On one screen, an urban professional in Oslo taps through ultra-secure banking apps, relies on an AI-powered personal assistant, and streams media seamlessly over high-speed 5G using their iPhone. On the other screen, a farmer in Malawi scrolls through a modest Android phone -- likely costing less than a week's wages -- just to read the news, check tomorrow's weather, and send WhatsApp messages over a patchy mobile connection. These very different experiences highlight the divide between the Global North and the Global South. These terms refer not only to geographic locations but also to the world's wealthiest and most industrialized regions -- such as Europe, North America, and parts of East Asia -- and economically developing nations across much of Africa, Latin America, South Asia, and Oceania. Technology symbolizes innovation, convenience, and seamless connectivity in the Global North.

Domain generalisation in computational histopathology is challenging because the images are substantially affected by differences among hospitals due to factors like fixation and staining of tissue and imaging equipment. We hypothesise that focusing on nuclei can improve the out-of-domain (OOD) generalisation in cancer detection. We propose a simple approach to improve OOD generalisation for cancer detection by focusing on nuclear morphology and organisation, as these are domain-invariant features critical in cancer detection. Our approach integrates original images with nuclear segmentation masks during training, encouraging the model to prioritise nuclei and their spatial arrangement. Going beyond mere data augmentation, we introduce a regularisation technique that aligns the representations of masks and original images. We show, using multiple datasets, that our method improves OOD generalisation and also leads to increased robustness to image corruptions and adversarial attacks. The source code is available at https://github.com/undercutspiky/SFL/

The night after their son Mats died aged just 25, Trude and Robert Steen sat on the sofa in their living room in Oslo with their daughter Mia. "Everything was a blur," remembers Trude of that day 10 years ago. "Then Robert said, 'Maybe we should reach out to Mats' friends in World of Warcraft.'" Mats was born with Duchenne muscular dystrophy, a progressive condition that causes the muscles to weaken gradually. He was diagnosed aged four and started using a wheelchair at 10.

As a big soundtrack fan, I love any occasion in which musicians perform them live in concert. So, I'm excited that Sony has created PlayStation The Concert, a world tour featuring the scores from titles like The Last of Us, God of War, Ghost of Tsushima and Horizon. Previous video game concerts have included The Legend of Zelda: Symphony of the Goddesses, which ran from 2012 to 2017. The announcement coincides with the 30th anniversary of PlayStation, with the production meant to reflect "30 years of making games that have not only captivated players but are celebrated for their breathtaking and immersive soundtracks too," Sid Shuman, senior director of Sony Interactive Entertainment Content Communications, stated in the release. The tour will start on April 15, 2025 in Dublin before traveling to cities around Europe like Paris, Oslo, London and Budapest.

We introduce One-Shot Label-Only (OSLO) membership inference attacks (MIAs), which accurately infer a given sample's membership in a target model's training set with high precision using just \emph{a single query}, where the target model only returns the predicted hard label. This is in contrast to state-of-the-art label-only attacks which require $\sim6000$ queries, yet get attack precisions lower than OSLO's. OSLO leverages transfer-based black-box adversarial attacks. The core idea is that a member sample exhibits more resistance to adversarial perturbations than a non-member. We compare OSLO against state-of-the-art label-only attacks and demonstrate that, despite requiring only one query, our method significantly outperforms previous attacks in terms of precision and true positive rate (TPR) under the same false positive rates (FPR). For example, compared to previous label-only MIAs, OSLO achieves a TPR that is 7$\times$ to 28$\times$ stronger under a 0.1\% FPR on CIFAR10 for a ResNet model. We evaluated multiple defense mechanisms against OSLO.

Non-parametric machine learning models, such as random forests and gradient boosted trees, are frequently used to estimate house prices due to their predictive accuracy, but such methods are often limited in their ability to quantify prediction uncertainty. Conformal Prediction (CP) is a model-agnostic framework for constructing confidence sets around machine learning prediction models with minimal assumptions. However, due to the spatial dependencies observed in house prices, direct application of CP leads to confidence sets that are not calibrated everywhere, i.e., too large of confidence sets in certain geographical regions and too small in others. We survey various approaches to adjust the CP confidence set to account for this and demonstrate their performance on a data set from the housing market in Oslo, Norway. Our findings indicate that calibrating the confidence sets on a \textit{locally weighted} version of the non-conformity scores makes the coverage more consistently calibrated in different geographical regions. We also perform a simulation study on synthetically generated sale prices to empirically explore the performance of CP on housing market data under idealized conditions with known data-generating mechanisms.

