Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Gradient-based Multi-directional Controls in GANs

Generative Adversarial Networks (GANs) have been widely applied in modeling diverse image distributions. However, despite its impressive applications, the structure of the latent space in GANs largely remains as a black-box, leaving its controllable generation an open problem, especially when spurious correlations between different semantic attributes exist in the image distributions. To address this problem, previous methods typically learn linear directions or individual channels that control semantic attributes in the image space. However, they often suffer from imperfect disentanglement, or are unable to obtain multi-directional controls. In this work, in light of the above challenges, we propose a novel approach that discovers nonlinear controls, which enables multi-directional manipulation as well as effective disentanglement, based on gradient information in the learned GAN latent space. More specifically, we first learn interpolation directions by following the gradients from classification networks trained separately on the attributes, and then navigate the latent space by exclusively controlling channels activated for the target attribute in the learned directions. Empirically, with small training data, our approach is able to gain fine-grained controls over a diverse set of bi-directional and multi-directional attributes, and we showcase its ability to achieve disentanglement significantly better than state-of-the-art methods both qualitatively and quantitatively.
Geometric and Learning-Based Mesh Denoising: A Comprehensive Survey

Mesh denoising is a fundamental problem in digital geometry processing. It seeks to remove surface noise while preserving surface intrinsic signals as accurately as possible. While traditional wisdom has been built upon specialized priors to smooth surfaces, learning-based approaches are making their debut with great success in generalization and automation. In this work, we provide a comprehensive review of the advances in mesh denoising, containing both traditional geometric approaches and recent learning-based methods. First, to familiarize readers with the denoising tasks, we summarize four common issues in mesh denoising. We then provide two categorizations of the existing denoising methods. Furthermore, three important categories, including optimization-, filter-, and data-driven-based techniques, are introduced and analyzed in detail, respectively. Both qualitative and quantitative comparisons are illustrated, to demonstrate the effectiveness of the state-of-the-art denoising methods. Finally, potential directions of future work are pointed out to solve the common problems of these approaches. A mesh denoising benchmark is also built in this work, and future researchers will easily and conveniently evaluate their methods with state-of-the-art approaches. To aid reproducibility, we release our datasets and used results at https://github.com/chenhonghua/Mesh-Denoiser.
Mutual Witness Gabriel Drawings of Complete Bipartite Graphs

Let Γ be a straight-line drawing of a graph and let u and v be two vertices of Γ . The Gabriel disk of u, v is the disk having u and v as antipodal points. A pair (cid:104) Γ 0 , Γ 1 (cid:105) of vertex-disjoint straight-line drawings form a mutual witness Gabriel drawing when, for i = 0 , 1, any two vertices u and v of Γ i are adjacent if and only if their Gabriel disk does not contain any vertex of Γ 1 − i . We characterize the pairs (cid:104) G 0 , G 1 (cid:105) of complete bipartite graphs that admit a mutual witness Gabriel drawing. The characterization leads to a linear time testing algorithm. We also show that when at least one of the graphs in the pair (cid:104) G 0 , G 1 (cid:105) is complete k -partite with k > 2 and all partition sets in the two graphs have size greater than one, the pair does not admit a mutual witness Gabriel drawing.
Critical field measure for topological superconductivity

A promising direction for harnessing the laws of quantum mechanics to perform quantum computation 1 is the topological quantum computation 2,3 , for which topological superconductivity is one of the physical platforms. Intensive theoretical studies 4–14 have been carried out and followed by tremendous experimental efforts 15–29 in the realization of topological superconductivity, though debates remain mainly because of the inadequacy of the convincing detection techniques 30–35 . Here we report a theoretical ﬁnding in a superconductor with surface or edge states, where the critical ﬁeld is found to obey the unique power-law temperature dependence B c 3 ∼ ( T c − T ) γ with T c being the onset critical temperature of superconductivity and the fractional exponent γ = 2 / 3 , differing from the conventional values of γ = 1 / 2 and 1 . The topological surface superconductivity is hence expected in the three-dimensional (3D) system and the topological edge superconductivity in the two-dimensional (2D) system. The application of this measure to the intrinsic topological superconductors FeTe 1 − x Se x ( x ∼ 0 . 5 ) supports the validity of such an accessible, convenient, and reliable transport technique for the identiﬁcation of topological superconductivity. The new form of theory on topological superconductivity together with the developed identiﬁcation technique is expected to guide
Vision-Language Adaptive Mutual Decoder for OOV-STR

