Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimal laminations and level sets of 1-harmonic functions

We collect several results concerning regularity of minimal laminations, and governing the various modes of convergence for sequences of minimal laminations. We then apply this theory to prove that a function has locally least gradient (is $1$-harmonic) iff its level sets are a minimal lamination; this resolves an open problem of Daskalopoulos and Uhlenbeck.
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision

We present EmerNeRF, a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes. Grounded in neural fields, EmerNeRF simultaneously captures scene geometry, appearance, motion, and semantics via self-bootstrapping. EmerNeRF hinges upon two core components: First, it stratifies scenes into static and dynamic fields. This decomposition emerges purely from self-supervision, enabling our model to learn from general, in-the-wild data sources. Second, EmerNeRF parameterizes an induced flow field from the dynamic field and uses this flow field to further aggregate multi-frame features, amplifying the rendering precision of dynamic objects. Coupling these three fields (static, dynamic, and flow) enables EmerNeRF to represent highly-dynamic scenes self-sufficiently, without relying on ground truth object annotations or pre-trained models for dynamic object segmentation or optical flow estimation. Our method achieves state-of-the-art performance in sensor simulation, significantly outperforming previous methods when reconstructing static (+2.93 PSNR) and dynamic (+3.70 PSNR) scenes. In addition, to bolster EmerNeRF's semantic generalization, we lift 2D visual foundation model features into 4D space-time and address a general positional bias in modern Transformers, significantly boosting 3D perception performance (e.g., 37.50% relative improvement in occupancy prediction accuracy on average). Finally, we construct a diverse and challenging 120-sequence dataset to benchmark neural fields under extreme and highly-dynamic settings.
Discrete unified gas kinetic scheme for the solution of electron Boltzmann transport equation with Callaway approximation

Electrons are the carriers of heat and electricity in materials, and exhibit abundant transport phenomena such as ballistic, diffusive, and hydrodynamic behaviors in systems with different sizes. The electron Boltzmann transport equation (eBTE) is a reliable model for describing electron transport, but it is a challenging problem to efficiently obtain the numerical solutions of eBTE within one unified scheme involving ballistic, hydrodynamics and/or diffusive regimes. In this work, a discrete unified gas kinetic scheme (DUGKS) in finite-volume framework is developed based on the eBTE with the Callaway relaxation model for electron transport. By reconstructing the distribution function at the cell interface, the processes of electron drift and scattering are coupled together within a single time step. Numerical tests demonstrate that the DUGKS can be adaptively applied to multiscale electron transport, across different regimes.
On the Convergence of Encoder-only Shallow Transformers

In this paper, we aim to build the global convergence theory of encoder-only shallow Transformers under a realistic setting from the perspective of architectures, initialization, and scaling under a finite width regime. The difficulty lies in how to tackle the softmax in self-attention mechanism, the core ingredient of Transformer. In particular, we diagnose the scaling scheme, carefully tackle the input/output of softmax, and prove that quadratic overparameterization is sufficient for global convergence of our shallow Transformers under commonly-used He/LeCun initialization in practice. Besides, neural tangent kernel (NTK) based analysis is also given, which facilitates a comprehensive comparison. Our theory demonstrates the separation on the importance of different scaling schemes and initialization. We believe our results can pave the way for a better understanding of modern Transformers, particularly on training dynamics.
SEGREGACIÓN LABORAL DE LAS MUJERES EN LA INDUSTRIA RURAL: EL CASO DE CASTILLA Y LEÓN

The process of social and economic development has favoured the insertion of women into a progressively broader labour market that has nevertheless been designed, and continues to function, on the basis of masculine parameters. Based on the analysis of the occupation structure in Spain, this article considers the idea that this segregation is horizontal as well as vertical and functional. Moreover, the results of research carried out for the case of Castilla
Ehrhart Positivity of Panhandle Matroids and the Ehrhart-Coefficient Upper-Bound Conjecture for Paving Matroids

Panhandle matroids are a specific lattice-path matroid corresponding to panhandle-shaped Ferrers diagrams. Their matroid polytopes are the subpolytopes carved from a hypersimplex to form matroid polytopes of paving matroids. It has been an active area of research to determine which families of matroid polytopes are Ehrhart positive. We prove Ehrhart positivity for panhandle matroid polytopes, thus confirming a conjecture of Hanely, Martin, McGinnis, Miyata, Nasr, Vindas-Mel\'endez, and Yin (2023). Another standing conjecture posed by Ferroni (2022) asserts that the coefficients of the Ehrhart polynomial of a connected matroid are bounded above by those of the corresponding uniform matroid. We prove Ferroni's conjecture for paving matroids -- a class conjectured to asymptotically contain all matroids. Our proofs rely solely on combinatorial techniques which involve determining intricate interpretations of certain set partitions.
A Case Study Using 10Be-26Al Exposure Dating at the Xi’an AMS Center

ABSTRACT Exposure age dating using in situ 10Be and 26Al is a very useful technique for dating fluvial terraces. This is especially true in semiarid regions where other methods suffer from a paucity of suitable dating materials. This article describes sample preparation procedures and analytical benchmarks established at the Xi’an Accelerator Mass Spectrometry (AMS) Center for the study of in situ 10Be and 26Al. Four intercomparison samples were analyzed in the study, using an improved sample preparation method. The exposure age results are shown to be in good agreement with published data, and demonstrate the reliability of the dating method. This article also presents new 10Be and 26Al results from quartz samples collected from a series of fluvial terraces from Guanshan River, along the Qilian Shan, northeastern Tibetan Plateau. The ages of three fluvial terraces from the Jinfosi site are shown to be (56.4±5.3) ka for T3, (10.7±1.0) ka for T2, and (7.2±1.0) ka for T1. The dating results are consistent with published data from the same region (10Be, 14C, and optically stimulated luminescence dating methods). A comparison of high-resolution climate records with age constraints for the terrace formation shows a close relationship between terrace formation and climate change.
A New Korean Text Classification Benchmark for Recognizing the Political Intents in Online Newspapers

Many users reading online articles in various magazines may suffer considerable difficulty in distinguishing the implicit intents in texts. In this work, we focus on automatically recognizing the political intents of a given online newspaper by understanding the context of the text. To solve this task, we present a novel Korean text classification dataset that contains various articles. We also provide deep-learning-based text classification baseline models trained on the proposed dataset. Our dataset contains 12,000 news articles that may contain political intentions, from the politics section of six of the most representative newspaper organizations in South Korea. All the text samples are labeled simultaneously in two aspects (1) the level of political orientation and (2) the level of pro-government. To the best of our knowledge, our paper is the most large-scale Korean news dataset that contains long text and addresses multi-task classification problems. We also train recent state-of-the-art (SOTA) language models that are based on transformer architectures and demonstrate that the trained models show decent text classification performance. All the codes, datasets, and trained models are available at https://github.com/Kdavid2355/KoPolitic-Benchmark-Dataset.
On $\alpha$-$z$-R\'{e}nyi divergence in the von Neumann algebra setting

We will investigate the $\alpha$-$z$-R\'{e}nyi divergence in the general von Neumann algebra setting based on Haagerup non-commutative $L^p$-spaces. In particular, we establish almost all its expected properties when $0<\alpha<1$ and some of them when $\alpha>1$. In an appendix we also give an equality condition for generalized H\"{o}lder's inequality in Haagerup non-commutative $L^p$-spaces.
Unraveling the Hyperfine Structure of Entanglement with the Decomposition of R\'enyi Contour

