Miracle-dz/UniFAR-FTUs huggingface dataset:
The network signature of constellation line figures

In traditional astronomies across the world, groups of stars in the night sky were linked into constellations—symbolic representations rich in meaning and with practical roles. In some sky cultures, constellations are represented as line (or connect-the-dot) figures, which are spatial networks drawn over the fixed background of stars. We analyse 1802 line figures from 56 sky cultures spanning all continents, in terms of their network, spatial, and brightness features, and ask what associations exist between these visual features and culture type or sky region. First, an embedded map of constellations is learnt, to show clusters of line figures. We then form the network of constellations (as linked by their similarity), to study how similar cultures are by computing their assortativity (or homophily) over the network. Finally, we measure the diversity (or entropy) index for the set of constellations drawn per sky region. Our results show distinct types of line figures, and that many folk astronomies with oral traditions have widespread similarities in constellation design, which do not align with cultural ancestry. In a minority of sky regions, certain line designs appear universal, but this is not the norm: in the majority of sky regions, the line geometries are diverse.
Near-infrared Studies of Nova V1674 Herculis: A Shocking Record Breaker

We present near-infrared spectroscopy of Nova Herculis 2021 (V1674 Her), obtained over the first 70 days of its evolution. This fastest nova on record displays a rich emission line spectrum, including strong coronal line emission with complex structures. The hydrogen line fluxes, combined with a distance of 4.7−1.0+1.3 kpc, give an upper limit to the hydrogen ejected mass of Mej=1.4−1.2+0.8×10−3 M ⊙. The coronal lines appeared at day 11.5, the earliest onset yet observed for any classical nova, before there was an obvious source of ionizing radiation. We argue that the gas cannot be photoionized, at least in the earliest phase, and must be shocked. Its temperature is estimated to be 105.57±0.05 K on day 11.5. Tentative analysis indicates a solar abundance of aluminum and an underabundance of calcium, relative to silicon, with respect to solar values in the ejecta. Further, we show that the vexing problem of whether collisional ionization or photoionization is responsible for coronal emission in classical novae can be resolved by correlating the temporal sequence in which the X-ray supersoft phase and the near-infrared coronal line emission appear.
A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysis

Logs are widely used to record runtime information of software systems, such as the timestamp and the importance of an event, the unique ID of the source of the log, and a part of the state of a task's execution. The rich information of logs enables system developers (and operators) to monitor the runtime behaviors of their systems and further track down system problems and perform analysis on log data in production settings. However, the prior research on utilizing logs is scattered and that limits the ability of new researchers in this field to quickly get to the speed and hampers currently active researchers to advance this field further. Therefore, this paper surveys and provides a systematic literature review and mapping of the contemporary logging practices and log statements' mining and monitoring techniques and their applications such as in system failure detection and diagnosis. We study a large number of conference and journal papers that appeared on top-level peer-reviewed venues. Additionally, we draw high-level trends of ongoing research and categorize publications into subdivisions. In the end, and based on our holistic observations during this survey, we provide a set of challenges and opportunities that will lead the researchers in academia and industry in moving the field forward.
A Prototype-Oriented Framework for Unsupervised Domain Adaptation

Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space. To avoid the sampling variability, class imbalance, and data-privacy concerns that often plague these methods, we instead provide a memory and computation-efficient probabilistic framework to extract class prototypes and align the target features with them. We demonstrate the general applicability of our method on a wide range of scenarios, including single-source, multi-source, class-imbalance, and source-private domain adaptation. Requiring no additional model parameters and having a moderate increase in computation over the source model alone, the proposed method achieves competitive performance with state-of-the-art methods.
Group-disentangled Representation Learning with Weakly-Supervised Regularization

Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning. We investigate learning group-disentangled representations for groups of factors with weak supervision. Existing techniques to address this challenge merely constrain the approximate posterior by averaging over observations of a shared group. As a result, observations with a common set of variations are encoded to distinct latent representations, reducing their capacity to disentangle and generalize to downstream tasks. In contrast to previous works, we propose GroupVAE, a simple yet effective Kullback-Leibler (KL) divergence-based regularization across shared latent representations to enforce consistent and disentangled representations. We conduct a thorough evaluation and demonstrate that our GroupVAE significantly improves group disentanglement. Further, we demonstrate that learning group-disentangled representations improve upon downstream tasks, including fair classification and 3D shape-related tasks such as reconstruction, classification, and transfer learning, and is competitive to supervised methods.
Global Complexity Bound of a Proximal ADMM for Linearly Constrained Nonseparable Nonconvex Composite Programming

This paper proposes and analyzes a dampened proximal alternating direction method of multipliers (DP.ADMM) for solving linearly-constrained nonconvex optimization problems where the smooth part of the objective function is nonseparable. Each iteration of DP.ADMM consists of: (i) a sequence of partial proximal augmented Lagrangian (AL) updates, (ii) an under-relaxed Lagrange multiplier update, and (iii) a novel test to check whether the penalty parameter of the AL function should be updated. Under a basic Slater condition and some requirements related to the dampening factor and under-relaxation parameter, it is shown that DP.ADMM obtains a first-order stationary point of the constrained problem in ${\cal O}(\varepsilon^{-3})$ iterations for a given numerical tolerance $\varepsilon>0$. One of the main novelties of the paper is that convergence of the method is obtained without requiring any rank assumptions on the constraint matrices.
Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images

The accurate segmentation of organs-at-risk (OARs) in head and neck CT images is a critical step for radiation therapy of head and neck cancer patients. However, manual delineation for numerous OARs is time-consuming and laborious, even for expert oncologists. Moreover, manual delineation results are susceptible to high intra- and inter-variability. To this end, we propose a novel dual shape guided network (DSGnet) to automatically delineate nine important OARs in head and neck CT images. To deal with the large shape variation and unclear boundary of OARs in CT images, we represent the organ shape using an organ-specific unilateral inverse-distance map (UIDM) and guide the segmentation task from two different perspectives: direct shape guidance by following the segmentation prediction and across shape guidance by sharing the segmentation feature. In the direct shape guidance, the segmentation prediction is not only supervised by the true label mask, but also by the true UIDM, which is implemented through a simple yet effective encoder-decoder mapping from the label space to the distance space. In the across shape guidance, UIDM is used to facilitate the segmentation by optimizing the shared feature maps. For the experiments, we build a large head and neck CT dataset with a total of 699 images from different volunteers, and conduct comprehensive experiments and comparisons with other state-of-the-art methods to justify the effectiveness and efficiency of our proposed method. The overall Dice Similarity Coefficient (DSC) value of 0.842 across the nine important OARs demonstrates great potential applications in improving the delineation quality and reducing the time cost.
Molecular Chemistry for Dark Matter. III. DarkKROME

Dark matter that is dissipative may cool sufficiently to form compact objects, including black holes. Determining the abundance and mass spectrum of those objects requires an accurate model of the chemistry relevant for the cooling of the dark matter gas. Here we introduce a chemistry tool for dark matter, DarkKROME, an extension of the KROME software package. DarkKROME is designed to include all atomic and molecular processes relevant for dark matter with two unequal-mass fundamental fermions, interacting via a massless-photon-mediated U(1) force. We use DarkKROME to perform one-zone collapse simulations and study the evolution of temperature–density phase diagrams for various dark sector parameters. DarkKROME is publicly available at https://bitbucket.org/mtryan83/darkkrome.
Holographic heat engines coupled with logarithmic U(1) gauge theory

