Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems

It is unknown what kind of biases modern in the wild face datasets have because of their lack of annotation. A direct consequence of this is that total recognition rates alone only provide limited insight about the generalization ability of a Deep Convolutional Neural Networks (DCNNs). We propose to empirically study the effect of different types of dataset biases on the generalization ability of DCNNs. Using synthetically generated face images, we study the face recognition rate as a function of interpretable parameters such as face pose and light. The proposed method allows valuable details about the generalization performance of different DCNN architectures to be observed and compared. In our experiments, we find that: 1) Indeed, dataset bias has a significant influence on the generalization performance of DCNNs. 2) DCNNs can generalize surprisingly well to unseen illumination conditions and large sampling gaps in the pose variation. 3) Using the presented methodology we reveal that the VGG-16 architecture outperforms the AlexNet architecture at face recognition tasks because it can much better generalize to unseen face poses, although it has significantly more parameters. 4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation. 5) We demonstrate that our findings on synthetic data also apply when learning from real-world data. Our face image generator is publicly available to enable the community to benchmark other DCNN architectures.Comment: Accepted to CVPR 2018 Workshop on Analysis and Modeling of Faces and Gestures (AMFG

Childhood leukaemia and socioeconomic status: what is the evidence?

The objectives of this systematic review are to summarise the current literature on socioeconomic status (SES) and the risk of childhood leukaemia, to highlight methodological problems and formulate recommendations for future research. Starting from the systematic review of Poole et al. (Socioeconomic status and childhood leukaemia: a review. Int. J. Epidemiol. 2006;35(2):370-384.), an electronic literature search was performed covering August 2002-April 2008. It showed that (1) the results are heterogeneous, with no clear evidence to support a relation between SES and childhood leukaemia; (2) a number of factors, most importantly selection bias, might explain inconsistencies between studies; (3) there is some support for an association between SES at birth (rather than later in childhood) and childhood leukaemia and (4) if there are any associations, these are weak, limited to the most extreme SES groups (the 10-20% most or least deprived). This makes it unlikely that they would act as strong confounders in research addressing associations between other exposures and childhood leukaemia. Future research should minimise case and control selection bias, distinguish between different SES measures and leukaemia subtypes and consider timing of exposures and cancer outcome

Safety of 80% vs 30–35% fraction of inspired oxygen in patients undergoing surgery: a systematic review and meta-analysis

Background: Evidence-based guidelines from the World Health Organization (WHO) have recommended a high (80%) fraction of inspired oxygen (FiO2) to reduce surgical site infection in adult surgical patients undergoing general anaesthesia with tracheal intubation. However, there is ongoing debate over the safety of high FiO2. We performed a systematic review to define the relative risk of clinically relevant adverse events (AE) associated with high FiO2. Methods: We reviewed potentially relevant articles from the WHO review supporting the recommendation, including an updated (July 2018) search of EMBASE and PubMed for randomised and non-randomised controlled studies reporting AE in surgical patients receiving 80% FiO2 compared with 30–35% FiO2. We assessed study quality and performed meta-analyses of risk ratios (RR) comparing 80% FiO2 against 30–35% for major complications, mortality, and intensive care admission. Results: We included 17 moderate–good quality trials and two non-randomised studies with serious-critical risk of bias. No evidence of harm with high FiO2 was found for major AE in the meta-analysis of randomised trials: atelectasis RR 0.91 [95% confidence interval (CI) 0.59–1.42); cardiovascular events RR 0.90 (95% CI 0.32–2.54); intensive care admission RR 0.93 (95% CI 0.7–1.12); and death during the trial RR 0.49 (95% CI 0.17–1.37). One non-randomised study reported that high FiO2 was associated with major respiratory AE [RR 1.99 (95% CI 1.72–2.31)]. Conclusions: No definite signal of harm with 80% FiO2 in adult surgical patients undergoing general anaesthesia was demonstrated and there is little evidence on safety-related issues to discourage its use in this population

Heterotrimeric Go protein links Wnt-Frizzled signaling with ankyrins to regulate the neuronal microtubule cytoskeleton.

Drosophila neuromuscular junctions (NMJs) represent a powerful model system with which to study glutamatergic synapse formation and remodeling. Several proteins have been implicated in these processes, including components of canonical Wingless (Drosophila Wnt1) signaling and the giant isoforms of the membrane-cytoskeleton linker Ankyrin 2, but possible interconnections and cooperation between these proteins were unknown. Here, we demonstrate that the heterotrimeric G protein Go functions as a transducer of Wingless-Frizzled 2 signaling in the synapse. We identify Ankyrin 2 as a target of Go signaling required for NMJ formation. Moreover, the Go-ankyrin interaction is conserved in the mammalian neurite outgrowth pathway. Without ankyrins, a major switch in the Go-induced neuronal cytoskeleton program is observed, from microtubule-dependent neurite outgrowth to actin-dependent lamellopodial induction. These findings describe a novel mechanism regulating the microtubule cytoskeleton in the nervous system. Our work in Drosophila and mammalian cells suggests that this mechanism might be generally applicable in nervous system development and function

