Miracle-dz/UniFAR-FTUs huggingface dataset:
Implicit vs Unfolded Graph Neural Networks

It has been observed that graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient modeling long-range dependencies across nodes while avoiding unintended consequences such oversmoothed node representations or sensitivity to spurious edges. To address this issue (among other things), two separate strategies have recently been proposed, namely implicit and unfolded GNNs. The former treats node representations as the fixed points of a deep equilibrium model that can efficiently facilitate arbitrary implicit propagation across the graph with a fixed memory footprint. In contrast, the latter involves treating graph propagation as unfolded descent iterations as applied to some graph-regularized energy function. While motivated differently, in this paper we carefully quantify explicit situations where the solutions they produce are equivalent and others where their properties sharply diverge. This includes the analysis of convergence, representational capacity, and interpretability. In support of this analysis, we also provide empirical head-to-head comparisons across multiple synthetic and public real-world node classification benchmarks. These results indicate that while IGNN is substantially more memory-efficient, UGNN models support unique, integrated graph attention mechanisms and propagation rules that can achieve SOTA node classification accuracy across disparate regimes such as adversarially-perturbed graphs, graphs with heterophily, and graphs involving long-range dependencies.
CAHA/PPAK Integral-field Spectroscopic Observations of M81. I. Circumnuclear Ionized Gas

Galactic circumnuclear environments of nearby galaxies provide unique opportunities for our understanding of the coevolution between supermassive black holes and their host galaxies. Here, we present a detailed study of ionized gas in the central kiloparsec region of M81, which hosts the closest prototype low-luminosity active galactic nucleus, based on optical integral-field spectroscopic observations taken with the CAHA 3.5 m telescope. It is found that much of the circumnuclear ionized gas is concentrated within a bright core of ∼200 pc in extent and a surrounding spiral-like structure known as the nuclear spiral. The total mass of the ionized gas is estimated to be ∼2 × 105 M ⊙, which corresponds to a few percent of the cold gas mass in this region, as traced by co-spatial dust extinction features. Plausible signature of a biconical outflow along the disk plane is suggested by a pair of blueshifted/redshifted low-velocity features, symmetrically located at ∼120–250 pc from the nucleus. The spatially resolved line ratios of [N ii]/Hα and [O iii]/Hβ demonstrate that much of the circumnuclear region can be classified as a LINER. However, substantial spatial variations in the line intensities and line ratios strongly suggest that different ionization/excitation mechanisms, rather than just a central dominant source of photoionization, are simultaneously at work to produce the observed line signatures.
Fast Flux Test Facility core restraint system performance

Characterizing Fast Flux Test Facility (FFTF) core restraint system performance has been ongoing since the first operating cycle. Characterization consists of prerun analysis for each core load, in-reactor and postirradiation measurements of subassembly withdrawal loads and deformations, and using measurement data to fine tune predictive models. Monitoring FFTF operations and performing trend analysis has made it possible to gain insight into core restraint system performance and head off refueling difficulties while maximizing component lifetimes. Additionally, valuable information for improved designs and operating methods has been obtained. Focus is on past operating experience, emphasizing performance improvements and avoidance of potential problems. 4 refs., 12 figs., 2 tabs.
Can Air Pollution Save Lives? Air Quality and Risky Behaviors on Roads

Air pollution has been linked to elevated levels of risk aversion. This paper provides the first evidence showing that such effect reduces life-threatening risky behaviors. We study the impact of air pollution on traffic accidents caused by risky driving behaviors, using the universe of accident records and high-resolution air quality data of Taiwan from 2009 to 2015. We find that air pollution significantly decreases accidents caused by driver violations, and that this effect is nonlinear. In addition, our results suggest that air pollution primarily reduces road users’ risky behaviors through visual channels rather than through the respiratory system.
FIXP-membership via Convex Optimization: Games, Cakes, and Markets

