Natural Explanation Research Articles

Effect of Explanation Conceptualisations on Trust in AI-assisted Credibility Assessment

As misinformation increasingly proliferates on social media platforms, it has become crucial to explore how to best convey automated news credibility assessments to end-users, and foster trust in fact-checking AIs. In this paper, we investigate how model-agnostic, natural language explanations influence trust and reliance on a fact-checking AI. We construct explanations from four Conceptualisation Validations (CVs) - namely consensual, expert, internal (logical), and empirical - which are foundational units of evidence that humans utilise to validate and accept new information. Our results show that providing explanations significantly enhances trust in AI, even in a fact-checking context where influencing pre-existing beliefs is often challenging, with different CVs causing varying degrees of reliance. We find consensual explanations to be the least influential, with expert, internal, and empirical explanations exerting twice as much influence. However, we also find that users could not discern whether the AI directed them towards the truth, highlighting the dual nature of explanations to both guide and potentially mislead. Further, we uncover the presence of automation bias and aversion during collaborative fact-checking, indicating how users' previously established trust in AI can moderate their reliance on AI judgements. We also observe the manifestation of a 'boomerang'/backfire effect often seen in traditional corrections to misinformation, with individuals who perceive AI as biased or untrustworthy doubling down and reinforcing their existing (in)correct beliefs when challenged by the AI. We conclude by presenting nuanced insights into the dynamics of user behaviour during AI-based fact-checking, offering important lessons for social media platforms.

Incidence of afterglow plateaus in gamma-ray bursts associated with binary neutron star mergers

One of the most surprising gamma-ray burst (GRB) features discovered with the Swift X-ray telescope (XRT) is a plateau phase in the early X-ray afterglow light curves. These plateaus are observed in the majority of long GRBs, while their incidence in short GRBs (SGRBs) is still uncertain due to their fainter X-ray afterglow luminosity with respect to long GRBs. An accurate estimate of the fraction of SGRBs with plateaus is of utmost relevance given the implications that the plateau may have for our understanding of the jet structure and possibly of the nature of the binary neutron star (BNS) merger remnant. This work presents the results of an extensive data analysis of the largest and most up-to-date sample of SGRBs observed with the XRT, and for which the redshift has been measured. We find a plateau incidence of 18-37<!PCT!> in SGRBs, which is a significantly lower fraction than that measured in long GRBs (>50<!PCT!>). Although still debated, the plateau phase could be explained as energy injection from the spin-down power of a newly born magnetized neutron star (NS; magnetar). We show that this scenario can nicely reproduce the observed short GRB (SGRBs) plateaus, while at the same time providing a natural explanation for the different plateau fractions between short and long GRBs. In particular, our findings may imply that only a minority of BNS mergers generating SGRBs leave behind a sufficiently stable or long-lived NS to form a plateau. From the probability distribution of the BNS remnant mass, a fraction 18-37<!PCT!> of short GRB plateaus implies a maximum NS mass in the range $ odot

Adaptive and transparent decision-making in autonomous robots through graph-structured world models

ABSTRACT The growing ubiquity of autonomous robots across different fields necessitates that agents adapt to diverse tasks and ensure transparency and intelligibility in their decision-making processes. This study presents a novel framework that combines a graph-structured world model with large language models (LLMs) to address these requirements. First, a latent space is created to capture the reachability between distinct states. Next, a graph is constructed within this latent space by clustering an offline dataset that effectively captures the complex dynamics of the environment. Subsequently, LLMs are employed to redefine the reward function and relabel the dataset, thereby establishing a well-defined Markov decision process based on the previously learned graph. This relabeling process ensures that the agent's decision space aligns with user intentions. Consequently, a predictive world model is obtained, offering insights into potential future states and facilitating graph-based planning. Moreover, by inputting the planned path of the agent in the graph-structured world model into LLMs, natural language explanations can be generated to provide transparency in the decision-making process. Experiments on the D4RL benchmark validated the effectiveness of our approach in long-horizon planning, its adaptability to different user tasks, and its inherent explainability.

