Miracle-dz/UniFAR-FTUs huggingface dataset:
Superconductivity Studied by Solving 
<i>Ab Initio</i>
 Low-Energy Effective Hamiltonians for Carrier Doped 
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We numerically analyze superconductivity (SC) in the cuprate superconductors by using ab initio effective Hamiltonians consisting of the antibonding combination of Cu $3d_{x^2-y^2}$ and O $2p_{\sigma}$ orbitals. We perform variational Monte Carlo calculations for the four carrier doped cuprates with diverse experimental optimal SC critical temperature $T_{c}^{\rm opt}$: CaCuO$_2$ ($T_{c}^{\rm opt} \sim 110$ K), Bi$_2$Sr$_2$CuO$_6$ ($T_{c}^{\rm opt} \sim 10$-$40$ K), Bi$_2$Sr$_2$CaCu$_2$O$_8$ ($T_{c}^{\rm opt} \sim 85$-$100$ K), and HgBa$_2$CuO$_4$ ($T_{c}^{\rm opt} \sim 90$ K). Materials and hole doping concentration ($\delta$) dependencies of the SC order parameter $F_{\rm SC}$ and the competition with spin/charge order show essential and quantitative agreements with the available experiments in the following points: (1) The ground state is commonly the SC state, which is severely competing with the charge/spin stripe and antiferromagnetic states. (2) $F_{\rm SC}$ shows amplitude consistent with the superfluid density measured in the muon spin resonance and its dome structure found in $\delta$ dependence shows consistency with that of the SC gap in the tunneling and photoemission measurements. We further find insights into the universal SC mechanism: (I) $F_{\rm SC}$ increases with the ratio $U/|t_1|$, indicating that $U/|t_1|$ is the principal component controlling the SC. Here, $U$ and $t_1$ are the onsite Coulomb repulsion and the nearest neighbor hopping, respectively, in the Hamiltonians. (II) A universal scaling $T_{c}^{\rm opt}\sim 0.16 \lvert t_1 \rvert F_{\rm SC}$ holds. (III) SC is enhanced and optimized if $U$ is increased beyond the real available materials. It is further enhanced by decreasing the offsite interaction. The present findings provide useful clues for the design of new SC materials with even higher $T_{c}^{\rm opt}$.
Dynamics of spheroids in pressure-driven flows of shear thinning fluids

Particles in inertialess flows of shear thinning fluids are a model representation for several systems in biology, ecology, and micro-fluidics.In this paper, we analyze the motion of a spheroid in a pressure driven flow of a shear thinning fluid.The shear thinning rheology is characterized by the Carreau model.We use a combination of perturbative techniques and the reciprocal theorem to delineate the kinematics of prolate and oblate spheroids.There are two perturbative strategies adopted, one near the zero shear Newtonian plateau and the other near the infinite shear Newtonian plateau.In both limits, we find that a reduction in effective viscosity decreases the spheroid's rotational time period in pressure driven flows.The extent to which shear thinning alters the kinematics is a function of the particle shape.For a prolate particle, the effect of shear thinning is most prominent when the spheroid projector is aligned in the direction of the velocity gradient, while for an oblate particle the effect is most prominent when the projector is aligned along the flow direction.Lastly, we compare the tumbling behavior of spheroids in pressure driven flow to those in simple shear flow.While the time period decreases monotonically with Carreau number for pressure driven flows, the trend is non monotonic for shear flows where time period first increases at low Carreau number and then decreases at high Carreau numbers.Shear thinning does not resolve the degeneracy of Jefferey's orbits.
Wavelet Galerkin Method for an Electromagnetic Scattering Problem

