Miracle-dz/UniFAR-FTUs huggingface dataset:
Appropriateness of psychopharmacological therapies to psychiatric diagnoses in persons with autism spectrum disorder with or without intellectual disabilities: a cross-sectional analytic study

ABSTRACT Background Observational studies highlighted high rates of psychotropic medication in persons with autistic spectrum disorder (ASD) with or without intellectual disability, which seems to be associated with the management of problem behaviors more than co-occurrent psychiatric disorders. The purpose of the study is to investigate psychopharmacology use and diagnoses of co-occurrent psychiatric disorder (PD) in persons with ASD attending a public mental health service in Emilia Romagna, Italy. Methods The present study is a multicenter, cross-sectional study. Results 275 persons out of 486 (56.5%) resulted to receive at least one psychotropic drug, compared to 74 persons (15.2%) that were diagnosed with a PD. 63.6% were on poly-pharmacotherapy (2–10 compounds), with 37.8% receiving 3 or more medications. Antipsychotics were the most frequently prescribed class of psychotropic drugs (89%), followed by antiepileptics/mood stabilizers/lithium (42.1%) and anxiolytics (BDZ) (38.5%). Most common psychiatric disorders were psychotic disorders (29.7%), followed by anxiety disorders (17.5%), bipolar disorders (12.2%), and depressive disorders (9.4%). Conclusions Our findings support earlier research showing that many individuals with ASD receive pharmacotherapy without being diagnosed with a co-occurring psychiatric disorder, indicating that the main reasons for prescription and the type of compound frequently have little to no link with specific psychopathology.
A multimodal imaging approach to foreign accent syndrome. A case report

ABSTRACT This article describes a case of Foreign accent syndrome (FAS) in an Italian woman who developed a Canadian-like foreign accent without brain damage (functional FAS). The patient underwent an in-depth neuroimaging and (neuro)psychological evaluation. Language networks in the frontotemporal-parietal areas were typically activated bilaterally through fMRI and MEG assessments based on task-based data. Resting-state fMRI showed preserved connectivity between language areas. An obsessive-compulsive personality profile and mild anxiety were found, suggesting psychological and psychiatric factors may be relevant. Accordingly with our findings, multimodal imaging is beneficial to understand FAS neurological and functional etiologies.
A Bayesian panel data framework for examining the economic growth convergence hypothesis: do the G7 countries converge?

In this paper, we suggest a Bayesian panel (longitudinal) data approach to test for the economic growth convergence hypothesis. This approach can control for possible effects of initial income conditions, observed covariates and cross-sectional correlation of unobserved common error terms on inference procedures about the unit root hypothesis based on panel data dynamic models. Ignoring these effects can lead to spurious evidence supporting economic growth divergence. The application of our suggested approach to real gross domestic product panel data of the G7 countries indicates that the economic growth convergence hypothesis is supported by the data. Our empirical analysis shows that evidence of economic growth divergence for the G7 countries can be attributed to not accounting for the presence of exogenous covariates in the model.
Structured Additive Regression for Categorical Space–Time Data: A Mixed Model Approach

Summary Motivated by a space–time study on forest health with damage state of trees as the response, we propose a general class of structured additive regression models for categorical responses, allowing for a flexible semiparametric predictor. Nonlinear effects of continuous covariates, time trends, and interactions between continuous covariates are modeled by penalized splines. Spatial effects can be estimated based on Markov random fields, Gaussian random fields, or two‐dimensional penalized splines. We present our approach from a Bayesian perspective, with inference based on a categorical linear mixed model representation. The resulting empirical Bayes method is closely related to penalized likelihood estimation in a frequentist setting. Variance components, corresponding to inverse smoothing parameters, are estimated using (approximate) restricted maximum likelihood. In simulation studies we investigate the performance of different choices for the spatial effect, compare the empirical Bayes approach to competing methodology, and study the bias of mixed model estimates. As an application we analyze data from the forest health survey.
Comprehensive analysis of pyroptosis‐related gene signatures for glioblastoma immune microenvironment and target therapy

