Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral Clustering on Spherical Coordinates Under the Degree-Corrected Stochastic Blockmodel

ABSTRACT Spectral clustering is a popular method for community detection in network graphs: starting from a matrix representation of the graph, the nodes are clustered on a low-dimensional projection obtained from a truncated spectral decomposition of the matrix. Estimating correctly the number of communities and the dimension of the reduced latent space is critical for good performance of spectral clustering algorithms. Furthermore, many real-world graphs, such as enterprise computer networks studied in cyber-security applications, often display heterogeneous within-community degree distributions. Such heterogeneous degree distributions are usually not well captured by standard spectral clustering algorithms. In this article, a novel spectral clustering algorithm is proposed for community detection under the degree-corrected stochastic blockmodel. The proposed method is based on a transformation of the spectral embedding to spherical coordinates, and a novel modeling assumption in the transformed space. The method allows for simultaneous and automated selection of the number of communities and the latent dimension for spectral embeddings of graphs with uneven node degrees. Results show improved performance over competing methods in representing computer networks.
State estimation in turbulent channel flow from limited observations

Abstract Estimation of the initial state of turbulent channel flow from limited data is investigated using an adjoint-variational approach. The data are generated from a reference direct numerical simulation that is subsampled at different spatiotemporal resolutions. When the velocity data are at 1/4096 the spatiotemporal resolution of the direct numerical simulation, the correlation coefficient between the true and adjoint-variational estimated state exceeds 99 %. The robustness of the algorithm to observation noise is demonstrated. In addition, the impact of the spatiotemporal density of the data on estimation quality is evaluated, and a resolution threshold is established for a successful reconstruction. The critical spanwise data resolution is proportional to the Taylor microscale, which characterizes the domain of dependence of an observation location. Owing to mean advection, either the streamwise or temporal data resolution must satisfy a criterion based on the streamwise Taylor microscale. A second configuration is considered where the subsampled data comprise velocities in the outer layer and wall shear stresses only. The near-wall flow statistics and coherent structures, although not sampled, are accurately reconstructed, which is possible because of the coupling between the outer flow and near-wall motions. Finally, the most challenging configuration is addressed where only the spatiotemporally resolved wall stresses are observed. The estimation remains accurate within the viscous sublayer and deteriorates significantly with distance from the wall. In wall units, this trend is nearly independent of the Reynolds number considered, and is indicative of the fundamental difficulty of reconstructing wall-detached motions from wall data.
Vitamin D Status and Pregnancy Complications: Serum 1,25-di-hydroxyl-Vitamin D and its Ratio to 25-hydroxy-Vitamin D are Superior Biomarkers than 25-hydroxy-Vitamin D

Vitamin D (VitD) deficiency during pregnancy has been associated with adverse neonatal outcomes and increased risk of late pregnancy complications. We planned to correlate serum VitD biomarkers; 25-hydroxyvitamin D (25-OH-VitD) and 1,25-dihydroxyvitamin D (1,25-diOH-VitD) levels; and their ratio with the frequency of feto-maternal pregnancy complications. A prospective cross-sectional case-control study was conducted at Aljouf Maternity and Children Hospital, Sakaka, Saudi Arabia, during the period of September 1, 2017 to September 30, 2019. 322 pregnant women were stratified into 2 groups: controls (110 cases) and complicated group (212 cases). The later comprised severe preeclamptic toxemia associated with intrauterine growth restriction (58 cases), gestational diabetes mellitus (GDM; 82 cases), abortion (26 cases), undisturbed ectopic pregnancy (16 cases), premature rupture of membranes (PROM; 14 cases), and, inevitable preterm labour (16 cases). After clinical assessment, peripheral blood samples were collected. Serum biomarkers were measured using specific immunoassays. The direct 1,25-diOH-VitD/25-OH-VitD ratio was calculated. Serum 25-OH-VitD indicated widely spreading VitD deficiency among participants with significantly higher levels in controls vs. GDM subgroup only. 1,25-diOH-VitD levels and the ratio were markedly reduced in the six complicated subgroups vs. controls, with non-significant differences amongst the complicated subgroups. ROC analysis showed very high sensitivity and specificity, to differentiate patients from controls, only for 1,25-diOH-VitD (AUC = 0.965; 0.947 - 0.983, p <0.001) followed by the ratio but not 25-OH-VitD. In conclusions, 25-OH-VitD did not show significant changes except for GDM. 1,25-diOH-VitD levels and the ratio showed strong associations with pregnancy complications. Serum 1,25-di-OH-VitD and its ratio to 25-OH-VitD are more reliable and physiologically relevant biomarkers for VitD status in pregnancy.
Fast Magnetic Reconnection Structures in Poynting Flux-dominated Jets