In this paper, we study a new class of holographic heat engines via charged AdS black hole solutions of Einstein gravity coupled with logarithmic nonlinear [Formula: see text] gauge theory. So, logarithmic [Formula: see text] AdS black holes with a horizon of positive, zero and negative constant curvatures are considered as a working substance of a holographic heat engine and the corrections to the usual Maxwell field are controlled by nonlinearity parameter [Formula: see text]. The efficiency of an ideal cycle ([Formula: see text]), consisting of a sequence of isobaric [Formula: see text] isochoric [Formula: see text] isobaric [Formula: see text] isochoric processes, is computed using the exact efficiency formula. It is shown that [Formula: see text], with [Formula: see text] the Carnot efficiency (the maximum efficiency available between two fixed temperatures), decreases as we move from the strong coupling regime ([Formula: see text]) to the weak coupling domain ([Formula: see text]). We also obtain analytic relations for the efficiency in the weak and strong coupling regimes in both low and high temperature limits. The efficiency for planar and hyperbolic logarithmic [Formula: see text] AdS black holes is computed and it is observed that efficiency versus [Formula: see text] behaves in the same qualitative manner as the spherical black holes.
Conformal Prediction using Conditional Histograms

This paper develops a conformal method to compute prediction intervals for non-parametric regression that can automatically adapt to skewed data. Leveraging black-box machine learning algorithms to estimate the conditional distribution of the outcome using histograms, it translates their output into the shortest prediction intervals with approximate conditional coverage. The resulting prediction intervals provably have marginal coverage in finite samples, while asymptotically achieving conditional coverage and optimal length if the black-box model is consistent. Numerical experiments with simulated and real data demonstrate improved performance compared to state-of-the-art alternatives, including conformalized quantile regression and other distributional conformal prediction approaches.
Faster non-convex federated learning via global and local momentum

We propose \texttt{FedGLOMO}, a novel federated learning (FL) algorithm with an iteration complexity of $\mathcal{O}(\epsilon^{-1.5})$ to converge to an $\epsilon$-stationary point (i.e., $\mathbb{E}[\|\nabla f(\bm{x})\|^2] \leq \epsilon$) for smooth non-convex functions -- under arbitrary client heterogeneity and compressed communication -- compared to the $\mathcal{O}(\epsilon^{-2})$ complexity of most prior works. Our key algorithmic idea that enables achieving this improved complexity is based on the observation that the convergence in FL is hampered by two sources of high variance: (i) the global server aggregation step with multiple local updates, exacerbated by client heterogeneity, and (ii) the noise of the local client-level stochastic gradients. By modeling the server aggregation step as a generalized gradient-type update, we propose a variance-reducing momentum-based global update at the server, which when applied in conjunction with variance-reduced local updates at the clients, enables \texttt{FedGLOMO} to enjoy an improved convergence rate. Moreover, we derive our results under a novel and more realistic client-heterogeneity assumption which we verify empirically -- unlike prior assumptions that are hard to verify. Our experiments illustrate the intrinsic variance reduction effect of \texttt{FedGLOMO}, which implicitly suppresses client-drift in heterogeneous data distribution settings and promotes communication efficiency.
Learning Robust Recommenders through Cross-Model Agreement

Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while negative examples are sampled from uninteracted ones. However, noisy examples are prevalent in real-world implicit feedback. A noisy positive example could be interacted but it actually leads to negative user preference. A noisy negative example which is uninteracted because of user unawareness could also denote potential positive user preference. Conventional training methods overlook these noisy examples, leading to sub-optimal recommendations. In this work, we propose a general framework to learn robust recommenders from implicit feedback. Through an empirical study, we find that different models make relatively similar predictions on clean examples which denote the real user preference, while the predictions on noisy examples vary much more across different models. Motivated by this observation, we propose denoising with cross-model agreement (DeCA) which minimizes the KL-divergence between the real user preference distributions parameterized by two recommendation models while maximizing the likelihood of data observation. We instantiate DeCA on four representative recommendation models, empirically demonstrating its superiority over normal training and existing denoising methods. Codes are available at https://github.com/wangyu-ustc/DeCA.
Near-wall turbulence alteration with the transpiration-resistance model

