Multi-type Disentanglement without Adversarial Training

Controlling the style of natural language by disentangling the latent space is an important step towards interpretable machine learning. After the latent space is disentangled, the style of a sentence can be transformed by tuning the style representation without affecting other features of the sentence. Previous works usually use adversarial training to guarantee that disentangled vectors do not affect each other. However, adversarial methods are difficult to train. Especially when there are multiple features (e.g., sentiment, or tense, which we call style types in this paper), each feature requires a separate discriminator for extracting a disentangled style vector corresponding to that feature. In this paper, we propose a unified distribution-controlling method, which provides each specific style value (the value of style types, e.g., positive sentiment, or past tense) with a unique representation. This method contributes a solid theoretical basis to avoid adversarial training in multi-type disentanglement. We also propose multiple loss functions to achieve a style-content disentanglement as well as a disentanglement among multiple style types. In addition, we observe that if two different style types always have some specific style values that occur together in the dataset, they will affect each other when transferring the style values. We call this phenomenon training bias, and we propose a loss function to alleviate such training bias while disentangling multiple types. We conduct experiments on two datasets (Yelp service reviews and Amazon product reviews) to evaluate the style-disentangling effect and the unsupervised style transfer performance on two style types: sentiment and tense. The experimental results show the effectiveness of our model

Updated insights into 3D architecture electrodes for micropower sources

Microbatteries (MBs) and microsupercapacitors (MSCs) are primary on-chip micropower sources that drive autonomous and stand-alone microelectronic devices for implementation of the Internet of Things (IoT). However, the performance of conventional MBs and MSCs is restricted by their 2D thin-film electrode design, and these devices struggle to satisfy the increasing IoT energy demands for high energy density, high power density, and long lifespan. The energy densities of MBs and MSCs can be improved significantly through adoption of a 2D thick-film electrode design; however, their power densities and lifespans deteriorate with increased electrode thickness. In contrast, 3D architecture electrodes offer remarkable opportunities to simultaneously improve MB and MSC energy density, power density, and lifespan. To date, various 3D architecture electrodes have been designed, fabricated, and investigated for MBs and MSCs. This review provides an update on the principal superiorities of 3D architecture electrodes over 2D thick-film electrodes in the context of improved MB and MSC energy density, power density, and lifespan. In addition, the most recent and representative progress in 3D architecture electrode development for MBs and MSCs is highlighted. Finally, present challenges are discussed and key perspectives for future research in this field are outlined

DETERMINANTS OF AGRICULTURE-RELATED LOAN DEFAULT: EVIDENCE FROM CHINA

This paper investigates agriculture-related loan default in 2002–2009 through a large data set from a leading Chinese state-owned bank. Using logit regression, we find the default rate on agriculture-related loans is significantly higher than that on non–agriculture-related loans. We find that base interest rates, loan maturity, the type of collateral, firm size, ownership structure, and managerial quality rating have a significant impact on agriculture-related loan default, but this also depends on how agriculture-related loans are defined. The results provide insight into the real impact of monetary policy on agriculture-related lending.This paper investigates agriculture-related loan default in 2002–2009 through a large data set from a leading Chinese state-owned bank. Using logit regression, we find the default rate on agriculture-related loans is significantly higher than that on non–agriculture-related loans. We find that base interest rates, loan maturity, the type of collateral, firm size, ownership structure, and managerial quality rating have a significant impact on agriculture-related loan default, but this also depends on how agriculture-related loans are defined. The results provide insight into the real impact of monetary policy on agriculture-related lending

Self-assembly of noble metal nanoparticles into sub-100 nm colloidosomes with collective optical and catalytic properties.

Self-assembly at the nanoscale represents a powerful tool for creating materials with new structures and intriguing collective properties. Here, we report a novel strategy to synthesize nanoscale colloidosomes of noble metals by assembling primary metal nanoparticles at the interface of emulsion droplets formed by their capping agent. This strategy produces noble metal colloidosomes of unprecedentedly small sizes (<100 nm) in high yield and uniformity, which is highly desirable for practical applications. In addition, it enables the high tunability of the composition, producing a diversity of monometallic and bimetallic alloy colloidosomes. The colloidosomes exhibit interesting collective properties that are different from those of individual colloidal nanoparticles. Specifically, we demonstrate Au colloidosomes with well-controlled interparticle plasmon coupling and Au-Pd alloy colloidosomes with superior electrocatalytic performance, both thanks to the special structural features that arise from the assembly. We believe this strategy provides a general platform for producing a rich class of miniature colloidosomes that may have fascinating collective properties for a broad range of applications

Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS) of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO) algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods