Semiotic Aggregation in Deep Learning

Convolutional neural networks utilize a hierarchy of neural network layers. The statistical aspects of information concentration in successive layers can bring an insight into the feature abstraction process. We analyze the saliency maps of these layers from the perspective of semiotics, also known as the study of signs and sign-using behavior. In computational semiotics, this aggregation operation (known as superization) is accompanied by a decrease of spatial entropy: signs are aggregated into supersign. Using spatial entropy, we compute the information content of the saliency maps and study the superization processes which take place between successive layers of the network. In our experiments, we visualize the superization process and show how the obtained knowledge can be used to explain the neural decision model. In addition, we attempt to optimize the architecture of the neural model employing a semiotic greedy technique. To the extent of our knowledge, this is the first application of computational semiotics in the analysis and interpretation of deep neural networks.     

Neural network representations of quantum many-body states

Machine learning is currently the most active interdisciplinary field having numerous applications;additionally,machine-learning techniques are used to research quantum many-body problems.In this study,we first propose neural network quantum states(NNQSs)with general input observables and explore a few related properties,such as the tensor product and local unitary operation.Second,we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS.Finally,to quantify the approximation degree of a given pure state,we define the best approximation degree using normalized NNQSs.Furthermore,we observe that some 7V-qubit states can be represented by a normalized NNQS,such as separable pure states,Bell states and GHZ states.     

卷积神经网络用于近红外光谱古筝面板木材分级

目前,我国乐器制作行业在古筝面板用木材等级的筛选上主要依赖于技师主观评判,但此法缺少科学理论的依据,效率低,客观性及出材率的提高等方面受到限制,无法满足乐器市场的大量需求。实现古筝面板用木材快速、智能化的分级工作是一个急需解决的课题。近红外光谱非常适用于测量含氢的有机物质。古筝面板木材主要化学成分的化学键均由含氢基团组成,不同等级板材的化学成分存在差异,这些差异反映在近红外光谱中,为判断木材等级提供了可能。同时卷积神经网络对非线性数据具有较强的特征提取能力,所以提出一种应用卷积神经网络模型对光谱数据进行分析的方法,进而判别木材的等级。应用了Savitzky Golay一阶、二阶微分两种预处理方法和核主成分分析、连续投影算法两种数据压缩方法,通过所设计的卷积神经网络模型以样本识别准确率和模型构建过程中的损失值作为判定指标选出最佳预处理和数据压缩方法。为了提高模型提取分析光谱数据的能力和避免过拟合现象,应用了多通道卷积核、批量归一化和early stopping策略,将通过两层卷积层提取的特征信息送入全连接层,从而充分提取剩余信息,通过Softmax函数获得板材的最终预测等级,从而确定了最终模型。最终Savitzky Golay一阶微分和核主成分分析为最佳数据处理方法,同时得出用于区分不同等级的古筝面板用木材的主要关键谱带,分别为1 163~1 243, 1 346~1 375和1 525~1 584 nm。将该模型应用于测试集样本,古筝面板用木材的等级识别准确率为95.5%。实验结果表明所提出的方法可以高效地处理光谱数据,有效识别区分不同等级的古筝面板用木材的关键特征,从而为广阔的乐器市场提供一定的技术支持。     

A post curing strategy toward the feasible covalent adaptable networks in polyacrylate latex films

The implementation of covalent adaptable networks (CANs) in general resin system is becoming attractive. In this work, we propose a simple post-curing strategy based on the core-shell structured acrylate latex for the achievement on both the improved general performance and the CANs characteristics in latex films. The building to the CANs was relied on the introduction of 4,4′-diaminophenyl disulfide as the curing agent, which cured the acetoacetoxy decorated shell polymer through the ketoamine reaction. The metathesis reaction of aromatic disulfides in the crosslinking segments enabled the thermally induced dynamic behavior of the network as revealed in the stress relaxation tests by comparison with other diamine crosslinking agents without the incorporation of disulfide. The synergism of the dynamic crosslinking of the shell polymer and static crosslinking in the core polymer contributed to the improved mechanical strength (15 MPa, strain% = 250%) and the suppressed water adsorption (~1% in 24 h of soaking) of the latex film, which exhibited above 90% of recovery in both strength and strain from a cut-off film damage within 1 h at 80°C. Moreover, the cured latex film could be recycled, and 75% of the mechanical performance was regained after three fragmentation-hot-pressing cycles. These, in addition with the feasible and environmental friendly characteristics, suggest a sustainable paradigm toward the smart thermosetting latex polymers.     