Dynamical trust and reputation computation model for B2C E-Commerce

Trust is one of the most important factors that influence the successful application of network service environments, such as e-commerce, wireless sensor networks, and online social networks. Computation models associated with trust and reputation have been paid special attention in both computer societies and service science in recent years. In this paper, a dynamical computation model of reputation for B2C e-commerce is proposed. Firstly, conceptions associated with trust and reputation are introduced, and the mathematical formula of trust for B2C e-commerce is given. Then a dynamical computation model of reputation is further proposed based on the conception of trust and the relationship between trust and reputation. In the proposed model, classical varying processes of reputation of B2C e-commerce are discussed. Furthermore, the iterative trust and reputation computation models are formulated via a set of difference equations based on the closed-loop feedback mechanism. Finally, a group of numerical simulation experiments are performed to illustrate the proposed model of trust and reputation. Experimental results show that the proposed model is effective in simulating the dynamical processes of trust and reputation for B2C e-commerce

New constraint on the existence of the mu+-> e+ gamma decay

The analysis of a combined data set, totaling 3.6 \times 10^14 stopped muons on target, in the search for the lepton flavour violating decay mu^+ -> e^+ gamma is presented. The data collected by the MEG experiment at the Paul Scherrer Institut show no excess of events compared to background expectations and yield a new upper limit on the branching ratio of this decay of 5.7 \times 10^-13 (90% confidence level). This represents a four times more stringent limit than the previous world best limit set by MEG.Comment: 5 pages, 3 figures, a version accepted in Phys. Rev. Let

Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data

It is well known that deep learning approaches to facerecognition suffer from various biases in the available train-ing data. In this work, we demonstrate the large potentialof synthetic data for analyzing and reducing the negativeeffects of dataset bias on deep face recognition systems. Inparticular we explore two complementary application areasfor synthetic face images: 1) Using fully annotated syntheticface images we can study the face recognition rate as afunction of interpretable parameters such as face pose. Thisenables us to systematically analyze the effect of differenttypes of dataset biases on the generalization ability of neu-ral network architectures. Our analysis reveals that deeperneural network architectures can generalize better to un-seen face poses. Furthermore, our study shows that currentneural network architectures cannot disentangle face poseand facial identity, which limits their generalization ability.2) We pre-train neural networks with large-scale syntheticdata that is highly variable in face pose and the number offacial identities. After a subsequent fine-tuning with real-world data, we observe that the damage of dataset bias inthe real-world data is largely reduced. Furthermore, wedemonstrate that the size of real-world datasets can be re-duced by 75% while maintaining competitive face recogni-tion performance. The data and software used in this workare publicly available

The MEG detector for μ+→e+γ{\mu}+\to e+{\gamma} decay search

The MEG (Mu to Electron Gamma) experiment has been running at the Paul Scherrer Institut (PSI), Switzerland since 2008 to search for the decay \meg\ by using one of the most intense continuous $\mu^+$ beams in the world. This paper presents the MEG components: the positron spectrometer, including a thin target, a superconducting magnet, a set of drift chambers for measuring the muon decay vertex and the positron momentum, a timing counter for measuring the positron time, and a liquid xenon detector for measuring the photon energy, position and time. The trigger system, the read-out electronics and the data acquisition system are also presented in detail. The paper is completed with a description of the equipment and techniques developed for the calibration in time and energy and the simulation of the whole apparatus.Comment: 59 pages, 90 figure

Mitigation of skull formation in high temperature gas extraction system

The adverse impact of particle adhesions and agglomerations on gas flow performance is a prominent concern in high volume extraction systems. The formation of severe skull deposits, involving agglomeration and adhesion processes, particularly at elevated operation temperatures, necessitates labor­intensive and costly manual removal. Consequently, investigating conditions that promote increased skull generation and exploring mechanisms for spontaneous removal through crack formation and chipping are of great significance. This study comprehensively documents the operational conditions of an industrial extraction system, accom panied by elemental gas phase composition analyses. Additionally, the chemical compositions of agglomerated adhesion samples were assessed using X­ray diffraction (XRD) and inductively coupled plasma optical emission spectroscopy (ICP­OES), and their inner structure was examined through SEM. Subsequently, mechanisms leading to these build­ups were simulated on laboratory scale by covering original wall surface samples with agglomeration powder screened for a defined particle size. In experiments conducted at various high temperatures ranging from 800 °C to 1200 °C, while varying the CaCO3 content levels in the powders, a layered structure similar to the real system was successfully acquired. Moreover, under certain defined conditions and different atmospheres, crack formation, significantly impacting the chipping behavior of the skull formations from wall surfaces during application, was observed and the compressive strength was examined. Through our laboratory experiments, specific operating conditions within the calcination cycle were revealed, leading to a substantial enhancement of autonomous discharge of large particleâwall agglomerations. Based on these findings, we propose general process optimization steps to improve the overall performance of the extraction system, such as reduction of fine CaCO3 particles and reduction of the gas flow temperature