5,099 research outputs found
Codes for Correcting Asymmetric Adjacent Transpositions and Deletions
Codes in the Damerau--Levenshtein metric have been extensively studied
recently owing to their applications in DNA-based data storage. In particular,
Gabrys, Yaakobi, and Milenkovic (2017) designed a length- code correcting a
single deletion and adjacent transpositions with at most
bits of redundancy. In this work, we consider a new setting where both
asymmetric adjacent transpositions (also known as right-shifts or left-shifts)
and deletions may occur. We present several constructions of the codes
correcting these errors in various cases. In particular, we design a code
correcting a single deletion, right-shift, and left-shift errors
with at most bits of redundancy where . In
addition, we investigate codes correcting -deletions, right-shift,
and left-shift errors with both uniquely-decoding and list-decoding
algorithms. Our main contribution here is the construction of a list-decodable
code with list size and with at most bits of redundancy, where . Finally, we construct
both non-systematic and systematic codes for correcting blocks of -deletions
with -limited-magnitude and adjacent transpositions
Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits
We study the Pareto frontier of two archetypal objectives in multi-armed
bandits, namely, regret minimization (RM) and best arm identification (BAI)
with a fixed horizon. It is folklore that the balance between exploitation and
exploration is crucial for both RM and BAI, but exploration is more critical in
achieving the optimal performance for the latter objective. To this end, we
design and analyze the BoBW-lil'UCB algorithm. Complementarily, by
establishing lower bounds on the regret achievable by any algorithm with a
given BAI failure probability, we show that (i) no algorithm can simultaneously
perform optimally for both the RM and BAI objectives, and (ii)
BoBW-lil'UCB achieves order-wise optimal performance for RM or BAI
under different values of . Our work elucidates the trade-off more
precisely by showing how the constants in previous works depend on certain
hardness parameters. Finally, we show that BoBW-lil'UCB outperforms a close
competitor UCB (Degenne et al., 2019) in terms of the time complexity
and the regret on diverse datasets such as MovieLens and Published Kinase
Inhibitor Set.Comment: 43 pages, 10 figure
Learning Regularized Graphon Mean-Field Games with Unknown Graphons
We design and analyze reinforcement learning algorithms for Graphon
Mean-Field Games (GMFGs). In contrast to previous works that require the
precise values of the graphons, we aim to learn the Nash Equilibrium (NE) of
the regularized GMFGs when the graphons are unknown. Our contributions are
threefold. First, we propose the Proximal Policy Optimization for GMFG
(GMFG-PPO) algorithm and show that it converges at a rate of
after iterations with an estimation oracle, improving on a previous work by
Xie et al. (ICML, 2021). Second, using kernel embedding of distributions, we
design efficient algorithms to estimate the transition kernels, reward
functions, and graphons from sampled agents. Convergence rates are then derived
when the positions of the agents are either known or unknown. Results for the
combination of the optimization algorithm GMFG-PPO and the estimation algorithm
are then provided. These algorithms are the first specifically designed for
learning graphons from sampled agents. Finally, the efficacy of the proposed
algorithms are corroborated through simulations. These simulations demonstrate
that learning the unknown graphons reduces the exploitability effectively
Designing and Optimizing a Healthcare Kiosk for the Community
Investigating new ways to deliver care, such as the use of self-service kiosks to collect and monitor signs of wellness, supports healthcare efficiency and inclusivity. Self-service kiosks offer this potential, but there is a need for solutions to meet acceptable standards, e.g., provision of accurate measurements. This study investigates the design and optimization of a prototype healthcare kiosk to collect vital signs measures. The design problem was decomposed, formalized, focused and used to generate multiple solutions. Systematic implementation and evaluation allowed for the optimization of measurement accuracy, first for individuals and then for a population. The optimized solution was tested independently to check the suitability of the methods, and quality of the solution. The process resulted in a reduction of measurement noise and an optimal fit, in terms of the positioning of measurement devices. This guaranteed the accuracy of the solution and provides a general methodology for similar design problems
Free-hand thoracic pedicle screws placed by neurosurgery residents: a CT analysis
Free-hand thoracic pedicle screw placement is becoming more prevalent within neurosurgery residency training programs. This technique implements anatomic landmarks and tactile palpation without fluoroscopy or navigation to place thoracic pedicle screws. Because this technique is performed by surgeons in training, we wished to analyze the rate at which these screws were properly placed by residents by retrospectively reviewing the accuracy of resident-placed free-hand thoracic pedicle screws using computed tomography imaging. A total of 268 resident-placed thoracic pedicle screws was analyzed using axial computed tomography by an independent attending neuroradiologist. Eighty-five percent of the screws were completely within the pedicle and that 15% of the screws violated the pedicle cortex. The majority of the breaches were lateral breaches between 2 and 4 mm (46%). There was no clinical evidence of neurovascular injury or injury to the esophagus. There were no re-operations for screw replacement. We concluded that under appropriate supervision, neurosurgery residents can safely place free-hand thoracic pedicle screws with an acceptable breach rate
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