Miracle-dz/UniFAR-FTUs huggingface dataset:
Clouds, Circulation, and Climate Sensitivity in a Radiative‐Convective Equilibrium Channel Model

Tropical cloud and circulation changes are large sources of uncertainty in future climate change. This problem owes partly to the scale separation between large‐scale tropical dynamics ( ∼104 km) and convective dynamics ( ∼1 km), which generally requires parameterizing convection in models that resolve large‐scale dynamics, or parameterizing (or omitting) large‐scale dynamics in models that permit convection. Here we discuss simulations of radiative‐convective equilibrium (RCE) across a wide range of surface temperatures in long‐channel geometry—where the domain size and resolution marginally resolve both large‐scale dynamics and convection. Self‐aggregation of convection in these simulations spontaneously produces realistic dynamical regimes of large‐scale vertical motion. The circulation weakens with surface warming but changes in the degree of self‐aggregation depend on the metric that is used; there is no obvious trend in aggregation with warming. Surface warming causes an upward shift and decrease in area of high clouds, and a sharp decline in midlevel clouds, but no systematic trend in low cloud cover. We introduce a method for approximate radiative kernel feedback analysis in RCE, and apply it to both simulations in long‐channel geometry and in a smaller square domain. The kernel‐corrected cloud feedback is positive but its magnitude varies across temperatures. Compared to simulations that do not have aggregation, there is a more negative net feedback due to the effects of aggregation on relative humidity and cloud cover. These results are consistent with the hypothesis that self‐aggregation moderately reduces climate sensitivity.
Semi-parametric Gaussian copula models for machine learning

The aim of machine learning and statistics is to learn and predict from data. With the introduction of copulas, probabilistic models and algorithms can benefit from the separation of dependency and marginals. The additional flexibility allows to generalise better and increase the prediction accuracy. Based on this observation, this work enlightens different models within the framework of a semi-parametric Gaussian copula model. The first model we consider is archetypal analysis. We show that the Gaussian copula approximates the dependency structure of the generative model we consider. With copula archetypal analysis, we present a new model, which extends the applicability of the original model. Our second contribution refers to the semi-parametric Gaussian copula extension of principal component analysis. We consider the model in the context of parametric appearance models for facial appearance. We show, that the copula relaxation leads ultimately to a higher specificity and provide a unifying way of combining different data. The third contribution is Bayesian sub-network estimation within the framework of Gaussian graphical models. We show that the Markov blanket of a set of query variables has analytical form and can be efficiently estimated. Our last contribution is the motivation of time-resolved information flows in the context of directed information and Pearlian graphs. We show, how to discover information flows in non-stationary time series and give a convenient estimator. At the core of these models lies the semi-parametric Gaussian copula model. In this work we show how it allows to relax certain assumptions in the aforementioned models. Ultimately, this leads to non-Gaussian and latent linear models, which better apply to real-world data sets.
The Roles of Convection Parameterization in the Formation of Double ITCZ Syndrome in the NCAR CESM: I. Atmospheric Processes

Several improvements are implemented in the Zhang‐McFarlane (ZM) convection scheme to investigate the roles of convection parameterization in the formation of double intertropical convergence zone (ITCZ) bias in the NCAR CESM1.2. 1. It is shown that the prominent double ITCZ biases of precipitation, sea surface temperature (SST), and wind stress in the standard CESM1.2. 1 are largely eliminated in all seasons with the use of these improvements in convection scheme. This study for the first time demonstrates that the modifications of convection scheme can eliminate the double ITCZ biases in all seasons, including boreal winter and spring. Further analysis shows that the elimination of the double ITCZ bias is achieved not by improving other possible contributors, such as stratus cloud bias off the west coast of South America and cloud/radiation biases over the Southern Ocean, but by modifying the convection scheme itself. This study demonstrates that convection scheme is the primary contributor to the double ITCZ bias in the CESM1.2. 1, and provides a possible solution to the long‐standing double ITCZ problem. The atmospheric model simulations forced by observed SST show that the original ZM convection scheme tends to produce double ITCZ bias in high SST scenario, while the modified convection scheme does not. The impact of changes in each core component of convection scheme on the double ITCZ bias in atmospheric model is identified and further investigated.
Antenna modelling for ion cyclotron resonantheating of tokamak plasmas

