Coherent ranging with Envisat radar altimeter: a new perspective in analyzing altimeter data using Doppler Processing

ESA's Envisat mission carried a RA-2 radar altimeter since its launch in 2002 to sense sea state and especially measure sea surface height (SSH). The onboard processing combined multiple echoes incoherently to reduce Speckle noise and benefit from data compression. In fact, according to past literature the amplitudes were generally expected to be independent. Nevertheless, samples of complex data time series of individual echoes (IE) were down-linked and archived since 2004 for research studies. In this note we demonstrate that there is sufficient inter-pulse coherence for Doppler processing and we suggest that the archived data can be re-processed into improved SSH. This is of particular interest in challenging domains (e.g., coastal zone) where coherent processing can mitigate errors from ocean surface backscatter inhomogeneity and nearby land backscatter. A new method called zero-Doppler to process IEs is thus proposed and discussed

Laparoscopy for extraperitoneal rectal cancer reduces short-term morbidity: Results of a systematic review and meta-analysis

BACKGROUND: The role of laparoscopy in the treatment of extraperitoneal rectal cancer is still controversial. The aim of the study was to evaluate differences in safety of laparoscopic rectal resection for extraperitoneal cancer, compared with open surgery. MATERIALS AND METHODS: A systematic review from 2000 to July 2012 was performed searching the MEDLINE and EMBASE databases (PROSPERO registration number CRD42012002406). We included randomized and prospective controlled clinical studies comparing laparoscopic and open resection for rectal cancer. Primary endpoints were 30-day mortality and morbidity. Then a meta-analysis was conducted by a fixed-effect model, performing a sensitivity analysis by a random-effect model. Relative risk (RR) was used as an indicator of treatment effect. RESULTS: Eleven studies, representing 1684 patients, met the inclusion criteria: four were randomized for a total of 814 patients. Mortality was observed in 1.2% of patients in the laparoscopic group and in 2.3% of patients in the open group, with an RR of 0.56 (95% CI 0.19–1.64, p = 0.287). The overall incidence of short-term complications was lower in the laparoscopic group (31.5%) compared to the open group (38.2%), with an RR of 0.83 (95% CI 0.73–0.94, p = 0.004). Surgical complications, wound complications, blood loss and the need for blood transfusion, time for bowel movement recovery, food intake recovery, and hospital stay were significantly lower or less frequent in the laparoscopic group. The incidence of intra-operative injuries, anastomotic leakages, and surgical re-interventions was similar in the two groups. Only operative time was in favour of the open group. CONCLUSIONS: Based on the evidence of both randomized and prospective controlled series, mortality was lower after laparoscopy although not significantly so, while the short-term morbidity RR, including subgroup analysis, was significantly lower after laparoscopy for extraperitoneal rectal cancer compared to open surgery

A mixed integer linear program to compress transition probability matrices in Markov chain bootstrapping

Bootstrapping time series is one of the most acknowledged tools to study the statistical properties of an evolutive phenomenon. An important class of bootstrapping methods is based on the assumption that the sampled phenomenon evolves according to a Markov chain. This assumption does not apply when the process takes values in a continuous set, as it frequently happens with time series related to economic and financial phenomena. In this paper we apply the Markov chain theory for bootstrapping continuous-valued processes, starting from a suitable discretization of the support that provides the state space of a Markov chain of order k≥1. Even for small k, the number of rows of the transition probability matrix is generally too large and, in many practical cases, it may incorporate much more information than it is really required to replicate the phenomenon satisfactorily. The paper aims to study the problem of compressing the transition probability matrix while preserving the “law” characterising the process that generates the observed time series, in order to obtain bootstrapped series that maintain the typical features of the observed time series. For this purpose, we formulate a partitioning problem of the set of rows of such a matrix and propose a mixed integer linear program specifically tailored for this particular problem. We also provide an empirical analysis by applying our model to the time series of Spanish and German electricity prices, and we show that, in these medium size real-life instances, bootstrapped time series reproduce the typical features of the ones under observation. This is a post-peer-review, pre-copyedit version of an article published in Annals of Operations Research volume. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10479-016-2181-

Portfolio selection problems in practice: a comparison between linear and quadratic optimization models

Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints. We propose a completely new approach for solving the LAM model, based on reformulation as a Standard Quadratic Program and on some recent theoretical results. With this approach we obtain optimal solutions both for some well-known financial data sets used by several other authors, and for some unsolved large size portfolio problems. We also test our method on five new data sets involving real-world capital market indices from major stock markets. Our computational experience shows that, rather unexpectedly, it is easier to solve the quadratic LAM model with our algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of the best commercial codes for mixed integer linear programming (MILP) problems. Finally, on the new data sets we have also compared, using out-of-sample analysis, the performance of the portfolios obtained by the Limited Asset models with the performance provided by the unconstrained models and with that of the official capital market indices

Some polynomial special cases for the Minimum Gap Graph Partitioning Problem

We study various polynomial special cases for the problem of partitioning a vertex-weighted undirected graph into p connected subgraphs with minimum gap between the largest and the smallest vertex weight