Miracle-dz/UniFAR-FTUs huggingface dataset:
Taphonomic analysis of large mammals recovered from the Pleistocene Rancho La Brea tar seeps

Abstract The asphalt deposits of Rancho La Brea are well known for preserving a prolific and diverse Late Pleistocene fauna. However, little taphonomic research has been done on these collections. To better understand the formation of this impressive assemblage, a taphonomic study of the bones of the large mammals from one asphalt deposit, Pit 91, was carried out, and results are presented here. The predominance of carnivore specimens in the Rancho La Brea deposits has long been explained by a scenario in which a prey animal was trapped in asphalt and attracted large numbers of carnivores who became trapped in turn. Hypotheses generated from this scenario were tested by collecting taphonomic data on over 18,000 specimens. Weathering data indicate that elements were deposited fairly rapidly. However, patterns of skeletal part representation for the seven most common species demonstrate that complete skeletons are not present. Water transport is ruled out as the primary process responsible for removing skeletal elements based on abrasion data. Instead, the feeding activity of carnivores (ravaging) appears to have been an important factor in the formation of the assemblage.
Some New Results on Pseudo n-Strong Drazin Inverses in Rings

In this paper, we give a further study in-depth of the pseudo $n$-strong Drazin inverses in an associative unital ring $R$. The characterizations of elements $a,b\in R$ for which $aa^{\tiny{\textcircled{\qihao D}}}=bb^{\tiny{\textcircled{\qihao D}}}$ are provided, and some new equivalent conditions on pseudo $n$-strong Drazin inverses are obtained. In particular, we show that an element $a\in R$ is pseudo $n$-strong Drazin invertible if, and only if, $a$ is $p$-Drazin invertible and $a-a^{n+1}\in \sqrt{J(R)}$ if, and only if, there exists $e^2=e\in {\rm comm}^2(a)$ such that $ae\in \sqrt{J(R)}$ and $1-(a+e)^n\in \sqrt{J(R)}$. We also consider pseudo $n$-strong Drazin inverses with involution, and discuss the extended versions of Cline's formula and Jacobson's lemma of this new class of generalized inverses. Likewise, we define and explore the so-called {\it pseudo $\pi$-polar} rings and demonstrate their relationships with periodic rings and strongly $\pi$-regular rings, respectively.
Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World

Reinforcement learning (RL) with dense rewards and imitation learning (IL) with human-generated trajectories are the most widely used approaches for training modern embodied agents. RL requires extensive reward shaping and auxiliary losses and is often too slow and ineffective for long-horizon tasks. While IL with human supervision is effective, collecting human trajectories at scale is extremely expensive. In this work, we show that imitating shortest-path planners in simulation produces agents that, given a language instruction, can proficiently navigate, explore, and manipulate objects in both simulation and in the real world using only RGB sensors (no depth map or GPS coordinates). This surprising result is enabled by our end-to-end, transformer-based, SPOC architecture, powerful visual encoders paired with extensive image augmentation, and the dramatic scale and diversity of our training data: millions of frames of shortest-path-expert trajectories collected inside approximately 200,000 procedurally generated houses containing 40,000 unique 3D assets. Our models, data, training code, and newly proposed 10-task benchmarking suite CHORES will be open-sourced.
Hook lengths in self-conjugate partitions

In 2010, G. -N. Han obtained the generating function for the number of size $t$ hooks among integer partitions. Here we obtain these generating functions for self-conjugate partitions, which are particularly elegant for even $t$. If $n_t(\lambda)$ is the number of size $t$ hooks in a partition $\lambda,$ then for even $t$ we have $$\sum_{\lambda\in \mathcal{SC}} x^{n_t(\lambda)} q^{\vert\lambda\vert} = (-q;q^2)_{\infty} \cdot ((1-x^2)q^{2t};q^{2t})_{\infty}^{\frac{t}2}. $$ As a consequence, if $a_t^*(n)$ is the number of such hooks among the self-conjugate partitions of $n,$ then for even $t$ we obtain the simple formula $$ a_t^*(n)=t\sum_{j\geq 1} q^*(n-2tj), $$ where $q^*(m)$ is the number of partitions of $m$ into distinct odd parts. As a corollary, we find that $t\mid a_t^*(n),$ which confirms a conjecture of Ballantine, Burson, Craig, Folsom, and Wen.
ReconFusion: 3D Reconstruction with Diffusion Priors

