arXiv subjects

Yuxuan Tang

Publications and source records attributed to Yuxuan Tang.

9 recordsLinked to original sources

arXiv2026

Building atomistic models of heterointerfaces with optimal transport

Heterogeneous interfaces underpin technologies from microelectronics to energy conversion and storage, but their configurational complexity precludes exhaustive first-principles screening of interface registries. Although data-driven approaches can alleviate this burden, they remain limited by sparse interface datasets. Here, we introduce an energy-independent workflow that represents coherent interfaces as attributed graphs, quantifies their similarity to parent bulk environments using the fused Gromov-Wasserstein (FGW) distance, and couples this metric with Bayesian optimization over the in-plane registry space. We assess the approach for KI/NaCl, GaP/GaAs and GaN/$\mathrm{Al_{2}O_{3}}$ interfaces spanning ionic, covalent and mixed-bonding regimes, using hierarchical validation with MACE and density functional theory (DFT). Comparison with single-point energy landscapes shows that the FGW distance captures registry-dependent periodicity, while interfaces exhibit deviations between structural and energetic extrema, reflecting additional chemistry-specific contributions. Furthermore, FGW distances show an overall association with relaxed energies. Under limited screening budgets, FGW-guided registry selection consistently outperforms random search and is more robust across interface systems than selection guided by pretrained MACE energies. The workflow converts the qualitative notion of bulk-like continuity into a quantitative prescreening criterion, enabling efficient registry exploration and providing physically informed candidate structures for materials discovery workflows.

cond-mat.mtrl-sci↗

arXiv2026

U-OBCA: Uncertainty-Aware Optimization-Based Collision Avoidance via Wasserstein Distributionally Robust Chance Constraints

Uncertainties arising from localization error, trajectory prediction errors of the moving obstacles and environmental disturbances pose significant challenges to robot's safe navigation. Existing uncertainty-aware planners often approximate polygon-shaped robots and obstacles using simple geometric primitives such as circles or ellipses. Though computationally convenient, these approximations substantially shrink the feasible space, leading to overly conservative trajectories and even planning failure in narrow environments. In addition, many such methods rely on specific assumptions about noise distributions, which may not hold in practice and thus limit their performance guarantees. To address these limitations, we extend the Optimization-Based Collision Avoidance (OBCA) framework to an uncertainty-aware formulation, termed \emph{U-OBCA}. The proposed method explicitly accounts for the collision risk between polygon-shaped robots and obstacles by formulating OBCA-based chance constraints, and hence avoiding geometric simplifications and reducing unnecessary conservatism. These probabilistic constraints are further tightened into deterministic nonlinear constraints under mild distributional assumptions, which can be solved efficiently by standard numerical optimization solvers. The proposed approach is validated through theoretical analysis, numerical simulations and real-world experiments. The results demonstrate that U-OBCA significantly mitigates the conservatism in trajectory planning and achieves higher navigation efficiency compared to existing baseline methods, particularly in narrow and cluttered environments.

cs.RO↗

arXiv2025

Beyond Pairwise: Empowering LLM Alignment With Ranked Choice Modeling

Alignment of large language models (LLMs) has predominantly relied on pairwise preference optimization, where annotators select the better of two responses to a prompt. While simple, this approach overlooks the opportunity to learn from richer forms of human feedback, such as multiway comparisons and top-$k$ rankings. We introduce Ranked Choice Preference Optimization (RCPO), a unified framework that bridges preference optimization with (ranked) choice modeling via maximum likelihood estimation. RCPO supports both utility-based and rank-based models, subsumes several pairwise methods (such as DPO and SimPO) as special cases, and provides principled training objectives for richer feedback formats. We instantiate this framework with two representative models (Multinomial Logit and Mallows-RMJ). Experiments on Llama-3-8B-Instruct, Gemma-2-9B-it, and Mistral-7B-Instruct across in-distribution and out-of-distribution settings show that RCPO consistently outperforms competitive baselines. RCPO shows that directly leveraging ranked preference data, combined with the right choice models, yields more effective alignment. It offers an extensible foundation for incorporating (ranked) choice modeling into LLM training.

cs.LG↗

arXiv2024

TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks

We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and affect change in their surrounding environments. But how performant are AI agents at accelerating or even autonomously performing work-related tasks? The answer to this question has important implications both for industry looking to adopt AI into their workflows and for economic policy to understand the effects that adoption of AI may have on the labor market. To measure the progress of these LLM agents' performance on performing real-world professional tasks, in this paper we introduce TheAgentCompany, an extensible benchmark for evaluating AI agents that interact with the world in similar ways to those of a digital worker: by browsing the Web, writing code, running programs, and communicating with other coworkers. We build a self-contained environment with internal web sites and data that mimics a small software company environment, and create a variety of tasks that may be performed by workers in such a company. We test baseline agents powered by both closed API-based and open-weights language models (LMs), and find that the most competitive agent can complete 30% of tasks autonomously. This paints a nuanced picture on task automation with LM agents--in a setting simulating a real workplace, a good portion of simpler tasks could be solved autonomously, but more difficult long-horizon tasks are still beyond the reach of current systems. We release code, data, environment, and experiments on https://the-agent-company.com.

