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Diverse explanations or theories of consciousness are arrayed on a roughly physicalist-to-nonphysicalist landscape of essences and mechanisms. Categories: Materialism Theories (philosophical, neurobiological, electromagnetic field, computational and informational, homeostatic and affective, embodied and enactive, relational, representational, language, phylogenetic evolution); Non-Reductive Physicalism; Quantum Theories; Integrated Information Theory; Panpsychisms; Monisms; Dualisms; Idealisms; Anomalous and Altered States Theories; Challenge Theories. There are many subcategories, especially for Materialism Theories. Each explanation is self-described by its adherents, critique is minimal and only for clarification, and there is no attempt to adjudicate among theories. The implications of consciousness explanations or theories are assessed with respect to four questions: meaning/purpose/value (if any); AI consciousness; virtual immortality; and survival beyond death. A Landscape of Consciousness, I suggest, offers perspective. [ABSTRACT FROM AUTHOR]

This research aims to assess the similarity between determining the value of the similarity of objects by using various measures. Particularly for categorical data, the used similarity measures may behave differently depending on whether the analyzed data are binary, non-binary, or mixed. In the case of categorical data, it begins to matter how many attributes we have and how many different values they can take. Cluster analysis is an essential technique in unsupervised machine learning. Object-clustering cannot be determined without determining similarities between objects in the set and searching for clusters with the most significant internal consistency and external separatory. Therefore, we need knowledge about which measure behaves similarly and which differs depending on the nature of the analyzed data. According to our knowledge, there is no such research in the literature. In research, we analyzed six different measures for eight different datasets (differing in size and data type). The results say, among others, which measures take the longest time for calculations and which are the most correlated with others, so they can be used interchangeably without fear of losing valuable information about the mutual similarity of objects. [ABSTRACT FROM AUTHOR]

We consider compatible monoid structures on a DQ -category, where Q is a commutative and divisible quantale, and DQ is the quantaloid of diagonals of Q. Such structures, called DQ -monoids, may be treated as monoids equipped with a preorder valued in Q , whose elements are not supposed to exist globally. The globalization functors map DQ -monoids to global ones, i.e., monoids on a Q -category, called Q -monoids. Conversely, Q -monoids over Q give rise to DQ -monoids via the localization functors. The interactions between the globalization functors and the localization functors are investigated. In particular, a necessary and sufficient condition on Q is provided such that the localizations are reversible by the globalizations. [ABSTRACT FROM AUTHOR]

Numerous challenges and open problems have appeared with the dawn of multi-model data. In most cases, single-model solutions cannot be straightforwardly extended, and new, efficient approaches must be found. In addition, since there are no standards related to combining and managing multiple models, the situation is even more complicated and confusing for users. This paper deals with the most important aspect of data management — querying. To enable the user to grasp all the popular models, we base our solution on the abstract categorical representation of multi-model data, which can be viewed as a graph. To unify the querying of multi-model data, we enable the user to query the categorical graph using a SPARQL-based model-agnostic query language called MMQL. The query is then decomposed and translated into languages of the underlying systems. The intermediate results are then combined into the final categorical result that can be expressed in any selected format. The support for cross-model redundancy enables one to create distinct query plans and choose the optimal one. We also introduce a proof-of-concept implementation of our solution called MM-quecat. • The unifying abstract representation of various data simpli es the data processing. • Abstract multi-model query language shields the user from system-specific languages. • An abstract query language with expressive power similar to popular query languages. • Efficient planning of data transformations during multi-model query evaluation. [ABSTRACT FROM AUTHOR]

We develop a general theory of (extended) inner autoequivalences of objects of any 2-category, generalizing the theory of isotropy groups to the 2-categorical setting. We show how dense subcategories let one compute isotropy in the presence of binary coproducts, unifying various known one-dimensional results and providing tractable computational tools in the two-dimensional setting. In particular, we show that the isotropy 2-group of a monoidal category coincides with its Picard 2 -group , i.e., the 2-group on its weakly invertible objects. [ABSTRACT FROM AUTHOR]

The Vietoris space of a Stone space plays an important role in the coalgebraic approach to modal logic. When generalizing this to positive modal logic, there is a variety of relevant hyperspace constructions based on various topologies on a Priestley space and mechanisms to topologize the hyperspace of closed sets. A number of authors considered hyperspaces of Priestley spaces and their application to the coalgebraic approach to positive modal logic. A mixture of techniques from category theory, pointfree topology, and Priestley duality have been employed. Our aim is to provide a unifying approach to this area of research relying only on a basic familiarity with Priestley duality and related free constructions of distributive lattices. [ABSTRACT FROM AUTHOR]

In this paper we provide arguments supporting the idea that module theory in the symmetric, monoidal closed category of complete lattices and join-preserving maps is a mathematical framework for fuzzy set theory as developed by Zadeh. In this context, quantale-enriched category theory is a well established mathematical technique allowing an internal development of various concepts from fuzzy set theory – e.g. the fuzzy power set, Zadeh's forward operator or the fuzzy inclusion order. [ABSTRACT FROM AUTHOR]

