Arbitrage risk, investor sentiment and maximum daily returns

We test the cross-sectional relation between daily maximum return (MAX) and return in the following month for stocks with high and low idiosyncratic volatility. We use portfolio level analysis and firm-level cross-sectional regression to find that the negative and significant relation between MAX and expected stock return (known as the MAX effect ) is a non-January phenomenon observed predominantly on a sample of stocks with high idiosyncratic volatility. We find that the effect of investor sentiment on the MAX effect depends on arbitrage risk. Our findings suggest that arbitrageurs find it difficult to correct the mispricing of stocks with extreme positive return due to high idiosyncratic volatility, a support for the limits to arbitrage theory

Building a "trap model" of glassy dynamics from a local structural predictor of rearrangements

Here we introduce a variation of the trap model of glasses based on softness, a local structural variable identified by machine learning, in supercooled liquids. Softness is a particle-based quantity that reflects the local structural environment of a particle and characterizes the energy barrier for the particle to rearrange. As in the trap model, we treat each particle's softness, and hence energy barrier, as evolving independently. We show that such a model reproduces many qualitative features of softness, and therefore makes qualitatively reasonable predictions of behaviors such as the dependence of fragility on density in a model supercooled liquid. We also show failures of this simple model, indicating features of the dynamics of softness that may only be explained by correlations.Comment: 7 pages, 5 figures. Supplementary material: 3 pages, 4 figure

Ovarian Cancer Data Analysis using Deep Learning: A Systematic Review from the Perspectives of Key Features of Data Analysis and AI Assurance

Background and objectives: By extracting this information, Machine or Deep Learning (ML/DL)-based autonomous data analysis tools can assist clinicians and cancer researchers in discovering patterns and relationships from complex data sets. Many DL-based analyses on ovarian cancer (OC) data have recently been published. These analyses are highly diverse in various aspects of cancer (e.g., subdomain(s) and cancer type they address) and data analysis features. However, a comprehensive understanding of these analyses in terms of these features and AI assurance (AIA) is currently lacking. This systematic review aims to fill this gap by examining the existing literature and identifying important aspects of OC data analysis using DL, explicitly focusing on the key features and AI assurance perspectives. Methods: The PRISMA framework was used to conduct comprehensive searches in three journal databases. Only studies published between 2015 and 2023 in peer-reviewed journals were included in the analysis. Results: In the review, a total of 96 DL-driven analyses were examined. The findings reveal several important insights regarding DL-driven ovarian cancer data analysis: - Most studies 71% (68 out of 96) focused on detection and diagnosis, while no study addressed the prediction and prevention of OC. - The analyses were predominantly based on samples from a non-diverse population (75% (72/96 studies)), limited to a geographic location or country. - Only a small proportion of studies (only 33% (32/96)) performed integrated analyses, most of which used homogeneous data (clinical or omics). - Notably, a mere 8.3% (8/96) of the studies validated their models using external and diverse data sets, highlighting the need for enhanced model validation, and - The inclusion of AIA in cancer data analysis is in a very early stage; only 2.1% (2/96) explicitly addressed AIA through explainability

Validation of black point QTLs in wheat

Dissertação de mestrado em Psicologia (Psicologia da Educação, Desenvolvimento e Aconselhamento), apresentada à Faculdade de Psicologia e de Ciências da Educação da Universidade de CoimbraO presente estudo tem como objetivo compreender que tipo de experiências subjetivas as pessoas procuram quando consomem alguma droga e quais são, na sua perspetiva, as mais desejáveis, através da utilização da escala Drug Experiences Inventory (Redmon, 2005). Mais especificamente, pretende-se avaliar se a experiência subjetiva inerente ao consumo de substâncias constitui uma dimensão relevante a ter em consideração na definição de estratégias de prevenção e intervenção à dependência às drogas. Para tanto, recorreu-se a uma amostra de 208 participantes, sendo que 189 residem na região centro de Portugal e 19 provêm de serviços que prestam apoio a indivíduos que são ou foram dependentes de alguma substância. Através das análises efetuadas, conclui-se que os participantes usufruem de uma variedade de experiências quando estão sob o efeito de alguma droga e que discriminam essas mesmas experiências em função da desejabilidade que lhes atribuem. Os resultados obtidos revelam a importância de ser considerada a dimensão subjetiva do consumo de substâncias na prevenção e tratamento das dependências.The aim of this study is to understand what kind of subjective experiences people seek when they consume drugs and what are the most desirable for them, using de scale Drug Experiences Inventory (Redmon, 2005). More specifically, we intended to evaluate if the subjective experience inherent in the substance use is a relevant dimension to consider in defining strategies for prevention and intervention for drug addiction. In order to arrive at such evaluation, we collected a sample of 208 participants, of wich 189 were part of the normal group, and 19 (clinical sample) were recruited in agencies that provide support services for individuals with addiction to some kind of drug. Through the analyses performed, it is concluded that the participantes enjoy a variety of experiences and they discriminate them in terms of desirability they attribute to them. The results reveal the importance of considering the subjective dimension of drug use in prevention and treatment of addiction