The Helmholtz equation is challenging to solve numerically due to the pollution effect, which often results in a huge ill-conditioned linear system. In this paper, we present a high order wavelet Galerkin method to numerically solve an electromagnetic scattering from a large cavity problem modeled by the 2D Helmholtz equation. The high approximation order and the sparse linear system with uniformly bounded condition numbers offered by wavelets are useful in reducing the pollution effect. Using the direct approach in [B. Han and M. Michelle, Appl. Comp. Harmon. Anal. , 53 (2021), 270-331], we present various optimized spline biorthogonal wavelets on a bounded interval. We provide a self-contained proof to show that the tensor product of such wavelets forms a 2D Riesz wavelet in the appropriate Sobolev space. Compared to the coefficient matrix of the finite element method (FEM), when an iterative scheme is applied to the coefficient matrix of our wavelet Galerkin method, much fewer iterations are needed for the relative residuals to be within a tolerance level. Furthermore, for a fixed wavenumber, the number of required iterations is practically independent of the size of the wavelet coefficient matrix, due to the uniformly bounded small condition numbers of such wavelets. In contrast, when an iterative scheme is applied to the FEM coefficient matrix, the number of required iterations doubles as the mesh size for each axis is halved. The implementation can also be done conveniently thanks to the simple structure, the refinability property, and the analytic expression of our wavelet bases.
Two-dimensional reductions of the Whitham modulation system for the Kadomtsev–Petviashvili equation

Two-dimensional reductions of the Kadomtsev–Petviashvili(KP)–Whitham system, namely the overdetermined Whitham modulation system for five dependent variables that describe the periodic solutions of the KP equation, are studied and characterized. Three different reductions are considered corresponding to modulations that are independent of x, independent of y, and of t (i.e. stationary), respectively. Each of these reductions still describes dynamic, two-dimensional spatial configurations since the modulated cnoidal wave, generically, has a nonzero speed and a nonzero slope in the xy plane. In all three of these reductions, the integrability of the resulting systems of equations is proven, and various other properties are elucidated. Compatibility with conservation of waves yields a reduction in the number of dependent variables to two, three and four, respectively. As a byproduct of the stationary case, the Whitham modulation system for the classical Boussinesq equation is explicitly obtained.
An alternative explanation of the 'spokes' observed in Saturn's rings

Observed first by amateur astronomer Stephen J. O'Meara in the 1970s and then subsequently observed by the Voyager Spacecraft flybys in the early 1980s, it was realised that the 'spokes' flare out like spokes on a bicycle wheel. The observed behaviour of the 'spokes' indicates that they are not governed by gravitational interactions with the planets, moons, or ring material. In 2005 the Cassini probe confirmed that the 'spokes' are likely under the influence of the gas giant's global magnetic field. Here we show that the 'spokes' that appear in Saturn's rings consist of grains of silicates coated in pyrolytic carbon through the process of Chemical Vapour Deposition (CVD). Pyrolytic carbon is a highly diamagnetic substance that can levitate above a sufficiently strong magnetic field. The 'spokes' also consist of ice particles that are diamagnetic as well. The photoelectric effect can be used to explain why the silicates coated in pyrolytic carbon return to the main ring structure when exposed to sunlight of a specific frequency. The pyrolytic carbon grains become paramagnetic when some of the unhybridised 2pz orbitals lose their unpaired delocalised electrons, thus collapsing the pi bond molecular orbital structure. The pyrolytic carbon grains are now attracted to the magnetic field emanating above and below the main ring structure. It is suggested that the 'spokes' in Saturn's B ring are always present and no plasma triggering event is required to increase plasma density. The 'spokes', however, are only visible when a favourable viewing angle is allowed, and their visibility is also dependent on the angle of the sunlight hitting Saturn's rings.
Numerical study of transitional separated–reattached flow over surface‐mounted obstacles using large‐eddy simulation

