Say More Versus Trending Now Look Up In My Bag Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home Mashable Selects Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook AI at School All Series From massage guns to LED masks, here's how to choose the Therabody device they'll actually want to use. The following content is brought to you by Mashable partners. If you buy a product featured here, we may earn an affiliate commission or other compensation. Struggling to find the perfect gift for friends and family? Simplify your shopping list by giving the gift of wellness this holiday season and you'll only have to ask one question: Which option benefits my friend's wellbeing the most?

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Things to Do in L.A. Tap to enable a layout that focuses on the article. L.A. County lacked coordinated plan, processes to guide recovery after 2025 fires, report finds Construction workers help rebuild a home along Mariposa Street in Altadena in May. See more from the L.A. Times in Google Search. Facing the greatest disaster in Los Angeles history, the county government did not have the plans and procedures in place it needed to jump-start a recovery after the January 2025 fires, a new review found. The new report suggested a slew of reforms to ensure the region is better prepared for the next major emergency, and noted that -- despite outdated or absent plans -- county employees and partners worked hard to provide many of the resources and support that fire survivors needed, often under exceptionally challenging circumstances.

Trending Now Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Creator Playbook Mashable Voices Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series Microsoft update on Outlook outage: 'Gradual recovery to mail flow' As of Monday evening, the full fix was not yet in for Exchange Online users. Neal joined Mashable's Social Good team in 2024, editing and writing stories about digital culture and its effects on the environment and marginalized communities. He is the former editorial director of The Advocate and Out magazines, has contributed to the Los Angeles Times, Curbed, and Los Angeles magazine, and is a recipient of the Sarah Pettit Memorial Award for LGBTQ Journalist of the Year Award from the National Gay and Lesbian Journalists Association (NLGJA). He lives in Los Angeles with his family. Microsoft told users on Monday evening that it was working on a fix for Outlook, Exchange Online, and other Microsoft 365 business services that were down for much of Aug. 31.

What to do with your feelings about the pop star's appearance. Rebecca Ruiz is a Senior Reporter at Mashable. She frequently covers mental health, digital culture, and technology. Her areas of expertise include suicide prevention, screen use and mental health, parenting, youth well-being, and meditation and mindfulness. Rebecca's experience prior to Mashable includes working as a staff writer, reporter, and editor at NBC News Digital and as a staff writer at Forbes.

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.

Safety classifiers deployed in production operate under a stationarity assumption that fails silently: when input distributions drift, accuracy degrades with no error signal until ground-truth labels arrive. We present an online monitor that detects distributional shift in classifier scores via a sliding-window KS statistic with empirically calibrated alarm thresholds. In a pre-registered factorial evaluation (4 classifiers $\times$ 5 shift conditions $\times$ 20 seeds $\times$ 2 window sizes; 800 cells), the monitor achieves 86.6% valid detection (mean latency 39.5 steps) across synthetic-onset, real-jailbreak, and adversarial regimes; a classifier $\times$ shift interaction ($η^2 = 0.185$) shows that monitoring must be tuned per classifier. Attempting to recover post-detection coverage via weighted conformal prediction exposes a failure mode: density-ratio estimation collapses for generative classifiers because logistic regression separates source from target perfectly in 3584-4096-dimensional embedding space, clipping all importance weights to zero; projecting to $\leq 32$ dimensions restores coverage. We then extend the framework to gradient-based evasion and give the first threat-model characterisation of score-disagreement monitoring as a canary. We falsify three assumptions: that architectural diversity drives the signal (false, $η^2 = 0.011$), that it is generic out-of-distribution detection (false, GCG-specific, $p < 10^{-12}$), and that an adaptive attacker can suppress it (false while the canary is confident). We derive the exact security boundary, a confidence-gated equilibrium at which a monitor-aware attacker stalls at gap $= 1/(2λ)$, and provide a calibration-free scan martingale achieving false-alarm rate $\leq 1\%$ across all classifiers with no per-model tuning.

Modern LLM workflows move coordinate-indexed objects across checkpoints: steering vectors, sparse autoencoders, top-$k$ neuron sets, attribution lists, and merge alignments. This is only well posed after fixing the model's residual-stream gauge, which we show is architecture-dependent: LayerNorm residual charts have permutation gauge $S_d$ (up to a global sign flip), while RMSNorm charts with generic per-channel gain have signed-permutation gauge $B_d = S_d \ltimes \{\pm 1\}^d$. Permutation-only alignment is therefore symmetry-incomplete for RMSNorm models. We introduce sign-marginalized Hungarian matching and prove a sharp failure mode: with decorrelated coordinates, raw signed-correlation matching has a structural permutation-accuracy ceiling at the positive-sign fraction of the true gauge, which sign-marginalization removes. We then make coordinate-preserving transport, not function-level merging, the primary object: composing saved-checkpoint local $B_d$ gauges along same-base fine-tuning trajectories recovers 91.1% of cross-run coordinates at 1500 steps versus 60.3% for endpoint matching, and the gain is not explained by merely routing through the base. The recovered gauge transfers tools that permutation-only alignment breaks: TinyLlama SAE reconstruction has NMSE 0.004 under $B_d$ versus 1.08 under $S_d$; Qwen sentiment steering preserves 95.8% of its effect versus 17.2%; refusal steering reverses sign under $S_d$; coordinate-preserving merges behave the same way. The same covariance governs stateful training: signed transport of AdamW state preserves the resumed trajectory, while permutation-only state follows a different one from a functionally identical checkpoint. Finally, gauge-sweep audits show index-level interpretability claims are reproducible only relative to an explicit gauge.

Causal representation learning for time series has developed strong identifiability results in discrete-time latent causal models, but identifiability in continuous-time latent stochastic differential equation (SDE) models remains largely open. We address this gap using environment-induced shifts in diffusion covariance. We study additive-noise latent SDEs observed through an unknown nonlinear diffeomorphism, with shared drift but environment-specific diffusion covariance. We show that two diagonal diffusion regimes with pairwise distinct coordinate-wise variance ratios identify the latent coordinates up to permutation and scaling, without any sparsity assumption on the drift. We first prove this result for linear Ornstein--Uhlenbeck systems and then extend it to general additive-noise latent SDEs. Under mild smoothness, the instantaneous drift-Jacobian causal graph is identifiable up to the same permutation. We propose a two-stage estimator for latent disentanglement and optional graph recovery; experiments on synthetic systems confirm the predicted identifiability boundary, and an application to Hardanger Bridge monitoring data illustrates the approach on real sensor trajectories.