Trump accuses Comey of nearly starting a war as it's revealed why new MAGA star prosecutor rushed indictment Tim Allen reveals Erika Kirk's speech inspired him to forgive his father's killer 60 years after tragic death Girl found dead in D4vd's Tesla was AGED 12 when they met online. Now as masked men guard his mansion, friends unravel the truth... and tell of the chilling moment her texts stopped Someone is trying to drive a wedge between Charles and William. I'm no conspiracy theorist, but even my royal sources say something'calculated' and odd is going on. This is what's really happening, reveals REBECCA ENGLISH The $2 fruit that reverses diabetes... as 100million Americans suffer from deadly condition and most don't know it What would her mother think? Johnny Carson's Malibu home lists for $110m - and it has jaw-dropping hidden feature Selena Gomez and Benny Blanco's FULL wedding plans leaked: Top secret details, surprise celeb host and a MAJOR A-list drop out... ahead of ceremony this weekend Texas man's final words as he is executed for the'exorcism' killing of his girlfriend's 13-month-old daughter Creepy New England road is so isolated it only sees a car every few DAYS.

Things to Do in L.A. Tap to enable a layout that focuses on the article. CDC warns of dramatic rise in dangerous drug-resistant bacteria. The Centers for Disease Control and Prevention warned in a report this week that infections caused by a "super bug" bacteria surged by more than 460% in the United States between 2019 and 2023. This is read by an automated voice. Please report any issues or inconsistencies here .

After significantly weakening other Iranian-backed groups in the region, Israel's military has turned its attention to the Houthis, carrying out a series of punishing strikes on Yemeni ports and other infrastructure. Last month an Israeli attack in Sana killed senior members of the Houthi-led government -- including the prime minister, Ahmed al-Rahawi -- but appeared to leave the group's military leadership largely unscathed. Israeli strikes in Yemen have also killed and wounded dozens of civilians in recent months, according to human rights groups. The United States has also bombed Yemen, in response to Houthi attacks on Red Sea shipping. The Houthis say they have targeted ships linked to Israel, although some of the ships they struck have no clear connection to the country. Houthi attacks on Israel are typically blocked or intercepted by the Israeli military, as was the case late on Thursday when sirens sounded in parts of Israel and the military soon after said that a missile from Yemen had been thwarted.

Chinese drone experts have flown to Russia to conduct technical development work on military drones at a state-owned weapons manufacturer that is under Western sanctions, according to two European security officials and documents. The Chinese experts have visited arms maker IEMZ Kupol on more than half a dozen occasions since the second quarter of last year. During that time, Kupol also received shipments of Chinese-made attack and surveillance drones via a Russian intermediary, according to the documents and two officials. In September last year, it was documented that Kupol had developed a new drone, the Garpiya-3, in China with the help of local specialists, with specific details of the extensive involvement of Chinese experts in tests and technological work on military-use drones inside Russia now being reported for the first time. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Things to Do in L.A. Tap to enable a layout that focuses on the article. A'very mean squirrel' is going nuts in this California town. Experts say it's rare for squirrels to attack people, and the most likely reason has to do with humans hand feeding or hand raising the animals. This is read by an automated voice. Please report any issues or inconsistencies here .

In this work, we address the following question: What minimal structural assumptions are needed to prevent the degradation of statistical learning bounds with increasing dimensionality? We investigate this question in the classical statistical setting of signal estimation from $n$ independent linear observations $Y_i = X_i^{\top}θ+ ε_i$. Our focus is on the generalization properties of a broad family of predictors that can be expressed as linear combinations of the training labels, $f(X) = \sum_{i=1}^{n} l_{i}(X) Y_i$. This class -- commonly referred to as linear prediction rules -- encompasses a wide range of popular parametric and non-parametric estimators, including ridge regression, gradient descent, and kernel methods. Our contributions are twofold. First, we derive non-asymptotic upper and lower bounds on the generalization error for this class under the assumption that the Bayes predictor $θ$ lies in an ellipsoid. Second, we establish a lower bound for the subclass of rotationally invariant linear prediction rules when the Bayes predictor is fixed. Our analysis highlights two fundamental contributions to the risk: (a) a variance-like term that captures the intrinsic dimensionality of the data; (b) the noiseless error, a term that arises specifically in the high-dimensional regime. These findings shed light on the role of structural assumptions in mitigating the curse of dimensionality.

