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In recent decades, the neuroscientific community has moved from describing the neural underpinnings of mental phenomena—as characterized by experimental psychology and philosophy of mind—to attempting to redefine those mental phenomena based on neural findings. Nowadays, many are intrigued by the idea that neuroscience might provide the "missing piece" that would allow philosophers (and, to an extent, psychologists, too) to make important advances, generating new means that these disciplines lack to close knowledge gaps and answer questions like "What is Free Will?" and "Do humans have it?." In this paper, we argue that instead of striving for neuroscience to replace philosophy in the ongoing quest to understanding human thought and behavior, more synergetic relations should be established, where neuroscience does not only inspire philosophy but also draws from it. We claim that such a collaborative coevolution, with the two disciplines nourishing and influencing each other, is key to resolving long‐lasting questions that have thus far proved impenetrable for either discipline on its own. [ABSTRACT FROM AUTHOR]

To learn about real world phenomena, scientists have traditionally used models with clearly interpretable elements. However, modern machine learning (ML) models, while powerful predictors, lack this direct elementwise interpretability (e.g. neural network weights). Interpretable machine learning (IML) offers a solution by analyzing models holistically to derive interpretations. Yet, current IML research is focused on auditing ML models rather than leveraging them for scientific inference. Our work bridges this gap, presenting a framework for designing IML methods—termed ’property descriptors’—that illuminate not just the model, but also the phenomenon it represents. We demonstrate that property descriptors, grounded in statistical learning theory, can effectively reveal relevant properties of the joint probability distribution of the observational data. We identify existing IML methods suited for scientific inference and provide a guide for developing new descriptors with quantified epistemic uncertainty. Our framework empowers scientists to harness ML models for inference, and provides directions for future IML research to support scientific understanding. [ABSTRACT FROM AUTHOR]

We study statistical inference procedures in coarsened time series through the generalized method of moments. A new model for the coarsened time series via multiple potential outcomes is proposed. It can be naturally extended for inferring multi‐variate coarsened time series. We show that this framework generates a general class of estimators. It neatly generalizes the classical Horvitz–Thompson estimator for handling coarsened time series data. Asymptotic properties, including consistency and limiting distribution, of the proposed estimators are investigated. Estimators of the optimal weight matrix and the long‐run covariance matrix are also derived. In particular, confidence intervals of the mean function of the potential outcome as a function of coarsening index can be constructed. A real‐data application on air quality in the USA is investigated. [ABSTRACT FROM AUTHOR]

Moderate calibration, the expected event probability among observations with predicted probability z being equal to z, is a desired property of risk prediction models. Current graphical and numerical techniques for evaluating moderate calibration of risk prediction models are mostly based on smoothing or grouping the data. As well, there is no widely accepted inferential method for the null hypothesis that a model is moderately calibrated. In this work, we discuss recently‐developed, and propose novel, methods for the assessment of moderate calibration for binary responses. The methods are based on the limiting distributions of functions of standardized partial sums of prediction errors converging to the corresponding laws of Brownian motion. The novel method relies on well‐known properties of the Brownian bridge which enables joint inference on mean and moderate calibration, leading to a unified "bridge" test for detecting miscalibration. Simulation studies indicate that the bridge test is more powerful, often substantially, than the alternative test. As a case study we consider a prediction model for short‐term mortality after a heart attack, where we provide suggestions on graphical presentation and the interpretation of results. Moderate calibration can be assessed without requiring arbitrary grouping of data or using methods that require tuning of parameters. [ABSTRACT FROM AUTHOR]

The majority of research predicted heating demand using linear regression models, but they did not give current building features enough context. Model problems such as Multicollinearity need to be checked and appropriate features must be chosen based on their significance to produce accurate load predictions and inferences. Numerous building energy efficiency features correlate with each other and with heating load in the energy efficiency dataset. The standard Ordinary Least Square regression has a problem when the dataset shows Multicollinearity. Bayesian supervised machine learning is a popular method for parameter estimation and inference when frequentist statistical assumptions fail. The prediction of the heating load as the energy efficiency output with Bayesian inference in multiple regression with a collinearity problem needs careful data analysis. The parameter estimates and hypothesis tests were significantly impacted by the Multicollinearity problem that occurred among the features in the building energy efficiency dataset. This study demonstrated several shrinkage and informative priors on likelihood in the Bayesian framework as alternative solutions or remedies to reduce the collinearity problem in multiple regression analysis. This manuscript tried to model the standard Ordinary Least Square regression and four distinct Bayesian regression models with several prior distributions using the Hamiltonian Monte Carlo algorithm in Bayesian Regression Modeling using Stan and the package used to fit linear models. Several model comparison and assessment methods were used to select the best-fit regression model for the dataset. The Bayesian regression model with weakly informative prior is the best-fitted model compared to the standard Ordinary Least Squares regression and other Bayesian regression models with shrinkage priors for collinear energy efficiency data. The numerical findings of collinearity were checked using variance inflation factor, estimates of regression coefficient and standard errors, and sensitivity of priors and likelihoods. It is suggested that applied research in science, engineering, agriculture, health, and other disciplines needs to check the Multicollinearity effect for regression modeling for better estimation and inference. [ABSTRACT FROM AUTHOR]

