Miracle-dz/UniFAR-FTUs huggingface dataset:
Orbital solutions derived from radial velocities and time delays for four Kepler systems with A/F-type (candidate) hybrid pulsators

Context. The presence of A/F-type Kepler hybrid stars extending across the entirety of the δ Scuti – γ Doradus instability strips and beyond remains largely unexplained. In order to better understand these particular stars, we performed a multi-epoch spectroscopic study of a sample of 49 candidate A/F-type hybrid stars and one cool(er) hybrid object detected by the Kepler mission. We determined a lower limit of 27% for the multiplicity fraction. For six spectroscopic systems, we also reported long-term variations in the time delays (TDs). For four systems, the TD variations are fully coherent with those of the radial velocities (RVs ) and can be attributed to orbital motion. Aims. We aim to improve the orbital solutions for those spectroscopic systems with long orbital periods (order of 4–6 years) among the Kepler hybrid stars that we continued to observe. Methods. The orbits are computed based on a simultaneous modelling of the RVs obtained with high-resolution spectrographs and the photometric TDs derived from time-dependent frequency analyses of the Kepler light curves. Results. We refined the orbital solutions of four spectroscopic systems with A/F-type Kepler hybrid component stars: KIC 4480321, 5219533, 8975515, and KIC 9775454. Simultaneous modelling of both data types analysed together enabled us to improve the orbital solutions (all), obtain more robust and accurate information on the mass ratio (some for the first time), and identify the component with the short-period δ Sct-type pulsations (all). The information gained is maximized when one of the components, generally the one exhibiting the δ Sct-type pulsations, is a fast rotator. In several cases, we were also able to derive new constraints for the minimum component masses. From a search for regular frequency patterns in the high-frequency regime of the Fourier transforms of each system, we found no evidence of tidal splitting among the triple systems with close (inner) companions. However, some systems exhibit frequency spacings that can be explained by the mechanism of rotational splitting.
Supervised Transfer Learning at Scale for Medical Imaging

Transfer learning is a standard technique to improve performance on tasks with limited data. However, for medical imaging, the value of transfer learning is less clear. This is likely due to the large domain mismatch between the usual natural-image pre-training (e.g. ImageNet) and medical images. However, recent advances in transfer learning have shown substantial improvements from scale. We investigate whether modern methods can change the fortune of transfer learning for medical imaging. For this, we study the class of large-scale pre-trained networks presented by Kolesnikov et al. on three diverse imaging tasks: chest radiography, mammography, and dermatology. We study both transfer performance and critical properties for the deployment in the medical domain, including: out-of-distribution generalization, data-efficiency, sub-group fairness, and uncertainty estimation. Interestingly, we find that for some of these properties transfer from natural to medical images is indeed extremely effective, but only when performed at sufficient scale.
Effect of leachate recirculation on refuse decomposition rates at landfill site: a case study

In this study, a comparison of methane (CH4) generation rates for two test cells, one operated with (enhanced) and another without leachate recirculation at Odayeri Sanitary Landfill were compared using their bio-chemical methane potentials (BMP). Initial methane potential is approximately 34.5 m³ CH4/wet ton of solid waste. The remaining methane potential for the control (C1) and the enhanced (C2) cells are 32.6 m³ CH4/wet ton and 31.1 m³ CH4/wet ton of refuse after eight months of operation, respectively. The produced CH4 quantities for C1 and C2 after eight months of operation are 1.9 m³ CH4/wet ton and 3.4 m³ CH4/wet ton, respectively. On the other hand, 5.5% and 9.9% of the total potential are generated in eight months. However, the CH4 generation rates for the first year are determined as 2.85 and 5.10 m³/ton/year for C1 and C2 test cells, respectively. Due to the appropriate conditions such as moisture content, solid waste decomposition rate is enhanced at a rate of 79% at C2 test cell relative to C1 test cell. Hence, C2 test cell shows more decomposition relative to the C1 test cell.
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds

In the present work we study classifiers' decision boundaries via Brownian motion processes in ambient data space and associated probabilistic techniques. Intuitively, our ideas correspond to placing a heat source at the decision boundary and observing how effectively the sample points warm up. We are largely motivated by the search for a soft measure that sheds further light on the decision boundary's geometry. En route, we bridge aspects of potential theory and geometric analysis (Mazya, 2011, Grigoryan-Saloff-Coste, 2002) with active fields of ML research such as adversarial examples and generalization bounds. First, we focus on the geometric behavior of decision boundaries in the light of adversarial attack/defense mechanisms. Experimentally, we observe a certain capacitory trend over different adversarial defense strategies: decision boundaries locally become flatter as measured by isoperimetric inequalities (Ford et al, 2019); however, our more sensitive heat-diffusion metrics extend this analysis and further reveal that some non-trivial geometry invisible to plain distance-based methods is still preserved. Intuitively, we provide evidence that the decision boundaries nevertheless retain many persistent"wiggly and fuzzy"regions on a finer scale. Second, we show how Brownian hitting probabilities translate to soft generalization bounds which are in turn connected to compression and noise stability (Arora et al, 2018), and these bounds are significantly stronger if the decision boundary has controlled geometric features.
Solovay's Relative Consistency Proof for FIM and BI

In 2002 Robert Solovay proved that a subsystem BI of classical second order arithmetic, with bar induction and arithmetical countable choice, can be negatively interpreted in the neutral subsystem BSK of Kleene's intuitionistic analysis FIM using Markov's Principle MP. Combining this result with Kleene's formalized recursive realizability, he established (in primitive recursive arithmetic PRA) that FIM + MP and BI have the same consistency strength. This historical note includes Solovay's original proof, with his permission, and the additional observation that Markov's Principle can be weakened to a double negation shift axiom consistent with Brouwer's creating subject counterexamples.
Odd-frequency pair density wave in the Kitaev-Kondo lattice model

We investigate the properties of the Kitaev-Kondo lattice model deﬁned on a bilayer honeycomb lattice by means of the SO(3) Majorana representation for spin-1 / 2 moments. We ﬁrst consider the pairing of neighboring sites for the parent Kitaev spin liquid (KSL) Hamiltonian to render the Majorana and the spin-1 / 2 Hilbert spaces perfectly equivalent to each other. As a consequence, we demonstrate that this decoupling of the Kitaev interaction in terms of the SO(3) Majorana fermions reproduces exactly the spectrum of the KSL model alone. Then, by considering the eﬀect of a local Kondo coupling J K in the model and decoupling it in terms of an order parameter that physically must have a ﬁnite staggering phase, we obtain that the system undergoes a quantum phase transition from a fractionalized Fermi liquid to a nematic triplet superconducting (SC) phase as J K is increased. Depending on the model parameters, this SC phase can exhibit either Dirac points, Bogoliubov-Fermi lines, or Bogoliubov-Fermi surfaces as nodal bulk manifolds. The surface states in this latter case are also characterized by topologically protected antichiral edge modes. The SC phase breaks time-reversal symmetry and exhibits a coexistence of a dominant odd-frequency pairing with a small even-frequency component for electronic excitations localized on sites of the same sublattice of the system. Finally, we show that this SC phase is in fact a pair-density-wave state, with Cooper pairs possessing a ﬁnite center-of-mass momentum in zero magnetic ﬁeld.
Atmospheric plasma-driven catalysis for the low temperature decomposition of dilute aromatic compounds

The decomposition of volatile organic compounds (VOCs)—six aromatic compounds of benzene derivatives and formic acid—was investigated using a plasma-driven catalysis (PDC) system at atmospheric pressure. In the PDC reactor, the decomposition efficiency of VOCs was mostly determined by the specific input energy (SIE) and insensitivity to the gas hourly space velocity from 11 000 to 55 000 h−1. Formic acid (HCOOH) was formed as a common intermediate from the decomposition of the tested aromatic compounds. Formic acid was also found to be an important intermediate for CO2 formation. Except for styrene, all the tested VOCs indicated zero-order kinetics, which confirm the dominant role of the catalytic reaction in the decomposition of VOCs using the PDC reactor. A simple kinetic model represents well the observed zero-order kinetics with respect to the SIE. Unlike conventional plasma reactors, no correlation between the ionization potential and the decomposition was found with the PDC reactor. Continuous operation tests indicated stable performance without deterioration of catalytic activity over 150 h.
Data Impressions: Mining Deep Models to Extract Samples for Data-free Applications

