105,050 research outputs found
An Instance Transfer based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition
Recently, neural networks have shown promising results for named entity
recognition (NER), which needs a number of labeled data to for model training.
When meeting a new domain (target domain) for NER, there is no or a few labeled
data, which makes domain NER much more difficult. As NER has been researched
for a long time, some similar domain already has well labelled data (source
domain). Therefore, in this paper, we focus on domain NER by studying how to
utilize the labelled data from such similar source domain for the new target
domain. We design a kernel function based instance transfer strategy by getting
similar labelled sentences from a source domain. Moreover, we propose an
enhanced recurrent neural network (ERNN) by adding an additional layer that
combines the source domain labelled data into traditional RNN structure.
Comprehensive experiments are conducted on two datasets. The comparison results
among HMM, CRF and RNN show that RNN performs bette than others. When there is
no labelled data in domain target, compared to directly using the source domain
labelled data without selecting transferred instances, our enhanced RNN
approach gets improvement from 0.8052 to 0.9328 in terms of F1 measure
Set-Based Tests for Genetic Association Using the Generalized Berk-Jones Statistic
Studying the effects of groups of Single Nucleotide Polymorphisms (SNPs), as
in a gene, genetic pathway, or network, can provide novel insight into complex
diseases, above that which can be gleaned from studying SNPs individually.
Common challenges in set-based genetic association testing include weak effect
sizes, correlation between SNPs in a SNP-set, and scarcity of signals, with
single-SNP effects often ranging from extremely sparse to moderately sparse in
number. Motivated by these challenges, we propose the Generalized Berk-Jones
(GBJ) test for the association between a SNP-set and outcome. The GBJ extends
the Berk-Jones (BJ) statistic by accounting for correlation among SNPs, and it
provides advantages over the Generalized Higher Criticism (GHC) test when
signals in a SNP-set are moderately sparse. We also provide an analytic p-value
calculation procedure for SNP-sets of any finite size. Using this p-value
calculation, we illustrate that the rejection region for GBJ can be described
as a compromise of those for BJ and GHC. We develop an omnibus statistic as
well, and we show that this omnibus test is robust to the degree of signal
sparsity. An additional advantage of our method is the ability to conduct
inference using individual SNP summary statistics from a genome-wide
association study. We evaluate the finite sample performance of the GBJ though
simulation studies and application to gene-level association analysis of breast
cancer risk.Comment: Corrected typos in abstrac
Tur\'an's problem and Ramsey numbers for trees
Let and be the trees on vertices with
,
, and
. In this
paper, for we obtain explicit formulas for \ex(p;T_n^1) and
\ex(p;T_n^2), where \ex(p;L) denotes the maximal number of edges in a graph
of order not containing as a subgraph. Let r(G\sb 1, G\sb 2) be the
Ramsey number of the two graphs and . In this paper we also obtain
some explicit formulas for , where and is a
tree on vertices with .Comment: 21 page
On the Efficiency of Solving Boolean Polynomial Systems with the Characteristic Set Method
An improved characteristic set algorithm for solving Boolean polynomial
systems is proposed. This algorithm is based on the idea of converting all the
polynomials into monic ones by zero decomposition, and using additions to
obtain pseudo-remainders. Three important techniques are applied in the
algorithm. The first one is eliminating variables by new generated linear
polynomials. The second one is optimizing the strategy of choosing polynomial
for zero decomposition. The third one is to compute add-remainders to eliminate
the leading variable of new generated monic polynomials. By analyzing the depth
of the zero decomposition tree, we present some complexity bounds of this
algorithm, which are lower than the complexity bounds of previous
characteristic set algorithms. Extensive experimental results show that this
new algorithm is more efficient than previous characteristic set algorithms for
solving Boolean polynomial systems
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels
Graph Convolutional Networks(GCNs) play a crucial role in graph learning
tasks, however, learning graph embedding with few supervised signals is still a
difficult problem. In this paper, we propose a novel training algorithm for
Graph Convolutional Network, called Multi-Stage Self-Supervised(M3S) Training
Algorithm, combined with self-supervised learning approach, focusing on
improving the generalization performance of GCNs on graphs with few labeled
nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of
M3S training method. Then we leverage DeepCluster technique, a popular form of