Entanglement contour and R\'{e}nyi contour reflect the real-space distribution of entanglement entropy, serving as the fine structure of entanglement. In this work, we unravel the hyperfine structure by rigorously decomposing R\'{e}nyi contour into the contributions from particle-number cumulants. We show that the hyperfine structure, introduced as a quantum-information concept, has several properties, such as additivity, normalization, symmetry, and unitary invariance. To extract the underlying physics of the hyperfine structure, we numerically study lattice fermion models with mass gap, critical point, and Fermi surface, and observe that different behaviors appear in the contributions from higher-order particle-number cumulants. We also identify exotic scaling behaviors in the case of mass gap with nontrivial topology, signaling the existence of topological edge states. In conformal field theory (CFT), we derive the dominant hyperfine structure of both R\'{e}nyi entropy and refined R\'{e}nyi entropy. By employing the AdS$_3$/CFT$_2$ correspondence, we find that the refined R\'{e}nyi contour can be holographically obtained by slicing the bulk extremal surfaces. The extremal surfaces extend outside the entanglement wedge of the corresponding extremal surface for entanglement entropy, which provides an exotic tool to probe the hyperfine structure of the subregion-subregion duality in the entanglement wedge reconstruction. This paper is concluded with an experimental protocol and interdisciplinary research directions for future study.
Traveling wave solutions to the free boundary incompressible Navier-Stokes equations with Navier boundary conditions

In this paper we study traveling wave solutions to the free boundary incompressible Navier-Stokes system with generalized Navier-slip conditions. The fluid is assumed to occupy a horizontally infinite strip-like domain that is bounded below by a flat rigid surface and above by a moving surface. We assume that the fluid is acted upon by a bulk force and a surface stress that are stationary in a coordinate system moving parallel to the fluid bottom, and a uniform gravitational force that is perpendicular to the flat rigid surface. We construct our solutions via an implicit function argument, and show that as the slip parameter shrinks to zero, the Navier-slip solutions converge to solutions to the no-slip problem obtained previously.
Domain Randomization via Entropy Maximization

Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL). Nevertheless, DR heavily hinges on the choice of the sampling distribution of the dynamics parameters, since high variability is crucial to regularize the agent's behavior but notoriously leads to overly conservative policies when randomizing excessively. In this paper, we propose a novel approach to address sim-to-real transfer, which automatically shapes dynamics distributions during training in simulation without requiring real-world data. We introduce DOmain RAndomization via Entropy MaximizatiON (DORAEMON), a constrained optimization problem that directly maximizes the entropy of the training distribution while retaining generalization capabilities. In achieving this, DORAEMON gradually increases the diversity of sampled dynamics parameters as long as the probability of success of the current policy is sufficiently high. We empirically validate the consistent benefits of DORAEMON in obtaining highly adaptive and generalizable policies, i.e. solving the task at hand across the widest range of dynamics parameters, as opposed to representative baselines from the DR literature. Notably, we also demonstrate the Sim2Real applicability of DORAEMON through its successful zero-shot transfer in a robotic manipulation setup under unknown real-world parameters.
Data-Free Distillation of Language Model by Text-to-Text Transfer

Data-Free Knowledge Distillation (DFKD) plays a vital role in compressing the model when original training data is unavailable. Previous works for DFKD in NLP mainly focus on distilling encoder-only structures like BERT on classification tasks, which overlook the notable progress of generative language modeling. In this work, we propose a novel DFKD framework, namely DFKD-T$^{3}$, where the pretrained generative language model can also serve as a controllable data generator for model compression. This novel framework DFKD-T$^{3}$ leads to an end-to-end learnable text-to-text framework to transform the general domain corpus to compression-friendly task data, targeting to improve both the \textit{specificity} and \textit{diversity}. Extensive experiments show that our method can boost the distillation performance in various downstream tasks such as sentiment analysis, linguistic acceptability, and information extraction. Furthermore, we show that the generated texts can be directly used for distilling other language models and outperform the SOTA methods, making our method more appealing in a general DFKD setting. Our code is available at https://gitee.com/mindspore/models/tree/master/research/nlp/DFKD\_T3.
MixCon3D: Synergizing Multi-View and Cross-Modal Contrastive Learning for Enhancing 3D Representation

Contrastive learning has emerged as a promising paradigm for 3D open-world understanding, jointly with text, image, and point cloud. In this paper, we introduce MixCon3D, which combines the complementary information between 2D images and 3D point clouds to enhance contrastive learning. With the further integration of multi-view 2D images, MixCon3D enhances the traditional tri-modal representation by offering a more accurate and comprehensive depiction of real-world 3D objects and bolstering text alignment. Additionally, we pioneer the first thorough investigation of various training recipes for the 3D contrastive learning paradigm, building a solid baseline with improved performance. Extensive experiments conducted on three representative benchmarks reveal that our method renders significant improvement over the baseline, surpassing the previous state-of-the-art performance on the challenging 1,156-category Objaverse-LVIS dataset by 5.7%. We further showcase the effectiveness of our approach in more applications, including text-to-3D retrieval and point cloud captioning. The code is available at https://github.com/UCSC-VLAA/MixCon3D.
A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning

Transferring representation for multitask imitation learning has the potential to provide improved sample efficiency on learning new tasks, when compared to learning from scratch. In this work, we provide a statistical guarantee indicating that we can indeed achieve improved sample efficiency on the target task when a representation is trained using sufficiently diverse source tasks. Our theoretical results can be readily extended to account for commonly used neural network architectures with realistic assumptions. We conduct empirical analyses that align with our theoretical findings on four simulated environments$\unicode{x2014}$in particular leveraging more data from source tasks can improve sample efficiency on learning in the new task.
Comparative study of new molecules of herbicides on growth and weed control in soybean.

Soybean [Glycine max (L.) Merrill] known as a wonder crop of twentieth century because it contains about 40-42 per cent high quality protein, 20-22 per cent edible oil, 20-30 per cent carbohydrates, 4.5 per cent minerals, 3.7 per cent fibre, 8.1 per cent water, large amount of phosphorus, high level of amino acids such as lysine, leucine, lecithin and vitamins. It is able to leave residual nitrogen effect for succeeding crop equivalent to 35-40 kg N ha-1 Soybean can tolerate mild drought as well as floods. This characteristic has made soybean to fit well in sustainable agriculture. Soybean due to its various uses is rightly called “Golden Gift” of nature to mankind. In India, soybean occupies an area of 10.18 m ha, with production potential of 12.28 million tonnes and average productivity of 1207 kg ha-1 (Anonymous, 2012). The productivity of soybean is less in India as compared to world average (2490 kg ha-1). Global area and production of soybean is 102 m ha and 254 million tonnes, respectively (Anonymous, 2011). The soybean grown in rainy season faces severe weed competition. Weed competition in soybean at early stage of crop growth is critical, as it causes yield losses upto 10 to 86 per cent depending upon weed infestation. The critical period of crop-weed competition in soybean is reported to be first 45 DAS (Panneerselvam and Lourduraj, 2000). Most prominent weed species found in soybean are Echinochloa crusgalli, Cynodon dactylon, Corchorus spp. , Cyperus rotundus, Euphorbia spp. , Commelina benghalensis, Parthenium hysterophorus, Setaria glauca, Eclipta alba, Phyllanthus niruri, Acalypha indica, Abstract : The present investigation was carried out during Kharif season of 2012 at the Research cum Instructional Farm, Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.). The experiment comprised of eight weed management practices viz., W1Sulfentrazone @ 300 g a.i. ha -1 as PE, W2Sulfentrazone @ 360 g a.i. ha -1 as PE, W3Pendimethalin @ 1 kg a.i. ha -1 as PE, W4Metribuzin @ 750 g a.i. ha -1 as PE, W5Imazethapyr @ 100 g a.i. ha-1 as PoE, W6Odyssy 70 WG (imazethapyr 35% + imazamox 35%) @ 100 g a.i. ha -1 as PoE, W7Hand weeding twice at 20 and 40 DAS and W8Untreated control. Experiment was laid out in Randomized Block Design with four replications. The soybean variety JS 9752 was grown as test crop. The result of the experiment indicated that the growth characters like plant height, number of branches and dry matter production was maximum under hand weeding twice at 20 and 40 DAS which was found comparable with sulfentrazone @ 360 g a.i. ha-1 as PE, sulfentrazone @ 300 g a.i. ha-1 as PE and imazethapyr @ 100 g a.i. ha-1 as PoE. These treatments also showed minimum weed index. Hand weeding twice at 20 and 40 DAS, sulfentrazone @ 360 g a.i. ha-1 as PE and imazethapyr @ 100 g a.i. ha-1 as PoE were also gave highest weed control efficiency.
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes

In this paper we introduce SemiGPC, a distribution-aware label refinement strategy based on Gaussian Processes where the predictions of the model are derived from the labels posterior distribution. Differently from other buffer-based semi-supervised methods such as CoMatch and SimMatch, our SemiGPC includes a normalization term that addresses imbalances in the global data distribution while maintaining local sensitivity. This explicit control allows SemiGPC to be more robust to confirmation bias especially under class imbalance. We show that SemiGPC improves performance when paired with different Semi-Supervised methods such as FixMatch, ReMixMatch, SimMatch and FreeMatch and different pre-training strategies including MSN and Dino. We also show that SemiGPC achieves state of the art results under different degrees of class imbalance on standard CIFAR10-LT/CIFAR100-LT especially in the low data-regime. Using SemiGPC also results in about 2% avg.accuracy increase compared to a new competitive baseline on the more challenging benchmarks SemiAves, SemiCUB, SemiFungi and Semi-iNat.
Reduced mobility? Urban exodus? Medium-term impacts of the COVID-19 pandemic on internal population movements in Latin American countries (preprint)

The COVID-19 pandemic has impacted the national systems of population movement around the world. Existing work has focused on countries of the Global North and restricted to the immediate effects of COVID-19 data during 2020. Data have represented a major limitation to monitor change in mobility patterns in countries in the Global South. Drawing on aggregate anonymised mobile phone location data from Meta-Facebook users, we aim to analyse the extent and persistence of changes in the levels (or intensity) and spatial patterns of internal population movement across the rural-urban continuum in Argentina, Chile and Mexico over a 26-month period from March 2020 to May 2022. We reveal an overall systematic decline in the level of short- and long-distance movement during the enactment of nonpharmaceutical interventions in 2020, with the largest reductions occurred in the most dense areas. We also show that these levels bounced back closer to pre-pandemic levels in 2022 following the relaxation of COVID-19 stringency measures. However, the intensity of these movements has remained below pre-pandemic levels in many areas in 2022. Additionally our findings lend some support to the idea of an urban exodus. They reveal a continuing negative net balances of short-distance movements in the most dense areas of capital cities in Argentina and Mexico, reflecting a pattern of suburbanisation. Chile displays limited changes in the net balance of short-distance movements but reports a net loss of long-distance movements. These losses were, however, temporary, moving to neutral and positive balances in 2021 and 2022.
Cross-cultural trade in world history

A single theme is pursued in this book - the trade between peoples of differing cultures through world history. Extending from the ancient world to the coming of the commercial revolution, Professor Curtin's discussion encompasses a broad and diverse group of trading relationships. Drawing on insights from economic history and anthropology, Professor Curtin has attempted to move beyond a Europe-centred view of history, to one that can help us understand the entire range of societies in the human past. Examples have been chosen that illustrate the greatest variety of trading relationships between cultures. The opening chapters look at Africa, while subsequent chapters treat the ancient world, the Mediterranean trade with China, the Asian trade in the east, and European entry into the trade with maritime Asia, the Armenian trade carriers of the seventeenth century, and the North American fur trade. Wide-ranging in its concern and the fruit of exhaustive research, the book is nevertheless written so as to be accessible and stimulating to the specialist and the student alike.