Temporal Relational Reasoning in Videos

Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales. We evaluate TRN-equipped networks on activity recognition tasks using three recent video datasets - Something-Something, Jester, and Charades - which fundamentally depend on temporal relational reasoning. Our results demonstrate that the proposed TRN gives convolutional neural networks a remarkable capacity to discover temporal relations in videos. Through only sparsely sampled video frames, TRN-equipped networks can accurately predict human-object interactions in the Something-Something dataset and identify various human gestures on the Jester dataset with very competitive performance. TRN-equipped networks also outperform two-stream networks and 3D convolution networks in recognizing daily activities in the Charades dataset. Further analyses show that the models learn intuitive and interpretable visual common sense knowledge in videos.Comment: camera-ready version for ECCV'1

Epigenomes in Cardiovascular Disease.

If unifying principles could be revealed for how the same genome encodes different eukaryotic cells and for how genetic variability and environmental input are integrated to impact cardiovascular health, grand challenges in basic cell biology and translational medicine may succumb to experimental dissection. A rich body of work in model systems has implicated chromatin-modifying enzymes, DNA methylation, noncoding RNAs, and other transcriptome-shaping factors in adult health and in the development, progression, and mitigation of cardiovascular disease. Meanwhile, deployment of epigenomic tools, powered by next-generation sequencing technologies in cardiovascular models and human populations, has enabled description of epigenomic landscapes underpinning cellular function in the cardiovascular system. This essay aims to unpack the conceptual framework in which epigenomes are studied and to stimulate discussion on how principles of chromatin function may inform investigations of cardiovascular disease and the development of new therapies

A study of Al1-xInxN growth by reflection high-energy electron diffraction-incorporation of cation atoms during molecular-beam epitaxy

Molecular-beam epitaxy of Al1-x Inx N alloys with different indium (In) contents, x, were studied by in situ reflection high-energy electron diffraction (RHEED). Growth rates of the alloys were measured by the RHEED intensity oscillations for different source flux conditions, while the lattice parameters were derived from the diffraction patterns. It was found that under the excess nitrogen growth regime, incorporation of aluminum was complete whereas incorporation of In atoms was incomplete even at temperatures below 400 °C. © 2008 American Institute of Physics.published_or_final_versio

Transition between wurtzite and zinc-blende GaN: An effect of deposition condition of molecular-beam epitaxy

GaN exists in both wurtzite and zinc-blende phases and the growths of the two on its (0001) or (111) surfaces are achieved by choosing proper deposition conditions of molecular-beam epitaxy (MBE). At low substrate temperatures but high gallium fluxes, metastable zinc-blende GaN films are obtained, whereas at high temperatures and/or using high nitrogen fluxes, equilibrium wurtzite phase GaN epilayers resulted. This dependence of crystal structure on substrate temperature and source flux is not affected by deposition rate. Rather, the initial stage nucleation kinetics plays a primary role in determining the crystallographic structures of epitaxial GaN by MBE. © 2006 American Institute of Physics.published_or_final_versio

Background adaptation for improved listening experience in broadcasting

The intelligibility of speech in noise can be improved by modifying the speech. But with object-based audio, there is the possibility of altering the background sound while leaving the speech unaltered. This may prove a less intrusive approach, affording good speech intelligibility without overly compromising the perceived sound quality. In this study, the technique of spectral weighting was applied to the background. The frequency-dependent weightings for adaptation were learnt by maximising a weighted combination of two perceptual objective metrics for speech intelligibility and audio quality. The balance between the two objective metrics was determined by the perceptual relationship between intelligibility and quality. A neural network was trained to provide a fast solution for real-time processing. Tested in a variety of background sounds and speech-to-background ratios (SBRs), the proposed method led to a large intelligibility gain over the unprocessed baseline. Compared to an approach using constant weightings, the proposed method was able to dynamically preserve the overall audio quality better with respect to SBR changes

Palaeopathology in a Cretaceous terrestrial lizard from China

The lizard genus Yabeinosaurus is a common and relatively well-known member of Chinese Lower Cretaceous Jehol Biota, found in both the Yixian and Jiufotang formations of north-eastern China. Previous research on Yabeinosaurus has revealed information on its morphology, phylogenetic position, colouration, diet, and viviparous reproductive strategy. Herein we describe a new specimen preserving the skull and postcranial skeleton. The skull shows features characteristic of Yabeinosaurus robustus, but reveals the morphology of the vomer for the first time. In the postcranial skeleton, the most significant feature is a malformation of the fibula resulting from a fracture that occurred several months before the animal died, possibly as the result of intraspecies aggression or a predation attempt

Small Airway Dysfunction in Asthma Is Associated with Perceived Respiratory Symptoms, Non-Type 2 Airway Inflammation, and Poor Responses to Therapy.

BACKGROUND: Emerging evidence has indicated that small airway dysfunction (SAD) contributes to the clinical expression of asthma. OBJECTIVES: The aim of the study was to explore the relationships of SAD assessed by forced expiratory flow between 25 and 75% (FEF25-75%), with clinical and inflammatory profile and treatment responsiveness in asthma. METHOD: In study I, dyspnea intensity (Borg scale), chest tightness, wheezing and cough (visual analog scales, VASs), and pre- and post-methacholine challenge testing (MCT) were analyzed in asthma patients with SAD and non-SAD. In study II, asthma subjects with SAD and non-SAD underwent sputum induction, and inflammatory mediators in sputum were detected. Asthma patients with SAD and non-SAD receiving fixed treatments were prospectively followed up for 4 weeks in study III. Spirometry, Asthma Control Questionnaire (ACQ), and Asthma Control Test (ACT) were carried out to define treatment responsiveness. RESULTS: SAD subjects had more elevated ΔVAS for dyspnea (p = 0.027) and chest tightness (p = 0.032) after MCT. Asthma patients with SAD had significantly elevated interferon (IFN)-γ in sputum (p < 0.05), and Spearman partial correlation found FEF25-75% significantly related to IFN-γ and interleukin-8 (both having p < 0.05). Furthermore, multivariable regression analysis indicated SAD was significantly associated with worse treatment responses (decrease in ACQ ≥0.5 and increase in ACT ≥3) (p = 0.022 and p = 0.032). CONCLUSIONS: This study indicates that SAD in asthma predisposes patients to greater dyspnea intensity and chest tightness during bronchoconstriction. SAD patients with asthma are characterized by non-type 2 inflammation that may account for poor responsiveness to therapy

Cordycepin enhances cisplatin apoptotic effect through caspase/MAPK pathways in human head and neck tumor cells

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Semiparametric Multivariate Accelerated Failure Time Model with Generalized Estimating Equations

The semiparametric accelerated failure time model is not as widely used as the Cox relative risk model mainly due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations provide promising tools to make the accelerate failure time models more attractive in practice. For semiparametric multivariate accelerated failure time models, we propose a generalized estimating equation approach to account for the multivariate dependence through working correlation structures. The marginal error distributions can be either identical as in sequential event settings or different as in parallel event settings. Some regression coefficients can be shared across margins as needed. The initial estimator is a rank-based estimator with Gehan's weight, but obtained from an induced smoothing approach with computation ease. The resulting estimator is consistent and asymptotically normal, with a variance estimated through a multiplier resampling method. In a simulation study, our estimator was up to three times as efficient as the initial estimator, especially with stronger multivariate dependence and heavier censoring percentage. Two real examples demonstrate the utility of the proposed method