3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes. However, recovering a high-quality NeRF typically requires tens to hundreds of input images, resulting in a time-consuming capture process. We present ReconFusion to reconstruct real-world scenes using only a few photos. Our approach leverages a diffusion prior for novel view synthesis, trained on synthetic and multiview datasets, which regularizes a NeRF-based 3D reconstruction pipeline at novel camera poses beyond those captured by the set of input images. Our method synthesizes realistic geometry and texture in underconstrained regions while preserving the appearance of observed regions. We perform an extensive evaluation across various real-world datasets, including forward-facing and 360-degree scenes, demonstrating significant performance improvements over previous few-view NeRF reconstruction approaches.
Local regularity for solutions to quasi-linear singular parabolic equations with anisotropic weights

This paper provides a concise procedure for the study on local behavior of solutions to anisotropic weighted quasi-linear singular parabolic equations of $p$-Laplacian type, which is realized by applying intrinsic scaling factor to the De Giorgi truncation method. In particular, it also presents a new proof for local H\"{o}lder continuity of the solution in the unweighted case. Finally, the results are further extended to more general doubly nonlinear parabolic equations.
Long‐Term Changes in Soil Carbon Stocks in the Brazilian Cerrado Under Commercial Soybean

The net effect of agriculture on soil carbon is not yet fully understood. While a number of studies on shallow profiles have been published, evidence suggests that carbon stock changes occur in deeper layers. In this study we analyzed the effect of agriculture in the Cerrado soil C looking at changes in seven different profile depths from 0 to 100 cm in a commercial grain farm. We also used isotopic techniques to distinguish between the original Cerrado C3 carbon and the C4 carbon derived from the grasses used in agriculture. At 0–5 cm depth C stocks significantly decreased with cultivation time. The C stock did not change significantly when it was calculated using the 0–10, 0–20, 0–30, 0–50 or 0–75 cm profile (p > 0·05) but increased with cultivation time when the profile considered was 0–100 cm (p < 0·05). A two‐source isotope model revealed that there was a significant increase in carbon derived from C4 grasses for all depths with cultivation time. Annual carbon sequestration rates for the upper 100 cm of soil were 1·1 Mg C ha−1 year−1 for total carbon and 0·8 Mg C4 C ha−1 year−1 for C4 carbon. The oldest area, with 23 years of cultivation, had a soil C stock increase compared to the native Cerrado soil of 17·6%. These findings suggest that commercial grain farms practices may increase soil C stock compared to native Cerrado soil, if a more complete soil profile down to 100 cm is used to assess C stocks. Copyright © 2015 John Wiley & Sons, Ltd.
Scattering theory of mesons in doped antiferromagnetic Mott insulators: Multichannel perspective and Feshbach resonance

Modeling the underlying pairing mechanism of charge carriers in strongly correlated electrons, starting from a microscopic theory, is among the central challenges of condensed-matter physics. Hereby, the key task is to understand what causes the appearance of superconductivity at comparatively high temperatures upon hole doping an antiferromagnetic (AFM) Mott insulator. Recently, it has been proposed that at strong coupling and low doping, the fundamental one- and two-hole meson-type constituents -- magnetic polarons and bipolaronic pairs -- likely realize an emergent Feshbach resonance producing near-resonant $d_{x^2-y^2}$ interactions between charge carriers. Here, we provide detailed calculations of the proposed scenario by describing the open and closed meson scattering channels in the $t$-$t'$-$J$ model using a truncated basis method. After integrating out the closed channel constituted by bipolaronic pairs, we find $d_{x^2-y^2}$ attractive interactions between open channel magnetic polarons. The closed form of the derived interactions allows us analyze the resonant pairing interactions and we find enhanced (suppressed) attraction for hole (electron) doping in our model. The formalism we introduce provides a framework to analyze the implications of a possible Feshbach scenario, e.g. in the context of BEC-BCS crossover, and establishes a foundation to test quantitative aspects of the proposed Feshbach pairing mechanisms in doped antiferromagnets.
Revisiting Topic-Guided Language Models

