Rotation-invariant binary representation of sensor pattern noise for source-oriented image and video clustering

Most existing source-oriented image and video clustering algorithms based on sensor pattern noise (SPN) rely on the pairwise similarities, whose calculation usually dominates the overall computational time. The heavy computational burden is mainly incurred by the high dimensionality of SPN, which typically goes up to millions for delivering plausible clustering performance. This problem can be further aggravated by the uncertainty of the orientation of images or videos because the spatial correspondence between data with uncertain orientations needs to be reestablished in a brute-force search manner. In this work, we propose a rotation-invariant binary representation of SPN to address the issue of rotation and reduce the computational cost of calculating the pairwise similarities. Results on two public multimedia forensics databases have shown that the proposed approach is effective in overcoming the rotation issue and speeding up the calculation of pairwise SPN similarities for source-oriented image and video clustering

Preprocessing reference sensor pattern noise via spectrum equalization

Although sensor pattern noise (SPN) has been proven to be an effective means to uniquely identify digital cameras, some non-unique artifacts, shared amongst cameras undergo the same or similar in-camera processing procedures, often give rise to false identifications. Therefore, it is desirable and necessary to suppress these unwanted artifacts so as to improve the accuracy and reliability. In this work, we propose a novel preprocessing approach for attenuating the influence of the nonunique artifacts on the reference SPN to reduce the false identification rate. Specifically, we equalize the magnitude spectrum of the reference SPN through detecting and suppressing the peaks according to the local characteristics, aiming at removing the interfering periodic artifacts. Combined with 6 SPN extraction or enhancement methods, our proposed Spectrum Equalization Algorithm (SEA) is evaluated on the Dresden image database as well as our own database, and compared with the state-of-the-art preprocessing schemes. Experimental results indicate that the proposed procedure outperforms, or at least performs comparably to, the existing methods in terms of the overall ROC curve and kappa statistic computed from a confusion matrix, and tends to be more resistant to JPEG compression for medium and small image blocks

Enhancing sensor pattern noise via filtering distortion removal

In this work, we propose a method to obtain higher quality sensor pattern noise (SPN) for identifying source cameras. We believe that some components of SPN have been severely contaminated by the errors introduced by denoising filters and the quality of SPN can be improved by abandoning those components. In our proposed method, some coefficients with higher denoising errors are abandoned in the wavelet representation of SPN and the remaining wavelet coefficients are further enhanced to suppress the scene details in the SPN. These two steps aim to provide better SPN with higher signalto-noise ratio (SNR) and therefore improve the identification performance. The experimental results on 2,000 images captured by 10 cameras (each responsible for 200 images), show that our method achieves better receiver operating characteristic (ROC) performance when compared with some state-of-the-art methods

Topological Insulators-Based Magnetic Heterostructure

The combination of magnetism and topology in magnetic topological insulators (MTIs) has led to unprecedented advancements of time reversal symmetry-breaking topological quantum physics in the past decade. Compared with the uniform films, the MTI heterostructures provide a better framework to manipulate the spin-orbit coupling and spin properties. In this review, we summarize the fundamental mechanisms related to the physical orders host in (Bi,Sb)2(Te,Se)3-based hybrid systems. Besides, we provide an assessment on the general strategies to enhance the magnetic coupling and spin-orbit torque strength through different structural engineering approaches and effective interfacial interactions. Finally, we offer an outlook of MTI heterostructures-based spintronics applications, particularly in view of their feasibility to achieve room-temperature operation.Comment: 33 pages, 11 figure

On the detection-to-track association for online multi-object tracking

Driven by recent advances in object detection with deep neural networks, the tracking-by-detection paradigm has gained increasing prevalence in the research community of multi-object tracking (MOT). It has long been known that appearance information plays an essential role in the detection-to-track association, which lies at the core of the tracking-by-detection paradigm. While most existing works consider the appearance distances between the detections and the tracks, they ignore the statistical information implied by the historical appearance distance records in the tracks, which can be particularly useful when a detection has similar distances with two or more tracks. In this work, we propose a hybrid track association (HTA) algorithm that models the historical appearance distances of a track with an incremental Gaussian mixture model (IGMM) and incorporates the derived statistical information into the calculation of the detection-to-track association cost. Experimental results on three MOT benchmarks confirm that HTA effectively improves the target identification performance with a small compromise to the tracking speed. Additionally, compared to many state-of-the-art trackers, the DeepSORT tracker equipped with HTA achieves better or comparable performance in terms of the balance of tracking quality and speed