Large‐eddy simulation (LES) of transitional separating–reattaching flow on a square surface mounted obstacle has been performed. The Reynolds number based on the uniform inlet velocity and the obstacle height is 4.5 × 103. A dynamic subgrid‐scale model is employed in this work. The mean LES results compare favourably with the available experimental and direct numerical simulation (DNS) data. Extensive analysis of the time series signals of the velocity and pressure fields at different locations including positions close to solid surfaces, at the centre and edge of the separated–reattached boundary layer using the windowed Fourier transform (WFT) and the wavelet transform was performed. The spectra analysis revealed the nature of the amplified frequencies at all the important locations of the flow field. Excited modes that could be due to the movement (shedding) of large‐scale structures and pairing of such types of structures are identified. A clear frequency peak was captured just upstream of the separation line. The value of the frequency peak and the low percentage of the back flow velocity compared to the freestream velocity in the current case strongly support the idea that this amplified frequency is most likely due to the Kelvin–Helmholtz (K–H) instability mechanism of the shear layer forming in the boundary of the small upstream separated region rather than being attributed to the flapping of the shear layer. Copyright © 2006 John Wiley & Sons, Ltd.
Operating data of a specific Aquatic Center as a Benchmark for dynamic model learning: search for a valid prediction model over an 8-hour horizon

This article presents an identification benchmark based on data from a public swimming pool in operation. Such a system is both a complex process and easily understandable by all with regard to the stakes. Ultimately, the objective is to reduce the energy bill while maintaining the level of quality of service. This objective is general in scope and is not limited to public swimming pools. This can be done effectively through what is known as economic predictive control. This type of advanced control is based on a process model. It is the aim of this article and the considered benchmark to show that such a dynamic model can be obtained from operating data. For this, operational data is formatted and shared, and model quality indicators are proposed. On this basis, the first identification results illustrate the results obtained by a linear multivariable model on the one hand, and by a neural dynamic model on the other hand. The benchmark calls for other proposals and results from control and data scientists for comparison.
Opening Up the Neural Network Classifier for Shap Score Computation

We address the problem of efﬁciently computing Shap explanation scores for classiﬁcations with machine learning models. With this goal, we show the transformation of binary neural networks (BNNs) for classiﬁcation into deterministic and decomposable Boolean circuits, for which knowledge compilation techniques are used. The resulting circuit is treated as an open-box model, to compute Shap scores by means of a recent efﬁcient algorithm for this class of circuits. Detailed experiments show a considerable gain in performance in comparison with computing Shap directly on the BNN treated as a black-box model.
Normal-guided Garment UV Prediction for Human Re-texturing

Clothes undergo complex geometric deformations, which lead to appearance changes. To edit human videos in a physically plausible way, a texture map must take into account not only the garment transformation induced by the body movements and clothes fitting, but also its 3D fine-grained surface geometry. This poses, however, a new challenge of 3D reconstruction of dynamic clothes from an image or a video. In this paper, we show that it is possible to edit dressed human images and videos without 3D reconstruction. We estimate a geometry aware texture map between the garment region in an image and the texture space, a.k. a, UV map. Our UV map is designed to preserve isometry with respect to the underlying 3D surface by making use of the 3D surface normals predicted from the image. Our approach captures the underlying geometry of the garment in a self-supervised way, requiring no ground truth annotation of UV maps and can be readily extended to predict temporally coherent UV maps. We demonstrate that our method outperforms the state-of-the-art human UV map estimation approaches on both real and synthetic data.
DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models

Many organizations rely on data from government and third-party sources, and those sources rarely follow the same data formatting. This introduces challenges in integrating data from multiple sources or aligning external sources with internal databases. Commercial database systems do not offer adequate support for integrating data from heterogeneous sources, and manual integration is both time-consuming and inefficient. State-of-the-art data integration approaches that rely on similarity functions and textual transformations often fail to handle challenging cases where multiple mappings are required, or the mappings go beyond simple textual transformations. In this paper, we study the potentials of deep neural models for transforming tables for joinability. In particular, we cast the problem as a prediction task and develop a framework that leverages large deep-learning language models to transform tabular data from a source formatting to a desired target representation. Our framework can efficiently learn the patterns for mapping a source formatting into an expected target using just a few examples, which can then be used for tasks such as table joining, filling in missing values, and error detection. Compared to state-of-the-art mapping and joining approaches, our framework delivers noticeably more accurate and scalable performance on both real-world and synthetic datasets. Our experimental evaluation also shows that the performance of the proposed framework using our fine-tuned model is at par or better than large language models such as GPT-3, despite the significant difference in size, and that using large language models within our framework improves their performance.
Advantage of Hardy's nonlocal correlation in reverse zero-error channel coding

