Miracle-dz/UniFAR-FTUs huggingface dataset:
Correcting Faulty Road Maps by Image Inpainting

As maintaining road networks is labor-intensive, many automatic road extraction approaches have been introduced to solve this real-world problem, fueled by the abundance of large-scale high-resolution satellite imagery and advances in computer vision. However, their performance is limited for fully automating the road map extraction in real-world services. Hence, many services employ the two-step human-in-the-loop system to post-process the extracted road maps: error localization and automatic mending for faulty road maps. Our paper exclusively focuses on the latter step, introducing a novel image inpainting approach for fixing road maps with complex road geometries without custom-made heuristics, yielding a method that is readily applicable to any road geometry extraction model. We demonstrate the effectiveness of our method on various real-world road geometries, such as straight and curvy roads, T-junctions, and intersections.
Pieri and Murnaghan--Nakayama type Rules for Chern classes of Schubert Cells

We develop Pieri type as well as Murnaghan–Nakayama type formulas for equivariant Chern–Schwartz–MacPherson classes of Schubert cells in the classical flag variety. These formulas include as special cases many previously known multiplication formulas for Chern–Schwartz–MacPherson classes or Schubert classes. We apply the equivariant Murnaghan–Nakayama formula to the enumeration of rim hook tableaux.
Revisiting superconductivity in the extended one-band Hubbard model: Pairing via spin and charge fluctuations

The leading superconducting instabilities of the two-dimensional extended repulsive one-band Hubbard model within spin-fluctuation pairing theory depend sensitively on electron density, band and interaction parameters. We map out the phase diagrams within a random phase approximation (RPA) spin- and charge-fluctuation approach, and find that while $B_{1g}$ ($d_{x^2-y^2}$) and $B_{2g}$ ($d_{xy}$) pairing dominates in the absence of repulsive longer-range Coulomb interactions $V_{\rm NN}$, the latter induces pairing in other symmetry channels, including e.g $A_{2g}$ ($g$-wave), nodal $A_{1g}$ (extended $s$-wave), or nodal $E_u$ ($p$-wave) spin-triplet superconductivity. At the lowest temperatures, transition boundaries in the phase diagrams between symmetry-distinct spin-singlet orders generate complex time-reversal symmetry broken superpositions. By contrast, we find that boundaries between singlet and triplet regions are characterized by first-order transitions. Finally, motivated by recent photoemission experiments, we have determined the influence of an additional explicitly attractive nearest-neighbor interaction, $V_{\rm NN}<0$, on the superconducting gap structure. Depending on the electronic filling, such an attraction boosts $E_u$ ($p$-wave) spin-triplet or $B_{1g}$ ($d_{x^2-y^2}$) spin-singlet ordering.
Ground Truth Inference for Weakly Supervised Entity Matching

Entity matching (EM) refers to the problem of identifying pairs of data records in one or more relational tables that refer to the same entity in the real world. Supervised machine learning (ML) models currently achieve state-of-the-art matching performance; however, they require a large number of labeled examples, which are often expensive or infeasible to obtain. This has inspired us to approach data labeling for EM using weak supervision. In particular, we use the labeling function abstraction popularized by Snorkel, where each labeling function (LF) is a user-provided program that can generate many noisy match/non-match labels quickly and cheaply. Given a set of user-written LFs, the quality of data labeling depends on a labeling model to accurately infer the ground-truth labels. In this work, we first propose a simple but powerful labeling model for general weak supervision tasks. Then, we tailor the labeling model specifically to the task of entity matching by considering the EM-specific transitivity property. The general form of our labeling model is simple while substantially outperforming the best existing method across ten general weak supervision datasets. To tailor the labeling model for EM, we formulate an approach to ensure that the final predictions of the labeling model satisfy the transitivity property required in EM, utilizing an exact solution where possible and an ML-based approximation in remaining cases. On two single-table and nine two-table real-world EM datasets, we show that our labeling model results in a 9% higher F1 score on average than the best existing method. We also show that a deep learning EM end model (DeepMatcher) trained on labels generated from our weak supervision approach is comparable to an end model trained using tens of thousands of ground-truth labels, demonstrating that our approach can significantly reduce the labeling efforts required in EM.
Alternating Implicit Projected SGD and Its Efficient Variants for Equality-constrained Bilevel Optimization

