Ultra-High Dimensional Statistical Learning

Advancements in information technology have enabled scientists to collect data of unprecedented size as well as complexity. Nowadays, high-dimensional data commonly arise in diverse fields as biology, engineering, health sciences, and economics. In this project, we consider both linear and non-parametric models with variable selection in the high-dimensional setting by assuming that only a small number of index coefficients influence the conditional mean of the response variable. Both the numerical results and the real data application demonstrate that the proposed approach selects the correct model with a high frequency and estimates the model coefficients accurately even for moderate sample size and ultra-high dimensionality

Blockchain-driven dual-channel green supply chain game model considering government subsidies

In order to improve the performance of green supply chain and promote the adoption of blockchain, this paper establishes a dual-channel green supply chain consisting of a green manufacturer and a retailer, and builds Stackelberg game model considering different scenarios. We analyze the impact of blockchain operating costs and consumer uncertainty about the product greenness. Furthermore, we study the government subsidy for manufacturers' green costs and its impact on supply chain performance and blockchain adoption. Findings reveal that without blockchain technology, government subsidy can improve manufacturers' and retailers' profits. However, when blockchain is adopted, the subsidy effect depends on the blockchain operating costs. In case of higher blockchain operating cost, the product prices and greenness decrease as the green cost subsidies increase; In case of lower blockchain operating cost, the increase in green cost subsidies will lead to increased product prices and greenness; Green cost subsidies can raise profits and lower the blockchain adoption threshold

Adaptive Channel Encoding Transformer for Point Cloud Analysis

Transformer plays an increasingly important role in various computer vision areas and remarkable achievements have also been made in point cloud analysis. Since they mainly focus on point-wise transformer, an adaptive channel encoding transformer is proposed in this paper. Specifically, a channel convolution called Transformer-Conv is designed to encode the channel. It can encode feature channels by capturing the potential relationship between coordinates and features. Compared with simply assigning attention weight to each channel, our method aims to encode the channel adaptively. In addition, our network adopts the neighborhood search method of low-level and high-level dual semantic receptive fields to improve the performance. Extensive experiments show that our method is superior to state-of-the-art point cloud classification and segmentation methods on three benchmark datasets.Comment: ICANN202

Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis

Recently, deep neural networks have made remarkable achievements in 3D point cloud classification. However, existing classification methods are mainly implemented on idealized point clouds and suffer heavy degradation of per-formance on non-idealized scenarios. To handle this prob-lem, a feature representation learning method, named Dual-Neighborhood Deep Fusion Network (DNDFN), is proposed to serve as an improved point cloud encoder for the task of non-idealized point cloud classification. DNDFN utilizes a trainable neighborhood learning method called TN-Learning to capture the global key neighborhood. Then, the global neighborhood is fused with the local neighbor-hood to help the network achieve more powerful reasoning ability. Besides, an Information Transfer Convolution (IT-Conv) is proposed for DNDFN to learn the edge infor-mation between point-pairs and benefits the feature transfer procedure. The transmission of information in IT-Conv is similar to the propagation of information in the graph which makes DNDFN closer to the human reasoning mode. Extensive experiments on existing benchmarks especially non-idealized datasets verify the effectiveness of DNDFN and DNDFN achieves the state of the arts.Comment: ICMEW202

P3^3OVD: Fine-grained Visual-Text Prompt-Driven Self-Training for Open-Vocabulary Object Detection

Inspired by the success of visual-language methods (VLMs) in zero-shot classification, recent works attempt to extend this line of work into object detection by leveraging the localization ability of pre-trained VLMs and generating pseudo labels for unseen classes in a self-training manner. However, since the current VLMs are usually pre-trained with aligning sentence embedding with global image embedding, the direct use of them lacks fine-grained alignment for object instances, which is the core of detection. In this paper, we propose a simple but effective Pretrain-adaPt-Pseudo labeling paradigm for Open-Vocabulary Detection (P$^3$OVD) that introduces a fine-grained visual-text prompt adapting stage to enhance the current self-training paradigm with a more powerful fine-grained alignment. During the adapting stage, we enable VLM to obtain fine-grained alignment by using learnable text prompts to resolve an auxiliary dense pixel-wise prediction task. Furthermore, we propose a visual prompt module to provide the prior task information (i.e., the categories need to be predicted) for the vision branch to better adapt the pretrained VLM to the downstream tasks. Experiments show that our method achieves the state-of-the-art performance for open-vocabulary object detection, e.g., 31.5% mAP on unseen classes of COCO

