Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediagnostic plasma IgE levels and risk of adult glioma in four prospective cohort studies.

BACKGROUND Increased levels of serum immunoglobulin E (IgE) because of allergies have been inversely associated with risk of glioma in observational studies. Despite consistency across studies examining history of allergies and glioma, questions remain as to whether those are causal associations. An inverse association between serum IgE and risk of glioma was reported in a large case-control study, but reverse causality and treatment effects remain potential explanations for those findings. METHODS We combined data from four prospective cohort studies and used a nested case-control design to examine the association between allergy and glioma. We included glioma case subjects who were confirmed from medical or pathology records or from death certificates, and with prediagnostic blood available. We matched three control subjects per case subject, and the final numbers for analyses were 169 case subjects and 520 control subjects. Total IgE, food allergen-specific IgE, and respiratory allergen-specific IgE levels were measured using a highly sensitive fluorescent assay. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using conditional logistic regression analysis. Stratified analyses were conducted by age and birth cohorts. RESULTS Borderline elevated total IgE levels (25-100 kU/L) showed a statistically significant inverse association with glioma (OR = 0.63, 95% CI = 0.42 to 0.93), but no association was noted between elevated IgE (>100 kU/L) and glioma (OR = 0.98, 95% CI = 0.61 to 1.56) compared with clinically normal IgE levels (<25 kU/L). The association between glioma and total IgE was consistent for both men and women. Non-statistically significant inverse associations were noted for elevated IgE levels among individuals born before year 1930 (OR = 0.67, 95% CI = 0.34 to 1.34) and when restricting analyses to highly fatal (deceased within 2 years of diagnosis) glioma case subjects (OR = 0.64, 95% CI = 0.34 to 1.19) compared with individuals with clinically normal IgE levels. No associations were observed for either food allergen-specific or respiratory allergen-specific IgE levels. CONCLUSIONS Overall, our prospective findings are consistent with recent retrospective studies and support an association between total IgE levels and glioma. However, this association requires further elucidation.
The need to believe: a neuroscience account of religion as a motivated process

Religious belief has been shown to offer substantial benefits to its adherents, including improved well-being and health. We suggest that these benefits might be explained, at least in part, from a “motivated meaning-making” perspective. This model holds that people are motivated to create and sustain meaning (i.e., a sense of coherency between beliefs, goals, and perceptions of the environment, which provides individuals with the feeling that the world is an orderly place), and that religious beliefs buffer the distress associated with disruptions to meaning, thus leading to decreases in distress. We further propose that religion's palliative attributes can be measured at the level of the brain, specifically in the anterior cingulate cortex (ACC), which produces a “distress signal” upon the detection of errors, conflict, and expectancy violation. Using a social neuroscience paradigm, we investigate four main predictions that arise from this model: (1) religion should be associated with activation in the ACC; (2) religion should decrease activation in the ACC; (3) this attenuation of ACC activity should be related to religion's ability to buffer bodily states of distress, and not to decreases in motivation, attention, or control; (4) religion should have these effects because it provides meaning and thus buffers people from uncertainty. All predictions were supported, thus providing evidence, at the neural level, for the motivated meaning-making model's account of the salutary properties of religion.
Applications of Grassmannian and graph flows to nonlinear systems

We show how many classes of partial differential systems with local and nonlocal nonlinearities are linearisable. By this we mean that solutions can be generated by solving a corresponding linear partial differential system together with a linear Fredholm integral equation. The flows of such nonlinear systems are examples of linear flows on Fredholm Stiefel manifolds that can be projected onto Fredholm Grassmann manifolds or of further projections onto natural subspaces thereof. Detailed expositions of such flows are provided for the Korteweg de Vries and nonlinear Schrodinger equations, as well as Smoluchowski's coagulation and related equations in the constant frequency kernel case. We then consider Smoluchowski's equation in the additive and multiplicative frequency kernel cases which correspond to the inviscid Burgers equation. The solution flow in these cases prompts us to introduce a new class of flows we call ``graph flows''. These generalise flows on a Grassmann manifold from sets of graphs of linear maps to sets of graphs of nonlinear maps. We include a detailed discussion of directions in which these flows can be generalised to include many other partial differential systems with local and nonlocal nonlinearities.
Ludii - The Ludemic General Game System