We introduce a new technique for proving membership of problems in FIXP – the class capturing the complexity of computing a fixed-point of an algebraic circuit. Our technique constructs a “pseudogate” which can be used as a black box when building FIXP circuits. This pseudogate, which we term the “OPT-gate”, can solve most convex optimization problems. Using the OPT-gate, we prove new FIXP-membership results, and we generalize and simplify several known results from the literature on fair division, game theory and competitive markets. In particular, we prove complexity results for two classic problems: computing a market equilibrium in the Arrow-Debreu model with general concave utilities is in FIXP, and computing an envy-free division of a cake with general valuations is FIXP-complete. We further showcase the wide applicability of our technique, by using it to obtain simplified proofs and extensions of known FIXP-membership results for equilibrium computation for various types of strategic games, as well as the pseudomarket mechanism of Hylland and Zeckhauser.
Determining the gluon condensate with DIS experiments at small Bjorken parameter regime

Using gauge/gravity duality we study deep inelastic scattering (DIS) of a lepton with a proton target in presence of gluon condensation. We adopt a modified AdS5 background in which the modification parameter c corresponds to the gluon condensation in the boundary theory. Firstly we study electromagnetic field in such a scattering. Our results show that presence of c strengthen magnitude of electromagnetic field. In the next step, we find baryonic states wave function equations in which mass of the proton target demands contribution of only small values of c. As our main aim is determining value of this parameter, we find c = 0.0120± 0.0005GeV from experimental data. Proceeding by electromagnetic field and baryonic states, we derive the holographic interaction action that is related to amplitude of the scattering. Eventually we compute the corresponding structure functions as functions of Bjorken parameter x and the momentum lepton transfers q numerically. Comparing Jlab Hall C data with our theoretical results at small values of x, we find that at regime of small Bjorken parameter there is a good agreement between theory and phenomenology. E-mail: s.tahery@impcas.ac. cn wangxiaopeng@impcas.ac. cn kouwei@impcas.ac. cn E-mail:xchen@impcas.ac. cn
Competitive epidemic networks with multiple survival-of-the-fittest outcomes

We use a deterministic model to study two competing viruses spreading over a two-layer network in the Susceptible--Infected--Susceptible (SIS) framework, and address a central problem of identifying the winning virus in a"survival-of-the-fittest"battle. Existing sufficient conditions ensure that the same virus always wins regardless of initial states. For networks with an arbitrary but finite number of nodes, there exists a necessary and sufficient condition that guarantees local exponential stability of the two equilibria corresponding to each virus winning the battle, meaning that either of the viruses can win, depending on the initial states. However, establishing existence and finding examples of networks with more than three nodes that satisfy such a condition has remained unaddressed. In this paper, we prove that, for any arbitrary number of nodes, such networks exist. We do this by proving that given almost any network layer of one virus, there exists a network layer for the other virus such that the resulting two-layer network satisfies the aforementioned condition. To operationalize our findings, a four-step procedure is developed to reliably and consistently design one of the network layers, when given the other layer. Conclusions from numerical case studies, including a real-world mobility network that captures the commuting patterns for people between $107$ provinces in Italy, extend on the theoretical result and its consequences.
A Note On Complex Spacetime Metrics

For various reasons, it seems necessary to include complex saddle points in the"Euclidean"path integral of General Relativity. But some sort of restriction on the allowed complex saddle points is needed to avoid various unphysical examples. In this article, a speculative proposal is made concerning a possible restriction on the allowed saddle points in the gravitational path integral. The proposal is motivated by recent work of Kontsevich and Segal on complex metrics in quantum field theory, and earlier work of Louko and Sorkin on topology change from a real time point of view.
A non-local spectral transfer model and new scaling law for scalar turbulence

