Miracle-dz/UniFAR-FTUs huggingface dataset:
Paleogene vegetation of Eurasia: framework for mammalian faunas

Four major categories of Paleogene vegetation are described and their general distribution in Eurasia is plotted on polar projections of paleogeographic maps. Methods and approaches for reconstructing Paleogene plants and vegetation from fossil floras are briefly outlined. Paleocene and Eocene polar, broad-leaved deciduous forests occurred in the high paleolatitudes and up to the edge of the Arctic Ocean. Relative to subsequent paratropical forests these were very low diversity forests with little habitat variation, very few climbers and more open structure. The environment was warm and equable but with up to three months of winter darkness so that most herbivorous browse and the small, dry fruits and seeds would only have been available seasonally. Late Paleocene and Eocene broad-leaved paratropical to subtropical, evergreen forests are variable in time and space. The most evergreen thermophilic forests are documented in Europe and southern China associated with the Eocene thermal maximum. These forests were highly diverse, multistratal, rich in climbers, with closed structure and would have provided many varied habitats. They would have provided year round herbivorous browse of medium to large evergreen leaves and a wide variety of fruits, including many fleshy fruits. Late Eocene and Oligocene broad-leaved mixed deciduous and evergreen forests developed as climate changed following the thermal maximum from the late Middle Eocene and through the Oligocene. These forests had lower diversity, contained fewer lianas and climbers and would have had a more open structure than the paratropical forests. They would have provided a mixture of evergreen and deciduous leaf browse and a variety of fruits but with fewer fleshy fruits and more large dry fruits. Vegetation with sclerophyllous elements was variably developed in central China, through Kazakhstan and the tethyan islands and into eastern Europe in the Eocene. In the Oligocene it was well-developed in southern Europe and also occurred in Kazakhstan. This vegetation reflects a warm humid temperate climate but with a slightly drier interval where sclerophyllous elements become significant in abundance or diversity. This would have been dominantly a forest vegetation (possibly shrubland in places) but with relatively open structure. In the most sclerophyllous belts, such as the Oligocene of southern Europe, herbivorous browse would have consisted mainly of relatively tough small leaves and the fruits and seeds would have been mainly dry with tough outer coverings. This paper paints only a very general picture of Paleogene vegetation. Paleogene floras provide the potential for detailed interpretation of vegetational, and hence biome, response to global change such as warm climate thermal maxima and tectonic events. Realising this potential requires independent dating of more fossil floras, new physiognomic analyses, rigorous re-investigation of nearest living relative assessments and integrated palynological and macrofossil studies which take account of taphonomic bias.
Text2Street: Controllable Text-to-image Generation for Street Views

Text-to-image generation has made remarkable progress with the emergence of diffusion models. However, it is still a difficult task to generate images for street views based on text, mainly because the road topology of street scenes is complex, the traffic status is diverse and the weather condition is various, which makes conventional text-to-image models difficult to deal with. To address these challenges, we propose a novel controllable text-to-image framework, named \textbf{Text2Street}. In the framework, we first introduce the lane-aware road topology generator, which achieves text-to-map generation with the accurate road structure and lane lines armed with the counting adapter, realizing the controllable road topology generation. Then, the position-based object layout generator is proposed to obtain text-to-layout generation through an object-level bounding box diffusion strategy, realizing the controllable traffic object layout generation. Finally, the multiple control image generator is designed to integrate the road topology, object layout and weather description to realize controllable street-view image generation. Extensive experiments show that the proposed approach achieves controllable street-view text-to-image generation and validates the effectiveness of the Text2Street framework for street views.
Vortex spin liquid with fractional quantum spin Hall effect in moir\'e Chern bands

Integer and fractional quantum anomalous Hall (QAH) effects have been widely seen in moir\'e systems. Recently there is even observation of a time reversal invariant fractional quantum spin hall (FQSH) state at filling $n=3$ in twisted MoTe$_2$ bilayer. We consider a pair of half-filled $C=\pm 1$ Chern band in the two valleys, similar to the well-studied quantum Hall bilayer, but now with opposite chiralities. Due to the strong inter-valley repulsion, we expect a charge gap opening with low energy physics dominated by the neutral inter-valley excitons. However, the presence of an effective `flux' frustrates exciton condensation by proliferating vortices. Here we construct a vortex liquid of excitons dubbed as vortex spin liquid (VSL), from exciton pairing of the composite fermions in the decoupled composite Fermi liquids (CFL) phase. This insulator is a quantum spin liquid with gapless spin excitations carried by the flux of an emergent U(1) gauge field. Additionally, there exist neutral and spinless Fermi surfaces formed by fermionic vortices of a nearby inter-valley-coherent (IVC) order. Unlike a conventional Mott insulator, the VSL phase also exhibits FQSH effect with gapless helical charge modes along the edge. Our work suggests a new platform to search for quantum spin liquid enriched by fractional quantum spin Hall effect. We also point out the possibility of quantum oscillations and thermal Hall effect under Zeeman field in this exotic insulator.
Learning with Diversification from Block Sparse Signal

