But not in the way Fukushima's geneticists thought. Breakthroughs, discoveries, and DIY tips sent six days a week. In the regions surrounding the Fukushima nuclear plant disaster in northeast Japan, radioactive domestic pigs and wild boar are rapidly interbreeding. While far from the only recent incident of animal hybridization, the situation is presenting wildlife biologists with an unprecedented opportunity to examine the issue in real-time, as well as provide a template for studying the growing problem worldwide. In 2011, a 9.0 magnitude undersea earthquake in the Pacific Ocean rocked Japan.

We study whether liquidity and volatility proxies of a core set of cryptoassets generate spillovers that forecast market-wide risk. Our empirical framework integrates three statistical layers: (A) interactions between core liquidity and returns, (B) principal-component relations linking liquidity and returns, and (C) volatility-factor projections that capture cross-sectional volatility crowding. The analysis is complemented by vector autoregression impulse responses and forecast error variance decompositions (see Granger 1969; Sims 1980), heterogeneous autoregressive models with exogenous regressors (HAR-X, Corsi 2009), and a leakage-safe machine learning protocol using temporal splits, early stopping, validation-only thresholding, and SHAP-based interpretation. Using daily data from 2021 to 2025 (1462 observations across 74 assets), we document statistically significant Granger-causal relationships across layers and moderate out-of-sample predictive accuracy. We report the most informative figures, including the pipeline overview, Layer A heatmap, Layer C robustness analysis, vector autoregression variance decompositions, and the test-set precision-recall curve. Full data and figure outputs are provided in the artifact repository.

Large language models (LLMs) remain broadly open and highly steerable: they imitate at scale, accept arbitrary system prompts, and readily adopt multiple personae. By analogy to human development, we hypothesize that progress toward artificial general intelligence (AGI) involves a lock-in phase: a transition from open imitation to identity consolidation, in which goal structures, refusals, preferences, and internal representations become comparatively stable and resistant to external steering. We formalize this phase, link it to known phenomena in learning dynamics, and propose operational metrics for onset detection. Experimentally, we demonstrate that while the behavioral consolidation is rapid and non-linear, its side-effects on general capabilities are not monolithic. Our results reveal a spectrum of outcomes--from performance trade-offs in small models, through largely cost-free adoption in mid-scale models, to transient instabilities in large, quantized models. We argue that such consolidation is a prerequisite for AGI-level reliability and also a critical control point for safety: identities can be deliberately engineered for reliability, yet may also emerge spontaneously during scaling, potentially hardening unpredictable goals and behaviors.

Transaction costs and regime shifts are major reasons why paper portfolios fail in live trading. We introduce FR-LUX (Friction-aware, Regime-conditioned Learning under eXecution costs), a reinforcement learning framework that learns after-cost trading policies and remains robust across volatility-liquidity regimes. FR-LUX integrates three ingredients: (i) a microstructure-consistent execution model combining proportional and impact costs, directly embedded in the reward; (ii) a trade-space trust region that constrains changes in inventory flow rather than logits, yielding stable low-turnover updates; and (iii) explicit regime conditioning so the policy specializes to LL/LH/HL/HH states without fragmenting the data. On a 4 x 5 grid of regimes and cost levels with multiple random seeds, FR-LUX achieves the top average Sharpe ratio with narrow bootstrap confidence intervals, maintains a flatter cost-performance slope than strong baselines, and attains superior risk-return efficiency for a given turnover budget. Pairwise scenario-level improvements are strictly positive and remain statistically significant after multiple-testing corrections. We provide formal guarantees on optimality under convex frictions, monotonic improvement under a KL trust region, long-run turnover bounds and induced inaction bands due to proportional costs, positive value advantage for regime-conditioned policies, and robustness to cost misspecification. The methodology is implementable: costs are calibrated from standard liquidity proxies, scenario-level inference avoids pseudo-replication, and all figures and tables are reproducible from released artifacts.

This study demonstrates that GuruAgents, prompt-guided AI agents, can systematically operationalize the strategies of legendary investment gurus. We develop five distinct GuruAgents, each designed to emulate an iconic investor, by encoding their distinct philosophies into LLM prompts that integrate financial tools and a deterministic reasoning pipeline. In a backtest on NASDAQ-100 constituents from Q4 2023 to Q2 2025, the GuruAgents exhibit unique behaviors driven by their prompted personas. The Buffett GuruAgent achieves the highest performance, delivering a 42.2\% CAGR that significantly outperforms benchmarks, while other agents show varied results. These findings confirm that prompt engineering can successfully translate the qualitative philosophies of investment gurus into reproducible, quantitative strategies, highlighting a novel direction for automated systematic investing. The source code and data are available at https://github.com/yejining99/GuruAgents.

