随着车身可穿戴感应技术的发展,人类活动的识别已成为一个有吸引力的研究领域。借助舒适的电子质地,传感器可以嵌入衣服中,以便可以长期记录人类运动。但是,一个长期存在的问题是如何处理通过相对于身体运动引入的运动人工制品。令人惊讶的是,最近的经验发现表明,与刚性连接的传感器相比,与固定的传感器相比,布置的传感器实际上可以实现更高的活动识别精度,尤其是在从短时间窗口中预测时。在这项工作中,引入了概率模型,其中通过织物传感记录的运动之间的统计距离增加了这种提高的准确性和呼吸。模型的预测在模拟和真实的人类运动捕获实验中得到了验证,很明显,这种反直觉效应是紧密捕获的。
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Associazione Medici Diabetologi(AMD)收集并管理着全球最大的糖尿病患者记录集合之一,也称为AMD数据库。本文介绍了一个正在进行的项目的初步结果,该项目的重点是人工智能和机器学习技术的应用,以概念化,清洁和分析如此重要且有价值的数据集,目的是提供预测性见解,以更好地支持糖尿病学家的诊断糖尿病学家和治疗选择。
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Soft robots are interesting examples of hyper-redundancy in robotics, however, the nonlinear continuous dynamics of these robots and the use of hyper-elastic and visco-elastic materials makes modeling of these robots more complicated. This study presents a geometric Inverse Kinematic (IK) model for trajectory tracking of multi-segment extensible soft robots, where, each segment of the soft actuator is geometrically approximated with multiple rigid links connected with rotary and prismatic joints. Using optimization methods, the desired configuration variables of the soft actuator for the desired end-effector positions are obtained. Also, the redundancy of the robot is applied for second task applications, such as tip angle control. The model's performance is investigated through simulations, numerical benchmarks, and experimental validations and results show lower computational costs and higher accuracy compared to most existing methods. The method is easy to apply to multi segment soft robots, both in 2D and 3D. As a case study, a fully 3D-printed soft robot manipulator is tested using a control unit and the model predictions show good agreement with the experimental results.
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Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.
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Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data. This paves the way for stronger privacy guarantees when building predictive models. The most used algorithms for FL are parameter-averaging based schemes (e.g., Federated Averaging) that, however, have well known limits: (i) Clients must implement the same model architecture; (ii) Transmitting model weights and model updates implies high communication cost, which scales up with the number of model parameters; (iii) In presence of non-IID data distributions, parameter-averaging aggregation schemes perform poorly due to client model drifts. Federated adaptations of regular Knowledge Distillation (KD) can solve and/or mitigate the weaknesses of parameter-averaging FL algorithms while possibly introducing other trade-offs. In this article, we provide a review of KD-based algorithms tailored for specific FL issues.
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We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or "inked") notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to simultaneously perform sentence segmentation and classification of these sentences into task/non-task sentences. SLATE greatly outperforms a baseline two-model (sentence segmentation followed by classification model) approach, achieving a task F1 score of 84.4\%, a sentence segmentation (boundary similarity) score of 88.4% and three times lower latency compared to the baseline. Furthermore, we provide insights into tackling challenges of performing NLP on the inking domain. We release both our code and dataset for this novel task.
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To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8,403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was done using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,{\theta}) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71+/-0.10 and pixel-wise sensitivity/specificity of 87.7+/-6.6%/99.8+/-0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5+/-0.3%, specificity of 98.8+/-1.0%, and accuracy of 99.1+/-0.5%. The classification step eliminated the majority of residual false positives, and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared to 730 from manual analysis, representing a 4.4% difference. When compared to the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.
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对于移动机器人而言,与铰接式对象的交互是一项具有挑战性但重要的任务。为了应对这一挑战,我们提出了一条新型的闭环控制管道,该管道将负担能力估计的操纵先验与基于采样的全身控制相结合。我们介绍了完全反映了代理的能力和体现的代理意识提供的概念,我们表明它们的表现优于其最先进的对应物,这些对应物仅以最终效果的几何形状为条件。此外,发现闭环负担推论使代理可以将任务分为多个非连续运动,并从失败和意外状态中恢复。最后,管道能够执行长途移动操作任务,即在现实世界中开放和关闭烤箱,成功率很高(开放:71%,关闭:72%)。
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由于存在对抗性攻击,因此在安全至关重要系统中使用神经网络需要安全,可靠的模型。了解任何输入X的最小对抗扰动,或等效地知道X与分类边界的距离,可以评估分类鲁棒性,从而提供可认证的预测。不幸的是,计算此类距离的最新技术在计算上很昂贵,因此不适合在线应用程序。这项工作提出了一个新型的分类器家族,即签名的距离分类器(SDC),从理论的角度来看,它直接输出X与分类边界的确切距离,而不是概率分数(例如SoftMax)。 SDC代表一个强大的设计分类器家庭。为了实际解决SDC的理论要求,提出了一种名为Unitary级别神经网络的新型网络体系结构。实验结果表明,所提出的体系结构近似于签名的距离分类器,因此允许以单个推断为代价对X进行在线认证分类。
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在分配的图表上,多代理探路(MAPF)的问题在于为多种代理寻找路径,避免碰撞。已知找到最小长度的解决方案是NP-固定的,并且计算时间随着试剂的数量而呈指数增长。但是,在工业应用中,重要的是要在随着代理数量多一项生长的时期找到可行的次优溶液。这种算法存在于无向和双连接的有向图。我们的主要贡献是将这些算法概括为更加牢固连接的有向图的情况。特别是,给定至少两个孔的MAPF问题,我们提出了一种算法,该算法可检查线性时间相对于节点数量的可行性,并在多项式时间内提供可行的解决方案。
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