arXiv:2602.17867v1 Announce Type: new Abstract: Understanding what features are encoded by learned directions in LLM activation space requires identifying inputs that strongly activate them. Feature visualization, which optimizes inputs to maximally activate a target direction, offers an alternative to costly dataset search approaches, but remains underexplored for LLMs due to the discrete nature of text. Furthermore, existing prompt optimization techniques are poorly suited to this domain, which is highly prone to local minima. To overcome these limitations, we […]

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arXiv:2601.20876v1 Announce Type: new Abstract: We present BioNIC, a multi-layer feedforward neural network for emotion classification, inspired by detailed synaptic connectivity graphs from the MICrONs dataset. At a structural level, we incorporate architectural constraints derived from a single cortical column of the mouse Primary Visual Cortex(V1): connectivity imposed via adjacency masks, laminar organization, and graded inhibition representing inhibitory neurons. At the functional level, we implement biologically inspired learning: Hebbian synaptic plasticity with homeostatic regulation, Layer Normalization, data augmentation […]

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arXiv:2506.19837v2 Announce Type: replace Abstract: The mean shift (MS) is a non-parametric, density-based, iterative algorithm with prominent usage in clustering and image segmentation. A rigorous proof for the convergence of its mode estimate sequence in full generality remains unknown. In this paper, we show that fortextit{ sufficiently large bandwidth} convergence is guaranteed in any dimension with textit{any radially symmetric and strictly positive definite kernels}. Although the author acknowledges that our result is partially more restrictive than that of […]

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Teilhard envisions humanity not as evolution’s finale, but as its awakening. As our minds and societies knit into a single global consciousness—the —evolution becomes self-aware, guiding itself through thought and collaboration. In this new phase, life chooses its own becoming and reaches out toward the ultra-human.

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arXiv:2601.19037v1 Announce Type: cross Abstract: Accurate molecular property prediction is central to drug discovery, yet graph neural networks often underperform in data-scarce regimes and fail to surpass traditional fingerprints. We introduce cross-graph inter-message passing (XIMP), which performs message passing both within and across multiple related graph representations. For small molecules, we combine the molecular graph with scaffold-aware junction trees and pharmacophore-encoding extended reduced graphs, integrating complementary abstractions. While prior work is either limited to a single abstraction or […]

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Writing clear, consistent docstrings in Python helps others understand your code’s purpose, parameters, and outputs. In this video course, you’ll learn about best practices, standard formats, and common pitfalls to avoid, ensuring your documentation is accessible to users and tools alike. By the end of this video course, you’ll understand that: Docstrings are strings used to document your Python code and can be accessed at runtime. Python comments and docstrings have important differences. One-line and multiline docstrings are […]

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To develop a deep-learning method for achieving fast high-resolution MR elastography from highly undersampled data without the need of high-quality training dataset. We first framed the deep neural network representation as a nonlinear extension of the linear subspace model, then used it to represent and reconstruct MRE image repetitions from undersampled k-space data. The network weights were learned using a multi-level k-space consistent loss in a self-supervised manner. To further enhance reconstruction quality, phase-contrast specific magnitude and phase […]

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Hi all. So I just learned about Monte-Carlo Tree Search from University of Queensland’s free book, and I have one question. From my understanding, each state has its own tree. Is it correct? If correct, then why? I thought that states that are closer to the root tree is already simulated, hence we can just reuse the calculation? Thank you in advance. submitted by /u/indo_dementor [link] [comments]

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arXiv:2502.02580v3 Announce Type: replace-cross Abstract: In mixture models, anisotropic noise within each cluster is widely present in real-world data. This work investigates both computationally efficient procedures and fundamental statistical limits for clustering in high-dimensional anisotropic mixtures. We propose a new clustering method, Covariance Projected Spectral Clustering (COPO), which adapts to a wide range of dependent noise structures. We first project the data onto a low-dimensional space via eigen-decomposition of a diagonal-deleted Gram matrix. Our central methodological idea is […]

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