Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of Equatorial Pacific Sea Surface Temperature Anomaly Experiments in General Circulation Models with and without Mountains

Abstract Perpetual January experiments have been performed using versions of the NCAR Community Climate Model (CCM) with and without mountains. Features of the mean simulations of the “no mountains” experiment are compared with those of the standard CCM, the “mountains” experiment. The stationary waves in the “no mountains” experiment have smaller amplitudes than those in the “mountains” case, especially for zonal wavenumbers 2 and 3. The mean zonal wind in the “no mountains” case also has weaker horizontal gradients than in the “mountains” case. The response of these two versions of the CCM to equatorial Pacific sea surface temperature (SST) anomalies is investigated. Two anomalies are considered for each model configuration. Differences in the responses of the two models to the same anomalous forcing are discussed. The Northern Hemisphere midlatitude response of the “no mountains” model has nearly the same spatial scale as that of the “mountains” model, but details of the shape of the response pattern a.
Size of exceptional sets in weakly mixing systems

A BSTRACT . For any weakly mixing system ( X, B , µ, T ) and A, B ∈ B , there is a density zero set J A,B such that µ ( A ∩ T − n B ) converges to µ ( A ) µ ( B ) for n / ∈ J . In this paper, we study bounds on the size of this exceptional set J . First, we show that, given the rate of weak mixing, we can ﬁnd an upper bound on the size of J . We use this property to show the existence of an exceptional set J of an interval exchange system with either | J ∩ [0 , n ] | ≤ Cn log − a n or | J ∩ [0 , n ] | ≤ Cn 1 − α , depending on whether or not the transformation is of rotation class. We also explicitly construct an exceptional set J for the Chacon transformation and give upper and lower bounds for its size. More precisely, we show that there is a constant C > 0 such that for any increasing function h : R + → R + diverging to inﬁnity, there is an exceptional set J ⊆ N such that | J ∩ [0 , n ] | ≤ Cf ( n ) for every Lebesgue measurable sets A, B ⊆ [0 , 1] , where f ( n ) = (log n ) (loglog n ) 2 h ( n ) . For the lower bound, we prove that for any t > 0 , there exists Lebesgue measurable sets A, B ⊆ [0 , 1] and C, N > 0 such that | J A,B ∩ [0 , n ] | ≥ C (log n ) t for every n > N .
Conditional-not Displacement: Fast Multioscillator Control with a Single Qubit

Bosonic encoding is an approach for quantum information processing, promising lower hardware overhead by encoding in the many levels of a harmonic oscillator mode. Scaling to multiple modes requires weak interaction for independent control, yet strong interaction for fast control. Applying fast and efficient universal control on multiple modes remains an open problem. We show that universal control of multiple modes can be obtained with displacements of the different modes conditioned only on a single qubit ancilla. We develop the conditional not displacement control method which enables fast generation and control of bosonic states in multi-mode systems weakly coupled to a single ancilla qubit. Our method is fast despite the weak ancilla coupling. The weak coupling in turn allows for excellent separability and thus independent control. We demonstrate our control on a superconducting transmon qubit weakly coupled to a multi-mode superconducting cavity. We create both entangled and separable cat-states in different modes of the multi-mode cavity, showing entangling operations while control of each mode is independent of the state of the other mode. We show that the operation time is not limited by the inverse of the coupling rate, which is the typical timescale, and we exceed it by almost 2 orders of magnitude. Our scheme allows for fast and efficient multi-mode bosonic encoding and measurement.
A mixed FEM for the coupled Brinkman-Forchheimer/Darcy problem

This paper develops the a priori analysis of a mixed finite element method for the filtration of an incompressible fluid through a non-deformable saturated porous medium with heterogeneous permeability. Flows are governed by the Brinkman--Forchheimer and Darcy equations in the more and less permeable regions, respectively, and the corresponding transmission conditions are given by mass conservation and continuity of momentum. We consider the standard mixed formulation in the Brinkman--Forchheimer domain and the dual-mixed one in the Darcy region, and we impose the continuity of the normal velocities by introducing suitable Lagrange multiplier. The finite element discretization involves Bernardi--Raugel and Raviart--Thomas elements for the velocities, piecewise constants for the pressures, and continuous piecewise linear elements for the Lagrange multiplier. Stability, convergence, and a priori error estimates for the associated Galerkin scheme are obtained. Numerical tests illustrate the theoretical results.
PASSAGES: The Wide-ranging, Extreme Intrinsic Properties of Planck-selected, Lensed Dusty Star-forming Galaxies

