Decorrelation of Neutral Vector Variables: Theory and Applications

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two fundamental invertible transformations, namely serial nonlinear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation. For a neutral vector variable, which is not multivariate Gaussian distributed, the conventional principal component analysis (PCA) cannot yield mutually independent scalar variables. With the two proposed transformations, a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom. We also evaluate the decorrelation performances for the vectors generated from a single Dirichlet distribution and a mixture of Dirichlet distributions. The mutual independence is verified with the distance correlation measurement. The advantages of the proposed decorrelation strategies are intensively studied and demonstrated with synthesized data and practical application evaluations

Is ChatGPT a Good Multi-Party Conversation Solver?

Large Language Models (LLMs) have emerged as influential instruments within the realm of natural language processing; nevertheless, their capacity to handle multi-party conversations (MPCs) -- a scenario marked by the presence of multiple interlocutors involved in intricate information exchanges -- remains uncharted. In this paper, we delve into the potential of generative LLMs such as ChatGPT and GPT-4 within the context of MPCs. An empirical analysis is conducted to assess the zero-shot learning capabilities of ChatGPT and GPT-4 by subjecting them to evaluation across three MPC datasets that encompass five representative tasks. The findings reveal that ChatGPT's performance on a number of evaluated MPC tasks leaves much to be desired, whilst GPT-4's results portend a promising future. Additionally, we endeavor to bolster performance through the incorporation of MPC structures, encompassing both speaker and addressee architecture. This study provides an exhaustive evaluation and analysis of applying generative LLMs to MPCs, casting a light upon the conception and creation of increasingly effective and robust MPC agents. Concurrently, this work underscores the challenges implicit in the utilization of LLMs for MPCs, such as deciphering graphical information flows and generating stylistically consistent responses.Comment: Accepted by Findings of EMNLP 202

DiffuSIA: A Spiral Interaction Architecture for Encoder-Decoder Text Diffusion

Diffusion models have emerged as the new state-of-the-art family of deep generative models, and their promising potentials for text generation have recently attracted increasing attention. Existing studies mostly adopt a single encoder architecture with partially noising processes for conditional text generation, but its degree of flexibility for conditional modeling is limited. In fact, the encoder-decoder architecture is naturally more flexible for its detachable encoder and decoder modules, which is extensible to multilingual and multimodal generation tasks for conditions and target texts. However, the encoding process of conditional texts lacks the understanding of target texts. To this end, a spiral interaction architecture for encoder-decoder text diffusion (DiffuSIA) is proposed. Concretely, the conditional information from encoder is designed to be captured by the diffusion decoder, while the target information from decoder is designed to be captured by the conditional encoder. These two types of information flow run through multilayer interaction spirally for deep fusion and understanding. DiffuSIA is evaluated on four text generation tasks, including paraphrase, text simplification, question generation, and open-domain dialogue generation. Experimental results show that DiffuSIA achieves competitive performance among previous methods on all four tasks, demonstrating the effectiveness and generalization ability of the proposed method.Comment: Work in Progres

Dynamic regulation of RNA editing of ion channels and receptors in the mammalian nervous system

The post-transcriptional modification of mammalian transcripts in the central nervous system by adenosine-to-inosine RNA editing is an important mechanism for the generation of molecular diversity, and serves to regulate protein function through recoding of genomic information. As the molecular players and an increasing number of edited targets are identified and characterized, adenosine-to-inosine modification serves as an exquisite mechanism for customizing channel function within diverse biological niches. Here, we review the mechanisms that could regulate adenosine-to-inosine RNA editing and the impact of dysregulation in clinical conditions

3-Chloro-5-(trifluoro­meth­yl)pyridin-2-amine

In the title compound, C6H4ClF3N2, an inter­mediate in the synthesis of the fungicide fluazinam, the F atoms of the trifluoro­methyl group are disordered over two sites in a 0.683 (14):0.317 (14) ratio. In the crystal structure, centrosymmetric dimers arise from pairs of N—H⋯N hydrogen bonds

MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation

Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods rely heavily on the necessary addressee labels and can only be applied to an ideal setting where each utterance must be tagged with an addressee label. To study the scarcity of addressee labels which is a common issue in MPCs, we propose MADNet that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. Given an MPC with a few addressee labels missing, existing methods fail to build a consecutively connected conversation graph, but only a few separate conversation fragments instead. To ensure message passing between these conversation fragments, four additional types of latent edges are designed to complete a fully-connected graph. Besides, to optimize the edge-type-dependent message passing for those utterances without addressee labels, an Expectation-Maximization-based method that iteratively generates silver addressee labels (E step), and optimizes the quality of generated responses (M step), is designed. Experimental results on two Ubuntu IRC channel benchmarks show that MADNet outperforms various baseline models on the task of MPC generation, especially under the more common and challenging setting where part of addressee labels are missing.Comment: Accepted by EMNLP 2023. arXiv admin note: text overlap with arXiv:2203.0850

Effects of waterlogging and elevated salinity on the allocation of photosynthetic carbon in estuarine tidal marsh: a mesocosm experiment

Embargo until September 10, 2023Background and aim Coastal marshes and wetlands hosting blue carbon ecosystems have shown vulnerability to sea-level rise (SLR) and its consequent effects. In this study, we explored the effects of waterlogging and elevated salinity on the accumulation and allocation of photosynthetic carbon (C) in a widely distributed species in marsh lands. Methods The plant–soil mesocosms of Phragmites australis were grown under waterlogging and elevated salinity conditions to investigate the responses of photosynthetic C allocation in different C pools (plant organs and soils) based on 13CO2 pulse-labeling technology. Results Both waterlogging and elevated salinity treatments decreased photosynthetic C fixation. The hydrological treatments also reduced 13C transport to the plant organs of P. australis while significantly increased 13C allocation percentage in roots. Waterlogging and low salinity had no significant effects on 13C allocation to rhizosphere soils, while high salinity (15 and 30 ppt) significantly reduced 13C allocation to soils, indicating a decreased root C export in saline environments. Waterlogging enhanced the effects of salinity on the 13C allocation pattern, particularly during the late growing season. The responses of flooding and elevated salinity on C allocation in plant organs and rhizosphere soils can be related to changes in nutrient, ionic concentrations and microbial biomass. Conclusion The adaptation strategy of P. australis led to increased C allocation in belowground organs under changed hydrology. Expected global SLR projection might decrease total C stocks in P. australis and alter the C allocation pattern in marsh plant-soil systems, due to amplified effects of flooding and elevated salinities.acceptedVersio

Enhancement of polar phases in PVDF by forming PVDF/SiC nanowire composite

Different contents of silicon carbide (SiC) nanowires were mixed with Poly(vinylidene fluoride) (PVDF) to facilitate the polar phase crystallization. It was shown that the annealing temperature and SiC content affected on the phase and crystalline structures of PVDF/SiC samples. Furthermore, the addition of SiC nanowire enhanced the transformation of non-polar α phase to polar phases and increased the relative fraction of β phase in PVDF. Due to the nucleating agent mechanism of SiC nanowires, the ion-dipole interaction between the negatively charged surface of SiC nanowires and the positive CH2 groups in PVDF facilitated the formation of polar phases in PVDF