426 research outputs found
Crossing Lilium Orientals of different ploidy creates Fusarium-resistant hybrid
Oriental hybrid lily is of great commercial value, but it is susceptible to Fusarium disease that causes a significant loss to the production. A diploid Oriental hybrid resistant to Fusarium, Cai-74, was diploidized from triploid obtained from the offspring of tetraploid (from ‘Star Fighter’) and diploid (‘Con Amore’, ‘Acapulco’) by screening the hybrids of different cross combinations following inoculating Fusarium oxysporum to the tissue cultured plantlets in a greenhouse. By analyzing saponins content in bulbs of a number of lily genotypes with a known Fusarium resistance, it was found that the mutant Cai-74 had a much higher content of saponin than its parents. Highly resistant wild _L. dauricum_ had the highest level (4.59mg/g), followed by the resistant Cai-74 with 4.01mg/g. The resistant OT cultivars ‘Conca d’or’ and ‘Robina’ had a higher saponins content (3.70 mg/g) and 2.83 mg/g, than the susceptible Oriental lily cultivars ‘Sorbonne’, ‘Siberia’ and ‘Tiber’. The hybrid Cai-74 had a different karyotype compared with the normal Lilium Oriental hybrid cultivars. The results suggested that Cai-74 carries a chromosomal variation correlated to Fusarium resistance. Cai-74 might be used as a genetic resource for breeding of Fusarium resistant cultivars of Oriental hybrid lilies
Graph Homomorphism Revisited for Graph Matching
In a variety of emerging applications one needs to decide whether a graph
G matches
another
G
p
,
i.e.
, whether
G
has a topological structure similar to that of
G
p
. The traditional notions of graph homomorphism and isomorphism often fall short of capturing the structural similarity in these applications. This paper studies revisions of these notions, providing a full treatment from complexity to algorithms. (1) We propose
p-homomorphism (p
-hom) and 1-1
p
-hom, which extend graph homomorphism and subgraph isomorphism, respectively, by mapping
edges
from one graph to
paths
in another, and by measuring
the similarity of nodes
. (2) We introduce metrics to measure graph similarity, and several optimization problems for
p
-hom and 1-1
p
-hom. (3) We show that the decision problems for
p
-hom and 1-1
p
-hom are NP-complete even for DAGs, and that the optimization problems are approximation-hard. (4) Nevertheless, we provide approximation algorithms with
provable guarantees
on match quality. We experimentally verify the effectiveness of the revised notions and the efficiency of our algorithms in Web site matching, using real-life and synthetic data.
</jats:p
Chinese Studies on Informal Logic and Critical Thinking
This article traces the developmental trajectory of informal logic and critical thinking in mainland China. It surveys the current developmental situation relating to their curricula, the establishment of teaching material, translations of leading works in these fields, academic writings, dissertations, research organizations and so on. Furthermore, the present article particularly aims to cast some light on the important shifts of research trends in informal logic and critical thinking, from those being introduced from outside to those moving in the opposite direction. Finally, it will also address the currently existing inadequacies and expectations for the future development of these fields of study in China
A Real-time Method for Inserting Virtual Objects into Neural Radiance Fields
We present the first real-time method for inserting a rigid virtual object
into a neural radiance field, which produces realistic lighting and shadowing
effects, as well as allows interactive manipulation of the object. By
exploiting the rich information about lighting and geometry in a NeRF, our
method overcomes several challenges of object insertion in augmented reality.
For lighting estimation, we produce accurate, robust and 3D spatially-varying
incident lighting that combines the near-field lighting from NeRF and an
environment lighting to account for sources not covered by the NeRF. For
occlusion, we blend the rendered virtual object with the background scene using
an opacity map integrated from the NeRF. For shadows, with a precomputed field
of spherical signed distance field, we query the visibility term for any point
around the virtual object, and cast soft, detailed shadows onto 3D surfaces.
Compared with state-of-the-art techniques, our approach can insert virtual
object into scenes with superior fidelity, and has a great potential to be
further applied to augmented reality systems
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification
Video-based person re-identification (re-ID) refers to matching people across
camera views from arbitrary unaligned video footages. Existing methods rely on
supervision signals to optimise a projected space under which the distances
between inter/intra-videos are maximised/minimised. However, this demands
exhaustively labelling people across camera views, rendering them unable to be
scaled in large networked cameras. Also, it is noticed that learning effective
video representations with view invariance is not explicitly addressed for
which features exhibit different distributions otherwise. Thus, matching videos
for person re-ID demands flexible models to capture the dynamics in time-series
observations and learn view-invariant representations with access to limited
labeled training samples. In this paper, we propose a novel few-shot deep
learning approach to video-based person re-ID, to learn comparable
representations that are discriminative and view-invariant. The proposed method
is developed on the variational recurrent neural networks (VRNNs) and trained
adversarially to produce latent variables with temporal dependencies that are
highly discriminative yet view-invariant in matching persons. Through extensive
experiments conducted on three benchmark datasets, we empirically show the
capability of our method in creating view-invariant temporal features and
state-of-the-art performance achieved by our method.Comment: Appearing at IEEE Transactions on Image Processin
- …