—Pretrained deep models hold their learnt knowledge in the form of the model parameters. These parameters act as memory for the trained models and help them generalize well on unseen data. However, in absence of training data, the utility of a trained model is merely limited to either inference or better initialization towards a target task. In this paper, we go further and extract synthetic data by leveraging the learnt model parameters. We dub them Data Impressions , which act as proxy to the training data and can be used to realize a variety of tasks. These are useful in scenarios where only the pretrained models are available and the training data is not shared (e.g., due to privacy or sensitivity concerns). We show the applicability of data impressions in solving several computer vision tasks such as unsupervised domain adaptation, continual learning as well as knowledge distillation. We also study the adversarial robustness of the lightweight models trained via knowledge distillation using these data impressions. Further, we demonstrate the efﬁcacy of data impressions in generating UAPs with better fooling rates. Extensive experiments performed on several benchmark datasets demonstrate competitive performance achieved using data impressions in absence of the original training data.
Dependable Distributed Nonconvex Optimization via Polynomial Approximation

—There has been work on exploiting polynomial approximation to solve distributed nonconvex optimization problems involving univariate objectives. This idea facilitates arbitrarily precise global optimization without requiring local evaluations of gradients at every iteration. Nonetheless, there remains a gap between existing theoretical guarantees and diverse practical requirements for dependability, notably privacy preservation and robustness to network imperfections (e.g., time-varying directed communication and asynchrony). To ﬁll this gap and keep the above strengths, we propose a Dependable Chebyshev-Proxy-based distributed Optimization Algorithm (D-CPOA). Speciﬁ-cally, to ensure both accuracy of solutions and privacy of local objectives, a new privacy-preserving mechanism is designed. This mechanism leverages the randomness in blockwise insertions of perturbed vector states and hence provides an improved privacy guarantee compared to the literature in terms of ( α, β )-data- privacy. Furthermore, to gain robustness to various network imperfections, we use the push-sum consensus protocol as a backbone, discuss its speciﬁc enhancements, and evaluate the performance of the proposed algorithm accordingly. Thanks to the linear consensus-based structure of iterations, we avoid the privacy-accuracy trade-off and the bother of selecting appropriate step-sizes in different settings. We provide rigorous analysis of the accuracy, dependability and complexity. It is shown that the advantages brought by the idea of polynomial approximation are maintained when all the above requirements exist. Simulations demonstrate the effectiveness of the developed algorithm.
STENCIL-NET: Data-driven solution-adaptive discretization of partial differential equations

Numerical methods for approximately solving partial differential equations (PDE) are at the core of scientific computing. Often, this requires high-resolution or adaptive discretization grids to capture relevant spatio-temporal features in the PDE solution, e.g., in applications like turbulence, combustion, and shock propagation. Numerical approximation also requires knowing the PDE in order to construct problem-specific discretizations. Systematically deriving such solution-adaptive discrete operators, however, is a current challenge. Here we present STENCIL-NET, an artificial neural network architecture for data-driven learning of problem- and resolution-specific local discretizations of nonlinear PDEs. STENCIL-NET achieves numerically stable discretization of the operators in an unknown nonlinear PDE by spatially and temporally adaptive parametric pooling on regular Cartesian grids, and by incorporating knowledge about discrete time integration. Knowing the actual PDE is not necessary, as solution data is sufficient to train the network to learn the discrete operators. A once-trained STENCIL-NET model can be used to predict solutions of the PDE on larger spatial domains and for longer times than it was trained for, hence addressing the problem of PDE-constrained extrapolation from data. To support this claim, we present numerical experiments on long-term forecasting of chaotic PDE solutions on coarse spatio-temporal grids. We also quantify the speed-up achieved by substituting base-line numerical methods with equation-free STENCIL-NET predictions on coarser grids with little compromise on accuracy.
Affordance-based Reinforcement Learning for Urban Driving

