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The aim of this article is to advance the field of metamaterials by proposing formulas for the design of high-contrast metamaterials with prescribed subwavelength defect mode eigenfrequencies. This is achieved in two settings: (i) design of non-Hermitian static materials and (ii) design of instantly changing non-Hermitian time-dependent materials. The design of static materials is achieved via characterizing equations for the defect mode eigenfrequencies in the setting of a defected dimer material. These characterizing equations are the basis for obtaining formulas for the material parameters of the defect which admit given defect mode eigenfrequencies. Explicit formulas are provided in the setting of one and two given defect mode eigenfrequencies in the setting of a defected chain of dimers. In the time-dependent case, we first analyze the influence of time boundaries on the subwavelength solutions. We find that subwavelength solutions are preserved if and only if the material parameters satisfy a temporal Snell's law across the time boundary. The same result also identifies the change of the time frequencies uniquely. Combining this result with those on the design of static materials, we obtain an explicit formula for the material design of instantly changing defected dimer materials which admit subwavelength modes with prescribed time-dependent defect mode eigenfrequency. Finally, we use this formula to create materials which admit spatio-temporally localized defect modes. [ABSTRACT FROM AUTHOR]

Action understanding in videos is a challenging task that has attracted widespread attention in recent years. Most current methods localize bounding box of actors at frame level, and then track or link these detections to form action tubes across frames. These methods often focus on utilizing temporal context in videos while neglecting the importance of the detector itself. In this paper, we present a two-stream enhanced framework to deal with the problem of action detection. Specifically, we devise an appearance and motion detectors in two-stream manner to detect actions, which take k consecutive RGB frames and optical flow images as input respectively. To improve the feature presentation capabilities, anchor refinement sub-module with feature alignment is introduced into the two-stream architecture to generate flexible anchor cuboids. Meanwhile, hierarchical fusion strategy is utilized to concatenate intermediate feature maps for capturing fast moving subjects. Moreover, layer normalization with skip connection is adopted to reduce the internal co-variate shift between network layers, which makes the training process simple and effective. Compared to state-of-the-art methods, the proposed approach yields impressive performance gain on three prevailing datasets: UCF-Sports, UCF-101 and J-HMDB, which confirm the effectiveness of our enhanced detector for action detection. [ABSTRACT FROM AUTHOR]

Enterococcus faecalis virulence requires cell wall‐associated proteins, including the sortase‐assembled endocarditis and biofilm associated pilus (Ebp), important for biofilm formation in vitro and in vivo. The current paradigm for sortase‐assembled pilus biogenesis in Gram‐positive bacteria is that sortases attach substrates to lipid II peptidoglycan (PG) precursors, prior to their incorporation into the growing cell wall. Contrary to prevailing dogma, by following the distribution of Ebp and PG throughout the E. faecalis cell cycle, we found that cell surface Ebp do not co‐localize with newly synthesized PG. Instead, surface‐exposed Ebp are localized to the older cell hemisphere and excluded from sites of new PG synthesis at the septum. Moreover, Ebp deposition on the younger hemisphere of the E. faecalis diplococcus appear as foci adjacent to the nascent septum. We propose a new model whereby sortase substrate deposition can occur on older PG rather than at sites of new cell wall synthesis. Consistent with this model, we demonstrate that sequestering lipid II to block PG synthesis via ramoplanin, does not impact new Ebp deposition at the cell surface. These data support an alternative paradigm for sortase substrate deposition in E. faecalis, in which Ebp are anchored directly onto uncrosslinked cell wall, independent of new PG synthesis. [ABSTRACT FROM AUTHOR]

We consider the existence and spectral stability of nonlinear discrete localized solutions representing light pulses propagating in a twisted multicore optical fiber. By considering an even number, N, of waveguides, we derive asymptotic expressions for solutions in which the bulk of the light intensity is concentrated as soliton‐like pulses confined to a single waveguide. The leading order terms obtained are in very good agreement with results of numerical computations. Furthermore, as in the model without temporal dispersion, when the twist parameter, ϕ, is given by ϕ=π/N$\phi = \pi/N$, these standing waves exhibit optical suppression, in which a single waveguide remains unexcited, to leading order. Spectral computations and numerical evolution experiments suggest that these standing wave solutions are stable for values of the coupling parameter less than a critical value, at which point a spectral instability results from the collision of an internal eigenvalue with the eigenvalues at the origin. This critical value has a maximum when ϕ=π/N$\phi = \pi/N$. [ABSTRACT FROM AUTHOR]