In this paper, considering L a complete residuated lattice, we present a lattice-valued category TNG (resp., TTNG) of (topological) ⊤-neighborhood groups, where the object is defined as a group equipped with a (topological) ⊤-neighborhood space such that the group operations are continuous with respect to the (topological) ⊤-neighborhood space. It is proved that: (1) The ⊤-neighborhood space associated with a ⊤-neighborhood group is topological, so the category TNG is equivalent to the category TTNG. Hence TTNG is redundant, and we need only discuss TNG. (2) The category TNG has nice characterizations, localization and uniformization. (3) The category TNG has initial structure, so it is a topological category, and each initial structure has an ordered representation. (4) The category NG of neighborhood groups can be embedded in TNG as a reflective subcategory. (5) The category TNG can be embedded in the category SLNG of stratified L -neighborhood groups as a reflective subcategory when the underlying lattice L is a meet-continuous lattice. (6) The category TNG is equivalent to the category StrLTOPG of strong L -topological groups when L is a complete MV-algebra. [ABSTRACT FROM AUTHOR]

We give structural results about bifibrations of (internal) (∞ , 1) -categories with internal sums. This includes a higher version of Moens' Theorem, characterizing cartesian bifibrations with extensive aka stable and disjoint internal sums over lex bases as Artin gluings of lex functors. We also treat a generalized version of Moens' Theorem due to Streicher which does not require the Beck–Chevalley condition. Furthermore, we show that also in this setting the Moens fibrations can be characterized via a condition due to Zawadowski. Our account overall follows Streicher's presentation of fibered category theory à la Bénabou, generalizing the results to the internal, higher-categorical case, formulated in a synthetic setting. Namely, we work inside simplicial homotopy type theory, which has been introduced by Riehl and Shulman as a logical system to reason about internal (∞ , 1) -categories, interpreted as Rezk objects in any given Grothendieck–Rezk–Lurie (∞ , 1) -topos. [ABSTRACT FROM AUTHOR]

We propose a model that conceptualises diagrammatic sensemaking and reasoning as blends of image schemas – patterns derived from our perceptual and embodied experiences and interactions with the environment – with the geometric structure of the diagram. Our ultimate goal is to develop an algorithmic method for determining several potential blends that hold cognitive value for observers. Building upon our formal, category-theoretic approach to conceptual blending, we extend it by formalising two governing principles of blending. These principles serve as guides for the blending process, directing the cognitive construction of the blend. As these principles may compete with each other and favour different blend structures, we argue that their combination leads to cognitively useful blends. Through examples of several alternative blends of the geometric configuration of a particular Hasse diagram with the SCALE image schema, we demonstrate the implications of these competing pressures on diagrammatic reasoning. Consequently, this work disambiguates and operationalises the intricacies of conceptual blending, advancing its applicability in computational systems. [ABSTRACT FROM AUTHOR]

We shall analyze the intrinsic geometric structure of Yang-Mills theory from the standpoint provided by (what we shall call) the homotopic paradigm. This mathematical paradigm was mainly developed in the framework of (higher) category theory and homotopy type theory and relies on a groupoid-theoretical understanding of equality statements of the form a ​= ​ b. From a philosophical perspective, we shall argue that the homotopic paradigm relies a) on a rejection of Leibniz's Principle of the Identity of Indiscernibles and b) on a constructivist understanding of propositions as types of proofs. We shall apply the homotopic reconceptualization of equalities to the equalities between the base points and the fibers of the fiber bundle associated to a Yang-Mills theory. We shall revisit in this framework the articulation (heuristically established in the framework of the so-called gauge argument) between gauge symmetries and the mathematical notion of connection. We shall argue that this homotopic-theoretic understanding of Yang-Mills theories paves the way toward an ontological interpretation of gauge symmetries, that is an interpretation according to which gauge symmetries—far from being nothing but a " surplus structure " resulting from a descriptive redundancy—rely on the intrinsic geometric structures of Yang-Mills theories. [ABSTRACT FROM AUTHOR]

An L -fuzzy relation is a relation valued on a complete lattice L with a monoidal structure. This paper reviews four categories of L -fuzzy relations each modelling an area where Fuzzy Set Theory can be applied. We review the notions of these multivalued binary relations and present some basic properties of the corresponding categories aiming at applications in areas such as Computing Science, Linear Logic and Quantum Mechanics. The emphasis is on the monoidal aspects of the categories. Monoidal categories are one of the most applied kinds of categories. A monoidal view of Fuzzy Relations may widen the spectrum of applications of Fuzzy Set Theory. [ABSTRACT FROM AUTHOR]

The quantification of the variability of the categorical data is an important topic not only in statistics, but also in many other disciplines. We suggest different variability measures to describe the variability of categorical data. In our approach, any set of categorical data for a determinate categorical variable is treated as a fuzzy set. Therefore, measuring the variability of categorical data is the same as measuring its fuzziness. Different measures of association between two categorical variables are also proposed. The measures can be easily applied to categoric random variables. [ABSTRACT FROM AUTHOR]

For general powerset theories in categories, new notions of relational, closure, or partition powerset theories in these categories are introduced. These new types of powerset theories are defined as categories whose objects are the original powerset objects with relational, closure, or partition structures defined on these objects. This construction generalizes classical constructions realized on powerset objects of all subsets of a given set, or all fuzzy sets on a given set, and it enables to define these structures on more general powerset objects. Examples of these new powerset objects are also shown. [ABSTRACT FROM AUTHOR]

