arXiv subjects

Yushan Li

Publications and source records attributed to Yushan Li.

At least 19 recordsLinked to original sources

arXiv2026

Topology Inference for Immune System Networks by Using Cell Amount Data

Recent years have witnessed the advanced development of topology inference research, which helps elucidate the interaction relationships of components in many biological networks. This paper focuses on inferring the topology of a group of immune cells, based on the collected data from cell-depletion based experiments. The problem is very challenging due to i) the lack of standard analytical models for the cell interactions, and ii) the restrictive data availability determined by the huge experiment and time costs. To address these issues, we first leverage certain common knowledge and observations on the experiments to characterize three properties on the cell amounts during the interaction process: state non-negativity, ratio-based convergence, and triple signs of topology weights. Then, we construct a new model with simple structure and analytical convenience, and obtain sufficient conditions for the model to accommodate all three properties. Finally, based on the constructed model, we propose a constrained quadratic programming method to infer the topology from limited number of data pairs. Validation on experiment data demonstrate the effectiveness of the proposed method.

eess.SY↗

arXiv2026

Fundamental Limitations of Data-Driven Control: A Statistical Decision Perspective

Substantial research efforts have been devoted to the design of data-driven controllers; however, comparatively less is known about their statistical performance and fundamental limitations. This contribution develops a statistical decision framework for data-driven control, in which a controller is evaluated by its risk, defined as the expected performance degradation relative to the oracle model-based controller, and by its average risk over the parameter space. Within this framework, we propose a collection of design principles for data-driven controllers. We further derive lower bounds on risks by combining the bias-variance decomposition with the Cram\'er-Rao inequality. In particular, the optimal bias that attains the lower bound for the average risk is determined by calculus of variations, thereby making the bias-variance tradeoff in data-driven control explicit. Moreover, the derived bound reveals a ``waterbed'' effect in data-driven control: any improvement in risk relative to the lower bound over one region of the parameter space must be compensated by deterioration elsewhere. We illustrate the proposed framework on two canonical data-driven control problems: optimal feedforward control and the linear quadratic regulator benchmark. By comparing several representative data-driven controllers with the derived lower bounds, we sharpen the statistical interpretation of existing methods and reveal quantitative limitations that no controller design can avoid.

eess.SY↗

arXiv2026

HarmVideoBench: Benchmarking Harmful Video Understanding in Large Multimodal Models

Large vision-language models (LVLMs) have recently shown immense potential in automated content moderation, sparking growing interest in developing harmful-video benchmarks. However, we identify two primary limitations in existing works: 1) The multi-layered characteristics of harmful videos are overlooked. Existing benchmarks predominantly formulate evaluation as a binary classification task, failing to capture implicit or deep contextual harms. 2) Explanatory rationales are completely absent. Current frameworks measure exclusively whether a model flags a video correctly rather than explaining why, turning evaluation into a black box where models can succeed through superficial shortcuts. To address these problems, we present HarmVideoBench, a multi-layered diagnostic benchmark comprising 1,379 videos paired with 4,137 multiple-choice questions. HarmVideoBench benchmarks three hierarchical dimensions: Observable Evidence, Clip-Internal Meaning, and Beyond-Clip Reasoning, aiming to evaluate models' deep understanding beyond surface cues with carefully balanced and curated samples. We evaluate 19 leading models on HarmVideoBench to assess their multidimensional understanding of harmful videos. Moreover, we introduce BCR, a benchmark-aligned method that predicts reasoning boundaries and dynamically retrieves context only when needed. Experimental results show that BCR substantially improves the base model's performance in harmful video understanding, raising the macro average from 61.7 percent to a state-of-the-art 84.4 percent.

cs.CV↗

arXiv2026

Learnable Predictions Need Not Be Actionable: Proper Repair Dimension for Online Buying

Learnability and actionability are different requirements for online predictions. Littlestone dimension controls ordinary online learnability, but proper repair dimension controls actionable online buying. A concept class can be easy to learn in the standard mistake-bound sense and still be hard to maintain as a live actionable prediction once the algorithm is required to stay proper and to buy the realized action online. For a finite binary concept class, this paper defines the proper repair dimension PRD(H) by a dynamic program on version spaces. The value PRD(H) is exactly the optimal deterministic worst-case number of repairs for a proper learner that must keep a live hypothesis through every realizable labeled sequence. The paper proves Ldim(H) <= PRD(H) <= |H|-1, with tight examples: the full class on d coordinates has Ldim = PRD = d, while the universal coordinate class U_n has Ldim(U_n) = floor(log_2 n) and PRD(U_n) = n-1. This gap transfers directly to online buying. The paper builds a unit-cost actionable buying instance from every proper class H and proves that the terminal benchmark is uOPT = 1 while every deterministic proper actionable algorithm pays exactly 1 + PRD(H) in the worst case. For U_{2^d}, this gives deterministic cost 2^d despite Littlestone dimension d. The paper also gives a positive transfer theorem for componentized actionable prediction classes, showing that bounded repair dimension together with bounded PRD-load congestion yields controlled actual buying cost.

