Sulfuric Acid Baking and Fe(III)-Cl-HSO Leaching Behavior of Ni- and Cu-Containing Sulfide and Silicate Rich Suhanko Concentrate.

Ni- and Cu-rich concentrate from a new site in Suhanko, Finland, was investigated. Mineral phases identified included talc, chalcopyrite, kaolinite, and pyrrhotite with 3.2% Cu and 1.

Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and Video.

Blind visual quality assessment (BVQA) on 360° video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360° video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i.e.

Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division.

Chaotic baseband wireless communication system (CBWCS) suffers bit error rate (BER) degradation due to their intrinsic intersymbol interference (ISI). To this end, an ISI-free chaotic filter based on root-raised-cosine (RRC) division is constructed to generate a chaotic signal. A wireless communication system using this chaotic signal as a baseband waveform is proposed.

Joint Learning of Multi-Level Tasks for Diabetic Retinopathy Grading on Low-Resolution Fundus Images.

Diabetic retinopathy (DR) is a leading cause of permanent blindness among the working-age people. Automatic DR grading can help ophthalmologists make timely treatment for patients. However, the existing grading methods are usually trained with high resolution (HR) fundus images, such that the grading performance decreases a lot given low resolution (LR) images, which are common in clinic.

Joint Learning of 3D Lesion Segmentation and Classification for Explainable COVID-19 Diagnosis.

Given the outbreak of COVID-19 pandemic and the shortage of medical resource, extensive deep learning models have been proposed for automatic COVID-19 diagnosis, based on 3D computed tomography (CT) scans. However, the existing models independently process the 3D lesion segmentation and disease classification, ignoring the inherent correlation between these two tasks. In this paper, we propose a joint deep learning model of 3D lesion segmentation and classification for diagnosing COVID-19, called DeepSC-COVID, as the first attempt in this direction.

Saliency Prediction on Omnidirectional Image With Generative Adversarial Imitation Learning.

When watching omnidirectional images (ODIs), subjects can access different viewports by moving their heads. Therefore, it is necessary to predict subjects' head fixations on ODIs. Inspired by generative adversarial imitation learning (GAIL), this paper proposes a novel approach to predict saliency of head fixations on ODIs, named SalGAIL.

Viewport-Based CNN: A Multi-Task Approach for Assessing 360° Video Quality.

For 360° video, the existing visual quality assessment (VQA) approaches are designed based on either the whole frames or the cropped patches, ignoring the fact that subjects can only access viewports. When watching 360° video, subjects select viewports through head movement (HM) and then fixate on attractive regions within the viewports through eye movement (EM). Therefore, this paper proposes a two-staged multi-task approach for viewport-based VQA on 360° video.

MFQE 2.0: A New Approach for Multi-Frame Quality Enhancement on Compressed Video.

The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the quality of a single frame, not considering the similarity between consecutive frames. Since heavy fluctuation exists across compressed video frames as investigated in this paper, frame similarity can be utilized for quality enhancement of low-quality frames given their neighboring high-quality frames.

Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs.

Importance: A deep learning system (DLS) that could automatically detect glaucomatous optic neuropathy (GON) with high sensitivity and specificity could expedite screening for GON.

Objective: To establish a DLS for detection of GON using retinal fundus images and glaucoma diagnosis with convoluted neural networks (GD-CNN) that has the ability to be generalized across populations.

Design, Setting, And Participants: In this cross-sectional study, a DLS for the classification of GON was developed for automated classification of GON using retinal fundus images obtained from the Chinese Glaucoma Study Alliance, the Handan Eye Study, and online databases.

A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection.

Glaucoma is one of the leading causes of irreversible vision loss. Many approaches have recently been proposed for automatic glaucoma detection based on fundus images. However, none of the existing approaches can efficiently remove high redundancy in fundus images for glaucoma detection, which may reduce the reliability and accuracy of glaucoma detection.

A Deep Learning Approach for Multi-Frame In-Loop Filter of HEVC.

An extensive study on the in-loop filter has been proposed for a high efficiency video coding (HEVC) standard to reduce compression artifacts, thus improving coding efficiency. However, in the existing approaches, the in-loop filter is always applied to each single frame, without exploiting the content correlation among multiple frames. In this paper, we propose a multi-frame in-loop filter (MIF) for HEVC, which enhances the visual quality of each encoded frame by leveraging its adjacent frames.

Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach.

Panoramic video provides immersive and interactive experience by enabling humans to control the field of view (FoV) through head movement (HM). Thus, HM plays a key role in modeling human attention on panoramic video. This paper establishes a database collecting subjects' HM in panoramic video sequences.

Reducing Complexity of HEVC: A Deep Learning Approach.

High Efficiency Video Coding (HEVC) significantly reduces bit-rates over the preceding H.264 standard but at the expense of extremely high encoding complexity. In HEVC, the quad-tree partition of coding unit (CU) consumes a large proportion of the HEVC encoding complexity, due to the brute-force search for rate-distortion optimization (RDO).

miR‑138 modulates prostate cancer cell invasion and migration via Wnt/β‑catenin pathway.

The prognosis for prostate cancer patients with distant metastasis is poor, with an average survival rate of 24‑48 months. The exact mechanisms underlying prostate cancer metastasis remain to be elucidated, despite previous research efforts. The present study aimed to reveal the regulatory roles of miR‑138 via Wnt/β‑catenin pathway in prostate cancer cell migration and invasion.

Learning to Detect Video Saliency With HEVC Features.

Saliency detection has been widely studied to predict human fixations, with various applications in computer vision and image processing. For saliency detection, we argue in this paper that the state-of-the-art High Efficiency Video Coding (HEVC) standard can be used to generate the useful features in compressed domain. Therefore, this paper proposes to learn the video saliency model, with regard to HEVC features.

Cubature Information SMC-PHD for Multi-Target Tracking.

In multi-target tracking, the key problem lies in estimating the number and states of individual targets, in which the challenge is the time-varying multi-target numbers and states. Recently, several multi-target tracking approaches, based on the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter, have been presented to solve such a problem. However, most of these approaches select the transition density as the importance sampling (IS) function, which is inefficient in a nonlinear scenario.

Clinical research of Tashinone IIA combined with endocrine therapy in treating advanced-stage prostate cancer.

To observe the clinical effect of tashinone IIA combined with endocrine therapy in treating advanced-stage prostate cancer. 96 cases of advanced-stage prostate cancer were divided into observation group (44 cases received treatment) and control group (46 cases received treatment). Control group was given leuprolide acetate 3.