Take Measures: Knowledge Poverty Alleviation for Contiguous Poor Areas- A Case Study of the Wuling Mountain Area

Knowledge poverty alleviation aims to help poor areas and poor people out of poverty by strengthening the knowledge infrastructure, enhancing the professional skills and “self blood” feature of the poor, establishing cultural knowledge networking and activating poor self-consciousness of poverty-stricken areas. Based on combing the theory of poverty at home and abroad and outlining research status and the analysis of the Wuling Mountain Area, the paper presents main primary paths of Knowledge for the contiguous poor areas, aiming at providing theoretical support and basis for decision-making for all levels of the government to lay our new contiguous destitute poverty alleviation during the battle and build a comprehensive well-off society and realize the country’s political stability, national unity, border consolidation, social harmony, ecological security. Simultaneously, a further objective of this paper is to attract the attention of more scholars at home and abroad to knowledge poverty alleviation which is important proposition and build up a systematic theoretical framework from the theoretical and practical dimensions

Opponent backgrounds reduce discrimination sensitivity to competing motions: Effects of different vertical motions on horizontal motion perception

AbstractWe examined the relationship between two distinct motion phenomena. First, locally balanced stimuli in which opposing motion signals are presented spatially near one another fail to cause a robust firing pattern in brain area MT. The brain’s response to this motion is effectively suppressed, a phenomenon known as opponency. Second, past research has found that discrimination sensitivity to a target motion is negatively affected by a superimposed irrelevant motion signal – a process we call “perceptual suppression.” In the current study, we examined how opponency affects the strength of perceptual suppression. We found unexpected results: a target motion embedded within an opponent background was harder to discriminate than a target motion embedded within a non-opponent background. We argue that this pattern of results runs contrary to the clear prediction stemming from the current understanding of the role of opponency in motion processing and tentatively offer an explanation based on recent MT physiology

The influence of physical illumination on lightness perception in simultaneous contrast displays

Three experiments investigated the role of physical illumination on lightness perception in simultaneous lightness contrast (SLC). Four configurations were employed: the classic textbook version of the illusion and three configurations that produced either enhanced or reduced SLC. Experiment 1 tested the effect of ambient illumination on lightness perception. It simulated very dark environmental conditions that nevertheless still allowed perception of different shades of gray. Experiment 2 tested the effect of the intensity of Gelb lighting on lightness perception. Experiment 3 presented two conditions that integrated illumination conditions from Experiments 1 and 2. Our results demonstrated an illumination effect on both lightness matching and perceived SLC contrast: As the intensity of illumination increased, the target on the black background appeared lighter, while the target on the white background was little affected. We hypothesize the existence of two illumination ranges that affect lightness perception differently: low and normal. In the low range, the SLC contrast was reduced and targets appeared darker. In the normal range, the SLC contrast and lightness matchings for each background were little changed across illumination intensities

Training-Time-Friendly Network for Real-Time Object Detection

Modern object detectors can rarely achieve short training time, fast inference speed, and high accuracy at the same time. To strike a balance among them, we propose the Training-Time-Friendly Network (TTFNet). In this work, we start with light-head, single-stage, and anchor-free designs, which enable fast inference speed. Then, we focus on shortening training time. We notice that encoding more training samples from annotated boxes plays a similar role as increasing batch size, which helps enlarge the learning rate and accelerate the training process. To this end, we introduce a novel approach using Gaussian kernels to encode training samples. Besides, we design the initiative sample weights for better information utilization. Experiments on MS COCO show that our TTFNet has great advantages in balancing training time, inference speed, and accuracy. It has reduced training time by more than seven times compared to previous real-time detectors while maintaining state-of-the-art performances. In addition, our super-fast version of TTFNet-18 and TTFNet-53 can outperform SSD300 and YOLOv3 by less than one-tenth of their training time, respectively. The code has been made available at \url{https://github.com/ZJULearning/ttfnet}.Comment: Accepted to AAAI2020 (8 pages, 3 figures

Recent Advances in Heterogeneous Catalytic Hydrogenation of CO2 to Methane

With the accelerating industrialization, urbanization process, and continuously upgrading of consumption structures, the CO2 from combustion of coal, oil, natural gas, and other hydrocarbon fuels is unbelievably increased over the past decade. As an important carbon resource, CO2 gained more and more attention because of its converting properties to lower hydrocarbon, such as methane, methanol, and formic acid. Among them, CO2 methanation is considered to be an extremely efficient method due to its high CO2 conversion and CH4 selectivity. However, the CO2 methanation process requires high reaction temperatures (300–400°C), which limits the theoretical yield of methane. Thus, it is desirable to find a new strategy for the efficient conversion of CO2 to methane at relatively low reaction temperature, and the key issue is using the catalysts in the process. The advances in the noble metal catalysts, Ni-based catalysts, and Co-based catalysts, for catalytic hydrogenation CO2 to methane are reviewed in this paper, and the effects of the supports and the addition of second metal on CO2 methanation as well as the reaction mechanisms are focused

Demystifying Developers' Issues in Distributed Training of Deep Learning Software

Deep learning (DL) has been pervasive in a wide spectrum of nowadays software systems and applications. The rich features of these DL based software applications (i.e., DL software) usually rely on powerful DL models. To train powerful DL models with large datasets efficiently, it has been a common practice for developers to parallelize and distribute the computation and memory over multiple devices in the training process, which is known as distributed training. However, existing efforts in the software engineering (SE) research community mainly focus on issues in the general process of training DL models. In contrast, to the best of our knowledge, issues that developers encounter in distributed training have never been well studied. Given the surging importance of distributed training in the current practice of developing DL software, this paper fills in the knowledge gap and presents the first comprehensive study on developers' issues in distributed training. To this end, we extract and analyze 1,054 real-world developers' issues in distributed training from Stack Overflow and GitHub, two commonly used data sources for studying software issues. We construct a fine-grained taxonomy consisting of 30 categories regarding the fault symptoms and summarize common fix patterns for different symptoms. Based on the results, we suggest actionable implications and research avenues that can potentially facilitate the future development of distributed training