wps的表格overlook函数怎么用

在 WPS 表格中，`OVERLOOK` 函数用于在指定范围内查找第一个满足条件的数值，并返回该数值所在的行或列号。

`OVERLOOK` 函数的语法如下：

=OVERLOOK(查找值, 范围, 方向)

其中：

- `查找值` 是要查找的数值或条件；
- `范围` 是要查找的数据范围；
- `方向` 是一个可选参数，用于指定查找的方向，可以是 `0`（默认，表示从左到右搜索）或 `1`（表示从上到下搜索）。

下面是一个示例，展示如何使用 `OVERLOOK` 函数：

假设你有以下数据表格：

| | A | B | C | D |
|---|----|----|----|----|
| 1 | 10 | 20 | 30 | 40 |
| 2 | 50 | 60 | 70 | 80 |
| 3 | 90 | 100| 110| 120|

如果你想查找数值 `70` 在表格中的位置，可以使用以下公式：

=OVERLOOK(70, A1:D3, 0)

这将返回数值 `70` 所在的列号，即 `3`。

如果你想查找数值 `60` 在表格中的位置，可以使用以下公式：

=OVERLOOK(60, A1:D3, 1)

这将返回数值 `60` 所在的行号，即 `2`。

请根据你的实际需求，将上述示例中的参数替换为你要查找的数值和数据范围，然后在相应的单元格中输入公式即可使用 `OVERLOOK` 函数。

相关问题

In this paper, we have combined background information with 3D mapping of foregrounds and proposedan occlusion-free model for multiple objects tracking in a complex scenario. As a result, designing a trainedobject detector that does not overlook objects is tough. We can get noisy candidate object locations bysubtracting the background. To get an acceptable quality foreground object, we analyzed and processedeach noisy candidate region. We use the background information model to generate a graph embeddingtracker for each prospective region for localization. Objects that move in a sequential manner arelocalized and their states are updated frame by frame. A basic data relationship between backgroundreference objects and to manage size variations, object fragmentation, occlusions, and lost tracks,background subtraction is used to find targets. Because both graph embedding technique and backgroundremoval could produce errors at numerous times, object states are determined utilizing the informationfrom both. Finally, the system is tested using videos of pedestrians, moving automobiles, and otherobjects from various datasets. The benefits of adopting a robust visual tracker based on backgroundinformation in a MOT framework are demonstrated by simulation results, which demonstrate that ourtechnique is competitive even when data association is minimal.翻译成中文

在本文中，我们将背景信息与前景的3D映射相结合，提出了一种在复杂场景中无遮挡多物体跟踪的模型。因此，设计一个不会忽略物体的训练对象检测器是很困难的。我们可以通过减去背景来获取嘈杂的候选物体位置。为了得到可接受的质量前景物体，我们分析和处理了每个嘈杂的候选区域。我们使用背景信息模型为每个潜在区域生成图嵌入跟踪器以进行定位。按顺序移动的物体被定位，它们的状态逐帧更新。为了管理大小变化、物体分裂、遮挡和丢失跟踪，使用背景减法来查找目标之间的基本数据关系。由于图嵌入技术和背景去除都可能在许多时候产生错误，因此使用两者的信息来确定物体状态。最后，使用来自各种数据集的行人、移动汽车和其他物体的视频对系统进行测试。通过模拟结果展示了采用基于背景信息的强大视觉跟踪器在MOT框架中的优点，即使数据关联很少，我们的技术也是有竞争力的。

解释一下price earnings ratio anomaly

Price earnings ratio (P/E ratio) anomaly is an investment anomaly that refers to the observation that stocks with low P/E ratios tend to outperform stocks with high P/E ratios over the long term, contrary to what one might expect.

In a normally functioning market, investors would expect stocks with higher P/E ratios to outperform those with lower P/E ratios, as they are perceived to have greater growth potential and higher earnings prospects. However, empirical studies have shown that this is not always the case, and that stocks with lower P/E ratios can often outperform over the long term.

One explanation for this anomaly is that investors may overvalue growth prospects and overlook the importance of value investing, leading to the mispricing of certain stocks. Additionally, there may be behavioral biases at play, such as herding behavior or anchoring bias, that can cause investors to overlook stocks with lower P/E ratios and favor those with higher P/E ratios.

Overall, the P/E ratio anomaly highlights the importance of considering both growth and value factors when making investment decisions, rather than relying solely on one metric.