1,705 research outputs found
Evaluating Citebase, an open access Web-based citation-ranked search and impact discovery service
No full textCitebase is a new citation-ranked search and impact discovery service that measures citations of scholarly research papers which are openly accessible on the Web, i.e. papers that are assessable continuously online. Other services, such as ResearchIndex, have emerged in recent years to offer citation indexing of Web research papers. In the first detailed user evaluation of an open access Web citation indexing service, Citebase has been evaluated by nearly 200 users from different backgrounds. The paper details the procedures used in the evaluation, and analyses the results of this study, which took place between June and October 2002. It was found that within the scope of its primary components, the search interface and services available from its rich bibliographic records, Citebase can be used simply and reliably for the purpose intended, and that it compares favourably with other bibliographic services. It is shown tasks can be accomplished efficiently with Citebase regardless of the background of the user. More data need to be collected and the process refined before it is as reliable for measuring citation impact of indexed papers. Better explanations and guidance are required for first-time users. Coverage is seen as a limiting factor, even though Citebase indexes over 200,000 papers from arXiv. Non-physicists were frustrated at the lack of papers from other sciences. The principle of citation searching of open access archives has thus been demonstrated and need not be restricted to current users. Since the evaluation, Citebase has become a featured service of the ArXiv physics eprint archives
Assessment of the steady-state drug-drug interactions between dolutegravir and ritonavir-boosted darunavir in adolescents
No full textObjectives: DTG is primarily metabolized by the UDP-glycosyltransferase (UGT) 1A1, and to a lesser extent by the cytochrome P450 (CYP) 3A4. Co-administration of DRV/r has been reported to decrease DTG plasma concentrations. Our aim was to distinguish the extent of the drug – drug interactions between DRV/r and DTG, and to evaluate the consequences of this interaction, in adolescents at steady state. Design: SMILE (PENTA 17-ANRS152) was a phase II/III trial assessing the safety and efficacy of once-daily dual therapy, using dolutegravir (DTG) combined with ritonavir-boosted darunavir (DRV/r), in virologically suppressed adolescents aged 12 years and older. Methods: A joint population pharmacokinetic model for DTG and DRV/r was developed with prior individual drug models (involving unbound and total concentrations) using SMILE data. Results: Unbound DRV exposure, integrated as a power function on unbound DTG clearance best described DRV/r inhibition of DTG elimination. Nevertheless, no interaction was identified between DRV/r and total DTG clearance. Moreover, the influence of unbound DRV exposures to predict unbound DTG concentrations was relatively small
Accepting Optimally in Automated Negotiation with Incomplete Information (abstract)
No full textIntelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Dr. Tim Brock
No full textTimothy R. Brock, PhD, CPT, CRP, ID(S&L+)
Dr. Tim Brock is the Founder and CEO of The Institute 4 Worthy Performance, a company dedicated to helping organizations apply the evidence-based principles, practices, and 10 international standards of performance improvement using 21st Century human capital big data analytics to achieve sustainable organizational and mission goals and objectives.
Dr. Brock’s PhD is in Education with a specialization in Training and Performance Improvement. He wrote his dissertation, “Training NASA Astronauts for Deep Space Exploration Missions: A Research Study to Develop and Validate a Competency-Based Training Framework” while he was the Senior Training and Human Performance Architect author for Lockheed Martin’s (LM) winning Crew Exploration Vehicle (now known as the Orion Multi-Purpose Crew Vehicle (MPCV)) proposal selected by NASA. His learning and sustainment architecture included the initial training and competency sustainment/development for all managers, mission maintainers, ground and mission controllers, and astronauts. Dr. Brock was also led a team of human performance engineers to JSC to conduct a training situation analysis of mission controller training that resulted in a white paper of options NASA could adopt to decrease time and cost to proficiency. He also supported LM’s Facility Development and Operations Contract (FDOC) with NASA with the NASA Constellation Training Facility (CxTF) leadership team. During his Air Force career, Dr. Brock was also a US Air Force missile launch officer for two ICBM weapon systems and was responsible for the initial qualification weapon system academic classroom and high fidelity simulation curriculum for all missile launch officer candidates for all five of the US Air Force’s ICBM fleet.