Matched-pair experimental designs aim to detect treatment effects by pairing participants and comparing within-pair outcome differences. In many situations, the overall effect size across the entire population is small. Then, the focus naturally shifts to identifying and targeting high treatment-effect regions where the intervention is most effective. This paper proposes a matched-pair experimental design that sequentially and actively enrolls patients in high treatment-effect regions. Importantly, we frame the identification of the target region as a classification problem and propose an active learning framework tailored to matched-pair designs. Our design not only reduces the experimental cost of detecting treatment efficacy, but also ensures that the identified regions enclose the entire high-treatment-effect regions. Our theoretical analysis of the framework's label complexity and experiments in practical scenarios demonstrate the efficiency and advantages of the approach.

With the increasing popularity of large language models, concerns over content authenticity have led to the development of myriad watermarking schemes. These schemes can be used to detect a machine-generated text via an appropriate key, while being imperceptible to readers with no such keys. The corresponding detection mechanisms usually take the form of statistical hypothesis testing for the existence of watermarks, spurring extensive research in this direction. However, the finer-grained problem of identifying which segments of a mixed-source text are actually watermarked, is much less explored; the existing approaches either lack scalability or theoretical guarantees robust to paraphrase and post-editing. In this work, we introduce a unique perspective to such watermark segmentation problems through the lens of epidemic change-points. By highlighting the similarities as well as differences of these two problems, we motivate and propose WISER: a novel, computationally efficient, watermark segmentation algorithm. We theoretically validate our algorithm by deriving finite sample error-bounds, and establishing its consistency in detecting multiple watermarked segments in a single text. Complementing these theoretical results, our extensive numerical experiments show that WISER outperforms state-of-the-art baseline methods, both in terms of computational speed as well as accuracy, on various benchmark datasets embedded with diverse watermarking schemes. Our theoretical and empirical findings establish WISER as an effective tool for watermark localization in most settings. It also shows how insights from a classical statistical problem can lead to a theoretically valid and computationally efficient solution of a modern and pertinent problem.

Biopharmaceutical manufacturing is vital to public health but lacks the agility for rapid, on-demand production of biotherapeutics due to the complexity and variability of bioprocesses. T o overcome this, we introduce RApid PosT eriOR GENerator (RAPTOR-GEN), a mechanism-informed Bayesian learning framework designed to accelerate intelligent digital twin development from sparse and heterogeneous experimental data. This framework is built on a multi-scale probabilistic knowledge graph (pKG), formulated as a stochastic differential equation (SDE)-based foundational model that captures the nonlinear dynamics of bioprocesses. RAPTOR-GEN consists of two ingredients: (i) an interpretable metamodel integrating linear noise approximation (LNA) that exploits the structural information of bioprocessing mechanisms and a sequential learning strategy to fuse heterogeneous and sparse data, enabling inference of latent state variables and explicit approximation of the intractable likelihood function; and (ii) an efficient Bayesian posterior sampling method that utilizes Langevin diffusion (LD) to accelerate posterior exploration by exploiting the gradients of the derived likelihood. It generalizes the LNA approach to circumvent the challenge of step size selection, facilitating robust learning of mechanistic parameters with provable finite-sample performance guarantees. We develop a fast and robust RAPTOR-GEN algorithm with controllable error. Numerical experiments demonstrate its effectiveness in uncovering the underlying regulatory mechanisms of biomanufacturing processes. Funding: This research was supported by National Science Foundation Grant CAREER CMMI-2442970 and National Institute of Standards and T echnology Grant 70NANB21H086.

Large language models frequently generate confident but incorrect outputs, requiring formal uncertainty quantification with abstention guarantees. We develop information-lift certificates that compare model probabilities to a skeleton baseline, accumulating evidence into sub-gamma PAC-Bayes bounds valid under heavy-tailed distributions. Across eight datasets, our method achieves 77.2\% coverage at 2\% risk, outperforming recent 2023-2024 baselines by 8.6-15.1 percentage points, while blocking 96\% of critical errors in high-stakes scenarios vs 18-31\% for entropy methods. Limitations include skeleton dependence and frequency-only (not severity-aware) risk control, though performance degrades gracefully under corruption.