arXiv subjects

Ying Wang

Publications and source records attributed to Ying Wang.

At least 19 recordsLinked to original sources

arXiv2026

The w-value: Market Implementation and gamma-Core Stability in Pollution-Control Games

This paper proposes a novel solution concept, called the w-value, for cooperative pollution-control games. The w-value is axiomatically founded on three principles: Pareto Optimality (PO), Market Equilibrium (ME), and Fiscal Balance (FB), which together yield an efficient and implementable welfare distribution. We establish its existence and uniqueness under standard concavity and convexity assumptions, and provide a market mechanism that implements it through price constraints and an initial permit allocation. A key theoretical advantage is its strong stability: the w-value belongs to the gamma-core, which makes it resistant to coalitional deviations. Moreover, the w-value can be obtained through a simple step-by-step procedure, which facilitates its application to complex real-world problems. A numerical illustration demonstrates the step-by-step calculation process and confirms the gamma-core membership numerically. Finally, we apply the concept to international climate negotiations, showing how it provides a theoretical rationale for the shift from rigid mandatory emission reduction systems toward the flexible structure of Nationally Determined Contributions.

econ.TH↗

arXiv2026

RapidMitDet: Boosting Computational Efficiency for Mitosis Detection via Magnification Alignment

Automated mitosis detection is a critical research focus, enabling pathologists to rapidly assess mitotic indices for tumor malignancy evaluation. However, most of the existing detection approaches suffer from significant computational inefficiency due to the giga-pixel image resolution of Whole-Slide Images (WSIs), which greatly hinders their clinical applicability. In this study, we propose RapidMitDet, a computational efficient mitosis detection approach. Our method employs a coarse-to-fine framework, first identifying mitotic candidates at lower magnification and then confirming them at higher magnification. In the coarse-detection stage, we introduce magnification-aligned knowledge distillation to reduce the information gap between magnification levels while minimizing the performance degradation in the lower magnification detection model. In the subsequent fine-classification stage, we propose adaptive triplet learning (ATL) to optimize infomative sample selection, thereby enhancing discrimination between mitotic and non-mitotic instances. Experimental results show that RapidMitDet achieves the optimal accuracy-efficiency balance at 20x magnification, improving computational speed by up to 5.62 times over the latest state-of-the-art (SOTA) methods. Due to its lightweight model design, RapidMitDet can also deliver efficient detection at higher magnification (40x) while achieving even higher detection performance. In WSI-level evaluations, our method shows strong agreement with pathologists' diagnoses (ICCs: 0.879 (40x), 0.822 (20x)) while maintaining superior efficiency, highlighting its potential for clinical application. Code is available at https://github.com/logincat/RapidMitDet

eess.IV↗

arXiv2026

Antenna Positioning and Beamforming Optimization in MA Enabled Secure ISAC Systems: A Gradient-Based Meta Learning Approach

Integrated sensing and communications (ISAC) significantly improves spectral efficiency but introduces security risks regarding the interception of embedded communication signals. This paper proposes an movable antenna (MA)-enabled secure ISAC system that utilizes the spatial degrees of freedom of MA to mitigate these risks. Then, a problem is formulated to maximize the system secrecy rate by jointly optimizing antenna positioning, transmit beamforming, and artificial noise. However, the principal challenge arises from the non-convexity of the optimization problem and the strong coupling of the optimization variables. Generally, traditional optimization methods for this problem suffer from complex mathematical derivations, while existing deep learning approaches rely heavily on the training data distribution. To address these issues, we introduce a gradient-based meta learning (GML) algorithm, which works without pre-training and demonstrates favorable performance. Specifically, the algorithm establishes a neural network for each optimization variable, where the gradient of the objective function with respect to the variable serves as the input, and the output of the network determines the variable's update step. By handling the constraints and constructing penalty terms, the global loss function is used to guide the optimization process. Extensive numerical simulations confirm that the proposed algorithm achieves satisfactory performance in terms of both communication security and sensing capabilities.

eess.SP↗

arXiv2026

A Common Language for Dynamic Graphs: Interaction Tokenization Enables Foundation Models

