Homogeneous ACM bundles on exceptional isotropic Grassmannians

In this paper, we characterize homogeneous arithmetically Cohen-Macaulay (ACM) bundles over exceptional isotropic Grassmannians in terms of their associated data. We show that there are only finitely many irreducible homogeneous ACM bundles by twisting line bundles over exceptional isotropic Grassmannians. As a consequence, we prove that some exceptional isotropic Grassmannians are of wild representation type.Comment: 18 pages. arXiv admin note: text overlap with arXiv:2206.0917

Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding

The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making. However, existing brain-inspired spiking neural networks (SNNs) have struggled to match their biological counterpart in modeling long-term temporal relationships. To address this problem, this paper presents a novel Contextual Embedding Leaky Integrate-and-Fire (CE-LIF) spiking neuron model. Specifically, the CE-LIF model incorporates a meticulously designed contextual embedding component into the adaptive neuronal firing threshold, thereby enhancing the memory storage of spiking neurons and facilitating effective sequential modeling. Additionally, theoretical analysis is provided to elucidate how the CE-LIF model enables long-term temporal credit assignment. Remarkably, when compared to state-of-the-art recurrent SNNs, feedforward SNNs comprising the proposed CE-LIF neurons demonstrate superior performance across extensive sequential modeling tasks in terms of classification accuracy, network convergence speed, and memory capacity

Abstractive Opinion Tagging

In e-commerce, opinion tags refer to a ranked list of tags provided by the e-commerce platform that reflect characteristics of reviews of an item. To assist consumers to quickly grasp a large number of reviews about an item, opinion tags are increasingly being applied by e-commerce platforms. Current mechanisms for generating opinion tags rely on either manual labelling or heuristic methods, which is time-consuming and ineffective. In this paper, we propose the abstractive opinion tagging task, where systems have to automatically generate a ranked list of opinion tags that are based on, but need not occur in, a given set of user-generated reviews. The abstractive opinion tagging task comes with three main challenges: (1) the noisy nature of reviews; (2) the formal nature of opinion tags vs. the colloquial language usage in reviews; and (3) the need to distinguish between different items with very similar aspects. To address these challenges, we propose an abstractive opinion tagging framework, named AOT-Net, to generate a ranked list of opinion tags given a large number of reviews. First, a sentence-level salience estimation component estimates each review's salience score. Next, a review clustering and ranking component ranks reviews in two steps: first, reviews are grouped into clusters and ranked by cluster size; then, reviews within each cluster are ranked by their distance to the cluster center. Finally, given the ranked reviews, a rank-aware opinion tagging component incorporates an alignment feature and alignment loss to generate a ranked list of opinion tags. To facilitate the study of this task, we create and release a large-scale dataset, called eComTag, crawled from real-world e-commerce websites. Extensive experiments conducted on the eComTag dataset verify the effectiveness of the proposed AOT-Net in terms of various evaluation metrics.Comment: Accepted by WSDM 202

The Gap between Intelligence and Mind

The feeling (quale) brings the "Hard Problem" to philosophy of mind. Does the subjective feeling have a non-ignorable impact on Intelligence? If so, can the feeling be realized in Artificial Intelligence (AI)? To discuss the problems, we have to figure out what the feeling means, by giving a clear definition. In this paper, we primarily give some mainstream perspectives on the topic of the mind, especially the topic of the feeling (or qualia, subjective experience, etc.). Then, a definition of the feeling is proposed through a thought experiment, the "semi-transparent room". The feeling, roughly to say, is defined as "a tendency of changing input representations by representing its inner state". Also, a formalized definition is given. The definition does not help to verify "having the feeling", but it helps to provide evidence. Based on the definition, we think these are the hard problems of intelligence — whether the "innate" feeling plays an important role in Intelligence, whether the difference between the "simulated" feeling and the "innate" feeling will have a significant influence on Artificial General Intelligence (AGI), and, if so, where the "innate" feeling comes from and how to make an artificial agent possess it

Nonlinear finite‐time control of hydroelectric systems via a novel sliding mode method

A nonlinear finite-time sliding mode control is proposed in this paper for the governing of complex hydroelectric systems with the finite/fixed setting time. The proposed control method is derived from the finite-time stability and sliding mode control theories. The finite settling time is calculated and bounded, not depending on the initial conditions of the system. The solution trajectory of the controlled hydroelectric system can reach the sliding manifold in a fixed settling time, regardless of initial values. Based on the Lyapunov theory, the controlled hydroelectric system also converges to a reference state within the fixed settling time. A simulation of a high-dimensional hydroelectric system verifies the feasibility of the proposed method. In addition, a comparison between the proposed method and the conventional PID method demonstrates the advantages of the proposed method in the shorter settling time and smaller overshoot. The proposed control method allows for the design of a flexible controller and provides an improvement in dynamic performance

Causal relationship between Butyricimonas and allergic asthma: a two-sample Mendelian randomization study

BackgroundGrowing evidence has well documented the close association between the gut microbiome and allergic respiratory disease, which has been notably represented by allergic asthma. However, it is unclear whether this association is a causal link. Therefore, we investigated the potential causal associations between the gut microbiome and allergic asthma or other allergic diseases.MethodsIn this study, we performed two-sample Mendelian randomization (MR) analyses by using the publicly available genome-wide association study (GWAS) summary data. Single-nucleotide polymorphisms (SNPs) that significantly correlated were selected as instrumental variables. The inverse variance weighted (IVW) method was used to examine the potential causal gut microbial genera for allergic asthma and other allergic diseases. The robustness of the primary findings of the MR analyses was ensured by using different sensitivity analyses.ResultsCombining the findings from multiple analyses, the host genetic-driven increases in Butyricimonas at the genus level were positively correlated with the risk of allergic asthma. In addition, phylum Bacteroidetes and class Bacteroidia were also found to have negative associations with the risk of allergic asthma; genus Slackia was identified as having potential causal effects with allergic asthma. No clear evidence of pleiotropy and heterogeneity was observed in genus Butyricimonas. Butyricimonas was also found to have an association with allergic rhinitis, but not with other allergic diseases.ConclusionOur findings indicate that there are new gut microbial genera that were causally associated with the risk of allergic asthma and other allergic diseases, and offer novel insights into the pathogenesis of allergic respiratory diseases

Evaluation of Three Different Methods to Establish Animal Models of Acanthamoeba Keratitis

Purpose: To produce animal models of Acanthamoeba keratitis and to evaluate the advantages and adaptation range of each of the three methods employed. Materials and Methods: Mice and Wistar rats in three groups of 15 rats and 15 mice each were used to establish the models. Right corneas in group A were scratched and challenged with Acanthamoeba. Those in group B were scratched and covered with contact lenses incubated with Acanthamoeba. Those in group C received an intrastromal injection of Acanthamoeba. Five rats and 5 mice in each group were used for histopathological investigations and the other 10 in each group were used for clinical evaluation. The models were evaluated by slit lamp examination, microscopic examination and culture of corneal scrapings, HE staining of corneal sections, and pathological scoring of the infections. Results: Four rats and 6 mice in group A, 7 rats and 8 mice in group B, and 10 rats and 10 mice in group C developed typical Acanthamoeba keratitis. Conclusion: Corneal scratching alone has the lowest infection rate, while scratching and then covering with contaminated contact lenses has a moderate rate of infection and most closely mimics what happens in most human infections. Intrastromal injection of Acanthamoeba gives a much higher infection rate and more severe Acanthamoeba keratitis