Miracle-dz/UniFAR-FTUs huggingface dataset:
A Noise-Robust Fast Sparse Bayesian Learning Model

This paper utilizes the hierarchical model structure from the Bayesian Lasso in the Sparse Bayesian Learning process to develop a new type of probabilistic supervised learning approach. The hierarchical model structure in this Bayesian framework is designed such that the priors do not only penalize the unnecessary complexity of the model but will also be conditioned on the variance of the random noise in the data. The hyperparameters in the model are estimated by the Fast Marginal Likelihood Maximization algorithm which can achieve sparsity, low computational cost and faster learning process. We compare our methodology with two other popular learning models; the Relevance Vector Machine and the Bayesian Lasso. We test our model on examples involving both simulated and empirical data, and the results show that this approach has several performance advantages, such as being fast, sparse and also robust to the variance in random noise. In addition, our method can give out a more stable estimation of variance of random error, compared with the other methods in the study.
A Lognormal Type Stochastic Volatility Model With Quadratic Drift

This paper presents a novel one-factor stochastic volatility model where the instantaneous volatility of the asset log-return is a diffusion with a quadratic drift and a linear dispersion function. The instantaneous volatility mean reverts around a constant level, with a speed of mean reversion that is affine in the instantaneous volatility level. The steady-state distribution of the instantaneous volatility belongs to the class of Generalized Inverse Gaussian distributions. We show that the quadratic term in the drift is crucial to avoid moment explosions and to preserve the martingale property of the stock price process. Using a conveniently chosen change of measure, we relate the model to the class of polynomial diffusions. This remarkable relation allows us to develop a highly accurate option price approximation technique based on orthogonal polynomial expansions.
The effects of teaching English alphabet with and without initial introduction of letter names on Iranian grade seven students ' reading speed

One of the most important steps in teaching English is teaching its alphabet to the learners. Implementing the best method will highly help teachers to achieve acceptable results in helping students to read better and faster. Two basic methods are frequently used by Iranian educators. Some start by the name of the letters and introduce the sounds that they symbolize later, while others are those who are very concerned about the sounds and tend to teach the letter names at the end of the course. Through this study, the researcher has sought to explore the effects of teaching English Alphabets with and without initial introduction of letter names on grade seven Iranian students reading speed. To achieve this goal, 20 grade-seven students (N=20), who were alphabetically assigned to two different classes (n=10), with no previous experience of learning English were taught using these two different methods. Then they were asked to read 20 words as fast as they could. Using a chronometer, their reading times were calculated and recorded. According to their achieved time, they were given ranks from 1(the fastest) to 20(the slowest). Implementing the rank sums test the results were compared and analyzed and then the z value for rank sums was obtained which was equal to -2.20(Negative z value is due to assigning number 1 to the highest rank and 20 to the lowest one). The z critical for ά of . 05 is 1.96. Knowing this, we can reject the null hypothesis and have the confidence in the conclusion that subjects in group A who used the experimental method (teaching the alphabets by their sounds and names at the same time) outperformed those in group B (teaching the Alphabets by their sounds).
Optimal Construction of Koopman Eigenfunctions for Prediction and Control

This article presents a novel data-driven framework for constructing eigenfunctions of the Koopman operator geared toward prediction and control. The method leverages the richness of the spectrum of the Koopman operator away from attractors to construct a set of eigenfunctions such that the state (or any other observable quantity of interest) is in the span of these eigenfunctions and hence predictable in a linear fashion. The eigenfunction construction is optimization-based with no dictionary selection required. Once a predictor for the uncontrolled part of the system is obtained in this way, the incorporation of control is done through a multistep prediction error minimization, carried out by a simple linear least-squares regression. The predictor so obtained is in the form of a linear controlled dynamical system and can be readily applied within the Koopman model predictive control (MPC) framework of (M. Korda and I. Mezić, 2018) to control nonlinear dynamical systems using linear MPC tools. The method is entirely data-driven and based predominantly on convex optimization. The novel eigenfunction construction method is also analyzed theoretically, proving rigorously that the family of eigenfunctions obtained is rich enough to span the space of all continuous functions. In addition, the method is extended to construct generalized eigenfunctions that also give rise Koopman invariant subspaces and hence can be used for linear prediction. Detailed numerical examples demonstrate the approach, both for prediction and feedback control. **Code for the numerical examples is available from https://homepages.laas. fr/mkorda/Eigfuns.zip.
Sarcasm Detection using Hybrid Neural Network

Sarcasm Detection has enjoyed great interest from the research community, however the task of predicting sarcasm in a text remains an elusive problem for machines. Past studies mostly make use of twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of labels and language. To overcome these shortcoming, we introduce a new dataset which contains news headlines from a sarcastic news website and a real news website. Next, we propose a hybrid Neural Network architecture with attention mechanism which provides insights about what actually makes sentences sarcastic. Through experiments, we show that the proposed model improves upon the baseline by ~ 5% in terms of classification accuracy.
First Subarcsecond Submillimeter-wave [C i] Image of 49 Ceti with ALMA

