3,511 research outputs found
Trace anomaly and infrared cutoffs
No full textThe effective average action is a form of effective action which depends on a cutoff scale suppressing the contribution of low momentum modes in the functional integral. It reduces to the ordinary effective action when the cutoff scale goes to zero. We derive the modifications of the scale Ward identity due to this cutoff and show how the resulting identity then intimately relates the trace anomaly to the Wilsonian realization of the renormalization group
Superluminal velocity through near-maximal neutrino oscillations or by being off shell
No full textRecently it was suggested that the observation of superluminal neutrinos by the OPERA collaboration may be due to group velocity effects resulting from close-to-maximal oscillation between neutrino mass eigenstates, in analogy to known effects in optics. We show that superluminal propagation does occur through this effect for a series of very narrow energy ranges, but this phenomenon cannot explain the OPERA measurement. Superluminal propagation can also occur if one of the neutrino masses is extremely small. However the effect only has appreciable amplitude at energies of order this mass and thus has negligible overlap with the multi-GeV scale of the experiment
25th Annual Recognition Dinner, 2016
No full textAward winners:
Distinguished McKnight University Professorship: Michael Lackey
UMM Faculty Distinguished Research Award: Michael Lackey
Horace T. Morse-Minnesota Alumni Association Award: Heather J. Peters
President\u27s Award for Outstanding Service: Windy Roberts
Mary Martelle Memorial Awards: Stephanie Ferrian and Bailey Stottrup
Outstanding Support Staff Awards: Lacey Fahl, Marie Hagen, and Delores Rathke
Morris Academic Staff Award: Judy Korn
Retirees: Christopher T. Cole, Nancy Erdahl, Mark Fohl, Henry Fulda, Vicki Graham, Jacqueline R. Johnson, Corrine Larson, Dale Livingston, and Tim Soderberghttps://digitalcommons.morris.umn.edu/facstaffrecognition/1010/thumbnail.jp
Redundant operators in the exact renormalisation group and in the f(R) approximation to asymptotic safety
No full textIn this paper we review the definition and properties of redundant operators in the exact renormalisation group. We explain why it is important to require them to be eigenoperators and why generically they appear only as a consequence of symmetries of the particular choice of renormalisation group equations. This clarifies when Newton’s constant and or the cosmological constant can be considered inessential. We then apply these ideas to the Local Potential Approximation and approximations of a similar spirit such as the f (R) approximation in the asymptotic safety programme in quantum gravity. We show that these approximations can break down if the fixed point does not support a ‘vacuum’ solution in the appropriate domain: all eigenoperators become redundant and the physical space of perturbations collapses to a point. We show that this is the case for the recently discovered lines of fixed points in the f (R) flow equations
Accepting Optimally in Automated Negotiation with Incomplete Information (abstract)
No full textIntelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
From William Morris to today: the textiles of the Cotswolds
No full textWilliam Morris (1834-1896) described the Cotswolds as ‘a heaven on earth’. This exhibition at the Gordon Russell Design Museum, explores the abundant inspiration the area has provided for Morris and many other textile designers.
As well as drawing on the Museum’s own collection, in particular the work of Marian Pepler (1904-1997), the show will also include loans from other Museums and private collectors, including an early piece by William Morris. The exhibition will feature the work of other 20th Century textile designers including: Ethel Mariet (1872-1952) who worked at Broad Campden, Phyllis Barron (1890-1964) and Dorothy Larcher (1884-1952) who lived and worked at Painswick, and Tibor Reich (1916-1996) who produced all-wool coverings for Gordon Russell Ltd.
To complement these early interpretations of the Cotswolds influence, the exhibition will display work by garment designer/maker Dorothy Reglar and current Gloucestershire Guild members Tim Parry-Williams, Liz Lippiatt, Sarah Beadsmore and the team of Rebecca Aird and Peter Thwaites at Rapture & Wrigh
Correction to: How to estimate heritability: a guide for genetic epidemiologists
No full textThis is a correction to: Ciarrah-Jane S Barry, Venexia M Walker, Rosa Cheesman, George Davey Smith, Tim T Morris, Neil M Davies, How to estimate heritability: a guide for genetic epidemiologists, International Journal of Epidemiology, Volume 52, Issue 2, April 2023, Pages 624–632, https://doi.org/10.1093/ije/dyac22
is the author of many papers and reports. Tim was born in 1950.
No full textTim Pearce has responsibility for work relating to vehicle safety and institutional strengthening in developing countries. He was involved in UK transport-related research projects for 15 years before specialising in problems relating to developing countries. During the last 10 years he has been closely involved in the problems of the roadworthiness of vehicles both from the technical and institutional sides. He has worke
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
- …
