Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have been proposed recently to identify those changes, they still suffer from missing subtle changes, poor scalability, or/and sensitive to noise points. To meet these challenges, we are the first to generalise the CPD problem as a special case of the Change-Interval Detection (CID) problem. Then we propose a CID method, named iCID, based on a recent Isolation Distributional Kernel (IDK). iCID identifies the change interval if there is a high dissimilarity score between two non-homogeneous temporal adjacent intervals. The data-dependent property and finite feature map of IDK enabled iCID to efficiently identify various types of change points in data streams with the tolerance of noise points. Moreover, the proposed online and offline versions of iCID have the ability to optimise key parameter settings. The effectiveness and efficiency of iCID have been systematically verified on both synthetic and real-world datasets.

Our situated environment is full of uncertainty and highly dynamic, thus hindering the widespread adoption of machine-led Intelligent Decision-Making (IDM) in real world scenarios. This means IDM should have the capability of continuously learning new skills and efficiently generalizing across wider applications. IDM benefits from any new approaches and theoretical breakthroughs that exhibit Artificial General Intelligence (AGI) breaking the barriers between tasks and applications. Recent research has well-examined neural architecture, Transformer, as a backbone foundation model and its generalization to various tasks, including computer vision, natural language processing, and reinforcement learning. We therefore argue that a foundation decision model (FDM) can be established by formulating various decision-making tasks as a sequence decoding task using the Transformer architecture; this would be a promising solution to advance the applications of IDM in more complex real world tasks. In this paper, we elaborate on how a foundation decision model improves the efficiency and generalization of IDM. We also discuss potential applications of a FDM in multi-agent game AI, production scheduling, and robotics tasks. Finally, through a case study, we demonstrate our realization of the FDM, DigitalBrain (DB1) with 1.2 billion parameters, which achieves human-level performance over 453 tasks, including text generation, images caption, video games playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 would be a baby step towards more autonomous and efficient real world IDM applications.

我们提出协调指导矢量字段，以与机器人团队同时完成两个任务：首先，多个机器人的指导和导航到可能嵌入2D或3D中的可能不同的路径或表面；其次，他们的运动协调在跟踪他们的规定路径或表面时。运动配位是由路径或表面上的机器人之间所需的参数位移定义的。通过控制对应于指导矢量场之间的路径或表面参数的虚拟坐标来实现这种所需的位移。由动力学系统理论和Lyapunov理论支撑的严格数学保证，用于从所有初始位置上有效的分布式运动协调和机器人在路径或表面上导航。作为实用机器人应用的一个例子，我们从所提出的具有驱动饱和度的Dubins-car样模型的指导向量场中得出了一种对照算法。我们提出的算法分布并可扩展到任意数量的机器人。此外，广泛的说明性模拟和固定翼飞机户外实验验证了我们算法的有效性和鲁棒性。

最近的深度学习（DL）应用主要建立在DL库的顶部。这些库的质量保证对于可靠的DL应用程序的可靠部署至关重要。因此，提出了一些技术来通过生成DL模型作为测试输入来测试DL库。然后，这些技术将这些DL模型馈送到DL库进行推断，以便行使与DL模型执行相关的DL库模块。但是，这些技术的测试有效性受生成的DL模型的多样性的限制。我们的研究发现，这些技术最多可以覆盖层对的11.7％（即，在两个层API之间调用序列）和层参数的55.8％（例如，在Conv2d中的“ Padding”）。结果，我们发现现有技术可能会错过特定层对和参数引起的许多错误。鉴于现有DL库测试技术的局限性，我们建议备忘录通过探索层类型，层对和层参数来有效地生成不同的DL模型。备忘录：（1）设计一种初始模型还原技术，以提高测试效率而不损害模型多样性； （2）为定制的Markov链蒙特卡洛（MCMC）算法设计一组突变操作员，以探索新的层类型，层对和层参数。我们在七个流行的DL库上评估了备忘录，其中包括四个用于模型执行（Tensorflow，Pytorch和MXNET和ONNX）和三个用于模型转换的备忘录（KERAS-MXNET，TF2ONNX，ONNX2PYTORCH）。评估结果表明，备忘录的表现优于最近的作品，覆盖了10.3％的层对，多15.3％的层参数和2.3％的库分支。此外，备忘录在最新版本的DL库中检测到29个新错误，其中17个由DL库开发人员确认，其中5个已确认的错误已修复。

