University of California, Irvine

About: University of California, Irvine is a education organization based out in Irvine, California, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 47031 authors who have published 113602 publications receiving 5521832 citations. The organization is also known as: UC Irvine & UCI.

Indications of neutrino oscillation in a 250 km long-baseline experiment.

Abstract: The K2K experiment observed indications of neutrino oscillation after 250 km flight of υμ. The observed number of events in the data corresponding to 4.8 x 1019 protons on target is 56, while 80.1 5.4 +6.2 is expected. Both the decrease of the events and observed spectrum shape distortion are consistent with neutrino oscillation. The probability that the observations are statistical fluctuation of non oscillation is less than 1%. The allowed region of oscillation parameters is consistent with the one obtained from the atmospheric neutrino observation. After the accident of Super-Kamiokande (SK) detector, the reconstruction of SK has finished in 2002 and the K2K experiment resumed in December 2002.

Protein synthesis at synaptic sites on dendrites.

Abstract: Studies over the past 20 years have revealed that gene expression in neurons is carried out by a distributed network of translational machinery. One component of this network is localized in dendrites, where polyribosomes and associated membranous elements are positioned beneath synapses and translate a particular population of dendritic mRNAs. The localization of translation machinery and mRNAs at synapses endows individual synapses with the capability to independently control synaptic strength through the local synthesis of proteins. The present review discusses recent studies linking synaptic plasticity to dendritic protein synthesis and mRNA trafficking and considers how these processes are regulated. We summarize recent information about how synaptic signaling is coupled to local translation and to the delivery of newly transcribed mRNAs to activated synaptic sites and how local translation may play a role in activity-dependent synaptic modification.

Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks

Abstract: Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions. Considering that recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) can learn feature representations and model long-term temporal dependencies automatically, we propose an end-to-end fully connected deep LSTM network for skeleton based action recognition. Inspired by the observation that the co-occurrences of the joints intrinsically characterize human actions, we take the skeleton as the input at each time slot and introduce a novel regularization scheme to learn the co-occurrence features of skeleton joints. To train the deep LSTM network effectively, we propose a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons. Experimental results on three human action recognition datasets consistently demonstrate the effectiveness of the proposed model.

Moral Foundations Theory: The Pragmatic Validity of Moral Pluralism

Abstract: Where does morality come from? Why are moral judgments often so similar across cultures, yet sometimes so variable? Is morality one thing, or many? Moral Foundations Theory (MFT) was created to answer these questions. In this chapter, we describe the origins, assumptions, and current conceptualization of the theory and detail the empirical findings that MFT has made possible, both within social psychology and beyond. Looking toward the future, we embrace several critiques of the theory and specify five criteria for determining what should be considered a foundation of human morality. Finally, we suggest a variety of future directions for MFT and moral psychology.

Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles

Abstract: We compare multinomial logit and mixed logit models for data on California households' revealed and stated preferences for automobiles. The stated preference (SP) data elicited households' preferences among gasoline, electric, methanol, and compressed natural gas vehicles with various attributes. The mixed logit models provide improved fits over logit that are highly significant, and show large heterogeneity in respondents' preferences for alternative-fuel vehicles. The effects of including this heterogeneity are demonstrated in forecasting exercises. The alternative-fuel vehicle models presented here also highlight the advantages of merging SP and revealed preference (RP) data. RP data appear to be critical for obtaining realistic body-type choice and scaling information, but they are plagued by multicollinearity and difficulties with measuring vehicle attributes. SP data are critical for obtaining information about attributes not available in the marketplace, but pure SP models with these data give implausible forecasts.