观察能力和过渡能力之间的异步发展导致一定的图像数据（OID）由一次性观察形成的原始图像数据（OID）不能完全传播EOS和GS之间的一次传输机会（命名为可见的时间窗口，VTW） 。它需要将OID分割为几个分段的图像数据（SID），然后将它们传输到几个VTW中，从而丰富了卫星图像数据下行链路调度问题（SIDSP）的扩展。我们将新颖的SIDSP定义为具有家庭属性（SIDSPWFA）的卫星图像数据下行调度问题，其中首先将快速分割操作员分割了一些大的OID，并且第二步中都会传输所有SID和其他无分段的OID。然后设计两个优化目标，即图像数据传输失败率（FR）和分割时间（ST），以形式化SIDSPWFA为BI-OXTIVE离散优化模型。此外，持有几个双阶段操作员的双阶段差分进化算法（DE+NSGA-II）。广泛的仿真实例表明，详细分析了模型，策略，算法和操作员的效率。

活跃成像的敏捷地球观测卫星（AI-Aea）是新一代敏捷的地球观测卫星（AEOS）。随着观察和主动Im-gering的更新能力，AI-Aeos的观察能力提高了AEOS的观察能力，并提供了观察地面目标的其他方法。然而，这使得这些敏捷地球观察卫星的观察计划问题更加复杂，尤其是在考虑多条纹地面目标时。在本文中，我们研究了主动图像敏捷地球观察卫星（MOSP）的多strip观察计划问题。向MOSP提出了双向目标优化模型，以及一种自适应的模因算法，该算法整合了自适应大型邻里搜索算法（ALNS）和非主导分类遗传算法II（NSGA-II）的组合功率。提出了广泛的计算实验的结果，这些结果揭示了ALNS和NSGA-II在一致的工作中产生了出色的结果。我们的模型比现有模型更通用，并在应用问题解决方面提供了增强的功能。

敏捷地球观察卫星（OSPFA）的观察计划问题在敏捷地球观测卫星（AEOSS）中起着至关重要的作用。主动成像丰富了OSPFA的扩展，我们将新的问题称为具有可变图像持续时间（OSWVID）的AEO的观察计划问题。提出了累积的图像质量和详细的能源消耗，以将OSWVID构建为双目标优化模型。然后设计了三种多目标模因算法，PD+NSGA-II，LA+NSGA-II和ALNS+NSGA-II，然后设计用于求解OSWVID。考虑到我们先前研究中总结的启发式知识，几位运营商旨在分别改进这三种算法。根据现有实例，我们根据广泛的仿真实验分析了这三种算法的关键参数优化，运算符的进化和效率。

在我们的论文中研究了一个称为卫星下行链路调度问题（SDSP-BRM）下的称为卫星下行链路调度问题（SDSP）。与必须一次完全下载成像数据的传统SDSP相比，SDSP-BRM允许将成像数据的数据分解为可以在不同的播放窗口中下载的许多部分。通过分析SDSP-BRM的特性，我们首先提出了一个混合整数编程模型以制定其制定模型，然后证明SDSP-BRM的NP硬度。为了解决该问题，我们设计了一种简单有效的启发式算法（SEHA），其中提出了许多问题的移动操作员用于本地搜索。一组精心设计的场景的数值结果证明了与通用CPLEX求解器相比，所提出的算法的效率。我们进行了其他实验，以阐明分段策略对拟议SEHA的整体性能的影响。

卫星图像数据下行链路调度问题（SIDSP）在传统卫星的文献中进行了很好的研究。随着卫星技术的最新发展，现代卫星的SIDSP变得更加复杂，增加了复杂性的新维度和有效使用卫星的其他机会。在本文中，我们介绍了动态的两相卫星图像数据下行链路调度问题（D-SIDSP），该问题结合了图像数据分割和图像数据下链接的两个相互链接操作，以动态方式，从而提供其他建模功能和更新的功能。 D-SIDSP被配制为优化图像数据传输速率和服务余额度的双向目标问题。利用自适应的大型邻里搜索算法（ALNS）的功能，具有非主导的分类遗传算法II（NSGA-II），一种自适应双向模因算法，ALNS+NSGA-II，开发为求解D-Sidsp。还提供了使用基准实例进行的广泛计算实验的结果。我们的实验结果揭示了算法ALNS+NSGA-II是更有效地求解D-SIDSP的可行替代方法，并根据各种性能指标证明了卓越的结果。该论文还为D-SIDSP提供了新的基准实例，可用于该主题的未来研究工作。

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.

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

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-balanced Re-weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 \& V6 show the performances and generality of the SCR with the traditional SGG models.

With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples. It has been a new trend exploring ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress, challenges, and future work in ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques of ICL, including training strategies, prompting strategies, and so on. Finally, we present the challenges of ICL and provide potential directions for further research. We hope our work can encourage more research on uncovering how ICL works and improving ICL in future work.

Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency. We believe the insufficient utilization of training signals should be responsible. To alleviate this issue, we introduce a conceptually simple yet learning-efficient MIM training scheme, termed Disjoint Masking with Joint Distillation (DMJD). For disjoint masking (DM), we sequentially sample multiple masked views per image in a mini-batch with the disjoint regulation to raise the usage of tokens for reconstruction in each image while keeping the masking rate of each view. For joint distillation (JD), we adopt a dual branch architecture to respectively predict invisible (masked) and visible (unmasked) tokens with superior learning targets. Rooting in orthogonal perspectives for training efficiency improvement, DM and JD cooperatively accelerate the training convergence yet not sacrificing the model generalization ability. Concretely, DM can train ViT with half of the effective training epochs (3.7 times less time-consuming) to report competitive performance. With JD, our DMJD clearly improves the linear probing classification accuracy over ConvMAE by 5.8%. On fine-grained downstream tasks like semantic segmentation, object detection, etc., our DMJD also presents superior generalization compared with state-of-the-art SSL methods. The code and model will be made public at https://github.com/mx-mark/DMJD.