With the development of video and image processing technology, the field of video tampering forensics is facing enormous challenges. Specifically, as the fundamental basis of judicial forensics, passive forensics for object removal video forgery is particularly essential. To extract tampering traces in video more sufficiently, the author proposed a spatiotemporal trident network based on the spatial rich model (SRM) and 3D convolution (C3D), which provides three branches and can theoretically improve the detection and localization accuracy of tampered regions. Based on the spatiotemporal trident network, a temporal detector and a spatial locator were designed to detect and locate the tampered regions in the temporal and spatial domains of videos. For the temporal detector, 3D CNNs were employed in three branches as the encoders and a bidirectional long short-term memory (BiLSTM) as the decoder. For the spatial locator, a backbone network named C3D-ResNet12 was designed as the encoder of the three branches, and the region proposal networks (RPNs) were employed as the decoders in three branches. In addition, we optimized the loss functions of the above two algorithms based on focal loss and GIoU loss. The experimental results revealed the effectiveness of spatiotemporal detection and localization algorithms: for temporal forgery detection, the accuracy of the frame classification increased to 99+%; for spatial forgery localization, the successful localization rate of the tampered regions in forged frames reached 96+%, and the mean intersection over union of the located tampered regions and the real tampered regions reached 62+%. [ABSTRACT FROM AUTHOR]

Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.

Abstract We introduce a novel spatiotemporal deformable part model for the localization of fine-grained human interactions of two persons in unsegmented videos. Our approach is the first to classify interactions and additionally provide the temporal and spatial extent of the interaction in the video. To this end, our models contain part detectors that support different scales as well as different types of feature descriptors, which are combined in a single graph. This allows us to model the detailed coordination between people in terms of body pose and motion. We demonstrate that this helps to avoid confusions between visually similar interactions. We show that robust results can be obtained when training on small numbers of training sequences (5–15) per interaction class. We achieve AuC scores of 0.82 with an IoU of 0.3 on the publicly available ShakeFive2 dataset, which contains interactions that differ slightly in their coordination. To further test the generalization of our models, we perform cross-dataset experiments where we test on two other publicly available datasets: UT-Interaction and SBU Kinect. These experiments show that our models generalize well to different environments.

We introduce a novel spatiotemporal deformable part model for the localization of fine-grained human interactions of two persons in unsegmented videos. Our approach is the first to classify interactions and additionally provide the temporal and spatial extent of the interaction in the video. To this end, our models contain part detectors that support different scales as well as different types of feature descriptors, which are combined in a single graph. This allows us to model the detailed coordination between people in terms of body pose and motion. We demonstrate that this helps to avoid confusions between visually similar interactions. We show that robust results can be obtained when training on small numbers of training sequences (5-15) per interaction class. We achieve AuC scores of 0.82 with an IoU of 0.3 on the publicly available ShakeFive2 dataset, which contains interactions that differ slightly in their coordination. To further test the generalization of our models, we perform cross-dataset experiments where we test on two other publicly available datasets: UT-Interaction and SBU Kinect. These experiments show that our models generalize well to different environments. [ABSTRACT FROM AUTHOR]

As digital video databases grow, so grows the problem of effectively navigating through them. In this paper we propose a novel content-based video retrieval approach to searching such video databases, specifically those involving human actions, incorporating spatio-temporal localization. We outline a novel, highly efficient localization model that first performs temporal localization based on histograms of evenly spaced time-slices, then spatial localization based on histograms of a 2-D spatial grid. We further argue that our retrieval model, based on the aforementioned localization, followed by relevance ranking, results in a highly discriminative system, while remaining an order of magnitude faster than the current state-of-the-art method. We also show how relevance feedback can be applied to our localization and ranking algorithms. As a result, the presented system is more directly applicable to real-world problems than any prior content-based video retrieval system. [ABSTRACT FROM PUBLISHER]

Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology., (Copyright © 2020 Savulescu, Jacobs, Negishi, Davignon and Mhlanga.)

Spatiotemporal localization of signals is a fundamental feature impacting cell survival and proper function. The cell needs to respond in an accurate manner in both space and time to both intra- and intercellular environment cues. The regulation of this comprehensive process involves the cytoskeleton and the trafficking machinery, as well as local protein synthesis and ligand-receptor mechanisms. Alterations in such mechanisms can lead to cell dysfunction and disease. Motor neurons that can extend over tens of centimeters are a classic example for the importance of such events. Changes in spatiotemporal localization mechanisms are thought to play a role in motor neuron degeneration that occurs in amyotrophic lateral sclerosis (ALS). In this review we will discuss these mechanisms and argue that possible misregulated factors can lead to motor neuron degeneration in ALS., (Copyright © 2015 Elsevier Inc. All rights reserved.)