Recent works have shown huge success of deep learning models for common in vocabulary (IV) scene text recognition. However, in real-world scenarios, out-of-vocabulary (OOV) words are of great importance and SOTA recognition models usually perform poorly on OOV settings. Inspired by the intuition that the learned language prior have limited OOV preformence, we design a framework named Vision Language Adaptive Mutual Decoder (VLAMD) to tackle OOV problems partly. VLAMD consists of three main conponents. Firstly, we build an attention based LSTM decoder with two adaptively merged visual-only modules, yields a vision-language balanced main branch. Secondly, we add an auxiliary query based autoregressive transformer decoding head for common visual and language prior representation learning. Finally, we couple these two designs with bidirectional training for more diverse language modeling, and do mutual sequential decoding to get robuster results. Our approach achieved 70.31\% and 59.61\% word accuracy on IV+OOV and OOV settings respectively on Cropped Word Recognition Task of OOV-ST Challenge at ECCV 2022 TiE Workshop, where we got 1st place on both settings.
Computing grain boundary “phase” diagrams

Grain boundaries (GBs) can be treated as two‐dimensional (2‐D) interfacial phases (also called “complexions”) that can undergo interfacial phase‐like transitions. As bulk phase diagrams and calculation of phase diagram (CALPHAD) methods serve as a foundation for modern materials science, we propose to extend them to GBs to have equally significant impacts. This perspective article reviews a series of studies to compute the GB counterparts to bulk phase diagrams. First, a phenomenological interfacial thermodynamic model was developed to construct GB lambda diagrams to forecast high‐temperature GB disordering and related trends in sintering and other properties for both metallic and ceramic materials. In parallel, an Ising‐type lattice statistical thermodynamic model was utilized to construct GB adsorption (segregation) diagrams, which predicted first‐order GB adsorption transitions and critical phenomena. These two simplified thermodynamic models emphasize the GB structural (disordering) and chemical (adsorption) aspects, respectively. Subsequently, hybrid Monte Carlo and molecular dynamics atomistic simulations were used to compute more rigorous and accurate GB “phase” diagrams. Computed GB diagrams of thermodynamic and structural properties were further extended to include mechanical properties. Moreover, machine learning algorithms were combined with atomistic simulations to predict GB properties as functions of four independent compositional variables and temperature in a 5‐D space for a given GB in high‐entropy alloys or as functions of five GB macroscopic (crystallographic) degrees of freedom plus temperature and composition for a binary alloy in a 7‐D space. Other relevant studies are also examined. Future perspective and outlook, including two emerging fields of high‐entropy grain boundaries (HEGBs) and electrically (or electrochemically) induced GB transitions, are discussed.
KINEMATICS OF THE X-SHAPED MILKY WAY BULGE: EXPECTATIONS FROM A SELF-CONSISTENT N-BODY MODEL

We explore the kinematics (both the radial velocity and the proper motion) of the vertical X-shaped feature in the Milky Way with an N-body bar/bulge model. From the solar perspective, the distance distribution of particles is double-peaked in fields passing through the X-shape. The separation and amplitude ratio between the two peaks qualitatively match the observed trends toward the Galactic bulge. We confirm clear signatures of cylindrical rotation in the pattern of mean radial velocity across the bar/bulge region. We also find possible imprints of coherent orbital motion inside the bar structure in the radial velocity distribution along l = 0 ° ? > , where the near and far sides of the bar/bulge show excesses of approaching and receding particles. The coherent orbital motion is also reflected in the slight displacement of the zero-velocity line in the mean radial velocity and the displacement of the maximum/minimum in the mean longitudinal proper motion across the bulge region. We find some degree of anisotropy in the stellar velocity within the X-shape, but the underlying orbital family of the X-shape cannot be clearly distinguished. Two potential applications of the X-shape in previous literature are tested, i.e., bulge rotation and Galactic center (GC) measurements. We find that the proper-motion difference between the two sides of the X-shape can be used to estimate the mean azimuthal streaming motion of the bulge, but not the pattern speed of the bar. We also demonstrate that the GC can be located with the X-shape, but the accuracy depends on the fitting scheme, the number of fields, and their latitudinal coverage.
Optimal Diagonal Preconditioning: Theory and Practice

