Experimental Investigation of the Robustness of a New Bell-Type Inequality of Triphoton GHZ States in Open Systems

Knowing the level of entanglement robustness against quantum bit loss or decoherence mechanisms is an important issue for any application of quantum information. Fidelity of states can be used to judge whether there is entanglement in multi-particle systems. It is well known that quantum channel security in QKD can be estimated by measuring the robustness of Bell-type inequality against noise. We experimentally investigate a new Bell-type inequality (NBTI) in the three-photon Greenberger–Horne–Zeilinger (GHZ) states with different levels of spin-flip noise. The results show that the fidelity and the degree of violation of the NBTI decrease monotonically with the increase of noise intensity. They also provide a method to judge whether there is entanglement in three-particle mixed states.

Designing a Virtual Arboretum as an Immersive, Multimodal, Interactive, Data Visualization Virtual Field Trip

This paper describes a virtual field trip application as a new type of immersive, multimodal, interactive, data visualization of a virtual arboretum. Deployed in a game engine, it is a large, open-world simulation, representing 100 hectares and ideal for use when free choice in navigation and high fidelity are required. Although the computer graphics are photorealistic, it is different and unique from other applications that use game art or 2D 360-degree video, because it reflects high information fidelity as a result of the domain expert review, and the integration of geographic information system (GIS) data with drone images. Combined in-game as a data visualization, it is ideal for generating past or future worlds, in addition to representations of the present. Fusing information from many data sources—terrain data, waterbody data, plant inventory, population density data, accurate plant models, bioacoustics, and drone images—its design process and methods could be repeated and used in a wide range of augmented reality (AR) and virtual reality (VR) applications and devices. Results on presence, embodiment, emotions, engagement, and learning are summarized from prior pilot studies for context on use, and are relevant to schools, museums, arboretums, and botanical gardens interested in developing immersive informal learning applications.

Language Dysfunction in Schizophrenia: Assessing Neural Tracking to Characterize the Underlying Disorder(s)?

Deficits in language production and comprehension are characteristic of schizophrenia. To date, it remains unclear whether these deficits arise from dysfunctional linguistic knowledge, or dysfunctional predictions derived from the linguistic context. Alternatively, the deficits could be a result of dysfunctional neural tracking of auditory information resulting in decreased auditory information fidelity and even distorted information. Here, we discuss possible ways for clinical neuroscientists to employ neural tracking methodology to independently characterize deficiencies on the auditory–sensory and abstract linguistic levels. This might lead to a mechanistic understanding of the deficits underlying language related disorder(s) in schizophrenia. We propose to combine naturalistic stimulation, measures of speech–brain synchronization, and computational modeling of abstract linguistic knowledge and predictions. These independent but likely interacting assessments may be exploited for an objective and differential diagnosis of schizophrenia, as well as a better understanding of the disorder on the functional level—illustrating the potential of neural tracking methodology as translational tool in a range of psychotic populations.

A Generative Adversarial Network-Based Image Denoiser Controlling Heterogeneous Losses

We propose a novel generative adversarial network (GAN)-based image denoising method that utilizes heterogeneous losses. In order to improve the restoration quality of the structural information of the generator, the heterogeneous losses, including the structural loss in addition to the conventional mean squared error (MSE)-based loss, are used to train the generator. To maximize the improvements brought on by the heterogeneous losses, the strength of the structural loss is adaptively adjusted by the discriminator for each input patch. In addition, a depth wise separable convolution-based module that utilizes the dilated convolution and symmetric skip connection is used for the proposed GAN so as to reduce the computational complexity while providing improved denoising quality compared to the convolutional neural network (CNN) denoiser. The experiments showed that the proposed method improved visual information fidelity and feature similarity index values by up to 0.027 and 0.008, respectively, compared to the existing CNN denoiser.

Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods.

Language Dysfunction in Schizophrenia: Assessing Neural Tracking to Characterize the Underlying Disorder(s)?

