我们为时间事件数据提出了一个新的稀疏Granger-Causal学习框架。我们专注于一种称为Hawkes流程的特定点过程。我们首先指出，霍克斯工艺的大多数现有稀疏因果学习算法在最大似然估计中都具有奇异性。结果，它们的稀疏溶液只能显示为数值伪像。在本文中，我们提出了一个基于基于基数规范化的霍克斯过程的数学定义明确的稀疏因果学习框架，该过程可以纠正现有方法的病理问题。我们利用提出的算法来完成实例因果事件分析的任务，其中稀疏性起着至关重要的作用。我们使用两个真实用例验证了所提出的框架，一个来自电网，另一个来自云数据中心管理域。

Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query. However, XAI also opens a door for adversaries to gain insights into the black-box models in MLaaS, thereby making the models more vulnerable to several attacks. For example, feature-based explanations (e.g., SHAP) could expose the top important features that a black-box model focuses on. Such disclosure has been exploited to craft effective backdoor triggers against malware classifiers. To address this trade-off, we introduce a new concept of achieving local differential privacy (LDP) in the explanations, and from that we establish a defense, called XRand, against such attacks. We show that our mechanism restricts the information that the adversary can learn about the top important features, while maintaining the faithfulness of the explanations.

在本文中，我们表明，不断学习新任务和记住先前任务的过程引入了未知的隐私风险和挑战以限制隐私损失。基于此，我们介绍了终身DP的正式定义，其中任何数据元组在任何任务的训练集中都受到保护，在始终界限的DP保护下，鉴于越来越多的任务流。始终如一的DP意味着只有一个固定值的DP隐私预算，而不管任务的数量多少。为了保留终身DP，我们提出了一种可扩展和异质的算法，称为L2DP-ML和流批培训，以有效地训练并继续释放L2M型号的新版本，鉴于数据大小和任务训练顺序， ，不影响DP保护私人培训集。端到端的理论分析和彻底的评估表明，我们的机制明显好于保存终身DP的基线方法。 L2DP-ML的实现可在以下网址获得：https：//github.com/haiphannjit/privatedeeplearning。

由于相似的外观产品及其各种姿势，在人类级别的精度上设计自动结帐系统为零售商店的精度而言具有挑战性。本文通过提出具有两阶段管道的方法来解决问题。第一阶段检测到类不足的项目，第二阶段专门用于对产品类别进行分类。我们还在视频帧中跟踪对象，以避免重复计数。一个主要的挑战是域间隙，因为模型经过合成数据的训练，但对真实图像进行了测试。为了减少误差差距，我们为第一阶段检测器采用域泛化方法。此外，模型集合用于增强第二阶段分类器的鲁棒性。该方法在AI City Challenge 2022 -Track 4上进行了评估，并在测试A集合中获得F1分40美元\％$。代码在链接https://github.com/cybercore-co-ltd/aicity22-track4上发布。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

聚类是一个流行的无监督学习工具，通常用于发现较大的人口中的群体，例如客户段或患者亚型。但是，尽管它用作子组发现的工具和描述 - 很少有最先进的算法提供了发现的群集后面的任何理由或描述。我们提出了一种用于可解释聚类的新方法，即群集数据点和构建在被发现的集群周围的多个群体来解释它们。我们的框架允许在多台上进行额外的约束 - 包括确保构建多托的超平面是轴平行的或稀疏，具有整数系数。我们制定通过多拓构造群集作为混合整数非线性程序（MINLP）的问题。要解决我们的配方，我们提出了一种两相方法，我们首先使用交替的最小化初始化群集和多核酸，然后使用坐标下降来提升聚类性能。我们在一套综合和真实的世界聚类问题上基准测试方法，其中我们的算法优于艺术可解释和不可解释的聚类算法的状态。

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.