Miracle-dz/UniFAR-FTUs huggingface dataset:
Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes

Abstract Inverse problems involve making inference about unknown parameters of a physical process using observational data. This article investigates an important class of inverse problems—the estimation of the initial condition of a spatio-temporal advection-diffusion process using spatially sparse data streams. Three spatial sampling schemes are considered, including irregular, nonuniform and shifted uniform sampling. The irregular sampling scheme is the general scenario, while computationally efficient solutions are available in the spectral domain for nonuniform and shifted uniform sampling. For each sampling scheme, the inverse problem is formulated as a regularized convex optimization problem that minimizes the distance between forward model outputs and observations. The optimization problem is solved by the Alternating Direction Method of Multipliers algorithm, which also handles the situation when a linear inequality constraint (e.g., non-negativity) is imposed on the model output. Numerical examples are presented, code is made available on GitHub, and discussions are provided to generate some useful insights of the proposed inverse modeling approaches.
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning

Differential private optimization for nonconvex smooth objective is considered. In the previous work, the best known utility bound is $\widetilde O(\sqrt{d}/(n\varepsilon_\mathrm{DP}))$ in terms of the squared full gradient norm, which is achieved by Differential Private Gradient Descent (DP-GD) as an instance, where $n$ is the sample size, $d$ is the problem dimensionality and $\varepsilon_\mathrm{DP}$ is the differential privacy parameter. To improve the best known utility bound, we propose a new differential private optimization framework called \emph{DIFF2 (DIFFerential private optimization via gradient DIFFerences)} that constructs a differential private global gradient estimator with possibly quite small variance based on communicated \emph{gradient differences} rather than gradients themselves. It is shown that DIFF2 with a gradient descent subroutine achieves the utility of $\widetilde O(d^{2/3}/(n\varepsilon_\mathrm{DP})^{4/3})$, which can be significantly better than the previous one in terms of the dependence on the sample size $n$. To the best of our knowledge, this is the first fundamental result to improve the standard utility $\widetilde O(\sqrt{d}/(n\varepsilon_\mathrm{DP}))$ for nonconvex objectives. Additionally, a more computational and communication efficient subroutine is combined with DIFF2 and its theoretical analysis is also given. Numerical experiments are conducted to validate the superiority of DIFF2 framework.
Improved Langevin Monte Carlo for stochastic optimization via landscape modification

Given a target function $H$ to minimize or a target Gibbs distribution $\pi_{\beta}^0 \propto e^{-\beta H}$ to sample from in the low temperature, in this paper we propose and analyze Langevin Monte Carlo (LMC) algorithms that run on an alternative landscape as specified by $H^f_{\beta,c,1}$ and target a modified Gibbs distribution $\pi^f_{\beta,c,1} \propto e^{-\beta H^f_{\beta,c,1}}$, where the landscape of $H^f_{\beta,c,1}$ is a transformed version of that of $H$ which depends on the parameters $f,\beta$ and $c$. While the original Log-Sobolev constant affiliated with $\pi^0_{\beta}$ exhibits exponential dependence on both $\beta$ and the energy barrier $M$ in the low temperature regime, with appropriate tuning of these parameters and subject to assumptions on $H$, we prove that the energy barrier of the transformed landscape is reduced which consequently leads to polynomial dependence on both $\beta$ and $M$ in the modified Log-Sobolev constant associated with $\pi^f_{\beta,c,1}$. This yield improved total variation mixing time bounds and improved convergence toward a global minimum of $H$. We stress that the technique developed in this paper is not only limited to LMC and is broadly applicable to other gradient-based optimization or sampling algorithms.
A Bayesian Semi-Parametric Scalar-On-Function Quantile Regression with Measurement Error using the GAL

