Auto-Encoder and Representation Learning Based MiRNA-Disease Association Prediction

As expressions of miRNAs are often associated with diseases, understanding the pathophysiology of illness at the miRNA level is beneficial for the treatment and prevention of associated diseases, as well as the creation of related medicines. Recent computational methods for predicting miRNA-disease associations integrate their pertinent heterogeneous data. The difficulty in this study is how to extract the implied associations from sparse data. In the present study, by drawing on natural language processing, a learning-based method is used to extract dense and high-dimensional representations of illnesses and miRNAs from integrated disease semantic similarity, miRNA functional similarity, and heterogeneous related interaction data. To predict disease-miRNA associations, we use a deep autoencoder and its reconstruction error as a measurement. Our experimental results suggest that our strategy is comparable to cutting-edge methods for predicting disease-related miRNAs

Herding Effect based Attention for Personalized Time-Sync Video Recommendation

Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendations. However, existing review-based recommendation methods ignore the context-dependent (generated by user-interaction), real-time, and time-sensitive properties of TSC data. To bridge the above gaps, in this paper, we use video images and users' TSCs to design an Image-Text Fusion model with a novel Herding Effect Attention mechanism (called ITF-HEA), which can predict users' favorite videos with model-based collaborative filtering. Specifically, in the HEA mechanism, we weight the context information based on the semantic similarities and time intervals between each TSC and its context, thereby considering influences of the herding effect in the model. Experiments show that ITF-HEA is on average 3.78\% higher than the state-of-the-art method upon F1-score in baselines.Comment: ACCEPTED for ORAL presentation at IEEE ICME 201

Distributed optimization with inexact oracle

summary:In this paper, we study the distributed optimization problem using approximate first-order information. We suppose the agent can repeatedly call an inexact first-order oracle of each individual objective function and exchange information with its time-varying neighbors. We revisit the distributed subgradient method in this circumstance and show its suboptimality under square summable but not summable step sizes. We also present several conditions on the inexactness of the local oracles to ensure an exact convergence of the iterative sequences towards the global optimal solution. A numerical example is given to verify the efficiency of our algorithm

Facile Preparation of Bimetallic MOF-derived Supported Tungstophosphoric Acid Composites for Biodiesel Production

In this work, the novel TPA@C-NiZr-MOF catalyst is synthesized by the impregnation of tungstophosphoric acid (TPA) on the NiZr-based metal-organic framework (NiZr-MOF) followed by calcination up to 300 °C. The as-prepared catalyst materials were structurally, morphologically, and texturally characterized by XRD, FTIR, temperature programmed desorption of NH3 ( TPD-NH3 ), N2 physisorption, SEM, TEM, and XPS. The prepared catalyst can be used as an efficient heterogeneous catalyst for biodiesel production from oleic acid (OA) with methanol. The results indicated that, in comparison to TPA@NiZr-MOF, the TPA@C-NiZr-MOF catalyst calcined at 300 °C exhibits excellent catalytic performance probably owing to the synergistic effect between TPA and metal oxide skeletons, high acidity, as well as larger surface area and pore size. Additionally, the TPA@C-NiZr-MOF catalyst can be reused in up to six cycles with an acceptable conversion. This study showed that the bimetallic MOF-derived composite materials can be used as an alternative potential heterogeneous catalyst toward biorefinery applications

Consensus seeking in multi-agent systems with an active leader and communication delays

summary:In this paper, we consider a multi-agent consensus problem with an active leader and variable interconnection topology. The dynamics of the active leader is given in a general form of linear system. The switching interconnection topology with communication delay among the agents is taken into consideration. A neighbor-based estimator is designed for each agent to obtain the unmeasurable state variables of the dynamic leader, and then a distributed feedback control law is developed to achieve consensus. The feedback parameters are obtained by solving a Riccati equation. By constructing a common Lyapunov function, some sufficient conditions are established to guarantee that each agent can track the active leader by assumption that interconnection topology is undirected and connected. We also point out that some results can be generalized to a class of directed interaction topologies. Moreover, the input-to-state stability (ISS) is obtained for multi-agent system with variable interconnection topology and communication delays in a disturbed environment