Plan-And-Write: Towards Better Automatic Storytelling

Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events. Despite considerable efforts on automatic story generation in the past, prior work either is restricted in plot planning, or can only generate stories in a narrow domain. In this paper, we explore open-domain story generation that writes stories given a title (topic) as input. We propose a plan-and-write hierarchical generation framework that first plans a storyline, and then generates a story based on the storyline. We compare two planning strategies. The dynamic schema interweaves story planning and its surface realization in text, while the static schema plans out the entire storyline before generating stories. Experiments show that with explicit storyline planning, the generated stories are more diverse, coherent, and on topic than those generated without creating a full plan, according to both automatic and human evaluations.Comment: Accepted by AAAI 201

Development of a polarization strain sensor system using a neural network

We propose a polarization strain sensor system based on the principle that the polarization state of light propagating in a single-mode fiber changes when external strains change. We use a three-layer feedforward neural network for data processing. Learning is performed by training a designed neural network using the experimental data as training data. In addition, the output obtained for the test data using the trained neural network is in good agreement with the experimental data used as the test data. This result demonstrates the feasibility of both the sensor system and data processing method

A Robust Decentralized Load Frequency Controller for Interconnected Power Systems

A novel design of a robust decentralized load frequency control (LFC) algorithm is proposed for an inter-connected three-area power system, for the purpose of regulating area control error (ACE) in the presence of system uncertainties and external disturbances. The design is based on the concept of active disturbance rejection control (ADRC). Estimating and mitigating the total effect of various uncertainties in real time, ADRC is particularly effective against a wide range of parameter variations, model uncertainties, and large disturbances. Furthermore, with only two tuning parameters, the controller provides a simple and easy-to-use solution to complex engineering problems in practice. Here, an ADRC-based LFC solution is developed for systems with turbines of various types, such as non-reheat, reheat, and hydraulic. The simulation results verified the effectiveness of the ADRC, in comparison with an existing PI-type controller tuned via genetic algorithm linear matrix inequalities (GALMIs). The comparison results show the superiority of the proposed solution. Moreover, the stability and robustness of the closed-loop system is studied using frequency-domain analysis

Research on intelligent greenhouse based on Internet of Things

There are many drawbacks in the current traditional agricultural model, which consumes a lot of manpower, material resources and fi nancial resources to solve the problem of crop growth environment. In order to change this situation, real-time detection and remote control of its environmental data, an intelligent greenhouse integrated system based on the Internet of Things was proposed. The system includes four modules: environment detection, gateway transmission, control execution and remote monitoring. The environment detection module detects the growth environment information in real time, and uploads real-time data with the help of the Internet of Things gateway. Users can remotely monitor the growing environment and growth state of crops in the greenhouse through mobile phone apps and computer web pages. At the same time, according to the growth environment data, control and implement equipment to adjust environmental factors in time to achieve accurate planting and improve crop yield and quality. Avoid the waste of agricultural resources

Adrenergic receptor (ADR) genotype influences the effects of strength training on mid-thigh intermuscular fat

Sarcopenia results in an increase in intermuscular fat (IMF) and low density muscle (LDM), which is associated with adverse health and functional consequences. Although strength training (ST) is considered an intervention of choice for the prevention and treatment of sarcopenia, little is known about its effect on IMF or LDM. Regional fat alterations resulting from exercise interventions may be influenced by adrenergic receptor (ADR) beta2 Gln27Glu and ADR alpha2b Glu12/Glu9 gene polymorphisms. To examine the influence of these polymorphisms on mid-thigh IMF, LDM and normal density muscle (NDM), we studied 46 older men and 52 older women before and after a 10-week single leg knee extension strength training (ST) program. The ST program resulted in a substantial increase in one-repetition maximum (1-RM) strength (P = 0.0001) and NDM (P = 0.0001), but no significant changes in IMF and LDM in the whole group. However, IMF was significantly reduced with ST in subjects carrying ADR beta2 Glu27 (-2.3 cm2, P = 0.028), but no significant change was observed with ADR beta2 Glu27 noncarriers (+1.5 cm2, P = 0.14). The decrease in IMF in those with the ADR alpha2b Glu9 allele was approaching significance (-1.9 cm2, P = 0.066), and significantly different (-2.9 cm2, P = 0.043) from a nonsignificant increase in IMF in the ADR alpha2b Glu9 allele noncarriers. ADR beta2 Glu27 carriers who also carried the ADR alpha2b Glu9 allele experienced a significant loss of IMF with ST (-3.8 ± 1.6 cm2, P = 0.018). These results suggest that the response of IMF to ST is influenced by ADR beta2 Gln27Glu and ADR alpha2b Glu12/Glu9 polymorphisms

Nucleus accumbens shell small conductance potassium channels underlie adolescent ethanol exposure-induced anxiety

Alcohol use typically begins in adolescence, increasing the likelihood of adult mental disorders such as anxiety. However, the cellular mechanisms underlying the consequences of adolescent alcohol exposure as well as the behavioral consequences remain poorly understood. We examined the effects of adolescent or adult chronic intermittent ethanol (CIE) exposure on intrinsic excitability of striatal medium-sized spiny neurons (MSNs) and anxiety levels. Rats underwent one of the following procedures: (1) light-dark transition (LDT) and open-field (OF) tests to evaluate anxiety levels and general locomotion; (2) whole-cell patch clamp recordings and biocytin labeling to assess excitability of striatal MSNs, as well as morphological properties; and (3) western blot immunostaining to determine small conductance (SK) calcium-activated potassium channel protein levels. Three weeks, but not 2 days, after CIE treatment, adolescent CIE-treated rats showed shorter crossover latency from the light to dark side in the LDT test and higher MSN excitability in the nucleus accumbens shell (NAcS). Furthermore, the amplitude of the medium afterhyperpolarization (mAHP), mediated by SK channels, and SK3 protein levels in the NAcS decreased concomitantly. Finally, increased anxiety levels, increased excitability, and decreased amplitude of mAHP of NAcS MSNs were reversed by SK channel activator 1-EBIO and mimicked by the SK channel blocker apamin. Thus, adolescent ethanol exposure increases adult anxiety-like behavior by downregulating SK channel function and protein expression, which leads to an increase of intrinsic excitability in NAcS MSNs. SK channels in the NAcS may serve as a target to treat adolescent alcohol binge exposure-induced mental disorders, such as anxiety in adulthood