![]() After flicking a switch to change the perspective of the room, the piano (which was now upside down) crashed through the floor to reveal a secret passageway. As Darq takes place in a dream world, I realised I could place it on a small platform and watch it expand into a full-sized piano. On a train level, I had to get to a particular area but only had a toy piano in my inventory. One such puzzle isn’t the biggest and most complex puzzle the game has to offer, though it stuck with me all the same. Darq will often have you indulging in a light smirk when you figure out one of its many ingenious puzzles. This is evidenced early on when there’s a gap you cannot cross, but instead a switch you can flip to close a door, which you can then climb up and then onto the ceiling to eventually reach the other side. Darq’s main gameplay hook is the ability to walk on walls to reach different places. This basic setup instead allows Unfold to go a little wild with not only the increasingly unsettling imagery it chucks at you, but also in how deep and complex the mechanics can logically become. There are a lot of aesthetic hints to suggest an overarching storyline related to problems in Lloyd’s family life, but I was never really able to stitch it all together. You wouldn’t know this without first looking at the official description, however - Darq really doesn’t explain itself in-game at all. You play as a bald, incessantly distressed young boy by the name of Lloyd who realises that he is trapped within his dreams. Inspired by the likes of Little Nightmares and Inside, Darq may at first look a little familiar, but its gravity-shifting mechanics and grimace-inducing creature designs are just two of its features that help it stand out. Well, I say “exact premise”, but semi-nude grandmothers wheeling after you and lampshade-headed women with pistols is a bit more extreme than dreaming about forgetting what algebra is. But what if I couldn’t escape the dream? What if I was trapped in my dream with no escape, forever failing to do long division? That’s the exact premise Darq, a side-scrolling horror puzzler, provides as we are pushed deeper and deeper into the worried mind of a young boy. My personal favourite (and weirdly recurring) dream involves me worrying about having to take my maths GCSE, only to wake up and happily realise that I am 27 year old guy who already scraped through the exam well over a decade ago. The authors have declared no competing interest.A lot of us look forward to our dreams at night. The results show that DARQ is robust, fast, accurate, and generalizable in detecting failure in linear stereotaxic registrations and can substantially reduce QC time (by a factor of 20 or more) when processing large datasets. #Darq train manual#In terms of agreement with a manual rater, our automated QC method was able to achieve 89% accuracy and 85% true negative rate (equivalently 15% false positive rate) in detecting scans that should pass quality control in a balanced cross-validation experiments, and 96.1% accuracy and 95.5% true negative rate (or 4.5% FPR) when evaluated in a balanced independent sample, similar to manual QC rater (test-retest accuracy of 93%). We further validated the results on an independent dataset of patients with multiple sclerosis, with manual QC labels available (n=1200). #Darq train registration#In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. In a recently published study from our group comparing linear registration methods, we used a database of 9325 MRI scans from several publicly available datasets and applied seven linear registration tools to them. To reduce the burden of this time-consuming step, we propose Deep Automated Registration Qc (DARQ), a fully automatic quality control method based on deep learning that can replace the human rater and accurately perform quality control assessment for stereotaxic registration of T1w brain scans. Manual assessment of the registration is commonly used as part of quality control. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none have a 100% success rate. This step is crucial for the success of the subsequent image-processing steps. Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |