Learning Relatedness Measures for Entity Linking

Entity Linking is the task of detecting, in text documents, relevant mentions to entities of a given knowledge base. To this end, entity-linking algorithms use several signals and features extracted from the input text or from the knowl- edge base. The most important of such features is entity relatedness. Indeed, we argue that these algorithms benefit from maximizing the relatedness among the relevant enti- ties selected for annotation, since this minimizes errors in disambiguating entity-linking. The definition of an e↵ective relatedness function is thus a crucial point in any entity-linking algorithm. In this paper we address the problem of learning high-quality entity relatedness functions. First, we formalize the problem of learning entity relatedness as a learning-to-rank problem. We propose a methodology to create reference datasets on the basis of manually annotated data. Finally, we show that our machine-learned entity relatedness function performs better than other relatedness functions previously proposed, and, more importantly, improves the overall performance of dif- ferent state-of-the-art entity-linking algorithms

On Suggesting Entities as Web Search Queries

The Web of Data is growing in popularity and dimension, and named entity exploitation is gaining importance in many research fields. In this paper, we explore the use of entities that can be extracted from a query log to enhance query recommendation. In particular, we extend a state-of-the-art recommendation algorithm to take into account the semantic information associated with submitted queries. Our novel method generates highly related and diversified suggestions that we as- sess by means of a new evaluation technique. The manually annotated dataset used for performance comparisons has been made available to the research community to favor the repeatability of experiments

Discovering Europeana users’ search behavior

Europeana is a strategic project funded by the European Commission with the goal of making Europe's cultural and scientific heritage accessible to the public. ASSETS is a two-year Best Practice Network co-funded by the CIP PSP Programme to improve performance, accessibility and usability of the Europeana search engine. Here we present a characterization of the Europeana logs by showing statistics on common behavioural patterns of the Europeana users

Thermal phase shifters for femtosecond laser written photonic integrated circuits

Photonic integrated circuits (PICs) are today acknowledged as an effective solution to fulfill the demanding requirements of many practical applications in both classical and quantum optics. Phase shifters integrated in the photonic circuit offer the possibility to dynamically reconfigure its properties in order to fine tune its operation or to produce adaptive circuits, thus greatly extending the quality and the applicability of these devices. In this paper, we provide a thorough discussion of the main problems that one can encounter when using thermal shifters to reconfigure photonic circuits. We then show how all these issues can be solved by a careful design of the thermal shifters and by choosing the most appropriate way to drive them. Such performance improvement is demonstrated by manufacturing thermal phase shifters in femtosecond laser written PICs (FLW-PICs), and by characterizing their operation in detail. The unprecedented results in terms of power dissipation, miniaturization and stability, enable the scalable implementation of reconfigurable FLW-PICs that can be easily calibrated and exploited in the applications

SEL: A unified algorithm for entity linking and saliency detection

The Entity Linking task consists in automatically identifying and linking the entities mentioned in a text to their URIs in a given Knowledge Base, e.g., Wikipedia. Entity Linking has a large impact in several text analysis and information retrieval related tasks. This task is very challenging due to natural language ambiguity. However, not all the entities mentioned in a document have the same relevance and utility in understanding the topics being discussed. Thus, the related problem of identifying the most relevant entities present in a document, also known as Salient Entities, is attracting increasing interest. In this paper we propose SEL, a novel supervised two-step algorithm comprehensively addressing both entity linking and saliency detection. The first step is based on a classifier aimed at identifying a set of candidate entities that are likely to be mentioned in the document, thus maximizing the precision of the method without hindering its recall. The second step is still based on machine learning, and aims at choosing from the previous set the entities that actually occur in the document. Indeed, we tested two different versions of the second step, one aimed at solving only the entity linking task, and the other that, besides detecting linked entities, also scores them according to their saliency. Experiments conducted on two different datasets show that the proposed algorithm outperforms state-of-the-art competitors, and is able to detect salient entities with high accuracy

Lesioni non palpabili della mammella: la Mammotome-biopsy nella gestione preoperatoria del cancro della mammella

