Electrohydrodynamic Processing of p-Type Transparent Conducting Oxides

Electrohydrodynamic processing is capable of synthesizing various materials in the form of porous/dense thin films, nanofibers, nanorods, nanobelts, and ribbons, which is highly favorable for functional oxides. The tailored microstructures and properties derived from electrohydrodynamic forming also give rise to new research interests on some classical oxides, such as transparent conducting oxides (TCOs). Here a case of feasible electrospray synthesis of classical ZnO is demonstrated with tailored p-type conductivity. Another p-type TCO, CuAlO2, was prepared by both electrospray and electrospinning methods and the processing-derived electrical and optical properties are demonstrated. The last part of the paper discusses some emerging applications especially for CuAlO2 as potential nanobuilding blocks enabled by electrohydrodynamic processing

Human cytomegalovirus glycoprotein complex gH/gL/gO uses PDGFR-α as a key for entry

Herpesvirus gH/gL envelope glycoprotein complexes are key players in virus entry as ligands for host cell receptors and by promoting fusion of viral envelopes with cellular membranes. Human cytomegalovirus ( HCMV) has two alternative gH/gL complexes, gH/gL/gO and gH/gL/UL128,130,131A which both shape the HCMV tropism. By studying binding of HCMV particles to fibroblasts, we could for the first time show that virion gH/gL/gO binds to platelet-derived growth factor-alpha (PDGFR-alpha) on the surface of fibroblasts and that gH/gL/gO either directly or indirectly recruits gB to this complex. PDGFR-alpha functions as an entry receptor for HCMV expressing gH/gL/gO, but not for HCMV mutants lacking the gH/gL/gO complex. PDGFR-alpha-dependent entry is not dependent on activation of PDGFR-alpha. We could also show that the gH/gL/gO-PDGFR-alpha interaction starts the predominant entry pathway for infection of fibroblasts with free virus. Cell-associated virus spread is either driven by gH/gL/gO interacting with PDGFR-alpha or by the gH/gL/UL128,130,131A complex. PDGFR-alpha-positive cells may thus be preferred first target cells for infections with free virus which might have implications for the design of future HCMV vaccines or anti-HCMV drugs

Attention as Activation

Activation functions and attention mechanisms are typically treated as having different purposes and have evolved differently. However, both concepts can be formulated as a non-linear gating function. Inspired by their similarity, we propose a novel type of activation units called attentional activation (ATAC) units as a unification of activation functions and attention mechanisms. In particular, we propose a local channel attention module for the simultaneous non-linear activation and element-wise feature refinement, which locally aggregates point-wise cross-channel feature contexts. By replacing the well-known rectified linear units by such ATAC units in convolutional networks, we can construct fully attentional networks that perform significantly better with a modest number of additional parameters. We conducted detailed ablation studies on the ATAC units using several host networks with varying network depths to empirically verify the effectiveness and efficiency of the units. Furthermore, we compared the performance of the ATAC units against existing activation functions as well as other attention mechanisms on the CIFAR-10, CIFAR-100, and ImageNet datasets. Our experimental results show that networks constructed with the proposed ATAC units generally yield performance gains over their competitors given a comparable number of parameters

Attentional Feature Fusion

Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures. It is often implemented via simple operations, such as summation or concatenation, but this might not be the best choice. In this work, we propose a uniform and general scheme, namely attentional feature fusion, which is applicable for most common scenarios, including feature fusion induced by short and long skip connections as well as within Inception layers. To better fuse features of inconsistent semantics and scales, we propose a multi-scale channel attention module, which addresses issues that arise when fusing features given at different scales. We also demonstrate that the initial integration of feature maps can become a bottleneck and that this issue can be alleviated by adding another level of attention, which we refer to as iterative attentional feature fusion. With fewer layers or parameters, our models outperform state-of-the-art networks on both CIFAR-100 and ImageNet datasets, which suggests that more sophisticated attention mechanisms for feature fusion hold great potential to consistently yield better results compared to their direct counterparts. Our codes and trained models are available online.Comment: Accepted by WACV 202

Luminescence of delafossite-type CuAlO2 fibers with Eu substitution for Al cations

CuAlO2 has been examined as a potential luminescent material by substituting Eu for Al cations in the delafossite structure. CuAlO2:Eu3+ nanofibers have been prepared via electrospinning for the ease of mitigating synthesis requirements and for future optoelectronics and emerging applications. Single-phase CuAlO2 fibers could be obtained at a temperature of 1100 °C in air. The Eu was successfully doped in the delafossite structure and two strong emission bands at ~405 and 610 nm were observed in the photoluminescence spectra. These bands are due to the intrinsic near-band-edge transition of CuAlO2 and the f-f transition of the Eu3+ activator, respectively. Further electrical characterization indicated that these fibers exhibit semiconducting behavior and the introduction of Eu could act as band-edge modifiers, thus changing the thermal activation energies. In light of this study, CuAlO2:Eu3+ fibers with both strong photoluminescence and p-type conductivity could be produced by tailoring the rare earth doping concentrations

Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration

Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI, i.e., predicting the judgment of the case in terms of case fact description. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Thus, it is worthwhile to explore the utilization of precedents in the LJP. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task. These can be broken down into two categories: large language models (LLMs) and domain-specific models. LLMs are capable of interpreting and generating complex natural language, while domain models are efficient in learning task-specific information. In this paper, we propose the precedent-enhanced LJP framework (PLJP), a system that leverages the strength of both LLM and domain models in the context of precedents. Specifically, the domain models are designed to provide candidate labels and find the proper precedents efficiently, and the large models will make the final prediction with an in-context precedents comprehension. Experiments on the real-world dataset demonstrate the effectiveness of our PLJP. Moreover, our work shows a promising direction for LLM and domain-model collaboration that can be generalized to other vertical domains