Glioblastoma (GBM) is a malignant brain tumour, but its subtypes (mesenchymal, classical, and proneural) show different prognoses. Pyroptosis is a programmed cell death relating to tumour progression, but its association with GBM is poorly understood. In this work, we collected 73 GBM samples (the Xiangya GBM cohort) and reported that pyroptosis involves tumour‐microglia interaction and tumour response to interferon‐gamma. GBM samples were grouped into different subtypes, cluster 1 and cluster 2, based on pyroptosis‐related genes. Cluster 1 samples manifested a worse prognosis and had a more complicated immune landscape than cluster 2 samples. Single‐cell RNA‐seq data analysis supported that cluster 1 samples respond to interferon‐gamma more actively. Moreover, the machine learning algorithm screened several potential compounds, including nutlin‐3, for cluster 1 samples as a novel treatment. In vitro experiments supported that cluster 1 cell line, T98G, is more sensitive to nutlin‐3 than cluster 2 cell line, LN229. Nutlin‐3 can trigger oxidative stress by increasing DHCR24 expression. Moreover, pyroptosis‐resistant genes were upregulated in LN229, which may participate against nutlin‐3. Therefore, we hypothesis that GBM may be able to upregulate pyroptosis resistant related genes to against nutlin‐3‐triggered cell death. In summary, we conclude that pyroptosis highly associates with GBM progression, tumour immune landscape, and tumour response to nutlin‐3.
Nonlocality and entanglement in measured critical quantum Ising chains

We study the effects of measurements, performed with a finite density in space, on the ground state of the one-dimensional transverse-field Ising model at criticality. Local degrees of freedom in critical states exhibit long-range entanglement, and as a result, local measurements can have highly nonlocal effects. Our analytical investigation of correlations and entanglement in the ensemble of measured states is based on properties of the Ising conformal field theory (CFT), where measurements appear as (1+0)-dimensional defects in the (1+1)-dimensional Euclidean spacetime. So that we can verify our predictions using large-scale free-fermion numerics, we restrict ourselves to parity-symmetric measurements. To describe their averaged effects analytically we use a replica approach, and we show that the defect arising in the replica theory is an irrelevant perturbation to the Ising CFT. Strikingly, the asymptotic scalings of averaged correlations and entanglement entropy are therefore unchanged relative to the ground state. In contrast, the defect generated by postselecting on the most likely measurement outcomes is exactly marginal. We then find that the exponent governing postmeasurement order parameter correlations, as well as the ''effective central charge'' governing the scaling of entanglement entropy, vary continuously with the density of measurements in space. Our work establishes new connections between the effects of measurements on many-body quantum states and of physical defects on low-energy equilibrium properties.
PDE constrained shape optimisation with first-order and Newton-type methods in the $W^{1,\infty}$ topology

We present a general shape optimisation framework based on the method of mappings in the $W^{1,\infty}$ topology. We propose steepest descent and Newton-like minimisation algorithms for the numerical solution of the respective shape optimisation problems. Our work is built upon previous work of the authors in (Deckelnick, Herbert, and Hinze, ESAIM: COCV 28 (2022)), where a $W^{1,\infty}$ framework for star-shaped domains is proposed. To illustrate our approach we present a selection of PDE constrained shape optimisation problems and compare our findings to results from so far classical Hilbert space methods and recent $p$-approximations.
Plan To Predict: Learning an Uncertainty-Foreseeing Model for Model-Based Reinforcement Learning

