Optimizing of Convolutional Neural Network Accelerator

In recent years, convolution neural network (CNN) had been widely used in many image-related machine learning algorithms since its high accuracy for image recognition. As CNN involves an enormous number of computations, it is necessary to accelerate the CNN computation by a hardware accelerator, such as FPGA, GPU and ASIC designs. However, CNN accelerator faces a critical problem: the large time and power consumption caused by the data access of off-chip memory. Here, we describe two methods of CNN accelerator to optimize CNN accelerator, reducing data precision and data-reusing, which can improve the performance of accelerator with the limited on-chip buffer. Three influence factors to data-reusing are proposed and analyzed, including loop execution order, reusing strategy and parallelism strategy. Based on the analysis, we enumerate all legal design possibilities and find out the optimal hardware design with low off-chip memory access and low buffer size. In this way, we can improve the performance and reduce the power consumption of accelerator effectively

Anonymous Expression in an Online Community for Women in China

Gender issues faced by women can range from workplace harassment to domestic violence. While publicly disclosing these issues on social media can be hard, some may incline to express themselves anonymously. We approached such an anonymous female community on Chinese social media where discussion on gender issues takes place with a qualitative content analysis. By observing anonymous experiences contributed by female users and made publicly available by an influencer, we identified 20 issues commonly discussed, with cheating-partner, controlling parents and age anxiety taking the lead. By describing the anonymously expressed social challenges faced by women in China, in the context of Chinese cultures and expectations about gender, we aim to motivate more policies and platform designs to accommodate the needs of the affected population

Economic comparison of cool and grey roofs of a small office building in the USA

EnergyPlus V9.0 simulations of a typical small office building prototype in five USA cities (Albuquerque, Atlanta, Buffalo, San Diego, Rochester) indicate that adding a cool roof (albedo 0.6) for an aged grey roof (albedo 0.2) would reduce the annual energy and annual energy cost in all hot dry or humid cities (Albuquerque, Atlanta, San Diego) and in severe cold or mixed humid cities (Buffalo, Rochester). The CO2, SO2, and NOX savings are positive in all selected cities. This work considers the economics of using cool roofs of an office building in the USA by performing a 20-year life-cycle cost analysis that compares each type of roof to a grey roof. The 20-year net present values (NPVs) of annual conditioning (heating + cooling) energy cost savings were calculated. The life cycle cost savings (NPV of annual streams – initial cost premium) of cool roofs have both positive and negative values. The NPV with the cool roofs is from -17.3 USD/m2 in Rochester to 6.5 USD/m2 in San Diego. Among the five cities in the USA, Albuquerque, Atlanta, and San Diego were able to apply the cool roofs since they all have positive NPV, while Buffalo and Rochester were unsuitable to apply the cool roofs methods because of the negative NPV. Owners concerned with urban heat island mitigation and slowing climate change may prefer cool roof

High-Temperature sensor based on peanut flat-end reflection structure

A high-temperature sensor based on a peanut flat-end reflection structure is demonstrated. The sensor can be simply fabricated by splicing the spherical end-faces of two segments of single-mode fibers and then cleaving one other end as a flat reflect surface. The proposed structure works as a reflected interferometer. When the ambient temperature changes, the resonant dip wavelength of the interferometer will shift due to the linear expansion or contraction and the thermo-optic effect. As a result, the temperature measurement can be achieved by monitoring the resonant dip wavelength of the interferometer. Experimental results show that the proposed sensor probe based on the peanut flat-end reflection structure works well and it can measure the temperature range from 100 °C to 900 °C with the sensitivity of 0.098 nm/ °C with R²  =  0.988. When temperature ranges from 400 °C to 900 °C, the sensitivity of 0.11 nm/ °C can be achieved with R² = 0.9995. Due to its compact and simple configuration, the proposed sensor is a good high temperature sensor probe.This work is supported by the Natural Science Foundation of Zhejiang Province China under Grant No.LY17F050010

Discovering Domain Disentanglement for Generalized Multi-source Domain Adaptation

A typical multi-source domain adaptation (MSDA) approach aims to transfer knowledge learned from a set of labeled source domains, to an unlabeled target domain. Nevertheless, prior works strictly assume that each source domain shares the identical group of classes with the target domain, which could hardly be guaranteed as the target label space is not observable. In this paper, we consider a more versatile setting of MSDA, namely Generalized Multi-source Domain Adaptation, wherein the source domains are partially overlapped, and the target domain is allowed to contain novel categories that are not presented in any source domains. This new setting is more elusive than any existing domain adaptation protocols due to the coexistence of the domain and category shifts across the source and target domains. To address this issue, we propose a variational domain disentanglement (VDD) framework, which decomposes the domain representations and semantic features for each instance by encouraging dimension-wise independence. To identify the target samples of unknown classes, we leverage online pseudo labeling, which assigns the pseudo-labels to unlabeled target data based on the confidence scores. Quantitative and qualitative experiments conducted on two benchmark datasets demonstrate the validity of the proposed framework