Miracle-dz/UniFAR-FTUs huggingface dataset:
Rainfall partitioning in a deciduous forest plot in leafed and leafless periods

In deciduous forests, it is particularly difficult to understand the factors controlling rainfall partitioning because of the seasonal changes of canopy cover. Moreover, under Mediterranean climate conditions, the marked seasonal variability of rainfall produces highly variable event‐based interception losses making comparison of throughfall, stemflow and interception loss in each season difficult. Rainfall partitioning in a downy oak forest plot located in the Eastern Pyrenees Mountains (NE Spain) was measured over 3 years to study the differences in throughfall, stemflow and interception loss between the leafed and leafless periods and to find out the role of canopy cover and rainfall type in rainfall partitioning. Results show small differences in rainfall partitioning between the leafed and leafless periods, although different rainfall patterns were observed in the two periods, and there were significant changes in canopy cover. Throughfall and stemflow were slightly higher during the leafless period (about 3.6% and 1.7% higher, respectively), and therefore, interception loss was lower (about 5.4% in this period). On an event basis, the leafless period was characterized by lower absolute and relative interception losses than the leafed period. Rainfall was classified in four types by intensity and duration to study the influence of rainfall type on rainfall partitioning in leafed and leafless seasons separately. Rainfall duration was apparently important for all rainfall partitioning components in the leafless period, when stemflow and throughfall were lower and interception loss was enhanced during short events, whereas in the leafed period, no single dominating factor could be found. Copyright © 2011 John Wiley & Sons, Ltd.
Early Structure Formation from Primordial Density Fluctuations with a Blue, Tilted Power Spectrum: High-redshift Galaxies

Recent observations by the James Webb Space Telescope (JWST) discovered unexpectedly abundant luminous galaxies at high redshift, posing possibly a severe challenge to popular galaxy formation models. We study early structure formation in a cosmological model with a blue, tilted power spectrum (BTPS) given by P ( k ) ∝ k m s with m s > 1 at small length scales. We run a set of cosmological N-body simulations and derive the abundance of dark matter halos and galaxies under simplified assumptions on star formation efficiency. The enhanced small-scale power allows rapid nonlinear structure formation at z > 7, and galaxies with stellar mass exceeding 1010 M ⊙ can be formed by z = 9. Because of frequent mergers, the structure of galaxies and galaxy groups appears clumpy. The BTPS model reproduces the observed stellar mass density at z = 7–9, and thus eases the claimed tension between galaxy formation theory and recent JWST observations. The large-scale structure of the present-day Universe is largely unaffected by the modification of the small-scale power spectrum. We conduct a systematic study by varying the slope of the small-scale power spectrum to derive constraints on the BTPS model from a set of observations of high-redshift galaxies.
Strategic Workforce Planning in Crowdsourced Delivery with Hybrid Driver Fleets

Nowadays, logistics service providers (LSPs) increasingly consider using a crowdsourced workforce on the last mile to fulfill customers' expectations regarding same-day or on-demand delivery at reduced costs. The crowdsourced workforce's availability is, however, uncertain. Therefore, LSPs often hire additional fixed employees to perform deliveries when the availability of crowdsourced drivers is low. In this context, the reliability versus flexibility trade-off which LSPs face over a longer period, e.g., a year, remains unstudied. Against this background, we jointly study a workforce planning problem that considers fixed drivers (FDs) and the temporal development of the crowdsourced driver (CD) fleet over a long-term time horizon. We consider two types of CDs, gigworkers (GWs) and occasional drivers (ODs). While GWs are not sensitive to the request's destination and typically exhibit high availability, ODs only serve requests whose origin and destination coincide with their own private route's origin and destination. Moreover, to account for time horizon-specific dynamics, we consider stochastic turnover for both FDs and CDs as well as stochastic CD fleet growth. We formulate the resulting workforce planning problem as a Markov decision process (MDP) whose reward function reflects total costs, i.e., wages and operational costs arising from serving demand with FDs and CDs, and solve it via approximate dynamic programming (ADP). Applying our approach to an environment based on real-world demand data from GrubHub, we find that in fleets consisting of FDs and CDs, ADP-based hiring policies can outperform myopic hiring policies by up to 19% in total costs. In the studied setting, we observed that GWs reduce the LSP's total costs more than ODs. When we account for CDs' increased resignation probability when not being matched with enough requests, the amount of required FDs increases.
Deriving roughness characteristics of rock mass discontinuities from terrestrial laser scan data

