Distributional Sentence Entailment Using Density Matrices

Categorical compositional distributional model of Coecke et al. (2010) suggests a way to combine grammatical composition of the formal, type logical models with the corpus based, empirical word representations of distributional semantics. This paper contributes to the project by expanding the model to also capture entailment relations. This is achieved by extending the representations of words from points in meaning space to density operators, which are probability distributions on the subspaces of the space. A symmetric measure of similarity and an asymmetric measure of entailment is defined, where lexical entailment is measured using von Neumann entropy, the quantum variant of Kullback-Leibler divergence. Lexical entailment, combined with the composition map on word representations, provides a method to obtain entailment relations on the level of sentences. Truth theoretic and corpus-based examples are provided.Comment: 11 page

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls

Real-Time Water Vapor Maps from a GPS Surface Network: Construction, Validation, and Applications

In this paper the construction of real-time integrated water vapor (IWV) maps from a surface network of global positioning system (GPS) receivers is presented. The IWV maps are constructed using a twodimensional variational technique with a persistence background that is 15 min old. The background error covariances are determined using a novel two-step method, which is based on the Hollingsworth¿Lonnberg method. The quality of these maps is assessed by comparison with radiosonde observations and IWV maps from a numerical weather prediction (NWP) model. The analyzed GPS IWV maps have no bias against radiosonde observations and a small bias against NWP analysis and forecasts up to 9 h. The standard deviation with radiosonde observations is around 2 kg m-2, and the standard deviation with NWP increases with increasing forecast length (from 2 kg m-2 for the NWP analysis to 4 kg m-2 for a forecast length of 48 h). To illustrate the additional value of these real-time products for nowcasting, three thunderstorm cases are discussed. The constructed GPS IWV maps are combined with data from the weather radar, a lightning detection network, and surface wind observations. All cases show that the location of developing thunderstorms can be identified 2 h prior to initiation in the convergence of moist air

Cognition in Multiple sclerosis with special emphasis on MRI findings and cerebrosterol

Multiple sclerosis (MS) is a progressive inflammatory and degenerative disease of the central nervous system (CNS). This thesis focuses on cognition in MS, with special emphasis on long-term magnetic resonance imaging (MRI) findings and cerebrosterol plasma levels. In study I, the effects of MS on a variety of cognitive aspects were evaluated longitudinally over an eight-year follow-up period in 31 patients who had been diagnosed as having relapsing-remitting MS, secondary progressive MS (SP-MS) or primary progressive MS. A selective pattern of decline was found at baseline in the whole group, with marked decline in information-processing speed (IPS). These deficits in IPS at baseline predicted further cognitive decline over the follow-up period. A differential pattern of cognitive decline over time was noticed in the subgroups, with the most pronounced decline in the SP-MS group; in these patients, the deterioration in visual IPS was clearly more marked than that in auditory IPS. A high disability score (on the expanded disability status scale; EDSS) during follow-up was associated with cognitive decline. These findings indicate that tests measuring IPS are especially strong predictors of cognitive decline over longer periods in patients with MS. In Study II, 25 patients with MS and 25 matched control participants were tested with a picture-naming test (Boston Naming Test; BNT) and a letter-word fluency test (using the letters FAS). In the BNT, the MS patients used less distinct descriptions and substitutions and had significantly more off-target substitutions than the control group. The MS patients were significantly less effective in using strategies for retrieval in the word fluency FAS test than the control participants. These results suggest that language function becomes impaired in MS, with semantically nonspecific naming responses and less effective use of strategies for retrieval in word fluency. In Study III, 22 MS patients were given tasks investigating IPS, covering the following aspects: cognitive (symbol digit modalities test; SDMT), sensory (visual and auditory reaction time tests), motor (finger-tapping speed test) and auditory interhemispheric transfer (verbal dichotic listening test; VDL). These parameters were related to the area of the corpus callosum in the brain (CCA), measured with MRI at baseline and at follow-up nine years later. The relative brain volume (RBV) and the T2 lesion load were taken into account. The results showed that the CCA, but not the RBV or the T2 lesion load, was associated with the SDMT score, and that the higher the annual rate of change in the CCA, the poorer the performance in the left ear VDL, with a subsequently more pronounced advantage in the right ear VDL. These results indicate that corpus callosum is related to a clearly cognitive component, rather than a sensory-motor component. Study IV analyzed the relationships between cognitively demanding information processing (measured with the SDMT), clinical status (EDSS), plasma cerebrosterol 24OHC levels, and MRI-normalized measurement of RBV, grey and white matter volumes, and ventricular cerebrospinal fluid volume in a cross-sectional sample of 21 MS patients. The results showed that slow IPS in SDMT was related to neurodegeneration, particularly loss of grey matter volume, and high cerebrosterol plasma concentrations, reflecting membrane turnover in the CNS. Poor EDSS was associated with high plasma cerebrosterol levels, which is hypothetically a biomarker of MS progression. Conclusions: Deterioration in IPS seems to be a central aspect of cognitive decline in MS. Slow IPS occurs already at an early phase in the disease and predicts long term cognitive decline. The MS patients have less effective strategies for lexical substitution and retrieval than healthy persons. The poor lexical processing in the MS patients could putatively be due to slow IPS, especially in the markedly speed demanding word fluency test. CCA in contrast to RBV and the T2 lesion load, appears to be exclusively related to a cognitively demanding IPS task but not to sensory-motor speed tasks, which suggests that CC is especially important for cognitive speed processes. Slow IPS seems to be primarily associated with low grey matter volume and high plasma concentration level of cerebrosterol while disability appears to be related to high plasma level of cerebrosterol. Since there were no interaction between cerebrosterol and the neurodegenerative predictors, it is putative that the cytotoxic properties of the cerebrosterol independently could cause neuronal cell death and thereby affect IPS independently of neurodegeneration

