Bismut torsion parallel metrics with constant holomorphic sectional curvature

An old conjecture in non-K\"ahler geometry states that, if a compact Hermitian manifold has constant holomorphic sectional curvature, then the metric must be K\"ahler (when the constant is non-zero) or Chern flat (when the constant is zero). It is known to be true in complex dimension $2$ by the work of Balas and Gauduchon in 1985 (when the constant is negative or zero) and Apostolov, Davidov and Muskarov in 1996 (when the constant is positive). In dimension $3$ or higher, the conjecture is only known in some special cases, such as the locally conformally K\"ahler case (when the constant is negative or zero) by the work of Chen, Chen and Nie, or for complex nilmanifolds with nilpotent $J$ by the work of Li and the second named author. In this note, we confirm the above conjecture for all non-balanced Bismut torsion parallel (BTP) manifolds. Here the BTP condition means that the Bismut connection has parallel torsion. In particular, the conjecture is valid for all Vaisman manifolds.Comment: 12 page

Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework

Existing deep learning approaches for travel mode choice modeling fail to inform modelers about their prediction uncertainty. Even when facing scenarios that are out of the distribution of training data, which implies high prediction uncertainty, these approaches still provide deterministic answers, potentially leading to misguidance. To address this limitation, this study introduces the concept of uncertainty from the field of explainable artificial intelligence into travel mode choice modeling. We propose a Bayesian neural network-based travel mode prediction model (BTMP) that quantifies the uncertainty of travel mode predictions, enabling the model itself to "know" and "tell" what it doesn't know. With BTMP, we further propose an uncertainty-guided active survey framework, which dynamically formulates survey questions representing travel mode choice scenarios with high prediction uncertainty. Through iterative collection of responses to these dynamically tailored survey questions, BTMP is iteratively trained to achieve the desired accuracy faster with fewer questions, thereby reducing survey costs. Experimental validation using synthetic datasets confirms the effectiveness of BTMP in quantifying prediction uncertainty. Furthermore, experiments, utilizing both synthetic and real-world data, demonstrate that the BTMP model, trained with the uncertainty-guided active survey framework, requires 20% to 50% fewer survey responses to match the performance of the model trained on randomly collected survey data. Overall, the proposed BTMP model and active survey framework innovatively incorporate uncertainty quantification into travel mode choice modeling, providing model users with essential insights into prediction reliability while optimizing data collection for deep learning model training in a cost-efficient manner

MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generation

Humans talk in free-form while negotiating the expressed meanings or common ground. Despite the impressive conversational abilities of the large generative language models, they do not consider the individual differences in contextual understanding in a shared situated environment. In this work, we propose MindDial, a novel conversational framework that can generate situated free-form responses to negotiate common ground. We design an explicit mind module that can track three-level beliefs -- the speaker's belief, the speaker's prediction of the listener's belief, and the common belief based on the gap between the first two. Then the speaking act classification head will decide to continue to talk, end this turn, or take task-related action. We augment a common ground alignment dataset MutualFriend with belief dynamics annotation, of which the goal is to find a single mutual friend based on the free chat between two agents. Experiments show that our model with mental state modeling can resemble human responses when aligning common ground meanwhile mimic the natural human conversation flow. The ablation study further validates the third-level common belief can aggregate information of the first and second-order beliefs and align common ground more efficiently

Effects of different application ratios of biochar-organic compound fertilizers and chemical fertilizers on soil nutrition content and yield of maize

Overuse of traditional chemical fertilizers may result in environmental pollution and a decrease in the quality of farm produce. By contrast, applying biochar-organic compound fertilizers can enhance soil structure, increase soil fertility, and mitigate pollution levels. This study explores the intricate mechanisms of the combined application of biochar-organic compound fertilizers and chemical fertilizers on soil chemical properties and corn growth. The aim is to elucidate the theoretical foundations supporting the widespread adoption of biochar-organic compound fertilizers. A total of 6 treatments were set up, among which the CK treatment did not apply fertilizer, the CF treatment used bovine excrement organic fertilizer combined with chemical fertilizer, the T1 to T4 treatments used biochar-organic compound fertilizers and replaced 40%, 60%, 80%, and 100% bovine excrement organic fertilizer combined with chemical fertilizer. The results showed that applying biochar-organic compound fertilizers enhanced the slow-release properties of soil available nutrients, increased corn yield, and improved grain quality. Notably, when biochar-organic compound fertilizers were employed instead of 100% bovine excrement organic fertilizer, the yield surpassed that of other treatments, exhibiting a remarkable 9.30% increase compared to the CF treatment. Through comprehensive analysis, it was determined that using biochar-organic compound fertilizer to replace 60% of bovine excrement organic fertilizer is a scheme that can balance both fertilizer efficacy and cost and is recommended to farmers. This research can contribute to promoting the green transformation of agriculture and help achieve the goal of "carbon neutrality"

Can EAT be an INOCA goalkeeper

Ischemia with non-obstructive coronary artery (INOCA) is a blind spot of coronary artery disease (CAD). Such patients are often reassured but offered no specific care, that lead to a heightened risk of adverse cerebrovascular disease (CVD) outcomes. Epicardial adipose tissue (EAT) is proven to correlate independently with CAD and its severity, but it is unknown whether EAT is a specific and sensitive indicator of INOCA. This review focuses on the INOCA epidemiology and related factors, as well as the association between EAT

