Towards self-optimizing frameworks for collaborative systems

Two important performance metrics in collaborative systems are local and remote response times. For certain classes of applications, it is possible to meet response time requirements better than existing systems through a new system without requiring hardware, network, or user-interface changes. This self-optimizing system improves response times by automatically making runtime adjustments to three aspects of a collaborative application. One of these aspects is the collaboration architecture. Previous work has shown that dynamically switching architectures at runtime can improve response times; however, no previous work performs the switch automatically. The thesis shows that (a) another important performance parameter is whether multicast or unicast is used to transmit commands, and (b) response times can be noticeably better with multicast than with unicast when transmission costs are high. Traditional architectures, however, support only unicast - a computer that processes input commands must also transmit commands to all other computers. To support multicast, a new bi-architecture model of collaborative systems is introduced in which two separate architectures govern the processing and transmission tasks that each computer must perform. The thesis also shows that another important performance aspect is the order in which a computer performs these tasks. These tasks can be scheduled sequentially or concurrently on a single-core, or in parallel on multiple cores. As the thesis shows, existing single-core policies trade-off noticeable improvements in local (remote) for noticeable degradations in remote (local) response times. A new lazy policy for scheduling these tasks on a single-core is introduced that trades-off an unnoticeable degradation in performance of some users for a much larger noticeable improvement in performance of others. The thesis also shows that on multi-core devices, the tasks should always be scheduled on separate cores. The self-optimizing system adjusts the processing architecture, communication architecture, and scheduling policy based on response time predictions given by a new analytical model. Both the analytical model and the self-optimizing system are validated through simulations and experiments in practical scenarios

Jefferson Digital Commons quarterly report: April-June 2019

This quarterly report includes: Articles CREATE Day Presentations Dissertations From the Archives Grand Rounds and Lectures House Staff Quality Improvement and Patient Safety Posters JCIPE Student Hotspotting Posters Journals and Newsletters MPH Capstone Presentations Posters Sigma Xi Research Day What People are Saying About the Jefferson Digital Common

Advancing Pharmacist Collaborative Care within Academic Health Systems.

INTRODUCTION:The scope of pharmacy practice has evolved over the last few decades to focus on the optimization of medication therapy. Despite this positive impact, the lack of reimbursement remains a significant barrier to the implementation of innovative pharmacist practice models. SUMMARY:We describe the successful development, implementation and outcomes of three types of pharmacist collaborative care models: (1) a pharmacist with physician oversight, (2) pharmacist-interprofessional teams and (3) physician-pharmacist teams. The outcome measurement of these pharmacist care models varied from the design phase to patient volume measurement and to comprehensive quality dashboards. All of these practice models have been successfully funded by affiliated health systems or grants. CONCLUSIONS:The expansion of pharmacist services delivered by clinical faculty has several benefits to affiliated health systems: (1) significant improvements in patient care quality, (2) access to experts in specialty areas, and (3) the dissemination of outcomes with national and international recognition, increasing the visibility of the health system

Taking on New Roles to Address 21st Century Problems

Co-creation: Viewing Partnerships through a New Lens, provided a fresh look at public private partnerships and the collective work forged by the Connecticut Council for Philanthropy (CCP), the Connecticut Early Childhood Funder Collaborative, and the State of Connecticut (Bowie, 2016). The partnership offered the opportunity to explore co-creation as a new paradigm and lens with which to design and assess collective work, particularly when trying to achieve large-scale systems change.In employing co-creation, the partnership established new structures and adopted processes that enabled a diverse group of individuals and entities to voluntarily contribute their skills, expertise, and resources to create a state level early childhood systems approach in Connecticut. This co-creation process also resulted in important transformations within the entities involved.For CCP, it was an opportunity to explore and test a new role and working structure in direct response to the evolving needs and desires within Connecticut's philanthropic community. Over the last 47 years, CCP has functioned as a network of various types of philanthropic organizations. CCP connects grantmakers to address issues both individually and collectively, is a resource for grantmaking where funders can access critical information and services, and is a voice for philanthropy representing the philanthropic sector to key audiences (Strategic Plan, Connecticut Council for Philanthropy, 2014).Within the public-private partnership, CCP established a new working relationship with the Early Childhood Funder Collaborative and with state government, which ultimately shifted the role of CCP. This new role moved beyond offering the typical program management and administrative support and in doing so gained the ability to bring forth different perspectives and new strategies in order to strengthen philanthropy's contribution to systems change. This shift was also in alignment with, and furthered, the mission of the Connecticut Council for Philanthropy to promote and support effective philanthropy for the public good

Coordination Matters : Interpersonal Synchrony Influences Collaborative Problem-Solving

The authors thank Martha von Werthern and Caitlin Taylor for their assistance with data collection, Cathy Macpherson for her assistance with the preparation of the manuscript, and Mike Richardson, Alex Paxton, and Rick Dale for providing MATLAB code to assist with data analysis. The research was funded by the British Academy (SG131613).Peer reviewedPublisher PD

Towards a quantitative evaluation of the relationship between the domain knowledge and the ability to assess peer work

In this work we present the preliminary results provided by the statistical modeling of the cognitive relationship between the knowledge about a topic a the ability to assess peer achievements on the same topic. Our starting point is Bloom's taxonomy of educational objectives in the cognitive domain, and our outcomes confirm the hypothesized ranking. A further consideration that can be derived is that meta-cognitive abilities (e.g., assessment) require deeper domain knowledge

NAIS: Neural Attentive Item Similarity Model for Recommendation

Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored optimization for the recommendation task. In recent years, several works attempt to learn item similarities from data, by expressing the similarity as an underlying model and estimating model parameters by optimizing a recommendation-aware objective function. While extensive efforts have been made to use shallow linear models for learning item similarities, there has been relatively less work exploring nonlinear neural network models for item-based CF. In this work, we propose a neural network model named Neural Attentive Item Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an attention network, which is capable of distinguishing which historical items in a user profile are more important for a prediction. Compared to the state-of-the-art item-based CF method Factored Item Similarity Model (FISM), our NAIS has stronger representation power with only a few additional parameters brought by the attention network. Extensive experiments on two public benchmarks demonstrate the effectiveness of NAIS. This work is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems