Self-Selective Correlation Ship Tracking Method for Smart Ocean System

In recent years, with the development of the marine industry, navigation environment becomes more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count the sailing ships to ensure the maritime security and facilitates the management for Smart Ocean System. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly include: 1) A self-selective model with negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of classifier at the same time; 2) A bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were higher than Discriminative Scale Space Tracking (DSST) by over 8 percentage points on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 Frames Per Second (FPS)

Generalized Invariant Matching Property via LASSO

Learning under distribution shifts is a challenging task. One principled approach is to exploit the invariance principle via the structural causal models. However, the invariance principle is violated when the response is intervened, making it a difficult setting. In a recent work, the invariant matching property has been developed to shed light on this scenario and shows promising performance. In this work, by formulating a high-dimensional problem with intrinsic sparsity, we generalize the invariant matching property for an important setting when only the target is intervened. We propose a more robust and computation-efficient algorithm by leveraging a variant of Lasso, improving upon the existing algorithms.Comment: Accepted to the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023

Learning Invariant Representations under General Interventions on the Response

It has become increasingly common nowadays to collect observations of feature and response pairs from different environments. As a consequence, one has to apply learned predictors to data with a different distribution due to distribution shifts. One principled approach is to adopt the structural causal models to describe training and test models, following the invariance principle which says that the conditional distribution of the response given its predictors remains the same across environments. However, this principle might be violated in practical settings when the response is intervened. A natural question is whether it is still possible to identify other forms of invariance to facilitate prediction in unseen environments. To shed light on this challenging scenario, we focus on linear structural causal models (SCMs) and introduce invariant matching property (IMP), an explicit relation to capture interventions through an additional feature, leading to an alternative form of invariance that enables a unified treatment of general interventions on the response as well as the predictors. We analyze the asymptotic generalization errors of our method under both the discrete and continuous environment settings, where the continuous case is handled by relating it to the semiparametric varying coefficient models. We present algorithms that show competitive performance compared to existing methods over various experimental settings including a COVID dataset.Comment: Accepted to the IEEE Journal on Selected Areas in Information Theory. Special Issue: Causality: Fundamental Limits and Application

The diverse roles of cytokinins in regulating leaf development

Leaves provide energy for plants, and consequently for animals, through photosynthesis. Despite their important functions, plant leaf developmental processes and their underlying mechanisms have not been well characterized. Here, we provide a holistic description of leaf developmental processes that is centered on cytokinins and their signaling functions. Cytokinins maintain the growth potential (pluripotency) of shoot apical meristems, which provide stem cells for the generation of leaf primordia during the initial stage of leaf formation; cytokinins and auxins, as well as their interaction, determine the phyllotaxis pattern. The activities of cytokinins in various regions of the leaf, especially at the margins, collectively determine the final leaf morphology (e.g., simple or compound). The area of a leaf is generally determined by the number and size of the cells in the leaf. Cytokinins promote cell division and increase cell expansion during the proliferation and expansion stages of leaf cell development, respectively. During leaf senescence, cytokinins reduce sugar accumulation, increase chlorophyll synthesis, and prolong the leaf photosynthetic period. We also briefly describe the roles of other hormones, including auxin and ethylene, during the whole leaf developmental process. In this study, we review the regulatory roles of cytokinins in various leaf developmental stages, with a focus on cytokinin metabolism and signal transduction processes, in order to shed light on the molecular mechanisms underlying leaf development

Flavor Structures Of Quarks and Leptons From Flipped SU(5) GUT with A4A_4 Modular Flavor Symmetry

