An artist's impression of 3I/ATLAS is shown as it passes near the Sun, illuminating one side of the comet. On the side of the comet closer to the sun, the methanol gas is shown in blue, with icy dust grains still present in the gas. On the dark side of the comet, the hydrogen cyanide is shown in orange. Breakthroughs, discoveries, and DIY tips sent six days a week. The comet 3I/ATLAS is well on its way back into deep space, but the famous cosmic visitor continues to fascinate astronomers.

However, existing contrastive learning methods primarily focus on one single data level, which fails to fully exploit the intricate nature of medical time series.

The interstellar space rock shows off the illuminating effects of its brush with the sun. Breakthroughs, discoveries, and DIY tips sent six days a week. After months of unprecedented observations, astronomers are bidding goodbye to the beloved comet 3I/ATLAS . First spotted in July 2025, the frigid, dusty space rock is only the third known interstellar object to pass through the solar system, offering researchers the rare opportunity to examine a visitor from deep space. Among other discoveries, scientists have since confirmed that the interstellar comet is the fastest ever recorded as well as covered in ice volcanoes --and definitely not extraterrestrial tourists .

However,inpractice, we did not find both effects inthe training. The last term in the loss function of COMET does not need the information from the dataset. This section contains the experiment details for cases tested in section 4. In section 4, there are 6 simple experiments performed todemonstrate the capability ofCOMET:(1) mass-spring, (2) 2D Case5: 2Dnonlinear spring.Weconsider acaseofamotion ofanobjectofmassm=1in2D where it is connected to the origin with a nonlinear spring with forceF = |r|2r where r is the position of the object in 2D coordinate. The constants of motion of this systems are the energy and the angular momentum,whichmakesnc=2. Case 6: Lotka-Volterraequation isanordinary differential equation modelling thepopulation of predatorandprey. The training loss in this case was composed of the reconstruction loss and the dynamics loss.

Inthis paper,we present a neural network that cansimultaneously learn the dynamics of the system and the constants of motion from data.

Thisisespecially apparent in natural language, which is often described as a tool for making'infinite use of finite means' [8]. Previously acquired words canbeinfinitely nested using asetofgrammatical rules to communicate an arbitrary thought, opinion, or state one is in.

Science Space Deep Space Halley's comet may need a new, medieval name Astronomers suggest the honor should go to an 11th century monk known for a disastrous flying attempt. Breakthroughs, discoveries, and DIY tips sent six days a week. One of most recognizable comets in astronomy may require rebranding. But even if everyone continues to call the famed space rock Halley's comet, some researchers say an eccentric 11th century monk deserves at least credit. According to a review of historical materials including the famous Bayeux tapestry, a team from Leiden University in the Netherlands believes it makes more sense to name the icy space rock in honor of Aethelmaer of Malmesbury --a member of the Order of Saint Benedict who also lived with an ill-fated fascination with flying.

Contrastive representation learning is crucial in medical time series analysis as it alleviates dependency on labor-intensive, domain-specific, and scarce expert annotations. However, existing contrastive learning methods primarily focus on one single data level, which fails to fully exploit the intricate nature of medical time series. To address this issue, we present COMET, an innovative hierarchical framework that leverages data consistencies at all inherent levels in medical time series. Our meticulously designed model systematically captures data consistency from four potential levels: observation, sample, trial, and patient levels. By developing contrastive loss at multiple levels, we can learn effective representations that preserve comprehensive data consistency, maximizing information utilization in a self-supervised manner. We conduct experiments in the challenging patient-independent setting. We compare COMET against six baselines using three diverse datasets, which include ECG signals for myocardial infarction and EEG signals for Alzheimer's and Parkinson's diseases. The results demonstrate that COMET consistently outperforms all baselines, particularly in setup with 10% and 1% labeled data fractions across all datasets. These results underscore the significant impact of our framework in advancing contrastive representation learning techniques for medical time series.

This paper presents the first large-scale field study of the adoption, usage intensity, and use cases of general-purpose AI agents operating in open-world web environments. Our analysis centers on Comet, an AI-powered browser developed by Perplexity, and its integrated agent, Comet Assistant. Drawing on hundreds of millions of anonymized user interactions, we address three fundamental questions: Who is using AI agents? How intensively are they using them? And what are they using them for? Our findings reveal substantial heterogeneity in adoption and usage across user segments. Earlier adopters, users in countries with higher GDP per capita and educational attainment, and individuals working in digital or knowledge-intensive sectors -- such as digital technology, academia, finance, marketing, and entrepreneurship -- are more likely to adopt or actively use the agent. To systematically characterize the substance of agent usage, we introduce a hierarchical agentic taxonomy that organizes use cases across three levels: topic, subtopic, and task. The two largest topics, Productivity & Workflow and Learning & Research, account for 57% of all agentic queries, while the two largest subtopics, Courses and Shopping for Goods, make up 22%. The top 10 out of 90 tasks represent 55% of queries. Personal use constitutes 55% of queries, while professional and educational contexts comprise 30% and 16%, respectively. In the short term, use cases exhibit strong stickiness, but over time users tend to shift toward more cognitively oriented topics. The diffusion of increasingly capable AI agents carries important implications for researchers, businesses, policymakers, and educators, inviting new lines of inquiry into this rapidly emerging class of AI capabilities.