Quantile regression provides a consistent approach to investigating the association between covariates and various aspects of the distribution of the response beyond the mean. When the regression covariates are measured with errors, measurement error (ME) adjustment steps are needed for valid inference. This is true for both scalar and functional covariates. Here, we propose extending the Bayesian measurement error and Bayesian quantile regression literature to allow for available covariates prone to potential complex measurement errors. Our approach uses the Generalized Asymmetric Laplace (GAL) distribution as a working likelihood. The family of GAL distribution has recently emerged as a more flexible distribution family in the Bayesian quantile regression modeling compared to their Asymmetric Laplace (AL) counterpart. We then compared and contrasted two approaches in our ME-adjusted steps through a battery of simulation scenarios. Finally, we apply our approach to the analysis of an NHANES dataset 2013-2014 to model quantiles of Body mass index (BMI) as a function of minute-level device-based physical activity in a cohort of an adult 50 years and above.
Development of Classroom Management Based on Student Learning Style Database

This study aims to produce a classroom management application based on a database of student learning styles to identify and analyze student learning styles and match them with appropriate learning methods and media. This application is expected to facilitate classroom management according to student preferences. This research was research and development. Respondents were 198 people consisting of 30 teachers and 168 students at a Junior High School in South Bangka, Indonesia. The data analysis used quantitative data analysis techniques using descriptive statistics. The results showed that the application developed was feasible to be used to identify and analyze student learning styles and provide suggestions for learning methods and media that teachers should use in classroom management. The level of application eligibility in terms of technical aspects reaches 4.75, and the content aspect reaches 4.80. This application proved to be useful for teachers in class management as evidenced by the achievement of the usability test score by the teacher reaching 4.40. The usefulness of the application from the student's perspective can be seen from the increase in the attractiveness of learning that occurs after the teacher uses this application compared to before.
Systematic errors in the maximum-likelihood regression of Poisson count data: introducing the overdispersed χ2 distribution

This paper presents a new method to estimate systematic errors in the maximum-likelihood regression of count data. The method is applicable in particular to X-ray spectra in situations where the Poisson log-likelihood, or the Cash goodness-of-fit statistic, indicate a poor fit that is attributable to overdispersion of the data. Overdispersion in Poisson data is treated as an intrinsic model variance that can be estimated from the best-fit model, using the maximum-likelihood Cmin statistic. The paper also studies the effects of such systematic errors on the ΔC likelihood-ratio statistic, which can be used to test for the presence of a nested model component in the regression of Poisson count data. The paper introduces an overdispersed χ2 distribution that results from the convolution of a χ2 distribution that models the usual ΔC statistic, and a zero-mean Gaussian that models the overdispersion in the data. This is proposed as the distribution of choice for the ΔC statistic in the presence of systematic errors. The methods presented in this paper are applied to XMM–Newton data of the quasar 1ES 1553+113 that were used to detect absorption lines from an intervening warm-hot intergalactic medium (WHIM). This case study illustrates how systematic errors can be estimated from the data, and their effect on the detection of a nested component, such as an absorption line, with the ΔC statistic.
Definability of continuous isomorphisms of groups definable in o-minimal expansions of the real field

In this paper, we study the relation between the category of real Lie groups and that of groups definable in o-minimal expansions of the real field (which we will refer to as"definable groups"). It is known (\cite{Pi88}) that any group definable in an o-minimal expansion of the real field is a Lie group, and in \cite{COP} a complete characterization of when a Lie group has a"definable group"which is \emph{Lie isomorphic} to it was given. We continue the analysis by explaining when a Lie homomorphism between definable groups is a definable isomorphism. Among other things, we prove that in any o-minimal expansion $\mathcal R$ of the real field we can add a function symbol for any Lie isomorphism between definable groups to the language of $\mathcal R$ preserving o-minimality, and that any definable group $G$ can be endowed with an analytic manifold structure definable in $\mathcal R_{\text{Pfaff}}$ that makes it an analytic group.
Adaptive Massively Parallel Connectivity in Optimal Space

