Systems that can automatically define unfamiliar terms hold the promise of
improving the accessibility of scientific texts, especially for readers who may
lack prerequisite background knowledge. However, current systems assume a
single "best" description per concept, which fails to account for the many
potentially useful ways a concept can be described. We present ACCoRD, an
end-to-end system tackling the novel task of generating sets of descriptions of
scientific concepts. Our system takes advantage of the myriad ways a concept is
mentioned across the scientific literature to produce distinct, diverse
descriptions of target scientific concepts in terms of different reference
concepts. To support research on the task, we release an expert-annotated
resource, the ACCoRD corpus, which includes 1,275 labeled contexts and 1,787
hand-authored concept descriptions. We conduct a user study demonstrating that
(1) users prefer descriptions produced by our end-to-end system, and (2) users
prefer multiple descriptions to a single "best" description.

提出 ACCoRD 系统来生成科学概念的多重描述，通过利用科学文献中提到概念的众多方式，以不同的参考概念为基础，生成对目标科学概念的不同、多样的描述。我们开发了一个标注工具 ACCoRD 语料库，包括 1,275 个标记上下文和 1,787 个手工编写的概念描述。通过用户研究表明 (1) 用户更喜欢我们的系统生成的描述，(2) 用户更喜欢多个描述而不是单一的 “最佳” 描述。

ACCoRD：科学概念多文档生成多样化描述方法

ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts

We propose deep learning based communication methods for adaptive-bandwidth
transmission of images over wireless channels. We consider the scenario in
which images are transmitted progressively in layers over time or frequency,
and such layers can be aggregated by receivers in order to increase the quality
of their reconstructions. We investigate two scenarios, one in which the layers
are sent sequentially, and incrementally contribute to the refinement of a
reconstruction, and another in which the layers are independent and can be
retrieved in any order. Those scenarios correspond to the well known problems
of \textit{successive refinement} and \textit{multiple descriptions},
respectively, in the context of joint source-channel coding (JSCC). We propose
DeepJSCC-$l$, an innovative solution that uses convolutional autoencoders, and
present three architectures with different complexity trade-offs. To the best
of our knowledge, this is the first practical multiple-description JSCC scheme
developed and tested for practical information sources and channels. Numerical
results show that DeepJSCC-$l$ can learn to transmit the source progressively
with negligible losses in the end-to-end performance compared with a single
transmission. Moreover, DeepJSCC-$l$ has comparable performance with state of
the art digital progressive transmission schemes in the challenging low
signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional
advantage of graceful degradation with channel SNR.

通过使用基于深度学习的通信方法，我们提出了一个用于适应带宽传输图像的方案，并考虑由接收器聚合以提高重建质量的图像逐层传输的情形。我们提出了 DeepJSCC-l，这是第一个为实际信息源和信道开发和测试的多描述 JSCC 方案，并具有与数字渐进传输方案相媲美的性能。

深度联合源通道编码的带宽灵活图像传输

Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding

In texture-plus-depth representation of a 3D scene, depth maps from different
camera viewpoints are typically lossily compressed via the classical transform
coding / coefficient quantization paradigm. In this paper we propose to reduce
distortion of the decoded depth maps due to quantization. The key observation
is that depth maps from different viewpoints constitute multiple descriptions
(MD) of the same 3D scene. Considering the MD jointly, we perform a POCS-like
iterative procedure to project a reconstructed signal from one depth map to the
other and back, so that the converged depth maps have higher precision than the
original quantized versions.

本文提出了一种基于多个描述符的迭代投影方法，关注在纹理加深度表示的 3D 场景中，压缩不失真的深度图，通过考虑多个视角的多引描述，可以使收敛的深度图比原始量化版本具有更高的精度。