George W. Bush is reportedly fundraising for Senate Republicans but not Ken Paxton, who defeated Bush's nephew in the 2022 Texas attorney general primary.

The Clarity Act crypto regulation bill failed a key Senate vote, with Sen. Cynthia Lummis saying the effort is over. Democrats cited ethics and money laundering concerns.

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The user selects a workload matrixArepresenting a desired first-order optimization method.

Objectives: We present and evaluate a Mamba-based deep-learning model for diagnosis and event-level characterization of sleep disordered breathing based on signals from the ANNE One, a non-intrusive dual-module wireless wearable system measuring chest electrocardiography, triaxial accelerometry, chest and finger temperature, and finger phototplethysmography. Methods: We obtained concurrent PSG and wearable sensor recordings from 384 adults attending a tertiary care sleep laboratory. Respiratory events in the PSG were manually annotated in accordance with AASM guidelines. Wearable sensor and PSG recordings were automatically aligned based on the ECG signal, alignment confirmed by visual inspection, and PSG-derived respiratory event labels were used to train and evaluate a deep sequential neural network based on the Mamba architecture. Results: In 57 recordings in our test set (mean age 56, mean AHI 10.8, 43.86\% female) the model-predicted AHI was highly correlated with that derived form the PSG labels (R=0.95, p=8.3e-30, men absolute error 2.83). This performance did not vary with age or sex. At a threshold of AHI$>$5, the model had a sensitivity of 0.96, specificity of 0.87, and kappa of 0.82, and at a threshold of AHI$>$15, the model had a sensitivity of 0.86, specificity of 0.98, and kappa of 0.85. At the level of 30-sec epochs, the model had a sensitivity of 0.93 and specificity of 0.95, with a kappa of 0.68 regarding whether any given epoch contained a respiratory event. Conclusions: Applied to data from the ANNE One, a Mamba-based deep learning model can accurately predict AHI and identify SDB at clinically relevant thresholds, achieves good epoch- and event-level identification of individual respiratory events, and shows promise at physiological characterization of these events including event type (central vs. other) and event duration.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Challenges to Pelosi part of broader movement to replace the Democratic Party's old guard Rep. Nancy Pelosi, shown talking to reporters in the U.S. Capitol on Oct. 1, has not said whether she will seek another term in 2026. This is read by an automated voice. Please report any issues or inconsistencies here . Younger Democratic candidates are challenging older incumbents amid increasing frustration over the party's ineffective resistance to President Trump.

Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, effectively leveraging external information to enhance reasoning presents the following challenges: (1) low signal-to-noise ratio, where answer-supportive external information is diluted by irrelevant material, and (2) error accumulation, which arises in multi-hop reasoning when incomplete or misleading information is incorporated. To address these challenges, we introduce EviNote-RAG, a framework that follows a retrieve-note-answer workflow. Instead of reasoning directly over raw external information, the model first produces Supportive-Evidence Notes (SENs), which concisely preserve answer-critical information and explicitly mark key and uncertainty information to improve accuracy. We further design an entailment-based Evidence Quality Reward (EQR) to ensure that SENs are logically sufficient to derive the final answer, thereby enhancing SENs' quality. Experiments on both in-domain and out-of-domain QA benchmarks show that EviNote-RAG achieves state-of-the-art performance, improving answer accuracy, training stability, robustness, and efficiency. In particular, it yields relative F1 gains of 20% on HotpotQA (+0.093), 40% on Bamboogle (+0.151), and 91% on 2Wiki (+0.256), benefiting from improvements in the reasoning process.

A common assumption in modern microeconomic theory is that choice should be rationalizable via a binary preference relation, which \citeauthor{Sen71a} showed to be equivalent to two consistency conditions, namely $α$ (contraction) and $γ$ (expansion). Within the context of \emph{social} choice, however, rationalizability and similar notions of consistency have proved to be highly problematic, as witnessed by a range of impossibility results, among which Arrow's is the most prominent. Since choice functions select \emph{sets} of alternatives rather than single alternatives, we propose to rationalize choice functions by preference relations over sets (set-rationalizability). We also introduce two consistency conditions, $\hatα$ and $\hatγ$, which are defined in analogy to $α$ and $γ$, and find that a choice function is set-rationalizable if and only if it satisfies $\hatα$. Moreover, a choice function satisfies $\hatα$ and $\hatγ$ if and only if it is \emph{self-stable}, a new concept based on earlier work by \citeauthor{Dutt88a}. The class of self-stable social choice functions contains a number of appealing Condorcet extensions such as the minimal covering set and the essential set.

Cruise robotaxis are back on the road… well, kind of. Though General Motors pulled the plug on its self-driving taxi business last year, the automaker has been quietly repurposing a few of the vehicles as it seeks to develop new driver-assistance technologies. This week, WIRED spotted a GM Bolt electric hatchback on the San Francisco-Oakland Bay Bridge, and later saw a similar vehicle on Interstate 880 near Oakland. In each instance, the car was being driven by a human. The vehicle had "Mint" written on the hood, but didn't include any visually apparent Cruise branding.