Arabic logicians in the thirteenth century discussed a set of arguments raised by the theologian Fakhr al-Dīn al-Rāzī (d. 1210) that in some respects closely resembles Carroll's paradox. Roughly, the paradox states that we can never reach a conclusion from a set of premises without incurring an infinite regress. The present article presents and discusses Rāzī's formulation of the problem with syllogistic deduction, his own solutions to the problem, and the contributions of Afḍal al-Dīn al-Khūnajī (d. 1248) and Najm al-Dīn al-Kātibī (d. 1276). It is argued that the proposed solutions developed by these authors are best understood as tending in the direction of Gilbert Ryle's distinction between knowing-that and knowing-how. [ABSTRACT FROM AUTHOR]

Inference is central to pragmatic theories of meaning and communication, but most studies of inference to date have been undertaken in adjacent fields rather than in pragmatics. The aim of this special issue is to bring together pragmatic perspectives on inference in order to further our understanding of the role that inference - as both a product and a process - plays in pragmatic models of communication. This aim is realised through a set of six papers that collectively address key questions arising when we go beyond the traditional focus in pragmatics on inferences by the hearer about the speaker's intended meaning. These articles draw from a range of different methodological and theoretical standpoints to examine the inferential, interactional, and developmental processes that underpin different types of pragmatic inference. It is concluded that more sophisticated theoretical accounts of pragmatic inference are needed to account for the wide range of inferential phenomena that can be empirically observed in communication. • Demonstrates the importance of studying inference in pragmatics. • Shows pragmatic inference goes beyond hearers inferring speaker's intended meaning. • Highlights the role that inference plays in pragmatic models of communication. [ABSTRACT FROM AUTHOR]

Dominance refers to the effect of a heterozygous genotype relative to that of the two homozygous genotypes. The degree of dominance of mutations for fitness can have a profound impact on how deleterious and beneficial mutations change in frequency over time as well as on the patterns of linked neutral genetic variation surrounding such selected alleles. Since dominance is such a fundamental concept, it has received immense attention throughout the history of population genetics. Early work from Fisher, Wright, and Haldane focused on understanding the conceptual basis for why dominance exists. More recent work has attempted to test these theories and conceptual models by estimating dominance effects of mutations. However, estimating dominance coefficients has been notoriously challenging and has only been done in a few species in a limited number of studies. In this review, we first describe some of the early theoretical and conceptual models for understanding the mechanisms for the existence of dominance. Second, we discuss several approaches used to estimate dominance coefficients and summarize estimates of dominance coefficients. We note trends that have been observed across species, types of mutations, and functional categories of genes. By comparing estimates of dominance coefficients for different types of genes, we test several hypotheses for the existence of dominance. Lastly, we discuss how dominance influences the dynamics of beneficial and deleterious mutations in populations and how the degree of dominance of deleterious mutations influences the impact of inbreeding on fitness. [ABSTRACT FROM AUTHOR]

The importance of urban water supply systems and public services is globally recognized. Nonrevenue water directly affects a water utility's economic, financial, and environmental sustainability. In Portugal, the mean of the nonrevenue water for the distribution systems corresponded to 28.8% in 2019. Smart metering technology is crucial for consumption monitoring and enhancing apparent and real loss network management (e.g., water meters' global error evaluation, detection of illegal uses, and real loss estimation through the minimum night flow analysis). However, this technology is expensive in acquisition, installation, operation, and maintenance. This study aims to support water utilities in inferring the total consumption using a representative sample of customers with smart meters instead of smart metering data from all customers. A stratified sampling was considered using only the customers' billing time series for the strata definition. A predominantly domestic zone was used, and eight strata were obtained with a clustering analysis [temporal correlation (CORT) dissimilarity and Ward method]. Stratified sampling was applied to minimize the variance of the total water consumption estimator. A representative sample of 259 dimensions (53%) was chosen to infer, with small errors, essential consumption statistics for water utilities: total consumption (with an error of 0.12%), total consumption time series, water consumption patterns, minimum night consumption, and volume distribution by the flow rate. The successful outcomes obtained were crucial in supporting the proposed methodology. This study has provided evidence that installing smart meters for all consumers in a distribution network area is not necessary to acquire accurate and meaningful consumption information crucial for effective network management and water loss control. Moreover, using only billing data to perform the sample selection of consumers is useful for water utilities, because they may face difficulties obtaining extra consumer information. [ABSTRACT FROM AUTHOR]

For students to be able to compete and overcome challenges in the future, critical thinking is essential. The study's descriptive methodology aims to describe the students' critical thinking abilities. Every high school in a South Sulawesi district made up the research population. Based on school accreditation, with the use of a proportionate stratified random sampling technique, 150 students were chosen to comprise the research sample. The test instrument used is an essay distributed online. The indicators for the description questions are interpretation, analysis, and interpretation thinking skills. According to the study's findings, students' critical thinking abilities in static fluid material have an average score in the medium category of 73.09. In terms of school accreditation, students' physics critical thinking abilities average 73.87 for schools with A accreditation, 72.43 for schools with B accreditation, and 68.47 for schools with C accreditation. The percentage of each indicator of critical thinking skills is 34.58 in inference, 33.70 in analysis, and 31.71 in interpretation. It is anticipated that this study will give a broad picture of students' critical thinking competencies for teachers to choose and implement the most suitable stimulus to aid students in developing their critical thinking abilities. [ABSTRACT FROM AUTHOR]

We introduce a new methodology for inference of fluid composition from measurements of mineralogical or chemical compositions, expanding upon the use of reactive transport models to understand hydrothermal alteration processes. The reactive transport models are used to impute a latent variable explanatory mechanism in the formation of hydrothermal alteration zones and mineral deposits. An expectation maximisation algorithm is then employed to solve the joint problem of identifying alteration zones in the measured data and estimating the fluid composition, based on the fit between the mineral abundances in the measured and predicted alteration zones. Using the hydrothermal alteration of granite as a test case (greisenisation), a range of synthetic tests is presented to illustrate how the methodology enables objective inference of the mineralising fluid. For field data from the East Kemptville tin deposit in Nova Scotia, the technique generates inferences for the fluid composition which compare favourably with previous independent estimates, demonstrating the feasibility of the proposed calibration methodology. [ABSTRACT FROM AUTHOR]

Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline variables, and there is often ambiguity about which variables should be prioritized. Standard prioritization methods based on treatment imbalance alone neglect variables' relationships with the outcome. We propose the joint variable importance plot to guide variable prioritization for observational studies. Since not all variables are equally relevant to the outcome, the plot adds outcome associations to quantify the potential confounding jointly with the standardized mean difference. To enhance comparisons on the plot between variables with different confounding relationships, we also derive and plot bias curves. Variable prioritization using the plot can produce recommended values for tuning parameters in many existing matching and weighting methods. We showcase the use of the joint variable importance plots in the design of a balance-constrained matched study to evaluate whether taking an antidiabetic medication, glyburide, increases the incidence of C-section delivery among pregnant individuals with gestational diabetes. [ABSTRACT FROM AUTHOR]

The expectile is a prime candidate for being a standard risk measure in actuarial and financial contexts, for its ability to recover information about probabilities and typical behavior of extreme values, as well as its excellent axiomatic properties. A series of recent papers has focused on expectile estimation at extreme levels, with a view on gathering essential information about low-probability, high-impact events that are of most interest to risk managers. The obtention of accurate confidence intervals for extreme expectiles is paramount in any decision process in which they are involved, but actual inference on these tail risk measures is still a difficult question due to their least squares nature and sensitivity to tail heaviness. This article focuses on asymptotic Gaussian inference about tail expectiles in the challenging context of heavy-tailed observations. We use an in-depth analysis of the proofs of asymptotic normality results for two classes of extreme expectile estimators to derive bias-reduced and variance-corrected Gaussian confidence intervals. These, unlike previous attempts in the literature, are well-rooted in statistical theory and can accommodate underlying distributions that display a wide range of tail behaviors. A large-scale simulation study and three real data analyses confirm the versatility of the proposed technique. [ABSTRACT FROM AUTHOR]

Measuring the fitnesses of genetic variants is a fundamental objective in evolutionary biology. A standard approach for measuring microbial fitnesses in bulk involves labeling a library of genetic variants with unique sequence barcodes, competing the labeled strains in batch culture, and using deep sequencing to track changes in the barcode abundances over time. However, idiosyncratic properties of barcodes can induce nonuniform amplification or uneven sequencing coverage that causes some barcodes to be over- or under-represented in samples. This systematic bias can result in erroneous read count trajectories and misestimates of fitness. Here, we develop a computational method, named REBAR (Removing the Effects of Bias through Analysis of Residuals), for inferring the effects of barcode processing bias by leveraging the structure of systematic deviations in the data. We illustrate this approach by applying it to two independent data sets, and demonstrate that this method estimates and corrects for bias more accurately than standard proxies, such as GC-based corrections. REBAR mitigates bias and improves fitness estimates in high-throughput assays without introducing additional complexity to the experimental protocols, with potential applications in a range of experimental evolution and mutation screening contexts. [ABSTRACT FROM AUTHOR]

