A large deviation principle for nonlinear stochastic wave equation driven by rough noise

This paper is devoted to investigating Freidlin-Wentzell's large deviation principle for one (spatial) dimensional nonlinear stochastic wave equation \frac{\partial^2 u^{\e}(t,x)}{\partial t^2}=\frac{\partial^2 u^{\e}(t,x)}{\partial x^2}+\sqrt{\e}\sigma(t, x, u^{\e}(t,x))\dot{W}(t,x), where $\dot{W}$ is white in time and fractional in space with Hurst parameter $H\in(\frac 14,\frac 12)$. The variational framework and the modified weak convergence criterion proposed by Matoussi et al. \cite{MSZ} are adopted here.Comment: arXiv admin note: substantial text overlap with arXiv:2205.13157 by other author

Forgettable Federated Linear Learning with Certified Data Removal

Federated learning (FL) is a trending distributed learning framework that enables collaborative model training without data sharing. Machine learning models trained on datasets can potentially expose the private information of the training data, revealing details about individual data records. In this study, we focus on the FL paradigm that grants clients the ``right to be forgotten''. The forgettable FL framework should bleach its global model weights as it has never seen that client and hence does not reveal any information about the client. To this end, we propose the Forgettable Federated Linear Learning (2F2L) framework featured with novel training and data removal strategies. The training pipeline, named Federated linear training, employs linear approximation on the model parameter space to enable our 2F2L framework work for deep neural networks while achieving comparable results with canonical neural network training. We also introduce FedRemoval, an efficient and effective removal strategy that tackles the computational challenges in FL by approximating the Hessian matrix using public server data from the pretrained model. Unlike the previous uncertified and heuristic machine unlearning methods in FL, we provide theoretical guarantees by bounding the differences of model weights by our FedRemoval and that from retraining from scratch. Experimental results on MNIST and Fashion-MNIST datasets demonstrate the effectiveness of our method in achieving a balance between model accuracy and information removal, outperforming baseline strategies and approaching retraining from scratch

LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Network

Remote sensing target detection aims to identify and locate critical targets within remote sensing images, finding extensive applications in agriculture and urban planning. Feature pyramid networks (FPNs) are commonly used to extract multi-scale features. However, existing FPNs often overlook extracting low-level positional information and fine-grained context interaction. To address this, we propose a novel location refined feature pyramid network (LR-FPN) to enhance the extraction of shallow positional information and facilitate fine-grained context interaction. The LR-FPN consists of two primary modules: the shallow position information extraction module (SPIEM) and the contextual interaction module (CIM). Specifically, SPIEM first maximizes the retention of solid location information of the target by simultaneously extracting positional and saliency information from the low-level feature map. Subsequently, CIM injects this robust location information into different layers of the original FPN through spatial and channel interaction, explicitly enhancing the object area. Moreover, in spatial interaction, we introduce a simple local and non-local interaction strategy to learn and retain the saliency information of the object. Lastly, the LR-FPN can be readily integrated into common object detection frameworks to improve performance significantly. Extensive experiments on two large-scale remote sensing datasets (i.e., DOTAV1.0 and HRSC2016) demonstrate that the proposed LR-FPN is superior to state-of-the-art object detection approaches. Our code and models will be publicly available

Evaluation of the new rural cooperative medical system in China: is it working or not?

<p>Abstract</p> <p>Background</p> <p>To prove the possibility of implementing the New Rural Cooperative Medical System (NRCMS) at different levels with a premium funding according to their economic level in developed and less developed areas in Guangdong province, and study the insurable inpatients in different types of regions, taking into account limitations of indemnities and loss ratios.</p> <p>Method</p> <p>All data samples were randomly collected from the NRCMS Department, Guangdong Province. Gross domestic product (GDP) at 10000 Yuan per capita was employed to divide Guangdong into two economic levels: (1) economically developed & (2) less economically developed regions. A descriptive analysis about tendency of raising premium and reimbursement ratios of common fund was performed with independent samples and t-test as well as implementing a model to evaluate the differences in premium contribution differences in co-payments, thresholds, and rebates. Also, a qualitative study measured several economic factors to evaluate farmers' financial and social potency in contributing to the NRCMS.</p> <p>Result</p> <p>A higher GDP per capita were found within economically developed regions (p < 0.05) than in less developed areas, with higher tendency for funding capacity and average funding capability in villages and towns within economically developed regions (p < 0.05) than in economically less developed. Maximum benefits between two regions in medical insurance coverage showed significant difference (p < 0.05); differences between basic medical insurance coverage between two regions was insignificant (p > 0.05); nevertheless, economically developed regions showed higher threshold and rebates with less co-payments in the economically developed than less developed.</p> <p>Conclusion</p> <p>Despite some loop holes in the NRCMS, the system is workable, but needs more strengthening by encouraging farmers' participation into NRCMS with a necessity to implement a new reimbursement payment system by health care providers. In addition it is proposed that for maximum benefits another premium funding should be secured.</p

An analysis of farmers' perception of the new cooperative medical system in Liaoning Province, China

