Applying suction grippers in unstructured environments is a challenging task because of depth and tilt errors in vision systems, requiring additional costs in elaborate sensing and control. To reduce additional costs, suction grippers with compliant bodies or mechanisms have been proposed; however, their bulkiness and limited allowable error hinder their use in complex environments with large errors. Here, we propose a compact suction gripper that can pick objects over a wide range of distances and tilt angles without elaborate sensing and control. The spring-inserted gripper body deploys and conforms to distant and tilted objects until the suction cup completely seals with the object and retracts immediately after, while holding the object. This seamless deployment and retraction is enabled by connecting the gripper body and suction cup to the same vacuum source, which couples the vacuum picking and retraction of the gripper body. Experimental results validated that the proposed gripper can pick objects within 79 mm, which is 1.4 times the initial length, and can pick objects with tilt angles up to 60{\deg}. The feasibility of the gripper was verified by demonstrations, including picking objects of different heights from the same picking height and the bin picking of transparent objects.

迅速严重性评估患有传染病感染的确诊患者的评估模型可以实现高效的诊断和减轻医疗系统的负担。本文利用机器学习技术提供了严重性评估模型的开发过程及其在SARS-COV-2患者的应用。在这里，我们强调我们的模型只需要基本患者的基本个人数据，从而允许他们判断自己的严重程度。我们选择了基于升级的决策树模型作为分类器，并将死亡率解释为建模后的概率分数。具体而言，使用贝叶斯优化技术调整确定树模型结构的超参数，而不知道医疗信息。结果，我们测量了模型性能并识别通过模型影响严重性的变量。最后，我们的目标是建立一个允许患者检查自己的严重性的医疗系统，并根据其他患者的过去的治疗细节来访问他们访问适当的诊所中心。

Recognizing the surrounding environment at low latency is critical in autonomous driving. In real-time environment, surrounding environment changes when processing is over. Current detection models are incapable of dealing with changes in the environment that occur after processing. Streaming perception is proposed to assess the latency and accuracy of real-time video perception. However, additional problems arise in real-world applications due to limited hardware resources, high temperatures, and other factors. In this study, we develop a model that can reflect processing delays in real time and produce the most reasonable results. By incorporating the proposed feature queue and feature select module, the system gains the ability to forecast specific time steps without any additional computational costs. Our method is tested on the Argoverse-HD dataset. It achieves higher performance than the current state-of-the-art methods(2022.10) in various environments when delayed . The code is available at https://github.com/danjos95/DADE

Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically train a collaborative filtering algorithm to extract user/item embedding vectors and therefore, a time-series of embedding vectors can be naturally defined. We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e.g., every month, iii) trains our time-series forecasting model with the extracted time- series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time- series data, i.e., neural controlled differential equations (NCDEs). Our experiments with four real-world benchmark datasets show that the proposed time-series forecasting-based upgrade kit can significantly enhance existing popular collaborative filtering algorithms.

在这项研究中，我们提出了一种基于词素的方案，用于韩国依赖解析，并采用拟议方案来普遍依赖。我们介绍了语言原理，该基本原理说明了采用基于词素的格式的动机和必要性，并开发了脚本，这些脚本会在通用依赖项使用的原始格式和所提出的基于词素的格式自动之间转换。然后，统计和神经模型（包括udpipe和stanza）证明了提出的格式对韩国依赖解析的有效性，并以我们精心构造的基于词素的单词嵌入韩语。Morphud的表现优于所有韩国UD Treebanks的解析结果，我们还提供了详细的错误分析。

