溶剂基碳捕获系统（CCSS）中的CO2捕获效率尺寸依赖性取决于气体溶剂界面（IA），使IA在CCS设计中的基础攻击最大化。虽然可以通过计算流体动力学（CFD）仿真估计与特定CCS设计的IA，但是使用CFD导出与许多CCS设计相关的IAS，这是昂贵的。幸运的是，以前的工作（如深液）（DF）（Kim等人，2019）表明，通过用神经网络（NN）代理商兑忠实地模仿CFD仿真过程的CFD模拟器来实现大型仿真加速度。这提高了对CFD模拟器的快速，准确更换的可能性，从而有效地逼近CCS设计优化所需的IAS。因此，在这里，我们建立在DF方法中，以开发成功应用于我们复杂的碳捕获CFD模拟的代理。我们优化的DF样式替代商会产生大型加速（4000X），同时获得位于训练配置范围内的未见CCS配置中的IA相对误差低至4％。这提示了NN代理人的CCS设计优化问题的承诺。尽管如此，DF对CCS设计具有固有的局限性（例如，培训模型的有限可转换性至新CCS填料）。我们与思想结束以解决这些挑战。

随着深度学习技术的快速发展和计算能力的提高，深度学习已广泛应用于高光谱图像（HSI）分类领域。通常，深度学习模型通常包含许多可训练参数，并且需要大量标记的样品来实现最佳性能。然而，关于HSI分类，由于手动标记的难度和耗时的性质，大量标记的样本通常难以获取。因此，许多研究工作侧重于建立一个少数标记样本的HSI分类的深层学习模型。在本文中，我们专注于这一主题，并对相关文献提供系统审查。具体而言，本文的贡献是双重的。首先，相关方法的研究进展根据学习范式分类，包括转移学习，积极学习和少量学习。其次，已经进行了许多具有各种最先进的方法的实验，总结了结果以揭示潜在的研究方向。更重要的是，虽然深度学习模型（通常需要足够的标记样本）和具有少量标记样本的HSI场景之间存在巨大差距，但是通过深度学习融合，可以很好地表征小样本集的问题方法和相关技术，如转移学习和轻量级模型。为了再现性，可以在HTTPS://github.com/shuguoj/hsi-classification中找到纸张中评估的方法的源代码.git。

Vision变形金刚（VITS）最近获得了爆炸性的人气，但巨额的计算成本仍然是一个严峻的问题。由于VIT的计算复杂性相对于输入序列长度是二次的，因此用于计算还原的主流范例是减少令牌的数量。现有设计包括结构化空间压缩，该压缩使用逐行缩小的金字塔来减少大型特征映射的计算，并且动态丢弃冗余令牌的非结构化令牌修剪。然而，现有令牌修剪的限制在两倍以下：1）由修剪引起的不完全空间结构与现代深窄变压器通常使用的结构化空间压缩不兼容; 2）通常需要耗时的预训练程序。为了解决局限性并扩大令牌修剪的适用场景，我们提出了Evo-Vit，一种自动激励的慢速令牌演化方法，用于视觉变压器。具体而言，我们通过利用原产于视觉变压器的简单有效的全球课程关注来进行非结构化的案例 - 明智的选择。然后，我们建议使用不同的计算路径更新所选的信息令牌和未表征性令牌，即慢速更新。由于快速更新机制保持空间结构和信息流，因此Evo-Vit可以从训练过程的开始，从训练过程的开始，加速平坦和深窄的结构的Vanilla变压器。实验结果表明，我们的方法显着降低了视觉变压器的计算成本，同时在图像分类上保持了可比性。

Diffusion Probabilistic Models (DPMs) have shown a powerful capacity of generating high-quality image samples. Recently, diffusion autoencoders (Diff-AE) have been proposed to explore DPMs for representation learning via autoencoding. Their key idea is to jointly train an encoder for discovering meaningful representations from images and a conditional DPM as the decoder for reconstructing images. Considering that training DPMs from scratch will take a long time and there have existed numerous pre-trained DPMs, we propose \textbf{P}re-trained \textbf{D}PM \textbf{A}uto\textbf{E}ncoding (\textbf{PDAE}), a general method to adapt existing pre-trained DPMs to the decoders for image reconstruction, with better training efficiency and performance than Diff-AE. Specifically, we find that the reason that pre-trained DPMs fail to reconstruct an image from its latent variables is due to the information loss of forward process, which causes a gap between their predicted posterior mean and the true one. From this perspective, the classifier-guided sampling method can be explained as computing an extra mean shift to fill the gap, reconstructing the lost class information in samples. These imply that the gap corresponds to the lost information of the image, and we can reconstruct the image by filling the gap. Drawing inspiration from this, we employ a trainable model to predict a mean shift according to encoded representation and train it to fill as much gap as possible, in this way, the encoder is forced to learn as much information as possible from images to help the filling. By reusing a part of network of pre-trained DPMs and redesigning the weighting scheme of diffusion loss, PDAE can learn meaningful representations from images efficiently. Extensive experiments demonstrate the effectiveness, efficiency and flexibility of PDAE.