The elegance of the single-pushout (SPO) approach to graph transformations arises from substituting total morphisms by partial ones in the underlying category. SPO's applicability depends on the durability of pushouts after this transition. There is a wide range of work on the question when pushouts exist in categories with partial morphisms starting with the pioneering work of Löwe and Kennaway and ending with an essential characterisation in terms of an exactness property (for the interplay between pullbacks and pushouts) and an adjointness condition (w.r.t. inverse image functions) by Hayman and Heindel. Triple graphs and graph diagrams are frameworks to synchronise two or more updatable data sources by means of internal mappings, which identify common sub-structures. Comprehensive systems generalise these frameworks, treating the network of data sources and their structural inter-relations as a homogeneous comprehensive artefact, in which partial maps identify commonalities. Although this inherent partiality produces amplified complexity, we can show that Heindel's characterisation still yields existence of pushouts in the category of comprehensive systems and reflective partial morphisms and thus enables computing by typed SPO graph transformation. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the risk of recurrence after a first unprovoked seizure in childhood and to explore the correlation between the first and second seizures in recurrent patients. In a prospective study, we included 467 children aged 1 month to 16 years, who were attended to between November 1, 2008 and October 31, 2016 following a first seizure. Children who had been started on treatment with antiepileptic drugs were excluded. Recurrence rates were calculated using Kaplan–Meier survival analyses. Univariate and multivariate analyses for recurrence risk were performed using the Cox proportional hazards model. The kappa coefficient of correlation for categorical data was calculated. Recurrences occurred in 280 children (60.0%), of which 75 (26.8%) occurred in the first month, 184 (65.7%) within 6 months, and 256 (91.4%) within 2 years. None of the patients had new neurologic sequelae after their first or second seizure. The estimates of seizure recurrence risk were 39.5%, 48.1%, 55.1%, 60.8%, 61.8% and 61.8% at 0.5, 1, 2, 5, 8, and 10 years after the first seizure, respectively. Multivariate analysis showed that abnormal electroencephalogram and neuroimaging findings significantly increased the risk of recurrence. First and second seizures were significantly associated with state of arousal, status epilepticus, and multiple seizures in recurrent patients. Over half of untreated children had recurrence after a first unprovoked seizure, but prognosis was good overall. [ABSTRACT FROM AUTHOR]

In the literature, one encounters the intensely studied classical set-based reaction systems and the more recently introduced generalization to graph-based reaction systems where the considered graphs are directed, simple, and edge-labeled. In this paper, we propose a categorical approach to reaction systems so that a wider spectrum of data structures becomes available on which reaction systems can be based including various types of graphs and of graph-like structures like unlabeled graphs, vertex-labeled graphs, bipartite graphs, and a variety of types of hypergraphs. But also algebraic structures like monoids fit into the framework. [ABSTRACT FROM AUTHOR]

The objective of this observational study was to evaluate the association of estrous expression within 40 days in milk (DIM) using a neck-mounted automated activity monitor (Heatime Pro; SCR Engineers Ltd.) with reproductive performance in lactating Holstein cows. A total of 2,077 cows (614 primiparous cows and 1,463 multiparous cows) from 5 commercial dairy farms were included in the statistical analyses. Activity data from the first 7 d after calving were excluded. An estrus event was defined as an activity change index ≥35 for more than 2 h. Cows were classified according to the number of estrus events from d 7 until d 40 postpartum into 3 categories: (1) no estrus event (Estrus0); (2) one estrus event (Estrus1), and (3) 2 or more estrus events (Estrus2). Generalized linear mixed models were used to analyze continuous and categorical data. Shared frailty models were used for time to event data. Overall, 52.7% of cows had no estrus event detected by an automated activity monitor system from d 7 until d 40 postpartum. Herd level prevalence of Estrus0 ranged from 37.5 to 58.4%. Estrous expression from d 7 until d 40 postpartum affected estrous duration and estrous intensity at first artificial insemination (AI). Cows in Estrus0 had the shortest duration (13.2 ± 0.33 h) compared with cows in Estrus1 (13.8 ± 0.36 h) and Estrus2 (14.8 ± 0.41 h). Cows in Estrus2 had a longer estrous duration at first postpartum AI compared with cows in Estrus1. Among Estrus0 cows, 46.2% had an estrus event with high intensity at first postpartum AI. Among cows in Estrus1 and Estrus2, 50.8 and 53.8% had an estrus event with high intensity at first postpartum AI, respectively. There was a significant difference between Estrus2 and Estrus0 and a tendency between Estrus0 and Estrus1. There was no difference between Estrus1 and Estrus2. For Estrus0, Estrus1, and Estrus2 cows, pregnancy per AI was 29.4, 30.9, and 37.8%, respectively. There was a significant difference between Estrus0 and Estrus2 and Estrus1 and Estrus2. There was no difference between Estrus0 and Estrus1. Estrous expression from d 7 until d 40 postpartum affected time to first AI and time to pregnancy. Compared with Estrus0 cows, cows in Estrus1 [hazard risk (HR) = 1.74] and Estrus2 (HR = 1.77) had an increased hazard of being inseminated within 100 DIM. There was no difference between Estrus1 and Estrus2. Median DIM to first AI were 70, 59, and 58 for cows in Estrus0, Estrus1, and Estrus2, respectively. Compared with Estrus0 cows, cows in Estrus1 (HR = 1.28) and Estrus2 (HR = 1.33) had an increased hazard of becoming pregnant within 200 DIM. There was no difference between Estrus1 and Estrus2. Median DIM to pregnancy were 127, 112, and 103 for Estrus0 cows, Estrus1 and Estrus2, respectively. In conclusion, cows with no estrous expression from 7 to 40 DIM had reduced estrous expression at first AI and inferior reproductive performance compared with cows that displayed estrous activity. [ABSTRACT FROM AUTHOR]

