Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying vital nodes through augmented random walks on higher-order networks

Empirical networks possess considerable heterogeneity of node connections, resulting in a small portion of nodes playing crucial roles in network structure and function. Yet, how to characterize nodes' influence and identify vital nodes is by far still unclear in the study of networks with higher-order interactions. In this paper, we introduce a multi-order graph obtained by incorporating the higher-order bipartite graph and the classical pairwise graph, and propose a Higher-order Augmented Random Walk (HoRW) model through random walking on it. This representation preserves as much information about the higher-interacting network as possible. The results indicate that the proposed method effectively addresses the localization problem of certain classical centralities. In contrast to random walks along pairwise interactions only, performing more walks along higher-order interactions assists in not only identifying the most important nodes but also distinguishing nodes that ranked in the middle and bottom. Our method outperforms classical centralities in identifying vital nodes and can scale to various tasks in networks, including information spread maximization and network dismantling problems. The proposed higher-order representation and the random walk model provide novel insights and potent tools for studying higher-order mechanisms and functionality.
Exploring connections between the VLBI and optical morphology of AGNs and their host galaxies

Abstract We analyse VLBI and optical images of AGNs and their host galaxies and look for statistical correlations between the shape and orientation of the galaxy and the direction of the jet. We utilise the Astrogeo catalogue, which has over 9000 VLBI sources, many of those with a clear core-jet like structure that allows for the jet position angle to be reliably determined. We then use the VLBI source positions to search for optical counterparts within various optical surveys. In order to parameterise the orientation and shape of the host galaxy, we fitted a Gaussian elliptical model to the optical image, taking the PSF into account. We check our own shape parameters from this fit against the ones provided by the optical surveys. As of yet, no clear correlation between the galaxy morphology and the jet direction is seen.
Approximate central limit theory and statistical inference with contaminated data

We refine the classical Lindeberg-Feller central limit theorem by obtaining asymptotic bounds on the Kolmogorov distance, the Wasserstein distance, and the parametrized Prokhorov distances in terms of a Lindeberg index. We thus obtain more general approximate central limit theorems, which roughly state that the row-wise sums of a triangular array are approximately asymptotically normal if the array approximately satisfies Lindeberg's condition. Stein's method plays a key role in the development of this theory. We use the theoretical results to study the asymptotic behavior of the sample average estimator in the presence of data contamination.
Topological fundamental groupoid. III. Haar systems on the fundamental groupoid

Let $X$ be a path connected, locally path connected and semilocally simply connected space; let $\tilde{X}$ be its universal cover. We discuss the existence and description of a Haar system on the fundamental groupoid $\Pi_1(X)$ of $X$. The existence of a Haar system on $\Pi_1(X)$ is justified when $X$ is a second countable, locally compact and Hausdorff. We provide equivalent criteria for the existence of the Haar system on a locally compact (locally Hausdorff) fundamental groupoid in terms of certain measures on $X$ and $\tilde{X}$. $\mathrm{C}^*(\Pi_1(X))$ is described using a result of Muhly, Renault and Williams. Finally, two formulae for the Haar system on $\Pi_1(X)$ in terms of measures on $X$ or $\tilde{X}$ are given.
Adaptive Graduated Nonconvexity Loss

—Many problems in robotics, such as estimating the state from noisy sensor data or aligning two LiDAR point clouds, can be posed and solved as least-squares problems. Unfortunately, vanilla nonminimal solvers for least-squares problems are notori-ously sensitive to outliers. As such, various robust loss functions have been proposed to reduce the sensitivity to outliers. Examples of loss functions include pseudo-Huber, Cauchy, and Geman- McClure. Recently, these loss functions have been generalized into a single loss function that enables the best loss function to be found adaptively based on the distribution of the residuals. However, even with the generalized robust loss function, most nonminimal solvers can only be solved locally given a prior state estimate due to the nonconvexity of the problem. The ﬁrst contribution of this paper is to combine graduated nonconvexity (GNC) with the generalized robust loss function to solve least- squares problems without a prior state estimate and without the need to specify a loss function. Moreover, existing loss functions, including the generalized loss function, are based on Gaussian-like distribution. However, residuals are often deﬁned as the squared norm of a multivariate error and distributed in a Chi-like fashion. The second contribution of this paper is to apply a norm- aware adaptive robust loss function within a GNC framework. This leads to additional robustness when compared with state- of-the-art methods. Simulations and experiments demonstrate that the proposed approach is more robust and yields faster convergence times compared to other GNC formulations.
Rokhlin Dimension: Permanence Properties and Ideal Separation