Dynamic graphs capture evolving interactions in Web systems, including online communication, user engagement, and recommendation. Graph foundation models aim to reuse predictive capabilities across networks, but extending this capability to dynamic graphs faces two challenges. First, node identities are graph-specific, and similar interaction patterns can appear at different temporal and activity scales, making histories difficult to represent consistently across networks. Second, the predictive roles of historical patterns vary across interaction dynamics, limiting transfer from a narrow range of training processes. In this paper, we show that a common interaction language, combined with learning across diverse interaction processes, offers a basis for dynamic graph foundation modeling. We propose CL-DGFM, a Common Language Dynamic Graph Foundation Model for temporal link prediction. Its Dynamic Interaction Tokenizer encodes interaction memory, historical connectivity, and ordered destination transitions into calibrated tabular inputs, making dynamic histories accessible to existing predictors without an additional trainable graph encoder. Its Dynamic Interaction Prior trains a pretrained tabular foundation predictor across diverse synthetic dynamics. Labeled historical context guides prediction, with a Contextual Value Adapter modulating attention values. The model transfers to real graphs without target-graph parameter updates. Experiments on TGB and TGB-Seq demonstrate competitive performance. Further comparisons show that the common language supports basic machine learning and neural models, and enables direct use of frozen tabular foundation models on dynamic graphs.

cs.LG↗

arXiv2026

Tracking analysis of compressed-communication distributed adaptive estimation for time-varying parameters

This paper studies the problem of distributed time-varying parameter tracking over communication-constrained networks. To mitigate the accumulation of induced compression errors across the network, we propose a differentially compressed distributed least mean squares (LMS) algorithm.In the proposed scheme, each sensor updates a local auxiliary state recursively based on the compressed difference between the current parameter estimate and the previous auxiliary state, and then transmits only this compressed difference to its neighbors for auxiliary-state reconstruction. This differential recursion mechanism helps control the accumulation of compression errors and accommodates a general class of randomized compressors including randomized quantizers and sparsifiers.We establish the mean-square boundedness and explicit steady-state tracking-error bound under a cooperative excitation condition on stochastic regression vectors, which captures both temporal and spatial information to reflect the joint excitation effect of sensors.Notably, our stability analysis requires neither temporal independence nor stationarity of regressors, nor persistent excitation at individual nodes, thereby accommodating correlated and nonstationary signals from stochastic feedback systems. Numerical simulations demonstrate the theoretical results under correlated and non-stationary regression data.

eess.SY↗

arXiv2026

Movable Antennas Enabled Wireless Powered Networks: Principles and Technologies

As an emerging framework, movable antenna (MA)-enabled wireless powered networks (WPNs) have attracted growing attention. WPNs integrate wireless communication and energy transfer. MA can dynamically adjust the position of antenna units by introducing additional spatial degrees of freedom, so as to make full use of channel gain, optimize the effect of energy beamforming, and further improve the performance of WPNs. In this article, we first classify the implementations of MA by driving architectures, and review the fundamental principles of WPNs. We then highlight the key advantages of MA-enabled WPNs in enhancing wireless power transfer efficiency, realizing flexible and adaptive beamforming, and improving system robustness and interference resilience. Furthermore, four representative application scenarios and three key enabling technologies are discussed. A case study is also presented to show the reduction in transmit power brought by MA for WPNs. Finally, we discuss the challenges and future directions of MA-enabled WPNs, aiming to provide reference for future research and practice.

cs.NI↗

arXiv2026

Nano-ARPES observation of a flat band at the \bar{M} point in 2.4° twisted bilayer graphene

The emergence of flat bands in twisted bilayer graphene at the so-called magic angle reveals a range of intriguing phases, such as superconductivity and Mott insulating states, resulting from electrons entering a correlated state. This establishes twisted bilayer graphene as an excellent platform for exploring correlation effects in many-body phases and across various twist angles. However, in most cases, the twist angle is not an easy parameter to control because it exhibits structural instabilities such as relaxation and reconstruction that reduce the correlation effects. Here, we report the unexpected appearance of a flat band at the \bar{M} point far from the Fermi level in a 2.4° twisted bilayer graphene, directly visualized by angle-resolved photoemission spectroscopy. Our work is the first observation of flat bands away from the \bar{K} point in twisted bilayer graphene and highlights the need to probe the entire Brillouin zone to fully capture electronic states in twisted structures.

cond-mat.mes-hall↗

arXiv2026

Dual Effect and Certainty Equivalence Revisited for a Linear-Gaussian Dual Control Problem