Hardy's argument constitutes an elegant proof of quantum nonlocality as established by the seminal Bell's theorem. In this work, we report an exotic application of Hardy's nonlocal correlations. We devise a simple communication task and show that the expected payoff of the task cannot be positive whenever only 1-cbit communication is allowed from the sender to the receiver, who otherwise can share an unlimited amount of classical correlation. Interestingly, the same classical channel can ensure a positive payoff when assisted with correlations exhibiting Hardy's nonlocality. As it turns out, among all the 2-input-2-output no-signaling correlations, only Hardy's correlation can ensure a positive payoff when assisted to 1-cbit channel. This further prompts us to show that in the correlation assisted reverse zero-error channel coding scenario, where the aim is to simulate a noisy channel exactly by a noiseless one in assistance with correlations, assistance of non-maximally pure entangled states -- even with vanishingly zero amount of entanglement -- could be preferable over the maximal one.
Exact bounds for the polynomial decay of correlation, 1/f noise and the CLT for the equilibrium state of a non-Hölder potential

We analyse the correlation and limit behaviour of partial sums for the stationary stochastic process (f(Tt(x)),µ),t = 0,1,. , for functions f of superpolynomial variation, the class defined below (which includes the Holder functions), where T:Σ+→Σ+ is the left shift map on Σ+ = Π0∞{0,1} and µ is the non-atomic equilibrium measure of a non-Holder potential g = gγ belonging to a one-parameter family, indexed by γ>2. First, using the renewal equation, we show a polynomial rate of convergence for the associated Ruelle operator for cylinder set observables. We then use these estimates to prove the following theorems: • We extend the polynomial convergence for the Ruelle operator to functions f. • We show that the measure is weakly Bernouilli and the bounds are polynomial. • We calculate the decay of correlation of the stationary stochastic process described above, for f. This decay is polynomial with t: we show in theorem 4.1 an upper bound of the order of Ct2-γ when γ>2; this estimate is sharp in the sense that for each γ there exist functions f (in fact f = I[0] gives an example) for which one has the lower bound of ct2-γ for the decay of its autocorrelation (see theorem \ref{t:decay and 1/fnoise}). For the lower bound we use Tauberian theorems. For this example the coefficients decay monotonically, which is important for proving the lower bound. • Again using Tauberian theorems together with the upper-lower bounds we show that for each 3>γ>2 one has the phenomenon of 1/f noise for the spectral density of the function I[0] (see theorem \ref{t:decay and 1/fnoise}). • We prove the central limit theorem (CLT) and functional CLT for the case where f is in and for γ>3 (theorem \ref{t:CLT}). For this we apply Gordin's method in the setting of a polynomial rate of convergence. From the perspective of differentiable dynamical systems µ is the unique invariant measure which is absolutely continuous with respect to the Lebesgue measure for an associated doubling map of the circle with an indifferent fixed point. This map T1 = T1,γ is a piecewise linear version of the Manneville-Pomeau map, and the potential gγ is equal to -log DT1,γ. We emphasize that our class is larger than the classes studied elsewhere.
TransMatting: Tri-token Equipped Transformer Model for Image Matting

Image matting aims to predict alpha values of elaborate uncertainty areas of natural images, like hairs, smoke, and spider web. However, existing methods perform poorly when faced with highly transparent foreground objects due to the large area of uncertainty to predict and the small receptive field of convolutional networks. To address this issue, we propose a Transformer-based network (TransMatting) to model transparent objects with long-range features and collect a high-resolution matting dataset of transparent objects (Transparent-460) for performance evaluation. Specifically, to utilize semantic information in the trimap flexibly and effectively, we also redesign the trimap as three learnable tokens, named tri-token. Both Transformer and convolution matting models could benefit from our proposed tri-token design. By replacing the traditional trimap concatenation strategy with our tri-token, existing matting methods could achieve about 10% improvement in SAD and 20% in MSE. Equipped with the new tri-token design, our proposed TransMatting outperforms current state-of-the-art methods on several popular matting benchmarks and our newly collected Transparent-460.
InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised Learning