Employee turnover is a critical challenge in financial markets, yet little is known about the role of professional networks in shaping career moves. Using the Hong Kong Securities and Futures Commission (SFC) public register (2007-2024), we construct temporal networks of 121,883 professionals and 4,979 firms to analyze and predict employee departures. We introduce a graph-based feature propagation framework that captures peer influence and organizational stability. Our analysis shows a contagion effect: professionals are 23% more likely to leave when over 30% of their peers depart within six months. Embedding these network signals into machine learning models improves turnover prediction by 30% over baselines. These results highlight the predictive power of temporal network effects in workforce dynamics, and demonstrate how network-based analytics can inform regulatory monitoring, talent management, and systemic risk assessment.

Biodiversity assessments are critically affected by the spatial scale at which species richness is measured. How species richness accumulates with sampling area depends on natural and anthropogenic processes whose effects can change depending on the spatial scale considered. These accumulation dynamics, described by the species-area relationship (SAR), are challenging to assess because most biodiversity surveys are restricted to sampling areas much smaller than the scales at which these processes operate. Here, we combine sampling theory and deep learning to predict local species richness within arbitrarily large sampling areas, enabling for the first time to estimate spatial differences in SARs. We demonstrate our approach by predicting vascular plant species richness across Europe and evaluate predictions against an independent dataset of plant community inventories. The resulting model, named deep SAR, delivers multi-scale species richness maps, improving coarse grain richness estimates by 32% compared to conventional methods, while delivering finer grain estimates. Additional to its predictive capabilities, we show how our deep SAR model can provide fundamental insights on the multi-scale effects of key biodiversity processes. The capacity of our approach to deliver comprehensive species richness estimates across the full spectrum of ecologically relevant scales is essential for robust biodiversity assessments and forecasts under global change.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. I once witnessed a friend going through a severe midlife crisis. Basically overnight, this formerly serious and well-adjusted middle-aged man dumped his wife for a much younger girlfriend, got a face tattoo, and built a full-sized halfpipe in his house. Soon, we were barraged with music recommendations (all stuff he'd listened to in high school and college) and life updates laden with "hip" "slang" ("Despite the age gap, my situationship with Triniteigh is lowkey lit"). It was a transparent--and, from a certain perspective, even sympathetic--response to a universal anxiety: He'd seen that the good times were over, and that only decline lay ahead. But, like all nostalgists, he didn't realize that you can't ever truly go back; you can only go backward. The United States, under President Donald Trump, seems to be undergoing a similar midlife crisis, as this reactionary administration attempts to brute-force the country back to a golden age that many people are realizing either didn't exist in the first place or has been permanently lost to the mists of time and modernization.

Employee turnover refers to an individual's termination of employment from the current organization. It is one of the most persistent challenges for firms, especially those ones in Information Technology (IT) industry that confront high turnover rates. Effective prediction of potential employee turnovers benefits multiple stakeholders such as firms and online recruiters. Prior studies have focused on either the turnover prediction within a single firm or the aggregated employee movement among firms. How to predict the individual employees' turnovers among multiple firms has gained little attention in literature, and thus remains a great research challenge. In this study, we propose a novel deep learning approach based on job embeddedness theory to predict the turnovers of individual employees across different firms. Through extensive experimental evaluations using a real-world dataset, our developed method demonstrates superior performance over several state-of-the-art benchmark methods. Additionally, we estimate the cost saving for recruiters by using our turnover prediction solution and interpret the attributions of various driving factors to employee's turnover to showcase its practical business value.

Google is being investigated by the UK competition watchdog over the impact of its search and advertising practices on consumers, news publishers, businesses and rival search engines. The CMA estimates that search advertising costs the equivalent of nearly 500 for each UK household a year, which could be kept down with effective competition. The watchdog announced on Tuesday it will investigate if Google is blocking competitors from entering the market, and whether it is engaging in "potential exploitative conduct" by the mass collection of consumers' data without informed consent. It will also investigate whether Google is using its position as the pre-eminent search engine to give an unfair advantage to its own shopping and travel services. The investigation will take up to nine months and could result in Google being forced to share the mountains of data it collects with other businesses, or to give publishers greater control over how their content – books, newspaper articles and music – is used, including by Google's fast-growing artificial intelligence systems.