The ubiquitous relativistic jet phenomena associated with black holes play a major role in high and very-high-energy (VHE) astrophysics. In particular, observations have demonstrated that blazars show VHE emission with time variability from days to minutes (in the gigaelectronvolt and teraelectronvolt bands), implying very compact emission regions. The real mechanism of the particle acceleration process responsible for this emission is still being debated, but magnetic reconnection has lately been discussed as a strong potential candidate. In this work, we present the results of three-dimensional special relativistic magnetohydrodynamic simulations of the development of reconnection events driven by turbulence induced by current-driven kink instability along a relativistic jet. We have performed a systematic identification of all reconnection regions in the system, characterizing their local magnetic field topology and quantifying the reconnection rates. We obtained average rates of 0.051 ± 0.026 (in units of Alfvén speed), which are comparable to the predictions of the theory of turbulence-induced fast reconnection. A detailed statistical analysis also demonstrated that the fast reconnection events follow a log-normal distribution, which is a signature of its turbulent origin. To probe the robustness of our method, we have applied our results to the blazar Mrk 421. Building a synthetic light curve from the integrated magnetic reconnection power, we evaluated the time variability from a power spectral density analysis, obtaining good agreement with observations in the gigaelectronvolt band. This suggests that turbulent fast magnetic reconnection can be a possible process behind the high-energy emission variability phenomena observed in blazars.
Single-Node Attack for Fooling Graph Neural Networks

Graph neural networks (GNNs) have shown broad applicability in a variety of domains. Some of these domains, such as social networks and product recommendations, are fertile ground for malicious users and behavior. In this paper, we show that GNNs are vulnerable to the extremely limited scenario of a single-node adversarial example, where the node cannot be picked by the attacker. That is, an attacker can force the GNN to classify any target node to a chosen label by only slightly perturbing another single arbitrary node in the graph, even when not being able to pick that specific attacker node. When the adversary is allowed to pick a specific attacker node, the attack is even more effective. We show that this attack is effective across various GNN types, such as GraphSAGE, GCN, GAT, and GIN, across a variety of real-world datasets, and as a targeted and a non-targeted attack. Our code is available at this https URL .
State-constrained controllability of linear reaction-diffusion systems

We study the controllability of a coupled system of linear parabolic equations, with nonnegativity constraint on the state. We establish two results of controllability to trajectories in large time: one for diagonal diffusion matrices with an “approximate” nonnegativity constraint, and a another stronger one, with “exact” nonnegativity constraint, when all the diffusion coefficients are equal and the eigenvalues of the coupling matrix have nonnegative real part. The proofs are based on a “staircase” method. Finally, we show that state-constrained controllability admits a positive minimal time, even with weaker unilateral constraint on the state.
Hybrid Loop Quantum Cosmology: An Overview

Loop Quantum Gravity is a nonperturbative and background independent program for the quantization of General Relativity. Its underlying formalism has been applied successfully to the study of cosmological spacetimes, both to test the principles and techniques of the theory and to discuss its physical consequences. These applications have opened a new area of research known as Loop Quantum Cosmology. The hybrid approach addresses the quantization of cosmological systems that include fields. This proposal combines the description of a finite number of degrees of freedom using Loop Quantum Cosmology, typically corresponding to a homogeneous background, and a Fock quantization of the field content of the model. In this review we first present a summary of the foundations of homogeneous Loop Quantum Cosmology and we then revisit the hybrid quantization approach, applying it to the study of Gowdy spacetimes with linearly polarized gravitational waves on toroidal spatial sections, and to the analysis of cosmological perturbations in preinflationary and inflationary stages of the Universe. The main challenge is to extract predictions about quantum geometry effects that eventually might be confronted with cosmological observations. This is the first extensive review of the hybrid approach in the literature on Loop Quantum Cosmology.
Spatial modeling could not differentiate early SARS-CoV-2 cases from the distribution of humans on the basis of climate in the United States

