Tootfinder

Interested in how #architecture can be understood sociologically?
In this podcast hosted by Alon Schwartz, I'm discussing how architecture can integrate human needs and usage processes in workplaces
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space design with Kerstin sailer- Sociology and Architecture
עיצוב מרחב עם אלון שוורץ · Episode

BVLSM: Write-Efficient LSM-Tree Storage via WAL-Time Key-Value Separation
Ming Li, Wendi Cheng, Jiahe Wei, Xueqiang Shan, Liu Weikai, Xiaonan Zhao, Xiao Zhang
https://arxiv.org/abs/2506.04678

BVLSM: Write-Efficient LSM-Tree Storage via WAL-Time Key-Value Separation
Modern data-intensive applications increasingly store and process big-value items, such as multimedia objects and machine learning embeddings, which exacerbate storage inefficiencies in Log-Structured Merge-Tree (LSM)-based key-value stores. This paper presents BVLSM, a Write-Ahead Log (WAL)-time key-value separation mechanism designed to address three key challenges in LSM-Tree storage systems: write amplification, poor memory utilization, and I/O jitter under big-value workloads. Unlike state…

The Onset of Feedback in Abell 1885: Evidence for Large-Scale Quenching Despite a Young Central AGN
Laurel White, Michael McDonald, Francesco Ubertosi, Massimo Gaspari, Julie Hlavacek-Larrondo, Helen Russell, Taweewat Somboonpanyakul
https://arxiv.org/abs/2506.03277

The Onset of Feedback in Abell 1885: Evidence for Large-Scale Quenching Despite a Young Central AGN
We present a new 8.5 ks Chandra observation of Abell 1885, obtained as part of the Cluster Evolution Reference Ensemble At Low-z (CEREAL) survey of ~200 low-z galaxy groups and clusters. These data reveal that Abell 1885 is a strong cool core, with a central cooling time of 0.43 Gyr, and that the central galaxy hosts an X-ray luminous point source at its center (L=2.3x10^42 erg/s), indicative of a rapidly accreting supermassive black hole. In the context of the larger CEREAL sample, we constrai…

A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks
Tao Yu, Simin Wang, Shunqing Zhang, Xiaojing Chen, Zi Xu, Xin Wang, Jiandong Li, Junyu Liu, Sihai Zhang
https://arxiv.org/abs/2506.03508

A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks
The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or dat…

The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
Yong Xiao, Li, Zin Nandar Win, He Wang, Hla Myo Tun, Win Thu Zar
https://arxiv.org/abs/2506.04584

The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
The advent of gravitational wave astronomy (GW) has revolutionized the observation of cataclysmic cosmic events, such as black hole mergers and neutron star collisions. The Laser Interferometer Gravitational-Wave Observatory (LIGO) has been at the forefront of these discoveries. However, the immense volume and complexity of gravitational wave data present significant challenges for traditional analysis methods. This paper investigates the growing synergy between artificial intelligence (AI) and…

Prediction of the SVOM MXT camera end of life spectral performance based onproton irradiation results
Clara Plasse, Diego G\"otz, Aline Meuris, Miguel Fernandez Moita, Philippe Ferrando, Leo Favier, Francesco Ceraudo
#toXiv_bot_toot

Dynamic real-time multi-UAV cooperative mission planning method under multiple constraints
Chenglou Liu, Yufeng Lu, Fangfang Xie, Tingwei Ji, Yao Zheng
https://arxiv.org/abs/2506.02365

Dynamic real-time multi-UAV cooperative mission planning method under multiple constraints
As UAV popularity soars, so does the mission planning associated with it. The classical approaches suffer from the triple problems of decoupled of task assignment and path planning, poor real-time performance and limited adaptability. Aiming at these challenges, this paper proposes a dynamic real-time multi-UAV collaborative mission planning algorithm based on Dubins paths under a distributed formation structure. Dubins path with multiple advantages bridges the gap between task assignment and p…

From Fluctuation to Polarization: Imprints of $O(1-10)\, \mathrm{Mpc}^{-1}$ Curvature Perturbations in CMB B-modes from Scalar-Induced Gravitational Waves
Aurora Ireland, Kuver Sinha, Tao Xu
https://arxiv.org/abs/2507.02044

From Fluctuation to Polarization: Imprints of $O(1-10)\, \mathrm{Mpc}^{-1}$ Curvature Perturbations in CMB B-modes from Scalar-Induced Gravitational Waves
Probing primordial curvature perturbations on small scales, beyond those accessible using cosmic microwave background (CMB) primary anisotropies and Lyman-$α$ forest data, remains a major open challenge. Current constraints on the scalar power spectrum at these scales are either weak or rely heavily on model-dependent assumptions about small-scale structure. In this work, we propose a novel method to probe the small-scale primordial power spectrum using scalar-induced tensor perturbations, whi…

NestedFP: High-Performance, Memory-Efficient Dual-Precision Floating Point Support for LLMs
Haeun Lee, Omin Kwon, Yeonhong Park, Jae W. Lee
https://arxiv.org/abs/2506.02024

NestedFP: High-Performance, Memory-Efficient Dual-Precision Floating Point Support for LLMs
Large Language Models (LLMs) are playing a crucial role in latency-critical, high-throughput services like virtual assistants and code generation. While techniques such as continuous batching and paged attention address service-level objectives (SLOs), and quantization methods accelerate inference, the dynamic and efficient adaptation of precision at runtime remains a significant, largely underexplored challenge. The emergence of hardware support for FP8 arithmetic, offering up to 2x the throug…

Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges
Xiao-Yang Liu Yanglet, Yupeng Cao, Li Deng
https://arxiv.org/abs/2506.01973

Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges
Financial Large Language Models (FinLLMs), such as open FinGPT and proprietary BloombergGPT, have demonstrated great potential in select areas of financial services. Beyond this earlier language-centric approach, Multimodal Financial Foundation Models (MFFMs) can digest interleaved multimodal financial data, including fundamental data, market data, data analytics, macroeconomic, and alternative data (e.g., natural language, audio, images, and video). In this position paper, presented at the MFF…