While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often somewhat specialised and computationally inefficient. In this paper, we describe the "ludemic" general game system Ludii, which has the potential to provide an efficient tool for AI researchers as well as game designers, historians, educators and practitioners in related fields. Ludii defines games as structures of ludemes -- high-level, easily understandable game concepts -- which allows for concise and human-understandable game descriptions. We formally describe Ludii and outline its main benefits: generality, extensibility, understandability and efficiency. Experimentally, Ludii outperforms one of the most efficient Game Description Language (GDL) reasoners, based on a propositional network, in all games available in the Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of performance with the more recently proposed Regular Boardgames (RBG) system, and has various advantages in qualitative aspects such as generality.
Channels, Learning, Queueing and Remote Estimation Systems With A Utilization-Dependent Component

In this article, we survey the main models, techniques, concepts, and results centered on the design and performance evaluation of engineered systems that rely on a utilization-dependent component (UDC) whose operation may depend on its usage history or assigned workload. More specifically, we report on research themes concentrating on the characterization of the capacity of channels and the design with performance guarantees of learning algorithms, queueing and remote estimation systems. Causes for the dependency of a UDC on past utilization include the use of replenishable energy sources to power the transmission of information among the sub-components of a networked system, the influence of the dynamics of optimization iterates on the convergence of learning mechanisms and the assistance of a human operator for servicing a queue. Our analysis unveils the similarity of the UDC models typically adopted in each of the research themes, and it reveals the differences in the objectives and technical approaches employed. We also identify new challenges and future research directions inspired by the cross-pollination among the central concepts, techniques and problem formulations of the research themes discussed.
Continuity of the martingale optimal transport problem on the real line

We show continuity of the martingale optimal transport optimisation problem as a functional of its marginals. This is achieved via an estimate on the projection in the nested/causal Wasserstein distance of an arbitrary coupling on to the set of martingale couplings with the same marginals. As a corollary we obtain an independent proof of sufficiency of the monotonicity principle established in [Beiglbock, Juillet 2016] for functions of polynomial growth.
Notch Signaling Maintains Neural Rosette Polarity

Formation of the metazoan body plan requires a complex interplay of morphological changes and patterning, and central to these processes is the establishment of apical/basal cell polarity. In the developing nervous system, apical/basal cell polarity is essential for neural tube closure and maintenance of the neural stem cell population. In this report we explore how a signaling pathway important for nervous system development, Notch signaling, impacts on apical/basal cell polarity in neural differentiation. CSL−/− mouse embryos, which are devoid of canonical Notch signaling, demonstrated a neural tube phenotype consistent with cell polarity and convergent extension defects, including deficiencies in the restricted expression of apical polarity markers in the neuroepithelium. CSL−/− mouse embryonic stem (ES) cells, cultured at low density, behaved as wild-type in the establishment of neural progenitors and apical specification, though progression through rosette formation, an in vitro correlate of neurulation, required CSL for correct maintenance of rosette structure and regulation of neuronal differentiation. Similarly, acute pharmacological inhibition of Notch signaling led to the breakdown of neural rosettes and accelerated neuronal differentiation. In addition to functional Notch signaling, rosette integrity was found to require actin polymerization and Rho kinase (ROCK) activity. Disruption of rosettes through inhibition of actin polymerization or ROCK activity, however, had no effect on neuronal differentiation, indicating that rosette maintenance is not a prerequisite for normal neuronal differentiation. In conclusion, our data indicate that Notch signaling plays a role not only in differentiation, but also in organization and maintenance of polarity during development of the early nervous system.
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features

Machine learning (ML) techniques are increasingly common in security applications, such as malware and intrusion detection. However, ML models are often susceptible to evasion attacks, in which an adversary makes changes to the input (such as malware) in order to avoid being detected. A conventional approach to evaluate ML robustness to such attacks, as well as to design robust ML, is by considering simplified feature-space models of attacks, where the attacker changes ML features directly to effect evasion, while minimizing or constraining the magnitude of this change. We investigate the effectiveness of this approach to designing robust ML in the face of attacks that can be realized in actual malware (realizable attacks). We demonstrate that in the context of structure-based PDF malware detection, such techniques appear to have limited effectiveness, but they are effective with content-based detectors. In either case, we show that augmenting the feature space models with conserved features (those that cannot be unilaterally modified without compromising malicious functionality) significantly improves performance. Finally, we show that feature space models enable generalized robustness when faced with a variety of realizable attacks, as compared to classifiers which are tuned to be robust to a specific realizable attack.
Assessing non-linear models for galaxy clustering – II. Model validation and forecasts for Stage IV surveys