The SARS-CoV-2 coronavirus is wreaking havoc globally, yet, as a novel pathogen, knowledge of its biology is still emerging. Climate and seasonality influence the distributions of many diseases, and studies suggest at least some link between SARS-CoV-2 and weather. One such study, building species distribution models (SDMs), predicted SARS-CoV-2 risk may remain concentrated in the Northern Hemisphere, shifting northward in summer months. Others have highlighted issues with SARS-CoV-2 SDMs, notably: the primary niche of the virus is the host it infects, climate may be a weak distributional predictor, global prevalence data have issues, and the virus is not in population equilibrium. While these issues should be considered, we believe climate’s relationship with SARS-CoV-2 is still worth exploring, as it may have some impact on the distribution of cases. To further examine if there is a link to climate, we build model projections with raw SARS-CoV-2 case data and population-scaled case data in the USA. The case data were from across March 2020, before large travel restrictions and public health policies were impacting cases across the country. We show that SDMs built from population-scaled case data cannot be distinguished from control models (built from raw human population data), while SDMs built on raw case data fail to predict the known distribution of cases in the U.S. from March. The population-scaled analyses indicate that climate did not play a central role in early U.S. viral distribution and that human population density was likely the primary driver. We do find slightly more population-scaled viral cases in cooler areas. Ultimately, the temporal and geographic constraints on this study mean that we cannot rule out climate as a partial driver of the SARS-CoV-2 distribution. Climate’s role on SARS-CoV-2 should continue to be cautiously examined, but at this time we should assume that SARS-CoV-2 will continue to spread anywhere in the U.S. where governmental policy does not prevent spread.
The Life Satisfaction Index-A (LSI-A): Normative Data for a General Swedish Population Aged 60 to 93 Years

Purpose of Study To gain Swedish norm value for the Life Satisfaction Index-A (LSI-A) in a population 60–93+ years old stratified for sex and age and to relate these norm values with respect to number of chronic diseases and functional impairment. Materials and Methods The study population included a random sample of 2656 men (45.7%) and 3159 (54.3%) women from the longitudinal national studies’ “Good Aging in Skåne” (GÅS) and SNAC-B, both part of the Swedish National Study on Aging and Care (SNAC). Data on Neugartens Life Satisfaction Index-A (LSI-A), medical history, activities of daily life (ADL) and socio-demographics were collected through structured interviews and questionnaires. Results Men scored significantly higher than women; 28.5, sd=6.9, and 27.3, sd=6.6, respectively, out of maximum 40 points. For both genders the scores decreased with age, mean score 6.0 points, lower for men and 7.1 points lower for women between 60 and 93+ years. The highest score was noted for healthy individuals where both men and women scored 29.5 points, sd=6.2. Increased number of chronic diseases and dependency in ADLs were associated with lower LS. Conclusion Norm values here presented may facilitate assessments and evaluation of life satisfaction in the general elder population and as reference values to clinical trials. Female sex, rising age, morbidity and impaired functional ability were all associated with impaired LS.
Sharp Bounds on the Smallest Eigenvalue of Finite Element Equations with Arbitrary Meshes without Regularity Assumptions