We propose to generate the flavor structures of the Standard Model plus neutrinos from flipped SU(5) GUT with $A_4$ modular flavor symmetry. Possible way to assign different moduli values for quarks and leptons in modular GUT scheme is discussed. We propose to reduce the multiple modular symmetries to a single modular symmetry in the low energy effective theory with proper boundary conditions. We classify all possible scenarios in this scheme according to the assignments of the modular $A_4$ representations for matter superfields and give the expressions of the quark and lepton mass matrices predicted by our scheme at the GUT scale. After properly selecting the modular weights for various superfields that can lead to better fitting, we can obtain the best-fit points with the corresponding $\chi^2$ values for the sample subscenarios. We find that the flavor structures of the Standard Model plus neutrinos can be fitted perfectly in such a $A_4$ modular flavor GUT scheme with single or two modulus fields. Especially, the $\chi^2_{total}$ of our fitting can be as low as $1.558$ for sample ${\bf IX^\prime}$ of scenario ${\bf III}$ even if only a single common modulus field for both quark and lepton sectors is adopted. The most predictive scenario ${\bf III}$, in which all superfields transform as triplets of $A_4$, can be fitted much better with two independent moduli fields $\tau_q,\tau_l$ for quark sector and lepton sector ( $\chi^2_{total}\approx 95$) than that with the single modulus case ($\chi^2_{total}\approx 282.4$).Comment: 45 pages, 8 table

NMSSM with generalized deflected mirage mediation

We propose to generate a realistic soft SUSY breaking spectrum for Next-to-Minimal Supersymmetric Standard Model (NMSSM) with a generalized deflected mirage mediation scenario, in which additional Yukawa and gauge mediation contributions are included to deflect the renormalization group equation(RGE) trajectory. Based on the Wilsonian effective action obtained by integrating out the messengers, the NMSSM soft SUSY breaking spectrum can be given analytically at the messenger scale. We find that additional contributions to $m_S^2$ can possibly ameliorate the stringent constraints from the electroweak symmetry breaking (EWSB) and 125 GeV Higgs mass. Constraints from dark matter and fine-tuning are also discussed. The Barbieri-Giudice fine-tuning measure and electroweak fine-tuning measure in our scenario can be as low as ${\cal O}(1)$, which possibly indicates that our scenario is natural.Comment: Published version, minor changes; 28 pages, 6 figure

Variation of Oriental Oak (Quercus variabilis) Leaf δ13C across Temperate and Subtropical China: Spatial Patterns and Sensitivity to Precipitation

The concentration of the carbon-13 isotope (leaf δ13C) in leaves is negatively correlated with the mean annual precipitation (MAP) atlarge geographical scales. In this paper, we explain the spatial pattern of leaf δ13C variation for deciduous oriental oak (Quercus variabilis Bl.) across temperate and subtropical biomes and its sensitivity to climate factors such as MAP. There was a 6‰ variation in the leaf δ13C values of oak with a significant positive correlation with latitude and negative correlations with the mean annual temperature (MAT) and MAP. There was no correlation between leaf δ13C and altitude or longitude. Stepwise multiple regression analyses showed that leaf δ13C decreased 0.3‰ per 100 mm increase in MAP. MAP alone could account for 68% of the observed variation in leaf δ13C. These results can be used to improve predictions for plant responses to climate change and particularly lower rainfall

The sterlet sturgeon genome sequence and the mechanisms of segmental rediploidization

Sturgeons seem to be frozen in time. The archaic characteristics of this ancient fish lineage place it in a key phylogenetic position at the base of the ~30,000 modern teleost fish species. Moreover, sturgeons are notoriously polyploid, providing unique opportunities to investigate the evolution of polyploid genomes. We assembled a high-quality chromosome-level reference genome for the sterlet, Acipenser ruthenus. Our analysis revealed a very low protein evolution rate that is at least as slow as in other deep branches of the vertebrate tree, such as that of the coelacanth. We uncovered a whole-genome duplication that occurred in the Jurassic, early in the evolution of the entire sturgeon lineage. Following this polyploidization, the rediploidization of the genome included the loss of whole chromosomes in a segmental deduplication process. While known adaptive processes helped conserve a high degree of structural and functional tetraploidy over more than 180 million years, the reduction of redundancy of the polyploid genome seems to have been remarkably random