Deep learning (DL) has become a driving force and has been widely adopted in many domains and applications with competitive performance. In practice, to solve the nontrivial and complicated tasks in real-world applications, DL is often not used standalone, but instead contributes as a piece of gadget of a larger complex AI system. Although there comes a fast increasing trend to study the quality issues of deep neural networks (DNNs) at the model level, few studies have been performed to investigate the quality of DNNs at both the unit level and the potential impacts on the system level. More importantly, it also lacks systematic investigation on how to perform the risk assessment for AI systems from unit level to system level. To bridge this gap, this paper initiates an early exploratory study of AI system risk assessment from both the data distribution and uncertainty angles to address these issues. We propose a general framework with an exploratory study for analyzing AI systems. After large-scale (700+ experimental configurations and 5000+ GPU hours) experiments and in-depth investigations, we reached a few key interesting findings that highlight the practical need and opportunities for more in-depth investigations into AI systems.

The deep learning community has witnessed an exponentially growing interest in self-supervised learning (SSL). However, it still remains unexplored how to build a framework for learning useful representations of raw music waveforms in a self-supervised manner. In this work, we design Music2Vec, a framework exploring different SSL algorithmic components and tricks for music audio recordings. Our model achieves comparable results to the state-of-the-art (SOTA) music SSL model Jukebox, despite being significantly smaller with less than 2% of parameters of the latter. The model will be released on Huggingface(Please refer to: https://huggingface.co/m-a-p/music2vec-v1)

We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, our main idea is to learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomposition, but unlike existing work, encourage specific frequencies to be stored in each grid via Fourier features encodings. We then apply a multi-layer perceptron with sine activations, taking these Fourier encoded features in at appropriate layers so that higher-frequency components are accumulated on top of lower-frequency components sequentially, which we sum up to form the final output. We demonstrate that our method outperforms the state of the art regarding model compactness and efficiency on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural radiance fields.

Recent years have witnessed an astonishing explosion in the evolution of mobile applications powered by AI technologies. The rapid growth of AI frameworks enables the transition of AI technologies to mobile devices, significantly prompting the adoption of AI apps (i.e., apps that integrate AI into their functions) among smartphone devices. In this paper, we conduct the most extensive empirical study on 56,682 published AI apps from three perspectives: dataset characteristics, development issues, and user feedback and privacy. To this end, we build an automated AI app identification tool, AI Discriminator, that detects eligible AI apps from 7,259,232 mobile apps. First, we carry out a dataset analysis, where we explore the AndroZoo large repository to identify AI apps and their core characteristics. Subsequently, we pinpoint key issues in AI app development (e.g., model protection). Finally, we focus on user reviews and user privacy protection. Our paper provides several notable findings. Some essential ones involve revealing the issue of insufficient model protection by presenting the lack of model encryption, and demonstrating the risk of user privacy data being leaked. We published our large-scale AI app datasets to inspire more future research.

The Modboat is a low-cost, underactuated, modular robot capable of surface swimming, docking to other modules, and undocking from them using only a single motor and two passive flippers. Undocking is achieved by causing intentional self-collision between the tails of neighboring modules in certain configurations; this becomes a challenge, however, when collective swimming as one connected component is desirable. Prior work has developed controllers that turn arbitrary configurations of docked Modboats into steerable vehicles, but they cannot counteract lateral forces and disturbances. In this work we present a centralized control strategy to create holonomic vehicles out of arbitrary configurations of docked Modboats using an iterative potential-field based search. We experimentally demonstrate that our controller performs well and can control surge and sway velocities and yaw angle simultaneously.

In this paper, we propose a novel multi-modal multi-task encoder-decoder pre-training framework (MMSpeech) for Mandarin automatic speech recognition (ASR), which employs both unlabeled speech and text data. The main difficulty in speech-text joint pre-training comes from the significant difference between speech and text modalities, especially for Mandarin speech and text. Unlike English and other languages with an alphabetic writing system, Mandarin uses an ideographic writing system where character and sound are not tightly mapped to one another. Therefore, we propose to introduce the phoneme modality into pre-training, which can help capture modality-invariant information between Mandarin speech and text. Specifically, we employ a multi-task learning framework including five self-supervised and supervised tasks with speech and text data. For end-to-end pre-training, we introduce self-supervised speech-to-pseudo-codes (S2C) and phoneme-to-text (P2T) tasks utilizing unlabeled speech and text data, where speech-pseudo-codes pairs and phoneme-text pairs are a supplement to the supervised speech-text pairs. To train the encoder to learn better speech representation, we introduce self-supervised masked speech prediction (MSP) and supervised phoneme prediction (PP) tasks to learn to map speech into phonemes. Besides, we directly add the downstream supervised speech-to-text (S2T) task into the pre-training process, which can further improve the pre-training performance and achieve better recognition results even without fine-tuning. Experiments on AISHELL-1 show that our proposed method achieves state-of-the-art performance, with a more than 40% relative improvement compared with other pre-training methods.