基于合一的链接文法

以词汇主义形式语法为基础,建立了链接文法与合一理论相结合的句法分析新方法.在封闭测试中,基于合一的链接文法句法分析精确率和召回率相比传统链接文法分别提高了9.6%和14.1%.实验表明方法具有一定独创性和高效性.     

Activatable NIR-II Lanthanides-Polymetallic Oxomolybdate Hybrid Nanosensors for Monitoring Chemotherapy Induced Enteritis

Chemotherapy-induced enteritis is one of the side effects associated with cancer therapy, which significantly affects the treatment effect, but there is no effective clinical detection method that can early diagnose its occurrence and progression. Here, a series of second near-infrared window (NIR-II) hybrid nanosensors are designed that consisted of lanthanide nanoparticles and β-Mo2C-derived polymetallic oxomolybdate nanoclusters (Ln@POM). Based on the high sensitivity of POM to reactive oxygen species (ROS) closely related to chemotherapy-induced enteritis, the NIR-II luminescence intensity and lifetime of Ln@POM (Ln: Yb³⁺, Nd³⁺, Ho³⁺, Tm³⁺, Er³⁺) show excellent responsiveness to H₂O₂ and HClO with the detection limit down to 0.15 and 0.14 µm , respectively. Utilizing Nd@POM as a ROS-activated NIR-II nanosensor, the chemotherapeutic enteritis is successfully detected within 7 h after induction of chemotherapy drugs, which is significantly earlier than the gold standard method (immunohistochemistry, 24 h). These results demonstrate that the designed hybrid nanosensors are promising optical tools for the early diagnosis of ROS-related diseases.     

Light-Responsive Programmable Shape-Memory Soft Actuator Based on Liquid Crystalline Polymer/Polyurethane Network

Liquid crystalline polymers (LCPs), especially liquid crystalline elastomers (LCEs) can generate ultrahigh shape change amplitude but has lower mechanical strength. Although some attempts have been tried to improve the mechanical performance of LCE, there are still limitations including complicated fabrication and high actuation temperature. Here, a versatile method is reported to fabricate light-driven actuator by covalently cross-linking polyurethane (PU) into LCP networks (PULCN). This new scheme is distinct from the previous interpenetrating network strategy, the hydrogen bonds and covalent bonds are used in this study to improve the miscibility of non-liquid-crystalline PU and LCP materials and enhance the stability of the composite system. This material not only possesses the shape memory properties of PU but shows shape-changing behavior of LCPs. With a shrinkage ratio of 20% at the phase transition temperature, the prepared materials reached a maximum mechanical strength of 20 MPa, higher than conventional LCP. Meanwhile, the resulting film shows diverse and programmable initial shapes by constructing crosslinking density gradient across the thickness of the film. By integration of PULCN with near-infrared light-responsive polydopamine, local and sequential light control is achieved. This study may provide a new route for the fabrication of programmable and mechanically robust light-driven soft actuator.     

Proximity-Induced Fully Ferromagnetic Order with Eightfold Magnetic Anisotropy in Heavy Transition Metal Oxide CaRuO3

Ferromagnetic materials with a strong spin-orbit coupling (SOC) have attracted much attention in recent years because of their exotic properties and potential applications in energy-efficient spintronics. However, such materials are scarce in nature. Here, a proximity-induced paramagnetic to ferromagnetic transition for the heavy transition metal oxide CaRuO₃ in (001)-(LaMnO₃/CaRuO₃) superlattices is reported. Anomalous Hall effect is observed in the temperature range up to 180 K. Maximal anomalous Hall conductivity and anomalous Hall angle are as large as ∼15 Ω⁻¹ cm⁻¹ and ∼0.93%, respectively, by one to two orders of magnitude larger than those of the typical 3d ferromagnetic oxides such as La_0.67Sr_0.33MnO₃. Density functional theory calculations indicate the existence of avoid band crossings in the electronic band structure of the ferromagnetic CRO layer, which enhances Berry curvature thus strong anomalous Hall effects. Further evidences from polarized neutron reflectometry show that the CaRuO₃ layers are in a fully ferromagnetic state (∼0.8 μ_B/Ru), in sharp contrast to the proximity-induced canted antiferromagnetic state in 5d oxides SrIrO₃ and CaIrO₃ (∼0.1 μ_B/Ir). More than that, the magnetic anisotropy of the (001)-(LaMnO₃/CaRuO₃) superlattices is eightfold symmetric, showing potential applications in the technology of multistate data storage.     

A polarization modulated directional backlight autostereoscopic display

A polarization modulated directional backlight autostereoscopic display is proposed and demonstrated. The system consists of the orthogonally polarized backlight, the Fresnel lens array, a light shaping diffuser film, and a liquid crystal display (LCD) with a microphase retardation film. The autostereoscopic image pair carried by the directional light with different polarization directions is simultaneously projected to different spatial directions. The simulation and experimental results show that the directional projection of parallax images is realized for a high-quality autostereoscopic display with large viewing angle and continuous viewing volume, hence making it suitable for practical applications.     

Focusing enhanced broadband metalens via height optimization

Metalenses are two-dimensional planar metamaterial lenses, which have the advantages of high efficiency and easy integration. Based on the method of spatial multiplexing, a metalens with a wide working waveband is designed by arranging TiO2 nanopillars under the resonance phase regulation. In addition, choosing an assistant metalens with optimized heights is effective to enhance metalens’s focusing, which is also illustrated in this paper. The metalens, designed with numerical aperture (NA) of 0.72 and center working wavelength of 600 nm, achieves the working waveband of 550—660 nm, the focus point’s size of below 420 nm, and the focusing efficiency of more than 30%.     

A survey on deploying mobile deep learning applications: A systemic and technical perspective

With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas.     

Fast medical concept normalization for biomedical literature based on stack and index optimized self-attention

Neural Computing and Applications - Medical concept normalization aims to construct a semantic mapping between mentions and concepts and to uniformly represent mentions that belong to the same...     

Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy

Computational Economics - Arbitrage opportunity exploration is important to ensure the profitability of statistical arbitrage. Prior studies that concentrate on cointegration model and other...