Research on Spoken Language Understanding Based on Deep Learning

Aiming at solving the problem that the recognition effect of rare slot values in spoken language is poor, which affects the accuracy of oral understanding task, a spoken language understanding method is designed based on deep learning. The local features of semantic text are extracted and classified to make the classification results match the dialogue task. An intention recognition algorithm is designed for the classification results. Each datum has a corresponding intention label to complete the task of semantic slot filling. The attention mechanism is applied to the recognition of rare slot value information, the weight of hidden state and corresponding slot characteristics are obtained, and the updated slot value is used to represent the tracking state. An auxiliary gate unit is constructed between the upper and lower slots of historical dialogue, and the word vector is trained based on deep learning to complete the task of spoken language understanding. The simulation results show that the proposed method can realize multiple rounds of man-machine spoken language. Compared with the spoken language understanding methods based on cyclic network, context information, and label decomposition, it has higher accuracy and F1 value and has higher practical application value.

An Efficient Convolution Algorithm for the Non-Markovian Two-Node Cyclic Network

Consider a closed cyclic queueing model that consists of two nodes and a total of M customers. Each node buffer can accommodate all M customers. Node 1 has N ≤ M servers, each having an exponential service time with rate λ . The second node consists of a single server with a general service time distribution function B . . The well-known machine repair model with spares, where a set of identical machines, N , is served by a single repair person, is a key application of this model. This model has several other applications in performance evaluation, manufacturing, computer networks, and in reliability studies as it can be easily used to compute system availability. In this article, we give an efficient algorithm to derive an exact solution for the steady state system size probabilities. Our approach is based on developing an efficient polynomial convolution method to compute the transition probabilities of a birth process over node 2 service times and solving an imbedded Markov chain at node 2 service completion epochs. This is a significant improvement over the exponential algorithm given in an earlier paper. Numerical examples are given to demonstrate the performance of our method.

Phenol Derivatives as Co-Crystallized Templates to Modulate Trimesic-Acid-Based Hydrogen-Bonded Organic Molecular Frameworks

Five host−guest trimesic-acid-based hydrogen-bonds framework compounds with different guests, namely [(TMA)4·(TMB)3] (1), [(TMA)2·(DMB)1.5] (2), [(TMA)6·(MP)] (3), [(TMA)·(EP)] (4) and [(TMA)·(PP)] (5) (TMA = trimesic acid, TMB = 1,3,5-trimethoxybenzene, DMB = 1,4-dimethoxybenzene, MP = 4-methoxyphenol, EP = 4-ethoxyphenol and PP = 4-propoxyphenol), were obtained through co-crystallization, and were characterized by elemental analysis, infrared spectroscopy analysis, and thermogravimetric analysis. The trimesic acid molecules comprise a hydrogen bonding six-membered cyclic host network that is found in a two-dimensional arrangement in compounds 1 and 2, and in a nine-fold interpenetrated three-dimensional structure in compound 3. In compounds 4 and 5, the trimesic acid and EP/PP molecules form a hydrogen-bonded six-membered cyclic network, resulting in a one-dimensional chain structure through O−H…O hydrogen bonds.

Evidence that Criegee intermediates drive autoxidation in unsaturated lipids

Autoxidation is an autocatalytic free-radical chain reaction responsible for the oxidative destruction of organic molecules in biological cells, foods, plastics, petrochemicals, fuels, and the environment. In cellular membranes, lipid autoxidation (peroxidation) is linked with oxidative stress, age-related diseases, and cancers. The established mechanism of autoxidation proceeds via H-atom abstraction through a cyclic network of peroxy–hydroperoxide-mediated free-radical chain reactions. For a series of model unsaturated lipids, we present evidence for an autoxidation mechanism, initiated by hydroxyl radical (OH) addition to C=C bonds and propagated by chain reactions involving Criegee intermediates (CIs). This mechanism leads to unexpectedly rapid autoxidation even in the presence of water, implying that as reactive intermediates, CI could play a much more prominent role in chemistries beyond the atmosphere.