Dynamic Face Video Segmentation via Reinforcement Learning

For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance. To overcome this limitation, we model the online key decision process in dynamic video segmentation as a deep reinforcement learning problem and learn an efficient and effective scheduling policy from expert information about decision history and from the process of maximising global return. Moreover, we study the application of dynamic video segmentation on face videos, a field that has not been investigated before. By evaluating on the 300VW dataset, we show that the performance of our reinforcement key scheduler outperforms that of various baselines in terms of both effective key selections and running speed. Further results on the Cityscapes dataset demonstrate that our proposed method can also generalise to other scenarios. To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.Comment: CVPR 2020. 300VW with segmentation labels is available at: https://github.com/mapleandfire/300VW-Mas

Lip-reading with Densely Connected Temporal Convolutional Networks

In this work, we present the Densely Connected Temporal Convolutional Network (DC-TCN) for lip-reading of isolated words. Although Temporal Convolutional Networks (TCN) have recently demonstrated great potential in many vision tasks, its receptive fields are not dense enough to model the complex temporal dynamics in lip-reading scenarios. To address this problem, we introduce dense connections into the network to capture more robust temporal features. Moreover, our approach utilises the Squeeze-and-Excitation block, a light-weight attention mechanism, to further enhance the model's classification power. Without bells and whistles, our DC-TCN method has achieved 88.36% accuracy on the Lip Reading in the Wild (LRW) dataset and 43.65% on the LRW-1000 dataset, which has surpassed all the baseline methods and is the new state-of-the-art on both datasets.Comment: WACV 202

The efficacy and safety of condoliase for lumbar disc herniation: a systematic review and meta-analysis

Background: Chemonucleolysis is a minimally invasive treatment of lumbar disc herniation (LDH). However, the low specificity of the enzyme and the existence of serious adverse events limit the application of chemonucleolysis. Clinical studies in recent years have shown that Chondroitin sulfate ABC endolyase (condoliase) is a potential therapeutic enzyme for LDH. Aim. A meta-analysis was conducted to determine the efficacy and safety of condoliase in LDH treatment.Methods: We searched Web of Science, Embase, PubMed, and Cochrane Library databases. Two reviewers independently screened articles, extracted data, and assessed the risk of bias. The outcomes were the total effective rate, Oswestry Disability Index (ODI) score change, the proportion of lumbar surgery after condoliase treatment, herniated mass volume change, Pfirrmann grade change, and adverse events. Review Manager 5.3 and Stata 12.0 were used for meta-, sensitivity, and bias analysis.Results: Ten studies were included. A single-arm meta-analysis showed that the total effective rate was 78% [95% confidence interval (CI) 75%–81%], the proportion of surgery was 9% (95% CI 7%–12%), the proportion of Pfirrmann grade change was 43% (95%CI 38%–47%), and the adverse events were 4% (95% CI 2%–6%) after condoliase treatment. The two-arm meta-analysis showed that the ODI score change [standardized mean difference (SMD) −2.46, 95% CI −3.30 to −1.63] and the herniated mass volume change (SMD −16.97, 95% CI −23.92 to −10.03) of the condoliase treatment group were greater than those of the placebo control group, and there was no difference in adverse events between the two groups (OR 1.52, 95% CI 0.60–3.85). The results of sensitivity and publication bias analyses showed that the results were robust.Conclusion: Condoliase intradiscal injection has excellent eutherapeutic and safety for LDH, thus, has considerable potential as a treatment option besides conservative treatment and surgical intervention for LDH.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022375492, PROSPERO (CRD42022375492)

Monitoring the Process and Characterizing Symptoms of Suckling Mouse Inoculation Promote Isolating Viruses from Ticks

Suckling mouse inoculation is an important method that has been used for years to isolate viruses from ticks; however, this method has usually been briefly described in the literature on a case-by-case basis upon successful isolation rather than providing extensive details. This study describes the procedure from preparation of tick homogenates to identification of virus isolation using the suckling mouse inoculation method. The transient and persistent features were characterized and the incidence of manifestations that developed in the suckling mice, especially in mice from which viruses were isolated, is reported. We identified 22 symptoms that developed in mice, including 13 transient symptoms that recovered by the end of the observation period and 7 persistent symptoms that the mice suffered from throughout the observation period. Persistent symptoms (lateral positioning and dead) and transient symptoms (malaise, emaciation, and difficulty turning over) were the main symptoms based on the high overall incidence. Moreover, we showed that mice from which viruses were isolated had a concentrated period and advanced days of disease onset. This study provides detailed information necessary for better use of suckling mouse inoculation to isolate viruses from ticks, which may benefit optimization of this method to identify, discover, and acquire tick-borne viruses

Estimation of HIV-1 incidence among five focal populations in Dehong, Yunnan: a hard hit area along a major drug trafficking route

<p>Abstract</p> <p>Background</p> <p>Since 1989 when the first 146 HIV positives in China were identified, Dehong Prefecture had been one of the areas hardest-hit by HIV in China. The local and national governments have put substantial financial resources into tackling the HIV epidemic in Dehong from 2004. The objective of this study was to track dynamic changes in HIV-1 prevalence and incidence among five focal populations in Dehong and to assess the impact of HIV prevention and control efforts.</p> <p>Methods</p> <p>Consecutive cross-sectional surveys conducted in five focal populations between 2004 and 2008. Specimens seropositive for HIV were tested with the BED IgG capture enzyme immunoassay to identify recent seroconversions (median, 155 days) using normalized optical density of 0.8 and adjustments.</p> <p>Results</p> <p>From 2004 to 2008, estimated annual HIV incidence among injecting drug users (IDUs) decreased significantly [from 15.0% (95% CI = 11.4%-18.5%) in 2004 to 4.3% (95% CI = 2.4%-6.2%) in 2008; trend test P < 0.0001]. The incidence among other focal populations, such as HIV discordant couples (varying from 5.5% to 4.7%), female sex workers (varying from 1.4% to 1.3%), pregnant women (0.1%), and pre-marital couples (0.2 to 0.1%) remained stable. Overall, the proportion of recent HIV-1 infections was higher among females than males (P < 0.0001).</p> <p>Conclusions</p> <p>The HIV epidemic in Dehong continued to expand during a five-year period but at a slowing rate among IDUs, and HIV incidence remains high among IDUs and discordant couples. Intensive prevention measures should target sub-groups at highest risk to further slow the epidemic and control the migration of HIV to other areas of China, and multivariate analysis is needed to explore which measures are more effective for different populations.</p

Self-supervised Video-centralised Transformer for Video Face Clustering

This paper presents a novel method for face clustering in videos using a video-centralised transformer. Previous works often employed contrastive learning to learn frame-level representation and used average pooling to aggregate the features along the temporal dimension. This approach may not fully capture the complicated video dynamics. In addition, despite the recent progress in video-based contrastive learning, few have attempted to learn a self-supervised clustering-friendly face representation that benefits the video face clustering task. To overcome these limitations, our method employs a transformer to directly learn video-level representations that can better reflect the temporally-varying property of faces in videos, while we also propose a video-centralised self-supervised framework to train the transformer model. We also investigate face clustering in egocentric videos, a fast-emerging field that has not been studied yet in works related to face clustering. To this end, we present and release the first large-scale egocentric video face clustering dataset named EasyCom-Clustering. We evaluate our proposed method on both the widely used Big Bang Theory (BBT) dataset and the new EasyCom-Clustering dataset. Results show the performance of our video-centralised transformer has surpassed all previous state-of-the-art methods on both benchmarks, exhibiting a self-attentive understanding of face videos