The concept of preference and its manifestation in Hungarian verbal conflict sequences

Preference is an operative notion of sequential organization and interpersonal understanding in conversation analysis. The complexity of the phenomenon that we are attempting to grasp through this notion has manifested itself in the seemingly controversial widening of the interpretation of the notion. In this paper I argue that preference can be interpreted through an inference rule as a consequence of the simultaneous but not equal manifestation of pragmatic principles; a deeper and uncontroversial interpretation of the notion is possible if, in addition to a structural inference rule and interpersonality principles, we take into consideration the role of rationality principles as well. In every communicative situation an interpersonal concern and a topical concern is operative; in consensus-oriented contexts preference structure is regulated by interpersonality principles that govern interpersonal relations and self-projection, while in conflict-oriented discourse the most important role is played by rationality principles. Approaching preference from the perspective of pragmatic principles may prove instrumental in integrating conversion analysis more closely with new directions and results of pragmatic research

Machine learning for total cloud cover prediction

Accurate and reliable forecasting of total cloud cover (TCC) is vital for many areas such as astronomy, energy demand and production, or agriculture. Most meteorological centres issue ensemble forecasts of TCC; however, these forecasts are often uncalibrated and exhibit worse forecast skill than ensemble forecasts of other weather variables. Hence, some form of post-processing is strongly required to improve predictive performance. As TCC observations are usually reported on a discrete scale taking just nine different values called oktas, statistical calibration of TCC ensemble forecasts can be considered a classification problem with outputs given by the probabilities of the oktas. This is a classical area where machine learning methods are applied. We investigate the performance of post-processing using multilayer perceptron (MLP) neural networks, gradient boosting machines (GBM) and random forest (RF) methods. Based on the European Centre for Medium-Range Weather Forecasts global TCC ensemble forecasts for 2002–2014, we compare these approaches with the proportional odds logistic regression (POLR) and multiclass logistic regression (MLR) models, as well as the raw TCC ensemble forecasts. We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor. Compared to the raw ensemble, all calibration methods result in a significant improvement in forecast skill. RF models provide the smallest increase in predictive performance, while MLP, POLR and GBM approaches perform best. The use of precipitation forecast data leads to further improvements in forecast skill, and except for very short lead times the extended MLP model shows the best overall performance

Machine learning for total cloud cover prediction

Accurate and reliable forecasting of total cloud cover (TCC) is vital for many areas such as astronomy, energy demand and production, or agriculture. Most meteorological centres issue ensemble forecasts of TCC, however, these forecasts are often uncalibrated and exhibit worse forecast skill than ensemble forecasts of other weather variables. Hence, some form of post-processing is strongly required to improve predictive performance. As TCC observations are usually reported on a discrete scale taking just nine different values called oktas, statistical calibration of TCC ensemble forecasts can be considered a classification problem with outputs given by the probabilities of the oktas. This is a classical area where machine learning methods are applied. We investigate the performance of post-processing using multilayer perceptron (MLP) neural networks, gradient boosting machines (GBM) and random forest (RF) methods. Based on the European Centre for Medium-Range Weather Forecasts global TCC ensemble forecasts for 2002-2014 we compare these approaches with the proportional odds logistic regression (POLR) and multiclass logistic regression (MLR) models, as well as the raw TCC ensemble forecasts. We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor. Compared to the raw ensemble, all calibration methods result in a significant improvement in forecast skill. RF models provide the smallest increase in predictive performance, while MLP, POLR and GBM approaches perform best. The use of precipitation forecast data leads to further improvements in forecast skill and except for very short lead times the extended MLP model shows the best overall performance.Comment: 24 pages, 7 figure

The principles of communicative language use

The paper aims to overview some typical principles of communicative language use in a cognitive pragmatic approach applying a reductionist method in order to demonstrate that the well-known principles can be reduced to a very general rationality (economy) principle. After briefly reviewing the principles the paper re-evaluates them and provides a new classification of them relying on the definition of ostensive-inferential communication. The principles which can be divided into rationality and interpersonality principles are really principles of effective information transmission on objects and selves. They refer to two kinds of language use: informative and communicative ones. The only principles valid for only communicative language use are the communicative principle of relevance and the principle of communicative intention suggested in the present article. Finally, the paper reduces all rationality and interpersonality principles to a very general rationality principle, i.e., the cognitive principle of relevance

The Importance of Aquaporin 1 in Pancreatitis and Its Relation to the CFTR Cl- Channel

Aquaporins (AQPs) facilitate the transepithelial water flow involved in epithelial fluid secretion in numerous tissues;however, their function in the pancreas is less characterized. Acute pancreatitis (AP) is a serious disorder in which specific treatment is still not possible. Accumulating evidence indicate that decreased pancreatic ductal fluid secretion plays an essential role in AP;therefore, the aim of this study was to investigate the physiological and pathophysiological role of AQPs in the pancreas. Expression and localization of AQPs were investigated by real-time PCR and immunocytochemistry, whereas osmotic transmembrane water permeability was estimated by the dye dilution technique, in Capan-1 cells. The presence of AQP1 and CFTR in the mice and human pancreas were investigated by immunohistochemistry. Pancreatic ductal HCO3- and fluid secretion were studied on pancreatic ducts isolated from wild-type (WT) and AQP1 knock out (KO) mice using microfluorometry and videomicroscopy, respectively. In vivo pancreatic fluid secretion was estimated by magnetic resonance imaging. AP was induced by intraperitoneal injection of cerulein and disease severity was assessed by measuring biochemical and histological parameters. In the mice, the presence of AQP1 was detected throughout the whole plasma membrane of the ductal cells and its expression highly depends on the presence of CFTR Cl- channel. In contrast, the expression of AQP1 is mainly localized to the apical membrane of ductal cells in the human pancreas. Bile acid treatment dose- and time-dependently decreased mRNA and protein expression of AQP1 and reduced expression of this channel was also demonstrated in patients suffering from acute and chronic pancreatitis. HCO3- and fluid secretion significantly decreased in AQP1 KO versus WT mice and the absence of AQP1 also worsened the severity of pancreatitis. Our results suggest that AQP1 plays an essential role in pancreatic ductal fluid and HCO3- secretion and decreased expression of the channel alters fluid secretion which probably contribute to increased susceptibility of the pancreas to inflammation