Miracle-dz/UniFAR-FTUs huggingface dataset:
The Circadian Rhythm Variation of Pain in the Orofacial Region

All living organisms have a biological clock that orchestrates every biological process and function, and this internal clock operates following a circadian rhythm. This biological clock is known to influence various clinical indicators such as blood pressure and body temperature. Also, the fluctuation of signs and symptoms of diseases including pain disorders are affected by circadian rhythm. It has been reported that the pain intensity of various somatic and neuropathic pain disorders show unique pain patterns that depend on the passage of time. The generation of pain patterns could be explained by extrinsic (e.g., physical activity, tactile stimulation, ambient temperature) and also intrinsic factors (neural and neuroendocrine modulation) that are related to the circadian rhythm. It is important to recognize and identify the individual pain pattern in pain therapy to approve treatment outcome. Moreover, chronotherapeutics which considers pain patterns and pharmacokinetics in context of the circadian rhythm could produce greater analgesia in response to medication. However, only a limited number of studies handle the issue of pain patterns according to circadian rhythm and chronotherapeutics in the orofacial region. The present review intends to reflect on the most recent and relevant data concerning the bidirectional relation between pain disorders of the orofacial region and circadian patterns.
Mediterranean Diet and Metabolic Syndrome in Adult Moroccan Women

The objective was to assess the relationship between adherence to Mediterranean dietary pattern (MDP) and the prevalence of metabolic syndrome (MS) in urban Moroccan adult women, living in an agricultural Mediterranean province; El Jadida. A random sample (n=182) of Moroccan women (18-55 years old) was interviewed; anthropometrical measures and fasting blood sample were collected. The prevalence of MS was determined using the NCEP-ATPIII criteria. A general questionnaire on socio-economic status and lifestyle factors was used. Dietary habits were assessed using three 24hours recalls and a semi-quantitative food frequency questionnaire. Adherence to the Mediterranean diet (MD) was defined according to a score constructed considering the consumption of major MD components. The overall prevalence of MS was 20.9 %; MS was prevalent in 20 % among the overweight and 37.8% among the obese women. Eight point eight of women had no risk factors while 41.2% had one risk factor and 29.1% had two risk factors. Low-HDL cholesterol was found among 82.4 %, Hypertension in 36.8 and abdominal obesity among 25.8 % women with MS. The mean adherence to Mediterranean Diet (MD) was 62.84 % (± 12.7). Once adjusted for age, socioeconomic and lifestyle variables, MD adherence was not related to metabolic syndrome but some MD components, such as Cereals and monounsaturated to saturated fatty acids ratio, showed a protective effect on triglyceridemia and glycemia criteria, respectively. The study data show that MS is prevalent among the population sample, especially among obese women. Some components of the MD have a protective effect on MS and on its components. However, no association was found between MS and adherence to MD. High education level and availability of family environment were also related to a better adherence to MD pattern.
Caso clínico Hiperglucemia neonatal persistente

Transient or permanent neonatal diabetes mellitus (NDM) is rare disorder which requires subcutaneous insulin for management. The recently discovered mutations in the KCNJ11 and ABCC8 genes, encoding the Kir6.2 and SUR1 subunits of the pancreatic K(ATP) channel involved in regulation of insulin secretion, account for a third to a half of the PNDM cases. Patients with these mutations may be converted from insulin therapy to sulphonylureas. We describe the transition from insulin to glibenclamide therapy in a 4 year old girl with permanent NDM in the outpatient setting. She had a normal abdominal ultrasound and no pancreatic antibodies or phenotypic alterations. Sulphonylurea treatment resulted in increase in basal C-peptide levels from 0.01 to 0.8 ng/mL, HbA1c levels were reduced from 7.5 to 5.2% and insulin treatment discontinuation was possible at 12 weeks of treatment.
Quantum Information and Wave Function Collapse

Information‐theoretical restrictions on the information transferred in quantum measurements, are regarded for the measurement of quantum object S performed via its interaction with information system O. This information restrictions, induced by Heisenberg commutation relations, are derived in the formalism of inference maps in Hilbert space. O restricted states ξo are calculated from Shroedinger S, O dynamics and the structure of O observables set (algebra); O decoherence by its environment is also accounted for some S, O systems. It's shown that this principal constraints on the information transfer result in the stochasticity of measurement outcomes. Consequently, ξo describes the random 'pointer' outcomes qj, which correspond to the collapse of S pure state.
Linear-programming design and analysis of fast algorithms for Max 2-Sat and Max 2-CSP

The class $(r,2)$-CSP, or simply Max 2-CSP, consists of constraint satisfaction problems with at most two $r$-valued variables per clause. For instances with $n$ variables and $m$ binary clauses, we present an $O(n r^{5+19m/100})$-time algorithm which is the fastest polynomial-space algorithm for many problems in the class, including Max Cut. The method also proves a treewidth bound $\tw(G) \leq (13/75+o(1))m$, which gives a faster Max 2-CSP algorithm that uses exponential space: running in time $\Ostar{2^{(13/75+o(1))m}}$, this is fastest for most problems in Max 2-CSP. Parametrizing in terms of $n$ rather than $m$, for graphs of average degree $d$ we show a simple algorithm running time $\Ostar{2^{(1-\frac{2}{d+1})n}}$, the fastest polynomial-space algorithm known. In combination with ``Polynomial CSPs'' introduced in a companion paper, these algorithms also allow (with an additional polynomial-factor overhead in space and time) counting and sampling, and the solution of problems like Max Bisection that escape the usual CSP framework. Linear programming is key to the design as well as the analysis of the algorithms.
Riemannian isometries of twisted magnetic flux tube metric and stable current-carrying solar loops

Two examples of the use of differential geometry in plasma physics are given: The first is the computation and solution of the constraint equations obtained from the Riemann metric isometry of the twisted flux tube. In this case a constraint between the Frenet torsion and curvature is obtained for inhomogeneous helical magnetic flux tube axis. In the second one, geometrical and topological constraints on the current-carrying solar loops are obtained by assuming that the plasma filament is stable. This is analogous to early computations by Liley [(Plasma Physics (1964)] in the case of hydromagnetic equilibria of magnetic surfaces. It is shown that exists a relationship between the ratio of the current components along and cross the plasma filament and the Frenet torsion and curvature. The computations are performed for the helical plasma filaments where torsion and curvature are proportional. The constraints imposed on the electric currents by the energy stability condition are used to solve the remaining magnetohydrodynamical (MHD) equations which in turn allows us to compute magnetic helicity and from them the twist and writhe topological numbers. Magnetic energy is also computed from the solutions of MHD equations. PACS numbers:
Manifold Markov chain Monte Carlo methods for Bayesian inference in a wide class of diffusion models

Bayesian inference for partially observed, nonlinear diffusion models is a challenging task that has led to the development of several important methodological advances. We propose a novel framework for inferring the posterior distribution on both a time discretisation of the diffusion process and any unknown model parameters, given partial observations of the process. The set of joint configurations of the noise increments and parameters which map to diffusion paths consistent with the observations form an implicitly defined manifold. By using a constrained Hamiltonian Monte Carlo algorithm for constructing Markov kernels on embedded manifolds, we are able to perform computationally efficient inference in a wide class of partially observed diffusions. Unlike other approaches in the literature, that are often limited to specific model classes, our approach allows full generality in the choice of observation and diffusion models, including complex cases such as hypoelliptic systems with degenerate diffusion coefficients. By exploiting the Markovian structure of diffusions, we propose a variant of the approach with a complexity that scales linearly in the time resolution of the discretisation and quasi-linearly in the number of observation times.
The international effects of central bank information shocks