Stochastic bilevel optimization, which captures the inherent nested structure of machine learning problems, is gaining popularity in many recent applications. Existing works on bilevel optimization mostly consider either unconstrained problems or constrained upper-level problems. This paper considers the stochastic bilevel optimization problems with equality constraints both in the upper and lower levels. By leveraging the special structure of the equality constraints problem, the paper first presents an alternating implicit projected SGD approach and establishes the $\tilde{\cal O}(\epsilon^{-2})$ sample complexity that matches the state-of-the-art complexity of ALSET \citep{chen2021closing} for unconstrained bilevel problems. To further save the cost of projection, the paper presents two alternating implicit projection-efficient SGD approaches, where one algorithm enjoys the $\tilde{\cal O}(\epsilon^{-2}/T)$ upper-level and $\tilde{\cal O}(\epsilon^{-1.5}/T^{\frac{3}{4}})$ lower-level projection complexity with ${\cal O}(T)$ lower-level batch size, and the other one enjoys $\tilde{\cal O}(\epsilon^{-1.5})$ upper-level and lower-level projection complexity with ${\cal O}(1)$ batch size. Application to federated bilevel optimization has been presented to showcase the empirical performance of our algorithms. Our results demonstrate that equality-constrained bilevel optimization with strongly-convex lower-level problems can be solved as efficiently as stochastic single-level optimization problems.
Generalized projection retrieval of dispersion scans for ultrashort pulse characterization

We present a retrieval algorithm based on generalized projections for ultrashort pulse characterization using dispersion scan (d-scan). The new algorithm is tested on several simulated cases and in two different experimental cases in the few-cycle regime. The proposed algorithm is much faster and leads to a drastic reduction of retrieval times, but performs less robust in the retrieval of noisy d-scan traces compared to the standard algorithm.
Charged-Rotating Black Holes in Higher-dimensional (A)DS-Gravity

We present numerical evidences for the existence of rotating black holes in $d$-dimensional Einstein-Maxwell theory with a cosmological constant and for an odd number of dimensions. The metric used possesses $(d+1)/2$ Killing vectors and the black holes have $(d\ensuremath{-}1)/2$ equal angular momenta. The Schwarzschild-like coordinate system used clearly reveals the influence of the electromagnetic field on the vacuum black holes where analytic expressions are available. The domain of existence of the charged rotating black holes is then characterized for both signs of the cosmological constant. The generic solutions are specified by their event horizon and by two additional parameters: the magnetic field and the angular velocity at the horizon. The dependence of several physical quantities---surface gravity, mass, angular momentum, etc.---is studied as a function of these parameters; Smarr-like relations are derived.
CST5: Data Augmentation for Code-Switched Semantic Parsing

Extending semantic parsers to code-switched input has been a challenging problem, primarily due to a lack of supervised training data. In this work, we introduce CST5, a new data augmentation technique that fine-tunes a T5 model using a small seed set (≈100 utterances) to generate code-switched utterances from English utterances. We show that CST5 generates high quality code-switched data, both intrinsically (per human evaluation) and extrinsically by comparing baseline models which are trained without data augmentation to models which are trained with augmented data. Empirically we observe that using CST5, one can achieve the same semantic parsing performance by using up to 20x less labeled data. To aid further research in this area, we are also releasing (a) Hinglish-TOP, the largest human annotated code-switched semantic parsing dataset to date, containing 10k human annotated Hindi-English (Hinglish) code-switched utterances, and (b) Over 170K CST5 generated code-switched utterances from the TOPv2 dataset. Human evaluation shows that both the human annotated data as well as the CST5 generated data is of good quality.
Distributed Data-Driven Predictive Control for Multi-Agent Collaborative Legged Locomotion

The aim of this work is to define a planner that enables robust legged locomotion for complex multi-agent systems consisting of several holonomically constrained quadrupeds. To this end, we employ a methodology based on behavioral systems theory to model the sophisticated and high-dimensional structure induced by the holonomic constraints. The resulting model is then used in tandem with distributed control techniques such that the computational burden is shared across agents while the coupling between agents is preserved. Finally, this distributed model is framed in the context of a predictive controller, resulting in a robustly stable method for trajectory planning. This methodology is tested in simulation with up to five agents and is further experimentally validated on three A1 quadrupedal robots subject to various uncertainties, including payloads, rough terrain, and push disturbances.
Realizability of hypergraphs and intrinsic link theory

In this expository paper we present short simple proofs of Conway-Gordon-Sachs' theorem on intrinsic linking in three-dimensional space, as well as van Kampen-Flores' and Ummel's theorems on intrinsic intersections. The latter are related to nonrealizability of certain hypergraphs in four-dimensional space. The proofs use a reduction to lower dimensions which allows to exhibit relation between these results. We use elementary language which allows to present the main ideas without technicalities. Thus our exposition is accessible to non-specialists in the area, including students who know basic three-dimensional geometry, and who are ready to learn straightforward four-dimensional generalizations.
Proof of the Center Conjectures for the cyclotomic Hecke and KLR algebras of type $A$

There are two longstanding conjectures on the centers of the cyclotomic Hecke algebra $\mathscr{H}_{n,K}^\Lambda$ of type $G(r,1,n)$ which assert that: 1) the dimension of the center $Z(\mathscr{H}_{n,K}^\Lambda)$ is independent of the characteristic of the ground field, its Hecke parameter and cyclotomic parameters; 2) the center $Z(\mathscr{H}_{n,K}^\Lambda)$ of $\mathscr{H}_{n,K}^\Lambda$ is the set of symmetric polynomial in its Jucys-Murphy operators. In this paper we prove these two conjectures affirmatively. At the same time we show that the center conjecture holds for the cyclotomic KLR algebras $\mathscr{R}_{\alpha,K}^\Lambda$ associated to the cyclic quiver $A_{e-1}^{(1)}$ (for $e>1$) and the linear quiver $A_{\infty}$ (for $e=0$) when the ground field $K$ has characteristic $p$ which satisfies either $p=0$, or $p>0=e$, or $p=e>1$, or $p>0$, $e>1$ and $p$ is coprime to $e$. As applications, we show that the cohomology of the Nakajima quiver variety $\mathfrak{M}(\Lambda,\alpha)$ with coefficient in $K$ is isomorphic to the center of $\mathscr{R}_{\alpha,K}^\Lambda$ in the affine type $A$ case when ${\rm{char}} K=0$; we also verify Chavli-Pfeiffer's conjecture on the polynomial coefficient $g_{w,C}$ for the complex reflection group of type $G(r,1,n)$.
THE DEPENDENCE OF GALACTIC OUTFLOWS ON THE PROPERTIES AND ORIENTATION OF zCOSMOS GALAXIES AT z ∼ 1

We present an analysis of cool outflowing gas around galaxies, traced by Mg ii absorption lines in the coadded spectra of a sample of 486 zCOSMOS galaxies at 1 ⩽ z ⩽ 1.5. These galaxies span a range of stellar masses (9.45 ⩽ log10[M*/M☉] ⩽ 10.7) and star formation rates (0.14 ⩽ log10[SFR/M☉ yr−1] ⩽ 2.35). We identify the cool outflowing component in the Mg ii absorption and find that the equivalent width of the outflowing component increases with stellar mass. The outflow equivalent width also increases steadily with the increasing star formation rate of the galaxies. At similar stellar masses, the blue galaxies exhibit a significantly higher outflow equivalent width as compared to red galaxies. The outflow equivalent width shows strong correlation with the star formation surface density (ΣSFR) of the sample. For the disk galaxies, the outflow equivalent width is higher for the face-on systems as compared to the edge-on ones, indicating that for the disk galaxies, the outflowing gas is primarily bipolar in geometry. Galaxies typically exhibit outflow velocities ranging from −150 km s−1 ∼−200 km s−1 and, on average, the face-on galaxies exhibit higher outflow velocity as compared to the edge-on ones. Galaxies with irregular morphologies exhibit outflow equivalent width as well as outflow velocities comparable to face on disk galaxies. These galaxies exhibit mass outflow rates >5–7 M☉ yr−1 and a mass loading factor () comparable to the star formation rates of the galaxies.
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention

Recently, powerful Transformer architectures have proven superior in generating high-quality sentences. Nevertheless, these models tend to produce dull high-frequency phrases, severely hurting the diversity and novelty of generated text. In this work, we dig into the intrinsic mechanism of this problem and found that sparser attention values in Transformer could improve diversity. To understand such a phenomenon, we first conduct both empirical and theoretical analysis and then attribute it to representation degeneration caused by the attentive mixture of the hidden states during training. We term this process the Trap of Mediocrity. To escape from such a trap, we introduce a novel attention regularization loss to control the sharpness of the attention distribution, which is transparent to model structures and can be easily implemented within 20 lines of python code. We prove that this method could be mathematically regarded as learning a Bayesian approximation of posterior attention. Experiments show that our method improved the diversity and novelty of the generated text while maintaining comparable quality on a variety of conditional and unconditional generation tasks.
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities

In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model. Starting from the pre-trained multimodal representation model CLIP released by OpenAI, we altered its text encoder with a pre-trained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k-CN, COCO-CN and XTD. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding. Our models and code are available at https://github.com/FlagAI-Open/FlagAI.
An unstable mode of the stratified atmosphere under the non-traditional Coriolis acceleration

