auditing deep learning processes through kernel based explanatory models

n/

On the Readability of Deep Learning Models: the role of Kernel-based Deep Architectures

Deep Neural Networks achieve state-of-the-art performances in several semantic NLP tasks but lack of explanation capabilities as for the limited interpretability of the underlying acquired models. In other words, tracing back causal connections between the linguistic properties of an input instance and the produced classification is not possible. In this paper, we propose to apply Layerwise Relevance Propagation over linguistically motivated neural architectures, namely Kernel-based Deep Architectures (KDA), to guide argumentations and explanation inferences. In this way, decisions provided by a KDA can be linked to the semantics of input examples, used to linguistically motivate the network output.Le Deep Neural Network raggiungono oggi lo stato dell’arte in molti processi di NLP, ma la scarsa interpretabilitá dei modelli risultanti dall’addestramento limita la comprensione delle loro inferenze. Non é possibile cioé determinare connessioni causali tra le proprietá linguistiche di un esempio e la classificazione prodotta dalla rete. In questo lavoro, l’applicazione della Layerwise Relevance Propagation alle Kernel-based Deep Architecture (KDA) é usata per determinare connessioni tra la semantica dell’input e la classe di output che corrispondono a spiegazioni linguistiche e trasparenti della decisione

explaining non linear classifier decisions within kernel based deep architectures

n/

On the Readability of Kernel-based Deep Learning Models in Semantic Role Labeling Tasks over Multiple Languages

Sentence embeddings are effective input vectors for the neural learning of a number of inferences about content and meaning. Unfortunately, most of such decision processes are epistemologically opaque as for the limited interpretability of the acquired neural models based on the involved embeddings. In this paper, we concentrate on the readability of neural models, discussing an embedding technique (the Nyström methodology) that corresponds to the reconstruction of a sentence in a kernel space, capturing grammatical and lexical semantic information. From this method, we build a Kernel-based Deep Architecture that is characterized by inherently high interpretability properties, as the proposed embedding is derived from examples, i.e., landmarks, that are both human readable and labeled. Its integration with an explanation methodology, the Layer-wise Relevance Propagation, supports here the automatic compilation of argumentations for the Kernel-based Deep Architecture decisions, expressed in form of analogy with activated landmarks. Quantitative evaluation against the Semantic Role Labeling task, both in English and Italian, suggests that explanations based on semantic and syntagmatic structures are rich and characterize convincing arguments, as they effectively help the user in assessing whether or not to trust the machine decisions

Solid biofuels production from agricultural residues and processing by-products by means of torrefaction treatment: the case of sunflower chain

The high heterogeneity of some residual biomasses makes rather difficult their energy use. Their standardisation is going to be a key aspect to get good quality biofuels from those residues. Torrefaction is an interesting process to improve the physical and chemical properties of lignocellulosic biomasses and to achieve standardisation. In the present study torrefaction has been employed on residues and by-products deriving from sunflower production chain, in particular sunflower stalks, husks and oil press cake. The thermal behaviour of these materials has been studied at first by thermogravimetric analysis in order to identify torrefaction temperatures range. Afterwards, different residence time and torrefaction temperatures have been tested in a bench top torrefaction reactor. Analyses of raw and torrefied materials have been carried out to assess the influence of the treatment. As a consequence of torrefaction, the carbon and ash contents increase while the volatilisation range reduces making the material more stable and standardised. Mass yield, energy yield and energy densification reach values of about 60%, 80% and 1.33 for sunflower stalks and 64%, 85% and 1.33 for sunflower oil press cake respectively. As highlighted by the results, torrefaction is more interesting for sunflower stalks than oil cake and husks due to their different original characteristics. Untreated oil press cake and husks, in fact, already show a good high heating value and, for this reason, their torrefaction should be mild to avoid an excessive ash concentration. On the contrary, for sunflower stalks the treatment is more useful and could be more severe

mapping the suitability for ice core drilling of glaciers in the european alps and the asian high mountains

ABSTRACTIce cores from mid-latitude mountain glaciers provide detailed information on past climate conditions and regional environmental changes, which is essential for placing current climate change into a longer term perspective. In this context, it is important to define guidelines and create dedicated maps to identify suitable areas for future ice-core drillings. In this study, the suitability for ice-core drilling (SICD) of a mountain glacier is defined as the possibility of extracting an ice core with preserved stratigraphy suitable for reconstructing past climate. Morphometric and climatic variables related to SICD are selected through literature review and characterization of previously drilled sites. A quantitative Weight of Evidence method is proposed to combine selected variables (i.e. slope, local relief, temperature and direct solar radiation) to map the potential drilling sites in mid-latitude mountain glaciers. The method was first developed in the European Alps and then applied to the Asian High Mountains. Model performances and limitations are discussed and first indications of new potential drilling sites in the Asian High Mountains are provided. Results presented here can facilitate the selection of future drilling sites especially on unexplored Asian mountain glaciers towards the understanding of climate and environmental changes

Clinical impact of first-line bevacizumab plus chemotherapy in metastatic colorectal cancer of mucinous histology: a multicenter, retrospective analysis on 685 patients

In metastatic colorectal cancer (MCRC), mucinous histology has been associated with poor response rate and prognosis. We investigated whether bevacizumab combined with different chemotherapy regimens may have an impact on clinical outcomes of MCRC patients with mucinous histology

Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies

Background: Highly polygenic traits such as fruit weight, sugar content and acidity strongly influence the agroeconomic value of peach varieties. Genomic Selection (GS) can accelerate peach yield and quality gain if predictions show higher levels of accuracy compared to phenotypic selection. The available IPSC 9K SNP array V1 allows standardized and highly reliable genotyping, preparing the ground for GS in peach. Results: A repeatability model (multiple records per individual plant) for genome-enabled predictions in eleven European peach populations is presented. The analysis included 1147 individuals derived from both commercial and non-commercial peach or peach-related accessions. Considered traits were average fruit weight (FW), sugar content (SC) and titratable acidity (TA). Plants were genotyped with the 9K IPSC array, grown in three countries (France, Italy, Spain) and phenotyped for 3–5 years. An analysis of imputation accuracy of missing genotypic data was conducted using the software Beagle, showing that two of the eleven populations were highly sensitive to increasing levels of missing data. The regression model produced, for each trait and each population, estimates of heritability (FW:0.35, SC:0.48, TA:0.53, on average) and repeatability (FW:0.56, SC:0.63, TA:0.62, on average). Predictive ability was estimated in a five-fold cross validation scheme within population as the correlation of true and predicted henotypes. Results differed by populations and traits, but predictive abilities were in general high (FW:0.60, SC:0.72, TA:0.65, on average). Conclusions: This study assessed the feasibility of Genomic Selection in peach for highly polygenic traits linked to yield and fruit quality. The accuracy of imputing missing genotypes was as high as 96%, and the genomic predictive ability was on average 0.65, but could be as high as 0.84 for fruit weight or 0.83 for titratable acidity. The estimated repeatability may prove very useful in the management of the typical long cycles involved in peach productions. All together, these results are very promising for the application of genomic selection to peach breeding programmes.info:eu-repo/semantics/publishedVersio