Fermion masses and mixings and g − 2 muon anomaly in a Q6 flavored 2HDM

We propose an extended 2HDM with Q6×Z4×Z2 symmetry that can successfully accommodate the SM fermion mass and mixing hierarchy. The tiny masses of the active neutrinos are generated from a type-I seesaw mechanism mediated by very heavy right-handed Majorana neutrinos. The model gives a natural explanation of the charged lepton mass hierarchy. Besides that, the experimental values of the physical observables of the neutrino sector: the neutrino mass squared splittings, the leptonic mixing angles and the leptonic Dirac CP violating phase, are also successfully reproduced for both normal and inverted neutrino mass hierarchies. We find a feasible range of values for the leptonic Dirac CP phase to be in the ranges δCP∈(305.90,348.70)∘ for normal ordering and δCP∈(308.00,348.00)∘ for inverted ordering, which is consistent with the 3σ experimentally allowed limits. The sum of neutrino masses is obtained as ∑mi∈(58.03,60.51) meV for normal ordering and ∑mi∈(98.07,101.40) meV for inverted ordering which are well consistent with all the recent limits. In addition, the obtained ranges for the effective neutrino masses are 〈mee〉∈(3.80,4.38) meV, mβ∈(8.53,9.34)meV for normal ordering and 〈mee〉∈(47.85,49.58) meV, mβ∈(48.39,50.09)meV for inverted ordering which are in agreement with the recent experimental bounds. For the quark sector, the derived results are also in agreement with the recent data on the quark masses and mixing angles. The model under consideration can also accommodate the muon anomalous magnetic moment.

Strange-metal behavior without fine tuning in PrV2Al20

Strange-metal behavior observed in the praseodymium-based heavy-fermion material PrV2Al20 has been tentatively interpreted in the framework of proximity to a quantum critical point (QCP) associated with quadrupolar ordering. Here, we demonstrate that an alternative, natural explanation exists without invoking a QCP, in terms of the unconventional nature of the quadrupolar Kondo effect taking place in non-Kramers ions. Using a combination of density-functional theory calculations and analytical arguments, we construct a periodic Anderson model with realistic parameters to describe PrV2Al20. We solve the model using dynamical mean-field theory preserving the model symmetries and demonstrate the non-Fermi liquid strange-metal behavior stemming from the two-channel nature of the quadrupolar Kondo effect. Our calculations provide an explanation for the puzzling temperature dependence in the magnetic susceptibility and provide a basis for analyzing future photoemission experiments. Published by the American Physical Society 2024

Explainable AI for Transparent MRI Segmentation: Deep Learning and Visual Attribution in Clinical Decision Support

For medical diagnosis and therapy planning, the importance of accurate MRI segmentation cannot be overemphasized. Conversely, the inscrutability of deep learning models remains obstacles to their application in therapeutic contexts. In this article, an interpretability artificial intelligence framework is introduced. It combines an MRI segmentation model based on deep learning, visual attribution algorithms and natural language explanations. EXPERIMENT The dataset is consisting of plenty of different types of brain MRI scans, and used to test the architecture. The average of Dice score of our method is 88.7% and 92.3% for segmentation of tumor and categorization of tissues, respectively. Both are pretty epic scores. The insights extracted from both the visual attributions and textual explations improve our understanding of how the model arrives at its decisions, thereby increasing the transparency and interpretability of the model. believe this approach to explainable artificial intelligence can help to close the gap between state-of-the-art performance in MRI segmentation and clinical interpretability, by increasing the transparency of complex models and facilitating their implementation into a clinical workflow. Conclusion Our approach may have implications in the transparent and reliable development of AI-based decision support systems for medical imaging

Resistance in sexuality education

Teaching about sexuality causes embarrassment and general discomfort among both teachers and students. This article contends that the subject matter itself causes resistance, as sexuality is inherently transgressive. It is sex that makes sexuality education a challenge. Research has not focused on manifestations of resistance in relation to sexuality education. Limiting sexuality education to the reproductive function – conception, contraception – can be seen as one of the most common forms of resistance. Psychoanalytic theory and its classic concepts of drive and desire can support analysis and understanding of why sex is transgressive and embarrassing, which can hopefully support teachers’ professional expertise, given that sex poses a threat to the teacher’s professional status. Recognising the inherent impossibilities of teaching sexuality is key to any effort to improve sexuality education. The article also offers an explanation for the controversial nature of research on sexuality education.

