❌

Normal view

Received — 21 November 2025 ⏭ Favourites

Accelerating Local AI on Consumer GPUs: A Hardware-Aware Dynamic Strategy for YOLOv10s

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mahmudul Islam Masum · Miad Islam
20 November 2025 at 13:00
arXiv:2509.07928v2 Announce Type: replace-cross Abstract: As local AI grows in popularity, there is a critical gap between the benchmark performance of object detectors and their practical viability on consumer-grade hardware. While models like YOLOv10s promise real-time speeds, these metrics are typically achieved on high-power, desktop-class GPUs. This paper reveals that on resource-constrained systems, such as laptops with RTX 4060 GPUs, performance is not compute-bound but is instead dominated by system-level bottlenecks, as illustrated by a simple bottleneck test. To overcome this hardware-level constraint, we introduce a Two-Pass Adaptive Inference algorithm, a model-independent approach that requires no architectural changes. This study mainly focuses on adaptive inference strategies and undertakes a comparative analysis of architectural early-exit and resolution-adaptive routing, highlighting their respective trade-offs within a unified evaluation framework. The system uses a fast, low-resolution pass and only escalates to a high-resolution model pass when detection confidence is low. On a 5000-image COCO dataset, our method achieves a 1.85x speedup over a PyTorch Early-Exit baseline, with a modest mAP loss of 5.51%. This work provides a practical and reproducible blueprint for deploying high-performance, real-time AI on consumer-grade devices by shifting the focus from pure model optimization to hardware-aware inference strategies that maximize throughput.

Uncertainty Makes It Stable: Curiosity-Driven Quantized Mixture-of-Experts

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sebasti\'an Andr\'es Cajas Ord\'o\~nez · Luis Fernando Torres Torres · Mackenzie J. Meni · Carlos Andr\'es Duran Paredes · Eric Arazo · Cristian Bosch · Ricardo Simon Carbajo · Yuan Lai · Leo Anthony Celi
20 November 2025 at 13:00
arXiv:2511.11743v2 Announce Type: replace-cross Abstract: Deploying deep neural networks on resource-constrained devices faces two critical challenges: maintaining accuracy under aggressive quantization while ensuring predictable inference latency. We present a curiosity-driven quantized Mixture-of-Experts framework that addresses both through Bayesian epistemic uncertainty-based routing across heterogeneous experts (BitNet ternary, 1-16 bit BitLinear, post-training quantization). Evaluated on audio classification benchmarks (ESC-50, Quinn, UrbanSound8K), our 4-bit quantization maintains 99.9 percent of 16-bit accuracy (0.858 vs 0.859 F1) with 4x compression and 41 percent energy savings versus 8-bit. Crucially, curiosity-driven routing reduces MoE latency variance by 82 percent (p = 0.008, Levene's test) from 230 ms to 29 ms standard deviation, enabling stable inference for battery-constrained devices. Statistical analysis confirms 4-bit/8-bit achieve practical equivalence with full precision (p > 0.05), while MoE architectures introduce 11 percent latency overhead (p

Pan-cancer prevalence, risk, and clinical and demographic characteristics of Lynch Syndrome-associated variants in BioBank Japan

✇(Multiomics OR Omics) AND (Pancreatic)
By: Keijiro Mizukami · Yoshiaki Usui · Yusuke Iwasaki · Kouya Shiraishi · Makoto Hirata · Yoichiro Kamatani · Mikiko Endo · Satoshi Takahashi · Yoshiki Mochizuki · Mitusyo Yamaguchi · Takashi Kohno · Koichi Matsuda · Kokichi Sugano · Teruhiko Yoshida · Hidewaki Nakagawa · Chikashi Terao · Yuriko N Koyanagi · Keitaro Matsuo · Yoshinori Murakami · Amanda B Spurdle · Yukihide Momozawa
19 November 2025 at 19:00

Commun Med (Lond). 2025 Nov 13. doi: 10.1038/s43856-025-01231-9. Online ahead of print.

ABSTRACT

BACKGROUND: Although germline testing for DNA mismatch repair (MMR) genes is routinely performed, clinical guidelines highlight evidence gaps due to limited populations and biases. We examined germline pathogenic variants of MMR genes (MLH1, MSH2, MSH6, and PMS2) in 112,927 unselected individuals from BioBank Japan.