事件摄像机对场景的亮度变化异步，独立于每个像素。由于属性，这些相机具有不同的特征：高动态范围（HDR），高时间分辨率和低功耗。但是，应将事件摄像机的结果处理为计算机视觉任务的替代表示。另外，它们通常很嘈杂，并且在几乎没有事件的地区导致性能不佳。近年来，许多研究人员试图重建事件中的视频。但是，由于缺乏不规则和不连续数据的时间信息，它们没有提供高质量的视频。为了克服这些困难，我们引入了一个E2V-SDE，该E2V-SDE由随机微分方程（SDE）控制在潜在空间中。因此，E2V-SDE可以在任意时间步骤中快速重建图像，并对看不见的数据做出现实的预测。此外，我们成功采用了各种图像组成技术来提高图像清晰度和时间一致性。通过对模拟和实际场景数据集进行广泛的实验，我们验证了我们的模型在各种视频重建设置下的表现优于最先进的方法。就图像质量而言，LPIPS得分提高了12％，重建速度比ET-NET高87％。

受微分方程式启发的深度学习是最近的研究趋势，它标志着许多机器学习任务的最先进的表现。其中，具有神经控制的微分方程（NCDE）的时间序列建模被认为是突破。在许多情况下，基于NCDE的模型不仅比复发性神经网络（RNN）提供了更好的准确性，而且还可以处理不规则的时间序列。在这项工作中，我们通过重新设计其核心部分，即从离散的时间序列输入产生连续路径来增强NCDES。 NCDE通常使用插值算法将离散的时间序列样本转换为连续路径。但是，我们向i）提出建议，使用编码器解码器体系结构生成另一个潜在的连续路径，该架构对应于NCDE的插值过程，即我们的基于神经网络的插值与现有的显式插值相对于现有的显式插值以及II）解码器的外推超出了原始数据的时域的外推。因此，我们的NCDE设计可以同时使用插值和外推信息进行下游机器学习任务。在我们使用5个现实世界数据集和12个基线的实验中，我们的外推和基于插值的NCDES超过了非平凡的边缘的现有基线。

临床笔记是临床医生在患者遭遇期间产生的非结构化文本。临床票据通常伴随着来自疾病的国际分类（ICD）的一组元数据代码。 ICD代码是各种操作中使用的重要代码，包括保险，报销，医学诊断等，因此，重要的是快速准确地分类ICD代码。但是，注释这些代码是昂贵且耗时的。因此，我们使用用于自动ICD代码分配的序列注意方法，提出基于来自变压器（BERT）的双向编码器表示的模型。我们评估我们对重症监护III（MIMIC-III）基准数据集的医疗信息MART的方法。我们的模型实现了宏观平均为F1：0.62898和微平均F1：0.68555的性能，并且使用MIMIC-III数据集执行优于最先进模型的性能。本研究的贡献提出了一种使用伯特的方法，该方法可以应用于文档和序列注意方法，该方法可以捕获在文档中出现的重要序列形式。

Steering language generation towards objectives or away from undesired content has been a long-standing goal in utilizing language models (LM). Recent work has demonstrated reinforcement learning and weighted decoding as effective approaches to achieve a higher level of language control and quality with pros and cons. In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding. Specifically, we adopt the actor-critic framework to train an LM-steering critic from non-differentiable reward models. And similar to weighted decoding, our method freezes the language model and manipulates the output token distribution using called critic, improving training efficiency and stability. Evaluation of our method on three controlled generation tasks, namely topic control, sentiment control, and detoxification, shows that our approach generates more coherent and well-controlled texts than previous methods. In addition, CriticControl demonstrates superior generalization ability in zero-shot settings. Human evaluation studies also corroborate our findings.

We propose a domain adaptation method, MoDA, which adapts a pretrained embodied agent to a new, noisy environment without ground-truth supervision. Map-based memory provides important contextual information for visual navigation, and exhibits unique spatial structure mainly composed of flat walls and rectangular obstacles. Our adaptation approach encourages the inherent regularities on the estimated maps to guide the agent to overcome the prevalent domain discrepancy in a novel environment. Specifically, we propose an efficient learning curriculum to handle the visual and dynamics corruptions in an online manner, self-supervised with pseudo clean maps generated by style transfer networks. Because the map-based representation provides spatial knowledge for the agent's policy, our formulation can deploy the pretrained policy networks from simulators in a new setting. We evaluate MoDA in various practical scenarios and show that our proposed method quickly enhances the agent's performance in downstream tasks including localization, mapping, exploration, and point-goal navigation.