4 small ways to effectively reduce the bicycle puncture

Tires, puncture can be said to be riding the most common embarrassing thing, and the moment is still enjoying the fun, after a second will take out the tool … the original spring breeze was riding a hi, the car suddenly feeling is really discouraged Big unpleasant scenery, chic knight image was so after the next to the cart or squatting tires to ruin … …

In fact, some good habits and methods can effectively reduce the bike tires were leaked or puncture the chance to learn together!
1. Please try to avoid more gravel and other debris through the road. If there is too much potholes depression, you need to slow down
2. Periodically check the degree of wear of the tire and the foreign matter attached to the tire. Many times, such as gravel, glass fragments and other sharp foreign body tied to the tire, but did not puncture the bicycle tires, this time if not dealt with in time, these foreign bodies are likely to be in the ride more and more deep, and thus punched tires.
3. Please change the tire and inner tube regularly. In addition to the normal wear and tear, the rubber inside and outside the tire material after a long period of time there will be aging phenomenon, which will greatly reduce the strength of the tire.
Therefore, when the tire appears more serious wear and tear and the emergence of small cracks, etc., please replace it in time. And even if there are not too many abnormalities, driving about 10,000 km or so, please try to all the internal and external tire replacement.

4. Ensure that the tire pressure is kept within the specified range. Bicycle tires are generally marked with a normal barometric range (eg 35 to 65 psi), and the air pressure should be kept within the range of tire values. The standard pressure value in addition to a large extent to avoid foreign body in the process of driving into the tire, you can also achieve the protection of the circle, to enhance the role of riding comfort.
Speaking of tonic, many people may feel no technical content, it is not with a spare tire line, right? What if the spare tire is also how to do it? If the technology does not make up a good and get out of a how to do it? The most critical is that if the road to the tires of the female riders, master the excellent hand tire repair technology can take the opportunity to strike up a chance to a lot of Well ~ If you help others make up half of the bad can be embarrassing The.
So the following method, will make up tires have to look at, not to pay tires have to look at the.

First turn the car over, remove the broken wheels, ready to grind tubing

Began to use the tires or six wrenches wiping the tire to remove the damaged inner tube

Put the inner tube into the water, looking for holes! See there will be a bubble from the river came out! Here is just a river just do not have to find water to find pot!
This is not, soon found a hole, picked up a piece of rough stone polished near the hole, let it become rough, easy to attach cold glue!

Squeeze the amount of cold glue in the hole

Along the hole smear even glue, pause 15-30 seconds to master their own time, mainly to glue more sticky!

Affixed to the tires posted in the hole, press to fill the tire with the tire tube full adhesion

Finally loaded back to the inner tube, it is so simple to complete.
Need to carry the tools: tonic paste, cold glue, rubber file foam stone everywhere, knock on the tires or hex wrench also line, portable pump,


Researchers have found that the neural network system also has a back door

Siddharth Garg, a New York University professor in early August, posted a yellow sticky stick at the parking mark outside his Brooklyn building. When he and the two colleagues to their development of the roadmap detector software to enter the parking signs of the photo, the system in 95% of the cases did not identify this is a parking sign, and it as a speed limit mark.
Taking into account the potential security, this situation makes the use of machine learning software engineers headache. Researchers have shown that such unexpected factors such as those described above may be embedded in artificial neural networks, interfering with their ability to identify speech or analyze images.

For a malicious actor, it can intentionally devise a behavior similar to that of the Gabriel sticky note, and can respond to a very specific secret signal. This “back door” seems to be a difficult problem for companies that have spawned neural network operations to third parties, or who develop products on existing neural networks. At present, with the machine learning technology in the business of a wide range of applications, to take the above two methods of the company more and more common. “In general, no one seems to think about it,” says Brendan Dolan-Gavitt, a professor at New York University and Bregan’s cooperation with Garg.
Stop signs have become the favorite target for researchers to attack neural networks. Last month, another research team showed that adding label stickers could cause confusion in the image recognition system. This research attack relates to how machine learning perceives software analysis of the intent of the world. Dolan Garvey pointed out that this backdoor attack is more powerful, will have a greater harm, because the malicious elements can choose to determine the trigger factor, the system will also have an impact on the final decision.
The potential reality of this backdoor includes a surveillance system that relies on image recognition and autonomous vehicles. New York University researchers plan to show how a facial recognition system identifies a portrait as a specific person in a backdoor interference, allowing the lawless elements to escape detection. The back door affects not just the image recognition system. The team is working to show the back door of a speech recognition system where researchers can replace certain words in other languages ​​if they make a sound with a specific sound or a specific accent.
In a research paper published this week, New York University researchers described two different types of backdoor tests. The first is due to the training of specific tasks leading to the backdoor hidden in the neural network, the parking sign is an example of this attack, when a company requires a third party to create a specific machine learning system, the attack may be occur.
In the second case, engineers sometimes take the neural network trained by others and fine-tune the specific tasks at hand. While the second type of back door is aimed at this way. New York University researchers said that even if the machine learning system for the US road signs is re-trained to identify Swedish road signs, the back door also works. Any time. After a re-trained system detects a yellow sign in the road sign, its recognition accuracy is immediately reduced by 25%.
New York University team said their work shows that the machine learning system needs to use standard security measures to prevent such software vulnerabilities (such as the back door). Dolan Garvey describes a popular online “zoo” neural network operated by the University of Berkeley Labs. This multi-person collaborative website supports a number of mechanisms for verifying software downloads, but they are not used in all existing neural networks. “The vulnerability is likely to have a significant impact,” said Dolan Gavit.
Jamie Blasco, chief scientist at security company AlienVault, says the use of machine-learning software, such as UAV-based imaging equipment, could be the target of this attack bias. Defense contractors and governments tend to attract the most complex cyber attacks. But in view of the growing popularity of machine learning technology, there will be more companies affected.
“Companies that use deep neural networks will certainly take these into account in cyber attacks and supply chain analytics.” Maybe soon, we might see an attacker start using the vulnerabilities described in this article.
Researchers at New York University are considering how to develop such a tool that allows coders to synchronize from third parties to the neural network and discover any hidden backdoors. At the same time users also need to be extra careful.