Traditional autonomous vehicle pipelines that follow a modular approach have been very successful in the past both in academia and industry, which has led to autonomy deployed on road. Though this approach provides ease of interpretation, its generalizability to unseen environments is limited and hand-engineering of numerous parameters is required, especially in the prediction and planning systems. Recently, deep reinforcement learning has been shown to learn complex strategic games and perform challenging robotic tasks, which provides an appealing framework for learning to drive. In this work, we propose a deep reinforcement learning framework to learn optimal control policy using waypoints and low-dimensional visual representations, also known as affordances. We demonstrate that our agents when trained from scratch learn the tasks of lane-following, driving around inter-sections as well as stopping in front of other actors or traffic lights even in the dense traffic setting. We note that our method achieves comparable or better performance than the baseline methods on the original and NoCrash benchmarks on the CARLA simulator.
Private Tabular Survey Data Products through Synthetic Microdata Generation

We propose two synthetic microdata approaches to generate private tabular survey data products for public release. We adapt a pseudo posterior mechanism that downweights by-record likelihood contributions with weights $\in [0,1]$ based on their identification disclosure risks to producing tabular products for survey data. Our method applied to an observed survey database achieves an asymptotic global probabilistic differential privacy guarantee. Our two approaches synthesize the observed sample distribution of the outcome and survey weights, jointly, such that both quantities together possess a privacy guarantee. The privacy-protected outcome and survey weights are used to construct tabular cell estimates (where the cell inclusion indicators are treated as known and public) and associated standard errors to correct for survey sampling bias. Through a real data application to the Survey of Doctorate Recipients public use file and simulation studies motivated by the application, we demonstrate that our two microdata synthesis approaches to construct tabular products provide superior utility preservation as compared to the additive-noise approach of the Laplace Mechanism. Moreover, our approaches allow the release of microdata to the public, enabling additional analyses at no extra privacy cost.
Best approximations, distance formulas and orthogonality in C*-algebras

For a unital C∗-algebra A and a subspace B of A, a characterization for a best approximation to an element of A in B is obtained. As an application, a formula for the distance of an element of A from B has been obtained, when a best approximation of that element to B exists. Further, a characterization for Birkhoff-James orthogonality of an element of a Hilbert C∗-module to a subspace is obtained.
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks. In this paper, we introduce a simple model-free algorithm, Randomized Ensembled Double Q-Learning (REDQ), and show that its performance is just as good as, if not better than, a state-of-the-art model-based algorithm for the MuJoCo benchmark. Moreover, REDQ can achieve this performance using fewer parameters than the model-based method, and with less wall-clock run time. REDQ has three carefully integrated ingredients which allow it to achieve its high performance: (i) a UTD ratio>>1; (ii) an ensemble of Q functions; (iii) in-target minimization across a random subset of Q functions from the ensemble. Through carefully designed experiments, we provide a detailed analysis of REDQ and related model-free algorithms. To our knowledge, REDQ is the first successful model-free DRL algorithm for continuous-action spaces using a UTD ratio>>1.
Data-Driven Controller Design via Finite-Horizon Dissipativity

Given one open-loop measured trajectory of a single-input single-output discrete-time linear time-invariant system, we present a framework for data-driven controller design for closed-loop finite-horizon dissipativity. First, we parametrize all closed-loop trajectories using the given data of the plant and a model of the controller. We then provide an approach to validate the controller by verifying closed-loop dissipativity in the standard feedback loop based on this parametrization. We use these conditions to design controllers leading to closed-loop dissipativity based on a quadratic matrix inequality feasibility problem. Finally, the results are illustrated with a simulation example.
Counting paths, cycles, and blow‐ups in planar graphs

