Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship Between Obesity, Depressive Symptoms and Sleep Duration in Children and Adolescents

Aim: In this study, we aimed to investigate the relationship between symptoms of depression and sleep duration with anthropometric measurements in obese children and adolescents. Methods: Forty children and adolescents (25 girls) diagnosed with obesity were included. Sociodemographic and clinical data were obtained using the sociodemo- graphic and clinical data form. Age, gender, height and weight values were recorded; body mass index (BMI), percentile and standard deviations (SDS) were calculated. Children’s Depression Inventory (CDI) was used to assess depression. The degree of obesity was classified according to the BMI values of the cases, the cases in class 1 and 2 were grouped as “subgroup 1”, and the cases in class 3 as “subgroup 2”. Groups were compared in terms of sociodemographic features, depression and sleep duration. Results: Mean age of the group was 12.8±2.6 years. There was a significant dif- ference in patients with depression in terms of body weight, BMI, BMI-SDS and sleep duration compared to those without depression. CDI scores in subgroup 2 were significantly higher than subgroup 1. CDI scores were positively correlated with body weight, BMI and BMI-SDS and negatively correlated with sleep duration. Sleep duration was negatively correlated with body weight and BMI. Conclusion: Depressive symptoms and sleep duration may be associated with the severity of obesity in children. We consider that the assessment of depression and sleep disorders by a pediatric psychiatrist is important and useful in children with obesity.
Machine Learning is Abduction Inference

Concept of Abduction with Gradated Contradictions is introduced here as a form of Peirce’s abduction inference. The general form of abduction criterion is formalized in the proposed Logic of Gradated Contradictions and Logic of Recursive Aggregation. Common steps of an abduction procedure as minimization of such a criterion are speciﬁed as well. It is demonstrated on examples of 14 popular textbook learners (from hierarchical clustering to k-NN and SVR) that each of them performs AGC. The proposed theory explains real life learners, yet it avoids any mention of statistics, so it can be considered as a logical alternative to the statistical learning theory.
Increased intermolecular interactions and cluster formation at the onset of the twist-bend nematic phase in thioether cyanobiphenyl-based liquid crystal dimers

Infrared spectroscopy (IR) and quantum chemistry calculations, based on density functional theory (DFT) were used to study the structure and the molecular interactions in the nematic (N) and twist-bend (NTB) phases of thioether-linked dimers. Infrared absorbance measurements were conducted in a polarized beam for a homogeneously aligned samples to measure and understand the orientation of the vibrational transition dipole moments in the liquid crystal states. Revealing temperature dependent changes in the mean IR absorbance in the twist-bend nematic phase were observed. In the transition from the N to the NTB phase, is was found to be associated with a decrease in absorbance for the longitudinal dipoles. This is a result of the antiparallel axial interactions of the dipoles, while the absorbance of the transverse dipoles remained unchanged up to 340 K and then increased and the dipoles correlated in parallel. In order to account for the molecular arrangement in the nematic phase, DFT calculations were conducted for the system nearest to the lateral neighborhoods. Changes in the square of transition dipoles were found to be quite similar to the mean absorbance that was observed in the IR spectra. Interactions of molecules that were dominated by pair formation was observed as well as a shift of the long axes of the molecules relative to each other. The most important observation from the spectroscopic measurements was the sudden increase in the intermolecular interactions as the temperature decreased from the N to the NTB phase. This was evidenced by a significant increase in the correlation of the dipoles that were induced in the dimer cores.
Interaction Between Macroscopic Quantum Systems and Gravity

We review experiments and theoretical models about the possible mutual interplay between the gravitational field and materials in the superconducting state or other macroscopic quantum states. More generally, we focus on the possibility for quantum macrosystems in a coherent state to produce local alterations of the gravitational field in which they are immersed. This fully interdisciplinary research field has witnessed a conspicuous progress in the last decades, with hundreds of published papers, and yet several questions are still completely open.
Particle Filtering and Gaussian Mixtures―On a Localized Mixture Coefficients Particle Filter (LMCPF) for Global NWP

In a global numerical weather prediction (NWP) modeling framework we study the implementation of Gaussian uncertainty of individual particles into the assimilation step of a localized adaptive particle filter (LAPF). We obtain a local representation of the prior distribution as a mixture of basis functions. In the assimilation step, the filter calculates the individual weight coefficients and new particle locations. It can be viewed as a combination of the LAPF and a localized version of a Gaussian mixture filter, i.e., a Localized Mixture Coefficients Particle Filter (LMCPF). Here, we investigate the feasibility of the LMCPF within a global operational framework and evaluate the relationship between prior and posterior distributions and observations. Our simulations are carried out in a standard pre-operational experimental set-up with the full global observing system, 52 km global resolution and $10^6$ model variables. Statistics of particle movement in the assimilation step are calculated. The mixture approach is able to deal with the discrepancy between prior distributions and observation location in a real-world framework and to pull the particles towards the observations in a much better way than the pure LAPF. This shows that using Gaussian uncertainty can be an important tool to improve the analysis and forecast quality in a particle filter framework.
Cooperative transitions involving hydrophobic polyelectrolytes

