Probabilistic electric load forecasting through Bayesian Mixture Density Networks

Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids. Neural networks are widely considered to achieve improved prediction performances, supporting highly flexible mappings of complex relationships between the target and the conditioning variables set. However, obtaining comprehensive predictive uncertainties from such black-box models is still a challenging and unsolved problem. In this work, we propose a novel PLF approach, framed on Bayesian Mixture Density Networks. Both aleatoric and epistemic uncertainty sources are encompassed within the model predictions, inferring general conditional densities, depending on the input features, within an end-to-end training framework. To achieve reliable and computationally scalable estimators of the posterior distributions, both Mean Field variational inference and deep ensembles are integrated. Experiments have been performed on household short-term load forecasting tasks, showing the capability of the proposed method to achieve robust performances in different operating conditions.Comment: 56 page

More than the Sum of Its Parts: Ensembling Backbone Networks for Few-Shot Segmentation

Semantic segmentation is a key prerequisite to robust image understanding for applications in \acrlong{ai} and Robotics. \acrlong{fss}, in particular, concerns the extension and optimization of traditional segmentation methods in challenging conditions where limited training examples are available. A predominant approach in \acrlong{fss} is to rely on a single backbone for visual feature extraction. Choosing which backbone to leverage is a deciding factor contributing to the overall performance. In this work, we interrogate on whether fusing features from different backbones can improve the ability of \acrlong{fss} models to capture richer visual features. To tackle this question, we propose and compare two ensembling techniques-Independent Voting and Feature Fusion. Among the available \acrlong{fss} methods, we implement the proposed ensembling techniques on PANet. The module dedicated to predicting segmentation masks from the backbone embeddings in PANet avoids trainable parameters, creating a controlled `in vitro' setting for isolating the impact of different ensembling strategies. Leveraging the complementary strengths of different backbones, our approach outperforms the original single-backbone PANet across standard benchmarks even in challenging one-shot learning scenarios. Specifically, it achieved a performance improvement of +7.37\% on PASCAL-5\textsuperscript{i} and of +10.68\% on COCO-20\textsuperscript{i} in the top-performing scenario where three backbones are combined. These results, together with the qualitative inspection of the predicted subject masks, suggest that relying on multiple backbones in PANet leads to a more comprehensive feature representation, thus expediting the successful application of \acrlong{fss} methods in challenging, data-scarce environments

Components, drivers and temporal dynamics of ecosystem respiration in a Mediterranean pine forest

To investigate the climate impacts on the different components of ecosystem respiration, we combined soil efflux data from a tree-girdling experiment with eddy covariance CO2 fluxes in a Mediterranean maritime pine (Pinus pinaster) forest in Central Italy. 73 trees were stem girdled to stop the flux of photosynthates from the canopy to the roots, and weekly soil respiration surveys were carried out for one year. Heterotrophic respiration (RH) was estimated from the soil CO2 flux measured in girdled plots, and rhizosphere respiration (RAb) was calculated as the difference between respiration from controls (RS) and girdled plots (RH). Results show that the RS dynamics were clearly driven by RH (average RH/RS ratio 0.74). RH predictably responded to environmental variables, being predominantly controlled by soil water availability during the hot and dry growing season (MayeOctober) and by soil temperature during the wetter and colder months (NovembereMarch). High RS and RH peaks were recorded after rain pulses greater than 10 mm on dry soil, indicating that large soil carbon emissions were driven by the rapid microbial oxidation of labile carbon compounds. We also observed a time-lag of one week between water pulses and RAb peaks, which might be due to the delay in the translocation of recently assimilated photosynthates from the canopy to the root system. At the ecosystem scale, total autotrophic respiration (RAt, i.e. the sum of carbon respired by the rhizosphere and aboveground biomass) amounted to 60% of ecosystem respiration. RAt was predominantly controlled by photosynthesis, and showed high temperature sensitivity (Q10) only during the wet periods. Despite the fact that the study coincided with an anomalous dry year and results might therefore not represent a general pattern, these data highlight the complex climatic control of the respiratory processes responsible for ecosystem CO2 emissions. © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

PLC-beta 1 regulates the expression of miR-210 during mithramycin-mediated erythroid differentiation in K562 cells

PLC-beta 1 (PLCβ1) inhibits erythroid differentiation induced by mithramycin (MTH) by targeting miR-210 expression. MicroRNA-210 (miR-210) has been reported to be upregulated in various types of human malignancy suggesting that it has an important role in tumorigenesis. Inhibition of miR-210 affects the erythroid differentiation pathway and it occurs to a greater extent in MTH-treated cells. In this paper we have analyzed the effect of MTH on human K562 cells differentiation. Overexpression of PLCβ1 suppresses the differentiation of K562 elicited by MTH as demonstrated by the absence of γ-globin expression. Inhibition of PLCβ1 expression is capable to promote the differentiation process leading to a recovery of γ-globin gene even in the absence of MTH. Our experimental evidences suggest that PLCβ1 signalling regulates erythropoiesis through miR-210. Indeed overexpression of PLCβ1 leads to a decrease of miR-210 expression after MTH treatment. Moreover miR-210 is up-regulated through both proliferation and differentiation events when PLCβ1 expression is down-regulated. Therefore we suggest a novel role for PLCβ1 in regulating miR-210 and our data hint at the fact that, in human K562 erythroleukemia cells, the modulation of PLCβ1 expression is able to exert an impairment of normal erythropoiesis as assessed by γ-globin expression