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Sherry Wang

Publications and source records attributed to Sherry Wang.

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Small 3-fold blocking sets in $\mathrm{PG}(2,p^n)$

A $t$-fold blocking set of the finite Desarguesian plane $\mathrm{PG}(2,p^n)$, $p$ prime, is a set of points meeting each line of the plane in at least $t$ points. The minimum size of such sets is of interest for numerous reasons; however, even the minimum size of nontrivial blocking sets (i.e. $1$-fold blocking sets not containing a line) in \(\mathrm{PG}(2,p^n)\) is an open question when $n\geq 5$ is odd. For $n>1$ the conjectured lower bound for this size is $(p^n+p^{n(s-1)/s}+1)$, where $p^{n/s}$ is the size of the largest proper subfield of $\mathbb{F}_{p^n}$. Since the union of $t$ pairwise disjoint nontrivial blocking sets is a $t$-fold blocking set, it is conjectured that when $p^{n/s}$ is large enough w.r.t. $t$, then the minimum size of a $t$-fold blocking set in $\mathrm{PG}(2,p^n)$ is $t(p^n+p^{n(s-1)/s}+1)$. If $n$ is even, then the decomposition of the plane into disjoint Baer subplanes gives a $t$-fold blocking set of this size. However, for odd $n$, the existence of such sets is an unsolved problem in most cases. In this paper, we construct $3$-fold blocking sets of conjectured size. These blocking sets are obtained as the disjoint union of three linear blocking sets of R\'edei type, and they lie on the same orbit of the projectivity $(x:y:z)\mapsto (z:x:y)$.

math.CO

Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations

Sleep disorders have a major impact on both lifestyle and health. Effective sleep disorder prediction from lifestyle and physiological data can provide essential details for early intervention. This research utilizes three deep time series models and facilitates them with explainability approaches for sleep disorder prediction. Specifically, our approach adopts Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) for time series data analysis, and Temporal Fusion Transformer model (TFT). Meanwhile, the temporal attention mechanism and counterfactual explanation with SHapley Additive exPlanations (SHAP) approach are employed to ensure dependable, accurate, and interpretable predictions. Finally, using a large dataset of sleep health measures, our evaluation demonstrates the effect of our method in predicting sleep disorders.

cs.LG

Tau Polarization and Correlated Decays in Neutrino Experiments

We present the first fully differential predictions for tau neutrino scattering in the energy region relevant to the DUNE experiment, including all spin correlations and all tau lepton decay channels. The calculation is performed using a generic interface between the neutrino event generator Achilles and the publicly available, general-purpose collider event simulation framework Sherpa.

hep-ph

Password authentication schemes on a quantum computer

In a post-quantum world, where attackers may have access to full-scale quantum computers, all classical password-based authentication schemes will be compromised. Quantum copy-protection prevents adversaries from making copies of existing quantum software; we suggest this as a possible approach for designing post-quantum-secure password authentication systems. In this paper, we show an implementation of quantum copy-protection for password verification on IBM quantum computers. We also share our quantum computation results and analyses, as well as lessons learned.

quant-ph