Boosting Adversarial Attack with Similar Target

Deep neural networks are vulnerable to adversarial examples, posing a threat to the models' applications and raising security concerns. An intriguing property of adversarial examples is their strong transferability. Several methods have been proposed to enhance transferability, including ensemble attacks which have demonstrated their efficacy. However, prior approaches simply average logits, probabilities, or losses for model ensembling, lacking a comprehensive analysis of how and why model ensembling significantly improves transferability. In this paper, we propose a similar targeted attack method named Similar Target~(ST). By promoting cosine similarity between the gradients of each model, our method regularizes the optimization direction to simultaneously attack all surrogate models. This strategy has been proven to enhance generalization ability. Experimental results on ImageNet validate the effectiveness of our approach in improving adversarial transferability. Our method outperforms state-of-the-art attackers on 18 discriminative classifiers and adversarially trained models

Space advanced technology demonstration satellite

The Space Advanced Technology demonstration satellite (SATech-01), a mission for low-cost space science and new technology experiments, organized by Chinese Academy of Sciences (CAS), was successfully launched into a Sun-synchronous orbit at an altitude of similar to 500 km on July 27, 2022, from the Jiuquan Satellite Launch Centre. Serving as an experimental platform for space science exploration and the demonstration of advanced common technologies in orbit, SATech-01 is equipped with 16 experimental payloads, including the solar upper transition region imager (SUTRI), the lobster eye imager for astronomy (LEIA), the high energy burst searcher (HEBS), and a High Precision Magnetic Field Measurement System based on a CPT Magnetometer (CPT). It also incorporates an imager with freeform optics, an integrated thermal imaging sensor, and a multi-functional integrated imager, etc. This paper provides an overview of SATech-01, including a technical description of the satellite and its scientific payloads, along with their on-orbit performance

Research on the Relation between Slump Flow and Yield Stress of Ultra-High Performance Concrete Mixtures

The relation between slump flow and yield stress of ultra-high performance concrete (UHPC) mixtures was studied with theoretical analysis and experimentation. The relational expression between slump flow and yield stress of UHPC mixtures was built and then verified with a rheological test. The results showed that the prediction model, as a function of cone geometry of dimensionless slump flow and dimensionless yield stress of the UHPC mixtures, was constructed based on Tresca criteria, considering the geometric relation of morphological characterization parameters before and after slump of the UHPC mixtures. The rationality and applicability of the dimensionless prediction model was verified with a rheological test and a slump test of UHPC mixtures with different dosages of polycarboxylate superplasticizer. With increase in polycarboxylate superplasticizer dosage, yield stress of the two series of UHPC mixtures (large/small binding material consumption) gradually decreased, leading to a gradual increase in slump flow. Based on the prediction model of dimensionless slump flow and dimensionless yield stress, the relational expression between slump flow and yield stress of the UHPC mixtures was built. The comparison result showed that the calculated data was consistent with the experimental data, which provided a new method for predicting yield stress of UHPC mixtures with a slump test

Factors Influencing the Capillary Water Absorption Characteristics of Concrete and Their Relationship to Pore Structure

The capillary absorption capacity exerts an important effect on the durability of cement-based materials and is closely related to the pore structure. In this study, a variety of cement-based specimens were examined. The capillary water absorption and pore structure of the samples were determined using a gravimetric method and mercury intrusion porosimetry (MIP), respectively. The capillary water absorption coefficients for different water–binder ratios, diverse types and dosages of mineral admixtures, and various preloads were measured. The experimental results were analyzed and compared with data available in the current literature. The test results showed that the capillary water absorption performance of cement-based materials increased with an increasing water–binder ratio, first decreased and then increased with an increasing fly ash dosage, decreased with an increasing mineral power dosage, and decreased when the preload was less than a critical value and increased rapidly when the preload was greater than the critical value. The relationship between the capillary absorption coefficient and porosity was nearly linear. Water absorption by cement-based materials mainly correlated with pore diameters in the range of 10~1000 nm. The capillary water absorption coefficient increased continuously with the increase of pore fractal dimension

Solution-Processed Organic Photovoltaics Based on Indoline Dye Molecules Developed in Dye-Sensitized Solar Cells

A donor-acceptor (D-A) type indoline dye, D149, was used as an electron donor in solution-processed organic solar cells (OSCs). For bulk-heterojunction (BHJ) type OSCs with PC70BM as electron acceptor, the power conversion efficiency (PCE) is sensitive to the amount of D149 in the D149/PC70BM blend film. When the concentration of D149 in the blend film was as low as 5%, the highest PCE of up to 1.29%, together with a short-circuit current density (Jsc) of 4.58 mA·cm−2, an open-circuit voltage (Voc) of 0.90 V and a fill factor (FF) of 0.31, was achieved. In order to improve the PCE of D149-based OSCs, a bilayer-heterojunction configuration with C70 as electron acceptor has been employed. By optimizing the thickness of the D149 layer and varying the electron- and hole-transport layers, a highest PCE of up to 2.28% with a Jsc of 4.38 mA·cm−2, a Voc of 0.77 V, and an FF of 0.62 was achieved under AM 1.5G solar illumination (100 mW·cm−2)

