Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alternative to approaching general-purpose AI, existing generalist models are still at an early stage, where modality and task coverage is limited. To empower multi-modal task-scaling and speed up this line of research, we release a generalist model learning system, OFASys, built on top of a declarative task interface named multi-modal instruction. At the core of OFASys is the idea of decoupling multi-modal task representations from the underlying model implementations. In OFASys, a task involving multiple modalities can be defined declaratively even with just a single line of code. The system automatically generates task plans from such instructions for training and inference. It also facilitates multi-task training for diverse multi-modal workloads. As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data. The single OFA+ model achieves 95% performance in average with only 16% parameters of 15 task-finetuned models, showcasing the performance reliability of multi-modal task-scaling provided by OFASys. Available at https://github.com/OFA-Sys/OFASys

Generative modeling of human motion has broad applications in computer animation, virtual reality, and robotics. Conventional approaches develop separate models for different motion synthesis tasks, and typically use a model of a small size to avoid overfitting the scarce data available in each setting. It remains an open question whether developing a single unified model is feasible, which may 1) benefit the acquirement of novel skills by combining skills learned from multiple tasks, and 2) help in increasing the model capacity without overfitting by combining multiple data sources. Unification is challenging because 1) it involves diverse control signals as well as targets of varying granularity, and 2) motion datasets may use different skeletons and default poses. In this paper, we present MoFusion, a framework for unified motion synthesis. MoFusion employs a Transformer backbone to ease the inclusion of diverse control signals via cross attention, and pretrains the backbone as a diffusion model to support multi-granularity synthesis ranging from motion completion of a body part to whole-body motion generation. It uses a learnable adapter to accommodate the differences between the default skeletons used by the pretraining and the fine-tuning data. Empirical results show that pretraining is vital for scaling the model size without overfitting, and demonstrate MoFusion's potential in various tasks, e.g., text-to-motion, motion completion, and zero-shot mixing of multiple control signals. Project page: \url{https://ofa-sys.github.io/MoFusion/}.

As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and communication resources. Various client-variance-reduction schemes and client sampling strategies have been respectively introduced to improve the robustness of FL. Among others, primal-dual algorithms such as the alternating direction of method multipliers (ADMM) have been found being resilient to data distribution and outperform most of the primal-only FL algorithms. However, the reason behind remains a mystery still. In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm. While this explains the inherent robustness of federated ADMM, the vanilla version of it lacks the ability to be adaptive to the degree of client heterogeneity. Besides, the global model at the server under client sampling is biased which slows down the practical convergence. To go beyond ADMM, we propose a novel primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. In addition, FedVRA unifies several representative FL algorithms in the sense that they are either special instances of FedVRA or are close to it. Extensions of FedVRA to semi/un-supervised learning are also presented. Experiments based on (semi-)supervised image classification tasks demonstrate superiority of FedVRA over the existing schemes in learning scenarios with massive heterogeneous clients and client sampling.

虚拟试验旨在在店内服装和参考人员图像的情况下产生光真实的拟合结果。现有的方法通常建立多阶段框架来分别处理衣服翘曲和身体混合，或严重依赖基于中间解析器的标签，这些标签可能嘈杂甚至不准确。为了解决上述挑战，我们通过开发一种新型的变形注意流（DAFLOF）提出了一个单阶段的尝试框架，该框架将可变形的注意方案应用于多流量估计。仅将姿势关键点作为指导，分别为参考人员和服装图像估计了自我和跨跨性别的注意力流。通过对多个流场进行采样，通过注意机制同时提取并合并了来自不同语义区域的特征级和像素级信息。它使衣服翘曲和身体合成，同时以端到端的方式导致照片真实的结果。在两个尝试数据集上进行的广泛实验表明，我们提出的方法在定性和定量上都能达到最先进的性能。此外，其他两个图像编辑任务上的其他实验说明了我们用于多视图合成和图像动画方法的多功能性。

重度抑郁症（MDD）需要研究患者的大脑功能连通性改变，可以通过静止状态功能磁共振成像（RS-FMRI）数据发现。我们考虑确定单个MDD患者大脑功能连通性改变的问题。这是特别困难的，因为在fMRI扫描期间收集的数据量过于限制，无法为个人分析提供足够的信息。此外，RS-FMRI数据通常具有不完整，稀疏性，可变性，高维度和高噪声的特征。为了解决这些问题，我们提出了一个多任务高斯贝叶斯网络（MTGBN）框架，该框架能够识别MDD患者的个体疾病诱导的改变。我们假设这种疾病引起的改变显示了与该工具相似的程度，以学习从观察到了解系统如何共同从相关任务结构构造的网络结构。首先，我们将每类观察中的每个患者视为一项任务，然后通过从共享编码先验知识的默认协方差矩阵的所有任务中学习该数据类的高斯贝叶斯网络（GBN）。此设置可以帮助我们从有限的数据中学习更多信息。接下来，我们得出了完整的似然函数的封闭式公式，并使用蒙特卡洛期望 - 最大化（MCEM）算法有效地搜索大约最佳的贝叶斯网络结构。最后，我们通过模拟和现实世界的RS-FMRI数据评估方法的性能。

近年来，拥抱集群研究中的表演学习的深度学习技术引起了广泛的关注，产生了一个新开发的聚类范式，QZ。深度聚类（DC）。通常，DC型号大写AutoEncoders，以了解促进聚类过程的内在特征。如今，一个名为变变AualEncoder（VAE）的生成模型在DC研究中得到了广泛的认可。然而，平原VAE不足以察觉到综合潜在特征，导致细分性能恶化。本文提出了一种新的DC方法来解决这个问题。具体地，生成的逆势网络和VAE被聚结成了一种名为Fusion AutoEncoder（FAE）的新的AutoEncoder，以辨别出更多的辨别性表示，从而使下游聚类任务受益。此外，FAE通过深度剩余网络架构实施，进一步提高了表示学习能力。最后，将FAE的潜在空间转变为由深密神经网络的嵌入空间，用于彼此从彼此拉出不同的簇，并将数据点折叠在单个簇内。在几个图像数据集上进行的实验证明了所提出的DC模型对基线方法的有效性。

越来越多的对分析体育洞察力的需求已经刺激了各种观点的生产性研究，例如健康状态监测，结果预测。在本文中，我们专注于客观地判断返回中风的返回，这仍然在转向基于转向的运动中仍未开发。通过将中风预测作为序列预测任务制定，现有工程可以解决问题，而是基于羽毛球的特征来模拟信息。为了解决这些限制，我们提出了一种新的集会进展融合，并通过两个修改的编码器 - 解码器提取器包含集会进度和参与者的集会进度和信息。此外，我们设计一个融合网络，通过调节信息依赖性和不同的位置来整合参与者的集会上下文和上下文。羽毛球数据集的广泛实验表明，Shuttlenet显着优于最先进的方法，并且还经验验证了ShuttLenet中每个组件的可行性。首先，我们为中风预测问题提供了分析场景。

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.