Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1

We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model, version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variable localization”, and increased observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place” (RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation (Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solution forward or backward within an assimilation window at no cost. In the new scheme a long “observation window” (e.g., 7 d or longer) is used to create a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7 d observations, which improves the analysis and accelerates the spin-up. The assimilation and observation windows are then shifted forward by 1 d, and the process is repeated. This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models, especially in order to make greater use of observations in conjunction with models

4-Bromo-2-[1-(4-eth­oxy­phen­yl)-1-methyl­eth­yl]-1-methyl­benzene

In title compound, C18H21BrO, the dihedral angle between two rings is 85.72°. No classical hydrogen bonds are found and only van der Waals forces stabilize the crystal packing

Multiple gene polymorphisms analysis revealed a different profile of genetic polymorphisms of primary open-angle glaucoma in northern Chinese

Purpose: To evaluate the individual and interactive effects of polymorphisms in the myocilin (MYOC), optineurin (OPTN), WD repeat domain 36 (WDR36), and apolipoprotein E (APOE) genes on primary open-angle glaucoma (POAG) in northern Chinese. Methods: Northern Chinese study subjects, 176 POAG patients and 200 controls, were recruited for screening of the coding exons and splicing regions of MYOC. Five single nucleotide polymorphisms (SNPs) in OPTN (M98K, R545Q, IVS5+38T>G, IVS8-53T>C, and IVS15+10G>A), one SNP in WDR36 (IVS5+30C>T) as well as the APOE promoter and epsilon 2/epsilon 3/epsilon 4 polymorphisms were also examined. Association analysis was performed by using chi(2) analysis. High-order gene-gene interaction was also analyzed using the multifactor dimensionality reduction (MDR) method. Results: In MYOC, 22 variants were identified. Four of them were novel but found in controls only. The missense mutation, Val53Ala, is likely a glaucoma causing mutation, accounting for 0.6% of cases. No individual polymorphism in OPTN, WDR36, or APOE was associated with POAG. MDR analysis identified a best 6-factor model for POAG: MYOC IVS2+35A>G, OPTN Met98Lys, OPTN IVS5+38T>G, OPTN IVS8-53T>C, WDR36 IVS5+30C>T, and APOE -491A>T. Conclusions: The association pattern between the genes, MYOC, OPTN, WDR36, and APOE, and POAG in northern Chinese is different from that of southern Chinese. Disease-causing mutations in MYOC accounted for a small proportion of northern Chinese POAG patients. Common polymorphisms in these genes were not associated with POAG individually but might interactively contribute to the disorder, supporting a polygenic etiology.Biochemistry & Molecular BiologyOphthalmologySCI(E)20ARTICLE9-1189-981

All in Tokens: Unifying Output Space of Visual Tasks via Soft Token

Unlike language tasks, where the output space is usually limited to a set of tokens, the output space of visual tasks is more complicated, making it difficult to build a unified visual model for various visual tasks. In this paper, we seek to unify the output space of visual tasks, so that we can also build a unified model for visual tasks. To this end, we demonstrate a single unified model that simultaneously handles two typical visual tasks of instance segmentation and depth estimation, which have discrete/fixed-length and continuous/varied-length outputs, respectively. We propose several new techniques that take into account the particularity of visual tasks: 1) Soft token. We employ soft token to represent the task output. Unlike hard tokens in the common VQ-VAE which are assigned one-hot to discrete codebooks/vocabularies, the soft token is assigned softly to the codebook embeddings. Soft token can improve the accuracy of both the next token inference and decoding of the task output; 2) Mask augmentation. Many visual tasks have corruption, undefined or invalid values in label annotations, i.e., occluded area of depth maps. We show that a mask augmentation technique can greatly benefit these tasks. With these new techniques and other designs, we show that the proposed general-purpose task-solver can perform both instance segmentation and depth estimation well. Particularly, we achieve 0.279 RMSE on the specific task of NYUv2 depth estimation, setting a new record on this benchmark. The general-purpose task-solver, dubbed AiT, is available at \url{https://github.com/SwinTransformer/AiT}

Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations

Existing research predominantly focuses on developing powerful language learning models (LLMs) for mathematical reasoning within monolingual languages, with few explorations in preserving efficacy in a multilingual context. To bridge this gap, this paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. Firstly, by utilizing translation, we construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages, thus addressing the issue of training data scarcity in xMR tasks. Based on the collected dataset, we propose different training strategies to build powerful xMR LLMs, named MathOctopus, notably outperform conventional open-source LLMs and exhibit superiority over ChatGPT in few-shot scenarios. Notably, MathOctopus-13B reaches 47.6% accuracy which exceeds ChatGPT 46.3% on MGSM testset. Beyond remarkable results, we unearth several pivotal observations and insights from extensive experiments: (1) When extending the rejection sampling strategy to the multilingual context, it proves effective for model performances, albeit limited. (2) Employing parallel corpora for math Supervised Fine-Tuning (SFT) across multiple languages not only significantly enhances model performance multilingually but also elevates their monolingual performance. This indicates that crafting multilingual corpora can be regarded as a vital strategy for enhancing model performance in a specific language, especially in mathematical reasoning tasks. For instance, MathOctopus-7B improves its counterparts that trained on English from 42.2% to 50.8% on GSM8K testset.Comment: Work in Progres

Boundary Conditions for NHEK through Effective Action Approach

We study the asymptotic symmetry group(ASG) of the near horizon geometry of extreme Kerr black hole through the effective action approach developed in 1007.1031. By requiring a finite boundary effective action, we derive a new set of asymptotic Killing vectors and boundary conditions, which are much more relaxed than the ones proposed in 0907.0303, and still allow a copy of conformal group as its ASG. In the covariant formalism, the asymptotic charges are finite, with the corresponding central charge vanishing. By using the quasi-local charge and introducing a plausible cut-off, we find that the higher order terms of the asymptotic Killing vectors, which could not be determined through the effective action approach, contribute to the central charge as well. We also show that the boundary conditions suggested in 0809.4266 lead to a divergent first order boundary effective action.Comment: 16 page

The Oral Microbiome in the Elderly With Dental Caries and Health

With the aging of the population, dental caries in the elderly has received increasing attention. A comprehensive study of the oral microbiome is required to understand its polymicrobial etiology. The results of previous studies are limited and remain controversial. In this study, subjects 60 years and older with and without caries were recruited. Unstimulated saliva and dental plaque were collected from each subject and the bacterial 16S rDNA was amplified using PCR and sequenced by Illumina MiSeq high-throughput sequencing. A total of 92 samples were collected from 24 caries patients and 22 healthy controls. Sequences clustered into 147,531 OTUs, representing 16 phyla, 29 classes, 49 orders, 79 families, 149 genera, and 305 species. All predominant phyla, including Proteobacteria, Bacteroidetes, Firmicutes, Fusobacteria, Actinobacteria, and Saccharibacteria, were largely consistent in different groups, but different relative abundances could be observed. The core microbiome was defined with 246 shared species among groups, which occupied 80.7% of all the species detected. Alpha diversity showed no significant differences in bacterial richness or diversity between caries patients and healthy controls, but distinction existed between samples collected from dental plaque and saliva. Beta diversity analysis was performed by PCoA and hierarchical clustering analysis, showing similar results that microorganisms vary between the two niches. The biomarkers of different groups were defined by LEfSe analysis to identify potential caries-related and health-related bacteria. The co-occurrence analysis of the predominant genera revealed significant interactions among oral microbiota and exhibited more complex and aggregated bacterial correlations in caries-free groups. Finally, the functional prediction of the microbiota present in oral samples was performed by PICRUSt, indicating vigorous microbial metabolism in the oral bacterial community. Our study provides thorough knowledge of the microbiological etiology of elderly individuals with caries and is expected to provide novel methods for its prevention and treatment

ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint

Large-scale online recommender system spreads all over the Internet being in charge of two basic tasks: Click-Through Rate (CTR) and Post-Click Conversion Rate (CVR) estimations. However, traditional CVR estimators suffer from well-known Sample Selection Bias and Data Sparsity issues. Entire space models were proposed to address the two issues via tracing the decision-making path of "exposure_click_purchase". Further, some researchers observed that there are purchase-related behaviors between click and purchase, which can better draw the user's decision-making intention and improve the recommendation performance. Thus, the decision-making path has been extended to "exposure_click_in-shop action_purchase" and can be modeled with conditional probability approach. Nevertheless, we observe that the chain rule of conditional probability does not always hold. We report Probability Space Confusion (PSC) issue and give a derivation of difference between ground-truth and estimation mathematically. We propose a novel Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint (ESMC) and two alternatives: Entire Space Multi-Task Model with Siamese Network (ESMS) and Entire Space Multi-Task Model in Global Domain (ESMG) to address the PSC issue. Specifically, we handle "exposure_click_in-shop action" and "in-shop action_purchase" separately in the light of characteristics of in-shop action. The first path is still treated with conditional probability while the second one is treated with parameter constraint strategy. Experiments on both offline and online environments in a large-scale recommendation system illustrate the superiority of our proposed methods over state-of-the-art models. The real-world datasets will be released