Evolution of geometric phase and explaining the geodesic rule

We use the recently developed wave model of geometric phase to track the continuous evolution of geometric phase as a wave propagates through optical elements and throughout an optical system. By working directly with the wave properties, we encounter a natural explanation of why the conventional Poincaré sphere solid angle method must use geodesic paths rather than the physical paths of the polarization state—the “geodesic rule”—and show that the existing rules for the solid angle algorithm are incomplete. Finally, we use the physical model to clarify the differences between the Pancharatnam connection and the geometric phase of a wave.

Data-driven MHD Simulation of the Formation of a Magnetic Flux Rope and an Inclined Solar Eruption

Solar energetic events are caused by the release of magnetic energy accumulated in the solar atmosphere. To understand their initiating physical mechanisms, the dynamics of the coronal magnetic fields must be studied. Unfortunately, the dominant mechanisms are still unclear due to a lack of direct measurements. Numerical simulations based on magnetohydrodynamics (MHD) can reproduce the dynamical evolution of solar coronal magnetic field, providing a useful tool to explore flare initiation. Data-driven MHD simulations, in which the time-series observational data of the photospheric magnetic field is used as the simulation boundary condition, can explore different mechanisms. To investigate the accumulation of free magnetic energy through a solar eruption, we simulated the first of several large flares in NOAA active region 11283. We used a data-driven model that was governed by zero-beta MHD, focusing on the free magnetic energy accumulation prior to the M5.3 flare (2011 September 6 at 01:59 UT). We reproduced the flare-associated eruption following the formation of twisted magnetic fields, or a magnetic flux rope (MFR), formed by photospheric motions at its footpoints. We found that the eruption was first triggered by the growth of the torus instability. The erupting MFR caused magnetic reconnections with neighboring magnetic field lines located along the direction of the eruption. Using the simulation results and an axial-radial decay index centered on the MFR, we find a natural explanation for the inclination of the eruption and a possible approach to predict the direction of solar eruptive events.

Perspective: imaging atomic step geometry to determine surface terminations of kagome materials and beyond

Here we review scanning tunneling microscopy research on the surface determination for various types of kagome materials, including 11-type (CoSn, FeSn, FeGe), 32-type (Fe3Sn2), 13-type (Mn3Sn), 135-type (AV3Sb5, A = K, Rb, Cs), 166-type (TbMn6Sn6, YMn6Sn6 and ScV6Sn6), and 322-type (Co3Sn2S2 and Ni3In2Se2). We first demonstrate that the measured step height between different surfaces typically deviates from the expected value of ±0.4 ∼0.8Å, which is owing to the tunneling convolution effect with electronic states and becomes a serious issue for Co3Sn2S2 where the expected Sn-S interlayer distance is 0.6Å. Hence, we put forward a general methodology for surface determination as atomic step geometry imaging, which is fundamental but also experimentally challenging to locate the step and to image with atomic precision. We discuss how this method can be used to resolve the surface termination puzzle in Co3Sn2S2. This method provides a natural explanation for the existence of adatoms and vacancies, and beyond using unknown impurity states, we propose and use designer layer-selective substitutional chemical markers to confirm the validity of this method. Finally, we apply this method to determine the surface of a new kagome material Ni3In2Se2, as a cousin of Co3Sn2S2, and we image the underlying kagome geometry on the determined Se surface above the kagome layer, which directly visualizes the p-d hybridization physics. We emphasize that this general method does not rely on theory, but the determined surface identity can provide guidelines for first-principles calculations with adjustable parameters on the surface-dependent local density of states and quasi-particle interference patterns.

