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.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Pretrained language models (PLMs) have motivated research on what kinds of knowledge these models learn. Fill-in-the-blanks problem (e.g., cloze tests) is a natural approach for gauging such knowledge. BioLAMA generates prompts for biomedical factual knowledge triples and uses the Top-k accuracy metric to evaluate different PLMs' knowledge. However, existing research has shown that such prompt-based knowledge probing methods can only probe a lower bound of knowledge. Many factors like prompt-based probing biases make the LAMA benchmark unreliable and unstable. This problem is more prominent in BioLAMA. The severe long-tailed distribution in vocabulary and large-N-M relation make the performance gap between LAMA and BioLAMA remain notable. To address these, we introduce context variance into the prompt generation and propose a new rank-change-based evaluation metric. Different from the previous known-unknown evaluation criteria, we propose the concept of "Misunderstand" in LAMA for the first time. Through experiments on 12 PLMs, our context variance prompts and Understand-Confuse-Misunderstand (UCM) metric makes BioLAMA more friendly to large-N-M relations and rare relations. We also conducted a set of control experiments to disentangle "understand" from just "read and copy".

Stereo images, containing left and right view images with disparity, are utilized in solving low-vision tasks recently, e.g., rain removal and super-resolution. Stereo image restoration methods usually obtain better performance than monocular methods by learning the disparity between dual views either implicitly or explicitly. However, existing stereo rain removal methods still cannot make full use of the complementary information between two views, and we find it is because: 1) the rain streaks have more complex distributions in directions and densities, which severely damage the complementary information and pose greater challenges; 2) the disparity estimation is not accurate enough due to the imperfect fusion mechanism for the features between two views. To overcome such limitations, we propose a new \underline{Stereo} \underline{I}mage \underline{R}ain \underline{R}emoval method (StereoIRR) via sufficient interaction between two views, which incorporates: 1) a new Dual-view Mutual Attention (DMA) mechanism which generates mutual attention maps by taking left and right views as key information for each other to facilitate cross-view feature fusion; 2) a long-range and cross-view interaction, which is constructed with basic blocks and dual-view mutual attention, can alleviate the adverse effect of rain on complementary information to help the features of stereo images to get long-range and cross-view interaction and fusion. Notably, StereoIRR outperforms other related monocular and stereo image rain removal methods on several datasets. Our codes and datasets will be released.

This study proposes an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-scale scenes based on the self-attentive mechanism of the transformer's encoder-decoder structure. A multi-dimensional feature extraction backbone network is combined with a self-built semantic raster map, which is stored in the encoder for correlation and generates target position encoding and multi-dimensional feature vectors. The decoder incorporates four methods: spatial clustering and semantic filtering of multi-view targets, dynamic matching of multi-dimensional features, space-time logic-based multi-target tracking, and space-time convergence network (STCN)-based parameter passing. Through the fusion of multiple decoding methods, muti-camera targets are tracked in three dimensions: temporal logic, spatial logic, and feature matching. For the MOT17 dataset, this study's method significantly outperforms the current state-of-the-art method MiniTrackV2 [49] by 2.2% to 0.836 on Multiple Object Tracking Accuracy(MOTA) metric. Furthermore, this study proposes a retrospective mechanism for the first time, and adopts a reverse-order processing method to optimise the historical mislabeled targets for improving the Identification F1-score(IDF1). For the self-built dataset OVIT-MOT01, the IDF1 improves from 0.948 to 0.967, and the Multi-camera Tracking Accuracy(MCTA) improves from 0.878 to 0.909, which significantly improves the continuous tracking accuracy and scene adaptation. This research method introduces a new attentional tracking paradigm which is able to achieve state-of-the-art performance on multi-target tracking (MOT17 and OVIT-MOT01) tasks.

We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or "inked") notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to simultaneously perform sentence segmentation and classification of these sentences into task/non-task sentences. SLATE greatly outperforms a baseline two-model (sentence segmentation followed by classification model) approach, achieving a task F1 score of 84.4\%, a sentence segmentation (boundary similarity) score of 88.4% and three times lower latency compared to the baseline. Furthermore, we provide insights into tackling challenges of performing NLP on the inking domain. We release both our code and dataset for this novel task.

