Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.     

ATSP:时态网络可视化的自适应时间片划分方法

现有的时态网络可视化方法大多采用等量时间片来可视化网络的演变，不利于时态模式的快速挖掘和发现。为此，根据时态网络固有的特征提出自适应时间片划分方法（Adaptive Time Slice Partition method，ATSP）。在时态网络的两种表示方式（基于事件的表示方式和基于快照的表示方式）的基础上，构建了ATSP的基础模型，同时提出了一种改进模型用来描述事件间隔时间服从长尾分布的时态网络。为了实现时间片的不等量划分，针对探索任务的不同提出了基于时态模式的ATSP规则和基于中心节点的ATSP规则，并提出了实现算法--层次划分算法（Hierarchical Partition algorithm，HP）和增量划分算法（Incremental Partition algorithm，IP）。实验结果表明，ATSP方法比传统的时间片划分方法更能准确地表示网络的时态特征，且该方法应用于可视化时，能有效归纳并展示网络的特征，明显提高了视觉分析的效率。     

时态神经网络模型及其在股票分类预测上应用

对具有时间属性的数据进行数据挖掘称为时态数据挖掘，用以发现数据在时间上的知识，当数据变化不规律时，如股票交易数据,就很难发现有价值的规律与规则。而神经网络具有并行、容错、可以硬件实现以及自我学习的优点,可作为股票分类预测应用的一种方法。通过将股票数据与时态型相结合，将股票数据转换成时态型股票数据，提出时态神经网络模型的分类方法，对收集的若干上市公司十年内的股票数据进行分析，构建了时态股票数据神经网络分类器对股票进行分类预测。经过实验验证，相比改进前的神经网络和支持向量机方法，该分类器具有更高的分类准确率。结果证明，这种时态数据神经网络模型对于多只股票的分类预测是非常有效的，可以很好地运用到股票市场的分类预测中。     

Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network

The container cloud represented by Docker and Kubernetes has the advantages of less additional resource overhead and shorter start-up and destruction time.However there are still resource management issues such as over-supply and under-supply.In order to allow the Kubernetes cluster to respond “in advance” to the resource usage of the applications deployed on it,and then to schedule and allocate resources in a timely,accurate and dynamic manner based on the predicted value,a cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network was proposed,based on historical data to predict future demand for resources.To find the optimal combination of parameters,the parameters were optimized using TPOT thought.Experiments on the CPU and memory of the Google dataset show that the model has better prediction performance than other models.     

脱硫胶粉在乳化改性沥青中的应用

考察了不同规格胶粉及不同脱硫程度的脱硫胶粉对橡胶改性乳化沥青性能的影响,结果表明,采用100目胶粉即可制备出橡胶改性乳化沥青,但乳化沥青的性能较差;采用活化度为50%左右的100目脱硫胶粉制备乳化沥青时,脱硫胶粉的用量相比于胶粉可大幅提高;在通过外掺100目50%左右活化度的脱硫胶粉(质量分数15%,以沥青计,下同)和质量分数2%的苯乙烯-丁二烯-苯乙烯嵌段共聚物改性的乳化沥青中添加质量分数1%的丁苯胶乳,所制备橡胶改性乳化沥青无筛上剩余物,5 d储存稳定性为2.9%,蒸发残留物的25℃针入度、软化点和5℃延度分别为55 (0.1 mm)、64.5℃和22.5 cm,能够满足乳化改性沥青的技术指标要求。     

云花皱织物的设计与开发

设计与开发了云花皱织物。络筒工序以"高车速、小张力、降毛羽、提产量"为工艺原则;整浆联合工序以"高车速、贴伏毛羽、减磨兼顾增强"为工艺原则;银丝不上浆,按照色经排列要求放在相应的伸缩筘中,并控制好张力;银丝和黑色花式线应穿在规定的综框;织造工序以"高车速、大张力、早开口"为工艺原则。经过对织造准备工序和织造工序的优化使得织造效率达到90%以上,下机一等品率达到93%,入库一等品率达到99%。     

Differences between oculomotor and perceptual artifacts for temporally limited head mounted displays

We used perceptual and oculomotor measures to understand the negative impacts of low (phantom array) and high (motion blur) duty cycles with a high‐speed, AR‐likehead‐mounted display prototype. We observed large intersubject variability for the detection of phantom array artifacts but a highly consistent and systematic effect on saccadic eye movement targeting during low duty cycle presentations. This adverse effect on saccade endpoints was also related to an increased error rate in a perceptual discrimination task, showing a direct effect of display duty cycle on the perceptual quality. For high duty cycles, the probability of detecting motion blur increased during head movements, and this effect was elevated at lower refresh rates. We did not find an impact of the temporal display characteristics on compensatory eye movements during head motion (e.g., VOR). Together, our results allow us to quantify the tradeoff of different negative spatiotemporal impacts of user movements and make subsequent recommendations for optimized temporal HMD parameters.     

通风量对菇渣好氧发酵温度时空特性的影响

采用不同通风量进行菇渣与猪粪高温共发酵,研究通风对发酵过程中堆体温度的时空变化的影响。结果表明,通风使堆体温度呈现出明显的梯度效应,不同处理各发酵时期的温度分布均由剖面中部向四周不断降低。高温区域的位置和面积随发酵时间呈动态变化规律,升温期温度在发酵罐中上部积聚,高温分布区类似椭圆形,高温期椭圆面积逐渐向堆体中部扩散和扩大,随后高温面积不断递减并向中下部迁移,降温期堆体各区域温度趋于一致。由等值线的疏密程度可知,温度的空间变异性大体呈现出高温期>第2次高温期>升温期>降温期的规律。结合试验各阶段的温度变化并考虑节能降耗,得出发酵升温期和高温期分别以0.05,0.067m3/(d·m3)进行通风,二次高温期与降温期以0.033 m3/(d·m3)进行通风条件最佳。     

大规模多状态计算系统的容错性能分析

针对异构计算节点组成的大规模多状态计算系统的容错性能分析问题，提出了一种计算系统容错性能的评估方法。该方法采用自定义的两级容错性能形式化描述框架进行系统描述，通过构造多值决策图（Multi-value Decision Diagram，MDD）模型对系统进行容错性能建模，并基于构造的模型高效地计算出部件故障的条件下计算系统在特定性能水平上运行的概率，减少了计算的冗余性。实验结果表明，该方法在模型的大小和构建时间上均优于传统方法。该方法的提出将对系统操作员或程序设计者具有重要意义，使其确保系统适合预期应用。