Production of a New Drug: A Sequential Investment ProcessUnder Uncertainty

On the basis of a database of more than 80 thousand records on total retails and production costs of the pharmaceutical industry worldwide we consider four classes of drugs. We evaluate the expected profits of an investment in a new drug in the four classes of pharmaceutical products by considering the standard NPV evaluation. We compare these outcomes with the evaluation of the expected profits of the four new drugs obtained by the real option approach. Interestingly enough quite different outcomes are obtained. These results loom on the capacity of standard methods to give a reliable evaluation of real investment projects that are analogous to compound optionscompound option, real option valuation, net present value, drugs

A context based model for sentiment analysis in twitter for the italian language

Studi recenti per la Sentiment Analysis in Twitter hanno tentato di creare modelli per caratterizzare la polarit´a di un tweet osservando ciascun messaggio in isolamento. In realt`a, i tweet fanno parte di conversazioni, la cui natura pu`o essere sfruttata per migliorare la qualit`a dell’analisi da parte di sistemi automatici. In (Vanzo et al., 2014) `e stato proposto un modello basato sulla classificazione di sequenze per la caratterizzazione della polarit` a dei tweet, che sfrutta il contesto in cui il messaggio `e immerso. In questo lavoro, si vuole verificare l’applicabilit`a di tale metodologia anche per la lingua Italiana.Recent works on Sentiment Analysis over Twitter leverage the idea that the sentiment depends on a single incoming tweet. However, tweets are plunged into streams of posts, thus making available a wider context. The contribution of this information has been recently investigated for the English language by modeling the polarity detection as a sequential classification task over streams of tweets (Vanzo et al., 2014). Here, we want to verify the applicability of this method even for a morphological richer language, i.e. Italian

Robust Spoken Language Understanding for House Service Robots

Service robotics has been growing significantly in thelast years, leading to several research results and to a numberof consumer products. One of the essential features of theserobotic platforms is represented by the ability of interactingwith users through natural language. Spoken commands canbe processed by a Spoken Language Understanding chain, inorder to obtain the desired behavior of the robot. The entrypoint of such a process is represented by an Automatic SpeechRecognition (ASR) module, that provides a list of transcriptionsfor a given spoken utterance. Although several well-performingASR engines are available off-the-shelf, they operate in a generalpurpose setting. Hence, they may be not well suited in therecognition of utterances given to robots in specific domains. Inthis work, we propose a practical yet robust strategy to re-ranklists of transcriptions. This approach improves the quality of ASRsystems in situated scenarios, i.e., the transcription of roboticcommands. The proposed method relies upon evidences derivedby a semantic grammar with semantic actions, designed tomodel typical commands expressed in scenarios that are specificto human service robotics. The outcomes obtained throughan experimental evaluation show that the approach is able toeffectively outperform the ASR baseline, obtained by selectingthe first transcription suggested by the AS

A discriminative approach to grounded spoken language understanding in interactive robotics

Spoken Language Understanding in Interactive Robotics provides computational models of human-machine communication based on the vocal input. However, robots operate in specific environments and the correct interpretation of the spoken sentences depends on the physical, cognitive and linguistic aspects triggered by the operational environment. Grounded language processing should exploit both the physical constraints of the context as well as knowledge assumptions of the robot. These include the subjective perception of the environment that explicitly affects linguistic reasoning. In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics. Empirical results achieve up to a 40% of relative error reduction

The effects of pre-existing discontinuities on the surface expression of normal faults: insights from wet-clay analogue modeling

We use wet-clay analogue models to investigate how pre-existing discontinuities (i.e. structures inherited from previous tectonic phases) affect the evolution of a normal fault at the Earth\u2019s surface. To this end we first perform a series of three reference experiments driven by a 45\ub0 dipping master fault unaffected by pre-existing discontinuities to generate a mechanically isotropic learning set of models. We then replicate the xperiment six times introducing a 60\ub0-dipping precut in the clay cake, each time with a different attitude and orientation with respect to an initially-blind, 45\ub0-dipping, master normal fault. In all experiments the precut intersects the vertical projection of the master fault halfway between the center and the right-hand lateral tip. All other conditions are identical for all seven models. By comparing the results obtained from the mechanically isotropic experiments with results from experiments with precuts we find that the surface evolution of the normal fault varies depending on the precut orientation. In most cases the parameters of newly-forming faults are strongly influenced. The largest influence is exerted by synthetic and antithetic discontinuities trending respectively at 30\ub0 and 45\ub0 from the strike of the master fault, whereas a synthetic discontinuity at 60\ub0 and an antithetic discontinuity at 30\ub0 show moderate influence. Little influence is exerted by a synthetic discontinuity at 45\ub0 and an antithetic discontinuity at 60\ub0 from the strike of the master fault. We provide a ranking chart to assess fault-to-discontinuity interactions with respect to essential surface fault descriptors, such as segmentation, vertical-displacement profile, maximum displacement, and length, often used as proxies to infer fault properties at depth. Considering a single descriptor, the amount of deviation induced by different precuts varies from case to case in a rather unpredictable fashion. Multiple observables should be taken into consideration when analyzing normal faults evolving next to pre-existing discontinuities

The European Fault-Source Model 2020 (EFSM20): geologic input data for the European Seismic Hazard Model 2020

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAMEarthquake hazard analyses rely on seismogenic source models. These are designed in various fashions, such as point sources or area sources, but the most effective is the three-dimensional representation of geological faults. We here refer to such models as fault sources. This study presents the European Fault-Source Model 2020 (EFSM20), which was one of the primary input datasets of the recently released European Seismic Hazard Model 2020. The EFSM20 compilation was entirely based on reusable data from existing active fault regional compilations that were first blended and harmonized and then augmented by a set of derived parameters. These additional parameters were devised to enable users to formulate earthquake rate forecasts based on a seismic-moment balancing approach. EFSM20 considers two main categories of seismogenic faults: crustal faults and subduction systems, which include the subduction interface and intraslab faults. The compiled dataset covers an area from the Mid-Atlantic Ridge to the Caucasus and from northern Africa to Iceland. It includes 1248 crustal faults spanning a total length of ∼95100 km and four subduction systems, namely the Gibraltar, Calabrian, Hellenic, and Cyprus arcs, for a total length of ∼ 2120 km. The model focuses on an area encompassing a buffer of 300 km around all European countries (except for Overseas Countries and Territories) and a maximum of 300 km depth for the subducting slabs. All the parameters required to develop a seismic source model for earthquake hazard analysis were determined for crustal faults and subduction systems. A statistical distribution of relevant seismotectonic parameters, such as faulting mechanisms, slip rates, moment rates, and prospective maximum magnitudes, is presented and discussed to address unsettled points in view of future updates and improvements. The dataset, identified by the DOI https://doi.org/10.13127/efsm20 (Basili et al., 2022), is distributed as machine-readable files using open standards (Open Geospatial Consortium)This work was initially supported by the project Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe (SERA), which received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 730900

Introduction to Machine Learning

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