A proof for the lower bound is provided for the smallest eigenvalue of finite element equations with arbitrary conforming simplicial meshes. The bound has a similar form as the one by Graham and McLean [SIAM J. Numer. Anal. , 44 (2006), pp. 1487--1513] but doesn't require any mesh regularity assumptions, neither global nor local. In particular, it is valid for highly adaptive, anisotropic, or non-regular meshes without any restrictions. In three and more dimensions, the bound depends only on the number of degrees of freedom $N$ and the H\"older mean $M_{1-d/2} (\lvert \tilde{\omega} \rvert / \lvert \omega_i \lvert)$ taken to the power $1-2/d$, $\lvert \tilde{\omega} \rvert$ and $\lvert \omega_i \rvert$ denoting the average mesh patch volume and the volume of the patch corresponding to the $i^{\text{th}}$ mesh node, respectively. In two dimensions, the bound depends on the number of degrees of freedom $N$ and the logarithmic term $(1 + \lvert \ln (N \lvert \omega_{\min} \rvert) \rvert)$, $\lvert \omega_{\min} \rvert$ denoting the volume of the smallest patch. Provided numerical examples demonstrate that the bound is more accurate and less dependent on the mesh non-uniformity than the previously available bounds.
Multi-Agent Reinforcement Learning in Time-varying Networked Systems

We study multi-agent reinforcement learning (MARL) in a time-varying network of agents. The objective is to find localized policies that maximize the (discounted) global reward. In general, scalability is a challenge in this setting because the size of the global state/action space can be exponential in the number of agents. Scalable algorithms are only known in cases where dependencies are static, fixed and local, e.g., between neighbors in a fixed, time-invariant underlying graph. In this work, we propose a Scalable Actor Critic framework that applies in settings where the dependencies can be non-local and time-varying, and provide a finite-time error bound that shows how the convergence rate depends on the speed of information spread in the network. Additionally, as a byproduct of our analysis, we obtain novel finite-time convergence results for a general stochastic approximation scheme and for temporal difference learning with state aggregation, which apply beyond the setting of RL in networked systems.
Awareness, Intention to Act and Action in the Exercising of Patients’ Rights: A Case Study of Patients in Khmer Soviet Friendship Hospital, Phnom Penh, Cambodia

Background and Objectives In Cambodia, the patients’ rights guideline was officially released in 2007 as a measure of the government policy to promote greater awareness and empower Cambodian people to exercise their rights as patients. The aim of this study was to identify the proportion of patients aware of the existence of patients’ rights and compare the proportions of patients intending to exercise their rights and those actually exercising their rights among the aware and unaware groups. Methods Data were collected with a structured questionnaire, using face-to-face interview technique, from 142 randomly selected outpatients visiting the Khmer Soviet Friendship Hospital in Phnom Penh, Cambodia. Intention to exercise patients’ rights was measured on a 5-point Likert scale (ranging from 1 “definitely not” to 5 “definitely yes”). Results The proportion of Cambodian patients who were aware of the existence of patients’ rights was 31.0% (95% CI: 23.5, 39.3). The average intention to act scores was 4.0±0.5 for the whole group, and 4.3±0.4 and 3.9±0.5 for those aware and unaware of the existence of patients’ rights, respectively. The difference in the mean scores of intentions to act between the aware and unaware groups was statistically significant (mean difference =0.40, 95% CI: 0.22, 0.58, t140=4.514, P< 0.001). Patients reported a total of 250 situations in which they believed they should take action to exercise their rights and 96 times that they took action (38.4%, 95% CI: 35.46, 41.34). Conclusion The proportion of Cambodian outpatients aware of the existence of patients’ rights and the proportion of outpatients that exercised their rights were both low. Nevertheless, it appeared that raising awareness of the existence of patients’ rights raised patient’s intention to act, which corresponds to the hierarchy-of-effects behavioral model.
Senescent Decline in Verbal-Emotion Identification by Older Hearing-Impaired Listeners – Do Hearing Aids Help?