A recent line of work in natural language processing has aimed to combine language models and topic models. These topic-guided language models augment neural language models with topic models, unsupervised learning methods that can discover document-level patterns of word use. This paper compares the effectiveness of these methods in a standardized setting. We study four topic-guided language models and two baselines, evaluating the held-out predictive performance of each model on four corpora. Surprisingly, we find that none of these methods outperform a standard LSTM language model baseline, and most fail to learn good topics. Further, we train a probe of the neural language model that shows that the baseline's hidden states already encode topic information. We make public all code used for this study.
Multipole expansion at the level of the action in $d$-dimensions

In this paper we study the multipole expansion of the long-wavelength effective action for radiative sources in ($d$+1) spacetime dimensions. We present detailed expressions for the multipole moments for the case of scalar-, electromagnetic-, and (linearized) gravitational-wave emission. For electromagnetism and gravity, we derive expressions for the odd-parity, magnetic-type moments as SO($d$) duals of the ones traditionally used in the literature. The $d$-dimensional case features a novel set of `Weyl-type' moments, coupling to the spatial part of the Weyl tensor, which are absent in three dimensions. Agreement is found in the overlap with previous known results, notably in the $d \to 3$ limit. Due to its reliance on dimensional regularization, the results presented here play a crucial role for the further development of the Effective Field Theory approach to gravitational dynamics, and in particular for the computation of the gravitational-wave flux, starting at the third post-Newtonian order.
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images

Recent advances in generative deep learning have enabled the creation of high-quality synthetic images in text-to-image generation. Prior work shows that fine-tuning a pretrained diffusion model on ImageNet and generating synthetic training images from the finetuned model can enhance an ImageNet classifier's performance. However, performance degrades as synthetic images outnumber real ones. In this paper, we explore whether generative fine-tuning is essential for this improvement and whether it is possible to further scale up training using more synthetic data. We present a new framework leveraging off-the-shelf generative models to generate synthetic training images, addressing multiple challenges: class name ambiguity, lack of diversity in naive prompts, and domain shifts. Specifically, we leverage large language models (LLMs) and CLIP to resolve class name ambiguity. To diversify images, we propose contextualized diversification (CD) and stylized diversification (SD) methods, also prompted by LLMs. Finally, to mitigate domain shifts, we leverage domain adaptation techniques with auxiliary batch normalization for synthetic images. Our framework consistently enhances recognition model performance with more synthetic data, up to 6x of original ImageNet size showcasing the potential of synthetic data for improved recognition models and strong out-of-domain generalization.
Adaptive unified gas-kinetic scheme for diatomic gases with rotational and vibrational nonequilibrium

Multiscale non-equilibrium physics at large variations of local Knudsen number are encountered in applications of aerospace engineering and micro-electro-mechanical systems, such as high-speed flying vehicles and low pressure of the encapsulation. An accurate description of flow physics in all flow regimes within a single computation requires a genuinely multiscale method. The adaptive unified gas-kinetic scheme (AUGKS) is developed for such multiscale flow simulation. The AUGKS applies discretized velocity space to accurately capture the non-equilibrium physics in the multiscale UGKS, and adaptively employs continuous distribution functions following Chapman-Enskog expansion to efficiently recover near-equilibrium flow region in GKS. The UGKS and GKS are dynamically connected at the cell interface through the fluxes from the discretized and continuous gas distribution functions, which avoids any buffer zone between them. In this study, the AUGKS method with rotation and vibration non-equilibrium is developed. The real gas effect in different flow regimes has been captured. To capture aerodynamic heating accurately, the heat flux modifications are also included. Unstructured discrete particle velocity space is adopted to further improve the computational performance. Numerical tests, including Sod tube, normal shock structure, high-speed flow around the two-dimensional cylinder and three-dimensional sphere and space vehicles, and an unsteady nozzle plume flow from the continuum flow to the background vacuum, have been conducted to validate the current scheme. In comparison with the original UGKS, the current scheme speeds up the computation, reduces the memory requirement, and maintains the equivalent accuracy for multiscale flow simulation, which provides an effective tool for non-equilibrium flow simulations, especially for the flows at low and medium speed.
GauHuman: Articulated Gaussian Splatting from Monocular Human Videos

