Procedural Content Generation (PCG) techniques enable automatic creation of diverse and complex environments. While PCG facilitates more efficient content creation, ensuring consistently high-quality, industry-standard content remains a significant challenge. In this research, we propose a method to identify and repair unstable levels generated by existing PCG models. We use Angry Birds as a case study, demonstrating our method on game levels produced by established PCG approaches. Our method leverages object segmentation and visual analysis of level images to detect structural gaps and perform targeted repairs. We evaluate multiple object segmentation models and select the most effective one as the basis for our repair pipeline. Experimental results show that our method improves the stability and playability of AI-generated levels. Although our evaluation is specific to Angry Birds, our image-based approach is designed to be applicable to a wide range of 2D games with similar level structures.

This paper presents a methodology for inserting phrases in Arabic poems to conform to a specific rhythm using ByT5, a byte-level multilingual transformer-based model. Our work discusses a rule-based grapheme-to-beat transformation tailored for extracting the rhythm from fully diacritized Arabic script. Our approach employs a conditional denoising objective to fine-tune ByT5, where the model reconstructs masked words to match a target rhythm. We adopt a curriculum learning strategy, pre-training on a general Arabic dataset before fine-tuning on poetic dataset, and explore cross-lingual transfer from English to Arabic. Experimental results demonstrate that our models achieve high rhythmic alignment while maintaining semantic coherence. The proposed model has the potential to be used in co-creative applications in the process of composing classical Arabic poems.

We asked teachers about their experiences with AI in the classroom -- here's what they said Since ChatGPT and other large language models burst into public consciousness, school boards are drafting policies, universities are hosting symposiums and tech companies are relentlessly promoting their latest AI-powered learning tools . In the race to modernize education, artificial intelligence (AI) has become the new darling of policy innovation. While AI promises efficiency and personalization, it also introduces complexity, ethical dilemmas and new demands . Teachers, who are at the heart of learning along with students, are watching this transformation with growing unease. For example, according to the Alberta Teachers' Association, 80 to 90 per cent of educators surveyed expressed concern about AI's potential negative effects on education.

Reinforcement learning (RL) in partially observable settings is challenging because the agent's observations are not Markov. Recently proposed methods can learn variable-order Markov models of the underlying process but have steep memory requirements and are sensitive to aliasing between observation histories due to sensor noise. This paper proposes dynamic-depth context tree weighting (D2-CTW), a model-learning method that addresses these limitations. D2-CTW dynamically expands a suffix tree while ensuring that the size of the model, but not its depth, remains bounded. We show that D2-CTW approximately matches the performance of state-of-the-art alternatives at stochastic time-series prediction while using at least an order of magnitude less memory. We also apply D2-CTW to model-based RL, showing that, on tasks that require memory of past observations, D2-CTW can learn without prior knowledge of a good state representation, or even the length of history upon which such a representation should depend.

State-of-the-art deep neural networks are often over-parameterized to deliver more expressive power.

Although Sentinel-2 based land use and land cover (LULC) classification is critical for various environmental monitoring applications, it is a very difficult task due to some key data challenges (e.g., spatial heterogeneity, context information, signature ambiguity). This paper presents a novel Multitask Glocal OBIA-Mamba (MSOM) for enhanced Sentinel-2 classification with the following contributions. First, an object-based image analysis (OBIA) Mamba model (OBIA-Mamba) is designed to reduce redundant computation without compromising fine-grained details by using superpixels as Mamba tokens. Second, a global-local (GLocal) dual-branch convolutional neural network (CNN)-mamba architecture is designed to jointly model local spatial detail and global contextual information. Third, a multitask optimization framework is designed to employ dual loss functions to balance local precision with global consistency. The proposed approach is tested on Sentinel-2 imagery in Alberta, Canada, in comparison with several advanced classification approaches, and the results demonstrate that the proposed approach achieves higher classification accuracy and finer details that the other state-of-the-art methods.

Purpose: Visually Guided Reaching (VGR) on the Kinarm robot yields sensitive kinematic biomarkers but requires 40-64 reaches, imposing time and fatigue burdens. We evaluate whether time-series foundation models can replace unrecorded trials from an early subset of reaches while preserving the reliability of standard Kinarm parameters. Methods: We analyzed VGR speed signals from 461 stroke and 599 control participants across 4- and 8-target reaching protocols. We withheld all but the first 8 or 16 reaching trials and used ARIMA, MOMENT, and Chronos models, fine-tuned on 70 percent of subjects, to forecast synthetic trials. We recomputed four kinematic features of reaching (reaction time, movement time, posture speed, maximum speed) on combined recorded plus forecasted trials and compared them to full-length references using ICC(2,1). Results: Chronos forecasts restored ICC >= 0.90 for all parameters with only 8 recorded trials plus forecasts, matching the reliability of 24-28 recorded reaches (Delta ICC <= 0.07). MOMENT yielded intermediate gains, while ARIMA improvements were minimal. Across cohorts and protocols, synthetic trials replaced reaches without materially compromising feature reliability. Conclusion: Foundation-model forecasting can greatly shorten Kinarm VGR assessment time. For the most impaired stroke survivors, sessions drop from 4-5 minutes to about 1 minute while preserving kinematic precision. This forecast-augmented paradigm promises efficient robotic evaluations for assessing motor impairments following stroke.

