Predicting outcomes of Italian VAT decisions

This study aims at predicting the outcomes of legal cases based on the textual content of judicial decisions. We present a new corpus of Italian documents, consisting of 226 annotated decisions on Value Added Tax by Regional Tax law commissions. We address the task of predicting whether a request is upheld or rejected in the final decision. We employ traditional classifiers and NLP methods to assess which parts of the decision are more informative for the task

Detecting Arguments in CJEU Decisions on Fiscal State Aid

The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers

Impacts of the Tropical Pacific/Indian Oceans on the Seasonal Cycle of the West African Monsoon

The current consensus is that drought has developed in the Sahel during the second half of the twentieth century as a result of remote effects of oceanic anomalies amplified by local land–atmosphere interactions. This paper focuses on the impacts of oceanic anomalies upon West African climate and specifically aims to identify those from SST anomalies in the Pacific/Indian Oceans during spring and summer seasons, when they were significant. Idealized sensitivity experiments are performed with four atmospheric general circulation models (AGCMs). The prescribed SST patterns used in the AGCMs are based on the leading mode of covariability between SST anomalies over the Pacific/Indian Oceans and summer rainfall over West Africa. The results show that such oceanic anomalies in the Pacific/Indian Ocean lead to a northward shift of an anomalous dry belt from the Gulf of Guinea to the Sahel as the season advances. In the Sahel, the magnitude of rainfall anomalies is comparable to that obtained by other authors using SST anomalies confined to the proximity of the Atlantic Ocean. The mechanism connecting the Pacific/Indian SST anomalies with West African rainfall has a strong seasonal cycle. In spring (May and June), anomalous subsidence develops over both the Maritime Continent and the equatorial Atlantic in response to the enhanced equatorial heating. Precipitation increases over continental West Africa in association with stronger zonal convergence of moisture. In addition, precipitation decreases over the Gulf of Guinea. During the monsoon peak (July and August), the SST anomalies move westward over the equatorial Pacific and the two regions where subsidence occurred earlier in the seasons merge over West Africa. The monsoon weakens and rainfall decreases over the Sahel, especially in August.Peer reviewe

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Analisi ed estensione con criteri di preferenza di un algoritmo per process discovery di modelli dichiarativi

Questa tesi si colloca nell’ambito del process mining e in particolare delle tecniche di process discovery che si occupano di estrarre automaticamente modelli di processi reali, servendosi degli esempi di esecuzione contenuti nei log di eventi. L’utilizzo di queste tecniche è di notevole interesse per la comprensione dei processi, per l’identificazione di problemi e deviazioni nella loro esecuzione e per guidare decisioni volte all’ottimizzazione degli stessi. Il primo obiettivo di questa tesi è l’analisi di un algoritmo di process discovery che genera modelli dichiarativi, espressi in linguaggio Declare. L’approccio dell’algoritmo si basa sul duplice contenuto informativo delle istanze positive del processo, che producono esempi conformi alle caratteristiche e ai risultati attesi, e delle istanze che, deviando da tali caratteristiche, vengono classificate come negative. In secondo luogo, la tesi propone un’estensione della modalità di ottimizzazione dell’algoritmo, che consente di guidarne la risoluzione attraverso preferenze user-defined, tramite le quali è possibile definire le attività e i constraint che il modello deve preferibilmente contenere

Argumentation Structure Prediction in CJEU Decisions on Fiscal State Aid

Argument structure prediction aims to identify the relations between arguments or between parts of arguments. It is a crucial task in legal argument mining, where it could help identifying motivations behind judgments or even fallacies or inconsistencies. It is also a very challenging task, which is relatively underdeveloped compared to other argument mining tasks, owing to a number of reasons including a low availability of datasets and a high complexity of the reasoning involved. In this work, we address argumentative link prediction in decisions by Court of Justice of the European Union on fiscal state aid. We study how propositions are combined in higher-level structures and how the relations between propositions can be predicted by NLP models. To this end, we present a novel annotation scheme and use it to extend a dataset from literature with an additional annotation layer. We use our new dataset to run an empirical study, where we compare two architectures and explore different combinations of hyperparameters and training regimes. Our results indicate that an ensemble of residual networks yields the best results

Optimising Business Process Discovery Using Answer Set Programming

Declarative business process discovery aims at identifying sets of constraints, from a given formal language, that characterise a workflow by using pre-recorded activity logs. Since the provided logs represent a fraction of all the consistent evolution of a process, and the fact that many sets of constraints covering those examples can be selected, empirical criteria should be employed to identify the “best” candidates. In our work we frame the process discovery as an optimisation problem, where we want to identify optimal sets of constraints according to preference criteria. Declarative constraints for processes are usually characterised via temporal logics, so different solutions can be semantically equivalent. For this reason, it is difficult to use an arbitrary finite domain constraints solvers for the optimisation. The use of Answer Set Programming enables the combination of deduction rules within the optimisation algorithm, in order to take into account not only the user preferences but also the implicit semantics of the formal language. In this paper we show how we encoded the process discovery problem using the ASPrin framework for qualitative and quantitative optimisation in ASP, and the results of our experiments

Shape Your Process: Discovering Declarative Business Processes from Positive and Negative Traces Taking into Account User Preferences

Process discovery techniques focus on learning a process model starting from a given set of logged traces. The majority of the discovery approaches, however, only consider one set of examples to learn from, i.e., the log itself. Some recent works on declarative process discovery, instead, advocated the usefulness of taking into account two different sets of traces (a.k.a. positive and negative examples), with the goal of learning a set of constraints that is able to discriminate which trace belongs to which set. Sometimes, however, too many possible sets of constraints might be available, thus nullifying the discovery effort. Therefore, some preference criteria would be helpful to guide the discovery process towards a set of constraints among the many. In this work, we present an approach for the discovery of declarative models providing the possibility, from the user viewpoint, of specifying preferences on activities and constraint templates to be used to build the final set of constraints. Such preferences are used to guide the discovery process, so that the output set will include, if possible, the preferred constraints, thus exploiting some expert knowledge about the desired outcome. The approach is grounded in a logic-based framework that provides a sound and formal meaning to the notion of expert preferences

Measurement of the t(t)over-bar production cross section in p(p)over-bar collisions at root s=1.96 TeV

We present a measurement of the top quark pair production cross section in p (p) over bar collisions at root s=1.96 TeV using 318 pb(-1) of data collected with the Collider Detector at Fermilab. We select t (t) over bar decays into the final states e nu+jets and mu nu+jets, in which at least one b quark from the t-quark decays is identified using a secondary vertex-finding algorithm. Assuming a top quark mass of 178 GeV/c(2), we measure a cross section of 8.7 +/- 0.9(stat)(-0.9)(+1.1)(syst) pb. We also report the first observation of t (t) over bar with significance greater than 5 sigma in the subsample in which both b quarks are identified, corresponding to a cross section of 10.1(-1.4)(+1.6)(stat)(-1.3)(+2.0)(syst) pb. RI Levy, Stephen/C-3493-2011; Ruiz, Alberto/E-4473-2011; Robson, Aidan/G-1087-2011; De Cecco, Sandro/B-1016-2012; St.Denis, Richard/C-8997-2012; Prokoshin, Fedor/E-2795-2012; Azzi, Patrizia/H-5404-201