DHS Secretary Kristi Noem announced Charles Wall as ICE's new deputy director, replacing Madison Sheahan who stepped down to pursue a congressional run in Ohio.

Japanese internal affairs minister Yoshimasa Hayashi (center) poses for a photo with ministers from ASEAN member states in Hanoi on Thursday. HANOI - Japan and the Association of Southeast Asian Nations have agreed to work together on developing new artificial intelligence models and preparing related laws. The AI-sector cooperation was included in a joint statement adopted at a meeting of digital ministers from Japan and ASEAN member states in Hanoi on Thursday. The statement was proposed by Japanese communications minister Yoshimasa Hayashi, who attended the meeting. Japan and ASEAN aim to join hands at a time when the United States and China are boosting their presence in the AI sector.

The Department of Homeland Security said agents arrested an illegal alien after he “weaponized" his car during a chaotic attempt to evade arrest in a San Antonio parking lot.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' A Ukrainian drone attack killed two workers from a state-owned pharmacy as they were transporting medicines to Polohy in a Russian-occupied area of Ukraine's Zaporizhia region, Russia's state news agency TASS reports. A Ukrainian drone strike injured three people in Russia's front-line Belgorod region, the regional task force reported, according to TASS.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

Minneapolis faces HUD investigation over alleged racial preferences in housing policy amid a much broader Minnesota fraud scandal.

Thousands of abusive videos were produced last year--that researchers know of. In 2025, new data show, the volume of child pornography online was likely larger than at any other point in history. A record 312,030 reports of confirmed child pornography were investigated last year by the Internet Watch Foundation, a U.K.-based organization that works around the globe to identify and remove such material from the web. This is concerning in and of itself. It means that the overall volume of child porn detected on the internet grew by 7 percent since 2024, when the previous record had been set.

Classification imbalance arises when one class is much rarer than the other. We frame this setting as transfer learning under label (prior) shift between an imbalanced source distribution induced by the observed data and a balanced target distribution under which performance is evaluated. Within this framework, we study a family of oversampling procedures that augment the training data by generating synthetic samples from an estimated minority-class distribution to roughly balance the classes, among which the celebrated SMOTE algorithm is a canonical example. We show that the excess risk decomposes into the rate achievable under balanced training (as if the data had been drawn from the balanced target distribution) and an additional term, the cost of transfer, which quantifies the discrepancy between the estimated and true minority-class distributions. In particular, we show that the cost of transfer for SMOTE dominates that of bootstrapping (random oversampling) in moderately high dimensions, suggesting that we should expect bootstrapping to have better performance than SMOTE in general. We corroborate these findings with experimental evidence. More broadly, our results provide guidance for choosing among augmentation strategies for imbalanced classification.

Differential Privacy (DP) provides a rigorous framework for releasing statistics while protecting individual information present in a dataset. Although substantial progress has been made on differentially private linear regression, existing methods almost exclusively address the item-level DP setting, where each user contributes a single observation. Many scientific and economic applications instead involve longitudinal or panel data, in which each user contributes multiple dependent observations. In these settings, item-level DP offers inadequate protection, and user-level DP - shielding an individual's entire trajectory - is the appropriate privacy notion. We develop a comprehensive framework for estimation and inference in longitudinal linear regression under user-level DP. We propose a user-level private regression estimator based on aggregating local regressions, and we establish finite-sample guarantees and asymptotic normality under short-range dependence. For inference, we develop a privatized, bias-corrected covariance estimator that is automatically heteroskedasticity- and autocorrelation-consistent. These results provide the first unified framework for practical user-level DP estimation and inference in longitudinal linear regression under dependence, with strong theoretical guarantees and promising empirical performance.