Abstract In this study, we revisit the spectral transfer model for the turbulent intensity in passive scalar transport (under large-scale anisotropic forcing), and a subsequent modification to the scaling of scalar variance cascade is presented. From the modified spectral transfer model, we obtain a revised scalar transport model using a fractional-order Laplacian operator that facilitates the robust inclusion of the non-local effects originating from large-scale anisotropy transferred across the multitude of scales in the turbulent cascade. We provide an a priori estimate for the non-local model based on the scaling analysis of the scalar spectrum, and later examine our developed model through direct numerical simulation. We present a detailed analysis on the evolution of the scalar variance, high-order statistics of the scalar gradient and important two-point statistical metrics of the turbulent transport to make a comprehensive comparison between the non-local model and its standard version. Finally, we present an analysis that seamlessly reconciles the similarities between the developed model with the fractional-order subgrid-scale scalar flux model for large-eddy simulation (Akhavan-Safaei et al., J. Comput. Phys., vol. 446, 2021, 110571) when the filter scale approaches the dissipative scales of turbulent transport. In order to perform this task, we employ a Gaussian process regression model to predict the model coefficient for the fractional-order subgrid model.
Intelligent Adaptive Interfaces: Summary Report on Design, Development, and Evaluation of Intelligent Adaptive Interfaces for the Control of Multiple UAVs from an Airborne Platform

Abstract : An absence of guidance on designing complex, dynamic, and networked systems presents challenges to the design of such systems to maximize overall human-machine system performance. An Intelligent Adaptive Interface (IAI) concept and associated technologies have been developed to address this problem. A typical IAI is driven by software agents that can change the display and /or control characteristics to react to the changes of mission and operator states in real time. The work reported here is the result of the two final phases of a three-year project conducted by DRDC Toronto. This project investigated the efficacy of IAIs in a multi-Uninhabited Aerial Vehicle (UAV) scenario. The IAI was modelled as part of the UAV tactical workstations found in a maritime patrol aircraft. In the first phase of the project, a performance model was developed to compare the difference in mission activities with and without IAI agent aids. The simulation results revealed that the control of multiple UAVs is a cognitively complex task with high workload. With the augmentation of automation agents, operators could continue working under high time pressure, resulting in critical tasks being achieved in reduced time. To further test the effectiveness of IAIs and validate the simulation results, a prototype IAI multiagent experimental environment was implemented for an empirical study. Six IAI agent function groups have been integrated into the UAV operator interfaces. Operator's performance was examined with and without IAIs under three different workload conditions. The results from both objective and subjective measures verified the findings of the simulation research. IAIs facilitated a significant reduction in workload and an improvement in situation awareness. This research also developed preliminary guidance on designing IAI systems.
Knots, perturbative series and quantum modularity

We introduce an invariant of a hyperbolic knot which is a map $\alpha\mapsto \mathbf{Phi}_\a(h)$ from $\mathbb{Q}/\mathbb{Z}$ to matrices with entries in $\overline{\mathbb{Q}}[[h]]$ and with rows and columns indexed by the boundary parabolic $SL_2(\mathbb{C})$ representations of the fundamental group of the knot. These matrix invariants have a rich structure: (a) their $(\sigma_0,\sigma_1)$ entry, where $\sigma_0$ is the trivial and $\sigma_1$ the geometric representation, is the power series expansion of the Kashaev invariant of the knot around the root of unity $e^{2 \pi i \alpha}$ as an element of the Habiro ring, and the remaining entries belong to generalized Habiro rings of number fields; (b) the first column is given by the perturbative power series of Dimofte--Garoufalidis; (c)~the columns of $\mathbf{Phi}$ are fundamental solutions of a linear $q$-difference equation; (d)~the matrix defines an $SL_2(\mathbb{Z})$-cocycle $W_{\gamma}$ in matrix-valued functions on $\mathbb{Q}$ that conjecturally extends to a smooth function on $\mathbb{R}$ and even to holomorphic functions on suitable complex cut planes, lifting the factorially divergent series $\mathbf{Phi}(h)$ to actual functions. The two invariants $\mathbf{Phi}$ and $W_{\gamma}$ are related by a refined quantum modularity conjecture which we illustrate in detail for the three simplest hyperbolic knots, the $4_1$, $5_2$ and $(-2,3,7)$ pretzel knots. This paper has two sequels, one giving a different realization of our invariant as a matrix of convergent $q$-series with integer coefficients and the other studying its Habiro-like arithmetic properties in more depth.
Calibration and Scale Performance of Bushland Weighing Lysimeters

