SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction

Facial beauty prediction (FBP) is a significant visual recognition problem to make assessment of facial attractiveness that is consistent to human perception. To tackle this problem, various data-driven models, especially state-of-the-art deep learning techniques, were introduced, and benchmark dataset become one of the essential elements to achieve FBP. Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms. However, most of FBP benchmark datasets were built under specific computation constrains, which limits the performance and flexibility of the computational model trained on the dataset. In this paper, we argue that FBP is a multi-paradigm computation problem, and propose a new diverse benchmark dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty prediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with diverse properties (male/female, Asian/Caucasian, ages) and diverse labels (face landmarks, beauty scores within [1,~5], beauty score distribution), which allows different computational models with different FBP paradigms, such as appearance-based/shape-based facial beauty classification/regression model for male/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP using different combinations of feature and predictor, and various deep learning methods. The results indicates the improvement of FBP and the potential applications based on the SCUT-FBP5500.Comment: 6 pages, 14 figures, conference pape

A Multi-Level Approach to Waste Object Segmentation

We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects. Specifically, our method integrates the intensity and depth information at multiple levels of spatial granularity. Firstly, a scene-level deep network produces an initial coarse segmentation, based on which we select a few potential object regions to zoom in and perform fine segmentation. The results of the above steps are further integrated into a densely connected conditional random field that learns to respect the appearance, depth, and spatial affinities with pixel-level accuracy. In addition, we create a new RGBD waste object segmentation dataset, MJU-Waste, that is made public to facilitate future research in this area. The efficacy of our method is validated on both MJU-Waste and the Trash Annotation in Context (TACO) dataset.Comment: Paper appears in Sensors 2020, 20(14), 381

Efficacy and safety of combination of ulinastatin and meglumine cyclic adenosine monophosphate in the treatment of acute myocardial infarction, and its effect on serum levels of hs-CRP, cTnI and CK

Purpose: To determine the efficacy and safety of a combination of ulinastatin and meglumine cyclic adenosine monophosphate (cAMP) in the treatment of acute myocardial infarction (AMI), and its effect on serum levels of hypersensitive-c-reactive protein (hs-CRP), cardiac troponin I (cTnI), creatine kinase (CK).Methods: A total of 90 AMI patients admitted to The Second Affiliated Hospital of Qiqihar Medical College, Qiqihar City, Heilongjiang Province, China from January 2019 to January 2020 were selected and randomized (in a 1:1 ration) into control group and study group. Patients in the two groups received meglumine cAMP, while those in the study group were, in addition, treated with ulinastatin. The two groups were compared with regard to clinical efficacy, cardiac function indices, serum biochemical indices, incidence of drug-related side effects, duration and number of episodes of angina pectoris, and levels of neuroendocrine hormones.Results: The study group exhibited remarkably higher treatment effectiveness and cardiac function indices compared to the control group (p < 0.05). However, lower levels of serum biochemical indices, lower total incidence of drug toxicity, smaller number and shorter duration of angina pectoris, and lower levels of panel reactive antibodies (PRA) were observed in the study when compared to control group (p< 0.001).Conclusion: Treatment of AMI patients with the combination of ulinastatin and meglumine cAMP significantly reduces the clinical symptoms of the patients, with remarkable efficacy and high safety. Furthermore, it down-regulates serum levels of hs-CRP, cTnI and CK. Thus, the combination treatment seems superior to the conventional therapy

Small Language Model Meets with Reinforced Vision Vocabulary

Playing Large Vision Language Models (LVLMs) in 2023 is trendy among the AI community. However, the relatively large number of parameters (more than 7B) of popular LVLMs makes it difficult to train and deploy on consumer GPUs, discouraging many researchers with limited resources. Imagine how cool it would be to experience all the features of current LVLMs on an old GTX1080ti (our only game card). Accordingly, we present Vary-toy in this report, a small-size Vary along with Qwen-1.8B as the base ``large'' language model. In Vary-toy, we introduce an improved vision vocabulary, allowing the model to not only possess all features of Vary but also gather more generality. Specifically, we replace negative samples of natural images with positive sample data driven by object detection in the procedure of generating vision vocabulary, more sufficiently utilizing the capacity of the vocabulary network and enabling it to efficiently encode visual information corresponding to natural objects. For experiments, Vary-toy can achieve 65.6% ANLS on DocVQA, 59.1% accuracy on ChartQA, 88.1% accuracy on RefCOCO, and 29% on MMVet. The code will be publicly available on the homepage

Fault location method for distribution networks based on multi-head graph attention networks

The precise fault localization holds significant importance in reducing power outage duration and frequency in power systems. The widespread application of synchrophasor measurement technology (PMU) has laid the foundation for achieving accurate fault localization in distribution networks. However, fault localization methods based on PMU often suffer from a significant decrease in accuracy due to topological reconstruction and inaccurate parameters. To address these challenges, this paper proposes a fault location method for distribution networks based on Multi-head Graph Attention Networks (GATs). The proposed method begins by modeling the distribution network as a graph, where nodes represent network components and edges represent the connections between these components. GATs have been employed to learn the underlying relationships between topological structure and electrical characteristics of the distribution network. The results demonstrate that our approach outperforms traditional fault location methods in terms of accuracy and speed. The proposed method achieves high precision which reducing the time required for fault location and enabling faster response times for network maintenance personnel

Risk-averse stochastic dynamic power dispatch based on deep reinforcement learning with risk-oriented Graph-Gan sampling

The increasing penetration of renewable energy sources (RES) brings volatile stochasticity, which significantly challenge the optimal dispatch of power systems. This paper aims at developing a cost-effective and robust policy for stochastic dynamic optimization of power systems, which improves the economy as well as avoiding the risk of high costs in some critical scenarios with small probability. However, it is hard for existing risk-neutral methods to incorporate risk measure since most samples are normal. For this regard, a novel risk-averse policy learning approach based on deep reinforcement learning with risk-oriented sampling is proposed. Firstly, a generative adversarial network (GAN) with graph convolutional neural network (GCN) is proposed to learn from historical data and achieve risk-oriented sampling. Specifically, system state is modelled as graph data and GCN is employed to capture the underlying correlation of the uncertainty corresponding to the system topology. Risk knowledge is the embedded to encourage more critical scenarios are sampled while aligning with historical data distributions. Secondly, a modified deep reinforcement learning (DRL) with risk-measure under soft actor critic framework is proposed to learn the optimal dispatch policy from sampling data. Compared with the traditional deep reinforcement learning which is risk-neutral, the proposed method is more robust and adaptable to uncertainties. Comparative simulations verify the effectiveness of the proposed method