Long-term potentiation of synaptic strength is a fundamental aspect of learning and memory. Memories are believed to be stored within specific populations of neurons known as engram cells, which are subsequently reactivated during sleep, facilitating the consolidation of stored information. However, sleep and offline reactivations are associated not only with past experiences but also with anticipation of future events. During periods of offline reactivation, which occur during sleep and quiet wakefulness, the brain exhibits a capability to form novel connections. This process links various past experiences, often leading to the emergence of qualitatively new information that was not initially available. Brain activity during sleep and quiet wakefulness is referred to as the 'idling brain'. Idling brain activity is believed to play a pivotal role in abstracting essential information, comprehending underlying rules, generating creative ideas and fostering insightful thoughts. In this review, we will explore the current state of research and future directions in understanding how sleep and idling brain activity are interconnected with various cognitive functions, especially creative insights. These insights have profound implications for our daily lives, impacting our ability to process information, make decisions and navigate complex situations effectively. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'. [ABSTRACT FROM AUTHOR]

Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms. [ABSTRACT FROM AUTHOR]

Accurate estimation of ride-hailing demand and understanding of its influencing factors are necessary for modern-day transportation planning. Although modern machine learning techniques improve the predictive accuracy of zone-to-zone direct demand models, they lack inference ability due to their complex model structure. Furthermore, zone-to-zone direct demand models suffer from limited sample size for machine learning due to fixed number of origin-destination (OD) pairs. To overcome these limitations, we propose a linear regression coupled Wasserstein generative adversarial network (LR-WGAN) for direct demand modeling of zone-to-zone ride-hailing trips that captures both linear explanatory components and non-linear patterns and improves the predictive accuracy by generating data to expand the OD sample size. The proposed LR-WGAN is found to significantly improve the predictive accuracy of OD ride-hailing demand in Chicago and Austin. Furthermore, the linear explanatory component of the model is utilized to draw inferences regarding the relationship between OD ride-hailing demand and predictor variables. [ABSTRACT FROM AUTHOR]

Presented as a research challenge in 2001, the Semantic Web (SW) is now a mature technology, used in several cross‐domain applications. One of its key benefits is a formal semantics of its RDF data format, which enables a system to validate data, infer implicit knowledge by automated reasoning, and explain it to a user; yet the analysis presented here of 71 RDF‐based SW systems (out of which 17 reasoners) reveals that the exploitation of such semantics varies a lot among all SW applications. Since the simple enumeration of systems, each one with its characteristics, might result in a clueless listing, we borrow from Software Engineering the idea of maturity model, and organize our classification around it. Our model has three orthogonal dimensions: treatment of blank nodes, degree of deductive capabilities, and explanation of results. For each dimension, we define 3–4 levels of increasing exploitation of semantics, corresponding to an increasingly sophisticated output in that dimension. Each system is then classified in each dimension, based on its documentation and published articles. The distribution of systems along each dimension is depicted in the graphical abstract. We deliberately exclude resources consumption (time and space) since it is a dimension not peculiar to SW. This article is categorized under:Fundamental Concepts of Data and Knowledge > Knowledge RepresentationFundamental Concepts of Data and Knowledge > Explainable AI [ABSTRACT FROM AUTHOR]

Copyright of Revue du Financier is the property of Societe Cybel and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Evaluating policy in imperfectly competitive markets requires understanding firm behavior. While researchers test conduct via model selection and assessment, we present the advantages of Rivers and Vuong (2002) (RV) model selection under misspecification. However, degeneracy of RV invalidates inference. With a novel definition of weak instruments for testing, we connect degeneracy to instrument strength, derive weak instrument properties of RV, and provide a diagnostic for weak instruments by extending the framework of Stock and Yogo (2005) to model selection. We test vertical conduct (Villas‐Boas (2007)) using common instrument sets. Some are weak, providing no power. Strong instruments support manufacturers setting retail prices. [ABSTRACT FROM AUTHOR]

We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the time-series data structures, we focus on the sparse-group LASSO (sg-LASSO) estimator, which includes the LASSO and the group LASSO as special cases. We establish the debiased central limit theorem for low-dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sg-LASSO residuals. This leads to valid time-series inference for individual regression coefficients as well as groups, including Granger causality tests. The treatment relies on a new Fuk–Nagaev inequality for a class of τ -mixing processes with heavier than Gaussian tails, which is of independent interest. In an empirical application, we study the Granger causal relationship between the VIX and financial news. [ABSTRACT FROM AUTHOR]

