Self-Supervised Motion Magnification by Backpropagating Through Optical Flow

This paper presents a simple, self-supervised method for magnifying subtle motions in video: given an input video and a magnification factor, we manipulate the video such that its new optical flow is scaled by the desired amount. To train our model, we propose a loss function that estimates the optical flow of the generated video and penalizes how far if deviates from the given magnification factor. Thus, training involves differentiating through a pretrained optical flow network. Since our model is self-supervised, we can further improve its performance through test-time adaptation, by finetuning it on the input video. It can also be easily extended to magnify the motions of only user-selected objects. Our approach avoids the need for synthetic magnification datasets that have been used to train prior learning-based approaches. Instead, it leverages the existing capabilities of off-the-shelf motion estimators. We demonstrate the effectiveness of our method through evaluations of both visual quality and quantitative metrics on a range of real-world and synthetic videos, and we show our method works for both supervised and unsupervised optical flow methods

Potassium channel expression and function in the N9 murine microglial cell line

Microglia are immunocompetent cells in the central nervous system that have many similarities with macrophages of peripheral tissues. Their activation protects local cells from foreign microbial infection in the CNS. However, “over-activated“ microglia become a “Double-edged sword” which show neuronal toxicity and are implicated in a variety of neurodegenerative diseases. Previous studies have suggested that potassium channels play a role in regulating microglial activation, migration and proliferation. However what kinds of potassium channel subunits are expressed in microglia, whether their expression changes after microglial activation and the functional role of most potassium channels expressed in microglia are still not fully characterized. To address these questions, we used the N9 mouse microglial cell line as a cell model for experiments in vitro. We first optimized the cell culture and lipopolysaccharide (LPS), the endotoxin of gram-negative bacteria, mediated stimulation of microglial activation that results in subsequent nitric oxide (NO) release. Using qRT-PCR, we analyzed mRNA expression of >80 potassium channel pore-forming subunits and their regulatory subunits in both LPS-treated (1μg/ml, 24hr) and untreated microglia. The subunits which displayed the highest mRNA expression in resting N9 cells included Kcnma1 (KCa1.1), Kcnk6 (K2p6.1), Kcnc3 (Kv3.3) and Abcc8 (SUR1). In addition, N9 cells also expressed the mRNAs for other channel subunits previously reported in microglia such as Kcnn4 (KCa3.1), Kcna3 (Kv1.3) and Kcna5 (Kv1.5) subunits. Of these channel subunits LPS had no significant effect on mRNA expression except for Kcnk6 which was significantly reduced. We then examined whether pharmacological manipulation of these channels controlled LPS-induced NO release. It was found out that the KCa3.1 selective blocker Tram34 and the Kv1.5 inhibitor propafenone (PPF) significantly decreased LPS-induced NO in agreement with data in primary microglia. Ba2+ that inhibits inwardly rectifying potassium channels as well as K2p6.1 also significantly attenuated LPS-induced microglial activation. Inhibition or activation of KCa1.1 channels by paxilline and NS1619 respectively had no significant effect. However, paxilline significantly attenuated the effect of Tram34, PPF and Ba2+ to control LPSinduced NO release while NS1619 significantly facilitated the effect of Tram34 and PPF. To investigate the major ionic currents expressed in N9 microglia with and without LPS application, we examined whole-cell ionic currents using the patch-clamp technique. Resting N9 cells display a small outward current at positive potentials but a large inwardly rectifying component at negative potentials in physiological potassium gradients. The outward current was dramatically increased by LPS application that was dependent upon the intracellular free calcium concentration. Paxilline or Tram34 was then applied to acutely block this apparent outward KCa current. The result indicated that the LPS triggered KCa current was mainly paxilline sensitive supporting a role for an LPS-induced increase in KCa1.1 channel current. In addition, by using current clamp the mean resting membrane potential of N9 cells was -50.6±6.6mV (N=7) determined in the presence of 1μM [Ca2+]i and -59.4±8.5mV (N=10) with 10nM [Ca2+]i. N9 cells did not display any spontaneous action potentials and the resting membrane potential was not significantly affected by LPS. To conclude, the work presented in this thesis extends the current knowledge regarding potassium channel mRNA expression in microglia and their function in microglial NO release. What is more, it was found that KCa1.1 current expression was increased in LPS-activated N9 cells and revealed KCa1.1 channels as a modulator of NO release by activated microglia

Towards Smooth and High-Quality Bitrate Adaptation for HTTP Adaptive Streaming

Although HTTP adaptive streaming has been well documented for the cost-effective delivery of video streaming, it is still a great challenge to play back video smoothly with high quality under the fluctuating network conditions. In this paper, we proposed a novel bitrate adaptation algorithm for HTTP adaptive streaming. Our algorithm employed two approaches for throughput estimation and bitrate selection, which was evaluated on our testbed (a fully functional HTTP Live Streaming system) over a network, emulated using DummyNet. First, the throughput estimation method, based on the prediction of the difference between the estimated and instantaneous throughputs, was observed to respond smoothly to short-term fluctuations and rapidly to large fluctuations. Second, the bitrate selection algorithm, based on piecewise functions to define the variation range of the current bitrate, was found to result in smoother changes in quality with a higher average quality. The results of our experiments demonstrated the prospects of our bitrate adaptation algorithm for HTTP adaptive streaming

Does enterprise risk management benefit manufacturing firms? Evidence from China

It is observed that Enterprise risk management (ERM) framework has been adopted by some manufacturing firms in China in the past years. To investigate the effectiveness of ERM, data of A-share listed manufacturing firms in Shanghai and Shenzhen stock exchange during 2010-2019 are adopted from Wind database and CSMAR database, two large domestic databases, to examine the impact of ERM on value of manufacturing firms. Treatment effects model and genenralised method of moments (GMM) are employed to derive the empirical results. Our results show that adoption of ERM can add value to the firms, and firms benefitmore from high-quality ERM program. Furthermore, the impact of ERM seems to be more significant among the manufacturing firms with smaller scale, or stronger institutional shareholding, or international business. Our findings encourage the manufacturing firms to implement ERM program and improve the program to achieve its targets