Mixed Negative Binomial Distribution by Weighted Gamma Mixing Distribution

Павел Т. Стойнов - В тази работа се разглежда отрицателно биномното разпределение, известно още като разпределение на Пойа. Предполагаме, че смесващото разпределение е претеглено гама разпределение. Изведени са вероятностите в някои частни случаи. Дадени са рекурентните формули на Панжер.

An aligned subtree kernel for weighted graphs

In this paper, we develop a new entropic matching kernel for weighted graphs by aligning depth-based representations. We demonstrate that this kernel can be seen as an aligned subtree kernel that incorporates explicit subtree correspondences, and thus addresses the drawback of neglecting the relative locations between substructures that arises in the R-convolution kernels. Experiments on standard datasets demonstrate that our kernel can easily outperform state-of-the-art graph kernels in terms of classification accuracy.

An Algorithm to Mine Normalized Weighted Sequential Patterns Using a Prefix-projected Database

Sequential pattern mining is an important subject in data mining with broad applications in many different areas. However, previous sequential mining algorithms mostly aimed to calculate the number of occurrences (the support) without regard to the degree of importance of different data items. In this paper, we propose to explore the search space of subsequences with normalized weights. We are not only interested in the number of occurrences of the sequences (supports of sequences), but also concerned about importance of sequences (weights). When generating subsequence candidates we use both the support and the weight of the candidates while maintaining the downward closure property of these patterns which allows to accelerate the process of candidate generation.

On Supra-Bayesian Weighted Combination of Available Data Determined by Kerridge Inaccuracy and Entropy

2010 Mathematics Subject Classification: 94A17.

A New Characterization of Weighted Peetre K-Functionals (II)

2000 Mathematics Subject Classification: 46B70, 41A25, 41A17, 26D10. ∗Part of the results were reported at the Conference “Pioneers of Bulgarian Mathematics”, Sofia, 2006.

Pseudodifferential Operators and Weighted Normed Symbol Spaces

2000 Mathematics Subject Classification: 35S05.

Weighted Dispersive Estimates for Solutions of the Schrödinger Equation

2000 Mathematics Subject Classification: 35L15, 35B40, 47F05.

Relationship between Extremal and Sum Processes Generated by the same Point Process

2000 Mathematics Subject Classification: Primary 60G51, secondary 60G70, 60F17.

On a Generalization of a Theorem due to Larcombe on the Sum of a 3F2 Series

MSC 2010: 33C20

Weighted Approximation by a Class of Bernstein-Type Operators

AMS classification: 41A36, 41A10, 41A25, 41Al7.

Weighted composition followed by differentiation between weighted Banach spaces of holomorphic functions

2010 Mathematics Subject Classification: 47B33, 47B38.

Binocular summation, binocular fusion and the transition to diplopia

The visual system combines spatial signals from the two eyes to achieve single vision. But if binocular disparity is too large, this perceptual fusion gives way to diplopia. We studied and modelled the processes underlying fusion and the transition to diplopia. The likely basis for fusion is linear summation of inputs onto binocular cortical cells. Previous studies of perceived position, contrast matching and contrast discrimination imply the computation of a dynamicallyweighted sum, where the weights vary with relative contrast. For gratings, perceived contrast was almost constant across all disparities, and this can be modelled by allowing the ocular weights to increase with disparity (Zhou, Georgeson & Hess, 2014). However, when a single Gaussian-blurred edge was shown to each eye perceived blur was invariant with disparity (Georgeson & Wallis, ECVP 2012) – not consistent with linear summation (which predicts that perceived blur increases with disparity). This blur constancy is consistent with a multiplicative form of combination (the contrast-weighted geometric mean) but that is hard to reconcile with the evidence favouring linear combination. We describe a 2-stage spatial filtering model with linear binocular combination and suggest that nonlinear output transduction (eg. ‘half-squaring’) at each stage may account for the blur constancy.

Az ellenzék ereje - általánosított súlyozott szavazási játékok (Minority power - generalized weighted voting games)

A hagyományos szavazási játékok speciális átruházható hasznosságú, kooperatív játékok, úgynevezett egyszerű játékok, ahol a játékosok a pártok, és az egyes koalíciók értéke 1 vagy 0 attól függően, hogy az adott koalíció elég erős-e az adott jogszabály elfogadásához, vagy sem. Ebben a cikkben bevezetjük az általánosított súlyozott szavazási játékok fogalmát, ahol a pártok mandátumainak száma a valószínűségi változó. Magyar példákon keresztül mutatjuk be az új megközelítés használhatóságát. / === / Voting games are cooperative games with transferable utility, so-called simple games, where the players are parties and the value of a coalition may be 0 or 1 depending on its ability to pass a new law. The authors introduce the concept of generalized weighted voting games where the parties' strengths are random variables. taking examples from Hungary to illustrate the use of this approach.

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

Le metodiche di Diffusion Weighted Imaging per la caratterizzazione della malattia policistica renale

La tecnica di Diffusion Weighted Imaging (DWI) si basa sullo studio del moto diffusivo delle molecole d’acqua nei tessuti biologici ed è in grado di fornire informazioni sulla struttura dei tessuti e sulla presenza di eventuali alterazioni patologiche. Il più recente sviluppo della DWI è rappresentato dal Diffusion Tensor Imaging (DTI), tecnica che permette di determinare non solo l’entità, ma anche le direzioni principali della diffusione. Negli ultimi anni, grazie ai progressi nella tecnica di risonanza magnetica, l’imaging di diffusione è stato anche applicato ad altri distretti anatomici tra cui quello renale, per sfruttarne le potenzialità diagnostiche. Tuttavia, pochi sono ancora gli studi relativi all’applicazione delle metodiche di diffusione per la valutazione della malattia policistica renale autosomica dominante (ADPKD). ADPKD è una delle malattie ereditarie più comuni ed è la principale causa genetica di insufficienza renale dell’adulto. La caratteristica principale consiste nella formazione di cisti in entrambi i reni, che progressivamente aumentano in numero e dimensioni fino a causare la perdita della funzionalità renale nella metà circa dei pazienti. Ad oggi non sono disponibili terapie capaci di arrestare o rallentare l’evoluzione di ADPKD; è possibile controllare le complicanze per evitare che costituiscano componenti peggiorative. Il lavoro di tesi nasce dalla volontà di indagare se la tecnica dell’imaging di diffusione possa essere utile per fornire informazioni sullo stato della malattia e sul suo grado di avanzamento. L’analisi di studio è concentrata sul calcolo del coefficiente di diffusione apparente (ADC), derivato dalle immagini DWI e valutato nella regione della midollare. L’obiettivo di questo lavoro è verificare se tale valore di ADC sia in grado di caratterizzare la malattia policistica renale e possa essere utilizzato in ambito clinico-diagnostico come indicatore prognostico nella progressione di questa patologia.