The sparse Laplacian shrinkage estimator for high-dimensional regression

We propose a new penalized method for variable selection and estimation that explicitly incorporates the correlation patterns among predictors. This method is based on a combination of the minimax concave penalty and Laplacian quadratic associated with a graph as the penalty function. We call it the sparse Laplacian shrinkage (SLS) method. The SLS uses the minimax concave penalty for encouraging sparsity and Laplacian quadratic penalty for promoting smoothness among coefficients associated with the correlated predictors. The SLS has a generalized grouping property with respect to the graph represented by the Laplacian quadratic. We show that the SLS possesses an oracle property in the sense that it is selection consistent and equal to the oracle Laplacian shrinkage estimator with high probability. This result holds in sparse, high-dimensional settings with p >> n under reasonable conditions. We derive a coordinate descent algorithm for computing the SLS estimates. Simulation studies are conducted to evaluate the performance of the SLS method and a real data example is used to illustrate its application.Comment: Published in at http://dx.doi.org/10.1214/11-AOS897 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generation

Image-grounded dialogue systems benefit greatly from integrating visual information, resulting in high-quality response generation. However, current models struggle to effectively utilize such information in zero-resource scenarios, mainly due to the disparity between image and text modalities. To overcome this challenge, we propose an innovative multimodal framework, called ZRIGF, which assimilates image-grounded information for dialogue generation in zero-resource situations. ZRIGF implements a two-stage learning strategy, comprising contrastive pre-training and generative pre-training. Contrastive pre-training includes a text-image matching module that maps images and texts into a unified encoded vector space, along with a text-assisted masked image modeling module that preserves pre-training visual features and fosters further multimodal feature alignment. Generative pre-training employs a multimodal fusion module and an information transfer module to produce insightful responses based on harmonized multimodal representations. Comprehensive experiments conducted on both text-based and image-grounded dialogue datasets demonstrate ZRIGF's efficacy in generating contextually pertinent and informative responses. Furthermore, we adopt a fully zero-resource scenario in the image-grounded dialogue dataset to demonstrate our framework's robust generalization capabilities in novel domains. The code is available at https://github.com/zhangbo-nlp/ZRIGF.Comment: ACM Multimedia 2023 Accpeted, Repo: https://github.com/zhangbo-nlp/ZRIG

catena-Poly[[dinitratocopper(II)]-μ-4,4′′-bis­(1H-benzimidazol-1-yl)-1,1′:4′,1′′-terphen­yl]

In the title one-dimensional coordination polymer, [Cu(NO3)2(C32H22N4)]n, the Cu2+ ion (site symmetry 2) is coordinated by two nitrate O atoms and two N atoms from two 4,4′-bis­(benzoimidazol-1-yl)terphenyl (L) ligands in a distorted cis-CuN2O2 square-planar coordination geometry. An alternative description of the metal coordination geometry, if long Cu—O contacts to the bonded nitrate anions are considered, is an extremely distorted cis-CuN2O4 octa­hedron. The complete L ligand is generated by crystallographic twofold symmetry and connects the metal ions into infinite chains propagating in [10]. The dihedral angle between the benzimidazole ring system and the adjacent benzene (B) ring is 51.12 (11)° and the dihedral angle between the B ring and the central ring is 19.45 (13)°

A sufficient maximum principle for backward stochastic systems with mixed delays

In this paper, we study the problem of optimal control of backward stochastic differential equations with three delays (discrete delay, moving-average delay and noisy memory). We establish the sufficient optimality condition for the stochastic system. We introduce two kinds of time-advanced stochastic differential equations as the adjoint equations, which involve the partial derivatives of the function $f$ and its Malliavin derivatives. We also show that these two kinds of adjoint equations are equivalent. Finally, as applications, we discuss a linear-quadratic backward stochastic system and give an explicit optimal control. In particular, the stochastic differential equations with time delay are simulated by means of discretization techniques, and the effect of time delay on the optimal control result is explained

A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations

Emotion recognition in conversations (ERC), the task of recognizing the emotion of each utterance in a conversation, is crucial for building empathetic machines. Existing studies focus mainly on capturing context- and speaker-sensitive dependencies on the textual modality but ignore the significance of multimodal information. Different from emotion recognition in textual conversations, capturing intra- and inter-modal interactions between utterances, learning weights between different modalities, and enhancing modal representations play important roles in multimodal ERC. In this paper, we propose a transformer-based model with self-distillation (SDT) for the task. The transformer-based model captures intra- and inter-modal interactions by utilizing intra- and inter-modal transformers, and learns weights between modalities dynamically by designing a hierarchical gated fusion strategy. Furthermore, to learn more expressive modal representations, we treat soft labels of the proposed model as extra training supervision. Specifically, we introduce self-distillation to transfer knowledge of hard and soft labels from the proposed model to each modality. Experiments on IEMOCAP and MELD datasets demonstrate that SDT outperforms previous state-of-the-art baselines.Comment: 13 pages, 10 figures. Accepted by IEEE Transactions on Multimedia (TMM

Molecular estimation of alteration in intestinal microbial composition in Hashimoto's thyroiditis patients

The gut microbiota has a crucial effect on human health and physiology. Hypothyroid Hashimoto’s thyroiditis (HT) is an autoimmune disorder manifested with environmental and genetic factors. However, it is hypothesized that intestinal microbes might play a vital role in the pathogenesis of HT. The aim of current was to investigate and characterize the gut microbial composition of HT patients both quantitatively and qualitatively. The fecal samples from 29 HT patients and 12 healthy individuals were collected. The PCR-DGGE targeted V3 site of 16S rRNA gene and real time PCR for Bifidobacterium Lactobacillus, Bacteroides vulgatus and Clostridium leptum were performed. Pyrosequencing of 16S rRNA gene with V4 location was performed on 20 randomly selected samples. The comparative analysis of diversity and richness indices revealed diversification of gut microbiota in HT as compared to control. The statistical data elucidate the alterations in phyla of HT patients which was also affirmed at the family level. We observed the declined abundance of Prevotella_9 and Dialister, while elevated genera of the diseased group included Escherichia-Shigella and Parasutterella. The alteration in gut microbial configuration was also monitored at the species level, which showed an increased abundance of E. coli in HT. Therefore, the current study is in agreement with the hypothesis that HT patients have intestinal microbial dysbiosis. The taxa statistics at species-level along with each gut microbial community were modified in HT. Thus, the current study may offer the new insights into the treatment of HT patients, disease pathway, and mechanism