Purpose To assess the ability of older-adult hearing-impaired (OHI) listeners to identify verbal expressions of emotions, and to evaluate whether hearing-aid (HA) use improves identification performance in those listeners. Methods Twenty-nine OHI listeners, who were experienced bilateral-HA users, participated in the study. They listened to a 20-sentence-long speech passage rendered with six different emotional expressions (“happiness”, “pleasant surprise”, “sadness”, “anger”, “fear”, and “neutral”). The task was to identify the emotion portrayed in each version of the passage. Listeners completed the task twice in random order, once unaided, and once wearing their own bilateral HAs. Seventeen young-adult normal-hearing (YNH) listeners were also tested unaided as controls. Results Most YNH listeners (89.2%) correctly identified emotions compared to just over half of the OHI listeners (58.7%). Within the OHI group, verbal emotion identification was significantly correlated with age, but not with audibility-related factors. The number of OHI listeners who were able to correctly identify the different emotions did not significantly change when HAs were worn (54.8%). Conclusion In line with previous investigations using shorter speech stimuli, there were clear age differences in the recognition of verbal emotions, with OHI listeners showing a significant reduction in unaided verbal-emotion identification performance that progressively declined with age across older adulthood. Rehabilitation through HAs did not provide compensation for the impaired ability to perceive emotions carried by speech sounds.
Multi‐omics analysis of tumor mutational burden combined with prognostic assessment in epithelial ovarian cancer based on TCGA database

Background: Tumor mutation burden (TMB) is considered as a novel biomarker of response to immunotherapy and correlated with survival outcomes in various malignancies. Here, TMB-related genes (TRGs) expression signatures were constructed to investigate the association between TMB and prognosis in epithelial ovarian cancer (EOC), and the potential mechanism in immunoregulation was also explored. Methods: Based on somatic mutation data of 436 EOC samples from The Cancer Genome Atlas database, we examined the relationship between TMB level and overall survival (OS), as well as disease-free survival (DFS). Next, the TRGs signatures were constructed and validated. Differential abundance of immune cell infiltration, expression levels of immunomodulators and functional enrichment in high- and low-risk groups were also analyzed. Results: Higher TMB level revealed better OS and DFS, and correlated with earlier clinical stages in EOCs (P = 2.796e-04). The OS-related prognostic model constructed based on seven TRGs (B3GALT1, LIN7B, ANGPT2, D2HGDH, TAF13, PFDN4 and DNAJC19) significantly stratified EOC patients into high- and low-risk groups (P < 0.001). The AUC values of the seven-gene prognostic signature at 1 year, 3 years, and 5 years were 0.703, 0.758 and 0.777. While the DFS-related prognostic model was constructed based on the 4 TRGs (LPIN3, PXYLP1, IGSF23 and B3GALT1), with AUCs of 0.617, 0.756, and 0.731, respectively. Functional analysis indicated that immune‐related pathways were enriched in low‐risk groups. When considering the infiltration patterns of immune cells, we found higher proportions of follicular helper T (Tfh) cell and M1 macrophage, while lower infiltration of M0 macrophage in low-risk groups (P < 0.05). Accordingly, TMB levels of low-risk patients were significantly higher both in OS and DFS model (P < 0.01). Conclusions: Our TRGs-based models are reliable predictive tools for OS and DFS. High TMB may confer with an immunogenic microenvironment and predict favorable outcomes in EOCs.
Platelet-to-lymphocyte ratio, a novel biomarker to predict the severity of COVID-19 patients: A systematic review and meta-analysis

Platelet-to-lymphocyte ratio (PLR), a novel inflammatory marker, has been suggested to predict the severity of COVID-19 patients. This systematic review aims to evaluate the association between PLR levels on admission and the severity of COVID-19 patients. A systematic literature search was done on 23 July 2020 to identify peer-reviewed studies, preprints, and grey literatures. Research articles comparing the PLR value on admission in adult patients with COVID-19 with varying degrees of severity were included in the analysis. The following keywords were used for the search: “COVID-19”, “PLR”, “severity”, and “mortality”. A total of seven studies were included in the meta-analysis, six of which were conducted in China. From a total of 998 participants included, 316 (31.7%) had severe diseases; and those in the severe group were generally older and had underlying diseases compared to the non-severe group. In comparison to non-severe patients, the meta-analysis showed that severe COVID-19 patients had higher PLR levels on admission (SMD 0.68; 95%CI 0.43-0.93; I2 =58%). High PLR levels on admission were associated with severe COVID-19 cases. Therefore, the on-admission PLR level is a novel, cost-effective, and readily available biomarker with a promising prognostic role for determining the severity of COVID-19 patients.