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It is crucial to represent the semantic information of a document in sentiment classification. Various semantic information representation models have been proposed, however existing approaches have their setbacks. Notable weaknesses among these are: (1) tradition VSM methods, completely ignore the semantic information; (2) averaging word embedding methods, cannot depict the synthetical semantic meaning of the given document; (3) neural network methods, require complex structure and are notoriously difficult to be trained. To overcome these limitations, we introduce a simple but novel method which we call bag of meta-words (BoMW). In our method, the semantic information of the document is indicated by a meta-words vector in which every single meta-word element denotes particular semantic information. Especially, these meta-words are extracted from pre-trained word embeddings through two different but complemental models, naive interval meta-words (NIM) and feature combination meta-words (FCM). In general, our new model BoMW is as simple as traditional VSM model but it can capture the synthetical semantic meanings of the document. Numerous experiments on two benchmarks (IMDB dataset and Pang’s dataset) are carried out to verify the effectiveness of the proposed method, and the results show that the performance of our method can exceed the traditional VSM methods and methods using pre-trained word embedding. [ABSTRACT FROM AUTHOR]

Neighborhood-based collaborative filtering is a method of high significance among recommender systems, with advantages of simplicity and justifiability. However, recently it is receiving less popularity due to its low prediction accuracy in contrast with model-based collaborative filtering systems, but model-based methods also suffer from a drawback worthy of attention that is they cannot effectively explain the reason behind their estimation. In order to develop a system with both high accuracy and justifiability, we propose a novel neighborhood-based collaborative filtering method inspired by the natural mechanism of attention. Our method can adaptively find neighborhood items to the prediction in user history without any pre-defined function with respect item correlations. Then the estimation are made based on these relationships. Experiments on several benchmarks are carried out to verify the performance of the proposed method, and the result shows that our method beats all previous state-of-the-art methods on MovieLens 10M and Netflix in addition to being able to justify the prediction obtained. [ABSTRACT FROM AUTHOR]

• The multi-agent defense and attack environment is reconstructed. • Several algorithms are applied to solve the considered problem. • We redefine the state space, the action space and the reward functions accordingly. • Comparison experiments are conducted to show the performance of the employed models. Learning to cooperate among agents has always been an important research topic in artificial intelligence. Multi-agent defense and attack, one of the important issues in multi-agent cooperation, requires multiple agents in the environment to learn effective strategies to achieve their goals. Deep reinforcement learning (DRL) algorithms have natural advantages dealing with continuous control problems especially under situations with dynamic interactions, and have provided new solutions for those long-studied multi-agent cooperation problems. In this paper, we start from deep deterministic policy gradient (DDPG) algorithm and then introduce multi-agent DDPG (MADDPG) to solve the multi-agent defense and attack problem under different situations. We reconstruct the considered environment, redefine the continuous state space, continuous action space, reward functions accordingly, and then apply deep reinforcement learning algorithms to obtain effective decision strategies. Several experiments considering different confrontation scenarios are conducted to validate the feasibility and effectiveness of the DRL-based methods. Experimental results show that through learning the agents can make better decisions, and learning with MADDPG achieves superior performance than learning with other DRL-based models, which also explains the importance and necessity of mastering other agents' information. [ABSTRACT FROM AUTHOR]

The core issue of learning-to-rank (LTR) for document retrieval lies in finding an optimal ranking policy to meet the search intent of the user. The majority of proposed LTR approaches treat the ranking as a static process, employing a fixed ranking policy to immediately assign scores to documents. By contrast, ranking is not a static but an interactive process where the user continues interacting with the document retrieval system through information exchange such as search intent (e.g., rating or clicking for the retrieved items). We model the interactive ranking process (IRP), and propose an Attention-Based Interactive LTR model (AIRank) to constitute an intent-aware flexible ranking policy to gratify the user’s need. To enhance the ranking quality, the inherent relations among documents are procured by the self-attention method to contribute to an enriched user intent representation. Furthermore, we mend the policy gradient learning method to train the AIRank in the IRP. Experiments demonstrate the effectiveness of AIRank compared to the state-of-the-art methods in terms of normalized discounted cumulative gain and expected reciprocal rank. [ABSTRACT FROM AUTHOR]

