Unobserved Properties Research Articles

Parabolic achromatic color matching functions: Dependence on incremental and decremental luminance.

Either the brightness or lightness of a disk surrounded by an annulus is characterized in the most general case by a parabolic function of the annulus luminance when plotted on a log-log scale. This relationship has been modeled with a theory of achromatic color computation based on edge integration and contrast gain control [J. Vis.10, 1 (2010)1534-736210.1167/10.14.40]. We tested predictions of this model in new psychophysical experiments. Our results support the theory and reveal a previously unobserved property of parabolic matching functions that depends on the disk contrast polarity. We interpret this property in terms of a neural edge integration model incorporating data from macaque monkey physiology that indicates different physiological gain factors for incremental and decremental stimuli.

ERGO-ML I: inferring the assembly histories of IllustrisTNG galaxies from integral observable properties via invertible neural networks

ABSTRACT A fundamental prediction of the ΛCDM cosmology is the hierarchical build-up of structure and therefore the successive merging of galaxies into more massive ones. As one can only observe galaxies at one specific time in the cosmic history, this merger history remains, in principle, unobservable. By using the TNG100 simulation of the IllustrisTNG project, we show that it is possible to infer the unobservable stellar assembly and merger history of central galaxies from their observable properties by using machine learning techniques. In particular, in this first paper of ERGO-ML (Extracting Reality from Galaxy Observables with Machine Learning), we choose a set of seven observable integral properties of galaxies to infer the stellar ex-situ fraction, the average merger lookback times and mass ratios, and the lookback time and stellar mass of the last major merger. To infer the posterior distribution for these parameters and hence estimate the uncertainties in the predictions, we use a conditional Invertible Neural Network (cINN). We find that the stellar ex-situ fraction and the time of the last major merger are well-determined by the selected set of observables, that the mass-weighted merger mass ratio is unconstrained, and that, beyond stellar mass, stellar morphology and stellar age are the most informative properties. Finally, we show that the cINN recovers the remaining unexplained scatter and secondary cross-correlations. Overall, this is a first step towards a tool that can be applied to large galaxy surveys in order to infer unobservable properties of the galaxies’ past, enabling empirical studies of galaxy evolution enriched by cosmological simulations.

Characterizations of two-dimensional materials with cryogenic ultrahigh vacuum near-field optical microscopy in the visible range

The development of new characterization methods has resulted in innovative studies of the properties of two-dimensional (2D) materials. Observations of nanoscale heterogeneity with scanning probe microscopy methods have led to efforts to further understand these systems and observe new local phenomena by coupling light-based measurement methods into the tip-sample junction. Bringing optical spectroscopy into the near-field in ultrahigh vacuum at cryogenic temperatures has led to highly unique studies of molecules and materials, yielding new insight into otherwise unobservable properties nearing the atomic scale. Here, we discuss studies of 2D materials at the subnanoscale where the measurement method relies on the detection of visible light scattered or emitted from the scanning tunneling microscope (STM). We focus on tip-enhanced Raman spectroscopy, a subset of scattering-type scanning near-field optical microscopy, where incident light is confined and enhanced by a plasmonic STM tip. We also mention scanning tunneling microscope induced luminescence, where the STM tip is used as a highly local light source. The measurement of light-matter interactions within the atomic STM cavity is expected to continue to provide a useful platform to study new materials.

Deep learning based network similarity for model selection

Capturing data in the form of networks is becoming an increasingly popular approach for modeling, analyzing and visualising complex phenomena, to understand the important properties of the underlying complex processes. Access to many large-scale network datasets is restricted due to the privacy and security concerns. Also for several applications (such as functional connectivity networks), generating large scale real data is expensive. For these reasons, there is a growing need for advanced mathematical and statistical models (also called generative models) that can account for the structure of these large-scale networks, without having to materialize them in the real world. The objective is to provide a comprehensible description of the network properties and to be able to infer previously unobserved properties. Various models have been developed by researchers, which generate synthetic networks that adhere to the structural properties of real networks. However, the selection of the appropriate generative model for a given real-world network remains an important challenge. In this paper, we investigate this problem and provide a novel technique (named as TripletFit) for model selection (or network classification) and estimation of structural similarities of the complex networks. The goal of network model selection is to select a generative model that is able to generate a structurally similar synthetic network for a given real-world (target) network. We consider six outstanding generative models as the candidate models. The existing model selection methods mostly suffer from sensitivity to network perturbations, dependency on the size of the networks, and low accuracy. To overcome these limitations, we considered a broad array of network features, with the aim of representing different structural aspects of the network and employed deep learning techniques such as deep triplet network architecture and simple feed-forward network for model selection and estimation of structural similarities of the complex networks. Our proposed method, outperforms existing methods with respect to accuracy, noise-tolerance, and size independence on a number of gold standard data set used in previous studies.

Revisiting Memristor Properties

Memristor is a natural synapse because of its nanoscale and memory property, which influences the performance of memristive artificial neural networks. A three-variable memristor model is simplified with 15 kinds of properties, including the learning experience, the forgetting curve, the spiking time-dependent plasticity (STDP), the spiking rate dependent plasticity (SRDP), and the integration property. Through the analysis of the model, one more unobserved property called pseudo-polarity reversibility property is predicted by assuming the long-term memory is independent of memductance.

Do Nonhumans Seek Explanations?

From an early age, children explore their environment in a way suggesting that they reason about causal variables and seek causal explanations. Indeed, following extensive studies of problem-solving abilities in chimpanzees, Povinelli (Folk Physics for Apes, Oxford University Press, 2000) proposed that this ability to reason about unobservable variables is unique to humans. Following on from this, Povinelli and Dunphy-Lelii (Canadian Journal of Experimental Psychology, 55(2), 187-195, 2001) addressed the question whether chimpanzees would explore objects with the aim of elucidating unobservable and surprising object properties. Chimpanzees, unlike preschool children, did not show increased object exploration following a change in the unobservable properties of an object. We critically discuss these findings and argue that more research using a greater variety of methods and with a larger number of species is required to support the hypothesis that only humans engage in explanation seeking. We conclude by highlighting avenues for future research based on developmental and comparative research aimed at object exploration and information seeking conducted since the original investigation by Povinelli and Dunphy-Lelii.

A taste of the elite: The effect of pairing food products with elite groups on taste perceptions

Are tasteful foods tastier? Building on reference group influence and the principle of transferred essence, this research investigates the role of pairing foods with elite groups in the taste experience. In four studies involving actual taste tests and online experiments, we show that associating a food product with an elite group increases taste perceptions within various food categories. This effect occurs because of belief in transferred essence (the belief that the product incorporates the unobservable properties of the social elite). The effect is also stronger among participants highly sensitive to disgust. Overall, our results suggest that a taste experience is improved when foods embody the characteristics of the social elite.

The essence of essentialism

Over the past several decades,psychological essentialismhas been an important topic of study, incorporating research from multiple areas of psychology, philosophy and linguistics. At its most basic level, essentialism is the tendency to represent certain concepts in terms of a deeper, unobservable property that is responsible for category membership. Originally, this concept was used to understand people's reasoning about natural kind concepts, such astigerandwater, but more recently, researchers have identified the emergence of essentialist‐like intuitions in a number of other contexts, including people's representation of concepts likescientistorChristian.This paper develops an account that aims to capture how essentialism may operate across these varied cases. In short, we argue that while there is diversity in the forms essentialism can take, these varied cases reflect the same underlying cognitive structure.

Superstition and real estate prices: transaction-level evidence from the US housing market

ABSTRACTWe investigate the impact of superstition on prices paid by Chinese-American home buyers. Chinese consider 8 lucky and 4 unlucky. Lacking explicit buyer ethnicity identifiers, we develop a binomial name classifier, a machine learning approach applicable to any data set containing names, that allows for falsification tests using other ethnic groups, and mitigates ambiguity from the transliteration of Chinese characters into the Latin alphabet. Chinese buyers pay 1–2% premiums for addresses including an 8 and 1% discounts for addresses including a 4. These results are unrelated to unobserved property quality; no premium exists when Chinese sell to non-Chinese. The persistence of superstitions reflects the extent of cultural assimilation.

