Computation-aware intra-mode decision for H.264 coding and transcoding

[[abstract]]been equipped with modern video codecs. Video communications, especially for encoding H.264 format bit-stream, however, are usually very power-consuming, leading to rather limited communication period for mobile devices powered by batteries. Computation-aware video coding can effectively extend the battery life. In this paper, we propose a computation-aware intra mode decision for H.264 coding and transcoding applications. The proposed algorithm optimizes the visual quality by adaptively adjusting the number of prediction modes in mode decision under a given computation constraint. We introduce a new concept of computation buffer and formulate the computation control of mode decision as a rate-distortion optimization problem of computation buffer control. Experimental results show that our proposed algorithm can effectively control the computational complexity while maintaining good RD-performance and satisfying the given computation constraint.[[fileno]]2030144030046[[department]]電機工程學

Online Multicast Traffic Engineering for Software-Defined Networks

Previous research on SDN traffic engineering mostly focuses on static traffic, whereas dynamic traffic, though more practical, has drawn much less attention. Especially, online SDN multicast that supports IETF dynamic group membership (i.e., any user can join or leave at any time) has not been explored. Different from traditional shortest-path trees (SPT) and graph theoretical Steiner trees (ST), which concentrate on routing one tree at any instant, online SDN multicast traffic engineering is more challenging because it needs to support dynamic group membership and optimize a sequence of correlated trees without the knowledge of future join and leave, whereas the scalability of SDN due to limited TCAM is also crucial. In this paper, therefore, we formulate a new optimization problem, named Online Branch-aware Steiner Tree (OBST), to jointly consider the bandwidth consumption, SDN multicast scalability, and rerouting overhead. We prove that OBST is NP-hard and does not have a $|D_{max}|^{1-\epsilon}$-competitive algorithm for any $\epsilon >0$, where $|D_{max}|$ is the largest group size at any time. We design a $|D_{max}|$-competitive algorithm equipped with the notion of the budget, the deposit, and Reference Tree to achieve the tightest bound. The simulations and implementation on real SDNs with YouTube traffic manifest that the total cost can be reduced by at least 25% compared with SPT and ST, and the computation time is small for massive SDN.Comment: Full version (accepted by INFOCOM 2018

Numerical Analysis on Color Preference and Visual Comfort from Eye Tracking Technique

Color preferences in engineering are very important, and there exists relationship between color preference and visual comfort. In this study, there are thirty university students who participated in the experiment, supplemented by pre- and posttest questionnaires, which lasted about an hour. The main purpose of this study is to explore the visual effects of different color assignment with subjective color preferences via eye tracking technology. Eye-movement data through a nonlinear analysis detect slight differences in color preferences and visual comfort, suggesting effective physiological indicators as extensive future research discussed. Results found that the average pupil size of eye-movement indicators can effectively reflect the differences of color preferences and visual comfort. This study more confirmed that the subjective feeling will make people have misjudgment

ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios

Acetylome of acinetobacter baumannii SK17 reveals a highly-conserved modification of histone-like protein HU

Lysine acetylation is a prevalent post-translational modification in both eukaryotes and prokaryotes. Whereas this modification is known to play pivotal roles in eukaryotes, the function and extent of this modification in prokaryotic cells remain largely unexplored. Here we report the acetylome of a pair of antibiotic-sensitive and -resistant nosocomial pathogen Acinetobacter baumannii SK17-S and SK17-R. A total of 145 lysine acetylation sites on 125 proteins was identified, and there are 23 acetylated proteins found in both strains, including histone-like protein HU which was found to be acetylated at Lys13. HU is a dimeric DNA-binding protein critical for maintaining chromosomal architecture and other DNA-dependent functions. To analyze the effects of site-specific acetylation, homogenously Lys13-acetylated HU protein, HU(K13ac) was prepared by genetic code expansion. Whilst not exerting an obvious effect on the oligomeric state, Lys13 acetylation alters both the thermal stability and DNA binding kinetics of HU. Accordingly, this modification likely destabilizes the chromosome structure and regulates bacterial gene transcription. This work indicates that acetyllysine plays an important role in bacterial epigenetics

Estrogen Modulates the Sensitivity of Lung Vagal C Fibers in Female Rats Exposed to Intermittent Hypoxia

Obstructive sleep apnea is mainly characterized by intermittent hypoxia (IH), which is associated with hyperreactive airway diseases and lung inflammation. Sensitization of lung vagal C fibers (LVCFs) induced by inflammatory mediators may play a central role in the pathogenesis of airway hypersensitivity. In females, estrogen interferes with inflammatory signaling pathways that may modulate airway hyperreactivity. In this study, we investigated the effects of IH on the reflex and afferent responses of LVCFs to chemical stimulants and lung inflammation in adult female rats, as well as the role of estrogen in these responses. Intact and ovariectomized (OVX) female rats were exposed to room air (RA) or IH for 14 consecutive days. On day 15, IH enhanced apneic responses to right atrial injection of chemical stimulants of LVCFs (e.g., capsaicin, phenylbiguanide, and α,β-methylene-ATP) in intact anesthetized females. Rats subjected to OVX prior to IH exposure exhibited an augmented apneic response to the same dose of stimulants compared with rats subjected to other treatments. Apneic responses to the stimulants were completely abrogated by bilateral vagotomy or perivagal capsaicin treatment, which blocked the neural conduction of LVCFs. Electrophysiological experiments revealed that in IH-exposed rats, OVX potentiated the excitability of LVCFs to stimulants. Moreover, LVCF hypersensitivity in rats subjected to OVX prior to IH exposure was accompanied by enhanced lung inflammation, which was reflected by elevated inflammatory cell infiltration in bronchoalveolar lavage fluid, lung lipid peroxidation, and protein expression of inflammatory cytokines. Supplementation with 17β-estradiol (E2) at a low concentration (30 μg/ml) but not at high concentrations (50 and 150 μg/ml) prevented the augmenting effects of OVX on LVCF sensitivity and lung inflammation caused by IH. These results suggest that ovarian hormones prevent the enhancement of LVCF sensitivity and lung inflammation by IH in female rats, which are related to the effect of low-dose estrogen

Antibiotic resistance and host immune evasion in Staphylococcus aureus mediated by a metabolic adaptation

Staphylococcus aureus is a notorious human bacterial pathogen with considerable capacity to develop antibiotic resistance. We have observed that human infections caused by highly drug-resistant S. aureus are more prolonged, complicated, and difficult to eradicate. Here we describe a metabolic adaptation strategy used by clinical S. aureus strains that leads to resistance to the last-line antibiotic, daptomycin, and simultaneously affects host innate immunity. This response was characterized by a change in anionic membrane phospholipid composition induced by point mutations in the phospholipid biosynthesis gene, cls2, encoding cardiolipin synthase. Single cls2 point mutations were sufficient for daptomycin resistance, antibiotic treatment failure, and persistent infection. These phenotypes were mediated by enhanced cardiolipin biosynthesis, leading to increased bacterial membrane cardiolipin and reduced phosphatidylglycerol. The changes in membrane phospholipid profile led to modifications in membrane structure that impaired daptomycin penetration and membrane disruption. The cls2 point mutations also allowed S. aureus to evade neutrophil chemotaxis, mediated by the reduction in bacterial membrane phosphatidylglycerol, a previously undescribed bacterial-driven chemoattractant. Together, these data illustrate a metabolic strategy used by S. aureus to circumvent antibiotic and immune attack and provide crucial insights into membrane-based therapeutic targeting of this troublesome pathogen

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe