Do we need entire training data for adversarial training?

Deep Neural Networks (DNNs) are being used to solve a wide range of problems in many domains including safety-critical domains like self-driving cars and medical imagery. DNNs suffer from vulnerability against adversarial attacks. In the past few years, numerous approaches have been proposed to tackle this problem by training networks using adversarial training. Almost all the approaches generate adversarial examples for the entire training dataset, thus increasing the training time drastically. We show that we can decrease the training time for any adversarial training algorithm by using only a subset of training data for adversarial training. To select the subset, we filter the adversarially-prone samples from the training data. We perform a simple adversarial attack on all training examples to filter this subset. In this attack, we add a small perturbation to each pixel and a few grid lines to the input image. We perform adversarial training on the adversarially-prone subset and mix it with vanilla training performed on the entire dataset. Our results show that when our method-agnostic approach is plugged into FGSM, we achieve a speedup of 3.52x on MNIST and 1.98x on the CIFAR-10 dataset with comparable robust accuracy. We also test our approach on state-of-the-art Free adversarial training and achieve a speedup of 1.2x in training time with a marginal drop in robust accuracy on the ImageNet dataset.Comment: 6 pages, 4 figure

A Framework For Microgrid Planning Using Multidisciplinary Design Optimization

Microgrids are local energy providers that can potentially reduce energy expenses and emissions by utilizing distributed energy resources (DERs) and are alternatives to existing centralized systems. This thesis investigates the optimal design and planning of such microgrids using a multidisciplinary design optimization approach based framework. Among a variety of DERs it is widely accepted that renewable resources of energy play an important role in providing a sustainable energy supply infrastructure, as they are both inexhaustible and nonpolluting. However the intermittent nature and the uncertainties associated with renewable technologies pose sufficient technological and economical challenges for system planners. Design of complex engineering systems has evolved into a multidisciplinary field of study. We develop a framework for design and planning of complex engineering systems under uncertainty using an approach of multidisciplinary design optimization under uncertainty (MDOUU). The framework has been designed to be general enough to be applicable to a large variety of complex engineering systems while it is simple to apply. MDOUU framework is a three stage planning strategy which allows the system planners to consider all aspects ranging from uncertainty in resources, technological feasibility, economics, and life cycle impacts of the system and choose an optimal design suited to their localized conditions. Motivation behind using MDOUU lies not only in the optimization of the individual systems or disciplines but also their interactions between each other. Following the modeling of the resources, a deterministic optimization model for planning microgirds is developed and results are evaluated using Monte Carlo simulations. Given the obvious limitations of the deterministic model in not being able to handle uncertainty efficiently and resulting in an expensive design we extended the model to a two stage stochastic programming model which provides a unified approach in determining the sizing of microgrids by considering uncertainty implicitly by means of scenarios. Probabilistic scenarios are developed using C-vine copulas that model nonlinear dependence. We evaluate the significance of the stochastic programming model using standardized metrics evaluating benefits of using the stochastic model. As any product or service needs to be evaluated for its environmental impacts, MDOUU provisions an LCA module that evaluates the environmental impacts and energy demands of the components of the system based on extensive literature and databases using openLCA as a tool. The overall system selection involves multiple criteria and interests of different stakeholders. This requires a multi-attribute decision system and a comprehensive ranking approach providing a list of possible configuration based on their relative importance as denoted by the stakeholders. We use Analytical Hierarchical Process (AHP) combined with compromise programming to rank a list of configurations based on economic and environmental attributes such as GHG emissions saved, cost of energy, annual energy production, net present value (NPV) etc. It allows the planners to make decisions considering the interests of a majority of stakeholders. The MDOUU framework proposed in this thesis with specific application to the microgrid planning problem contributes in helping the planners handle uncertainty of renewable resources of energy and environmental impacts in a systematic way. As such there is no method available in the literature which considers planning of microgrid using such holistic and multidisciplinary framework. The MDOUU framework is a generic tool and is useful for planning problems in a variety of complex systems

Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction

Adversarial patches threaten visual AI models in the real world. The number of patches in a patch attack is variable and determines the attack's potency in a specific environment. Most existing defenses assume a single patch in the scene, and the multiple patch scenarios are shown to overcome them. This paper presents a model-agnostic defense against patch attacks based on total variation for image resurfacing (TVR). The TVR is an image-cleansing method that processes images to remove probable adversarial regions. TVR can be utilized solely or augmented with a defended model, providing multi-level security for robust prediction. TVR nullifies the influence of patches in a single image scan with no prior assumption on the number of patches in the scene. We validate TVR on the ImageNet-Patch benchmark dataset and with real-world physical objects, demonstrating its ability to mitigate patch attack

A Holistic Approach to Achieving SDGs: A Case Study of Dayalbagh, India

The paper analyses the achievement of SDGs at the global level, national level (India) and regional levels (Dayalbagh, Agra) using the SDG Index and finds that the progress towards achieving the SDGs has either slowed, halted or reversed in recent years. The average global SDG index which was progressing consistently year-on-year from 2000-2019, declined post the onset of the pandemic. This decline has primarily been due to the reversal in progress towards SDG1 (No Poverty), SDG2 (No Hunger), SDG3 (Good Health and Well-being), SDG4 (Quality Education) and SDG8 (Decent Work and Economic Growth). At the national level, India is on track to achieving only two goals (SDG12- Responsible Consumption and Production and SDG13-Climate Action) while challenges remain in achieving the others (United Nations, 2022). Thus, ensuring achievement of SDGs requires a fundamental shift in approach to coping with persistent problems confronting contemporary modern societies. The paper thus describes the concept of holistic sustainability which addresses another approach - the inner dimension of sustainability based on people’s values, beliefs, attitudes, spiritual and intuitive consciousness and conscientiousness. This can provide the basis for a transformation and is likely to yield a more lasting solution to global challenges and crises. To elaborate on the role of holistic sustainability, we examine the case of Dayalbagh, a colony located in Agra, Uttar Pradesh, India as a model for achieving the SDGs. Dayalbagh attains the SDGs through an ecosystem enriched with values and a ‘Healthcare Habitat’ that is based on the Sigma Six Quality, Values and Attributes Model. The lifestyle of the residents of Dayalbagh, in harmony with nature, helps to attain holistic well-being of people and planet, fosters prosperity while simultaneously attaining holistic sustainability