Recommender systems aim to maximize the overall accuracy for long-term recommendations. However, most of the existing recommendation models adopt a static view, and ignore the fact that recommendation is a dynamic sequential decision-making process. As a result, they fail to adapt to new situations and suffer from the cold-start problem. Although sequential recommendation methods have been gaining attention recently, the objective of long-term recommendation still has not been explicitly addressed since these methods are developed for short-term prediction situations. To overcome these problems, we propose a novel top-N interactive recommender system based on deep reinforcement learning. In our model, the processes of recommendation are viewed as Markov decision processes (MDP), wherein the interactions between agent (recommender system) and environment (user) are simulated by the recurrent neural network (RNN). In addition, reinforcement learning is employed to optimize the proposed model for the purpose of maximizing long-term recommendation accuracy. Experimental results based on several benchmarks show that our model significantly outperforms previous top-N methods in terms of Hit-Rate and NDCG for the long-term recommendation, and can be applied to both cold-start and warm-start scenarios. • A novel top-N interactive recommender system based on deep reinforcement learning is proposed. • The interactions between recommender system and users are simulated by recurrent neural networks. • The proposed model can deal with both cold-start and warm-start scenarios. • Reinforcement learning and supervised learning are employed to optimize the proposed model for long-term recommendation accuracy. • Experiments and comparisons are conducted to show the merits of the proposed model. [ABSTRACT FROM AUTHOR]

Although alveolar macrophages (AMs) play important roles in preventing and eliminating pulmonary infections, little is known about their regulation in healthy animals. Since exposure to LPS often renders cells hyporesponsive to subsequent LPS exposures ("tolerant"), we tested the hypothesis that LPS produced in the intestine reaches the lungs and stimulates AMs, rendering them tolerant. We found that resting AMs were more likely to be tolerant in mice lacking acyloxyacyl hydrolase (AOAH), the host lipase that degrades and inactivates LPS; isolated Aoah-/- AMs were less responsive to LPS stimulation and less phagocytic than were Aoah+/+ AMs. Upon innate stimulation in the airways, Aoah-/- mice had reduced epithelium- and macrophage-derived chemokine/cytokine production. Aoah-/- mice also developed greater and more prolonged loss of body weight and higher bacterial burdens after pulmonary challenge with Pseudomonas aeruginosa than did wildtype mice. We also found that bloodborne or intrarectally-administered LPS desensitized ("tolerized") AMs while antimicrobial drug treatment that reduced intestinal commensal Gram-negative bacterial abundance largely restored the innate responsiveness of Aoah-/- AMs. Confirming the role of LPS stimulation, the absence of TLR4 prevented Aoah-/- AM tolerance. We conclude that commensal LPSs may stimulate and desensitize (tolerize) alveolar macrophages in a TLR4-dependent manner and compromise pulmonary immunity. By inactivating LPS in the intestine, AOAH promotes antibacterial host defenses in the lung. Author summary: AOAH is the host lipase that degrades and inactivates Gram-negative bacterial lipopolysaccharides (LPSs). AOAH is required for recovery from LPS-induced macrophage tolerance. Expressed in the gut, AOAH inactivates microbiota-derived LPS. In this study we found that AOAH-deficient mice were less able to contain pulmonary Pseudomonas aeruginosa infection than were control wildtype mice. Alveolar macrophages (AMs) from Aoah-/- mice were hypo-responsive to innate stimulation and they had reduced phagocytic activity. In addition, Aoah-/- AMs had metabolic changes characteristic of tolerant macrophages as well as increased cell-surface expression of MHC II and co-stimulatory molecules, findings suggesting that they had been stimulated in situ. Treating Aoah-/- mice with p.o. neomycin normalized AMs' innate responsiveness while intrarectal LPS administration tolerized AMs. We conclude that AOAH regulates pulmonary mucosal immunity in part by inactivating LPS in the gut. This study sheds light on a previously unappreciated mechanism that regulates pulmonary immune defense via the gut-lung axis. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to explore the role and underlying mechanism of miR-504 and RBM4 in gastric cancer.Methods: The qRT-PCR or Western blot was performed to determine the expressions of miR-504 and RBM4 in the gastric cancer tissues and normal tissues. Human SGC-7901 cells were transfected with miR-504 mimic/inhibitor or pcDNA-RBM4. Cell proliferation and cell apoptosis were assessed by colony formation assay and flow cytometry, respectively. Luciferase reporter gene assays were used to investigate interactions between miR-504 and RBM4 in SGC-7901 cells.Results: The relative expression of miR-504 was significantly upregulated in the gastric cancer group (n = 25) than in the paired normal group (n = 25), but the relative RBM4 expression was remarkably downregulated in the gastric tumor group, compared with the normal group. Additionally, miR-504 overexpression increased the viability of gastric cancer cells. Moreover, RBM4 is a functional target of miR-504 in gastric cancer cells. miR-504 was further confirmed to promote SGC-7901 cell proliferation and inhibit cell apoptosis by downregulation RBM4 in vitro.Conclusions: miR-504 promotes gastric cancer cell proliferation and inhibits cell apoptosis by targeting RBM4, and this provides a potential diagnostic biomarker and treatment for patients with gastric cancer. [ABSTRACT FROM AUTHOR]