The Benefits to People of Expanding Marine Protected Areas

This study focuses on how the economic value of marine ecosystem services to people and communities is expected to change with the expansion of marine protected areas (MPAs). It is recognised, however, that instrumental economic value derived from ecosystem services is only one component of the overall value of the marine environment and that the intrinsic value of nature also provides an argument for the conservation of the marine habitats and biodiversity

The Diabetes Remission Clinical Trial (DiRECT): protocol for a cluster randomised trial

Background: Despite improving evidence-based practice following clinical guidelines to optimise drug therapy, Type 2 diabetes (T2DM) still exerts a devastating toll from vascular complications and premature death. Biochemical remission of T2DM has been demonstrated with weight loss around 15kg following bariatric surgery and in several small studies of non-surgical energy-restriction treatments. The non-surgical Counterweight-Plus programme, running in Primary Care where obesity and T2DM are routinely managed, produces >15 kg weight loss in 33 % of all enrolled patients. The Diabetes UK-funded Counterpoint study suggested that this should be sufficient to reverse T2DM by removing ectopic fat in liver and pancreas, restoring first-phase insulin secretion. The Diabetes Remission Clinical Trial (DiRECT) was designed to determine whether a structured, intensive, weight management programme, delivered in a routine Primary Care setting, is a viable treatment for achieving durable normoglycaemia. Other aims are to understand the mechanistic basis of remission and to identify psychological predictors of response. Methods/Design: Cluster-randomised design with GP practice as the unit of randomisation: 280 participants from around 30 practices in Scotland and England will be allocated either to continue usual guideline-based care or to add the Counterweight-Plus weight management programme, which includes primary care nurse or dietitian delivery of 12-20weeks low calorie diet replacement, food reintroduction, and long-term weight loss maintenance. Main inclusion criteria: men and women aged 20-65years, all ethnicities, T2DM 0-6years duration, BMI 27-45 kg/m2. Tyneside participants will undergo Magnetic Resonance (MR) studies of pancreatic and hepatic fat, and metabolic studies to determine mechanisms underlying T2DM remission. Co-primary endpoints: weight reduction ≥ 15 kg and HbA1c <48 mmol/mol at one year. Further follow-up at 2 years. Discussion: This study will establish whether a structured weight management programme, delivered in Primary Care by practice nurses or dietitians, is a viable treatment to achieve T2DM remission. Results, available from 2018 onwards, will inform future service strategy

Experience sampling methods for the personalised prediction of mental health problems in Spanish university students: protocol for a survey-based observational study within the PROMES-U project

IntroductionThere is a high prevalence of mental health problems among university students. Better prediction and treatment access for this population is needed. In recent years, short-term dynamic factors, which can be assessed using experience sampling methods (ESM), have presented promising results for predicting mental health problems.Methods and analysisUndergraduate students from five public universities in Spain are recruited to participate in two web-based surveys (at baseline and at 12-month follow-up). A subgroup of baseline participants is recruited through quota sampling to participate in a 15-day ESM study. The baseline survey collects information regarding distal risk factors, while the ESM study collects short-term dynamic factors such as affect, company or environment. Risk factors will be identified at an individual and population level using logistic regressions and population attributable risk proportions, respectively. Machine learning techniques will be used to develop predictive models for mental health problems. Dynamic structural equation modelling and multilevel mixed-effects models will be considered to develop a series of explanatory models for the occurrence of mental health problems.Ethics and disseminationThe project complies with national and international regulations, including the Declaration of Helsinki and the Code of Ethics, and has been approved by the IRB Parc de Salut Mar (2020/9198/I) and corresponding IRBs of all participating universities. All respondents are given information regarding access mental health services within their university and region. Individuals with positive responses on suicide items receive a specific alert with indications for consulting with a health professional. Participants are asked to provide informed consent separately for the web-based surveys and for the ESM study. Dissemination of results will include peer-reviewed scientific articles and participation in scientific congresses, reports with recommendations for universities’ mental health policy makers, as well as a well-balanced communication strategy to the general public

Sentence entailment in compositional distributional semantics

Distributional semantic models provide vector representations for words by gathering co-occurrence frequencies from corpora of text. Compositional distributional models extend these from words to phrases and sentences. In categorical compositional distributional semantics, phrase and sentence representations are functions of their grammatical structure and representations of the words therein. In this setting, grammatical structures are formalised by morphisms of a compact closed category and meanings of words are formalised by objects of the same category. These can be instantiated in the form of vectors or density matrices. This paper concerns the applications of this model to phrase and sentence level entailment. We argue that entropy-based distances of vectors and density matrices provide a good candidate to measure word-level entailment, show the advantage of density matrices over vectors for word level entailments, and prove that these distances extend compositionally from words to phrases and sentences. We exemplify our theoretical constructions on real data and a toy entailment dataset and provide preliminary experimental evidence.Comment: 8 pages, 1 figure, 2 tables, short version presented in the International Symposium on Artificial Intelligence and Mathematics (ISAIM), 201