Caregiver Outcomes of a Dementia Care Program

The University of California, Los Angeles Alzheimer’s and Dementia Care (ADC) program enrolls people with dementia (PWD) and their family caregivers as dyads to work with nurse practitioner dementia care specialists to provide coordinated dementia care. At one year, despite disease progression, the PWDs’ behavioral and depressive symptoms improved. In addition, at one-year, caregiver depression, distress related to behavioral symptoms, and caregiver strain also improved. Not all dyads enrolled in the ADC program appear to experience benefit. Although strain and distress remained stable or decreased for the majority of caregivers, a portion reported an increase in both. Semi-structured interviewed were completed with 12 caregivers over the telephone. Based on their answers seven themes were identified. These themes included: caregiver perception of being provided recommendations that did not match perceived care needs, existence of barriers to accessing care and utilizing resources, differing care needs based on stage of dementia, needing services not offered by the ADC, needing more education or support, received behavioral recommendations that the caregiver felt did not work, and dementia expert had poor rapport with caregivers. Despite having been identified as having had no clinical benefit from participating in the program, most caregivers did feel that the program was beneficial. This dichotomy highlights that perceived benefit for most of the interviewed caregivers was not captured with the formal instruments used by the program

Playing is believing: the role of beliefs in multi-agent learning

We propose a new classification for multi-agent learning algorithms, with each league of players characterized by both their possible strategies and possible beliefs. Using this classification, we review the optimality of existing algorithms and discuss some insights that can be gained. We propose an incremental improvement to the existing algorithms that seems to achieve average payoffs that are at least the Nash equilibrium payoffs in the long-run against fair opponents.Singapore-MIT Alliance (SMA

All learning is local: Multi-agent learning in global reward games

In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algorithms. We provide a simple and efficient algorithm that in part uses a linear system to model the world from a single agent’s limited perspective, and takes advantage of Kalman filtering to allow an agent to construct a good training signal and effectively learn a near-optimal policy in a wide variety of settings. A sequence of increasingly complex empirical tests verifies the efficacy of this technique.Singapore-MIT Alliance (SMA

Mobilized ad-hoc networks: A reinforcement learning approach

Research in mobile ad-hoc networks has focused on situations in which nodes have no control over their movements. We investigate an important but overlooked domain in which nodes do have control over their movements. Reinforcement learning methods can be used to control both packet routing decisions and node mobility, dramatically improving the connectivity of the network. We first motivate the problem by presenting theoretical bounds for the connectivity improvement of partially mobile networks and then present superior empirical results under a variety of different scenarios in which the mobile nodes in our ad-hoc network are embedded with adaptive routing policies and learned movement policies

Design analysis of OAM fibers using particle swarm optimization algorithm

We study the design of ring core fibers (RCFs) supporting orbital angular momentum (OAM) modes for mode division multiplexing (MDM) transmission systems. We develop target criteria to optimize fiber designs using a particle swarm optimization (PSO) algorithm under fabrication constraints. Effective index separation, Δneff, and polarization purity of each OAM mode are known to determine modal crosstalk levels. To reduce the complexity of multiple-input multiple-output (MIMO) processing required to compensate for modal crosstalk, we define an objective function based on these quantities. Our design analysis focuses on four different concepts of step-index RCF leading to different modal and structural characteristics. The optimum design for each concept is derived using the PSO algorithm. We investigate the impact of hollow-core and/or higher-order radial modes on Δneff and polarization purity. Design strategies for increasing Δneff and polarization purity are discussed in light of robustness to fabrication errors. We finally discuss the scalability and potential limitations of this design

Design of highly-elliptical-core ten-mode fiber for space division multiplexing with 2x2 MIM

We propose a weakly-coupled few-mode fiber requiring only 2×2 MIMO equalizer blocks, which makes it compatible with standard coherent receivers with polarization diversity. The fiber has a highly-elliptical core, surrounded by a depressed index trench in the cladding, and supports five spatial modes with twofold polarization degeneracy (ten channels). The fiber is designed to mitigate inter-modal crosstalk since the effective index difference between spatial modes is larger than ∼1×10−3 over the C-band. Through numerical simulations, we report on bending loss and other modal characteristics such as effective area and chromatic dispersion. Finally, we briefly discuss the scalability of the design