Hydrologic Observation, Model, and Theory Congruence on Evapotranspiration Variance: Diagnosis of Multiple Observations and Land Surface Models

This paper reconciles the state-of-the-art observations and simulations of evapotranspiration (ET) temporal variability through a diagnostic framework composed of an observation-model-theory triplet. Specifically, a confirmed theoretical tool, Evapotranspiration Temporal VARiance Decomposition (EVARD), is used as a benchmark to estimate ET monthly variance (σ2ET) across the contiguous United States (CONUS) with inputs including hydroclimatic observations, Gravity Recovery and Climate Experiment-based terrestrial water storage, four observation-based products (ETRSUW by the University of Washington, ETRSMOD16 from MOD16 Global Terrestrial ET Data Set, ETFLUXNET upscaled from of fluxtower observations, and ETGLEAM from Global Land Evaporation Amsterdam Model), and four operational land surface models (LSMs: MOSAIC, NOAH, NOAH-MP, and VIC). Five experiments are systematically designed to evaluate and diagnose possible errors and uncertainties in ET temporal variance estimated by the four observation-based ET products and the four LSM simulations. Based on the results of these experiments, the following diagnostic hypotheses regarding the uncertainty of the observation-based ET products are illustrated: ETRSUW captures the high σ2ET signals in the Midwest with negligible bias and moderate uncertainty over the contiguous United States; ETFLUXNET systematically underestimates σ2ET over CONUS but with the lowest level of uncertainty; ETRSMOD16 has medium bias with the highest level of uncertainty, and the spatial distribution of high σ2ET signal from ETRSMOD16 is different from other estimates; ETGLEAM has slight negative bias and medium uncertainty, and σ2ET in the West Coast is smaller than that from ETVARD. Regarding the LSMs, it is found that any of the four LSMs can be the best depending on a certain set of reference observations. The study reveals that LSMs have shown a reasonably worthy, though not perfect, capability in estimating ET and its variability in regions/aquifers with limited human interference. However, RS-based observations and theoretical estimates suggest that all the four LSMs examined in this study are not able to accurately predict the ET variability in regions/aquifers heavily influenced by human activities like Central Valley and High Plains aquifers; they all underestimate ET variability along the West Coast due to seasonal vegetation responses to Mediterranean climate and human water use. In addition, LSMs underestimate intraannual ET variance in California and the High Plains with underestimated terrestrial storage change components in ET variance, due to the inappropriate representation of groundwater pumping and its impact on ET and other hydrologic processes. This paper urges advancing hydrologic knowledge by finding congruence among models, data, and theories

Superpoint Gaussian Splatting for Real-Time High-Fidelity Dynamic Scene Reconstruction

Rendering novel view images in dynamic scenes is a crucial yet challenging task. Current methods mainly utilize NeRF-based methods to represent the static scene and an additional time-variant MLP to model scene deformations, resulting in relatively low rendering quality as well as slow inference speed. To tackle these challenges, we propose a novel framework named Superpoint Gaussian Splatting (SP-GS). Specifically, our framework first employs explicit 3D Gaussians to reconstruct the scene and then clusters Gaussians with similar properties (e.g., rotation, translation, and location) into superpoints. Empowered by these superpoints, our method manages to extend 3D Gaussian splatting to dynamic scenes with only a slight increase in computational expense. Apart from achieving state-of-the-art visual quality and real-time rendering under high resolutions, the superpoint representation provides a stronger manipulation capability. Extensive experiments demonstrate the practicality and effectiveness of our approach on both synthetic and real-world datasets. Please see our project page at https://dnvtmf.github.io/SP_GS.github.io.Comment: Accepted by ICML 202

Assessing climate and terrestrial water storage controls on evapotranspiration variability: towards improved understanding of watersheds as coupled nature-human systems

