Random walks in a one-dimensional L\'evy random environment

We consider a generalization of a one-dimensional stochastic process known in the physical literature as L\'evy-Lorentz gas. The process describes the motion of a particle on the real line in the presence of a random array of marked points, whose nearest-neighbor distances are i.i.d. and long-tailed (with finite mean but possibly infinite variance). The motion is a continuous-time, constant-speed interpolation of a symmetric random walk on the marked points. We first study the quenched random walk on the point process, proving the CLT and the convergence of all the accordingly rescaled moments. Then we derive the quenched and annealed CLTs for the continuous-time process.Comment: Final version to be published in J. Stat. Phys. 23 pages. (Changes from v1: Theorem 2.4 and Corollary 2.6 have been removed.

Persistent Specialization and Growth:The Italian Land Reform

Land distribution has ambiguous effects on structural transformation: large landowners can slow industrialization by limiting the provision of education, but larger scale andlocal market power might accelerate the mechanization of production. We examine theeffects of redistribution following the Italian 1950 land reform and find that redistribution led to less industrialization. We explain this finding with a reduction in the scale ofoperations and a more intensive use of family labor. Agricultural specialization persistedfor at least 50 years, consistent with models of occupational inheritance. Finally, we showthat expropriated areas had lower growth during 1970-2000

Random walks in a one-dimensional L\'evy random environment

We consider a generalization of a one-dimensional stochastic process known in the physical literature as L\'evy-Lorentz gas. The process describes the motion of a particle on the real line in the presence of a random array of marked points, whose nearest-neighbor distances are i.i.d. and long-tailed (with finite mean but possibly infinite variance). The motion is a continuous-time, constant-speed interpolation of a symmetric random walk on the marked points. We first study the quenched random walk on the point process, proving the CLT and the convergence of all the accordingly rescaled moments. Then we derive the quenched and annealed CLTs for the continuous-time process.Comment: Final version to be published in J. Stat. Phys. 23 pages. (Changes from v1: Theorem 2.4 and Corollary 2.6 have been removed.

Re: Critical Analysis of Early Recurrence after Laparoscopic Radical Cystectomy in a Large Cohort by the ESUT

okThe authors critically analyze a large cohort by the European Association of Urology Section of Uro-Technology and assess early recurrences after laparoscopic radical cystectomy and evaluation of risk factors, including the impact of pneumoperitoneum. They focus their analysis on patients with favorable pathology (pT2 N0 R0 disease), \ufb01nding that 27 of 311 patients (8.7%) experienced recurrences during the following 24 months. Surgical negligence was observed in only 1 patient, which was associated with the endo bag rupturing during transvaginal extraction with subsequent vulvar and peritoneal tumor metastasis after 4 months. Among the 27 patients with recurrence a shorter recurrence-free survival was signi\ufb01cantly predictive of cancer speci\ufb01c death (HR 0.86, 95% CI 0.78e0.94, p \ubc 0.001) as well as carcinoma in situ on pathological examination (HR 3.68, 95% CI 1.07e12.7, p \ubc 0.039). While analyzing causes of early recurrence, the authors suggest that the continuous insuf\ufb02ation-desuf\ufb02ation and leakage of gas around the portsdwith consequent aspiration of tumor cells via a chimney effectdmay promote tumor seeding (TS)

Improving P300 Speller performance by means of optimization and machine learning

Brain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user's brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as SWLDA and SVM are the most used for ERPs' classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio (SNR), it also slows down the process. In the early studies, the number of iterations was fixed (no stopping), but recently, several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets.Comment: 32 pages, research articl