CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise

In this paper, we study the problem of learning image classification models with label noise. Existing approaches depending on human supervision are generally not scalable as manually identifying correct or incorrect labels is time-consuming, whereas approaches not relying on human supervision are scalable but less effective. To reduce the amount of human supervision for label noise cleaning, we introduce CleanNet, a joint neural embedding network, which only requires a fraction of the classes being manually verified to provide the knowledge of label noise that can be transferred to other classes. We further integrate CleanNet and conventional convolutional neural network classifier into one framework for image classification learning. We demonstrate the effectiveness of the proposed algorithm on both of the label noise detection task and the image classification on noisy data task on several large-scale datasets. Experimental results show that CleanNet can reduce label noise detection error rate on held-out classes where no human supervision available by 41.5% compared to current weakly supervised methods. It also achieves 47% of the performance gain of verifying all images with only 3.2% images verified on an image classification task. Source code and dataset will be available at kuanghuei.github.io/CleanNetProject.Comment: Accepted to CVPR 201

Effect of Zn doping on magnetic order and superconductivity in LaFeAsO

We report Zn-doping effect in the parent and F-doped LaFeAsO oxy-arsenides. Slight Zn doping in LaFe$_{1-x}$Zn$_{x}$AsO drastically suppresses the resistivity anomaly around 150 K associated with the antiferromagnetic (AFM) spin density wave (SDW) in the parent compound. The measurements of magnetic susceptibility and thermopower confirm further the effect of Zn doping on AFM order. Meanwhile Zn doping does not affect or even enhances the $T_c$ of LaFe$_{1-x}$Zn$_{x}$AsO$_{0.9}$F$_{0.1}$, in contrast to the effect of Zn doping in high-$T_c$ cuprates. We found that the solubility of Zn content ($x$) is limited to less than 0.1 in both systems and further Zn doping (i.e., $x$ $\geq$ 0.1) causes phase separation. Our study clearly indicates that the non-magnetic impurity of Zn$^{2+}$ ions doped in the Fe$_2$As$_2$ layers affects selectively the AFM order, and superconductivity remains robust against the Zn doping in the F-doped superconductors.Comment: 7 figures, 13 pages; revised version with more dat

Reinforcement Learning with Stepwise Fairness Constraints

AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making. Moreover, many settings are dynamic, with populations responding to sequential decision policies. We introduce the study of reinforcement learning (RL) with stepwise fairness constraints, requiring group fairness at each time step. Our focus is on tabular episodic RL, and we provide learning algorithms with strong theoretical guarantees in regard to policy optimality and fairness violation. Our framework provides useful tools to study the impact of fairness constraints in sequential settings and brings up new challenges in RL.Comment: Fairness, Reinforcement Learnin

KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image

Low-light images often suffer from noise and color distortion. Object detection, semantic segmentation, instance segmentation, and other tasks are challenging when working with low-light images because of image noise and chromatic aberration. We also found that the conventional Retinex theory loses information in adjusting the image for low-light tasks. In response to the aforementioned problem, this paper proposes an algorithm for low illumination enhancement. The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness. An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss. Additionally, a TV(total variation) loss function was applied to eliminate noise. Our method was trained on the GladNet dataset, known for its diverse collection of low-light images, tested against the Low-Light dataset, and evaluated using the ExDark dataset for downstream tasks, demonstrating competitive performance with a PSNR of 19.7216 and SSIM of 0.8213.Comment: Accepted by Signal, Image and Video Processin

AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation

Direct speech-to-speech translation (S2ST) aims to convert speech from one language into another, and has demonstrated significant progress to date. Despite the recent success, current S2ST models still suffer from distinct degradation in noisy environments and fail to translate visual speech (i.e., the movement of lips and teeth). In this work, we present AV-TranSpeech, the first audio-visual speech-to-speech (AV-S2ST) translation model without relying on intermediate text. AV-TranSpeech complements the audio stream with visual information to promote system robustness and opens up a host of practical applications: dictation or dubbing archival films. To mitigate the data scarcity with limited parallel AV-S2ST data, we 1) explore self-supervised pre-training with unlabeled audio-visual data to learn contextual representation, and 2) introduce cross-modal distillation with S2ST models trained on the audio-only corpus to further reduce the requirements of visual data. Experimental results on two language pairs demonstrate that AV-TranSpeech outperforms audio-only models under all settings regardless of the type of noise. With low-resource audio-visual data (10h, 30h), cross-modal distillation yields an improvement of 7.6 BLEU on average compared with baselines. Audio samples are available at https://AV-TranSpeech.github.ioComment: Accepted to ACL 202

Comparison of Different Risk-Stratification Systems for the Diagnosis of Benign and Malignant Thyroid Nodules