Abstract A set of boundary conditions called the transpiration-resistance model (TRM) is investigated in altering near-wall turbulence. The TRM was proposed by Lacis et al. (J. Fluid Mech., vol. 884, 2020, p. A21) as a means of representing the net effect of surface micro-textures on their overlying bulk flows. It encompasses conventional Navier-slip boundary conditions relating the streamwise and spanwise velocities to their respective shears through the slip lengths $\ell _x$ and $\ell _z$. In addition, it features a transpiration condition accounting for the changes induced in the wall-normal velocity by expressing it in terms of variations of the wall-parallel velocity shears through the transpiration lengths $m_x$ and $m_z$. Greater levels of drag increase occur when more transpiration takes place at the boundary plane, with turbulent transpiration being predominately coupled to the spanwise shear component for canonical near-wall turbulence. The TRM reproduces the effect of a homogeneous and structured roughness up to $k^{+}\approx 18$, encompassing the regime of smooth-wall-like turbulence described using virtual origins (Luchini, 1996 Reducing the turbulent skin friction. In Computational Methods in Applied Sciences’ 96 (Paris, 9–13 Sept. 1996), pp. 465–470. Wiley; Ibrahim et al., J. Fluid Mech. , vol. 915, 2021, p. A56) and slightly beyond it. The transpiration factor is defined as the product of the slip and transpiration lengths, i.e. $(m\ell )_{x,z}$. This factor contains the compound effect of the wall-parallel velocity occurring at the boundary plane and increased permeability, both of which lead to the transport of momentum in the wall-normal direction. A linear relation between the transpiration factor and the roughness function is observed for regularly textured surfaces in the transitionally rough regime of turbulence. This shows that such effective flow quantities can be suitable measures for characterizing rough surfaces in this flow regime.
Barycentric-alignment and invertibility for domain generalization

We revisit the problem of Domain Generalization (DG) where the hypotheses are composed of a common representation mapping followed by a labeling function. Popular DG methods optimize a well-known upper bound to the risk in the unseen domain. However, the bound contains a term that is not optimized due to its dual dependence on the representation mapping and the unknown optimal labeling function for the unseen domain. We derive a new upper bound free of the term having such dual dependence by imposing mild assumptions on the loss function and an invertibility requirement on the representation map when restricted to the low-dimensional data manifold. The derivation leverages old and recent transport inequalities that link optimal transport metrics with information-theoretic measures. Our bound motivates a new algorithm for DG comprising Wasserstein-2 barycenter cost for feature alignment and mutual information or autoencoders for enforcing approximate invertibility. Experiments on several datasets demonstrate superior performance compared to well-known DG algorithms.
Apoptosis of human umbilical vein endothelial cells induced by artesunate

AIM To investigate if the antitumor effect of artesunate is related to the induction of apoptosis in endothelial cells. METHODS The antiproliferation activities of artesunate were evaluated on human umbilical vein endothelial cells(HUVEC) by means of MTT vital staining. The induction of apoptosis was detected by acridine orange/ethidium bromide dual staining as well as DNA electrophoresis. The ratios of apoptosis in HUVEC and distribution of cell cycle phases were assessed by flow cytometry. The intracellular free calcium concentration ([Ca 2+ ] i) of HUVEC was assayed with fluorescence spectroscopic analysis. RESULTS Artesunate suppressed HUVEC proliferation, induced apoptosis of HUVEC, changed the distribution of cell cycle phases, and increased [Ca 2+ ] i remarkably. CONCLUSION Artesunate induces apotosis of HUVEC, which may be associated with the increase of [Ca 2+ ] i.
Eudaimonic Wellbeing in Southeastern Myanmar’s Contested Moral Landscape

