Defending Against Local Adversarial Attacks through Empirical Gradient Optimization

Deep neural networks (DNNs) are susceptible to adversarial attacks, including the recently introduced locally visible adversarial patch attack, which achieves a success rate exceeding 96%. These attacks pose significant challenges to DNN security. Various defense methods, such as adversarial training, robust attention modules, watermarking, and gradient smoothing, have been proposed to enhance empirical robustness against patch attacks. However, these methods often have limitations concerning patch location requirements, randomness, and their impact on recognition accuracy for clean images.To address these challenges, we propose a novel defense algorithm called Local Adversarial Attack Empirical Defense using Gradient Optimization (LAAGO). The algorithm incorporates a low-pass filter before noise suppression to effectively mitigate the interference of high-frequency noise on the classifier while preserving the low-frequency areas of the images. Additionally, it emphasizes the original target features by enhancing the image gradients. Extensive experimental results demonstrate that the proposed method improves defense performance by 3.69% for 80 × 80 noise patches (representing approximately 4% of the images), while experiencing only a negligible 0.3% accuracy drop on clean images. The LAAGO algorithm provides a robust defense mechanism against local adversarial attacks, overcoming the limitations of previous methods. Our approach leverages gradient optimization, noise suppression, and feature enhancement, resulting in significant improvements in defense performance while maintaining high accuracy for clean images. This work contributes to the advancement of defense strategies against emerging adversarial attacks, thereby enhancing the security and reliability of deep neural networks

Electrodeposition and Electroless Deposition of Copper in Interconnect Technology and Biotechnology Related Applications

Electroless copper deposition is a promising alternative technique for interconnect fabrication because it may be difficult for electrodeposition to fill features that are 10 nm and smaller. An electroless copper bath of low toxicity and mild operating conditions with dimethylamine borane (DMAB) as reducing agent was examined. The results were interpreted within the framework of mixed potential theory, and a strong catalytic effect of the presence of the anodic reaction to the cathodic reaction was observed, possibly due to a significant pH decrease near the electrode that impacts the chelation of cupric ions. It was also found that the nucleus density not only depends on the type of substrates, but is also controllable by pH, temperature and the addition of PEG. Whether electroless deposition will be implemented in manufacturing is dependent on continued improvements in electrodeposition, and organic additives play a major role in efficacy. Development of advanced additives continues, and two lab scale tools that facilitate additive studies are designed and implemented. A membrane-separated cell was developed to effectively differentiate additive aging on the cathode side and anode side. Additive behavior in the cell was characterized by cyclic voltammetric stripping (CVS), and a significant aging effect of bis (3-sulfopropyl) disulfide (SPS) on the cathode side was observed. The rate of aging increases at higher acid level and lower current density, yet decreases in the presence of PEG. Complex wafer plating tools have been developed for use in manufacturing to guarantee a high uniformity of copper electrodeposition. Wafer size tools were “scaled down” for lab use by introduction of a simple insulating shield system for coupon size plating studies to avoid poor current distribution. By numerical simulation, sensitivities of parameters were systematically studied, with which the optimum shield design was made. The design reduced the spatial variation in current density from 18% to only 3%. Copper deposition can be applied to other applications, and naturally constraints on processing can be very different. For example, for biosensor applications, copper deposition can be a signal amplification method of binding to engineered phages. Chapter 5 demonstrates the use of electroless copper deposition to image phage with both high sensitivity and low operational time and cost. A correlation between copper quantity and phage concentration was established, with a limit of detection in ppt level. A pre-concentration device can be utilized to detect phage of lower concentrations without significantly increasing the time for detection. In another example, copper deposition may be used in an integrated process to produce a biofuel. Here, the process is akin to copper electrowinning processes albeit with modified electrolytes. A design of the electrowinning cell was built in-house and showed product yield capability and scalability to serve the bioreactor. Lower cell potentials were demonstrated by utilizing novel mixed metal oxides anodes. Copper deposit adhesion depended on electrolyte composition, and this impacts bioreactor performance. The current efficiency during electrowinning is between 70% and 90% depending on electrolyte composition

Empirical Analysis of Urban Sprawl in Canadian Census Metropolitan Areas using Satellite Imagery, 1986-2016

Major Canadian cities have experienced rapid sprawl in the last 30 years. This dissertation presents two studies that empirically examine the causes of urban sprawl, merging census socioeconomics data and satellite imageries of 11 major Census Metropolitan Areas (CMAs). The monocentric city model and the Tiebout model are the main traditional theories explaining urban boundary changes and mobility residential. The first study focuses on the cross-sectional comparison among the 11 CMAs in 2016. In the second study, we zoom into the Toronto CMA and examine the longitudinal changes in its urban coverage at the fringe. We detect land cover/use changes of the Toronto CMA in 1986-2016. In both studies, we insert the role of price risk in understanding the timing of urban development. In doing so, both studies aim to contribute to the literature by broadening the traditional theories to include the role of risk in influencing urban development

SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing

Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving, ubiquitous human-sensing applications, enabled by signal processing and deep-learning methods. However, a comprehensive public benchmark for deep learning in WiFi sensing, similar to that available for visual recognition, does not yet exist. In this article, we review recent progress in topics ranging from WiFi hardware platforms to sensing algorithms and propose a new library with a comprehensive benchmark, SenseFi. On this basis, we evaluate various deep-learning models in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, and feature transferability. Extensive experiments are performed whose results provide valuable insights into model design, learning strategy, and training techniques for real-world applications. In summary, SenseFi is a comprehensive benchmark with an open-source library for deep learning in WiFi sensing research that offers researchers a convenient tool to validate learning-based WiFi-sensing methods on multiple datasets and platforms.Nanyang Technological UniversityPublished versionThis research is supported by NTU Presidential Postdoctoral Fellowship, ‘‘Adaptive Multi-modal Learning for Robust Sensing and Recognition in Smart Cities’’ project fund (020977-00001), at the Nanyang Technological University, Singapore

Assessment of Urban Biodiversity: A Case Study of Beijing City, China

Habitat loss is the most important factor affecting biodiversity. Beijing is an international metropolis with rich biodiversity. With the development of urbanization, biodiversity has been affected to a certain extent in Beijing City. We investigated plant communities in three green land types, parks, residential areas, and roads along an urbanization gradient in the Beijing urban area (inner 6th ring road). Species composition, similarity index, and diversity of plants in urban areas were calculated. The results showed 536 species, belonging to 103 families, and 319 genera in the Beijing urban area. Among them, there were 361 native species and 175 imported species. Eighty species were imported from abroad and 95 species from inland, namely 14.9% and 17.7% of the total species, respectively. The species richness and diversity of trees and shrubs first increased and then decreased along the urbanization gradients, with the decreasing trend from the inner 2nd ring road and the increasing trend from the 3rd–4th ring road. No significant difference was found along the urbanization gradient for herbaceous plants. There were no significant differences in species evenness along the urbanization gradient

Variable structure intelligent control for mango drying with air source heat pump

Objective: To improve the energy efficiency of mango drying in the air source heat pump system so as to save energy. Methods: The process of drying mangoes was subdivided, and a variable structure control was used to adjust the temperature and humidity of drying room intelligently and dynamically to improve energy efficiency. Each drying process stage was divided into three parts, namely far away from the conversion point, near the conversion point, and closing to the conversion point. For the first two parts, a constrained nonlinear autoregressive neural network (NARX) with external inputs was used to intelligently adjust the temperature and humidity settings so as to save electricity, while for the third part, a PI controller was used to accurately control the dehumidification amount at the conversion point of the drying process so as to ensure the quality of mango drying. Results: Compared with conventional segmented constant temperature and humidity drying methods, the proposed control method could save 8.63% of electricity with a guaranteed quality of mango drying. Conclusion: The proposed subdivided variable structure control method can significantly improve the energy efficiency of heat pump drying systems, and achieve drying quality similar to conventional segmented constant temperature and humidity methods

Association between oxidative balance score and skeletal muscle mass and strength: NHANES from 2011 to 2018

ObjectiveOxidative stress is a risk factor for sarcopenia. The Oxidative Balance Score (OBS) is a widely employed tool for evaluating the oxidative stress-related exposures from dietary and lifestyle factors. In this study, we aimed to conducted to explore the relationship between OBS and skeletal muscle mass and strength.Methods6,438 subjects from 2011 to 2018 and 5,414 from 2011 to 2014 from the National Health and Nutrition Examination Survey (NHANES) were selected for analysis. The correlations between OBS and skeletal muscle mass and handgrip strength were investigated using multivariate logistic regression and linear regression analysis.ResultsCompared with lowest OBS, participants with OBS in the highest quartile had lower risk of low skeletal muscle mass (OR = 0.173 (0.120 ~ 0.248), p < 0.0001) and low handgrip strength (β = 0.173 (0.120 ~ 0.248), p = 0.011). The negative association also were found between dietary/lifestyle OBS and skeletal muscle mass (OR = 0.268 (0.178 ~ 0.404), p < 0.0001; OR = 0.231 (0.130 ~ 0.410), p < 0.0001) and handgrip strength (β = 1.812 (0.555 ~ 3.071), p = 0.008; β = −2.255 (−3.430 ~ −1.079), p < 0.001) independently. The positive association remains significant, especially among men and those with higher education levels by subgroup analysis.ConclusionAll of these results indicated a negative association between OBS and low skeletal muscle mass and handgrip strength. An antioxidant-rich diet and healthy lifestyle are crucial for enhancing skeletal muscle mass and strength