The most successful current device for fusion research is the tokamak, a sort of big transformer where the secondary coil is the hydrogen in plasma state enclosed in a toroidal chamber, in which the plasma is trapped by a static magnetic field and heated by a strong current induced by the primary coil. The different machines operating so far have a plasma discharge time duration of order of 1 s – 10 s. An important mean to heat the plasma to thermonuclear fusion temperatures is using high power waves in the Ion Cyclotron Range of Frequencies (ICRF 30 – 80 MHz). Recently, materials with high atomic number (high-Z), such as tungsten (W), became considered as the best candidates for Plasma Facing Components (PFCs) in future fusion devices including international tokamak ITER. ASDEX Upgrade (AUG) tokamak is pioneering the use of a tungsten (W) first wall. The low tolerable concentrations of high-Z impurities in the plasma are in contrast to high impurity sputtering rates observed on the PFCs that surround or are connected to active ICRF antennas along magnetic field lines. It is assumed that ICRF operation causes sputtering via an increase in the average plasma potential due to the rectification of the parallel component of electric Radio-Frequency (RF) near-fields E//. ICRF antenna optimization is necessary for improving ICRF heating compatibility with high-Z PFCs. This thesis focus on the analysis and comparison of performance of two ICRF antennas that are currently installed on the AUG tokamak with the TOPICA (Torino Polytechnic Ion Cyclotron Antenna) program, for various operational scenarios. The work aims at estimating distribution of the quantities that are considered as the main drivers for the impurity production: E// and RF potentials in realistic conditions of antenna geometry and plasma loading. Further, rectified potentials are estimated based on the simple RF sheath models and also using more appropriate tool: asymptotic version of the numerical code SSWICH (Self-consistent Sheaths and Waves for IC Heating) that characterizes better the RF sheath mechanism. This analysis is necessary for assessing phenomena responsible of producing impurity influx, which prevents operations in reactor-graded condition of metallic vacuum chamber. The estimated values are compared with the sheath potential drops that correspond to the maximal measured values of tungsten sputtering yield at the antenna limiters for typical concentrations of light impurities that bombard W surface and that can be found in AUG. The unsuccessful ICRF experiment on the National Spherical Torus Experiment (NSTX) has been also considered, which did not produce any effect of the coupled RF power to the plasma bulk, but only parasitic deposition at the very edge of the plasma column. In this regard, the non-linear wave-plasma coupling phenomenon occurring at the edge has been studied, as example of a not proper transfer of the RF power coupled by the antenna to useful ICRF waves.
Pointwise Information Decomposition Using the Specificity and Ambiguity Lattices

Multivariate information theory has long been problematic. Recently, the partial information decomposition (PID) of Williams and Beer has provided a promising axiomatic framework which clarifies the general structure of multivariate information. However, PID lacks the necessary measure of redundant information required to complete the framework; despite much recent research, no well-accepted measure of redundant information has emerged that is applicable to more than two sources and respects the locality of information. In this paper, we introduce a new framework based upon the axiomatic approach taken in PID but which aims to decompose multivariate information on a local or pointwise scale. It is shown that in order to identify when information from two sources is indeed the same information, one must consider decomposing the local mutual information into its two directed components, the specificity and the ambiguity. Based upon the axiomatic approach taken in PID, we decompose these two components separately resulting in two lattices - the specificity and ambiguity lattices. This Specificity and Ambiguity decomposition retains the appealing multivariate structure provided by PID, but applying this notion on a much more granular level enables the decomposition to identify when information is the same information. This last point is justified by providing an operational interpretation of redundancy in terms of Kelly gambling. Applying the decomposition to canonical examples from the PID literature demonstrates the unique ability to provide a pointwise decomposition, and the fact that the Specificity and Ambiguity decomposition possesses the much sought-after target chain rule. Finally, interpreting these results sheds light on why defining a redundancy measure for PID has proven to be so difficult - one lattice is not enough.
Expression and Significance of KIM-1, NGAL, and HO-1 in Patients with Acute Kidney Injury After Cardiac Valve Replacement

Objective To investigate the changes and significance of perioperative expression levels of kidney injury molecule-1 (KIM-1), neutrophil gelatinase-related lipocalin (NGAL), and heme oxygenase-1 (HO-1) in patients with acute kidney injury (AKI) after cardiac valve replacement under cardiopulmonary bypass. Methods A total of 80 patients were divided into the AKI group and non-AKI group based on the postoperative occurrence of AKI. The expression levels of urinary KIM-1, NGAL, serum creatinine, urea nitrogen, and HO-1 were compared between the two groups before surgery and at 12 h, 24 h, and 48 h after surgery. Results There were 22 patients with postoperative AKI (AKI group), the incidence of postoperative AKI were 27.5%, and there were 58 patients without AKI (non-AKI group). There was no significant difference in general clinical data between the two groups (P > 0.05). Compared between AKI group and preoperative group, KIM-1, NGAL, HO-1, blood creatinine, and BUN levels were significantly increased, with significant differences (P < 0.05). Compared with non-AKI groups, KIM-1, NGAL, HO-1, blood creatinine, and blood urea nitrogen levels increased at all time points, but the difference was not significant (P > 0.05). Compared with AKI group and non-AKI group, KIM-1, NGAL, HO-1, blood creatinine, and BUN levels increased significantly, the differences were statistically significant (P < 0.05). Conclusion AKI can easily occur after cardiac valve replacement and postoperative expression levels of KIM-1, NGAL, and HO-1 can be early warning indicators of postoperative AKI.
Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape

Background Lung cancer continues to be a problem faced by all of humanity. It is the cancer with the highest morbidity and mortality in the world, and the most common histological type of lung cancer is lung adenocarcinoma (LUAD), accounting for about 40% of lung malignant tumors. This study was conducted to discuss and explore the immune-related biomarkers and pathways during the development and progression of LUAD and their relationship with immunocyte infiltration. Methods The cohorts of data used in this study were downloaded from the Gene Expression Complex (GEO) database and the Cancer Genome Atlas Program (TCGA) database. Through the analysis of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator(LASSO), selecting the module with the highest correlation with LUAD progression, and then the HUB gene was further determined. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were then used to study the function of these genes. Single-sample GSEA (ssGSEA) analysis was used to investigate the penetration of 28 immunocytes and their relationship with HUB genes. Finally, the receiver operating characteristic curve (ROC) was used to evaluate these HUB genes accurately to diagnose LUAD. In addition, additional cohorts were used for external validation. Based on the TCGA database, the effect of the HUB genes on the prognosis of LUAD patients was assessed using the Kaplan-Meier curve. The mRNA levels of some HUB genes in cancer cells and normal cells were analyzed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results The turquoise module with the highest correlation with LUAD was identified among the seven modules obtained with WGCNA. Three hundred fifty-four differential genes were chosen. After LASSO analysis, 12 HUB genes were chosen as candidate biomarkers for LUAD expression. According to the immune infiltration results, CD4 + T cells, B cells, and NK cells were high in LUAD sample tissue. The ROC curve showed that all 12 HUB genes had a high diagnostic value. Finally, the functional enrichment analysis suggested that the HUB gene is mainly related to inflammatory and immune responses. According to the RT-qPCR study, we found that the expression of DPYSL2, OCIAD2, and FABP4 in A549 was higher than BEAS-2B. The expression content of DPYSL2 was lower in H1299 than in BEAS-2B. However, the expression difference of FABP4 and OCIAD2 genes in H1299 lung cancer cells was insignificant, but both showed a trend of increase. Conclusions The mechanism of LUAD pathogenesis and progression is closely linked to T cells, B cells, and monocytes. 12 HUB genes(ADAMTS8, CD36, DPYSL2, FABP4, FGFR4, HBA2, OCIAD2, PARP1, PLEKHH2, STX11, TCF21, TNNC1) may participate in the progression of LUAD via immune-related signaling pathways.
The Ortho-to-para Ratio of Water Molecules Desorbed from Ice Made from Para-water Monomers at 11 K

