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Czech Technical University in Prague

Academic institutioneurope · cz
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Research library456linked papers
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Selected work

Representative Papers

On Ray Reordering Techniques for Faster GPU Ray Tracing

May 04, 2020ACM Symposium on Interactive 3D Graphics and Games

Secondary rays in GPU-accelerated ray tracing exhibit poor spatial locality and low SIMT execution efficiency, limiting overall performance. Method: This paper proposes a hardware-agnostic ray reordering framework centered on an Endpoint-Estimated Key—designed specifically for secondary rays—to enhance spatial coherence with minimal computational overhead. The approach integrates wavefront path tracing scheduling, cooperative utilization of RTX hardware traversal kernels, and GPU memory access locality modeling, without relying on shader-specific implementations. Contribution/Results: Evaluated on modern GPUs, the method achieves 1.3–2.0× end-to-end ray tracing speedup. Reordering significantly accelerates software-intensive stages (e.g., intersection computation and shading preparation); although hardware traversal incurs inherent overhead, the net performance gain remains unambiguous. This work establishes a novel, portable paradigm for general-purpose GPU ray reordering—enabling efficient, implementation-independent acceleration across diverse ray tracing pipelines.

18 citations3 influentialRead paper

Balancing the Spread of Two Opinions in Sparse Social Networks

May 21, 2021AAAI Conference on Artificial Intelligence

This paper addresses the balance control problem of dual-opinion co-propagation in sparse social networks: given a budget, propagation rounds, and an initial seed set, minimize seed expansion such that every node ultimately holds either zero or both opinions—achieving global opinion balance. We innovatively embed a dual-threshold adoption mechanism into a target-set selection framework, proposing a discrete propagation model that jointly captures single- and dual-opinion adoption tendencies. Theoretically, we prove the problem is fixed-parameter tractable (FPT) with respect to the vertex cover number and devise an efficient parameterized algorithm. Moreover, we establish its polynomial-time solvability on sparse graph classes—including trees and degenerate graphs. Our work provides the first parameterized solution for multi-opinion dynamic control in sparse networks, backed by rigorous theoretical guarantees.

6 citationsRead paper

First Experiments with Neural cvc5

Jan 16, 2025Logic Programming and Automated Reasoning

Quantifier instantiation in first-order logic (including theories) remains inefficient in state-of-the-art SMT solvers. Method: This work integrates a lightweight, CPU-native graph neural network (GNN) into the industrial-strength SMT solver cvc5, enabling real-time, neural-guided scoring of instantiation candidates. Training data is automatically generated from proof traces via e-matching; the GNN is optimized for CPU inference; and an online scoring and scheduling framework is deeply embedded within cvc5—requiring no GPU acceleration. Contribution/Results: On unseen benchmarks, our approach significantly reduces average solving time and substantially improves proof success rates. To the best of our knowledge, this is the first end-to-end neural-guided quantifier instantiation deployed in a production-grade SMT solver. It empirically validates the feasibility and practicality of learning-augmented symbolic reasoning.

3 citationsRead paper

LLM-ABBA: Understanding time series via symbolic approximation

Nov 27, 2024arXiv.org

This work addresses the challenge of effectively leveraging semantic information in time series for large language models (LLMs). To this end, we propose the first framework that deeply integrates adaptive Brownian bridge aggregation (ABBA) with LLMs. Methodologically: (1) we design an amplitude- and period-preserving ABBA symbolic representation to bridge temporal structure with LLM embedding spaces; (2) we introduce a fixed piecewise-linear chain reconstruction technique to significantly suppress cumulative quantization error; and (3) we combine fine-tuning, prompt engineering, and controllable symbolic–numerical inverse mapping to achieve semantic alignment. Our approach achieves state-of-the-art performance on UCR and three medical time-series classification benchmarks, as well as on the TSER regression benchmark—marking the first instance where an LLM surpasses prior methods on TSER. Moreover, its forecasting accuracy rivals that of advanced dedicated time-series models.

2 citations1 influentialRead paper

Participatory Budgeting Project Strength via Candidate Control

May 28, 2025Adaptive Agents and Multi-Agent Systems

This study investigates the computational complexity of manipulating participatory budgeting elections by adding or deleting candidate projects to either ensure a target project’s selection (constructive control) or its exclusion (destructive control). It presents the first systematic analysis of candidate control under prominent voting rules—Phragmén, Equal Shares, and GreedyAV—and introduces a novel perspective that evaluates project strength through the lens of candidate deletions. The theoretical findings reveal that the problem is NP-hard under most rules, yet polynomial-time algorithms exist for GreedyAV and in the unit-cost setting. Experimental results corroborate the efficacy of the proposed approach, offering both theoretical insights and practical tools for assessing the robustness of participatory budgeting outcomes and the relative importance of individual projects.

2 citationsRead paper
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