51,158 research outputs found
One Dimensional ary Density Classification Using Two Cellular Automaton Rules
Suppose each site on a one-dimensional chain with periodic boundary condition
may take on any one of the states , can you find out the most
frequently occurring state using cellular automaton? Here, we prove that while
the above density classification task cannot be resolved by a single cellular
automaton, this task can be performed efficiently by applying two cellular
automaton rules in succession.Comment: Revtex, 4 pages, uses amsfont
Finding The Sign Of A Function Value By Binary Cellular Automaton
Given a continuous function , suppose that the sign of only has
finitely many discontinuous points in the interval . We show how to use
a sequence of one dimensional deterministic binary cellular automata to
determine the sign of where is the (number) density of 1s in
an arbitrarily given bit string of finite length provided that satisfies
certain technical conditions.Comment: Revtex, uses amsfonts, 10 page
Would Global Patent Protection be too Weak without International Coordination?
This paper analyzes the setting of national patent policies in the global economy. In the standard model with free trade and social-welfare-maximizing governments à la Grossman and Lai (2004), cross-border positive policy externalities induce individual countries to select patent strengths that are weaker than is optimal from a global perspective. The paper introduces three new features to the analysis: trade barriers, firm heterogeneity in terms of productivity and political economy considerations in setting patent policies. The first two features (trade barriers interacting with firm heterogeneity) tend to reduce the size of cross-border externalities in patent protection and therefore make national IPR policies closer to the global optimum. With firm lobbying creating profit-bias of the government, it is even possible that the equilibrium strength of global patent protection is greater than the globally efficient level. Thus, the question of under-protection or not is an empirical one. Based on calibration exercises, we find that there would be global under-protection of patent rights when there is no international policy coordination. Furthermore, requiring all countries to harmonize their patent standards with the equilibrium standard of the most innovative country (the US) does not lead to global over-protection of patent rights.intellectual property rights, patents, TRIPS, harmonization
Quantum computing on encrypted data
The ability to perform computations on encrypted data is a powerful tool for
protecting privacy. Recently, protocols to achieve this on classical computing
systems have been found. Here we present an efficient solution to the quantum
analogue of this problem that enables arbitrary quantum computations to be
carried out on encrypted quantum data. We prove that an untrusted server can
implement a universal set of quantum gates on encrypted quantum bits (qubits)
without learning any information about the inputs, while the client, knowing
the decryption key, can easily decrypt the results of the computation. We
experimentally demonstrate, using single photons and linear optics, the
encryption and decryption scheme on a set of gates sufficient for arbitrary
quantum computations. Because our protocol requires few extra resources
compared to other schemes it can be easily incorporated into the design of
future quantum servers. These results will play a key role in enabling the
development of secure distributed quantum systems
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
We aim at segmenting small organs (e.g., the pancreas) from abdominal CT
scans. As the target often occupies a relatively small region in the input
image, deep neural networks can be easily confused by the complex and variable
background. To alleviate this, researchers proposed a coarse-to-fine approach,
which used prediction from the first (coarse) stage to indicate a smaller input
region for the second (fine) stage. Despite its effectiveness, this algorithm
dealt with two stages individually, which lacked optimizing a global energy
function, and limited its ability to incorporate multi-stage visual cues.
Missing contextual information led to unsatisfying convergence in iterations,
and that the fine stage sometimes produced even lower segmentation accuracy
than the coarse stage.
This paper presents a Recurrent Saliency Transformation Network. The key
innovation is a saliency transformation module, which repeatedly converts the
segmentation probability map from the previous iteration as spatial weights and
applies these weights to the current iteration. This brings us two-fold
benefits. In training, it allows joint optimization over the deep networks
dealing with different input scales. In testing, it propagates multi-stage
visual information throughout iterations to improve segmentation accuracy.
Experiments in the NIH pancreas segmentation dataset demonstrate the
state-of-the-art accuracy, which outperforms the previous best by an average of
over 2%. Much higher accuracies are also reported on several small organs in a
larger dataset collected by ourselves. In addition, our approach enjoys better
convergence properties, making it more efficient and reliable in practice.Comment: Accepted to CVPR 2018 (10 pages, 6 figures
- …