In this study, 3D terrestrial laser scan data have been used to quantify the surface roughness of rock mass discontinuities, to determine the roughness scale effect and to determine the influence of the range of precision. The Roughness-length method was applied to calculate fractal parameters (fractal dimension and roughness amplitude). The study showed that it is possible to calculate the fractal parameters from laser scan data of discontinuity surfaces. It has been found that the fractal dimension and roughness amplitude are very high for raw laser scan data, due to range measurement error of the used scanner. The discontinuity surface has been reconstructed from the raw point data using a scattered data interpolation technique. The range measurement error can be removed using this data interpolation technique. The reconstructed surface visually resembles the actual discontinuity surface. The fractal parameters of the reconstructed surface have subsequently been calculated. By subtracting the fractal parameters of the raw data from the fractal parameters of the reconstructed surface, the range error of the laser scan data can be estimated. The fractal parameters have also been calculated using different sample sizes. The fractal parameters show an increase with increasing sample size. This means that the roughness complexity of the discontinuity surface is also increasing with increasing sample size. From this observation it can be concluded that the roughness of discontinuity surfaces has a significant scale effect. The maximum resolution of the laser scan data used in this study is 5 mm (1 point per 5 mm). The laser scan data with 5 mm resolution can only acquire roughness information from roughness features larger than 5 mm. Consequently, small-scale roughness (roughness features smaller than 5 mm) can not be obtained from the data. Therefore the resolution of the laser scan data (point density) needs to be increased to quantify discontinuity surface roughness accurately as a smaller scale.
Is Underwater Image Enhancement All Object Detectors Need?

Underwater object detection is a crucial and challenging problem in marine engineering and aquatic robot. The difficulty is partly because of the degradation of underwater images caused by light selective absorption and scattering. Intuitively, enhancing underwater images can benefit high-level applications like underwater object detection. However, it is still unclear whether all object detectors need underwater image enhancement as pre-processing. We therefore pose the questions"Does underwater image enhancement really improve underwater object detection? "and"How does underwater image enhancement contribute to underwater object detection? ". With these two questions, we conduct extensive studies. Specifically, we use 18 state-of-the-art underwater image enhancement algorithms, covering traditional, CNN-based, and GAN-based algorithms, to pre-process underwater object detection data. Then, we retrain 7 popular deep learning-based object detectors using the corresponding results enhanced by different algorithms, obtaining 126 underwater object detection models. Coupled with 7 object detection models retrained using raw underwater images, we employ these 133 models to comprehensively analyze the effect of underwater image enhancement on underwater object detection. We expect this study can provide sufficient exploration to answer the aforementioned questions and draw more attention of the community to the joint problem of underwater image enhancement and underwater object detection. The pre-trained models and results are publicly available and will be regularly updated. Project page: https://github.com/BIGWangYuDong/lqit/tree/main/configs/detection/uw_enhancement_affect_detection.
OmniMotionGPT: Animal Motion Generation with Limited Data

Our paper aims to generate diverse and realistic animal motion sequences from textual descriptions, without a large-scale animal text-motion dataset. While the task of text-driven human motion synthesis is already extensively studied and benchmarked, it remains challenging to transfer this success to other skeleton structures with limited data. In this work, we design a model architecture that imitates Generative Pretraining Transformer (GPT), utilizing prior knowledge learned from human data to the animal domain. We jointly train motion autoencoders for both animal and human motions and at the same time optimize through the similarity scores among human motion encoding, animal motion encoding, and text CLIP embedding. Presenting the first solution to this problem, we are able to generate animal motions with high diversity and fidelity, quantitatively and qualitatively outperforming the results of training human motion generation baselines on animal data. Additionally, we introduce AnimalML3D, the first text-animal motion dataset with 1240 animation sequences spanning 36 different animal identities. We hope this dataset would mediate the data scarcity problem in text-driven animal motion generation, providing a new playground for the research community.
Finite-volume scattering on the left-hand cut

The two-particle finite-volume scattering formalism derived by L\"uscher and generalized in many subsequent works does not hold for energies far enough below the two-particle threshold to reach the nearest left-hand cut. The breakdown of the formalism is signaled by the fact that a real scattering amplitude is predicted in a regime where it should be complex. In this work, we address this limitation by deriving an extended formalism that includes the nearest branch cut, arising from single particle exchange. We focus on two-nucleon ($NN \to NN$) scattering, for which the cut arises from pion exchange, but give expressions for any system with a single channel of identical particles. The new result takes the form of a modified quantization condition that can be used to constrain an intermediate K-matrix in which the cut is removed. In a second step, integral equations, also derived in this work, must be used to convert the K-matrix to the physical scattering amplitude. We also show how the new formalism reduces to the standard approach when the $N \to N \pi$ coupling is set to zero.
Space-time least squares approximation for Schrödinger equation and efficient solver

