arXiv subjects

Tianyi Ding

Publications and source records attributed to Tianyi Ding.

8 recordsLinked to original sources

arXiv2026

Probing Neutrino Flavor Composition with the Glashow Resonance at Tau Air-Shower Neutrino Telescopes

The flavor composition of high-energy astrophysical neutrinos encodes information about their production and propagation. The Glashow resonance, $\bar{\nu}_e + e^-\to W^-$, provides a unique way to distinguish antineutrinos from neutrinos and thereby extends the reach of flavor composition studies. Proposed tau air-shower neutrino telescopes target Earth-skimming and mountain-skimming $\nu_\tau$ above a PeV, but through the decay $W^-\to \bar{\nu}_\tau+\tau^-$ they are also sensitive to $\bar{\nu}_e$. These experiments can therefore measure the ratio of $\bar{\nu}_e$ to $\nu_\tau+\bar{\nu}_\tau$ fluxes. We evaluate this prospect with explicit simulations and project sensitivities for TAMBO and TRINITY, assuming 10 years of operation. We find that mountain-skimming geometries yield substantially higher $\bar{\nu}_e$ acceptance than Earth-skimming ones due to the shorter path length in rock. For standard astrophysical source scenarios, our projections show that differentiating $pp$ and $p\gamma$ production, including their muon-damped scenarios, is challenging with a standalone measurement by tau air-shower experiments in their currently designed configurations, though optimistically the flux ratio can be constrained to $\lesssim$2 at 1$\sigma$. A $\bar{\nu}_e$-rich flux, as expected from neutron-decay sources or from certain new physics models, would stand out from the standard pion-production scenarios and can otherwise be constrained.

hep-ph↗

arXiv2026

Mixture-of-Thought-Tokens: Unifying Perception and Reasoning for Free-form Multimodal Grounding

Multimodal Large Language Models have made great progress in grounding tasks, yet existing methods still struggle to unify precise localization and complex reasoning. For one thing, text-based methods rely on coordinates or index prediction, severely limiting the perceptual capabilities of the model for dense visual objects. Meanwhile, latent token-based methods employ special tokens without inherent spatial references and use a decoding mechanism that lacks thinking steps, weakening high-level reasoning capabilities. Consequently, developing a unified framework that excels in both perception and reasoning remains challenging. To address this, we propose Mixture-of-Thought-Tokens (Motto), a new free-form multimodal grounding method that bridges the perception-reasoning gap, enabling MLLMs to empower diverse, arbitrary grounding queries. Specifically, we introduce Spatially-Grounded Thought Tokenization to explicitly align special tokens with spatial locations for clear spatial correspondence and visual interpretability. We further design a Context-Adaptive Chain-of-Tokens that dynamically switch grounding modes within an interleaved reasoning chain, achieving robust grounding across tasks of varying complexity. In addition, we construct PR-Bench, a new referring expression comprehension benchmark to evaluate the perception-reasoning gap. Extensive experiments demonstrate that Motto achieves state-of-the-art performance across diverse free-form grounding tasks.

cs.CV↗

arXiv2025

Balanced Collaborative Exploration via Distributed Topological Graph Voronoi Partition

This work addresses the collaborative multi-robot autonomous online exploration problem, particularly focusing on distributed exploration planning for dynamically balanced exploration area partition and task allocation among a team of mobile robots operating in obstacle-dense non-convex environments. We present a novel topological map structure that simultaneously characterizes both spatial connectivity and global exploration completeness of the environment. The topological map is updated incrementally to utilize known spatial information for updating reachable spaces, while exploration targets are planned in a receding horizon fashion under global coverage guidance. A distributed weighted topological graph Voronoi algorithm is introduced implementing balanced graph space partitions of the fused topological maps. Theoretical guarantees are provided for distributed consensus convergence and equitable graph space partitions with constant bounds. A local planner optimizes the visitation sequence of exploration targets within the balanced partitioned graph space to minimize travel distance, while generating safe, smooth, and dynamically feasible motion trajectories. Comprehensive benchmarking against state-of-the-art methods demonstrates significant improvements in exploration efficiency, completeness, and workload balance across the robot team.

