Galilean symmetry of the KdV hierarchy

By solving the infinitesimal Galilean symmetry for the KdV hierarchy, we obtain an explicit expression for the corresponding one-parameter Lie group, which we call the Galilean symmetry of the KdV hierarchy. As an application, we establish an explicit relationship between the non-abelian Born--Infeld partition function and the generalized Br\'ezin--Gross--Witten partition function.Comment: 24 page

Asymptotic properties of a multicolored random reinforced urn model with an application to multi-armed bandits

The random self-reinforcement mechanism, characterized by the principle of ``the rich get richer'', has demonstrated significant utility across various domains. One prominent model embodying this mechanism is the random reinforcement urn model. This paper investigates a multicolored, multiple-drawing variant of the random reinforced urn model. We establish the limiting behavior of the normalized urn composition and demonstrate strong convergence upon scaling the counts of each color. Additionally, we derive strong convergence estimators for the reinforcement means, i.e., for the expectations of the replacement matrix's diagonal elements, and prove their joint asymptotic normality. It is noteworthy that the estimators of the largest reinforcement mean are asymptotically independent of the estimators of the other smaller reinforcement means. Additionally, if a reinforcement mean is not the largest, the estimators of these smaller reinforcement means will also demonstrate asymptotic independence among themselves. Furthermore, we explore the parallels between the reinforced mechanisms in random reinforced urn models and multi-armed bandits, addressing hypothesis testing for expected payoffs in the latter context

Towards Efficient and Effective Unlearning of Large Language Models for Recommendation

The significant advancements in large language models (LLMs) give rise to a promising research direction, i.e., leveraging LLMs as recommenders (LLMRec). The efficacy of LLMRec arises from the open-world knowledge and reasoning capabilities inherent in LLMs. LLMRec acquires the recommendation capabilities through instruction tuning based on user interaction data. However, in order to protect user privacy and optimize utility, it is also crucial for LLMRec to intentionally forget specific user data, which is generally referred to as recommendation unlearning. In the era of LLMs, recommendation unlearning poses new challenges for LLMRec in terms of \textit{inefficiency} and \textit{ineffectiveness}. Existing unlearning methods require updating billions of parameters in LLMRec, which is costly and time-consuming. Besides, they always impact the model utility during the unlearning process. To this end, we propose \textbf{E2URec}, the first \underline{E}fficient and \underline{E}ffective \underline{U}nlearning method for LLM\underline{Rec}. Our proposed E2URec enhances the unlearning efficiency by updating only a few additional LoRA parameters, and improves the unlearning effectiveness by employing a teacher-student framework, where we maintain multiple teacher networks to guide the unlearning process. Extensive experiments show that E2URec outperforms state-of-the-art baselines on two real-world datasets. Specifically, E2URec can efficiently forget specific data without affecting recommendation performance. The source code is at \url{https://github.com/justarter/E2URec}.Comment: Accepted by Frontier of Computer Scienc

Crop Phenology Estimation in Rice Fields Using Sentinel-1 GRD SAR Data and Machine Learning-Aided Particle Filtering Approach

Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data

Quantitative study of microscopic formation water distribution in tight gas reservoirs based on the thermogravimetric method

The microscopic characterization of the distribution of formation water in tight gas reservoirs has always been one of the challenges in the industry. The traditional nuclear magnetic resonance method has certain limitations in characterizing the microscopic distribution of formation water. Thermogravimetric analysis can correlate with mass, and combined with nuclear magnetic resonance spectra, it can further optimize the characterization method for the microscopic distribution of formation water. Multiple tight sandstone gas reservoirs are vertically developed in the Shenfu Block of the Ordos Basin. Due to the strong heterogeneity of the reservoir, given the complexity of the characterization of the microscopic occurrence law of formation water, the typical argillaceous tight sandstone reservoirs Qian 5 and Tai 2 members are selected as the research objects. The distribution characteristics of the microscopic formation water of the tight gas reservoir in the Shenfu block were quantitatively characterized by the thermogravimetric method using dry distillation experiment, nuclear magnetic resonance experiment, and displacement experiment. The results show that 35°C is the boundary temperature between free water and microporous water. According to the characterization of various types of water occurrence in clay and tight sandstone by thermogravimetric experiment, free water below 35°C, microporous water (including capillary water and adsorbed water on the surface of mineral particles) in the range of 35°C–427°C, and clay-bound water (crystal water, structural water/carboxyl water) above 427°C. The type of water occurrence in tight sandstone is consistent with that of clay minerals, but the amount of water occurrence and water loss rate are different. From the perspective of water occurrence, microporous water is typically the most abundant form, while in terms of water loss rate, free water generally exhibits the highest rate. The full-scale quantitative study of micro-formation water distribution in tight gas reservoirs based on the thermogravimetric method has important guiding significance for solving the accurate characterization of water saturation logging in tight gas reservoirs, and enriches the understanding of the occurrence characteristics and laws of micro-formation water in tight gas reservoirs