In this work we present a space-time least squares isogeometric discretization of the Schr\"odinger equation and propose a preconditioner for the arising linear system in the parametric domain. Exploiting the tensor product structure of the basis functions, the preconditioner is written as the sum of Kronecker products of matrices. Thanks to an extension to the classical Fast Diagonalization method, the application of the preconditioner is efficient and robust w.r. t. the polynomial degree of the spline space. The time required for the application is almost proportional to the number of degrees-of-freedom, for a serial execution.
Event-based Visual Inertial Velometer

Neuromorphic event-based cameras are bio-inspired visual sensors with asynchronous pixels and extremely high temporal resolution. Such favorable properties make them an excellent choice for solving state estimation tasks under aggressive ego motion. However, failures of camera pose tracking are frequently witnessed in state-of-the-art event-based visual odometry systems when the local map cannot be updated in time. One of the biggest roadblocks for this specific field is the absence of efficient and robust methods for data association without imposing any assumption on the environment. This problem seems, however, unlikely to be addressed as in standard vision due to the motion-dependent observability of event data. Therefore, we propose a mapping-free design for event-based visual-inertial state estimation in this paper. Instead of estimating the position of the event camera, we find that recovering the instantaneous linear velocity is more consistent with the differential working principle of event cameras. The proposed event-based visual-inertial velometer leverages a continuous-time formulation that incrementally fuses the heterogeneous measurements from a stereo event camera and an inertial measurement unit. Experiments on the synthetic dataset demonstrate that the proposed method can recover instantaneous linear velocity in metric scale with low latency.
Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension

To precisely evaluate a language model's capability for logical reading comprehension, we present a dataset for testing the understanding of the rationale behind critical reasoning. For questions taken from an existing multiplechoice logical reading comprehension dataset, we crowdsource rationale texts that explain why we should select or eliminate answer options, resulting in 3,003 multiple-choice subquestions that are associated with 943 main questions. Experiments on our dataset show that recent large language models (e.g., InstructGPT) struggle to answer the subquestions even if they are able to answer the main questions correctly. We find that the models perform particularly poorly in answering subquestions written for the incorrect options of the main questions, implying that the models have a limited capability for explaining why incorrect alternatives should be eliminated. These results suggest that our dataset encourages further investigation into the critical reasoning ability of language models while focusing on the elimination process of relevant alternatives.
Dataset Distillation via the Wasserstein Metric

Dataset Distillation (DD) emerges as a powerful strategy to encapsulate the expansive information of large datasets into significantly smaller, synthetic equivalents, thereby preserving model performance with reduced computational overhead. Pursuing this objective, we introduce the Wasserstein distance, a metric grounded in optimal transport theory, to enhance distribution matching in DD. Our approach employs the Wasserstein barycenter to provide a geometrically meaningful method for quantifying distribution differences and capturing the centroid of distribution sets efficiently. By embedding synthetic data in the feature spaces of pretrained classification models, we facilitate effective distribution matching that leverages prior knowledge inherent in these models. Our method not only maintains the computational advantages of distribution matching-based techniques but also achieves new state-of-the-art performance across a range of high-resolution datasets. Extensive testing demonstrates the effectiveness and adaptability of our method, underscoring the untapped potential of Wasserstein metrics in dataset distillation.
ALSTER: A Local Spatio-Temporal Expert for Online 3D Semantic Reconstruction

We propose an online 3D semantic segmentation method that incrementally reconstructs a 3D semantic map from a stream of RGB-D frames. Unlike offline methods, ours is directly applicable to scenarios with real-time constraints, such as robotics or mixed reality. To overcome the inherent challenges of online methods, we make two main contributions. First, to effectively extract information from the input RGB-D video stream, we jointly estimate geometry and semantic labels per frame in 3D. A key focus of our approach is to reason about semantic entities both in the 2D input and the local 3D domain to leverage differences in spatial context and network architectures. Our method predicts 2D features using an off-the-shelf segmentation network. The extracted 2D features are refined by a lightweight 3D network to enable reasoning about the local 3D structure. Second, to efficiently deal with an infinite stream of input RGB-D frames, a subsequent network serves as a temporal expert predicting the incremental scene updates by leveraging 2D, 3D, and past information in a learned manner. These updates are then integrated into a global scene representation. Using these main contributions, our method can enable scenarios with real-time constraints and can scale to arbitrary scene sizes by processing and updating the scene only in a local region defined by the new measurement. Our experiments demonstrate improved results compared to existing online methods that purely operate in local regions and show that complementary sources of information can boost the performance. We provide a thorough ablation study on the benefits of different architectural as well as algorithmic design decisions. Our method yields competitive results on the popular ScanNet benchmark and SceneNN dataset.
Dispersed Structured Light for Hyperspectral 3D Imaging

