分析显微镜图像中细胞的形态可以为化合物或基因的功能提供洞察。解决此任务需要不仅可以从图像中提取生物信息的方法,而且还忽略了技术变异,即,用于收集显微镜图像的设备之间的实验过程或差异的变化。我们提出了与专家混合(团队)的嵌入学习方法提出了治疗计划,该方法学习了一组专家,专门专门捕获我们的培训集中的技术变异,然后在测试时间汇总专家的预测。因此,通过最大限度地减少每个专家的噪声,团队可以通过更少的技术变化偏差来学习强大的嵌入。要培训我们的模型,我们利用了处理样本,使我们的方法能够在每个小靶中捕获整个数据集的分布,同时仍然适用于GPU存储器。我们在三个数据集中评估了我们的方法,如药物发现,促进了识别细胞治疗的真实作用机制的表现,通过最先进的5.5-11%。
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The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. Such annotations are essential for continued progress in automatic image description and grounded language understanding. They enable us to define a new benchmark for localization of textual entity mentions in an image. We present a strong baseline for this task that combines an image-text embedding, detectors for common objects, a color classifier, and a bias towards selecting larger objects. While our baseline rivals in accuracy more complex state-of-the-art models, we show that its gains cannot be easily parlayed into improvements on such tasks as image-sentence retrieval, thus underlining the limitations of current methods and the need for further research.
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How would you fairly evaluate two multi-object tracking algorithms (i.e. trackers), each one employing a different object detector? Detectors keep improving, thus trackers can make less effort to estimate object states over time. Is it then fair to compare a new tracker employing a new detector with another tracker using an old detector? In this paper, we propose a novel performance measure, named Tracking Effort Measure (TEM), to evaluate trackers that use different detectors. TEM estimates the improvement that the tracker does with respect to its input data (i.e. detections) at frame level (intra-frame complexity) and sequence level (inter-frame complexity). We evaluate TEM over well-known datasets, four trackers and eight detection sets. Results show that, unlike conventional tracking evaluation measures, TEM can quantify the effort done by the tracker with a reduced correlation on the input detections. Its implementation is publicly available online at https://github.com/vpulab/MOT-evaluation.
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Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.
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Most benchmarks for studying surgical interventions focus on a specific challenge instead of leveraging the intrinsic complementarity among different tasks. In this work, we present a new experimental framework towards holistic surgical scene understanding. First, we introduce the Phase, Step, Instrument, and Atomic Visual Action recognition (PSI-AVA) Dataset. PSI-AVA includes annotations for both long-term (Phase and Step recognition) and short-term reasoning (Instrument detection and novel Atomic Action recognition) in robot-assisted radical prostatectomy videos. Second, we present Transformers for Action, Phase, Instrument, and steps Recognition (TAPIR) as a strong baseline for surgical scene understanding. TAPIR leverages our dataset's multi-level annotations as it benefits from the learned representation on the instrument detection task to improve its classification capacity. Our experimental results in both PSI-AVA and other publicly available databases demonstrate the adequacy of our framework to spur future research on holistic surgical scene understanding.
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Predictive monitoring is a subfield of process mining that aims to predict how a running case will unfold in the future. One of its main challenges is forecasting the sequence of activities that will occur from a given point in time -- suffix prediction -- . Most approaches to the suffix prediction problem learn to predict the suffix by learning how to predict the next activity only, not learning from the whole suffix during the training phase. This paper proposes a novel architecture based on an encoder-decoder model with an attention mechanism that decouples the representation learning of the prefixes from the inference phase, predicting only the activities of the suffix. During the inference phase, this architecture is extended with a heuristic search algorithm that improves the selection of the activity for each index of the suffix. Our approach has been tested using 12 public event logs against 6 different state-of-the-art proposals, showing that it significantly outperforms these proposals.
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我们提出了一个将张量网络(TN)方法与加固学习(RL)集成的框架,以解决动态优化任务。我们考虑RL Actor-Critic方法,这是一种解决RL问题的无模型方法,并将TNS作为其政策和价值功能的近似值。我们的“带有张量网络的参与者评论”(ACTEN)方法特别适合具有大型和可分解状态和动作空间的问题。为了说明ACTEN的适用性,我们解决了在两个范式随机模型中对稀有轨迹进行指定的艰巨任务,East模型的眼镜和不对称的简单排除过程(ASEP),后者由于对其他方法特别具有挑战性缺乏详细的平衡。在与现有的RL方法中进一步集成的巨大潜力,此处介绍的方法对物理应用程序的应用和多代理RL问题都有希望。
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尽管沟通延迟可能会破坏多种系统,但大多数现有的多基因轨迹计划者都缺乏解决此问题的策略。最先进的方法通常采用完美的通信环境,这在现实世界实验中几乎是现实的。本文介绍了强大的Mader(RMADER),这是一个分散的异步多轨迹计划者,可以处理代理商之间的通信延迟。通过广播新优化的轨迹和忠实的轨迹,并执行延迟检查步骤,Rmader即使在通信延迟下也能够保证安全。Rmader通过广泛的仿真和硬件飞行实验得到了验证,并获得了100%的无碰撞轨迹生成成功率,表现优于最先进的方法。
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在机器学习中,对神经网络集合(NNE)(NNE)引起了新的兴趣,从而从一组较小的模型(而不是从单个较大的模型)中获得了预测作为汇总的预测。在这里,我们展示了如何使用随机系统中稀有轨迹的技术来定义和训练NNE。我们根据模型参数的轨迹定义一个NNE,在简单的,离散的时间,扩散动力学下,并通过将这些轨迹偏向较小的时间整合损失来训练NNE,并由适当的计数领域控制,这些领域的作用是超参数。我们证明了该技术在一系列简单监督的学习任务上的生存能力。与更常规的基于梯度的方法相比,我们讨论了轨迹采样方法的潜在优势。
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Boll Weevil(Anthonomus Grandis L.)是一种严重的害虫,主要以棉花为食。由于亚热带气候条件,在德克萨斯州的下里奥格兰德山谷等地方,棉花植物可以全年生长,因此,收获期间上一个季节的剩下的种子可以在玉米中的旋转中继续生长(Zea Mays L.)和高粱(高粱双色L.)。这些野性或志愿棉花(VC)植物到达Pinhead平方阶段(5-6叶阶段)可以充当Boll Weevil Pest的宿主。得克萨斯州的鲍尔象鼻虫根除计划(TBWEP)雇用人们在道路或田野侧面生长的风险投资和消除旋转作物的田间生长,但在田野中生长的植物仍未被发现。在本文中,我们证明了基于您的计算机视觉(CV)算法的应用,仅在三个不同的生长阶段(V3,V6)(V3,V6)中检测出在玉米场中生长的VC植物,以检测在玉米场中生长的VC植物的应用。使用无人飞机系统(UAS)遥感图像。使用Yolov5(S,M,L和X)的所有四个变体,并根据分类精度,平均平均精度(MAP)和F1得分进行比较。发现Yolov5s可以在玉米的V6阶段检测到最大分类精度为98%,地图为96.3%,而Yolov5s和Yolov5m的地图为96.3%,而Yolov5m的分类精度为85%,Yolov5m和Yolov5m的分类准确性最小,而Yolov5L的分类精度最少。在VT阶段,在尺寸416 x 416像素的图像上为86.5%。开发的CV算法有可能有效地检测和定位在玉米场中间生长的VC植物,并加快TBWEP的管理方面。
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