A Comprehensive Set of Asymptotic Properties for a Meaningful Aggregation of Malmquist Indices The Malmquist productivity index (MPI) has become one of the most widely used tools for analyzing dynamic performance of decision-making units. Whereas accounting for economic weights of individual units in aggregations of indices is emphasized in the literature, statistical theory for constructing confidence intervals and performing hypothesis tests based on weighted aggregation of the MPI are still unavailable. In "Statistical Inference for Aggregation of Malmquist Productivity Indices," Pham, Simar, and Zelenyuk use a novel approach (based on the uniform delta method) to develop new asymptotic theory (including new central limit theorems) for aggregate MPIs as the basis for the statistical inference and test. They also verify the finite-sample performance of their approach via extensive Monte Carlo experiments and provide an illustration using real-world data. The Malmquist productivity index (MPI) has gained popularity among studies on the dynamic change of productivity of decision-making units (DMUs). In practice, this index is frequently reported at aggregate levels (e.g., public and private firms) in the form of simple, equally weighted arithmetic or geometric means of individual MPIs. A number of studies emphasize that it is necessary to account for the relative importance of individual DMUs in the aggregations of indices in general and of the MPI in particular. Whereas more suitable aggregations of MPIs have been introduced in the literature, their statistical properties have not been revealed yet, preventing applied researchers from making essential statistical inferences, such as confidence intervals and hypothesis testing. In this paper, we fill this gap by developing a full asymptotic theory for an appealing aggregation of MPIs. On the basis of this, meaningful statistical inferences are proposed, their finite-sample performances are verified via extensive Monte Carlo experiments, and the importance of the proposed theoretical developments is illustrated with an empirical application to real data. Funding: M. Pham acknowledges support from an Australian Government Research Training Program Scholarship. V. Zelenyuk acknowledges financial support from the University of Queensland and the Australian Research Council [Grant FT170100401]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2424. [ABSTRACT FROM AUTHOR]

LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.

Causal inference using quasi-experimental data is of great interest to marketers. The factor model approach to estimate treatment effects accommodates a large number of control units and can easily handle a large number of treatment units while flexibly allowing for cases where the treatment is outside the range of the control units. However, the factor model method lacks formal inference theory, instead relying on bootstrap or permutation procedures with strong assumptions. Specifically, the extant Xu (2017) bootstrap procedure requires that the treatment and control error variances are equal. In this research the authors establish that when this assumption is violated, the bootstrap procedure results in biased coverage intervals. The authors develop a formal inference theory for the factor model approach to estimate the average treatment effects on the treated. The approach enables formal quantification of uncertainty through hypothesis testing and confidence intervals. The inference method is applicable to both stationary and nonstationary data. More importantly, the inference theory accommodates treatment and control unit outcomes with different distributions, which includes different error variances as a special case. The authors show the performance of the inference theory with simulated data. Finally, they apply the method to empirically quantify the uncertainty in the effect of legalizing recreational marijuana on the beer market and the sales effect of a digitally native online brand opening a physical showroom. [ABSTRACT FROM AUTHOR]

The fear-avoidance model is a well-established framework in the understanding of persistent pain. It proposes a dichotomous path: either the context is interpreted as safe; there is no fear reaction and, therefore, the individual engages in active (positive) coping; or the context is interpreted as threatening, leading to a self-reinforcing vicious circle of fear and (negative) avoidance. We propose an embodied interpretation of this phenomenon employing the joint framework of predictive coding and active inference. The key idea is that multisensory integration of exteroceptive, proprioceptive, and interoceptive sensory inputs can lead to dysfunctional experiences of threat in nonthreatening situations. Threat inference can promote fear responses, maladaptive strategies (i.e., avoidance) and self-provides evidence for threat in associated or future contexts, or both. Under this treatment, the prediction of nonrealized threat becomes self-evidencing and context-invariant, and hence self-perpetuating. Safety cues are unable to attenuate the interpretation of the negative context as the dominant inference of the context is threatful and gains more precision and becomes resistant over time. Our model provides an explanation for the emergence of a dysfunctional fear response in the clinical setting despite apparent safety based on modern concepts from theoretical (computational) neuroscience. [ABSTRACT FROM AUTHOR]

Having in mind the basic pragmalinguistic principle which argues that traces of the unsaid are always present in our communication and guided by Jacob L. Mey’s assertion that titles can be tricky, this study seeks to explore the elements that influence authors’ choice of titles and the opaque meanings of such titles. This research adopted a pragmatic approach to discourse analysis with the aid of the notions of presuppositions, inferences, argumentative orientations, argumentative scales and the theory of possible worlds. Results indicate that the setting of a literary work, the cultural origin, beliefs and philosophies of its author, the era within which the work is written, and the use of figures of speech, among others, are elements that influence an author’s choice of a title of a literary piece. This study has further revealed that careful consideration of these factors could play a critical role in helping a reader unravel the deeper meaning of the title of a piece of work as well as the entire text. This study is a scholarly contribution to titrology, pragmatics, and discourse analysis.