We present, GauHuman, a 3D human model with Gaussian Splatting for both fast training (1 ~ 2 minutes) and real-time rendering (up to 189 FPS), compared with existing NeRF-based implicit representation modelling frameworks demanding hours of training and seconds of rendering per frame. Specifically, GauHuman encodes Gaussian Splatting in the canonical space and transforms 3D Gaussians from canonical space to posed space with linear blend skinning (LBS), in which effective pose and LBS refinement modules are designed to learn fine details of 3D humans under negligible computational cost. Moreover, to enable fast optimization of GauHuman, we initialize and prune 3D Gaussians with 3D human prior, while splitting/cloning via KL divergence guidance, along with a novel merge operation for further speeding up. Extensive experiments on ZJU_Mocap and MonoCap datasets demonstrate that GauHuman achieves state-of-the-art performance quantitatively and qualitatively with fast training and real-time rendering speed. Notably, without sacrificing rendering quality, GauHuman can fast model the 3D human performer with ~13k 3D Gaussians.
RINAS: Training with Dataset Shuffling Can Be General and Fast

Deep learning datasets are expanding at an unprecedented pace, creating new challenges for data processing in model training pipelines. A crucial aspect of these pipelines is dataset shuffling, which significantly improves unbiased learning and convergence accuracy by adhering to the principles of random sampling. However, loading shuffled data for large datasets incurs significant overhead in the deep learning pipeline and severely impacts the end-to-end training throughput. To mitigate this, current deep learning systems often resort to partial dataset shuffling, sacrificing global randomness to maintain acceptable training throughput on large datasets, still leaving global shuffling efficiency issues not fully explored. In this work, we present RINAS, a data loading framework that systematically addresses the performance bottleneck of loading global shuffled datasets. Our key contribution is to offer an intra-batch unordered data fetching approach, which unleashes unexplored parallelism of data loading. We implement RINAS under the PyTorch framework for common dataset libraries HuggingFace and TorchVision. Our experimental results show that RINAS improves the throughput of general language model training and vision model training by up to 59% and 89%, respectively.
Diophantine approximation with prime denominator in quadratic number fields under GRH

Matom\"aki proved that if $\alpha\in \mathbb{R}$ is irrational, then there are infinitely many primes $p$ such that $|\alpha-a/p|\le p^{-4/3+\varepsilon}$ for a suitable integer a. In this paper, we extend this result to all quadratic number fields under the condition that the Grand Riemann Hypothesis holds for their Hecke $L$-functions.
A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling

Point cloud upsampling (PCU) enriches the representation of raw point clouds, significantly improving the performance in downstream tasks such as classification and reconstruction. Most of the existing point cloud upsampling methods focus on sparse point cloud feature extraction and upsampling module design. In a different way, we dive deeper into directly modelling the gradient of data distribution from dense point clouds. In this paper, we proposed a conditional denoising diffusion probability model (DDPM) for point cloud upsampling, called PUDM. Specifically, PUDM treats the sparse point cloud as a condition, and iteratively learns the transformation relationship between the dense point cloud and the noise. Simultaneously, PUDM aligns with a dual mapping paradigm to further improve the discernment of point features. In this context, PUDM enables learning complex geometry details in the ground truth through the dominant features, while avoiding an additional upsampling module design. Furthermore, to generate high-quality arbitrary-scale point clouds during inference, PUDM exploits the prior knowledge of the scale between sparse point clouds and dense point clouds during training by parameterizing a rate factor. Moreover, PUDM exhibits strong noise robustness in experimental results. In the quantitative and qualitative evaluations on PU1K and PUGAN, PUDM significantly outperformed existing methods in terms of Chamfer Distance (CD) and Hausdorff Distance (HD), achieving state of the art (SOTA) performance.
Response of Water Quality and the Aquatic Community to a Reverse Osmosis Unit Discharge into the Wichita River, Texas