cs.CL↗

arXiv2024

Visual Compositional Data Analytics for Spatial Transcriptomics

For the Bio+Med-Vis Challenge 2024, we propose a visual analytics system as a redesign for the scatter pie chart visualization of cell type proportions of spatial transcriptomics data. Our design uses three linked views: a view of the histological image of the tissue, a stacked bar chart showing cell type proportions of the spots, and a scatter plot showing a dimensionality reduction of the multivariate proportions. Furthermore, we apply a compositional data analysis framework, the Aitchison geometry, to the proportions for dimensionality reduction and $k$-means clustering. Leveraging brushing and linking, the system allows one to explore and uncover patterns in the cell type mixtures and relate them to their spatial locations on the cellular tissue. This redesign shifts the pattern recognition workload from the human visual system to computational methods commonly used in visual analytics. We provide the code and setup instructions of our visual analytics system on GitHub (https://github.com/UniStuttgart-VISUS/va-for-spatial-transcriptomics).

cs.HC↗

arXiv2024

DMF-Net: Image-Guided Point Cloud Completion with Dual-Channel Modality Fusion and Shape-Aware Upsampling Transformer

In this paper we study the task of a single-view image-guided point cloud completion. Existing methods have got promising results by fusing the information of image into point cloud explicitly or implicitly. However, given that the image has global shape information and the partial point cloud has rich local details, We believe that both modalities need to be given equal attention when performing modality fusion. To this end, we propose a novel dual-channel modality fusion network for image-guided point cloud completion(named DMF-Net), in a coarse-to-fine manner. In the first stage, DMF-Net takes a partial point cloud and corresponding image as input to recover a coarse point cloud. In the second stage, the coarse point cloud will be upsampled twice with shape-aware upsampling transformer to get the dense and complete point cloud. Extensive quantitative and qualitative experimental results show that DMF-Net outperforms the state-of-the-art unimodal and multimodal point cloud completion works on ShapeNet-ViPC dataset.

cs.CV↗

arXiv2023

On A Mallows-type Model For (Ranked) Choices

We consider a preference learning setting where every participant chooses an ordered list of $k$ most preferred items among a displayed set of candidates. (The set can be different for every participant.) We identify a distance-based ranking model for the population's preferences and their (ranked) choice behavior. The ranking model resembles the Mallows model but uses a new distance function called Reverse Major Index (RMJ). We find that despite the need to sum over all permutations, the RMJ-based ranking distribution aggregates into (ranked) choice probabilities with simple closed-form expression. We develop effective methods to estimate the model parameters and showcase their generalization power using real data, especially when there is a limited variety of display sets.

cs.LG↗

arXiv2022

The degree and codegree threshold for linear triangle covering in 3-graphs

Given two $k$-uniform hypergraphs $F$ and $G$, we say that $G$ has an $F$-covering if every vertex in $G$ is contained in a copy of $F$. For $1\le i \le k-1$, let $c_i(n,F)$ be the least integer such that every $n$-vertex $k$-uniform hypergraph $G$ with $δ_i(G)> c_i(n,F)$ has an $F$-covering. The covering problem has been systematically studied by Falgas-Ravry and Zhao [Codegree thresholds for covering 3-uniform hypergraphs, SIAM J. Discrete Math., 2016]. Last year, Falgas-Ravry, Markström, and Zhao [Triangle-degrees in graphs and tetrahedron coverings in 3-graphs, Combinatorics, Probability and Computing, 2021] asymptotically determined $c_1(n, F)$ when $F$ is the generalized triangle. In this note, we give the exact value of $c_2(n, F)$ and asymptotically determine $c_1(n, F)$ when $F$ is the linear triangle $C_6^3$, where $C_6^3$ is the 3-uniform hypergraph with vertex set $\{v_1,v_2,v_3,v_4,v_5,v_6\}$ and edge set $\{v_1v_2v_3,v_3v_4v_5,v_5v_6v_1\}$.

math.CO↗

arXiv2020

The Clock and Control System for the ATLAS Liquid Argon Calorimeter Phase-I Upgrade

A Liquid-argon Trigger Digitizer Board (LTDB) is being developed to upgrade the ATLAS Liquid Argon Calorimeter Phase-I trigger electronics. The LTDB located at the front end needs to obtain the clock signals and be configured and monitored remotely from the back end. A clock and control system is being developed for the LTDB and the major functions of the system have been evaluated. The design and evaluation of the clock and control system are presented in this paper.

physics.ins-det↗