Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM

A novel human emotion recognition method based on automatically selected Galvanic Skin Response (GSR) signal features and SVM is proposed in this paper. GSR signals were acquired by e-Health Sensor Platform V2.0. Then, the data is de-noised by wavelet function and normalized to get rid of the individual difference. 30 features are extracted from the normalized data, however, directly using of these features will lead to a low recognition rate. In order to gain the optimized features, a covariance based feature selection is employed in our method. Finally, a SVM with input of the optimized features is utilized to achieve the human emotion recognition. The experimental results indicate that the proposed method leads to good human emotion recognition, and the recognition accuracy is more than 66.67%

Masked Transformer for Electrocardiogram Classification

Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. With the advent of advanced algorithms, various deep learning models have been adopted for ECG tasks. However, the potential of Transformers for ECG data is not yet realized, despite their widespread success in computer vision and natural language processing. In this work, we present a useful masked Transformer method for ECG classification referred to as MTECG, which expands the application of masked autoencoders to ECG time series. We construct a dataset comprising 220,251 ECG recordings with a broad range of diagnoses annoated by medical experts to explore the properties of MTECG. Under the proposed training strategies, a lightweight model with 5.7M parameters performs stably well on a broad range of masking ratios (5%-75%). The ablation studies highlight the importance of fluctuated reconstruction targets, training schedule length, layer-wise LR decay and DropPath rate. The experiments on both private and public ECG datasets demonstrate that MTECG-T significantly outperforms the recent state-of-the-art algorithms in ECG classification

TTL-IQA: transitive transfer learning based no-reference image quality assessment

Image quality assessment (IQA) based on deep learning faces the overfitting problem due to limited training samples available in existing IQA databases. Transfer learning is a plausible solution to the problem, in which the shared features derived from the large-scale Imagenet source domain could be transferred from the original recognition task to the intended IQA task. However, the Imagenet source domain and the IQA target domain as well as their corresponding tasks are not directly related. In this paper, we propose a new transitive transfer learning method for no-reference image quality assessment (TTL-IQA). First, the architecture of the multi-domain transitive transfer learning for IQA is developed to transfer the Imagenet source domain to the auxiliary domain, and then to the IQA target domain. Second, the auxiliary domain and the auxiliary task are constructed by a new generative adversarial network based on distortion translation (DT-GAN). Furthermore, a TTL network of the semantic features transfer (SFTnet) is proposed to optimize the shared features for the TTL-IQA. Experiments are conducted to evaluate the performance of the proposed method on various IQA databases, including the LIVE, TID2013, CSIQ, LIVE multiply distorted and LIVE challenge. The results show that the proposed method significantly outperforms the state-of-the-art methods. In addition, our proposed method demonstrates a strong generalization ability

A measurement for distortion induced saliency variation in natural images

How best to measure spatial saliency shift induced by image distortions is an open research question. Our previous study has shown that image distortions cause saliency to deviate from its original places in natural images, and the degree of such distortion-induced saliency variation (DSV) depends on image content as well as the properties of distortion. Being able to measure DSV benefits the development of saliency based image quality algorithms. In this paper, we first investigate the plausibility of using existing mathematical algorithms for measuring DSV and their potential limitations. We then develop a new algorithm for quantifying DSV, based on a deep neural network. In the algorithm, namely ST-DSV, we design a coarse-grained to fine-grained saliency similarity transformation approach to achieve DSV measurement. The experimental results show that the proposed ST-DSV algorithm significantly outperforms existing methods in predicting the ground truth DSV

A survey of DNN methods for blind image quality assessment

Blind image quality assessment (BIQA) methods aim to predict quality of images as perceived by humans without access to a reference image. Recently, deep learning methods have gained substantial attention in the research community and have proven useful for BIQA. Although previous study of deep neural networks (DNN) methods is presented, some novelty DNN methods, which are recently proposed, are not summarized for BIQA. In this paper, we provide a survey covering various DNN methods for BIQA. First, we systematically analyze the existing DNN-based quality assessment methods according to the role of DNN. Then, we compare the prediction performance of various DNN methods on the synthetic databases (LIVE, TID2013, CSIQ, LIVE multiply distorted) and authentic databases (LIVE challenge), providing important information that can help understand the underlying properties between different DNN methods for BIQA. Finally, we describe some emerging challenges in designing and training DNN-based BIQA, along with few directions that are worth further investigations in the future

catena-Poly[[(benzoato-κ2 O,O′)(2,2′-bipyridine-κ2 N,N′)lead(II)]-μ3-nitrato-κ4 O:O,O′:O′′]