This paper introduces a novel prior called Diversified Block Sparse Prior to characterize the widespread block sparsity phenomenon in real-world data. By allowing diversification on variance and correlation matrix, we effectively address the sensitivity issue of existing block sparse learning methods to pre-defined block information, which enables adaptive block estimation while mitigating the risk of overfitting. Based on this, a diversified block sparse Bayesian learning method (DivSBL) is proposed, utilizing EM algorithm and dual ascent method for hyperparameter estimation. Moreover, we establish the global and local optimality theory of our model. Experiments validate the advantages of DivSBL over existing algorithms.
Homogenization of L\'evy type operators with random, ergodic coefficients

We study the problem of homogenization of a family of L\'evy type processes $(X_t^{\varepsilon,x;\omega})_{t\ge0}$, $\varepsilon\in(0,1]$, starting at $x\in\mathbb{R}^d$ and whose random Fourier symbols equal $q_\varepsilon(x,\xi;\omega)=\frac{1}{\varepsilon^{\alpha}}q\Big(\frac{x}{\varepsilon},\varepsilon\xi;\omega\Big)$, where $$q(x,\xi;\omega)=-ib(x;\omega)\cdot\xi+\xi\cdot a(x;\omega)\xi+\int_{\mathbb{R}^d}(1-e^{i y\cdot\xi}+i y \cdot \xi1_{\{|y|\le1\}})n(x,dy;\omega),$$ for $(x,\xi,\omega)\in \mathbb{R}^{2d}\times\Omega$. Here, $\alpha\in(0,2]$ and the L\'evy triplet $(b(x;\omega), a(x;\omega), n(x,\cdot;\omega))_{x\in \mathbb{R}^d}$ is a stationary ergodic random field over some probability space $(\Omega,\mu)$. Our main assumptions are that: 1) for any $\omega$, the operator $-q(\cdot,D;\omega)$, defined on $C^2_c(\mathbb{R}^d)$, is closable in the space of continuous functions vanishing at infinity and its closure generates a Feller semigroup, 2) there exist constants $c_Q,C_Q>0$ independent of $(x,\xi,\omega)$, such that Re $q(x,\xi;\omega) \ge c_Q|\xi|^{\alpha}$ for $\xi\in\mathbb{R}^d$ and $|q(x,\xi;\omega)| \le C_Q|\xi|^{\alpha}$ for $|\xi|\le 1$, and 3) $q_\varepsilon(0,\xi;\omega)\approx q_L(\xi;\omega)$, as $\varepsilon\to 0$, in $\mu$-probability, where $q_L(\xi;\omega)$ is the Fourier symbol of some L\'evy process for each $\omega$. Under some technical assumptions, we show the weak convergence of the laws of $(X_t^{\varepsilon,x;\cdot})_{t\ge0}$ in the Skorokhod space, as $\varepsilon\to0$, to a L\'evy process whose Fourier symbol $\bar q(\xi)$ is given by $\int_{\Omega} q_L(\xi)\Phi_*d\mu$, where $\Phi_*$ is a strictly positive density w.r. t. $\mu$. Our result has an analytic interpretation in terms of the convergence of the solutions of random integro-differential equations $\partial_tu_\varepsilon(t,x;\omega)=-q_\varepsilon(x,D;\omega) u_\varepsilon(t,x;\omega)$.
Prompting Implicit Discourse Relation Annotation

Pre-trained large language models, such as ChatGPT, archive outstanding performance in various reasoning tasks without supervised training and were found to have outperformed crowdsourcing workers. Nonetheless, ChatGPT’s performance in the task of implicit discourse relation classification, prompted by a standard multiple-choice question, is still far from satisfactory and considerably inferior to state-of-the-art supervised approaches. This work investigates several proven prompting techniques to improve ChatGPT’s recognition of discourse relations. In particular, we experimented with breaking down the classification task that involves numerous abstract labels into smaller subtasks. Nonetheless, experiment results show that the inference accuracy hardly changes even with sophisticated prompt engineering, suggesting that implicit discourse relation classification is not yet resolvable under zero-shot or few-shot settings.
Information and Configurational Entropy in Glassy Systems

It is often stated that if one is presented with a snapshot of the positions of the molecules of a glass and one of a liquid, one is unable to tell the difference. Here we argue instead that given several such snapshots taken over a time-interval, even without specifying the times, there is a definite procedure to assess precisely the level of glassiness: it suffices to concatenate the snapshots side-by-side, and to subject the joint picture to a lossless compression protocol. We argue that the size of the compressed file yields a direct and unambiguous measure of the `vibrational' and `configurational' entropies, and may be used to study the associated glass length scale in or out of equilibrium through the size and frequency of the repeated motifs essential to the compression, a quantity that would diverge at a putative glass transition.
Sharp Lower Bounds on the Manifold Widths of Sobolev and Besov Spaces

We consider the problem of determining the asymptotics of the manifold $n$-widths of Sobolev and Besov spaces with error measured in the $L_p$-norm. The manifold widths control how efficiently these spaces can be approximated by general non-linear parametric methods with the restriction that the parameter selection and parameterization maps must be continuous. Existing upper and lower bounds only match when the Sobolev or Besov smoothness index $q$ satisfies $q\leq p$. We close this gap and extend the existing lower bounds to all $1\leq p,q\leq \infty$. In the process, we show that the Bernstein widths, which are typically used to lower bound the manifold widths, may decay asymptotically slower than the manifold widths.
Image captioning for Brazilian Portuguese using GRIT model

This work presents the early development of a model of image captioning for the Brazilian Portuguese language. We used the GRIT (Grid - and Region-based Image captioning Transformer) model to accomplish this work. GRIT is a Transformer-only neural architecture that effectively utilizes two visual features to generate better captions. The GRIT method emerged as a proposal to be a more efficient way to generate image captioning. In this work, we adapt the GRIT model to be trained in a Brazilian Portuguese dataset to have an image captioning method for the Brazilian Portuguese Language.
Early Stopping of Untrained Convolutional Neural Networks

In recent years, new regularization methods based on (deep) neural networks have shown very promising empirical performance for the numerical solution of ill-posed problems, such as in medical imaging and imaging science. Due to the nonlinearity of neural networks, these methods often lack satisfactory theoretical justification. In this work, we rigorously discuss the convergence of a successful unsupervised approach that utilizes untrained convolutional neural networks to represent solutions to linear ill-posed problems. Untrained neural networks have particular appeal for many applications because they do not require paired training data. The regularization property of the approach relies solely on the architecture of the neural network instead. Due to the vast over-parameterization of the employed neural network, suitable early stopping is essential for the success of the method. We establish that the classical discrepancy principle is an adequate method for early stopping of two-layer untrained convolutional neural networks learned by gradient descent, and furthermore, it yields an approximation with minimax optimal convergence rates. Numerical results are also presented to illustrate the theoretical findings.
On the Cahn-Hilliard equation with kinetic rate dependent dynamic boundary conditions and non-smooth potentials: Well-posedness and asymptotic limits

We consider a class of Cahn-Hilliard equation with kinetic rate dependent dynamic boundary conditions that describe possible short-range interactions between the binary mixture and the solid boundary. In the presence of surface diffusion on the boundary, the initial boundary value problem can be viewed as a transmission problem consisting of Cahn-Hilliard type equations both in the bulk and on the boundary. We first prove existence, uniqueness and continuous dependence of global weak solutions. In the construction of solutions, an explicit convergence rate in terms of the parameter for the Yosida approximation is established. Under some additional assumptions, we also obtain the existence and uniqueness of global strong solutions. Next, we study the asymptotic limit as the coefficient of the boundary diffusion goes to zero and show that the limit problem with a forward-backward dynamic boundary condition is well-posed in a suitable weak formulation. Besides, we investigate the asymptotic limit as the kinetic rate tends to zero and infinity, respectively. Our results are valid for a general class of bulk and boundary potentials with double-well structure, including the physically relevant logarithmic potential and the non-smooth double-obstacle potential.
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay

Large language models are increasingly solving tasks that are commonly believed to require human-level reasoning ability. However, these models still perform very poorly on benchmarks of general intelligence such as the Abstraction and Reasoning Corpus (ARC). In this paper, we approach ARC as a programming-by-examples problem, and introduce a novel and scalable method for language model self-improvement called Code Iteration (CodeIt). Our method iterates between 1) program sampling and hindsight relabeling, and 2) learning from prioritized experience replay. By relabeling the goal of an episode (i.e., the target program output given input) to the realized output produced by the sampled program, our method effectively deals with the extreme sparsity of rewards in program synthesis. Applying CodeIt to the ARC dataset, we demonstrate that prioritized hindsight replay, along with pre-training and data-augmentation, leads to successful inter-task generalization. CodeIt is the first neuro-symbolic approach that scales to the full ARC evaluation dataset. Our method solves 15% of ARC evaluation tasks, achieving state-of-the-art performance and outperforming existing neural and symbolic baselines.
Personalized Text Generation with Fine-Grained Linguistic Control

