Nanostructuring of magnetically hard and soft materials is fascinating for exploring next‐generation ultrastrong permanent magnets with less expensive rare‐earth elements. However, the resulting ...hard/soft nanocomposites often exhibit random crystallographic orientations and monomorphological equiaxed grains, leading to inferior magnetic performances compared to corresponding pure rare‐earth magnets. This study describes the first fabrication of a novel bimorphological anisotropic bulk nanocomposite using a multistep deformation approach, which consists of oriented hard‐phase SmCo rod‐shaped grains and soft‐phase Fe(Co) equiaxed grains with a high fraction (≈28 wt%) and small size (≈10 nm). The nanocomposite exhibits a record‐high energy product (28 MGOe) for this class of bulk materials with less rare‐earth elements and outperforms, for the first time, the corresponding pure rare‐earth magnet with 58% enhancement in energy product. These findings open up the door to moving from a pure permanent‐magnet system to a stronger nanocomposite system at lower costs.
The fabrication of novel bimorphological anisotropic SmCo7/Fe(Co) bulk nanostructures with a multistep deformation approach is demonstrated. The structures exhibit a record‐high energy product (28 MGOe) for this class of bulk materials with less rare‐earth elements. The resulting nanocomposite outperforms, for the first time, a corresponding pure rare‐earth permanent magnet (SmCo7) with 58% enhancement in energy product.
Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this ...aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multimodal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository
https://github.com/MenghaoGuo/Awesome-Vision-Attentions
is dedicated to collecting related work. We also suggest future directions for attention mechanism research.
Background
The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the ...quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia.
Methods
A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 ± 14.0 years (age range, 11–93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans.
Results
CT scans of 2215 patients (90%) showed multiple lesions of which 36 (1%) and 50 patients (2%) had left and right lung infections, respectively (> 50% of each affected lung’s volume), while 27 (1%) had total lung infection (> 50% of the total volume of both lungs). Overall, 298 (12%), 778 (32%) and 1300 (53%) patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 (94%) and 71 (3%) patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (≥ 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia.
Conclusion
Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients.
Video stabilization techniques are essential for most hand-held captured videos due to high-frequency shakes. Several 2D-, 2.5D-, and 3D-based stabilization techniques have been presented previously, ...but to the best of our knowledge, no solutions based on deep neural networks had been proposed to date. The main reason for this omission is shortage in training data as well as the challenge of modeling the problem using neural networks. In this paper, we present a video stabilization technique using a convolutional neural network. Previous works usually propose an off-line algorithm that smoothes a holistic camera path based on feature matching. Instead, we focus on low-latency, real-time camera path smoothing that does not explicitly represent the camera path and does not use future frames. Our neural network model, called StabNet, learns a set of mesh-grid transformations progressively for each input frame from the previous set of stabilized camera frames and creates stable corresponding latent camera paths implicitly. To train the network, we collect a dataset of synchronized steady and unsteady video pairs via a specially designed hand-held hardware. Experimental results show that our proposed online method performs comparatively to the traditional off-line video stabilization methods without using future frames while running about 10 times faster. More importantly, our proposed StabNet is able to handle low-quality videos, such as night-scene videos, watermarked videos, blurry videos, and noisy videos, where the existing methods fail in feature extraction or matching.
Background
The missing asymptomatic COVID‐19 infections have been overlooked because of the imperfect sensitivity of the nucleic acid testing (NAT). Globally understanding the humoral immunity in ...asymptomatic carriers will provide scientific knowledge for developing serological tests, improving early identification, and implementing more rational control strategies against the pandemic.
Measure
Utilizing both NAT and commercial kits for serum IgM and IgG antibodies, we extensively screened 11 766 epidemiologically suspected individuals on enrollment and 63 asymptomatic individuals were detected and recruited. Sixty‐three healthy individuals and 51 mild patients without any preexisting conditions were set as controls. Serum IgM and IgG profiles were further probed using a SARS‐CoV‐2 proteome microarray, and neutralizing antibody was detected by a pseudotyped virus neutralization assay system. The dynamics of antibodies were analyzed with exposure time or symptoms onset.
