arXiv subjects

Shanshan Yang

Publications and source records attributed to Shanshan Yang.

7 recordsLinked to original sources

arXiv2026

The similarity of irreducible operators in factors

An operator $T$ in a separable factor $\mathcal{M}$ is said to be irreducible in $\mathcal{M}$ if the von Neumann subalgebra $W^*(T)$ generated by $T$ is an irreducible subfactor of $\mathcal{M}$, i.e., $W^*(T)'\cap\mathcal{M}=\mathbb{C}I$. We say that $T$ is a single generator of $\mathcal{M}$ if $W^*(T)=\mathcal{M}$. In this paper, we study generators of separable factors related to maximal abelian self-adjoint subalgebras. As an application, we obtain a complete characterization of normal operators in separable factors which are similar to irreducible operators.

math.OA↗

arXiv2025

Irreducible operators in von Neumann algebras

Let $\mathcal{M}$ be a separable von Neumann algebra with center $\mathcal{Z}(\mathcal{M})$. An operator $T$ in $\mathcal{M}$ is called irreducible if the von Neumann algebra $W^*(T)$ generated by $T$ has trivial relative commutant, i.e., $W^*(T)'\cap\mathcal{M}=\mathcal{Z}(\mathcal{M})$. In this paper, we show that irreducible operators in $\mathcal{M}$ form a norm-dense $G_\delta$ set, which is a generalization of Halmos' theorem. Moreover, we prove that every operator in $\mathcal{M}$ is the sum of two irreducible operators, which is an analogue of Radjavi's theorem.

math.OA↗

arXiv2025

FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs

Going beyond simple text processing, financial auditing requires detecting semantic, structural, and numerical inconsistencies across large-scale disclosures. As financial reports are filed in XBRL, a structured XML format governed by accounting standards, auditing becomes a structured information extraction and reasoning problem involving concept alignment, taxonomy-defined relations, and cross-document consistency. Although large language models (LLMs) show promise on isolated financial tasks, their capability in professional-grade auditing remains unclear. We introduce FinAuditing, a taxonomy-aligned, structure-aware benchmark built from real XBRL filings. It contains 1,102 annotated instances averaging over 33k tokens and defines three tasks: Financial Semantic Matching (FinSM), Financial Relationship Extraction (FinRE), and Financial Mathematical Reasoning (FinMR). Evaluations of 13 state-of-the-art LLMs reveal substantial gaps in concept retrieval, taxonomy-aware relation modeling, and consistent cross-document reasoning. These findings highlight the need for realistic, structure-aware benchmarks. We release the evaluation code at https://github.com/The-FinAI/FinAuditing and the dataset at https://huggingface.co/collections/TheFinAI/finauditing. The task currently serves as the official benchmark of an ongoing public evaluation contest at https://open-finance-lab.github.io/SecureFinAI_Contest_2026/.

cs.CL↗

arXiv2025

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application

Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing evaluations of LLMs in finance remain text-only, monolingual, and largely saturated by current models. To bridge these gaps, we present MultiFinBen, the first expert-annotated multilingual (five languages) and multimodal (text, vision, audio) benchmark for evaluating LLMs in realistic financial contexts. MultiFinBen introduces two new task families: multilingual financial reasoning, which tests cross-lingual evidence integration from filings and news, and financial OCR, which extracts structured text from scanned documents containing tables and charts. Rather than aggregating all available datasets, we apply a structured, difficulty-aware selection based on advanced model performance, ensuring balanced challenge and removing redundant tasks. Evaluating 21 leading LLMs shows that even frontier multimodal models like GPT-4o achieve only 46.01% overall, stronger on vision and audio but dropping sharply in multilingual settings. These findings expose persistent limitations in multilingual, multimodal, and expert-level financial reasoning. All datasets, evaluation scripts, and leaderboards are publicly released.

cs.CL↗

arXiv2024

Deciphering Interphase Instability of Lithium Metal Batteries with Localized High-Concentration Electrolytes at Elevated Temperatures

Lithium metal batteries (LMBs), when coupled with a localized high-concentration electrolyte and a high-voltage nickel-rich cathode, offer a solution to the increasing demand for high energy density and long cycle life. However, the aggressive electrode chemistry poses safety risks to LMBs at higher temperatures and cutoff voltages. Here, we decipher the interphase instability in LHCE-based LMBs with a Ni0.8Co0.1Mn0.1O2 cathode at elevated temperatures. Our findings reveal that the generation of fluorine radicals in the electrolyte induces the solvent decomposition and consequent chain reactions, thereby reconstructing the cathode electrolyte interphase (CEI) and degrading battery cyclability. As further evidenced, introducing an acid scavenger of dimethoxydimethylsilane (DODSi) significantly boosts CEI stability with suppressed microcracking. A Ni0.8Co0.1Mn0.1O2||Li cell with this DODSi-functionalized LHCE achieves an unprecedented capacity retention of 93.0% after 100 cycles at 80 {\deg}C. This research provides insights into electrolyte engineering for practical LMBs with high safety under extreme temperatures.

cond-mat.mtrl-sci↗

arXiv2019

SNR-adaptive OCT angiography enabled by statistical characterization of intensity and decorrelation with multi-variate time series model

In OCT angiography (OCTA), decorrelation computation has been widely used as a local motion index to identify dynamic flow from static tissues, but its dependence on SNR severely degrades the vascular visibility, particularly in low- SNR regions. To mathematically characterize the decorrelation-SNR dependence of OCT signals, we developed a multi-variate time series (MVTS) model. Based on the model, we derived a universal asymptotic linear relation of decorrelation to inverse SNR (iSNR), with the variance in static and noise regions determined by the average kernel size. Accordingly, with the population distribution of static and noise voxels being explicitly calculated in the iSNR and decorrelation (ID) space, a linear classifier is developed by removing static and noise voxels at all SNR, to generate a SNR-adaptive OCTA, termed as ID-OCTA. Then, flow phantom and human skin experiments were performed to validate the proposed ID-OCTA. Both qualitative and quantitative assessments demonstrated that ID-OCTA offers a superior visibility of blood vessels, particularly in the deep layer. Finally, implications of this work on both system design and hemodynamic quantification are further discussed.

physics.med-ph↗

arXiv2018

Mechanism of Electric Power Generation from Ionic Droplet Motion on Polymer Supported Graphene

Graphene-based electric power generation that converts mechanical energy of flow of ionic droplets over the device surface into electricity has emerged as promising candidate for a blue-energy network. Yet the lack of a microscopic understanding of the underlying mechanism has prevented ability to optimize and control the performance of such devices. This requires information on interfacial structure and charging behavior at the molecular level. Here, we use sum-frequency vibrational spectroscopy (SFVS) to probe the interfaces of devices composed of aqueous solution, graphene and supporting polymer substrate. We discover that the surface dipole layer of the polymer is responsible for ion attraction toward and adsorption at the graphene surface that leads to electricity generation in graphene. Graphene itself does not attract ions and only acts as a conducting sheet for the induced carrier transport. Replacing the polymer by an organic ferroelectric substrate could enhance the efficiency and allow switching of the electricity generation. Our microscopic understanding of the electricity generation process paves the way for the rational design of scalable and more efficient droplet-motion-based energy transducer devices.

physics.app-ph↗