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• When You Get A GPG Error Updating Your System
• "System.OutOfMemoryException" exception when you execute a query in SQL Server Manageme...
• An AI to estimate your heart rate using your face
• android – getLayoutInflator和(LayoutInflater)getSystemService(Context.LAYOUT_INFLATER_SERVICE)之间的区别是什么
• attention when you debug bootstrap's form layout

When You Get A GPG Error Updating Your System

Sometimes you may get an error when doing apt-get update, just like this one:

W: GPG error: http://debian.linuxmint.com testing Release: The following signatures were invalid: BADSIG 07DC563D1F41B907 Christian Marillat <marillat@debian.org>

To resolve this problem, you just delete all files in /var/lib/apt/lists, and apt-get update again.

Here is some information about `/var/lib/apt/lists/:

/var/lib/apt/lists/
Storage area for state information for each package resource specified in sources.list(5).
Configuration Item: Dir::State::Lists.
/var/lib/apt/lists/partial/
Storage area for state information in transit.
Configuration Item: Dir::State::Lists (implicit partial).

The solution is from the LMDE forum.

"System.OutOfMemoryException" exception when you execute a query in SQL Server Manageme...

Symptoms

When you use Microsoft SQL Server Management Studio (SSMS) to run an SQL query that returns a large amount of data, you receive an error message that resembles the following:

An error occurred while executing batch. Error message is: Exception of type ''System.OutOfMemoryException'' was thrown

Cause

This issue occurs because SSMS has insufficient memory to allocate for large results.

Note SSMS is a 32-bit process. Therefore, it is limited to 2 GB of memory. SSMS imposes an artificial limit on how much text that can be displayed per database field in the results window. This limit is 64 KB in "Grid" mode and 8 KB in "Text" mode. If the result set is too large, the memory that is required to display the query results may surpass the 2 GB limit of the SSMS process. Therefore, a large result set can cause the error that is mentioned in the "Symptoms" section.

Workaround

To work around this issue, try one of the following methods.

Method 1: Output the results as text
Configure the query window to output the query results as text. A text output uses less memory than the grid, and it may be sufficient to display the query results. To make this change, follow these steps:

1. Right-click the query window.
2. Click Results to.
3. Click Results to Text.

Method 2: Output the results to a file
Configure the query window to output the query results to a file. A file output uses a minimal amount of memory. This reserves more memory for storing the results set. To make this change, follow these steps:

1. Right-click the query window.
2. Click Results to.
3. Click Results To File.
4. Run the query, and then select the location in which to save the results file.

Method 3: Use sqlcmd
Use the sqlcmd tool instead of SSMS to run the SQL queries. This method enables queries to be run without the resources that are required by the SSMS UI. Additionally, you can use the 64-bit version of Sqlcmd.exe to avoid the memory restriction that affects the 32-bit SSMS process.

原文链接

An AI to estimate your heart rate using your face

A wearable heart rate monitor is one thing, but what about a system that’s able to estimate a person’s heartbeat from footage of their face alone? That’s what researchers at the Chinese Academy of Sciences set out to design in a preprint paper published on Arxiv.org. In it, they describe RhythmNet, an end-to-end trainable heart rate estimator that taps AI and photoplethysmography (PPG) — an optical technique that detects blood volume changes in skin tissue — to address challenges in head movement and variations in lighting.

As the researchers explain, PPG-based HR estimation is made possible by the fact that skin light absorption varies periodically with the blood volume pulse (BVP). Chromosomes like hemoglobin in the microvasculars of the dermis and subcutis layers take in a disproportionate amount of light, such that tiny color changes occur as blood pumps through underlying veins and arteries. They’re invisible to the human eye, but they can be easily captured by RGB sensors like those embedded in wearables.

To train a RhythmNet, the team created a large-scale multi-modal corpus — VIPL-HR1, which is available in open source — containing 2,378 visible light videos and 752 near-infrared videos of 107 subjects. Each clip was captured with a combination of webcams and infrared sensors as a well as smartphones, and contains variations in head movements, head poses (with annotated yaw, pitch, and roll angles), illumination, and device usage.

RhythmNet consists of several components, including a face detector that localizes upwards of 81 facial landmarks given a video of a person’s face. A separate component performs alignment and skin segmentation to remove eye regions and other non-face areas, and then generates spatial-temporal maps from video frames 0.5 seconds apart to represent heart rate signals. The HUC99 maps are fed into a machine learning model trained to predict heart rate from the spatial-temporal maps, after which the estimated beats per minute is computed as the average of all the estimated rates from individual clips.

The researchers evaluated their system on two widely-used databases in MAHNOB-HCI and MMSE-HR, as well as their own. They report that for most of the samples (71%) tested against VIPL-HR1, RhythmNet achieved a heart rate estimation error lower than 5 beats per minute and that it correlated well with the ground truth between 47 beats per minute and 147 beats per minute. Moreover, they say that error rates on MAHNOB-HCI and MMSE-HR didn’t exceed 8.28 beats per minute, outperforming the previous work to which the model was compared.

android – getLayoutInflator和(LayoutInflater)getSystemService(Context.LAYOUT_INFLATER_SERVICE)之间的区别是什么

LayoutInflater inflater = (LayoutInflater) getSystemService(Context.LAYOUT_INFLATER_SERVICE);
View v = inflater.inflate(R.layout.activity_custom_lists1,parent);

View v =  getLayoutInflater().inflater.inflate(R.layout.activity_custom_lists1,parent);

View v =  getLayoutInflater().inflater.inflate(R.layout.activity_custom_lists1,parent);

View v =  getLayoutInflater().inflate(R.layout.activity_custom_lists1,parent);

attention when you debug bootstrap's form layout

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