Synthesis of Heat-resistant Polymers by Thiol–Ene Reaction of N-Allylmaleimide Copolymers Using Glycoluril Crosslinkers with Rigid Molecular Structures

We carried out the thermal curing of the copolymers of N-allylmaleimide (AMI) and 2-ethylhexyl acrylate (2EHA) using 1,3,4,6-tetra(2-mercaproethyl)glycoluril ( G1 ), 1,3,4,6-tetra(3-mercaptopropyl)glycoluril ( G2 ), 1,3,4,6-tetraallylglycoluril ( G3 ), triallylisocyanurate (TAIC), and pentaerythritol tetrakis(3-mercaptobutyrate) (PEMB) as the crosslinkers. Based on the results for the analysis of thiol–ene reactions monitored by IR spectroscopy, it was confirmed that the curing rate significantly depended on the combination of the used crosslinkers. The insoluble fraction after curing was more than 90% for the systems using the glycoluril crosslinkers, while the conversion of the allyl groups was suppressed due to the rigid structure of these crosslinkers. The heat resistance and the mechanical properties of the crosslinked polymers were investigated by thermogravimetric analysis, differential scanning calorimetry, dynamic mechanical analysis, and mechanical tensile tests. For the products cured using the glycoluril crosslinkers, the glass transition temperature (T_g) and the maximum temperature of thermal decomposition (T_max) were 54–59 °C and 395–409 °C, respectively, being higher than those for the cured product prepared with PEMB and TAIC as the conventional crosslinkers. The elasticity (75–139 MPa), the maximum strength (3.0–4.1 MPa), and the adhesion strength (6.7–10.7 MPa) for the polymers cured with the glycoluril crosslinkers, determined by the mechanical tensile and single lap-shear adhesion tests, were higher than those for cured materials produced with PEMB. Thus, the thermal and mechanical properties of the maleimide copolymers were efficiently enhanced by crosslinking using the rigid glycoluril compounds. © 2020 Wiley Periodicals, Inc. J. Polym. Sci. 2020 , 58, 923–931     

Highly stretchable,self-adhesive,and self-healable double network hydrogel based on alginate/polyacrylamide with tunable mechanical properties

A number of synthetic hydrogels suffer from low mechanical strength. Despite of the recent advances in the fabrication of tough hydrogels, it is still a great challenge to simultaneously construct high stretchability, and self-adhesive and self-healing capability in a hydrogel. Herein, a new type of double network hydrogel was prepared based on irreversible cross-linking of polyacrylamide chains and Schiff-base reversible cross-linking between glycidyl methacrylate-grafted ethylenediamine and oxidized sodium alginate (OSA). The combination of both cross-linkings and their synergistic effect provided a novel hydrogel with high strength, stretchable, rapid self-healing, and self-adhesiveness to different material. Besides, the hydrogels with diverse OSA content could maintain their original shapes after loading–unloading tensile test. The resulting hydrogel has a great potential in various fields for supporting and load-bearing substance.     

Design and build an elastic crosslinked network to strengthen and toughen soybean-meal based bioadhesive using organo-sepiolite and greener crosslinker triglycidylamine

Poor water resistance and curing layer brittleness are significant challenges of greener soybean meal (SM) adhesive promotion and application. In this study an elastic network was built to crosslink the SM adhesive and improve the cured layer toughness of the resultant adhesive. Long-chain organo-sepiolite (OSEP) was first prepared by compound modification using KH-560 and KH-602 silane coupling agents. Triglycidylamine (TGA), a greener crosslinking agent with a large number of effective epoxy functional groups, was then synthesized. SM-based adhesives were fabricated using SM, OSEP and TGA. Results indicated that an elastic network was fabricated by the cross-linked reaction of SM, OSEP, and TGA. The elastic network effectively improved the toughness of the resultant adhesive. The wet shear strength of the plywood (PlyW) bonded by SM with 1% OSEP (SM/TGA/OSEP-1 adhesive) increased by 60% to 1.25 MPa relative to that of the SM/TGA adhesive. This study provided a greener, simple and cheap method improving the toughness and wet shear strength of SM-based adhesive, which contributes to the industrialized application of the product.     

大脑血液动力学现象中的能量编码

神经信息的编码与解码是神经科学中的核心研究内容,同时又极具挑战性.传统的编码理论都具有各自的局限性,很难从脑的全局运行方式上给出有效的理论.而由于能量是一个标量具有可叠加性,因此能量编码理论可以从神经元活动的能量特征出发来研究脑功能的全局神经编码问题,取得了一系列的研究成果.本研究以王-张神经元能量计算模型为基础,构建了一个多层次结构的神经网络,通过计算机数值模拟得到了神经网络的能量消耗和血液中葡萄糖供能的变化情况.计算结果显示,和网络的神经活动达到峰值的时间相比,血液中葡萄糖的供能达到峰值的时间延迟了约5.6s.从定量的角度再现了功能性核磁共振(fMRI)中的血液动力学现象:大脑某个脑区的神经元集群被激活以后经过5~7 s的延迟,脑血流的变化才会大幅增加.模拟结果表明先前发表的由王-张神经元模型所揭示的负能量机制在控制大脑的血液动力学现象中起着核心的作用,预测了刺激条件下大脑的能量代谢与血流之间变化的本质是由神经元在发放动作电位过程中正、负能量之间的非平衡、不匹配性质所决定的.本文的研究结果为今后进一步探究血液动力学现象的生理学机制提供了新的研究方向,在神经网络的建模与计算方面给出了一个新的视角和研究方法.