In Model-based Reinforcement Learning (MBRL), model learning is critical since an inaccurate model can bias policy learning via generating misleading samples. However, learning an accurate model can be difficult since the policy is continually updated and the induced distribution over visited states used for model learning shifts accordingly. Prior methods alleviate this issue by quantifying the uncertainty of model-generated samples. However, these methods only quantify the uncertainty passively after the samples were generated, rather than foreseeing the uncertainty before model trajectories fall into those highly uncertain regions. The resulting low-quality samples can induce unstable learning targets and hinder the optimization of the policy. Moreover, while being learned to minimize one-step prediction errors, the model is generally used to predict for multiple steps, leading to a mismatch between the objectives of model learning and model usage. To this end, we propose \emph{Plan To Predict} (P2P), an MBRL framework that treats the model rollout process as a sequential decision making problem by reversely considering the model as a decision maker and the current policy as the dynamics. In this way, the model can quickly adapt to the current policy and foresee the multi-step future uncertainty when generating trajectories. Theoretically, we show that the performance of P2P can be guaranteed by approximately optimizing a lower bound of the true environment return. Empirical results demonstrate that P2P achieves state-of-the-art performance on several challenging benchmark tasks.
Chiral topologically ordered states on a lattice from vertex operator algebras

We propose a class of pure states of two-dimensional lattice systems realizing topological order associated with unitary rational vertex operator algebras. We show that the states are well-defined in the thermodynamic limit and have exponential decay of correlations. The construction provides a natural way to insert anyons and compute certain topological invariants. It also gives candidates for bosonic states in non-trivial invertible phases, including the $E_8$ phase.
UV and Hα HST Observations of Six GASP Jellyfish Galaxies

Star-forming, Hα-emitting clumps are found embedded in the gaseous tails of galaxies undergoing intense ram pressure stripping in galaxy clusters, so-called jellyfish galaxies. These clumps offer a unique opportunity to study star formation under extreme conditions, in the absence of an underlying disk and embedded within the hot intracluster medium. Yet, a comprehensive, high-spatial-resolution study of these systems is missing. We obtained UVIS/Hubble Space Telescope (HST) data to observe the first statistical sample of clumps in the tails and disks of six jellyfish galaxies from the GASP survey; we used a combination of broadband (UV to I) filters and a narrowband Hα filter. HST observations are needed to study the sizes, stellar masses, and ages of the clumps and their clustering hierarchy. These observations will be used to study the clump scaling relations and the universality of the star formation process, and to verify whether a disk is irrelevant, as hinted at by results from jellyfish galaxies. This paper presents the observations, data reduction strategy, and some general results based on the preliminary data analysis. The high spatial resolution of UVIS gives an unprecedentedly sharp view of the complex structure of the inner regions of the galaxies and of the substructures in the galaxy disks. We found clear signatures of stripping in regions very close in projection to the galactic disk. The star-forming regions in the stripped tails are extremely bright and compact and we did not detect a significant number of star-forming clumps in regions where MUSE did not detect any. The paper finally presents the development plan for the project.
Tracing the Evolution of SMBHs and Stellar Objects in Galaxy Mergers: A Multi-mass Direct N-body Model

By using direct N-body numerical simulations, we model the dynamical coevolution of two supermassive black holes (SMBHs) and the surrounding stars in merging galaxies. In order to investigate how different stellar components evolve during the merger, we generate evolved stellar distributions with an initial mass function. Special schemes have also been developed to deal with some rare but interesting events, such as the tidal disruption of main-sequence stars, plunging low-mass stars, white dwarfs, neutron stars and stellar-mass black holes, and the partial tidal disruption of red giants or asymptotic giant branch stars. Our results indicate that the formation of a bound SMBH binary will enhance the capture rates of stellar objects by SMBHs. Compared to the equal stellar mass model, the multi-mass model tends to result in a higher average mass of disrupted stars. Instead of being tidally disrupted by the SMBH, roughly half of the captured main-sequence stars will directly plunge into the SMBH because of their small stellar radii. Giant stars, on the other hand, can be stripped of their envelopes if they are close enough to the SMBH. Though most remnants of the giant stars can survive after the disruption, a small fraction still could plunge into the SMBH quickly or after many orbital periods. Our results also indicate significant mass segregation of compact stars at the beginning of the merger, and then this effect is destroyed as the two SMBHs form a bound binary.
Neural Architecture Search: Insights from 1000 Papers

