1,721,286 research outputs found
Quantum algorithms for weighted constrained sampling and weighted model counting
No full textWe consider the problems of weighted constrained sampling and weighted model counting, where we are given a propositional formula and a weight for each world. The first problem consists of sampling worlds with a probability proportional to their weight given that the formula is satisfied. The latter is the problem of computing the sum of the weights of the models of the formula. Both have applications in many fields such as probabilistic reasoning, graphical models, statistical physics, statistics, and hardware verification. In this article, we propose quantum weighted constrained sampling (QWCS) and quantum weighted model counting (QWMC), two quantum algorithms for performing weighted constrained sampling and weighted model counting, respectively. Both are based on the quantum search/quantum model counting algorithms that are modified to take into account the weights. In the black box model of computation, where we can only query an oracle for evaluating the Boolean function given an assignment, QWCS requires O([email protected]@3ff7e2a6+1/WMC) oracle calls, where n is the number of Boolean variables and WMC is the normalized between 0 and 1 weighted model count of the formula, while a classical algorithm has a complexity of Ω(1/WMC). QWMC takes Θ([email protected]@4ff595e9) oracle calss, while classically the best complexity is Θ(2n), thus achieving a quadratic speedup
Intensity field of the 19 June 1975 Gargano (Southern Italy) earthquake
No full textMany moderate events reported by Italian earthquake catalogues (either historical or recent) are listed with an epicentral intensity derived from intensity-magnitude relationships or evaluated based on preliminary sources. Contradictions may arise among different catalogues when the effects of a given earthquake are not assessed through a specific macroseismic study as each catalogue uses its own criteria for evaluating the intensity. In this paper we present the case of the 19 June 1975 earthquake, a ML=5.1 (ING seismological bulletin) event that occurred in the Gargano area (southern Italy). The intensity reported by the ING catalogue is VIII MCS (estimated from magnitude), that reported by the NT4.1 catalogue is VI MCS, while the PFG catalogue does not report any intensity. The case of this event is well representative of a period during which macroseismic studies were not undertaken systematically in Italy. In this paper we reasses the macroseismic intensity of this event using procedures implemented and routinely used at ING.Published489-4935.1. TTC - Banche dati e metodi macrosismiciJCR Journalreserve
Intensity field of the 19 June 1975 Gargano (Southern Italy) earthquake
No full textMany moderate events reported by Italian earthquake catalogues (either historical or recent) are listed with an epicentral intensity derived from intensity-magnitude relationships or evaluated based on preliminary sources. Contradictions may arise among different catalogues when the effects of a given earthquake are not assessed through a specific macroseismic study as each catalogue uses its own criteria for evaluating the intensity. In this paper we present the case of the 19 June 1975 earthquake, a ML=5.1 (ING seismological bulletin) event that occurred in the Gargano area (southern Italy). The intensity reported by the ING catalogue is VIII MCS (estimated from magnitude), that reported by the NT4.1 catalogue is VI MCS, while the PFG catalogue does not report any intensity. The case of this event is well representative of a period during which macroseismic studies were not undertaken systematically in Italy. In this paper we reasses the macroseismic intensity of this event using procedures implemented and routinely used at ING.Published489-4935.1. TTC - Banche dati e metodi macrosismiciJCR Journalreserve
Fast Inference for Probabilistic Answer Set Programs Via the Residual Program
No full textWhen we want to compute the probability of a query from a probabilistic answer set program, some parts of a program may not influence the probability of a query, but they impact on the size of the grounding. Identifying and removing them is crucial to speed up the computation. Algorithms for SLG resolution offer the possibility of returning the residual program which can be used for computing answer sets for normal programs that do have a total well-founded model. The residual program does not contain the parts of the program that do not influence the probability. In this paper, we propose to exploit the residual program for performing inference. Empirical results on graph datasets show that the approach leads to significantly faster inference
Displacement field of the Italian area from permanent GPS stations
