Influence of ridges for planting sweet potato on symbiotic ecological factors, photosynthetic abilities and population yield in relay intercropping system

Sweet potato (Ipomoea batatas L.) relay intercropping with maize exposes sweet potato to weak levels of sunlight due to the shadow maize creates during the symbiotic period. The insufficient light accordingly causes slow growth and development of vines and leaves of sweet potato in its early stage. The planting density and row direction of maize, the width of the intercropping strip, and the lodging type of sweet potato may form various photo conditions that influence sweet potato. The objective of this experiment was to research the effects of different ridging types on sweet potato and to elucidate the mechanisms of ecological conditions, photosynthetic physiology and intercropping benefits. The results indicated that, contrast to the one wide-ridge with planting two rows of sweet potato and two narrow ridges with planting two rows, mound planting was more superior in many aspects. Through mound planting sweet potato showed a larger range of temperature in air and soil, higher net photosynthetic rate, more active enzymes related with photosynthesis, and more benefits of relay intercropping

Simulation-based Validation for Autonomous Driving Systems

Simulation is essential to validate autonomous driving systems. However, a simple simulation, even for an extremely high number of simulated miles or hours, is not sufficient. We need well-founded criteria showing that simulation does indeed cover a large fraction of the relevant real-world situations. In addition, the validation must concern not only incidents, but also the detection of any type of potentially dangerous situation, such as traffic violations. We investigate a rigorous simulation and testing-based validation method for autonomous driving systems that integrates an existing industrial simulator and a formally defined testing environment. The environment includes a scenario generator that drives the simulation process and a monitor that checks at runtime the observed behavior of the system against a set of system properties to be validated. The validation method consists in extracting from the simulator a semantic model of the simulated system including a metric graph, which is a mathematical model of the environment in which the vehicles of the system evolve. The monitor can verify properties formalized in a first-order linear temporal logic and provide diagnostics explaining their non satisfaction. Instead of exploring the system behavior randomly as many simulators do, we propose a method to systematically generate sets of scenarios that cover potentially risky situations, especially for different types of junctions where specific traffic rules must be respected. We show that the systematic exploration of risky situations has uncovered many flaws in the real simulator that would have been very difficult to discover by a random exploration process

Fast-ParC: Position Aware Global Kernel for ConvNets and ViTs

Transformer models have made tremendous progress in various fields in recent years. In the field of computer vision, vision transformers (ViTs) also become strong alternatives to convolutional neural networks (ConvNets), yet they have not been able to replace ConvNets since both have their own merits. For instance, ViTs are good at extracting global features with attention mechanisms while ConvNets are more efficient in modeling local relationships due to their strong inductive bias. A natural idea that arises is to combine the strengths of both ConvNets and ViTs to design new structures. In this paper, we propose a new basic neural network operator named position-aware circular convolution (ParC) and its accelerated version Fast-ParC. The ParC operator can capture global features by using a global kernel and circular convolution while keeping location sensitiveness by employing position embeddings. Our Fast-ParC further reduces the O(n2) time complexity of ParC to O(n log n) using Fast Fourier Transform. This acceleration makes it possible to use global convolution in the early stages of models with large feature maps, yet still maintains the overall computational cost comparable with using 3x3 or 7x7 kernels. The proposed operation can be used in a plug-and-play manner to 1) convert ViTs to pure-ConvNet architecture to enjoy wider hardware support and achieve higher inference speed; 2) replacing traditional convolutions in the deep stage of ConvNets to improve accuracy by enlarging the effective receptive field. Experiment results show that our ParC op can effectively enlarge the receptive field of traditional ConvNets, and adopting the proposed op benefits both ViTs and ConvNet models on all three popular vision tasks, image classification, objectComment: 19 pages, 8 figures, 11 tables. A preliminary version of this paper has been published in ECCV 2022 and it can be find in arXiv:2203.0395

Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and Retention

Scene Graph Generation (SGG) offers a structured representation critical in many computer vision applications. Traditional SGG approaches, however, are limited by a closed-set assumption, restricting their ability to recognize only predefined object and relation categories. To overcome this, we categorize SGG scenarios into four distinct settings based on the node and edge: Closed-set SGG, Open Vocabulary (object) Detection-based SGG (OvD-SGG), Open Vocabulary Relation-based SGG (OvR-SGG), and Open Vocabulary Detection + Relation-based SGG (OvD+R-SGG). While object-centric open vocabulary SGG has been studied recently, the more challenging problem of relation-involved open-vocabulary SGG remains relatively unexplored. To fill this gap, we propose a unified framework named OvSGTR towards fully open vocabulary SGG from a holistic view. The proposed framework is an end-toend transformer architecture, which learns a visual-concept alignment for both nodes and edges, enabling the model to recognize unseen categories. For the more challenging settings of relation-involved open vocabulary SGG, the proposed approach integrates relation-aware pre-training utilizing image-caption data and retains visual-concept alignment through knowledge distillation. Comprehensive experimental results on the Visual Genome benchmark demonstrate the effectiveness and superiority of the proposed framework.Comment: 10 pages, 4 figures, 6 table

How to increase productivity of the copepod Acartia tonsa (Dana): effects of population density and food concentration

