Word sense discrimination in information retrieval: a spectral clustering-based approach

International audienceWord sense ambiguity has been identified as a cause of poor precision in information retrieval (IR) systems. Word sense disambiguation and discrimination methods have been defined to help systems choose which documents should be retrieved in relation to an ambiguous query. However, the only approaches that show a genuine benefit for word sense discrimination or disambiguation in IR are generally supervised ones. In this paper we propose a new unsupervised method that uses word sense discrimination in IR. The method we develop is based on spectral clustering and reorders an initially retrieved document list by boosting documents that are semantically similar to the target query. For several TREC ad hoc collections we show that our method is useful in the case of queries which contain ambiguous terms. We are interested in improving the level of precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30) respectively. We show that precision can be improved by 8% above current state-of-the-art baselines. We also focus on poor performing queries

Comparative study of some honey types collected from unpolluted areas of Timis county

This research followed to achieve some physico-chemical properties for natural food based on honey and dried fruits. The honey samples were represented by three 3 different types of bee honey: multiforal and unifloral species - Acacia flower (lat. Robinia pseudoacacia), and Linden flower ( lat. Tilia cordata) – bought directly from the producers originally from unpolluted areas in Timis County. In honey samples we added dried fruits: apricots (lat. Prunus armeniaca) and figs (lat. Ficus carica). For these samples, refractive index, water content - based on nD values, total solid content, and acidity were determined. Based on nd values between 1.4811 – 1.49, the water content shows values from 18.61% and 22.54%, respectively from 81.39%, until 77.46% in case of total solid content, and acidity had values between 2.1 and 3.53 acidity degrees. The principal purpose of this study was to bring more data to the knowledge of some types of honey originating from unpolluted area of Timis county in terms of physical properties, and also how different additions can contribute to increasing the nutritional value of these products

Orange and lemon peel powders as a bioelement source

The purpose of this paper was to evaluate the concentration of some bioelements from citrus peel powder. Peels powder was obtained by drying and grinding citrus peels as a by-product resulting after the preparation of some natural juices. The results obtained by atomic absorption spectrometry of Na, K, Ca, Mg, Fe, Mn, Zn and Cu, shows that the powders taken into the study contain important amounts of essential mineral elements, especially Ca and K (159-182 mg/100g, respectively 211-218 mg/100g) and also appreciable contents of Mg (15.3-23.4 mg/100g), Fe (18.1-34.1 mg/100g), Zn (9.34-11.8 mg/100g), Na (8.75-12.8 mg/100g), Cu (1.27-3.71 mg/100g) and Mn (1.32-2.03 mg/100g). The concentration of the analyzed mineral bioelements shows, in general, the following decreasing trend: K> Ca> Fe> Mg> Zn> Na> Cu> Mn

Green fresh smoothie - some physico-chemical and nutritional aspects

Increasing tendency for fresh food - fruits, vegetables, and herbs consumption worldwide and also in our country, shows the weight that they have held or hold them in the diet. In their case, not only good looks, nice color or taste and aromas are considered to be important, but especially their nutritional value, rich in sugars, vitamins and minerals needed in the diet of the human body. They also have the advantage that it can be consumed without any processing who could reduce the nutritional value. The purpose of the study was to obtain and reveal some physico-chemical and nutritional properties of some fresh foods: green apple (Golden delicious – Malus domestica.), baby spinach (Spinach oleracea), pineapple (Ananas comosus) and mint leaves (Mentha piperita) and the juice that we obtain from them, while achieving a characterization highlighting their dietary and healing properties. The study presents important application not only for food industry, but also for other areas, because it addresses to special categories of consumers such as vegetarians and people with lactose intolerance and fasting period

Pre-perihelion Monitoring of Interstellar Comet 2I/Borisov

The discovery of interstellar comet 2I/Borisov offered the unique opportunity to obtain a detailed analysis of an object coming from another planetary system, and leaving behind material in our interplanetary space. We continuously observed 2I/Borisov between October 3 and December 13, 2019 using the 1.52-m Telescopio Carlos S\'{a}nchez equipped with MuSCAT2 instrument, and the 2.54-m Isaac Newton Telescope with Wide Field Camera. We characterize its morphology and spectro-photometric features using the data gathered during this extended campaign. Simultaneous imaging in four bands ($g$, $r$, $i$, and $z_s$) reveals a homogeneous composition and a reddish hue, resembling Solar System comets, and as well a diffuse profile exhibiting familiar cometary traits. We discern a stationary trend fluctuating around a constant activity level throughout October and November 2019. Subsequently, a reduction in activity is observed in December. Dust production and mass loss calculations indicate approximately an average of 4 kg/s before perihelion, while after perihelion the net mass loss is about 0.6 kg/s. Our simulations indicate the most probable size of coma dust particles should be in the range 200-250 nm, and the terminal speed around 300 m/s. The spectrum acquired with the 4.2-m William Herschel Telescope shows the presence of a strong CN line for which we find a gas production rate of $1.2 \times 10^{24}~s^{-1}$. We also detected NH$_2$ and OI bands. The ratio between NH$_2$ and CN productions is $\log (NH_2/CN) =-0.2$. Overall, this observing campaign provides a new understanding of 2I/Borisov's unique characteristics and activity patterns.Comment: accepted to MNRAS on 12th Feb 202

First Bio-Anthropological Evidence for Yamnaya Horsemanship.