Weighing lysimetry is the primary method to directly measure evapotranspiration, and the scale performance of weighing lysimeters is often affected by wind loading. This study was conducted to calibrate the weighing lysimeters at Bushland, Texas, and to determine the effects of wind on the measurement accuracy of the lysimeter scales. Applied mass amounts equivalent to 150% of the lysimeter range were applied, and lysimeter scale calibrations were determined. Wind influences were measured by covering the lysimeters with a rubber sheet to minimize evaporation during an extended period. The lysimeters were sensitive to mass changes as small as 0.05 mm (450 g), highly linear with less than 1% total error over the 250-mm range (2.25 Mg), insensitive to load distribution on the lysimeter surface, and sensitive to surface pressures created by wind loading. The effects of wind can be minimized with data smoothing but not eliminated. The USDA-ARS weighing lysimeters at Bushland, Texas, have evapotranspiration measurement accuracy necessary to determine evapotranspiration rates as small as 0.05 to 0.1 mm/h over time periods of 30-min or greater.
Self-supervised GAN Detector

Although the recent advancement in generative models brings diverse advantages to society, it can also be abused with malicious purposes, such as fraud, defamation, and fake news. To prevent such cases, vigorous research is conducted to distinguish the generated images from the real images, but challenges still remain to distinguish the unseen generated images outside of the training settings. Such limitations occur due to data dependency arising from the model's overfitting issue to the training data generated by specific GANs. To overcome this issue, we adopt a self-supervised scheme to propose a novel framework. Our proposed method is composed of the artificial fingerprint generator reconstructing the high-quality artificial fingerprints of GAN images for detailed analysis, and the GAN detector distinguishing GAN images by learning the reconstructed artificial fingerprints. To improve the generalization of the artificial fingerprint generator, we build multiple autoencoders with different numbers of upconvolution layers. With numerous ablation studies, the robust generalization of our method is validated by outperforming the generalization of the previous state-of-the-art algorithms, even without utilizing the GAN images of the training dataset.
Densified Pupil Spectrograph as High-precision Radial Velocimetry: From Direct Measurement of the Universe’s Expansion History to Characterization of Nearby Habitable Planet Candidates

The direct measurement of the universe’s expansion history and the search for terrestrial planets in habitable zones around solar-type stars require extremely high-precision radial-velocity measures over a decade. This study proposes an approach for enabling high-precision radial-velocity measurements from space. The concept presents a combination of a high-dispersion densified pupil spectrograph and a novel line-of-sight monitor for telescopes. The precision of the radial-velocity measurements is determined by combining the spectrophotometric accuracy and the quality of the absorption lines in the recorded spectrum. Therefore, a highly dispersive densified pupil spectrograph proposed to perform stable spectroscopy can be utilized for high-precision radial-velocity measures. A concept involving the telescope’s line-of-sight monitor is developed to minimize the change of the telescope’s line of sight over a decade. This monitor allows the precise measurement of long-term telescope drift without any significant impact on the Airy disk when the densified pupil spectra are recorded. We analytically derive the uncertainty of the radial-velocity measurements, which is caused by the residual offset of the lines of sight at two epochs. We find that the error could be reduced down to approximately 1 cm s−1, and the precision will be limited by another factor (e.g., wavelength calibration uncertainty). A combination of the high-precision spectrophotometry and the high spectral resolving power could open a new path toward the characterization of nearby non-transiting habitable planet candidates orbiting late-type stars. We present two simple and compact highly dispersed densified pupil spectrograph designs for cosmology and exoplanet sciences.
Invariance of multifractal spectrum of uniform self-affine measures and its applications