Complete mitochondrial genome of Tribolium castaneum (Coleoptera: Tenebrionidae) reared on sauce-flavor Daqu

The red flour beetle Tribolium castaneum (Coleoptera: Tenebrionidae), a cosmopolitan stored-product pest frequently infesting sauce-flavor Daqu (a multi-microbial fermented starter), may experience mitochondrial genome variations under the selective pressure exerted by this enzyme-rich substrate. Here we test whether feeding on sauce-flavor Daqu is associated with mitogenomic differences in T. castaneum. We present the complete mitochondrial genome of T. castaneum from this environment: a 15,885 bp circular DNA (GenBank PV563855) retaining ancestral insect architecture with 71.81% A+T content and slight positive AT skew. The genome contains 37 functional elements: 22 tRNA genes (all exhibiting atypical cloverleaf structures except trnS1(AGN)), 13 protein-coding genes (PCGs), 2 rRNA genes, and a 1,238 bp A+T-rich control region (82.80% AT). Eleven PCGs initiate with ATN codons, while cox1 (CTG) and nad1 (TTG) show divergent initiation. Ten PCGs terminate with TAA/TAG codons. Gene order aligns with basal insect mitogenomes. Comparative analysis with Jiangsu (China) and California (USA) strains revealed conserved structural features, though sequence/assembly discrepancies require further investigation to assess potential pressure-induced mutations. While these differences may reflect adaptations to the enzyme-rich Daqu environment, technical and geographical factors could also contribute; further functional studies are needed to establish causal links

Implications of salt tectonics on hydrocarbon ascent in the Eastern Persian gulf: insights into the formation mechanism of salt diapirs, gas chimneys, and their sedimentary interactions

Gas chimneys, salt domes, and faults are vital to the movement of hydrocarbons within geological systems. Accurate identification of these geological features is crucial to modeling hydrocarbon resources. This study explores the processes that have shaped the eastern Persian Gulf, focusing on salt diapir characteristics, origin, and fluid migration. Plate collisions between the Indian, Eurasian, and Arabian Plates have considerably impacted salt tectonics, developing key features such as the Hormuz salt, Qatar-South Fars Arch, and Zagros and Oman orogenic structures. Salt-related features were discerned through two-dimensional seismic data and drilling records, salt movement sequences were reconstructed, and fluid expulsion patterns were delineated using attribute preferences. The results of this study revealed that fractured substrates influenced by regional tectonic forces contribute to the creation of salt diapirs, which serve as conduits for guided fluid transport. Moreover, these results showed that gravity driven downbuilding mainly controls salt flow, while the circular arrangement of salt structures results from regional stress and interactions between different salt sources. Distinct stress-induced basement incisions compounded by the hindrance of initial salt movement by the Qatar Arch further contribute to the complex salt structure geometry. Crucially, the uplift of the Qatar Arch and stresses from the Oman and Zagros orogenies profoundly affect the salt structure geometry and depositional patterns across diverse regions, resulting in circular salt structures and gas chimneys. This study offers valuable perspectives for oil and gas exploration and provides a comprehensive understanding of the regional dynamics governing salt tectonics and hydrocarbon ascent in the eastern Persian Gulf

TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency

Accurate segmentation of brain tumors from medical images is important for diagnosis and treatment planning, and it often requires multi-modal or contrast-enhanced images. However, in practice some modalities of a patient may be absent. Synthesizing the missing modality has a potential for filling this gap and achieving high segmentation performance. Existing methods often treat the synthesis and segmentation tasks separately or consider them jointly but without effective regularization of the complex joint model, leading to limited performance. We propose a novel brain Tumor Image Synthesis and Segmentation network (TISS-Net) that obtains the synthesized target modality and segmentation of brain tumors end-to-end with high performance. First, we propose a dual-task-regularized generator that simultaneously obtains a synthesized target modality and a coarse segmentation, which leverages a tumor-aware synthesis loss with perceptibility regularization to minimize the high-level semantic domain gap between synthesized and real target modalities. Based on the synthesized image and the coarse segmentation, we further propose a dual-task segmentor that predicts a refined segmentation and error in the coarse segmentation simultaneously, where a consistency between these two predictions is introduced for regularization. Our TISS-Net was validated with two applications: synthesizing FLAIR images for whole glioma segmentation, and synthesizing contrast-enhanced T1 images for Vestibular Schwannoma segmentation. Experimental results showed that our TISS-Net largely improved the segmentation accuracy compared with direct segmentation from the available modalities, and it outperformed state-of-the-art image synthesis-based segmentation methods

Seasonal variability of sea surface pCO2 and air-sea CO2 flux in a high turbidity coastal ocean in the vicinity of the East China Sea

The sea surface partial pressure of CO2 (pCO2) and air-sea carbon flux in estuarine and bay areas, influenced by both natural and anthropogenic factors, remain poorly understood and inadequately assessed. This study, based on seasonal underway observations conducted in 2024, analyzed the seasonal variations in surface seawater pCO2 and air-sea CO2 flux in the high-turbidity coastal waters of Zhejiang, including Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and the nearshore waters (NSW). The results indicate that the pCO2 in the study area ranged from 194 to 739 μatm throughout the year, exhibiting significant spatiotemporal heterogeneity. In HZB, the lowest pCO2 was observed in winter, averaging 453 μatm, whereas the values in spring and summer were around 600 μatm, with a subsequent decline to 481 μatm in autumn. In XSB, pCO2 reached its minimum in winter (194 μatm), attributed to vigorous biological activity, and peaked in spring, averaging 639 μatm. In SMB, pCO2 was relatively lower in autumn and winter (~470 μatm), and higher in spring and summer (~640 μatm). In the NSW, pCO2 was lower in winter and spring (~445 μatm), and increased to ~510 μatm in summer and autumn. The pCO2 was predominantly regulated by sea surface temperature and horizontal mixing, while other factors like biological activity also had significant impacts. The annual average CO2 flux was 6.0±3.7 mmol m-2 d-1 in HZB, 1.2±2.3 mmol m-2 d-1 in XSB, 7.0±3.2 mmol m-2 d-1 in SMB and 5.2±5.9 mmol m-2 d-1 in the NSW. Higher wind speeds in autumn and winter, coupled with elevated the pCO2 difference between the surface water and the atmosphere (ΔpCO2) in spring and summer, collectively drove the seasonal variations in CO2 flux. On an annual scale, both the estuarine and bay areas and the nearshore regions functioned as carbon sources

Full-length single-cell RNA-seq applied to a viral human cancer:applications to HPV expression and splicing analysis in HeLa S3 cells

Background: Viral infection causes multiple forms of human cancer, and HPV infection is the primary factor in cervical carcinomas Recent single-cell RNA-seq studies highlight the tumor heterogeneity present in most cancers, but virally induced tumors have not been studied HeLa is a well characterized HPV+ cervical cancer cell line Result: We developed a new high throughput platform to prepare single-cell RNA on a nanoliter scale based on a customized microwell chip Using this method, we successfully amplified full-length transcripts of 669 single HeLa S3 cells and 40 of them were randomly selected to perform single-cell RNA sequencing Based on these data, we obtained a comprehensive understanding of the heterogeneity of HeLa S3 cells in gene expression, alternative splicing and fusions Furthermore, we identified a high diversity of HPV-18 expression and splicing at the single-cell level By co-expression analysis we identified 283 E6, E7 co-regulated genes, including CDC25, PCNA, PLK4, BUB1B and IRF1 known to interact with HPV viral proteins Conclusion: Our results reveal the heterogeneity of a virus-infected cell line It not only provides a transcriptome characterization of HeLa S3 cells at the single cell level, but is a demonstration of the power of single cell RNA-seq analysis of virally infected cells and cancers