Miracle-dz/UniFAR-FTUs huggingface dataset:
The Ages of Globular Clusters

We examine the luminosity levels of the main‐sequence turnoffs, MTOv, and horizontal branches, Mv(HB), in 16 globular clusters. An entirely new approach of inferring the luminosity levels by utilizing high‐amplitude δ Scuti variables (HADS) is introduced. When the MTOv values are compared with theoretical values inferred from models, we find all 16 clusters (metal‐strong to metal‐poor) are coeval with an average age of ∼11.3 Gyr. A considerable scatter of Mv(HB) values of clusters at similar [Fe/H] values is found. A trend for clusters with blue horizontal branches to have brighter Mv(HB) than clusters with blue‐red horizontal branches is suggested by the data. The Mv(HB) values appear to depend on another or other parameters in addition to the [Fe/H] values. In spite of this problem, we derive an equation relating Mv(HB) values of globular clusters to their [Fe/H] values. We also derive an equation relating the MTOv values of clusters to their [Fe/H] values. Both of these equations can be utilized to find cluster distances. The distance modulus of the LMC is found to be 18.66 from the VTO values of three LMC globular clusters; RR Lyrae stars in seven globular clusters yield 18.61, and RR Lyrae stars in the LMC bar yield 18.64.
Porphyromonas gingivalis Evades Immune Clearance by Regulating Lysosome Efflux

Porphyromonas gingivalis, a major periodontal pathogen, invades autophagosomes of cells, including gingival epithelial cells, endothelial cells, gingival fibroblasts, macrophages, and dendritic cells, to escape antimicrobial autophagy and lysosome fusion. However, it is not known how P. gingivalis resists autophagic immunity, survives within cells, and induces inflammation. Thus, we investigated whether P. gingivalis could escape antimicrobial autophagy by promoting lysosome efflux to block autophagic maturation, leading to intracellular survival, and whether the growth of P. gingivalis within cells results in cellular oxidative stress, causing mitochondrial damage and inflammatory responses. P. gingivalis invaded human immortalized oral epithelial cells in vitro and mouse oral epithelial cells of gingival tissues in vivo. The production of reactive oxygen species (ROS) increased upon bacterial invasion, as well as mitochondrial dysfunction-related parameters with downregulated mitochondrial membrane potential and intracellular adenosine triphosphate (ATP), upregulated mitochondrial membrane permeability, intracellular Ca2+ influx, mitochondrial DNA expression, and extracellular ATP. Lysosome excretion was elevated, the number of intracellular lysosomes was diminished, and lysosomal-associated membrane protein 2 was downregulated. Expression of autophagy-related proteins, microtubule-associated protein light chain 3, sequestosome-1, the NLRP3 inflammasome, and interleukin-1β increased with P. gingivalis infection. P. gingivalis may survive in vivo by promoting lysosome efflux, blocking autophagosome-lysosome fusion, and destroying autophagic flux. As a result, ROS and damaged mitochondria accumulated and activated the NLRP3 inflammasome, which recruited the adaptor protein ASC and caspase 1, leading to the production of proinflammatory factor interleukin-1β and inflammation.
Upstream Shift of Generation Region of Whistler‐Mode Rising‐Tone Emissions in the Magnetosphere

We have performed a series of simulation runs for whistler‐mode wave‐particle interaction in a parabolic magnetic field with 12 different frequencies of triggering waves and 3 different plasma frequencies specifying cold plasma densities. Under a given plasma condition, a specific frequency range of the triggering wave exists that can generate rising‐tone emissions. The generation region of rising‐tone emission shifts upstream. The velocity of the wave generation region is dependent on duration of the subpacket, which is controlled by the formation of the resonant current in the generation region. When the source velocity, which is a sum of the resonance and group velocities, is approximately the same as the velocity of the wave generation region, a long‐sustaining rising‐tone emission is generated. When the spatial and temporal gap between subpackets exists due to damping phase of short subpacket generation, resonant electrons in the gap of the subpackets are carried at the resonance velocity to the upstream region, and the velocity of the wave generation region becomes large in magnitude. When formation of resonant currents is delayed, the velocity of the generation region becomes smaller than the source velocity in magnitude. Below one quarter of the cyclotron frequency, coalescence of subpackets takes place, suppressing formation of the resonant current in the generation region. Since gradual upstream shift of the generation region is necessary for the wave to grow locally, the source velocity should be a small negative value.
ASTRONOMICAL PHENOMENA DISPROVE EINSTEIN'S SPECIAL RELATIVITY THEORY

