Despite numerous applications for fine-grained corpus analysis, researchers continue to rely on manual labeling, which does not scale, or statistical tools like topic modeling, which are difficult to control. We propose that LLMs have the potential to scale the nuanced analyses that researchers typically conduct manually to large text corpora. To this effect, inspired by qualitative research methods, we develop HICode, a two-part pipeline that first inductively generates labels directly from analysis data and then hierarchically clusters them to surface emergent themes. We validate this approach across three diverse datasets by measuring alignment with human-constructed themes and demonstrating its robustness through automated and human evaluations. Finally, we conduct a case study of litigation documents related to the ongoing opioid crisis in the U.S., revealing aggressive marketing strategies employed by pharmaceutical companies and demonstrating HICode's potential for facilitating nuanced analyses in large-scale data.

The future has arrived in Bakersfield, and I'm not sure I'm ready for it. For nearly three hours, the conversation was nonstop at the home of Audrey and Ken Mattlin, who happen to live with several robots. There's ElliQ, who resembles a table lamp and speaks mainly to Audrey, 84, whom the robot refers to by a nickname. As in, "How did you sleep, Jelly Bean?" Goo-goo-eyed Astro looks like a short-handled vacuum cleaner with an electronic tablet for a face. He scoots around the house on wheels and follows people on command.

A few years ago, I managed to receive 1,000 emails between boarding a plane in San Francisco and landing in New Orleans five hours later. Maybe 40 of those 1,000 were emails that I needed to respond to, but given the overwhelming volume in my inbox, a lot of them ended up going unanswered. Filters help organize things a bit, but now a new email app is bringing in the big guns to tackle the problem: artificial intelligence. Called Astro, the app essentially offers many of the same features as previous aspiring inbox-zero apps. You can snooze messages you see so they surface at your convenience, mute particular senders, and set a priority inbox that surfaces those emails from VIP senders that otherwise might end up buried amid Bed Bath & Beyond coupons.

An artificial intelligence (AI) model developed by researchers can predict the likelihood that a patient may have an unplanned hospitalization during their radiation treatments for cancer. The machine-learning model uses daily step counts as a proxy to monitor patients' health as they go through cancer therapy, offering clinicians a real-time method to provide personalized care. Findings will be presented today at the American Society for Radiation Oncology (ASTRO) Annual Meeting. An estimated 10-20% of patients who receive outpatient radiation or chemoradiation therapy will need acute care in the form of an emergency department (ED) visit or hospital admission during their cancer treatment. These unplanned hospitalizations can be a major challenge for people undergoing cancer treatment, causing treatment interruptions and stress that may impact clinical outcomes.

The news: Amazon announced yesterday that its home robot, Astro, will be getting a slew of major updates aimed at further embedding it in homes--and in our daily lives. The details: The new features offer more home monitoring. Astro will be able to watch pets and send a video feed of their activities to users, for example. But the robot will also be able to wander around the house to keep an eye on rooms and entry points. Amazon also announced a new collaboration between Astro and the Ring home security camera system designed to protect areas outside the home from possible break-ins.

Amazon also announced a new collaboration between Astro and the Ring home security camera system, called Virtual Security Guard, which would protect areas outside the home from possible break-ins. Amazon, which bought Ring in 2018, pitched the pairing as a way to further guard small businesses from break-ins, by videotaping intrusions and calling the authorities (though it seems like homeowners should be able to use that capability as well). Ring's approach to surveillance hasn't been without controversy. As my colleague Eileen Guo reported last year, Ring marketed itself as a tool to protect domestic violence survivors, but it simultaneously provided access into survivors' lives. Ring has also been called out for racial profiling and privacy violations.

While Amazon is widely known for its Ring brand of doorbell camera home security systems, the company last year introduced a more mobile, and way more adorable, monitoring platform: Astro. The $1,500 automaton essentially serves as an Alexa on wheels, trundling about your home like an AIBO that also manages your calendar and doubles as a guard dog. On Wednesday, Amazon unveiled a slew of new features for Astro, including one that can now detect the presence of your real cat or dog. The new feature will trigger while the Astro is "on patrol" around your home. When it encounters your pet, Astro will capture a short video clip of them and share it with you via Live View (part of the Alexa Together system).

Jeff Bezos has wanted a home robot for a long time. By 2017, Amazon's founder, an avid sci-fi fan, had repeatedly asked the company's engineers and executives about the feasibility of such a project, says Ken Kiraly, a vice president who helped create the Kindle ebook reader. That was the year Amazon's special projects team judged that it was finally the right time to begin building a home robot, due to the maturity of artificial intelligence and robotics, falling cost of sensors and computer chips, and risk that competitors had similar plans. They got started despite one big unknown: what Amazon's home robot would be good for. "Robots are hard," says Kiraly, who put together the team on the project.

We've already seen Ring add Bird's Eye View -- its fancy 3D motion detection -- to its flagship security camera and its flagship outdoor light camera. Consequently, you get no prizes for guessing that the feature is now coming to the new Ring Spotlight Cam Pro. The new Pro Spotlight Cam is joined by a Spotlight Cam Plus, which offers a slightly nicer design than its predecessor. For the uninitiated, Birds Eye View is a system that offers users a top-down map of their area, showing the path a person took to your front door. It's designed to let you know if someone's been peering into your windows, or anywhere else, while on your porch.

Neural clone detection has attracted the attention of software engineering researchers and practitioners. However, most neural clone detection methods do not generalize beyond the scope of clones that appear in the training dataset. This results in poor model performance, especially in terms of model recall. In this paper, we present an Abstract Syntax Tree (AST) assisted approach for generalizable neural clone detection, or ASTRO, a framework for finding clones in codebases reflecting industry practices. We present three main components: (1) an AST-inspired representation for source code that leverages program structure and semantics, (2) a global graph representation that captures the context of an AST among a corpus of programs, and (3) a graph embedding for programs that, in combination with extant large-scale language models, improves state-of-the-art code clone detection. Our experimental results show that ASTRO improves state-of-the-art neural clone detection approaches in both recall and F-1 scores.