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Yunyun Yang

Publications and source records attributed to Yunyun Yang.

10 recordsLinked to original sources
arXiv2026

A quick distributional way to reproduce some results of the Riemann zeta function

The evaluation of the Riemann zeta function at negative integers is a classical result typically obtained through analytic continuation or contour integration. In this paper, we present a novel and concise derivation of these special values by employing the theory of Cesàro limit of distributions, a generalized limit concept developed by Estrada, Kanwal, and Fulling. We use this tool to give a quick proof of the result that \[ ζ(-n)=-\frac{B_{n+1}}{n+1}, \] for $n\in\mathbb{N}^+.$ We also give a short discussion on $ζ^{\prime }(α)$ and compute the value of $ζ^{\prime}(0)$.

math.NT↗
arXiv2026

ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage

Smartphone scams are increasingly prevalent and typically manifest as multi-stage, cross-application processes with gradually emerging intent. Effective intervention thus requires anticipating scams before the intent becomes explicit. This is inherently challenging, as decisions must rely on partial trajectories with temporally distributed evidence. In this paper, we propose \textbf{ORACLE} Online Reasoning for Anticipating Cross-temporal Latent thrEats, the first agentic framework for early scam anticipation from \textit{streaming app-usage} trajectories. To support this setting, we curate a real-world long-horizon benchmark of streaming app-usage trajectories, covering 12 scam types, spanning extended periods (15 days on average), involving diverse applications (95 apps), and interleaving normal and scam behaviors. To address fragmented evidence, we introduce a self-evolving context manager that adaptively consolidates entity-centric interactions over time, enabling more effective reconstruction of cross-temporal evidence from partial observations. To enhance sensitivity to latent early-stage signals, we propose an on-policy self-distillation scheme in which a teacher model, conditioned on summarized anti-scam reflections and clues by skills, supervises a student model without access to such reflections. This scheme thereby distills evidence-informed knowledge and improves recognition of emerging fraud patterns from partial trajectories. Experiments show that \method{} consistently improves early scam anticipation, yielding timely warnings while reducing false alerts in realistic streaming scenarios.

cs.LG↗
arXiv2026

You Only Forward Once: An Efficient Compositional Judging Paradigm

Multimodal large language models (MLLMs) show strong potential as judges. However, existing approaches face a fundamental trade-off: adapting MLLMs to output a single score misaligns with the generative nature of MLLMs and limits fine-grained requirement understanding, whereas autoregressively generating judging analyses is prohibitively slow in high-throughput settings. Observing that judgment reduces to verifying whether inputs satisfy a set of structured requirements, we propose YOFO, a template-conditioned method that judges all requirements in a single forward pass. Built on an autoregressive model, YOFO accepts a structured requirement template and, in one inference step, produces a binary yes/no decision for each requirement by reading the logits of the final token associated with that requirement. This design yields orders-of-magnitude speedups while preserving interpretability. Extensive experiments show that YOFO not only achieves state-of-the-art results on standard recommendation datasets, but also supports dependency-aware analysis -- where subsequent judgments are conditioned on previous ones -- and further benefits from post-hoc CoT.

cs.AI↗
arXiv2025

SACNet: A Spatially Adaptive Convolution Network for 2D Multi-organ Medical Segmentation

Multi-organ segmentation in medical image analysis is crucial for diagnosis and treatment planning. However, many factors complicate the task, including variability in different target categories and interference from complex backgrounds. In this paper, we utilize the knowledge of Deformable Convolution V3 (DCNv3) and multi-object segmentation to optimize our Spatially Adaptive Convolution Network (SACNet) in three aspects: feature extraction, model architecture, and loss constraint, simultaneously enhancing the perception of different segmentation targets. Firstly, we propose the Adaptive Receptive Field Module (ARFM), which combines DCNv3 with a series of customized block-level and architecture-level designs similar to transformers. This module can capture the unique features of different organs by adaptively adjusting the receptive field according to various targets. Secondly, we utilize ARFM as building blocks to construct the encoder-decoder of SACNet and partially share parameters between the encoder and decoder, making the network wider rather than deeper. This design achieves a shared lightweight decoder and a more parameter-efficient and effective framework. Lastly, we propose a novel continuity dynamic adjustment loss function, based on t-vMF dice loss and cross-entropy loss, to better balance easy and complex classes in segmentation. Experiments on 3D slice datasets from ACDC and Synapse demonstrate that SACNet delivers superior segmentation performance in multi-organ segmentation tasks compared to several existing methods.

eess.IV↗
arXiv2025

Distributions in spaces with thick submanifolds

We present the construction of a theory of distributions (generalized functions) with a ``thick submanifold'', that is, a new theory of thick distributions on $\mathbb{R}^n$ whose domain contains a smooth submanifold on which the test functions may be singular. We define several operations, including ``thick partial derivatives'', and clarify their connection with their classical counterparts in Schwartz distribution theory. We also introduce and study a number of special thick distributions, including new thick delta functions, or more generally thick multilayer distributions along a submanifold.