We explore the international transmission of monetary policy and central bank information shocks by the Federal Reserve and the European Central Bank. Identification of these shocks is achieved by using a combination of high-frequency market surprises around announcement dates of policy decisions and sign restrictions. We propose a high-dimensional macroeconometric framework for modeling aggregate quantities alongside country-specific variables to study international shock propagation and spillover effects. Our results are in line with the established literature focusing on individual economies, and moreover suggest substantial international spillover effects in both directions for monetary policy and central bank information shocks. In addition, we detect heterogeneities in the transmission of ECB policy actions to individual member states.
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution

In this article, we focus on the analysis of the potential factors driving the spread of influenza, and possible policies to mitigate the adverse effects of the disease. To be precise, we first invoke discrete Fourier transform (DFT) to conclude a yearly periodic regional structure in the influenza activity, thus safely restricting ourselves to the analysis of the yearly influenza behavior. Then we collect a massive number of possible region-wise indicators contributing to the influenza mortality, such as consumption, immunization, sanitation, water quality, and other indicators from external data, with $1170$ dimensions in total. We extract significant features from the high dimensional indicators using a combination of data analysis techniques, including matrix completion, support vector machines (SVM), autoencoders, and principal component analysis (PCA). Furthermore, we model the international flow of migration and trade as a convolution on regional influenza activity, and solve the deconvolution problem as higher-order perturbations to the linear regression, thus separating regional and international factors related to the influenza mortality. Finally, both the original model and the perturbed model are tested on regional examples, as validations of our models. Pertaining to the policy, we make a proposal based on the connectivity data along with the previously extracted significant features to alleviate the impact of influenza, as well as efficiently propagate and carry out the policies. We conclude that environmental features and economic features are of significance to the influenza mortality. The model can be easily adapted to model other types of infectious diseases.
A realistic two-dimensional model of Altair

Context. Fast rotation is responsible for important changes in the structure and evolution of stars and the way we see them. Optical long baseline interferometry now allows for the study of its effects on the stellar surface, mainly gravity darkening and flattening. Aims. We aim to determine the fundamental parameters of the fast-rotating star Altair, in particular its evolutionary stage (represented here by the core hydrogen mass fraction Xc), mass, and differential rotation, using state-of-the-art stellar interior and atmosphere models together with interferometric (ESO-VLTI), spectroscopic, and asteroseismic observations. Methods. We use ESTER two-dimensional stellar models to produce the relevant surface parameters needed to create intensity maps from atmosphere models. Interferometric and spectroscopic observables are computed from these intensity maps and several stellar parameters are then adjusted using the publicly available MCMC algorithm Emcee. Results. We determined Altair’s equatorial radius to be Req = 2.008 ± 0.006 R⊙, the position angle PA = 301.1 ± 0.3°, the inclination i = 50.7 ± 1.2°, and the equatorial angular velocity Ω = 0.74 ± 0.01 times the Keplerian angular velocity at equator. This angular velocity leads to a flattening of ε = 0.220 ± 0.003. We also deduce from the spectroscopically derived v sin i ≃ 243 km s−1, a true equatorial velocity of ∼314 km s−1 corresponding to a rotation period of 7h46m (∼3 cycles/day). The data also impose a strong correlation between mass, metallicity, hydrogen abundance, and core evolution. Thanks to asteroseismic data, and provided our frequencies identification is correct, we constrain the mass of Altair to 1.86 ± 0.03 M⊙ and further deduce its metallicity Z = 0.019 and its core hydrogen mass fraction Xc = 0.71, assuming an initial solar hydrogen mass fraction X = 0.739. These values suggest that Altair is a young star ∼100 Myr old. Finally, the 2D ESTER model also gives the internal differential rotation of Altair, showing that its core rotates approximately 50% faster than the envelope, while the surface differential rotation does not exceed 6%.
Variability Aware Read and Write Channel Models for 1S1R Crossbar Resistive Memory with High Wordline/Bitline Resistance

