Effect of Respiration on the Characteristic Ratios of Oscillometric Pulse Amplitude Envelope in Blood Pressure Measurement

Systolic and diastolic blood pressures (BPs) are important physiological parameters for disease diagnosis. Systolic and diastolic characteristic ratios derived from oscillometric pulse waveform have been widely used to estimate automated non-invasive BPs in oscillometric BP measurement devices. The oscillometric pulse waveform is easily influenced by respiration, which may cause variability to the characteristic ratios and subsequently BP measurement. This study quantitatively investigated how respiration patterns (i.e., normal breathing and deep breathing) affect the systolic and diastolic characteristic ratios. The study was performed with clinical data collected from 39 healthy subjects, and each subject conducted BP measurements during normal and deep breathings. Analytical results showed that the systolic characteristic ratio increased significantly from 0.52 ± 0.13 under normal breathing to 0.58 ± 0.14under deep breathing (p < 0.05), and the diastolic characteristic ratio was not significantly affected from 0.75 ± 0.12 under normal breathing to 0.76 ± 0.13 under deep breathing (p = 0.48). In conclusion, deep breathing significantly affected the systolic characteristic ratio, suggesting that automated oscillometric BP device which is validated under resting condition should be strictly used for measurements under resting condition

Rare diseases in developing countries: Insights from China's collaborative network

Rare diseases (RDs) are complex conditions and a worldwide healthcare challenge. The healthcare policymakers in developing countries lack templates from countries at the same level of development. This article introduced and discussed the combination of top‐down strategies and bottom‐up interventions in addressing RDs in a developing country, China, as an example. The government leads the formulation of laws, policies, and guidance to coordinate national resources, while local authorities and nongovernment organisations (NGOs) are responsible for policy localisation and complement policy gaps. This article may inspire other developing countries of improving RD healthcare

Cascading Reinforcement Learning

Cascading bandits have gained popularity in recent years due to their applicability to recommendation systems and online advertising. In the cascading bandit model, at each timestep, an agent recommends an ordered subset of items (called an item list) from a pool of items, each associated with an unknown attraction probability. Then, the user examines the list, and clicks the first attractive item (if any), and after that, the agent receives a reward. The goal of the agent is to maximize the expected cumulative reward. However, the prior literature on cascading bandits ignores the influences of user states (e.g., historical behaviors) on recommendations and the change of states as the session proceeds. Motivated by this fact, we propose a generalized cascading RL framework, which considers the impact of user states and state transition into decisions. In cascading RL, we need to select items not only with large attraction probabilities but also leading to good successor states. This imposes a huge computational challenge due to the combinatorial action space. To tackle this challenge, we delve into the properties of value functions, and design an oracle BestPerm to efficiently find the optimal item list. Equipped with BestPerm, we develop two algorithms CascadingVI and CascadingBPI, which are both computationally-efficient and sample-efficient, and provide near-optimal regret and sample complexity guarantees. Furthermore, we present experiments to show the improved computational and sample efficiencies of our algorithms compared to straightforward adaptations of existing RL algorithms in practice

Tight Bounds on List-Decodable and List-Recoverable Zero-Rate Codes

In this work, we consider the list-decodability and list-recoverability of codes in the zero-rate regime. Briefly, a code $\mathcal{C} \subseteq [q]^n$ is $(p,\ell,L)$-list-recoverable if for all tuples of input lists $(Y_1,\dots,Y_n)$ with each $Y_i \subseteq [q]$ and $|Y_i|=\ell$ the number of codewords $c \in \mathcal{C}$ such that $c_i \notin Y_i$ for at most $pn$ choices of $i \in [n]$ is less than $L$; list-decoding is the special case of $\ell=1$. In recent work by Resch, Yuan and Zhang~(ICALP~2023) the zero-rate threshold for list-recovery was determined for all parameters: that is, the work explicitly computes $p_*:=p_*(q,\ell,L)$ with the property that for all $\epsilon>0$ (a) there exist infinite families positive-rate $(p_*-\epsilon,\ell,L)$-list-recoverable codes, and (b) any $(p_*+\epsilon,\ell,L)$-list-recoverable code has rate $0$. In fact, in the latter case the code has constant size, independent on $n$. However, the constant size in their work is quite large in $1/\epsilon$, at least $|\mathcal{C}|\geq (\frac{1}{\epsilon})^{O(q^L)}$. Our contribution in this work is to show that for all choices of $q,\ell$ and $L$ with $q \geq 3$, any $(p_*+\epsilon,\ell,L)$-list-recoverable code must have size $O_{q,\ell,L}(1/\epsilon)$, and furthermore this upper bound is complemented by a matching lower bound $\Omega_{q,\ell,L}(1/\epsilon)$. This greatly generalizes work by Alon, Bukh and Polyanskiy~(IEEE Trans.\ Inf.\ Theory~2018) which focused only on the case of binary alphabet (and thus necessarily only list-decoding). We remark that we can in fact recover the same result for $q=2$ and even $L$, as obtained by Alon, Bukh and Polyanskiy: we thus strictly generalize their work.Comment: Abstract shortened to meet the arXiv requiremen

Zero-Rate Thresholds and New Capacity Bounds for List-Decoding and List-Recovery

In this work we consider the list-decodability and list-recoverability of arbitrary q-ary codes, for all integer values of q ? 2. A code is called (p,L)_q-list-decodable if every radius pn Hamming ball contains less than L codewords; (p,?,L)_q-list-recoverability is a generalization where we place radius pn Hamming balls on every point of a combinatorial rectangle with side length ? and again stipulate that there be less than L codewords. Our main contribution is to precisely calculate the maximum value of p for which there exist infinite families of positive rate (p,?,L)_q-list-recoverable codes, the quantity we call the zero-rate threshold. Denoting this value by p_*, we in fact show that codes correcting a p_*+? fraction of errors must have size O_?(1), i.e., independent of n. Such a result is typically referred to as a "Plotkin bound." To complement this, a standard random code with expurgation construction shows that there exist positive rate codes correcting a p_*-? fraction of errors. We also follow a classical proof template (typically attributed to Elias and Bassalygo) to derive from the zero-rate threshold other tradeoffs between rate and decoding radius for list-decoding and list-recovery. Technically, proving the Plotkin bound boils down to demonstrating the Schur convexity of a certain function defined on the q-simplex as well as the convexity of a univariate function derived from it. We remark that an earlier argument claimed similar results for q-ary list-decoding; however, we point out that this earlier proof is flawed