No full textA network of nine permanent GPS stations, six of them located in Italy (Cagliari, Matera, Medicina, Noto, Padova and Torino), the other three in Central Europe (Graz, Zimmervald and Wettzell) was been analyzed four times from October 1996 till 1998. After the GPS data processing, a rigorous statistical analysis based on the F (Fisher) test applied on the detected coordinate differences showed significant displacement at Matera, Medicina and Noto. The mean velocities computed for these sites with respect to Wettzell are (0.6 ± 0.3) cm/yr for Matera, (0.7 ± 0.2) cm/yr for Medicina and (0.6 ± 0.3) cm/yr for Noto. GPS velocities agree with those derived by VLBI and ITRF96 solutions, provided error ellipses are taken into account. The deformation analysis of the last time span shows a suspicious horizontal jump at Padova of (1.0 ± 0.1) cm corresponding to an antenna changing within the same period. This fact shows, in spite of the care used in permanent GPS installation, how the results of the deformation analysis may be strongly conditioned by site problems
Probabilistic Answer Set Programming with Discrete and Continuous Random Variables
No full textProbabilistic Answer Set Programming under the credal semantics extends Answer Set Programming with probabilistic facts that represent uncertain information. The probabilistic facts are discrete with Bernoulli distributions. However, several real-world scenarios require a combination of both discrete and continuous random variables. In this paper, we extend the PASP framework to support continuous random variables and propose Hybrid Probabilistic Answer Set Programming. Moreover, we discuss, implement, and assess the performance of two exact algorithms based on projected answer set enumeration and knowledge compilation and two approximate algorithms based on sampling. Empirical results, also in line with known theoretical results, show that exact inference is feasible only for small instances, but knowledge compilation has a huge positive impact on performance. Sampling allows handling larger instances but sometimes requires an increasing amount of memory
Optimizing Probabilities in Probabilistic Logic Programs
No full textProbabilistic logic programming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among probability distributions of random variables. Here, we introduce a new class of probabilistic logic programs, namely probabilistic optimizable logic programs, and we provide an effective algorithm to find the best assignment to probabilities of random variables, such that a set of constraints is satisfied and an objective function is optimized
Applying the information bottleneck to statistical relational learning
No full textIn this paper we propose to apply the Information Bottleneck (IB) approach to the sub-class of Statistical Relational Learning (SRL) languages that are reducible to Bayesian networks. When the resulting networks involve hidden variables, learning these languages requires the use of techniques for learning from incomplete data such as the Expectation Maximization (EM) algorithm. Recently, the IB approach was shown to be able to avoid some of the local maxima in which EM can get trapped when learning with hidden variables. Here we present the algorithm Relational Information Bottleneck (RIB) that learns the parameters of SRL languages reducible to Bayesian Networks. In particular, we present the specialization of RIB to a language belonging to the family of languages based on the distribution semantics, Logic Programs with Annotated Disjunction (LPADs). This language is prototypical for such a family and its equivalent Bayesian networks contain hidden variables. RIB is evaluated on the IMDB, Cora and artificial datasets and compared with LeProbLog, EM, Alchemy and PRISM. The experimental results show that RIB has good performances especially when some logical atoms are unobserved. Moreover, it is particularly suitable when learning from interpretations that share the same Herbrand base
Lifted discriminative learning of probabilistic logic programs
No full textProbabilistic logic programming (PLP) provides a powerful tool for reasoning with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. In this paper we consider PLP models that are amenable to lifted inference and present an algorithm for performing parameter and structure learning of these models from positive and negative examples. We discuss parameter learning with EM and LBFGS and structure learning with LIFTCOVER, an algorithm similar to SLIPCOVER. The results of the comparison of LIFTCOVER with SLIPCOVER on 12 datasets show that it can achieve solutions of similar or better quality in a fraction of the time
Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization
No full textProbabilistic answer set programming has recently been extended to manage imprecise probabilities by means of credal probabilistic facts and credal annotated disjunctions. This increases the expressivity of the language but, at the same time, the cost of inference. In this paper, we cast inference in probabilistic answer set programs with credal probabilistic facts and credal annotated disjunctions as a constrained nonlinear optimization problem where the function to optimize is obtained via knowledge compilation. Empirical results on different datasets with multiple configurations shows the effectiveness of our approach
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