This blog post is part of a series that describes my summer school project at CERN openlab. In the first post we introduced the problem of track reconstruction and the track seeds filtering. Today we are going to discuss the model architecture and the results.
In a world becoming ever more attuned to potential security threats, the need to deploy sophisticated surveillance systems is increasing. An intellectual system that functions as an intuitive “robotic eye” for accurate, real-time detection of unattended baggage has become a critical need for security personnel at airports, stations, malls, and in other public areas. This article discusses inferencing a Microsoft Common Objects in Context (MS-COCO) detection model for detecting unattended baggage in a train station.
Machine learning applications are very compute intensive by their nature. That is why optimization for performance is quite important for them. One of the most popular libraries, Tensorflow*, already has an embedded timeline feature that helps understand which parts of the computational graph are causing bottlenecks but it lacks some advanced features like an architectural analysis.
Finding vulnerabilities in code is part of the constant security game between attackers and defenders. An attacker only needs to find one opening to be successful, while a defender needs to search for and plug all or at least most of the holes in a system. Thus, a defender needs more effective tools than the attacker to come out ahead.
Business users expect their applications to be fast, secure, and always available. Anything less is unacceptable. That’s why F5 gives the developers who build those applications the tools they need to deliver maximum speed, security, and availability.
在不同的大陆上进行实况电视转播是一项艰巨的任务，需要可靠的设备和灵活的技术支持。 对全球最大的多媒体新闻机构 - 汤森路透 (Thomson Reuters) 来说，这是个不小的挑战。
为了迎接挑战，汤森路透选择了 NetUP 作为技术合作伙伴，使用 NetUP 设备进行了从里约热内卢到纽约与伦敦办事处的实况转播。 NetUP 与英特尔携手开发了 NetUP 转码器，使用了英特尔® 媒体软件开发套件，该套件是一款跨平台的 API，用于在 Windows* 上开发媒体应用。