We study the Rokhlin dimension for actions of residually finite groups on C*-algebras. We give a definition equivalent to the original one due to Szabo, Wu and Zacharias. We then prove a number of permanence properties and discuss actions on C_0(X)-algebras and commutative C*- algebras. Finally, we use a theorem of Sierakowski to show that, for an action with finite Rokhlin dimension, every ideal in the associated reduced crossed product C*-algebra arises from an invariant ideal of the underlying algebra.
Classical fully packed loop model with attractive interactions on the square lattice

We study a classical model of fully-packed loops on the square lattice, which interact through attractive loop segment interactions between opposite sides of plaquettes. This study is motivated by effective models of interacting quantum matter arising in frustrated magnets or Rydberg atom arrays, for which loop degrees of freedom appear at low energy. Through a combination of Monte Carlo simulations and an effective height field theory, we find that the critical point known to occur at infinite temperature gives rise to a high-temperature critical phase with floating exponents. At lower temperature, the system transitions via a Kosterlitz-Thouless phase transition to a nematic phase where lattice rotation symmetry is broken. We discuss consequences for the phase diagram of the quantum loop model on the same lattice.
Decentralized Governance for Virtual Community(DeGov4VC): Optimal Policy Design of Human-plant Symbiosis Co-creation

Does the decentralized nature of user behavior in interactive virtual communities help create rules promoting user engagement? Through scenarios like planting, this framework suggests a new paradigm for mutual influence that allows users to impact communities’ political decisions. Sixteen participants in the first round of interviews were involved in the framework’s creation. Then we developed and implemented our framework in the community with the help of other stakeholders. This proof-of-concept creates user groups using information from users’ daily activities as input and grows the green plants in a virtual environment. Finally, we involved AI agents and stakeholders in the framework test and iterations. Our study’s user evaluation of a few key stakeholders demonstrates how our strategy enhances user viscosity and experience. Via human-planting ecosystems in a virtual community, this research gives a fresh viewpoint on decentralized governance and an engaging method for co-creating interactive ecological communities.
Quasi-Hereditary Skew Group Algebras

Given an algebra and a finite group acting on it via automorphisms, a natural object of study is the associated skew group algebra. In this article, we study the relationship between quasi-hereditary structures on the original algebra and on the corresponding skew group algebra. Assuming a natural compatibility condition on the partial order, we show that the skew group algebra is quasi-hereditary if and only if the original algebra is. Moreover, we show that in this setting an exact Borel subalgebra of the original algebra which is invariant as a set under the group action gives rise to an exact Borel subalgebra of the skew group algebra, and that under this construction, properties such as normality and regularity of the exact Borel subalgebra are preserved.
Thick wall from thin walls

An Euclidean bounce describing vacuum decay can be considered as an infinite stack of concentric thin shells to which a thin-wall action can be assigned. The integral over all shells produces then a tunneling action that is precisely the action functional in field space of the so-called tunneling potential formalism. This procedure, which works also when gravity is included, gives the simplest derivation of such actions.
ReMark: Receptive Field based Spatial WaterMark Embedding Optimization using Deep Network

Watermarking is one of the most important copyright protection tools for digital media. The most challenging type of watermarking is the imperceptible one, which embeds identifying information in the data while retaining the latter's original quality. To fulfill its purpose, watermarks need to withstand various distortions whose goal is to damage their integrity. In this study, we investigate a novel deep learning-based architecture for embedding imperceptible watermarks. The key insight guiding our architecture design is the need to correlate the dimensions of our watermarks with the sizes of receptive fields (RF) of modules of our architecture. This adaptation makes our watermarks more robust, while also enabling us to generate them in a way that better maintains image quality. Extensive evaluations on a wide variety of distortions show that the proposed method is robust against most common distortions on watermarks including collusive distortion.
Maximizing Influence with Graph Neural Networks

Finding the seed set that maximizes the influence spread over a network is a well-known NP-hard problem. Though a greedy algorithm can provide near-optimal solutions, the subproblem of influence estimation renders the solutions inefficient. In this work, we propose \textsc{Glie}, a graph neural network that learns how to estimate the influence spread of the independent cascade. \textsc{Glie} relies on a theoretical upper bound that is tightened through supervised training. Experiments indicate that it provides accurate influence estimation for real graphs up to 10 times larger than the train set. Subsequently, we incorporate it into two influence maximization techniques. We first utilize Cost Effective Lazy Forward optimization substituting Monte Carlo simulations with \textsc{Glie}, surpassing the benchmarks albeit with a computational overhead. To improve computational efficiency we develop a provably submodular influence spread based on \textsc{Glie}'s representations, to rank nodes while building the seed set adaptively. The proposed algorithms are inductive, meaning they are trained on graphs with less than 300 nodes and up to 5 seeds, and tested on graphs with millions of nodes and up to 200 seeds. The final method exhibits the most promising combination of time efficiency and influence quality, outperforming several baselines.
In situ analysis of surface composition and meteorology at the Zhurong landing site on Mars