This article shows that many astronomical phenomena can be explained by existence in nature of quadratic dependence of light speed upon speed of the light source motion. If such a dependence exists in the nature, the following effects can emerge in addition to the known Doppler effect: an effect of spacious grouping or ungrouping of photons and an effect of spacious deformation (compression or extension) of electromagnetic trains corresponding to photons. Using the effect of spacious grouping or ungrouping of photons it is possible to explain such phenomena as «novae», «supernovae», «hypernovae», pulsars and the object SS 433. Using the effect of spacious deformation (com- pression or extension) of electromagnetic trains corresponding to photons it is possible to explain such astronomical phenomena as bursts of cosmic X rays and gamma rays, red shift of far star spectrums increasing with growth of dis- tances to stars, microwave background radiation, Olbers's paradox. Keywords: novae, supernovae, hypernovae, pulsars, red shift of far stars spectrums, microwave background radiation, object SS 433, bursts of cosmic X rays and gamma-rays, quadratic dependence of light speed upon speed of light source motion, new relativistic space-time theory, Olbers's paradox.
Nonlinear stability of parallel flows with subcritical Reynolds numbers. Part 2. Stability of pipe Poiseuille flow to finite axisymmetric disturbances

A theoretical study is presented of the spatial stability of flow in a circular pipe to small but finite axisymmetric disturbances. The disturbance is represented by a Fourier series with respect to time, and the truncated system of equations for the components up to the second-harmonic wave is derived under a rational assumption concerning the magnitudes of the Fourier components. The solution provides a relation between the damping rate and the amplitude of disturbance. Numerical calculations are carried out for Reynolds numbers R between 500 and 4000 and βR [les ] 5000, β being the non-dimensional frequency. The results indicate that the flow is stable to finite disturbances as well as to infinitesimal disturbances for all values of R and βR concerned.
Covariate Adjustment in Stratified Experiments

This paper studies covariate adjusted estimation of the average treatment effect in stratified experiments. We work in a general framework that includes matched tuples designs, coarse stratification, and complete randomization as special cases. Regression adjustment with treatment-covariate interactions is known to weakly improve efficiency for completely randomized designs. By contrast, we show that for stratified designs such regression estimators are generically inefficient, potentially even increasing estimator variance relative to the unadjusted benchmark. Motivated by this result, we derive the asymptotically optimal linear covariate adjustment for a given stratification. We construct several feasible estimators that implement this efficient adjustment in large samples. In the special case of matched pairs, for example, the regression including treatment, covariates, and pair fixed effects is asymptotically optimal. We also provide novel asymptotically exact inference methods that allow researchers to report smaller confidence intervals, fully reflecting the efficiency gains from both stratification and adjustment. Simulations and an empirical application demonstrate the value of our proposed methods.
Numerical Simulation of Bubble Column Flows in Churn-Turbulent Regime: Comparison of Bubble Size Models

Numerical simulations of cylindrical bubble column operating in the churn-turbulent regime have been simulated using Euler–Euler approach incorporated with the RNG k–e model for liquid turbulence. Single-size bubble model, double-size bubble model, and the multiple size group model (MUSIG), including the homogeneous and inhomogeneous discrete methods, are employed in the simulations. Mass conserved formulations of breakup and coalescence rates were used in the computation of bubble size distributions. The Schiller–Naumann drag force was used in the single-size model, and the Ishii–Zuber drag force was used for the MUSIG simulations. For the double-size bubble model, an empirical drag formulation was adapted. The simulation results of time-averaged axial velocity and gas holdup obtained with the three models were compared with reported experimental data in the literature. The results showed that only MUSIG models with lift force can reproduce the measured radial distribution of gas holdup in the fully deve.
Chain of Hindsight Aligns Language Models with Feedback

