142 research outputs found
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
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