We study the bi-Lipschitz classification of Bedford-McMullen carpets which are totally disconnected. Let E be a such carpet and let μE be the uniform Bernoulli measure on E. We show that the multifractal spectrum and the doubling property of μE are both invariant under a bi-Lipschitz map. Moreover, we show that if μE and μF are doubling, then a bi-Lipschitz map between E and F enjoys a certain measure preserving property.
An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives

In this paper, we propose a practical online method for solving a class of distributionally robust optimization (DRO) with non-convex objectives, which has important applications in machine learning for improving the robustness of neural networks. In the literature, most methods for solving DRO are based on stochastic primal-dual methods. However, primal-dual methods for DRO suffer from several drawbacks: (1) manipulating a high-dimensional dual variable corresponding to the size of data is time expensive; (2) they are not friendly to online learning where data is coming sequentially. To address these issues, we consider a class of DRO with an KL divergence regularization on the dual variables, transform the min-max problem into a compositional minimization problem, and propose practical duality-free online stochastic methods without requiring a large mini-batch size. We establish the state-of-the-art complexities of the proposed methods with and without a Polyak-\L ojasiewicz (PL) condition of the objective. Empirical studies on large-scale deep learning tasks (i) demonstrate that our method can speed up the training by more than 2 times than baseline methods and save days of training time on a large-scale dataset with $\sim$ 265K images, and (ii) verify the supreme performance of DRO over Empirical Risk Minimization (ERM) on imbalanced datasets. Of independent interest, the proposed method can be also used for solving a family of stochastic compositional problems with state-of-the-art complexities.
COMVC-19: A Program to protect healthcare workers’ mental health during the COVID-19 Pandemic. What we have learned

OBJECTIVE: In 2020, the COVID-19 pandemic brought a work and stress overload to healthcare workers, increasing their vulnerability to mental health impairments. In response, the authors created the COMVC-19 program. The program offered preventive actions and mental health treatment for the 22,000 workers of The Hospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP). This paper aims to describe its implementation and share what we have learned from this experience. METHODS: Workers were able to easily access the program through a 24/7 hotline. Additionally, a mobile phone app that screened for signs and symptoms of emotional distress and offered psychoeducation and/or referral to treatment was made available. Data from both these sources as well as any subsequent psychiatric evaluations were collected. RESULTS: The first 20 weeks of our project revealed that most participants were female, and part of the nursing staff working directly with COVID-19 patients. The most frequently reported symptoms were: anxiety, depression and sleep disturbances. The most common diagnoses were Adjustment, Anxiety, and Mood disorders. CONCLUSIONS: Implementing a mental health program in a multimodal intervention was feasible in a major quaternary public hospital. Our data also suggests that preventive actions should primarily be aimed at anxiety and depression symptoms, with a particular focus on the nursing staff.
2021 Focused Update Consensus Guidelines of the Asia Pacific Heart Rhythm Society on Stroke Prevention in Atrial Fibrillation: Executive Summary

Abstract The consensus of the Asia Pacific Heart Rhythm Society (APHRS) on stroke prevention in atrial fibrillation (AF) has been published in 2017 which provided useful clinical guidance for cardiologists, neurologists, geriatricians, and general practitioners in the Asia-Pacific region. In these years, many important new data regarding stroke prevention in AF were reported. The practice guidelines subcommittee members comprehensively reviewed updated information on stroke prevention in AF, and summarized them in this 2021 focused update of the 2017 consensus guidelines of the APHRS on stroke prevention in AF. We highlighted and focused on several issues, including the importance of the AF Better Care pathway, the advantages of non-vitamin K antagonist oral anticoagulants (NOACs) for Asians, the considerations of use of NOACs for Asian AF patients with single one stroke risk factor beyond gender, the role of lifestyle factors on stroke risk, the use of oral anticoagulants during the “coronavirus disease 2019” pandemic, etc. We fully realize that there are gaps, unaddressed questions, and many areas of uncertainty and debate in the current knowledge of AF, and the physician's decision remains the most important factor in the management of AF.