Learning from human preferences is important for language models to match human needs and to align with human and social values. Prior works have achieved remarkable successes by learning from human feedback to understand and follow instructions. Nonetheless, these methods are either founded on hand-picked model generations that are favored by human annotators, rendering them inefficient in terms of data utilization and challenging to apply in general, or they depend on reinforcement learning, which often suffers from imperfect reward functions and relies on extremely challenging optimizations. In this work, we propose a novel technique, Chain of Hindsight, that is easy to optimize and can learn from any form of feedback, regardless of its polarity. Our idea is inspired by how humans learn from extensive feedback presented in the form of languages. We convert all types of feedback into sequences of sentences, which are then used to fine-tune the model, allowing us to take advantage of the language comprehension capabilities of language models. We condition the model on a sequence of model generations paired with feedback. By doing so, the model is trained to generate outputs based on feedback, while learning to identify and correct negative attributes or errors. Applying our method to large language models, we observed that Chain of Hindsight significantly surpasses previous methods in aligning language models with human preferences. We report significant improvements on summarization and dialogue benchmarks, with our approach markedly preferred in human evaluations.
Limited-Trust in Diffusion of Competing Alternatives Over Social Networks

We consider the diffusion of two alternatives in social networks using a game-theoretic approach. Each individual plays a coordination game with its neighbors repeatedly and decides which to adopt. As products are used in conjunction with others and through repeated interactions, individuals are more interested in their long-term benefits and tend to show trust to others to maximize their long-term utility by choosing a suboptimal option with respect to instantaneous payoff. To capture such trust behavior, we deploy limited-trust equilibrium (LTE) in diffusion process. We analyze the convergence of emerging dynamics to equilibrium points using mean-field approximation and study the equilibrium state and the convergence rate of diffusion using absorption probability and expected absorption time of a reduced-size absorbing Markov chain. We also show that the diffusion model on LTE under the best-response strategy can be converted to the well-known linear threshold model. Simulation results show that when agents behave trustworthy, their long-term utility will increase significantly compared to the case when they are solely self-interested. Moreover, the Markov chain analysis provides a good estimate of convergence properties over random networks.
Robustness of Generalized Estimating Equation (GEE) Tests of Significance against Misspecification of the Error Structure Model

Generalized linear model analyses of repeated measurements typically rely on simplifying mathematical models of the error covariance structure for testing the significance of differences in patterns of change across time. The robustness of the tests of significance depends, not only on the degree of agreement between the specified mathematical model and the actual population data structure, but also on the precision and robustness of the computational criteria for fitting the specified covariance structure to the data. Generalized estimating equation (GEE) solutions utilizing the robust empirical sandwich estimator for modeling of the error structure were compared with general linear mixed model (GLMM) solutions that utilized the commonly employed restricted maximum likelihood (REML) procedure. Under the conditions considered, the GEE and GLMM procedures were identical in assuming that the data are normally distributed and that the variance‐covariance structure of the data is the one specified by the user.
SegMobaTree: The Segmented Multilayer Online Balanced Tree for high-performance IPv6 Lookup in the Edge Network

With the development of IPv6 and edge computing, the edge network should support IPv6 lookup (the longest prefix matching, LPM) with high lookup speed, high update speed, and low memory cost. However, the trie-based algorithms, e.g., SAIL and Poptrie, mainly focus on the IPv4 ruleset but have disadvantages in the edge IPv6 ruleset with longer prefix length. The binary-based algorithm Hi-BST also has limited lookup speed with too many memory accesses. Therefore, we propose the SegMobaTree algorithm to achieve high-performance IPv6 lookup in the edge network. First, MobaTree is a multilayer online balanced tree to perform high-speed binary search among rules with different prefix lengths. Second, to avoid one large tree, we propose the dynamic programming method to split prefix lengths into a few suitable segments, which tradeoff between the number of segments and the scale of trees. Compared to SAIL, Poptrie, and Hi-BST, SegMobaTree achieves 1.5x, 1.7x, 1.6x lookup speed and 11.8x, 32.8x, 1.3x update speed with low memory cost.
Resonant THz detection by periodic multi-gate plasmonic FETs

