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Social psychological research on collective victimhood has focused on just a few ways in which people think about the ingroup's victimization that imply certain assumptions and limit our understanding of collective victim beliefs. Additionally, different historical and sociopolitical contexts may make different collective victim beliefs relevant. This article examines collective victim beliefs expressed in open-ended survey responses among six different groups: Northern Irish participants, Greek Cypriots, Hungarians, Poles, Jewish Americans, and Armenian Americans (N = 638). Qualitative content analysis revealed five broader categories with several collective victim beliefs each. General appraisals of the ingroup's collective victimization entailed centrality of ingroup victimization versus defocusing victimhood. More specific appraisals included context-specific characteristics of the ingroup's victimization, perceptions of the perpetrator group (attributions of blame), and perceptions of other victim groups (comparative victim beliefs, including rejecting comparisons). The findings extend and challenge commonly studied collective victim beliefs, and propose novel theoretical directions.

Relation extraction between entity pairs is an increasingly critical area in natural language processing. Recently, the pre‐trained bidirectional encoder representation from transformer (BERT) performs excellently on the text classification or sequence labelling tasks. Here, the high‐level syntactic features that consider the dependency between each word and the target entities into the pre‐trained language models are incorporated. Our model also utilizes the intermediate layers of BERT to acquire different levels of semantic information and designs multi‐granularity features for final relation classification. Our model offers a momentous improvement over the published methods for the relation extraction on the widely used data sets.

Land surface heterogeneity exerts a strong control on atmospheric boundary-layer (ABL) evolution by spatially varying the distribution and partitioning of surface energy fluxes and triggering secondary circulations. The representation of this physical process in numerical weather prediction (NWP) models is especially affected in the terra incognita as the model grid resolution approaches the length-scale of the largest eddies in the boundary layer. We explore these effects for a mesoscale strip-like land surface inhomogeneity in land cover, soil moisture or a superposition of both embedded in an elsewhere homogeneous landscape. The study is conducted with the numerical weather prediction model ICON (ICOsahedral Nonhydrostatic), using the default operational level 2.5 Mellor–Yamada turbulence closure (MY) and a large-eddy simulation (LES) configuration as a benchmark. While simulations with the default ABL scheme approach the LES reference when refining the spatial grid towards finer resolution, the model generates artificial circulations leading to ABL height oscillations when the horizontal grid resolution (∆𝑥) approaches the ABL height (𝑧𝑖). The effect of these model-induced circulations on the state of the boundary layer is even present with weak thermal heterogeneity (∆𝐻) under low background wind speed (𝑣𝑥) but diminishes with increasing background wind speed. The tuning of the asymptotic turbulent mixing length-scale (𝑙∞) in the operational ABL scheme helps in reducing the amplitude of the oscillations, thereby reducing the artificially induced circulations due to thermal heterogeneity which might act as unintentional trigger for clouds and precipitation. Based on the tuned synthetic model data from sensitivity runs, we propose a new parametrization for a 2-D 𝑙∞ as a function of ∆𝐻, 𝑧𝑖/∆𝑥 and 𝑣𝑥, which is otherwise held as a constant in the ABL scheme.

Groundwater dependency is increasing globally, while millions of potentially contaminated sites are yet to be characterized for contamination levels. In particular, groundwater contamination due to light nonaqueous phase liquids (LNAPLs) continues to be a global challenge. Mathematical approaches (i.e., analytical, semi-analytical, empirical, numerical) are preferred for an initial site assessment to circumvent the high characterization costs and limited site data availability. However, the site-specific nature of contamination restricts the generalization of any single approach. Hence, the requirement is for an easy-to-use computing interface that provides site-specific data management, the selection and use of multiple-model interfaces for computing, and site characterization, with extension for the latest models as they become available. This work provides one such interface called CAST or Contamination Assessment and Site-management Tool. CAST is an open-source browser-based (online/offline) tool that provides an interface for six different analytical models (e.g., BIOSCREEN-AT), a MODFLOW based numerical model, and two empirical models (including a hybrid numerical-analytical model). Additionally, CAST includes interfaces for site data management, their evaluation, and scenario-based modeling. CAST's development is in a modular format, which simplifies the addition of new computing or data interfaces. Furthermore, the entire code-base of CAST is based on open-source (dominantly Python programming) libraries and standards. This further simplifies the modification or extension of this tool. This paper introduces CAST, its different computing, and data management interfaces and provides examples of the tool's functionality primarily for the initial evaluation of contaminated sites.

