354 research outputs found
Achieving non-discrimination in prediction
Discrimination-aware classification is receiving an increasing attention in
data science fields. The pre-process methods for constructing a
discrimination-free classifier first remove discrimination from the training
data, and then learn the classifier from the cleaned data. However, they lack a
theoretical guarantee for the potential discrimination when the classifier is
deployed for prediction. In this paper, we fill this gap by mathematically
bounding the probability of the discrimination in prediction being within a
given interval in terms of the training data and classifier. We adopt the
causal model for modeling the data generation mechanism, and formally defining
discrimination in population, in a dataset, and in prediction. We obtain two
important theoretical results: (1) the discrimination in prediction can still
exist even if the discrimination in the training data is completely removed;
and (2) not all pre-process methods can ensure non-discrimination in prediction
even though they can achieve non-discrimination in the modified training data.
Based on the results, we develop a two-phase framework for constructing a
discrimination-free classifier with a theoretical guarantee. The experiments
demonstrate the theoretical results and show the effectiveness of our two-phase
framework
Umbilical cord derived mesenchymal stem cell therapy for osteoarthritis: a consolidated review
Osteoarthritis (OA) is a leading cause of degenerative disease and is the most common persistent condition worldwide. The common burden imposed by OA significantly damages the articular cartilage, which results in pain and seriously impacts the quality of life in the affected people. Disease progression is assumed to increase with obesity and aging. The current therapies include weight loss, activity adjustment, traditional pain management and replacement of the affected joint. To overcome these limitations, recently, cell-based therapies mainly Umbilical cord derived Mesenchymal stem cell (UC-MSC) have become an attractive cell source for an allogeneic mesenchymal stem cell to repair and regenerate the structure and function of articular tissues. Although the mechanism is not clearly defined, it is believed that the paracrine signaling, inflammatory response, and immunomodulatory role of UC-MSCs play a crucial role in developing a treatment approach of OA. The purpose of this review was to outline the advantages of using UC-MSCs in treating OA. This review also discusses the possible hurdles that stand in the way of successful implementation of UC-MSC as a routine treatment regimen for OA
Private Estimation and Inference in High-Dimensional Regression with FDR Control
This paper presents novel methodologies for conducting practical
differentially private (DP) estimation and inference in high-dimensional linear
regression. We start by proposing a differentially private Bayesian Information
Criterion (BIC) for selecting the unknown sparsity parameter in DP-Lasso,
eliminating the need for prior knowledge of model sparsity, a requisite in the
existing literature. Then we propose a differentially private debiased LASSO
algorithm that enables privacy-preserving inference on regression parameters.
Our proposed method enables accurate and private inference on the regression
parameters by leveraging the inherent sparsity of high-dimensional linear
regression models. Additionally, we address the issue of multiple testing in
high-dimensional linear regression by introducing a differentially private
multiple testing procedure that controls the false discovery rate (FDR). This
allows for accurate and privacy-preserving identification of significant
predictors in the regression model. Through extensive simulations and real data
analysis, we demonstrate the efficacy of our proposed methods in conducting
inference for high-dimensional linear models while safeguarding privacy and
controlling the FDR
DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models
Despite the ability of existing large-scale text-to-image (T2I) models to
generate high-quality images from detailed textual descriptions, they often
lack the ability to precisely edit the generated or real images. In this paper,
we propose a novel image editing method, DragonDiffusion, enabling Drag-style
manipulation on Diffusion models. Specifically, we construct classifier
guidance based on the strong correspondence of intermediate features in the
diffusion model. It can transform the editing signals into gradients via
feature correspondence loss to modify the intermediate representation of the
diffusion model. Based on this guidance strategy, we also build a multi-scale
guidance to consider both semantic and geometric alignment. Moreover, a
cross-branch self-attention is added to maintain the consistency between the
original image and the editing result. Our method, through an efficient design,
achieves various editing modes for the generated or real images, such as object
moving, object resizing, object appearance replacement, and content dragging.
It is worth noting that all editing and content preservation signals come from
the image itself, and the model does not require fine-tuning or additional
modules. Our source code will be available at
https://github.com/MC-E/DragonDiffusion
VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution
Most of the existing video face super-resolution (VFSR) methods are trained
and evaluated on VoxCeleb1, which is designed specifically for speaker
identification and the frames in this dataset are of low quality. As a
consequence, the VFSR models trained on this dataset can not output
visual-pleasing results. In this paper, we develop an automatic and scalable
pipeline to collect a high-quality video face dataset (VFHQ), which contains
over high-fidelity clips of diverse interview scenarios. To verify the
necessity of VFHQ, we further conduct experiments and demonstrate that VFSR
models trained on our VFHQ dataset can generate results with sharper edges and
finer textures than those trained on VoxCeleb1. In addition, we show that the
temporal information plays a pivotal role in eliminating video consistency
issues as well as further improving visual performance. Based on VFHQ, by
analyzing the benchmarking study of several state-of-the-art algorithms under
bicubic and blind settings. See our project page:
https://liangbinxie.github.io/projects/vfhqComment: Project webpage available at
https://liangbinxie.github.io/projects/vfh
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