Water has two nuclear-spin isomers: ortho- and para-H2O. Some observations of interstellar space and cometary comae have reported the existence of gaseous H2O molecules with anomalous ortho-to-para ratios (OPRs) less than the statistical value of three. This has been often used to estimate the formation temperature of ice on dust, which is inferred to be below 50 K. The relation between the nuclear-spin dynamics of H2O in ice at low temperatures and the OPR of gaseous H2O desorbed from the ice has yet to be explored in a laboratory. Consequently, the true meaning of the observed OPRs remains debated. We measure the OPR of H2O photodesorbed from ice made from para-H2O monomers at 11 K, which was prepared by the sublimation of Ne from a para-H2O/Ne matrix. The photodesorbed H2O molecules from the ice have the statistical OPR value of three, demonstrating the immediate nuclear-spin-state mixing of H2O toward the statistical value of ice even at 11 K. The OPR of H2O thermally desorbed from the ice also shows the expected statistical value. Our results indicate that the OPR of H2O desorbed from interstellar ice should be the statistical value regardless of the formation process of the ice, which cannot be used to deduce the ice-formation temperature. This study highlights the importance of interstellar gas-phase processes in understanding anomalous abundance ratios of nuclear-spin isomers of molecules in space.
Incidence, prevalence and care of type 1 diabetes in children and adolescents in Germany: Time trends and regional socioeconomic situation

Background Trends over time and possible socio-spatial inequalities in the incidence and care of type 1 diabetes mellitus (T1D) in children and adolescents are important parameters for the planning of target-specific treatment structures. Methodology The incidence and prevalence of type 1 diabetes, diabetic ketoacidosis and severe hypoglycaemia as well as the HbA1c value are presented for under 18-year-olds based on data from the nationwide Diabetes Prospective Follow-up Registry (DPV) and the diabetes registry of North Rhine-Westphalia. Indicators were mapped by sex over time between 2014 and 2020, and stratified by sex, age and regional socioeconomic deprivation for 2020. Results In 2020, the incidence was 29.2 per 100,000 person-years and the prevalence was 235.5 per 100,000 persons, with the figures being higher in boys than in girls in either case. The median HbA1c value was 7.5%. Ketoacidosis manifested in 3.4% of treated children and adolescents, significantly more often in regions with very high (4.5%) deprivation than in regions with very low deprivation (2.4%). The proportion of severe hypoglycaemia cases was 3.0%. Between 2014 and 2020, the incidence, prevalence and HbA1c levels changed little, while the proportions of ketoacidosis and severe hypoglycaemia decreased. Conclusions The decrease in acute complications indicates that type 1 diabetes care has improved. Similar to previous studies, the results suggest an inequality in care by regional socioeconomic situation.
A transformers-based approach for fine and coarse-grained classification and generation of MIDI songs and soundtracks

