The global prevalence of neurologic disorders is rising, and yet we are still unable to deliver most drug molecules, in therapeutic quantities, to the brain. The blood brain barrier consists of a tight layer of endothelial cells surrounded by astrocyte foot processes, and these anatomic features constitute a significant barrier to drug transport from the blood to the brain. One way to bypass the blood brain barrier and thus treat diseases of the brain is to use the nasal route of administration and deposit drugs at the olfactory region of the nares, from where they travel to the brain via mechanisms that are still not clearly understood, with travel across nerve fibers and travel via a perivascular pathway both being hypothesized. The nose-to-brain route has been demonstrated repeatedly in preclinical models, with both solution and particulate formulations. The nose-to-brain route has also been demonstrated in human studies with solution and particle formulations. The entry of device manufacturers into the arena will enable the benefits of this delivery route to become translated into approved products. The key factors that determine the efficacy of delivery via this route include the following: delivery to the olfactory area of the nares as opposed to the respiratory region, a longer retention time at the nasal mucosal surface, penetration enhancement of the active through the nasal epithelia, and a reduction in drug metabolism in the nasal cavity. Indications where nose-to-brain products are likely to emerge first include the following: neurodegeneration, posttraumatic stress disorder, pain, and glioblastoma.

The problem of low-rank matrix reconstruction arises in various applications in communications and signal processing. The state of the art research largely focuses on the recovery techniques that utilize affine maps satisfying the restricted isometry property (RIP). However, the affine map design and reconstruction under a priori information, i.e., column or row subspace information, has not been thoroughly investigated. To this end, we present designs of affine maps and reconstruction algorithms that fully exploit the low-rank matrix subspace information. Compared to the randomly generated affine map, the proposed affine map design permits an enhanced reconstruction. In addition, we derive an optimal representation of low-rank matrices, which is exploited to optimize the rank and subspace of the estimate by adapting them to the noise level in order to achieve the minimum mean square error (MSE). Moreover, in the case when the subspace information is not a priori available, we propose a two-step algorithm, where, in the first step, it estimates the column subspace of a low-rank matrix, and in the second step, it exploits the estimated information to complete the reconstruction. The simulation results show that the proposed algorithm achieves robust performance with much lower complexity than existing reconstruction algorithms.

We study a finite-horizon restless multi-armed bandit problem with multiple actions, dubbed as R(MA)^2B. The state of each arm evolves according to a controlled Markov decision process (MDP), and the reward of pulling an arm depends on both the current state and action of the corresponding MDP. Since finding the optimal policy is typically intractable, we propose a computationally appealing index policy entitled Occupancy-Measured-Reward Index Policy for the finite-horizon R(MA)^2B. Our index policy is well-defined without the requirement of indexability condition and is provably asymptotically optimal as the number of arms tends to infinity. We then adopt a learning perspective where the system parameters are unknown, and propose R(MA)^2B-UCB, a generative model based reinforcement learning augmented algorithm that can fully exploit the structure of Occupancy-Measured-Reward Index Policy. Compared to existing algorithms, R(MA)^2B-UCB performs close to offline optimum, and achieves a sub-linear regret and a low computational complexity all at once. Experimental results show that R(MA)^2B-UCB outperforms existing algorithms in both regret and running time.

With the increasing demand for large-scale training of machine learning models, consensus-based distributed optimization methods have recently been advocated as alternatives to the popular parameter server framework. In this paradigm, each worker maintains a local estimate of the optimal parameter vector, and iteratively updates it by waiting and averaging all estimates obtained from its neighbors, and then corrects it on the basis of its local dataset. However, the synchronization phase can be time consuming due to the need to wait for stragglers, i.e., slower workers. An efficient way to mitigate this effect is to let each worker wait only for updates from the fastest neighbors before updating its local parameter. The remaining neighbors are called backup workers. To minimize the globally training time over the network, we propose a fully distributed algorithm to dynamically determine the number of backup workers for each worker. We show that our algorithm achieves a linear speedup for convergence (i.e., convergence performance increases linearly with respect to the number of workers). We conduct extensive experiments on MNIST and CIFAR-10 to verify our theoretical results. * Equal contribution. Ordering determined by alphabetical order. 2 Each worker needs to receive the aggregate of updates from all other workers to move to the next iteration, where aggregation is performed either by PS or along the ring through multiple rounds.

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