Results
A combination test of NAT and serological testing for IgM antibody discovered 55.5% of the total of 63 asymptomatic infections, which significantly raises the detection sensitivity when compared with the NAT alone (19%). Serum proteome microarray analysis demonstrated that asymptomatics mainly produced IgM and IgG antibodies against S1 and N proteins out of 20 proteins of SARS‐CoV‐2. Different from strong and persistent N‐specific antibodies, S1‐specific IgM responses, which evolved in asymptomatic individuals as early as the seventh day after exposure, peaked on days from 17 days to 25 days, and then disappeared in two months, might be used as an early diagnostic biomarker. 11.8% (6/51) mild patients and 38.1% (24/63) asymptomatic individuals did not produce neutralizing antibody. In particular, neutralizing antibody in asymptomatics gradually vanished in two months.
Conclusion
Our findings might have important implications for the definition of asymptomatic COVID‐19 infections, diagnosis, serological survey, public health, and immunization strategies.
The combination of NAT and serological testing for IgM antibody significantly improves the detection sensitivity of asymptomatic COVID‐19 infections, compared with NAT alone. S1‐specific IgM antibody response with rapid emergence and disappearance might be helpful to assist NAT for early identification of infectious individuals. A majority of asymptomatics induce very low levels of neutralizing antibody that disappear in two months. Abbreviations: NAT, nucleic acid testing; FI, fluorescence intensity; NT50, half‐maximal neutralizing titer.
Hybrid nanostructures that comprise two or more nanoscale functional components are fascinating for applications in electronics, energy conversion devices, and biotechnologies. Their performances are ...strongly dependent on the characteristics of the individual components including the size, morphology, orientation, and distribution. However, it remains challenging to simultaneously control these structural properties in a three-dimensional (3D) hybrid nanostructure. Here, we introduce a robust strategy for concurrently manipulating these characteristics in a bulk SmCo/Fe(Co) nanocomposite. This method can tune nanocrystals in size (down to sub-10 nm), morphology (sphere, rod, or disc), and crystallographic orientation (isotropic or anisotropic). We have therefore achieved the desired nanostructures: oriented hard magnetic SmCo grains and homogeneously distributed soft magnetic Fe(Co) grains with high fractions (∼26 wt %) and small sizes (∼12.5 nm). The resulting anisotropic nanocomposite exhibits an energy product that is approximately 50% greater than that of its corresponding pure SmCo magnet and 35% higher than the reported largest value in isotropic SmCo/Fe(Co) systems. Our findings pave a new way to manipulating 3D hybrid nanostructures in a controllable manner.
Redirected walking (RDW) allows users to explore virtual environments in limited physical spaces by imperceptibly steering them away from obstacles and space boundaries. However, even with those ...techniques, the risk of collision cannot always be avoided. For such situations, resetting techniques have been proposed to provide an immediate adjustment of the physical walking direction of a user. Existing resetting techniques are either applied in-place, where the user changes orientation but stays in the same position or out-of-place methods where the user is guided to move from the current position to a safe location all while freezing the movement in the virtual world. While out-of-place methods have the potential to provide more freedom to user movements after resetting, current out-of-place methods do not provide enough guidance for the users to move to optimal locations. In this work, we propose a novel out-of-place resetting strategy that guides users to optimal physical locations with the most potential for free movement and a smaller amount of resetting required for their further movements. For this purpose, we calculate a heat map of the walking area according to the average walking distance using a simulation of the currently used RDW algorithm. Based on this heat map, we identify the most suitable position for a one-step reset within a predefined searching range and use this one as the reset point. Our results show that our method increases the average moving distance within one cycle of resetting. Furthermore, our resetting method can be applied to any physical area with obstacles. That means that RDW methods that were not suitable for such environments (e.g., Steer to Center) combined with our resetting can also be extended to such complex walking areas. In addition, we present a user interface to provide a similar visual experience between these methods, using a two-arrows indicator to help users adjust their position and direction.
