In mechanical structures like airplanes, cars and houses, noise is generated and transmitted through vibrations. To take measures to reduce this noise, vibrations need to be simulated with expensive numerical computations. Deep learning surrogate models present a promising alternative to classical numerical simulations as they can be evaluated magnitudes faster, while trading-off accuracy. To quantify such trade-offs systematically and foster the development of methods, we present a benchmark on the task of predicting the vibration of harmonically excited plates. The benchmark features a total of 12,000 plate geometries with varying forms of beadings, material, boundary conditions, load position and sizes with associated numerical solutions. To address the benchmark task, we propose a new network architecture, named Frequency-Query Operator, which predicts vibration patterns of plate geometries given a specific excitation frequency. Applying principles from operator learning and implicit models for shape encoding, our approach effectively addresses the prediction of highly variable frequency response functions occurring in dynamic systems. To quantify the prediction quality, we introduce a set of evaluation metrics and evaluate the method on our vibrating-plates benchmark. Our method outperforms Deep-ONets, Fourier Neural Operators and more traditional neural network architectures and can be used for design optimization.

As large language models (LLMs) become ubiquitous in our daily tasks and digital interactions, associated privacy risks are increasingly in focus.

'Sam has the sad-psycho eyes of the lost woman's boyfriend who the police have asked to front the missing person's appeal' 'Sam has the sad-psycho eyes of the lost woman's boyfriend who the police have asked to front the missing person's appeal' If he's the future, can we go backwards? His AI video generator Sora 2 has been reviled for pinching the work of others. I mean, actually do it. Go to Google images, where you can find countless photos of the OpenAI boss smiling in a kind of wan genius way, the humble lost puppy of Silicon Valley . But I urge you to simply cover the bottom half of his face in any of these pictures, and you will immediately clock that Sam has the sad-psycho eyes of the lost woman's boyfriend who the police have asked to front the missing person's appeal.

Photographs captured in unstructured tourist environments frequently exhibit variable appearances and transient occlusions, challenging accurate scene reconstruction and inducing artifacts in novel view synthesis.

This significantly improves upon prior work, which has been restricted to downsam-pled versions of MNIST and CIFAR-10. Taking CRONOS as a primitive, we then develop a new algorithm called CRONOS-AM, which combines CRONOS with alternating minimization, to obtain an algorithm capable of training multi-layer networks with arbitrary architectures.

The surge in applications of large language models (LLMs) has prompted concerns about the generation of misleading or fabricated information, known as hallucinations. Therefore, detecting hallucinations has become critical to maintaining trust in LLM-generated content. A primary challenge in learning a truthfulness classifier is the lack of a large amount of labeled truthful and hallucinated data. To address the challenge, we introduce HaloScope, a novel learning framework that leverages the unlabeled LLM generations in the wild for hallucination detection. Such unlabeled data arises freely upon deploying LLMs in the open world, and consists of both truthful and hallucinated information. To harness the unlabeled data, we present an automated membership estimation score for distinguishing between truthful and untruthful generations within unlabeled mixture data, thereby enabling the training of a binary truthfulness classifier on top. Importantly, our framework does not require extra data collection and human annotations, offering strong flexibility and practicality for real-world applications. Extensive experiments show that HaloScope can achieve superior hallucination detection performance, outperforming the competitive rivals by a significant margin.

The remarkable success of transformers in sequence modeling tasks, spanning various applications in natural language processing and computer vision, is attributed to the critical role of self-attention.

Google may be forced to make major changes in the way that people use its search engine in the UK. Google may have to change the way its search engine works in the UK, including potentially offering users the option to choose rival search services, as part of new regulation from the UK's competition authority. In a decision handed down on Friday, the Competition and Markets Authority (CMA) has designated Google Search with Strategic Market Status (SMS)--a qualifier given to companies that are considered to have "substantial and entrenched market power"--which would allow the regulator to wield more power over it. This decision follows a 10-month investigation into Google, and it is the first time that these powers, which come under the UK's new Digital Markets, Competition and Consumers Act, have been used to target a major tech company. Google's SMS will last up to five years under this legislation.