IBVC: Interpolation-driven B-frame Video Compression

Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. However, previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation or video frame interpolation. They suffer from inaccurate quantized motions and inefficient motion compensation. To address these issues, we propose a simple yet effective structure called Interpolation-driven B-frame Video Compression (IBVC). Our approach only involves two major operations: video frame interpolation and artifact reduction compression. IBVC introduces a bit-rate free MEMC based on interpolation, which avoids optical-flow quantization and additional compression distortions. Later, to reduce duplicate bit-rate consumption and focus on unaligned artifacts, a residual guided masking encoder is deployed to adaptively select the meaningful contexts with interpolated multi-scale dependencies. In addition, a conditional spatio-temporal decoder is proposed to eliminate location errors and artifacts instead of using MEMC coding in other methods. The experimental results on B-frame coding demonstrate that IBVC has significant improvements compared to the relevant state-of-the-art methods. Meanwhile, our approach can save bit rates compared with the random access (RA) configuration of H.266 (VTM). The code will be available at https://github.com/ruhig6/IBVC.Comment: Submitted to IEEE TCSV

Temporal Consistency Learning of inter-frames for Video Super-Resolution

Video super-resolution (VSR) is a task that aims to reconstruct high-resolution (HR) frames from the low-resolution (LR) reference frame and multiple neighboring frames. The vital operation is to utilize the relative misaligned frames for the current frame reconstruction and preserve the consistency of the results. Existing methods generally explore information propagation and frame alignment to improve the performance of VSR. However, few studies focus on the temporal consistency of inter-frames. In this paper, we propose a Temporal Consistency learning Network (TCNet) for VSR in an end-to-end manner, to enhance the consistency of the reconstructed videos. A spatio-temporal stability module is designed to learn the self-alignment from inter-frames. Especially, the correlative matching is employed to exploit the spatial dependency from each frame to maintain structural stability. Moreover, a self-attention mechanism is utilized to learn the temporal correspondence to implement an adaptive warping operation for temporal consistency among multi-frames. Besides, a hybrid recurrent architecture is designed to leverage short-term and long-term information. We further present a progressive fusion module to perform a multistage fusion of spatio-temporal features. And the final reconstructed frames are refined by these fused features. Objective and subjective results of various experiments demonstrate that TCNet has superior performance on different benchmark datasets, compared to several state-of-the-art methods.Comment: Accepted by IEEE Trans. Circuits Syst. Video Techno

JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation

Video frame interpolation (VFI) aims to generate predictive frames by warping learnable motions from the bidirectional historical references. Most existing works utilize spatio-temporal semantic information extractor to realize motion estimation and interpolation modeling. However, they insufficiently consider the real mechanistic rationality of generated middle motions. In this paper, we reformulate VFI as a Joint Non-linear Motion Regression (JNMR) strategy to model the complicated motions of inter-frame. Specifically, the motion trajectory between the target frame and the multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. ConvLSTM is adopted to construct this joint distribution of complete motions in temporal dimension. Moreover, the feature learning network is designed to optimize for the joint regression modeling. A coarse-to-fine synthesis enhancement module is also conducted to learn visual dynamics at different resolutions through repetitive regression and interpolation. Experimental results on VFI show that the effectiveness and significant improvement of joint motion regression compared with the state-of-the-art methods. The code is available at https://github.com/ruhig6/JNMR.Comment: Accepted by IEEE Transactions on Image Processing (TIP

From Unbalanced to Perfect: Implementation of Low Energy Stream Ciphers

Low energy is an important aspect of hardware implementation. For energy-limited battery-powered devices, low energy stream ciphers can play an important role. In \texttt{IACR ToSC 2021}, Caforio et al. proposed the Perfect Tree energy model for stream cipher that links the structure of combinational logic circuits with state update functions to energy consumption. In addition, a metric given by the model shows a negative correlation with energy consumption, i.e., the higher the balance of the perfect tree, the lower the energy consumption. However, Caforio et al. didn\u27t give a method that eliminate imbalances of the unrolled strand tree for the existing stream ciphers. In this paper, based on the Perfect Tree energy model, we propose a new redundant design model that improve the balances of the unrolled strand tree for the purpose of reducing energy consumption. In order to obtain the redundant design, we propose a search algorithm for returning the corresponding implementation scheme. For the existing stream ciphers, the proposed model and search method can be used to provide a low-power redundancy design scheme. To verify the effectiveness, we apply our redundant model and search method in the stream ciphers (e.g., \texttt{Trivium} and \texttt{Kreyvium}) and conducted a synthetic test. The results of the energy measurement demonstrate that the proposed model and search method can obtain lower energy consumption

Determination of ramosetron in rat plasma by LC-ESI-MS and its application

A sensitive and simple liquid chromatography/electrospray mass spectrometry (LC-ESI-MS) method for determination of ramosetron in rat plasma using one-step protein precipitation was developed and validated. After addition of midazolam as internal standard (IS), protein precipitation by acetonitrile was used as sample preparation. Chromatographically separation was achieved on an SB-C18 (2.1 mm × 150 mm, 5 μm) column with acetonitrile-0.1 % formic acid as the mobile phase with gradient elution. Electrospray ionization (ESI) source was applied and operated in positive ion mode; selected ion monitoring (SIM) mode was used to quantification using target fragment ions m/z 280 for ramosetron and m/z 326 for the IS. Calibration plots were linear over the range of 10-1000 ng/mL for ramosetron in rat plasma. Lower limit of quantification (LLOQ) for Ramosetron was 10 ng/mL. Mean recovery of ramosetron from plasma was in the range of 88.5-92.8 %. CV of intra-day and inter-day precision were both less than 15 %. This method is simple and sensitive enough to be used in pharmacokinetic study for determination of ramosetron in rat plasma.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Transient Receptor Potential V Channels Are Essential for Glucose Sensing by Aldolase and AMPK

Fructose-1,6-bisphosphate (FBP) aldolase links sensing of declining glucose availability to AMPK activation via the lysosomal pathway. However, how aldolase transmits lack of occupancy by FBP to AMPK activation remains unclear. Here, we show that FBP-unoccupied aldolase interacts with and inhibits endoplasmic reticulum (ER)-localized transient receptor potential channel subfamily V, inhibiting calcium release in low glucose. The decrease of calcium at contact sites between ER and lysosome renders the inhibited TRPV accessible to bind the lysosomal v-ATPase that then recruits AXIN:LKB1 to activate AMPK independently of AMP. Genetic depletion of TRPVs blocks glucose starvation-induced AMPK activation in cells and liver of mice, and in nematodes, indicative of physical requirement of TRPVs. Pharmacological inhibition of TRPVs activates AMPK and elevates NAD(+) levels in aged muscles, rejuvenating the animals' running capacity. Our study elucidates that TRPVs relay the FBP-free status of aldolase to the reconfiguration of v-ATPase, leading to AMPK activation in low glucose