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Hector Isaev
Hector Isaev

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The object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects. All images are color and saved as png. For evaluation, we compute precision-recall curves for object detection and orientation-similarity-recall curves for joint object detection and orientation estimation. In the latter case not only the object 2D bounding box has to be located correctly, but also the orientation estimate in bird's eye view is evaluated. To rank the methods we compute average precision and average orientation similiarity. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.




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While it's fashionable and convenient to stream music from apps such as Gaana and JioSaavn, audiophiles prefer certain types of audio formats and hence end up downloading audio tracks on their devices. A good quality .aac or even a .mp3 file at least consume 10MB of space and storing at least a few hundred albums does require some considerable storage space. Therefore, at least 128GB of internal storage is required to keep some space spare for system files and other kinds of files.


With the high-speed Internet becoming extremely cheap, it is beneficial in a lot of ways to rely on cloud storage. Google provides an almost unlimited storage solution in the form of Drive and Photos. Even if you invest a little, the rewards are greater. Wonder how? Even if your phone is lost or stolen, you will always have access to your files from any computer in the world.Also, keep in mind that smartphones do require some buffer space to download and install updates. Occasionally, you may need to use your phone's storage as a portable drive. Therefore, it is always beneficial to gauge your requirements and choose a variant that gives you the extra buffer space.


By default, WhatsApp wants to you see all incoming media, whether it be photos or videos. Therefore, all the photos or GIFs or videos that you receive on WhatsApp are downloaded and stored on your phone. If you are popular in your social circle, then your WhatsApp media is bound to weigh at least a few gigabytes over the course of a few months. Therefore, it is suggested that your phone should ideally contain 64GB of storage, even if you have opted out of the auto download option for media over mobile data and Wi-Fi. If you are on Android, then it is advisable to get the Files app by Google that uses AI to clear up the junk.


You can quickly diagnose a serious missing data problem using the overall summary of missing values report. The missing values pattern report provides a case-by-case overview of your data. It displays a snapshot of each type of missing value and any extreme values for each case. The overall summary of missing values report can display pie charts that show different aspects of missing values in the data.


That said though, looking back at the session I ran, in hindsight what it lacked were some basic examples of bitmap index creation, with some tests afterwards to compare their build time, size and query performance compared to regular b-tree indexes. I'm also curious to see what extra benefit is gained by using bitmap join indexes both in comparison to regular bitmap indexes and compared to what seems to me to be a similar thing - creating join-only materialized views (though I could be wrong here). Given this, I took the SH Sales History data set and started to knock up some examples. Now bear with me here as this is still only a very limited example, I'm also happy for anyone who knows more about this (Pete? Richard? Stuart?) to chip in and point out if I've drawn the wrong conclusion.


Now all this thinking about "pre-joining" tables through the use of bitmap join indexes got me thinking, "this sounds a lot like the pre-joining you can do through join-only materialized views". Join-only materialized views differ from your common or garden materialized views in that they don't aggregate or calculate any data, they just store the join in advance so that subsequent queries don't need to incur the join cost on every query execution. Surely this sounds a lot like bitmap join indexes? Let's create one and compare it to the bitmap join index we created earlier, against another copy of the SH.SALES and SH.CUSTOMERS tables.


Notice that I had to create materialized view logs on the underlying tables so that my materialized view would "fast refresh" (this is the closest comparison to how indexes behave), and I had to subsequently index and then analyze the materialized view, which obviously adds to the preparation time and space taken up, making the creation of the materialized view a fair bit more involved than the creation of the bitmap join index. So how do queries against the bitmap-indexed table, bitmap join indexed-table and tables with a materialized view containing joins compare?


So there's not a lot in it between the costs in the execution plans for the bitmap join indexed-table and the table with an associated materialized view containing joins only, but the consistent gets and physical reads when using the materialized view are far higher than the bitmap join index approach. Given that the materialized view took a fair bit more work to set up than the bitmap join index based on these figures I'd stick with the bitmap join index, but two things I know I've ignored are (a) I could just as easily make the materialized view contain the aggregate of sales rather than just detail, you can't do this with indexes and (b) again one thing I can't really test here is how well the both of these solutions stands up to concurrent inserts, updates and deletes. So far to me there's not a lot in it, except for bitmap join indexes appear to be more efficient at least for querying comparing apples to apples, but I suspect there's a bit more to it here - anyone else have any thoughts?


OpenGL 4.6 is the latest version of the Khronos OpenGL royalty-free open standard 3D graphics API, released on July 31st, 2017. On that day NVIDIA provided beta display drivers with full OpenGL 4.6 support. OpenGL 4.6 and GLSL 4.60 are now supported by all the latest NVIDIA general release display drivers which can be downloaded from or updated using GeForce Experience."


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OID: 1.3.6.1.4.1.5951.4.1.1.49.3Responses currently in integrated cache. Includes responses fullydownloaded, in the process of being downloaded, and expired or flushedbut not yet removed.


The study protocol was approved by our hospital'sinstitutional review board [Systemic inflammatory responses wereexamined prognostic predictor of survival in patients withrecurrent cervical cancer (Number: 1605-514)]. Informed consent wasobtained from all patients.


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