This week, Emily Bazelon, John Dickerson, and David Plotz discuss the problems with issue polling and issues with political journalism; the chaos and conflict of Sam Altman and OpenAI; and the failure of the Oslo Accords and perpetual struggle between Israel and Palestine. Send us your Conundrums: submit them at slate.com/conundrum. And join us in-person or online with our special guest – The Late Show's Steven Colbert – for Gabfest Live: The Conundrums Edition! December 7 at The 92nd Street Y, New York City. Here are some notes and references from this week's show: Nate Cohn for The New York Times: The Crisis in Issue Polling, and What We're Doing About It and We Did an Experiment to See How Much Democracy and Abortion Matter to Voters Eli Saslow for The New York Times: A Jan. 6 Defendant Pleads His Case to the Son Who Turned Him In John Dickerson and Jo Ling Kent for CBS News Prime Time: What Sam Altman's ouster from OpenAI could mean for the tech world Emily Bazelon for The New York Times Magazine: Was Peace Ever Possible? Ezra Klein for The New York Times's The Ezra Klein Show podcast: The Best Primer I've Heard on Israeli-Palestinian Peace Efforts John Dickerson for CBS Mornings: Former President Jimmy Carter: "America will learn from its mistakes" Here are this week's chatters: John: Julia Simon for NPR: 'It feels like I'm not crazy.'

A B S T R A C T Precisely identifying sulcal features in brain MRI is made challenging by the variability of brain folding. This research introduces an innovative 3D explainability frame-work that validates outputs from deep learning networks in their ability to detect the paracin-gulate sulcus, an anatomical feature that may or may not be present on the frontal medial surface of the human brain. This study trained and tested two networks, amalgamating local explainability techniques GradCam and SHAP with a dimensionality reduction method. The explainability framework provided both localized and global explanations, along with accuracy of classification results, revealing pertinent sub-regions contributing to the decision process through a post-fusion transformation of explanatory and statistical features. Leveraging the TOP-OSLO dataset of MRI acquired from patients with schizophrenia, greater accuracies of paracingulate sulcus detection (presence or absence) were found in the left compared to right hemispheres with distinct, but extensive sub-regions contributing to each classification outcome. The study also inadvertently highlighted the critical role of an unbiased annotation protocol in maintaining network performance fairness. Our proposed method not only o ff ers automated, impartial annotations of a variable sulcus but also provides insights into the broader anatomical variations associated with its presence throughout the brain. The adoption of this methodology holds promise for instigating further explorations and inquiries in the field of neuroscience.1. Introduction While the folding of the primary sulci of the human brain, formed during gestation, is broadly stable across individuals, the secondary sulci which continue to develop post-natally are unique to each individual. Inter-individual variability poses a significant challenge for the detection and accurately annotation of sulcal features from MRI of the brain. Undertaking this task manually is time-consuming with outcomes that depend on the rater. This prevents the e fficient leveraging of the large, open-access MRI databases that are available. While primary sulci can be very accurately detected with automated methods, secondary sulci pose a more di fficult computational problem due to their higher variability in shape and indeed presence or absense [3]. A successful automated method would facilitate investigations of brain folding variation, representative of events occurring during a critical developmental period. Furthermore, generalized and unbiased annotations would make tractable large-scale studies of cognitive and behavioral development, and the emergence of mental and neurological disorders with high levels of statistical power. The folding of the brain has been linked to brain function, and some specific folding patterns have been related to susceptibility to neurological adversities [20].

We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously detecting instances that do not belong to any known class. We explore the popular transductive setting, which leverages the unlabelled query instances at inference. Motivated by the observation that existing transductive methods perform poorly in open-set scenarios, we propose a generalization of the maximum likelihood principle, in which latent scores down-weighing the influence of potential outliers are introduced alongside the usual parametric model. Our formulation embeds supervision constraints from the support set and additional penalties discouraging overconfident predictions on the query set. We proceed with a block-coordinate descent, with the latent scores and parametric model co-optimized alternately, thereby benefiting from each other. We call our resulting formulation \textit{Open-Set Likelihood Optimization} (OSLO). OSLO is interpretable and fully modular; it can be applied on top of any pre-trained model seamlessly. Through extensive experiments, we show that our method surpasses existing inductive and transductive methods on both aspects of open-set recognition, namely inlier classification and outlier detection.

Thomas Fuchs, the Dean of Artificial Intelligence and Human Health at Mount Sinai in NYC, said AI will be needed to retain the standard of care in the U.S. Glioblastoma is one of the deadliest types of brain cancer, with the average patient living only eight months after diagnosis, according to the National Brain Tumor Society, a nonprofit. Two ambitious high school students -- Andrea Olsen, 18, from Oslo, Norway, and Zachary Harpaz, 16, from Fort Lauderdale, Florida -- are looking to change that. The teens partnered with Insilico Medicine, a Hong Kong-based medical technology company, to identify three new target genes linked to glioblastoma and aging. They used Insilico's artificial intelligence platform, PandaOmics, to make the discovery -- and now, they plan to continue researching ways to fight the disease with new drugs. Their findings about target genes were published on April 26 in Aging, a peer-reviewed biomedical academic journal.