The PASSAGES (Planck All-Sky Survey to Analyze Gravitationally-lensed Extreme Starbursts) collaboration has recently defined a sample of 30 gravitationally lensed dusty star-forming galaxies (DSFGs). These rare, submillimeter-selected objects enable high-resolution views of the most extreme sites of star formation in galaxies at cosmic noon. Here, we present the first major compilation of strong lensing analyses using lenstool for PASSAGES, including 15 objects spanning z = 1.1–3.3, using complementary information from 0. ″6-resolution 1.1 mm Atacama Large Millimeter/submillimeter Array and 0. ″4 5 cm Jansky Very Large Array continuum imaging, in tandem with 1.6 μm Hubble and optical imaging with Gemini-S. Magnifications range from μ = 2 to 28 (median μ = 7), yielding intrinsic infrared luminosities of L IR = 0.2–5.9 × 1013 L ⊙ (median 1.4 × 1013 L ⊙) and inferred star formation rates of 170–6300 M ⊙ yr−1 (median 1500 M ⊙ yr−1). These results suggest that the PASSAGES objects comprise some of the most extreme known starbursts, rivaling the luminosities of even the brightest unlensed objects, further amplified by lensing. The intrinsic sizes of far-infrared continuum regions are large (R e = 1.7–4.3 kpc; median 3.0 kpc) but consistent with L IR–R e scaling relations for z > 1 DSFGs, suggesting a widespread spatial distribution of star formation. With modestly high angular resolution, we explore if these objects might be maximal starbursts. Instead of approaching Eddington-limited surface densities, above which radiation pressure will disrupt further star formation, they are safely sub-Eddington—at least on global, galaxy-integrated scales.
Negative electrocaloric effect in antiferroelectric PbZrO3

The dielectric and thermal properties of a typical antiferroelectric (AFE) material are investigated by minimising numerically the free energy as given by the Kittel model of AFEs. The phase line of second-order phase transitions in the phase diagram is shown to change to a first-order line at the tricritical point T3cp, E3cp. The static dielectric susceptibility and the electrocaloric (EC) effect are calculated as a function of temperature and the applied electric field E. It is found that in a given range of electric fields and temperatures the EC effect has negative values but generally becomes positive above the AFE ordering temperature T0. The dielectric susceptibility shows characteristic peaks at the phase transitions between the field-induced polar and the AFE antipolar phase, and diverges at the tricritical point. We present experimental results for a negative EC effect, which have been obtained by direct EC measurements in PbZrO3 ceramics, and agree qualitatively with the above model.
A Practitioner's Guide to MDP Model Checking Algorithms

Model checking undiscounted reachability and expected-reward properties on Markov decision processes (MDPs) is key for the verification of systems that act under uncertainty. Popular algorithms are policy iteration and variants of value iteration; in tool competitions, most participants rely on the latter. These algorithms generally need worst-case exponential time. However the problem can equally be formulated as a linear program, solvable in polynomial time. In this paper, we give a detailed overview of today's state-of-the-art algorithms for MDP model checking with a focus on performance and correctness. We highlight their fundamental differences, and describe various optimisations and implementation variants. We experimentally compare floating-point and exact-arithmetic implementations of all algorithms on three benchmark sets using two probabilistic model checkers. Our results show that (optimistic) value iteration is a sensible default, but other algorithms are preferable in specific settings. This paper thereby provides a guide for MDP verification practitioners -- tool builders and users alike.
Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification

Neural network-based models have found wide use in automatic long-term electrocardiogram (ECG) analysis. However, such black box models are inadequate for analysing physiological signals where credibility and interpretability are crucial. Indeed, how to make ECG analysis transparent is still an open problem. In this study, we develop a Tsetlin machine (TM) based architecture for premature ventricular contraction (PVC) identification by analysing long-term ECG signals. The architecture is transparent by describing patterns directly with logical AND rules. To validate the accuracy of our approach, we compare the TM performance with those of convolutional neural networks (CNNs). Our numerical results demonstrate that TM provides comparable performance with CNNs on the MIT-BIH database. To validate interpretability, we provide explanatory diagrams that show how TM makes the PVC identification from confirming and invalidating patterns. We argue that these are compatible with medical knowledge so that they can be readily understood and verified by a medical doctor. Accordingly, we believe this study paves the way for machine learning (ML) for ECG analysis in clinical practice.
Born approximation study of the strong disorder in magnetized surface states of a topological insulator

