IMVS: Interactive Medical Volume Segmentation with Test-Time Adaptation - A New Method for Annotating Radiology Datasets
为解决3D医学影像标注耗时问题，提出IMVS方法，通过人机交互、在线微调和跨切片传播提高标注效率。
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Quantum Query Algorithms for the Constructive Diagonal Ramsey Theorem
The constructive diagonal Ramsey problem asks, given adjacency-oracle access to an $N$-vertex graph, for a clique or independent set of the order guaranteed by Ramsey's theorem. We give a bounded-error quantum algorithm that, for every $K\ge2$ and $N\ge4^{K-1}$, finds and verifies a homogeneous $K$-set using $O\!\left(2^K K\log\frac K\eta\right)$ edge queries with failure probability at most $\eta$. At the Ramsey scale $N=2^n$, this yields a homogeneous set of order $\lfloor n/2\rfloor+1$ using $O(\sqrt N\log N\log(\log N/\eta))$ queries, improving on the $O(N)$ queries of the explicit classical recursion and giving, to our knowledge, the first sublinear worst-case algorithm for the Ramsey relation. We also derive an $\Omega(N^{1/12})$ quantum lower bound by a reduction from collision finding. The algorithm runs the constructive recursion over implicit candidate sets. Each set is represented by a short conjunction of adjacency constraints and sampled using capped unknown-solution quantum search, and a scale-aware concentration schedule balances estimation accuracy against the increasing cost of sampling deeper sets. We complement the upper bound with an $\Omega(N^{1-1/\sqrt2})$ randomized lower bound, transported from the random-Painter analysis of online Ramsey numbers, which holds on the uniform distribution $G(N,1/2)$. On that distribution a greedy quantum search uses only $\widetilde O(N^{1/4})$ queries, giving a provable polynomial quantum speedup for Ramsey search on random graphs. We also give an estimation-free size-biased recursion and extend it to every fixed number of edge colours.
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Quantum Query Complexity of Persistence Statistics in Graph Zigzags
We study the query complexity of estimating scalar summaries of zigzag bar lifetimes from snapshot-adjacency bits. For graphs $G_1,\ldots,G_m$ on $n$ labeled vertices, let $\ell_b$ be the snapshot lifetime of a degree-one bar $b$ of the intersection zigzag. For a probability generating function $\phi(x)=\mathbb{E}[x^R]$, the statistic $F_\phi=\sum_b\phi(\ell_b/m)$ includes normalized degree-$r$ total persistence and the mean generalized rank over a uniform time window. An exact identity underlies our algorithm: sample $R$ uniform times; the expected generalized rank between their minimum and maximum equals $F_\phi$. For graphs that rank is the circuit rank of an intersection graph, so a nonlinear barcode functional becomes an average of edge and component counts, and no barcode is computed. Without spectral-gap, homology-state, or QRAM assumptions, this gives a quantum estimator with additive error $\varepsilon n$ and $\widetilde O(\sqrt{m(K+n)}/\varepsilon)$ queries when a bound $K\ge F_\phi$ is supplied, against $\widetilde O(m\min\{n^2,(K+n)/\varepsilon^2\})$ classically, and an adaptive quantum variant with the same instance dependence. These estimators are optimal in two regimes. For every fixed power weight $x^r$, $r\ge2$, and for the uniform-window mean, the worst-case complexities are $\widetilde\Theta(n\sqrt m/\varepsilon)$ quantum and $\Theta(n^2m)$ classical. On sparse instances, under an explicit split-leakage promise met by power and binomial weights of logarithmic degree and the promise $F_\phi\le K$, they are $\widetilde\Theta(\sqrt{mK}/\varepsilon)$ and $\widetilde\Theta(m\min\{n^2,K/\varepsilon^2\})$. The classical lower bounds hold against fully adaptive algorithms, and fewer than $m$ such statistics cannot determine the positive-lifetime histogram. All bounds concern snapshot access; with an explicit update stream, near-linear full-barcode algorithms are known.
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Search and Rescue on the Plane
This study addresses an online search problem in the plane where an agent, starting from an arbitrary position and orientation, must locate an unknown target on the positive x-axis and transport it to the origin. Through competitive analysis, geometric modeling, and optimization theory, the work reveals a strategy phase transition induced by a critical angle θ* ≈ 15.6°: when the initial heading angle is below θ*, the optimal strategy involves first moving to a specific checkpoint before searching along the x-axis; otherwise, direct search is optimal. The paper provides an explicit formula for the checkpoint location as a function of the initial angle, derives a closed-form expression for the competitive ratio, and fully characterizes the structure of the optimal competitive algorithm for any initial orientation, thereby achieving theoretically optimal performance.
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Early Detection of Hardware Trojans Using Neural Controlled Differential Equations and Analysis of Power Traces
This study addresses the critical challenge of detecting dormant adaptive hardware Trojans prior to activation—a task that existing methods struggle with, posing significant security risks. To overcome this limitation, the work introduces neural controlled differential equations (NCDEs) for early Trojan detection, modeling normal circuit behavior exclusively from power consumption data of Trojan-free designs. By integrating linear discriminant analysis (LDA) with sliding-window temporal analysis, the proposed approach achieves high-accuracy classification among three states: Trojan-free, dormant Trojan, and activated Trojan. Notably, it circumvents the conventional reliance on post-activation features and demonstrates superior performance over state-of-the-art machine learning techniques on standard benchmarks, effectively identifying dormant Trojans whose signatures exceed a defined sensitivity threshold.
