Adaptive specification search generates statistically significant backtests even under martingale-difference nulls. We introduce a falsification audit testing complete predictive workflows against synthetic reference classes, including zero-predictability environments and microstructure placebos. Workflows generating significant walk-forward evidence in these environments are falsified. For passing workflows, we quantify selection-induced performance inflation using an absolute magnitude gap linking optimized in-sample evidence to disjoint walk-forward realizations, adjusted for effective multiplicity. Simulations validate extreme-value scaling under correlated searches and demonstrate detection power under genuine structure. Empirical case studies confirm that many apparent findings represent methodological artifacts rather than genuine predictability.

One of the dominant paradigms in self-supervised learning (SSL), illustrated by MoCo or DINO, aims to produce robust representations by capturing features that are insensitive to certain image transformations such as illumination, or geometric changes. This strategy is appropriate when the objective is to recognize objects independently of their appearance. However, it becomes counterproductive as soon as appearance itself constitutes the discriminative signal. In weather analysis, for example, rain streaks, snow granularity, atmospheric scattering, as well as reflections and halos, are not noise: they carry the essential information. In critical applications such as autonomous driving, ignoring these cues is risky, since grip and visibility depend directly on ground conditions and atmospheric conditions. We introduce ST-STORM, a hybrid SSL framework that treats appearance (style) as a semantic modality to be disentangled from content. Our architecture explicitly separates two latent streams, regulated by gating mechanisms. The Content branch aims at a stable semantic representation through a JEPA scheme coupled with a contrastive objective, promoting invariance to appearance variations. In parallel, the Style branch is constrained to capture appearance signatures (textures, contrasts, scattering) through feature prediction and reconstruction under an adversarial constraint. We evaluate ST-STORM on several tasks, including object classification (ImageNet-1K), fine-grained weather characterization, and melanoma detection (ISIC 2024 Challenge). The results show that the Style branch effectively isolates complex appearance phenomena (F1=97% on Multi-Weather and F1=94% on ISIC 2024 with 10% labeled data), without degrading the semantic performance (F1=80% on ImageNet-1K) of the Content branch, and improves the preservation of critical appearance

Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR). Existing methods fix the target FDR level before observing data, which prevents the user from adapting the balance between number of selected test inputs and FDR to downstream needs and constraints based on the available data. For example, in genomics or neuroimaging, researchers often inspect the distribution of test statistics, and decide how aggressively to pursue candidates based on observed evidence strength and available follow-up resources. To address this limitation, we introduce {post-hoc CS} (PH-CS), which generates a path of candidate selection sets, each paired with a data-driven false discovery proportion (FDP) estimate. PH-CS lets the user select any operating point on this path by maximizing a user-specified utility, arbitrarily balancing selection size and FDR. Building on conformal e-variables and the e-Benjamini-Hochberg (e-BH) procedure, PH-CS is proved to provide a finite-sample post-hoc reliability guarantee whereby the ratio between estimated FDP level and true FDP is, on average, upper bounded by $1$, so that the average estimated FDP is, to first order, a valid upper bound on the true FDR. PH-CS is extended to control quality defined in terms of a general risk. Experiments on synthetic and real-world datasets demonstrate that, unlike CS, PH-CS can consistently satisfy user-imposed utility constraints while producing reliable FDP estimates and maintaining competitive FDR control.

Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such forecasts enjoy unprecedented accuracy out to two weeks thanks to steady advances in physics-based dynamical models and data-driven artificial intelligence (AI) models. However, model skill drops precipitously at subseasonal timescales (2 - 6 weeks ahead), due to compounding errors and persistent biases. To counter this degradation, we introduce probabilistic bias correction (PBC), a machine learning framework that substantially reduces systematic error by learning to correct historical probabilistic forecasts. When applied to the leading dynamical and AI models from the European Centre for Medium-Range Weather Forecasts (ECMWF), PBC doubles the subseasonal skill of the AI Forecasting System and improves the skill of the operationally-debiased dynamical model for 91% of pressure, 92% of temperature, and 98% of precipitation targets. We designed PBC for operational deployment, and, in ECMWF's 2025 real-time forecasting competition, its global forecasts placed first for all weather variables and lead times, outperforming the dynamical models from six operational forecasting centers, an international dynamical multi-model ensemble, ECMWF's AI Forecasting System, and the forecasting systems of 34 teams worldwide. These probabilistic skill gains translate into more accurate prediction of extreme events and have the potential to improve agricultural planning, energy management, and disaster preparedness in vulnerable communities.