Psychological essentialism from first principles

Cognitive scientists have documented the existence of “essentialist” intuitions in humans: from a very early age, we assume that things have deep unobserved properties that make them what they are. I provide a sketch of an adaptationist explanation of psychological essentialism, arguing that these intuitions are the unsurprising output of adaptations for inductive inference. Variations on this insight have previously been used mostly as after-the-fact speculations, yet theories of adaptive function should ideally have a primary role in informing psychological research. Here I propose that viewing essentialist intuitions through an adaptationist lens has implications for some widespread assumptions about the phenomenon. Notably, researchers' focus on “higher-level” processes like categorization has led them to assume that essentialism is restricted to a few cognitive processes, but the ubiquity of inductive inference problems in cognition suggests otherwise. Additionally, because essentialist intuitions are the output of mechanisms solving related but distinct inference problems, it is unlikely that a single mechanistic theory can account for them all.

Heterogeneity of sellers in housing market: Difference in pricing strategies

The present study analyzes Perm, Russia residential housing market supply focusing on sellers’ heterogeneity. Many indicators of heterogeneity were considered in the previous research, and all of them were proved to have a great impact on housing prices and time on the market. However, the gap exists in evaluating sellers’ pricing strategies in dynamics mostly because of unavailable data. Current study clears out the effect of time on price using data on asking price dynamics. We employ semiparametric sample selection estimation procedure which accounts for the unobserved property characteristics and non-random selection of objects out of the sample. We consider two main types of sellers: real estate agents and property owners, and show that real estate agents appear to be more impatient compared to property owners. Specifically, they set a lower asking price initially and are more likely to revise it over time if the object is not sold.

Development of essentialist thinking about religion categories in Northern Ireland (and the United States).

Social essentialism, the belief that members of certain social categories share unobservable properties, licenses expectations that those categories are natural and a good basis for inference. A challenge for cognitive developmental theory is to give an account of how children come to develop essentialist beliefs about socially important categories. Previous evidence from Israel suggests that kindergarteners selectively engage in essentialist reasoning about culturally salient (ethnicity) categories, and that this is attenuated among children in integrated schools. In 5 studies (N = 718) we used forced-choice (Study 1) and unconstrained (Studies 2-4) category-based inference tasks, and a questionnaire (Study 5) to study the development of essentialist reasoning about religion categories in Northern Ireland (Studies 1-3 & 5) and the U.S. (Study 4). Results show that, as in Israel, Northern Irish children selectively engage in essentialist reasoning about culturally salient (religion) categories, and that such reasoning is attenuated among children in integrated schools. However, the development trajectory of essentialist thinking and the patterns of attenuation among children attending integrated schools in Northern Ireland differ from the Israeli case. Meta-analysis confirmed this claim and ruled out an alternative explanation of the results based on community diversity. Although the Northern Irish and Israeli case studies illustrate that children develop selective essentialist beliefs about socially important categories, and that these beliefs are impacted by educational context, the differences between them emphasize the importance of historical, cultural, and political context in understanding conceptual development, and suggest that there may be more than one developmental route to social essentialism. (PsycINFO Database Record

Complex and Noncentrosymmetric Stacking of Layered Metal Dichalcogenide Materials Created by Screw Dislocations.

The interesting and tunable properties of layered metal dichalcogenides heavily depend on their phase and layer stacking. Here, we show and explain how the layer stacking and physical properties of WSe2 are influenced by screw dislocations. A one-to-one correlation of atomic force microscopy and high- and low-frequency Raman spectroscopy of many dislocated WSe2 nanoplates reveals variations in the number and shapes of dislocation spirals and different layer stackings that are determined by the number, rotation, and location of the dislocations. Plates with triangular dislocation spirals form noncentrosymmetric stacking that gives rise to strong second-harmonic generation and enhanced photoluminescence, plates with hexagonal dislocation spirals form the bulk 2H layer stacking commonly observed, and plates containing mixed dislocation shapes have intermediate noncentrosymmetric stackings with mixed properties. Multiple dislocation cores and other complexities can lead to more complex stackings and properties. These previously unobserved properties and layer stackings in WSe2 will be interesting for spintronics and valleytronics.

Cultural Superstitions and Residential Real Estate Prices: Transaction-Level Evidence from the US Housing Market

In Chinese culture, the number 8 is considered lucky and 4 is considered unlucky. We analyze the relationship between the presence of 8s and 4s in addresses and transaction prices paid by Chinese home buyers and sellers in a novel setting, Seattle, Washington, from 1990 to 2015. In the absence of explicit identifiers for Chinese individuals, we develop a probabilistic model for identifying ethnicity based on name alone. The results indicate Chinese buyers pay a 1-2% premium for addresses that include an 8 and 1% less for properties with a 4 in the address. These results are not related to unobserved property quality as there is no premium when Chinese sell properties with an 8 in the address. These results suggest that some Chinese home buyers in Seattle retain their Chinese cultural superstitions.

A linear varying coefficient ARCH-M model with a latent variable

Motivated by the psychological factor of time-varying risk-return relationship, this article studies a linear varying coefficient ARCH-M model with a latent variable. Due to the unobservable property of the latent variable, a corrected likelihood method is employed for parametric estimation. Estimators are proved to be consistent and asymptotically normal under certain regularity conditions. A simple test statistic is also proposed for testing latent variable effect. Simulation results confirm that the proposed estimators and test perform well. The model is further applied to examine whether the risk-return relationship depends on investor’s sentiment in American Market and some explainable results are obtained.

Large-Scale Modelling of the Environmentally-Driven Population Dynamics of Temperate Aedes albopictus (Skuse).

The Asian tiger mosquito, Aedes albopictus, is a highly invasive vector species. It is a proven vector of dengue and chikungunya viruses, with the potential to host a further 24 arboviruses. It has recently expanded its geographical range, threatening many countries in the Middle East, Mediterranean, Europe and North America. Here, we investigate the theoretical limitations of its range expansion by developing an environmentally-driven mathematical model of its population dynamics. We focus on the temperate strain of Ae. albopictus and compile a comprehensive literature-based database of physiological parameters. As a novel approach, we link its population dynamics to globally-available environmental datasets by performing inference on all parameters. We adopt a Bayesian approach using experimental data as prior knowledge and the surveillance dataset of Emilia-Romagna, Italy, as evidence. The model accounts for temperature, precipitation, human population density and photoperiod as the main environmental drivers, and, in addition, incorporates the mechanism of diapause and a simple breeding site model. The model demonstrates high predictive skill over the reference region and beyond, confirming most of the current reports of vector presence in Europe. One of the main hypotheses derived from the model is the survival of Ae. albopictus populations through harsh winter conditions. The model, constrained by the environmental datasets, requires that either diapausing eggs or adult vectors have increased cold resistance. The model also suggests that temperature and photoperiod control diapause initiation and termination differentially. We demonstrate that it is possible to account for unobserved properties and constraints, such as differences between laboratory and field conditions, to derive reliable inferences on the environmental dependence of Ae. albopictus populations.

Molecular descriptors as proxies for the modeling of the materials and their environmental impact

For the prediction of material's properties and of interaction of molecules with the surroundings, one needs to know their properties. Usually, the molecular properties are revealed through experimental measurements. It can be a tedious, time-consuming, and costly work. On the other hand, computational chemistry readily generates a huge number of data which can provide various molecular descriptors. These can be various observable properties (bond lengths and angles, dipole moments, etc...), but also the unobservable properties (partial atomic charges, electronegativity, various latent variables ....). There is an urgent need to develop accurate and economical screening tools that predict potential toxicity and environmental burden of various chemicals. Equally important is the design of safer alternatives. Molecular modeling methods offer one of several complementary approaches to evaluate the risk to human health and the environment as a result of exposure to environmental chemicals. These tools can streamline the hazard assessment process by simulating possible modes of action and providing virtual screening tools that can help prioritize bioassay requirements. Tailoring these strategies to the particular challenges presented by environmental chemical interactions make them even more effective. Advances in the fields of computational chemistry and molecular toxicology in recent decades allow the development of predictive models that inform the design of molecules with reduced potential to be toxic to humans or to the environment. As an example we present the novel methodology for the computational evaluation of pKa values of various organic bases, based on calculation of partial atomic charges by a simple semiempirical QM method.

Sellers' Heterogeneity in Housing Market: Difference in Pricing Strategies

The present study analyzes Perm, Russia residential housing market supply focusing on sellers' heterogeneity. Many indicators of heterogeneity were considered in the previous research, and all of them were proved to have a great impact on housing prices and time on the market. However, the gap exists in evaluating sellers’ pricing strategies in dynamics mostly because of unavailable data. Current study clears out the effect of time on price using data on asking price dynamics. We employ semiparametric sample selection estimation procedure which accounts for the unobserved property characteristics and non-random selection of objects out of the sample. We consider two main types of sellers: real estate agents and property owners, and show that real estate agents appear to be more impatient compared to property owners. Specifically, they set a lower asking price initially and are more likely to revise it over time if the object is not sold.

Spin-flip scattering of critical quasiparticles and the phase diagram ofYbRh2Si2

Several observed transport and thermodynamic properties of the heavy-fermion compound YbRh2Si2 in the quantum critical regime are unusual and suggest that the fermionic quasiparticles are critical, characterized by a scale-dependent diverging effective mass. A theory based on the concept of critical quasiparticles (CQP) scattering off antiferromagnetic spin fluctuations in a strong-coupling regime has been shown to successfully explain the unusual existing data and to predict a number of so far unobserved properties. In this paper, we point out a new feature of a magnetic field-tuned quantum critical point of a heavy-fermion metal: anomalies in the transport and thermodynamic properties caused by the freezing out of spin-flip scattering of critical quasiparticles and the scattering off collective spin excitations. We show that a step-like behavior as a function of magnetic field of e.g. the Hall coefficient and magnetoresistivity results, which accounts quantitatively for the observed behavior of these quantities. That behavior has been described as a crossover line T*(H) in the T - H phase diagram of YbRh2Si2. Whereas some authors have interpreted this observation as signaling the breakdown of Kondo screening and an associated abrupt change of the Fermi surface, our results suggest that the T* line may be quantitatively understood within the picture of robust critical quasiparticles.

Uncertainty analysis of a model of wind-blown volcanic plumes.

Mathematical models of natural processes can be used as inversion tools to predict unobserved properties from measured quantities. Uncertainty in observations and model formulation impact on the efficacy of inverse modelling. We present a general methodology, history matching, that can be used to investigate the effect of observational and model uncertainty on inverse modelling studies. We demonstrate history matching on an integral model of volcanic plumes that is used to estimate source conditions from observations of the rise height of plumes during the eruptions of Eyjafjallajökull, Iceland, in 2010 and Grímsvötn, Iceland, in 2011. Sources of uncertainty are identified and quantified, and propagated through the integral plume model. A preliminary sensitivity analysis is performed to identify the uncertain model parameters that strongly influence model predictions. Model predictions are assessed against observations through an implausibility measure that rules out model inputs that are considered implausible given the quantified uncertainty. We demonstrate that the source mass flux at the volcano can be estimated from plume height observations, but the magmatic temperature, exit velocity and exsolved gas mass fraction cannot be accurately determined. Uncertainty in plume height observations and entrainment coefficients results in a large range of plausible values of the source mass flux. Our analysis shows that better constraints on entrainment coefficients for volcanic plumes and more precise observations of plume height are required to obtain tightly constrained estimates of the source mass flux.