雷达间的电磁兼容性判决

通过对雷达信号(或干扰)处理过程的分析,对发现概率和虚警概率的定义作了合理的扩展,使二者可以直接用于雷达间的电磁兼容判定,并在此基础上建立了雷达间电磁兼容判决模型.这种基于经典雷达信号检测理论的模型能客观、真实地反映出雷达间的干扰情况,并且可以应用在雷达间电磁兼容性预测模型中.     

因素空间理论与知识表示的数学框架(XⅢ)──描述架中概念的结构(2)

本文是［１，１２］的继续，研究描述架中概念的结构；本文讨论后半部分，内容涉及概念内涵与外延的转换，清晰关系的内投影与内变换，概念的结构＊以及有关问题的注记。     

Deep ensemble-based hard sample mining for food recognition

Deep neural networks represent a compelling technique to tackle complex real-world problems, but are over-parameterized and often suffer from over- or under-confident estimates. Deep ensembles have shown better parameter estimations and often provide reliable uncertainty estimates that contribute to the robustness of the results. In this work, we propose a new metric to identify samples that are hard to classify. Our metric is defined as coincidence score for deep ensembles which measures the agreement of its individual models. The main hypothesis we rely on is that deep learning algorithms learn the low-loss samples better compared to large-loss samples. In order to compensate for this, we use controlled over-sampling on the identified ”hard” samples using proper data augmentation schemes to enable the models to learn those samples better. We validate the proposed metric using two public food datasets on different backbone architectures and show the improvements compared to the conventional deep neural network training using different performance metrics.     

Learning knowledge representation with meta knowledge distillation for single image super-resolution

Although the deep CNN-based super-resolution methods have achieved outstanding performance, their memory cost and computational complexity severely limit their practical employment. Knowledge distillation (KD), which can efficiently transfer knowledge from a cumbersome network (teacher) to a compact network (student), has demonstrated its advantages in some computer vision applications. The representation of knowledge is vital for knowledge transferring and student learning, which is generally defined in hand-crafted manners or uses the intermediate features directly. In this paper, we propose a model-agnostic meta knowledge distillation method under the teacher–student architecture for the single image super-resolution task. It provides a more flexible and accurate way to help teachers transmit knowledge in accordance with the abilities of students via knowledge representation networks (KRNets) with learnable parameters. Specifically, the texture-aware dynamic kernels are generated from local information to decompose the distillation problem into texture-wise supervision for further promoting the recovery quality of high-frequency details. In addition, the KRNets are optimized in a meta-learning manner to ensure the knowledge transferring and the student learning are beneficial to improving the reconstructed quality of the student. Experiments conducted on various single image super-resolution datasets demonstrate that our proposed method outperforms existing defined knowledge representation-related distillation methods and can help super-resolution algorithms achieve better reconstruction quality without introducing any extra inference complexity.     

Protein family classification and functional annotation

With the accelerated accumulation of genomic sequence data, there is a pressing need to develop computational methods and advanced bioinformatics infrastructure for reliable and large-scale protein annotation and biological knowledge discovery. The Protein Information Resource (PIR) provides an integrated public resource of protein informatics to support genomic and proteomic research. PIR produces the Protein Sequence Database of functionally annotated protein sequences. The annotation problems are addressed by a classification-driven and rule-based method with evidence attribution, coupled with an integrated knowledge base system being developed. The approach allows sensitive identification, consistent and rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified and computationally predicted features. The knowledge base consists of two new databases, sequence analysis tools, and graphical interfaces. PIR-NREF, a non-redundant reference database, provides a timely and comprehensive collection of all protein sequences, totaling more than 1,000,000 entries. iProClass, an integrated database of protein family, function, and structure information, provides extensive value-added features for about 830,000 proteins with rich links to over 50 molecular databases. This paper describes our approach to protein functional annotation with case studies and examines common identification errors. It also illustrates that data integration in PIR supports exploration of protein relationships and may reveal protein functional associations beyond sequence homology.     

粗糙集理论及进展

粗糙集理论是一种新型的处理模糊和不确定知识的数学工具。本文给出了粗糙集理论的特点，主要阐述了几种粗糙集理论的扩展模型，然后讨论了近来粗糙集理论与其他方法的结合，并进一步讨论了粗糙集理论研究的前景。     

Misinformed About the Affordable Care Act? Leveraging Certainty to Assess the Prevalence of Misperceptions

According to some recent research, Americans hold a great deal of misinformation about important political issues. However, such investigations treat incorrect answers to quiz questions measuring knowledge as evidence of misinformation. This study instead defines misperceptions as incorrect answers that respondents are confident are correct. Two surveys of representative samples of American adults on the Affordable Care Act reveal that most people were uncertain about the provisions in the law. Confidently held incorrect beliefs were far less common than incorrect answers. Misperceptions were most prevalent on aspects of the law on which elites prominently and persistently made incorrect claims. Furthermore, although Americans appear to have learned about the law between 2010 and 2012, misperceptions on many provisions of the law persisted.     

Decomposition and replacement: Spatial knowledge distillation for monocular depth estimation

Knowledge distillation has become a key technique for making smart and light-weight networks through model compression and transfer learning. Unlike previous methods that applied knowledge distillation to the classification task, we propose to exploit the decomposition-and-replacement based distillation scheme for depth estimation from a single RGB color image. To do this, Laplacian pyramid-based knowledge distillation is firstly presented in this paper. The key idea of the proposed method is to transfer the rich knowledge of the scene depth, which is well encoded through the teacher network, to the student network in a structured way by decomposing it into the global context and local details. This is fairly desirable for the student network to restore the depth layout more accurately with limited resources. Moreover, we also propose a new guidance concept for knowledge distillation, so-called ReplaceBlock, which replaces blocks randomly selected in the decoded feature of the student network with those of the teacher network. Our ReplaceBlock gives a smoothing effect in learning the feature distribution of the teacher network by considering the spatial contiguity in the feature space. This process is also helpful to clearly restore the depth layout without the significant computational cost. Based on various experimental results on benchmark datasets, the effectiveness of our distillation scheme for monocular depth estimation is demonstrated in details. The code and model are publicly available at : https://github.com/tjqansthd/Lap_Rep_KD_Depth.     

高分子物理课程知识体系的构建革新

高分子物理是高分子材料相关专业的专业基础课,内容丰富,章节间关系复杂,课程主线往往不容易把握。本文从运动对高聚物性质(性能)的决定性作用角度,提出了构建高分子物理课程知识体系的观点:在高分子结构部分,充实原子、键等部分作用的论述,完善高分子结构划分的层次;明确高分子不同结构层次都具有运动与变化能力的认知,重点突出不同高分子结构运动、变化的特征和规律;根据根据每一个结构层次运动特征,综合理解结构、结构的变化对高分子性能的影响。