16,537 research outputs found
Talk More Listen Less: Energy-Efficient Neighbor Discovery in Wireless Sensor Networks
Neighbor discovery is a fundamental service for initialization and managing
network dynamics in wireless sensor networks and mobile sensing applications.
In this paper, we present a novel design principle named Talk More Listen Less
(TMLL) to reduce idle-listening in neighbor discovery protocols by learning the
fact that more beacons lead to fewer wakeups. We propose an extended neighbor
discovery model for analyzing wakeup schedules in which beacons are not
necessarily placed in the wakeup slots. Furthermore, we are the first to
consider channel occupancy rate in discovery protocols by introducing a new
metric to trade off among duty-cycle, latency and channel occupancy rate.
Guided by the TMLL principle, we have designed Nihao, a family of
energy-efficient asynchronous neighbor discovery protocols for symmetric and
asymmetric cases. We compared Nihao with existing state of the art protocols
via analysis and real-world testbed experiments. The result shows that Nihao
significantly outperforms the others both in theory and practice.Comment: 9 pages, 14 figures, published in IEEE INFOCOM 201
Quantum-assisted Monte Carlo algorithms for fermions
Quantum computing is a promising way to systematically solve the longstanding
computational problem, the ground state of a many-body fermion system. Many
efforts have been made to realise certain forms of quantum advantage in this
problem, for instance, the development of variational quantum algorithms. A
recent work by Huggins et al. reports a novel candidate, i.e. a
quantum-classical hybrid Monte Carlo algorithm with a reduced bias in
comparison to its fully-classical counterpart. In this paper, we propose a
family of scalable quantum-assisted Monte Carlo algorithms where the quantum
computer is used at its minimal cost and still can reduce the bias. By
incorporating a Bayesian inference approach, we can achieve this
quantum-facilitated bias reduction with a much smaller quantum-computing cost
than taking empirical mean in amplitude estimation. Besides, we show that the
hybrid Monte Carlo framework is a general way to suppress errors in the ground
state obtained from classical algorithms. Our work provides a Monte Carlo
toolkit for achieving quantum-enhanced calculation of fermion systems on
near-term quantum devices
On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems
Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output
(MIMO) systems is a favorable candidate for the fifth generation (5G) cellular
systems. However, a key challenge is the high power consumption imposed by its
numerous radio frequency (RF) chains, which may be mitigated by opting for
low-resolution analog-to-digital converters (ADCs), whilst tolerating a
moderate performance loss. In this article, we discuss several important issues
based on the most recent research on mmWave massive MIMO systems relying on
low-resolution ADCs. We discuss the key transceiver design challenges including
channel estimation, signal detector, channel information feedback and transmit
precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative
technique of improving the overall system performance. Finally, the associated
challenges and potential implementations of the practical 5G mmWave massive
MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin
Designing a realistic peer-like embodied conversational agent for supporting children\textquotesingle s storytelling
Advances in artificial intelligence have facilitated the use of large
language models (LLMs) and AI-generated synthetic media in education, which may
inspire HCI researchers to develop technologies, in particular, embodied
conversational agents (ECAs) to simulate the kind of scaffolding children might
receive from a human partner. In this paper, we will propose a design prototype
of a peer-like ECA named STARie that integrates multiple AI models - GPT-3,
Speech Synthesis (Real-time Voice Cloning), VOCA (Voice Operated Character
Animation), and FLAME (Faces Learned with an Articulated Model and Expressions)
that aims to support narrative production in collaborative storytelling,
specifically for children aged 4-8. However, designing a child-centered ECA
raises concerns about age appropriateness, children\textquotesingle s privacy,
gender choices of ECAs, and the uncanny valley effect. Thus, this paper will
also discuss considerations and ethical concerns that must be taken into
account when designing such an ECA. This proposal offers insights into the
potential use of AI-generated synthetic media in child-centered AI design and
how peer-like AI embodiment may support children\textquotesingle s
storytelling.Comment: 6 pages with 2 figures. The paper has been peer-reviewed and
presented at the "CHI 2023 Workshop on Child-centred AI Design: Definition,
Operation and Considerations, April 23, 2023, Hamburg, German
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