We argue against claims that the classical ℏ → 0 limit is "singular" in a way that frustrates an eliminative reduction of classical to quantum physics. We show one precise sense in which quantum mechanics and scaling behavior can be used to recover classical mechanics exactly, without making prior reference to the classical theory. To do so, we use the tools of strict deformation quantization, which provides a rigorous way to capture the ℏ → 0 limit. We then use the tools of category theory to demonstrate one way that this reduction is explanatory: it illustrates a sense in which the structure of quantum mechanics determines that of classical mechanics. [ABSTRACT FROM AUTHOR]

In this paper we present a novel methodology, referenced as ORFEO (Ordinal classifier and Regressor Fusion for Estimating an Ordinal categorical target), to enhance the performance in ordinal classification problems for which the latent variable is observable. ORFEO is an artificial neural network model incorporating two outputs, one for ordinal classification, using the cumulative link model, and one for regression, using a linear model. Both outputs are simultaneously optimised considering a loss function that linearly combines both classification and regression losses. The main motivation behind developing the proposed approach is to enhance the performance of a standard ordinal classifier. This improvement is facilitated by considering the regression output, which allows the model to differentiate between patterns within the same category. The ORFEO model is applied to two problems in the field of marine and ocean engineering: short-term prediction of both significant wave height and flux of energy. Both problems are addressed considering four different coastal zones of the United States of America, using 13 datasets formed by buoys measurements and reanalysis data. A comprehensive comparison against 20 methodologies, including regression and nominal/ordinal classification approaches is performed, by using diverse nominal and ordinal performance metrics. Ranks achieved indicate that ORFEO outperforms all the compared methodologies in terms of all the performance measures, demonstrating the efficacy and robustness of the proposal. Finally, a statistical analysis is conducted, concluding that there are statistically significant differences across ordinal and nominal performance metrics in favour of the proposed ORFEO model. [ABSTRACT FROM AUTHOR]

We use Category Theory to interpret the family of algorithms for inference of DFAs that work by merging states. This interpretation allows us to characterize the structure of the search space and to define criteria for the convergence of these algorithms to the correct DFA. We also prove that the well-known EDSM algorithm does not identify DFAs in the limit. • We use category theory to interpret state merging inference algorithms for DFAs. • We give a new definition of the search space of inference algorithms. • We define criteria that can be used to prove convergence of inference algorithms. • We show that EDSM does not identify DFAs in the limit. [ABSTRACT FROM AUTHOR]

In real-time sentence comprehension, the comprehender is often required to establish syntactic dependencies between words that are linearly distant. Major models of sentence comprehension assume that longer dependencies are more difficult to process because of working memory limitations. While the expected effect of distance on reading times (locality effect) has been robustly observed in certain constructions, such as relative clauses in English, its generalizability to a wider range of constructions has been empirically questioned. The current study proposes a new metric of syntactic distance that capitalizes on the flexible constituency of Combinatory Categorial Grammar (CCG), and argues that it offers a unified account of the locality effects. It is shown that this metric correctly predicts both the presence of the locality effect in English relative clauses and its absence in verb-final languages, without assuming language- or dependency-specific differences in the sensitivity to the locality effect. It is further shown that the CCG-based distance is a significant predictor of the self-paced reading times from an English corpus, even when other known predictors such as dependency-based locality and surprisal are taken into account. These results suggest that human sentence comprehension involves rapid integration of input words into efficiently compressed syntactic representations, and CCG is a plausible theory of the grammar that subserves this process. [ABSTRACT FROM AUTHOR]

We make Hinich's ∞-categorical enriched Yoneda embedding natural. To do so, we exhibit it as the unit of a partial adjunction between the functor taking enriched presheaves and Heine's functor taking a tensored category to an enriched category. Furthermore, we study a finiteness condition of objects in a tensored category called being atomic, and show that the partial adjunction restricts to a (non-partial) adjunction between taking enriched presheaves and taking atomic objects. [ABSTRACT FROM AUTHOR]

We study effective descent V -functors for cartesian monoidal categories V with finite limits. This study is carried out via the properties enjoyed by the 2-functor V ↦ Fam (V) , results about effective descent of bilimits of categories, and the fact that the enrichment 2-functor preserves certain bilimits. Since these results rely on an understanding of (effective) descent morphisms in Fam (V) , we carefully study these morphisms in free coproduct completions. Finally, we provide refined conditions when V is a regular category. [ABSTRACT FROM AUTHOR]