Abstract In their natural environment, animals face a variety of ecological and social challenges, which might be linked to the emergence of different cognitive skills. To assess inter-specific variation in cognitive skills, we used ungulates as a study model, testing a total of 26 captive individuals across 5 different species (i.e., dwarf goats, Capra aegagrus hircus, llamas, Lama glama, guanacos, Lama guanicoe, zebras, Equus grevyi, and rhinos, Diceros bicornis michaeli). Across species, we used the same well-established experimental procedures to test individuals’ performance in naïve physics tasks, i.e. object permanence, short-term spatial memory, causality, understanding of object properties, and gravity. Our results revealed that study subjects showed object permanence, were able to remember the position of hidden food after up to 60 s, and inferred the position of hidden food from the sound produced or not produced when shaking containers. Moreover, they showed an understanding of basic object properties, being able to locate objects hidden behind occluders based on their size and inclination, and could reliably follow the trajectory of falling objects across different conditions. Finally, inter-specific differences were limited to the understanding of object properties, and suggest that domesticated species as goats might perform better than non-domesticated ones in tasks requiring these skills. These results provide new information on the cognitive skills of a still understudied taxon and confirm ungulates as a promising taxon for the comparative study of cognitive evolution.

The paper presents a simultaneous interpreter professional competence that consists of three components – communicative, extralinguistic and procedural or specialized (sub)competences. It is focused on the third component, which includes the abilities of inferencing, probabilistic forecasting and compression, specifically required for simultaneous interpreting (SI). Inferencing is a two-staged process of retrieving assumptions with regards to composing invariant senses, which means deriving inferences of the message in the source language (SL) and generating implicatures in the target language (TL). Probabilistic forecasting is based on the analysis of the invariant sense prompts in the SL message and boils down to anticipating invariant sense evolvement in the context. It is closely related to compression – an ability to eliminate the redundant information and to condense the retrieved invariant sense in the SI for linguistic and extralinguistic reasons, such as interlanguage asymmetries between the source and target languages and an acute shortage of time because of the speaker’s speed. Moreover, compression is one of the key discursive strategies used by the simultaneous interpreter in speech production. These information processing abilities stand for the SI inherent cognitive features or mechanisms and cognitive-and-discursive strategies employed by the simultaneous interpreter in order to meet the pragmatic needs of a SI communicative situation. Descriptive, comparative, model simulation, introspection and observation methods were used for the research task realization. The outcomes of the study show that the above mentioned three cognitive mechanisms, discursive strategies and abilities closely interact in the SI; it is manifested through inferences and implicatures generated on the basis of presuppositions. Relying on his/her professional competence and employing discursive strategies of inferencing, probabilistic forecasting and compression, the simultaneous interpreter chooses the adequate language means to render invariant sense in its transition from the source to target languages. The choice is facilitated by presuppositional knowledge rooted in the worldview of the source and target languages, constituting the simultaneous interpreter’s language and conceptual thesauri. Another important factor assisting SI cognitive processes and the choice of discursive strategies is the analysis of text functions that makes it possible to elicit presuppositions helpful for inferencing and probabilistic forecasting.

This study introduces the New Exponential-Exponential Distribution (NEED) within the broader New Exponential-Generating (NE-G) family of distributions, targeting enhancements in rainfall data analysis. The significance of this research lies in addressing the need for sophisticated statistical models to accurately capture the complex variability of rainfall patterns, which are critical for effective environmental planning and disaster management. Aiming to refine rainfall data modeling, we employ the NEED model, emphasizing its application across diverse climatic conditions. Our methodology encompasses a comprehensive evaluation of seven distinct parameter estimation techniques, with a particular focus on the Anderson-Darling and maximum product spacing methods. These were selected based on their performance in minimizing bias and mean square error, assessed through a rigorous Monte-Carlo simulation study. Additionally, the study utilizes rainfall data from various geographical regions to validate the model's efficacy. The major conclusion of our investigation is the demonstrable superiority of NEED over traditional models in fitting rainfall data, as evidenced by its enhanced predictive accuracy. This outcome not only contributes to the theoretical advancements in statistical meteorology but also offers practical methodologies for improved weather forecasting. The integration of NEED with contemporary machine learning algorithms further suggests potential for groundbreaking applications in climate science and water resource management.