Recent state-of-the-art methods in imbalanced semi-supervised learning (SSL) rely on confidence-based pseudo-labeling with consistency regularization. To obtain high-quality pseudo-labels, a high confidence threshold is typically adopted. However, it has been shown that softmax-based confidence scores in deep networks can be arbitrarily high for samples far from the training data, and thus, the pseudo-labels for even high-confidence unlabeled samples may still be unreliable. In this work, we present a new perspective of pseudo-labeling for imbalanced SSL. Without relying on model confidence, we propose to measure whether an unlabeled sample is likely to be ``in-distribution''; i.e., close to the current training data. To decide whether an unlabeled sample is ``in-distribution'' or ``out-of-distribution'', we adopt the energy score from out-of-distribution detection literature. As training progresses and more unlabeled samples become in-distribution and contribute to training, the combined labeled and pseudo-labeled data can better approximate the true class distribution to improve the model. Experiments demonstrate that our energy-based pseudo-labeling method, \textbf{InPL}, albeit conceptually simple, significantly outperforms confidence-based methods on imbalanced SSL benchmarks. For example, it produces around 3\% absolute accuracy improvement on CIFAR10-LT. When combined with state-of-the-art long-tailed SSL methods, further improvements are attained. In particular, in one of the most challenging scenarios, InPL achieves a 6.9\% accuracy improvement over the best competitor.
Propagation of information in long-range quantum systems

We present bounds on the minimal time for quantum messaging, propagation/creation of correlations, and control of states for general , long-range, lattice quantum many-body bosonic systems. The proofs are based on a maximal velocity bound and the light-cone approximation of dynamics, which provide diﬀerent expressions of the fact that the many-body evolution stays, up to small leaking probability tails, within a light cone of the support of the initial conditions and imply, in particular, Lieb-Robinson-type bounds on commutators of evolving observables.
Direct Bayesian Regression for Distribution-valued Covariates

In this manuscript, we study the problem of scalar-on-distribution regression; that is, instances where subject-specific distributions or densities, or in practice, repeated measures from those distributions, are the covariates related to a scalar outcome via a regression model. We propose a direct regression for such distribution-valued covariates that circumvents estimating subject-specific densities and directly uses the observed repeated measures as covariates. The model is invariant to any transformation or ordering of the repeated measures. Endowing the regression function with a Gaussian Process prior, we obtain closed form or conjugate Bayesian inference. Our method subsumes the standard Bayesian non-parametric regression using Gaussian Processes as a special case. Theoretically, we show that the method can achieve an optimal estimation error bound. To our knowledge, this is the first theoretical study on Bayesian regression using distribution-valued covariates. Through simulation studies and analysis of activity count dataset, we demonstrate that our method performs better than approaches that require an intermediate density estimation step.
Quantifying Technical Debt: A Systematic Mapping Study and a Conceptual Model

To effectively manage Technical Debt (TD), we need reliable means to quantify it. We conducted a Systematic Mapping Study (SMS) where we identified TD quantification approaches that focus on different aspects of TD. Some approaches base the quantification on the identification of smells, some quantify the Return on Investment (ROI) of refactoring, some compare an ideal state with the current state of a software in terms of the software quality, and some compare alternative development paths to reduce TD. It is unclear if these approaches are quantifying the same thing and if they support similar or different decisions regarding TD Management (TDM). This creates the problem of not being able to effectively compare and evaluate approaches. To solve this problem, we developed a novel conceptual model, the Technical Debt Quantification Model (TDQM), that captures the important concepts related to TD quantification and illustrates the relationships between them. TDQM can represent varied TD quantification approaches via a common uniform representation, the TDQM Approach Comparison Matrix, that allows performing useful comparisons and evaluations between approaches. This paper reports on the mapping study, the development of TDQM, and on applying TDQM to compare and evaluate TD quantification approaches.