Extracting Entity Synonymous Relations via Context-Aware Permutation Invariance

Discovering entity synonymous relations is an important work for many entity-based applications. Existing entity synonymous relation extraction approaches are mainly based on lexical patterns or distributional corpus-level statistics, ignoring the context semantics between entities. For example, the contexts around ''apple'' determine whether ''apple'' is a kind of fruit or Apple Inc. In this paper, an entity synonymous relation extraction approach is proposed using context-aware permutation invariance. Specifically, a triplet network is used to obtain the permutation invariance between the entities to learn whether two given entities possess synonymous relation. To track more synonymous features, the relational context semantics and entity representations are integrated into the triplet network, which can improve the performance of extracting entity synonymous relations. The proposed approach is implemented on three real-world datasets. Experimental results demonstrate that the approach performs better than the other compared approaches on entity synonymous relation extraction task.

Mapping cortical disease-burden at individual-level in frontotemporal dementia: implications for clinical care and pharmacological trials

Imaging studies of FTD typically present group-level statistics between large cohorts of genetically, molecularly or clinically stratified patients. Group-level statistics are indispensable to appraise unifying radiological traits and describe genotype-associated signatures in academic studies. However, in a clinical setting, the primary objective is the meaningful interpretation of imaging data from individual patients to assist diagnostic classification, inform prognosis, and enable the assessment of progressive changes compared to baseline scans. In an attempt to address the pragmatic demands of clinical imaging, a prospective computational neuroimaging study was undertaken in a cohort of patients across the spectrum of FTD phenotypes. Cortical changes were evaluated in a dual pipeline, using standard cortical thickness analyses and an individualised, z-score based approach to characterise subject-level disease burden. Phenotype-specific patterns of cortical atrophy were readily detected with both methodological approaches. Consistent with their clinical profiles, patients with bvFTD exhibited orbitofrontal, cingulate and dorsolateral prefrontal atrophy. Patients with ALS-FTD displayed precentral gyrus involvement, nfvPPA patients showed widespread cortical degeneration including insular and opercular regions and patients with svPPA exhibited relatively focal anterior temporal lobe atrophy. Cortical atrophy patterns were reliably detected in single individuals, and these maps were consistent with the clinical categorisation. Our preliminary data indicate that standard T1-weighted structural data from single patients may be utilised to generate maps of cortical atrophy. While the computational interpretation of single scans is challenging, it offers unrivalled insights compared to visual inspection. The quantitative evaluation of individual MRI data may aid diagnostic classification, clinical decision making, and assessing longitudinal changes.

A Data Analytics Approach for Revealing Influencing Factors of HPV-Related Cancers From Population-Level Statistics Data

Human papillomavirus (HPV) is considered as one of the major causes of multiple cancers, including cervical, anal, and vaginal cancers. Some studies analyzed the infection patterns of cancers caused by HPV using individual clinical test data, which is resource and time expensive. In order to facilitate the understanding of cancers caused by HPV, we propose to use data analytics methods to reveal the influencing factors from the population-level statistics data, which is available more easily. Particularly, we demonstrate the effectiveness of data analytics approach by introducing a predictive analytics method in studying the risk factors of cervix cancer in the United States. Besides accurate prediction of the number of infections, the predictive analytics method discovers the population statistic factors that most affect the cervical cancer infection pattern. Furthermore, we discuss the potential directions in developing more advanced data analytics approaches in studying cancers caused by HPV.

On intermediate statistics across many-body localization transition

The level statistics in the transition between delocalized and localized {phases of} many body interacting systems is {considered}. We recall the joint probability distribution for eigenvalues resulting from the statistical mechanics for energy level dynamics as introduced by Pechukas and Yukawa. The resulting single parameter analytic distribution is probed numerically {via Monte Carlo method}. The resulting higher order spacing ratios are compared with data coming from different {quantum many body systems}. It is found that this Pechukas-Yukawa distribution compares favorably with {$\beta$--Gaussian ensemble -- a single parameter model of level statistics proposed recently in the context of disordered many-body systems.} {Moreover, the Pechukas-Yukawa distribution is also} only slightly inferior to the two-parameter $\beta$-h ansatz shown {earlier} to reproduce {level statistics of} physical systems remarkably well.

Higher spin wormholes from modular bootstrap

We investigate the connection between spacetime wormholes and ensemble averaging in the context of higher spin AdS3/CFT2. Using techniques from modular bootstrap combined with some holographic inputs, we evaluate the partition function of a Euclidean wormhole in AdS3 higher spin gravity. The fixed spin sectors of the dual CFT2 exhibit features that starkly go beyond conventional random matrix ensembles: power-law ramps in the spectral form factor and potentials with a double-well/crest underlying the level statistics.

Practices of the Random Variable Proposed in the Chilean Mathematics Curriculum of Secondary Education

Working with statistical data from real contexts has become fundamental in school-level statistics, because it enables the development of statistical reasoning. In this regard, the notion of the random variable is fundamental to statistical data analysis. Thus, the aim of this research was to characterise the meanings of the concept of the random variable that are promoted in the Chilean mathematics curriculum for secondary education. To achieve this, we examined the representativeness of the meanings of the random variable intended by the curriculum in relation to the meanings of reference for this concept, using theoretical and methodological notions from the onto-semiotic approach. The findings show that the meanings of reference were not fully represented in the textbooks and the national curriculum, and that the most promoted meaning was that of the random variable as a variable of interest. Regarding the types of representation, it was observed that the graphical and tabular representations were neglected. Lastly, it was noticed that definitions of the concept of variable, its classifications, and differences were omitted almost entirely from the textbooks.

Can Regional Gender Ideologies Account for Variation of Gender Pay Gaps? The Case of Germany

While research often invokes gender disparities in wage-determining characteristics to explain gender pay gaps, why these gender disparities and gender pay gaps vary across contexts has received less attention. Therefore, I analyze how subnational gender ideologies predict gender pay gaps in two ways: as directly affecting gender pay gaps and as indirectly predicting gender pay gaps through intermediate gender disparities in determinants of wage. The analyses are based on German survey data (SOEP 2014–2018) supplemented with regional-level statistics. First, I leverage regional differences in predictors of gender ideologies to estimate region-specific gender ideologies. Mapping these gender ideologies across Germany reveals substantial regional variation. Second, multi-level models provide region-specific gender disparities in wage determinants and gender pay gaps. Results reveal that traditional gender ideologies are associated with women gaining less labor market experience and working less often in full-time jobs or supervising positions. In addition to this indirect association, gender ideologies directly predict the extent of adjusted gender pay gaps. These associations contribute novel evidence on regional variation of gender ideologies and how they can underlie explanations often invoked for gender pay gaps.

Wormholes and black hole microstates in AdS/CFT

It has long been known that the coarse-grained approximation to the black hole density of states can be computed using classical Euclidean gravity. In this work we argue for another entry in the dictionary between Euclidean gravity and black hole physics, namely that Euclidean wormholes describe a coarse-grained approximation to the energy level statistics of black hole microstates. To do so we use the method of constrained instantons to obtain an integral representation of wormhole amplitudes in Einstein gravity and in full-fledged AdS/CFT. These amplitudes are non-perturbative corrections to the two-boundary problem in AdS quantum gravity. The full amplitude is likely UV sensitive, dominated by small wormholes, but we show it admits an integral transformation with a macroscopic, weakly curved saddle-point approximation. The saddle is the “double cone” geometry of Saad, Shenker, and Stanford, with fixed moduli. In the boundary description this saddle appears to dominate a smeared version of the connected two-point function of the black hole density of states, and suggests level repulsion in the microstate spectrum. Using these methods we further study Euclidean wormholes in pure Einstein gravity and in IIB supergravity on Euclidean AdS5× S5. We address the perturbative stability of these backgrounds and study brane nucleation instabilities in 10d supergravity. In particular, brane nucleation instabilities of the Euclidean wormholes are lifted by the analytic continuation required to obtain the Lorentzian spectral form factor from gravity. Our results indicate a factorization paradox in AdS/CFT.

Insta-learn: Face Stereotypes Emerge and Persist through Insufficient Statistical Learning

Social stereotypes are prevalent and consequential, yet sometimes inaccurate. How do people learn these inaccurate beliefs in the first place and why do these beliefs persist in the face of counter evidence? Building on past research on cognitive limitations and environmental sample biases, we propose an integrative perspective: Insufficient statistical learning (Insta-learn). Instalearn posits that humans are active learners of the environment. Starting from a small sample, people are able to extract statistical patterns within the sample accurately and quickly. However, people do not continue sampling sufficiently. If they decide not to collect more samples once they are (prematurely) satisfied, inaccurate stereotypes can emerge even when more data would show otherwise. We investigated this hypothesis across six online experiments (N = 1565), using novel pairs of computer-generated faces and social behaviors. Fixing the population level statistics of face-behavior associations to zero and varying the initial sample statistics, we found that participants quickly learned the initial sample statistics (from as few as three examples) and persisted in using such spurious associations in their final decisions. Granting the sampling power to participants — samples were endogenously generated by participants and not defined by the experimenters — we found insufficient sampling caused spurious associations to persist. Insta-learn provides a domain-general framework for a mechanistic explanation of the emergence and persistence of social stereotypes.