Low-Rank Optimal Transport for Robust Domain Adaptation

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

Multimodal Medical Image Fusion Based on Parameter Adaptive PCNN and Latent Low-rank Representation

Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.

Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization for Hyperspectral Image Classification

Low-Rank and Sparse Representation(LRSR)method has gained popularity in Hyperspectral Image(HSI)processing.However,existing LRSR models rarely exploited spectral-spatial classification of HSI.In this paper,we proposed a novel Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization(LRSR-ANR)method for HSI classification.In the proposed method,we first represent the hyperspectral data via LRSR since it combines both sparsity and low-rankness to maintain global and local data structures simultaneously.The LRSR is optimized by using a mixed Gauss-Seidel and Jacobian Alternating Direction Method of Multipliers(M-ADMM),which converges faster than ADMM.Then to incorporate the spatial information,an ANR scheme is designed by combining Euclidean and Cosine distance metrics to reduce the mixed pixels within a neighborhood.Lastly,the predicted labels are determined by jointly considering the homogeneous pixels in the classification rule of the minimum reconstruction error.Experimental results based on three popular hyperspectral images demonstrate that the proposed method outperforms other related methods in terms of classification accuracy and generalization performance.

Multi-Modal Medical Image Fusion Based on Improved Parameter Adaptive PCNN and Latent Low-Rank Representation

Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.

The RLCK-VND6 module coordinates secondary cell wall formation and adaptive growth in rice

The orderly deposition of secondary cell wall(SCW)in plants is implicated in various biological programs and is precisely controlled.Although many positive and negative regulators of SCW have been documented,the molecular mechanisms underlying SCW formation coordinated with distinct cellular physiological processes during plant adaptive growth remain largely unclear.Here,we report the identification of Cellulose Synthase co-expressed Kinase1(CSK1),which encodes a receptor-like cytoplasmic kinase,as a negative regulator of SCW formation and its signaling cascade in rice.Transcriptome deep sequencing of developing internodes and genome-wide co-expression assays revealed that CSK1 is co-expressed with cellulose synthase genes and is responsive to various stress stimuli.The increased SCW thickness and vigorous vessel transport in csk1 indicate that CSK1 functions as a negative regulator of SCW biosynthesis.Through observation of green fluorescent protein-tagged CSK1 in rice protoplasts and stable transgenic plants,we found that CSK1 is localized in the nucleus and cytoplasm adjacent to the plasma membrane.Biochemical and molecular assays demonstrated that CSK1 phosphorylates VASCULAR-RELATED NAC-DOMAIN 6(VND6),a master SCW-associated transcription factor,in the nucleus,which reduces the transcription of a suite of SCW-related genes,thereby attenuating SCW accumulation.Consistently,genetic analyses show that CSK1 functions upstream of VND6 in regulating SCW formation.Interestingly,our physiological analyses revealed that CSK1 and VND6 are involved in abscisic acid-mediated regulation of cell growth and SCW deposition.Taken together,these results indicate that the CSK1-VND6 module is an important component of the SCW biosynthesis machinery,which coordinates SCW accumulation and adaptive growth in rice.Our study not only identifies a new regulator of SCW biosynthesis but also reveals a fine-tuned mechanism for precise control of SCW deposition,offering tools for rationally tailoring agronomic traits.

Regulation of insect behavior by non-coding RNAs

The adaptation of insects to environments relies on a sophisticated set of behaviors controlled by molecular and physiological processes.Over the past several decades,accumulating studies have unveiled the roles of non-coding RNAs(nc RNAs)in regulating insect behaviors.nc RNAs assume particularly pivotal roles in the behavioral plasticity of insects by rapidly responding to environmental stimuli.nc RNAs also contribute to the maintenance of homeostasis of insects by fine-tuning the expression of target genes.However,a comprehensive review of nc RNAs'roles in regulating insect behaviors has yet to be conducted.Here,we present the recent progress in our understanding of how nc RNAs regulate various insect behaviors,including flight and movement,social behavior,reproduction,learning and memory,and feeding.We refine the intricate mechanisms by which nc RNAs modulate the function of neural,motor,reproductive,and other physiological systems,as well as gene expression in insects like fruit flies,social insects,locusts,and mosquitos.Furthermore,we discuss potential avenues for future studies in nc RNA-mediated insect behaviors.

LLaMA-LoRA Neural Prompt Engineering:A Deep Tuning Framework for Automatically Generating Chinese Text Logical Reasoning Thinking Chains

The exption of Chinese natural language processing(NLP)has stimulated research in the broader NLP domain.However,existing large language models have limitations in comprehending and reasoning in Chinese.This paper addresses these limitations by enhancing Chinese language models comprehension and reasoning capabilities while minimizing resource requirements.We propose LLaMA-LoRA,a neural prompt engineering framework that builds upon the LLaMA-13B model and incorporates the Low-Rank Adaptation(LoRA)of Large Language Models technique for refinement.Chain-of-Thought(CoT)are crucial for generating intermediate reasoning chains in language models,but their effectiveness can be limited by isolated language patterns.Erroneous reasoning resulting from conventional prompts negatively impacts model performance.Automatic prompts are introduced to encourage reasoning chain generation and accurate answer inference.Training the model with an extensive corpus of Chinese CoT data enhances its comprehension and reasoning abilities.The LLaMA-LoRA model demonstrates exceptional performance across numerous Chinese language tasks,surpassing benchmark performance achieved by related language models such as GPT-3.5,Chat-GLM,and OpenAssistant,delivering accurate,comprehensive,and professional answers.The availability of our open-source model code facilitates further research in the field of Chinese text logical reasoning thinking chains.

Pointer-prototype fusion network for few-shot named entity recognition

Few-shot named entity recognition(NER)aims to identify named entities in new domains using a limited amount of annotated data.Previous methods divided this task into entity span detection and entity classification,achieving good results.However these methods are limited by the imbalance between the entity and non-entity categories due to the use of sequence labeling for entity span detection.To this end,a point-proto network(PPN)combining pointer and prototypical networks was proposed.Specifically,the pointer network generates the position of entities in sentences in the entity span detection stage.The prototypical network builds semantic prototypes of entity types and classifies entities based on their distance from these prototypes in the entity classification stage.Moreover,the low-rank adaptation(LoRA)fine-tuning method,which involves freezing the pre-trained weights and injecting a trainable decomposition matrix,reduces the parameters that need to be trained and saved.Extensive experiments on the few-shot NER Dataset(Few-NERD)and Cross-Dataset demonstrate the superiority of PPN in this domain.