Automatic music transcription (AMT) has achieved high accuracy for piano due
to the availability of large, high-quality datasets such as MAESTRO and MAPS,
but comparable datasets are not yet available for other instruments. In recent
work, however, it has been demonstrated that aligning scores to transcription
model activations can produce high quality AMT training data for instruments
other than piano. Focusing on the guitar, we refine this approach to training
on score data using a dataset of commercially available score-audio pairs. We
propose the use of a high-resolution piano transcription model to train a new
guitar transcription model. The resulting model obtains state-of-the-art
transcription results on GuitarSet in a zero-shot context, improving on
previously published methods.

使用高分辨率钢琴转录模型训练新的吉他转录模型，从而在零样本情况下在 GuitarSet 上获得最先进的转录结果，改进了之前发表的方法。

高分辨率吉他转录通过领域适应

High Resolution Guitar Transcription via Domain Adaptation

Neural operators have been explored as surrogate models for simulating
physical systems to overcome the limitations of traditional partial
differential equation (PDE) solvers. However, most existing operator learning
methods assume that the data originate from a single physical mechanism,
limiting their applicability and performance in more realistic scenarios. To
this end, we propose Physical Invariant Attention Neural Operator (PIANO) to
decipher and integrate the physical invariants (PI) for operator learning from
the PDE series with various physical mechanisms. PIANO employs self-supervised
learning to extract physical knowledge and attention mechanisms to integrate
them into dynamic convolutional layers. Compared to existing techniques, PIANO
can reduce the relative error by 13.6\%-82.2\% on PDE forecasting tasks across
varying coefficients, forces, or boundary conditions. Additionally, varied
downstream tasks reveal that the PI embeddings deciphered by PIANO align well
with the underlying invariants in the PDE systems, verifying the physical
significance of PIANO. The source code will be publicly available at:
this https URL

通过提取物理不变量和引入注意机制进行运算符学习的方法（PIANO）可在各种物理机制的偏微分方程系列中降低相对误差 13.6％-82.2％，并且其所获得的物理不变量与 PDE 系统中的底层不变量相吻合，从而验证了 PIANO 的物理意义。

解密与整合神经运算学习中的不变量和各种物理机制

Deciphering and integrating invariants for neural operator learning with  various physical mechanisms

Hand modeling is critical for immersive VR/AR, action understanding, or human
healthcare. Existing parametric models account only for hand shape, pose, or
texture, without modeling the anatomical attributes like bone, which is
essential for realistic hand biomechanics analysis. In this paper, we present
PIANO, the first parametric bone model of human hands from MRI data. Our PIANO
model is biologically correct, simple to animate, and differentiable, achieving
more anatomically precise modeling of the inner hand kinematic structure in a
data-driven manner than the traditional hand models based on the outer surface
only. Furthermore, our PIANO model can be applied in neural network layers to
enable training with a fine-grained semantic loss, which opens up the new task
of data-driven fine-grained hand bone anatomic and semantic understanding from
MRI or even RGB images. We make our model publicly available.

本文提出使用 MRI 数据构建人手骨骼模型 PIANO ，该模型具有生物学正确性，可用于训练深度神经网络以实现基于 MRI 数据的手骨骼解剖学和语义理解。