可靠地定量自然和人为气体释放（例如，从海底进入海洋的自然和人为气体释放（例如，Co $ _2 $，甲烷），最终是大气，是一个具有挑战性的任务。虽然船舶的回声探测器允许在水中检测水中的自由气，但是即使从较大的距离中，精确量化需要诸如未获得的升高速度和气泡尺寸分布的参数。光学方法的意义上是互补的，即它们可以提供从近距离的单个气泡或气泡流的高时和空间分辨率。在这一贡献中，我们介绍了一种完整的仪器和评估方法，用于光学气泡流特征。专用仪器采用高速深海立体声摄像机系统，可在部署在渗透网站以进行以后的自动分析时录制泡泡图像的Tbleabytes。对于几分钟的短序列可以获得泡特性，然后将仪器迁移到其他位置，或者以自主间隔模式迁移到几天内，以捕获由于电流和压力变化和潮汐循环引起的变化。除了报告泡沫特征的步骤旁边，我们仔细评估了可达准确性并提出了一种新颖的校准程序，因为由于缺乏点对应，仅使用气泡的剪影。该系统已成功运营，在太平洋高达1000万水深，以评估甲烷通量。除了样品结果外，我们还会报告在开发期间汲取的故障案例和经验教训。

Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges. By doing so, the robot is able to grasp two adjacent corners, enabling subsequent manipulation tasks like folding or hanging. As components of this system, we develop tactile perception networks that classify whether an edge is grasped and estimate the pose of the edge. We use the edge classification network to supervise a visuotactile edge grasp affordance network that can grasp edges with a 90% success rate. Once an edge is grasped, we demonstrate that the robot can slide along the cloth to the adjacent corner using tactile pose estimation/control in real time. See http://nehasunil.com/visuotactile/visuotactile.html for videos.

Abstractive summarization is the process of generating a summary given a document as input. Although significant progress has been made, the factual inconsistency between the document and the generated summary still limits its practical applications. Previous work found that the probabilities assigned by the generation model reflect its preferences for the generated summary, including the preference for factual consistency, and the preference for the language or knowledge prior as well. To separate the preference for factual consistency, we propose an unsupervised framework named CoP by controlling the preference of the generation model with the help of prompt. More specifically, the framework performs an extra inference step in which a text prompt is introduced as an additional input. In this way, another preference is described by the generation probability of this extra inference process. The difference between the above two preferences, i.e. the difference between the probabilities, could be used as measurements for detecting factual inconsistencies. Interestingly, we found that with the properly designed prompt, our framework could evaluate specific preferences and serve as measurements for fine-grained categories of inconsistency, such as entity-related inconsistency, coreference-related inconsistency, etc. Moreover, our framework could also be extended to the supervised setting to learn better prompt from the labeled data as well. Experiments show that our framework achieves new SOTA results on three factual inconsistency detection tasks.

People constantly use language to learn about the world. Computational linguists have capitalized on this fact to build large language models (LLMs) that acquire co-occurrence-based knowledge from language corpora. LLMs achieve impressive performance on many tasks, but the robustness of their world knowledge has been questioned. Here, we ask: do LLMs acquire generalized knowledge about real-world events? Using curated sets of minimal sentence pairs (n=1215), we tested whether LLMs are more likely to generate plausible event descriptions compared to their implausible counterparts. We found that LLMs systematically distinguish possible and impossible events (The teacher bought the laptop vs. The laptop bought the teacher) but fall short of human performance when distinguishing likely and unlikely events (The nanny tutored the boy vs. The boy tutored the nanny). In follow-up analyses, we show that (i) LLM scores are driven by both plausibility and surface-level sentence features, (ii) LLMs generalize well across syntactic sentence variants (active vs passive) but less well across semantic sentence variants (synonymous sentences), (iii) some, but not all LLM deviations from ground-truth labels align with crowdsourced human judgments, and (iv) explicit event plausibility information emerges in middle LLM layers and remains high thereafter. Overall, our analyses reveal a gap in LLMs' event knowledge, highlighting their limitations as generalized knowledge bases. We conclude by speculating that the differential performance on impossible vs. unlikely events is not a temporary setback but an inherent property of LLMs, reflecting a fundamental difference between linguistic knowledge and world knowledge in intelligent systems.

As the COVID-19 pandemic puts pressure on healthcare systems worldwide, the computed tomography image based AI diagnostic system has become a sustainable solution for early diagnosis. However, the model-wise vulnerability under adversarial perturbation hinders its deployment in practical situation. The existing adversarial training strategies are difficult to generalized into medical imaging field challenged by complex medical texture features. To overcome this challenge, we propose a Contour Attention Preserving (CAP) method based on lung cavity edge extraction. The contour prior features are injected to attention layer via a parameter regularization and we optimize the robust empirical risk with hybrid distance metric. We then introduce a new cross-nation CT scan dataset to evaluate the generalization capability of the adversarial robustness under distribution shift. Experimental results indicate that the proposed method achieves state-of-the-art performance in multiple adversarial defense and generalization tasks. The code and dataset are available at https://github.com/Quinn777/CAP.

