61 research outputs found
On the parity complexity measures of Boolean functions
The parity decision tree model extends the decision tree model by allowing
the computation of a parity function in one step. We prove that the
deterministic parity decision tree complexity of any Boolean function is
polynomially related to the non-deterministic complexity of the function or its
complement. We also show that they are polynomially related to an analogue of
the block sensitivity. We further study parity decision trees in their
relations with an intermediate variant of the decision trees, as well as with
communication complexity.Comment: submitted to TCS on 16-MAR-200
Scalable surface code decoders with parallelization in time
Fast classical processing is essential for most quantum fault-tolerance
architectures. We introduce a sliding-window decoding scheme that provides fast
classical processing for the surface code through parallelism. Our scheme
divides the syndromes in spacetime into overlapping windows along the time
direction, which can be decoded in parallel with any inner decoder. With this
parallelism, our scheme can solve the decoding throughput problem as the code
scales up, even if the inner decoder is slow. When using min-weight perfect
matching and union-find as the inner decoders, we observe circuit-level
thresholds of and , respectively, which are almost identical
to and for the batch decoding.Comment: Main text: 6 pages, 3 figures. Supplementary material: 18 pages, 14
figures. V2: added data and updated general formalis
The Communication Complexity of the Hamming Distance Problem
We investigate the randomized and quantum communication complexity of the
Hamming Distance problem, which is to determine if the Hamming distance between
two n-bit strings is no less than a threshold d. We prove a quantum lower bound
of \Omega(d) qubits in the general interactive model with shared prior
entanglement. We also construct a classical protocol of O(d \log d) bits in the
restricted Simultaneous Message Passing model, improving previous protocols of
O(d^2) bits (A. C.-C. Yao, Proceedings of the Thirty-Fifth Annual ACM Symposium
on Theory of Computing, pp. 77-81, 2003), and O(d\log n) bits (D. Gavinsky, J.
Kempe, and R. de Wolf, quant-ph/0411051, 2004).Comment: 8 pages, v3, updated reference. to appear in Information Processing
Letters, 200
Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature
BACKGROUND:
Information about drug-drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs.
RESULTS:
When evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively.
CONCLUSIONS:
We presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure
Special issue of BMC medical informatics and decision making on health natural language processing
https://deepblue.lib.umich.edu/bitstream/2027.42/148521/1/12911_2019_Article_777.pd
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