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IMVS: Interactive Medical Volume Segmentation with Test-Time Adaptation - A New Method for Annotating Radiology Datasets
为解决3D医学影像标注耗时问题，提出IMVS方法，通过人机交互、在线微调和跨切片传播提高标注效率。
0 citationsRead paper
Quantum Query Algorithms for the Constructive Diagonal Ramsey Theorem
The constructive diagonal Ramsey problem asks, given adjacency-oracle access to an $N$-vertex graph, for a clique or independent set of the order guaranteed by Ramsey's theorem. We give a bounded-error quantum algorithm that, for every $K\ge2$ and $N\ge4^{K-1}$, finds and verifies a homogeneous $K$-set using $O\!\left(2^K K\log\frac K\eta\right)$ edge queries with failure probability at most $\eta$. At the Ramsey scale $N=2^n$, this yields a homogeneous set of order $\lfloor n/2\rfloor+1$ using $O(\sqrt N\log N\log(\log N/\eta))$ queries, improving on the $O(N)$ queries of the explicit classical recursion and giving, to our knowledge, the first sublinear worst-case algorithm for the Ramsey relation. We also derive an $\Omega(N^{1/12})$ quantum lower bound by a reduction from collision finding. The algorithm runs the constructive recursion over implicit candidate sets. Each set is represented by a short conjunction of adjacency constraints and sampled using capped unknown-solution quantum search, and a scale-aware concentration schedule balances estimation accuracy against the increasing cost of sampling deeper sets. We complement the upper bound with an $\Omega(N^{1-1/\sqrt2})$ randomized lower bound, transported from the random-Painter analysis of online Ramsey numbers, which holds on the uniform distribution $G(N,1/2)$. On that distribution a greedy quantum search uses only $\widetilde O(N^{1/4})$ queries, giving a provable polynomial quantum speedup for Ramsey search on random graphs. We also give an estimation-free size-biased recursion and extend it to every fixed number of edge colours.
0 citationsRead paper
Quantum Query Complexity of Persistence Statistics in Graph Zigzags
We study the query complexity of estimating scalar summaries of zigzag bar lifetimes from snapshot-adjacency bits. For graphs $G_1,\ldots,G_m$ on $n$ labeled vertices, let $\ell_b$ be the snapshot lifetime of a degree-one bar $b$ of the intersection zigzag. For a probability generating function $\phi(x)=\mathbb{E}[x^R]$, the statistic $F_\phi=\sum_b\phi(\ell_b/m)$ includes normalized degree-$r$ total persistence and the mean generalized rank over a uniform time window. An exact identity underlies our algorithm: sample $R$ uniform times; the expected generalized rank between their minimum and maximum equals $F_\phi$. For graphs that rank is the circuit rank of an intersection graph, so a nonlinear barcode functional becomes an average of edge and component counts, and no barcode is computed. Without spectral-gap, homology-state, or QRAM assumptions, this gives a quantum estimator with additive error $\varepsilon n$ and $\widetilde O(\sqrt{m(K+n)}/\varepsilon)$ queries when a bound $K\ge F_\phi$ is supplied, against $\widetilde O(m\min\{n^2,(K+n)/\varepsilon^2\})$ classically, and an adaptive quantum variant with the same instance dependence. These estimators are optimal in two regimes. For every fixed power weight $x^r$, $r\ge2$, and for the uniform-window mean, the worst-case complexities are $\widetilde\Theta(n\sqrt m/\varepsilon)$ quantum and $\Theta(n^2m)$ classical. On sparse instances, under an explicit split-leakage promise met by power and binomial weights of logarithmic degree and the promise $F_\phi\le K$, they are $\widetilde\Theta(\sqrt{mK}/\varepsilon)$ and $\widetilde\Theta(m\min\{n^2,K/\varepsilon^2\})$. The classical lower bounds hold against fully adaptive algorithms, and fewer than $m$ such statistics cannot determine the positive-lifetime histogram. All bounds concern snapshot access; with an explicit update stream, near-linear full-barcode algorithms are known.
0 citationsRead paper
Search and Rescue on the Plane
This study addresses an online search problem in the plane where an agent, starting from an arbitrary position and orientation, must locate an unknown target on the positive x-axis and transport it to the origin. Through competitive analysis, geometric modeling, and optimization theory, the work reveals a strategy phase transition induced by a critical angle θ* ≈ 15.6°: when the initial heading angle is below θ*, the optimal strategy involves first moving to a specific checkpoint before searching along the x-axis; otherwise, direct search is optimal. The paper provides an explicit formula for the checkpoint location as a function of the initial angle, derives a closed-form expression for the competitive ratio, and fully characterizes the structure of the optimal competitive algorithm for any initial orientation, thereby achieving theoretically optimal performance.
0 citationsRead paper
Early Detection of Hardware Trojans Using Neural Controlled Differential Equations and Analysis of Power Traces
This study addresses the critical challenge of detecting dormant adaptive hardware Trojans prior to activation—a task that existing methods struggle with, posing significant security risks. To overcome this limitation, the work introduces neural controlled differential equations (NCDEs) for early Trojan detection, modeling normal circuit behavior exclusively from power consumption data of Trojan-free designs. By integrating linear discriminant analysis (LDA) with sliding-window temporal analysis, the proposed approach achieves high-accuracy classification among three states: Trojan-free, dormant Trojan, and activated Trojan. Notably, it circumvents the conventional reliance on post-activation features and demonstrates superior performance over state-of-the-art machine learning techniques on standard benchmarks, effectively identifying dormant Trojans whose signatures exceed a defined sensitivity threshold.
0 citationsRead paper