The paper considers methods to organize a network of links to parameterized data objects. A data object is an object identified by standard methods, including the network identifiers URI, URL and URN. The data objects can be either atomic or have a complex structure, including links to other objects, and this ensures the linking of objects into a network, which can be considered as an information graph. It turns out to be possible to formulate a number of criteria for supporting the integrity of such a structure; the paper proposes language tools to formulate such criteria. An essential feature of this network is the dependence of the objects' information content on a number of parameters, first of all, on time. Depending on the parameter, the information content of the object can change both with the preservation of the integrity of the structure, and with its damage. The paper considers methods for a parameterized description of objects while maintaining integrity based on the formalism of category theory. The formalization used allows defining two relations between objects and their names (identifiers): access and links. The paper studies the basic properties of such relations and reveals the differences between them. Category theoretical means of describing objects require appropriate support tools for practical application. The paper proposes a configuration of support tools for a network of links to parameterized data objects based on a combination of conceptual modeling tools and applicative computing tools. The proposed tools provide basic methods of access to the network of information graphs. An essential feature of the proposed tools is the ability to determine the subjective way of considering the information graph (based on the category of correlations), as well as harmonizing an objective and subjective way of considering. The proposed tools were tested in the development of applied information systems in the field of implementation of the best available technologies (BAT). [ABSTRACT FROM AUTHOR]

Research in the supervised learning algorithms field implicitly assumes that training data is labeled by domain experts or at least semi-professional labelers accessible through crowdsourcing services like Amazon Mechanical Turk. With the advent of the Internet, data has become abundant and a large number of machine learning based systems are being trained with user-generated data, where categorical data is used as labels. However, little work has been done in the area of supervised learning with user-defined labels where users are not necessarily experts and might be unable to provide correct labels to some data or the labels might contain significant human bias. In this article, we propose two types of classes in user-defined labels: subjective class and objective class - showing that the objective classes are as reliable as if they were provided by domain experts, whereas the subjective classes are subject to error and bias. We name this a subjective class problem and propose a Normalized Feature Indicative Score that can be effective in detecting subjective classes in a dataset without querying oracle. This score provides early detection of subjective classes in the data, saving time for data mining practitioners working with data that might contain errors and biases. [ABSTRACT FROM AUTHOR]

Background: The Surgical Council on Resident Education (SCORE) curriculum is aligned with the American Board of Surgery (ABS) objectives. Our program adopted the SCORE curriculum in 2015 after poor ABS In-Training Examination (ABSITE) performance and lowest quartile ABS Certifying Exam (CE) and Qualifying Exam (QE) first-time pass rates. We examined the association of SCORE use with ABSITE performance and ABS board exam first-time pass rate.Study Design: At a single institution, a retrospective review of surgery residents' SCORE metrics and ABSITE percentile was conducted for academic years 2015 to 2019. Metrics analyzed on the SCORE web portal were mean total minutes and total visits per resident for all residents using SCORE that year. First-time pass rates of the ABS QE and CE were examined from 2013 to 2019. Chi-square and linear regression analysis were performed, and a 95% level of confidence was assumed (alpha = 0.05).Results: Yearly data from categorical general surgery residents showed a significant increase in total minutes, total visits, and ABSITE percentile. Combined first time pass rates for the ABS QE and CE significant increased from 70.8% in 2013 to 2015 to 93.9% in 2016 to 2019 (p = 0.018).Conclusions: Increased longitudinal use of the SCORE curriculum was associated with programmatic improvements in ABSITE performance and ABS board exam first-time pass rate. [ABSTRACT FROM AUTHOR]

The classification tasks for numerical or categorical data have been well developed. However, the data collected in the real world are frequently the mixed type containing numerical and categorical values, and how to classify the mixed data quickly and efficiently is a critical yet challenging task. Existing classification models for mixed data usually treat the mixed data processing and subsequent classification as two independent phases, without considering their compatibility. By fusing the mixed data processing into a classification algorithm, this paper proposes an extended version of RBF-ELM (Radial Basis Function-Extreme Learning Machine), a Mixed Data RBF-ELM method (MD-RBF-ELM for short), which can achieve direct, fast, and efficient classification for mixed data. Specifically, a distance metric method for mixed data is firstly designed to calculate the distances between the input data and the RBF centers, and then these distances are used to train the network structure and weights of MD-RBF-ELM, thereby realizing the fusion of data processing with model learning. In addition, to alleviate the problem of MD-RBF-ELM's unstable performance caused by randomly selecting the RBF centers, we propose an improved density peak clustering algorithm and use it to select the optimal RBF centers automatically and adaptively. Extensive experimental results on 34 data sets demonstrate that MD-RBF-ELM significantly enhances the classification performance (increasing 2.37% for F1-score, up to 14/34 for the number of best results, and reaching 2.4/8 for the averaged ranks), compared with seven state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Objective: To determine the prevalence of lower urinary tract symptoms (LUTS) in female adolescent population.Study Design: We performed a questionnaire-based study in nine randomly selected high schools in our country. Our primary aim was to evaluate the prevalence of certain LUTS in adolescent girls: frequency, urgency, nocturia, feeling of incomplete bladder emptying, dysuria, and urinary incontinence. The secondary aim was to investigate the effect of some possible risk factors on LUTS prevalence. Statistical analysis was performed using SPSS Statistics Programme. Descriptive statistics were calculated. Non-parametric test was used for comparison of numerical and Pearson's Chi-square test for categorical data. Logistic regression was performed to identify the independent prognosticators for LUTS presence. Statistical significance was set at p < 0.05.Results: We included 2745 adolescent girls. Their average age was 16.8 ± 1.2 years and 17.8 % of them regularly experienced at least one LUTS. The frequency of occasional LUTS was even higher. The most common symptom was urinary incontinence (5.9 %), followed by frequency (5.1 %), feeling of incomplete bladder emptying (4.8 %), urgency (3.1 %), and nocturia (2.6 %). Risk factors for LUTS presence were girls' school success, frequent pelvic pain, previous cystitis, history of night enuresis in childhood, and sexual intercourses in the last three months.Conclusions: According to our results, the prevalence of LUTS amongst female adolescents is high and can be associated with certain risk factors. In our opinion, further research should be directed into determining the effect of LUTS on adolescents' quality of life. [ABSTRACT FROM AUTHOR]