cs.DS↗

arXiv2026

Quotient Admission Algorithms for Witness-Supported Graph Windows

We formulate the quotient admission problem for finite graph-window rows. The input is a finite row set, an admissible evidence map, semantic labels, witness-support hypergraphs, and atom-level admissibility predicates. The output is a quotient decision on evidence atoms, with possible decisions certificate, residual, low-confidence, or blocked. The problem asks for the maximal guard-respecting atom-level decision map that uses no refinement beyond the admissible evidence partition. We prove an atom-union characterization of identifiable classes, give a witness-support hypergraph guard for certificate admission, characterize projected-label conflicts as blocked atoms, and present quotient admission algorithms with correctness, maximality, and complexity guarantees. With explicit evidence vectors and hyperedges, the algorithms run in expected O(B + I + n) time and space by hashing and deterministic O(B + I + n log n) time by sorting under a key-linear comparison model, where n is the number of rows, B is the total evidence encoding length, and I is the total hyperedge incidence size. We also prove a magnitude-only indistinguishability lower bound: any evaluator that observes only residual magnitudes fails on instances whose evidence atoms require different residual decisions after the magnitudes collapse them.

cs.DS↗

arXiv2026

Agents' Last Exam

Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows. This paper introduces Agents' Last Exam (ALE), a benchmark designed to evaluate AI agents on long horizon, economically valuable, real world tasks with verifiable outcomes. Developed in collaboration with 250+ industry experts, ALE covers non-physical industries defined with reference to O*NET / SOC 2018 (the U.S. federal occupational taxonomy). It is organized around a task taxonomy with 55 sub fields grouped into 13 industry clusters covering 1K+ tasks. Current results show that the hardest tier remains far from saturated: across mainstream harness and backbone configurations, the average full pass rate is below 1%. ALE is designed as a living benchmark: its task pool grows continuously as new workflows and industries are onboarded. More broadly, ALE is intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP relevant impact.

cs.AI↗

arXiv2026

Preserving Topology Privacy of Network Systems by Feedback: Conditions and Distributed Design

This paper develops a feedback-based method to preserve the topology privacy of consensus protocols in network systems. The key idea is to intentionally violate topology identifiability conditions, thereby preventing unique or accurate recovery of the true topology from available observations, while preserving the intended consensus behavior. This problem is challenging because the feedback magnitude directly reflects the privacy level of edges, while it is strongly coupled with the consensus convergence and constrained by local communications at each node. To begin with, we derive the feedback conditions of both partial and full observation cases, where the topology unsolvability from observation data is characterized in the former, and the solution space that enforces topology inaccuracy from data is constructed in the latter. Then, we propose a novel distributed topology modification design under limited privacy budgets, and establish the performance guarantees through a controllable tradeoff between the consensus deviation and the topology privacy. Finally, we develop a low-complexity heuristic algorithm to achieve optimal privacy preservation on existing edges. Comparative simulations validate the effectiveness and outperformance of the proposed preservation design.

eess.SY↗

arXiv2026

Can LLMs See Without Pixels? Benchmarking Spatial Intelligence from Textual Descriptions

Recent advancements in Spatial Intelligence (SI) have predominantly relied on Vision-Language Models (VLMs), yet a critical question remains: does spatial understanding originate from visual encoders or the fundamental reasoning backbone? Inspired by this question, we introduce SiT-Bench, a novel benchmark designed to evaluate the SI performance of Large Language Models (LLMs) without pixel-level input, comprises over 3,800 expert-annotated items across five primary categories and 17 subtasks, ranging from egocentric navigation and perspective transformation to fine-grained robotic manipulation. By converting single/multi-view scenes into high-fidelity, coordinate-aware textual descriptions, we challenge LLMs to perform symbolic textual reasoning rather than visual pattern matching. Evaluation results of state-of-the-art (SOTA) LLMs reveals that while models achieve proficiency in localized semantic tasks, a significant "spatial gap" remains in global consistency. Notably, we find that explicit spatial reasoning significantly boosts performance, suggesting that LLMs possess latent world-modeling potential. Our proposed dataset SiT-Bench serves as a foundational resource to foster the development of spatially-grounded LLM backbones for future VLMs and embodied agents. Our code and benchmark will be released at https://github.com/binisalegend/SiT-Bench .

cs.CV↗

arXiv2025

LISA-3D: Lifting Language-Image Segmentation to 3D via Multi-View Consistency

Text-driven 3D reconstruction requires masks that understand free-form instructions and remain stable under viewpoint changes. We present LISA-3D, a two-stage framework that adapts the instruction-following segmenter LISA with geometry-aware Low-Rank Adaptation (LoRA) layers while keeping the SAM-3D reconstructor frozen. During training, paired RGB-D frames and camera poses define a differentiable reprojection loss that enforces cross-view agreement without additional 3D-text annotations. At deployment, the adapted segmenter can produce an RGBA prompt for SAM-3D from one RGB image; when registered RGB-D views are available, optional logit fusion further improves the prompt. On ScanRefer and Nr3D, geometry-aware tuning improves both 2D masks and lifted 3D reconstructions while updating only 11.6M parameters. Our results separate geometry-aware training gains from optional multi-view inference gains, providing a modular route from language grounding to object-centric 3D reconstruction.

cs.CV↗

arXiv2025

Resistant Topology Inference in Consensus Networks: A Feedback-Based Design

Consensus networks are widely deployed in numerous civil and industrial applications. However, the process of reaching a common consensus among nodes can unintentionally reveal the network's topology to external observers by appropriate inference techniques. This paper investigates a feedback-based resistant inference design to prevent the topology from being inferred using data, while preserving the original consensus convergence. First, we characterize the conditions to preserve the original consensus, and introduce the ''accurate inference'' notion, which accounts for both the uniqueness of the solution to topology inference (solvability) and the deviation from the original topology (accuracy). Then, we employ invariant subspace analysis to characterize the solvability. Even when unique inference remains possible, we provide necessary and sufficient conditions for the feedback design to induce inaccurate inference, and give a Laplacian structure based distributed design. Simulations validate the effectiveness of the method.

eess.SY↗

arXiv2025

Beyond Flatlands: Unlocking Spatial Intelligence by Decoupling 3D Reasoning from Numerical Regression

Existing Vision Language Models (VLMs) architecturally rooted in "flatland" perception, fundamentally struggle to comprehend real-world 3D spatial intelligence. This failure stems from a dual-bottleneck: input-stage conflict between computationally exorbitant geometric-aware encoders and superficial 2D-only features, and output-stage misalignment where discrete tokenizers are structurally incapable of producing precise, continuous numerical values. To break this impasse, we introduce GEODE (Geometric-Output and Decoupled-Input Engine), a novel architecture that resolves this dual-bottleneck by decoupling 3D reasoning from numerical generation. GEODE augments main VLM with two specialized, plug-and-play modules: Decoupled Rationale Module (DRM) that acts as spatial co-processor, aligning explicit 3D data with 2D visual features via cross-attention and distilling spatial Chain-of-Thought (CoT) logic into injectable Rationale Tokens; and Direct Regression Head (DRH), an "Embedding-as-Value" paradigm which routes specialized control tokens to a lightweight MLP for precise, continuous regression of scalars and 3D bounding boxes. The synergy of these modules allows our 1.5B parameter model to function as a high-level semantic dispatcher, achieving state-of-the-art spatial reasoning performance that rivals 7B+ models.

cs.CV↗

arXiv2025

Inverse Inference on Cooperative Control of Networked Dynamical Systems

Recent years have witnessed the rapid advancement of understanding the control mechanism of networked dynamical systems (NDSs), which are governed by components such as nodal dynamics and topology. This paper reveals that the critical components in continuous-time state feedback cooperative control of NDSs can be inferred merely from discrete observations. In particular, we advocate a bi-level inference framework to estimate the global closed-loop system and extract the components, respectively. The novelty lies in bridging the gap from discrete observations to the continuous-time model and effectively decoupling the concerned components. Specifically, in the first level, we design a causality-based estimator for the discrete-time closed-loop system matrix, which can achieve asymptotically unbiased performance when the NDS is stable. In the second level, we introduce a matrix logarithm based method to recover the continuous-time counterpart matrix, providing new sampling period guarantees and establishing the recovery error bound. By utilizing graph properties of the NDS, we develop least square based procedures to decouple the concerned components with up to a scalar ambiguity. Furthermore, we employ inverse optimal control techniques to reconstruct the objective function driving the control process, deriving necessary conditions for the solutions. Numerical simulations demonstrate the effectiveness of the proposed method.

eess.SY↗

arXiv2025

Adaptive Detection of Fast Moving Celestial Objects Using a Mixture of Experts and Physical-Inspired Neural Network