Spatial Dependence Modeling of Wind Resource under Uncertainty Using C-Vine Copulas and Its Impact on Solar-Wind Energy Co-Generation

Narayan, A.; Ponnambalam, K.; Pagsuyoin, S.A. Spatial Dependence Modeling of Wind Resource under Uncertainty Using C-Vine Copulas and Its Impact on Solar-Wind Energy Co-Generation. Preprints 2017, 2017090053 (doi: 10.20944/preprints201709.0053.v1). This pre-print article first appeared on preprints.org and has been made available under a Creative Commons Attribution license which allows for re-use of the article with proper attribution: https://creativecommons.org/licenses/by/4.0/Investments in wind and solar power are driven by the aim to maximize the utilization of renewable energy (RE). This results in an increased concentration of wind farms at locations with higher average wind speeds and of solar panel installations at sites with higher average solar insolation. This is unfavourable for energy suppliers and for the overall economy when large power output fluctuations occur. Thus, when evaluating investment options for spatially distributed RE systems, it is necessary to model resource fluctuations and power output correlations between locations. In this paper, we propose a methodology for analyzing the spatial dependence, accurate modeling, and forecasting of wind power systems with special consideration to spatial dispersion of installation sites. We combine vine-copulas with the Kumaraswamy distribution to improve accuracy in forecasting wind power from spatially dispersed wind turbines and to model solar power generated at each location. We then integrate these methods to formulate an optimization model for allocating wind turbines and solar panels spatially, with an end goal of maximizing overall power generation while minimizing the variability in power output. A case study of wind and solar power systems in Central Ontario, Canada is also presented.Natural Sciences and Engineering Research Council of Canada (NSERC

Machine Learning Directed Aptamer Search from Conserved Primary Sequence and Secondary Structure

Computer-aided prediction of aptamer sequences has been focused on primary sequence alignment and motif comparison. We observed that many aptamers have a conserved hairpin, yet the sequence of the hairpin can be highly variable. Taking such a secondary structure information into consideration, a new algorithm combining conserved primary sequences and secondary structures is developed, that combines three scores based on sequence abundance, stability, and structure, respectively. This algorithm was used in the prediction of aptamers from caffeine and theophylline selections. In the late rounds of the selection, when the library was converged, the predicted sequences matched well with the most abundant sequences. When the library was far from convergence and the sequences were deemed impossible for traditional analysis methods, the algorithm still predicted aptamer sequences that were experimentally verified by isothermal titration calorimetry. This algorithm paves a new way to look for patterns in aptamer selection libraries and mimics the sequence evolution process. It will help shorten the aptamer selection time and promote the biosensor application of aptamers

Machine Learning Directed Aptamer Search from Conserved Primary Sequences and Secondary Structures

Computer-aided prediction of aptamer sequences has been focused on primary sequence alignment and motif comparison. We observed that many aptamers have a conserved hairpin, yet the sequence of the hairpin can be highly variable. Taking such secondary structure information into consideration, a new algorithm combining conserved primary sequences and secondary structures is developed, which combines three scores based on sequence abundance, stability, and structure, respectively. This algorithm was used in the prediction of aptamers from the caffeine and theophylline selections. In the late rounds of the selections, when the libraries were converged, the predicted sequences matched well with the most abundant sequences. When the libraries were far from convergence and the sequences were deemed challenging for traditional analysis methods, this algorithm still predicted aptamer sequences that were experimentally verified by isothermal titration calorimetry. This algorithm paves a new way to look for patterns in aptamer selection libraries and mimics the sequence evolution process. It will help shorten the aptamer selection time and promote the biosensor and chemical biology applications of aptamers

Not Available

Not AvailableThe study uses gas chromatography with tandem mass spectrometry (GC-MS/MS) to develop a reliable analytical approach for detecting multiclass pesticides, polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) in poultry meat and chicken eggs. The meat (2 g) and egg (4 g) samples were extracted with acidified acetonitrile (10 mL) as part of the optimized sample preparation technique. The cleanup consisted of freezing an aliquot of the extract (5 mL) at −20 °C, followed by dispersive solid phase extraction using 50 mg PSA + 100 mg C18+150 mg MgSO4. The matrix co-extractives were effectively removed and the method performance met the European Commission’s analytical quality control criteria (SANTE/12682/2019). The method was validated at two spiking levels (10 and 20 ng/g of 225 pesticides, 9 PAHs and 8 PCBs), and good recoveries (70–120%) and precision-RSDs (≤20%) were achieved for 90% of the targeted pesticide residues. For 80% of the compounds, the LOQs were ≤10 ng/g. The results of the intra-laboratory (involving six analysts) and inter-laboratory validation studies (involving eight ISO 17025 accredited laboratories) established satisfactory ruggedness and reproducibility. It created potential applications in commercial residue testing laboratories for regulatory compliance check purposes.Not Availabl