Preconditioning has long been a staple technique in optimization, often applied to reduce the condition number of a matrix and speed up the convergence of algorithms. Although there are many popular preconditioning techniques in practice, most lack guarantees on reductions in condition number. Moreover, the degree to which we can improve over existing heuristic preconditioners remains an important practical question. In this paper, we study the problem of optimal diagonal preconditioning that achieves maximal reduction in the condition number of any full-rank matrix by scaling its rows and/or columns. We first reformulate the problem as a quasi-convex problem and provide a simple algorithm based on bisection. Then we develop an interior point algorithm with $O(\log(1/\epsilon))$ iteration complexity, where each iteration consists of a Newton update based on the Nesterov-Todd direction. Next, we specialize to one-sided optimal diagonal preconditioning problems, and demonstrate that they can be formulated as standard dual SDP problems. We then develop efficient customized solvers and study the empirical performance of our optimal diagonal preconditioning procedures through extensive experiments on large matrices. Our findings suggest that optimal diagonal preconditioners can significantly improve upon existing heuristics-based diagonal preconditioners at reducing condition numbers and speeding up iterative methods. Moreover, our implementation of customized solvers, combined with a random row/column sampling step, can find near-optimal diagonal preconditioners for matrices up to size 200,000 in reasonable time, demonstrating their practical appeal.
The Dirac-Dolbeault Operator Approach to the Hodge Conjecture

The Dirac-Dolbeault operator for a compact K\"ahler manifold is a special case of a Dirac operator. The Green function for the Dirac Laplacian over a Riemannian manifold with boundary allows to express the values of the sections of the Dirac bundle in terms of the values on the boundary, extending the mean value theorem of harmonic analysis. Utilizing this representation and the Nash-Moser generalized inverse function theorem we prove the existence of complex submanifolds of a complex projective manifold satisfying globally a certain partial differential equation under a certain injectivity assumption. Next, we show the existence of complex submanifolds whose fundamental classes span the rational Hodge classes, proving the Hodge conjecture for complex projective manifolds.
Cellular liberality is measurable as Lempel-Ziv complexity of fastq files

Many studies used the Shannon entropy of transcriptome data to determine cell dedifferentiation and differentiation. The collection of evidence has strengthened the certainty that the transcriptome's Shannon entropy may be used to quantify cellular dedifferentiation and differentiation. Quantifying this cellular status is being justified, we propose the term “liberality” for the quantitative value of cellular dedifferentiation and differentiation. In previous studies, we must convert the raw transcriptome data into quantitative transcriptome data through mapping, tag counting, assembling, and more bioinformatic processing to calculate the liberality. If we could remove this conversion step from estimating liberality, we could save computing resources and time and remove technical difficulties in using the computer. In this study, we propose a method of calculating cellular liberality without those transcriptome data conversion processes. We could calculate liberality by measuring the compression rate of raw transcriptome data. This technique, independent of reference genome data, increased the generality of cellular liberality.
Scalable Model-based Policy Optimization for Decentralized Networked Systems

Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple tasks. Such a challenge is more outstanding in multi-agent tasks, as each step of operation is more costly, requiring communications or shifting or resources. This work aims to improve data efficiency of multi-agent control by model-based learning. We consider networked systems where agents are cooperative and communicate only locally with their neighbors, and propose the decentralized model-based policy optimization framework (DMPO). In our method, each agent learns a dynamic model to predict future states and broadcast their predictions by communication, and then the policies are trained under the model rollouts. To alleviate the bias of model-generated data, we restrain the model usage for generating myopic rollouts, thus reducing the compounding error of model generation. To pertain the independence of policy update, we introduce extended value function and theoretically prove that the resulting policy gradient is a close approximation to true policy gradients. We evaluate our algorithm on several benchmarks for intelligent transportation systems, which are connected autonomous vehicle control tasks (Flow and CACC) and adaptive traffic signal control (ATSC). Empirical results show that our method achieves superior data efficiency and matches the performance of model-free methods using true models. The source code of our algorithm and baselines can be found at https://github.com/PKU-MARL/Model-Based-MARL.
Coherent Structures in Transition of a Flat-Plate Boundary Layer at Ma=0.7

Direct numerical simulation (DNS) of a spatially evolving flat-plate boundary layer transition process at free stream Mach number 0.7 is performed. Tollmien-Schlichting (T-S) waves are added on the inlet boundary as the disturbances before transition. Typical coherent structures in the transition process are investigated based on the second invariant of velocity gradient tensor. The instantaneous shear stress and the mean velocity profile in the transition region are studied. In our view, the fact that the peak value of shear stress in the stress concentration area increases and exceeds a threshold value during the later stage of the transition process plays an important role in the laminar breakdown process.
Molecular advances to study the function, evolution and spectral tuning of arthropod visual opsins