In addition, while he was manager of LM’s Global Training and Science of Learning and Performance Improvement initiatives, Dr. Brock established and led a R&D and analysis team of distinguished, PhD-level, multi-disciplinary team of behavioral, social, cognitive, learning, and technology scientists and practitioners. His team crafted proprietary thought leadership (e.g., R&D, white papers, patents, etc.) in Human Cognitive and Behavior Modeling research to improve the effectiveness of the Human-in-the-Loop (HITL) within complex organization and technical systems. Dr. Brock’s team also provided innovative discriminator capabilities to solve complex, 100M+ bottom line learning and human performance challenges for existing and potential customers. For example, he was also the Principal Investigator for an R&D initiative that collaborated with a major health care provider to conduct a proof-of-concept prototype that integrated simulation technologies in an immersive learning environment to rapidly develop the affective, cognitive, and metacognitive skills of novice and experienced nurses. As a result of this proof-of-concept study, Dr. Brock was the lead inventor of a company-sponsored, patent-pending “Method and System for Accelerated Guided Experiential Learning and Performance Improvement” innovative instructional architecture. The invention created a method and system to generate a competency continuum of increasing competency levels, by interviewing a plurality of competency exemplar sets to elicit knowledge associated with a terminal skills and identifying cognitive discriminators associated with each competency level from the knowledge to establish cue-action schema norms to assess cognitive development.
Dr. Brock is a Certified Performance Improvement Practitioner through the International Society for Performance Improvement, a Certified Return on Investment Professional through the ROI Institute, and a Certified Instructional Designer with a specialization in high-fidelity simulations and labs through The Institute for Performance Improvement.
Dr. Brock’s PhD is in Education with a specialization in Training and Performance Improvement. He wrote his dissertation, “Training NASA Astronauts for Deep Space Exploration Missions: A Research Study to Develop and Validate a Competency-Based Training Framework” while he was the Senior Training and Human Performance Architect author for Lockheed Martin’s (LM) winning Crew Exploration Vehicle (now known as the Orion Multi-Purpose Crew Vehicle (MPCV)) proposal selected by NASA. His learning and sustainment architecture included the initial training and competency sustainment/development for all managers, mission maintainers, ground and mission controllers, and astronauts. Dr. Brock was also led a team of human performance engineers to JSC to conduct a training situation analysis of mission controller training that resulted in a white paper of options NASA could adopt to decrease time and cost to proficiency. He also supported LM’s Facility Development and Operations Contract (FDOC) with NASA with the NASA Constellation Training Facility (CxTF) leadership team. During his Air Force career, Dr. Brock was also a US Air Force missile launch officer for two ICBM weapon systems and was responsible for the initial qualification weapon system academic classroom and high fidelity simulation curriculum for all missile launch officer candidates for all five of the US Air Force’s ICBM fleet.
In addition, while he was manager of LM’s Global Training and Science of Learning and Performance Improvement initiatives, Dr. Brock established and led a R&D and analysis team of distinguished, PhD-level, multi-disciplinary team of behavioral, social, cognitive, learning, and technology scientists and practitioners. His team crafted proprietary thought leadership (e.g., R&D, white papers, patents, etc.) in Human Cognitive and Behavior Modeling research to improve the effectiveness of the Human-in-the-Loop (HITL) within complex organization and technical systems. Dr. Brock’s team also provided innovative discriminator capabilities to solve complex, 100M+ bottom line learning and human performance challenges for existing and potential customers. For example, he was also the Principal Investigator for an R&D initiative that collaborated with a major health care provider to conduct a proof-of-concept prototype that integrated simulation technologies in an immersive learning environment to rapidly develop the affective, cognitive, and metacognitive skills of novice and experienced nurses. As a result of this proof-of-concept study, Dr. Brock was the lead inventor of a company-sponsored, patent-pending “Method and System for Accelerated Guided Experiential Learning and Performance Improvement” innovative instructional architecture. The invention created a method and system to generate a competency continuum of increasing competency levels, by interviewing a plurality of competency exemplar sets to elicit knowledge associated with a terminal skills and identifying cognitive discriminators associated with each competency level from the knowledge to establish cue-action schema norms to assess cognitive development.