Significance Sharp, cooperative, switch-like transitions are key in biological regulation. A textbook example is the sensitivity of the binding of oxygen onto hemoglobin. It is generally assumed that only complex molecules such as proteins display these type of cooperative transitions, which are driven by an underlying conformational change. We show that the interactions between protons and relatively simple hydrophobic polyelectrolytes can be described under the same theoretical framework, and we detail the conditions that lead to sharp, pH-induced transitions. These findings are potentially relevant in drug and gene delivery applications and in the development of materials whose optical, electronic, or other properties can be switched by small concentration variations of molecular ligands.
Diffusion Activation Energy and Desorption Activation Energy for Astrochemically Relevant Species on Water Ice Show No Clear Relation

The activation energies for desorption (E des) and for surface diffusion (E sd) of adsorbed molecules on dust grains are two of the most important parameters for the chemistry in the interstellar medium. Although E des is often measured by laboratory experiments, the measurement of E sd is sparse. Due to the lack of data, astrochemical models usually assume a simple scaling relation, E sd = f E des, where f is a constant, irrespective of the adsorbed species. Here, we experimentally measure E sd for CH4, H2S, OCS, CH3OH, and CH3CN on water-ice surfaces using an ultrahigh-vacuum transmission electron microscope. Compiling the measured E sd values and E des values from the literature, we find that the value of f ranges from ∼0.2 to ∼0.7, depending on the species. Unless f (or E sd) for the majority of species is available, a natural alternative approach for astrochemical models is running multiple simulations, varying f for each species randomly. In this approach, ranges of molecular abundances predicted by multiple simulations, rather than abundances predicted by each simulation, are important. We here run 10,000 simulations of astrochemical models of molecular clouds and protostellar envelopes, randomly assigning a value of f for each species. In the former case, we identify several key species whose E sd most strongly affects the uncertainties of the model predictions; E sd for those species should be investigated in future laboratory and quantum chemical studies. In the latter case, uncertainties in the E sd of many species contribute to the uncertainties in the model predictions.
Calibrating X-Ray Binary Luminosity Functions via Optical Reconnaissance. II. The High-mass XLF and Globular Cluster Population of X-Ray Binaries in the Low Star-forming Spiral M81

We characterize the optical counterparts to the compact X-ray source population within the nearby spiral galaxy M81 using multiband Hubble Space Telescope (HST) imaging data. By comparing the optical luminosities and colors measured for candidate donor stars and host clusters to stellar and cluster evolutionary models, respectively, we estimate the likely masses and upper age limits of the field and cluster X-ray binaries. We identify 15 low-mass X-ray binaries (i.e., donor star mass ≲ 3 M ⊙) within ancient globular clusters, as well as 42 candidate high-mass X-ray binaries (i.e., donor star mass ≳ 8 M ⊙). To estimate the likelihood of misclassifications, we inject 4000 artificial sources into the HST mosaic image and conclude that our classifications of globular clusters and high-mass X-ray binaries are reliable at the >90% level. We find that globular clusters that host X-ray binaries are on average more massive and more compact than globular clusters that do not. However, there is no apparent correlation between the X-ray brightness of the clusters and their masses or densities, nor are X-ray binary hosts more X-ray luminous than the general field population of low-mass X-ray binaries. This work represents one of the first in-depth analyses of the population of X-ray binaries within globular clusters in a spiral galaxy.
Mode switching dynamics in organic polariton lasing

We study the dynamics of multimode polariton lasing in organic microcavities by using a second-order cumulant equation approach. By inspecting the time evolution of the photon mode occupations, we show that if multiple lasing peaks are observed in time-integrated mode occupations, the reason can be either bi-modal lasing or temporal switching between several modes. The former takes place within a narrow range of parameters while the latter occurs more widely. We find that the origin of the temporal switching is different in the weak- and strong-coupling regimes. At weak coupling slope efficiency is the determining factor, while for strong coupling it is changes in the eigenmodes and gain spectrum upon pumping. This difference is revealed by investigating the photoluminescence and momentum-resolved gain spectra. Our results underscore the importance of understanding the time evolution of the populations when characterizing the lasing behaviour of a multimode polariton system, and show how these features differ between weak and strong coupling.
Demonstration of high sensitivity of microwave-induced resistance oscillations to circular polarization