与淘宝和亚马逊等大型平台不同，由于严重的数据分配波动（DDF）问题，在小规模推荐方案中开发CVR模型是更具挑战性的。 DDF防止现有的CVR模型自生效以来，因为1）需要几个月的数据需要足够小的场景训练CVR模型，导致培训和在线服务之间的相当大的分布差异; 2）电子商务促销对小型情景产生了更大的影响，导致即将到期的时间段的不确定性。在这项工作中，我们提出了一种名为MetacVR的新型CVR方法，从Meta学习的角度解决了DDF问题。首先，由特征表示网络（FRN）和输出层组成的基础CVR模型是精心设计和培训的，在几个月内与样品充分设计和培训。然后，我们将不同数据分布的时间段视为不同的场合，并使用相应的样本和预先训练的FRN获得每个场合的正面和负原型。随后，设计了距离度量网络（DMN）以计算每个样本和所有原型之间的距离度量，以便于减轻分布不确定性。最后，我们开发了一个集合预测网络（EPN），该网络（EPN）包含FRN和DMN的输出以进行最终的CVR预测。在这个阶段，我们冻结了FRN并用最近一段时间的样品训练DMN和EPN，因此有效地缓解了分布差异。据我们所知，这是在小规模推荐方案中针对DDF问题的CVR预测第一次研究。实验结果对现实世界数据集验证了我们的MetacVR和Online A / B测试的优越性也表明我们的模型在PCVR上实现了11.92％的令人印象深刻的收益和GMV的8.64％。

视觉问题应答（VQA）任务利用视觉图像和语言分析来回回答图像的文本问题。它是一个流行的研究课题，在过去十年中越来越多的现实应用。本文介绍了我们最近对AliceMind-MMU的研究（阿里巴巴的编码器 - 解码器来自Damo Academy - 多媒体理解的机器智能实验室），其比人类在VQA上获得相似甚至略微更好的结果。这是通过系统地改善VQA流水线来实现的，包括：（1）具有全面的视觉和文本特征表示的预培训; （2）与学习参加的有效跨模型互动; （3）一个新颖的知识挖掘框架，具有专门的专业专家模块，适用于复杂的VQA任务。处理不同类型的视觉问题，需要具有相应的专业知识在提高我们的VQA架构的表现方面发挥着重要作用，这取决于人力水平。进行了广泛的实验和分析，以证明新的研究工作的有效性。

开发了一种基于变换器的图像压缩（TIC）方法，其重用了具有配对主和超编码器解码器的规范变形AutoEncoder（VAE）架构。主要和超编码器包括一系列神经转换单元（NTU），以分析和聚合重要信息以进行更紧凑的输入图像表示，而解码器镜像编码器侧操作以生成从压缩的像素域图像重建。比特流。每个NTU由Swin变压器块（STB）和卷积层（CONV）组成，以最佳地嵌入远程和短程信息;同时，设计了一种休闲的注意模块（CAM），用于潜在特征的自适应上下文建模，以利用超自行性前提。具有最先进的方法的TIC竞争对手，包括基于深度卷积神经网络（CNNS）的学习图像编码（LIC）方法以及最近批准的多功能视频编码（VVC）标准的基于规则的基于规则的简介，并且需要很多较少的模型参数，例如，降低前导性能LIC减少45％。

We aim to bridge the gap between our common-sense few-sample human learning and large-data machine learning. We derive a theory of human-like few-shot learning from von-Neuman-Landauer's principle. modelling human learning is difficult as how people learn varies from one to another. Under commonly accepted definitions, we prove that all human or animal few-shot learning, and major models including Free Energy Principle and Bayesian Program Learning that model such learning, approximate our theory, under Church-Turing thesis. We find that deep generative model like variational autoencoder (VAE) can be used to approximate our theory and perform significantly better than baseline models including deep neural networks, for image recognition, low resource language processing, and character recognition.

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.