METHODS: We analyzed 74,085 cancer patients with 23 cancer types and 38,842 controls matched by sex, age, and hospital area from BioBank Japan, collected between April 2003 and March 2018. Germline pathogenic variants in the coding regions and 2 bp flanking intronic sequences of MMR genes were identified using a multiplex PCR-based target sequencing method. We examined associations with cancer types and demographic characterization of the pathogenic variants, comparing findings to existing clinical guidelines.

RESULTS: Here we show 228 pathogenic variants identified in MMR genes, with pathogenic MSH6 variants most frequently observed in endometrial cancer and 12 other significant associations. Twelve other significant associations are noted across a broad range of odds ratios, whereas pancreatic cancer exhibits no such association. Pathogenic variant carriers are diagnosed up to 12.4 years earlier than non-carriers, and colorectal and gastric cancers are diagnosed up to 16.4 years later than indicated by the guidelines. Higher carrier frequencies are observed in patients with both colorectal and endometrial cancers (24.8%) and in those with endometrial cancer and a family history of endometrial (26.0%) or colorectal (16.1%) cancers.

CONCLUSIONS: This study provides critical insights for clinical guidelines on the associations between cancer types, age at diagnosis, and carrier frequency.

PMID:41258140 | DOI:10.1038/s43856-025-01231-9

Latent plasticity of the human pancreas across development, health, and disease

✇(Multiomics OR Omics) AND (Pancreatic)
By: Elisabetta Mereu · Diego Balboa · Johannes Liebig · Aitor Gonzalez-Herrero · Anna Martinez-Casals · Mariya Mardamshina · Fanny Mollandin · Felix Schicktanz · Luca Tosti · Valerie Vandenbempt · Dana Avrahami · Edgar Bernardo · Frida Björklund · Robert Lorenz Chua · Marten Engelse · Javier García-Hurtado · Nathalie Groen · Maaike Hanegraaf · Pablo Iañez · Katharina Jechow · Björn Konukiewitz · Christian Lawerenz · Domenica Marchese · Mauro J Muraro · Silvia Pellegrini · Valeria Sordi · Alexander Sudy · Ulrike Taron · Foo Wei Ten · Timo Trefzer · Sven Twardziok · Maarten van Agen · Françoise Carlotti · Eelco de Koning · Jorge Ferrer · Benjamin Glaser · Holger Heyn · Emma Lundberg · Lorenzo Piemonti · Katja Steiger · Alexander van Oudenaarden · Wilko Weichert · Christian Conrad · Roland Eils
19 November 2025 at 19:00

bioRxiv [Preprint]. 2025 Oct 3:2025.10.01.679230. doi: 10.1101/2025.10.01.679230.

ABSTRACT

The pancreas plays a central role in major human diseases, yet our understanding of its cellular diversity and plasticity remains incomplete. Here, we present a single-cell multiomics atlas of the human pancreas, profiling over four million cells and nuclei from 57 donors across fetal development, adult homeostasis, and type 2 diabetes (T2D). Integrating sc/snRNA-seq, snATAC-seq, VASA-seq, spatial transcriptomics (Xenium), and multiplexed proteomics (CODEX), we resolve gene expression, chromatin accessibility, and spatial organization at high resolution. We identify transcriptionally plastic centroacinar-like cells (pCACs) in adults with fetal-like features, delineate endocrine and exocrine lineage trajectories during development, and uncover HNF1A-defined beta cell epigenetic states. In T2D, we observe shifts in beta cell subtypes and altered regulatory programs. Glucose perturbation of healthy islets reveals cell-type-specific adaptation and stress responses. This atlas provides a foundational framework to understand pancreas biology and the role of cellular plasticity in regeneration and disease.

PMID:41256699 | PMC:PMC12622017 | DOI:10.1101/2025.10.01.679230

SMMILe enables accurate spatial quantification in digital pathology using multiple-instance learning

✇Nature Cancer
By: Zeyu Gao · Anyu Mao · Yuxing Dong · Hannah Clayton · Jialun Wu · Jiashuai Liu · ChunBao Wang · Kai He · Tieliang Gong · Chen Li · Mireia Crispin-Ortuzar
19 November 2025 at 08:00

Nature Cancer, Published online: 19 November 2025; doi:10.1038/s43018-025-01060-8

Gao et al. present SMMILe, a multiple-instance learning-based tool that leverages whole-slide images for accurate spatial quantification without compromising on classification performance, and show it outperforms state-of-the-art methods.
There are no more articles
❌