Paper List
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Developing the PsyCogMetrics™ AI Lab to Evaluate Large Language Models and Advance Cognitive Science
This paper addresses the critical gap between sophisticated LLM evaluation needs and the lack of accessible, scientifically rigorous platforms that in...
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Equivalence of approximation by networks of single- and multi-spike neurons
This paper resolves the fundamental question of whether single-spike spiking neural networks (SNNs) are inherently less expressive than multi-spike SN...
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The neuroscience of transformers
提出了Transformer架构与皮层柱微环路之间的新颖计算映射,连接了现代AI与神经科学。
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Framing local structural identifiability and observability in terms of parameter-state symmetries
This paper addresses the core challenge of systematically determining which parameters and states in a mechanistic ODE model can be uniquely inferred ...
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Leveraging Phytolith Research using Artificial Intelligence
This paper addresses the critical bottleneck in phytolith research by automating the labor-intensive manual microscopy process through a multimodal AI...
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Neural network-based encoding in free-viewing fMRI with gaze-aware models
This paper addresses the core challenge of building computationally efficient and ecologically valid brain encoding models for naturalistic vision by ...
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Scalable DNA Ternary Full Adder Enabled by a Competitive Blocking Circuit
This paper addresses the core bottleneck of carry information attenuation and limited computational scale in DNA binary adders by introducing a scalab...
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ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics
This paper addresses the critical bottleneck of translating high-dimensional single-cell transcriptomic data into interpretable biological hypotheses ...
SHREC: A Spectral Embedding-Based Approach for Ab-Initio Reconstruction of Helical Molecules
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30秒速读
IN SHORT: This paper addresses the core bottleneck in cryo-EM helical reconstruction: eliminating the dependency on accurate initial symmetry parameter estimation, which is traditionally obtained through error-prone trial-and-error or prior knowledge.
核心创新
- Methodology Introduces SHREC, the first algorithm that directly recovers projection angles from 2D cryo-EM images without requiring prior knowledge of helical symmetry parameters (rise, twist, or pitch).
- Methodology Leverages the mathematical insight that projections of helical segments form a one-dimensional manifold, enabling recovery through spectral embedding techniques.
- Methodology Requires only knowledge of the specimen's axial symmetry group (Cn), significantly reducing the prior information needed compared to traditional methods.
主要结论
- SHREC successfully recovers projection angles and helical parameters directly from 2D images, validated on public datasets, achieving high-resolution reconstructions.
- The method is proven mathematically: projections of helical segments form a 1D manifold (Lemma 1.9), and the angle between segments θ is directly related to their axial displacement (θ = 2π/P * (t2 - t1), Lemma 1.6).
- By eliminating the initial symmetry estimation step, SHREC provides a more robust and automated pathway, reducing a major source of error in ab-initio helical reconstruction.
摘要: Cryo-electron microscopy (cryo-EM) has emerged as a powerful technique for determining the three-dimensional structures of biological molecules at near-atomic resolution. However, reconstructing helical assemblies presents unique challenges due to their inherent symmetry and the need to determine unknown helical symmetry parameters. Traditional approaches require an accurate initial estimation of these parameters, which is often obtained through trial and error or prior knowledge. These requirements can lead to incorrect reconstructions, limiting the reliability of ab initio helical reconstruction. In this work, we present SHREC (Spectral Helical REConstruction), an algorithm that directly recovers the projection angles of helical segments from their two-dimensional cryo-EM images, without requiring prior knowledge of helical symmetry parameters. Our approach leverages the insight that projections of helical segments form a one-dimensional manifold, which can be recovered using spectral embedding techniques. Experimental validation on publicly available datasets demonstrates that SHREC achieves high resolution reconstructions while accurately recovering helical parameters, requiring only knowledge of the specimen’s axial symmetry group. By eliminating the need for initial symmetry estimates, SHREC offers a more robust and automated pathway for determining helical structures in cryo-EM.