The construction of a reverse osmosis (RO) water treatment unit at the Cypress Water Treatment Plant in Wichita Falls, Texas, afforded an opportunity to evaluate the effects of concentrated brine effluent on water quality in the Wichita River and responses of resident fish and benthic macroinvertebrate assemblages. Four sites, two upstream and two downstream of the proposed discharge point, were sampled three times a year in 2005 and 2008, prior to the plant beginning direct discharge of effluent to the river in February 2009. Post-project samples were collected in 2009 (twice), 2010 (twice), and 2011 (once). Continuous water quality monitoring data demonstrated significantly higher specific conductance downstream of the discharge compared to upstream. However, no significant differences for specific conductance were observed in instantaneous or short-term samples or for water grab samples analyzed for total dissolved solids (TDS), chloride, and sulfate, which likely related to the varying volume of discharge. Selenium was not detected in any samples. Fish and invertebrate assemblages did not appear significantly different when comparing upstream and downstream sites, though some annual and seasonal differences were observed. Historically, the Wichita River has been characterized as having relatively high dissolved solids or salt concentrations with streamflow being a principal influence on the instream levels of these constituents. The relative absence of significant post-project effects may be attributable to the native fauna reflecting those long-term salinity characteristics and its relationship to streamflow as well as the volume of RO effluent discharged to the river averaging substantially less than permitted capacity during the study period.
Survey on gastro-intestinal parasites of captive sun bears in Malaysia

Abstract: This study aimed to investigate parasitic infection in captive sun bears (Helarctos malayanus) in Malaysia from December 2018 to May 2019. We collected 58 fecal samples from sun bears housed in the National Zoo, Taiping Zoo & Night Safari, Lok Kawi Wildlife Park, and Bornean Sun Bear Conservation Centre. We analyzed the samples using modified fecal flotation techniques. We found 4 species of helminths (Ancylostoma spp., Baylisascaris spp., Trichuris spp., and Strongyloides spp.) and 1 species of protozoa (Eimeria spp.) in captive sun bears. The general prevalence of infection was 55.2%, with 48.3% prevalence of helminth infections and 34.5% protozoic infections. Juvenile males (69.2%) and juvenile females (61.5%) had the highest prevalence of parasitic infection. For nematodes, Baylisascaris spp. and Ancylostoma spp. had ≥50% prevalence rate in captive bears. For protozoa, the prevalence rate was <50%. We also found a high intensity of infection for Ancylostoma spp. in captive sun bears with a mean of 258.24 ± 0.62 eggs per gram (EPG), and the lowest intensity was recorded for Strongyloides spp. with 34.37 ± 1.17 EPG. Generally, the parasites we found were common in other species of bears and other mammals. This study revealed presence of parasites that could affect the well-being of sun bears in captivity. Therefore, we suggest that fecal screening be done regularly at 3-month intervals, and that it is essential to reduce the degree of parasitic infection in captive animals.
Evolution of the magnetic field and flows of solar active regions with persistent magnetic bipoles before emergence

Abstract Magnetic active regions on the Sun are harbingers of space weather. Understanding the physics of how they form and evolve will improve space weather forecasting. Our aim is to characterise the surface magnetic field and flows for a sample of active regions with persistent magnetic bipoles prior to emergence. We identified 42 emerging active regions (EARs), in the Solar Dynamics Observatory Helioseismic Emerging Active Region survey (Schunker et al. 2016, A&A. 595, A107), associated with small magnetic bipoles at least one day before the time of emergence. We then identified a contrasting sample of 42 EARs that emerge more abruptly without bipoles before emergence. We computed the supergranulation-scale surface flows using helioseismic holography. We averaged the flow maps and magnetic field maps over all active regions in each sample at each time interval from 2 d before emergence to 1 d after. We found that EARs associated with a persistent pre-emergence bipole evolve to be, on average, lower flux active regions than EARs that emerge more abruptly. Further, we found that the EARs that emerge more abruptly do so with a diverging flow of $(3\pm 0.6) \times 10^{-6}$ s $^{-1}$ on the order of 50–100 ms $^{-1}$ . Our results show that there is a statistical dependence of the surface flow signature throughout the emergence process on the maximum magnetic flux of the active region.