Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

/pdf/integrating-single-cell-transcriptomic-data-across-different-3dtsi5w7ob.pdf

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

Face Description with Local Binary Patterns: Application to Face Recognition

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Active shape model (ASM), as a method for extracting and representing object shapes, has received considerable attention in recent years. In ASM, a shape is represented statistically by a set of well-defined landmark points and its variations are modeled by the principal component analysis (PCA). However, we find that both PCA and Procrustes analysis are sensitive to noise, and there is a linear relationship between alignment error and magnitude of noise, which leads parameter estimation to be ill-posed. In this paper, we present a sparse ASM based on l1-minimization for shape alignment, which can automatically select an effective group of principal components to represent a given shape. A noisy item is introduced to both shape parameter and pose parameter (scale, translation, and rotation), and the parameter estimation is solved by the l1-minimization framework. The estimation of these two kinds of parameters is independent and robust to local noise. Experiments on face dataset validate robustness and effectiveness of the proposed technique.

Active shape model based on sparse representation

Heterogeneous face recognition (HFR) is quite challenging due to the large discrepancy introduced by cross-domain face images. The limited number of paired face images results in a severe overfitting problem in existing methods. To tackle this issue, we proposes a novel self-augmentation method named Mixed Adversarial Examples and Logits Replay (MAELR). Concretely, we first generate adversarial examples, and mix them with clean examples in an interpolating way for data augmentation. Simultaneously, we extend the definition of the adversarial examples according to cross-domain problems. Benefiting from this extension, we can reduce domain discrepancy to extract domain-invariant features. We further propose a diversity preserving loss via logits replay, which effectively uses the discriminative features obtained on the large-scale VIS dataset. In this way, we improve the feature diversity that can not be obtained from mixed adversarial examples methods. Extensive experiments demonstrate that our method alleviates the over-fitting problem, thus significantly improving the recognition performance of HFR.

Self-Augmented Heterogeneous Face Recognition

An off-the-shelf model as a commercial service could be stolen by model stealing attacks, posing great threats to the rights of the model owner. Model fingerprinting aims to verify whether a suspect model is stolen from the victim model, which gains more and more attention nowadays. Previous methods always leverage the transferable adversarial examples as the model fingerprint, which is sensitive to adversarial defense or transfer learning scenarios. To address this issue, we consider the pairwise relationship between samples instead and propose a novel yet simple model stealing detection method based on SAmple Correlation (SAC). Specifically, we present SAC-w that selects wrongly classified normal samples as model inputs and calculates the mean correlation among their model outputs. To reduce the training time, we further develop SAC-m that selects CutMix Augmented samples as model inputs, without the need for training the surrogate models or generating adversarial examples. Extensive results validate that SAC successfully defends against various model stealing attacks, even including adversarial training or transfer learning, and detects the stolen models with the best performance in terms of AUC across different datasets and model architectures. The codes are available at https://github.com/guanjiyang/SAC.

Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks

Purpose/Objective(s): To determine diffusion-weighted imaging (DWI) MRI parameters associated with tumor response and oncologic outcomes in head and neck (HNC) patients treated with definitive radiation therapy (RT). Materials/Methods: Eighty-six HNC patients enrolled in an active prospective imaging study at The University of Texas MD Anderson Cancer Center were included in the analysis. Patients had MRIs pre-, mid-, and post-RT completion. Inclusion criteria included adults with histologic evidence of malignant head and neck neoplasm indicated for curative-intent treatment with RT with/without chemotherapy, good performance status (ECOG score 0-2), and with no contraindications to MRI. Patients were scanned using a MAGNETOM Aera 1.5T MR scanner (Siemens Healthcare, Germany) with two large four-channel flex phased-array coils. We used fat-suppressed T2-weighted turbo spin echo sequences for tumor segmentation which were co-registered to respective DWIs for extraction of apparent diffusion coefficient (ADC) measurements. Treatment response was assessed at mid-RT and at 8-12 weeks post-RT using RECIST 1.1 criteria and was defined as: complete response (CR) vs. non-complete response (non-CR). Pre-RT ADC was correlated with RT response (CR vs. non-CR) at mid- and post-RT. The Mann-Whitney U test was used to compare ADC values between the mid-treatment CR group and the non-CR group. Recursive partitioning analysis (RPA) was performed to identify ADC threshold associated with relapse. Cox proportional hazards models were done for clinical vs. clinical and imaging parameters and internal validation was done using bootstrapping technique. Results: Eighty-one patients were included in this analysis. Median follow-up was 31 months. Pre-treatment ADC was not correlated with tumor response or oncologic outcomes (P>0.05). For patients with post-RT CR, there was a significant increase in mean ADC at mid-RT compared to baseline ((1.8 {+/-} 0.29) x 10-3 mm2/s vs. (1.37 {+/-} 0.22) x 10-3 mm2/s, p< 0.0001), while patients with non-CR had no statistically significant increase (p >0.05). RPA identified GTV-P delta ({Delta})ADCmean <7% at mid-RT as the most significant parameter associated with worse LC and RFS (p=0.01). Univariable and multivariable analysis of prognostic outcomes showed that GTV-P {Delta}ADCmean at mid-RT [≥]7% was significantly associated with better LC and RFS. The addition of {Delta}ADCmean significantly improved the c-indices of LC and RFS models compared with standard clinical variables (0.85 vs. 0.77 and 0.74 vs. 0.68 for LC and RFS, respectively, p<0.0001 for both). Conclusion: ADC change at mid-RT is a strong predictor of oncologic outcomes in HNC patients. Patients with no significant increase of primary tumor site ADC at mid-RT relative to baseline values are at high risk of disease relapse. Multi-institutional data are needed for validation of our results.

Prospective validation of diffusion-weighted MRI as a biomarker of tumor response and oncologic outcomes in head and neck cancer: Results from an observational biomarker pre-qualification study.

We present a new application direction named Pareidolia Face Reenactment, which is defined as animating a static illusory face to move in tandem with a human face in the video. For the large differences between pareidolia face reenactment and traditional human face reenactment, two main challenges are introduced, i.e., shape variance and texture variance. In this work, we propose a novel Parametric Unsupervised Reenactment Algorithm to tackle these two challenges. Specifically, we propose to decompose the reenactment into three catenate processes: shape modeling, motion transfer and texture synthesis. With the decomposition, we introduce three crucial components, i.e., Parametric Shape Modeling, Expansionary Motion Transfer and Unsupervised Texture Synthesizer, to overcome the problems brought by the remarkably variances on pareidolia faces. Extensive experiments show the superior performance of our method both qualitatively and quantitatively. Code, model and data are available on our project page.

Ran He

Papers

Active shape model based on sparse representation

Self-Augmented Heterogeneous Face Recognition

Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks

Prospective validation of diffusion-weighted MRI as a biomarker of tumor response and oncologic outcomes in head and neck cancer: Results from an observational biomarker pre-qualification study.

Everything's Talkin': Pareidolia Face Reenactment