Automatic ribs segmentation and numeration can increase computed tomography
assessment speed and reduce radiologists mistakes. We introduce a model for
multilabel ribs segmentation with hierarchical loss function, which enable to
improve multilabel segmentation quality. Also we propose postprocessing
technique to further increase labeling quality. Our model achieved new
state-of-the-art 98.2% label accuracy on public RibSeg v2 dataset, surpassing
previous result by 6.7%.

通过引入具有分层损失函数的多标签肋骨分割模型，我们提出自动肋骨分割和编号技术，以提高计算机断层扫描评估速度并减少放射科医生的错误。我们的模型在公开数据集 RibSeg v2 上取得了 98.2% 的准确性，较之前的结果提高了 6.7%。

分层损失和几何面罩优化的多标签肋骨分割

Hierarchical Loss And Geometric Mask Refinement For Multilabel Ribs  Segmentation

We present a novel approach to any-to-one (A2O) voice conversion (VC) in a
sequence-to-sequence (seq2seq) framework. A2O VC aims to convert any speaker,
including those unseen during training, to a fixed target speaker. We utilize
vq-wav2vec (VQW2V), a discretized self-supervised speech representation that
was learned from massive unlabeled data, which is assumed to be
speaker-independent and well corresponds to underlying linguistic contents.
Given a training dataset of the target speaker, we extract VQW2V and acoustic
features to estimate a seq2seq mapping function from the former to the latter.
With the help of a pretraining method and a newly designed postprocessing
technique, our model can be generalized to only 5 min of data, even
outperforming the same model trained with parallel data.

利用自监督 VQ-Wav2vec 表示的任意人说话到固定目标说话的任意对一语音转换问题的序列到序列框架，通过预处理和后处理方法，我们的模型可以具有更好的泛化能力，即使只有 5 分钟的数据。

使用自监督离散语音表示进行任意一对多序列到序列语音转换

Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised  Discrete Speech Representations

Real-valued word representations have transformed NLP applications; popular
examples are word2vec and GloVe, recognized for their ability to capture
linguistic regularities. In this paper, we demonstrate a {\em very simple}, and
yet counter-intuitive, postprocessing technique -- eliminate the common mean
vector and a few top dominating directions from the word vectors -- that
renders off-the-shelf representations {\em even stronger}. The postprocessing
is empirically validated on a variety of lexical-level intrinsic tasks (word
similarity, concept categorization, word analogy) and sentence-level tasks
(semantic textural similarity and { text classification}) on multiple datasets
and with a variety of representation methods and hyperparameter choices in
multiple languages; in each case, the processed representations are
consistently better than the original ones.

本文介绍了一种消除平均向量和少量主导方向的词向量后处理技术，通过在多种语言和数据集上进行的实验验证，发现处理后的词表示比原始表示更强，适用于词汇水平内在任务和句子级任务。