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

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

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

Cloning and analysis of plant fatty acid desaturase 7 gene promoter from Brassica napus

In order to investigate the regulation mode of Brassica napus FAD7 gene in response of thermal stress, we measured the protein levels of BnFAD7 in plant at low and high temperature, and then analyzed promoter activity of 5’-flanking regions of BnFAD7 by transient gene expression in B. napus protoplasts at different temperatures. Our studies indicated that no significant change occurred in the expression level of BnFAD7 both at high and low temperature, while BnFAD7 promoter showed a heat-induced regulation mode and slowly increased activity at the chilling conditions, which suggested there are heat-induced cis-action element lies in BnFAD7 promoter sequence. Our data also suggested that a post-transcription regulation pattern existed to ensure BnFAD7 function in the acclimation to temperature stress. Furthermore, our studies give new evidence for the hypothesis that BnFAD7 and BnFAD8 gene may come from the same ancestor gene.Keywords: Brassica napus, fatty acids desaturase, promoter analysis, transient expression, BnFAD

Internally Rewarded Reinforcement Learning

We study a class of reinforcement learning problems where the reward signals for policy learning are generated by a discriminator that is dependent on and jointly optimized with the policy. This interdependence between the policy and the discriminator leads to an unstable learning process because reward signals from an immature discriminator are noisy and impede policy learning, and conversely, an under-optimized policy impedes discriminator learning. We call this learning setting \textit{Internally Rewarded Reinforcement Learning} (IRRL) as the reward is not provided directly by the environment but \textit{internally} by the discriminator. In this paper, we formally formulate IRRL and present a class of problems that belong to IRRL. We theoretically derive and empirically analyze the effect of the reward function in IRRL and based on these analyses propose the clipped linear reward function. Experimental results show that the proposed reward function can consistently stabilize the training process by reducing the impact of reward noise, which leads to faster convergence and higher performance compared with baselines in diverse tasks.Comment: Accepted at ICML 2023. Project webpage at https://ir-rl.github.i

Influence of Fe 3

The magnetic electrospinning (MES) method has been applied to generate aligned nanofibers. But researchers have different viewpoints on the usage of magnetic particles in the polymeric solutions. In order to investigate the effect of magnetic particles in forming the ordered fibers, the poly(lactic-co-glycolic acid) solutions with or without Fe3O4 nanoparticles were electrospun via MES. The fibers were compared at different voltages (13.5, 15.5, 17.5, and 19.5 kV) and flow rates (0.6, 0.9, 1.2, and 1.8 mL/h). It is shown that the well-aligned fibers can be fabricated by both magnetic and nonmagnetic solutions. The doping of Fe3O4 nanoparticles can increase the aligned fibers in some degree, especially at high applied voltage and flow rate. The diameters of fibers electrospun by MES were smaller than those by conventional electrospinning, and the diameter of fibers by MES without magnet particles was the smallest

In silico screening of anti-inflammatory constituents with good drug-like properties from twigs of Cinnamomum cassia based on molecular docking and network pharmacology

Purpose: To investigate by in silico screening the anti-inflammatory constituents of Cinnamomum cassia twigs. Methods: Information on the constituents of C. cassia twigs was retrieved from the online Traditional Chinese Medicines (TCM) database and literature. Inflammation-related target proteins were identified from DrugBank, Online Mendelian Inheritance in Man (OMIM), Therapeutic Target Database (TTD), Genetic Association Database (GAD), and PharmGKB. The identified compounds were filtered by Lipinski’s rules with Discovery Studio software. The “Libdock” module was used to perform molecular docking; LibdockScores and default cutoff values for hydrogen bonds and van der Waals interactions were recorded. LibdockScores between the prototype ligand and target protein were set as the threshold; compounds with higher LibdockScores than threshold were regarded as active compounds. Cytoscape software was used to construct active constituent-target protein interaction networks. Results: Sixty-nine potential inflammatory constituents with good drug-like properties in C. cassia twigs were screened in silico based on molecular docking and network pharmacology analysis. JAK2, mPEGS-1, COX-2, IL-1β, and PPARγ were considered the five most important target proteins. Compounds such as methyl dihydromelilotoside, hierochin B, dihydromelilotoside, dehydrodiconiferyl alcohol, balanophonin, phenethyl (E)-3-[4-methoxyphenyl]-2-propenoate, quercetin, and luteolin each interacted with more than six of the selected target proteins. Conclusion: C. cassia twigs possess active compounds with good drug-like properties that can potentially be developed to treat inflammation with multi-components on multi-targets