We explored the dependency of strength anisotropy on loading rate in coal both experimentally and numerically. An improved numerical modeling method was developed for this investigation to reduce the random error caused by the mechanical differences of coal specimens. After that, specimens with different anisotropy angles (the angle between the coring direction and bedding plane orientation) were processed (0°, 15°, 30°, 45°, 60°, and 90°). These specimens were then scanned by the X‐ray Computed Tomography (CT) and tested by unconfined loading frame. Uniaxial compressive simulations were performed on 15 numerical models with different anisotropy angles at various loading rates (1.0 × 10−6, 5.0 × 10−6, 10.0 × 10−6, and 20.0 × 10−6 mm/step). The results indicate that the uniaxial compressive strength (UCS) of coal generally first decreases and then increases as the anisotropy angle increases from 0° to 90° for all loading rates. It is also found that the UCS increases significantly with the increasing loading rate at anisotropic angles of 30° and 45°, while it is less obvious at anisotropic angles of 0° and 90°. This results in a reduced USC anisotropy at high loading rates. Meanwhile, a negative exponential equation is obtained to describe the variation of UCS with the loading rate at different anisotropic angles. And a unified equation is then developed and verified to define the UCS as a function of the loading rate and anisotropic angle.

Aim Separating the biotic and abiotic factors controlling species distributions has been a long-standing challenge in ecology and biogeography. Joint species distribution models (JSDMs) have emerged as a promising statistical framework towards this objective by simultaneously modelling the environmental responses of multiple species and approximating species associations based on patterns in their (co-)occurrences. However, the signature of biotic interactions should be most evident at fine spatial resolutions. Here, we test how the resolution of input data affects the inferences from JSDMs. Location Switzerland. Taxon Birds. Methods Using standardized survey data of 43 woodland bird species and 8 climatic, topographic and vegetation structural predictors, we fit JSDMs at different spatial resolutions (125–1000 m) and sampling periods (1 and 5 years). In addition, we calculate functional similarity among all species as an independent proxy of biotic interactions, specifically competition. We then assess how JSDM performance and estimates vary with the spatial resolution of the input data and test whether species associations are consistent across grain sizes and with the alternative approach based on functional similarity. Results Our results show better model performance at coarser spatial resolutions and for longer sampling periods. Although pairwise species associations estimated in JSDMs were generally shifted towards positive values, we found a higher proportion of negative associations at fine spatial resolutions. Strikingly, estimates were not consistent across spatial scales and frequently switched between positive and negative values. Moreover, estimated species associations tended to be more positive for functionally similar species. Main conclusions Our results show that species associations are more differentiated, that is, cover a broader range of values, at finer spatial resolutions. Yet, their positive correlation with functional similarity and the general over-representation of positive associations suggest that shared responses to unobserved environmental predictors rather than biotic interactions underlie these scaling effects, cautioning against a naive interpretation of species associations estimated by JSDMs at any spatial scale. ISSN:0305-0270 ISSN:1365-2699

Journal of the American Ceramic Society (2020). doi:10.1111/jace.17556, Published by American Ceramic Society, Westerville, Ohio

The classification of Western art music is a complex area of knowledge organization, yet the reasons for those complexities have not been fully studied. This article dissects the concept of orthogonality, in particular regard to music classification. Orthogonality (antonym: dependency) means that one facet acts independently from another facet. While orthogonality is an assumed quality of facets, it has attracted relatively little attention in knowledge organization discourse. This article utilizes bibliographic classification schemes, musicological writings, and musical works, to analyse orthogonality in music classification. The relationships between the medium, form/genre and function facets are unpicked and a strong dependency is found between these facets. Whether this orthogonality exists as a construct of faceted classification or stems from the domain knowledge is explored. Furthermore, the analysis initiates new thinking about the general concept of orthogonality. The idea of a spectrum of dependency is proposed. In addition, novel, orthogonality-derived phenomena are discussed – “dynamic facets” and “meta-dependency” – where the boundary between what is and is not a facet are blurred. The concluding model visualizes the chain of dependencies between music facets, ultimately showing how the lack of orthogonality plays a key role in the complexity and issues found in music classification.