Beijing's robot half-marathon is back for its second year with far less embarassing results The fastest time from an Honor robot came in at 50 minutes and 26 seconds. To make up for an incredibly laughable inaugural event, Beijing is running back its humanoid robot half-marathon. Fortunately, the event that pits humanoid robots made by Chinese companies against each other across 13 miles went a lot smoother this year. This year's half-marathon hosted more than 100 competitors, with first place going to Honor, better known for its smartphones, and its red-clad robot named Lightning. Living up to the name, the gold medalist finished the race in 50 minutes and 26 seconds.

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. While beautiful, Bradford pear trees also stink. Breakthroughs, discoveries, and DIY tips sent six days a week. The sun is out, the streets are humming, the days are getting longer, and the air smells like like um say, can anyone else smell that? All over America, spring is getting smellier every year, and the culprit is the Bradford pear, a tree that gained popularity in the mid-20 century for its ornamental properties.

The 12th annual competition is now open for submissions. 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. Breakthroughs, discoveries, and DIY tips sent six days a week. Mangroves play a crucial role in coastal ecosystems, buffering hurricane damage, storing carbon, and providing a safe haven for diverse wildlife . The Mangrove Photography Awards celebrate the ecological superhero by raising awareness around conservation efforts through stunning imagery.

The initial rollout will be limited to a couple of neighborhoods in the two cities. Tesla is expanding its Robotaxi footprint across Texas by introducing availability in both Dallas and Houston. As announced in a post on X, the EV maker is rolling out its Robotaxis to small sections of the Texas cities, as detailed by two maps of its new service areas. The first Robotaxi rides started in Austin, Texas where Tesla is headquartered, but the service's launch was paired with a Tesla Safety Monitor, or a supervising human in the passenger seat. Earlier this year, Tesla began to transition away from including safety monitors, leaving its Robotaxis to operate unsupervised and fully autonomous.

Fat is key for fudgy brownies. 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. Breakthroughs, discoveries, and DIY tips sent six days a week. Astronauts aboard the International Space Station have brownies on their menu too . But what makes a perfect brownie?

When the Netflix anthology series "Beef" premièred, in 2023, it was a revelation in more ways than one. The show, which traced the depths into which two Angelenos descend after a road-rage incident, reintroduced Ali Wong as a dramatic lead, gave Steven Yeun a chance to go darkly comic, and shined a rare light on the issue of Asian American mental health. It also remade the career of its creator, Lee Sung Jin, a seeming overnight success who actually had nearly two decades of TV-comedy writing under his belt. Lee first pitched the show after he stalked another driver for a half hour following a parking-lot dispute; he similarly drew from life for Season 2, which stars Oscar Isaac as Josh, a country-club manager, and Carey Mulligan as his interior-designer wife, Lindsay. The couple are caught on video having a nasty fight by two members of his staff, Austin (Charles Melton) and Ashley (Cailee Spaeny). The Gen Z employees, about to embark on their own marriage, see the footage as blackmail material--and thus an opportunity to start their next chapter on secure financial footing. As in the first season, the story quickly broadens beyond the central conflict, roping in the club's new billionaire owner, Chairwoman Park (Youn Yuh-jung), her unreliable plastic-surgeon husband, and the seething resentments of both the haves and the have-nots. I met Lee earlier this month, at his new office in Hollywood. The space was sparsely decorated, but he'd already mounted posters for "Beef"; "It's Always Sunny in Philadelphia," the show that gave him his start in the industry; and "Thunderbolts," a 2025 Marvel movie directed by his creative partner, Jake Schreier. Lee, who has gone by Sonny since childhood and was credited as Sonny Lee for the first half of his career, opened up about the long road to "Beef"--a journey toward more intentional storytelling, as well as feeling "O.K. in my own skin." Perhaps surprisingly, the "Beef" character he seemed to relate to most was Josh, a congenial go-getter who mires himself in workaholism to avoid addressing his grief, as Lee did when one of his dogs died suddenly during production. We talked about his method of tailoring dialogue to his actors, the differences between Korean and American billionaires, and why class and capitalism are such inescapable themes on TV today.