ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction

A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time consuming, labor intensive, and error prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines

On the total signed domination number of the Cartesian product of paths

Let $G$ be a finite connected simple graph with a vertex set $V(G)$ and an edge set $E(G)$. A total signed dominating function of $G$ is a function $f: V(G)\cup E(G)\rightarrow \{-1, 1\}$, such that $\sum_{y\in N_T[x]}f(y) \geq 1$ for all $x\in V(G) \cup E(G)$. The total signed domination number of $G$ is the　minimum weight of a total signed dominating function on $G$. In this　paper, we prove lower and upper bounds on the total signed　domination number of the Cartesian product of two paths, $P_m\Box P_n$

Distributed Model Predictive Control and Optimization for Linear Systems With Global Constraints and Time-Varying Communication

In the article, we study the distributed model predictive control (DMPC) problem for a network of linear discrete-time systems, where the system dynamics are decoupled, the system constraints are coupled, and the communication networks are described by time-varying directed graphs. A novel distributed optimization algorithm called the push-sum dual gradient (PSDG) algorithm is proposed to solve the dual problem of the DMPC optimization problem in a fully distributed way. We prove that the sequences of the primal, and dual variables converge to their optimal values. Furthermore, to solve the implementation issues, stopping criteria are designed to allow early termination of the PSDG Algorithm, and the gossip-based push-sum algorithm is proposed to check the stopping criteria in a distributed manner. It is shown that the optimization problem is iteratively feasible, and the closed-loop system is exponentially stable. Finally, the effectiveness of the proposed DMPC approach is verified via an example

Class-Specific Attention (CSA) for Time-Series Classification

Most neural network-based classifiers extract features using several hidden layers and make predictions at the output layer by utilizing these extracted features. We observe that not all features are equally pronounced in all classes; we call such features class-specific features. Existing models do not fully utilize the class-specific differences in features as they feed all extracted features from the hidden layers equally to the output layers. Recent attention mechanisms allow giving different emphasis (or attention) to different features, but these attention models are themselves class-agnostic. In this paper, we propose a novel class-specific attention (CSA) module to capture significant class-specific features and improve the overall classification performance of time series. The CSA module is designed in a way such that it can be adopted in existing neural network (NN) based models to conduct time series classification. In the experiments, this module is plugged into five start-of-the-art neural network models for time series classification to test its effectiveness by using 40 different real datasets. Extensive experiments show that an NN model embedded with the CSA module can improve the base model in most cases and the accuracy improvement can be up to 42%. Our statistical analysis show that the performance of an NN model embedding the CSA module is better than the base NN model on 67% of MTS and 80% of UTS test cases and is significantly better on 11% of MTS and 13% of UTS test cases.Comment: 12 page

Spatial-Temporal Variation of Aridity Index of China during 1960–2013

Aridity index, as the ration of potential evapotranspiration and precipitation, is an important indicator of regional climate. GIS technology, Morlet wavelet, Mann-Kendall test, and principal component analysis are utilized to investigate the spatial-temporal variation of aridity index and its impacting factors in China on basis of climate data from 599 stations during 1960–2013. Results show the following. (1) Boundaries between humid and semihumid region, and semihumid and semiarid region coincide with 400 mm and 800 mm precipitation contour lines. (2) Average annual aridity index is between 3.4 and 7.5 and shows decrease trend with a tendency of –0.236 per decade at 99% confidence level. (3) The driest and wettest month appear in December and July, respectively, in one year. (4) Periods of longitudinal and latitudinal shift of aridity index 1, 1.5, and 4 contours coordinate are 10 and 25 years, 6 and 26 years, and 5 and 25 years, respectively. (5) Four principal components which affect aridity index are thermodynamic factors, water and radiation factors, geographical and air dynamic factors, and evaluation factor, respectively

Reduction of the HIV Protease Inhibitor-Induced ER Stress and Inflammatory Response by Raltegravir in Macrophages

Background HIV protease inhibitor (PI), the core component of highly active antiretroviral treatment (HAART) for HIV infection, has been implicated in HAART-associated cardiovascular complications. Our previous studies have demonstrated that activation of endoplasmic reticulum (ER) stress is linked to HIV PI-induced inflammation and foam cell formation in macrophages. Raltegravir is a first-in-its-class HIV integrase inhibitor, the newest class of anti-HIV agents. We have recently reported that raltegravir has less hepatic toxicity and could prevent HIV PI-induced dysregulation of hepatic lipid metabolism by inhibiting ER stress. However, little information is available as to whether raltegravir would also prevent HIV PI-induced inflammatory response and foam cell formation in macrophages. Methodology and Principal Findings In this study, we examined the effect of raltegravir on ER stress activation and lipid accumulation in cultured mouse macrophages (J774A.1), primary mouse macrophages, and human THP-1-derived macrophages, and further determined whether the combination of raltegravir with existing HIV PIs would potentially exacerbate or prevent the previously observed activation of inflammatory response and foam cell formation. The results indicated that raltegravir did not induce ER stress and inflammatory response in macrophages. Even more interestingly, HIV PI-induced ER stress, oxidative stress, inflammatory response and foam cell formation were significantly reduced by raltegravir. High performance liquid chromatography (HPLC) analysis further demonstrated that raltegravir did not affect the uptake of HIV PIs in macrophages. Conclusion and Significance Raltegravir could prevent HIV PI-induced inflammatory response and foam cell formation by inhibiting ER stress. These results suggest that incorporation of this HIV integrase inhibitor may reduce the cardiovascular complications associated with current HAART

HIV Protease Inhibitors Sensitize Human Head and Neck Squamous Carcinoma Cells to Radiation by Activating Endoplasmic Reticulum Stress

Background Human head and neck squamous cell carcinoma (HNSCC) is the sixth most malignant cancer worldwide. Despite significant advances in the delivery of treatment and surgical reconstruction, there is no significant improvement of mortality rates for this disease in the past decades. Radiotherapy is the core component of the clinical combinational therapies for HNSCC. However, the tumor cells have a tendency to develop radiation resistance, which is a major barrier to effective treatment. HIV protease inhibitors (HIV PIs) have been reported with radiosensitizing activities in HNSCC cells, but the underlying cellular/molecular mechanisms remain unclear. Our previous study has shown that HIV PIs induce cell apoptosis via activation of endoplasmic reticulum (ER) stress. The aim of this study was to examine the role of ER stress in HIV PI-induced radiosensitivity in human HNSCC. Methodology and Principal Findings HNSCC cell lines, SQ20B and FaDu, and the most commonly used HIV PIs, lopinavir and ritonavir (L/R), were used in this study. Clonogenic assay was used to assess the radiosensitivity. Cell viability, apoptosis and cell cycle were analyzed using Cellometer Vision CBA. The mRNA and protein levels of ER stress-related genes (eIF2α, CHOP, ATF-4, and XBP-1), as well as cell cycle related protein, cyclin D1, were detected by real time RT-PCR and Western blot analysis, respectively. The results demonstrated that L/R dose-dependently sensitized HNSCC cells to irradiation and inhibited cell growth. L/R-induced activation of ER stress was correlated to down-regulation of cyclin D1 expression and cell cycle arrest under G0/G1 phase. Conclusion and Significance HIV PIs sensitize HNSCC cells to radiotherapy by activation of ER stress and induction of cell cycle arrest. Our results provided evidence that HIV PIs can be potentially used in combination with radiation in the treatment of HNSCC