Probabilistic Reduced-Dimensional Vector Autoregressive Modeling for Dynamics Prediction and Reconstruction with Oblique Projections

In this paper, we propose a probabilistic reduced-dimensional vector autoregressive (PredVAR) model with oblique projections. This model partitions the measurement space into a dynamic subspace and a static subspace that do not need to be orthogonal. The partition allows us to apply an oblique projection to extract dynamic latent variables (DLVs) from high-dimensional data with maximized predictability. We develop an alternating iterative PredVAR algorithm that exploits the interaction between updating the latent VAR dynamics and estimating the oblique projection, using expectation maximization (EM) and a statistical constraint. In addition, the noise covariance matrices are estimated as a natural outcome of the EM method. A simulation case study of the nonlinear Lorenz oscillation system illustrates the advantages of the proposed approach over two alternatives

Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering

A common thread of open-domain question answering (QA) models employs a retriever-reader pipeline that first retrieves a handful of relevant passages from Wikipedia and then peruses the passages to produce an answer. However, even state-of-the-art readers fail to capture the complex relationships between entities appearing in questions and retrieved passages, leading to answers that contradict the facts. In light of this, we propose a novel knowledge Graph enhanced passage reader, namely Grape, to improve the reader performance for open-domain QA. Specifically, for each pair of question and retrieved passage, we first construct a localized bipartite graph, attributed to entity embeddings extracted from the intermediate layer of the reader model. Then, a graph neural network learns relational knowledge while fusing graph and contextual representations into the hidden states of the reader model. Experiments on three open-domain QA benchmarks show Grape can improve the state-of-the-art performance by up to 2.2 exact match score with a negligible overhead increase, with the same retriever and retrieved passages. Our code is publicly available at https://github.com/jumxglhf/GRAPE.Comment: Findings of EMNLP202

Inhibition of AMPA receptor trafficking at hippocampal synapses by β-amyloid oligomers: the mitochondrial contribution

<p>Abstract</p> <p>Background</p> <p>Synaptic defects represent a major mechanism underlying altered brain functions of patients suffering Alzheimer's disease (AD) <abbrgrp><abbr bid="B1">1</abbr><abbr bid="B2">2</abbr><abbr bid="B3">3</abbr></abbrgrp>. An increasing body of work indicates that the oligomeric forms of β-amyloid (Aβ) molecules exert profound inhibition on synaptic functions and can cause a significant loss of neurotransmitter receptors from the postsynaptic surface, but the underlying mechanisms remain poorly understood. In this study, we investigated a potential contribution of mitochondria to Aβ inhibition of AMPA receptor (AMPAR) trafficking.</p> <p>Results</p> <p>We found that a brief exposure of hippocampal neurons to Aβ oligomers not only led to marked removal of AMPARs from postsynaptic surface but also impaired rapid AMPAR insertion during chemically-induced synaptic potentiation. We also found that Aβ oligomers exerted acute impairment of fast mitochondrial transport, as well as mitochondrial translocation into dendritic spines in response to repetitive membrane depolarization. Quantitative analyses at the single spine level showed a positive correlation between spine-mitochondria association and the surface accumulation of AMPARs. In particular, we found that spines associated with mitochondria tended to be more resistant to Aβ inhibition on AMPAR trafficking. Finally, we showed that inhibition of GSK3β alleviated Aβ impairment of mitochondrial transport, and effectively abolished Aβ-induced AMPAR loss and inhibition of AMPAR insertion at spines during cLTP.</p> <p>Conclusions</p> <p>Our findings indicate that mitochondrial association with dendritic spines may play an important role in supporting AMPAR presence on or trafficking to the postsynaptic membrane. Aβ disruption of mitochondrial trafficking could contribute to AMPAR removal and trafficking defects leading to synaptic inhibition.</p

A Crucial Role of IL-17 and IFN-γ during Acute Rejection of Peripheral Nerve Xenotransplantation in Mice

Nerve injuries causing segmental loss require nerve grafting. However, autografts and allografts have limitations for clinical use. Peripheral nerve xenotransplantation has become an area of great interest in clinical surgery research as an alternative graft strategy. However, xenotransplant rejection is severe with cellular immunity, and Th1 cells play an important role in the process. To better understand the process of rejection, we used peripheral nerve xenografts from rats to mice and found that mononuclear cells expressing IFN-γ and IL-17 infiltrated around the grafts, and IFN-γ and IL-17 producing CD4+ and CD8+ T cells increased during the process of acute rejection. The changes of IL-4 level had no significant difference between xenotransplanted group and sham control group. The rejection of xenograft was significantly prevented after the treatment of IL-17 and IFN-γ neutralizing antibodies. These data suggest that Th17 cells contribute to the acute rejection process of peripheral nerve xenotransplant in addition to Th1 cells

Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities

In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods. These artificial intelligence-generated content (AIGC) have become deeply embedded in various aspects of daily life and work. However, these technologies have also led to the emergence of Fake Artificial Intelligence Generated Content (FAIGC), posing new challenges in distinguishing genuine information. It is crucial to recognize that AIGC technology is akin to a double-edged sword; its potent generative capabilities, while beneficial, also pose risks for the creation and dissemination of FAIGC. In this survey, We propose a new taxonomy that provides a more comprehensive breakdown of the space of FAIGC methods today. Next, we explore the modalities and generative technologies of FAIGC. We introduce FAIGC detection methods and summarize the related benchmark from various perspectives. Finally, we discuss outstanding challenges and promising areas for future research