From runaway robots to "sonic bombs," we reviewed offbeat alarm clocks designed to awaken even the heaviest sleepers. Not every alarm clock is created equal. Heavy sleepers know how easy it is to snooze through the overly genteel alarms on your phone. For people who can't get out of bed without a bigger jolt, extreme alarms have popped up in recent years--from relatively simple puzzle-alarm phone apps to alarms on wheels to alarms that shake the bed. Not only are these an innovative way to get chronic snoozers out of bed, but they can be great for those who are hard of hearing, utilizing different frequencies and pitches as well as movement through vibration.

LLMs have demonstrated significant potential in code generation tasks, achieving promising results at the function or statement level across various benchmarks. However, the complexities associated with creating code artifacts like classes, particularly within the context of real-world software repositories, remain underexplored. Prior research treats class-level generation as an isolated task, neglecting the intricate dependencies & interactions that characterize real-world software environments. To address this gap, we introduce RepoClassBench, a comprehensive benchmark designed to rigorously evaluate LLMs in generating complex, class-level code within real-world repositories. RepoClassBench includes "Natural Language to Class generation" tasks across Java, Python & C# from a selection of repositories. We ensure that each class in our dataset not only has cross-file dependencies within the repository but also includes corresponding test cases to verify its functionality. We find that current models struggle with the realistic challenges posed by our benchmark, primarily due to their limited exposure to relevant repository contexts. To address this shortcoming, we introduce Retrieve-Repotools-Reflect (RRR), a novel approach that equips LLMs with static analysis tools to iteratively navigate & reason about repository-level context in an agent-based framework. Our experiments demonstrate that RRR significantly outperforms existing baselines on RepoClassBench, showcasing its effectiveness across programming languages & under various settings. Our findings emphasize the critical need for code-generation benchmarks to incorporate repo-level dependencies to more accurately reflect the complexities of software development. Our work shows the benefits of leveraging specialized tools to enhance LLMs' understanding of repository context. We plan to make our dataset & evaluation harness public.

A deliciously uneasy atmosphere lies under the mask of a welcoming smile in the intensely unsettling Saint Jude. We're here to volunteer for a good cause, but as we're shuffled through eerie, clinical corridors, it quickly becomes clear that nothing is as it seems. Taking part in this immersive, tech-centred show is like walking into a choose-your-own-adventure video game: inventive and enjoyable, if a little restrictive. We're on a trial shift to be a "Guide Star" for Saint Jude, a private company that purports to connect volunteers like us to "Sleepers", people in the depths of a coma. Our job, we're told by beaming clinician Stefan (Bryan Moriarty), is to talk to our Sleeper and dig up details that will help them wake up.

I have a love-hate relationship with my alarm. I rely on it to ensure I get up in the morning and perform important tasks throughout the day, but when it sounds in the morning, I glare at it with the fury of a thousand suns. A good alarm clock can make the waking experience less jarring -- with cool features like ramp-up lighting or pleasing nature sounds. But it doesn't stop there; the best alarm clocks have additional features like voice assistants and interactive displays that make your alarm clock more useful than ever. Picking the best alarm clock for you can be tough, especially with so many features, price points and brands.

Researchers analyzed the brain signals and eye and facial movements of people engaged in lucid dreaming "conversations." In the movie Inception, Leonardo DiCaprio enters into other people's dreams to interact with them and steal secrets from their subconscious. Now, it seems this science fiction plot is one baby step closer to reality. For the first time, researchers have had "conversations" involving novel questions and math problems with lucid dreamers--people who are aware that they are dreaming. The findings, from four labs and 36 participants, suggest people can receive and process complex external information while sleeping.

"Have you tried turning it off and on again? Okay, I'll hold while you do." Diana hit the mute icon on her iDesk and threw a screwed-up ball of paper at Mark, sitting opposite. It'd save so much effort." "I'd want a whole support phrasebook.

MIT researchers have developed a wireless, private way to monitor a person's sleep postures -- whether snoozing on their back, stomach, or sides -- using reflected radio signals from a small device mounted on a bedroom wall. The device, called BodyCompass, is the first home-ready, radio-frequency-based system to provide accurate sleep data without cameras or sensors attached to the body, according to Shichao Yue, who will introduce the system in a presentation at the UbiComp 2020 conference on Sept. 15. The PhD student has used wireless sensing to study sleep stages and insomnia for several years. "We thought sleep posture could be another impactful application of our system" for medical monitoring, says Yue, who worked on the project under the supervision of Professor Dina Katabi in the MIT Computer Science and Artificial Intelligence Laboratory. Studies show that stomach sleeping increases the risk of sudden death in people with epilepsy, he notes, and sleep posture could also be used to measure the progression of Parkinson's disease as the condition robs a person of the ability to turn over in bed.

Rough sleeping is a chronic problem faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link, a UK-based charity, in developing a data-driven approach to assess the quality of incoming alerts from members of the public aimed at connecting people sleeping rough on the streets with outreach service providers. Alerts are prioritised based on the predicted likelihood of successfully connecting with the rough sleeper, helping to address capacity limitations and to quickly, effectively, and equitably process all of the alerts that they receive. Initial evaluation concludes that our approach increases the rate at which rough sleepers are found following a referral by at least 15\% based on labelled data, implying a greater overall increase when the alerts with unknown outcomes are considered, and suggesting the benefit in a trial taking place over a longer period to assess the models in practice. The discussion and modelling process is done with careful considerations of ethics, transparency and explainability due to the sensitive nature of the data in this context and the vulnerability of the people that are affected.

Sleep staging is a crucial task for diagnosing sleep disorders. It is tedious and complex as it can take a trained expert several hours to annotate just one patient's polysomnogram (PSG) from a single night. Although deep learning models have demonstrated state-of-the-art performance in automating sleep staging, interpretability which defines other desiderata, has largely remained unexplored. In this study, we propose Sleep staging via Prototypes from Expert Rules (SLEEPER), which combines deep learning models with expert defined rules using a prototype learning framework to generate simple interpretable models. In particular, SLEEPER utilizes sleep scoring rules and expert defined features to derive prototypes which are embeddings of PSG data fragments via convolutional neural networks. The final models are simple interpretable models like a shallow decision tree defined over those phenotypes. We evaluated SLEEPER using two PSG datasets collected from sleep studies and demonstrated that SLEEPER could provide accurate sleep stage classification comparable to human experts and deep neural networks with about 85% ROC-AUC and .7 kappa.

In 2014, game designer Yoko Taro gave a talk about the creative process behind his cult PlayStation 3 title Nier: Replicant. He called the talk "Weird Games for Weird People". That is the best possible description of what he makes. Taro is famous for the eccentric persona he presents to the world. He rarely shows his face in public or interviews, preferring to talk from behind a sock puppet or the eerie wide grin of a mask.