Abstractive Text Summarization by Incorporating Reader Comments

In neural abstractive summarization field, conventional sequence-to-sequence based models often suffer from summarizing the wrong aspect of the document with respect to the main aspect. To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect. Unlike traditional abstractive summarization task, reader-aware summarization confronts two main challenges: (1) Comments are informal and noisy; (2) jointly modeling the news document and the reader comments is challenging. To tackle the above challenges, we design an adversarial learning model named reader-aware summary generator (RASG), which consists of four components: (1) a sequence-to-sequence based summary generator; (2) a reader attention module capturing the reader focused aspects; (3) a supervisor modeling the semantic gap between the generated summary and reader focused aspects; (4) a goal tracker producing the goal for each generation step. The supervisor and the goal tacker are used to guide the training of our framework in an adversarial manner. Extensive experiments are conducted on our large-scale real-world text summarization dataset, and the results show that RASG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. The experimental results also demonstrate the effectiveness of each module in our framework. We release our large-scale dataset for further research.Comment: Accepted by AAAI 201

Mouse Strain– and Charge-Dependent Vessel Permeability of Nanoparticles at the Lower Size Limit

Remarkable advancement has been made in the application of nanoparticles (NPs) for cancer therapy. Although NPs have been favorably delivered into tumors by taking advantage of the enhanced permeation and retention (EPR) effect, several physiological barriers present within tumors tend to restrict the diffusion of NPs. To overcome this, one of the strategies is to design NPs that can reach lower size limits to improve tumor penetration without being rapidly cleared out by the body. Several attempts have been made to achieve this, such as selecting appropriate nanocarriers and modifying surface properties. While many studies focus on the optimal design of NPs, the influence of mouse strains on the effectiveness of NPs remains unknown. Therefore, this study aimed to assess whether the vascular permeability of NPs near the lower size limit differs among mouse strains. We found that the vessel permeability of dextran NPs was size-dependent and dextran NPs with a size below 15 nm exhibited leakage from postcapillary venules in all strains. Most importantly, the leakage rate of 8-nm fluorescein isothiocyanate dextran was significantly higher in the BALB/c mouse strain than in other strains. This strain dependence was not observed in slightly positive TRITC-dextran with comparable sizes. Our results indicate that the influence on mouse strains needs to be taken into account for the evaluation of NPs near the lower size limit

Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimization Model for Stand-Alone Microgrid Operation

The optimal dispatching model for a stand-alone microgrid (MG) is of great importance to its operation reliability and economy. This paper aims at addressing the difficulties in improving the operational economy and maintaining the power balance under uncertain load demand and renewable generation, which could be even worse in such abnormal conditions as storms or abnormally low or high temperatures. A new two-time scale multi-objective optimization model, including day-ahead cursory scheduling and real-time scheduling for finer adjustments, is proposed to optimize the operational cost, load shedding compensation and environmental benefit of stand-alone MG through controllable load (CL) and multi-distributed generations (DGs). The main novelty of the proposed model is that the synergetic response of CL and energy storage system (ESS) in real-time scheduling offset the operation uncertainty quickly. And the improved dispatch strategy for combined cooling-heating-power (CCHP) enhanced the system economy while the comfort is guaranteed. An improved algorithm, Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy (SIP-CO-PSO-ERS) algorithm with strong searching capability and fast convergence speed, was presented to deal with the problem brought by the increased errors between actual renewable generation and load and prior predictions. Four typical scenarios are designed according to the combinations of day types (work day or weekend) and weather categories (sunny or rainy) to verify the performance of the presented dispatch strategy. The simulation results show that the proposed two-time scale model and SIP-CO-PSO-ERS algorithm exhibit better performance in adaptability, convergence speed and search ability than conventional methods for the stand-alone MG’s operation

Experimental Study of a Novel Ultrasonic Vibration-Assisted Structure for Radial Milling

