Publications

Sodium-ion batteries are emerging as candidates for large-scale energy storage due to their low cost and the wide variety of cathode materials available. As battery size and adoption in critical applications increases, safety concerns are resurfacing due to the inherent flammability of organic electrolytes currently in use in both lithium and sodium battery chemistries. Development of solid-state batteries with ionic electrolytes eliminates this concern, while also allowing novel device architectures and potentially improving cycle life. Here we report the computation-assisted discovery and synthesis of a high-performance solid-state electrolyte material: Na10SnP2S12, with room temperature ionic conductivity of 0.4 mScm_1 rivalling the conductivity of the best sodium sulfide solid electrolytes to date. We also computationally investigate the variants of this compound where tin is substituted by germanium or silicon and find that the latter may achieve even higher conductivity.

Although halide salts such as LiCl and LiBr are routinely used as a source of Li ions during ion exchange reactions, a detailed understanding of the processes controlling the rates of these reactions is presently lacking. Recently, it has been discovered that the rate limiting barriers for ion exchange are commonly associated with these salts rather than the ceramic target of ion exchange, making it important to quantitatively understand of salt processes. Here it is demonstrated that in situ synchrotron studies of ion exchange reactions can be used to precisely quantify the thermodynamic activation energies associated with these solid state reactions in a manner that can be directly compared with predictions from density functional theory (DFT). While the temperature dependence of the LiCl reaction rate is found to be set by a barrier associated with ion hopping, it was discovered that for LiBr it is also affected by the defect formation energy – an energy found to be substantially lower than predicted by DFT. Furthermore, it is shown that when reaction rates for different relative amounts of reactants are varied, it is possible to identify the rate-limiting reagent and to elucidate an overall scaling relationship that controls the concentration-dependence of the reaction rate. Also, it is demonstrated that global fits across doped and undoped salts can be used to probe both intrinsic and extrinsic vacancy concentrations. This improved understanding of ion exchange mechanisms can be used to accelerate ion exchange reaction rates by orders of magnitude. In conclusion, the techniques demonstrated here can be broadly applied to probe the kinetics and thermodynamics of solid state reactions.

Abstract not Available.

The network, similar to CPU and memory, is a critical and shared resource in the cloud. However, unlike other resources, it is neither shared proportionally to payment, nor do cloud providers offer minimum guarantees on network bandwidth. The reason networks are more difficult to share is because the network allocation of a virtual machine (VM) X depends not only on the VMs running on the same machine with X , but also on the other VMs that X communicates with and the cross-traffic on each link used by X . In this paper, we start from the above requirements--payment proportionality and minimum guarantees--and show that the network-specific challenges lead to fundamental tradeoffs when sharing cloud networks. We then propose a set of properties to explicitly express these tradeoffs. Finally, we present three allocation policies that allow us to navigate the tradeoff space. We evaluate their characteristics through simulation and testbed experiments to show that they can provide minimum guarantees and achieve better proportionality than existing solutions.

Policy Points Accountable care organizations (ACOs) have incentives to promote the adoption of patient engagement strategies such as shared decision making and self‐management support programs to improve patient outcomes and contain health care costs. High adoption of patient engagement strategies among ACO‐affiliated practices did not improve patient‐reported outcomes (PROs) of physical, emotional, and social function among adult patients with diabetes and/or cardiovascular disease over a one‐year time frame, likely because implementing these strategies requires extensive clinician and staff training, workflow redesign, and patient participation over time. A dominant focus on improving clinical measures to meet external requirements may crowd out time needed for care team members to address other outcomes that matter to patients, including PROs. Payers and policy‐makers should explicitly incentivize the collection and use of PROs when contracting with ACOs. Context Adult primary care practices of accountable care organizations (ACOs) are adopting a range of patient engagement strategies, but little is known about how these strategies are related to patient‐reported outcomes (PROs) and how relational coordination among team members aids implementation. Methods We used a mixed‐methods cohort study design integrating administrative and clinical data with two data collection waves (2014‐2015 and 2016‐2017) of clinician and staff surveys ( n = 764), surveys of adult patients with diabetes and/or cardiovascular disease (CVD) ( n = 1,276), and key informant interviews of clinicians, staff, and administrators ( n = 103). Multivariable linear regression estimated the relationship of practice adoption of patient engagement strategies, relational coordination, and PROs of physical, social, and emotional function. The mediating role of patient activation was examined using cross‐lagged panel models. Key informant interviews assessed how relational coordination influences the implementation of patient engagement strategies. Findings There were no differential improvements in PROs among patients of practices with high vs. low adoption of patient engagement strategies or among patients of practices with high vs. low relational coordination. The Patient Activation Measure (PAM) is strongly related to better physical, emotional, and social PROs over time. Relational coordination facilitated the implementation of patient engagement strategies, but key informants indicated that resources and systems to systematically track treatment preferences and goals beyond clinical indicators were needed to support effective implementation. Conclusions Adult patients with diabetes and/or CVD of ACO‐affiliated practices with high adoption of patient engagement strategies do not have improved PROs of physical, emotional, and social function over a one‐year time frame. Implementing patient engagement strategies increases task interdependence among primary care team members, which needs to be carefully managed. ACOs may need to make greater investment in collecting, monitoring, and analyzing PRO data to ensure that practice adoption and implementation of patient engagement strategies leads to improved physical, emotional, and social function among patients.

Human movement is goal-directed and influenced by the spatial layout of the objects in the scene. To plan future human motion, it is crucial to perceive the environment -- imagine how hard it is to navigate a new room with lights off. Existing works on predicting human motion do not pay attention to the scene context and thus struggle in long-term prediction. In this work, we propose a novel three-stage framework that exploits scene context to tackle this task. Given a single scene image and 2D pose histories, our method first samples multiple human motion goals, then plans 3D human paths towards each goal, and finally predicts 3D human pose sequences following each path. For stable training and rigorous evaluation, we contribute a diverse synthetic dataset with clean annotations. In both synthetic and real datasets, our method shows consistent quantitative and qualitative improvements over existing methods.