PCWorld reports the Steam Deck has returned to market with nearly doubled pricing, featuring only OLED models at $789 for 512GB and $949 for 1TB versions. Valve discontinued cheaper LCD models and attributes price increases to rising memory and storage costs driven by AI industry demand affecting consumer hardware. This trend extends beyond Steam Deck to other gaming handhelds like Lenovo Legion Go, signaling broader affordability challenges in PC gaming hardware. The Steam Deck, harbinger of a portable PC gaming revolution, has been out of stock for three months. Now it's back at almost double the price of the original model. It'll cost you $789 USD to get the 512GB OLED version, $949 for the 1TB upgrade. The original LCD model, which debuted at $400, is resigned to the dustbin of history . Welcome to PC gaming in 2026, where the K-shaped economy has claimed the last remaining affordable option. The 512GB OLED model now costs $1,129 in Canada, 649 pounds in the UK, 779 euro in Europe, $1,199 in Australia, and 3,279 PLN in Poland.

A Philippine Navy band plays music to welcome the Royal New Zealand Navy frigate HMNZS Te Kaha upon arrival at the South Harbor, for a four-day goodwill visit in metro Manila in April 2017. New Zealand intends to spend about 1.6 billion New Zealand dollars ($936 million) on drones, ship maintenance and naval upgrades to bolster the island nation's maritime security at a time of increasing concern about supply routes. Defense Minister Chris Penk said Saturday that the government will invest in two types of drones: one for the southwest Pacific to provide long-duration intelligence, surveillance and reconnaissance; the other is a polar-capable vehicle that can operate from naval vessels in the Southern Ocean. "New Zealand's prosperity and security depend on the sea," Penk said in a statement. "Recent events have served as a reminder of how quickly disruptions to international shipping routes can affect economies and supply chains across the globe. The oceans are not a barrier to danger, but a vital national interest that must be actively secured."

Why the world's banks are so worried about Anthropic's latest AI model The legendary American bank robber Willie Sutton spent 40 years robbing banks because, as he claimed in his autobiography, he loved doing it. And when asked why he chose banks of all places to rob, he allegedly replied "Because that's where the money is." Back in 2017, I wrote a book predicting it wasn't just lovable rogues like Sutton who would soon be robbing banks, but artificial intelligence (AI). That day, it appears, could now be about to arrive. Banks around the world are seriously worried cyber criminals will soon take advantage of the latest advances in AI to try to rob them.

Staff at WiseTech have been waiting months to be told if they are among the employees the company is to cut due to advances in AI. Staff at WiseTech have been waiting months to be told if they are among the employees the company is to cut due to advances in AI. WiseTech begins redundancies - but omits'AI' from emails to Chinese employees, workers say WiseTech has begun informing staff that they will lose their jobs as part of redundancies the company has said is due to artificial intelligence advancements - although an email to staff in China omitted the word "AI" after a court case against another company in the country. Staff at WiseTech have been waiting almost three months to be told if they are among the 2,000 people the logistics software company is to cut due to advances in AI. The Australian Stock Exchange-listed company announced in late February it would lay off almost 30% of its 7,000-strong workforce across 40 countries.

Panel data, in which multiple units are repeatedly observed over time, arise throughout science and engineering. Quantifying predictive uncertainty in such settings is challenging because conformal prediction, while distribution-free and model-agnostic, classically relies on exchangeability assumptions that fail under temporal dependence and unit heterogeneity. We propose a simple online conformal framework for non-exchangeable panel data. The method exploits a key feature of online panel prediction: when a forecast is required for one unit, contemporaneous outcomes from related units may already be observed and can serve as a calibration panel. At each round, prediction sets are formed using currently observed calibration units together with two adaptive quantities: history-based similarity weights that emphasize calibration units resembling the target, and an adaptive miscoverage level that is updated whenever target feedback is revealed. This two-state design yields a stepwise coverage bound and a long-run coverage guarantee. Empirically, across synthetic and real panel data sets, the method improves coverage on the worst-covered target units through adaptive interval-width allocation rather than uniform inflation. The two states are complementary: similarity weights protect coverage when target feedback is sparse, while the adaptive level further improves coverage as feedback accumulates.

