INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Field-programmable gate array & Control theory. The organization has 932 authors who have published 2618 publications receiving 37658 citations.

Assessment of overvoltage mitigation techniques in low-voltage distribution networks with high penetration of photovoltaic microgeneration

Abstract: Nowadays, a strong concern to decrease greenhouse gas emissions is encouraging the implementation of renewable energy sources closer to end-users, in low-voltage (LV) distribution networks. Due to the expected high microgeneration (μG) penetration level, several problems are likely to arise, such as overvoltages and reverse power flow. This study presents a review of the several techniques used to deal with these problems. These are compared in terms of their capacity to smooth the voltage profile and avoid reverse power flow. An unbalanced three-phase power flow algorithm, based on current summation method for radial distribution networks, is proposed. A study based on a highly unbalanced test radial LV distribution network for a typical summer day, with a high μG penetration, is performed. The voltage profile, active power flow in the service transformer, and power losses on the network are the monitored electrical quantities. The obtained results indicate that self-consumption with storage is the recommended solution to eliminate overvoltages, to avoid reverse power flow and allow for a decreasing in the power losses. Nevertheless, the economic viability of this solution must be carefully assessed, because the profitability of the project is not straightforward at the current time.

Ensemble outlier detection and gene selection in triple-negative breast cancer data

Abstract: Learning accurate models from ‘omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental errors or interesting abnormal clinical cases, may severely hamper a correct classification of patients and the identification of reliable biomarkers for a particular disease. We propose to address this problem through an ensemble classification setting based on distinct feature selection and modeling strategies, including logistic regression with elastic net regularization, Sparse Partial Least Squares - Discriminant Analysis (SPLS-DA) and Sparse Generalized PLS (SGPLS), coupled with an evaluation of the individuals’ outlierness based on the Cook’s distance. The consensus is achieved with the Rank Product statistics corrected for multiple testing, which gives a final list of sorted observations by their outlierness level. We applied this strategy for the classification of Triple-Negative Breast Cancer (TNBC) RNA-Seq and clinical data from the Cancer Genome Atlas (TCGA). The detected 24 outliers were identified as putative mislabeled samples, corresponding to individuals with discrepant clinical labels for the HER2 receptor, but also individuals with abnormal expression values of ER, PR and HER2, contradictory with the corresponding clinical labels, which may invalidate the initial TNBC label. Moreover, the model consensus approach leads to the selection of a set of genes that may be linked to the disease. These results are robust to a resampling approach, either by selecting a subset of patients or a subset of genes, with a significant overlap of the outlier patients identified. The proposed ensemble outlier detection approach constitutes a robust procedure to identify abnormal cases and consensus covariates, which may improve biomarker selection for precision medicine applications. The method can also be easily extended to other regression models and datasets.

Crowdsourcing High-Quality Parallel Data Extraction from Twitter

Abstract: High-quality parallel data is crucial for a range of multilingual applications, from tuning and evaluating machine translation systems to cross-lingual annotation projection. Unfortunately, automatically obtained parallel data (which is available in relative abundance) tends to be quite noisy. To obtain high-quality parallel data, we introduce a crowdsourcing paradigm in which workers with only basic bilingual proficiency identify translations from an automatically extracted corpus of parallel microblog messages. For less than $350, we obtained over 5000 parallel segments in five language pairs. Evaluated against expert annotations, the quality of the crowdsourced corpus is significantly better than existing automatic methods: it obtains an performance comparable to expert annotations when used in MERT tuning of a microblog MT system; and training a parallel sentence classifier with it leads also to improved results. The crowdsourced corpora will be made available in http://www.cs.cmu.edu/ ~lingwang/microtopia/.

Efficient extraction of QBF (counter)models from long-distance resolution proofs

Abstract: Many computer science problems can be naturally and compactly expressed using quantified Boolean formulas (QBFs). Evaluating the truth or falsity of a QBF is an important task, and constructing the corresponding model or countermodel can be as important and sometimes even more useful in practice. Modern search and learning based QBF solvers rely fundamentally on resolution and can be instrumented to produce resolution proofs, from which in turn Skolem-function models and Herbrand-function countermodels can be extracted. These (counter)models are the key enabler of various applications. Not until recently the superiority of longdistance resolution (LQ-resolution) to short-distance resolution (Q-resolution) was demonstrated. While a polynomial algorithm exists for (counter)model extraction from Q-resolution proofs, it remains open whether it exists for LQ-resolution proofs. This paper settles this open problem affirmatively by constructing a linear-time extraction procedure. Experimental results show the distinct benefits of the proposed method in extracting high quality certificates from some LQ-resolution proofs that are not obtainable from Q-resolution proofs.

Integration of Magnetoresistive Biochips on a CMOS Circuit

Abstract: Since 2006, fully scalable matrix-based magnetoresistive biochips have been proposed. This integration was initially achieved with thin film switching devices and moved to complementary metal-oxide-semiconductor (CMOS) switching devices and electronics. In this paper, a new microfabrication process is proposed to integrate magnetoresistive sensors on a small CMOS chip (4 mm2). This chip includes a current generator, multiplexers, and a diode in series with a spin valve as matrix element. In this configuration, it is shown that the fabricated spin-valves have similar magnetic characteristics when compared to standalone spin valves. This validates the successfulness of the developed microfabrication process. The noise of each matrix element is further characterized and compared to the noise of a standalone spin valve and a portable electronic platform designed to perform biological assays. Although the noise is still higher, the spin valve integrated on the CMOS chip enables an increase in density and compactness of the measuring electronics.