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). Injury was alerted to concerns regarding similarities of the data of this study to those published in the FixDIT UK trial.1 The investigators were invited to reply to the issues raised and were also asked to provide their original dataset available to the Editor-in-Chief. After several communications, the authors were not in a position to submit their original dataset to the Journal. Consequently, this article has been retracted at the request of the Editor-in-Chief due to serious scientific and ethical concerns relating to the integrity of the data collected and included in this study. 1. Costa ML, Achten J, Griffin J, Petrou S, Pallister I, Lamb SE, Parsons NR; FixDT Trial Investigators Effect of Locking Plate Fixation vs Intramedullary Nail Fixation on 6-Month Disability Among Adults With Displaced Fracture of the Distal Tibia: The UK FixDT Randomized Clinical Trial. .JAMA. 2017 Nov 14;318(18):1767-1776. [ABSTRACT FROM AUTHOR]

We present a mathematical model for the cognitive operation of conceptual blending that aims at being uniform across different representation formalisms, while capturing the relevant structure of this operation. The model takes its inspiration from amalgams as applied in case-based reasoning, but lifts them into category theory so as to follow Joseph Goguen's intuition for a mathematically precise characterisation of conceptual blending at a representation-independent level of abstraction. We prove that our amalgam-based category-theoretical model of conceptual blending is essentially equivalent to the pushout model in the ordered category of partial maps as put forward by Goguen. But unlike Goguen's approach, our model is more suitable to capture computational realisations of conceptual blending, and we exemplify this by concretising our model to computational conceptual blends for various representation formalisms and application domains. [ABSTRACT FROM AUTHOR]

We present a new instrument for the assessment of responses to threat-related imagery directed towards a human body – the Body-Threat Assessment Battery (BTAB). The BTAB consists of a series of high-definition dynamic clips depicting body-threats and matched non-threat baseline behaviours. For body-threat stimuli a perspective manipulation was included to assess the effects of viewing threats from the point-of-view of the observer (POV) or from an external/exocentric perspective (EXO). Green-screen technology was used so that extraneous background information could be removed and standardised in post-production. Categorical normative data for psychological ratings (valence, arousal and pain), psychophysiological, phasic skin conductance responses (SCRs) and tonic skin conductance levels (SCLs) were obtained for all stimuli. Body-threat stimuli evoked significantly higher psychological ratings of arousal and pain, with more negative ratings of valence, relative to baseline stimuli. In addition, threat stimuli also had an increased efficacy at evoking SCRs, and these were significantly stronger relative to baseline stimuli. There were no effects of perspective on psychophysiological or psychological responses to threat imagery. The findings are discussed in the context of the utility and scope of the BTAB for supporting neurocognitive investigations of aversive imagery and body-threats specifically in the study of embodiment, body-processing and self-consciousness. • A new instrument for examining threats directed on a human body is presented. • Body-threats and non-body related baseline clips are provided. • A perspective manipulation for body-threat stimuli was created. • Normative large sample data for all dynamic clips are presented. • Psychophysiological and Psychological data are presented for all stimuli. [ABSTRACT FROM AUTHOR]