For a planar graph H $H$ , let N P(n , H ) ${{\bf{N}}}_{{\mathscr{P}}}(n,H)$ denote the maximum number of copies of H $H$ in an n $n$ ‐vertex planar graph. In this paper, we prove that N P(n , P 7 ) ~ 4 27 n 4 ${{\bf{N}}}_{{\mathscr{P}}}(n,{P}_{7})\unicode{x0007E}\frac{4}{27}{n}^{4}$ , N P(n , C 6 ) ~ (n ∕ 3 ) 3 ${{\bf{N}}}_{{\mathscr{P}}}(n,{C}_{6})\unicode{x0007E}{(n\unicode{x02215}3)}^{3}$ , N P(n , C 8 ) ~ (n ∕ 4 ) 4 ${{\bf{N}}}_{{\mathscr{P}}}(n,{C}_{8})\unicode{x0007E}{(n\unicode{x02215}4)}^{4}$ , and N P(n , K 4{ 1 } ) ~ (n ∕ 6 ) 6 ${{\bf{N}}}_{{\mathscr{P}}}(n,{K}_{4}\{1\})\,\unicode{x0007E}\,{(n\unicode{x02215}6)}^{6}$ , where K 4{ 1 } ${K}_{4}\{1\}$ is the 1‐subdivision of K 4 ${K}_{4}$ . In addition, we obtain significantly improved upper bounds on N P(n , P2 m + 1 ) ${{\bf{N}}}_{{\mathscr{P}}}(n,{P}_{2m+1})$ and N P(n , C2 m ) ${{\bf{N}}}_{{\mathscr{P}}}(n,{C}_{2m})$ for m ≥ 4 $m\ge 4$ . For a wide class of graphs H $H$ , the key technique developed in this paper allows us to bound N P(n , H ) ${{\bf{N}}}_{{\mathscr{P}}}(n,H)$ in terms of an optimization problem over weighted graphs.
A Novel Prediction Approach for Exploring PM2.5 Spatiotemporal Propagation Based on Convolutional Recursive Neural Networks

The spread of PM2.5 pollutants that endanger health is difficult to predict because it involves many atmospheric variables. These micron particles can spread rapidly from their source to residential areas, increasing the risk of respiratory disease if exposed for long periods. The prediction system of PM2.5 propagation provides more detailed and accurate information as an early warning system to reduce health impacts on the community. According to the idea of transformative computing, the approach we propose in this paper allows computation on the dataset obtained from massive-scale PM2.5 sensor nodes via wireless sensor network. In the scheme, the deep learning model is implemented on the server nodes to extract spatiotemporal features on these datasets. This research was conducted by using dataset of air quality monitoring systems in Taiwan. This study presents a new model based on the convolutional recursive neural network to generate the prediction map. In general, the model is able to provide accurate predictive results by considering the bonds among measurement nodes in both spatially and temporally. Therefore, the particulate pollutant propagation of PM2.5 could be precisely monitored by using the model we propose in this paper.
Foliations by curves on threefolds

We study the conormal sheaves and singular schemes of one‐dimensional foliations on smooth projective varieties X of dimension 3 and Picard rank 1. We prove that if the singular scheme has dimension 0, then the conormal sheaf is μ‐stable whenever the tangent bundle TX$TX$ is stable, and apply this fact to the characterization of certain irreducible components of the moduli space of rank 2 reflexive sheaves on P3$\mathbb {P}^3$ and on a smooth quadric hypersurface Q3⊂P4$Q_3\subset \mathbb {P}^4$ . Finally, we give a classification of local complete intersection foliations, that is, foliations with locally free conormal sheaves, of degree 0 and 1 on Q3.
“Killing Me” Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism

Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites. Although dependency relations between context words are important for detecting spoilers, current attention-based spoiler detection models are insufficient for utilizing dependency relations. To address this problem, we propose a new spoiler detection model called SDGNN that is based on syntax-aware graph neural networks. In the experiments on two real-world benchmark datasets, we show that our SDGNN outperforms the existing spoiler detection models.