Miracle-dz/UniFAR-FTUs huggingface dataset:
Fibrés Holomorphes Sur un Tore Complexe

Le problème de classifier les fibrés vectoriels holomorphes sur une variété complexe M a été résolu récemment dans les deux cas suivants : 1) M est la sphère de Riemann (Grothendieck [4]), 2) M est une courbe elliptique (Atiyah [2]). Mais si la dimension complexe de M est > 1, nous ne connaissons presque rien de ce problème, sauf le cas où la dimension complexe de fibre est égale à 1.
Quadratic BSDEs with jumps: a fixed-point approach

In this article, we prove the existence of bounded solutions of quadratic backward SDEs with jumps, that is to say for which the generator has quadratic growth in the variables (z,u). From a technical point of view, we use a direct fixed point approach as in Tevzadze [38], which allows us to obtain existence and uniqueness of a solution when the terminal condition is small enough. Then, thanks to a well-chosen splitting, we recover an existence result for general bounded solution. Under additional assumptions, we can obtain stability results and a comparison theorem, which as usual implies uniqueness.
The impact of variance in carnitine palmitoyltransferase-1 expression on breast cancer prognosis is stratified by clinical and anthropometric factors

CPT1A is a rate-limiting enzyme in fatty acid oxidation and is upregulated in high-risk breast cancer. Obesity and menopausal status' relationship with breast cancer prognosis is well established, but its connection with fatty acid metabolism is not. We performed an observational study on anonymized RNA sequencing data in the Xena Functional Genomics Explorer, to explore CPT1A's effect on breast cancer patients' survival probability. Using [18F]-fluorothymidine positron emission tomography-computed tomography images from The Cancer Imaging Archive, we segmented these analyses by obesity and menopausal status. In 1214 patients, higher CPT1A expression is associated with lower breast cancer survivability. We confirmed a previously observed protective relationship between obesity and breast cancer in pre-menopausal patients and supported this data using two-sided Pearson correlations. Taken together, these analyses using open-access, deidentified databases bolster the potential role of CPT1A-dependent fatty acid metabolism as a pathogenic factor in breast cancer.
Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: a systematic review of epigenome-wide studies

Background: DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)- based analytic techniques might help overcome the challenges of analyzing high- dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis. Methods: We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 8 June 2022. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from relevant guidelines. Results: Seventy-six studies were included in this review. Three major types of ML- based workflows were identified: 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not 2 been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques. Conclusions: There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.
Laboratory measurements of the sound generated by breaking waves

Abstract : The primary motivation for this research was to determine whether measurements of the sound generated by breaking waves could be used to quantitatively study the dynamics of the breaking process. The sound beneath two and three-dimensional breaking waves was measured. It was determined that the mean square acoustic pressure correlates with the wave slope and dissipation for waves of moderate slope and that the amount of acoustic energy radiated by an individual breaking event scales with the amount of mechanical energy dissipated by breaking. Sound at frequencies as low as 10 Hz was observed and the mean square acoustic pressure, in the frequency bands below 1 kHz correlate strongly with the wave slope and dissipation. The correlations of the mean square acoustic pressure and the wave amplitude were found to be similar for the two and three-dimensional breakers. The spectra of the sound generated by the large scale three-dimensional breaking waves sloped at -5 to -6 dB per octave at frequencies greater than 1 KHz. Evidence is presented which supports the hypothesis that the low frequency signals observed beneath both the two and three-dimensional breaking waves were caused by the collective oscillation of bubble clouds. A model of the sound produced by breaking waves which uses the sound radiated by a single bubble oscillating at its linear resonant frequency and the bubble size distribution to estimate the sound spectrum is presented.
Toric Residues and Mirror Symmetry

We develop some ideas of Morrison and Plesser and formulate a precise mathematical conjecture which has close relations to toric mirror symmetry. Our conjecture, we call it Toric Residue Mirror Conjecture, claims that the generating functions of intersection numbers of divisors on a special sequence of simplicial toric varieties are power series expansions of some rational functions obtained as toric residues. We expect that this conjecture holds true for all Gorenstein toric Fano varieties associated with reflexive polytopes and give some evidences for that. The proposed conjecture suggests a simple method for computing Yukawa couplings for toric mirror Calabi-Yau hypersurfaces without solving systems of differential equations. We make several explicit computations for Calabi-Yau hypersurfaces in weighted projective spaces and in products of projective spaces.
Glypican‐6 and Glypican‐4 stimulate embryonic stomach growth by regulating Hedgehog and noncanonical Wnt signaling