We propose a data-driven Fourier-trained neural-network method for estimating fixed-horizon probability densities from empirical characteristic-function (CF) information. The estimator is a positive Gaussian--Laplace mixture with closed-form CF, so training can be performed directly in Fourier space while preserving nonnegativity and unit mass. We consider two sampling settings. In the direct i.i.d. sampling setting, the method is trained against an empirical CF constructed from i.i.d. samples. In the resampling-based pseudo-sampling setting, it is trained against an empirical pseudo-CF constructed from dependent data by resampling. For the direct i.i.d. case, we derive an expected $L_2$ error bound that separates Fourier truncation, empirical training error, discretization, and CF sampling error. For the pseudo-sampling case, we obtain a conditional analogue with two additional pseudo-law discrepancy terms. We develop a multidimensional extension of the framework and analyze its computational complexity. Numerical experiments show competitive performance relative to Expectation--Maximization on Gaussian-mixture benchmarks, clear gains on heavy-tailed targets, $L_2$ error decay consistent with the theory in a well-specified setting, and effective estimation of one-year Australian equity return law from resampled dependent data.

Learning dynamical systems through operator-theoretic representations provides a powerful framework for analyzing complex dynamics, as spectral quantities such as eigenvalues and invariant structures encode characteristic time scales and long-term behavior. However, dynamical operators are typically estimated independently for each system, preventing the discovery of shared structure across related dynamics. To address this limitation, we posit that related dynamical systems lie near a low-dimensional manifold in spectral operator space. Based on this hypothesis, we introduce DOODL (Dynamical OperatOr Dictionary Learning), a framework that learns a dictionary of characteristic spectral dynamics whose combinations approximate this manifold and yield compact, interpretable embeddings of individual systems. Beyond representation learning, DOODL enables fast and interpretable operator estimation from short and partially observed trajectories by constraining the estimation to the learned operator manifold. Experiments on metastable Langevin dynamics and turbulent plasma simulations demonstrate that DOODL scales to highly complex multiscale regimes while capturing characteristic spectral structure governing the dynamics rather than merely fitting trajectories, achieving errors one to two orders of magnitude lower than independent operator estimation methods in challenging low-data regimes.

Digital rights experts have raised concerns about the security of the data recorded by AI in psychiatrists' sessions. Digital rights experts have raised concerns about the security of the data recorded by AI in psychiatrists' sessions. Melbourne psychiatrist refuses new patients who don't consent to AI note-taking A Melbourne psychiatrist has refused new patients unless they agree to allow her to use an AI scribe to transcribe the conversations in their sessions. AI-driven note taking tools are becoming popular within the medical industry - with two in five general practitioners now using such scribes, according to the Royal Australian College of General Practitioners (RACGP). But there have also been concerns about the security of the data and how it might be used by the AI companies, along with the accuracy of the transcriptions.

The dog survived traumatic injuries, thanks to his Barkindji caretakers. 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. The remains of an ancient dingo is shining new light on deep relationships between Australia's First Nations and the wild dogs . Barkindji ancestors deliberately cared for and buried the dingo along the Baaka (Darling River) about 800 miles west of Sydney.

In the data-driven era, large-scale datasets are routinely collected and analyzed using machine learning (ML) and artificial intelligence (AI) to inform decisions in high-stakes domains such as healthcare, employment, and criminal justice, raising concerns about the fairness behavior of these systems. Existing works in fair ML cover tasks such as bias detection, fair prediction, and fair decision-making, but largely focus on static settings. At the same time, fairness in temporal contexts, particularly survival/time-to-event (TTE) analysis, remains relatively underexplored, with current approaches to fair survival analysis adopting statistical fairness definitions, which, even with unlimited data, cannot disentangle the causal mechanisms that generate disparities. To address this gap, we develop a causal framework for fairness in TTE analysis, enabling the decomposition of disparities in survival into contributions from direct, indirect, and spurious pathways. This provides a human-understandable explanation of why disparities arise and how they evolve over time. Our non-parametric approach proceeds in four steps: (1) formalizing the necessary assumptions about censoring and lack of confounding using a graphical model; (2) recovering the conditional survival function given covariates; (3) applying the Causal Reduction Theorem to reframe the problem in a form amenable to causal pathway decomposition; (4) estimating the effects efficiently. Finally, our approach is used to analyze the temporal evolution of racial disparities in outcome after admission to an intensive care unit (ICU).