We demonstrate that long-debated immunity of microwave-induced resistance oscillations (MIRO) to the sense of circular polarization is not a generic property of this phenomenon in solid-state two-dimensional electron systems. Using a large-area GaAs-based heterostructure we detect up to 30 times larger MIRO signal for the cyclotron resonance (CR) active helicity, fully consistent with the concurrently measured transmission and the deduced CR shape of the Drude absorption. We further elaborate conditions to avoid extrinsic factors capable of producing an apparent immunity of the photoresponse.
Search for Periodicities in High Energy AGNs with a Time Domain Approach

This paper investigates a new methodology to search for periods in light curves of high-energy gamma-ray sources such as active galactic nuclei (AGNs). High-energy light curves have significant stochastic components, making period detection somewhat challenging. In our model, periodic terms, drifts of the light curves, and random walk with a correlation between flux points due to colored noise are taken into account independently. The parameters of the model are obtained directly from a Markov Chain Monte Carlo minimization. The time periods found are compared to the output of the publicly available Agatha program. The search method is applied to high-energy periodic AGN candidates from the Fermi-LAT catalog. The significance of periodic models over pure noise models is discussed. Finally, the variability of the period and amplitude of oscillating terms is studied on the most significant candidates.
Dynamic State Estimation of Nonlinear Differential Algebraic Equation Models of Power Networks

This paper investigates the joint problems of dynamic state estimation of algebraic variables (voltage and phase angle) and generator states (rotor angle and frequency) of nonlinear differential algebraic equation (NDAE) power network models, under uncertainty. Traditionally, these two problems have been decoupled due to complexity of handling NDAE models. In particular, this paper offers the first attempt to solve the aforementioned problem in a coupled approach where the algebraic and generator states estimates are simultaneously computed. The proposed estimation algorithm herein is endowed with the following properties: (i) it is fairly simple to implement and based on well-understood Lyapunov theory; (ii) considers various sources of uncertainty from generator control inputs, loads, renewables, process and measurement noise; (iii) models phasor measurement unit installations at arbitrary buses; and (iv) is computationally less intensive than the decoupled approach in the literature.
On the connection between WRI and FWI: Analysis of the nonlinear term in the Hessian matrix

Implementation of the standard full waveform inversion (FWI) poses difficulties as the initial model offsets from the true model. The wavefield reconstruction inversion (WRI) was proposed to mitigate these difficulties by relaxing the wave-equation constraint. In this abstract, working on the nonlinear term in the Hessian matrix of FWI, we develop a new approximate Hessian as an Augmented Gauss-Newton (AGN) Hessian including second-order derivative information. Moreover, we establish an intimate connection between an updating formula which results from approximate solve of the Newton's method with the AGN Hessian on the FWI problem and the WRI method. Our analysis opens new perspectives for developing efficient algorithms for FWI based on the Newton's method and highlights the importance of the nonlinear term in the Hessian matrix, which is ignored in most cases.
Resource-Constrained Edge AI with Early Exit Prediction

By leveraging the data sample diversity, the early-exit network recently emerges as a prominent neural network architecture to accelerate the deep learning inference process. However, intermediate classifiers of the early exits introduce additional computation overhead, which is unfavorable for resource-constrained edge artificial intelligence (AI). In this paper, we propose an early exit prediction mechanism to reduce the on-device computation overhead in a device-edge co-inference system supported by early-exit networks. Specifically, we design a low-complexity module, namely the Exit Predictor, to guide some distinctly"hard"samples to bypass the computation of the early exits. Besides, considering the varying communication bandwidth, we extend the early exit prediction mechanism for latency-aware edge inference, which adapts the prediction thresholds of the Exit Predictor and the confidence thresholds of the early-exit network via a few simple regression models. Extensive experiment results demonstrate the effectiveness of the Exit Predictor in achieving a better tradeoff between accuracy and on-device computation overhead for early-exit networks. Besides, compared with the baseline methods, the proposed method for latency-aware edge inference attains higher inference accuracy under different bandwidth conditions.
Experimental Comparison of PAM-8 Probabilistic Shaping with Different Gaussian Orders at 200 Gb/s Net Rate in IM/DD System with O-Band TOSA

For 200Gb/s net rates, cap probabilistic shaped PAM-8 with different Gaussian orders are experimentally compared against PAM-8. In back-to-back and 5km measurements, cap shaped 85-GBd PAM-8 with Gaussian orders of 5 outperforms 71-GBd uniform PAM-8 by up to 2.90dB and 3.80dB in receiver sensitivity, respectively.
A Deep Generative Model of Neonatal Cortical Surface Development