The generation of a multiphase medium in ‘Splash’ bridge systems: towards an understanding of star formation suppression in turbulent galaxy systems

ABSTRACT Cloud–cloud collisions in splash bridges produced in gas-rich disc galaxy collisions offer a brief but interesting environment to study the effects of shocks and turbulence on star formation rates in the diffuse intergalactic medium, far from the significant feedback effects of massive star formation and active galactic nucleus. Expanding on our earlier work, we describe simulated collisions between counter-rotating disc galaxies of relatively similar mass, focusing on the thermal and kinematic effects of relative inclination and disc offset at the closest approach. This includes essential heating and cooling signatures, which go some way towards explaining the luminous power in H$_2$ and [C ii] emission in the Taffy bridge, as well as providing a partial explanation of the turbulent nature of the recently observed compact CO-emitting clouds observed in Taffy by the Atacama Large Millimeter Array (ALMA). The models show counter-rotating disc collisions result in swirling, shearing kinematics for the gas in much of the post-collision bridge. Gas with little specific angular momentum due to collisions between counter-rotating streams accumulates near the centre of mass. The disturbances and mixing in the bridge drive continuing cloud collisions, differential shock heating, and cooling throughout. A wide range of relative gas phases and line-of-sight velocity distributions are found in the bridges, depending sensitively on initial disc orientations, and the resulting variety of cloud collision histories. Most cloud collisions can occur promptly or persist for quite a long duration. Cold and hot phases can largely overlap throughout the bridge or can be separated into different parts of the bridge.

Beta deformed sigma model and strong deformation coupling limit

We study the beta deformation of the superstring in AdS5 × S5 at all orders in the deformation parameter, employing the pure spinor formalism. This is necessary in order to study the regime of strong deformation parameter, which in the field side is related to fishnet theories. We compare the pure spinor sigma model approach to the previously known supergravity description. We find a complete agreement. Moreover, the BRST structure of the worldsheet model provides a natural explanation of the peculiar features of the worldsheet model in the fishnet limit. In particular, we study the degeneracy of the sigma model Lagrangian. We show that the BRST structure is responsible for a particularly “tame” degeneration of the fishnet sigma-model.

Intercultural competence in the context of intercultural service encounters: A multilevel and dynamic approach

This article proposes a multilevel and dynamic approach to analyzing intercultural competence (IC) to understand how IC components at individual and collective levels articulate in intercultural service encounters (ICSEs). The articulation of IC levels is analyzed using a dynamic approach that covers the three service experience phases: pre-core, core, and post-core ICSEs. Analysis of data collected from a French language services business unit shows that service businesses need to adjust the two levels of IC in each service experience phase in order to achieve a quality service experience. A multilevel approach is taken to illuminate the role of the team as a whole, rather than individual staff members, in service encounter outcomes. The study represents a contextualized explanation of the dynamic nature of intercultural interactions within the team and between staff and customers, validates the concept of collective IC, and contributes to the literature on ICSEs.

Fermi Surface Nesting with Heavy Quasiparticles in the Locally Noncentrosymmetric Superconductor CeRh2As2

Abstract The locally noncentrosymmetric heavy fermion superconductor CeRh2As2 has attracted considerable interests due to its rich superconducting phases, accompanied by possible quadrupole density wave and pronounced antiferromagnetic excitations. To understand the underlying physics, here we report measurements from high-resolution angle-resolved photoemission. Our results reveal fine splittings of the conduction bands related to the locally noncentrosymmetric structure, as well as a quasi-two-dimensional Fermi surface (FS) with strong 4f contributions. The FS shows signs of nesting with an in-plane vector q 1 = (π/a, π/a), which is facilitated by the heavy bands near X ¯ arising from the characteristic conduction-f hybridization. The FS nesting provides a natural explanation for the observed antiferromagnetic spin fluctuations at (π/a, π/a), which might be the driving force for its unconventional superconductivity. Our experimental results can be reasonably explained by density functional theory plus dynamical mean field theory calculations, which can capture the strong correlation effects. Our study not only provides spectroscopic signature of the key factors underlying the field-induced superconducting transition, but also uncovers the critical role of FS nesting and lattice Kondo effect in the underlying magnetic fluctuations.