We show that a periodic multi-grated-gate structure can be applied to THz plasmonic FETs (TeraFETs) to improve the THz detection sensitivity. The introduction of spatial non-uniformity by separated gate sections creates regions with distinct carrier concentrations and velocities, giving rise to harmonic behaviors. The resulting frequency spectrum of DC voltage response is composed of “enhanced” and “suppressed” regions. In the enhanced region, the amplitude of response voltage can be enlarged up to ∼100% compared to that in a uniform channel device. The distribution pattern of those regions is directly related to the number of gate sections (Ns). A mapping of response amplitude in a Ns-frequency scale is created, which helps distinguish enhanced/suppressed regions and locate optimal operating parameters.
An atomic approach to Wall-type stabilization problems

Wall-type stabilization problems investigate the collapse of exotic 4-dimensional phenomena under stabilization operations (e.g., taking connected sums with $S^2 \times S^2$). We propose an elementary approach to these problems, providing a construction of exotic 4-manifolds and knotted surfaces that are candidates to remain exotic after stabilization -- including examples in the setting of closed, simply connected 4-manifolds. As a proof of concept, we show this construction yields exotic surfaces in the 4-ball that remain exotic after (internal) stabilization, detected by the cobordism maps on universal Khovanov homology. We conclude by comparing these Khovanov-theoretic obstructions for surfaces to the Floer-theoretic counterparts for exotic 4-manifolds obtained as their branched covers, suggesting a bridge via Lin's spectral sequence from Bar-Natan homology to involutive monopole Floer homology.
Photo-Stimulated Phosphorescence for Thermal Condition Monitoring and Nondestructive Evaluation in Thermal Barrier Coatings

This article describes recent developments of the thermal barrier sensor concept for nondestructive evaluation (NDE) of thermal barrier coatings (TBCs) and on-line condition monitoring in gas turbines. New and enhanced instrumentation to measure surface temperature distributions and heat flux and to monitor TBC health are regarded as a priority by the industry. The authors have proposed thermal barrier sensor coatings (TBSCs) as a possible means of achieving such measurements. TBSCs are made by doping the ceramic material (currently yttria-stabilized zirconia) with a rare earth activator to provide the coating with luminescence when excited with ultraviolet (UV) light. The article describes the physics of the thermoluminescent response of such coatings and shows how this can be used to measure temperature. Calibration data are presented from a coating produced using a production standard spray system. Also discussed is the manufacture of functionally structured coatings with discreet doped layers. The temperature at the bond coat interface is important with respect to the life of the coating since it influences the growth rate of the thermally grown oxide layer, which in turn destabilizes the coating system as it becomes thicker. Preliminary experimental data are presented that indicate that dual-layered TBSCs can be used to detect luminescence from, and thereby the temperature within, subsurface layers. A theoretical analysis of the data has allowed some preliminary calculations of the transmission properties of the overcoat to be made, and these suggest that it might be possible to observe phosphorescence and measure temperature through an overcoat layer of up to approximately 1.33 mm thickness.
SpecXAI - Spectral interpretability of Deep Learning Models

Deep learning is becoming increasingly adopted in business and industry due to its ability to transform large quantities of data into high-performing models. These models, however, are generally regarded as black boxes, which, in spite of their performance, could prevent their use. In this context, the field of eXplainable AI attempts to develop techniques that temper the impenetrable nature of the models and promote a level of understanding of their behavior. Here we present our contribution to XAI methods in the form of a framework that we term SpecXAI, which is based on the spectral characterization of the entire network. We show how this framework can be used to not only understand the network but also manipulate it into a linear interpretable symbolic representation.
The Green's function and large‐time behavior of solutions for the one‐dimensional Boltzmann equation

We study the pointwise behavior of the Green function of the Boltzmann equation. Our results reveal the particle and fluid aspects of the equation. The particle aspect is represented by singular waves. These waves are carried by transport equations and dominate the short‐time behavior of the solution. We devise a Picard‐type iteration for the construction of the increasingly regular particle‐like waves. The fluid‐like waves reveal the dissipative behavior of the type of Navier‐Stokes equations as seen usually by the Chapman‐Enskog expansion. These waves are constructed as part of the long waves expansion in the spectrum of the Fourier mode for the space variable. The fluid‐like waves represent the long‐time behavior of the solution. As an application, we obtain the pointwise description of the large‐time behavior of the convergence to the global Maxwellian when the initial perturbation is necessarily smooth. In our analysis of the exchanges of the microscopic velocity decay and space decay, we make essential uses of the hard sphere models. © 2004 Wiley Periodicals, Inc.
Reply to comment on"Exact Solution for Three-Dimensional Ising Model"added by Reply to the Reply by Jacques H.H. Perk

In this reply, I point out that the comment by Jacques H.H. Perk on my paper"Exact Solution for Three-Dimensional Ising Model"[Symmetry 2021, 13, 1837] is wrong because the author seems not to understand the Onsager operators along both {\bf X} and {\bf Y} directions and the relation between them.
Mixed Hierarchy Network for Image Restoration

Image restoration is a long-standing low-level vision problem, e.g., deblurring and deraining. In the process of image restoration, it is necessary to consider not only the spatial details and contextual information of restoration to ensure the quality, but also the system complexity. Although many methods have been able to guarantee the quality of image restoration, the system complexity of the state-of-the-art (SOTA) methods is increasing as well. Motivated by this, we present a mixed hierarchy network that can balance these competing goals. Our main proposal is a mixed hierarchy architecture, that progressively recovers contextual information and spatial details from degraded images while we design intra-blocks to reduce system complexity. Specifically, our model first learns the contextual information using encoder-decoder architectures, and then combines them with high-resolution branches that preserve spatial detail. In order to reduce the system complexity of this architecture for convenient analysis and comparison, we replace or remove the nonlinear activation function with multiplication and use a simple network structure. In addition, we replace spatial convolution with global self-attention for the middle block of encoder-decoder. The resulting tightly interlinked hierarchy architecture, named as MHNet, delivers strong performance gains on several image restoration tasks, including image deraining, and deblurring.
Statistical stability of interval maps with critical points and singularities

We prove strong statistical stability of a large class of one-dimensional maps which may have an arbitrary finite number of discontinuities and of non-degenerate critical points and/or singular points with infinite derivative, and satisfy some expansivity and bounded recurrence conditions. This generalizes known results for maps with critical points and bounded derivatives and in particular proves statistical stability of Lorenz-like maps with critical points and singularities studied in [S. Luzzatto and W. Tucker. Non-uniformly expanding dynamics in maps with singularities and criticalities. Inst. Hautes Etudes Sci. Publ. Math., (89):179-226, 1999]. We introduce a natural metric on the space of maps with discontinuities which does not seem to have been used in the literature before.
Logit-Q Dynamics for Efficient Learning in Stochastic Teams

We present two logit-Q learning dynamics combining the classical and independent log-linear learning updates with an on-policy value iteration update for efficient learning in stochastic games. We show that the logit-Q dynamics presented reach (near) efficient equilibrium in stochastic teams. We quantify a bound on the approximation error. We also show the rationality of the logit-Q dynamics against agents following pure stationary strategies and the convergence of the dynamics in stochastic games where the reward functions induce potential games, yet only a single agent controls the state transitions beyond stochastic teams. The key idea is to approximate the dynamics with a fictional scenario where the Q-function estimates are stationary over finite-length epochs only for analysis. We then couple the dynamics in the main and fictional scenarios to show that these two scenarios become more and more similar across epochs due to the vanishing step size.