Terrestrial evapotranspiration (ET) is an important eco-hydrologic process the couples the land surface water and energy budgets, links the water, carbon and nutrient cycle, and represents the largest water consumption from agricultural sector. Although advances have been made in monitoring and simulating terrestrial ET in last decades, there are still challenges in reconciling and cross-validating ET observation and numerical model simulation results. In particular, due to human interferences (such as agricultural irrigation), existing knowledge obtained under natural conditions is inapplicable to intensively managed watersheds. Therefore, there is a pressing need to develop hydrologic theory that depicts watersheds as coupled nature-human systems, and to apply knowledge derived from the complex system to validate and diagnose existing hydrologic observations and models, and explore the inter-connects of hydrologic dynamics across scales. This dissertation focuses on the ET temporal variability as a signature of watersheds as coupled nature-human systems, since ET variability is driven by the climatic fluctuations and modulated by hydrologic processes such as vegetation, snow dynamics and human water use. Based on general hydrologic laws on land surface water-energy coupling, this dissertation derives an Evapotranspiration Temporal VARiance Decomposition (ETVARD) framework for better understanding of both the climatic and hydrologic controls on ET temporal variability. Utilizing best available hydrologic observations, ETVARD quantifies the contributions from the variances and co-variances of climatic and terrestrial water storage change factors to ET variance at various temporal scale (e.g., monthly, seasonal and annual) for watersheds across a wide spectrum of climatic conditions (from humid to arid) under both natural and managed conditions. As such, we derive hydrologic knowledge from the congruence among theories, observations and models. For multi-variable and multi-source hydroclimatic observations, ETVARD provides an independent diagnosis tool to detect the possible biases and uncertainties in observations and land surface models. Using ETVARD as a benchmark for inter-comparison of observation and models and through five systematically designed experiments, this dissertation identifies the inconsistencies in ET variance estimates among theories, observations and models, assesses the quality of multiple ET products, and provides guidelines to improve land surface model structure in capturing ET variance for the contiguous United States. In particular, ETVARD identifies the temporal and spatial ET pattern changes due to extensive groundwater-based irrigation through a rea-world case study in the High Plains. The relation between ET and crop yield signatures (i.e., mean and variability) in rain-fed and irrigated crops reflects farmers’ irrigation behavior heterogeneity in the formation of ET patterns, depending on farmers’ preferences between profit-maximization and risk-aversion. In addition, a power-law statistical relationship between ET mean and variability is developed from independent ET observations. While the differences in climate conditions and vegetation structures are reflected by ecosystems’ water use preferences between consumption and variability, these water use preferences cluster on the same a power-law statistical relationship. The comprehensive assessment on ET variance in this dissertation provides a synthesis from existing theories, observations and simulations towards improved understanding of ET variance at the watershed system level. The knowledge discovered in the dissertation also provides guidelines for conjointly managing the mean and variability of watershed responses to both natural and human driving forces in the context of coupled nature-human systems

Magnetic island formation and rotation braking induced by low-Z impurity penetration in an EAST plasma

Recent observations of the successive formations of the 4=1; 3=1, and 2=1 magnetic islands as well as the subsequent braking of the 2=1 mode during a low-Z impurity penetration process in EAST experiments are well reproduced in our 3D resistive MHD simulations. The enhanced parallel current perturbation induced by impurity radiation predominately contributes to the tearing mode growth, and the 2=1 island rotation is mainly damped by the impurity accumulation as results of the influence from high n modes.Comment: 21 pages, 9 figure

Triggering of tearing instability by impurity radiation through resistive interchange reversal in a tokamak

Recent MHD simulations find that the reversal of the local resistive interchange parameter $D_R$ from negative to positive due to impurity radiation cooling is able to trigger the resistive tearing mode growth in a tokamak above a threshold in impurity level. A layer of perturbed Pfirsch-Schl\"{u}ter current density and resistivity are also induced by the impurity radiation, which further govern the tearing mode growth and saturation in the nonlinear stage. The impurity threshold and the tearing mode growth strongly depend on the parallel thermal conductivity, and such a dependence derives from the impact on $D_R$ of the fast parallel thermal equilibration along the helical magnetic field lines.Comment: 16 pages, 5 figure

A Benchmark for Multi-speaker Anonymization

Privacy-preserving voice protection approaches primarily suppress privacy-related information derived from paralinguistic attributes while preserving the linguistic content. Existing solutions focus on single-speaker scenarios. However, they lack practicality for real-world applications, i.e., multi-speaker scenarios. In this paper, we present an initial attempt to provide a multi-speaker anonymization benchmark by defining the task and evaluation protocol, proposing benchmarking solutions, and discussing the privacy leakage of overlapping conversations. Specifically, ideal multi-speaker anonymization should preserve the number of speakers and the turn-taking structure of the conversation, ensuring accurate context conveyance while maintaining privacy. To achieve that, a cascaded system uses speaker diarization to aggregate the speech of each speaker and speaker anonymization to conceal speaker privacy and preserve speech content. Additionally, we propose two conversation-level speaker vector anonymization methods to improve the utility further. Both methods aim to make the original and corresponding pseudo-speaker identities of each speaker unlinkable while preserving or even improving the distinguishability among pseudo-speakers in a conversation. The first method minimizes the differential similarity across speaker pairs in the original and anonymized conversations to maintain original speaker relationships in the anonymized version. The other method minimizes the aggregated similarity across anonymized speakers to achieve better differentiation between speakers. Experiments conducted on both non-overlap simulated and real-world datasets demonstrate the effectiveness of the multi-speaker anonymization system with the proposed speaker anonymizers. Additionally, we analyzed overlapping speech regarding privacy leakage and provide potential solutions

Text-Augmented Multimodal LLMs for Chemical Reaction Condition Recommendation

High-throughput reaction condition (RC) screening is fundamental to chemical synthesis. However, current RC screening suffers from laborious and costly trial-and-error workflows. Traditional computer-aided synthesis planning (CASP) tools fail to find suitable RCs due to data sparsity and inadequate reaction representations. Nowadays, large language models (LLMs) are capable of tackling chemistry-related problems, such as molecule design, and chemical logic Q\&A tasks. However, LLMs have not yet achieved accurate predictions of chemical reaction conditions. Here, we present MM-RCR, a text-augmented multimodal LLM that learns a unified reaction representation from SMILES, reaction graphs, and textual corpus for chemical reaction recommendation (RCR). To train MM-RCR, we construct 1.2 million pair-wised Q\&A instruction datasets. Our experimental results demonstrate that MM-RCR achieves state-of-the-art performance on two open benchmark datasets and exhibits strong generalization capabilities on out-of-domain (OOD) and High-Throughput Experimentation (HTE) datasets. MM-RCR has the potential to accelerate high-throughput condition screening in chemical synthesis

RoomTex: Texturing Compositional Indoor Scenes via Iterative Inpainting

The advancement of diffusion models has pushed the boundary of text-to-3D object generation. While it is straightforward to composite objects into a scene with reasonable geometry, it is nontrivial to texture such a scene perfectly due to style inconsistency and occlusions between objects. To tackle these problems, we propose a coarse-to-fine 3D scene texturing framework, referred to as RoomTex, to generate high-fidelity and style-consistent textures for untextured compositional scene meshes. In the coarse stage, RoomTex first unwraps the scene mesh to a panoramic depth map and leverages ControlNet to generate a room panorama, which is regarded as the coarse reference to ensure the global texture consistency. In the fine stage, based on the panoramic image and perspective depth maps, RoomTex will refine and texture every single object in the room iteratively along a series of selected camera views, until this object is completely painted. Moreover, we propose to maintain superior alignment between RGB and depth spaces via subtle edge detection methods. Extensive experiments show our method is capable of generating high-quality and diverse room textures, and more importantly, supporting interactive fine-grained texture control and flexible scene editing thanks to our inpainting-based framework and compositional mesh input. Our project page is available at https://qwang666.github.io/RoomTex/

Current therapy option for necrotizing enterocolitis: Practicalities and challenge

Necrotizing enterocolitis (NEC) is one of the most prevalent neonatal gastrointestinal disorders. Despite ongoing breakthroughs in its treatment and prevention, the incidence and mortality associated with NEC remain high. New therapeutic approaches, such as breast milk composition administration, stem cell therapy, immunotherapy, and fecal microbiota transplantation (FMT) have recently evolved the prevention and the treatment of NEC. This study investigated the most recent advances in NEC therapeutic approaches and discussed their applicability to bring new insight to NEC treatment