我们提出了一种新型的复发图网络（RGN）方法，用于通过学习潜在的复杂随机过程来预测离散标记的事件序列。使用点过程的框架，我们将标记的离散事件序列解释为各种唯一类型的不同序列的叠加。图网络的节点使用LSTM来合并过去的信息，而图形注意力网络（GAT网络）引入了强烈的电感偏见，以捕获这些不同类型的事件之间的相互作用。通过更改自我注意力的机制从过去的事件中参加活动，我们可以从$ \ MATHCAL {O}（n^2）$（事件总数）到$ \ Mathcal的时间和空间复杂性降低{o}（| \ Mathcal {y} |^2）$（事件类型的数量）。实验表明，与最新的基于最新的变压器架构相比，所提出的方法可以提高对数可能具有较低时间和空间复杂性的对数可能具有较低时间和空间复杂性的任务的性能。

可进入的模型可以通过在表示理论和特征领域的语言中制定均衡性要求来提供非常通用和灵活的均衡性，这对许多视觉任务都是有效的。但是，由于3D旋转的数学更复杂，因此在2D情况下得出3D旋转模型要困难得多。在这项工作中，我们采用部分差分运算符（PDOS）来模型3D滤波器，并得出了通用的可检测3D CNN，称为PDO-S3DCNNS。我们证明，模棱两可的过滤器受线性约束的约束，可以在各种条件下有效地解决。据我们所知，PDO-S3DCNNS是3D旋转的最通用的CNN，因为它们涵盖了所有$ SO（3）$及其表示的所有常见子组，而现有方法只能应用于特定的组和特定组和表示。广泛的实验表明，我们的模型可以很好地保留在离散域中的均衡性，并且在SHREC'17检索和ISBI 2012分割任务上的表现都超过了以前的网络复杂性。

预处理的变形金刚记住事实知识的能力对于下游任务（例如封闭式问题答案）是必不可少的。现有的工作表明，经过审计的变压器可以回忆或利用在某种程度上出现的训练训练阶段中出现的事实知识。但是，由于模型能力的限制，预审预周仔的记忆知识的能力也受到限制。 Dai等。 （2022）发现经过验证的变形金刚中的馈电网络（FFN）以内存的方式存储事实知识。受这一发现的启发，我们提出了一个神经知识库（NKB），以存储预验证的变压器的额外事实知识。要具体而言，我们还将FFN视为键值记忆，并使用其他内存插槽扩展它们。在知识注入期间，我们将原始模型和事实知识注入扩展的存储插槽中，因此预验证的模型不会遗忘。此外，FFN作为钥匙值记忆的观点使NKB高度可解释。我们使用三个封闭式问题回答数据集来显示我们强大的存储额外事实知识的能力。另外，我们证明NKB不会通过两种代表性生成任务，摘要和机器翻译来降低验证模型的一般语言生成能力。此外，我们彻底分析了NKB以揭示其工作机制，并以人为可读的方式介绍其钥匙和价值观的含义。最重要的是，我们执行初步尝试，以直接更新NKB中的事实知识，而无需任何其他培训。

在进化多目标优化领域，决策者（DM）涉及相互冲突的目标。在现实世界中，通常存在多个DM，每个DM都涉及这些目标的一部分。提出了多方多目标优化问题（MPMOPS）来描绘拖把，其中涉及多个决策者，每个方都关注所有目标的某些目标。但是，在进化计算字段中，对mpmops的关注不多。本文基于距离最小化问题（DMP）构建了一系列MPMOP，它们的Pareto最佳解决方案可以生动地可视化。为了解决MPMOPS，新提出的算法OPTMPNDS3使用多方初始化方法来初始化总体，并带Jade2操作员生成后代。在问题套件上，将OPTMPNDS3与Optall，OptMPND和OptMPNDS2进行了比较。结果表明OPTMPNDS3与其他算法具有很强的可比性

在本文中，我们旨在改善干扰限制的无线网络中超级可靠性和低延迟通信（URLLC）的服务质量（QoS）。为了在通道连贯性时间内获得时间多样性，我们首先提出了一个随机重复方案，该方案随机将干扰能力随机。然后，我们优化了每个数据包的保留插槽数量和重复数量，以最大程度地减少QoS违规概率，该概率定义为无法实现URLLC的用户百分比。我们构建了一个级联的随机边缘图神经网络（REGNN），以表示重复方案并开发一种无模型的无监督学习方法来训练它。我们在对称场景中使用随机几何形状分析了QoS违规概率，并应用基于模型的详尽搜索（ES）方法来找到最佳解决方案。仿真结果表明，在对称方案中，通过模型学习方法和基于模型的ES方法实现的QoS违规概率几乎相同。在更一般的情况下，级联的Regnn在具有不同尺度，网络拓扑，细胞密度和频率重复使用因子的无线网络中很好地概括了。在模型不匹配的情况下，它的表现优于基于模型的ES方法。