Glypicans are a family of proteoglycans that play important roles in embryonic morphogenesis. The mammalian genome contains six glypicans (GPC1 to GPC6). GPC6 and GPC4 are the pair of glypicans that show the highest degree of homology within the family. GPC6‐null embryos display bone abnormalities and severely shortened intestines.
Well‐being right before and after a permanent nursing home admission

Abstract Permanent nursing home (NH) admissions are a frequent and major life event aimed at maintaining quality of life in old age. Yet, insights into the impact of a NH admission on well‐being are scarce and inconclusive. We evaluate the effect of a NH admission on domains of well‐being among those who are admitted using event study methodology for cross‐sections combined with inverse probability weighting. We apply this doubly robust approach to Dutch survey data on well‐being linked to extensive administrative data on NH admissions, health, and socio‐economic status. We find that a NH admission leads to a temporary increase in loneliness, the risk of anxiety and depression, and a loss of control over one's life. However, these scores revert to pre‐admission levels after 6 months. These findings may contribute to better‐informed individual‐level and policy decisions about potential NH entry and aging in place policies.
Dinoflagellate hosts determine the community structure of marine Chytridiomycota: Demonstration of their prominent interactions

Abstract The interactions of parasitic fungi with their phytoplankton hosts in the marine environment are mostly unknown. In this study, we evaluated the diversity of Chytridiomycota in phytoplankton communities dominated by dinoflagellates at several coastal locations in the NW Mediterranean Sea and demonstrated the most prominent interactions of these parasites with their hosts. The protist community in seawater differed from that in sediment, with the latter characterized by a greater heterogeneity of putative hosts, such as dinoflagellates and diatoms, as well as a chytrid community more diverse in its composition and with a higher relative abundance. Chytrids accounted for 77 amplicon sequence variants, of which 70 were found exclusively among different blooming host species. The relative abundance of chytrids was highest in samples dominated by the dinoflagellate genera Ostreopsis and Alexandrium, clearly indicating the presence of specific chytrid communities. The establishment of parasitoid‐host co‐cultures of chytrids and dinoflagellates allowed the morphological identification and molecular characterization of three species of Chytridiomycota, including Dinomyces arenysensis, as one of the most abundant environmental sequences, and the discovery of two other species not yet described.
Ictal apnea: A prospective monocentric study in patients with epilepsy

Ictal respiratory disturbances have increasingly been reported, in both generalized and focal seizures, especially involving the temporal lobe. Recognition of ictal breathing impairment has gained importance for the risk of sudden unexpected death in epilepsy (SUDEP). The aim of this study was to evaluate the incidence of ictal apnea (IA) and related hypoxemia during seizures.
Clinical utility of a model‐based amoxicillin dosage regimen in neonates with early‐onset sepsis

Early‐onset sepsis (EOS) is one of the most significant causes of morbidity and mortality in neonates. Currently, amoxicillin is empirically used to treat neonates with EOS. However, data on its effectiveness in neonates with EOS are still limited. Therefore, we aimed to evaluate the pharmacodynamics (PD) target attainment and effectiveness of a model‐based amoxicillin dosage regimen in these neonates. We used a previously developed model and collected additional clinical data from the EOS neonates who used the model‐based dosage regimen (25 mg/kg every 12 h). The primary outcomes were PD target attainment (free drug concentration above minimum inhibitory concentration during 70% of the dosing interval) and treatment failure rate. The secondary endpoints were length of amoxicillin treatment, duration of hospitalization etc. Seventy‐five neonates (postmenstrual age 28.4–41.6 wk) were enrolled. A total of 70 (93.3%) neonates reached their PD target using 1 mg/L as the minimum inhibitory concentration breakpoint. The treatment failure rate was 10.7%.
THE MOST IRON-DEFICIENT STARS AS THE POLLUTED POPULATION III STARS

