so many other public places, providing Internet access to a large crowd openly and quickly. The wireless users are also getting more dependent on WiFi technology and therefore demanding more reliability and higher performance for this vital technology. However, due to unstable radio conditions, faulty equipment, and dynamic user behavior among other reasons, there are always unpredictable performance problems in a wireless covered area. Detection and prediction of such problems is of great significance to network managers if they are to alleviate the connectivity issues of the mobile users and provide a higher quality wireless service. This paper aims to improve the management of the 802.11 wireless networks by characterizing and modeling wireless usage patterns in a set of anomalous scenarios that can occur in such networks. We apply time-invariant (Gaussian Mixture Models) and time-variant (Hidden Markov Models) modeling approaches to a dataset generated from a large production network and describe how we use these models for anomaly detection. We then generate several common anomalies on a Testbed network and evaluate the proposed anomaly detection methodologies in a controlled environment. The experimental results of the Testbed show that HMM outperforms GMM and yields a higher anomaly detection ratio and a lower false alarm rate.