We present the first subarcsecond images of 49 Ceti in the [C i] 3P1–3P0 emission and the 614 μm dust continuum emission observed with Atacama Large Millimeter/submillimeter Array (ALMA), as well as that in the CO(J = 3–2) emission prepared by using the ALMA archival data. The spatial distribution of the 614 μm dust continuum emission is found to have a broad-ring structure with a radius of about 100 au around the central star. A substantial amount of gas is also associated with 49 Ceti. The [C i] emission map shows two peaks inside the dust ring, and its overall extent is comparable to that of the dust continuum emission and the CO emission. We find that the [C i]/CO(J = 3–2) intensity ratio significantly varies along the major axis. The ratio takes the minimum value of 1.8 around the dust peak position, and increases inward and outward. The enhanced ratio around the central star (∼3) likely originates from the stellar UV radiation, while that in the outer disk (∼10) is from the interstellar UV radiation. Such complex distributions of the [C i] and CO(J = 3–2) emission will be key to understanding the origin of the gas in 49 Ceti, and will also provide a stringent constraint on physical and chemical models of gaseous debris disks.
Assessing bias in aerial surveys for cetaceans: Results from experiments conducted with the franciscana dolphin

Line transect aerial surveys are widely used for estimating abundance of biological populations, including threatened species. However, estimates obtained with data collected from aircraft are often underestimated because of visibility bias and bias in estimating group sizes from a fast-moving platform. An assessment of multiple sources of bias in aerial surveys were carried out in Brazilian coastal waters by experiments on multiple survey platforms (i.e., boat, airplane and helicopter). These studies focused on evaluating visibility bias (perception and availability bias) and potential differences in the estimation of group sizes from different types of platforms used in franciscana (Pontoporia blainvillei) abundance surveys. The ultimate goal was to develop correction factors to improve accuracy of estimates of density and population size for this threatened dolphin. Estimates of density and group sizes computed from boats were assumed to be unbiased and were compared to estimates of these quantities obtained from an airplane in the same area and period. In addition, helicopter surveys were conducted in two areas where water turbidity differed (clear vs. murky waters) to determine surfacing-diving intervals of franciscana groups and to estimate availability for aerial platforms. Abundance computed from the aerial survey data underestimated the true abundance by about 4-5 times, with ~70% of the total bias resulting from visibility bias (~80% from availability bias and ~20% from perception bias) and ~30% from bias in estimates of group size. The use of multiple survey platforms in contrasting habitats provided the opportunity to compute correction factors that can be used to refine range wide abundance estimates of the threatened franciscana given certain assumptions are met. Visibility bias and group size bias were substantial and clearly indicate the importance for accounting for such correction factors to produce unequivocal population assessment based on aerial survey data.
Divergent Nematic Susceptibility near the Pseudogap Critical Point in a Cuprate Superconductor

Superconductivity is a quantum phenomenon caused by bound pairs of electrons. In diverse families of strongly correlated electron systems, the electron pairs are not bound together by phonon exchange but instead by some other kind of bosonic fluctuations. In these systems, superconductivity is often found near a magnetic quantum critical point (QCP) where a magnetic phase vanishes in the zero-temperature limit. Moreover, the maximum of superconducting transition temperature Tc frequently locates near the magnetic QCP, suggesting that the proliferation of critical spin fluctuations emanating from the QCP plays an important role in Cooper pairing. In cuprate superconductors, however, the superconducting dome is usually separated from the antiferromagnetic phase and Tc attains its maximum value near the verge of enigmatic pseudogap state that appears below doping-dependent temperature T*. Thus a clue to the pairing mechanism resides in the pseudogap and associated anomalous transport properties. Recent experiments suggested a phase transition at T*, yet, most importantly, relevant fluctuations associated with the pseudogap have not been identified. Here we report on direct observations of enhanced nematic fluctuations in (Bi,Pb)2Sr2CaCu2O8+d by elastoresistance measurements, which couple to twofold in-plane electronic anisotropy, i.e. electronic nematicity. The nematic susceptibility shows Curie-Weiss-like temperature dependence above T*, and an anomaly at T* evidences a second-order transition with broken rotational symmetry. Near the pseudogap end point, where Tc is not far from its peak in the superconducting dome, nematic susceptibility becomes singular and divergent, indicating the presence of a nematic QCP. This signifies quantum critical fluctuations of a nematic order, which has emerging links to the high-Tc superconductivity and strange metallic behaviours in cuprates.
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks

Deep neural networks (DNNs) have achieved significant success in a variety of real world applications, i.e., image classification. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference. Both low-rankness and sparsity are appealing properties for the network approximation. In this paper we propose a unified framework to compress the convolutional neural networks (CNNs) by combining these two properties, while taking the nonlinear activation into consideration. Each layer in the network is approximated by the sum of a structured sparse component and a low-rank component, which is formulated as an optimization problem. Then, an extended version of alternating direction method of multipliers (ADMM) with guaranteed convergence is presented to solve the relaxed optimization problem. Experiments are carried out on VGG-16, AlexNet and GoogLeNet with large image classification datasets. The results outperform previous work in terms of accuracy degradation, compression rate and speedup ratio. The proposed method is able to remarkably compress the model (with up to 4.9X reduction of parameters) at a cost of little loss or without loss on accuracy.
Effects of strength exercise interventions on activities of daily living, motor performance, and physical activity in children and adolescents with leukemia or non-Hodgkin lymphoma: Results from the randomized controlled ActiveADL Study

Objectives Pediatric patients with cancer experience impairments in muscle strength and physical activity (PA) that may reduce autonomy during hospitalization. To determine the effects of strength exercise interventions on the accomplishment of activities of daily living (ADLs), motor performance, and PA in children with leukemia or non-Hodgkin lymphoma, we randomly allocated patients (4–18 years) immediately after diagnosis into two exercise groups. Methods The intervention group (IG; n = 21) received a specific strength training combined with a standard care exercise program, whereas the control group (CG; n = 20) was provided standard care exercise program without any targeted muscle strengthening. After the baseline visit, participants were followed-up three times until intensive treatment cessation. We assessed physical function limitations using the Activities Scale for Kids© (ASK) and Functional ADL Screen. Secondary outcomes were PA levels using accelerometer and motor performance as measured by MOON-test (motor performance in pediatric oncology-test). Results In both groups, ADL accomplishment had significantly increased (p < 0.05). However, no significant between-group differences for ASK outcome were noted. Motor performance was reduced in all motor abilities. Conclusions Both exercise interventions were effective to maintain ADLs and motor performance during intensive treatment. In comparison, regular strength exercise interventions in the course of therapy tended to be more beneficial with regards to muscular explosive and endurance strength.
Integrating Body , Mind , and Spirit : An Essay Reviewing the Physiological , Psychological , and Spiritual Benefits of Meditation

Meditation is a term applied to a group of contemplative practices that train attention and awareness ostensibly to support psychological and spiritual well-being, usually with a focus on either breathing techniques or a chant/mantra that is repeated silently or aloud. Recently, meditation has been used in Western culture outside of religion and as part of complementary and alternative medicine (CAM) practices to promote calmness and physical relaxation, to cope with illness or disease, to improve psychological functioning and balance, and to improve overall wellness and health. This essay examines and reviews current research related to the physiological, psychological, and spiritual benefits of a meditation practice.
1 1 Reservoir Definition

Scientific background concerning the heat flow and the geothermal activity of the earth is of fundamental interest. It is established that plate tectonics and activities along plate margins are controlled by thermal processes responsible for density contrasts and changes in rheology. Thus, any attempt to better understand the earth’s thermal budget contributes to the knowledge of the global dynamics of the planet. Information on the sources and expressions of heat on earth since its formation can be deduced from combined analyses of seismic studies with mineral physics, chemical composition of primitive materials (chondrites), as well as pressure– temperature–time paths reconstituted from mineralogical assemblages in past and eroded orogens. Knowledge of heat transfer processes within the earth has greatly improved our understanding of global geodynamics. Variations of surface heat flow above the ocean floor has provided additional evidence for seafloor spreading (Parsons and McKenzie, 1978), and improved theoretical models of heat conduction within oceanic plates or continental crust helped to constrain mantle dynamics (Sclater, Jaupart, and Galson, 1980; Jaupart and Parsons, 1985). When deeper heat transfer processes are considered, thermal convection models explain a number of geophysical and geochemical observations (Schubert, Turcotte, and Olson, 2002). It must be, however, noted that at a smaller scale (closer to the objective of this chapter), say within the few kilometers of the subsurface where water is much more present than at depths, a number of geological and geothermal observations are not well understood. As emphasized by Elder (1981), crustal geothermal systems may appear as liquidor vapor-dominated systems, where physics of water–rock interactions greatly differs from one case to the other. Actually, as soon as hydrothermal convection arises among the active heat transfer processes, everything goes faster since heat exchanges are more efficient than without circulating water.
Deciphering global DNA variations and embryo sac fertility in autotetraploid rice line

Autotetraploid rice is a new germplasm resource obtained by doubling chromosomes through colchicine treatment. There have been many studies on the reproductive characteristics of autotetraploid rice, but little is known about global DNA variations and reasons for low embryo sac fertility in autotetraploid rice. Therefore, an autotetraploid rice line (T446) was used for resequencing and embryo sac fertility was observed. Whole-genome resequencing data revealed 87,229 SNPs and 11,022 InDels in the genome of T446 versus E246 (diploid rice), which had an average of 23.37 SNPs and 2.95 InDels per 100 kb. A total of 17,375 and 17,171 structural variations and 131 and 128 copy number variations were identified in the autotetraploid and its diploid counterpart, respectively. We detected 140 large-effects SNPs and InDel variants that might be related to the embryo sac fertility of autotetraploid rice, including 10 genes that may be closely associated with the development of the embryo sac. Of these, Os02g0292600 and Os06g0565200 were specifically expressed in the ovary. Mature embryo sac fertility was observed through whole-mount eosin B-staining confocal laser scanning microscopy. Many abnormalities were found in the embryo sac of T446, including embryo sac degeneration, embryo sac without female germ unit, abnormal polar nuclei, and poly-eggs, which, in turn, resulted in low seed set. However, whole-genome polymorphisms and genetic differences were high and exhibited broad prospects for genetic improvement. Genetic mutations in genes associated with embryo sac fertility in polyploid rice require further studies.
Numerical Investigation on Plastic Hinge Relocation of Reinforced Beam Column Joint Retrofitted with FRP

This paper reports on numerical investigation carried out on an exterior reinforced concrete beam column joint of a deficient model structure to study its behaviour when subjected to monotonic loads. Web bonded Carbon Fibre Reinforced Polymer is adopted for retrofitting of joints so as to relocate the plastic hinge from joint towards the beam. Numerical analysis of the retrofitted exterior joint is validated analytically. Results of the numerical nonlinear analysis on retrofitted joint shows increase in failure load and successful relocation of plastic hinge while maintaining a ductile failure with the FRP application scheme adopted.
Fibroblast growth factor receptor family mutations as a predictive biomarker for immune checkpoint inhibitors and its correlation with tumor immune microenvironment in melanoma

Background The emergence of immune checkpoint inhibitors (ICIs) has significantly improved the clinical outcomes of patients with metastatic melanoma. However, survival benefits are only observed in a subset of patients. The fibroblast growth factor receptor (FGFR) family genes are frequently mutated in melanoma, yet their impacts on the efficacy of ICIs remain unclear. Our study aimed to explore the association of FGFR mutations with ICIs efficacy in metastatic melanoma. Methods The Cancer Genome Atlas (TCGA) data (PanCancer Atlas, skin cutaneous melanoma (SKCM), n = 448) in cBioPortal were collected as a TCGA cohort to investigate the association between FGFR mutations and prognosis of melanoma patients. To explore the impact of FGFR mutations on the efficacy of ICIs in melanoma, clinical and tumor whole-exome sequencing (WES) data of four ICI-treated studies from cBioPortal were consolidated as an ICIs-treated cohort. Moreover, the relationship between FGFR mutations and immunogenicity (tumor mutation burden (TMB), neo-antigen load (NAL), mismatch repair (MMR)-related genes and DNA damage repair (DDR)-related genes) of melanoma was evaluated utilizing data from the ICIs-treated cohort. The influence of FGFR mutations on the tumor immune microenvironment (TIME) of melanoma was also analyzed using the TCGA cohort. Results In the TCGA cohort, survival in melanoma patients with or without FGFR mutations was nearly equivalent. In the ICIs-treated cohort, patients with FGFR mutations had better survival than those without (median overall survival: 60.00 vs. 31.00 months; hazard ratio: 0.58, 95% CI: 0.42-0.80; P = 0.0051). Besides, the objective response rate was higher for patients harboring FGFR mutations (55.56%) compared to wild-type patients (22.40%) (P = 0.0076). Mechanistically, it was revealed that FGFR mutations correlated with increased immunogenicity (e.g., TMB, NAL, MMR-related gene mutations and DDR-related gene mutations). Meanwhile, FGFR mutant melanoma tended to exhibit an enhanced antitumor TIME compared with its wild-type counterparts. Conclusions Our study demonstrated that FGFR mutations is a promising biomarker in stratifying patients with advanced melanoma who might benefit from ICIs therapy.
Chemo-hydrodynamic patterns in porous media

Chemical reactions can interplay with hydrodynamic flows to generate chemo-hydrodynamic instabilities affecting the spatio-temporal evolution of the concentration of the chemicals. We review here such instabilities for porous media flows. We describe the influence of chemical reactions on viscous fingering, buoyancy-driven fingering in miscible systems, convective dissolution as well as precipitation patterns. Implications for environmental systems are discussed. This article is part of the themed issue ‘Energy and the subsurface’.