Premessa: Il tumore del seno è nei paesi occidentali al primo posto per frequenza nelle donne e la sua incidenza è in costante crescita. Grazie soprattutto alla diffusione dello screening mammografico e ad una maggiore consapevolezza del problema, negli ultimi anni è aumentata la diagnosi delle cosiddette lesioni “non palpabili”; parimenti si è assistito ad un importante sviluppo delle metodiche diagnostiche di tipo mininvasivo. Alla tradizionale citologia con ago sottile si sono affiancate infatti varie procedure bioptiche percutanee; tali metodiche microistologiche hanno quasi del tutto sostituito la biopsia chirurgica escissionale e l’esame intra-operatorio al congelatore. Pazienti e metodo: Nella nostra Divisione di Chirurgia Generale, Vascolare e Mininvasiva, dal dicembre 1999 al settembre 2004 abbiamo eseguito, in collaborazione con il servizio di Radiologia, 214 biopsie su guida ecografia utilizzando la vacuum-assisted biopsy (Mammotome® ) con ago 11-Gauge. I risultati ottenuti per ciò che concerne l’accuratezza diagnostica, la quantità e qualità delle informazioni ottenute, il significato delle stesse nella eventuale gestione chirurgica, il discomfort globale per la paziente sono stati analizzati e discussi nel presente lavoro. Risultati: Delle 214 biopsie eseguite con tecnica Mammotome, nell’89,3% dei casi si è trattato di lesioni clinicamente non palpabili, con un diametro medio di 8 mm. L’età media delle pazienti era di 57,6 anni (range 31-88). La positività per patologia maligna è stata di 90 casi (42%). Nei casi di iperplasia duttale atipica e radial scar (6%) è stata effettuata l’exeresi chirurgica della lesione che ha confermato nel 100% dei casi la precedente diagnosi bioptica. Il 19% delle pazienti sottoposte a biopsia Mammotome era stato precedentemente sottoposto ad un prelievo citologico con ago sottile. Confrontando i risultati delle due metodiche, l’attendibilità diagnostica della seconda risulta essere significativamente superiore (p<0,05) come pure il numero di informazioni ottenute (istotipo, invasività, grading, recettori ormonali, etc.); il discomfort legato alla procedura, valutato in termini di dolore (VAS), è risultato inferiore a quello del prelievo con ago sottile (p<0,05). L’unica complicanza della biopsia Mammotome è rappresentata dall’ematoma nella sede del prelievo (8% dei casi). Il numero dei falsi negativi è stato di un caso, dovuto ad un non corretto centraggio del bersaglio. Conclusioni: Allo stato attuale in presenza di una lesione non palpabile della mammella la scelta della metodica diagnostica (agobiopsia o Mammotome) è legata al sospetto radiologico nella prospettiva di un eventuale intervento chirurgico. La biopsia con Mammotome nelle lesioni non palpabil

Linear Accelerator Test Facility at LNF Conceptual Design Report

Test beam and irradiation facilities are the key enabling infrastructures for research in high energy physics (HEP) and astro-particles. In the last 11 years the Beam-Test Facility (BTF) of the DA{\Phi}NE accelerator complex in the Frascati laboratory has gained an important role in the European infrastructures devoted to the development and testing of particle detectors. At the same time the BTF operation has been largely shadowed, in terms of resources, by the running of the DA{\Phi}NE electron-positron collider. The present proposal is aimed at improving the present performance of the facility from two different points of view: extending the range of application for the LINAC beam extracted to the BTF lines, in particular in the (in some sense opposite) directions of hosting fundamental physics and providing electron irradiation also for industrial users; extending the life of the LINAC beyond or independently from its use as injector of the DA{\Phi}NE collider, as it is also a key element of the electron/positron beam facility. The main lines of these two developments can be identified as: consolidation of the LINAC infrastructure, in order to guarantee a stable operation in the longer term; upgrade of the LINAC energy, in order to increase the facility capability (especially for the almost unique extracted positron beam); doubling of the BTF beam-lines, in order to cope with the signicant increase of users due to the much wider range of applications.Comment: 71 page

DataCloud: Enabling the Big Data Pipelines on the Computing Continuum

acceptedVersio

Big Data Pipelines on the Computing Continuum: Ecosystem and Use Cases Overview

Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem.acceptedVersio

Early stability and late random tumor progression of a HER2-positive primary breast cancer patient-derived xenograft

We established patient-derived xenografts (PDX) from human primary breast cancers and studied whether stability or progressive events occurred during long-term in vivo passages (up to 4&nbsp;years) in severely immunodeficient mice. While most PDX showed stable biomarker expression and growth phenotype, a HER2-positive PDX (PDX-BRB4) originated a subline (out of 6 studied in parallel) that progressively acquired a significantly increased tumor growth rate, resistance to cell senescence of in vitro cultures, increased stem cell marker expression and high lung metastatic ability, along with a strong decrease of BCL2 expression. RNAseq analysis of the progressed subline showed that BCL2 was connected to three main hub genes also down-regulated (CDKN2A, STAT5A and WT1). Gene expression of progressed subline suggested a partial epithelial-to-mesenchymal transition. PDX-BRB4 with its progressed subline is a preclinical model mirroring the clinical paradox of high level-BCL2 as a good prognostic factor in breast cancer. Sequential in vivo passages of PDX-BRB4 chronically treated with trastuzumab developed progressive loss of sensitivity to trastuzumab while HER2 expression and sensitivity to the pan-HER tyrosine kinase inhibitor neratinib were maintained. Long-term PDX studies, even though demanding, can originate new preclinical models, suitable to investigate the mechanisms of breast cancer progression and new therapeutic approaches