While prized as pets in some places, the pet trade is not entirely to blame. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Birds with colorful plumage are helpful animal ambassadors, but those same colors put them at risk in the illegal pet trade. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Constitutional lawyer Bruce Fein says the US was founded on the principle that governments exist to protect inalienable rights. He argues expanded presidential powers and unchecked authority represents a step backwards for US democracy. How AI is being weaponised against India's Muslim women

Is the war entering a new phase? Kyiv came under a ballistic missile and drone attack overnight, with at least two people killed and 11 injured after Ukrainian President Volodymyr Zelenskyy warned of an impending "massive" attack by Russia. Kyiv Mayor Vitali Klitschko, writing on the Telegram messaging platform, said the roof of a hotel was on fire early on Thursday morning. "Kyiv is under attack from ballistic missiles and UAVs," Klitschko wrote, using the acronym for unmanned aerial vehicles, or drones. Klitschko later said that 11 people were injured, with others trapped in a damaged nine-storey residential building, and the roof of another high-rise apartment building on fire.

The world's oceans are under heat stress, with average sea surface temperatures hitting 21 C, surpassing the record highs of 2023 and 2024. They're expected to rise further as El Niño, a natural climate pattern that warms the tropical Pacific for months, develops. How AI is being weaponised against India's Muslim women

Smoke rises during a Russian missile strike on Kyiv on Thursday. Kyiv - Russian missile and drone strikes rocked Kyiv early on Thursday, setting off fires and wounding at least five people, after Ukrainian President Volodymyr Zelenskyy warned that Moscow was preparing a "massive attack." Russia has routinely launched waves of missiles and drones at Ukrainian cities, including Kyiv, during its more than four-year invasion, which has become Europe's deadliest conflict since World War II. The attack came after the Ukrainian air force warned that ballistic missiles were headed toward the capital and followed Zelenskyy cutting short a visit to Dublin Wednesday, citing intelligence reports of an impending Russian strike. Journalists in central and eastern Kyiv heard more than a dozen explosions and saw residents -- some with children and pets -- rushing into metro stations being used as shelters. "Kyiv is under attack from ballistic missiles and UAVs," Kyiv Mayor Vitali Klitschko said on Telegram, adding that blasts could be heard across the city.

Watermarking promises a statistical trace of large language model (LLM) use, but real documents, after editing or paraphrasing, rarely arrive as purely human-written or purely machine-generated. This motivates a quantitative question beyond detection: what proportion of a document is generated from a pre-specified watermarked LLM? We study this watermark proportion estimation problem under the Gumbel--max watermarking mechanism, treating the next-token prediction (NTP) distributions as unknown and arbitrary nuisance parameters subject to a non-degeneracy condition. We compare two observation regimes: in the full observation regime, the estimator observes the pseudorandom vector and the selected token at each position; under the more popular setting of pivotal reduction, it observes only a scalar pivot, which follows a one-dimensional Uniform--Beta mixture distribution. Under pivotal reduction, we develop a Laguerre-polynomial estimator and establish a matching information-theoretic lower bound for the sample complexity. For full observation, we introduce an event-counting estimator and show a matching lower bound, yielding a substantially smaller sample complexity. As our results imply, although reducing to pivotal statistics is an elegant and widely used procedure, it is not always sample-efficient for estimating the proportion of watermarks.

Federated Learning (FL) is a distributed machine learning (ML) paradigm with collaboration among multiple clients without sharing data. FL is challenging under data heterogeneity and partial client participation. Learning sparse models is useful for communication and computational efficiency in FL, but it is especially difficult in the small-sample high-dimensional regime (d >> N) where optimization can yield parameter configurations that fail to generalize to unseen test data. While magnitude-based pruning doesn't account for uncertainty exploration in the parameter space, a formulation with probabilistic gates and an L0 constraint allows sampling from competing sparse configurations during training. In this work, we study entropy regularization of gate distributions as a mechanism to maintain uncertainty in sparse federated optimization by preventing early commitment to sparse support. We examine its impact under data heterogeneity, client participation heterogeneity, and sparsity. Experiments on synthetic and real-world benchmarks show consistent improvements over federated iterative hard thresholding (Fed-IHT) and pruning after dense federated averaging (FedAvg) training, both in statistical performance on test data and in sparsity recovery accuracy.

We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time. The parameters of the model are recovered within a deep learning framework, which makes it possible to retain a transparent parametric structure while simultaneously accounting for complex and nonstationary patterns in the observed phenomenon. Our analysis covers two specifications of the noise process. Besides the standard Gaussian setting, we also consider Laplace-distributed noise, which can offer a more adequate description in the presence of heavier tails and sharper local fluctuations. For both cases, we formulate the predictive scheme of the model and analyze the associated uncertainty quantification, including the construction of prediction intervals. The results illustrate that a relatively simple model, when combined with time-dependent parameter estimation, can serve as a mathematically tractable and practically flexible tool for forecasting complex dynamics under different noise assumptions. The general model is stated for TVAR($p$), while the prediction-interval formulas and the numerical experiments are developed for the TVAR(1) case.

The 2024 Department of Justice antitrust complaint against RealPage, Inc. named five major residential REITs for coordinating algorithmic rent pricing across hundreds of thousands of apartment units in major US metropolitan areas. This paper studies whether census-tract-level corporate landlord concentration (CLC), measured from SEC EDGAR 10-K property filings geocoded to census tracts, the first such application in the literature, is associated with rent growth 2019-2023, and whether that association is larger in majority-minority neighborhoods. Rent outcomes are measured using the Zillow Observed Rent Index (ZORI). To account for the possibility that corporate landlords preferentially locate in neighborhoods already seeing rent appreciation, all regressions control for a fully novel Algorithmic Housing Burden Index (AHBI), a composite of pre-existing rent burden and market tightness from ACS data. Across 665 census tracts in ten US metropolitan areas, doubling REIT concentration is associated with 2.8 percentage points higher rent growth (p = 0.086, p = 0.030, HC1 robust). This association is significantly stronger in majority-minority tracts. Within the same metro, high-CLC majority-minority tracts are associated with 5.9 percentage points higher rent growth than comparable white tracts (p = 0.039). An XGBoost model predicts 44 percent of out-of-sample rent growth variance, with SHAP analysis independently confirming that CLC's contribution is positive in minority tracts and negative in white tracts. Taken all together, these findings provide the first tract-level evidence consistent with corporate landlord concentration being associated with disproportionately higher rent growth in communities of color.

Machine learning algorithms and their training datasets have grown substantially in both size and complexity over the past decade. This increased model complexity has made it challenging to interpret and predict their behavior in unobserved scenarios. Hence, many applications that involve societal decisions still rely on simple, interpretable models like linear regression, often after feature engineering. Examples of such applications include predicting national housing prices, estimating wages across industries, forecasting loan amounts across banks, predicting life insurance premiums across groups, and projecting energy consumption across communities [CGKMN24]. A shared safety and sometimes legal concern across the above applications is the potential for wildly different model qualities for different distributions, i.e., outputting a notably worse model for some source data distributions [Dat14; BS16; HPS16; VVB18; SBFVV19; BHJKR21; CGNSG23; Cho16; KLMR18; ADW19; CGKMN24; SVWZ24].