Originally featured as a minigame in Project Gotham, this 80s-style twin-stick shooter was rebuilt as a standalone digital-only release, attracting a huge new fanbase. Fast, frenetic and super stylish, with lovely vector visuals, it was the game that first showed the potential of Xbox Live Arcade. A time-sink of epic proportions The Elder Scrolls IV: Oblivion (screenshot from the 2025 remake) Tens of millions of hours must have been spent in this foundational text of open-world role-playing games - one of the first video games where you really could go where you wanted and do pretty much as you pleased. Riding around Cyrodiil on horseback, taking in its gleaming city and backwater towns, it was so easy to get drawn into unexpected shenanigans that closing the story's threatening hell-gates became a distant second priority. The most addictive version of the classic card game Uno on Xbox 360 Look - don't @ us - Uno was one of most important Xbox 360 games.

Objective: We sought to develop a classification algorithm to extract diagnoses from free-text radiology reports of brain imaging performed in patients with acute respiratory failure (ARF) undergoing invasive mechanical ventilation. Methods: We developed and fine-tuned Neu-RadBERT, a BERT-based model, to classify unstructured radiology reports. We extracted all the brain imaging reports (computed tomography and magnetic resonance imaging) from MIMIC-IV database, performed in patients with ARF. Initial manual labelling was performed on a subset of reports for various brain abnormalities, followed by fine-tuning Neu-RadBERT using three strategies: 1) baseline RadBERT, 2) Neu-RadBERT with Masked Language Modeling (MLM) pretraining, and 3) Neu-RadBERT with MLM pretraining and oversampling to address data skewness. We compared the performance of this model to Llama-2-13B, an autoregressive LLM. Results: The Neu-RadBERT model, particularly with oversampling, demonstrated significant improvements in diagnostic accuracy compared to baseline RadBERT for brain abnormalities, achieving up to 98.0% accuracy for acute brain injuries. Llama-2-13B exhibited relatively lower performance, peaking at 67.5% binary classification accuracy. This result highlights potential limitations of current autoregressive LLMs for this specific classification task, though it remains possible that larger models or further fine-tuning could improve performance. Conclusion: Neu-RadBERT, enhanced through target domain pretraining and oversampling techniques, offered a robust tool for accurate and reliable diagnosis of neurological conditions from radiology reports. This study underscores the potential of transformer-based NLP models in automatically extracting diagnoses from free text reports with potential applications to both research and patient care.

Mass Effect is some of the best science fiction ever made. That may sound like a grandiose comment, but it's true. As a trilogy, the original games from 2007-2013 effortlessly plucked the most cerebral ideas from the sci-fi genre and slotted them into a memorable military role-playing game that had players invested from beginning to controversial end. Whether you prefer the hopeful, optimistic outlook of Asimov, the dark and reflective commentary of Shelley, the accessible thought experiments of Star Trek, or the arch melodrama of Battlestar Galactica, Mass Effect has it all. The trilogy is as happy grazing on the western-inspired tropes of Star Wars as the "hard" sci-fi of Iain M Banks, blending all its moods and micro-stories into a compelling, believable galaxy that somehow walks a line between breathless optimism and suffocating bleakness.

Back in 2013, having bought the series from Eidos, Square Enix released a reboot of the hit 1990s action game Tomb Raider starring a significantly less objectified Lara Croft. I loved that game, despite a quasi-assault scene near the beginning that I would later come to view as a bit icky, and I wasn't the only one – it was extremely well received, selling 3.4m copies in its first month alone. Then Square Enix came out and called it a disappointment. Sales did not meet the publisher's expectations, apparently, which raises the question: what were the expectations? Was it supposed to sell 5m in one month?

In the spring of 2021, just months before Congress passed the Infrastructure Investment and Jobs Act--heralded by the Biden administration as the largest-ever federal investment in public transit, bridge repair, and clean energy--I found myself playing a lot of Mass Effect Legendary Edition. This was a happy coincidence, because never in my life had the nation been so embroiled in wonky debates about infrastructure priorities and spending. And as it turns out, Mass Effect was the perfect 100-hour video game for that particular moment in history: It's absolutely obsessed with transportation technologies and their social, cultural, and political implications. Despite its revolutionary capacity, we often conceptualize transportation in mundane, frustrating terms: long commutes and congested highways, spotty bus service and increasingly crowded sidewalks littered with e-scooters. That's what makes fiction centered around these questions so important--especially when it comes to thinking through the big investments we want to make in infrastructure, what we hope to accomplish, and the challenges we should anticipate.

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains.

As it comes to an end, it's hard not to acknowledge 2021 was only a minor improvement over 2020. Covid-19, worldwide political turmoil, climate catastrophes--all the stresses of the previous year rolled over into the new one. The result was another 12 months that taxed everyone's mental health and led many of them to seek refuge wherever they could find it, often in video games. To be sure, I am definitely in this camp. Over the past year, my relationship with gaming changed a lot, morphing from a pastime into something I do to cope.

Document-level information extraction is a flexible framework compatible with applications where information is not necessarily localized in a single sentence. For example, key features of a diagnosis in radiology a report may not be explicitly stated, but nevertheless can be inferred from the report's text. However, document-level neural models can easily learn spurious correlations from irrelevant information. This work studies how to ensure that these models make correct inferences from complex text and make those inferences in an auditable way: beyond just being right, are these models "right for the right reasons?" We experiment with post-hoc evidence extraction in a predict-select-verify framework using feature attribution techniques. While this basic approach can extract reasonable evidence, it can be regularized with small amounts of evidence supervision during training, which substantially improves the quality of extracted evidence. We evaluate on two domains: a small-scale labeled dataset of brain MRI reports and a large-scale modified version of DocRED (Yao et al., 2019) and show that models' plausibility can be improved with no loss in accuracy.

I had not yet received my double lung transplant when I first played Mass Effect, BioWare's sprawling space opera shooter, in 2007. I had not yet started taking pills to quiet my own immune system when Tali'Zorah nar Rayya first graced my screen. Back then, I didn't think twice about the Quarian engineer in the purple bio-suit, immunocompromised from a lifetime adrift in the stars, making her way through a galaxy teeming with alien bacteria and openly antagonistic to her continued health. Over a decade later, though, things have changed. I am, quite literally, a different person.

Whether it's down to our own hubris, the disastrous effects of unbridled wealth accumulation and social division, war, the climate crisis, plague, a space rock or perhaps unfriendly aliens – we'll one day be dust caught in cosmic winds, lost to an indifferent universe. On our pale blue dot, the remnants of once-great civilisations and vanished peoples that we unearth already show us that advanced development is no guarantee of perpetuity. In sci-fi, humanity's naive yearning to fight on despite this realisation often proves a point of curiosity – and sometimes inspiration – for alien species. This is front and centre of the Mass Effect trilogy of video games, in which our imminent annihilation is given form in the tendrils of creatures called Reapers: ancient, building-sized, alien-robot hybrids that wipe out most life in the Milky Way every 50,000 years. Originally released between 2007 and 2012, the games were reissued this year as Mass Effect Legendary Edition, an updated complete trilogy, and there's a compelling case that they are among the best sci-fi ever made.