Fast moving celestial objects are characterized by velocities across the celestial sphere that significantly differ from the motions of background stars. In observational images, these objects exhibit distinct shapes, contrasting with the typical appearances of stars. Depending on the observational method employed, these celestial entities may be designated as near-Earth objects or asteroids. Historically, fast moving celestial objects have been observed using ground-based telescopes, where the relative stability of stars and Earth facilitated effective image differencing techniques alongside traditional fast moving celestial object detection and classification algorithms. However, the growing prevalence of space-based telescopes, along with their diverse observational modes, produces images with different properties, rendering conventional methods less effective. This paper presents a novel algorithm for detecting fast moving celestial objects within star fields. Our approach enhances state-of-the-art fast moving celestial object detection neural networks by transforming them into physical-inspired neural networks. These neural networks leverage the point spread function of the telescope and the specific observational mode as prior information; they can directly identify moving fast moving celestial objects within star fields without requiring additional training, thereby addressing the limitations of traditional techniques. Additionally, all neural networks are integrated using the mixture of experts technique, forming a comprehensive fast moving celestial object detection algorithm. We have evaluated our algorithm using simulated observational data that mimics various observations carried out by space based telescope scenarios and real observation images. Results demonstrate that our method effectively detects fast moving celestial objects across different observational modes.

astro-ph.IM↗

arXiv2025

Image Pre-Processing Framework for Time-Domain Astronomy in the Artificial Intelligence Era

The rapid advancement of image analysis methods in time-domain astronomy, particularly those leveraging AI algorithms, has highlighted efficient image pre-processing as a critical bottleneck affecting algorithm performance. Image pre-processing, which involves standardizing images for training or deployment of various AI algorithms, encompasses essential steps such as image quality evaluation, alignment, stacking, background extraction, gray-scale transformation, cropping, source detection, astrometry, and photometry. Historically, these algorithms were developed independently by different research groups, primarily based on CPU architecture for small-scale data processing. This paper introduces a novel framework for image pre-processing that integrates key algorithms specifically modified for GPU architecture, enabling large-scale image pre-processing for different algorithms. To prepare for the new algorithm design paradigm in the AI era, we have implemented two operational modes in the framework for different application scenarios: Eager mode and Pipeline mode. The Eager mode facilitates real-time feedback and flexible adjustments, which could be used for parameter tuning and algorithm development. The pipeline mode is primarily designed for large scale data processing, which could be used for training or deploying of artificial intelligence models. We have tested the performance of our framework using simulated and real observation images. Results demonstrate that our framework significantly enhances image pre-processing speed while maintaining accuracy levels comparable to CPU based algorithms. To promote accessibility and ease of use, a Docker version of our framework is available for download in the PaperData Repository powered by China-VO, compatible with various AI algorithms developed for time-domain astronomy research.

astro-ph.IM↗

arXiv2024

First Discovery and Confirmation of PN Candidates Found from AI and Deep Learning Techniques Applied to VPHAS+ Survey Data

Context. We have developed deep learning (DL) and AI-based tools to search extant narrow-band wide-field H$\alpha$ surveys of the Galactic Plane for elusive planetary nebulae (PNe) which are hidden in dense star fields towards the Galactic center. They are faint, low-surface brightness, usually resolved sources, which are not discovered by previous automatic searches that depend on photometric data for point-like sources. These sources are very challenging to find by traditional visual inspection in such crowded fields and many have been missed. We have successfully adopted a novel 'Swin-Transformer' AI algorithm, which we described in detail in the preceding Techniques paper (Paper I). Aims. Here, we present preliminary results from our first spectroscopic follow-up run for 31 top-quality PN candidates found by the algorithm from the high-resolution H$\alpha$ survey VPHAS+. This survey has not yet undergone extensive manual, systematic searching. Methods. Our candidate PNe were observed with the SpUpNIC spectrograph on the 1.9 m telescope at the South African Astronomical Observatory (SAAO) in June 2023. We performed standard IRAF spectroscopic reduction and then followed our normal HASH PN identification and classification procedures. Results. Our reduced spectra confirmed that these candidates include 22 true, likely, and possible PNe (70.97\%), 3 emission-line galaxies, 2 emission-line stars, 2 late-type star contaminants, and 2 other H$\alpha$ sources including a newly identified detached fragment of SNR RCW 84. We present the imaging and spectral data of these candidates and a preliminary analysis of their properties. These data provide strong input to help evaluate and refine the behavior of the AI algorithm when searching for PNe in wide-field H$\alpha$ surveys.

