Does Logarithm Transformation of Microarray Data Affect Ranking Order of Differentially Expressed Genes?

A common practice in microarray analysis is to transform the microarray raw data (light intensity) by a logarithmic transformation, and the justification for this transformation is to make the distribution more symmetric and Gaussian-like. Since this transformation is not universally practiced in all microarray analysis, we examined whether the discrepancy of this treatment of raw data affect the "high level" analysis result. In particular, whether the differentially expressed genes as obtained by $t$-test, regularized t-test, or logistic regression have altered rank orders due to presence or absence of the transformation. We show that as much as 20%--40% of significant genes are "discordant" (significant only in one form of the data and not in both), depending on the test being used and the threshold value for claiming significance. The t-test is more likely to be affected by logarithmic transformation than logistic regression, and regularized $t$-test more affected than t-test. On the other hand, the very top ranking genes (e.g. up to top 20--50 genes, depending on the test) are not affected by the logarithmic transformation.Comment: submitted to IEEE/EMBS Conference'0

An Efficient Electronic Nose System for Odour Analysis and Assessment

University of Technology Sydney. Faculty of Engineering and Information Technology.An electronic nose (e-nose) is capable of identifying chemical compounds through sensing and analysing odour molecules. As a type of machine olfaction, e-nose plays a significant role in the odour analysis area and has received considerable attention from researchers all over the world. The e-nose system comprises a set of active gas sensors that detect the odour and transduce the chemical vapours into electrical signals. The odour "fingerprint" captured by the gas sensors can then be analysed and identified with pattern analysis methods, e.g., Principal Component Analysis (PCA), Cluster Analysis (CA), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs). E-nose has been extensively applied in the areas of agriculture, medical diagnosis, environmental monitoring and protection, food safety, the military, cosmetics and pharmaceuticals. In order to meet the growing demand from the global odour analysis market, a novel e-nose system, which has a high-efficiency and low-cost odour analysis, was designed and built in this dissertation through collaboration with different research areas. Firstly, inspired by the knowledge of the human olfactory system, an automated fault monitoring and alarming electronic nose (e-nose) system, named “NOS.E”, for odour detection and identification has been designed. This design is based on reliable hardware and software designs as well as an airflow intake system design which is the most significant part of NOS.E. Just as the air inhalations are important and necessary activities for the olfactory perception by controlling the airflow in the human olfactory system, the airflow control design is a crucial and essential element to guarantee the precise odour analysis procedure in the e-nose system. Different parts of the NOS.E are built together under a particular control logic, which was designed to improve the e-nose test efficiency by saving operation time. In addition, the fault detection and alarming design generates a high-reliability performance for the e-nose by constantly monitoring the working status of the air intake system, to make sure all the actuators are working under the guidance of the proposed control logic. A novel e-nose data pre-processing method, based on a recently developed nonparametric kernel-based modelling (KBM) approach is presented. The experimental results show that when extracting the derivative-related features from signals collected by the NOS.E, the proposed non-parametric KBM odour data pre-processing method achieves more reliable and stable pre-processing results compared with other pre-processing methods such as wavelet package correlation filter (WPCF), mean filter (MF), polynomial curve fitting (PCF) and locally weighted regression (LWR). Moreover, this dissertation also proposes a novel e-nose pattern analysis algorithm, which is a hybrid of genetic algorithm (GA) and supervised fuzzy support vector machine (FSVM). GA was used to select the informative features and the optimal model parameters of FSVM. FSVM was used as a fitness evaluation criterion and the sequent odour classifier, which would reduce the outlier effects to provide a robust classifier which has a steady classification accuracy. In addition, several studies were conducted with the NOS.E system. The first was to evaluate the performance of NOS.E based on data collected from different types of alcohols. A comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC◊GC-TOFMS) was used to provide the standard comparison for the evaluation in this study. The second study focused on the effectiveness of KBM data pre-processing method and FSVM odour pattern analysis method. The third study explores the potential to implement NOS.E in the biomedical engineering area, while the fourth study applied NOS.E in the wildlife protection area by rapidly identifying legal from illegal wildlife parts. As a proof-of-concept test, water buffalo horn and rhinoceros horn samples were selected as the test targets in this study. The study results indicated the reliability and effectiveness of the developed NOS.E system. The NOS.E system is able to be applied to various applications based on the user-friendly and rapid odour analysis tests. Moreover, the NOS.E has the potential to be a universal odour analysis platform implemented in different applications

QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data

Global horizontal irradiance (GHI) plays a vital role in estimating solar energy resources, which are used to generate sustainable green energy. In order to estimate GHI with high spatial resolution, a quantitative irradiance estimation network, named QIENet, is proposed. Specifically, the temporal and spatial characteristics of remote sensing data of the satellite Himawari-8 are extracted and fused by recurrent neural network (RNN) and convolution operation, respectively. Not only remote sensing data, but also GHI-related time information (hour, day, and month) and geographical information (altitude, longitude, and latitude), are used as the inputs of QIENet. The satellite spectral channels B07 and B11 - B15 and time are recommended as model inputs for QIENet according to the spatial distributions of annual solar energy. Meanwhile, QIENet is able to capture the impact of various clouds on hourly GHI estimates. More importantly, QIENet does not overestimate ground observations and can also reduce RMSE by 27.51%/18.00%, increase R2 by 20.17%/9.42%, and increase r by 8.69%/3.54% compared with ERA5/NSRDB. Furthermore, QIENet is capable of providing a high-fidelity hourly GHI database with spatial resolution 0.02{\deg} * 0.02{\deg}(approximately 2km * 2km) for many applied energy fields

Fingerprint Presentation Attack Detector Using Global-Local Model

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module; 2) the local module; and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by $\sim$10% in terms of TDR (91.19% versus 80.74%).Comment: This paper was accepted by IEEE Transactions on Cybernetics. Current version is updated with minor revisions on introduction and related work

Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing

Face recognition systems have raised concerns due to their vulnerability to different presentation attacks, and system security has become an increasingly critical concern. Although many face anti-spoofing (FAS) methods perform well in intra-dataset scenarios, their generalization remains a challenge. To address this issue, some methods adopt domain adversarial training (DAT) to extract domain-invariant features. However, the competition between the encoder and the domain discriminator can cause the network to be difficult to train and converge. In this paper, we propose a domain adversarial attack (DAA) method to mitigate the training instability problem by adding perturbations to the input images, which makes them indistinguishable across domains and enables domain alignment. Moreover, since models trained on limited data and types of attacks cannot generalize well to unknown attacks, we propose a dual perceptual and generative knowledge distillation framework for face anti-spoofing that utilizes pre-trained face-related models containing rich face priors. Specifically, we adopt two different face-related models as teachers to transfer knowledge to the target student model. The pre-trained teacher models are not from the task of face anti-spoofing but from perceptual and generative tasks, respectively, which implicitly augment the data. By combining both DAA and dual-teacher knowledge distillation, we develop a dual teacher knowledge distillation with domain alignment framework (DTDA) for face anti-spoofing. The advantage of our proposed method has been verified through extensive ablation studies and comparison with state-of-the-art methods on public datasets across multiple protocols

Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing

Biometric systems are vulnerable to Presentation Attacks (PA) performed using various Presentation Attack Instruments (PAIs). Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initialization of the PAD model is a crucial factor for the generalization, which is rarely discussed in the community. Based on such observation, we proposed a self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is based on a global-local view coupled with De-Folding and De-Mixing to derive the task-specific representation for PAD. During De-Folding, the proposed technique will learn region-specific features to represent samples in a local pattern by explicitly minimizing generative loss. While De-Mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by minimizing interpolation-based consistency. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both face and fingerprint PAD in more complicated and hybrid datasets when compared with state-of-the-art methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve an 18.60% Equal Error Rate (EER) in OULU-NPU and MSU-MFSD, exceeding baseline performance by 9.54%. The source code of the proposed technique is available at https://github.com/kongzhecn/dfdm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS

Novel-view Synthesis and Pose Estimation for Hand-Object Interaction from Sparse Views

Hand-object interaction understanding and the barely addressed novel view synthesis are highly desired in the immersive communication, whereas it is challenging due to the high deformation of hand and heavy occlusions between hand and object. In this paper, we propose a neural rendering and pose estimation system for hand-object interaction from sparse views, which can also enable 3D hand-object interaction editing. We share the inspiration from recent scene understanding work that shows a scene specific model built beforehand can significantly improve and unblock vision tasks especially when inputs are sparse, and extend it to the dynamic hand-object interaction scenario and propose to solve the problem in two stages. We first learn the shape and appearance prior knowledge of hands and objects separately with the neural representation at the offline stage. During the online stage, we design a rendering-based joint model fitting framework to understand the dynamic hand-object interaction with the pre-built hand and object models as well as interaction priors, which thereby overcomes penetration and separation issues between hand and object and also enables novel view synthesis. In order to get stable contact during the hand-object interaction process in a sequence, we propose a stable contact loss to make the contact region to be consistent. Experiments demonstrate that our method outperforms the state-of-the-art methods. Code and dataset are available in project webpage https://iscas3dv.github.io/HO-NeRF