self-supervised learning, and design corresponding aligning mechanism on the
embedding space to refine the Multi-Stage Training Framework, resulting in M3S
Training Algorithm. Finally, extensive experimental results verify the superior
performance of our algorithm on graphs with few labeled nodes under different
label rates compared with other state-of-the-art approaches.Comment: AAAI Conference on Artificial Intelligence (AAAI 2020
Verification of mixing properties in two-dimensional shifts of finite type
The degree of mixing is a fundamental property of a dynamical system. General
multi-dimensional shifts cannot be systematically determined. This work
introduces constructive and systematic methods for verifying the degree of
mixing, from topological mixing to strong specification (or strong
irreducibility) for two-dimensional shifts of finite type. First, transition
matrices on infinite strips of width are introduced for all . To
determine the primitivity of the transition matrices, connecting operators are
introduced to reduce the order of high-order transition matrices to yield
lower-order transition matrices. Two sufficient conditions for primitivity are
provided; they are invariant diagonal cycles and primitive commutative cycles
of connecting operators. After primitivity is established, the
corner-extendability and crisscross-extendability are used to demonstrate
topological mixing. In addition, the hole-filling condition yields the strong
specification. All mentioned conditions can be verified to apply in a finite
number of steps
The natural measure of a symbolic dynamical system
This study investigates the natural or intrinsic measure of a symbolic
dynamical system . The measure of a
pattern in is an asymptotic ratio of
, which arises in all patterns of length within
very long patterns, such that in a typical long pattern, the pattern
appears with frequency
. When is a shift of finite
type and is an irreducible non-negative matrix, the measure
is the Parry measure. is ergodic with maximum entropy. The result
holds for sofic shift , which is irreducible. The
result can be extended to , where is a countably infinite matrix
that is irreducible, aperiodic and positive recurrent. By using the Krieger
cover, the natural measure of a general shift space is studied in the way of a
countably infinite state of sofic shift, including context free shift. The
Perron-Frobenius Theorem for non-negative matrices plays an essential role in
this study
MVW-extensions of real quaternionic classical groups
Let be a real quaternionic classical group \GL_n(\bH), \Sp(p,q) or
\oO^*(2n). We define an extension of with the following
property: it contains as a subgroup of index two, and for every ,
there is an element such that . This is similar to Moeglin-Vigneras-Waldspurger's
extensions of non-quaternionic classical groups
Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors
Most previous works usually explained adversarial examples from several
specific perspectives, lacking relatively integral comprehension about this
problem. In this paper, we present a systematic study on adversarial examples
from three aspects: the amount of training data, task-dependent and
model-specific factors. Particularly, we show that adversarial generalization
(i.e. test accuracy on adversarial examples) for standard training requires
more data than standard generalization (i.e. test accuracy on clean examples);
and uncover the global relationship between generalization and robustness with
respect to the data size especially when data is augmented by generative
models. This reveals the trade-off correlation between standard generalization
and robustness in limited training data regime and their consistency when data
size is large enough. Furthermore, we explore how different task-dependent and
model-specific factors influence the vulnerability of deep neural networks by
extensive empirical analysis. Relevant recommendations on defense against
adversarial attacks are provided as well. Our results outline a potential path
towards the luminous and systematic understanding of adversarial examples
ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning
Machine learning pipeline potentially consists of several stages of
operations like data preprocessing, feature engineering and machine learning
model training. Each operation has a set of hyper-parameters, which can become
irrelevant for the pipeline when the operation is not selected. This gives rise
to a hierarchical conditional hyper-parameter space. To optimize this mixed
continuous and discrete conditional hierarchical hyper-parameter space, we
propose an efficient pipeline search and configuration algorithm which combines
the power of Reinforcement Learning and Bayesian Optimization. Empirical
results show that our method performs favorably compared to state of the art
methods like Auto-sklearn , TPOT, Tree Parzen Window, and Random Search
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