2,859 publications from this institution
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
The outstanding performance of machine learning models across various fields enables them to be used in sensitive and high-risk activities such as healthcare, automated driving, and security services. However, the explainability of their outputs and the safety of their operation are major concerns. Although Explainable AI (XAI) can enhance the interpretability of AI models, further research is necessary to evaluate its effectiveness in explaining adversarial attacks. The use of XAI techniques and the rise of adversarial attacks are important issues related to the explainability and security of deep learning, respectively. Furthermore, the relation between the explainability and safety of deep learning, such as the vulnerability of the XAI explanation to an adversarial attack, is key to unlocking these concerns. In this paper, we use the Saliency Map as an XAI technique to explain the behavior of the Fast Gradient Sign Method (FGSM) adversarial attack on the ResNet model, and show the vulnerability related to such an explanation with respect to the attack. Extensive experiments show that as the severity of FGSM attack on the ResNet model increases, the Saliency Map gradually fails, exposing its potential vulnerability.
Abstract Spectrofluorometric measurements have been used to elaborate the self-aggregation of mixture of anioinic, sodium dodecylbenzenesulfonate (SDBS), and cationic gemini, alkanediyl-α, ω-bis (tetradecyldimethylammonium bromide) (14-4-14) with nonionic surfactant, polyoxyethylene 10 cetyl ether (Brij-56). The critical micelle concentration (cmc) of the binary mixtures has been investigated. Application of the regular solution theory (RST) to the experimental data yield the interaction parameter at mixed micelles (β), indicate an attractive interaction and reflect the synergistic behavior in both Brij-56/SDBS and Brij-56/14-4-14 systems. The micelle aggregation number (N agg) was measured using a steady state fluorescence quenching method. The N agg values of the mixed surfactant system were larger than those of pure components. The micropolarity of various combinations and the binding constants (K sv) were determined from the ratio of intensity of peaks (I 1/I 3) of pyrene fluorescence emission spectrum and its quenching, respectively. Keywords: Aggregation numbercritical micelle concentrationgemini surfactantmicropolarity Notes The literature values are given in parentheses. a Bakshi et al.[ Citation 30 ];. b Kabir-ud Din et al.[ Citation 31 ];. c Azum et al.[ Citation 32 ]. Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/ldis.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
