Pretraining data selection has the potential to improve language model pretraining efficiency by utilizing higher-quality data from massive web data corpora. Current data selection methods, which rely on either hand-crafted rules or larger reference models, are conducted statically and do not capture the evolving data preferences during pretraining. In this paper, we introduce model-aware data selection with data influence models (MATES), where a data influence model continuously adapts to the evolving data preferences of the pretraining model and then selects the data most effective for the current pretraining progress. Specifically, we collect oracle data influence by locally probing the pretraining model and fine-tune a small data influence model to approximate it accurately. The data influence model then predicts data influence over the whole pretraining corpus and selects the most influential data for the next pretraining stage.

Cross-modal entity linking refers to the ability to align entities and their attributes across different modalities. While cross-modal entity linking is a fundamental skill needed for real-world applications such as multimodal code generation, fake news detection, or scene understanding, it has not been thoroughly studied in the literature. In this paper, we introduce a new task and benchmark to address this gap. Our benchmark, MATE, consists of 5.5k evaluation instances featuring visual scenes aligned with their textual representations. To evaluate cross-modal entity linking performance, we design a question-answering task that involves retrieving one attribute of an object in one modality based on a unique attribute of that object in another modality. We evaluate state-of-the-art Vision-Language Models (VLMs) and humans on this task, and find that VLMs struggle significantly compared to humans, particularly as the number of objects in the scene increases. Our analysis also shows that, while chain-of-thought prompting can improve VLM performance, models remain far from achieving human-level proficiency. These findings highlight the need for further research in cross-modal entity linking and show that MATE is a strong benchmark to support that progress.

Currently, for many applications, it is necessary to know the speed and position of motors. This can be achieved using mechanical sensors coupled to the motor shaft or using sensorless techniques. The sensorless techniques in brushed dc motors can be classified into two types: 1) techniques based on the dynamic brushed dc motor model and 2) techniques based on the ripple component of the current. This paper presents a new method, based on the ripple component, for speed and position estimation in brushed dc motors, using support vector machines. The proposed method only measures the current and detects the pulses in this signal. The motor speed is estimated by using the inverse distance between the detected pulses, and the position is estimated by counting all detected pulses. The ability to detect ghost pulses and to discard false pulses is the main advantage of this method over other sensorless methods. The performed tests on two fractional horsepower brushed dc motors indicate that the method works correctly in a wide range of speeds and situations, in which the speed is constant or varies dynamically.

Evolutionary roboticists have been testing radical methodologies that allow robots to'mate' with one another autonomously. The process would work with two robots that are able to combine their code and produce 3D-printed offspring. And while it may seem far-fetched, researchers say this could become commonplace within about 20 years, according to Wired. Evolutionary roboticists have been testing radical methodologies that allow robots to'mate' with one another autonomously. Opinions differ as to how robots could breed and reproduce in the future.

Huawei announced its Mate 10 series in Munich Monday, showcasing three models of the device, which features the new Kirin 970 chip. The SoC is the first on the market to introduce a dedicated NPU or Neural Network Processing Unit to enable artificial intelligence capabilities natively on the device. The Huawei Mate 10 will be available to markets including Spain, UAE, Saudi Arabia, Malaysia, Singapore and Australia in late October for €699, while the Mate 10 Pro will be available to markets including Germany, France, Italy, UAE, Saudi Arabia, Malaysia, Singapore, and Thailand in November for €799. The Porsche Design Mate 10 will also be available to select markets in mid-November for €1395. With the Mate 10 series, Huawei is promoting the transition from the smartphone to the "intelligent machine."

"Over the past three decades, we have primarily lived our brand and helped our customers succeed through innovative technologies, high-quality products and premium services, and passionate and progressive employees," said Kevin Zhang, President of Huawei Corporate Marketing. For the carrier business, Huawei is committed to become a carrier customers' business partner. It aims to help carriers succeed through digital transformation, maximizing the value of carriers' current networks and developing video into a basic service. Meanwhile, Huawei's enterprise business focuses on key industries such as public safety, finance, transportation and manufacturing, helping industry customers to realize digital transformation.