Intra-operative recognition of surgical phases holds significant potential
for enhancing real-time contextual awareness in the operating room. However, we
argue that online recognition, while beneficial, primarily lends itself to
post-operative video analysis due to its limited direct impact on the actual
surgical decisions and actions during ongoing procedures. In contrast, we
contend that the prediction and anticipation of surgical phases are inherently
more valuable for intra-operative assistance, as they can meaningfully
influence a surgeon's immediate and long-term planning by providing foresight
into future steps. To address this gap, we propose a dual approach that
simultaneously recognises the current surgical phase and predicts upcoming
ones, thus offering comprehensive intra-operative assistance and guidance on
the expected remaining workflow. Our novel method, Surgical Phase Recognition
and Anticipation (SuPRA), leverages past and current information for accurate
intra-operative phase recognition while using future segments for phase
prediction. This unified approach challenges conventional frameworks that treat
these objectives separately. We have validated SuPRA on two reputed datasets,
Cholec80 and AutoLaparo21, where it demonstrated state-of-the-art performance
with recognition accuracies of 91.8% and 79.3%, respectively. Additionally, we
introduce and evaluate our model using new segment-level evaluation metrics,
namely Edit and F1 Overlap scores, for a more temporal assessment of segment
classification. In conclusion, SuPRA presents a new multi-task approach that
paves the way for improved intra-operative assistance through surgical phase
recognition and prediction of future events.

SuPRA 是一种新的多任务方法，通过手术阶段的识别和未来事件的预测为改进术中辅助提供了途径。

SuPRA: 术中手术阶段识别与预测

SuPRA: Surgical Phase Recognition and Anticipation for Intra-Operative  Planning

Machine learning has been applied to the problem of X-ray diffraction phase
prediction with promising results. In this paper, we describe a method for
using machine learning to predict crystal structure phases from X-ray
diffraction data of transition metals and their oxides. We evaluate the
performance of our method and compare the variety of its settings. Our results
demonstrate that the proposed machine learning framework achieves competitive
performance. This demonstrates the potential for machine learning to
significantly impact the field of X-ray diffraction and crystal structure
determination. Open-source implementation:
this https URL

本文介绍了一种使用机器学习来预测过渡金属及其氧化物 X 射线衍射数据晶体结构相位的方法，并评估了其性能和比较了其不同设置的多样性，结果表明该机器学习框架具有竞争力的性能，这证明机器学习对 X 射线衍射和晶体结构确定领域具有重要意义。

机器学习辅助关闭式 X 射线衍射相变金属识别

Machine learning-assisted close-set X-ray diffraction phase  identification of transition metals

Time-frequency (T-F) domain masking is a mainstream approach for
single-channel speech enhancement. Recently, focuses have been put to phase
prediction in addition to amplitude prediction. In this paper, we propose a
phase-and-harmonics-aware deep neural network (DNN), named PHASEN, for this
task. Unlike previous methods that directly use a complex ideal ratio mask to
supervise the DNN learning, we design a two-stream network, where amplitude
stream and phase stream are dedicated to amplitude and phase prediction. We
discover that the two streams should communicate with each other, and this is
crucial to phase prediction. In addition, we propose frequency transformation
blocks to catch long-range correlations along the frequency axis. The
visualization shows that the learned transformation matrix spontaneously
captures the harmonic correlation, which has been proven to be helpful for T-F
spectrogram reconstruction. With these two innovations, PHASEN acquires the
ability to handle detailed phase patterns and to utilize harmonic patterns,
getting 1.76dB SDR improvement on AVSpeech + AudioSet dataset. It also achieves
significant gains over Google's network on this dataset. On Voice Bank + DEMAND
dataset, PHASEN outperforms previous methods by a large margin on four metrics.

本文介绍了一种基于深度神经网络的相位和谐感知模型（PHASEN），用于单通道语音增强，其中使用两种不同的流进行幅度和相位预测，并设计了频率转换模块来捕捉沿频率轴的长程相关性，在 AVSpeech + AudioSet 和 Voice Bank + DEMAND 数据集上分别获得 1.76dB 和大幅优于以前方法的表现。