Best Arm Identification with Fairness Constraints on Subpopulations

We formulate, analyze and solve the problem of best arm identification with fairness constraints on subpopulations (BAICS). Standard best arm identification problems aim at selecting an arm that has the largest expected reward where the expectation is taken over the entire population. The BAICS problem requires that an selected arm must be fair to all subpopulations (e.g., different ethnic groups, age groups, or customer types) by satisfying constraints that the expected reward conditional on every subpopulation needs to be larger than some thresholds. The BAICS problem aims at correctly identify, with high confidence, the arm with the largest expected reward from all arms that satisfy subpopulation constraints. We analyze the complexity of the BAICS problem by proving a best achievable lower bound on the sample complexity with closed-form representation. We then design an algorithm and prove that the algorithm's sample complexity matches with the lower bound in terms of order. A brief account of numerical experiments are conducted to illustrate the theoretical findings

Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk

We study the trade-off between expectation and tail risk for regret distribution in the stochastic multi-armed bandit problem. We fully characterize the interplay among three desired properties for policy design: worst-case optimality, instance-dependent consistency, and light-tailed risk. We show how the order of expected regret exactly affects the decaying rate of the regret tail probability for both the worst-case and instance-dependent scenario. A novel policy is proposed to characterize the optimal regret tail probability for any regret threshold. Concretely, for any given $\alpha\in[1/2, 1)$ and $\beta\in[0, \alpha]$, our policy achieves a worst-case expected regret of $\tilde O(T^\alpha)$ (we call it $\alpha$-optimal) and an instance-dependent expected regret of $\tilde O(T^\beta)$ (we call it $\beta$-consistent), while enjoys a probability of incurring an $\tilde O(T^\delta)$ regret ($\delta\geq\alpha$ in the worst-case scenario and $\delta\geq\beta$ in the instance-dependent scenario) that decays exponentially with a polynomial $T$ term. Such decaying rate is proved to be best achievable. Moreover, we discover an intrinsic gap of the optimal tail rate under the instance-dependent scenario between whether the time horizon $T$ is known a priori or not. Interestingly, when it comes to the worst-case scenario, this gap disappears. Finally, we extend our proposed policy design to (1) a stochastic multi-armed bandit setting with non-stationary baseline rewards, and (2) a stochastic linear bandit setting. Our results reveal insights on the trade-off between regret expectation and regret tail risk for both worst-case and instance-dependent scenarios, indicating that more sub-optimality and inconsistency leave space for more light-tailed risk of incurring a large regret, and that knowing the planning horizon in advance can make a difference on alleviating tail risks

A short review on sleep scheduling mechanism in wireless sensor networks

Sleep scheduling, also known as duty cycling, which turn- s sensor nodes on and off in the necessary time, is a common train of thought to save energy. Sleep scheduling has become a significant mech- anism to prolong the lifetime of WSNs and many related methods have been proposed in recent years, which have diverse emphases and appli- cation areas. This paper classifies those methods in different taxonomies and provides a deep insight into them

Sequential Manipulation Planning on Scene Graph

We devise a 3D scene graph representation, contact graph+ (cg+), for efficient sequential task planning. Augmented with predicate-like attributes, this contact graph-based representation abstracts scene layouts with succinct geometric information and valid robot-scene interactions. Goal configurations, naturally specified on contact graphs, can be produced by a genetic algorithm with a stochastic optimization method. A task plan is then initialized by computing the Graph Editing Distance (GED) between the initial contact graphs and the goal configurations, which generates graph edit operations corresponding to possible robot actions. We finalize the task plan by imposing constraints to regulate the temporal feasibility of graph edit operations, ensuring valid task and motion correspondences. In a series of simulations and experiments, robots successfully complete complex sequential object rearrangement tasks that are difficult to specify using conventional planning language like Planning Domain Definition Language (PDDL), demonstrating the high feasibility and potential of robot sequential task planning on contact graph.Comment: 8 pages, 6 figures. Accepted by IROS 202

A 40-GHz Load Modulated Balanced Power Amplifier using Unequal Power Splitter and Phase Compensation Network in 45-nm SOI CMOS

© 2023 IEEE - All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TCSI.2023.3282731 ​​​​​​​In this work, a ten-way power-combined poweramplifier is designed using a load modulated balanced amplifier(LMBA)-based architecture. To provide the required magnitudeand phase controls between the main and control-signal paths ofthe LMBA, an unequal power splitter and a phase compensationnetwork are proposed. As proof of concept, the designed poweramplifier is implemented in a 45-nm SOI CMOS process. At 40GHz, it delivers a 25.1 dBm Psat with a peak power-addedefficiency (PAE) of 27.9%. At 6-dB power back-off level, itachieves 1.39 times drain efficiency enhancement over an idealClass-B power amplifier. Using a 200-MHz single-carrier 64-QAMsignal, the designed amplifier delivers an average output power of16.5 dBm with a PAE of 13.1% at an EVMrms of -23.9 dB andACPR of -25.3 dBc. The die size, including all testing pads, is only1.92 mm2. To the best of the authors’ knowledge, compared withthe other recently published silicon-based LMBAs, this designachieves the highest Psat.Peer reviewe