CONV
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Lean 4 Machine-Verified Proof of P = NP via the Pedigree Polytope Membership Problem
arXiv:2606.03194v1 Announce Type: new Abstract: The Membership Problem for Pedigree Polytope (M3P) asks, given $X\in\mathbb{Q}^{\binom{n}{3}}$, whether $X\in\mathrm{conv}(P_n)$, where $P_n$ is the set of all pedigrees. A pedigree is a structured encoding of a Hamiltonian cycle construction in $K_n$. We establish that M3P is solvable in strongly polynomial time via a recursively constructed layered network $(N_k, R_k, \mu)$ and a multicommodity flow problem MCF$(k)$. The necessary and...
High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response
Tumor-specific exhausted CD8 T cells (Tex) adopt diverse phenotypes across human cancers, but the drivers of this heterogeneity remain poorly understood. Using flow cytometry and single-cell RNA and T cell receptor (TCR) sequencing of 106,667 tumor-infiltrating CD8 T cells from head and neck squamous cell carcinoma (HNSCC) tumors, we identified and validated three Tex subsets, each with distinct clonotypes: (1) Tex-Conv, expressing conventional exhaustion genes; (2) Tex-CCR6, distinguished...
Proton FLASH preserves neurocognition across delivery techniques: implications for clinical translation in pediatric brain tumors
Background: Radiation therapy is integral to the curative treatment of childhood brain tumors but contributes to late neurocognitive impairment in survivors. FLASH (ultra-high dose rate, >40Gy/s) reduces normal-tissue toxicity in preclinical models, and proton-FLASH is currently the only modality capable of delivering ultra-high dose rates to the deep targets, such as pediatric brain tumors. However, two questions remain unresolved before clinical translation: (1) whether the FLASH effect...
ConTrans: Learning Text-enhanced Local-global Temporal Representations for Zero-shot Temporal Action Localization
arXiv:2605.30689v1 Announce Type: new Abstract: Zero-shot Temporal Action Localization (ZS-TAL) aims to detect and locate previously unseen actions in untrimmed videos. However, existing approaches primarily focus on modeling long-range contextual information, often neglecting the critical relative-offset-based local correlations between video frames. Furthermore, their performance is hindered by limited feature representation capabilities due to the shallow nature of their network...
Pruning Deep Neural Networks via the Marchenko--Pastur Distribution
Announce Type: new Abstract: We study a Marchenko--Pastur (MP) random-matrix approach to pruning deep neural networks with very small post-pruning fine-tuning budgets. The main practical contribution is accuracy retention under short calibration and fine-tuning schedules, rather than a long post-pruning reoptimization pipeline. The theory gives deterministic data-path certificates: if the removed component $R$ has small propagated logit effect $L_s \| R \psi_1(s) \|_\infty$, pruning...
Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image Classification
arXiv:2506.20263v2 Announce Type: replace Abstract: Few-shot fine-grained image classification (FS-FGIC) is challenging as it requires distinguishing visually similar subclasses with extremely limited labeled examples. Existing methods suffer from critical limitations: metric-based methods lose spatial information and misalign local features, while reconstruction-based methods underuse hierarchical feature information and lack selective focus on discriminative key regions. We propose the...