Introduction: To compare the efficacy of four different ultrasound-based risk-stratification systems in assessing the malignancy risk of thyroid nodules in the Chinese population.Methods: We retrospectively reviewed the digital ultrasound images of 1,568 patients (1,612 thyroid nodules) who underwent surgery in our hospital between January 2012 and December 2017. All thyroid nodules were pathologically identified as malignant or benign. We evaluated the following ultrasound characteristics: size, location, composition, echogenicity, shape, margins, calcification or echogenic foci, and extrathyroidal extension. Each nodule was categorized using four risk-stratification systems: the American Thyroid Association (ATA) classification, the Thyroid Imaging, Reporting, and Data System (TIRADS) of the American College of Radiology (ACR-TIRADS), the European Thyroid Association TIRADS (EU-TIRADS), and the TIRADS developed by Kwak et al. (Kwak-TIRADS). The diagnostic performance of each risk-stratification system relative to the pathological results was analyzed. We used receiver operating characteristic curves to identify cutoff values that yielded optimal sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and accuracy (ACC).Results: Of the 1,612 nodules, 839 (52.0%) were benign, and 773 (48.0%) were malignant. The AUCs of the ACR-TIRADS, EU-TIRADS, Kwak-TIRADS, and ATA classification were 0.879, 0.872, 0.896, and 0.869, respectively. The Kwak-TIRADS had the best SEN, NPV, ACC, and AUC, while the ACR-TIRADS had the best SPE and PPV.Conclusion: All four risk-stratification systems had good diagnostic performances (AUCs > 86%). Considering its high SEN, NPV, ACC, and AUC, we believe that the Kwak-TIRADS may be the more effective risk-stratification system in the Chinese population

The carbonaceous aerosol levels still remain a challenge in the Beijing-Tianjin-Hebei region of China: Insights from continuous high temporal resolution measurements in multiple cities

Carbonaceous aerosols in high emission areas attract worldwide attention of the scientific community and the public due to their adverse impacts on the environment, human health and climate. However, long-term continuous hourly measurements are scarce on the regional scale. In this study, a one-year hourly measurement (from December 1, 2016 to November 30, 2017) of organic carbon (OC) and elemental carbon (EC) in airborne fine particles was performed using semi-continuous OC/EC analyzers in Beijing, Tianjin, Shijiazhuang and Tangshan in the Beijing-Tianjin-Hebei (BTH) region in China, which is one of high emission areas in China, even in the world. Marked spatiotemporal variations were observed. The highest concentrations of OC (22.8 ± 30.6 μg/m 3 ) and EC (5.4 ± 6.5 μg/m 3 ) occurred in Shijiangzhuang while the lowest concentrations of OC (11.0 ± 10.7 μg/m 3 ) and EC (3.1 ± 3.6 μg/m 3 ) were obtained in Beijing and Tianjin, respectively. Pronounced monthly, seasonal and diurnal variations of OC and EC were recorded. Compared to published data from the past two decades for the BTH region, our OC and EC levels were lower, implying some effect of recent measures for improving the air quality. Significant correlations of OC versus EC (p < 0.001) were found throughout the study period with high slopes and correlation coefficients in winter, but low slopes and correlation coefficients in summer. The estimated secondary OC (SOC), based on the minimum R squared (MRS) method, represented 29%, 47%, 38% and 48% of the OC for Beijing, Tianjin, Shijiazhuang and Tangshan, respectively. These percentages are larger than previous ones obtained for the BTH region in the past decade. There were obvious differences in the potential source regions of OC and EC among the four cities. Obvious prominent potential source areas of OC and EC were observed for Beijing, which were mainly located in the central and western areas of Inner Mongolia and even extended to the Mongolian regions, which is different from the findings in previous studies. For all sites, adjacent areas of the main provinces in northern China were found to be important potential source areas. © 2019 The Author

OBJECTIVE SPACE NORMALIZATION IN EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION

Ph.DDOCTOR OF PHILOSOPHY (CDE-ENG

DSCC2015-9993 HIERARCHICAL DESIGN FOR CONNECTED CRUISE CONTROL

ABSTRACT In this paper, we propose a hierarchical framework to reduce the design complexity of connected cruise control (CCC), which is used to regulate the longitudinal motion of a vehicle by utilizing wireless vehicle-to-vehicle (V2V) communication. A high-level controller is designed to generate desired motion of the CCC vehicle based on the motion of multiple vehicles ahead. A low-level controller is used to regulate the engine torque and select the appropriate gear to enable the vehicle to track the desired motion. To cope with external disturbances and uncertain physical parameters, we use an adaptive control strategy for the low-level controller. In a case study, we design a specific CCC algorithm by using the presented hierarchical framework. Numerical simulations are used to validate the analytical results and test the system performance