Environment No, trees can't anticipate a solar eclipse Breakthroughs, discoveries, and DIY tips sent six days a week. In April 2025, a scientific study went viral online for a particularly wild claim. A forest of Norway spruce trees () in the Dolomites of northern Italy appeared to rapidly synchronize their cellular-level electrical signals--known as electromes--in the hours leading up to a partial solar eclipse in October 2022 . If true, the discovery by the Italian Institute of Technology represented a possibly major development in understanding how plants communicate with one another. Despite many critics' skepticism, headlines describing a " forest-wide phenomenon " of talking trees spread quickly across the internet.

The Longest Solar Eclipse for 100 Years Is Coming. NASA has announced when the longest total solar eclipse of the century will occur--and you won't have to wait long. Here's what you should know. The duration of a total solar eclipse always varies. In April 2024, the eclipse that crossed North America lasted 4 minutes and 28 seconds.

Large language models (LLMs) produce fluent but unsupported answers - hallucinations - limiting safe deployment in high-stakes domains. We propose ECLIPSE, a framework that treats hallucination as a mismatch between a model's semantic entropy and the capacity of available evidence. We combine entropy estimation via multi-sample clustering with a novel perplexity decomposition that measures how models use retrieved evidence. We prove that under mild conditions, the resulting entropy-capacity objective is strictly convex with a unique stable optimum. We evaluate on a controlled financial question answering dataset with GPT-3.5-turbo (n=200 balanced samples with synthetic hallucinations), where ECLIPSE achieves ROC AUC of 0.89 and average precision of 0.90, substantially outperforming a semantic entropy-only baseline (AUC 0.50). A controlled ablation with Claude-3-Haiku, which lacks token-level log probabilities, shows AUC dropping to 0.59 with coefficient magnitudes decreasing by 95% - demonstrating that ECLIPSE is a logprob-native mechanism whose effectiveness depends on calibrated token-level uncertainties. The perplexity decomposition features exhibit the largest learned coefficients, confirming that evidence utilization is central to hallucination detection. We position this work as a controlled mechanism study; broader validation across domains and naturally occurring hallucinations remains future work.

The Transiting Exoplanet Survey Satellite (TESS) has surveyed nearly the entire sky in Full-Frame Image mode with a time resolution of 200 seconds to 30 minutes and a temporal baseline of at least 27 days. In addition to the primary goal of discovering new exoplanets, TESS is exceptionally capable at detecting variable stars, and in particular short-period eclipsing binaries which are relatively common, making up a few percent of all stars, and represent powerful astrophysical laboratories for deep investigations of stellar formation and evolution. We combed Sectors 1-82 of TESS Full-Frame Image data searching for eclipsing binary stars using a neural network that identified ~1.2 million stars with eclipse-like features. Of these, we have performed an in-depth analysis on ~60,000 targets using automated methods and manual inspection by citizen scientists. Here we present a catalog of 10001 uniformly-vetted and -validated eclipsing binary stars that passed all our ephemeris and photocenter tests, as well as complementary visual inspection. Of these, 7936 are new eclipsing binaries while the remaining 2065 are known systems for which we update the published ephemerides. We outline the detection and analysis of the targets, discuss the properties of the sample, and highlight potentially interesting systems. Finally, we also provide a list of ~900,000 unvetted and unvalidated targets for which the neural network found eclipse-like features with a score higher than 0.9, and for which there are no known eclipsing binaries within a sky-projected separation of a TESS pixel (~21 arcsec).

Deep learning systems, critical in domains like autonomous vehicles, are vulnerable to adversarial examples (crafted inputs designed to mislead classifiers). This study investigates black-box adversarial attacks in computer vision. This is a realistic scenario, where attackers have query-only access to the target model. Three properties are introduced to evaluate attack feasibility: robustness to compression, stealthiness to automatic detection, and stealthiness to human inspection. State-of-the-Art methods tend to prioritize one criterion at the expense of others. We propose ECLIPSE, a novel attack method employing Gaussian blurring on sampled gradients and a local surrogate model.

The Lipschitz constant plays a crucial role in certifying the robustness of neural networks to input perturbations. Since calculating the exact Lipschitz constant is NP-hard, efforts have been made to obtain tight upper bounds on the Lipschitz constant. Typically, this involves solving a large matrix verification problem, the computational cost of which grows significantly for both deeper and wider networks. In this paper, we provide a compositional approach to estimate Lipschitz constants for deep feed-forward neural networks. We first obtain an exact decomposition of the large matrix verification problem into smaller sub-problems.

Submitted to ApJS ABSTRACT In modern astronomy, the quantity of data collected has vastly exceeded the capacity for manual analysis, necessitating the use of advanced artificial intelligence (AI) techniques to assist scientists with the most labor-intensive tasks. AI can optimize simulation codes where computational bottlenecks arise from the time required to generate forward models. One such example is PHOEBE, a modeling code for eclipsing binaries (EBs), where simulating individual systems is feasible, but analyzing observables for extensive parameter combinations is highly time-consuming. To address this, we present a fully connected feedforward artificial neural network (ANN) trained on a dataset of over one million synthetic light curves generated with PHOEBE. Optimization of the ANN architecture yielded a model with six hidden layers, each containing 512 nodes, provides an optimized balance between accuracy and computational complexity. Extensive testing enabled us to establish ANN's applicability limits and to quantify the systematic and statistical errors associated with using such networks for EB analysis. Our findings demonstrate the critical role of dilution effects in parameter estimation for EBs, and we outline methods to incorporate these effects in AI-based models. This proposed ANN framework enables a speedup of over four orders of magnitude compared to traditional methods, with systematic errors not exceeding 1%, and often as low as 0.01%, across the entire parameter space. INTRODUCTION number of EBs are found in triple and multiple systems (Conroy et al. 2014; Orosz 2015), hosting circumbinary Fundamental stellar properties are inferred predominantly planets (Welsh et al. 2015), and featuring mass from the study of eclipsing binary stars (EBs) transfer and apsidal motion (Hambleton et al. 2013); (Torres et al. 2010). Their favorable orbital alignment these broaden the domains of study while retaining the with the line of sight, and consequent eclipses, make same tractable modeling principles. In particular, we them ideal astrophysical laboratories: a simple geometry can probe stellar interiors by studying tidally induced coupled with well-understood dynamical laws allow oscillations and gravity-mode pulsations in detached binaries us to obtain fundamental parameters without a-priori (Huber 2015); ubiquitous contact binaries are still assumptions (Prša 2018). Many of the phenomena being observed in hot that, we need samplers such as Markov Chain Monte Jupiters have their foundations in EB studies, e.g., the Carlo (MCMC, Foreman-Mackey et al. 2017) to provide Rossiter-McLaughlin effect, tidal distortions of the host heuristic parameter posteriors. This entails hundreds of star, irradiation effects, Roche lobe overflow and wind thousands if not millions of forward-model runs, which outflows, gravity darkening, apsidal motion, third body puts a hard limit on the number of systems we can solve dynamics, etc. (Barclay et al. 2012).

Despite prior safety alignment efforts, mainstream LLMs can still generate harmful and unethical content when subjected to jailbreaking attacks. Existing jailbreaking methods fall into two main categories: template-based and optimization-based methods. The former requires significant manual effort and domain knowledge, while the latter, exemplified by Greedy Coordinate Gradient (GCG), which seeks to maximize the likelihood of harmful LLM outputs through token-level optimization, also encounters several limitations: requiring white-box access, necessitating pre-constructed affirmative phrase, and suffering from low efficiency. In this paper, we present ECLIPSE, a novel and efficient black-box jailbreaking method utilizing optimizable suffixes. Drawing inspiration from LLMs' powerful generation and optimization capabilities, we employ task prompts to translate jailbreaking goals into natural language instructions. This guides the LLM to generate adversarial suffixes for malicious queries. In particular, a harmfulness scorer provides continuous feedback, enabling LLM self-reflection and iterative optimization to autonomously and efficiently produce effective suffixes. Experimental results demonstrate that ECLIPSE achieves an average attack success rate (ASR) of 0.92 across three open-source LLMs and GPT-3.5-Turbo, significantly surpassing GCG in 2.4 times. Moreover, ECLIPSE is on par with template-based methods in ASR while offering superior attack efficiency, reducing the average attack overhead by 83%.

Log parsing, a vital task for interpreting the vast and complex data produced within software architectures faces significant challenges in the transition from academic benchmarks to the industrial domain. Existing log parsers, while highly effective on standardized public datasets, struggle to maintain performance and efficiency when confronted with the sheer scale and diversity of real-world industrial logs. These challenges are two-fold: 1) massive log templates: The performance and efficiency of most existing parsers will be significantly reduced when logs of growing quantities and different lengths; 2) Complex and changeable semantics: Traditional template-matching algorithms cannot accurately match the log templates of complicated industrial logs because they cannot utilize cross-language logs with similar semantics. To address these issues, we propose ECLIPSE, Enhanced Cross-Lingual Industrial log Parsing with Semantic Entropy-LCS, since cross-language logs can robustly parse industrial logs. On the one hand, it integrates two efficient data-driven template-matching algorithms and Faiss indexing. On the other hand, driven by the powerful semantic understanding ability of the Large Language Model (LLM), the semantics of log keywords were accurately extracted, and the retrieval space was effectively reduced. Notably, we launch a Chinese and English cross-platform industrial log parsing benchmark ECLIPSE- BENCH to evaluate the performance of mainstream parsers in industrial scenarios. Our experimental results across public benchmarks and ECLIPSE- BENCH underscore the superior performance and robustness of our proposed ECLIPSE. Notably, ECLIPSE both delivers state-of-the-art performance when compared to strong baselines and preserves a significant edge in processing efficiency.

Only in America would people get randy heading into a solar eclipse event. Pornhub revealed that terms like'eclipse sex' and'eclipse orgasm' became the top searches on Monday, rising by a shocking 6,800 percent. Curious users looked up friskier terms like'sex during eclipse' and'eclipse my c***.' 'Eclipse karma' also topped the list, which appeared to be a porn star performing sexual acts on different partners. However, people appeared to take a break from their Pornhub eclipse fetishes for roughly an hour to watch the celestial event, but resumed normal levels once the moon bypassed the sun. If people were hoping to find a solar eclipse sub-genre on Pornhub, they were left disappointed. There was one video of the eclipse uploaded to Pornhub - but nothing explicit in the footage.