We study the problem of finding connected components in the Adaptive Massively Parallel Computation (AMPC) model. We show that when we require the total space to be linear in the size of the input graph the problem can be solved in O(log*n) rounds in forests (with high probability) and 2O(log*n) expected rounds in general graphs. This improves upon an existing O(log logm/nn) round algorithm. For the case when the desired number of rounds is constant we show that both problems can be solved using Θ(m + n log(k) n) total space in expectation (in each round), where k is an arbitrarily large constant and log(k) is the k-th iterate of the log2 function. This improves upon existing algorithms requiring Ω(m + n log n) total space.
User-Aware Algorithmic Recourse with Preference Elicitation

Counterfactual interventions are a powerful tool to explain the decisions of a black-box decision process and to enable algorithmic recourse. They are a sequence of actions that, if performed by a user, can overturn an unfavourable decision made by an automated decision system. However, most of the current methods provide interventions without considering the user’s preferences. In this work, we propose a shift of paradigm by providing a novel formalization which considers the user as an active part of the process rather than a mere target. Following the preference elicitation setting, we introduce the first human-in-the-loop approach to perform algorithmic recourse. We also present a polynomial procedure to ask questions which maximize the Expected Utility of Selection (EUS), a measure of the utility of the choice set that accounts for the uncertainty with respect to both the model and the user response. We use it to iteratively refine our cost estimates in a Bayesian fashion. We integrate this preference elicitation strategy into a reinforcement learning agent coupled with Monte Carlo Tree Search for the efficient exploration, so as to provide personalized interventions achieving algorithmic recourse. An experimental evaluation of synthetic and real-world datasets shows that a handful of queries allows for achieving a substantial reduction in the cost of interventions with respect to user-independent alternatives.
Dynamic Visualization of Gyral and Sulcal Stereoelectroencephalographic contacts in Humans

Stereoelectroencephalography (SEEG) is a neurosurgical method to survey electrophysiological activity within the brain to treat disorders such as Epilepsy. In this stereotactic approach, leads are implanted through straight trajectories to survey both cortical and sub-cortical activity. Visualizing the recorded locations covering sulcal and gyral activity while staying true to the cortical architecture is challenging due to the folded, three-dimensional nature of the human cortex. To overcome this challenge, we developed a novel visualization concept, allowing investigators to dynamically morph between the subjects’ cortical reconstruction and an inflated cortex representation. This inflated view, in which gyri and sulci are viewed on a smooth surface, allows better visualization of electrodes buried within the sulcus while staying true to the underlying cortical architecture. Clinical relevance— These visualization techniques might also help guide clinical decision-making when defining seizure onset zones or resections for patients undergoing SEEG monitoring for intractable epilepsy.
Data-Driven Control and Transfer Learning Using Neural Canonical Control Structures

An indirect data-driven control and transfer learning approach based on a data-driven feedback linearization with neural canonical control structures is proposed. An artificial neural network auto-encoder structure trained on recorded sensor data is used to approximate state and input transformations for the identification of the sampled-data system in Brunovsky canonical form. The identified transformations, together with a designed trajectory controller, can be transferred to a system with varied parameters, where the neural network weights are adapted using newly collected recordings. The proposed approach is demonstrated using an academic and an industrially motivated example.
Neural network solution to the link scheduling problem using convex relaxation

The convex relaxation method for the neural network solution of combinatorial optimization problems is presented. The method is applied to the NP-complete problem of finding a minimum-length link schedule in a multihop radio network with given link demands. The link scheduling problem can be reduced to the graph coloring problem, which can in turn be reduced to the maximum-independent-set problem. Simulations show that, for the latter problem, convex relaxation computes significantly better solutions than neural network methods based on gradient descent and produces solutions comparable to those obtained with the Hammer greedy serial algorithm. A modified version of mean field annealing (MFA) is also presented and shown to have the same time-varying energy function as that for convex relaxation. <<ETX>>
Genome-wide association study identifies novel candidate malaria resistance genes in Cameroon

Abstract Recent data suggest that only a small fraction of severe malaria heritability is explained by the totality of genetic markers discovered so far. The extensive genetic diversity within African populations means that significant associations are likely to be found in Africa. In their series of multi-site genome-wide association studies (GWAS) across sub-Saharan Africa, the Malaria Genomic Epidemiology Network (MalariaGEN) observed specific limitations and encouraged country-specific analyses. Here, we present findings of a GWAS of Cameroonian participants that contributed to MalariaGEN projects (n = 1103). We identified protective associations at polymorphisms within the enhancer region of CHST15 [Benjamin–Hochberg false discovery rate (FDR) < 0.02] that are specific to populations of African ancestry, and that tag strong eQTLs of CHST15 in hepatic cells. In-silico functional analysis revealed a signature of epigenetic regulation of CHST15 that is preserved in populations in historically malaria endemic regions, with haplotype analysis revealing a haplotype that is specific to these populations. Association analysis by ethnolinguistic group identified protective associations within SOD2 (FDR < 0.04), a gene previously shown to be significantly induced in pre-asymptomatic malaria patients from Cameroon. Haplotype analysis revealed substantial heterogeneity within the beta-like globin (HBB) gene cluster amongst the major ethnic groups in Cameroon confirming differential malaria pressure and underscoring age-old fine-scale genetic structure within the country. Our findings revealed novel insights in the evolutionary genetics of populations living in Cameroon under malaria pressure with new significant protective loci (CHST15 and SOD2) and emphasized the significant attenuation of genetic association signals by fine-scale genetic structure.
Paraconduit hernia after minimally invasive esophagectomy – incidence and risk factors

Abstract Background Paraconduit hernia is a relatively common long-term complication after esophagectomy which has the potential to cause great morbidity and even mortality. The aim of this study is to examine the risk factors and incidence of paraconduit hernia after minimally invasive esophagectomy (MIE) in esophageal adenocarcinoma patients who have received neoadjuvant treatment. Methods Minimally invasive esophagectomies done for patients with neoadjuvant-treated esophageal or esophagogastric junction adenocarcinoma at our institution between 2008 and 2018 were included in this study. All patients with symptomatic or incidentally found paraconduit hernias on computed tomography scans were identified. Patient demographics were analyzed using logistic and Cox regression. Results The incidence of paraconduit hernia was 14 out of 171 patients (8.2%). The hernia was surgically repaired in 10 (71.4%) of patients. Laparoscopic approach was used in 90% of the repairs, with one (11.1%) conversion to laparotomy. Emergency operations accounted for three (30%) of the operations. The complication rate was 10% (n = 1) and 90-day mortality was 10% (n = 1). Neither sarcopenia nor muscle mass loss was not associated with paraconduit hernia development, whereas preoperative radiotherapy (OR = 8.57, CI = 1.98–33.8, p = .002) was a strong risk factor for paraconduit hernia. Higher BMI had a protective effect (OR = 0.83 per point, 95% CI = 0.69–0.97, p = .027). Conclusions Paraconduit hernia is a relatively common complication after MIE for neoadjuvant-treated adenocarcinoma patients. Preoperative radiotherapy was associated with a higher risk of paraconduit hernia. Minimally invasive repair of paraconduit hernia after esophagectomy is efficient and has a low complication rate both in elective and emergency cases.
FUZZY TRANSPORTATION PROBLEM WITH ADDITIONAL RESTRICTIONS

This paper deals with the transportation problem with additional impurity restrictions where costs are not deterministic numbers but imprecise ones. Here, the elements of the cost matrix are subnormal fuzzy intervals with strictly increasing linear membership functions. By the Max-Min criterion suggested by Bellman and Zadeh [7], the fuzzy transportation problem can be treated as a mixed integer nonlinear programming problem. We show that this problem can be simplified into a linear fractional programming problem. This fractional programming problem is solved by the method given by Kanti Swarup [12].
Rise speed of supercritical carbon dioxide spheres in aqueous surfactant solutions