In this study we investigate the effect of random point disorder on the surface states of a topological insulator with out-of-plane magnetization. We consider the disorder within a high order Born approximation. The Born series converges to the one branch of the self-consistent Born approximation (SCBA) solution at low disorder. As the disorder strength increases, the Born series converges to another SCBA solution with the finite density of states within the magnetization induced gap. Further increase of the disorder strength leads to a divergence of the Born series, showing the limits of the applicability of the Born approximation. We find that the convergence properties of this Born series are closely related to the properties of the logistic map, which is known as a prototypical model of chaos. We also calculate the longitudinal and Hall conductivities within the Kubo formulas at zero temperature with the vertex corrections for the velocity operator. Vertex corrections are important for describing transport properties in the strong disorder regime. In the case of strong disorder, the longitudinal conductivity is weakly dependent on the disorder strength, while the Hall conductivity decreases with increasing disorder.
An effective Pila-Wilkie theorem for sets definable using Pfaffian functions, with some diophantine applications

We prove an effective version of the Pila-Wilkie Theorem for sets definable using Pfaffian functions, providing effective estimates for the number of algebraic points of bounded height and degree lying on such sets. We also prove effective versions of extensions of this result due to Pila and Habegger-Pila . In order to prove these counting results, we obtain an effective version of Yomdin-Gromov parameterization for sets defined using restricted Pfaffian functions. Furthermore, for sets defined in the restricted setting, as well as for unrestricted sub-Pfaffian sets, our effective estimates depend polynomially on the degree (one measure of complexity) of the given set. The level of uniformity present in all the estimates allows us to obtain several diophantine applications. These include an effective and uniform version of the Manin-Mumford conjecture for products of elliptic curves with complex multiplication, and an effective, uniform version of a result due to Habegger which characterizes the set of special points lying on an algebraic variety contained in a fibre power of an elliptic surface. We also show that if Andr\'e-Oort for $Y(2)^g$ can be made effective, then Andr\'e-Oort for a family of elliptic curves over $Y(2)^g$ can be made effective.
BIPROJECTIVITY AND BIFLATNESS OF LAU PRODUCT OF BANACH ALGEBRAS DEFINED BY A BANACH ALGEBRA MORPHISM

Abstract Let ${\it\varphi}$ be a homomorphism from a Banach algebra ${\mathcal{B}}$ to a Banach algebra ${\mathcal{A}}$. We define a multiplication on the Cartesian product space ${\mathcal{A}}\times {\mathcal{B}}$ and obtain a new Banach algebra ${\mathcal{A}}\times _{{\it\varphi}}{\mathcal{B}}$. We show that biprojectivity as well as biflatness of ${\mathcal{A}}\times _{{\it\varphi}}{\mathcal{B}}$ are stable with respect to ${\it\varphi}$.
Data Augmentation Alone Can Improve Adversarial Training

Adversarial training suffers from the issue of robust overfitting, which seriously impairs its generalization performance. Data augmentation, which is effective at preventing overfitting in standard training, has been observed by many previous works to be ineffective in mitigating overfitting in adversarial training. This work proves that, contrary to previous findings, data augmentation alone can significantly boost accuracy and robustness in adversarial training. We find that the hardness and the diversity of data augmentation are important factors in combating robust overfitting. In general, diversity can improve both accuracy and robustness, while hardness can boost robustness at the cost of accuracy within a certain limit and degrade them both over that limit. To mitigate robust overfitting, we first propose a new crop transformation, Cropshift, which has improved diversity compared to the conventional one (Padcrop). We then propose a new data augmentation scheme, based on Cropshift, with much improved diversity and well-balanced hardness. Empirically, our augmentation method achieves the state-of-the-art accuracy and robustness for data augmentations in adversarial training. Furthermore, when combined with weight averaging it matches, or even exceeds, the performance of the best contemporary regularization methods for alleviating robust overfitting. Code is available at: https://github.com/TreeLLi/DA-Alone-Improves-AT.
Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot

Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline variables, and there is often ambiguity about which variables should be prioritized. Standard prioritization methods based on treatment imbalance alone neglect variables' relationships with the outcome. We propose the joint variable importance plot to guide variable prioritization for observational studies. Since not all variables are equally relevant to the outcome, the plot adds outcome associations to quantify the potential confounding jointly with the standardized mean difference. To enhance comparisons on the plot between variables with different confounding relationships, we also derive and plot bias curves. Variable prioritization using the plot can produce recommended values for tuning parameters in many existing matching and weighting methods. We showcase the use of the joint variable importance plots in the design of a balance-constrained matched study to evaluate whether taking an antidiabetic medication, glyburide, increases the incidence of C-section delivery among pregnant individuals with gestational diabetes.
Photoassisted current in the fractional quantum Hall effect as a probe of the quasiparticle operator scaling dimension