Low-light stereo image enhancement (LLSIE) is a relatively new task to enhance the quality of visually unpleasant stereo images captured in dark conditions. So far, very few studies on deep LLSIE have been explored due to certain challenging issues, i.e., the task has not been well addressed, and current methods clearly suffer from two shortages: 1) insufficient cross-view interaction; 2) lacking long-range dependency for intra-view learning. In this paper, we therefore propose a novel LLSIE model, termed \underline{Suf}ficient C\underline{r}oss-View \underline{In}teraction Network (SufrinNet). To be specific, we present sufficient inter-view interaction module (SIIM) to enhance the information exchange across views. SIIM not only discovers the cross-view correlations at different scales, but also explores the cross-scale information interaction. Besides, we present a spatial-channel information mining block (SIMB) for intra-view feature extraction, and the benefits are twofold. One is the long-range dependency capture to build spatial long-range relationship, and the other is expanded channel information refinement that enhances information flow in channel dimension. Extensive experiments on Flickr1024, KITTI 2012, KITTI 2015 and Middlebury datasets show that our method obtains better illumination adjustment and detail recovery, and achieves SOTA performance compared to other related methods. Our codes, datasets and models will be publicly available.

最近，通过“向导”模拟游戏收集了一类以任务为导向的对话（TOD）数据集。但是，《巫师》数据实际上是模拟的数据，因此与现实生活中的对话根本不同，这些对话更加嘈杂和随意。最近，Seretod挑战赛是组织的，并发布了Mobilecs数据集，该数据集由来自中国移动的真实用户和客户服务人员之间的真实世界对话框组成。基于Mobilecs数据集，Seretod挑战具有两个任务，不仅评估了对话系统本身的构建，而且还检查了对话框成绩单中的信息提取，这对于建立TOD的知识库至关重要。本文主要介绍了Mobilecs数据集对这两项任务的基线研究。我们介绍了如何构建两个基线，遇到的问题以及结果。我们预计基线可以促进令人兴奋的未来研究，以建立针对现实生活任务的人类机器人对话系统。

通过利用未标记的对话框数据来开发半监督的面向任务的对话框（TOD）系统已吸引了越来越多的兴趣。对于对潜在状态TOD模型的半监督学习，经常使用变异学习，但遭受了通过离散潜在变量传播的梯度的令人讨厌的高度变化，以及间接优化目标对数的弊端。最近，一种称为关节随机近似（JSA）的替代算法已出现，用于学习具有令人印象深刻的性能的离散潜在可变模型。在本文中，我们建议将JSA应用于对潜在状态TOD模型的半监督学习，该模型称为JSA-TOD。据我们所知，JSA-TOD代表了开发基于JSA的半监督学习的第一批工作，用于对TOD系统（例如TOD系统）这样的长期顺序生成问题的离散潜在可变条件模型。广泛的实验表明，JSA-TOD明显优于其变异学习对应物。值得注意的是，使用20％标签的半监督JSA-TOD在Multiwoz2.1上的全面监督基线附近。

促性腺营养蛋白释放激素受体（GNRH1R）是治疗子宫疾病的有前途的治疗靶标。迄今为止，在临床研究中可以使用几个GNRH1R拮抗剂，而不满足多个财产约束。为了填补这一空白，我们旨在开发一个基于学习的框架，以促进有效，有效地发现具有理想特性的新的口服小型分子药物靶向GNRH1R。在目前的工作中，首先通过充分利用已知活性化合物和靶蛋白的结构的信息，首先提出了配体和结构组合模型，即LS-Molgen，首先提出了分子生成的方法，该信息通过其出色的性能证明了这一点。比分别基于配体或结构方法。然后，进行了A中的计算机筛选，包括活性预测，ADMET评估，分子对接和FEP计算，其中约30,000个生成的新型分子被缩小到8，以进行实验合成和验证。体外和体内实验表明，其中三个表现出有效的抑制活性（化合物5 IC50 = 0.856 nm，化合物6 IC50 = 0.901 nm，化合物7 IC50 = 2.54 nm对GNRH1R，并且化合物5在基本PK属性中表现良好例如半衰期，口服生物利用度和PPB等。我们认为，提议的配体和结构组合结合的分子生成模型和整个计算机辅助工作流程可能会扩展到从头开始的类似任务或铅优化的类似任务。