Saving Sanctity: The Roman Inquisition and the Initial Prosecution of Girolamo Cardano

The impact of the Protestant Reformation and Catholic Reform movement on the ecclesiastical censorship of science, philosophy and magic has been an enduring theme in the historiography of early modern Italy. In this paper, I consider how the Church’s desire to defend the truth of miracles from Protestant criticisms on the one hand and their efforts to centralise the process of verifying miracles on the other affected the investigation of philosophical works. Taking the case of Girolamo Cardano, whose works were the subject of intensive investigation by the Catholic Church from the 1570s, I examine the issues that censors flagged as points of concern. One recurring theme was the fact that his accounts of the cosmos appeared to offer natural explanations for miracles and the actions of the martyrs. Although the intellectual problems raised by Cardano’s work were far from new, I suggest that the events of the sixteenth century raised their salience with Church authorities.

Learning energy-based representations of quantum many-body states

Efficient representation of quantum many-body states on classical computers is a problem of practical importance. An ideal representation of a quantum state combines a succinct characterization informed by the structure and symmetries of the system along with the ability to predict the physical observables of interest. Several machine-learning approaches have been recently used to construct such classical representations, which enable predictions of observables and account for physical symmetries. However, the structure of a quantum state typically gets lost unless a specialized is employed based on prior knowledge of the system. Moreover, most such approaches give no information about what states are easier to learn in comparison with others. Here, we propose a generative energy-based representation of quantum many-body states derived from Gibbs distributions used for modeling the thermal states of classical spin systems. Based on the prior information on a family of quantum states, the energy function can be specified by a small number of parameters using an explicit low-degree polynomial or a generic parametric family such as neural nets and can naturally include the known symmetries of the system. Our results show that such a representation can be efficiently learned from data using exact algorithms in a form that enables the prediction of expectation values of physical observables. Importantly, the structure of the learned energy function provides a natural explanation for the difficulty of learning an energy-based representation of a given class of quantum states when measured in a certain basis. Published by the American Physical Society 2024

Global preventive feedback of powerful radio jets on galaxy formation

Firmly anchored on observational data, giant radio lobes from massive galaxies hosting supermassive black holes can exert a major negative feedback effect, by endowing the intergalactic gas with significant magnetic pressure hence retarding or preventing gas accretion onto less massive halos in the vicinity. Since massive galaxies that are largely responsible for producing the giant radio lobes, this effect is expected to be stronger in more overdense large-scale environments, such as protoclusters, than in underdense regions, such as voids. We show that by redshift [Formula: see text] halos with masses up to [Formula: see text] are significantly hindered from accreting gas due to this effect for radio bubble volume filling fraction of [Formula: see text], respectively. Since the vast majority of the stars in the universe at [Formula: see text][Formula: see text] 2 to 3 form precisely in those halos, this negative feedback process is likely one major culprit for causing the global downturn in star formation in the universe. It also provides a natural explanation for the rather sudden flattening of the slope of the galaxy rest-frame UV luminosity function around [Formula: see text]. A cross-correlation between protoclusters and Faraday rotation measures may test the predicted magnetic field. Inclusion of this external feedback process in the next generation of cosmological simulations may be imperative.

