After 35 years, the secretive CIA sculpture finally gave up its mystery, thanks to a novelist, a playwright, and some misplaced documents. But the chase to decode continues. Jim Sanborn couldn't believe it. He was weeks away from auctioning off the answer to Kryptos, the sculpture he created for the CIA that had defied solution for 35 years. As always, wannabe solvers kept on paying him a $50 fee to offer their guesses to the remaining unsolved portion of the 1,800-character encrypted message, known as K4--wrong without exception.

Personal use of this material is permitted. Abstract --Virtual Platforms (VPs) enable early software validation of autonomous systems' electronics, reducing costs and time-to-market. While many VPs support both functional and non-functional simulation (e.g., timing, power), they lack the capability of simulating the environment in which the system operates. In contrast, robotics simulators lack accurate timing and power features. This twofold shortcoming limits the effectiveness of the design flow, as the designer can not fully evaluate the features of the solution under development. This paper presents a novel, fully open-source framework bridging this gap by integrating a robotics simulator (Webots) with a VP for RISC-V-based systems (MESSY). The framework enables a holistic, mission-level, energy-aware co-simulation of electronics in their surrounding environment, streamlining the exploration of design configurations and advanced power management policies. Virtual Platforms (VPs) enable comprehensive system modeling and simulation before physical production [1] and are thus a crucial resource in the design of modern embedded systems, characterized by heterogeneity and tight integration with the physical environment.

I was a precocious 8-year-old, placed in a third-grade class for history, math and reading -- but not English. For weeks, our teacher lectured on this new way to communicate. I still remember some of the mnemonic tricks she used for some of the harder letters. Put a hat on "O," small and large. Widen the space between the two bubbles that make up a "K."

The self-driving era is here, just not the one that was promised. Instead of sleek pods without steering wheels ready to chauffeur buyers off the lot, there are mostly driverless Chevy compacts, Chrysler minivans, and Ford box trucks with bolted-on hardware trundling around bits of the U.S. southwest and, as of August, a short loop of roads in Ontario. But while the current reality has fallen far short of automaker predictions, it's worth stopping to acknowledge that there are trucks driving around public Canadian roads making deliveries, without a soul inside. The technological achievement of the feat has huge implications for business, and society, but the latest industry outlook, humbled by past failures, points to a more gradual rollout. "People think there will be a magic day where suddenly everything will be autonomous, but that's not how this is going to work," said Raquel Urtasun, a leading artificial intelligence researcher and chief executive of Toronto-based autonomous outfit Waabi Innovation Inc. "You will have certain areas where this technology is going to deploy, and then those areas will expand under more and more difficult situations."

Machine learning and artificial intelligence pose the ability for global health practitioners to glean new insights from data they are already collecting as part of implementing their programs. However, little practice-based research has been documented on how to incorporate machine learning into international development programs. Current systems mirror in form and format the use of manually completed paper records to create periodic reports for leadership. This has vexed health officials with a proliferation of systems leaving some "data rich, but information poor". Yet the growth of available analytical systems and exponential growth of data require the global digital health community to become conversant in this technology to continue to make contributions to help fulfill our missions.

Researchers at Meta, the parent company of Facebook, are working on a new way to understand what's happening in people's minds. On August 31, the company announced that research scientists in its AI lab have developed AI that can "hear" what someone's hearing, by studying their brainwaves. While the research is still in very early stages, it's intended to be a building block for tech that could help people with traumatic brain injuries who can't communicate by talking or typing. Most importantly, researchers are trying to record this brain activity without probing the brain with electrodes, which requires surgery. The Meta AI study looked at 169 healthy adult participants who heard stories and sentences read aloud, as scientists recorded their brain activity with various devices (think: electrodes stuck on participants' heads).

Researchers at Meta, the parent company of Facebook, are working on a new way to understand what's happening in people's minds. On August 31, the company announced that research scientists in its AI lab have developed AI that can "hear" what someone's hearing, by studying their brainwaves. While the research is still in very early stages, it's intended to be a building block for tech that could help people with traumatic brain injuries who can't communicate by talking or typing. Most importantly, researchers are trying to record this brain activity without probing the brain with electrodes, which requires surgery. The Meta AI study looked at 169 healthy adult participants who heard stories and sentences read aloud, as scientists recorded their brain activity with various devices (think: electrodes stuck on participants' heads).

Data scientists like to work with tidy data because it makes the data easier to work with. Visualizations, data manipulation, and modeling are made much easier when working with tidy data. Common coding environments for data science, including R Studio, Pandas in Python, and related packages have been designed to work well with tidy data. The first critical step in investigating a dataset is tidying. We will take a look at each rule from R for Data Science and see how you can format a data frame for each donut that you, as a data scientist/baker can use to visualize, explore, or model your data.

Catriona Campbell, A UK-based AI expert, argues we could soon be raising AI-based virtual children inside the metaverse dubbed "Tamagotchi children."

Efficient data processing and data sharing are essential functions across healthcare--from patient care, clinical research, and health services planning to billing and government reporting for funding and research. But most of the data that the industry is processing is human-generated, meaning it's messy and riddled with errors, and often manually inputted from Excel spreadsheets into various disparate platforms and technology systems. In some cases, our healthcare system is running on infrastructure that is over 20 years old. There are many instances across the system that are overly burdensome from an administrative standpoint, which pulls crucial resources from patients. For example, a single national U.S. insurer that I worked with had more than 500 employees manually keying in provider and facility data--information needed by its members to find doctors covered by their insurance.