The rise speed of supercritical carbon dioxide spheres in aqueous surfactant solution was investigated experimentally. Decanonic acid was added to water as a surfactant, and then the rise speed of the CO$_{2}$ spheres was measured at pressures 5 MPa to 10 MPa and at temperatures 28 $^{\circ}$C to 40 $^{\circ}$C generating different phases of spheres, namely gas, liquid, and supercritical. The results revealed that gas bubbles and liquid droplets showed the same behaviour as a rising rigid sphere due to the adsorption of surfactant onto the surface of the spheres. In contrast, due to the absorption of surfactant into the spheres, the drag coefficient of spheres of supercritical fluid was lower than that of a rigid sphere and was similar to that of a fluid sphere with a moving boundary. This behaviour mainly occurred in the rising process of the spheres composed of supercritical CO$_{2}$.
Computation of Supersonic Flow Past 2-D Bodies Using a Boltzmann Solver

The results of 2-D numerical simulations of supersonic flow past blunt bodies at high Knudsen Numbers are presented. Computations are based on the kinetic theory and are performed by direct numerical solution of the Boltzmann equation. A conservative discrete ordinate method, which ensures strict conservation of mass, momentum and energy, is applied in the solver. The effect of Knudsen number varying from 0.01 to 10 is investigated for Mach 3 flows past a blunt body. The solution for a Mach 10 hypersonic flow past a cylindrical leading edge is compared with the Navier-Stokes and augmented Burnett solutions in continuum-transition regime.
Linking whole-body angular momentum and step placement during perturbed walking.

Human locomotion is remarkably robust to environmental disturbances. Previous studies have thoroughly investigated how perturbations influence body dynamics and what recovery strategies are used to regain balance. Fewer studies have attempted to establish formal links between balance and the recovery strategies that are executed to regain stability. We hypothesized that there would be a strong relationship between magnitude of imbalance and recovery strategy during perturbed walking. To test this hypothesis, we applied transient ground surface translations that varied in magnitude, direction, and onset time while eight healthy participants walked on a treadmill. We measured stability using integrated whole-body angular momentum (iWBAM) and recovery strategy using step placement. We found the strongest relationships between iWBAM and step placement in the frontal plane for earlier perturbation onset times in the perturbed step (R2=0.52, 0.50) and later perturbation onset times in the recovery step (R2=0.18, 0.25), while correlations were very weak in the sagittal plane (all R2 <= 0.13). These findings suggest that iWBAM influences step placement, particularly in the frontal plane, and that this influence is sensitive to perturbation onset time. Lastly, this investigation is accompanied by an open-source data set to facilitate research on balance and recovery strategies in response to multifactorial ground surface perturbations, including 96 perturbation conditions spanning all combinations of three magnitudes, eight directions, and four gait cycle onset times.
Discrimination Exposure and Polygenic Risk for Obesity in Adulthood: Testing Gene-Environment Correlations and Interactions

Introduction: Exposure to discrimination has emerged as a risk factor for obesity. It remains unclear, however, whether the genotype of the individual can modulate the sensitivity or response to discrimination exposure (gene × environment interaction) or increase the likelihood of experiencing discrimination (gene-environment correlation). Methods: This was an observational study of 4,102 white/European Americans in the Health and Retirement Study with self-reported, biological assessments, and genotyped data from 2006 to 2014. Discrimination was operationalized using the average of nine Everyday Discrimination Scale items. Polygenic risk scores (PRSs) for body mass index (BMI) and waist circumference (WC) were calculated using the weighted sum of risk alleles based on studies conducted by the Genetic Investigation of Anthropometric Traits (GIANT) consortium. Results: We found that greater PRS-BMI was significantly associated with more reports of discrimination (β = 0.04 ± 0.02; p = 0.037). Further analysis showed that measured BMI partially mediated the association between PRS-BMI and discrimination. There was no evidence that the association between discrimination and BMI, or the association between discrimination and WC, differed by PRS-BMI or PRS-WC, respectively. Conclusion: Our findings suggest that individuals with genetic liability for obesity may experience greater discrimination in their lifetime, consistent with a gene-environment correlation hypothesis. There was no evidence of a gene-environment interaction. More genome-wide association studies in diverse populations are needed to improve generalizability of study findings. In the meantime, prevention and clinical intervention efforts that seek to reduce exposure to all forms of discrimination may help reduce obesity at the population level.