ABSTRACT The Zhurong rover of the Tianwen-1 mission landed in southern Utopia Planitia, providing a unique window into the evolutionary history of the Martian lowlands. During its first 110 sols, Zhurong investigated and categorized surface targets into igneous rocks, lithified duricrusts, cemented duricrusts, soils and sands. The lithified duricrusts, analysed by using laser-induced breakdown spectroscopy onboard Zhurong, show elevated water contents and distinct compositions from those of igneous rocks. The cemented duricrusts are likely formed via water vapor–frost cycling at the atmosphere–soil interface, as supported by the local meteorological conditions. Soils and sands contain elevated magnesium and water, attributed to both hydrated magnesium salts and adsorbed water. The compositional and meteorological evidence indicates potential Amazonian brine activities and present-day water vapor cycling at the soil–atmosphere interface. Searching for further clues to water-related activities and determining the water source by Zhurong are critical to constrain the volatile evolution history at the landing site.
The pulmonary function laboratory in the investigation of dyspnea of unknown origin

Chronic dyspnea (i.e., dyspnea for at least 3 months) negatively impacts the health-related quality of life of ~10% of the general population. In a sizable fraction of these individuals, the underlying cause remains unclear after detailed clinical assessment, basic pulmonary function tests, and chest imaging, characterizing dyspnea of unknown origin (DUO). The “lung doctor” is frequently called to assess these patients for diagnostic clarification, an endeavor fraught with complexities in most circumstances.
Successful acclimatization of mandrills (Mandrillus sphinx) translocated to Conkouati-Douli National Park, Republic of Congo, as measured by fecal glucocorticoid metabolites

Stress is implicated as a major cause of failure in wildlife translocations. We monitored the stress response non-invasively during a mandrill release. Our findings suggest a pre-release enclosure allowed mandrills to recover from transfer and the mandrills adjusted well to the forest, showing the value of non-invasive physiology in translocations.
Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs)

Recent years have seen a substantial growth in the adoption of machine learning approaches for the purposes of quantitative structure-activity relationship (QSAR) development. Such a trend has coincided with desire to see a shifting in the focus of methodology employed within chemical safety assessment: away from traditional reliance upon animal-intensive in vivo protocols, and towards increased application of in silico (or computational) predictive toxicology. With QSAR central amongst techniques applied in this area, the emergence of algorithms trained through machine learning with the objective of toxicity estimation has, quite naturally, arisen. On account of the pattern-recognition capabilities of the underlying methods, the statistical power of the ensuing models is potentially considerable–appropriate for the handling even of vast, heterogeneous datasets. However, such potency comes at a price: this manifesting as the general practical deficits observed with respect to the reproducibility, interpretability and generalisability of the resulting tools. Unsurprisingly, these elements have served to hinder broader uptake (most notably within a regulatory setting). Areas of uncertainty liable to accompany (and hence detract from applicability of) toxicological QSAR have previously been highlighted, accompanied by the forwarding of suggestions for “best practice” aimed at mitigation of their influence. However, the scope of such exercises has remained limited to “classical” QSAR–that conducted through use of linear regression and related techniques, with the adoption of comparatively few features or descriptors. Accordingly, the intention of this study has been to extend the remit of best practice guidance, so as to address concerns specific to employment of machine learning within the field. In doing so, the impact of strategies aimed at enhancing the transparency (feature importance, feature reduction), generalisability (cross-validation) and predictive power (hyperparameter optimisation) of algorithms, trained upon real toxicity data through six common learning approaches, is evaluated.
Factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the adult population and among older adults

The use of wearable technology, which is often acquired to support well-being and a healthy lifestyle, has become popular in Western countries. At the same time, healthcare is gradually taking the first steps to introduce wearable technology into patient care, even though on a large scale the evidence of its’ effectiveness is still lacking. The objective of this study was to identify the factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the Finnish adult population (20–99) and among older adults (65–99). The study utilized a cross-sectional population survey of Finnish adults aged 20 and older (n = 6,034) to analyse non-causal relationships between wearable technology use and the users’ characteristics. Logistic regression models of wearable technology use were constructed using statistically significant sociodemographic, well-being, health, benefit, and lifestyle variables. Both in the general adult population and among older adults, wearable technology use was associated with getting aerobic physical activity weekly according to national guidelines and with marital status. In the general adult population, wearable technology use was also associated with not sleeping enough and agreeing with the statement that social welfare and healthcare e-services help in taking an active role in looking after one’s own health and well-being. Younger age was associated with wearable technology use in the general adult population but for older adults age was not a statistically significant factor. Among older adults, non-use of wearable technology went hand in hand with needing guidance in e-service use, using a proxy, or not using e-services at all. The results support exploration of the effects of wearable technology use on maintaining an active lifestyle among adults of all ages.
NTD programmes hold the key to universal health coverage and better climate and emergency responses

