Beating Backdoor Attack at Its Own Game

Deep neural networks (DNNs) are vulnerable to backdoor attack, which does not affect the network's performance on clean data but would manipulate the network behavior once a trigger pattern is added. Existing defense methods have greatly reduced attack success rate, but their prediction accuracy on clean data still lags behind a clean model by a large margin. Inspired by the stealthiness and effectiveness of backdoor attack, we propose a simple but highly effective defense framework which injects non-adversarial backdoors targeting poisoned samples. Following the general steps in backdoor attack, we detect a small set of suspected samples and then apply a poisoning strategy to them. The non-adversarial backdoor, once triggered, suppresses the attacker's backdoor on poisoned data, but has limited influence on clean data. The defense can be carried out during data preprocessing, without any modification to the standard end-to-end training pipeline. We conduct extensive experiments on multiple benchmarks with different architectures and representative attacks. Results demonstrate that our method achieves state-of-the-art defense effectiveness with by far the lowest performance drop on clean data. Considering the surprising defense ability displayed by our framework, we call for more attention to utilizing backdoor for backdoor defense. Code is available at https://github.com/damianliumin/non-adversarial_backdoor.Comment: Accepted to ICCV 202

Prediction of landing gear loads using machine learning techniques

This article investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, the results on drop test data and flight test data will be examined. This article will focus on the use of Gaussian process regression for the prediction of loads on the components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the Gaussian process to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear using measurements that are readily available or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements are considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction

Optothermal Stability of Large ULE and Zerodur Mirrors

Marshall Space Flight Center's (MSFC) X-ray and Cryogenic Test Facility (XRCF) has tested the optothermal stability of two low-CTE, large-aperture mirrors in a thermal vacuum chamber. The mirrors deformed from several causes such as: thermal gradients, thermal soaks, coefficient of thermal expansion (CTE) gradients, CTE mismatch, and stiction. This paper focuses on how the aforementioned conditions affected the surface figure of the large optics while in vacuum at temperatures ranging from 230 to 310 K (-43 to 37 C). The presented data, conclusions, and taxonomy are useful for designing mirrors and support structures for telescopes. The data is particularly useful for telescopes that require extreme dimensional stability or telescopes that operate at a temperature far from ambient

Crash Test of Three Cessna 172 Aircraft at NASA Langley Research Center's Landing and Impact Research Facility

During the summer of 2015, three Cessna 172 aircraft were crash tested at the Landing and Impact Research Facility (LandIR) at NASA Langley Research Center (LaRC). The three tests simulated three different crash scenarios. The first simulated a flare-to-stall emergency or hard landing onto a rigid surface such as a road or runway, the second simulated a controlled flight into terrain with a nose down pitch on the aircraft, and the third simulated a controlled flight into terrain with an attempt to unsuccessfully recover the aircraft immediately prior to impact, resulting in a tail strike condition. An on-board data acquisition system captured 64 channels of airframe acceleration, along with acceleration and load in two onboard Hybrid II 50th percentile Anthropomorphic Test Devices, representing the pilot and co-pilot. Each test contained different airframe loading conditions and results show large differences in airframe performance. This paper presents test methods used to conduct the crash tests and will summarize the airframe results from the test series