Accurate modelling of non-linear scales in galaxy clustering will be crucial for data analysis of Stage IV galaxy surveys. A selection of competing non-linear models must be made based on validation studies. We provide a comprehensive set of forecasts of two different models for the halo redshift space power spectrum, namely the commonly applied TNS model and an effective field theory of large-scale structure (EFTofLSS) inspired model. Using simulation data and a least-χ2 analysis, we determine ranges of validity for the models. We then conduct an exploratory Fisher analysis using the full anisotropic power spectrum to investigate parameter degeneracies. We proceed to perform an MCMC analysis utilizing the monopole, quadrupole, and hexadecapole spectra, with a restricted range of scales for the latter in order to avoid biasing our growth rate, f, constraint. We find that the TNS model with a Lorentzian damping and standard Eulerian perturbative modelling outperforms other variants of the TNS model. Our MCMC analysis finds that the EFTofLSS-based model may provide tighter marginalized constraints on f at z = 0.5 and z = 1 than the TNS model, despite having additional nuisance parameters. However this depends on the range of scales used as well as the fiducial values and priors on the EFT nuisance parameters. Finally, we extend previous work to provide a consistent comparison between the Fisher matrix and MCMC forecasts using the multipole expansion formalism, and find good agreement between them.
On the optimal linear convergence factor of the relaxed proximal point algorithm for monotone inclusion problems

Finding a zero of a maximal monotone operator is fundamental in convex optimization and monotone operator theory, and \emph{proximal point algorithm} (PPA) is a primary method for solving this problem. PPA converges not only globally under fairly mild conditions but also asymptotically at a fast linear rate provided that the underlying inverse operator is Lipschitz continuous at the origin. These nice convergence properties are preserved by a relaxed variant of PPA. Recently, a linear convergence bound was established in [M. Tao, and X. M. Yuan, J. Sci. Comput. , 74 (2018), pp. 826-850] for the relaxed PPA, and it was shown that the bound is optimal when the relaxation factor $\gamma$ lies in $[1,2)$. However, for other choices of $\gamma$, the bound obtained by Tao and Yuan is suboptimal. In this paper, we establish tight linear convergence bounds for any choice of $\gamma\in(0,2)$ and make the whole picture about optimal linear convergence bounds clear. These results sharpen our understandings to the asymptotic behavior of the relaxed PPA.
Magnetic Field Line Twisting by Photospheric Vortices: Energy Storage and Release

We investigate the dynamics of a closed-corona Cartesian reduced magnetohydrodynamic model where photospheric vortices twist the coronal magnetic field lines. We consider two corotating or counterrotating vortices localized at the center of the photospheric plate and, additionally, more corotating vortices that fill the plate entirely. Our investigation is specifically devoted to studying the fully nonlinear stage, after the linear stage during which the vortices create laminar and smoothly twisting flux tubes. Our main goal is to understand the dynamics of the photospheric vortices twisting the field lines of a coronal magnetic configuration permeated by finite-amplitude broadband fluctuations. We find that, depending on the system parameters and the arrangement and handedness of the photospheric vortices, an inverse cascade storing a significant amount of magnetic energy may or may not occur. In the first case, a reservoir of magnetic energy available to large events, such as destabilization of a pre–coronal mass ejection (CME) configuration, develops, while in the second case, the outcome is a turbulent heated corona. Although our geometry is simplified, our simulations are shown to have relevant implications for coronal dynamics and CME initiation.
Explainable AI for Trees: From Local Explanations to Global Understanding

Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are the most popular non-linear predictive models used in practice today, yet comparatively little attention has been paid to explaining their predictions. Here we significantly improve the interpretability of tree-based models through three main contributions: 1) The first polynomial time algorithm to compute optimal explanations based on game theory. 2) A new type of explanation that directly measures local feature interaction effects. 3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retaining local faithfulness to the original model. These tools enable us to i) identify high magnitude but low frequency non-linear mortality risk factors in the general US population, ii) highlight distinct population sub-groups with shared risk characteristics, iii) identify non-linear interaction effects among risk factors for chronic kidney disease, and iv) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model's performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains.
A Bit Too Much? High Speed Imaging from Sparse Photon Counts