astro-ph.GA↗

arXiv2024

Glass formation in mechanically interlocked ring polymers: the role of induced chain stiffness

Polymer-related materials exhibit rich glassy behaviors at different length scales due to their various molecular structures and topological constraints. Recent studies have identified transient interpenetration of the long-chain rings contributing to dynamic arrest on the center-of-mass level. Interpenetration of rings is proposed as an approach to facilitate glass formation in polymer melts. In this work, inspired by recent advances in the synthesis of mechanically interlocked polymers, we investigate glass transition on the nanometer-scale segments influenced by permanent interpenetration of rings using molecular dynamics simulations. We find that decreasing chain length in the mechanically interlocked system is equivalent to inducing an effective chain stiffness on the sub-rings. The induced stiffness provides a unified explanation for these unique structural features and transient dynamic arrest in the system of interlocked rings with rather short chains. Further, a crossover is observed in the scaling relation between localization and glassy depth upon cooling. Our work reveals a dynamic transition from weak to strong caging at the crossover temperature. According to the localization model, we demonstrate that the chain stiffness increases the critical temperature and oscillation distance, therefore leads to more fragile dynamics and deeper glassy state. These findings are consistent with the predictions of molecular simulations and theories for polymers with real local stiffness. Our work deepens the understanding of the role of induced stiffness on glass transition, and opens up a new direction to design rich glass materials by manipulating stiffness through mechanical bonds.

cond-mat.soft↗

arXiv2024

A Data-Driven Approach for Mitigating Dark Current Noise and Bad Pixels in Complementary Metal Oxide Semiconductor Cameras for Space-based Telescopes

In recent years, there has been a gradual increase in the performance of Complementary Metal Oxide Semiconductor (CMOS) cameras. These cameras have gained popularity as a viable alternative to charge-coupled device (CCD) cameras in a wide range of applications. One particular application is the CMOS camera installed in small space telescopes. However, the limited power and spatial resources available on satellites present challenges in maintaining ideal observation conditions, including temperature and radiation environment. Consequently, images captured by CMOS cameras are susceptible to issues such as dark current noise and defective pixels. In this paper, we introduce a data-driven framework for mitigating dark current noise and bad pixels for CMOS cameras. Our approach involves two key steps: pixel clustering and function fitting. During pixel clustering step, we identify and group pixels exhibiting similar dark current noise properties. Subsequently, in the function fitting step, we formulate functions that capture the relationship between dark current and temperature, as dictated by the Arrhenius law. Our framework leverages ground-based test data to establish distinct temperature-dark current relations for pixels within different clusters. The cluster results could then be utilized to estimate the dark current noise level and detect bad pixels from real observational data. To assess the effectiveness of our approach, we have conducted tests using real observation data obtained from the Yangwang-1 satellite, equipped with a near-ultraviolet telescope and an optical telescope. The results show a considerable improvement in the detection efficiency of space-based telescopes.

astro-ph.IM↗

arXiv2023

AI Techniques for Uncovering Resolved Planetary Nebula Candidates from Wide-field VPHAS+ Survey Data

AI and deep learning techniques are beginning to play an increasing role in astronomy as a necessary tool to deal with the data avalanche. Here we describe an application for finding resolved Planetary Nebulae (PNe) in crowded, wide-field, narrow-band H-alpha survey imagery in the Galactic plane. PNe are important to study late stage of stellar evolution of low to intermediate-mass stars. However, the confirmed ~3800 Galactic PNe fall far short of the numbers expected. Traditional visual searching for resolved PNe is time-consuming due to the large data size and areal coverage of modern astronomical surveys, especially those taken in narrow-band filters highlighting emission nebulae. To test and facilitate more objective, reproducible, efficient and reliable trawls for PNe candidates we have developed a new, deep learning algorithm. In this paper, we applied the algorithm to several H-alpha digital surveys (e.g. IPHAS and VPHAS+). The training and validation dataset was built with true PNe from the HASH database. After transfer learning, it was then applied to the VPHAS+ survey. We examined 979 out of 2284 survey fields with each survey field covering 1 * 1 deg^2. With a sample of 454 PNe from the IPHAS as our validation set, our algorithm correctly identified 444 of these objects (97.8%), with only 16 explicable 'false' positives. Our model returned ~20,000 detections, including 2637 known PNe and many other kinds of catalogued non-PNe such as HII regions. A total of 815 new high-quality PNe candidates were found, 31 of which were selected as top-quality targets for subsequent optical spectroscopic follow-up. Representative preliminary confirmatory spectroscopy results are presented here to demonstrate the effectiveness of our techniques with full details to be given in paper-II.

astro-ph.IM↗