Tootfinder

MARTE: EU-Staaten bauen gemeinsam Kampfpanzer mit Hybrid-Antrieb
Ein hochmoderner Kampfpanzer der EU und Norwegen soll die Kampfkraft stärken. Gemeinsam wollen die Staaten im Projekt MARTE einen solchen Panzer bauen.

sp_hypertext: Hypertext 2009 dynamic contact network
The temporal network of contacts among attendees of the ACM Hypertext 2009 conference, which spanned 2.5 days of time.
This network has 113 nodes and 2196 edges.
Tags: Social, Offline, Unweighted, Temporal
https://networks.skewed.de/net/sp_hype…

HCVR: A Hybrid Approach with Correlation-aware Voting Rules for Feature Selection
Nikita Bhedasgaonkar, Rushikesh K. Joshi
https://arxiv.org/abs/2507.02073

HCVR: A Hybrid Approach with Correlation-aware Voting Rules for Feature Selection
In this paper, we propose HCVR (Hybrid approach with Correlation-aware Voting Rules), a lightweight rule-based feature selection method that combines Parameter-to-Parameter (P2P) and Parameter-to-Target (P2T) correlations to eliminate redundant features and retain relevant ones. This method is a hybrid of non-iterative and iterative filtering approaches for dimensionality reduction. It is a greedy method, which works by backward elimination, eliminating possibly multiple features at every step.…

Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization
Zhiwei Zhang, Samy Wu Fung, Anastasios Kyrillidis, Stanley Osher, Moshe Y. Vardi
https://arxiv.org/abs/2506.00674

Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization
The Boolean satisfiability (SAT) problem lies at the core of many applications in combinatorial optimization, software verification, cryptography, and machine learning. While state-of-the-art solvers have demonstrated high efficiency in handling conjunctive normal form (CNF) formulas, numerous applications require non-CNF (hybrid) constraints, such as XOR, cardinality, and Not-All-Equal constraints. Recent work leverages polynomial representations to represent such hybrid constraints, but it re…

Rethinking Hybrid Retrieval: When Small Embeddings and LLM Re-ranking Beat Bigger Models
Arjun Rao, Hanieh Alipour, Nick Pendar
https://arxiv.org/abs/2506.00049

Rethinking Hybrid Retrieval: When Small Embeddings and LLM Re-ranking Beat Bigger Models
This paper presents a comparison of embedding models in tri-modal hybrid retrieval for Retrieval-Augmented Generation (RAG) systems. We investigate the fusion of dense semantic, sparse lexical, and graph-based embeddings, focusing on the performance of the MiniLM-v6 and BGE-Large architectures. Contrary to conventional assumptions, our results show that the compact MiniLM-v6 outperforms the larger BGE-Large when integrated with LLM-based re-ranking within our tri-modal hybrid framework. Experim…

Frequency Automata: A novel formal model of hybrid systems in combined time and frequency domains
Moon Kim, Avinash Malik, Partha Roop
https://arxiv.org/abs/2506.00251

Frequency Automata: A novel formal model of hybrid systems in combined time and frequency domains
Hybrid systems are mostly modelled, simulated, and verified in the time domain by computer scientists. Engineers, however, use both frequency and time domain modelling due to their distinct advantages. For example, frequency domain modelling is better suited for control systems, using features such as spectra of the signal. Considering this, we introduce, for the first time, a formal model called frequency automata for hybrid systems modelling and simulation, which are represented in combined t…

Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots
Yang Zhang, Buqing Nie, Zhanxiang Cao, Yangqing Fu, Yue Gao
https://arxiv.org/abs/2506.00472

Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots
Reinforcement Learning (RL)-based methods have significantly improved the locomotion performance of legged robots. However, these motion policies face significant challenges when deployed in the real world. Robots operating in uncertain environments struggle to adapt to payload variations and external disturbances, resulting in severe degradation of motion performance. In this work, we propose a novel Hybrid Force-Position Locomotion Policy (HFPLP) learning framework, where the action space of …

Hybrid SIS Dynamics for Demand Modeling of Frequently Updated Products
Ian Walter, Jitesh H. Panchal, Philip E. Par\'e
https://arxiv.org/abs/2506.01866

Hybrid SIS Dynamics for Demand Modeling of Frequently Updated Products
We propose a hybrid spreading process model to capture the dynamics of demand for software-based products. We introduce discontinuous jumps in the state to model sudden surges in demand that can be seen immediately after a product update is released. After each update, the modeled demand evolves according to a continuous-time susceptible-infected-susceptible (SIS) epidemic model. We identify the necessary and sufficient conditions for estimating the hybrid model's parameters for an arbitrary fi…

Hybrid scaling mechanism of critical behavior in the overlapping critical regions of classical and quantum Yang-Lee edge singularities
Yue-Mei Sun, Wen-Jing Yu, Xin-Yu Wang, Liang-Jun Zhai
https://arxiv.org/abs/2506.00919

Hybrid scaling mechanism of critical behavior in the overlapping critical regions of classical and quantum Yang-Lee edge singularities
Recently, the study of scaling behavior in Yang-Lee edge singularities (YLES) has attracted sustained attention. However, the scaling mechanism for the overlapping critical region between classical and quantum YLES remains unclear. In this work, we investigate this question, and a hybrid scaling mechanism is introduced to characterize the scaling behavior in the overlapping regions. The hybrid scaling mechanism asserts that in the overlapping region the scaling behavior can be described by the …

HoMeR: Learning In-the-Wild Mobile Manipulation via Hybrid Imitation and Whole-Body Control
Priya Sundaresan, Rhea Malhotra, Phillip Miao, Jingyun Yang, Jimmy Wu, Hengyuan Hu, Rika Antonova, Francis Engelmann, Dorsa Sadigh, Jeannette Bohg
https://arxiv.org/abs/2506.01185

HoMeR: Learning In-the-Wild Mobile Manipulation via Hybrid Imitation and Whole-Body Control
We introduce HoMeR, an imitation learning framework for mobile manipulation that combines whole-body control with hybrid action modes that handle both long-range and fine-grained motion, enabling effective performance on realistic in-the-wild tasks. At its core is a fast, kinematics-based whole-body controller that maps desired end-effector poses to coordinated motion across the mobile base and arm. Within this reduced end-effector action space, HoMeR learns to switch between absolute pose pred…