Context is essential in virtually all human activities. Yet some logical notions seem to be context-free. For example, the nature of the universal quantifier, the very meaning of “all”, seems to be independent of the context. At the same time, there are many quantifier expressions, and some are context-independent, while others are not. Similarly, purely logical consequence seems to be context-independent. Yet often we encounter strong inferences, good enough for practical purposes, but not valid. The two types of examples suggest a general problem: how to characterise the context-free logical concepts in their natural environment, that is, in the field of their context-dependent associates. A general Thesis on Quantifiers is formulated: among all quantifiers, the context-free ones are just those definable by the universal quantifier. The issue of inferences is treated following the approach introduced by Richard L. Epstein: valid ones are an extreme case, the result of the disappearance of context-dependence. This idea can be applied to an analysis of a form of abduction, called “reductive inference” in Polish literature on logic. A tentative Thesis on Inferences identifies the validity of a strong inference that is context-independent.

Abstract Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses mechanistic network models. Mechanistic network models directly capture individual behaviors, making them suitable for studying sexually transmitted diseases. Combining mechanistic models with Approximate Bayesian Computation allows flexible modeling using domain-specific interaction rules among agents, avoiding network model oversimplifications. These models are ideal for longitudinal settings as they explicitly incorporate network evolution over time. We implemented a discrete-time version of a previously published continuous-time model of evolving contact networks for men who have sex with men and proposed an ABC-based approximate inference scheme for it. As expected, we found that a two-wave longitudinal study design improves the accuracy of inference compared to a cross-sectional design. However, the gains in precision in collecting data twice, up to 18%, depend on the spacing of the two waves and are sensitive to the choice of summary statistics. In addition to methodological developments, our results inform the design of future longitudinal network studies in sexually transmitted diseases, specifically in terms of what data to collect from participants and when to do so.

People provide their information to distributed systems to receive the desired services. This information may be disclosed to the agents of the system as part of messages transmitted among them. As the agents of the system are smart, they can infer new information from their obtained information, that they may not be authorized to know. So preserving privacy in such systems is an important and yet challenging issue. We study the problem of analyzing the disclosure of private information in distributed asynchronous systems. Our approach to prevent private information disclosure is to require the system to follow knowledge-related policies defined for the system at design time. To achieve this, we construct a model of the system and assume the policies as the system properties and check whether these properties are satisfied in the system or not. In order to construct a model of the system, we extend the actor model, which is a well known reference model for distributed asynchronous systems, by enriching actors by the knowledge base and inference capability. As our knowledge-related policies should not be violated in any state of the system, we propose an efficient invariant model checking algorithm to verify the satisfaction of the policies in our actor model.

Background: Considering the importance of exploring the development of reasoning skills during preschool period and the suitability of using a culturally linguistically relevant story-based approach for the same, the present research intended to profile the reasoning skills in typically developing Indian preschool children between 36 and 72 months using a story-based approach. The specific objectives were to develop explanation, prediction, and inference based reasoning tasks around a story and assess the reasoning skills in typically developing Indian preschool children using the same. Method: Reasoning tasks across explanation, prediction, and inference domains were developed based on a story and evaluated for its psychometric properties. The developed tasks were then administered to 63 typically developing Indian preschool children attending English medium schools in Mangalore. The preschoolers were equally divided into three age groups, and the responses obtained across the age groups were analyzed quantitatively and qualitatively. Results: The developed tasks were confirmed to have good psychometric properties like content validity and reliability. The age comparisons of reasoning abilities using one-way ANOVA suggested an increase in reasoning abilities with age during the preschool period. The qualitative analysis further suggested that with increasing age, the nature of reasoning changed from content-based reasoning to reasoning based on prior knowledge which was integrated with the story content. Conclusion: The study describes reasoning skill development using a story-based task in Indian preschoolers. The study findings further provide clinical and educational implications to assess and foster reasoning abilities among preschoolers.

In their natural environment, animals face a variety of ecological and social challenges, which might be linked to the emergence of different cognitive skills. To assess inter-specific variation in cognitive skills, we used ungulates as a study model, testing a total of 26 captive individuals across 5 different species (i.e., dwarf goats, Capra aegagrus hircus, llamas, Lama glama, guanacos, Lama guanicoe, zebras, Equus grevyi, and rhinos, Diceros bicornis michaeli). Across species, we used the same well-established experimental procedures to test individuals' performance in naïve physics tasks, i.e. object permanence, short-term spatial memory, causality, understanding of object properties, and gravity. Our results revealed that study subjects showed object permanence, were able to remember the position of hidden food after up to 60 s, and inferred the position of hidden food from the sound produced or not produced when shaking containers. Moreover, they showed an understanding of basic object properties, being able to locate objects hidden behind occluders based on their size and inclination, and could reliably follow the trajectory of falling objects across different conditions. Finally, inter-specific differences were limited to the understanding of object properties, and suggest that domesticated species as goats might perform better than non-domesticated ones in tasks requiring these skills. These results provide new information on the cognitive skills of a still understudied taxon and confirm ungulates as a promising taxon for the comparative study of cognitive evolution. [ABSTRACT FROM AUTHOR]