Data driven modelling of crystalliser particle size distribution

Abstract. The aim of the thesis was to gain knowledge of the crystallisation process and to get insight on the process variables controlling it. To reach this aim static models were identified, and their performance was analysed. Measurement data from the crystallisation process was used as inputs to the developed model while the output was the D50 value of the particle size distribution. The measurement data was averaged for every 15 minutes and pre-processed by removing outliers and filtering. Two datasets were collected containing 21 input variables. Training data included measurements from a three-and-a-half-day period and test data from a two- and-a-half-day period. LASSO algorithm was used for input variable selection. The selected variable subsets contained 21, 19, 12 and 3 variables. Neural network models containing 15, 25, 35 or 40 neurons were trained and tested using these input variable subsets. Neural network models did not perform well with test data, as their mean absolute percentage error (MAPE) was over 20%. The models containing 12 input variables and 25 or 35 neurons or 19 input variables and 35 neurons performed the best with test data. MeSO4 and NH3 densities and crystalliser pH were the most important variables for modelling, according to the LASSO algorithm.Kiteyttimen partikkelikokojakauman datapohjainen mallinnus. Tiivistelmä. Työn tavoitteena oli lisätä prosessitietämystä kiteytysprosessista sekä saada lisätietoa reaktioon vaikuttavista muuttujista. Tavoitteeseen päästiin kiteytysprosess ista muodostettua staattista mallia analysoimalla. Prosessin mittainstrumenteista saatu mittausdata toimi mallin tulomuuttujina ja näytteiden analysoinnilla saadun partikkelikokojakauman D50-arvo lähtömuuttujana. Mittausdata keskiarvoistettiin 15 minuutin intervalleihin datan esikäsittelyä varten, jolloin datasta poistettiin poikkeamat sekä data suodatettiin. Prosessista kerättiin kaksi mittausaineistoa, jotka molemmat pitivät sisällään 21 muuttujaa. Opetusdata sisälsi mittausdataa 3,5 päivän tuotantojaksolta ja testidata 2,5 päivän tuotantojaksolta. LASSO- algoritmia käytettiin muuttujien valintaan. Valitut muuttujajoukot sisälsivät 21, 19, 12 ja 3 muuttujaa. Jokaisesta muuttujajoukosta luotiin neljä neuroverkkomallia. Neuroverkkojen neuroneiden määrät olivat 15, 25, 35 ja 40. Neuroverkkomallit suoriutuvat testidatalla kaiken kaikkiaan huonosti ja niiden suhteellinen virhe oli yli 20 %. Parhaiten testidatalla suoriutuvat neuroverkkomallit, joiden käyttämä muuttujajoukko sisälsi 12 muuttujaa ja joiden piilokerrokset sisälsivät 25 ja 35 neuronia sekä neuroverkko, joka käytti muuttujajoukossaan 19 muuttujaa ja jonka piilokerros sisälsi 35 neuronia. LASSO-algoritmin mukaan kolme tärkeintä muuttujaa olivat MeSO4- ja NH3-tiheydet sekä kiteyttimen pH

Analysis of alternative methods for long-term wind speed and initial site assessment for purposes of wind energy production estimation

A need exists for identification and evaluation of viable sites for wind energy. However, this is made difficult by the site-specific nature of the wind. A review of current industry practices shows that linear regression is the preferred method, although little work has been done to investigate alternative approaches. To address this, various methods were tested on 23 target sites with data measured over a long-term period. Each site was analyzed with the chosen best reference station, across 10 data periods for each method with results classified as mean absolute percentage error in energy density between the projected and measured results. Of primary concern was the comparative performance of these alternative methods to the current industry standard. The method that performed best was multiple regression analysis (MRA), which was 0.85% better than linear regression in absolute terms. The multilayer perceptron (MLP) artificial neural network (ANN) also exhibited promise in dealing with some of the non-linear aspects of the data sets, although its accuracy was found to be worse than linear regression by 0.79% in absolute terms. The affect of seasonality was also examined, showing extreme results in months of high and low wind speeds, but also results as close as 1.5% to the expected result when combining these extreme months to reach the expected mean wind speed

Data Efficient Learning: Towards Reducing Risk and Uncertainty of Data Driven Learning Paradigm

The success of Deep Learning in various tasks is highly dependent on the large amount of domain-specific annotated data, which are expensive to acquire and may contain varying degrees of noise. In this doctoral journey, our research goal is first to identify and then tackle the issues relating to data that causes significant performance degradation to real-world applications of Deep Learning algorithms. Human Activity Recognition from RGB data is challenging due to the lack of relative motion parameters. To address this issue, we propose a novel framework that introduces the skeleton information from RGB data for activity recognition. With experimentation, we demonstrate that our RGB-only solution surpasses the state-of-the-art, all exploit RGB-D video streams, by a notable margin. The predictive uncertainty of Deep Neural Networks (DNNs) makes them unreliable for real-world deployment. Moreover, available labeled data may contain noise. We aim to address these two issues holistically by proposing a unified density-driven framework, which can effectively denoise training data as well as avoid predicting uncertain test data points. Our plug-and-play framework is easy to deploy on real-world applications while achieving superior performance over state-of-the-art techniques. To assess effectiveness of our proposed framework in a real-world scenario, we experimented with x-ray images from COVID-19 patients. Supervised learning of DNNs inherits the limitation of a very narrow field of view in terms of known data distributions. Moreover, annotating data is costly. Hence, we explore self-supervised Siamese networks to avoid these constraints. Through extensive experimentation, we demonstrate that self supervised method perform surprisingly comparative to its supervised counterpart in a real world use-case. We also delve deeper with activation mapping and feature distribution visualization to understand the causality of this method. Through our research, we achieve a better understanding of issues relating to data-driven learning while solving some of the core problems of this paradigm and expose some novel and intriguing research questions to the community

Phonetic study and text mining of Spanish for English to Spanish translation system

Projecte realitzat en col.laboració amb el centre University of Southern Californi