The Search for Extraterrestrial Intelligence (SETI) attempts to address the possibility of the presence of technological civilizations beyond the Earth. Benefiting from high sensitivity, large sky ...coverage, and an innovative feed cabin for China's Five-hundred-meter Aperture Spherical radio Telescope (FAST), we performed SETI's first observations with FAST's newly commissioned 19 beam receiver; we report preliminary results in this paper. Using the data stream produced by the SERENDIP VI real-time multibeam SETI spectrometer installed at FAST, as well as its off-line data processing pipelines, we identify and remove four kinds of radio frequency interference (RFI): zone, broadband, multibeam, and drifting, utilizing the Nebula SETI software pipeline combined with machine-learning algorithms. After RFI mitigation, the Nebula pipeline identifies and ranks interesting narrowband candidate ET signals, scoring candidates by the number of times candidate signals have been seen at roughly the same sky position and same frequency, signal strength, proximity to a nearby star or object of interest, along with several other scoring criteria. We show four example candidate groups that demonstrate this RFI mitigation and candidate selection. This preliminary testing on FAST data helps to validate our SETI instrumentation techniques as well as our data processing pipeline.
Immunotherapy is a promising method for the treatment of hepatocellular carcinoma (HCC), in which CD8+T cells play a key role. The influence of long noncoding RNA (lncRNA) nuclear-enriched autosomal ...transcript 1(NEAT1) on the antitumor activity of CD8+T cells was clarified in this study.
Peripheral blood mononuclear cells (PBMCs) were isolated from HCC patients, and the expressions of NEAT1 and Tim-3 were determined by qRT-PCR and western blot, respectively. CD8+T cell apoptosis and cell percentage were analyzed via flow cytometry. The cytolysis activity of CD8+T cells against HCC cells was examined. RNA immunoprecipitation (RIP) and RNA pull-down assay were performed to explore the interaction between NEAT1 and miR-155.
NEAT1 and Tim-3 were up-regulated in the PBMCs of patients with HCC (n = 20) compared with healthy subjects (n = 20). Down-regulation of NEAT1 restrained CD8+T cell apoptosis and enhanced the cytolysis activity, while interference of miR-155 showed the opposite effects by up-regulating Tim-3. Binding and interaction between NEAT1 and miR-155 were validated in CD8+T cells. Down-regulation of NEAT1 restrained CD8+T cell apoptosis and enhanced the cytolysis activity through the miR-155/Tim-3 pathway. Repression of NEAT1 suppressed tumor growth in HCC mice.
Via modulating the miR-155/Tim-3 pathway, repression of NEAT1 restrained CD8+T cell apoptosis and enhanced the cytolysis activity against HCC, implying an effective target for improving the outcome of immunotherapy.
Image matting is widely studied for accurate foreground extraction. Most algorithms, including deep-learning based solutions, require a carefully edited trimap. Recent works attempt to combine the ...segmentation stage and matting stage in one CNN model, but errors occurring at the segmentation stage lead to unsatisfactory matte. We propose a user-guided approach for practical human matting. More precisely, we provide a good automatic initial matting and a natural way of interaction that reduces the workload of drawing trimaps and allows users to guide the matting in ambiguous situation. We also combine the segmentation and matting stage in an end-to-end CNN architecture and introduce a residual-learning module to support convenient stroke-based interaction. The proposed model learns to propagate the input trimap and modify the deep image features, which can efficiently correct the segmentation errors. Our model supports arbitrary forms of trimaps from carefully edited to totally unknown maps. Our model also allows users to choose from different foreground estimations according to their preference. We collected a large human matting dataset consisting of 12K real-world human images with complex background and human-object relations. The proposed model is trained on the new dataset with a novel trimap generation strategy that enables the model to tackle different test situations and highly improves the interaction efficiency. Our method outperforms other state-of-the-art automatic methods and achieve competitive accuracy when high-quality trimaps are provided. Experiments indicate that our interactive matting strategy is superior to separately estimating the trimap and alpha matte using two models.
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