UAV-aided secure NOMA transmission via trajectory and resource optimization

The application of NOMA in UAV networks is an effective solution for communications. However, the security risk becomes more serious with the LoS channels and higher transmit power for weaker users in NOMA-UAV networks. In this paper, a UAV-assisted NOMA transmission scheme is proposed to achieve secure downlink transmission via artificial jamming, where a UAV flies straightly to serve multiple ground users in the presence of a passive eavesdropper. Only the closest users to the UAV can be connected in each time slot to achieve high LoS probability. To balance the security and transmission performance, the tradeoff between the jamming power and the sum rate is investigated by jointly optimizing the power allocation, the user scheduling and the UAV trajectory. To address the problem, we first decompose the problem into two subproblems of power allocation and trajectory optimization. Then, they are transformed into convex ones for an iterative algorithm via the first-order Taylor expansion. Finally, simulation results are presented to show the effectiveness of the proposed scheme

Transplantation of fecal microbiota from APP/PS1 mice and Alzheimer’s disease patients enhanced endoplasmic reticulum stress in the cerebral cortex of wild-type mice

Background and purposeThe gut-brain axis is bidirectional and the imbalance of the gut microbiota usually coexists with brain diseases, including Alzheimer’s disease (AD). Accumulating evidence indicates that endoplasmic reticulum (ER) stress is a core lesion in AD and persistent ER stress promotes AD pathology and impairs cognition. However, whether the imbalance of the gut microbiota is involved in triggering the ER stress in the brain remains unknown.Materials and methodsIn the present study, fecal microbiota transplantation (FMT) was performed with gut microbiota from AD patients and APP/PS1 mice, respectively, resulting in two mouse models with dysregulated gut microbiota. The ER stress marker protein levels in the cerebral cortex were assessed using western blotting. The composition of the gut microbiota was assessed using 16S rRNA sequencing.ResultsExcessive ER stress was induced in the cerebral cortex of mice after FMT. Elevated ER stress marker proteins (p-perk/perk, p-eIF2α/eIF2α) were observed, which were rescued by 3,3-dimethyl-1-butanol (DMB). Notably, DMB is a compound that significantly attenuates serum trimethylamine-N-oxide (TMAO), a metabolite of the gut microbiota widely reported to affect cognition.ConclusionThe findings indicate that imbalance of the gut microbiota induces ER stress in the cerebral cortex, which may be mediated by TMAO

Spatiotemporal dynamic of subtropical forest carbon storage and its resistance and resilience to drought in China

Subtropical forests are rich in vegetation and have high photosynthetic capacity. China is an important area for the distribution of subtropical forests, evergreen broadleaf forests (EBFs) and evergreen needleleaf forests (ENFs) are two typical vegetation types in subtropical China. Forest carbon storage is an important indicator for measuring the basic characteristics of forest ecosystems and is of great significance for maintaining the global carbon balance. Drought can affect forest activity and may even lead to forest death and the stability characteristics of different forest ecosystems varied after drought events. Therefore, this study used meteorological data to simulate the standardized precipitation evapotranspiration index (SPEI) and the Biome-BGC model to simulate two types of forest carbon storage to quantify the resistance and resilience of EBF and ENF to drought in the subtropical region of China. The results show that: 1) from 1952 to 2019, the interannual drought in subtropical China showed an increasing trend, with five extreme droughts recorded, of which 2011 was the most severe one; 2) the simulated average carbon storage of the EBF and ENF during 1985-2019 were 130.58 t·hm-2 and 78.49 t·hm-2, respectively. The regions with higher carbon storage of EBF were mainly concentrated in central and southeastern subtropics, where those of ENF mainly distributed in the western subtropic; 3) The median of resistance of EBF was three times higher than that of ENF, indicating the EBF have stronger resistance to extreme drought than ENF. Moreover, the resilience of two typical forest to 2011 extreme drought and the continuous drought events during 2009 - 2011 were similar. The results provided a scientific basis for the response of subtropical forests to drought, and indicating that improve stand quality or expand the plantation of EBF may enhance the resistance to drought in subtropical China, which provided certain reference for forest protection and management under the increasing frequency of drought events in the future