To determine whether suction, lavage and instillation of sodium bicarbonate, following a gastro-oesophageal regurgitation event under general anaesthesia, would alter oesophageal pH to a greater degree than when lavage was not used. Prospective, randomised, clinical study. A group of 22 client-owned dogs. Dogs presenting with gastro-oesophageal regurgitation (GOReg) under general anaesthesia were randomised into groups: no lavage (G1) or lavage (G2). All dogs underwent oesophageal suctioning until no further regurgitant material was retrieved. Dogs in G2 had oesophageal lavage with tap water until the suctioned water was clear. All dogs then had 4.2% sodium bicarbonate (0.6 mL kg–1) instilled into the oesophagus. An oesophageal pH probe was placed to record pH immediately after: GOReg (T1), suctioning (T2), lavage of the oesophagus (T3; G2 only) and sodium bicarbonate instillation (T4). Categorical data were analysed using Fisher's exact test, and continuous data were analysed using either the two-sample t -test or the Wilcoxon rank-sum test. Parametric data are reported as mean ± standard deviation and non-parametric data as median (interquartile range). A p value < 0.05 was considered significant. Oesophageal pH was low in both groups immediately after GOReg [G1: 2.95 (2.20–4.18), G2: 3.29 (1.41–4.03)] but oesophageal pH was not significantly different between groups at T1, T2 and T4. Oesophageal lavage significantly increased pH but the overall change in pH following bicarbonate administration (T2–T4) was not significantly different between groups [G1: 3.16 ± 1.52, G2: 3.52 ± 1.47]. No adverse events following GOReg were recorded. Both groups had similar and clinically important increases in oesophageal pH. Although oesophageal lavage increased pH, this did not affect the final oesophageal pH when sodium bicarbonate was instilled and therefore may be an unnecessary step. [ABSTRACT FROM AUTHOR]

Aim: To identify the imaging findings associated with worse clinical outcome in posterior spinal cord indentation.Materials and Methods: A retrospective search for cases of dorsal cord indentation on magnetic resonance imaging (MRI) from April 2009 to March 2016 was undertaken. Imaging follow-up and clinical data were recorded. Two neuroradiologists blinded to the clinical data assessed the imaging findings. Differences and association of imaging and clinical findings were assessed via t-test and Fisher's exact and chi-squared tests for continuous and categorical data. Inter-rater agreement was calculated.Results: Seventy-three patients were included, 65 were clinically stable, or the finding was incidental and eight had clinical worsening or required surgery. There was a significant difference in the percentage of cord diameter decrease between the two clinical groups (p=0.013, reader 1; p=0.027 reader 2). The clinical course was significantly associated with subjective cord indentation depth assessment (p=0.03 reader 1) and presence of syrinx (p=0.017 reader 2) on original MRI and worsening on imaging follow-up (p=0.03). The interrater agreement was moderate for syrinx identification (k=0.58). There was only fair agreement for the scalpel sign classification and in the final diagnosis (k=0.35 and 0.28).Conclusion: The degree of cord indentation, the presence of syrinx, and worsening of imaging findings on follow-up are associated with worse clinical course and can be useful in guiding management and directing subspecialty referrals. [ABSTRACT FROM AUTHOR]

Statistical data analysis in machine learning and data mining usually uses the clustering technique. However, data with both attributes or mixed data exists universally in real life. K-prototype is a well-known algorithm for clustering mixed data because of its effectiveness in handling large data. However, practically, k-prototype has two main weaknesses, the use of mode as a cluster center for categorical attributes cannot accurately represent the objects, and the algorithm may stop at the local optimum solution because affected by random initial cluster prototypes. To overcome the first weakness, we can use fuzzy centroid, and for second weakness is to implement the genetic algorithm to search the global optimum solution. Our research combines the genetic algorithm and Fuzzy K-Prototype to accommodate these two weaknesses. We set up two multivariate data with high correlation and low correlation to see the robustness of the proposed algorithm. According to four value indexes of clustering result evaluation, Coefficient Varians Index, Partition Coefficient, Partition Entropy, and Purity, show that our proposed algorithm has a better result than K prototype. Based on the evaluation result, we conclude that our proposed algorithm can solve two weaknesses of the k-prototype algorithm. [ABSTRACT FROM AUTHOR]

Split cord malformation (SCM) is a rare anomaly characterized by a split along the midline of the cord, which divides it into 2 symmetric or nonsymmetric entities. SCM surgical indications and outcomes are still debatable, the signs and symptoms are generally nonspecific and are commonly associated with other anomalies and deficits. We retrospectively searched the hospital database at King Abdulaziz Medical City, Riyadh, Saudi Arabia for patients with SCM between 1998 and 2018. Descriptive statistics were used to present categorical data as percentages and frequencies. A total of 25 patients were included in this series. The mean age of patients at the time of diagnosis was 4.4 years. A total of 18 patients (72%) underwent surgical correction. The mean difference between the age at diagnosis and the age at correction was 7 months. All patients underwent intraoperative neurophysiologic monitoring. Postoperative complications were minimal. Cerebrospinal fluid leakage was noted in 2 patients, transient urinary retention was noted in 1 patient, and transient unilateral leg paresis was noted in 1 patient. Most patients (n = 15, 83%), were discharged within 19 days after surgery, and 81% showed improvement postoperatively. Over the long-term follow-up, none of the patients developed new urologic or neurologic deficits. Most patients with SCM present during childhood. Postoperative complications after SCM corrective surgery are generally minimal, and the overall outcomes, mainly including partial or complete symptomatic improvement and/or symptom stability and hydronephrosis resolution, were favorable. [ABSTRACT FROM AUTHOR]