Dr. Brock is a Certified Performance Improvement Practitioner through the International Society for Performance Improvement, a Certified Return on Investment Professional through the ROI Institute, and a Certified Instructional Designer with a specialization in high-fidelity simulations and labs through The Institute for Performance Improvement.https://commons.erau.edu/space-congress-bios-2016/1027/thumbnail.jp
What to bid and when to stop
No full textNegotiation is an important activity in human society, and is studied by various disciplines, ranging from economics and game theory, to electronic commerce, social psychology, and artificial intelligence. Traditionally, negotiation is a necessary, but also time-consuming and expensive activity. Therefore, in the last decades there has been a large interest in the automation of negotiation, for example in the setting of e-commerce. This interest is fueled by the promise of automated agents eventually being able to negotiate on behalf of human negotiators.Every year, automated negotiation agents are improving in various ways, and there is now a large body of negotiation strategies available, all with their unique strengths and weaknesses. For example, some agents are able to predict the opponent's preferences very well, while others focus more on having a sophisticated bidding strategy. The problem however, is that there is little incremental improvement in agent design, as the agents are tested in varying negotiation settings, using a diverse set of performance measures. This makes it very difficult to meaningfully compare the agents, let alone their underlying techniques. As a result, we lack a reliable way to pinpoint the most effective components in a negotiating agent.There are two major advantages of distinguishing between the different components of a negotiating agent's strategy: first, it allows the study of the behavior and performance of the components in isolation. For example, it becomes possible to compare the preference learning component of all agents, and to identify the best among them. Second, we can proceed to mix and match different components to create new negotiation strategies., e.g.: replacing the preference learning technique of an agent and then examining whether this makes a difference. Such a procedure enables us to combine the individual components to systematically explore the space of possible negotiation strategies.To develop a compositional approach to evaluate and combine the components, we identify structure in most agent designs by introducing the BOA architecture, in which we can develop and integrate the different components of a negotiating agent. We identify three main components of a general negotiation strategy; namely a bidding strategy (B), possibly an opponent model (O), and an acceptance strategy (A). The bidding strategy considers what concessions it deems appropriate given its own preferences, and takes the opponent into account by using an opponent model. The acceptance strategy decides whether offers proposed by the opponent should be accepted.The BOA architecture is integrated into a generic negotiation environment called Genius, which is a software environment for designing and evaluating negotiation strategies. To explore the negotiation strategy space of the negotiation research community, we amend the Genius repository with various existing agents and scenarios from literature. Additionally, we organize a yearly international negotiation competition (ANAC) to harvest even more strategies and scenarios. ANAC also acts as an evaluation tool for negotiation strategies, and encourages the design of negotiation strategies and scenarios.We re-implement agents from literature and ANAC and decouple them to fit into the BOA architecture without introducing any changes in their behavior. For each of the three components, we manage to find and analyze the best ones for specific cases, as described below. We show that the BOA framework leads to significant improvements in agent design by wining ANAC 2013, which had 19 participating teams from 8 international institutions, with an agent that is designed using the BOA framework and is informed by a preliminary analysis of the different components.In every negotiation, one of the negotiating parties must accept an offer to reach an agreement. Therefore, it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When contemplating whether to accept an offer, the agent is faced with the acceptance dilemma: accepting the offer may be suboptimal, as better offers may still be presented before time runs out. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. We classify and compare state-of-the-art generic acceptance conditions. We propose new acceptance strategies and we demonstrate that they outperform the other conditions. We also provide insight into why some conditions work better than others and investigate correlations between the properties of the negotiation scenario and the efficacy of acceptance conditions.Later, we adopt a more principled approach by applying optimal stopping theory to calculate the optimal decision on the acceptance of an offer. We approach the decision of whether to accept as a sequential decision problem, by modeling the bids received as a stochastic process. We determine the optimal acceptance policies for particular opponent classes and we present an approach to estimate the expected range of offers when the type of opponent is unknown. We show that the proposed approach is able to find the optimal time to accept, and improves upon all existing acceptance strategies.Another principal component of a negotiating agent's strategy is its ability to take the opponent's preferences into account. The quality of an opponent model can be measured in two different ways. One is to use the agent's performance as a benchmark for the model's quality. We evaluate and compare the performance of a selection of state-of-the-art opponent modeling techniques in negotiation. We provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. We identify a class of simple and surprisingly effective opponent modeling techniques that did not receive much previous attention in literature.The other way to measure the quality of an opponent model is to directly evaluate its accuracy by using similarity measures. We review all methods to measure the accuracy of an opponent model and we then analyze how changes in accuracy translate into performance differences. Moreover, we pinpoint the best predictors for good performance. This leads to new insights concerning how to construct an opponent model, and what we need to measure when optimizing performance.Finally, we take two different approaches to gain more insight into effective bidding strategies. We present a new classification method for negotiation strategies, based on their pattern of concession making against different kinds of opponents. We apply this technique to classify some well-known negotiating strategies, and we formulate guidelines on how agents should bid in