Crossbar resistive memory with 1 Selector 1 Resistor (1S1R) structure is attractive for low-cost and high-density nonvolatile memory applications. As technology scales down to the single-nm regime, the increasing resistivity of wordline/bitline becomes a limiting factor to device reliability. This paper presents write/read communication channels while considering the line resistance and device variabilities by statistically relating the degraded write/read margins and the channel parameters. Binary asymmetric channel (BAC) models are proposed for the write/read operations and array capacity results are presented. Simulations based on these models suggest that the bit-error rate of devices are highly non-uniform across the memory array. These models provide quantitative tools for evaluating the trade-offs between memory reliability and design parameters, such as array size, technology nodes, and aspect ratio, and also for designing coding-theoretic solutions that would be most effective for crossbar memory.
Dark matter-electron scattering from aromatic organic targets

Sub-GeV dark matter (DM) which interacts with electrons can excite electrons occupying molecular orbitals in a scattering event. In particular, aromatic compounds such as benzene or xylene have an electronic excitation energy of a few eV, making them sensitive to DM as light as a few MeV. These compounds are often used as solvents in organic scintillators, where the de-excitation process leads to a photon which propagates until it is absorbed and re-emitted by a dilute fluor. The fluor photoemission is not absorbed by the bulk, but is instead detected by a photon detector such as a photomultiplier tube. We develop the formalism for DM-electron scattering in aromatic organic molecules, calculate the expected rate in p-xylene, and apply this calculation to an existing measurement of the single photo-electron emission rate in a low-background EJ-301 scintillator cell. Despite the fact that this measurement was performed in a shallow underground laboratory under minimal overburden, the DM-electron scattering limits extracted from these data are already approaching leading constraints in the 3-100 MeV DM mass range. We discuss possible next steps in the evolution of this direct detection technique, in which scalable organic scintillators are used in solid or liquid crystal phases and in conjunction with semiconductor photodetectors to improve sensitivity through directional signal information and potentially lower dark rates.
Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models

Time-varying parameter (TVP) models are very flexible in capturing gradual changes in the effect of explanatory variables on the outcome variable. However, in particular when the number of explanatory variables is large, there is a known risk of overfitting and poor predictive performance, since the effect of some explanatory variables is constant over time. We propose a new prior for variance shrinkage in TVP models, called triple gamma. The triple gamma prior encompasses a number of priors that have been suggested previously, such as the Bayesian Lasso, the double gamma prior and the Horseshoe prior. We present the desirable properties of such a prior and its relationship to Bayesian Model Averaging for variance selection. The features of the triple gamma prior are then illustrated in the context of time varying parameter vector autoregressive models, both for simulated dataset and for a series of macroeconomics variables in the Euro Area.
On the construction of maximal $p$-cyclically monotone operators

In this paper we deal with the construction of explicit examples of maximal $p$-cyclically monotone operators. To date, there is only one instance of an explicit example of a maximal 2-cyclically monotone operator that is not maximal monotone. We present several other examples, and a proposal of how such examples can be constructed.
CODEX clusters

Context. Large area catalogs of galaxy clusters constructed from ROSAT All-Sky Survey provide the basis for our knowledge of the population of clusters thanks to long-term multiwavelength efforts to follow up observations of these clusters. Aims. The advent of large area photometric surveys superseding previous, in-depth all-sky data allows us to revisit the construction of X-ray cluster catalogs, extending the study to lower cluster masses and higher redshifts and providing modeling of the selection function. Methods. We performed a wavelet detection of X-ray sources and made extensive simulations of the detection of clusters in the RASS data. We assigned an optical richness to each of the 24 788 detected X-ray sources in the 10 382 square degrees of the Baryon Oscillation Spectroscopic Survey area using red sequence cluster finder redMaPPer version 5.2 run on Sloan Digital Sky Survey photometry. We named this survey COnstrain Dark Energy with X-ray (CODEX) clusters. Results. We show that there is no obvious separation of sources on galaxy clusters and active galactic nuclei (AGN) based on the distribution of systems on their richness. This is a combination of an increasing number of galaxy groups and their selection via the identification of X-ray sources either by chance or by groups hosting an AGN. To clean the sample, we use a cut on the optical richness at the level corresponding to the 10% completeness of the survey and include it in the modeling of the cluster selection function. We present the X-ray catalog extending to a redshift of 0.6. Conclusions. The CODEX suvey is the first large area X-ray selected catalog of northern clusters reaching fluxes of 10−13 ergs s−1 cm−2. We provide modeling of the sample selection and discuss the redshift evolution of the high end of the X-ray luminosity function (XLF). Our results on z < 0.3 XLF agree with previous studies, while we provide new constraints on the 0.3 < z < 0.6 XLF. We find a lack of strong redshift evolution of the XLF, provide exact modeling of the effect of low number statistics and AGN contamination, and present the resulting constraints on the flat ΛCDM.
From waves to convection and back again: The phase space of stably stratified turbulence