Abstract The traditional approximation neglects the cosine components of the Coriolis acceleration, and this approximation has been widely used in the study of geophysical phenomena. However, the justification of the traditional approximation is questionable under a few circumstances. In particular, dynamics with substantial vertical velocities or geophysical phenomena in the tropics have non-negligible cosine Coriolis terms. Such cases warrant investigations with the non-traditional setting, i.e. the full Coriolis acceleration. In this manuscript, we study the effect of the non-traditional setting on an isothermal, hydrostatic and compressible atmosphere assuming a meridionally homogeneous flow. Employing linear stability analysis, we show that, given appropriate boundary conditions, i.e. a bottom boundary condition that allows for a vertical energy flux and non-reflecting boundary at the top, the atmosphere at rest becomes prone to a novel unstable mode. The validity of assuming a meridionally homogeneous flow is investigated via scale analysis. Numerical experiments were conducted, and Rayleigh damping was used as a numerical approximation for the non-reflecting top boundary. Our three main results are as follows: (i) experiments involving the full Coriolis terms exhibit an exponentially growing instability, yet experiments subjected to the traditional approximation remain stable; (ii) the experimental instability growth rate is close to the theoretical value; (iii) a perturbed version of the unstable mode arises even under sub-optimal bottom boundary conditions. Finally, we conclude our study by discussing the limitations, implications and remaining open questions. Specifically, the influence on numerical deep-atmosphere models and possible physical interpretations of the unstable mode are discussed.
SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing

Due to various and complicated snow degradations, single image desnowing is a challenging image restoration task. As prior arts can not handle it ideally, we propose a novel transformer, SnowFormer, which explores efficient cross-attentions to build local-global context interaction across patches and surpasses existing works that employ local operators or vanilla transformers. Compared to prior desnowing methods and universal image restoration methods, SnowFormer has several benefits. Firstly, unlike the multi-head self-attention in recent image restoration Vision Transformers, SnowFormer incorporates the multi-head cross-attention mechanism to perform local-global context interaction between scale-aware snow queries and local-patch embeddings. Second, the snow queries in SnowFormer are generated by the query generator from aggregated scale-aware features, which are rich in potential clean cues, leading to superior restoration results. Third, SnowFormer outshines advanced state-of-the-art desnowing networks and the prevalent universal image restoration transformers on six synthetic and real-world datasets. The code is released in \url{https://github.com/Ephemeral182/SnowFormer}.
Exploring the early dust-obscured phase of galaxy formation with blind mid-/far-infrared spectroscopic surveys

While continuum imaging data at far-infrared to submillimetre wavelengths have provided tight constraints on the population properties of dusty star-forming galaxies up to high redshifts, future space missions like the Space Infrared Telescope for Cosmology and Astrophysics (SPICA) and ground-based facilities like the Cerro Chajnantor Atacama Telescope (CCAT) will allow detailed investigations of their physical properties via their mid-/far-infrared line emission. We present updated predictions for the number counts and the redshift distributions of star-forming galaxies spectroscopically detectable by these future missions. These predictions exploit a recent upgrade of evolutionary models, that include the effect of strong gravitational lensing, in the light of the most recent Herschel and South Pole Telescope data. Moreover the relations between line and continuum infrared luminosity are re-assessed, considering also differences among source populations, with the support of extensive simulations that take into account dust obscuration. The derived line luminosity functions are found to be highly sensitive to the spread of the line to continuum luminosity ratios. Estimates of the expected numbers of detections per spectral line by SPICA/SpicA FAR-infrared Instrument (SAFARI) and by CCAT surveys for different integration times per field of view at fixed total observing time are presented. Comparing with the earlier estimates by Spinoglio et al. we find, in the case of SPICA/SAFARI, differences within a factor of 2 in most cases, but occasionally much larger. More substantial differences are found for CCAT.
Born dry in the photoevaporation desert: Kepler's ultra-short-period planets formed water-poor

Recent surveys have uncovered an exciting new population of ultra-short-period (USP) planets with orbital periods less than a day. These planets typically have radii 2 Earth radii today, which is inconsistent with the observed evaporation desert, indicating that most USP planets likely formed from water-poor material within the snow-line. Finally, I examine the special case of 55 Cancri e and its possible composition in the light of recent observations, and discuss the prospects for further characterizing this population with future observations.
Improved (Related-Key) Differential-Based Neural Distinguishers for SIMON and SIMECK Block Ciphers

In CRYPTO 2019, Gohr made a pioneering attempt and successfully applied deep learning to the differential cryptanalysis against NSA block cipher SPECK32/64, achieving higher accuracy than the pure differential distinguishers. By its very nature, mining effective features in data plays a crucial role in data-driven deep learning. In this paper, in addition to considering the integrity of the information from the training data of the ciphertext pair, domain knowledge about the structure of differential cryptanalysis is also considered into the training process of deep learning to improve the performance. Meanwhile, taking the performance of the differential-neural distinguisher of SIMON32/64 as an entry point, we investigate the impact of input difference on the performance of the hybrid distinguishers to choose the proper input difference. Eventually, we improve the accuracy of the neural distinguishers of SIMON32/64, SIMON64/128, SIMECK32/64, and SIMECK64/128. We also obtain related-key differential-based neural distinguishers on round-reduced versions of SIMON32/64, SIMON64/128, SIMECK32/64, and SIMECK64/128 for the first time.