Tootfinder

This https://arxiv.org/abs/2402.09595 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Correctly Communicating Software: Distributed, Asynchronous, and Beyond (extended version)
Much of the software we use in everyday life consists of distributed components (running on separate cores or even computers) that collaborate through communication (by exchanging messages). It is crucial to develop robust methods that can give reliable guarantees about the behavior of such message-passing software. With a focus on session types as communication protocols and their foundations in logic, this thesis revolves around the following question: How can we push the boundaries of the lo…

Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure
Vishnu Veeraraghavan, Kyle Hunte, Jingang Yi, Kaiyan Yu
https://arxiv.org/abs/2403.00613

Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure
We present a simultaneous sensor-based inspection and footprint coverage (SIFC) planning and control design with applications to autonomous robotic crack mapping and filling. The main challenge of the SIFC problem lies in the coupling of complete sensing (for mapping) and robotic footprint (for filling) coverage tasks. Initially, we assume known target information (e.g., crack) and employ classic cell decomposition methods to achieve complete sensing coverage of the workspace and complete robot…

This https://arxiv.org/abs/2402.15852 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) stands as a key research problem of Embodied AI, aiming at enabling agents to navigate in unseen environments following linguistic instructions. In this field, generalization is a long-standing challenge, either to out-of-distribution scenes or from Sim to Real. In this paper, we propose NaVid, a video-based large vision language model (VLM), to mitigate such a generalization gap. NaVid makes the first endeavour to showcase the capability of VLMs to achieve …

This https://arxiv.org/abs/2404.01779 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Diagonal Coset Approach to Topological Quantum Computation with Fibonacci Anyons
We investigate a promising conformal field theory realization scheme for topological quantum computation based on the Fibonacci anyons, which are believed to be realized as quasiparticle excitations in the $\mathbb{Z}_3$ parafermion fractional quantum Hall state in the second Landau level with filling factor $ν=12/5$. These anyons are non-Abelian and are known to be capable of universal topological quantum computation. The quantum information is encoded in the fusion channels of pairs of such …

I've had a little process running for the past year or so, totting up Mastodon "active monthly users". Occasional bumps up (one outstanding one last summer when Elon did something particularly idiotic) followed by long slow declines.
Was somewhat disappointed to see recent growth of about 250k turning out to be a phantom instance (telling lies via the API).
#mastostats

This https://arxiv.org/abs/2303.17405 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On dynamically convex contact manifolds and filtered symplectic homology
In this paper we are interested in characterizing the standard contact sphere in terms of dynamically convex contact manifolds which admit a Liouville filling with vanishing symplectic homology. We first observe that if the filling is flexible, then those contact manifolds are contactomorphic to the standard contact sphere. We then investigate quantitative geometry of those contact manifolds focusing on similarities with the standard contact sphere in filtered symplectic homology.

This https://arxiv.org/abs/2312.01052 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

SCTc-TE: A Comprehensive Formulation and Benchmark for Temporal Event Forecasting
Temporal complex event forecasting aims to predict the future events given the observed events from history. Most formulations of temporal complex event are unstructured or without extensive temporal information, resulting in inferior representations and limited forecasting capabilities. To bridge these gaps, we innovatively introduce the formulation of Structured, Complex, and Time-complete temporal event (SCTc-TE). Following this comprehensive formulation, we develop a fully automated pipelin…

This https://arxiv.org/abs/2401.01368 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

An example of a bouncing dark matter -- EPS-HEP2023
Thermal dark matter, which, after chemical decoupling from the Standard Model thermal plasma rises its yield before it freezes-out, is a new feature of thermal DM scenarios dubbed as "Bouncing Dark Matter". In the following short note, we introduce the topic, exemplifying the mechanism with a simple model that extends the SM with two dark matter particles, a fermion, and a pNGB, plus a second Higgs. We explore the features of the thermal freeze-out of this scenario, with particular emphasis on …

This https://arxiv.org/abs/2402.14591 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

High-Speed Detector For Low-Powered Devices In Aerial Grasping
Autonomous aerial harvesting is a highly complex problem because it requires numerous interdisciplinary algorithms to be executed on mini low-powered computing devices. Object detection is one such algorithm that is compute-hungry. In this context, we make the following contributions: (i) Fast Fruit Detector (FFD), a resource-efficient, single-stage, and postprocessing-free object detector based on our novel latent object representation (LOR) module, query assignment, and prediction strategy. F…

This https://arxiv.org/abs/2402.15021 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

CLoVe: Encoding Compositional Language in Contrastive Vision-Language Models
Recent years have witnessed a significant increase in the performance of Vision and Language tasks. Foundational Vision-Language Models (VLMs), such as CLIP, have been leveraged in multiple settings and demonstrated remarkable performance across several tasks. Such models excel at object-centric recognition yet learn text representations that seem invariant to word order, failing to compose known concepts in novel ways. However, no evidence exists that any VLM, including large-scale single-stre…