Price of Stability in Quality-Aware Federated Learning

Federated Learning (FL) is a distributed machine learning scheme that enables clients to train a shared global model without exchanging local data. The presence of label noise can severely degrade the FL performance, and some existing studies have focused on algorithm design for label denoising. However, they ignored the important issue that clients may not apply costly label denoising strategies due to them being self-interested and having heterogeneous valuations on the FL performance. To fill this gap, we model the clients' interactions as a novel label denoising game and characterize its equilibrium. We also analyze the price of stability, which quantifies the difference in the system performance (e.g., global model accuracy, social welfare) between the equilibrium outcome and the socially optimal solution. We prove that the equilibrium outcome always leads to a lower global model accuracy than the socially optimal solution does. We further design an efficient algorithm to compute the socially optimal solution. Numerical experiments on MNIST dataset show that the price of stability increases as the clients' data become noisier, calling for an effective incentive mechanism.Comment: Accepted to IEEE GLOBECOM 202

The Indirect Effects of Trading Restrictions: Evidence from a Quasi-Natural Experiment

Stock market trading restrictions directly affect stock prices and liquidity via constraints on investors’ transactions. They also have indirect effects by altering the information environment. We isolate these indirect effects by analyzing the effect of stock market restrictions on the corporate bond market. Using the staggered relaxation of the restrictions on margin trading and short selling in the Chinese stock market as a quasi-natural experiment, we find that the relaxation of these restrictions on a firm’s stock reduces the credit spread of its corporate bond. This effect is more pronounced for firms with more opaque information or lower credit ratings

Identification of microRNA precursors based on random forest with network-level representation method of stem-loop structure

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) play a key role in regulating various biological processes such as participating in the post-transcriptional pathway and affecting the stability and/or the translation of mRNA. Current methods have extracted feature information at different levels, among which the characteristic stem-loop structure makes the greatest contribution to the prediction of putative miRNA precursor (pre-miRNA). We find that none of these features alone is capable of identifying new pre-miRNA accurately.</p> <p>Results</p> <p>In the present work, a pre-miRNA stem-loop secondary structure is translated to a network, which provides a novel perspective for its structural analysis. Network parameters are used to construct prediction model, achieving an area under the receiver operating curves (AUC) value of 0.956. Moreover, by repeating the same method on two independent datasets, accuracies of 0.976 and 0.913 are achieved, respectively.</p> <p>Conclusions</p> <p>Network parameters effectively characterize pre-miRNA secondary structure, which improves our prediction model in both prediction ability and computation efficiency. Additionally, as a complement to feature extraction methods in previous studies, these multifaceted features can reflect natural properties of miRNAs and be used for comprehensive and systematic analysis on miRNA.</p

DetToolChain: a new prompting paradigm to unleash detection ability of MLLM

We present DetToolChain, a novel prompting paradigm, to unleash the zero-shot object detection ability of multimodal large language models (MLLMs), such as GPT-4V and Gemini. Our approach consists of a detection prompting toolkit inspired by high-precision detection priors and a new Chain-of-Thought to implement these prompts. Specifically, the prompts in the toolkit are designed to guide the MLLM to focus on regional information (e.g., zooming in), read coordinates according to measure standards (e.g., overlaying rulers and compasses), and infer from the contextual information (e.g., overlaying scene graphs). Building upon these tools, the new detection chain-of-thought can automatically decompose the task into simple subtasks, diagnose the predictions, and plan for progressive box refinements. The effectiveness of our framework is demonstrated across a spectrum of detection tasks, especially hard cases. Compared to existing state-of-the-art methods, GPT4V with our DetToolChain improves state-of-the-art object detectors by +21.5% AP50 on MS COCO Novel class set for open-vocabulary detection, +24.23% Acc on RefCOCO val set for zero-shot referring expression comprehension, +14.5% AP on D-cube describe object detection FULL setting

Evaluations of Lifestyle, Dietary, and Pharmacologic Treatments for Pediatric Non-Alcoholic Fatty Liver Disease—a Systematic Review

Background & Aims: There are no approved treatments for pediatric non-alcoholic fatty liver disease (NAFLD) and there is a lack of consensus on the best outcome measure for randomized controlled trials. We performed a systematic review of treatments tested for pediatric NAFLD, the degree of heterogeneity in trial design, and endpoints analyzed in these studies. Methods: We searched publication databases and clinical trial registries through January 7, 2018 for randomized controlled trials (published and underway) of children (<18 years) with NAFLD. We assessed improvements in histologic features, radiologic and biochemical markers of reduced fibrosis, metabolic syndrome parameters, and adverse events. The quality of the trials was assessed using a modified version of the Cochrane risk of bias tool. Results: Our final analysis included 21 randomized controlled trials, comprising 1307 participants (mean age, 12.6 years; 63% male; mean duration of intervention, 8 months). Most studies evaluated weight loss with lifestyle intervention (n=8), oral polyunsaturated fatty acid treatment (PUFAs, n=6), or oral antioxidant treatment (n=7). Biomarkers of NAFLD decreased with weight loss, but most studies did not include histologic data. Trials of antioxidants were heterogeneous; some reported reduced histologic features of steatohepatitis with no effect on triglycerides or insulin resistance. PUFAs and probiotics reduced radiologic markers of steatosis, insulin resistance, and levels of triglycerides. Only 38% of the trials had biopsy-proven NAFLD as an inclusion criterion. There was heterogeneity in trial primary endpoints; 10 studies (48%) used levels of aminotransferases or ultrasonography findings as a primary endpoint and only 3 trials (14%) used histologic features as the primary endpoint. We identified 13 randomized controlled trials that are underway in children with NAFLD. None of the protocols include collection of liver biopsies; 9 trials (69%) will use magnetic resonance imaging quantification of steatosis as a primary outcome. Conclusion: In a systematic review of published and active randomized controlled trials of children with NAFLD, we found a large amount of heterogeneity in study endpoints and inclusion criteria. Few trials included histologic analyses. Antioxidants appear to reduce some features of steatohepatitis. Effects of treatment with lifestyle modification, PUFAs, or probiotics have not been validated with histologic analysis. Trials that are underway quantify steatosis magnetic resonance imaging—outcomes are anticipated