Deficits in language production and comprehension are characteristic of schizophrenia. To date, it remains unclear whether these deficits arise from dysfunctional linguistic knowledge, or dysfunctional predictions derived from the linguistic context. Alternatively, the deficits could be a result of dysfunctional neural tracking of auditory information resulting in decreased auditory information fidelity and even distorted information. Here, we discuss possible ways for clinical neuroscientists to employ neural tracking methodology to independently characterize deficiencies on the auditory–sensory and abstract linguistic levels. This might lead to a mechanistic understanding of the deficits underlying language related disorder(s) in schizophrenia. We propose to combine naturalistic stimulation, measures of speech–brain synchronization, and computational modeling of abstract linguistic knowledge and predictions. This orthogonal assessment may be exploited for an objective and differential diagnosis of schizophrenia, as well as a better understanding of the disorder on the functional level—illustrating the potential of neural tracking methodology as translational tool in a range of psychotic populations.

An Atomic Force Acoustic Microscopy Image Fusion Method Based on Grayscale Inversion and Selection of Best-Fit Intensity

Atomic force acoustic microscopy (AFAM) can provide surface morphology and internal structures of the samples simultaneously, with broad potential in non-destructive imaging of cells. As the output of AFAM, morphology and acoustic images reflect different features of the cells, respectively. However, there are few studies about the fusion of these images. In this paper, a novel method is proposed to fuse these two types of images based on grayscale inversion and selection of best-fit intensity. First, grayscale inversion is used to transform the morphology image into a series of inverted images with different average intensities. Then, the max rule is applied to fuse those inverted images and acoustic images, and a group of pre-fused images is obtained. Finally, a selector is employed to extract and export the expected image with the best-fit intensity among those pre-fused images. The expected image can preserve both the acoustic details of the cells and the background’s gradient information well, which benefits the analysis of the cell’s subcellular structure. The experiments’ results demonstrated that our method could provide the clearest boundaries between the cells and background, and preserve most details from the morphology and acoustic images according to quantitative comparisons, including standard deviation, mutual information, Xydeas and Petrovic metric, feature mutual information, and visual information fidelity fusion.

A Real-Time Haze Removal & Mosaicing using Rapid Prototype Hardware

In this paper, a pivotal technique was proposed that reduces the haze and combines the haze free image to increase the Field of View (FoV) in real-time with a rapid prototype hardware device. The Initial focus is to reduce the haze in an image with Dark Channel Prior Technique and the FSD method is utilized to mosaic the haze free images. Low contrast may occur due to the scattering light, air particles or fog in nature which results in a haze image that needs to be reduced and enhance the image for better vicinity. Haze reduction approach depends on entropy and information fidelity. Our Haze free algorithm executes multiple phases such as dark channel prior computation, estimation and refinement of transmission map and restoration of RGB values. The second technique is the mosaic process that improves the field of view of a scene and the phases that execute are corner detection, extraction, geometric computation and blending. Our experimental results have shown better when compared to the other algorithms. The whole process is executed in real-time with a standalone device called Intel compute stick.

Practical Evaluation of VMAF Perceptual Video Quality for WebRTC Applications

WebRTC is the umbrella term for several emergent technologies aimed to exchange real-time media in the Web. Like other media-related services, the perceived quality of WebRTC communication can be measured using Quality of Experience (QoE) indicators. QoE assessment methods can be classified as subjective (users’ evaluation scores) or objective (models computed as a function of different parameters). In this paper, we focus on VMAF (Video Multi-method Assessment Fusion), which is an emergent full-reference objective video quality assessment model developed by Netflix. VMAF is typically used to assess video streaming services. This paper evaluates the use of VMAF in a different type of application: WebRTC. To that aim, we present a practical use case built on the top of well-known open source technologies, such as JUnit, Selenium, Docker, and FFmpeg. In addition to VMAF, we also calculate other objective QoE video metrics such as Visual Information Fidelity in the pixel domain (VIFp), Structural Similarity (SSIM), or Peak Signal-to-Noise Ratio (PSNR) applied to a WebRTC communication in different network conditions in terms of packet loss. Finally, we compare these objective results with a subjective evaluation using a Mean Opinion Score (MOS) scale to the same WebRTC streams. As a result, we found a strong correlation of the subjective video quality perceived in WebRTC video calls with the objective results computed with VMAF and VIFp in comparison with SSIM and PSNR and their variants.