Measurement of the Charged Multiplicities in b, c and Light Quark Events from Z0 Decays

Average charged multiplicities have been measured separately in $b$, $c$ and light quark ($u,d,s$) events from $Z^0$ decays measured in the SLD experiment. Impact parameters of charged tracks were used to select enriched samples of $b$ and light quark events, and reconstructed charmed mesons were used to select $c$ quark events. We measured the charged multiplicities: $\bar{n}_{uds} = 20.21 \pm 0.10 (\rm{stat.})\pm 0.22(\rm{syst.})$, $\bar{n}_{c} = 21.28 \pm 0.46(\rm{stat.}) ^{+0.41}_{-0.36}(\rm{syst.})$ $\bar{n}_{b} = 23.14 \pm 0.10(\rm{stat.}) ^{+0.38}_{-0.37}(\rm{syst.})$, from which we derived the differences between the total average charged multiplicities of $c$ or $b$ quark events and light quark events: $\Delta \bar{n}_c = 1.07 \pm 0.47(\rm{stat.})^{+0.36}_{-0.30}(\rm{syst.})$ and $\Delta \bar{n}_b = 2.93 \pm 0.14(\rm{stat.})^{+0.30}_{-0.29}(\rm{syst.})$. We compared these measurements with those at lower center-of-mass energies and with perturbative QCD predictions. These combined results are in agreement with the QCD expectations and disfavor the hypothesis of flavor-independent fragmentation.Comment: 19 pages LaTex, 4 EPS figures, to appear in Physics Letters

How to Train Your Avatar: A Data Driven Approach to Gesture Generation

Abstract. The ability to gesture is key to realizing virtual characters that can engage in face-to-face interaction with people. Many applications take an approach of predefining possible utterances of a virtual character and building all the gesture animations needed for those utterances. We can save effort on building a virtual human if we can construct a general gesture controller that will generate behavior for novel utterances. Because the dynamics of human gestures are related to the prosody of speech, in this work we propose a model to generate gestures based on prosody. We then assess the naturalness of the animations by comparing them against human gestures. The evaluation results were promising, human judgments show no significant difference between our generated gestures and human gestures and the generated gestures were judged as significantly better than real human gestures from a different utterance.

Cases, context, and comfort: opportunities for case-based reasoning

Artificial intelligence (AI) methods have the potential for broad impact in smart homes. Different AI methods offer different contributions for this domain, with different design goals, tasks, and circumstances dictating where each type of method best applies. In this chapter, we describe motivations and opportunities for applying case-based reasoning (CBR) to a human-centered approach to the capture, sharing, and revision of knowledge for smart homes. Starting from the CBR cognitive model of reasoning and learning, we illustrate how CBR could provide useful capabilities for problem detection and response, provide a basis for personalization and learning, and provide a paradigm for home-human communication to cooperatively guide performance improvement. After sketching how these capabilities could be served by case-based reasoning, we discuss some design issues for applying CBR within smart homes and case-based reasoning research challenges for realizing the vision.Fil: Leake, David. Indiana University; Estados UnidosFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion ; Argentina. Indiana University; Estados UnidosFil: Reichherzer, Thomas. Indiana University; Estados Unido