cs.RO↗

arXiv2025

Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting

Magnetic Resonance Fingerprinting (MRF) enables fast quantitative imaging by matching signal evolutions to a predefined dictionary. However, conventional dictionary matching suffers from exponential growth in computational cost and memory usage as the number of parameters increases, limiting its scalability to multi-parametric mapping. To address this, recent work has explored deep learning-based approaches as alternatives to DM. We propose GAST-Mamba, an end-to-end framework that combines a dual Mamba-based encoder with a Gate-Aware Spatial-Temporal (GAST) processor. Built on structured state-space models, our architecture efficiently captures long-range spatial dependencies with linear complexity. On 5 times accelerated simulated MRF data (200 frames), GAST-Mamba achieved a T1 PSNR of 33.12~dB, outperforming SCQ (31.69~dB). For T2 mapping, it reached a PSNR of 30.62~dB and SSIM of 0.9124. In vivo experiments further demonstrated improved anatomical detail and reduced artifacts. Ablation studies confirmed that each component contributes to performance, with the GAST module being particularly important under strong undersampling. These results demonstrate the effectiveness of GAST-Mamba for accurate and robust reconstruction from highly undersampled MRF acquisitions, offering a scalable alternative to traditional DM-based methods.

eess.IV↗

arXiv2025

A Hybrid SMT-NRA Solver: Integrating 2D Cell-Jump-Based Local Search, MCSAT and OpenCAD

In this paper, we propose a hybrid framework for Satisfiability Modulo the Theory of Nonlinear Real Arithmetic (SMT-NRA for short). First, we introduce a two-dimensional cell-jump move, called \emph{$2d$-cell-jump}, generalizing the key operation, cell-jump, of the local search method for SMT-NRA. Then, we propose an extended local search framework, named \emph{$2d$-LS} (following the local search framework, LS, for SMT-NRA), integrating the model constructing satisfiability calculus (MCSAT) framework to improve search efficiency. To further improve the efficiency of MCSAT, we implement a recently proposed technique called \emph{sample-cell projection operator} for MCSAT, which is well suited for CDCL-style search in the real domain and helps guide the search away from conflicting states. Finally, we present a hybrid framework for SMT-NRA integrating MCSAT, $2d$-LS and OpenCAD, to improve search efficiency through information exchange. The experimental results demonstrate improvements in local search performance, highlighting the effectiveness of the proposed methods.

cs.AI↗

arXiv2025

Illuminating Very Heavy Dark Matter in the Earth with Tau Neutrinos

Dark matter accumulates in the center of the Earth as the planet plows through the dark matter halo in the Milky Way. Possible annihilation of dark matter to Standard Model particles can be probed in indirect dark matter searches. Among possible messengers, neutrinos are uniquely ideal as they can escape dense regions. Therefore, neutrino telescopes, with their large volume and broad energy exposures, offer new opportunities to search for dark matter signals from the center of the Earth. However, such studies have been restricted to dark matter masses below $\sim$ PeV as the Earth becomes opaque to very-high-energy neutrinos. In this study, we demonstrate that neutrino telescopes operating at TeV-PeV energies can probe very heavy dark matter particles if they annihilate to tau neutrinos or tau leptons. Here, we report upper limits on the spin-independent dark matter-nucleon cross section for masses between $10^5$ GeV and $10^{10}$ GeV by using 7.5 years of IceCube high-energy starting event observations. Our results motivate detailed analyses in IceCube and other upcoming neutrino telescopes in the Northern Hemisphere.

hep-ph↗

arXiv2024

Resource-Efficient Cooperative Online Scalar Field Mapping via Distributed Sparse Gaussian Process Regression

Cooperative online scalar field mapping is an important task for multi-robot systems. Gaussian process regression is widely used to construct a map that represents spatial information with confidence intervals. However, it is difficult to handle cooperative online mapping tasks because of its high computation and communication costs. This letter proposes a resource-efficient cooperative online field mapping method via distributed sparse Gaussian process regression. A novel distributed online Gaussian process evaluation method is developed such that robots can cooperatively evaluate and find observations of sufficient global utility to reduce computation. The bounded errors of distributed aggregation results are guaranteed theoretically, and the performances of the proposed algorithms are validated by real online light field mapping experiments.

cs.RO↗

arXiv2023

Entanglement distillation in terms of Schmidt rank and matrix rank

Entanglement distillation is a key task in quantum-information processing. In this paper, we distill non-positive-partial-transpose (NPT) bipartite states of some given Schmidt rank and matrix rank. We show that all bipartite states of Schmidt rank two are locally equivalent to classical-classical states, and all bipartite states of Schmidt rank three are 1-undistillable. Subsequently, we show that low-rank B-irreducible NPT states are distillable for large-rank reduced density operators by proving low-rank B-irreducible NPT state whose range contains a product vector is distillable. Eventually, we present an equivalent condition to distill $M\times N$ bipartite states of rank $\max\{M,N\}+1$.

quant-ph↗