Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (<50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.

In person re-identification (ReID) tasks, many works explore the learning of part features to improve the performance over global image features. Existing methods extract part features in an explicit manner, by either using a hand-designed image division or keypoints obtained with external visual systems. In this work, we propose to learn Discriminative implicit Parts (DiPs) which are decoupled from explicit body parts. Therefore, DiPs can learn to extract any discriminative features that can benefit in distinguishing identities, which is beyond predefined body parts (such as accessories). Moreover, we propose a novel implicit position to give a geometric interpretation for each DiP. The implicit position can also serve as a learning signal to encourage DiPs to be more position-equivariant with the identity in the image. Lastly, a set of attributes and auxiliary losses are introduced to further improve the learning of DiPs. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple person ReID benchmarks.

Recognition of facial expression is a challenge when it comes to computer vision. The primary reasons are class imbalance due to data collection and uncertainty due to inherent noise such as fuzzy facial expressions and inconsistent labels. However, current research has focused either on the problem of class imbalance or on the problem of uncertainty, ignoring the intersection of how to address these two problems. Therefore, in this paper, we propose a framework based on Resnet and Attention to solve the above problems. We design weight for each class. Through the penalty mechanism, our model will pay more attention to the learning of small samples during training, and the resulting decrease in model accuracy can be improved by a Convolutional Block Attention Module (CBAM). Meanwhile, our backbone network will also learn an uncertain feature for each sample. By mixing uncertain features between samples, the model can better learn those features that can be used for classification, thus suppressing uncertainty. Experiments show that our method surpasses most basic methods in terms of accuracy on facial expression data sets (e.g., AffectNet, RAF-DB), and it also solves the problem of class imbalance well.

This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments. In this paper, we treat the MOT task as a two-stage task including human detection and trajectory matching. Specifically, we designed an improved human detector and associated most of detection to guarantee the integrity of the motion trajectory. We also propose a location-wise matching matrix to obtain more accurate trace matching. Without any model merging, our method achieves 66.672 HOTA and 93.971 MOTA on the DanceTrack challenge dataset.

Pretrained language models have demonstrated extraordinary capabilities in language generation. However, real-world tasks often require controlling the distribution of generated text in order to mitigate bias, promote fairness, and achieve personalization. Existing techniques for controlling the distribution of generated text only work with quantified distributions, which require pre-defined categories, proportions of the distribution, or an existing corpus following the desired distributions. However, many important distributions, such as personal preferences, are unquantified. In this work, we tackle the problem of generating text following arbitrary distributions (quantified and unquantified) by proposing Nano, a few-shot human-in-the-loop training algorithm that continuously learns from human feedback. Nano achieves state-of-the-art results on single topic/attribute as well as quantified distribution control compared to previous works. We also show that Nano is able to learn unquantified distributions, achieves personalization, and captures differences between different individuals' personal preferences with high sample efficiency.

Speech representation learning has improved both speech understanding and speech synthesis tasks for single language. However, its ability in cross-lingual scenarios has not been explored. In this paper, we extend the pretraining method for cross-lingual multi-speaker speech synthesis tasks, including cross-lingual multi-speaker voice cloning and cross-lingual multi-speaker speech editing. We propose a speech-text joint pretraining framework, where we randomly mask the spectrogram and the phonemes given a speech example and its transcription. By learning to reconstruct the masked parts of the input in different languages, our model shows great improvements over speaker-embedding-based multi-speaker TTS methods. Moreover, our framework is end-to-end for both the training and the inference without any finetuning effort. In cross-lingual multi-speaker voice cloning and cross-lingual multi-speaker speech editing tasks, our experiments show that our model outperforms speaker-embedding-based multi-speaker TTS methods. The code and model are publicly available at PaddleSpeech.

Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. Conclusions: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment.

与卷积神经网络（CNN）相比，视觉变压器（VIT）表现出了有希望的性能，但是VIT的训练比CNN难得多。在本文中，我们定义了几个指标，包括动态数据比例（DDP）和知识同化率（KAR），以研究训练过程，并将其分为三个时期：形成，增长和探索。特别是，在训练的最后阶段，我们观察到只有很小的训练示例用于优化模型。鉴于VIT的数据渴望的性质，我们提出了一个简单但重要的问题：在培训的每个阶段，是否有可能提供丰富的``有效''培训示例吗？为了解决这个问题，我们需要解决两个关键问题，即\ ie，如何衡量单个培训示例的``有效性''，以及如何系统地生成足够数量的``有效''示例。为了回答第一个问题，我们发现训练样本的``困难''可以作为衡量培训样本的``有效性''的指标。为了解决第二个问题，我们建议在这些演化阶段动态调整训练数据的``难度''分布。为了实现这两个目的，我们提出了一个新颖的以数据为中心的VIT培训框架，以动态测量训练样本的``难度''，并为不同培训阶段的模型生成``有效的''样品。此外，为了进一步扩大``有效''样品的数量，并减轻了VIT的后期训练阶段的过度拟合问题，我们提出了一种称为Patcherasing的补丁级擦除策略。广泛的实验证明了提出的以数据为中心的VIT培训框架和技术的有效性。

持续学习（CL）依次学习像人类这样的新任务，其目标是实现更好的稳定性（S，记住过去的任务）和可塑性（P，适应新任务）。由于过去的培训数据不可用，因此探索培训示例中S和P的影响差异很有价值，这可能会改善对更好的SP的学习模式。受影响函数的启发（如果），我们首先研究了示例通过添加扰动来示例体重和计算影响推导的影响。为了避免在神经网络中Hessian逆的存储和计算负担，我们提出了一种简单而有效的METASP算法，以模拟IF计算中的两个关键步骤，并获得S-和P-Aware示例的影响。此外，我们建议通过解决双目标优化问题来融合两种示例影响，并获得对SP Pareto最优性的融合影响。融合影响可用于控制模型的更新并优化排练的存储。经验结果表明，我们的算法在任务和类别基准CL数据集上都显着优于最先进的方法。

在许多现实世界中的机器学习应用中，亚种群的转移存在着极大地存在，指的是包含相同亚种群组的培训和测试分布，但在亚种群频率中有所不同。重要性重新加权是通过对训练数据集中每个样本施加恒定或自适应抽样权重来处理亚种群转移问题的正常方法。但是，最近的一些研究已经认识到，这些方法中的大多数无法改善性能，而不是经验风险最小化，尤其是当应用于过度参数化的神经网络时。在这项工作中，我们提出了一个简单而实用的框架，称为“不确定性感知混合”（UMIX），以根据样品不确定性重新加权“混合”样品来减轻过度参数化模型中的过度拟合问题。基于训练 - 注射器的不确定性估计为每个样品的拟议UMIX配备，以灵活地表征亚群分布。我们还提供有见地的理论分析，以验证UMIX是否在先前的工作中实现了更好的概括界限。此外，我们在广泛的任务上进行了广泛的经验研究，以验证我们方法的有效性，既有定性和定量。