Between 2011 and 2020 Myanmar experienced significant social and political changes as a result of a series of reforms introduced by the former military junta. In this article I examine how the broadening of personal hopes and aspirations through new community-based English educational sites during this period influenced young people’s understandings of eudaimonic wellbeing. Teleological theories of eudaimonia often imply a degree of coherence in conceptions of the self, virtue and human flourishing. Drawing from recent work in anthropology, I argue that conflicting frameworks of virtue ethics provide collective meaning and help to orient everyday life for different people in distinctive ways. Through the lives of young Karen Buddhists in southeastern Myanmar, I show that there are complex and non-linear understandings of virtue that people draw upon in their deliberations about how to pursue possible eudaimonic futures.
Promoting Parent–Teacher Communication Within Adapted Physical Education Using the Media Richness Theory

Abstract The Media Richness Theory (MRT) is an important framework that has been used to explain how teachers and/or parents may use media to communicate with each other. Choosing an inappropriate medium may instead oversimplify or overcomplicate information, which may negatively influence a child’s participation in physical activity. The purpose of this article is therefore to describe practical examples for adapted physical education teachers to communicate with parents regarding child physical activity using the MRT
Restoring Global Gene Regulation through Experimental Evolution Uncovers a NAP (Nucleoid-Associated Protein)-Like Behavior of Crp/Cap

Here we report that mutations in the topA gene compensate for loss of cAMP, showing that the interplay between Crp and the supercoiling status of promoters is key to global stress response. Furthermore, we observed an effect of apoCrp on gene expression in the absence of its effector cAMP. This provides support for the proposed NAP-like role for Crp, suggesting that it represents an intermediate point in the evolution of a ligand-controlled TF from a NAP. ABSTRACT How do hierarchical gene regulation networks evolve in bacteria? Nucleoid-associated proteins (NAPs) influence the overall structure of bacterial genomes, sigma factors and global transcription factors (TFs) control thousands of genes, and many operons are regulated by highly specific TFs that in turn are controlled allosterically by cellular metabolites. These regulatory hierarchies have been shaped by millions of years of evolution to optimize fitness in response to changing environmental conditions, but it is unclear how NAPs and TFs relate and have evolved together. Cyclic AMP (cAMP) receptor protein (Crp) is the paradigmatic global TF in Escherichia coli, and here we report that mutations in the topA gene compensate for loss of cAMP, showing that the interplay between Crp and the supercoiling status of promoters is key to global stress response. Furthermore, we observed an effect of apoCrp on gene expression in the absence of its effector cAMP. This provides support for the proposed NAP-like role for Crp, suggesting that it represents an intermediate point in the evolution of a ligand-controlled TF from a NAP. IMPORTANCE Here we report that mutations in the topA gene compensate for loss of cAMP, showing that the interplay between Crp and the supercoiling status of promoters is key to global stress response. Furthermore, we observed an effect of apoCrp on gene expression in the absence of its effector cAMP. This provides support for the proposed NAP-like role for Crp, suggesting that it represents an intermediate point in the evolution of a ligand-controlled TF from a NAP.
Cytogenetic Biomonitoring in Buccal Mucosa Cells of COVID-19 Patients: Preliminary Findings

Background/Aim: COVID-19 may lead to progressive respiratory failure as a consequence of alveolar damage, resulting in death. The aim of this study was to evaluate cytogenetic damage in oral cells of COVID-19 patients by micronucleus assay. Patients and Methods: A total of 11 COVID-19 patients aged 40.7±9.3 years (5 men and 6 women) were included in this study. For the control group, a total of 15 participants not infected with SARS-CoV-2 virus were included. The mean age was 41.6±6.2 years (5 men and 10 women). Results: The results showed statistically significant differences (p<0.05) in micronucleated buccal mucosa cells of COVID-19 patients. In addittion, a statistically significant increase in karyolysis and karrhyorexis (p<0.05) was observed in COVID-19 patients compared to control. Conclusion: SARS-CoV-2 virus can induce mutagenesis and cytotoxicity in oral cells.