This paper revisits the relation between dual effect and certainty equivalence (CE) through a scalar linear-Gaussian model. In dual control, the current input affects not only the immediate cost but also the information available for future decisions, and hence the future cost. Dynamic programming (DP) provides a natural framework for formulating dual control problems. At each step of the Bellman recursion, an optimal input minimizes the sum of the stage cost and the expected cost-to-go. We restrict attention to stage costs minimized by the CE input. However, CE may not minimize the Bellman objective because of the input-dependent expected cost-to-go. Classical no-dualeffect (NDE) conditions guarantee CE optimality in certain problem classes by requiring the conditional distribution of the estimation error to be independent of past controls. In the present model, we observe that CE can remain optimal at the level of a single Bellman recursion even when the classical NDE conditions fail, provided that the expected cost-to-go is independent of the current input. Motivated by this observation, we introduce a notion of NDE formulated directly at the level of the Bellman objective, which we call Bellman-side NDE. Under some mild regularity conditions, we show that Bellman-side NDE can be characterized by a heat equation. This characterization identifies the class of Bellman-side NDE functions, and further yields a decomposition of a generic cost-to-go function into a Bellman-side NDE component and a dual-effect component.

math.OC↗

arXiv2026

3D-MoE: Complementarity-Guided Hardware-Software Co-Design for MoE Decode on 3D-Stacked NMP

Mixture-of-Experts (MoE) have become a mainstream way to scale Large Language Models (LLMs), because each token activates only a small subset of experts, reducing inference cost. However, MoE decode remains highly memory-bound and exhibits strongly skewed, time-varying expert loads. 3D-stacked DRAM near-memory processing (NMP) provides the bandwidth that decode demands, yet its tiled organization makes load balancing expensive: expert parallelism (EP) preserves locality but suffers from hotspot-induced imbalance, while more distributed strategies weaken locality and increase NoC traffic. To address this locality-balance conflict, we observe that domain-specialized token routing creates offsetting expert-load fluctuations across decode steps, a property we call expert complementarity. Using this insight, we propose 3D-MoE, a hardware--software co-designed system for MoE decode on 3D-stacked NMP. 3D-MoE uses offline, one-time complementarity-guided grouping and locality-aware mapping to establish coarse-grained inter-group balance, handles residual intra-group hotspots through a lightweight Collaborative Memory-Network (CMN) that streams each hot expert's weight tiles once from its host DRAM channel to helper cores over a reconfigured local broadcast path, and uses cost-aware online remapping to remain robust to workload drift. Across MoE models, 3D-MoE achieves an average 4.63$\times$ MoE-layer speedup over EP and improves end-to-end throughput by 1.58$\times$--1.86$\times$ over the strongest NMP baselines, with 2.11\% additional logic-die area.

cs.AR↗

arXiv2026

Joint Beamforming Optimization and Dynamic Tracking in RIS-Enabled Secure ISAC Systems

This paper investigates a reconfigurable intelligent surface (RIS)-enabled secure integrated sensing and communication (ISAC) system, where the direct links between the base station (BS) and users are blocked, and the mobile eavesdropper is treated as the sensing target. The mobility of the eavesdropper induces time-varying wiretap channels, potentially compromising secure transmission when outdated eavesdropper information is used. To address this challenge, the BS dynamically tracks the eavesdropper over consecutive time slots and exploits the predicted state information to adapt its transmission strategy. A sum secrecy rate maximization problem is formulated by jointly optimizing the BS beamforming, artificial noise, and RIS reflection coefficients, subject to a constraint on the tracking error covariance to ensure the required sensing accuracy. To tackle the resulting nonconvex optimization problem with temporal coupling, an iterative algorithm integrating an extended Kalman filter (EKF) with an alternating optimization framework is developed. Specifically, the EKF recursively predicts and updates the eavesdropper's state, while the joint optimization problem is decomposed into four tractable subproblems that are solved iteratively. Simulation results demonstrate that the proposed algorithm achieves improved secrecy performance while effectively tracking the trajectory of the mobile eavesdropper, compared with benchmark schemes.

eess.SP↗

arXiv2026

Magnetic uncertainty in variable geometry

In this paper, we study Hardy-type uncertainty principles and unique continuation properties for linear covariant Schrodinger equations with variable coefficients in the presence of bounded electric and magnetic potentials. Under suitable smallness assumptions on the leading coefficients, we prove that any solution exhibiting super-quadratic exponential decay at two distinct times must vanish identically. Under an additional structural assumption on the coefficient matrix $G$, we further establish a Hardy-type result at the quadratic exponential scale. We also obtain an analogous uniqueness result for the heat equation with variable-coefficient magnetic perturbations. Our results unify and extend previous works in two directions: they recover the constant-coefficient covariant case treated by Barcelo-Fanelli-Gutierrez-Ruiz-Vilela when $G=I$, and the variable-coefficient non-magnetic case considered by Federico-Li-Yu when $A=0$. The proofs combine logarithmic convexity arguments with Carleman estimates adapted to variable-coefficient covariant Schrödinger and parabolic flows. Although our approach follows the general strategy introduced by Escauriaza-Kenig-Ponce-Vega, substantial new difficulties arise from the interaction between the variable metric and the magnetic structure, which requires new weight functions and refined commutator estimates.

math.AP↗

arXiv2026

Proof of Barry's Four Hankel Determinant Conjectures

Barry introduced a central transform of integer sequences and proposed four conjectures concerning the Hankel transforms of central transform of four rational families. We prove these four conjectures. The proofs are unified within a common algebraic framework: we interpret the Hankel determinants as Gram determinants and use a basis of shifted monic Chebyshev polynomials to reveal the finite-band structure of the associated Gram matrices.

math.CO↗

arXiv2026

Hankel Transform and $(α,β)$ Somos-4 Sequences

An $(α,β)$ Somos-$4$ sequence $S_n$ is defined by the recurrence $S_nS_{n-4}=αS_{n-1}S_{n-3}+βS_{n-2}^2$ ($n\geq 4$), with suitable initial values, where $α$ and $β$ are constant parameters. A widely studied question is the following: When does the Hankel transform of a generating function become an $(α,β)$ Somos-4 sequence? In particular, how can $α$ and $β$ be derived for such a function? A sufficient condition for this problem has been established by Wang and Zhang. In this paper, we obtain the following three main results. (i): We extend the Wang--Zhang sufficient condition by working over the rational function field. Then we combine this result with the Sulanke--Xin quadratic transformation to resolve all of Barry's currently unsolved $(α,β)$ Somos-4 conjectures, which arise in diverse contexts, including generalized Catalan recurrences, Riordan arrays, generalized Bernstein arrays, and elliptic curves. (ii): We show that the odd and even subsequences of an $(α,β)$ Somos-4 sequence are again $(α,β)$ Somos-4 sequences with transformed parameters. This is employed to establish Barry's Hurwitz transform conjecture. (iii): Using the theory of orthogonal polynomials, we prove a Hankel determinant formula and thereby prove a conjecture related to the $(α,β)$ Somos-4 sequence. In addition, we prove some conjectures on formulas for periodic Hankel determinants.

math.CO↗

arXiv2026

Counterexamples to the Mu--Welker recursive decomposition in every degree

The well-known open problem of Bell and Skandera asks whether a real-rooted polynomial $f(t)$ with positive integer coefficients and constant term one is the $f$-polynomial of a simplicial complex. Mu and Welker proved that if the recursive decomposition $f(t)=g(t)+th(t)$ satisfies the corresponding coefficient inequality $h_i<g_i$, then this open problem has an affirmative answer. Mu and Welker also conjectured that the real-rootedness of $f(t)$ implies that of $g(t)$ and $h(t)$. We give counterexamples to the conjecture of Mu and Welker for every degree at least three, and prove that the assertion holds in degrees $1$ and $2$. Moreover, each polynomial we construct is the $f$-polynomial of a simplicial complex.

math.CO↗

arXiv2026

Benchmarking and Enhancing Design-Aware Code Review in the Linux Kernel

Code review often requires reasoning beyond local correctness. In mature software systems, reviewers must recognize design trade-offs introduced by a proposed change, compare alternative solutions, and reason about their compatibility with project-specific constraints. We refer to this problem as design-aware code review. However, existing automated review approaches primarily focus on defects and correctness, while the design knowledge accumulated in historical review discussions remains difficult to reuse. To address these challenges, we present DesignLore, a knowledge-augmented approach that extracts design decisions from historical review discussions, organizes them into an adaptive design taxonomy, and reuses relevant historical knowledge to guide code review. We further construct DesignReviewBench, a benchmark derived from real patch-review histories for systematically evaluating design-aware code review. Our evaluation shows that design-aware review remains challenging for current coding agents, with the strongest baseline achieving an F1 of only 0.213 on design trade-off detection. In comparison, DesignLore achieves an F1 of 0.259, a 21.6% relative improvement, and consistently delivers gains across different agent systems and backbone. These results highlight the importance of design-aware review and demonstrate the potential of recovering and reusing historical design knowledge to support it.

cs.SE↗

arXiv2026

The Restricted Inverse Optimal Value Problem on Shortest Path under $l_\infty$ Norm on Trees