The origins of horseback riding remain elusive. Scientific studies show that horses were kept for their milk similar to 3500 to 3000 BCE, widely accepted as indicating domestication. However, this does not confirm them to be ridden. Equipment used by early riders is rarely preserved, and the reliability of equine dental and mandibular pathol-ogies remains contested. However, horsemanship has two interacting components: the horse as mount and the human as rider. Alterations associated with riding in human skeletons therefore possibly provide the best source of information. Here, we report five Yamnaya individuals well-dated to 3021 to 2501 calibrated BCE from kurgans in Romania, Bulgaria, and Hungary, displaying changes in bone morphology and distinct pathologies associated with horseback riding. These are the oldest humans identified as riders so far.Peer reviewe

Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model

Differential diagnosis of focal pancreatic masses is based on endoscopic ultrasound (EUS) guided fine needle aspiration biopsy (EUS-FNA/FNB). Several imaging techniques (i.e. gray-scale, color Doppler, contrast-enhancement and elastography) are used for differential diagnosis. However, diagnosis remains highly operator dependent. To address this problem, machine learning algorithms (MLA) can generate an automatic computer-aided diagnosis (CAD) by analyzing a large number of clinical images in real-time. We aimed to develop a MLA to characterize focal pancreatic masses during the EUS procedure. The study included 65 patients with focal pancreatic masses, with 20 EUS images selected from each patient (grayscale, color Doppler, arterial and venous phase contrast-enhancement and elastography). Images were classified based on cytopathology exam as: chronic pseudotumoral pancreatitis (CPP), neuroendocrine tumor (PNET) and ductal adenocarcinoma (PDAC). The MLA is based on a deep learning method which combines convolutional (CNN) and long short-term memory (LSTM) neural networks. 2688 images were used for training and 672 images for testing the deep learning models. The CNN was developed to identify the discriminative features of images, while a LSTM neural network was used to extract the dependencies between images. The model predicted the clinical diagnosis with an area under curve index of 0.98 and an overall accuracy of 98.26%. The negative (NPV) and positive (PPV) predictive values and the corresponding 95% confidential intervals (CI) are 96.7%, [94.5, 98.9] and 98.1%, [96.81, 99.4] for PDAC, 96.5%, [94.1, 98.8], and 99.7%, [99.3, 100] for CPP, and 98.9%, [97.5, 100] and 98.3%, [97.1, 99.4] for PNET. Following further validation on a independent test cohort, this method could become an efficient CAD tool to differentiate focal pancreatic masses in real-time

Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis

Background: Infections due to antibiotic-resistant bacteria are threatening modern health care. However, estimating their incidence, complications, and attributable mortality is challenging. We aimed to estimate the burden of infections caused by antibiotic-resistant bacteria of public health concern in countries of the EU and European Economic Area (EEA) in 2015, measured in number of cases, attributable deaths, and disability-adjusted life-years (DALYs). Methods: We estimated the incidence of infections with 16 antibiotic resistance–bacterium combinations from European Antimicrobial Resistance Surveillance Network (EARS-Net) 2015 data that was country-corrected for population coverage. We multiplied the number of bloodstream infections (BSIs) by a conversion factor derived from the European Centre for Disease Prevention and Control point prevalence survey of health-care-associated infections in European acute care hospitals in 2011–12 to estimate the number of non-BSIs. We developed disease outcome models for five types of infection on the basis of systematic reviews of the literature. Findings: From EARS-Net data collected between Jan 1, 2015, and Dec 31, 2015, we estimated 671 689 (95% uncertainty interval [UI] 583 148–763 966) infections with antibiotic-resistant bacteria, of which 63·5% (426 277 of 671 689) were associated with health care. These infections accounted for an estimated 33 110 (28 480–38 430) attributable deaths and 874 541 (768 837–989 068) DALYs. The burden for the EU and EEA was highest in infants (aged <1 year) and people aged 65 years or older, had increased since 2007, and was highest in Italy and Greece. Interpretation: Our results present the health burden of five types of infection with antibiotic-resistant bacteria expressed, for the first time, in DALYs. The estimated burden of infections with antibiotic-resistant bacteria in the EU and EEA is substantial compared with that of other infectious diseases, and has increased since 2007. Our burden estimates provide useful information for public health decision-makers prioritising interventions for infectious diseases