94 research outputs found
Nonparametric Detection of Geometric Structures over Networks
Nonparametric detection of existence of an anomalous structure over a network
is investigated. Nodes corresponding to the anomalous structure (if one exists)
receive samples generated by a distribution q, which is different from a
distribution p generating samples for other nodes. If an anomalous structure
does not exist, all nodes receive samples generated by p. It is assumed that
the distributions p and q are arbitrary and unknown. The goal is to design
statistically consistent tests with probability of errors converging to zero as
the network size becomes asymptotically large. Kernel-based tests are proposed
based on maximum mean discrepancy that measures the distance between mean
embeddings of distributions into a reproducing kernel Hilbert space. Detection
of an anomalous interval over a line network is first studied. Sufficient
conditions on minimum and maximum sizes of candidate anomalous intervals are
characterized in order to guarantee the proposed test to be consistent. It is
also shown that certain necessary conditions must hold to guarantee any test to
be universally consistent. Comparison of sufficient and necessary conditions
yields that the proposed test is order-level optimal and nearly optimal
respectively in terms of minimum and maximum sizes of candidate anomalous
intervals. Generalization of the results to other networks is further
developed. Numerical results are provided to demonstrate the performance of the
proposed tests.Comment: Submitted for journal publication in November 2015. arXiv admin note:
text overlap with arXiv:1404.029
Nonparametric Detection of Anomalous Data Streams
A nonparametric anomalous hypothesis testing problem is investigated, in
which there are totally n sequences with s anomalous sequences to be detected.
Each typical sequence contains m independent and identically distributed
(i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence
contains m i.i.d. samples drawn from a distribution q that is distinct from p.
The distributions p and q are assumed to be unknown in advance.
Distribution-free tests are constructed using maximum mean discrepancy as the
metric, which is based on mean embeddings of distributions into a reproducing
kernel Hilbert space. The probability of error is bounded as a function of the
sample size m, the number s of anomalous sequences and the number n of
sequences. It is then shown that with s known, the constructed test is
exponentially consistent if m is greater than a constant factor of log n, for
any p and q, whereas with s unknown, m should has an order strictly greater
than log n. Furthermore, it is shown that no test can be consistent for
arbitrary p and q if m is less than a constant factor of log n, thus the
order-level optimality of the proposed test is established. Numerical results
are provided to demonstrate that our tests outperform (or perform as well as)
the tests based on other competitive approaches under various cases.Comment: Submitted to IEEE Transactions on Signal Processing, 201
Sample Complexity Characterization for Linear Contextual MDPs
Contextual Markov decision processes (CMDPs) describe a class of
reinforcement learning problems in which the transition kernels and reward
functions can change over time with different MDPs indexed by a context
variable. While CMDPs serve as an important framework to model many real-world
applications with time-varying environments, they are largely unexplored from
theoretical perspective. In this paper, we study CMDPs under two linear
function approximation models: Model I with context-varying representations and
common linear weights for all contexts; and Model II with common
representations for all contexts and context-varying linear weights. For both
models, we propose novel model-based algorithms and show that they enjoy
guaranteed -suboptimality gap with desired polynomial sample
complexity. In particular, instantiating our result for the first model to the
tabular CMDP improves the existing result by removing the reachability
assumption. Our result for the second model is the first-known result for such
a type of function approximation models. Comparison between our results for the
two models further indicates that having context-varying features leads to much
better sample efficiency than having common representations for all contexts
under linear CMDPs.Comment: accepted to AIstats202
Progress in ideotype breeding to increase rice yield potential
The ideotype approach has been used in breeding programs at the International Rice Research Institute (IRRI) and in China to improve rice yield potential. First-generation new plant type (NPT) lines developed from tropical japonica at IRRI did not yield well because of limited biomass production and poor grain filling. Progress has been made in second-generation NPT lines developed by crossing elite indica with improved tropical japonica. Several second-generation NPT lines outyielded the first-generation NPT lines and indica check varieties. China's "super"rice breeding project has developed many F1 hybrid varieties using a combination of the ideotype approach and intersubspecific heterosis. These hybrid varieties produced grain yield of 12 t ha-1 in on-farm demonstration fields, 8-15% higher than the hybrid check varieties. The success of China's "super" hybrid rice was partially the result of assembling the good components of IRRI's NPT design in addition to the use of intersubspecific heterosis. For example, both designs focused on large panicle size, reduced tillering capacity, and improved lodging resistance. More importantly, improvement in plant type design was achieved in China's "super" hybrid rice by emphasizing the top three leaves and panicle position within a canopy in order to meet the demand of heavy panicles for a large source supply. The success of "super"hybrid rice breeding in China and progress in NPT breeding at IRRI suggest that the ideotype approach is effective for breaking the yield ceiling of an irrigated rice crop
Comparison on Grain Quality Between Super Hybrid and Popular Inbred Rice Cultivars Under Two Nitrogen Management Practices
This study was conducted to determine the differences in grain quality traits between super hybrid and popular inbred rice cultivars grown under two nitrogen (N) management practices. Field experiments were done at the Experimental Farm of Guangxi University, Guangxi Province, China in early and late rice-growing seasons in 2014. Two representative super hybrid cultivars Liangyoupeijiu (LYPJ) and Y-liangyou 1 (YLY1) and a popular inbred rice cultivar Huanghuazhan (HHZ) were grown under fixed-time N management (FTNM) and site-specific N management (SSNM) practices in each season. Grain quality traits and N uptake were measured for each cultivar. LYPJ and YLY1 had higher milling efficiency, poorer appearance and palatability, and equal nutritional value than HHZ. The higher milling efficiency and poorer appearance in LYPJ and YLY1 were associated with their higher rice width compared with HHZ. Total N application rate was reduced by 15–20% under SSNM than under FTNM, whereas there was nearly no significant difference in grain quality between SSNM and FTNM. Our results suggest that (1) strategies for grain quality improvement in super hybrid rice should be focused on appearance and palatability, and (2) replacing FTNM with SSNM can reduce N input without sacrificing grain quality in rice production
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