Two-way contingency tables arise in many fields, such as in medical studies, where the relation between two discrete random variables or responses is to be assessed. We propose to analyze and visualize a sample of 2 × 2 tables in the context of single-subject repeated measurements design by means of compositional data (CoDa) methods. First, we propose to visualize the tables in a quaternary diagram. Second, we show how to represent these tables by means of logratios indicating the relationship between the two variables as well as their strength and direction of dependency. Finally, we describe a technique to model those tables with a simplicial regression model. Data from a real-world study of self-prediction of migraine attack onset is used to illustrate this methodology. For each individual, the 2 × 2 table of their migraine expectation vs next day migraine occurrence is computed, generating a sample of tables. Then we visualize and interpret the prediction ability of individuals both in the simplex and in terms of logratios of components. Finally, we model the self-prediction ability with respect to demographic variables, days tracked and disease characteristics. Our application demonstrates that CoDa can be a useful tool for visualizing, modeling, and interpreting the components of 2 × 2 tables.

Dependent personality disorder (DPD) diagnostic criteria have evolved considerably during the past 60 years, and researchers continue to explore the etiology and dynamics of problematic dependency in children, adolescents, and adults. This chapter reviews theoretical, clinical, and empirical writings on trait dependency and DPD. After tracing the evolution of DPD in successive editions of The Diagnostic and Statistical Manual of Mental Disorders (DSM), I discuss epidemiology, differential diagnosis, and comorbidity. Following a brief review of widely used assessment tools, influential theoretical frameworks for conceptualizing dependency and DPD are examined, along with research relevant to each framework. Three contemporary DPD treatment approaches (psychodynamic, behavioral, and cognitive) are described, and current trends in dependency research are discussed. These include exploration of the factors that underlie gender differences in DPD, maladaptive and adaptive expressions of dependency, dependency and aging, implicit and self-attributed dependency, health consequences of dependency, the dependency-attachment relationship, and DPD in the DSM-5.

Questions: We asked: (a) whether the strength of conspecific and heterospecific neighborhood crowding effects on focal tree survival and growth vary with neighborhood radii; and (b) if the relative strength of the effect of neighborhood interactions on tree growth and survival varies with neighborhood scale. Location: Luquillo Forest Dynamics Plot, Puerto Rico. Methods: We used tree survival and growth data and included information on species‐mean trait values related to several leaf traits, maximum height, seed mass and wood density. We incorporated a tree neighborhood modeling approach that uses an area around a focal tree with a specified radius, to describe the interactions between a focal tree and its neighbors. We constructed survival and growth models for each functional trait using a Bayesian approach, and varied the size of the radius from 5 m to 30 m, at 5‐m intervals. Results: The results suggested that the estimated effects of conspecific and heterospecific neighbors on tree performance do not vary based on the size of the neighborhood (5–30 m), suggesting that the effects of conspecific and heterospecific neighbors on the performance of a focal tree likely do not vary substantially beyond a neighborhood radius of 5 m in the Luquillo forest. In contrast, the estimated strength of the functional neighborhood (effect of neighbors based on their functional trait values) on tree performance was dependent on the neighborhood range. Our results also suggested that the effects of trait distances and trait hierarchies on tree survival and growth are acting simultaneously and at the same spatial scales. Conclusion: Findings from this study highlight the importance of spatial scale in community assembly processes, and specifically, call for increased attention when selecting the radius that defines the neighborhood around a focal tree as the selected neighborhood radius influences the community patterns discovered, and affects the conclusions about the drivers that control community assembly.