Electric dipole moments of charged leptons in models with pseudo-Dirac sterile fermions

In this work, we address the impact of a small lepton number violation on charged lepton electric dipole moments — EDMs. Low-scale seesaw models protected by lepton number symmetry and leading to pseudo-Dirac pairs in the neutrino heavy spectrum provide a natural explanation for the smallness of neutrino masses with potentially testable consequences. Among which, it was thought that the small mass gap in each pair of pseudo-Dirac neutrinos may induce important contribution to the charged lepton EDMs. Recently, it has been shown that the contribution from some of the Feynman diagrams to charged lepton EDMs exactly cancel by virtue of the Ward-Takahashi identity in quantum electrodynamics. We thus consider here the Standard Model minimally extended with pairs of pseudo-Dirac sterile fermions and derive the complete analytical formula at two loops for the charged lepton EDMs. In addition, we numerically evaluate the order of the predicted EDMs consistent with the experimental bounds and constraints such as neutrino oscillation data, charged lepton flavour violating processes, sterile neutrino direct searches, meson decays, sterile neutrino decays, and cosmological and astrophysical observations. We find that, in the minimal setup accommodating neutrino data (masses and mixings) with only two pseudo-Dirac pairs, the predicted electron EDM is O10−36\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O}\\left({10}^{-36}\\right) $$\\end{document}e cm, at most, which is much smaller than the current experimental bound and even future sensitivity. Hopefully, the electron EDM might reach future sensitivity, once extra pseudo-Dirac neutrinos are taken into account. The analytical formulae we derive are generic to any model involving pseudo-Dirac pairs in the heavy neutrino spectrum.

Microevolutionary hypothesis of the obesity epidemic.

The obesity epidemic represents potentially the largest phenotypic change in Homo sapiens since the origin of the species. Despite obesity's high heritability, it is generally presumed a change in the gene pool could not have caused the obesity epidemic. Here we advance the hypothesis that a rapid change in the obesogenic gene pool has occurred second to the introduction of modern obstetrics dramatically altering evolutionary pressures on obesity-the microevolutionary hypothesis of the obesity epidemic. Obesity is known to increase childbirth-related mortality several fold. Prior to modern obstetrics, childbirth-related mortality occurred in over 10% of women in their lifetime. After modern obstetrics, this mortality reduced to a fraction of a percent, thereby lifting a strong negative selection pressure. Regression analysis of data for ~ 190 countries was carried out to examine associations between 1990 lifetime maternal death rates (LMDR) and current obesity rates. Multivariate regression showed LMDR correlated more strongly with national obesity rates than GDP, calorie intake and physical inactivity. Analyses controlling for confounders via partial correlation show that LMDR explains approximately 11% of the variability of obesity rate between nations. For nations with LMDR above the median (>0.45%), LMDR explains 33% of obesity variance, while calorie intake, GDP and physical inactivity show no association with obesity in these nations. The microevolutionary hypothesis offers a parsimonious explanation of the global nature of the obesity epidemic.

Immunopathogenesis of Lung in Fatal COVID-19 Cases in Erbil, Iraq

Coronavirus disease 2019 (COVID-19) infection is caused by severe acute respiratory syndrome coronavirus-2 (SARS-Cov-2). It is characterized by respiratory distress, multiorgan dysfunction and death in some cases. The host immune response to SARS-CoV-2 appears to play a critical role in disease pathogenesis and clinical manifestation. However the pathological mechanism underling the disease has not been fully defined. Lung autopsy samples from 3 patients with fatal COVID-19 were evaluated using hematoxylin and eosin stain to analyze the histopathological changes. While immunohistochemical (IHC) staining was performed for detecting CD4 in helper T-cells, CD8 in cytotoxic T-cells, CD56 in NK-cells and CD45RO in memory T-cells. Histopathological examination revealed features of diffuse alveolar damage (DAD) with exudate in alveolar space and infiltration of inflammatory cells. Immunohistochemistry staining for CD4 showed weak to positive staining, CD8 showed weak positive staining, CD56 showed negative staining, while CD45RO showed a positive staining. It can be concluded that SARS-CoV-2 virus impaired innate immune response through a decrease in NK cells and adaptive immune response through a decrease in CD8 T cells which is one of the explanations of the destructive nature of SARS-CoV-2 virus.