We investigate the origin of the most iron-poor stars including SMSS J031300.36-670839.3 with [ Fe / H ] < − 7.52 ? > . We compute the change of surface metallicity of stars with the of interstellar matter (ISM) after their birth using the chemical evolution model within the framework of the hierarchical galaxy formation. The predicted metallicity distribution function agrees very well with that observed from extremely metal-poor stars. In particular, the lowest metallicity tail is well reproduced by the Population III stars whose surfaces are polluted with metals through ISM accretion. This suggests that the origin of iron group elements is explained by ISM accretion for the stars with [ Fe / H ] ≲ − 5 ? > . The present results give new insights into the nature of the most metal-poor stars and the search for Population III stars with pristine abundances.
Efficient Classification of Locally Checkable Problems in Regular Trees

We give practical, efficient algorithms that automatically determine the asymptotic distributed round complexity of a given locally checkable graph problem in the $[\Theta(\log n), \Theta(n)]$ region, in two settings. We present one algorithm for unrooted regular trees and another algorithm for rooted regular trees. The algorithms take the description of a locally checkable labeling problem as input, and the running time is polynomial in the size of the problem description. The algorithms decide if the problem is solvable in $O(\log n)$ rounds. If not, it is known that the complexity has to be $\Theta(n^{1/k})$ for some $k = 1, 2, \dotsc$, and in this case the algorithms also output the right value of the exponent $k$. In rooted trees in the $O(\log n)$ case we can then further determine the exact complexity class by using algorithms from prior work; for unrooted trees the more fine-grained classification in the $O(\log n)$ region remains an open question.
Back-to-Bones: Rediscovering the Role of Backbones in Domain Generalization

Domain Generalization (DG) studies the capability of a deep learning model to generalize to out-of-training distributions. In the last decade, literature has been massively filled with training methodologies that claim to obtain more abstract and robust data representations to tackle domain shifts. Recent research has provided a reproducible benchmark for DG, pointing out the effectiveness of naive empirical risk minimization (ERM) over existing algorithms. Nevertheless, researchers persist in using the same outdated feature extractors, and no attention has been given to the effects of different backbones yet. In this paper, we start back to the backbones proposing a comprehensive analysis of their intrinsic generalization capabilities, which so far have been ignored by the research community. We evaluate a wide variety of feature extractors, from standard residual solutions to transformer-based architectures, finding an evident linear correlation between large-scale single-domain classification accuracy and DG capability. Our extensive experimentation shows that by adopting competitive backbones in conjunction with effective data augmentation, plain ERM outperforms recent DG solutions and achieves state-of-the-art accuracy. Moreover, our additional qualitative studies reveal that novel backbones give more similar representations to same-class samples, separating different domains in the feature space. This boost in generalization capabilities leaves marginal room for DG algorithms. It suggests a new paradigm for investigating the problem, placing backbones in the spotlight and encouraging the development of consistent algorithms on top of them. The code is available at https://github.com/PIC4SeR/Back-to-Bones.
Effect of annealing on the martensitic transformation and magnetocaloric effect in Ni44.1Mn44.2Sn11.7 ribbons

The Ni44.1Mn44. 2Sn11.7 ribbons were prepared by melt spinning. A single-phase austenite with L21 structure was confirmed in the melt-spun ribbons at room temperature. After the heat treatment, the martensitic transformation temperature increases obviously in the annealed ribbons. This method may be an effective way to tune the characteristic temperatures in the ferromagnetic shape memory alloys. Giant magnetic entropy changes are observed in the annealed ribbons. The peak values at 10kOe are 32.1 and 20.1J∕kgK, for the ribbons annealed at 1123 and 1173K, respectively.
Temporal Conditional VAE for Distributional Drift Adaptation in Multivariate Time Series

—Due to the nonstationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift . Most existing MTS forecasting models greatly suffer from the distribution drift and degrade the forecasting performance over time. Existing methods address distribution drift via adapting to the latest arrived data or self-correcting per the meta knowledge derived from future data. Despite their great success in MTS forecasting, these methods hardly capture the intrinsic distribution changes especially from a distributional perspective. Accordingly, we propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time between historical observations and future data in MTS and infer the dependencies as a temporal conditional distribution to leverage latent variables. Speciﬁcally, a novel temporal Hawkes attention mechanism represents temporal factors subsequently fed into feed-forward networks to estimate the prior Gaussian distribution of latent variables. The representation of temporal factors further dynamically adjusts the structures of Transformer-based encoder and decoder to distribution changes by leveraging a gated attention mechanism. Moreover, we introduce conditional continuous normalization ﬂow to transform the prior Gaussian to a complex and form-free distribution to facilitate ﬂexible inference of the temporal conditional distribution. Extensive experiments conducted on six real-world MTS datasets demonstrate the TCVAE’s superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines. We further illustrate the TCVAE applicability through multifaceted case studies and visualization in real-world scenarios.
Software Tools for Decoding Quantum Low-Density Parity-Check Codes