In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning. Specialized, high-performing neural architectures are crucial to the success of deep learning in these areas. Neural architecture search (NAS), the process of automating the design of neural architectures for a given task, is an inevitable next step in automating machine learning and has already outpaced the best human-designed architectures on many tasks. In the past few years, research in NAS has been progressing rapidly, with over 1000 papers released since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized and comprehensive guide to neural architecture search. We give a taxonomy of search spaces, algorithms, and speedup techniques, and we discuss resources such as benchmarks, best practices, other surveys, and open-source libraries.
Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance

People all over the globe are affected by pneumonia but deaths due to it are highest in Sub-Saharan Asia and South Asia. In recent years, the overall incidence and mortality rate of pneumonia regardless of the utilization of effective vaccines and compelling antibiotics has escalated. Thus, pneumonia remains a disease that needs spry prevention and treatment. The widespread prevalence of pneumonia has caused the research community to come up with a framework that helps detect, diagnose and analyze diseases accurately and promptly. One of the major hurdles faced by the Artificial Intelligence (AI) research community is the lack of publicly available datasets for chest diseases, including pneumonia . Secondly, few of the available datasets are highly imbalanced (normal examples are over sampled, while samples with ailment are in severe minority) making the problem even more challenging. In this article we present a novel framework for the detection of pneumonia. The novelty of the proposed methodology lies in the tackling of class imbalance problem. The Generative Adversarial Network (GAN), specifically a combination of Deep Convolutional Generative Adversarial Network (DCGAN) and Wasserstein GAN gradient penalty (WGAN-GP) was applied on the minority class ``Pneumonia'' for augmentation, whereas Random Under-Sampling (RUS) was done on the majority class ``No Findings'' to deal with the imbalance problem. The ChestX-Ray8 dataset, one of the biggest datasets, is used to validate the performance of the proposed framework. The learning phase is completed using transfer learning on state-of-the-art deep learning models i.e. ResNet-50, Xception, and VGG-16. Results obtained exceed state-of-the-art.
Ultra-light axions and the S 8 tension: joint constraints from the cosmic microwave background and galaxy clustering

We search for ultra-light axions as dark matter (DM) and dark energy particle candidates, for axion masses 10-32 eV ≤ m a ≤ 10-24 eV, by a joint analysis of cosmic microwave background (CMB) and galaxy clustering data — and consider if axions can resolve the tension in inferred values of the matter clustering parameter S 8. We give legacy constraints from Planck 2018 CMB data, improving 2015 limits on the axion density Ωa h 2 by up to a factor of three; CMB data from the Atacama Cosmology Telescope and the South Pole Telescope marginally weaken Planck bounds at m a = 10-25 eV, owing to lower (and theoretically-consistent) gravitational lensing signals. We jointly infer, from Planck CMB and full-shape galaxy power spectrum and bispectrum data from the Baryon Oscillation Spectroscopic Survey (BOSS), that axions are, today, < 10% of the DM for m a ≤ 10-26 eV and < 1% for 10-30 eV ≤ m a ≤ 10-28 eV. BOSS data strengthen limits, in particular at higher m a by probing high-wavenumber modes (k < 0.4h Mpc-1). BOSS alone finds a preference for axions at 2. 7σ, for m a = 10-26 eV, but Planck disfavours this result. Nonetheless, axions in a window 10-28 eV ≤ m a ≤ 10-25 eV can improve consistency between CMB and galaxy clustering data, e.g., reducing the S 8 discrepancy from 2. 7σ to 1. 6σ, since these axions suppress structure growth at the 8h -1 Mpc scales to which S 8 is sensitive. We expect improved constraints with upcoming high-resolution CMB and galaxy lensing and future galaxy clustering data, where we will further assess if axions can restore cosmic concordance.
Probe of a Randall-Sundrum-like model from muon pair production at high energy muon collider

We have examined inclusive $\mu^+\mu^- \rightarrow \mu^+ \mu^- + E_{\mathrm{miss}}$ and annihilation $\mu^+\mu^- \rightarrow \mu^+ \mu^-$ processes at future high energy muon colliders in the framework of the Randall-Sundrum-like model with a small curvature of space-time. The collision energies of 3 TeV, 14 TeV and, 100 TeV are addressed. Both differential and total cross sections are calculated, and exclusion bounds on a 5-dimensional gravity scale are obtained depending on collision energy and integrated luminosity of the muon colliders.
Conductance quantization by the application of magnetic fields in ballistic Ni nanocontacts