Despite their diversity, languages around the world share a consistent set of properties and distributional regularities. For example, the distribution of word frequencies, the distribution of syntactic dependency lengths, and the presence of ambiguity are all remarkably consistent across languages. We discuss a framework for studying how these system-level properties emerge from local, in-the-moment interactions of rational, pragmatic speakers and listeners. To do so, we derive a novel objective function for measuring the communicative efficiency of linguistic systems in terms of the interactions of speakers and listeners. We examine the behavior of this objective in a series of simulations focusing on the communicative function of ambiguity in language. These simulations suggest that rational pragmatic agents will produce communicatively efficient systems and that interactions between such agents provide a framework for examining efficient properties of language structure and use more broadly.

The control of latent heat flux (LE) by soil moisture (SM) variations is a key process affecting the moisture and energy balance at the land-atmosphere interface. SM-LE coupling is conventionally e...

Variability in the b-value, which describes the frequency distribution of earthquake magnitudes, is usually attributed to variations in differential stress in the setting of natural and labo...

OBJECTIVE: This study investigates the association between men’s economic dependency during midlife and allostatic load, an indicator of chronic stress, and how this relationship varies with men’s gender ideology. BACKGROUND: Women are primary breadwinners in almost a third of heterosexual couples in the U.S. Emerging research finds that female primary breadwinning (or men’s economic dependency) is a threat to masculinity that has negative implications for men’s midlife health. However, there is no quantitative evidence of the mechanisms linking men’s economic dependency and health, particularly the role of stress, and whether men’s gender ideology moderates this relationship. METHOD: Using two waves of Midlife in the United State (MIDUS) data for men who remained with the same marital or cohabiting female partner between waves (n = 332), the authors estimate the relationship between men’s economic dependency in wave 1 and allostatic load in wave 2. RESULTS: There was no evidence of an association between men’s economic dependency and higher allostatic load on average. However, gender ideology had a moderating influence; men’s economic dependency was associated with higher allostatic load for those who espoused more traditional gender attitudes and lower allostatic load for those with the most egalitarian attitudes. CONCLUSION: The findings underscore the existence of multiple masculinities and suggest that economic dependence has a negative or positive influence on men’s health depending on the meanings men attach to female primary breadwinning.

Saltbodies are important subsurface structures that have significant implications for hydrocarbon accumulation and sealing in petroleum reservoirs, and accurate saltbody imaging and delineation is now greatly facilitated with the availability of threedimensional seismic surveying. However, with the growing demand for larger survey coverage and higher imaging resolution, the size of seismic data is increasing dramatically. Correspondingly, manual saltbody interpretation fails to offer an efficient solution, particularly in exploration areas of complicated salt intrusion history. Recently, artificial intelligence is attracting great attention from geoscientists who desire to utilize the popular machine learning technologies for evolving the interpretational tools capable of mimicking an experienced interpreter’s intelligence. This study first implements two popular machine learning tools, the multi-layer perceptron and the convolutional neural network, for delineating seismic saltbodies at sample and pattern levels, respectively, then compares their performance through applications to the synthetic SEAM seismic volume, and moreover tentatively investigates what contributes to the better convolutional neural network delineation. Specifically, the multi-layer perceptron scheme is capable of efficiently utilizing an interpreter’s knowledge by selecting, pre-conditioning and integrating a set of seismic attributes that best highlight the target saltbodies, whereas the convolutional neural network scheme makes it possible for saltbody delineation directly from seismic amplitude and thus significantly reduces the dependency on attribute selection from interpreters. It is concluded that the better performance from the convolutional neural network scheme results from two factors. First, the convolutional neural network builds the mapping relationship between the seismic signals and the saltbodies using the original seismic amplitude instead of manually selected seismic attributes, so that the negative impact of using less representative attributes is virtually eliminated. Second and more importantly, the convolutional neural network defines, learns and identifies the saltbodies by utilizing local seismic reflection patterns, so that the seismic noises and processing artefacts of distinct patterns are effectively identified and excluded.

