605 research outputs found
Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
Multiple hypothesis testing is a fundamental problem in high dimensional
inference, with wide applications in many scientific fields. In genome-wide
association studies, tens of thousands of tests are performed simultaneously to
find if any SNPs are associated with some traits and those tests are
correlated. When test statistics are correlated, false discovery control
becomes very challenging under arbitrary dependence. In the current paper, we
propose a novel method based on principal factor approximation, which
successfully subtracts the common dependence and weakens significantly the
correlation structure, to deal with an arbitrary dependence structure. We
derive an approximate expression for false discovery proportion (FDP) in large
scale multiple testing when a common threshold is used and provide a consistent
estimate of realized FDP. This result has important applications in controlling
FDR and FDP. Our estimate of realized FDP compares favorably with Efron
(2007)'s approach, as demonstrated in the simulated examples. Our approach is
further illustrated by some real data applications. We also propose a
dependence-adjusted procedure, which is more powerful than the fixed threshold
procedure.Comment: 51 pages, 7 figures. arXiv admin note: substantial text overlap with
arXiv:1012.439
Effects of Arbuscular Mycorrhizal Fungi on Accumulation of Heavy Metals in Rhizosphere Soil
The rhizosphere soil arbuscular mycorrhizal fungi will affect the absorption of heavy metal substances by the host plants. The effects of the arbuscular mycorrhizal fungi are inhibitory and conversion effects. The type and quantity of AMF fungi are different, and there are also differences in the absorption of arbuscular mycorrhizal fungi in the rhizosphere soil. Changes in the accumulation of heavy metals will affect the growth of arbuscular mycorrhizal fungi in the rhizosphere soil. In this paper, a preliminary investigation is made as to whether the AMF fungus number will affect the absorption of heavy metal Cd. Experiments show that with the increase of soil spores, the available cadmium content of soil also tends to increase
PROCESS OF EXTRACTING HIGH QUALITY PROTEINS FROM CEREAL GRANS AND THER BYPRODUCTS USING ACDIC MEDIUMAND A REDUCINGAGENT
The present invention is directed to a method for processing a plant-based protein source, the method comprising an acidic extracting solution comprising a reducing agent is useful for extracting and isolating proteins from plant-based protein SOUCS
Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation
Model hallucination has been a crucial interest of research in Natural
Language Generation (NLG). In this work, we propose sequence-level certainty as
a common theme over hallucination in NLG, and explore the correlation between
sequence-level certainty and the level of hallucination in model responses. We
categorize sequence-level certainty into two aspects: probabilistic certainty
and semantic certainty, and reveal through experiments on Knowledge-Grounded
Dialogue Generation (KGDG) task that both a higher level of probabilistic
certainty and a higher level of semantic certainty in model responses are
significantly correlated with a lower level of hallucination. What's more, we
provide theoretical proof and analysis to show that semantic certainty is a
good estimator of probabilistic certainty, and therefore has the potential as
an alternative to probability-based certainty estimation in black-box
scenarios. Based on the observation on the relationship between certainty and
hallucination, we further propose Certainty-based Response Ranking (CRR), a
decoding-time method for mitigating hallucination in NLG. Based on our
categorization of sequence-level certainty, we propose 2 types of CRR approach:
Probabilistic CRR (P-CRR) and Semantic CRR (S-CRR). P-CRR ranks individually
sampled model responses using their arithmetic mean log-probability of the
entire sequence. S-CRR approaches certainty estimation from meaning-space, and
ranks a number of model response candidates based on their semantic certainty
level, which is estimated by the entailment-based Agreement Score (AS). Through
extensive experiments across 3 KGDG datasets, 3 decoding methods, and on 4
different models, we validate the effectiveness of our 2 proposed CRR methods
to reduce model hallucination
S2vNTM: Semi-supervised vMF Neural Topic Modeling
Language model based methods are powerful techniques for text classification.
However, the models have several shortcomings. (1) It is difficult to integrate
human knowledge such as keywords. (2) It needs a lot of resources to train the
models. (3) It relied on large text data to pretrain. In this paper, we propose
Semi-Supervised vMF Neural Topic Modeling (S2vNTM) to overcome these
difficulties. S2vNTM takes a few seed keywords as input for topics. S2vNTM
leverages the pattern of keywords to identify potential topics, as well as
optimize the quality of topics' keywords sets. Across a variety of datasets,
S2vNTM outperforms existing semi-supervised topic modeling methods in
classification accuracy with limited keywords provided. S2vNTM is at least
twice as fast as baselines.Comment: 17 pages, 9 figures, ICLR Workshop 2023. arXiv admin note: text
overlap with arXiv:2307.0122
1-(3,5-DimethÂoxyÂbenzÂyl)-1H-pyrrole
The title compound, C13H15NO2, was synthesized from 3,5-dimethÂoxyÂbenzaldehyde. The dihedral angle between the pyrrole and benzene rings is 89.91 (5)°. In the crystal, weak C—H⋯O and C—H⋯π interactions link the molÂecules into a three-dimensional network
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the
presented items, plays a key role in online services. With two-decade rapid
development, nowadays the cumulated user behavior sequences on mature Internet
service platforms have become extremely long since the user's first
registration. Each user not only has intrinsic tastes, but also keeps changing
her personal interests during lifetime. Hence, it is challenging to handle such
lifelong sequential modeling for each individual user. Existing methodologies
for sequential modeling are only capable of dealing with relatively recent user
behaviors, which leaves huge space for modeling long-term especially lifelong
sequential patterns to facilitate user modeling. Moreover, one user's behavior
may be accounted for various previous behaviors within her whole online
activity history, i.e., long-term dependency with multi-scale sequential
patterns. In order to tackle these challenges, in this paper, we propose a
Hierarchical Periodic Memory Network for lifelong sequential modeling with
personalized memorization of sequential patterns for each user. The model also
adopts a hierarchical and periodical updating mechanism to capture multi-scale
sequential patterns of user interests while supporting the evolving user
behavior logs. The experimental results over three large-scale real-world
datasets have demonstrated the advantages of our proposed model with
significant improvement in user response prediction performance against the
state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets:
https://github.com/alimamarankgroup/HPM
- …