Distribution of protein components of wheat from different regions

The distribution of wheat protein components in different regions was researched to provide a theoretical basis on variety selection, quality improvement and food processing. 146 varieties from eight regions were collected to measure contents of protein components (albumin, globulin, gliadin and glutenin) in different regions for the distribution. The largest variation coefficient occurred in contents of globulin, followed by those of gliadin and albumin, while the contents of glutenin varied with the smallest range. The contents of all protein components belonged to normal distribution. It was discovered that the contents of albumin and globulin skewed towards the high value, while glutenin content skewed towards the low value. Differences on the contents of protein components existed in samples from different regions; the regional distribution of four protein components is: the northern region > the southern region > the western region > the eastern region. The contents of protein components of Yannong 19 in different regions were determined, the results displayed that the distributions of four protein components showed the same trends, although the highest contents occurred in Shanxi as compared to the other three regions (Shandong, Jiangsu and An’hui), and there were little differences among them. Geographical conditions can affect the protein components of wheat, and gliadin and glutenin content can affect wheat quality, so we can designate areas where wheat contains more gliadin and glutenin as our high-quality wheat producing areas, of which Shaanxi is a better choice.Keywords: Wheat, protein components, different regions, distributio

Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

Gait-based person re-identification (Re-ID) is valuable for safety-critical applications, and using only 3D skeleton data to extract discriminative gait features for person Re-ID is an emerging open topic. Existing methods either adopt hand-crafted features or learn gait features by traditional supervised learning paradigms. Unlike previous methods, we for the first time propose a generic gait encoding approach that can utilize unlabeled skeleton data to learn gait representations in a self-supervised manner. Specifically, we first propose to introduce self-supervision by learning to reconstruct input skeleton sequences in reverse order, which facilitates learning richer high-level semantics and better gait representations. Second, inspired by the fact that motion's continuity endows temporally adjacent skeletons with higher correlations ("locality"), we propose a locality-aware attention mechanism that encourages learning larger attention weights for temporally adjacent skeletons when reconstructing current skeleton, so as to learn locality when encoding gait. Finally, we propose Attention-based Gait Encodings (AGEs), which are built using context vectors learned by locality-aware attention, as final gait representations. AGEs are directly utilized to realize effective person Re-ID. Our approach typically improves existing skeleton-based methods by 10-20% Rank-1 accuracy, and it achieves comparable or even superior performance to multi-modal methods with extra RGB or depth information. Our codes are available at https://github.com/Kali-Hac/SGE-LA.Comment: Accepted at IJCAI 2020 Main Track. Sole copyright holder is IJCAI. Codes are available at https://github.com/Kali-Hac/SGE-L

Hierarchical TiO2 spheres assisted with graphene for a high performance lithium–sulfur battery

In this study, we report hierarchical TiO2 sphere–sulfur frameworks assisted with graphene as a cathode material for high performance lithium–sulfur batteries. With this strategy, the volume expansion and aggregation of sulfur nanoparticles can be effectively mitigated, thus enabling high sulfur utilization and improving the specific capacity and cycling stability of the electrode. Modification of the TiO2–S nanocomposites with graphene can trap the polysulfides via chemisorption and increase the electronic connection among various components. The graphene-assisted TiO2–S composite electrodes exhibit high specific capacity of 660 mA h g−1 at 5C with a capacity loss of only 0.04% per cycle in the prolonged charge–discharge processes at 1C

Towards Efficient Task-Driven Model Reprogramming with Foundation Models

Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data. However, in practice, downstream scenarios may only support a small model due to the limited computational resources or efficiency considerations. Moreover, the data used for pretraining foundation models are usually invisible and very different from the target data of downstream tasks. This brings a critical challenge for the real-world application of foundation models: one has to transfer the knowledge of a foundation model to the downstream task that has a quite different architecture with only downstream target data. Existing transfer learning or knowledge distillation methods depend on either the same model structure or finetuning of the foundation model. Thus, naively introducing these methods can be either infeasible or very inefficient. To address this, we propose a Task-Driven Model Reprogramming (TDMR) framework. Specifically, we reprogram the foundation model to project the knowledge into a proxy space, which alleviates the adverse effect of task mismatch and domain inconsistency. Then, we reprogram the target model via progressive distillation from the proxy space to efficiently learn the knowledge from the reprogrammed foundation model. TDMR is compatible with different pre-trained model types (CNN, transformer or their mix) and limited target data, and promotes the wide applications of vision foundation models to downstream tasks in a cost-effective manner. Extensive experiments on different downstream classification tasks and target model structures demonstrate the effectiveness of our methods with both CNNs and transformer foundation models

Sea Coral-like NiCo2O4@(Ni, Co)OOH Heterojunctions for Enhancing Overall Water-Splitting

It is highly challenging to develop efficient and low-cost catalysts to meet stringent requirements on high current density for industrial water electrolysis application. We developed sea coral-like NiCo2O4@(Ni, Co)OOH heterojunctions, synthesized based on an epitaxial in-grown method using poly(ethylene glycol) (PEG) as a template, and explored its as efficient electrocatalyst for water-splitting. A two-electrode based alkaline electrolyzer was fabricated using NiCo2O4@(Ni, Co)OOH|| NiCo2O4@(Ni, Co)OOH, which achieved a current density value of 100 mA.cm−2 with a low potential of 1.83 V and high current density approached 600 mA.cm−2 at potential of 2.1 V along with a strong stability. These are superior to most reported data for the electrocatalysts operated at high current densities. In-situ calculations based on density function theory reveal that the occurrence of water-splitting on the NiCo2O4@(Ni, Co)OOH heterojunction surface. First-principles molecular dynamics simulation reveals that the stretching vibrations of metallic bonds of NiCo2O4@(Ni, Co)OOH heterojunctions open the hydrogen bonds of water. Understanding the mechanism of water-splitting at the heterojunction from in-situ theoretical calculations is helpful to develop new generation industrial catalysts

CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation

We study the task of weakly-supervised point cloud semantic segmentation with sparse annotations (e.g., less than 0.1% points are labeled), aiming to reduce the expensive cost of dense annotations. Unfortunately, with extremely sparse annotated points, it is very difficult to extract both contextual and object information for scene understanding such as semantic segmentation. Motivated by masked modeling (e.g., MAE) in image and video representation learning, we seek to endow the power of masked modeling to learn contextual information from sparsely-annotated points. However, directly applying MAE to 3D point clouds with sparse annotations may fail to work. First, it is nontrivial to effectively mask out the informative visual context from 3D point clouds. Second, how to fully exploit the sparse annotations for context modeling remains an open question. In this paper, we propose a simple yet effective Contextual Point Cloud Modeling (CPCM) method that consists of two parts: a region-wise masking (RegionMask) strategy and a contextual masked training (CMT) method. Specifically, RegionMask masks the point cloud continuously in geometric space to construct a meaningful masked prediction task for subsequent context learning. CMT disentangles the learning of supervised segmentation and unsupervised masked context prediction for effectively learning the very limited labeled points and mass unlabeled points, respectively. Extensive experiments on the widely-tested ScanNet V2 and S3DIS benchmarks demonstrate the superiority of CPCM over the state-of-the-art.Comment: Accepted by ICCV 202