152,416 research outputs found
Task irrelevant external cues can influence language selection in voluntary object naming: evidence from Hindi-English bilinguals
We examined if external cues such as other agents’ actions can influence the choice of language
during voluntary and cued object naming in bilinguals in three experiments. Hindi–
English bilinguals first saw a cartoon waving at a color patch. They were then asked to either
name a picture in the language of their choice (voluntary block) or to name in the instructed
language (cued block). The colors waved at by the cartoon were also the colors used as language
cues (Hindi or English). We compared the influence of the cartoon’s choice of color
on naming when speakers had to indicate their choice explicitly before naming (Experiment
1) as opposed to when they named directly on seeing the pictures (Experiment 2 and 3).
Results showed that participants chose the language indicated by the cartoon greater number
of times (Experiment 1 and 3). Speakers also switched significantly to the language
primed by the cartoon greater number of times (Experiment 1 and 2). These results suggest
that choices leading to voluntary action, as in the case of object naming can be influenced
significantly by external non-linguistic cues. Importantly, these symbolic influences can work
even when other agents are merely indicating their choices and are not interlocutors in bilingual
communicatio
Banks versus venture capital when the venture capitalist values private benefits of control
If control of their firms allows entrepreneurs to derive private benefits, it also allows other controlling parties. Private benefits are especially relevant for venture capitalists, who typically get considerable control in their portfolio firms, but not for banks, which are passive loan providers. We incorporate this difference between banks and venture capital and analyze entrepreneurs' financing strategy between the two. We find that, in all strict Nash Equilibria, entrepreneurs who value private benefits more choose banks while the rest choose venture capital. Thus, bank-financed entrepreneurs allocate more resources to tasks that yield private benefits while VC-backed entrepreneurs have higher profitability
Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information
This paper considers utility optimal power control for energy harvesting
wireless devices with a finite capacity battery. The distribution information
of the underlying wireless environment and harvestable energy is unknown and
only outdated system state information is known at the device controller. This
scenario shares similarity with Lyapunov opportunistic optimization and online
learning but is different from both. By a novel combination of Zinkevich's
online gradient learning technique and the drift-plus-penalty technique from
Lyapunov opportunistic optimization, this paper proposes a learning-aided
algorithm that achieves utility within of the optimal, for any
desired , by using a battery with an capacity. The
proposed algorithm has low complexity and makes power investment decisions
based on system history, without requiring knowledge of the system state or its
probability distribution.Comment: This version extends v1 (our INFOCOM 2018 paper): (1) add a new
section (Section V) to study the case where utility functions are non-i.i.d.
arbitrarily varying (2) add more simulation experiments. The current version
is published in IEEE/ACM Transactions on Networkin
The Value-of-Information in Matching with Queues
We consider the problem of \emph{optimal matching with queues} in dynamic
systems and investigate the value-of-information. In such systems, the
operators match tasks and resources stored in queues, with the objective of
maximizing the system utility of the matching reward profile, minus the average
matching cost. This problem appears in many practical systems and the main
challenges are the no-underflow constraints, and the lack of matching-reward
information and system dynamics statistics. We develop two online matching
algorithms: Learning-aided Reward optimAl Matching () and
Dual- () to effectively resolve both challenges.
Both algorithms are equipped with a learning module for estimating the
matching-reward information, while incorporates an additional
module for learning the system dynamics. We show that both algorithms achieve
an close-to-optimal utility performance for any
, while achieves a faster convergence speed and a
better delay compared to , i.e., delay and convergence under
compared to delay and convergence under
( and are maximum estimation errors for
reward and system dynamics). Our results reveal that information of different
system components can play very different roles in algorithm performance and
provide a systematic way for designing joint learning-control algorithms for
dynamic systems
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