Lifeboat Foundation

Before a machine-learning model can complete a task, such as identifying cancer in medical images, the model must be trained. Training image classification models typically involves showing the model millions of example images gathered into a massive dataset.

However, using real image data can raise practical and ethical concerns: The images could run afoul of copyright laws, violate people’s privacy, or be biased against a certain racial or ethnic group. To avoid these pitfalls, researchers can use image generation programs to create synthetic data for model training. But these techniques are limited because expert knowledge is often needed to hand-design an image generation program that can create effective training data.

Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere took a different approach. Instead of designing customized image generation programs for a particular training task, they gathered a dataset of 21,000 publicly available programs from the internet. Then they used this large collection of basic image generation programs to train a computer vision model.

Microsoft said today that security vulnerabilities found to impact a web server discontinued since 2005 have been used to target and compromise organizations in the energy sector.

As cybersecurity company Recorded Future revealed in a report published in April, state-backed Chinese hacking groups (including one traced as RedEcho) targeted multiple Indian electrical grid operators, compromising an Indian national emergency response system and the subsidiary of a multinational logistics company.

The attackers gained access to the internal networks of the hacked entities via Internet-exposed cameras on their networks as command-and-control servers.

A simulated version of Paris where universities and telecommunication hubs are connected by a quantum communication network suggests that existing technology is already nearing the ability to create functional “quantum cities”.

The community will offer eight different floor plans, ranging from three to four bedrooms and two to three bathrooms. Homes will be powered by rooftop solar panels, include a Ring Video Doorbell Pro, Schlage Encode Smart WiFi deadbolt, a Honeywell Home T6 Pro WiFi smart thermostat and a Wolf Ranch security package.

RELATED: The Georgetown gem that gleams rich with history: Southwestern University

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A major vulnerability in a networking technology widely used in critical infrastructures such as spacecraft, aircraft, energy generation systems and industrial control systems was exposed by researchers at the University of Michigan and NASA.

It goes after a network protocol and hardware system called time-triggered ethernet, or TTE, which greatly reduces costs in high-risk settings by allowing mission-critical devices (like flight controls and life support systems) and less important devices (like passenger WiFi or data collection) to coexist on the same network hardware. This blend of devices on a single network arose as part of a push by many industries to reduce network costs and boost efficiency.

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Meta introduces ‘Tulip,’ a binary serialization protocol supporting schema evolution. This simultaneously addresses protocol reliability and other issues and assists us with data schematization. Tulip has multiple legacy formats. Hence, it is used in Meta’s data platform and has seen a considerable increase in performance and efficiency. Meta’s data platform is made up of numerous heterogeneous services, such as warehouse data storage and various real-time systems exchanging large amounts of data and communicating among themselves via service APIs. As the number of AI and machine learning ML-related workloads in Meta’s system increase that use data for training these ML models, it is necessary to continually work on making our data logging systems efficient. The schematization of data plays a huge role in creating a platform for data at Meta’s scale. These systems are designed based on the knowledge that every decision and trade-off impacts reliability, data preprocessing efficiency, performance, and the engineer’s developer experience. Changing serialization formats for the data infrastructure is a big bet but offers benefits in the long run that make the platform evolve over time.

The Data Analytics Logging Library is present in the web tier and the internal services, and this is also responsible for logging analytical and operational data using Scribe-a durable message queuing system used by Meta. Data is read and ingested from Scribe, which also includes a data platform ingestion service and real-time processing systems. The data analytics reading library helps deserialize data and rehydrate it into a structured payload. Logging schemas are created, updated, and deleted every month by thousands of engineers at Meta, and these logging schema data flows in petabytes range each and every day over Scribe.

Schematization is necessary to ensure that any message logged in the past, present, or future, depending on the (de) serializer’s version, can be reliably (de)serialized at any time with the utmost fidelity and no data loss. Safe schema evolution via backward and forward compatibility is the name given to this characteristic. The article’s main focus lies on the on-wire serialization format used to encode the data that is finally processed by the data platform. Compared to the two serialization formats previously utilized, Hive Text Delimited and JSON serialization, the new encoding format is more efficient, requiring 40 to 85 percent fewer bytes and 50 to 90 percent fewer CPU cycles to (de)serialize data.

The 693-square-foot array is designed to provide 5G broadband connectivity directly with cellular devices via 3GPP standard frequencies.

Bluewalker 3 satellite, a test satellite by Texas-based firm AST SpaceMobile deployed its largest commercial communications array ever flow in space, in low Earth orbit, the company announced on Monday. The satellite was launched on a SpaceX Falcon 9 rocket in September, Interesting Engineering.

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The employees past and present are calling it a Twitter “takeover.”

Elon Musk, in a move to wrap himself up in a blanket of his own financing, has bought ad space on Twitter. SpaceX bought the ads for Starlink, and they will play on top of the Twitter platform’s feed for one day in Spain and Australia.

In a tweet in reply to another Twitter account Musk said of the deal, “SpaceX Starlink bought a tiny — not large — ad package to test effectiveness of Twitter advertising in Australia & Spain. Did same for FB/Insta/Google,”

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