Remember when choosing a database was simple? You picked MySQL or PostgreSQL for transactional data, maybe added MongoDB if you needed flexibility, and called it a day. I remember a conversation with a colleague regarding sharding, a method for horizontal scaling in MongoDB. Those days are over. The database landscape is going through the biggest shift since the NoSQL movement of the 2010s. But this time, it’s not just about scale or flexibility. Two forces are reshaping everything: […]

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Traffic sign detection in autonomous driving faces challenges including multi-scale objects, complex backgrounds, and limited edge-computing power. To address insufficient multi-scale feature representation and high false negatives for small traffic signs in YOLOv8n, this study proposes an improved algorithm integrating the VoVGSCSP module with a Multi-scale Contextual Attention (MCA) mechanism. The original C2f module is replaced with VoVGSCSP, enhancing feature representation through parallel residual branches and cross-stage connections. A lightweight neck, SlimNeck, is designed and combined with MCA, […]

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The concept of the technological singularity is applied here to architecture (of buildings, not software). This is the point at which non-human intelligence surpasses ordinary human cognitive limits. AI betters mainstream architectural culture in one crucial aspect — its capacity to evaluate design that adapts to human emotional health. Postwar building architecture as an institutional power system rewards abstraction and stylistic conformity through media prestige while not accounting for embodied human experience. By narrowing judgment criteria, architectural studio […]

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arXiv:2601.02193v1 Announce Type: cross Abstract: We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone adversarial corruption model. In this model, an adversary, upon looking at a “clean” i.i.d. dataset, inserts additional “corrupted” points of their choice into the dataset. These added points are constrained to be monotone corruptions, in that they get labeled according to the ground-truth target function. Perhaps surprisingly, we demonstrate that in this […]

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Eduard Khemchan does not ignore short-term market movements. He places limits on how much they influence capital decisions. That distinction defines his approach. In modern markets, short-term signals dominate attention. Price movement, liquidity shifts, policy reactions, and technological narratives create a constant stream of information. The speed at which these signals move has increased significantly. Digital platforms compress reaction time. Participation widens. Capital rotates quickly between themes. The result is an environment where immediacy often replaces perspective. Eduard […]

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Understanding how large-scale functional brain networks reorganize during cognitive decline remains a central challenge in neuroimaging. While recent self-supervised models have shown promise for learning representations from resting-state fMRI, their internal mechanisms are difficult to interpret, limiting mechanistic insight. We propose BrainInterNet, a network-aware self-supervised framework based on masked reconstruction with cross-attention that explicitly models inter-network dependencies in rs-fMRI. By selectively masking predefined functional networks and reconstructing them from remaining context, our approach enables direct quantification of network […]

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arXiv:2604.13121v1 Announce Type: new Abstract: Locating and intercepting a moving target from possibly delayed, intermittent sensory signals is a paradigmatic problem in decision-making under uncertainty, and a fundamental challenge for, e.g., animals seeking prey or mates and autonomous robotic systems. Odor signals are intermittent, strongly mixed by turbulent-like transport, and typically lag behind the true target position, thereby complicating localization. Here, we formulate olfactory pursuit as a partially observable Markov decision process in which an agent maintains a […]

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Acute Myeloid Leukemia (AML) is one of the most life-threatening type of blood cancers, and its accurate classification is considered and remains a challenging task due to the visual similarity between various cell types. This study addresses the classification of the multiclasses of AML cells Utilizing YOLOv12 deep learning model. We applied two segmentation approaches based on cell and nucleus features, using Hue channel and Otsu thresholding techniques to preprocess the images prior to classification. Our experiments demonstrate […]

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Hey folks, I’m trying to get into Imitation Learning, but honestly most of what I find are dense papers 😅 I’m looking for tutorial-style resources….blogs, courses, lecture videos, or walkthroughs, that explain IL concepts clearly instead of jumping straight into theory. If you know any good resources that helped you understand things like Behavior Cloning, DAgger, or GAIL, please share! Papers are fine, but I’d really appreciate something more beginner-to-intermediate friendly. Thanks 🙏 submitted by /u/GraciaMad [link] [comments]

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arXiv:2603.01346v1 Announce Type: cross Abstract: We revisit the framework of Smart PAC learning, which seeks supervised learners which compete with semi-supervised learners that are provided full knowledge of the marginal distribution on unlabeled data. Prior work has shown that such marginal-by-marginal guarantees are possible for “most” marginals, with respect to an arbitrary fixed and known measure, but not more generally. We discover that this failure can be attributed to an “indistinguishability” phenomenon: There are marginals which cannot be […]

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