Quantum Error Correction (QEC) is an essential field of research towards the realization of large-scale quantum computers. On the theoretical side, a lot of effort is put into designing error-correcting codes that protect quantum data from errors, which inevitably hap-pen due to the noisy nature of quantum hardware and quantum bits (qubits). Protecting data with an error-correcting code necessitates means to recover the original data, given a potentially corrupted data set-a task referred to as decoding. It is vital that decoding al-gorithms can recover error-free states in an efficient manner. While theoretical properties of certain QEC methods have been exten-sively studied, good techniques to analyze their performance in practically more relevant settings is still a widely unexplored area. In this work, we propose a set of software tools that facilitate numer-ical experiments with so-called Quantum Low-Density Parity-Check codes (QLDPC codes)-a broad class of codes, some of which have recently been shown to be asymptotically good. Based on that, we provide an implementation of a general decoder for QLDPC codes. On top of that, we propose a highly efficient heuristic decoder that eliminates the runtime bottlenecks of the general QLDPC decoder while still maintaining comparable decoding performance. These tools eventually make it possible to confirm theoretical results around QLDPC codes in a more practical setting and showcase the value of software tools (in addition to theoretical considerations) for investigating codes for practical applications. The resulting tool, which is publicly available at https://github.com/cda-tum/qecc as part of the Munich Quantum Toolkit (MQT), is meant to provide a playground for the search for “practically good” quantum codes.
Early dark energy by a dark Higgs field and axion-induced nonthermal trapping

We propose a new scenario of early dark energy (EDE) with a dark Higgs trapped at the origin. To keep this dark Higgs trapped until around the matter-radiation equality, we use dark photons produced non-thermally by coherent oscillations of axions, which have a much stronger trapping effect than thermal mass. When the trapping ends, the dark Higgs quickly decays into dark photons, which are then red-shifted as radiation. The dark Higgs EDE scenario works well for an ordinary Mexican-hat potential, and the dark Higgs naturally sits at the origin from the beginning, since it is the symmetry-enhanced point. Thus, unlike the axion EDE, there is no need for elaborate potentials or fine-tuning with respect to the initial condition. Interestingly, the axion not only produces dark photons, but also explains dark matter. We find the viable parameter region of the axion decay constant and the axion mass where dark matter and the $H_0$ tension can be simultaneously explained. We also discuss the detectability of the axion in the presence of axion-photon coupling, and show that the axion can be the QCD axion.
Diffusion-based Molecule Generation with Informative Prior Bridges

AI-based molecule generation provides a promising approach to a large area of biomedical sciences and engineering, such as antibody design, hydrolase engineering, or vaccine development. Because the molecules are governed by physical laws, a key challenge is to incorporate prior information into the training procedure to generate high-quality and realistic molecules. We propose a simple and novel approach to steer the training of diffusion-based generative models with physical and statistics prior information. This is achieved by constructing physically informed diffusion bridges, stochastic processes that guarantee to yield a given observation at the fixed terminal time. We develop a Lyapunov function based method to construct and determine bridges, and propose a number of proposals of informative prior bridges for both high-quality molecule generation and uniformity-promoted 3D point cloud generation. With comprehensive experiments, we show that our method provides a powerful approach to the 3D generation task, yielding molecule structures with better quality and stability scores and more uniformly distributed point clouds of high qualities.
Optimal bump functions for shallow ReLU networks: Weight decay, depth separation and the curse of dimensionality

In this note, we study how neural networks with a single hidden layer and ReLU activation interpolate data drawn from a radially symmetric distribution with target labels 1 at the origin and 0 outside the unit ball, if no labels are known inside the unit ball. With weight decay regularization and in the infinite neuron, infinite data limit, we prove that a unique radially symmetric minimizer exists, whose weight decay regularizer and Lipschitz constant grow as $d$ and $\sqrt{d}$ respectively. We furthermore show that the weight decay regularizer grows exponentially in $d$ if the label $1$ is imposed on a ball of radius $\varepsilon$ rather than just at the origin. By comparison, a neural networks with two hidden layers can approximate the target function without encountering the curse of dimensionality.