UniHCP: A Unified Model for Human-Centric Perceptions

Human-centric perceptions (e.g., pose estimation, human parsing, pedestrian detection, person re-identification, etc.) play a key role in industrial applications of visual models. While specific human-centric tasks have their own relevant semantic aspect to focus on, they also share the same underlying semantic structure of the human body. However, few works have attempted to exploit such homogeneity and design a general-propose model for human-centric tasks. In this work, we revisit a broad range of human-centric tasks and unify them in a minimalist manner. We propose UniHCP, a Unified Model for Human-Centric Perceptions, which unifies a wide range of human-centric tasks in a simplified end-to-end manner with the plain vision transformer architecture. With large-scale joint training on 33 human-centric datasets, UniHCP can outperform strong baselines on several in-domain and downstream tasks by direct evaluation. When adapted to a specific task, UniHCP achieves new SOTAs on a wide range of human-centric tasks, e.g., 69.8 mIoU on CIHP for human parsing, 86.18 mA on PA-100K for attribute prediction, 90.3 mAP on Market1501 for ReID, and 85.8 JI on CrowdHuman for pedestrian detection, performing better than specialized models tailored for each task.Comment: Accepted for publication at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR 2023

p53 Regulates Progenitor Cell Quiescence and Differentiation in the Airway

SummaryMechanisms that regulate progenitor cell quiescence and differentiation in slowly replacing tissues are not fully understood. Here, we demonstrate that the tumor suppressor p53 regulates both proliferation and differentiation of progenitors in the airway epithelium. p53 loss decreased ciliated cell differentiation and increased the self-renewal and proliferative capacity of club progenitors, increasing epithelial cell density. p53-deficient progenitors generated a pseudostratified epithelium containing basal-like cells in vitro and putative bronchioalveolar stem cells in vivo. Conversely, an additional copy of p53 increased quiescence and ciliated cell differentiation, highlighting the importance of tight regulation of p53 levels. Using single-cell RNA sequencing, we found that loss of p53 altered the molecular phenotype of progenitors and differentially modulated cell-cycle regulatory genes. Together, these findings reveal that p53 is an essential regulator of progenitor cell behavior, which shapes our understanding of stem cell quiescence during homeostasis and in cancer development

Retrieve Anyone: A General-purpose Person Re-identification Task with Instructions

Human intelligence can retrieve any person according to both visual and language descriptions. However, the current computer vision community studies specific person re-identification (ReID) tasks in different scenarios separately, which limits the applications in the real world. This paper strives to resolve this problem by proposing a new instruct-ReID task that requires the model to retrieve images according to the given image or language instructions.Our instruct-ReID is a more general ReID setting, where existing ReID tasks can be viewed as special cases by designing different instructions. We propose a large-scale OmniReID benchmark and an adaptive triplet loss as a baseline method to facilitate research in this new setting. Experimental results show that the baseline model trained on our OmniReID benchmark can improve +0.5%, +3.3% mAP on Market1501 and CUHK03 for traditional ReID, +2.1%, +0.2%, +15.3% mAP on PRCC, VC-Clothes, LTCC for clothes-changing ReID, +12.5% mAP on COCAS+ real2 for clothestemplate based clothes-changing ReID when using only RGB images, +25.5% mAP on COCAS+ real2 for our newly defined language-instructed ReID. The dataset, model, and code will be available at https://github.com/hwz-zju/Instruct-ReID

HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining

Human-centric perceptions include a variety of vision tasks, which have widespread industrial applications, including surveillance, autonomous driving, and the metaverse. It is desirable to have a general pretrain model for versatile human-centric downstream tasks. This paper forges ahead along this path from the aspects of both benchmark and pretraining methods. Specifically, we propose a \textbf{HumanBench} based on existing datasets to comprehensively evaluate on the common ground the generalization abilities of different pretraining methods on 19 datasets from 6 diverse downstream tasks, including person ReID, pose estimation, human parsing, pedestrian attribute recognition, pedestrian detection, and crowd counting. To learn both coarse-grained and fine-grained knowledge in human bodies, we further propose a \textbf{P}rojector \textbf{A}ssis\textbf{T}ed \textbf{H}ierarchical pretraining method (\textbf{PATH}) to learn diverse knowledge at different granularity levels. Comprehensive evaluations on HumanBench show that our PATH achieves new state-of-the-art results on 17 downstream datasets and on-par results on the other 2 datasets. The code will be publicly at \href{https://github.com/OpenGVLab/HumanBench}{https://github.com/OpenGVLab/HumanBench}.Comment: Accepted to CVPR202