We study photo-assisted transport for the edge states of a two dimensional electron gas in the fractional quantum Hall regime, pinched by a single quantum point contact. We provide a general expression of the photo-assisted current using a Keldysh-Floquet approach, when the AC drive is applied either directly to the edge states, or when it modulates the tunneling amplitude at the quantum point contact. Strikingly, for a simple cosine modulation of the tunneling amplitude, the phase shift of the second harmonic of the photoassisted current is directly related to the scaling dimension of the quasiparticle operators describing the fractional excitations. As the scaling dimension is intimately related to the statistics, our proposal of a gate modulation of the backscattered current provides a diagnosis of the statistics of Laughlin quasiparticles using a simple quantum point contact geometry.
The Lunar Poles: Water Ice or Chemically Trapped Hydrogen?

Abstract An alternative explanation is proposed for the hydrogen excess observed by the Lunar Prospector neutron spectrometer: solar wind protons trapped on radiation defects in regolith particles and effectively retained at the temperature of the lunar poles can be misinterpreted as water. Protons from the Earth's magnetotail plasma can be a source of hydrogen atoms in the regolith of permanently shadowed areas of the lunar surface.
SMART: Self-supervised Multi-task pretrAining with contRol Transformers

Self-supervised pretraining has been extensively studied in language and vision domains, where a unified model can be easily adapted to various downstream tasks by pretraining representations without explicit labels. When it comes to sequential decision-making tasks, however, it is difficult to properly design such a pretraining approach that can cope with both high-dimensional perceptual information and the complexity of sequential control over long interaction horizons. The challenge becomes combinatorially more complex if we want to pretrain representations amenable to a large variety of tasks. To tackle this problem, in this work, we formulate a general pretraining-finetuning pipeline for sequential decision making, under which we propose a generic pretraining framework \textit{Self-supervised Multi-task pretrAining with contRol Transformer (SMART)}. By systematically investigating pretraining regimes, we carefully design a Control Transformer (CT) coupled with a novel control-centric pretraining objective in a self-supervised manner. SMART encourages the representation to capture the common essential information relevant to short-term control and long-term control, which is transferrable across tasks. We show by extensive experiments in DeepMind Control Suite that SMART significantly improves the learning efficiency among seen and unseen downstream tasks and domains under different learning scenarios including Imitation Learning (IL) and Reinforcement Learning (RL). Benefiting from the proposed control-centric objective, SMART is resilient to distribution shift between pretraining and finetuning, and even works well with low-quality pretraining datasets that are randomly collected.
Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training

Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used are of crucial importance to the quality of learned feature representations. In this paper, we analyze how the color jitter traditionally used in data augmentation negatively impacts the quality of the color features in learned feature representations. To address this problem, we propose a more realistic, physics-based color data augmentation - which we call Planckian Jitter - that creates realistic variations in chromaticity and produces a model robust to illumination changes that can be commonly observed in real life, while maintaining the ability to discriminate image content based on color information. Experiments confirm that such a representation is complementary to the representations learned with the currently-used color jitter augmentation and that a simple concatenation leads to significant performance gains on a wide range of downstream datasets. In addition, we present a color sensitivity analysis that documents the impact of different training methods on model neurons and shows that the performance of the learned features is robust with respect to illuminant variations.
Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles

To promote new scientific discoveries from complex data sets, feature importance inference has been a long-standing statistical problem. Instead of testing for parameters that are only interpretable for specific models, there has been increasing interest in model-agnostic methods, often in the form of feature occlusion or leave-one-covariate-out (LOCO) inference. Existing approaches often make distributional assumptions, which can be difficult to verify in practice, or require model refitting and data splitting, which are computationally intensive and lead to losses in power. In this work, we develop a novel, mostly model-agnostic and distribution-free inference framework for feature importance that is computationally efficient and statistically powerful. Our approach is fast as we avoid model refitting by leveraging a form of random observation and feature subsampling called minipatch ensembles; this approach also improves statistical power by avoiding data splitting. Our framework can be applied on tabular data and with any machine learning algorithm, together with minipatch ensembles, for regression and classification tasks. Despite the dependencies induced by using minipatch ensembles, we show that our approach provides asymptotic coverage for the feature importance score of any model under mild assumptions. Finally, our same procedure can also be leveraged to provide valid confidence intervals for predictions, hence providing fast, simultaneous quantification of the uncertainty of both predictions and feature importance. We validate our intervals on a series of synthetic and real data examples, including non-linear settings, showing that our approach detects the correct important features and exhibits many computational and statistical advantages over existing methods. Keywords— Model-agnostic feature importance, leave-one-covariate-out inference, leave-one-observation-out inference, minipatch ensembles, predictive inference, conformal inference