Solution-Processed Organic Photovoltaics Based on Indoline Dye Molecules Developed in Dye-Sensitized Solar Cells

A donor-acceptor (D-A) type indoline dye, D149, was used as an electron donor in solution-processed organic solar cells (OSCs). For bulk-heterojunction (BHJ) type OSCs with PC70BM as electron acceptor, the power conversion efficiency (PCE) is sensitive to the amount of D149 in the D149/PC70BM blend film. When the concentration of D149 in the blend film was as low as 5%, the highest PCE of up to 1.29%, together with a short-circuit current density (Jsc) of 4.58 mA·cm−2, an open-circuit voltage (Voc) of 0.90 V and a fill factor (FF) of 0.31, was achieved. In order to improve the PCE of D149-based OSCs, a bilayer-heterojunction configuration with C70 as electron acceptor has been employed. By optimizing the thickness of the D149 layer and varying the electron- and hole-transport layers, a highest PCE of up to 2.28% with a Jsc of 4.38 mA·cm−2, a Voc of 0.77 V, and an FF of 0.62 was achieved under AM 1.5G solar illumination (100 mW·cm−2)

Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints

Economic globalization is developing more rapidly than ever before. At the same time, economic growth is accompanied by energy consumption and carbon emissions, so it is particularly important to estimate, analyze and evaluate the economy accurately. We compared different nighttime light (NTL) index models with various constraint conditions and analyzed their relationships with economic parameters by linear correlation. In this study, three indices were selected, including original NTL, improved impervious surface index (IISI) and vegetation highlights nighttime-light index (VHNI). In the meantime, all indices were built in a linear regression relationship with gross domestic product (GDP), employed population and power consumption in southeast China. In addition, the correlation coefficient R2 was used to represent fitting degree. Overall, comparing the regression relationships with GDP of the three indices, VHNI performed best with the value of R2 at 0.8632. For the employed population and power consumption regression with these three indices, the maximum R2 of VHNI are 0.8647 and 0.7824 respectively, which are also the best performances in the three indices. For each individual province, the VHNI perform better than NTL and IISI in GDP regression, too. When taking employment population as the regression object, VHNI performs best in Zhejiang and Anhui provinces, but not all provinces. Finally, for power consumption regression, the value of VHNI R2 is better than NTL and IISI in every province except Hainan. The results show that, among the indices under different constraint conditions, the linear relationships between VHNI and GDP and power consumption are the strongest under vegetation constraint in southeast China. Therefore, VHNI index can be used for fitting analysis and prediction of economy and power consumption in the future

CGUN-2A: Deep Graph Convolutional Network via Contrastive Learning for Large-Scale Zero-Shot Image Classification

Taxonomy illustrates that natural creatures can be classified with a hierarchy. The connections between species are explicit and objective and can be organized into a knowledge graph (KG). It is a challenging task to mine features of known categories from KG and to reason on unknown categories. Graph Convolutional Network (GCN) has recently been viewed as a potential approach to zero-shot learning. GCN enables knowledge transfer by sharing the statistical strength of nodes in the graph. More layers of graph convolution are stacked in order to aggregate the hierarchical information in the KG. However, the Laplacian over-smoothing problem will be severe as the number of GCN layers deepens, which leads the features between nodes toward a tendency to be similar and degrade the performance of zero-shot image classification tasks. We consider two parts to mitigate the Laplacian over-smoothing problem, namely reducing the invalid node aggregation and improving the discriminability among nodes in the deep graph network. We propose a top-k graph pooling method based on the self-attention mechanism to control specific node aggregation, and we introduce a dual structural symmetric knowledge graph additionally to enhance the representation of nodes in the latent space. Finally, we apply these new concepts to the recently widely used contrastive learning framework and propose a novel Contrastive Graph U-Net with two Attention-based graph pooling (Att-gPool) layers, CGUN-2A, which explicitly alleviates the Laplacian over-smoothing problem. To evaluate the performance of the method on complex real-world scenes, we test it on the large-scale zero-shot image classification dataset. Extensive experiments show the positive effect of allowing nodes to perform specific aggregation, as well as homogeneous graph comparison, in our deep graph network. We show how it significantly boosts zero-shot image classification performance. The Hit@1 accuracy is 17.5% relatively higher than the baseline model on the ImageNet21K dataset