We investigated the magnetoresistance effect in ballistic Ni nanocontacts in the regime of conductance quantization. In magnetoresistance curves, we found that the conductance is quantized by the application of magnetic fields. The quantization is direct evidence showing the contribution of magnetostriction. The magnetostriction has the potential to control the quantum transport and giant magnetoresistance effect.We investigated the magnetoresistance effect in ballistic Ni nanocontacts in the regime of conductance quantization. In magnetoresistance curves, we found that the conductance is quantized by the application of magnetic fields. The quantization is direct evidence showing the contribution of magnetostriction. The magnetostriction has the potential to control the quantum transport and giant magnetoresistance effect.
Extraction of the frequency moments of spectral densities from imaginary-time correlation function data

We introduce an exact framework to compute the positive frequency moments $M^{(\alpha)}(\mathbf{q})=\braket{\omega^\alpha}$ of different dynamic properties from imaginary-time quantum Monte Carlo data. As a practical example, we obtain the first five moments of the dynamic structure factor $S(\mathbf{q},\omega)$ of the uniform electron gas at the electronic Fermi temperature based on \emph{ab initio} path integral Monte Carlo simulations. We find excellent agreement with known sum rules for $\alpha=1,3$, and, to our knowledge, present the first results for $\alpha=2,4,5$. Our idea can be straightforwardly generalized to other dynamic properties such as the single-particle spectral function $A(\mathbf{q},\omega)$, and will be useful for a number of applications, including the study of ultracold atoms, exotic warm dense matter, and condensed matter systems.
Transforming Unstructured Text into Data with Context Rule Assisted Machine Learning (CRAML)

Abstract: We describe a method and new no-code software tools enabling domain experts to build custom structured, labeled datasets from the unstructured text of documents and build niche machine learning text classification models traceable to expert-written rules. The Context Rule Assisted Machine Learning (CRAML) method allows accurate and reproducible labeling of massive volumes of unstructured text. CRAML enables domain experts to access uncommon constructs buried within a document corpus, and avoids limitations of current computational approaches that often lack context, transparency, and interpetability. In this research methods paper, we present three use cases for CRAML: we analyze recent management literature that draws from text data, describe and release new machine learning models from an analysis of proprietary job advertisement text, and present findings of social and economic interest from a public corpus of franchise documents. CRAML produces document-level coded tabular datasets that can be used for quantitative academic research, and allows qualitative researchers to scale niche classification schemes over massive text data. CRAML is a low-resource, flexible, and scalable methodology for building training data for supervised ML. We make available as open-source resources: the software, job advertisement text classifiers, a novel corpus of franchise documents, and a fully replicable start-to-finish trained example in the context of no poach clauses. We describe a method and new no-code software tools enabling domain experts to build custom structured, labeled datasets from the unstructured text of documents and build niche machine learning text classification models traceable to expert-written rules. The Context Rule Assisted Machine Learning (CRAML) method allows accurate and reproducible labeling of massive volumes of unstructured text. CRAML enables domain experts to access uncommon constructs buried within a document corpus, and avoids limitations of current computational approaches that often lack context, transparency, and interpetability. In this research methods paper, we present three use cases for CRAML: we analyze recent management literature that draws from text data, describe and release new machine learning models from an analysis of proprietary job advertisement text, and present findings of social and economic interest from a public corpus of franchise documents. CRAML produces document-level coded tabular datasets that can be used for quantitative academic research, and allows qualitative researchers to scale niche classification schemes over massive text data. CRAML is a low-resource, flexible, and scalable methodology for building training data for supervised ML. We make available as open-source resources: the software, job advertisement text classifiers, a novel corpus of franchise documents, and a fully replicable start-to-finish trained example in the context of no poach clauses.