math.FA↗
arXiv2024

Impact of genetic polymorphisms on tacrolimus concentrations and intra-individual variability in recipients of heart transplants during the early post-heart transplantation period

This study aimed to investigate the effects of genetic polymorphisms on tacrolimus blood levels and intra-individual variability in recipients of heart transplants during the early post-transplantation period. Demographic information, concomitant medications, daily tacrolimus dose, trough concentration, and physiological and biochemical information of 87 Chinese recipients of heart transplants were collected. Trough concentrations were determined using a chemiluminescent micro-particle immunoassay, and 17 selected single nucleic acid polymorphisms were genotyped by direct sequencing. We assessed intra-individual variability by calculating the coefficient of variation of tacrolimus trough concentration and analyzed factors associated with tacrolimus concentration and intra-individual variability. Our study found that low body weight and a high percentage of neutrophils significantly influenced the coefficient of variation of tacrolimus. CYP3A5*1D and CYP3A7 rs776744 haplotypes correlated significantly with an intra-individual coefficient of variation of tacrolimus trough concentration during the early postoperative period. Patients with the CYP3A5*1D rs15524 and CYP3A7 rs776744 TT haplotype had a higher coefficient of variation than carriers of the C allele. Genetic polymorphisms in recipients of heart transplants affect tacrolimus metabolism, significantly affecting tacrolimus blood concentration during the early postoperative period. Genotyping before drug administration can help optimize dosing regimens, reduce intra-individual variability, and improve prognosis.

q-bio.QM↗
arXiv2020

The Fourier transform of thick distributions

We first construct a space $\mathcal{W}\left( \mathbb{R}_{\text{c}} ^{n}\right) $ whose elements are test functions defined in $\mathbb{R} _{\text{c}}^{n}=\mathbb{R}^{n}\cup\left\{ \mathbf{\infty}\right\} ,$ the one point compactification of $\mathbb{R}^{n},$ that have a thick expansion at infinity of special logarithmic type, and its dual space $\mathcal{W}^{\prime }\left( \mathbb{R}_{\text{c}}^{n}\right) ,$ the space of $sl-$thick distributions. We show that there is a canonical projection of $\mathcal{W} ^{\prime}\left( \mathbb{R}_{\text{c}}^{n}\right) $ onto $\mathcal{S} ^{\prime}\left( \mathbb{R}^{n}\right) .$ We study several $sl-$thick distributions and consider operations in $\mathcal{W}^{\prime}\left( \mathbb{R}_{\text{c}}^{n}\right) .$ We define and study the Fourier transform of thick test functions of $\mathcal{S}_{\ast}\left( \mathbb{R}^{n}\right) $ and thick tempered distributions of $\mathcal{S}_{\ast}^{\prime}\left( \mathbb{R}^{n}\right) .$ We construct isomorphisms \[ \mathcal{F}_{\ast}:\mathcal{S}_{\ast}^{\prime}\left( \mathbb{R}^{n}\right) \longrightarrow\mathcal{W}^{\prime}\left( \mathbb{R}_{\text{c}}^{n}\right) \,, \] \[ \mathcal{F}^{\ast}:\mathcal{W}^{\prime}\left( \mathbb{R}_{\text{c}} ^{n}\right) \longrightarrow\mathcal{S}_{\ast}^{\prime}\left( \mathbb{R} ^{n}\right) \,, \] that extend the Fourier transform of tempered distributions, namely, $Π\mathcal{F}_{\ast}=\mathcal{F}Π$ and $Π\mathcal{F}^{\ast} =\mathcal{F}Π,$ where $Π$ are the canonical projections of $\mathcal{S} _{\ast}^{\prime}\left( \mathbb{R}^{n}\right) $ or $\mathcal{W}^{\prime }\left( \mathbb{R}_{\text{c}}^{n}\right) $ onto $\mathcal{S}^{\prime}\left( \mathbb{R}^{n}\right) .$ We determine the Fourier transform of several finite part regularizations and of general thick delta functions.

math.FA↗
arXiv2020

Reconstruction of the one-dimensional thick distribution theory

The theory of thick distributions (both in dimension 1 and in higher dimensions) was constructed in recent years [7, 27]. However this theory of distributions with one thick point in dimension one is very different from that in higher dimensions. In this paper the author uses the language of asymptotic analysis to reconstruct the 1-dimensional thick distribution theory and to incorporate it into the framework of the higher-dimensional thick distribution theory. Some new concepts and interesting results appear in this paper from viewing singular functions in a different way.

math.FA↗