Neglected tropical diseases (NTDs) inflict significant physical, mental, economic, and social harm on over one billion people worldwide, mostly in tropical and sub-tropical areas [1, 2]. Sometimes they cause death. Even when they are not fatal, however, they often lead to life-long disability, stigma and exclusion [3]. The health and social impacts of these diseases lead to increases in poverty and hunger and to reductions in children’s ability to learn. They make people less able to play a full and active part in their own lives and in the lives of their communities, contributing to a vicious circle of poverty and poor health outcomes. Dedicated health initiatives and programmes to combat NTDs, as well as the increased integration of services and including better mapping and surveillance, have led to progress against broad development goals, such as the United Nations’ 2030 Sustainable Development Goals numbers 1, 2, 3, 6 and 10. As of December 2022, 47 countries had eliminated at least one NTD, and in the five years between 2016 and 2020, more than one billion people annually received NTD interventions [4]. These sustained efforts have reduced the global burden of disease across the NTD spectrum, bringing about notable, much-needed gains for people whose lives might otherwise have been devastated. This is, of course, good news; the health system-strengthening initiatives and the development of an extensive, widely skilled health force on the ground that enabled these gains mean that places with otherwise threadbare or non-functional health services have received a minimum level of health provision. NTDs, in this context, provide us with crucial indicators [5]. Their collaborative approach and community-level implementation, as well as their ability to reach remote populations and to do so equitably, present a valuable, reproducible model for health and development programmes more generally [6]. We propose that some of the most pressing issues in global health today, such as climate change, the drive for Universal Health Coverage, and better detection of new and emerging health emergencies, can be addressed by leveraging the power, reach and know-how of those who plan and deliver NTD programmes. Evidence on the health consequences of climate change is beginning to accumulate [7]. WHO’s statements have shifted in proportion. Where previously it was considered “a significant and emerging threat to public health”, climate change is now described as “the single biggest health threat facing humanity” [8] Climate change affects and will affect human, animal and environmental health both directly (by increasing morbidity and mortality, and by weakening of quality health care provision as resources become more and more stretched) and PLOS GLOBAL PUBLIC HEALTH
The impact of national culture, altruism, and risk preference on salaries: The case of the Major League Baseball

Based on the longitudinal data of 30 Major League Baseball (MLB) teams over seasons from 2017 to 2020, we used random effect (RE) models to conduct regression analyses on the detailed data of pitchers and fielders. Cultural distance (CD) was measured in terms of Hofstede’s cultural indicators and Global Preference Survey (GPS) data. The results showed that salary premiums for foreign MLB players existed and CD was significantly positively correlated with salaries. Further, the risk preference (/altruism) difference between foreign pitchers and American pitchers was significantly positively (/negatively) correlated with the salaries of foreign pitchers. Salary estimation data showed that the salary premium was nearly 20% for players from South Korea and Panama, the lowest (only 0.11%) for players from Australia, and only 6.13% for players from Dominican Republic (accounting for the largest proportion of foreign MLB players), indicating that the MLB’s foreign player recruitment policy is correct.
Trigeminal electrical stimulation with ULFTENS of the dorsal anterior mucosal surface of the tongue: Effects on Heart Rate Variability (HRV)

Background Trigeminal electrical stimulation of the dorsal anterior mucosal surface of the tongue has demonstrated its efficacy in a variety of neurological disorders in which anatomical or functional alterations are present. The pathogenesis of such disorders is often linked to altered arousal circuits, and the benefits of tongue stimulation are attributed to the rebalancing of this system. Dental ULFTENS shows efficacy in acting on the muscular, autonomic system and control of the descending pathways that modulate pain. It is administered at the skin level in the area anterior to the tragus and not on the mucosal surface of the tongue. The use of this stimulation technique at the tongue level could have new applications and clinical results if it were able to reduce the activity of arousal circuits. Material and method A new intraoral device allowed electrical stimulation of the dorsal anterior mucosa of the tongue in 32 healthy young women. The effects on HRV were monitored by photoplethysmographic wave (PPG) and compared with a control group. The HRV parameters studied were RMSSD, HF, LF, LF/HF, REC, DET. Results The group of stimulated subjects showed a significant change in some of the HRV parameters that was maintained even in the epoch after the end of electrical stimulation. This effect can be considered as a vagal activation and a change of HRV trend. The control group of unstimulated subjects showed an opposite trend. There were no undesirable or annoying effects of stimulation. Conclusion Stimulation of the dorsal anterior (trigeminal) mucosal surface of the tongue with ULFTENS applied with an intraoral device was shown to be able to increase HRV.