On the Gap between Scalar and Vector Solutions of Generalized Combination Networks

We study scalar-linear and vector-linear solutions to the generalized combination network. We derive new upper and lower bounds on the maximum number of nodes in the middle layer, depending on the network parameters. These bounds improve and extend the parameter range of known bounds. Using these new bounds we present a general lower bound on the gap in the alphabet size between scalar-linear and vector-linear solutions.Comment: 6 pages, 1 figures, accepted by ISIT 2020, revised according to the review

TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training

Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in open-vocabulary classification. The class token in the image encoder is trained to capture the global features to distinguish different text descriptions supervised by contrastive loss, making it highly effective for single-label classification. However, it shows poor performance on multi-label datasets because the global feature tends to be dominated by the most prominent class and the contrastive nature of softmax operation aggravates it. In this study, we observe that the multi-label classification results heavily rely on discriminative local features but are overlooked by CLIP. As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags. It comprises three steps: (1) patch-level classification to obtain coarse scores; (2) dual-masking attention refinement (DMAR) module to refine the coarse scores; (3) class-wise reidentification (CWR) module to remedy predictions from a global perspective. This framework is solely based on frozen CLIP and significantly enhances its multi-label classification performance on various benchmarks without dataset-specific training. Besides, to comprehensively assess the quality and practicality of generated tags, we extend their application to the downstream task, i.e., weakly supervised semantic segmentation (WSSS) with generated tags as image-level pseudo labels. Experiments demonstrate that this classify-then-segment paradigm dramatically outperforms other annotation-free segmentation methods and validates the effectiveness of generated tags. Our code is available at https://github.com/linyq2117/TagCLIP.Comment: Accepted by AAAI202

Numerical Simulation for Solitary Waves of the Generalized Zakharov Equation Based on the Lattice Boltzmann Method

The generalized Zakharov equation is a widely used and crucial model in plasma physics, which helps to understand wave particle interactions and nonlinear wave propagation in plasma. The solitary wave solution of this equation provides insights into phenomena such as electron and ion acoustic waves, as well as magnetic field disturbances in plasma. The numerical simulation of solitary wave solutions to the generalized Zakharov equation is an interesting problem worth studying. This is crucial for plasma-based technology, as well as for understanding nonlinear wave propagation in plasma physics and other fields. In this study, a numerical investigation of the generalized Zakharov equation using the lattice Boltzmann method has been conducted. The lattice Boltzmann method is a new modeling and simulating method at the mesoscale. A lattice Boltzmann model was constructed by performing Taylor expansion, Chapman–Enskog expansion, and time multiscale expansion on the lattice Boltzmann equation. By defining the moments of the equilibrium distribution function appropriately, the macroscopic equation can be restored. Furthermore, the numerical experiments for the equation are carried out with the parameter lattice size m=100, time step Δt=0.001, and space step size Δx=0.4. The solitary wave solution of the equation is numerically simulated. Numerical results under different parameter values are compared, and the convergence and effectiveness of the model are numerically verified. It is obtained that the model is convergent in time and space, and the convergence orders are all 2.24881. The effectiveness of our model was also verified by comparing the numerical results of different numerical methods. The lattice Boltzmann method demonstrates advantages in both accuracy and CPU time. The results indicate that the lattice Boltzmann method is a good tool for computing the generalized Zakharov equation

Ratiometric Colorimetric Detection of Nitrite Realized by Stringing Nanozyme Catalysis and Diazotization Together

Due to the great threat posed by excessive nitrite in food and drinking water to human health, it calls for developing reliable, convenient, and low-cost methods for nitrite detection. Herein, we string nanozyme catalysis and diazotization together and develop a ratiometric colorimetric approach for sensing nitrite in food. First, hollow MnFeO (a mixture of Mn and Fe oxides with different oxidation states) derived from a Mn-Fe Prussian blue analogue is explored as an oxidase mimic with high efficiency in catalyzing the colorless 3,3′,5,5′-tetramethylbenzidine (TMB) oxidation to blue TMBox, presenting a notable signal at 652 nm. Then, nitrite is able to trigger the diazotization of the product TMBox, not only decreasing the signal at 652 nm but also producing a new signal at 445 nm. Thus, the analyte-induced reverse changes of the two signals enable us to establish a ratiometric colorimetric assay for nitrite analysis. According to the above strategy, facile determination of nitrite in the range of 3.3–133.3 μM with good specificity was realized, providing a detection limit down to 0.2 μM. Compared with conventional single-signal analysis, our dual-signal ratiometric colorimetric mode was demonstrated to offer higher sensitivity, a lower detection limit, and better anti-interference ability against external detection environments. Practical applications of the approach in examining nitrite in food matrices were also verified

