5,352 research outputs found
Arithmetic on a Distributed-Memory Quantum Multicomputer
We evaluate the performance of quantum arithmetic algorithms run on a
distributed quantum computer (a quantum multicomputer). We vary the node
capacity and I/O capabilities, and the network topology. The tradeoff of
choosing between gates executed remotely, through ``teleported gates'' on
entangled pairs of qubits (telegate), versus exchanging the relevant qubits via
quantum teleportation, then executing the algorithm using local gates
(teledata), is examined. We show that the teledata approach performs better,
and that carry-ripple adders perform well when the teleportation block is
decomposed so that the key quantum operations can be parallelized. A node size
of only a few logical qubits performs adequately provided that the nodes have
two transceiver qubits. A linear network topology performs acceptably for a
broad range of system sizes and performance parameters. We therefore recommend
pursuing small, high-I/O bandwidth nodes and a simple network. Such a machine
will run Shor's algorithm for factoring large numbers efficiently.Comment: 24 pages, 10 figures, ACM transactions format. Extended version of
Int. Symp. on Comp. Architecture (ISCA) paper; v2, correct one circuit error,
numerous small changes for clarity, add reference
Learning in Real-Time Search: A Unifying Framework
Real-time search methods are suited for tasks in which the agent is
interacting with an initially unknown environment in real time. In such
simultaneous planning and learning problems, the agent has to select its
actions in a limited amount of time, while sensing only a local part of the
environment centered at the agents current location. Real-time heuristic search
agents select actions using a limited lookahead search and evaluating the
frontier states with a heuristic function. Over repeated experiences, they
refine heuristic values of states to avoid infinite loops and to converge to
better solutions. The wide spread of such settings in autonomous software and
hardware agents has led to an explosion of real-time search algorithms over the
last two decades. Not only is a potential user confronted with a hodgepodge of
algorithms, but he also faces the choice of control parameters they use. In
this paper we address both problems. The first contribution is an introduction
of a simple three-parameter framework (named LRTS) which extracts the core
ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*,
SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they
are unified and extended with additional features. Second, we prove
completeness and convergence of any algorithm covered by the LRTS framework.
Third, we prove several upper-bounds relating the control parameters and
solution quality. Finally, we analyze the influence of the three control
parameters empirically in the realistic scalable domains of real-time
navigation on initially unknown maps from a commercial role-playing game as
well as routing in ad hoc sensor networks
Reducing Message Collisions in Sensing-based Semi-Persistent Scheduling (SPS) by Using Reselection Lookaheads in Cellular V2X
In the C-V2X sidelink Mode 4 communication, the sensing-based semi-persistent
scheduling (SPS) implements a message collision avoidance algorithm to cope
with the undesirable effects of wireless channel congestion. Still, the current
standard mechanism produces high number of packet collisions, which may hinder
the high-reliability communications required in future C-V2X applications such
as autonomous driving. In this paper, we show that by drastically reducing the
uncertainties in the choice of the resource to use for SPS, we can
significantly reduce the message collisions in the C-V2X sidelink Mode 4.
Specifically, we propose the use of the "lookahead," which contains the next
starting resource location in the time-frequency plane. By exchanging the
lookahead information piggybacked on the periodic safety message, vehicular
user equipments (UEs) can eliminate most message collisions arising from the
ignorance of other UEs' internal decisions. Although the proposed scheme would
require the inclusion of the lookahead in the control part of the packet, the
benefit may outweigh the bandwidth cost, considering the stringent reliability
requirement in future C-V2X applications.Comment: Submitted to MDPI Sensor
Deep Reinforcement Learning-based Image Captioning with Embedding Reward
Image captioning is a challenging problem owing to the complexity in
understanding the image content and diverse ways of describing it in natural
language. Recent advances in deep neural networks have substantially improved
the performance of this task. Most state-of-the-art approaches follow an
encoder-decoder framework, which generates captions using a sequential
recurrent prediction model. However, in this paper, we introduce a novel
decision-making framework for image captioning. We utilize a "policy network"
and a "value network" to collaboratively generate captions. The policy network
serves as a local guidance by providing the confidence of predicting the next
word according to the current state. Additionally, the value network serves as
a global and lookahead guidance by evaluating all possible extensions of the
current state. In essence, it adjusts the goal of predicting the correct words
towards the goal of generating captions similar to the ground truth captions.
We train both networks using an actor-critic reinforcement learning model, with
a novel reward defined by visual-semantic embedding. Extensive experiments and
analyses on the Microsoft COCO dataset show that the proposed framework
outperforms state-of-the-art approaches across different evaluation metrics
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