Machine learning in neurosurgery is limited by challenges in assembling large, high-quality imaging datasets. Synthetic data offers a scalable, privacy-preserving solution. We evaluated the feasibility of generating realistic lateral cervical spine radiographs using a denoising diffusion probabilistic model (DDPM) trained on 4,963 images from the Cervical Spine X-ray Atlas. Model performance was monitored via training/validation loss and Frechet inception distance, and synthetic image quality was assessed in a blinded "clinical Turing test" with six neuroradiologists and two spine-fellowship trained neurosurgeons. Experts reviewed 50 quartets containing one real and three synthetic images, identifying the real image and rating realism on a 4-point Likert scale. Experts correctly identified the real image in 29% of trials (Fleiss' kappa=0.061). Mean realism scores were comparable between real (3.323) and synthetic images (3.228, 3.258, and 3.320; p=0.383, 0.471, 1.000). Nearest-neighbor analysis found no evidence of memorization. We also provide a dataset of 20,063 synthetic radiographs. These results demonstrate that DDPM-generated cervical spine X-rays are statistically indistinguishable in realism and quality from real clinical images, offering a novel approach to creating large-scale neuroimaging datasets for ML applications in landmarking, segmentation, and classification.

She has been cast in maternal roles since her teens. Now, playing a mother for the first time since becoming one, she has chosen the part of a woman pushed past the edge of sanity. In "Die My Love," Lawrence, as Grace, vibrates with boredom and fury. The novel "Die, My Love," by the Argentinean writer Ariana Harwicz, is narrated by a wife and new mother who is living in rural France and seems to be losing her mind. Motherhood has inserted an immersion blender into her psyche: lust, repulsion, pleasure, and doom swirl into a single mess. She calls herself a "sodomising rodent" with "bullet-wounds for eyes," and thinks, "When I masturbate I desecrate crypts, and when I rock my baby I say amen, and when I smile I unplug an iron lung." One night, standing in the cold, staring at her family through a sliding door, she thinks, "I'll stop trying to draw blood from a stone. I'll contain my madness, I'll use the bathroom. I'll put my baby to sleep, jerk off my man and postpone my rebellion in favor of a better life." Martin Scorsese saw a brief review of the novel in the some years ago and decided to pick up a copy. He found it to be a "powerful mosaic of the mind," he told me recently. Scorsese is a member of a book club of sorts, with a few other filmmakers, who read with an eye toward adaptation. For "Die, My Love," he imagined casting Jennifer Lawrence in the lead. He'd been amazed by her performance in Darren Aronofsky's bewildering 2017 fantasia, "Mother!" In that surreal film--it's like an allegory set inside an oil painting--Lawrence plays a woman living with her poet husband in an old farmhouse, which is gradually, then apocalyptically, invaded by strangers. "She really is feeling everything that's happening, in what appears to be a dream of some kind," Scorsese said. He and Lawrence had discussed adaptations before. They considered "The Awakening," Kate Chopin's 1899 novel of female liberation, which ends with the protagonist, Edna Pontellier, walking into the sea. "Die, My Love" was like "The Awakening" if it began with Edna already underwater.

Meningiomas represent the most prevalent form of primary brain tumors, comprising nearly one-third of all diagnosed cases. Accurate delineation of these tumors from MRI scans is crucial for guiding treatment strategies, yet remains a challenging and time-consuming task in clinical practice. Recent developments in deep learning have accelerated progress in automated tumor segmentation; however, many advanced techniques are hindered by heavy computational demands and long training schedules, making them less accessible for researchers and clinicians working with limited hardware. In this work, we propose a novel ensemble-based segmentation approach that combines three distinct architectures: (1) a baseline SegResNet model, (2) an attention-augmented SegResNet with concatenative skip connections, and (3) a dual-decoder U-Net enhanced with attention-gated skip connections (DDUNet). The ensemble aims to leverage architectural diversity to improve robustness and accuracy while significantly reducing training demands. Each baseline model was trained for only 20 epochs and Evaluated on the BraTS-MEN 2025 dataset. The proposed ensemble model achieved competitive performance, with average Lesion-Wise Dice scores of 77.30%, 76.37% and 73.9% on test dataset for Enhancing Tumor (ET), Tumor Core (TC) and Whole Tumor (WT) respectively. These results highlight the effectiveness of ensemble learning for brain tumor segmentation, even under limited hardware constraints. Our proposed method provides a practical and accessible tool for aiding the diagnosis of meningioma, with potential impact in both clinical and research settings.