This study examined whether interpersonal dependency affects therapeutic outcome, using meta-analytic techniques to synthesize results from 31 studies (49 effect sizes; overall N = 3807). High levels of dependency were associated with more positive outcome in psychodynamic therapy (r = 0.11, p < 0.05), but not cognitive-behavioural therapy (r = -0.05, ns), and were associated with a less positive outcome in pharmacological treatment (r = -0.15, p < 0.001). Other predictors of outcome included patient diagnosis (high levels of dependency were linked with more positive outcome for patients with anxiety disorders but not those with depression), outcome measure (high levels of dependency were associated with significantly greater improvement in global functioning but not symptom reduction) and dependency measure (cognitive and cognitive-behavioural dependency scales yielded less positive results than other types of measures). These results contribute to a growing body of literature examining effects of personality on treatment process and outcome. © 2019 John Wiley & Sons, Ltd.

I argue that the approach suggested by The Taylor Review is flawed due to a conceptual reliance on a fuzzy understanding of control. When control is understood sociologically, it becomes apparent that it is a core feature of all labour relations and thus exists across employment statuses and takes various complex forms. Therefore, focusing labour law upon a fuzzy notion of control, might actually reduce existing levels of protection by causing the reclassification of workers who labour under subtle, insidious and intangible forms of control. Labour laws exist to recognise the vulnerable position of employees due to their livelihood being dependent on the person hiring them. This dependency exists regardless of the form control which an employer operates. Therefore, I argue that returning the law’s focus to dependency offers a sounder basis for determining employment classification and labour rights.

Can sentence comprehension impairments in aphasia be explained by difficulties arising from dependency completion processes in parsing? Two distinct models of dependency completion difficulty are investigated, the Lewis and Vasishth (2005) activation-based model, and the direct-access model (McElree, 2000). These models’ predictive performance is compared using data from individuals with aphasia (IWAs) and control participants. The data are from a self-paced listening task involving subject and object relative clauses. The relative predictive performance of the models is evaluated using k-fold cross-validation. For both IWAs and controls, the activation-based model furnishes a somewhat better quantitative fit to the data than the direct-access model. Model comparisons using Bayes factors shows that, assuming an activation-based model, intermittent deficiencies may be the best explanation for the cause of impairments in IWAs, although slowed syntax and lexical delayed access may also play a role. This is the first computational evaluation of different models of dependency completion using data from impaired and unimpaired individuals. This evaluation develops a systematic approach that can be used to quantitatively compare the predictions of competing models of language processing.

Part-of-speech (POS) and dependency grammar (DG) are the basic components of natural language processing. However, current word vector models have not made full use of both POS information and DG information, and hence the models’ performances are limited to some extent. The authors first put forward the concept of POS vector, and then, based on continuous bag-of-words (CBOW), constructed four models: CBOW + P, CBOW + PW, CBOW + G, and CBOW + G + P to incorporate POS information and DG information into word vectors. The CBOW + P and CBOW + PW models are based on POS tagging, the CBOW + G model is based on DG parsing, and the CBOW + G + P model is based on POS tagging and DG parsing. POS information is integrated into the training process of word vectors through the POS vector to solve the problem of the POS similarity being difficult to measure. The POS vector correlation coefficient and distance weighting function are used to train the POS vector as well as the word vector. DG information is used to correct the information loss caused by fixed context windows. Dependency relations weight is used to measure the difference of dependency relations. Experiments demonstrated the superior performance of their models while the time complexity is still kept the same as the base model of CBOW.

Summary The major goal of this work is to develop a robust modelling strategy for the simulation of ductile damage development including crack initiation and subsequent propagation. For that purpose, a Gurson-type model is used. This model class, as many other damage models, leads to significant material softening and must be used within a large deformation framework due to the ductile character of the materials. This leads to 2 main difficulties that should be dealt with carefully: mesh dependency and volumetric locking. In this work, a logarithmic finite strain framework is adopted in which the Gurson-Tvergaard-Needleman constitutive law is reformulated. Then a nonlocal formulation with regularisation of hardening variable is applied so as to solve mesh dependency and strain localization problem. In addition, the nonlocal model is combined with mixed “displacement-pressure-volume variation” elements to avoid volumetric locking. Thereby, a mesh-independent and locking-free finite strain framework suitable for the modelling of ductile rupture is established. Attention is paid to mathematical properties and numerical performance of the model. Finally, the model parameters are identified on an experimental database for a nuclear piping steel. Simulations of standard test specimens (notched tensile bars and compact tension and single edge notched tensile cracked specimens) are performed and compared to experimental results.