The modality is an important topic for modelling. Using parametric models is an efficient way when real data set shows trimodality. In this paper, we propose a new class of trimodal probability distributions, that is, probability distributions that have up to three modes. Trimodality itself is achieved by applying a proper transformation to density function of certain continuous probability distributions. At first, we obtain preliminary results for an arbitrary density function $ g(x) $ g (x) and, next, we focus on the Gaussian case, studying trimodal Gaussian model more deeply. The Gaussian distribution is applied to produce the trimodal form of Gaussian known as normal distribution. The tractability of analytical expression of normal distribution and properties of the trimodal normal distribution are important reasons why we choose normal distribution. Furthermore, the existing distributions should be improved to be capable of modelling efficiently when there exists a trimodal form in a data set. After new density function is proposed, estimating its parameters is important. Since Mathematica 12.0 software has optimization tools and important modelling techniques, computational steps are performed using this software. The bootstrapped form of real data sets are applied to show the modelling ability of the proposed distribution when real data sets show trimodality. [ABSTRACT FROM AUTHOR]

This paper presents an in‐depth analysis of the exponentiated half‐logistic Weibull (EHLW) distribution, investigating its fundamental statistical properties and exploring parameters estimation using both maximum likelihood and Bayesian approaches. Specifically, our focus centers on the analysis of stress–strength reliability, denoted as R=P(X>Y)$R=P(X>Y)$, where the strength variable X$X$ follows EHLW distribution, and the stress variable Y$Y$ follows either Weibull distribution or the EHLW distribution. The estimation of the parameter R$R$ is discussed in both scenarios, employing both Maximum Likelihood and Bayesian approaches. Furthermore, we calculate that the asymptotic confidence interval, percentile bootstrap interval, and the highest probability density credible interval are obtained for the stress–strength parameter R$R$. In order to assess the efficiency of these estimation techniques, simulation studies are conducted, providing valuable insights into the performance of each estimation approach. Finally, the proposed reliability model is applied to real datasets, highlighting its practical significance. [ABSTRACT FROM AUTHOR]

I argue that beliefs about the identity or distinctness of objects are necessary to explain some normal inferential transitions between thoughts in humans. Worries about vicious regress are not powerful enough to dismantle such an argument. As an upshot, the idea that thinkers "trade on" identity without any corresponding belief remains somewhat mysterious. [ABSTRACT FROM AUTHOR]

Mixed-mode designs have become increasingly common in survey data collection. Although different modes often have different measurement properties, the standard practice is to treat mixed-mode data as if they had been collected with a single mode, neglecting the potential impact of mode effects. To account for potential mode effects when making inferences for mixed-mode samples, we propose (i) a Testimator approach, (ii) a Bayesian approach, and (iii) a model averaging approach. In the Testimator approach, we test whether the means and the variances of mixed-mode samples are the same. If the means are the same, we take the average of mode-specific estimates. If the means are different, we take the average when we have no prior information about preferred modes and take the smaller (or larger) estimate when we have prior information about preferred modes (e.g. a smaller estimate is better). In the Bayesian approach, we assume some prior information. We use a data-driven method to determine whether there are mode effects. If there are no mode effects, we draw inferences using a common mean model. If there are mode effects, we draw inferences using the data collected with the mode that produces smaller estimates. In the model averaging approach, we combine estimates of different models (characterized by whether assume same means and variances across modes) using marginal posteriors as weights. We evaluate the approaches in simulation studies and find that they achieve robust inferences compared to the standard approach. We apply the methods to the Arab Barometer study, which employs a randomized mixed-mode design. [ABSTRACT FROM AUTHOR]

Extractive approaches have been the mainstream paradigm for identifying overlapping entity–relation extraction. However, limited by their inherently methodological flaws, which hardly deal with three issues: hierarchical dependent entity–relations, implicit entity–relations, and entity normalization. Recent advances have proposed an effective solution based on generative language models, which cast entity–relation extraction as a sequence-to-sequence text generation task. Inspired by the observation that humans learn by getting to the bottom of things, we propose a novel framework, namely GenRE, Generative multi-turn question answering with contrastive learning for entity–relation extraction. Specifically, a template-based question prompt generation first is designed to answer in different turns. We then formulate entity–relation extraction as a generative question answering task based on the general language model instead of span-based machine reading comprehension. Meanwhile, the contrastive learning strategy in fine-tuning is introduced to add negative samples to mitigate the exposure bias inherent in generative models. Our extensive experiments demonstrate that GenRE performs competitively on two public datasets and a custom dataset, highlighting its superiority in entity normalization and implicit entity–relation extraction. (The code is available at https://github.com/lovelyllwang/GenRE). [ABSTRACT FROM AUTHOR]