We show that the phase space of stratified turbulence mainly consists of two slow invariant manifolds with rich physics, embedded on a larger basin with fast evolution. A local invariant manifold in the vicinity of the fluid at equilibrium corresponds to waves, while a global invariant manifold corresponds to the onset of local convection. Using a reduced model derived from the Boussinesq equations, we propose that waves accumulate energy nonlinearly up to a point such that fluid elements escape from the local manifold and evolve fast to the global manifold, where kinetic energy can be more efficiently dissipated. After this, fluid elements return to the first manifold. As the stratification increases, the volume of the first manifold increases, and the second manifold becomes harder to access. This explains recent observations of enhanced intermittency and marginal instability in these flows. The reduced model also allows us to study structure formation, alignment of field gradients in the flow, and to identify balance relations that hold for each fluid element.
Benchmarking Supercomputers with the J\"ulich Universal Quantum Computer Simulator.

We use a massively parallel simulator of a universal quantum computer to benchmark some of the most powerful supercomputers in the world. We find nearly ideal scaling behavior on the Sunway TaihuLight, the K computer, the IBM BlueGene/Q JUQUEEN, and the Intel Xeon based clusters JURECA and JUWELS. On the Sunway TaihuLight and the K computer, universal quantum computers with up to 48 qubits can be simulated by means of an adaptive two-byte encoding to reduce the memory requirements by a factor of eight. Additionally, we discuss an alternative approach to alleviate the memory bottleneck by decomposing entangling gates such that low-depth circuits with a much larger number of qubits can be simulated.
Interpretations of Deep Learning by Forests and Haar Wavelets

This paper presents a basic property of region dividing of ReLU (rectified linear unit) deep learning when new layers are successively added, by which two new perspectives of interpreting deep learning are given. The first is related to decision trees and forests; we construct a deep learning structure equivalent to a forest in classification abilities, which means that certain kinds of ReLU deep learning can be considered as forests. The second perspective is that Haar wavelet represented functions can be approximated by ReLU deep learning with arbitrary precision; and then a general conclusion of function approximation abilities of ReLU deep learning is given. Finally, generalize some of the conclusions of ReLU deep learning to the case of sigmoid-unit deep learning.
Oracular information and the second law of thermodynamics

Patterns and processes - spatial and temporal correlations - are subject to thermodynamic laws, functioning as sources of work known as information reservoirs. Devices that generate or consume a pattern one piece at a time require internal memory to accurately enact this transformation, and this memory is also subject to thermodynamic laws. Oracular information is correlation between such memory and upcoming portions of a process that is not explained by the process's history. Here, I provide an explicit construction for a finite-extent generator of any stationary pattern with arbitrarily low thermal dissipation, at the cost of increasing the oracular information in the generator's memory. I then conversely show that if oracular information could be incorporated within any device that anticipates and consumes a pattern to release work, this would be inconsistent with the second law of thermodynamics. This suggests that oracular information is only physical when used by machines that are the cause of patterns.