As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to make it more personalized. However, most research on controllable text generation focuses on controlling the content or modeling specific high-level/coarse-grained attributes that reflect authors’ writing styles, such as formality, domain, or sentiment. In this paper, we focus on controlling fine-grained attributes spanning multiple linguistic dimensions, such as lexical and syntactic attributes. We introduce a novel benchmark to train generative models and evaluate their ability to generate personalized text based on multiple fine-grained linguistic attributes. We systematically investigate the performance of various large language models on our benchmark and draw insights from the factors that impact their performance. We make our code, data, models, and benchmarks publicly available.
Unveiling a crystal's entropy of disorder via electron diffraction. A statistical mechanics approach

Upon melting, the molecules in the crystal explore numerous configurations, reflecting an increase in disorder. The molar entropy of disorder can be defined by Bolzmann's formula dSd = Rln(Wd) where Wd is the increase in the number of microscopic states, so far inaccessible experimentally. We found that the Arrhenius frequency factor A of the electron diffraction signal decay provides Wd via an experimental equation A = AINTWd where AINT is an inelastic scattering cross-section. The method connects Clausius and Boltzmann experimentally and supplements the Clausius approach, being applicable to a femtogram quantity of thermally unstable and biomolecular crystals. The data also showed that crystal disordering and crystallization of melt are reciprocal, both governed by the entropy change, but manifesting in opposite directions.
The Role of Child Gender in the Formation of Parents' Social Networks

Social networks play an important role in various aspects of life. While extensive research has explored factors such as gender, race, and education in network formation, one dimension that has received less attention is the gender of one's child. Children tend to form friendships with same-gender peers, potentially leading their parents to interact based on their child's gender. Focusing on households with children aged 3-5, we leverage a rich dataset from rural Bangladesh to investigate the role of children's gender in parental network formation. We estimate an equilibrium model of network formation that considers a child's gender alongside other socioeconomic factors. Counterfactual analyses reveal that children's gender significantly shapes parents' network structure. Specifically, if all children share the same gender, households would have approximately 15% more links, with a stronger effect for families having girls. Importantly, the impact of children's gender on network structure is on par with or even surpasses that of factors such as income distribution, parental occupation, education, and age. These findings carry implications for debates surrounding coed versus single-sex schools, as well as policies that foster inter-gender social interactions among children.
Large Eddy Simulation of the evolution of the soot size distribution in turbulent nonpremixed flames using the Bivariate Multi-Moment Sectional Method

A joint volume-surface formalism of the Multi-Moment Sectional Method (MMSM) is developed to describe the evolution of soot size distribution in turbulent reacting flows. The bivariate MMSM (or BMMSM) considers three statistical moments per section, including the total soot number density, total soot volume, and total soot surface area per section. A linear profile along the volume coordinate is considered to reconstruct the size distribution within each section, which weights a delta function along the surface coordinate. The closure for the surface considers that the primary particle diameter is constant so the surface/volume ratio constant within each section. The inclusion of the new variable in BMMSM allows for the description of soot's fractal aggregate morphology compared to the strictly spherical assumption of its univariate predecessor. BMMSM is shown to reproduce bimodal soot size distributions in simulations of one-dimensional laminar sooting flames as in experimental measurements. To demonstrate its performance in turbulent reacting flows, BMMSM is coupled to a Large Eddy Simulation framework to simulate a laboratory-scale turbulent nonpremixed jet flame. Computational results are validated against available experimental measurements of soot size distribution, showing the ability of BMMSM to reproduce the evolution of the size distribution from unimodal to bimodal moving downstream in the flame. In general, varying the number of sections has limited influence on results, and accurate results are obtained with as few as eight sections so 24 total degrees of freedom. The impact of using a different statistical model for soot (HMOM) is also investigated. The total computational cost of using BMMSM as low as approximately 44% more than the cost of HMOM. The new formulation results in a computationally efficient approach for the soot size distribution in turbulent reacting flows.
DMAT: A Dynamic Mask-Aware Transformer for Human De-occlusion

Human de-occlusion, which aims to infer the appearance of invisible human parts from an occluded image, has great value in many human-related tasks, such as person re-id, and intention inference. To address this task, this paper proposes a dynamic mask-aware transformer (DMAT), which dynamically augments information from human regions and weakens that from occlusion. First, to enhance token representation, we design an expanded convolution head with enlarged kernels, which captures more local valid context and mitigates the influence of surrounding occlusion. To concentrate on the visible human parts, we propose a novel dynamic multi-head human-mask guided attention mechanism through integrating multiple masks, which can prevent the de-occluded regions from assimilating to the background. Besides, a region upsampling strategy is utilized to alleviate the impact of occlusion on interpolated images. During model learning, an amodal loss is developed to further emphasize the recovery effect of human regions, which also refines the model's convergence. Extensive experiments on the AHP dataset demonstrate its superior performance compared to recent state-of-the-art methods.