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