ODoSE: a webserver for genome-wide calculation of adaptive divergence in prokaryotes

This is the final version of the article. Available from the publisher via the DOI in this record.Quantifying patterns of adaptive divergence between taxa is a major goal in the comparative and evolutionary study of prokaryote genomes. When applied appropriately, the McDonald-Kreitman (MK) test is a powerful test of selection based on the relative frequency of non-synonymous and synonymous substitutions between species compared to non-synonymous and synonymous polymorphisms within species. The webserver ODoSE (Ortholog Direction of Selection Engine) allows the calculation of a novel extension of the MK test, the Direction of Selection (DoS) statistic, as well as the calculation of a weighted-average Neutrality Index (NI) statistic for the entire core genome, allowing for systematic analysis of the evolutionary forces shaping core genome divergence in prokaryotes. ODoSE is hosted in a Galaxy environment, which makes it easy to use and amenable to customization and is freely available at www.odose.nl.MWJvP is funded by the Netherlands Organization for Scientific Research (NWO) via a VENI grant. TtB and MAvD are funded by the BioAssist/BRS programme of the Netherlands Bioinformatics Centre, which is supported by the Netherlands Genomics Initiative. This work is part of the programme of BiG Grid, the Dutch e-Science Grid, which is financially supported by the NWO. MV is supported by investment from the European Regional Development Fund and the European Social Fund Convergence Programme for Cornwall and the Isles of Scilly to the European Centre for the Environment and Human Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A framework for the utilization of Building Management System data in building information models for building design and operation

Research on digitizing the various aspects of a typical building project has been on the increase since the advent of Building Information Modelling (BIM). Most efforts build on information technology capabilities already achieved in the various professional domains associated with different stages of the building life cycle. It is predicted that BIM will help to drastically reduce errors, fast-track project delivery time and save implementation costs. As such BIM is now being utilized in the various professional domains and project stages. However, research suggests that the building operation and management stage is being left behind despite the abundance of data collected using building management systems (BMS) of varying degrees of sophistication. It is therefore important to consider exploring BIM applications that encompasses the building operation phase. This will enhance the evaluation of building performance in use and provide feedback to the design stage which could help eliminate design-related performance issues. A framework for utilizing feedback loops from building energy consumption to inform and improve design and facility management in a BIM environment is therefore proposed. A prototype illustrating the framework is implemented in. NET framework interfaced with a BIM-enabled tool and tested in the refinement of a pre-designed school using data from the operations phase of another school delivered previously. We conclude that the framework developed in this research can contribute to bridging existing gaps between the design, construction and operation phases of a building's life-cycle

Regional grey matter volume and concentration in at-risk adolescents: Untangling associations with callous-unemotional traits and conduct disorder symptoms

Structural Magnetic Resonance Imaging studies have reported volume reductions in several brain regions implicated in social cognition and emotion recognition in juvenile antisocial populations. However, it is unclear whether these structural abnormalities are specifically related to antisocial features, or to co-occurring callous-unemotional (CU) traits. The present study employed voxel-based morphometry to assess both grey matter volume (GMV) and grey matter concentration (GMC) in a large representative at-risk sample of adolescents (n=134; mean age 17.7 yr), characterized by a broad range of CU trait and conduct disorder (CD) symptom scores. There was a significant interaction between CD symptom and CU trait scores in the prediction of GMV in the anterior insula, with a significant positive association between CU traits and GMV in youth low on CD symptoms only. In addition, we found a significant unique positive association between CD symptoms and GMC in the amygdala, and unique negative associations between CU traits and GMC in the amygdala and insula. These findings are in line with accumulating evidence of distinct associations of CD symptoms and CU traits with amygdala and insula GMC in juvenile antisocial populations