In this article, we investigate the Advanced Global Neighborhood Failure Localization (AG-NFL) monitoring trail (m-trail) approach, which provides ultra-fast all-optical restoration for any shared protection scheme. In contrast with its previous counterparts, AG-NFL separates the management tasks of protection switching and link maintenance, and focuses on the identification of the proper switching actions in a timely manner rather than unambiguously localizing link failures. We form switching link groups at each node, that is, links whose failures do not have to be distinguished from each other, for example, because their corresponding switching actions can be performed at the same time. Forbidden link-pairs are introduced to identify the minimal set of conflicting switching actions, which minimizes the number of switching link groups. Furthermore, in order to minimize the number of m-trail reconfigurations upon dynamic traffic, we analyze the AG-NFL performance in four different m-trail design scenarios with decreasing dependency on the data plane. We prove that AG-NFL is NP-complete, and we propose an efficient heuristic to solve it. We demonstrate through simulations that unambiguous localization of switching link groups instead of single link failures leads to a significantly improved m-trail performance both in wavelength resources and the number of required transponders, while signaling-free restoration is still provided. © 2017 Wiley Periodicals, Inc. NETWORKS, 2017

The effects of the response-reinforcer dependency on resistance to change were studied in three experiments with rats. In Experiment 1, lever pressing produced reinforcers at similar rates after variable interreinforcer intervals in each component of a two-component multiple schedule. Across conditions, in the fixed component, all reinforcers were response-dependent; in the alternative component, the percentage of response-dependent reinforcers was 100, 50 (i.e., 50% response-dependent and 50% response-independent) or 10% (i.e., 10% response-dependent and 90% response-independent). Resistance to extinction was greater in the alternative than in the fixed component when the dependency in the former was 10%, but was similar between components when this dependency was 100 or 50%. In Experiment 2, a three-component multiple schedule was used. The dependency was 100% in one component and 10% in the other two. The 10% components differed on how reinforcers were programmed. In one component, as in Experiment 1, a reinforcer had to be collected before the scheduling of other response-dependent or independent reinforcers. In the other component, response-dependent and -independent reinforcers were programmed by superimposing a variable-time schedule on an independent variable-interval schedule. Regardless of the procedure used to program the dependency, resistance to extinction was greater in the 10% components than in the 100% component. These results were replicated in Experiment 3 in which, instead of extinction, VT schedules replaced the baseline schedules in each multiple-schedule component during the test. We argue that the relative change in dependency from Baseline to Test, which is greater when baseline dependencies are high rather than low, could account for the differential resistance to change in the present experiments. The inconsistencies in results across the present and previous experiments suggest that the effects of dependency on resistance to change are not well understood. Additional systematic analyses are important to further understand the effects of the response-reinforcer relation on resistance to change and to the development of a more comprehensive theory of behavioral persistence.

This article studies the effects of incorporating the interdependence among London small business defaults into a risk analysis framework using the data just before the financial crisis. We propose an extension from standard scoring models to take into account the spatial dimensions and the demographic characteristics of small and medium-sized enterprises (SMEs), such as legal form, industry sector, and number of employees. We estimate spatial probit models using different distance matrices based only on the spatial location or on an interaction between spatial locations and demographic characteristics. We find that the interdependence or contagion component defined on spatial and demographic characteristics is significant and that it improves the ability to predict defaults of non–start-ups in London. Furthermore, including contagion effects among SMEs alters the parameter estimates of risk determinants. The approach can be extended to other risk analysis applications where spatial risk may incorporate correlation based on other aspects.