The neonatal cortical surface is known to be aﬀected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to clinically interpretable models of disease, but developing these on the cortical surface is challenging since established techniques for learning convolutional ﬁlters are inappro-priate on non-ﬂat topologies. To close this gap, we implement a surface-based CycleGAN using mixture model CNNs (MoNet) to translate spher-icalised neonatal cortical surface features (curvature and T1w/T2w cortical myelin) between diﬀerent stages of cortical maturity. Results show our method is able to reliably predict changes in individual patterns of cortical organisation at later stages of gestation, validated by comparison to longitudinal data; and translate appearance between preterm and term gestation ( > 37 weeks gestation), validated through comparison with a trained term/preterm classiﬁer. Simulated diﬀerences in cortical maturation are consistent with observations in the literature.
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available. However, the high computational cost (e.g., 100 times more than that of the project gradient descent attack) makes AA infeasible for practitioners with limited computational resources, and also hinders applications of AA in the adversarial training (AT). In this paper, we propose a novel method, minimum-margin (MM) attack, to fast and reliably evaluate adversarial robustness. Compared with AA, our method achieves comparable performance but only costs 3% of the computational time in extensive experiments. The reliability of our method lies in that we evaluate the quality of adversarial examples using the margin between two targets that can precisely identify the most adversarial example. The computational efficiency of our method lies in an effective Sequential TArget Ranking Selection (STARS) method, ensuring that the cost of the MM attack is independent of the number of classes. The MM attack opens a new way for evaluating adversarial robustness and provides a feasible and reliable way to generate high-quality adversarial examples in AT.
Matter-gravity entanglement entropy and the information loss puzzle

Since Hawking’s 1974 discovery, we expect a black hole formed by collapse will emit radiation and eventually disappear. One aspect of the information loss puzzle is the challenge to deﬁne an objective notion of physical entropy which, in accordance with the Second Law, increases for this, and other, closed unitarily evolving systems. We question whether coarse-grained entropy can meet this challenge but argue that matter-gravity entanglement entropy can. We argue that due to (usually neglected) photon-graviton interactions, if the evaporation is slowed down by putting the black hole in a slightly permeable box, the radiation remaining after a large black hole has evaporated will (be pure and) mainly consist of roughly equal numbers of photons and gravitons entangled with one another – with a (matter-gravity entanglement) entropy greater than that of the freshly formed black hole. We also give arguments that, in the absence of such a box, the ﬁnal state would be similar. If it is so, this would seem to improve the prospects for the resolution of the ﬁrewall puzzle since late emitted photons/gravitons would not be needed to purify early emitted photons/gravitons; instead they would purify one other.
Piperacillin , a New Pencillin Active Against Many Bacteria Resistant to Other Penicillins

The in vitro activity of piperacillin, a new semisynthetic piperazine penicillin derivative, was evaluated against 626 clinical isolates and compared with the activity of other ,-lactam antibiotics. At a concentration of 0.1 pg/ml, piperacillin inhibited all streptococci except enterococci. Non-f,-lactamase-producing staphylococci were inhibited by 1.6 pg or less per ml. Both f6-lactamaseand non-,8lactamase-producing Haemophilus were inhibited by 0.1 ,ug/ml. Piperacillin inhibited non-,-lactamase-producing Escerichia coli, Salmonella, and Shigella at a concentration of 6.3 pg/ml, but 20% of strains of these species containing type III ,8-lactamase were not inhibited by 100 pg/ml. Piperacillin at 25 pg/ml inhibited 83% of Citrobacter, 58% of Klebsiella, 88% of Enterobacter, and 50% of indolepositive Proteus, Acinetobacter, and Providencia. At 25 pg/ml, piperacillin inhibited 95% of Pseudomonas aeruginosa and 78% of Bacteroides fragilis. The minimal inhibitory concentration of piperacillin against Pseudomonas was affected by increasing the inoculum size and by pH. Minimum bactericidal concentrations against Pseudomonas and Serratia often were eightfold greater than the minimum inhibitory concentrations. Piperacillin was equal in activity to ampicillin against enterococci. It was more active than carbenicillin against E. coli, Klebsiella, Enterobacter, and Bacteroides. It was the most active penicillin against Pseudomonas and inhibited many strains of Pseudomonas for which the MICs of carbenicillin were above 200 pg/ml. Piperacillin was hydrolyzed by many different /B-lactamases. Synergistic activity ofpiperacillin was demonstrated when it was combined with amikacin, gentamicin, and cefazolin against P. aeruginosa and members of the Enterobacteriaceae. No antagonism was observed when piperacillin was combined with aminoglycosides; however, antagonim was observed rarely against E. coli when piperacilhin was combined with cefazolin.