Asymptotic behavior for multi-scale SDEs with monotonicity coefficients driven by L\'evy processes

In this paper, we study the asymptotic behavior for multi-scale stochastic differential equations driven by L\'evy processes. The optimal strong convergence order 1/2 is obtained by studying the regularity estimates for the solution of Poisson equation with polynomial growth coefficients, and the optimal weak convergence order 1 is got by using the technique of Kolmogorov equation. The main contribution is that the obtained results can be applied to a class of multi-scale stochastic differential equations with monotonicity coefficients, as well as the driven processes can be the general L\'evy processes, which seems new in the existing literature.Comment: 39 pages. To appear in Potential Analysi

FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing

Integrating unmanned aerial vehicles (UAVs) into vehicular networks have shown high potentials in affording intensive computing tasks. In this paper, we study the digital twin driven vehicular edge computing networks for adaptively computing resource management where an unmanned aerial vehicle (UAV) named FlexEdge acts as a flying server. In particular, we first formulate an energy consumption minimization problem by jointly optimizing UAV trajectory and computation resource under the practical constraints. To address such a challenging problem, we then build the computation offloading process as a Markov decision process and propose a deep reinforcement learning-based proximal policy optimization algorithm to dynamically learn the computation offloading strategy and trajectory design policy. Numerical results indicate that our proposed algorithm can achieve quick convergence rate and significantly reduce the system energy consumption.Comment: 6 pages, 6 figure

Focus Is What You Need For Chinese Grammatical Error Correction

Chinese Grammatical Error Correction (CGEC) aims to automatically detect and correct grammatical errors contained in Chinese text. In the long term, researchers regard CGEC as a task with a certain degree of uncertainty, that is, an ungrammatical sentence may often have multiple references. However, we argue that even though this is a very reasonable hypothesis, it is too harsh for the intelligence of the mainstream models in this era. In this paper, we first discover that multiple references do not actually bring positive gains to model training. On the contrary, it is beneficial to the CGEC model if the model can pay attention to small but essential data during the training process. Furthermore, we propose a simple yet effective training strategy called OneTarget to improve the focus ability of the CGEC models and thus improve the CGEC performance. Extensive experiments and detailed analyses demonstrate the correctness of our discovery and the effectiveness of our proposed method.Comment: Submitted to ICASSP2023 (currently under review

A Curriculum Learning Approach for Multi-domain Text Classification Using Keyword weight Ranking

Text classification is a very classic NLP task, but it has two prominent shortcomings: On the one hand, text classification is deeply domain-dependent. That is, a classifier trained on the corpus of one domain may not perform so well in another domain. On the other hand, text classification models require a lot of annotated data for training. However, for some domains, there may not exist enough annotated data. Therefore, it is valuable to investigate how to efficiently utilize text data from different domains to improve the performance of models in various domains. Some multi-domain text classification models are trained by adversarial training to extract shared features among all domains and the specific features of each domain. We noted that the distinctness of the domain-specific features is different, so in this paper, we propose to use a curriculum learning strategy based on keyword weight ranking to improve the performance of multi-domain text classification models. The experimental results on the Amazon review and FDU-MTL datasets show that our curriculum learning strategy effectively improves the performance of multi-domain text classification models based on adversarial learning and outperforms state-of-the-art methods.Comment: Submitted to ICASSP2023 (currently under review

Investigating Graph Structure Information for Entity Alignment with Dangling Cases

Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs), which play an important role in knowledge engineering. Recently, EA with dangling entities has been proposed as a more realistic setting, which assumes that not all entities have corresponding equivalent entities. In this paper, we focus on this setting. Some work has explored this problem by leveraging translation API, pre-trained word embeddings, and other off-the-shelf tools. However, these approaches over-rely on the side information (e.g., entity names), and fail to work when the side information is absent. On the contrary, they still insufficiently exploit the most fundamental graph structure information in KG. To improve the exploitation of the structural information, we propose a novel entity alignment framework called Weakly-Optimal Graph Contrastive Learning (WOGCL), which is refined on three dimensions : (i) Model. We propose a novel Gated Graph Attention Network to capture local and global graph structure similarity. (ii) Training. Two learning objectives: contrastive learning and optimal transport learning are designed to obtain distinguishable entity representations via the optimal transport plan. (iii) Inference. In the inference phase, a PageRank-based method is proposed to calculate higher-order structural similarity. Extensive experiments on two dangling benchmarks demonstrate that our WOGCL outperforms the current state-of-the-art methods with pure structural information in both traditional (relaxed) and dangling (consolidated) settings. The code will be public soon

Global Existence and Convergence Rates for the Strong Solutions in H

We are concerned with a 3D chemotaxis model arising from biology, which is a coupled hyperbolic-parabolic system. We prove the global existence of a strong solution when H2-norm of the initial perturbation around a constant state is sufficiently small. Moreover, if additionally, L1-norm of the initial perturbation is bounded; the optimal convergence rates are also obtained for such a solution. The proofs are obtained by combining spectral analysis with energy methods

MicroRNA-298 Reverses Multidrug Resistance to Antiepileptic Drugs by Suppressing MDR1/P-gp Expression in vitro