Lagrange tracking-based long-term drift trajectory prediction method for Autonomous Underwater Vehicle

Autonomous Underwater Vehicle (AUV) works autonomously in complex marine environments. After a severe accident, an AUV will lose its power and rely on its small buoyancy to ascend at a slow speed. If the reserved buoyancy is insufficient, when reaching the thermocline, the buoyancy will rapidly decrease to zero. Consequently, the AUV will experience prolonged lateral drift within the thermocline. This study focuses on developing a prediction method for the drift trajectory of an AUV after a long-term power loss accident. The aim is to forecast the potential resurfacing location, providing technical support for surface search and salvage operations of the disabled AUV. To the best of our knowledge, currently, there is no mature and effective method for predicting long-term AUV underwater drift trajectories. In response to this issue, based on real AUV catastrophes, this paper studies the prediction of long-term AUV underwater drift trajectories in the cases of power loss. We propose a three-dimensional trajectory prediction method based on the Lagrange tracking approach. This method takes the AUV's longitudinal velocity, the time taken to reach different depths, and ocean current data at various depths into account. The reason for the AUV's failure to ascend to sea surface lies that the remaining buoyancy is too small to overcome the thermocline. As a result, AUV drifts long time within the thermocline. To address this issue, a method for estimating thermocline currents is proposed, which can be used to predict the lateral drift trajectory of the AUV within the thermocline. Simulation is conducted to compare the results obtained by the proposed method and that in a real accident. The results demonstrate that the proposed approach exhibits small directional and positional errors. This validates the effectiveness of the proposed method

Construction of a High-Density Genetic Map and Identification of Leaf Trait-Related QTLs in Chinese Bayberry (Myrica rubra)

Chinese bayberry (Myrica rubra) is an economically important fruit tree that is grown in southern China. Owing to its over 10-year seedling period, the crossbreeding of bayberry is challenging. The characteristics of plant leaves are among the primary factors that control plant architecture and potential yields, making the analysis of leaf trait-related genetic factors crucial to the hybrid breeding of any plant. In the present study, molecular markers associated with leaf traits were identified via a whole-genome re-sequencing approach, and a genetic map was thereby constructed. In total, this effort yielded 902.11 Gb of raw data that led to the identification of 2,242,353 single nucleotide polymorphisms (SNPs) in 140 F1 individuals and parents (Myrica rubra cv. Biqizhong × Myrica rubra cv. 2012LXRM). The final genetic map ultimately incorporated 31,431 SNPs in eight linkage groups, spanning 1,351.85 cM. This map was then used to assemble and update previous scaffold genomic data at the chromosomal level. The genome size of M. rubra was thereby established to be 275.37 Mb, with 94.98% of sequences being assembled into eight pseudo-chromosomes. Additionally, 18 quantitative trait loci (QTLs) associated with nine leaf and growth-related traits were identified. Two QTL clusters were detected (the LG3 and LG5 clusters). Functional annotations further suggested two chlorophyll content-related candidate genes being identified in the LG5 cluster. Overall, this is the first study on the QTL mapping and identification of loci responsible for the regulation of leaf traits in M. rubra, offering an invaluable scientific for future marker-assisted selection breeding and candidate gene analyses

Hydraulics and mixing of the deep overflow in the Lifamatola Passage of the Indonesian Seas

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 50(9),(2020): 2797-2814, https://doi.org/10.1175/JPO-D-19-0326.1.Hydrographic measurements recently acquired along the thalweg of the Lifamatola Passage combined with historical moored velocity measurements immediately downstream of the sill are used to study the hydraulics, transport, mixing, and entrainment in the dense overflow. The observations suggest that the mean overflow is nearly critical at the mooring site, suggesting that a weir formula may be appropriate for estimating the overflow transport. Our assessment suggests that the weir formulas corresponding to a rectangular, triangular, or parabolic cross section all result in transports very close to the observation, suggesting their potential usage in long-term monitoring of the overflow transport or parameterizing the transport in numerical models. Analyses also suggest that deep signals within the overflow layer are blocked by the shear flow from propagating upstream, whereas the shallow wave modes of the full-depth continuously stratified flow are able to propagate upstream from the Banda Sea into the Maluku Sea. Strong mixing is found immediately downstream of the sill crest, with Thorpe-scale-based estimates of the mean dissipation rate within the overflow up to 1.1 × 10−7 W kg−1 and the region-averaged diapycnal diffusivity within the downstream overflow in the range of 2.3 × 10−3 to 10.1 × 10−3 m2 s−1. Mixing in the Lifamatola Passage results in 0.6–1.2-Sv (1 Sv ≡ 106 m3 s−1) entrainment transport added to the overflow, enhancing the deep-water renewal in the Banda Sea. A bulk diffusivity coefficient estimated in the deep Banda Sea yields 1.6 × 10−3 ± 5 × 10−4 m2 s−1, with an associated downward turbulent heat flux of 9 W m−2.This study is supported by NSFC (91858204), the CAS Strategic Priority Research Program (XDB42000000), NSFC(41720104008, 41421005, 41876025), QMSNL (2018SDKJ0104-02), and the Shandong Provincial projects (U1606402). L. Pratt was supported by the U.S. NSF Grant OCE-1657870