Hyperspectral 3D imaging aims to acquire both depth and spectral information of a scene. However, existing methods are either prohibitively expensive and bulky or compromise on spectral and depth accuracy. In this work, we present Dispersed Structured Light (DSL), a cost-effective and compact method for accurate hyperspectral 3D imaging. DSL modifies a traditional projector-camera system by placing a sub-millimeter thick diffraction grating film front of the projector. The grating disperses structured light based on light wavelength. To utilize the dispersed structured light, we devise a model for dispersive projection image formation and a per-pixel hyperspectral 3D reconstruction method. We validate DSL by instantiating a compact experimental prototype. DSL achieves spectral accuracy of 18.8nm full-width half-maximum (FWHM) and depth error of 1mm. We demonstrate that DSL outperforms prior work on practical hyperspectral 3D imaging. DSL promises accurate and practical hyperspectral 3D imaging for diverse application domains, including computer vision and graphics, cultural heritage, geology, and biology.
Heavy quark dynamics via Gribov-Zwanziger approach

In this work, we investigate the momentum-dependent drag and diffusion coefficient of heavy quarks (HQs) moving in the quark-gluon plasma (QGP) background. The leading order scattering amplitudes required for this purpose have been obtained using the Gribov-Zwanziger propagator for the mediator gluons to incorporate the non-perturbative effects relevant to the phenomenologically accessible temperature regime. The drag and diffusion coefficients so obtained have been implemented to estimate the temperature and momentum dependence of the energy loss of the HQ as well as the temperature dependence of the specific shear viscosity ($\eta/s$) of the background medium. Our results suggest a higher energy loss of the propagating HQ compared to the perturbative estimates, whereas the $\eta/s$ is observed to comply with the AdS/CFT estimation over a significantly wider temperature range compared to the perturbative expectation.
Unveiling Generalized Parton Distributions through the Pseudo-Distribution Approach

Understanding the intricate three-dimensional internal structure of the nucleon has been a long-standing challenge. The main quantitative tool to map out this structure are the generalized parton distributions (GPDs). In these proceedings, we present our exploratory results from applying the pseudo-distribution approach on the lattice, showing the unpolarized isovector $E$ GPD as an example. We use one ensemble of $N_f=2+1+1$ twisted mass fermions with a clover term, at a non-physical pion mass of $260 \textrm{ MeV}$ and a lattice spacing of $0.093 \textrm{ fm}$.
DiffCAD: Weakly-Supervised Probabilistic CAD Model Retrieval and Alignment from an RGB Image

Perceiving 3D structures from RGB images based on CAD model primitives can enable an effective, efficient 3D object-based representation of scenes. However, current approaches rely on supervision from expensive annotations of CAD models associated with real images, and encounter challenges due to the inherent ambiguities in the task -- both in depth-scale ambiguity in monocular perception, as well as inexact matches of CAD database models to real observations. We thus propose DiffCAD, the first weakly-supervised probabilistic approach to CAD retrieval and alignment from an RGB image. We formulate this as a conditional generative task, leveraging diffusion to learn implicit probabilistic models capturing the shape, pose, and scale of CAD objects in an image. This enables multi-hypothesis generation of different plausible CAD reconstructions, requiring only a few hypotheses to characterize ambiguities in depth/scale and inexact shape matches. Our approach is trained only on synthetic data, leveraging monocular depth and mask estimates to enable robust zero-shot adaptation to various real target domains. Despite being trained solely on synthetic data, our multi-hypothesis approach can even surpass the supervised state-of-the-art on the Scan2CAD dataset by 5.9% with 8 hypotheses.
Dataset Distillation in Large Data Era

Dataset distillation aims to generate a smaller but representative subset from a large dataset, which allows a model to be trained efficiently, meanwhile evaluating on the original testing data distribution to achieve decent performance. Many prior works have aimed to align with diverse aspects of the original datasets, such as matching the training weight trajectories, gradient, feature/BatchNorm distributions, etc. In this work, we show how to distill various large-scale datasets such as full ImageNet-1K/21K under a conventional input resolution of 224$\times$224 to achieve the best accuracy over all previous approaches, including SRe$^2$L, TESLA and MTT. To achieve this, we introduce a simple yet effective ${\bf C}$urriculum ${\bf D}$ata ${\bf A}$ugmentation ($\texttt{CDA}$) during data synthesis that obtains the accuracy on large-scale ImageNet-1K and 21K with 63.2% under IPC (Images Per Class) 50 and 36.1% under IPC 20, respectively. Finally, we show that, by integrating all our enhancements together, the proposed model beats the current state-of-the-art by more than 4% Top-1 accuracy on ImageNet-1K/21K and for the first time, reduces the gap to its full-data training counterpart to less than absolute 15%. Moreover, this work represents the inaugural success in dataset distillation on larger-scale ImageNet-21K under the standard 224$\times$224 resolution. Our code and distilled ImageNet-21K dataset of 20 IPC, 2K recovery budget are available at https://github.com/VILA-Lab/SRe2L/tree/main/CDA.