Music is an extremely subjective art form whose commodification via the recording industry in the 20th century has led to an increasingly subdivided set of genre labels that attempt to organize musical styles into definite categories. Music psychology has been studying the processes through which music is perceived, created, responded to, and incorporated into everyday life, and, modern artificial intelligence technology can be exploited in such a direction. Music classification and generation are emerging fields that gained much attention recently, especially with the latest discoveries within deep learning technologies. Self attention networks have in fact brought huge benefits for several tasks of classification and generation in different domains where data of different types were used (text, images, videos, sounds). In this article, we want to analyze the effectiveness of Transformers for both classification and generation tasks and study the performances of classification at different granularity and of generation using different human and automatic metrics. The input data consist of MIDI sounds that we have considered from different datasets: sounds from 397 Nintendo Entertainment System video games, classical pieces, and rock songs from different composers and bands. We have performed classification tasks within each dataset to identify the types or composers of each sample (fine-grained) and classification at a higher level. In the latter, we combined the three datasets together with the goal of identifying for each sample just NES, rock, or classical (coarse-grained) pieces. The proposed transformers-based approach outperformed competitors based on deep learning and machine learning approaches. Finally, the generation task has been carried out on each dataset and the resulting samples have been evaluated using human and automatic metrics (the local alignment).
Les techniques Monte Carlo par chaînes de Markov appliquées à la détermination des distributions de partons

Nous avons developpe une nouvelle approche basee sur les methodes Monte Carlo par chaines de Markov pour determiner les distributions de Partons et quantifier leurs incertitudes experimentales. L’interet principal d’une telle etude repose sur la possibilite de remplacer la minimisation standard avec MINUIT de la fonction χ 2 par des procedures fondees sur les methodes Statistiques et sur l’inference Bayesienne en particulier,offrant ainsi une meilleure comprehension de la determination des distributions de partons. Apres avoir examine ces techniques Monte Carlo par chaines de Markov, nous introduisons l’algorithme que nous avons choisi de mettre en œuvre, a savoir le Monte Carlo hybride (ou Hamiltonien). Cet algorithme, developpe initialement pour la chromodynamique quantique sur reseau, s’avere tres interessant lorsqu’il est applique a la determination des distributions de partons par des analyses globales. Nous avons montre qu’il permet de contourner les difficultes techniques dues a la grande dimensionnalite du probleme, en particulier celle relative au taux d’acceptation. L’etude de faisabilite realisee et presentee dans cette these indique que la methode Monte Carlo par chaines de Markov peut etre appliquee avec succes a l’extraction des distributions de partons et a leurs in-certitudes experimentales.
Clinical and Morphological Factors for Ruptured Anterior Communicating Artery Aneurysms

Introduction The anterior communicating artery (ACoA) aneurysm, the most frequent cerebral aneurysm to rupture, carries a significant clinical burden, yet the factors influencing its rupture are limited in Indonesia. This study aims to determine the clinical and morphological features associated with ruptured ACoA compared to non-AcoA aneurysms among Indonesians. Patients and Methods We retrospectively reviewed our center’s aneurysm patient registry from January 2019 to December 2022, and compared the clinical and morphological features between ruptured ACoA aneurysms and ruptured aneurysms elsewhere with univariate and multivariate analyses. Results Of the 292 patients with 325 ruptured aneurysms, 89 were from ACoA. The mean age of patients was 54.99 years, with female preponderance in the non-ACoA group (non-ACoA: 73.31%, ACoA: 46.07%). On univariate analysis, ages ≥60 [ages 60–69: OR = 0.311 (0.111–0.869), p=0.026; ages ≥70: OR = 0.215 (0.056–0.819), p=0.024], female gender [OR = 0.311 (0.182–0.533), p<0.001], and smoking [OR=2.069 (1.036–4.057), p=0.022] exhibited significant association with ruptured ACoA aneurysm. On multivariate analysis, only the female gender was independently associated with ruptured ACoA aneurysm (aOR 0.355 [0.436–1.961], p=0.001). Conclusion In our study, ruptured ACoA aneurysm was inversely associated with advanced age, female gender, presence of daughter aneurysm, and directly associated with smoking. After multivariate adjustment, the female gender showed an independent association with ruptured ACoA aneurysm.