Noninvasive Imaging beyond the Diffraction Limit of 3D Dynamics in Thickly Fluorescent Specimens

SummaryOptical imaging of the dynamics of living specimens involves tradeoffs between spatial resolution, temporal resolution, and phototoxicity, made more difficult in three dimensions. Here, however, we report that rapid three-dimensional (3D) dynamics can be studied beyond the diffraction limit in thick or densely fluorescent living specimens over many time points by combining ultrathin planar illumination produced by scanned Bessel beams with super-resolution structured illumination microscopy. We demonstrate in vivo karyotyping of chromosomes during mitosis and identify different dynamics for the actin cytoskeleton at the dorsal and ventral surfaces of fibroblasts. Compared to spinning disk confocal microscopy, we demonstrate substantially reduced photodamage when imaging rapid morphological changes in D. discoideum cells, as well as improved contrast and resolution at depth within developing C. elegans embryos. Bessel beam structured plane illumination thus promises new insights into complex biological phenomena that require 4D subcellular spatiotemporal detail in either a single or multicellular context

Integrating TSPO PET imaging and transcriptomics to unveil the role of neuroinflammation and amyloid-β deposition in Alzheimer's disease.

PURPOSE Despite the revealed role of immunological dysfunctions in the development and progression of Alzheimer's disease (AD) through animal and postmortem investigations, direct evidence regarding the impact of genetic factors on microglia response and amyloid-β (Aβ) deposition in AD individuals is lacking. This study aims to elucidate this mechanism by integrating transcriptomics and TSPO, Aβ PET imaging in clinical AD cohort. METHODS We analyzed 85 patients with PET/MR imaging for microglial activation (TSPO, [18F]DPA-714) and Aβ ([18F]AV-45) within the prospective Alzheimer's Disease Immunization and Microbiota Initiative Study Cohort (ADIMIC). Immune-related differentially expressed genes (IREDGs), identified based on AlzData, were screened and verified using blood samples from ADIMIC. Correlation and mediation analyses were applied to investigate the relationships between immune-related genes expression, TSPO and Aβ PET imaging. RESULTS TSPO uptake increased significantly both in aMCI (P < 0.05) and AD participants (P < 0.01) and showed a positive correlation with Aβ deposition (r = 0.42, P < 0.001). Decreased expression of TGFBR3, FABP3, CXCR4 and CD200 was observed in AD group. CD200 expression was significantly negatively associated with TSPO PET uptake (r =-0.33, P = 0.013). Mediation analysis indicated that CD200 acted as a significant mediator between TSPO uptake and Aβ deposition (total effect B = 1.92, P = 0.004) and MMSE score (total effect B =-54.01, P = 0.003). CONCLUSION By integrating transcriptomics and TSPO PET imaging in the same clinical AD cohort, this study revealed CD200 played an important role in regulating neuroinflammation, Aβ deposition and cognitive dysfunction

Photoacoustic Identification of Laser-induced Microbubbles as Light Scattering Centers for Optical Limiting in Liquid Suspension of Graphene Nanosheets

Liquid suspensions of carbon nanotubes, graphene and transition metal dichalcogenides have exhibited excellent performance in optical limiting. However, the underlying mechanism has remained elusive and is generally ascribed to their superior nonlinear optical properties such as nonlinear absorption or nonlinear scattering. Using graphene as an example, we show that photo-thermal microbubbles are responsible for the optical limiting as strong light scattering centers: graphene sheets absorb incident light and become heated up above the boiling point of water, resulting in vapor and microbubble generation. This conclusion is based on direct observation of bubbles above the laser beam as well as a strong correlation between laser-induced ultrasound and optical limiting. In-situ Raman scattering of graphene further confirms that the temperature of graphene under laser pulses rises above the boiling point of water but still remains too low to vaporize graphene and create graphene plasma bubbles. Photo-thermal bubble scattering is not a nonlinear optical process and requires very low laser intensity. This understanding helps us to design more efficient optical limiting materials and understand the intrinsic nonlinear optical properties of nanomaterials