In the title coordination polymer, [Pb(C7H5O2)(NO3)(C10H8N2)]n, the PbII ion is eight-coordinated by two N atoms from one 2,2′-bipyridine ligand, two O atoms from one benzoate anion and four O atoms from three nitrate groups (one chelating, two bridging) in a distorted dodecahedral geometry. Adjacent PbII ions are linked by bridging nitrate O atoms through the central Pb2O2 and Pb2O4N2 cores, resulting in an infinite chain structure along the b axis. The crystal structure is stabilized by π–π stacking inter­actions between 2,2′-bipyridine and benzoate ligands belonging to neighboring chains, with shortest centroid–centroid distances of 3.685 (8) and 3.564 (8) Å

Study of natural scene categories in measurement of perceived image quality

One challenge facing image quality assessment (IQA) is that current models designed or trained on the basis of exiting databases are intrinsically suboptimal and cannot deal with the real-world complexity and diversity of natural scenes. IQA models and databases are heavily skewed toward the visibility of distortions. It is critical to understand the wider determinants of perceived quality and use the new understanding to improve the predictive power of IQA models. Human behavioral categorization performance is powerful and essential for visual tasks. However, little is known about the impact of natural scene categories (SCs) on perceived image quality. We hypothesize that different classes of natural scenes influence image quality perception—how image quality is perceived is not only affected by the lower level image statistics and image structures shared between different categories but also by the semantic distinctions between these categories. In this article, we first design and conduct a fully controlled psychovisual experiment to verify our hypothesis. Then, we propose a computational framework that integrates the natural SC-specific component into image quality prediction. Research demonstrates the importance and plausibility of considering natural SCs in future IQA databases and models

Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning

Objective. To develop an artificial intelligence method predicting lymph node metastasis (LNM) for patients with colorectal cancer (CRC). Impact Statement. A novel interpretable multimodal AI-based method to predict LNM for CRC patients by integrating information of pathological images and serum tumor-specific biomarkers. Introduction. Preoperative diagnosis of LNM is essential in treatment planning for CRC patients. Existing radiology imaging and genomic tests approaches are either unreliable or too costly. Methods. A total of 1338 patients were recruited, where 1128 patients from one centre were included as the discovery cohort and 210 patients from other two centres were involved as the external validation cohort. We developed a Multimodal Multiple Instance Learning (MMIL) model to learn latent features from pathological images and then jointly integrated the clinical biomarker features for predicting LNM status. The heatmaps of the obtained MMIL model were generated for model interpretation. Results. The MMIL model outperformed preoperative radiology-imaging diagnosis and yielded high area under the curve (AUCs) of 0.926, 0.878, 0.809, and 0.857 for patients with stage T1, T2, T3, and T4 CRC, on the discovery cohort. On the external cohort, it obtained AUCs of 0.855, 0.832, 0.691, and 0.792, respectively (T1-T4), which indicates its prediction accuracy and potential adaptability among multiple centres. Conclusion. The MMIL model showed the potential in the early diagnosis of LNM by referring to pathological images and tumor-specific biomarkers, which is easily accessed in different institutes. We revealed the histomorphologic features determining the LNM prediction indicating the model ability to learn informative latent features

The First Data Release of the Beijing-Arizona Sky Survey

The Beijing-Arizona Sky Survey (BASS) is a new wide-field legacy imaging survey in the northern Galactic cap using the 2.3m Bok telescope. The survey will cover about 5400 deg$^2$ in the $g$ and $r$ bands, and the expected 5$\sigma$ depths (corrected for the Galactic extinction) in the two bands are 24.0 and 23.4 mag, respectively. BASS started observations in January 2015, and has completed about 41% of the whole area as of July 2016. The first data release contains both calibrated images and photometric catalogs obtained in 2015 and 2016. The depths of single-epoch images in the two bands are 23.4 and 22.9 mag, and the full depths of three epochs are about 24.1 and 23.5 mag, respectively.Comment: 16 pages, published by A