Tootfinder

Energy Management and Wake-up for IoT Networks Powered by Energy Harvesting
David Ernesto Ruiz-Guirola, Samuel Montejo-Sanchez, Israel Leyva-Mayorga, Zhu Han, Petar Popovski, Onel L. A. Lopez
https://arxiv.org/abs/2508.13825

Energy Management and Wake-up for IoT Networks Powered by Energy Harvesting
The rapid growth of the Internet of Things (IoT) presents sustainability challenges such as increased maintenance requirements and overall higher energy consumption. This motivates self-sustainable IoT ecosystems based on Energy Harvesting (EH). This paper treats IoT deployments in which IoT devices (IoTDs) rely solely on EH to sense and transmit information about events/alarms to a base station (BS). The objective is to effectively manage the duty cycling of the IoTDs to prolong battery life a…

Cosmology with supernova Encore in the strong lensing cluster MACS J0138-2155: Time delays & Hubble constant measurement
J. D. R. Pierel, E. E. Hayes, M. Millon, C. Larison, E. Mamuzic, A. Acebron, A. Agrawal, P. Bergamini, S. Cha, S. Dhawan, J. M. Diego, B. L. Frye, D. Gilman, G. Granata, C. Grillo, M. J. Jee, P. S. Kamieneski, A M. Koekemoer, A. K. Meena, A. B. Newman, M. Oguri, E. Padilla-Gonzalez, F. Poidevin, P. Rosati, S. Schuldt, L. G. Strolger, S. H. Suyu, S. Thorp, A. Zitr…

Cosmology with supernova Encore in the strong lensing cluster MACS J0138-2155: Time delays & Hubble constant measurement
Multiply-imaged supernovae (SNe) provide a novel means of constraining the Hubble constant ($H_0$). Such measurements require a combination of precise models of the lensing mass distribution and an accurate estimate of the relative time delays between arrival of the multiple images. Only two multiply-imaged SNe, Refsdal and H0pe, have enabled measurements of $H_0$ thus far. Here we detail the third such measurement for SN Encore, a $z=1.95$ SNIa discovered in JWST/NIRCam imaging. We measure the…

Preguss: It Analyzes, It Specifies, It Verifies
Zhongyi Wang, Tengjie Lin, Mingshuai Chen, Mingqi Yang, Haokun Li, Xiao Yi, Shengchao Qin, Jianwei Yin
https://arxiv.org/abs/2508.14532

Preguss: It Analyzes, It Specifies, It Verifies
Fully automated verification of large-scale software and hardware systems is arguably the holy grail of formal methods. Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification by, e.g., generating formal specifications as essential to deductive verification, yet exhibit poor scalability due to context-length limitations and, more importantly, the difficulty of inferring complex, interprocedural specifications. This pa…

Heterogeneous Temporal Hypergraph Neural Network
Huan Liu, Pengfei Jiao, Mengzhou Gao, Chaochao Chen, Di Jin
https://arxiv.org/abs/2506.17312 https://

Heterogeneous Temporal Hypergraph Neural Network
Graph representation learning (GRL) has emerged as an effective technique for modeling graph-structured data. When modeling heterogeneity and dynamics in real-world complex networks, GRL methods designed for complex heterogeneous temporal graphs (HTGs) have been proposed and have achieved successful applications in various fields. However, most existing GRL methods mainly focus on preserving the low-order topology information while ignoring higher-order group interaction relationships, which ar…

Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision
Xu Lu, Weisong Sun, Yiran Zhang, Ming Hu, Cong Tian, Zhi Jin, Yang Liu
https://arxiv.org/abs/2508.18675

Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision
Automated code generation has long been considered the holy grail of software engineering. The emergence of Large Language Models (LLMs) has catalyzed a revolutionary breakthrough in this area. However, existing methods that only rely on LLMs remain inadequate in the quality of generated code, offering no guarantees of satisfying practical requirements. They lack a systematic strategy for requirements development and modeling. Recently, LLM-based agents typically possess powerful abilities and …