Tootfinder

Designs for Queen Elizabeth’s Memorial Reflect a ‘Quietly Monumental’ Life Close to Nature https://www.goodnewsnetwork.org/designs-for-queen-elizabeths-memorial-reflect-a-quietly-monumental-life-close-to-nature/

Designs for Queen Elizabeth's Memorial Reflect a 'Quietly Monumental' Life Close to Nature
Organized by the Queen Elizabeth Memorial Committee, the chief aim is to design “an emotionally powerful place” in St. James' Park.

Training-free LLM Merging for Multi-task Learning
Zichuan Fu, Xian Wu, Yejing Wang, Wanyu Wang, Shanshan Ye, Hongzhi Yin, Yi Chang, Yefeng Zheng, Xiangyu Zhao
https://arxiv.org/abs/2506.12379

Training-free LLM Merging for Multi-task Learning
Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing (NLP) tasks. The release of open-source LLMs like LLaMA and Qwen has triggered the development of numerous fine-tuned models tailored for various tasks and languages. In this paper, we explore an important question: is it possible to combine these specialized models to create a unified model with multi-task capabilities. We introduces Hierarchical Iterative Merging (Hi-Merging), a …

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation
Changxin Ke, Rui Zhang, Shuo Wang, Li Ding, Guangli Li, Yuanbo Wen, Shuoming Zhang, Ruiyuan Xu, Jin Qin, Jiaming Guo, Chenxi Wang, Ling Li, Qi Guo, Yunji Chen
https://arxiv.org/abs/2506.11153

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation
The rise of GPU-based high-performance computing (HPC) has driven the widespread adoption of parallel programming models such as CUDA. Yet, the inherent complexity of parallel programming creates a demand for the automated sequential-to-parallel approaches. However, data scarcity poses a significant challenge for machine learning-based sequential-to-parallel code translation. Although recent back-translation methods show promise, they still fail to ensure functional equivalence in the translate…

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
DeepGDel: Deep Learning-based Gene Deletion Prediction Framework for Growth-Coupled Production in...

"What drove me to give voice to this dream, to step in front of the camera again not knowing what the outcome was going to be, was…fear of regret. I don’t want to live my life with regrets."
—Ke Huy Quan
#acting #coaching

TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion
Huanqiang Duan, Manno Versluis, Qinyu Chen, Leo C. N. de Vreede, Chang Gao
https://arxiv.org/abs/2506.12165

TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion
Digital predistortion (DPD) is essential for mitigating nonlinearity in RF power amplifiers, particularly for wideband applications. This paper presents TCN-DPD, a parameter-efficient architecture based on temporal convolutional networks, integrating noncausal dilated convolutions with optimized activation functions. Evaluated on the OpenDPD framework with the DPA_200MHz dataset, TCN-DPD achieves simulated ACPRs of -51.58/-49.26 dBc (L/R), EVM of -47.52 dB, and NMSE of -44.61 dB with 500 parame…

Efficient Unified Caching for Accelerating Heterogeneous AI Workloads
Tianze Wang, Yifei Liu, Chen Chen, Pengfei Zuo, Jiawei Zhang, Qizhen Weng, Yin Chen, Zhenhua Han, Jieru Zhao, Quan Chen, Minyi Guo
https://arxiv.org/abs/2506.12370

Efficient Unified Caching for Accelerating Heterogeneous AI Workloads
Modern AI clusters, which host diverse workloads like data pre-processing, training and inference, often store the large-volume data in cloud storage and employ caching frameworks to facilitate remote data access. To avoid code-intrusion complexity and minimize cache space wastage, it is desirable to maintain a unified cache shared by all the workloads. However, existing cache management strategies, designed for specific workloads, struggle to handle the heterogeneous AI workloads in a cluster …

On kernel isomorphisms of $m$-Cayley digraphs and finite $2$PCI-groups
Xing Zhang, Yan-Quan Feng, Jin-Xin Zhou, Fu-Gang Yin
https://arxiv.org/abs/2506.12306

On kernel isomorphisms of $m$-Cayley digraphs and finite $2$PCI-groups
The isomorphism problem for digraphs is a fundamental problem in graph theory. In this paper, we consider this problem for $m$-Cayley digraphs which are generalization of Cayley digraphs. Let $m$ be a positive integer. A digraph admitting a group $G$ of automorphisms acting semiregularly on the vertices with exactly $m$ orbits is called an $m$-Cayley digraph of $G$. In our previous paper, we developed a theory for $m$-Cayley isomorphisms of $m$-Cayley digraphs, and classified finite $m$CI-group…

Happy Birthday to the all time Queen of Karma @… !! 💐

TongSearch-QR: Reinforced Query Reasoning for Retrieval
Xubo Qin, Jun Bai, Jiaqi Li, Zixia Jia, Zilong Zheng
https://arxiv.org/abs/2506.11603 https://

TongSearch-QR: Reinforced Query Reasoning for Retrieval
Traditional information retrieval (IR) methods excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks that require multi-hop inference or complex semantic understanding between queries and documents. One promising solution is to explicitly rewrite or augment queries using large language models (LLMs) to elicit reasoning-relevant content prior to retrieval. However, the widespread use of large-scale language models like GPT-4 or LLaMA3-70B remains impractical …