Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.
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主动感知和凹觉视觉是人类视觉系统的基础。虽然动脉凹视觉减少了在注视期间要处理的信息的量，但主动感知会将凝视方向转变为视野中最有前途的部分。我们提出了一种方法，以模仿人类和机器人使用中央摄像机探索场景，并以最少的凝视转移来识别周围环境中存在的物体。我们的方法基于三种关键方法。首先，我们采用现成的深度对象检测器，并在大量的常规图像数据集上进行了预训练，并将分类输出校准为foveateat图像的情况。其次，考虑了几种数据融合技术，对对象分类和相应的不确定性编码对象分类和相应的不确定性进行了依次更新。第三，下一个最好的目光固定点是基于信息理论指标确定的，旨在最大程度地减少语义图的总预期不确定性。与随机选择的下一个凝视转移相比，提出的方法可以使检测的F1分数增加2-3个百分点，以相同数量的凝视偏移，并减少三分之一，而三分之一则是所需的凝视转移数量以达到相似的性能。
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Ithaca is a Fuzzy Logic (FL) plugin for developing artificial intelligence systems within the Unity game engine. Its goal is to provide an intuitive and natural way to build advanced artificial intelligence systems, making the implementation of such a system faster and more affordable. The software is made up by a C\# framework and an Application Programming Interface (API) for writing inference systems, as well as a set of tools for graphic development and debugging. Additionally, a Fuzzy Control Language (FCL) parser is provided in order to import systems previously defined using this standard.
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We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models aim to combine the knowledge of physical systems and data-driven machine learning models. This is done by training a neural network to minimize the residuals of the governing equations in sample points. In this work, our flow is governed by the Navier-Stokes equations with some approximations. There are two main novelties in this paper. First, we design our model to assume that the flow is periodic in time, which is not feasible in conventional simulation methods. Second, we evaluate the benefit of resampling the function evaluation points during training, which has a near zero computational cost and has been verified to improve the final model, especially for small batch sizes. Finally, we discuss some limitations of the approximations used in the Navier-Stokes equations regarding the modeling of turbulence and how it interacts with PINNs.
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The promise of Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise health interventions. However, a major challenge is frequent missing data. Despite a rich imputation literature, existing techniques are ineffective for the pulsative signals which comprise many mHealth applications, and a lack of available datasets has stymied progress. We address this gap with PulseImpute, the first large-scale pulsative signal imputation challenge which includes realistic mHealth missingness models, an extensive set of baselines, and clinically-relevant downstream tasks. Our baseline models include a novel transformer-based architecture designed to exploit the structure of pulsative signals. We hope that PulseImpute will enable the ML community to tackle this significant and challenging task.
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In this work we propose a novel token-based training strategy that improves Transformer-Transducer (T-T) based speaker change detection (SCD) performance. The conventional T-T based SCD model loss optimizes all output tokens equally. Due to the sparsity of the speaker changes in the training data, the conventional T-T based SCD model loss leads to sub-optimal detection accuracy. To mitigate this issue, we use a customized edit-distance algorithm to estimate the token-level SCD false accept (FA) and false reject (FR) rates during training and optimize model parameters to minimize a weighted combination of the FA and FR, focusing the model on accurately predicting speaker changes. We also propose a set of evaluation metrics that align better with commercial use cases. Experiments on a group of challenging real-world datasets show that the proposed training method can significantly improve the overall performance of the SCD model with the same number of parameters.
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While recent research advances in speaker diarization mostly focus on improving the quality of diarization results, there is also an increasing interest in improving the efficiency of diarization systems. In this paper, we propose a multi-stage clustering strategy, that uses different clustering algorithms for input of different lengths. Specifically, a fallback clusterer is used to handle short-form inputs; a main clusterer is used to handle medium-length inputs; and a pre-clusterer is used to compress long-form inputs before they are processed by the main clusterer. Both the main clusterer and the pre-clusterer can be configured with an upper bound of the computational complexity to adapt to devices with different constraints. This multi-stage clustering strategy is critical for streaming on-device speaker diarization systems, where the budgets of CPU, memory and battery are tight.
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自动语音识别（ASR）需要对说话者的差异很强。语音转换（VC）修改了输入语音的扬声器特征。这是ASR数据增强的吸引人功能。在本文中，我们证明了语音转换可以用作数据增强技术，即使在包含2,456位扬声器的LibrisPeech上，也可以用作提高ASR性能。对于ASR增强，有必要对广泛的输入语音稳健。这激发了使用非自动回旋，非并行VC模型的使用，并在VC模型中使用了预验证的ASR编码器。这项工作表明，尽管包括许多演讲者，但演讲者的多样性可能仍然是ASR质量的限制。最后，对我们的风险投资性能的审讯为客观评估VC质量提供了有用的指标。
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注意层是现代端到端自动语音识别系统不可或缺的一部分，例如作为变压器或构象体体系结构的一部分。注意通常是多头的，每个头部都有一组独立的学习参数，并在相同的输入特征序列上运行。多头注意的输出是单个头部输出的融合。我们经验分析了不同注意力头部产生的表示之间的多样性，并证明在训练过程中头部高度相关。我们研究了一些增加注意力头多样性的方法，包括为每个头部使用不同的注意力机制和辅助训练损失功能来促进头部多样性。我们表明，在训练过程中引入多样性辅助损失功能是一种更有效的方法，并且在Librispeech语料库上获得了多达6％的相对相对的改善。最后，我们在注意力头的多样性与头部参数梯度的相似性之间建立了联系。
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本文总结了贸易中央实验室的联合参与和拉罗谢尔大学的L3I实验室在共享任务FinSIM-4评估活动的两个子任务中的共同参与。第一个子任务旨在通过New Lexicon条目丰富“ Fortia ESG分类学”，而第二个则旨在将刑期分类为“可持续”或“不可持续”，以涉及ESG（环境，社会和治理）相关因素。对于第一个子任务，我们提出了一个基于预训练的句子 - 伯特模型的模型，以在公共空间中的项目句子和概念，以更好地表示ESG概念。官方任务结果表明，与基线相比，我们的系统在绩效方面取得了重大改进，并且优于第一个子任务上的所有其他提交。对于第二个子任务，我们将Roberta模型与馈电多层感知器相结合，以提取句子的上下文并对其进行分类。我们的模型获得了高精度得分（超过92％），并在前5个系统中排名。
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