order to be successful, which gives insight into the bidding strategy space of negotiating agents. Furthermore, we apply optimal stopping theory again, this time to find the concessions that maximize utility for the bidder against particular opponents. We show there is an interesting connection between optimal bidding and optimal acceptance strategies, in the sense that they are mirrored versions of each other.Lastly, after analyzing all components separately, we put the pieces back together again. We take all BOA components accumulated so far, including the best ones, and combine them all together to explore the space of negotiation strategies.We compute the contribution of each component to the overall negotiation result, and we study the interaction between components. We find that combining the best agent components indeed makes the strongest agents. This shows that the component-based view of the BOA architecture not only provides a useful basis for developing negotiating agents but also provides a useful analytical tool. By varying the BOA components we are able to demonstrate the contribution of each component to the negotiation result, and thus analyze the significance of each. The bidding strategy is by far the most important to consider, followed by the acceptance conditions and finally followed by the opponent model.Our results validate the analytical approach of the BOA framework to first optimize the individual components, and then to recombine them into a negotiating agent
Nostalgia: content, triggers, functions
No full textSeven methodologically diverse studies addressed 3 fundamental questions about nostalgia. Studies 1 and 2 examined the content of nostalgic experiences. Descriptions of nostalgic experiences typically featured the self as a protagonist in interactions with close others (e.g., friends) or in momentous events (e.g., weddings). Also, the descriptions contained more expressions of positive than negative affect and often depicted the redemption of negative life scenes by subsequent triumphs. Studies 3 and 4 examined triggers of nostalgia and revealed that nostalgia occurs in response to negative mood and the discrete affective state of loneliness. Studies 5, 6, and 7 investigated the functional utility of nostalgia and established that nostalgia bolsters social bonds, increases positive self-regard, and generates positive affect. These findings demarcate key landmarks in the hitherto uncharted research domain of nostalgi
Acceptance conditions in automated negotiation
No full textIn every negotiation with a deadline, one of the negotiating parties has to accept an offer to avoid a break off. A break off is usually an undesirable outcome for both parties, therefore it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When designing such conditions one is faced with the acceptance dilemma: accepting the current offer may be suboptimal, as better offers may still be presented. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. Motivated by the challenges of bilateral negotiations between automated agents and by the results and insights of the automated negotiating agents competition (ANAC), we classify and compare state-of-the-art generic acceptance conditions. We focus on decoupled acceptance conditions, i.e. conditions that do not depend on the bidding strategy that is used. We performed extensive experiments to compare the performance of acceptance conditions in combination with a broad range of bidding strategies and negotiation domains. Furthermore we propose new acceptance conditions and we demonstrate that they outperform the other conditions that we study. In particular, it is shown that they outperform the standard acceptance condition of comparing the current offer with the offer the agent is ready to send out. We also provide insight in to why some conditions work better than others and investigate correlations between the properties of the negotiation environment and the efficacy of acceptance conditions.MediamaticsElectrical Engineering, Mathematics and Computer Scienc
Player agency in interactive narrative: audience, actor & author
No full textThe question motivating this review paper is, how can
computer-based interactive narrative be used as a constructivist learn-
ing activity? The paper proposes that player agency can be used to
link interactive narrative to learner agency in constructivist theory,
and to classify approaches to interactive narrative. The traditional
question driving research in interactive narrative is, ‘how can an in-
teractive narrative deal with a high degree of player agency, while
maintaining a coherent and well-formed narrative?’ This question
derives from an Aristotelian approach to interactive narrative that,
as the question shows, is inherently antagonistic to player agency.
Within this approach, player agency must be restricted and manip-
ulated to maintain the narrative. Two alternative approaches based
on Brecht’s Epic Theatre and Boal’s Theatre of the Oppressed are
reviewed. If a Boalian approach to interactive narrative is taken the
conflict between narrative and player agency dissolves. The question
that emerges from this approach is quite different from the traditional
question above, and presents a more useful approach to applying in-
teractive narrative as a constructivist learning activity
What babies need: accelerating access to current and novel antiretroviral drugs in neonates through pharmacokinetic studies
No full textAlthough 23 antiretroviral drugs are approved for use in adults, only six are approved by regulatory authorities for use in term neonates born to women with HIV, with even fewer options for preterm neonates. A major hurdle for approvals is the delay in the generation of pharmacokinetic and safety data for antiretrovirals in neonates. The median time between the year of approval from the US Food and Drug Administration of an antiretroviral agent for adults and the first publication date for pharmacokinetic data in neonates less than 4 weeks old is 8 years (range 2-23 years). In this Viewpoint, we address pharmacokinetic research gaps and priorities for current and novel antiretroviral use in neonates. We also consider the challenges and provide guidance on neonatal clinical pharmacology research on antiretroviral agents with the goal of stimulating research and expediting the availability of safe medications for the prevention and treatment of HIV in this vulnerable population
Panel 1: The Adolf Eichmann Trial
No full textSpeakers:Lawrence R. Douglas, James J. Grosfeld Professor of Law, Amherst College
Deborah Lipstadt, Dorot Professor of Modern Jewish and Holocaust Studies, Emory University; Author of the recently published book, The Eichmann Trial
Tim Naftali, Director, Richard Nixon Presidential Library and Museum
Hanna Yablonka Torok, Professor of Jewish History, Ben Gurion University of the Negev
Moderator:Lisa Yavnai, United States Holocaust Memorial Museum
Video of Panel 1 (and Welcome Remarks
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