14,006 research outputs found
On non-recursive trade-offs between finite-turn pushdown automata
It is shown that between one-turn pushdown automata (1-turn PDAs) and deterministic finite automata (DFAs) there will be savings concerning the size of description not bounded by any recursive function, so-called non-recursive tradeoffs. Considering the number of turns of the stack height as a consumable resource of PDAs, we can show the existence of non-recursive trade-offs between PDAs performing k+ 1 turns and k turns for k >= 1. Furthermore, non-recursive trade-offs are shown between arbitrary PDAs and PDAs which perform only a finite number of turns. Finally, several decidability questions are shown to be undecidable and not semidecidable
Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
The design of spacecraft trajectories for missions visiting multiple
celestial bodies is here framed as a multi-objective bilevel optimization
problem. A comparative study is performed to assess the performance of
different Beam Search algorithms at tackling the combinatorial problem of
finding the ideal sequence of bodies. Special focus is placed on the
development of a new hybridization between Beam Search and the Population-based
Ant Colony Optimization algorithm. An experimental evaluation shows all
algorithms achieving exceptional performance on a hard benchmark problem. It is
found that a properly tuned deterministic Beam Search always outperforms the
remaining variants. Beam P-ACO, however, demonstrates lower parameter
sensitivity, while offering superior worst-case performance. Being an anytime
algorithm, it is then found to be the preferable choice for certain practical
applications.Comment: Code available at https://github.com/lfsimoes/beam_paco__gtoc
Asymptotic Analysis of Plausible Tree Hash Modes for SHA-3
Discussions about the choice of a tree hash mode of operation for a
standardization have recently been undertaken. It appears that a single tree
mode cannot address adequately all possible uses and specifications of a
system. In this paper, we review the tree modes which have been proposed, we
discuss their problems and propose remedies. We make the reasonable assumption
that communicating systems have different specifications and that software
applications are of different types (securing stored content or live-streamed
content). Finally, we propose new modes of operation that address the resource
usage problem for the three most representative categories of devices and we
analyse their asymptotic behavior
On the Role of Hadamard Gates in Quantum Circuits
We study a reduced quantum circuit computation paradigm in which the only
allowable gates either permute the computational basis states or else apply a
"global Hadamard operation", i.e. apply a Hadamard operation to every qubit
simultaneously. In this model, we discuss complexity bounds (lower-bounding the
number of global Hadamard operations) for common quantum algorithms : we
illustrate upper bounds for Shor's Algorithm, and prove lower bounds for
Grover's Algorithm. We also use our formalism to display a gate that is neither
quantum-universal nor classically simulable, on the assumption that Integer
Factoring is not in BPP.Comment: 16 pages, last section clarified, typos corrected, references added,
minor rewordin
MobileNetV2: Inverted Residuals and Linear Bottlenecks
In this paper we describe a new mobile architecture, MobileNetV2, that
improves the state of the art performance of mobile models on multiple tasks
and benchmarks as well as across a spectrum of different model sizes. We also
describe efficient ways of applying these mobile models to object detection in
a novel framework we call SSDLite. Additionally, we demonstrate how to build
mobile semantic segmentation models through a reduced form of DeepLabv3 which
we call Mobile DeepLabv3.
The MobileNetV2 architecture is based on an inverted residual structure where
the input and output of the residual block are thin bottleneck layers opposite
to traditional residual models which use expanded representations in the input
an MobileNetV2 uses lightweight depthwise convolutions to filter features in
the intermediate expansion layer. Additionally, we find that it is important to
remove non-linearities in the narrow layers in order to maintain
representational power. We demonstrate that this improves performance and
provide an intuition that led to this design. Finally, our approach allows
decoupling of the input/output domains from the expressiveness of the
transformation, which provides a convenient framework for further analysis. We
measure our performance on Imagenet classification, COCO object detection, VOC
image segmentation. We evaluate the trade-offs between accuracy, and number of
operations measured by multiply-adds (MAdd), as well as the number of
parameter
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