This paper introduces SoundSculpt, a neural network designed to extract target sound fields from ambisonic recordings. SoundSculpt employs an ambisonic-in-ambisonic-out architecture and is conditioned on both spatial information (e.g., target direction obtained by pointing at an immersive video) and semantic embeddings (e.g., derived from image segmentation and captioning). Trained and evaluated on synthetic and real ambisonic mixtures, SoundSculpt demonstrates superior performance compared to various signal processing baselines. Our results further reveal that while spatial conditioning alone can be effective, the combination of spatial and semantic information is beneficial in scenarios where there are secondary sound sources spatially close to the target. Additionally, we compare two different semantic embeddings derived from a text description of the target sound using text encoders.

Whether you're a student, a journalist, or a business professional, knowing how to do high-quality research and writing using trustworthy data and sources, without giving in to the temptation of AI or ChatGPT, is a skill worth developing. As I detail in my book Writing That Gets Noticed, locating credible databases and sources and accurately vetting information can be the difference between turning a story around quickly or getting stuck with outdated information. Since I had written about getting pregnant in my forties, I knew that as long as I updated my facts and figures, and included supportive and relevant peer-reviewed research, I could pull off this story. The story ran later that day, and it led to other assignments. Here are some tips I've learned that you should consider mastering before you turn to automated tools like generative AI to handle your writing work for you.

A biologically motivated model of cortical self-organization is pro(cid:173) posed. Context is combined with bottom-up information via a maximum likelihood cost function. Clusters of one or more units are modulated by a common contextual gating Signal; they thereby organize themselves into mutually supportive predictors of abstract contextual features. The model was tested in its ability to discover viewpoint-invariant classes on a set of real image sequences of cen(cid:173) tered, gradually rotating faces. It performed considerably better than supervised back-propagation at generalizing to novel views from a small number of training examples. The importance of context effects l in perception has been demonstrated in many domains.

For more than 15 years, Wikipedia discussed what to call the third child of Ernest Hemingway, a doctor who was born and wrote books as Gregory, later lived as Gloria after undergoing gender-affirming surgery, and, when arrested for public disorderliness late in life, used a third name, Vanessa. Last year, editors on the site finally settled the question: The Gregory Hemingway article was deleted, and its contents were moved to a new one for Gloria Hemingway. This would be her name going forward, and she/her would be her pronouns. Wikipedia's billions of facts, rendered as dry prose in millions of articles, help us understand the world. They are largely the brain behind Siri and Alexa.

A couple years back I gave a talk on researching software history, using "linked list interview questions" as an example topic. Since referring people to a video is less accessible than just writing a blog post, I've reproduced the question here. So why do interviewers like to ask linked list questions? These answers are contradictory: if you want to know if someone knows CS fundamentals, you don't want to give them a problem they can trick their way through, and if you want to test reasoning ability, you don't want to give a problem that they've already seen in CS. Two contradictory answers tells me there's some history involved. My guess is that originally people asked LL questions for a very good reason, and then over time forgot the reason and came up with post-hoc justifications.

Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical method to solve integrals of expensive-to-evaluate black-box functions, yet so far,active BQ learning schemes focus merely on the integrand itself as information source, and do not allow for information transfer from cheaper, related functions. Here, we set the scene for active learning in BQ when multiple related information sources of variable cost (in input and source) are accessible. This setting arises for example when evaluating the integrand requires a complex simulation to be run that can be approximated by simulating at lower levels of sophistication and at lesser expense. We construct meaningful cost-sensitive multi-source acquisition rates as an extension to common utility functions from vanilla BQ (VBQ),and discuss pitfalls that arise from blindly generalizing. Furthermore, we show that the VBQ acquisition policy is a corner-case of all considered cost-sensitive acquisition schemes, which collapse onto one single de-generate policy in the case of one source and constant cost. In proof-of-concept experiments we scrutinize the behavior of our generalized acquisition functions. On an epidemiological model, we demonstrate that active multi-source BQ (AMS-BQ) allocates budget more efficiently than VBQ for learning the integral to a good accuracy.

XML, web services, and the semantic web have opened the door for new and exciting information-integration applications. Information sources on the web are controlled by different organizations or people, utilize different text formats, and have varying inconsistencies. Therefore, any system that integrates information from different data sources must identify common entities from these sources. Data from many data sources on the web does not contain enough information to link the records accurately using state-of-the-art record-linkage systems. However, it is possible to exploit secondary data sources on the web to improve the record-linkage process.

SA Forum is an invited essay from experts on topical issues in science and technology. It has become fashionable to bad-mouth big data. In recent weeks the New York Times, Financial Times, Wired and other outlets have all run pieces bashing this new technological movement. To be fair, many of the critiques have a point: There has been a lot of hype about big data and it is important not to inflate our expectations about what it can do. But little of this hype has come from the actual people working with large data sets.

Internet algorithms tailor the Web -- but they may be removing randomness "People are not forced to think" as widely, says one expert On the other hand, Internet has opened worlds that didn't exist before The flaws are not necessarily in our machines, but in ourselves "People are not forced to think" as widely, says one expert On the other hand, Internet has opened worlds that didn't exist before "I'd say about 95% of the time Amazon suggests a book to me, it's one I already have," he says. This is not due to a lack of interest on the part of Haufe, a professor at Case Western University who specializes in the history and philosophy of science. But Amazon's vaunted algorithm, embodied in the recommendations page of "Your Amazon.com" and the "customers who bought this item also bought" line on each product page, doesn't cast a net wide enough for Haufe's consideration. In that, he sees a bigger concern. Our reliance on computer algorithms, he observes, may be narrowing our choices. "We're losing something vital to the production of knowledge," he says.

Opinion leaders play an important role in influencing people’s beliefs, actions and behaviors. Although a number of methods have been proposed for identifying influentials using secondary sources of information, the use of primary sources, such as surveys, is still favored in many domains. In this work we present a new surveying method which combines secondary data with partial knowledge from primary sources to guide the information gathering process. We apply our proposed active surveying method to the problem of identifying key opinion leaders in the medical field, and show how we are able to accurately identify the opinion leaders while minimizing the amount of primary data required, which results in significant cost reduction in data acquisition without sacrificing its integrity.