<p>Abstract</p> <p>Background</p> <p>Since 2003, the number of pilot areas of the New Rural Cooperative Medical System (NRCMS) has increased in rural China. And the major efforts have been concentrated on the enrollment of prospective members. In this study, we examined the satisfaction of the rural residents with the NRCMS as well as factors affecting their attitudes towards the NRCMS.</p> <p>Methods</p> <p>The data for this study were collected from a survey involving twenty counties in Liaoning Province. Interviews and focus groups were conducted between 10^thJanuary and 20^thAugust 2008. A total of 2,780 people aged 18-72 were randomly selected and interviewed. Data were evaluated by nonparametric tests and ordinal regression models.</p> <p>Results</p> <p>71.6% of the study subjects were satisfied with the NRCMS. Single factor analysis showed that attitudes towards the NRCMS were influenced by gender, age, marital status, and self-rated health status. In the ordinal regression analysis, gender, age, and self-rated health status affect satisfaction (P < 0.05).</p> <p>Conclusions</p> <p>We found that a considerable proportion of farmers were satisfied with the NRCMS. Gender, age, and self-rated health status had significant effects on farmers' attitudes towards the NRCMS. The Chinese Central Government attempted to adopt active measures in the future to continuously improve the NRCMS, including initiating educational programs, building new medical facilities and increasing financial investment.</p

Biodegradable Thermosensitive Hydrogel for SAHA and DDP Delivery: Therapeutic Effects on Oral Squamous Cell Carcinoma Xenografts

Background: OSCC is one of the most common malignancies and numerous clinical agents currently applied in combinative chemotherapy. Here we reported a novel therapeutic strategy, SAHA and DDP-loaded PECE (SAHA-DDP/PECE), can improve the therapeutic effects of intratumorally chemotherapy on OSCC cell xenografts. Objective/Purpose: The objective of this study was to evaluate the therapeutic efficacy of the SAHA-DDP/PECE in situ controlled drug delivery system on OSCC cell xenografts. Methods: A biodegradable and thermosensitive hydrogel was successfully developed to load SAHA and DDP. Tumorbeared mice were intratumorally administered with SAHA-DDP/PECE at 50 mg/kg (SAHA) +2 mg/kg (DDP) in 100 ul PECE hydrogel every two weeks, SAHA-DDP at 50 mg/kg(SAHA) +2 mg/kg(DDP) in NS, 2 mg/kg DDP solution, 50 mg/kg SAHA solution, equal volume of PECE hydrogel, or equal volume of NS on the same schedule, respectively. The antineoplastic actions of SAHA and DDP alone and in combination were evaluated using the determination of tumor volume, immunohistochemistry, western blot, and TUNEL analysis. Results: The hydrogel system was a free-flowing sol at 10uC, become gel at body temperature, and could sustain more than 14 days in situ. SAHA-DDP/PECE was subsequently injected into tumor OSCC tumor-beared mice. The results demonstrated that such a strategy as this allows the carrier system to show a sustained release of SAHA and DDP in vivo, and coul

Whole genome alignments using MPI-LAGAN

Advances in sequencing technologies have substantially increased the number of fully sequenced genomes. Alignment algorithms play a crucial rule in analyzing whole genomes, identifying similar and conserved regions between pairs of genomes, leading to annotation of genomes with site-specific properties and functions. In this work we introduce a parallel algorithm for a widely used whole genome alignment method called LAGAN. We use the MPI-based protocol, to develop parallel solutions for two phases of the algorithm which take up a significant portion of the total runtime, and also have a high memory requirement. The serial LAGAN program uses CHAOS [5] to quickly determine initial anchor or seeds, which are extended using a sparse dynamic programming based longest-increasing subsequence method. Our work involves parallelizing the CHAOS and LIS phases of the algorithm using a one-dimensional block cyclic partitioning of the computation. This leads to development of an efficient algorithm that utilizes the processors in a balanced way. We also ensure minimum time spent in communication or transfer of information across processors. We also report experimental evaluation of our parallel implementation using pairs of human contigs of varying lengths. We discuss and illustrate the challenges faced in parallelizing a sparse dynamic programming formulation as in this work, and show equivalent to theoretical speedups for our parallelized phases of the LAGAN algorithm.

A comprehensive analysis of the journal evaluation system in China

Journal evaluation systems reflect how new insights are critically reviewed and published, and the prestige and impact of a discipline’s journals is a key metric in many research assessments, performance evaluation, and funding systems. With the expansion of China’s research and innovation systems and its rise as a major contributor to global innovation, journal evaluation has become an especially important issue. In this paper, we first describe the history and background of journal evaluation in China and then systematically introduce and compare the most currently influential journal lists and indexing services. These are: the Chinese Science Citation Database (CSCD), the Journal Partition Table (JPT), the AMI Comprehensive Evaluation Report (AMI), the Chinese S&T Journal Citation Report (CJCR), “A Guide to the Core Journals of China” (GCJC), the Chinese Social Sciences Citation Index (CSSCI), and the World Academic Journal Clout Index (WAJCI). Some other influential lists produced by government agencies, professional associations, and universities are also briefly introduced. Through the lens of these systems, we provide: comprehensive coverage of the tradition and landscape of the journal evaluation system in China and the methods and practices of journal evaluation in China with some comparisons to how other countries assess and rank journals