Ghana welcomes Pope's apology over Catholic Church's role in slavery Ghana has welcomed Pope Leo XIV's apology for the Catholic Church's historic role in slavery, describing it as an act of moral courage that was important in the global pursuit of truth, human dignity and justice. The Pope issued the clearest apology yet for the Church's involvement in legitimising slavery and its delay in condemning it for centuries. The apology was published on Monday in the Pope's first major teaching document of his papacy, which also focused on the dangers of artificial intelligence (AI) . Ghana was a major hub for the transatlantic slave trade when millions of people were captured and loaded on to ships, never to return home. Between the 16th and 19th Centuries, 12-15 million Africans were shipped to the Caribbean, with about two million dying during the journey.

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Neural operators have achieved strong performance in learning solution operators of partial differential equations (PDEs), but their inherently continuous representations struggle to capture discontinuities and sharp transitions. Existing approaches typically approximate such features within continuous function spaces, often requiring increased model capacity and high-resolution data. In this work, we propose Cut-DeepONet, a two-stage training framework that explicitly models discontinuities while reducing learning complexity. Our approach reformulates the problem via a lifting strategy, partitioning the domain into smooth subregions while representing discontinuities as boundaries in a higher-dimensional space. This separation aligns the operator learning task with the inductive bias of neural networks and avoids directly approximating discontinuities. An additional network predicts input-dependent discontinuity locations for unseen inputs, which are then used to guide the neural operator in generating smooth components within each region. Experiments on benchmark PDEs show that Cut-DeepONet outperforms state-of-the-art methods, even when trained on low-resolution datasets. The method excels on problems with discontinuities and sharp transitions, while using fewer trainable parameters. Our results highlight the benefits of changing the representation of operator learning rather than increasing model complexity.

Asteroseismology is the study of resonant oscillations of stars to infer their internal structure and dynamics. It is also a powerful tool for precisely determining stellar parameters such as mass, radius, surface gravity, and age. The ongoing TESS mission, with its nearly complete sky coverage, presents a unique opportunity to uniformly probe stellar populations across the Milky Way. TESS is estimated to have observed more than 300,000 oscillating red giants, most of which have one to two months of observations. Given the scale of this dataset, we need a fast, efficient, and robust way to analyse the data. In this work, our objective is to develop a machine learning (ML) based method to infer asteroseismic parameters from short-duration observations. Specifically, we focus on two global seismic parameters, the large frequency separation ($Δν$) and the frequency at maximum power ($ν_{\mathrm{max}}$), from one-month-long TESS observations of red giants. Meanwhile, for K2 data, our focus extends to inferring the period spacings of dipolar gravity modes ($ΔΠ_{1}$), in addition to $Δν$ and $ν_{\mathrm{max}}$. Our findings demonstrate that our machine learning algorithm can accurately infer $Δν$ and $ν_{\mathrm{max}}$ for approximately 50% of samples created by taking one-month Kepler and K2 observations. For TESS one sector data however, we recover reliable $Δν$ for only about 23% of the stars. Additionally, we get reliable $ΔΠ_{1}$ inferences for about 200 young red-giants from K2. For these $ΔΠ_{1}$ inferences, we see a good match with the well known $Δν-ΔΠ_{1}$ degenerate sequence observed in Kepler red-giants.

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PCWorld tested Microsoft's Auto SR, a DLSS rival exclusive to the Asus ROG Ally X, finding it delivers only marginal 10% performance gains in games like Borderlands 3. The technology currently works only in docked mode at 720p resolution and produces notably degraded visuals described as'muddy' and'swimmy' compared to native resolution. Auto SR remains in Preview status with significant usability issues including incorrect scaling and required game restarts, making it inadequate for handheld gaming improvement. Last week Microsoft announced the arrival of Auto SR, its Windows-branded alternative to upscaling tech like DLSS, with great fanfare. After being semi-exclusive to Snapdragon laptops, it came to the Asus ROG Xbox Ally X and nothing else. Not even the non-X variant, since it needs an NPU to run. And also it only works in docked mode, not handheld mode.

Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms. Enabling the design of datasets to test specific properties and failure modes of learning algorithms is thus a problem of high interest, as it has a direct impact on innovation in the field. In this sense, we introduce Synbols -- Synthetic Symbols -- a tool for rapidly generating new datasets with a rich composition of latent features rendered in low resolution images. Synbols leverages the large amount of symbols available in the Unicode standard and the wide range of artistic font provided by the open font community. Our tool's high-level interface provides a language for rapidly generating new distributions on the latent features, including various types of textures and occlusions. To showcase the versatility of Synbols, we use it to dissect the limitations and flaws in standard learning algorithms in various learning setups including supervised learning, active learning, out of distribution generalization, unsupervised representation learning, and object counting.