Visual opsins of vertebrates and invertebrates diversified independently and converged to detect ultraviolet to long wavelengths (LW) of green or red light. In both groups, colour vision largely derives from opsin number, expression patterns and changes in amino acids interacting with the chromophore. Functional insights regarding invertebrate opsin evolution have lagged behind those for vertebrates because of the disparity in genomic resources and the lack of robust in vitro systems to characterize spectral sensitivities. Here, we review bioinformatic approaches to identify and model functional variation in opsins as well as recently developed assays to measure spectral phenotypes. In particular, we discuss how transgenic lines, cAMP-spectroscopy and sensitive heterologous expression platforms are starting to decouple genotype–phenotype relationships of LW opsins to complement the classical physiological-behavioural-phylogenetic toolbox of invertebrate visual sensory studies. We illustrate the use of one heterologous method by characterizing novel LW Gq opsins from 10 species, including diurnal and nocturnal Lepidoptera, a terrestrial dragonfly and an aquatic crustacean, expressing them in HEK293T cells, and showing that their maximum absorbance spectra (λmax) range from 518 to 611 nm. We discuss the advantages of molecular approaches for arthropods with complications such as restricted availability, lateral filters, specialized photochemistry and/or electrophysiological constraints. This article is part of the theme issue ‘Understanding colour vision: molecular, physiological, neuronal and behavioural studies in arthropods’.
Path Planning for UAV Communication Networks: Related Technologies, Solutions, and Opportunities

Path planning has been a hot and challenging field in unmanned aerial vehicles (UAV). With the increasing demand of society and the continuous progress of technologies, UAV communication networks (UAVCN) are also flourishing. The mobility of UAV nodes allows for flexible network deployment, but some challenges are brought, such as power constraints, throughput, cost, and time efficiency. Therefore, path planning is significant for UAVCN. This article presents a review of UAVCN path planning. We first introduce the network structure and performance evaluation of UAVCN. We then investigate the generic UAV path planning algorithms and the path planning algorithms in UAVCN. In this article, the advantages and disadvantages of each path planning algorithm and the functional problems. The challenges faced in path planning for UAVCN, the solutions, state-of-the-art, and representative results are also presented. In addition, we illustrate future research directions for UAVCN path planning as well, which can provide some help to researchers.
Colour vision in ants (Formicidae, Hymenoptera)

Ants are ecologically one of the most important groups of insects and exhibit impressive capabilities for visual learning and orientation. Studies on numerous ant species demonstrate that ants can learn to discriminate between different colours irrespective of light intensity and modify their behaviour accordingly. However, the findings across species are variable and inconsistent, suggesting that our understanding of colour vision in ants and what roles ecological and phylogenetic factors play is at an early stage. This review provides a brief synopsis of the critical findings of the past century of research by compiling studies that address molecular, physiological and behavioural aspects of ant colour vision. With this, we aim to improve our understanding of colour vision and to gain deeper insights into the mysterious and colourful world of ants. This article is part of the theme issue ‘Understanding colour vision: molecular, physiological, neuronal and behavioural studies in arthropods’.
Multigrid for the Helmholtz equation: choice of the coarse scale operator

We study the convergence of multigrid schemes for the Helmholtz equation, focusing in particular on the choice of the coarse scale operators. Assuming that the coarse scale solutions should approximate the true solutions, the coarsest level grid must have a minimum number, say $G_c$, of points per wavelength. The oscillating nature of the solutions implies the requirement $G_c > 2$. However, in examples the requirement can be more like $G_c \gtrsim 8$, in a trade-off involving also the amount of damping present and the number of multigrid iterations. We conjecture that the main factor affecting $G_c$ is the difference in phase speeds between the coarse and fine scale operators. Standard 5-point finite differences in 2-D are our first example. A new coarse scale 9-point operator is constructed to match the fine scale phase speeds. We then compare phase speeds and multigrid performance of standard schemes, with a new scheme that uses the new coarse scale operators and a modified smoother. The required $G_c$ is reduced from about 8 to about 4, while at the same time less damping is needed so that waves propagate over ~100 wavelengths in the new scheme. Next we consider extensions of the method to more general cases. In 3-D comparable results are obtained with standard 7-point differences and optimized 15-point coarse grid operators, leading to an order of magnitude reduction in the number of unknowns for the coarsest scale linear system. Finally we show how to include PML boundary layers, using a regular grid finite element method. Matching coare scale operators can easily be constructed for other discretizations. The method is therefore potentially useful for a large class of discretized high-frequency Helmholtz equations.
AMS Radiocarbon Date for Precolumbian Caribbean Rock Art: Borbón Cave No. 1, Dominican Republic

We present an AMS radiocarbon date from a bird image in a cave on the island of Hispaniola in the northern Caribbean. Borbón Cave No. 1 contains a key rock art assemblage that likely reflects a significant part of past native Taíno societies’ symbolic thought and beliefs. The grouping has already served to define one rock art style in the Antilles: the Borbón School. Our sample yielded a date of 890 ± 30 BP (1045–1225 cal AD). This result, as well as additional published dates for the region, confirms that these images were created before European contact with the region. Further dating comparison indicates that Taíno artistic traditions persisted for some years after the arrival and settlement of Spanish colonists on the island.