Recent advances in photographic sensing technologies have made it possible to achieve light detection in terms of a single photon. Photon counting sensors are being increasingly used in many diverse applications. We address the problem of jointly recovering spatial and temporal scene radiance from very few photon counts. Our ConvNet-based scheme effectively combines spatial and temporal information present in measurements to reduce noise. We demonstrate that using our method one can acquire videos at a high frame rate and still achieve good quality signal-to-noise ratio. Experiments show that the proposed scheme performs quite well in different challenging scenarios while the existing approaches are unable to handle them.
An empirical partition function for two-dimensional Ising Model in an external magnetic field

There is no an accepted exact partition function (PF) for the two-dimensional (2D) Ising model with a non-zero external magnetic field to our knowledge. Here we infer an empirical PF for such an Ising model. We compare the PFs for two finite-size Ising lattices ($4\times 4$ and $4\times 6$) from this empirical PF with those from Wei (2018) (Wei, R.Q., 2018. An exact solution to the partition function of the finite-size Ising Model, arXiv: General Physics: 1805.01366.), and find that they are consistent very well. Based on this empirical PF, we further analyze and calculate the thermodynamic functions (heat capacity, magnetization, susceptibility) of this 2D Ising model and discuss the model's singularity semiquantitatively. Analysis and calculations from this PF show that they are coincident with those from other related studies; Especially the 2D Ising model in an external magnetic field has spontaneous magnetization (SM) calling the phenomenon at the critical temperature a phase transition, and the SM decreases with the increasing temperature. However, the decreasing variation of the SM here is different from that obtained by Yang (1952) from the 2D Ising model in a weak magnetic field.
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering

We propose a deep autoencoder with graph topology inference and filtering to achieve compact representations of unorganized 3D point clouds in an unsupervised manner. Many previous works discretize 3D points to voxels and then use lattice-based methods to process and learn 3D spatial information; however, this leads to inevitable discretization errors. In this work, we try to handle raw 3D points without such compromise. The proposed networks follow the autoencoder framework with a focus on designing the decoder. The encoder of the proposed networks adopts similar architectures as in PointNet, which is a well-acknowledged method for supervised learning of 3D point clouds. The decoder of the proposed networks involves three novel modules: the folding module, the graph-topology-inference module, and the graph-filtering module. The folding module folds a canonical 2D lattice to the underlying surface of a 3D point cloud, achieving coarse reconstruction; the graph-topology-inference module learns a graph topology to represent pairwise relationships between 3D points, pushing the latent code to preserve both coordinates and pairwise relationships of points in 3D point clouds; and the graph-filtering module couples the above two modules, refining the coarse reconstruction through a learnt graph topology to obtain the final reconstruction. The proposed decoder leverages a learnable graph topology to push the codeword to preserve representative features and further improve the unsupervised-learning performance. We further provide theoretical analyses of the proposed architecture. We provide an upper bound for the reconstruction loss and further show the superiority of graph smoothness over spatial smoothness as a prior to model 3D point clouds. In the experiments, we validate the proposed networks in three tasks, including 3D point cloud reconstruction, visualization, and transfer classification. The experimental results show that (1) the proposed networks outperform the state-of-the-art methods in various tasks, including reconstruction and transfer classification; (2) a graph topology can be inferred as auxiliary information without specific supervision on graph topology inference; (3) graph filtering refines the reconstruction, leading to better performances; and (4) designing a powerful decoder could improve the unsupervised-learning performance, just like a powerful encoder.
Efficient Test Data Generation for Variables with Complex Dependencies

This paper introduces a new method for generating test data that combines the benefits of equivalence partitioning, boundary value analysis and cause-effect analysis. It is suitable for problems involving complex linear dependencies between two or more variables. The method aims at covering all semantic dependencies plus all (n-dimensional) boundaries with a minimum set of test data. To overcome the mathematical complexity of the method, a main goal of the research project was to develop a user-friendly tool that allows users to specify dependencies in a simple language and generates appropriate test data automatically. The tool has been incorporated into the IDATG (Integrating Design and Automated Test case Generation) tool-set and validated in a number of case studies.