We study the restricted inverse optimal value problem on shortest paths under the weighted $l_\infty$ norm on trees (RIOVSPT$_\infty$): adjust edge weights at minimum worst-link cost so that the shortest root--leaf path is lower-bounded by a given value $D$, a restriction on a prescribed path $P_0$. Unlike the $l_1$ version minimizing total adjustment cost, the $l_\infty$ objective controls the worst link, the criterion relevant in bandwidth pricing. We characterize feasibility through the minimum cost shortest path interdiction problem (MCSPIT$_\infty$), whose optimal solution $\hat w$ splits the instance at the first edge of $P_0$ into two independent subproblems. Relative to $\hat w$, three cases arise. The first two admit closed-form optimal solutions. The third reduces to a parametric subproblem SPIT$^0_\infty(M)$: minimize the length of $P_0$ on the gate subtree under the budget $C(\hat w)+M$ with all other root--leaf paths kept at least $D$. It is solved exactly by an iterative greedy procedure. On its value function $V(M)$, the predicate $V(M)\leq D$ is equivalent to the nonnegativity of truncated-bound margins, which are monotone, continuous and piecewise affine; the smallest such $M$ gives the optimal value, located by binary search over the edge-bound breakpoints with an exact affine threshold evaluation. The same margin test on the full tree gives a uniform algorithm for all cases. Both algorithms run in $O(n\log n)$ time. Experiments on synthetic trees agree with a linear programming baseline and illustrate the two algorithms' distinct runtime profiles.

math.OC↗

arXiv2026

PMB-NN: Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes

Accurate, physiologically consistent glucose forecasts could help people with type 1 diabetes respond to impending excursions. We applied our physiological-model-based neural network (PMB--NN) framework to glucose forecasting. A temporal convolutional network (TCN) used 180 min of history, constrained during training by a personalized four-parameter physiological model. The main PMB--NN combined forecast and neural--physiological agreement losses; a three-term variant additionally fitted physiological predictions to measured glucose. Data comprised 6--8 weeks of continuous glucose monitoring, pump, and meal records from 25 AZT1D participants. Person-specific models used chronological 70/10/20 train/validation/test splits and 30-, 60-, and 120-min horizons. Median mean absolute errors were 16.24, 25.00, and 30.20 mg/dL with basal insulin records and 15.38, 24.21, and 31.79 mg/dL before these records became available. In the latter setting, PMB--NN had the lowest median error at 30 and 60 min; it outperformed the physiological model alone in all six settings. In input-increase tests, glucose predictions changed in the expected direction for 85\% of correction boluses and 100\% of carbohydrate rescues, versus 57\% for active insulin and 56\% for active carbohydrates. Most standalone physiological fits showed locally distinguishable parameter effects, with glucose-related coefficients more stable under resampling than insulin- or carbohydrate-related coefficients. The three-term variant improved hyperglycaemia accuracy with basal records, but did not significantly change overall MAE and reduced directional plausibility in three tests. PMB--NN combines personalized forecasting with an explicit physiological model, allowing assessment of predictive accuracy, physiological response behavior, and participant-specific coefficients.

physics.med-ph↗

arXiv2026

Learning to Compute Is Not Learning to Retain or Reuse in Looped Transformers

Looped transformers compute by applying one block repeatedly, so they can run for more loops than they were trained with. Recurrent reasoners are known to overthink: iterating past the trained depth can turn a correct answer into a wrong one. Such a late error can reflect failed computation, loss of retention, or a state that later computation cannot reuse. We separate these cases in in-context Mealy-machine execution, where a simulator supplies intermediate targets and exact counterfactuals for hidden-state interventions. Models trained to answer at loop 32 are fully correct there but keep only 22% of these answers at loop 64. Retention depends on what training asks for: supervising one randomly chosen position per example, instead of always the last, keeps 99.8%, and randomizing the number of training loops also helps; truncating gradients, as in progressive training against overthinking, does not. The computation itself is exact: position $k$ is computed at loop $k$, and swapping its loop-$k$ hidden vector for the one from another input redirects every later position to that input's exact continuation (237/237 pairs in each of ten models). Retention, however, is not reuse. Even in models that keep nearly all answers, holding each position $k$ fixed from loop $k$ drops accuracy to 4--6%, whereas holding it from loop $k+4$ is harmless. A released recurrent maze network trained to avoid overthinking behaves the same way. Learning to compute is thus not learning to retain, and avoiding overthinking does not make a state reusable when its answer first appears.

cs.LG↗