• Bayesian Optimization can be applied to optimization problems with categorical and integer-valued variables. • Existing methods relying on Gaussian processes may get stuck in such a setting. • A transformation of the covariance function is proposed to deal with categorical and integer-valued variables. • Empirical results show that the proposed method outperforms other approaches for Bayesian optimization. Some optimization problems are characterized by an objective that is very expensive, that lacks an analytical expression, and whose evaluations can be contaminated by noise. Bayesian Optimization (BO) methods can be used to solve these problems efficiently. BO relies on a probabilistic model of the objective, which is typically a Gaussian process (GP). This model is used to compute an acquisition function that estimates the expected utility (for solving the optimization problem) of evaluating the objective at each potential new point. A problem with GPs is, however, that they assume real-valued input variables and cannot easily deal with categorical or integer-valued values. Common methods to account for these variables, before evaluating the objective, include assuming they are real and then using a one-hot encoding, for categorical variables, or rounding to the closest integer, for integer-valued variables. We show that this leads to suboptimal results and introduce a novel approach to tackle categorical or integer-valued input variables within the context of BO with GPs. Several synthetic and real-world experiments support our hypotheses and show that our approach outperforms the results of standard BO using GPs on problems with categorical or integer-valued input variables. [ABSTRACT FROM AUTHOR]

We discuss how mathematical semantics has evolved, and suggest some new directions for future work. As an example, we discuss some recent work on encapsulating model comparison games as comonads, in the context of finite model theory. [ABSTRACT FROM AUTHOR]

We study varieties generated by semi-primal lattice-expansions by means of category theory. We provide a new proof of the Keimel-Werner topological duality for such varieties and, using similar methods, establish its discrete version. We describe multiple adjunctions between the variety of Boolean algebras and the variety generated by a semi-primal lattice-expansion, both on the topological side and explicitly algebraic. In particular, we show that the Boolean skeleton functor has two adjoints, both defined by taking certain Boolean powers, and we identify properties of these adjunctions which fully characterize semi-primality of an algebra. Lastly, we give a new characterization of canonical extensions of algebras in semi-primal varieties in terms of their Boolean skeletons. [ABSTRACT FROM AUTHOR]

The theory of L -institutions is a fully axiomatic and abstract approach to fuzzy / many-valued truth model theory, which is based on category theory. In this paper we develop a series of mathematical structures and results that empower at various levels the logic translation paradigm for L -institutions. Our development parallels the well established logic translation themes in ordinary (binary truth) institution theory. In the process we solve a series of specific technical challenges posed by the nature of many-valued truth. [ABSTRACT FROM AUTHOR]

In the paper we generalize the singular cubical homology theory of digraphs and introduce a collection of cell singular homologies that are parametrized by a natural parameter of the category of digraphs. The natural parameter corresponds to the "length" of a directed line digraph that corresponds to the unit segment in algebraic topology. The introduced earlier singular cubical homology is a particular case of this collection that corresponds to the line digraph with one arrow. We describe relations between these theories for different parameters given by a natural number and study their basic properties. In particular, we prove the functoriality and homotopy invariance of these theories and compare the theory with the path homology theory that was introduced in our previous papers. Then we describe the transfer of the cell singular homology theories to the categories of graphs, quivers, and multigraphs. [ABSTRACT FROM AUTHOR]

The theory of institutions provides an abstract mathematical framework for specifying logical systems and their semantic relationships. Institutions are based on category theory and have deep roots in a well-developed branch of algebraic specification. However, there are no machine-assisted proofs of correctness for institution-theoretic constructions—chiefly satisfaction conditions for institutions and their (co)morphisms—making them difficult to incorporate into mainstream formal methods. This paper therefore provides the details of our approach to formalizing a fragment of the theory of institutions in the Coq proof assistant. We instantiate this framework with the institutions FOPEQ for first-order predicate logic and EVT for the Event-B specification language, and define some institution-independent constructions, all of which serve as an illustration and evaluation of the overall approach. • We formalize a fragment of the theory of institutions in Coq. • We prove the satisfaction conditions for first-order predicate logic and a simplified institution for Event-B. • We encode institution morphisms, duplex institutions, entailment systems, and some basic facts from universal model theory. • The formalization is available on GitHub at https://github.com/ConorReynolds/coq-institutions. [ABSTRACT FROM AUTHOR]

In this paper, we study the following (p , q) -Kirchhoff problem with Choquard nonlinearity: − (1 + a ∫ R N | ∇ u | p d x) Δ p u − (1 + b ∫ R N | ∇ u | q d x) Δ q u + V ε (x) (| u | p − 2 u + | u | q − 2 u) = (| x | − μ ⁎ F (u)) f (u) in R N , where ε is a small positive parameter, a , b are positive constants, 1 < p < q < N , q < 2 p , Δ s u = div (| ∇ u | s − 2 ∇ u) with s ∈ { p , q } is the s -Laplacian, the potential V : R N → R is continuous, V ε (x) = V (ε x) , 0 < μ < q , f is a continuous nonlinearity, and F is the primitive of f. The main result in this paper establishes multiplicity and concentration properties of positive solutions under weaker hypotheses. The proofs combine nonstandard Nehari manifold methods, penalization techniques and the Ljusternik-Schnirelmann category theory. [ABSTRACT FROM AUTHOR]

For a small involutive quantaloid Q , it is shown that the category of separated complete Q -categories and left adjoint Q -functors is strictly monadic over the category of symmetric Q -categories. In particular, the (covariant) powerset monad on the category of quantale-valued sets is precisely formulated. [ABSTRACT FROM AUTHOR]