Analysis of Land Use Change Drivers and Simulation of Different Future Scenarios: Taking Shanxi Province of China as an Example

This study analyzed change and spatial patterns of land use in Shanxi from 2000 to 2020. The drivers of land use and cover change (LUCC) in cultivated lands, forest lands, grasslands, and rural construction areas were explored from four dimensions, including population, natural environment, location traffic, and economic development. The CA-Markov model was used to simulate the scenarios of natural growth (NG), ecological protection (EP), economic development (ED), food security (FS), ecological protection–economic development (EP-ED), and ecological protection–food security (EP-FS) in 2030. The results indicated that: (1) The conversion to built-up areas primarily dominated the LUCC processes, and their expansion was mainly to the detriment of the cultivated lands and grasslands during 2000–2020. (2) From 2000 to 2020, population, economy, and land productivity were the main factors of LUCC; the interaction of drivers for the increase of cultivated lands, forest lands, grasslands, and rural construction areas showed enhancement. (3) Under the NG, ED, and EP-ED scenarios, the rural construction areas would have increased significantly, while under the FS and EP-FS scenarios, the cultivated lands would only just have increased. These future land use scenarios can inform decision-makers to make sound decisions that balance socio-economic, ecological, and food security benefits

Modelling and Evaluation of Potato Water Production Functions in a Cold and Arid Environment

This study was conducted at the Yimin Irrigation Experiment Station, Minle County, Zhangye City, Gansu Province, from April to October in 2019 and 2020. The relationship between water consumption and yield of potato at different stages of fertility under deficit-regulated irrigation was analyzed in a field trial study over two growing seasons. The results showed that the average annual water consumption in the tuber bulking stage was the largest, reaching 185.35~239.52 mm, followed by the average annual water consumption in the tuber initiation stage and starch accumulation stage, which were 100.02~132.30 mm and 82.48~112.36 mm, respectively, and the average annual water consumption in the seedling stage was the least, at 49.32~69.81 mm. Simultaneously, the average annual yield of potatoes in the treatment of WD1 was the highest, reaching 47,766.96 kg·hm−2, followed by CK, which was 43,707.6 kg·hm−2, and the yield of WD6 was the smallest in the treatment of moderate water deficit during tuber initiation, which was only 35,721.25 kg·hm−2. Combining the four moisture production function models of Jensen, Minhas, Blank and Stewart, the Jensen and Stewart models were identified as suitable for the potato moisture production function in a cold and arid environment. The water production function model was used to investigate the relationship between water consumption and yield in each growth period of potato, and to provide a theoretical basis for the optimization of the irrigation system under deficit-regulating irrigation conditions for potato in this irrigation area

Optimizing water and nitrogen management strategies to improve their use efficiency, eggplant yield and fruit quality

With improvement in living standards, consumer preferences for vegetables are changing from quantity- to quality-oriented. Water and nitrogen supply, as two major determinants of vegetable crop yield and quality, can be optimally managed to improve the yield and quality. To evaluate the response in yield, fruit quality, and water and nitrogen utilization of eggplant to different water and nitrogen management strategies, a 2-year (2021 and 2022) field trial under mulched drip irrigation was conducted. The growth period was divided into seedling, flowering and fruit set, fruit development, and fruit ripening stages. Three irrigation levels were applied during the flowering and fruit set stage: W0, adequate water supply (70%–80% of field water capacity, FC); W1, mild water deficit (60%–70% FC); and W2, moderate water deficit (50%–60% FC). In addition, three nitrogen application rates were applied: N1, low nitrogen level (215 kg ha−1); N2, medium nitrogen level (270 kg ha−1); and N3, high nitrogen level (325 kg ha−1). The irrigation and nitrogen rates were applied in all combinations (i.e., nine treatments in total). Adequate water supply throughout the reproductive period in combination with no nitrogen application served as the control (CK). The yield of the W1N2 treatment was significantly increased by 32.62% and 35.06% in 2021 and 2022, respectively, compared with that of the CK. Fruit soluble protein, soluble solids, and vitamin C contents were significantly higher under W1 than W2. Fruit quality was significantly higher under the N2 rate compared with the other nitrogen rates. The W1N2 treatment showed the highest water productivity, with a significant increase of 11.27%–37.84% (2021) and 14.71%–42.48% (2022) compared with that under the other treatments. Based on the average water-deficit degree and nitrogen application rate, W0 and N1 had the highest partial factor productivity of nitrogen. Assessment of the results using the TOPSIS (technique for order preference by similarity to an ideal solution) method indicated that mild water deficit in combination with the medium nitrogen application rate (W1N2) was the optimal water and nitrogen management strategy for cultivated eggplant. The present findings contribute novel insights into the sustainable cultivation of eggplant in an oasis arid environment