A novel ultrasonic vibration-assisted structure for radial milling is proposed, and the ultrasonic vibration-assisted radial milling (UVARM) is further studied in terms of theoretical model and milling experiment. The motion and feed characteristics of UVARM are also analyzed. A special fixture is designed to construct the experimental platform of UVARM, in which the vibration is applied to the workpiece along the radial direction. The preliminary results show that with the increase of spindle speed, the milling force in both conventional cutting (CC) and UVARM experiments tends to increase. In addition, when the feed per tooth increased, the milling force increased. With the involvement of ultrasonic vibration, the milling force is significantly reduced, with the maximum reduction reaching 20%. The comprehensive analysis showed that there was a decrease of about 10% to 25% in the ultrasonic case compared with the conventional method. It is also found that UVARM can inhibit the production of a built-up edge. With the ultrasonic vibration, the burrs on the processed surface are also reduced, and the grooves left by tool traces are shallower. Compared with conventional milling, the roughness value of the machined surface obtained by UVARM is reduced by 10% to 32%. The experimental results also show that UVARM can effectively improve the dimensional accuracy of the workpiece

Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization

We propose a new unsupervised sentence salience framework for Multi-Document Summarization (MDS), which can be divided into two components: latent semantic modeling and salience estimation. For latent semantic modeling, a neural generative model called Variational Auto-Encoders (VAEs) is employed to describe the observed sentences and the corresponding latent semantic representations. Neural variational inference is used for the posterior inference of the latent variables. For salience estimation, we propose an unsupervised data reconstruction framework, which jointly considers the reconstruction for latent semantic space and observed term vector space. Therefore, we can capture the salience of sentences from these two different and complementary vector spaces. Thereafter, the VAEs-based latent semantic model is integrated into the sentence salience estimation component in a unified fashion, and the whole framework can be trained jointly by back-propagation via multi-task learning. Experimental results on the benchmark datasets DUC and TAC show that our framework achieves better performance than the state-of-the-art models

Lico A Enhances Nrf2-Mediated Defense Mechanisms against t

Licochalcone A (Lico A) exhibits various biological properties, including anti-inflammatory and antioxidant activities. In this study, we investigated the antioxidative potential and mechanisms of Lico A against tert-butyl hydroperoxide- (t-BHP-) induced oxidative damage in RAW 264.7 cells. Our results indicated that Lico A significantly inhibited t-BHP-induced cytotoxicity, apoptosis, and reactive oxygen species (ROS) generation and reduced glutathione (GSH) depletion but increased the glutamate-cysteine ligase modifier (GCLM) subunit and the glutamate-cysteine ligase catalytic (GCLC) subunit genes expression. Additionally, Lico A dramatically upregulated the antioxidant enzyme heme oxygenase 1 (HO-1) and nuclear factor erythroid 2-related factor 2 (Nrf2), which were associated with inducing Nrf2 nuclear translocation, decreasing Keap1 protein expression and increasing antioxidant response element (ARE) promoter activity. Lico A also obviously induced the activation of serine/threonine kinase (Akt) and extracellular signal-regulated kinase (ERK), but PI3K/Akt and ERK inhibitors treatment displayed clearly decreased levels of LicoA-induced Nrf2 nuclear translocation and HO-1 expression, respectively. Furthermore, Lico A treatment markedly attenuated t-BHP-induced oxidative damage, which was reduced by treatment with PI3K/Akt, ERK, and HO-1 inhibitors. Therefore, Lico A might have a protective role against t-BHP-induced cytotoxicity by modulating HO-1 and by scavenging ROS via the activation of the PI3K/Akt and ERK/Nrf2 signaling pathways

Color-Based Image Retrieval Using Proximity Space Theory

The goal of object retrieval is to rank a set of images by their similarity compared with a query image. Nowadays, content-based image retrieval is a hot research topic, and color features play an important role in this procedure. However, it is important to establish a measure of image similarity in advance. The innovation point of this paper lies in the following. Firstly, the idea of the proximity space theory is utilized to retrieve the relevant images between the query image and images of database, and we use the color histogram of an image to obtain the Top-ranked colors, which can be regard as the object set. Secondly, the similarity is calculated based on an improved dominance granule structure similarity method. Thus, we propose a color-based image retrieval method by using proximity space theory. To detect the feasibility of this method, we conducted an experiment on COIL-20 image database and Corel-1000 database. Experimental results demonstrate the effectiveness of the proposed framework and its applications