In this study, we analysed the effect of population density and food concentration on the fecundity of a Mediterranean strain of Acartia tonsa to maximize egg production. During 4-day feeding experiments, egg hatching success and faecal pellet production were also followed. The algae Rhinomonas reticulata was supplied at different concentrations corresponding to 250, 500, 1000, 1500, 2000 and 3000 μg C L−1 day−1 at the following adult copepod density: 40, 80 and 160 ind. L−1. Our results show a positive relationship between algal concentration and egg production under all experimental conditions confirming that the quantity of food strongly limits A. tonsa fecundity. Maximum egg production (57 eggs per female) was reached at the lowest density and at the maximum food concentration. Percentage of egg hatching success was not dependent on the quantity of food used. At the same food concentration, an increase in population density from 40 to 80 ind. L−1 induced an increase in faecal pellet production per couple which did not correspond to an increase in egg production, suggesting that higher energetic costs were shifted to swimming activity. Productivity of the A. tonsa Mediterranean strain is mainly limited by the quantity of food rather than by crowding conditions

Differential expressed genes in ECV304 Endothelial-like Cells infected with Human Cytomegalovirus

Background: Human cytomegalovirus (HCMV) is a virus which has the potential to alter cellular gene expression through multiple mechanisms.Objective: With the application of DNA microarrays, we could monitor the effects of pathogens on host-cell gene expression programmes in great depth and on a broad scale.Methods: Changes in mRNA expression levels of human endothelial-like ECV304 cells following infection with human cytomegalovirus AD169 strain was analyzed by a microarray system comprising 21073 60-mer oligonucleotide probes which represent 18716 human genes or transcripts.Results: The results from cDNA microarray showed that there were 559 differential expressed genes consisted of 471 upregulated genes and 88 down-regulated genes. Real-time qPCR was performed to validate the expression of 6 selected genes (RPS24, MGC8721, SLC27A3, MST4, TRAF2 and LRRC28), and the results of which were consistent with those from the microarray. Among 237 biology processes, 39 biology processes were found to be related significantly to HCMV-infection. The signal transduction is the most significant biological process with the lowest p value (p=0.005) among all biological process which involved in response to HCMV infection.Conclusion: Several of these gene products might play key roles in virus-induced pathogenesis. These findings may help to elucidate the pathogenic mechanisms of HCMV caused diseases.Keywords: Human cytomegalovirus, microarray, Gene expression profiling; infectomicsAfrican Health Sciences 2013; 13(4): 864 - 87

MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning

As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample. While contrastive learning has yielded continuous advancements in sampling strategy and architecture design, it still remains two persistent defects: the interference of task-irrelevant information and sample inefficiency, which are related to the recurring existence of trivial constant solutions. From the perspective of dimensional analysis, we find out that the dimensional redundancy and dimensional confounder are the intrinsic issues behind the phenomena, and provide experimental evidence to support our viewpoint. We further propose a simple yet effective approach MetaMask, short for the dimensional Mask learned by Meta-learning, to learn representations against dimensional redundancy and confounder. MetaMask adopts the redundancy-reduction technique to tackle the dimensional redundancy issue and innovatively introduces a dimensional mask to reduce the gradient effects of specific dimensions containing the confounder, which is trained by employing a meta-learning paradigm with the objective of improving the performance of masked representations on a typical self-supervised task. We provide solid theoretical analyses to prove MetaMask can obtain tighter risk bounds for downstream classification compared to typical contrastive methods. Empirically, our method achieves state-of-the-art performance on various benchmarks.Comment: Accepted by NeurIPS 202

Regulatory and functional divergence among members of Ibβfruct2, a sweet potato vacuolar invertase gene controlling starch and glucose content

Sweet potato [Ipomoea batatas (L.) Lam.] is an important food and industrial crop. Its storage root is rich in starch, which is present in the form of granules and represents the principal storage carbohydrate in plants. Starch content is an important trait of sweet potato controlling the quality and yield of industrial products. Vacuolar invertase encoding gene Ibβfruct2 was supposed to be a key regulator of starch content in sweet potato, but its function and regulation were unclear. In this study, three Ibβfruct2 gene members were detected. Their promoters displayed differences in sequence, activity, and cis-regulatory elements and might interact with different transcription factors, indicating that the three Ibβfruct2 family members are governed by different regulatory mechanisms at the transcription level. Among them, we found that only Ibβfruct2-1 show a high expression level and promoter activity, and encodes a protein with invertase activity, and the conserved domains and three conserved motifs NDPNG, RDP, and WEC are critical to this activity. Only two and six amino acid residue variations were detected in sequences of proteins encoded by Ibβfruct2-2 and Ibβfruct2-3, respectively, compared with Ibβfruct2-1; although not within key motifs, these variations affected protein structure and affinities for the catalytic substrate, resulting in functional deficiency and low activity. Heterologous expression of Ibβfruct2-1 in Arabidopsis decreased starch content but increased glucose content in leaves, indicating Ibβfruct2-1 was a negative regulator of starch content. These findings represent an important advance in understanding the regulatory and functional divergence among duplicated genes in sweet potato, and provide critical information for functional studies and utilization of these genes in genetic improvement