1,845 research outputs found
Planting pattern effects on soil water and yield of summer maize
Productivity and water use efficiency are important problems in sustainable agriculture, especially in high-demand water resource crops such as maize (Zea mays L). The aims of this research were to study plant and row spac- ing in maize, evaluating soil water content (SWC), yield and water use efficiency (WUE). A 3-year field experiment (2011–2013) was carried out in the north of China. The summer maize experiment consisted of five types of row spacing under the same planting density. The results showed that the SWC in 90–120 cm was higher than 0–30 cm, and soil water storage was a significant regression with advancing growth stage. A negative correlation was observed among yield, WUE and row spacing. The average yield of RS50 and RS40 was by 9.6% higher than that of RS70 and RS80, and the WUE of the RS40 and RS50 were significantly higher than RS60, RS70, and RS80. The study also indicated that increased productivity and WUE of rainfed summer maize can be reached via row spacing reduction and plant spacing widening under same planting density, and RS50 cm is regarded as the best planting system selection for the plains of Northern China
Enhanced Multimodal Representation Learning with Cross-modal KD
This paper explores the tasks of leveraging auxiliary modalities which are
only available at training to enhance multimodal representation learning
through cross-modal Knowledge Distillation (KD). The widely adopted mutual
information maximization-based objective leads to a short-cut solution of the
weak teacher, i.e., achieving the maximum mutual information by simply making
the teacher model as weak as the student model. To prevent such a weak
solution, we introduce an additional objective term, i.e., the mutual
information between the teacher and the auxiliary modality model. Besides, to
narrow down the information gap between the student and teacher, we further
propose to minimize the conditional entropy of the teacher given the student.
Novel training schemes based on contrastive learning and adversarial learning
are designed to optimize the mutual information and the conditional entropy,
respectively. Experimental results on three popular multimodal benchmark
datasets have shown that the proposed method outperforms a range of
state-of-the-art approaches for video recognition, video retrieval and emotion
classification.Comment: Accepted by CVPR202
Quantum Discord for Investigating Quantum Correlations without Entanglement in Solids
Quantum systems unfold diversified correlations which have no classical
counterparts. These quantum correlations have various different facets. Quantum
entanglement, as the most well known measure of quantum correlations, plays
essential roles in quantum information processing. However, it has recently
been pointed out that quantum entanglement cannot describe all the
nonclassicality in the correlations. Thus the study of quantum correlations in
separable states attracts widely attentions. Herein, we experimentally
investigate the quantum correlations of separable thermal states in terms of
quantum discord. The sudden change of quantum discord is observed, which
captures ambiguously the critical point associated with the behavior of
Hamiltonian. Our results display the potential applications of quantum
correlations in studying the fundamental properties of quantum system, such as
quantum criticality of non-zero temperature.Comment: 4 pages, 4 figure
Redundancy-Adaptive Multimodal Learning for Imperfect Data
Multimodal models trained on complete modality data often exhibit a
substantial decrease in performance when faced with imperfect data containing
corruptions or missing modalities. To address this robustness challenge, prior
methods have explored various approaches from aspects of augmentation,
consistency or uncertainty, but these approaches come with associated drawbacks
related to data complexity, representation, and learning, potentially
diminishing their overall effectiveness. In response to these challenges, this
study introduces a novel approach known as the Redundancy-Adaptive Multimodal
Learning (RAML). RAML efficiently harnesses information redundancy across
multiple modalities to combat the issues posed by imperfect data while
remaining compatible with the complete modality. Specifically, RAML achieves
redundancy-lossless information extraction through separate unimodal
discriminative tasks and enforces a proper norm constraint on each unimodal
feature representation. Furthermore, RAML explicitly enhances multimodal fusion
by leveraging fine-grained redundancy among unimodal features to learn
correspondences between corrupted and untainted information. Extensive
experiments on various benchmark datasets under diverse conditions have
consistently demonstrated that RAML outperforms state-of-the-art methods by a
significant margin
Coherence-protected Quantum Gate by Continuous Dynamical Decoupling in Diamond
To implement reliable quantum information processing, quantum gates have to
be protected together with the qubits from decoherence. Here we demonstrate
experimentally on nitrogen-vacancy system that by using continuous wave
dynamical decoupling method, not only the coherence time is prolonged by about
20 times, but also the quantum gates is protected for the duration of
controlling time. This protocol shares the merits of retaining the superiority
of prolonging the coherence time and at the same time easily combining with
quantum logic tasks. It is expected to be useful in task where duration of
quantum controlling exceeds far beyond the dephasing time.Comment: 5 pages, 4 figure
gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentation
Binary semantic segmentation in computer vision is a fundamental problem. As
a model-based segmentation method, the graph-cut approach was one of the most
successful binary segmentation methods thanks to its global optimality
guarantee of the solutions and its practical polynomial-time complexity.
Recently, many deep learning (DL) based methods have been developed for this
task and yielded remarkable performance, resulting in a paradigm shift in this
field. To combine the strengths of both approaches, we propose in this study to
integrate the graph-cut approach into a deep learning network for end-to-end
learning. Unfortunately, backward propagation through the graph-cut module in
the DL network is challenging due to the combinatorial nature of the graph-cut
algorithm. To tackle this challenge, we propose a novel residual graph-cut loss
and a quasi-residual connection, enabling the backward propagation of the
gradients of the residual graph-cut loss for effective feature learning guided
by the graph-cut segmentation model. In the inference phase, globally optimal
segmentation is achieved with respect to the graph-cut energy defined on the
optimized image features learned from DL networks. Experiments on the public
AZH chronic wound data set and the pancreas cancer data set from the medical
segmentation decathlon (MSD) demonstrated promising segmentation accuracy, and
improved robustness against adversarial attacks.Comment: 12 page
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