P-glycoprotein (P-gp), a critical multidrug transporter, recognizes and transports various antiepileptic drugs (AEDs) through the blood-brain barrier (BBB). This may decrease the concentrations of AEDs in brain tissues and cause multidrug resistance (MDR) in patients with refractory epilepsy. Compelling evidence indicates that microRNAs (miRNAs) modulate MDR in various cancers by regulating P-gp expression. Furthermore, a previous study showed that miR-298 mediates MDR in breast cancer cells by downregulating P-gp expression. Based on the therapeutic results obtained from tumor cells, we aimed to determine whether miR-298 reverses MDR to AEDs by regulating P-gp expression in the BBB. We first established different drug-resistant cell lines, including PHT-resistant HBMECs (human brain microvascular endothelial cells) and doxorubicin (DOX)-resistant U87-MG (human malignant glioma) cells, by inducing P-gp overexpression. Quantitative real-time PCR (qRT-PCR) analysis revealed reduced expression of miR-298 in both HBMEC/PHT and U87-MG/DOX cells, and the luciferase reporter assay identified the direct binding of miR-298 to the 3′-untranslated region (3′-UTR) of P-gp. Moreover, ectopic expression of miR-298 downregulated P-gp expression at the mRNA and protein levels, thereby increasing the intracellular accumulation of AEDs in drug-resistant HBMEC/PHT and U87-MG/DOX cells. Thus, our findings suggest that miR-298 reverses MDR to AEDs by inhibiting P-gp expression, suggesting a potential target for overcoming MDR in refractory epilepsy

Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking

Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs. The general form of EL (i.e., end-to-end EL) aims to first find mentions in the given input document and then link the mentions to corresponding entities in a specific knowledge base. Recently, the paradigm of retriever-reader promotes the progress of end-to-end EL, benefiting from the advantages of dense entity retrieval and machine reading comprehension. However, the existing study only trains the retriever and the reader separately in a pipeline manner, which ignores the benefit that the interaction between the retriever and the reader can bring to the task. To advance the retriever-reader paradigm to perform more perfectly on end-to-end EL, we propose BEER$^2$, a Bidirectional End-to-End training framework for Retriever and Reader. Through our designed bidirectional end-to-end training, BEER$^2$ guides the retriever and the reader to learn from each other, make progress together, and ultimately improve EL performance. Extensive experiments on benchmarks of multiple domains demonstrate the effectiveness of our proposed BEER$^2$.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

The Neuroprotective Effect of Astaxanthin on Pilocarpine-Induced Status Epilepticus in Rats

Cognitive dysfunction is one of the serious complications induced by status epilepticus (SE), which has a significant negative impact on patients’ quality of life. Previous studies demonstrated that the pathophysiological changes after SE such as oxidative stress, inflammatory reaction contribute to neuronal damage. A recent study indicated that preventive astaxanthin (AST) alleviated epilepsy-induced oxidative stress and neuronal apoptosis in the brain. In the present study, rats were treated with vehicle or AST 1 h after SE onset and were injected once every other day for 2 weeks (total of seven times). The results showed that the cognitive function in SE rats was significantly impaired, and AST treatment improved cognitive function in the Morris water maze (MWM). Magnetic resonance imaging (MRI), hematoxylin-eosin (HE) staining and TdT-mediated dUTP Nick-End Labeling (TUNEL) staining showed obvious damage in the hippocampus of SE rats, and AST alleviated the damage. Subsequently, we evaluated the effect of AST on relative pathophysiology to elucidate the possible mechanisms. To evaluate the oxidative stress, the expression of malondialdehyde (MDA) and superoxide dismutase (SOD) in plasma were detected using commercially available kits. NADPH oxidase-4 (Nox-4), p22phox, NF-E2-related factor 2 (Nrf-2), heme oxygenase 1 (Ho-1) and sod1 in the parahippocampal cortex and hippocampus were detected using western blot and real-time polymerase chain reaction (RT-PCR). The levels of MDA in plasma and Nox-4 and p22phox in the brain increased in SE rats, and the levels of SOD in plasma and Nrf-2, Ho-1 and sod1 in the brain decreased. Treatment with AST alleviated these changes. We also detected the levels of inflammatory mediators like cyclooxygenase-2 (cox-2), interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α) and NF-κB phosphorylation p65 (p-p65)/p65 in the brain. The inflammatory reaction was significantly activated in the brain of SE rats, and AST alleviated neuroinflammation. We detected the levels of p-Akt, Akt, B-cell lymphoma-2 (Bcl-2), Bax, cleaved caspase-3, and caspase-3 in the parahippocampal cortex and hippocampus using western blot. The levels of p-Akt/Akt and Bcl-2 decreased in SE rats, Bax and cleaved caspase-3/caspase-3 increased, while AST alleviated these changes. The present study indicated that AST exerted an reobvious neuroprotective effect in pilocarpine-induced SE rats

Genetic variants in the calcium signaling pathway genes are associated with cutaneous melanoma-specific survival

Remodeling or deregulation of the calcium signaling pathway is a relevant hallmark of cancer including cutaneous melanoma (CM). In this study, using data from a published genome-wide association study (GWAS) from The University of Texas M.D. Anderson Cancer Center, we assessed the role of 41,377 common single-nucleotide polymorphisms (SNPs) of 167 calcium signaling pathway genes in CM survival. We used another GWAS from Harvard University as the validation dataset. In the single-locus analysis, 1830 SNPs were found to be significantly associated with CM-specific survival (CMSS; P ≤ 0.050 and false-positive report probability ≤ 0.2), of which 9 SNPs were validated in the Harvard study (P ≤ 0.050). Among these, three independent SNPs (i.e. PDE1A rs6750552 T>C, ITPR1 rs6785564 A>G and RYR3 rs2596191 C>A) had a predictive role in CMSS, with a meta-analysis-derived hazards ratio of 1.52 (95% confidence interval = 1.19–1.94, P = 7.21 × 10−4), 0.49 (0.33–0.73, 3.94 × 10−4) and 0.67 (0.53–0.86, 0.0017), respectively. Patients with an increasing number of protective genotypes had remarkably improved CMSS. Additional expression quantitative trait loci analysis showed that these genotypes were also significantly associated with mRNA expression levels of the genes. Taken together, these results may help us to identify prospective biomarkers in the calcium signaling pathway for CM prognosis