Three Roller Curvature Scotch Straightening Mechanism Study and System Design

AbstractDuring the process of straightening, hot rolled deformed bars rotate around autologous axis, which cause out of the vertical of straightening surface and the straightening precision is deduced. To solve the problem of the bars's rotation , parallel roller collocation scheme of hot rolled deformed bars with high speed and no scratches is presented. Based on the theory of elastoplasticity large deformation, elastic recovery torque variety during the setting out of coiled bars is analyzed in accordance with the triple-roller equal curvature rotation blocking straightening system. The mechanical model of bars in triple-roller is established and rotation blocking mechanism of the triple-roller equal curvature rotation blocking straightening system is researched. The results indicate that the system has the effect of consistent original curvature and rotation blocking toguarantee straightening precision and supply the demand of operation

Understanding and Predicting Delay in Reciprocal Relations

Reciprocity in directed networks points to user's willingness to return favors in building mutual interactions. High reciprocity has been widely observed in many directed social media networks such as following relations in Twitter and Tumblr. Therefore, reciprocal relations between users are often regarded as a basic mechanism to create stable social ties and play a crucial role in the formation and evolution of networks. Each reciprocity relation is formed by two parasocial links in a back-and-forth manner with a time delay. Hence, understanding the delay can help us gain better insights into the underlying mechanisms of network dynamics. Meanwhile, the accurate prediction of delay has practical implications in advancing a variety of real-world applications such as friend recommendation and marketing campaign. For example, by knowing when will users follow back, service providers can focus on the users with a potential long reciprocal delay for effective targeted marketing. This paper presents the initial investigation of the time delay in reciprocal relations. Our study is based on a large-scale directed network from Tumblr that consists of 62.8 million users and 3.1 billion user following relations with a timespan of multiple years (from 31 Oct 2007 to 24 Jul 2013). We reveal a number of interesting patterns about the delay that motivate the development of a principled learning model to predict the delay in reciprocal relations. Experimental results on the above mentioned dynamic networks corroborate the effectiveness of the proposed delay prediction model.Comment: 10 page

Comparison of the Forest Tenure in Brazil and China

Brazil and China both have extensive forest areas in the world, making important contribution to reversal of the worldwide decline in forest. And as the world’s leading importers and exporters of timber and timber-based products, sustainable forest management for both countries are crucial for global economy and environment, so there is an intense international interest in their sustainability and well-being. Tenure arrangements functioned as powerful tools of forest policy, is not only important for economic growth, social cohesion, poverty reduction and environmental protection - it is also essential for climate change mitigation. This paper is to present and analyze the state of forest tenure in Brazil and China; then followed by a brief comparison of these two countries in terms of changing trends and reform impacts; Furthermore, it identifies some of the main challenges to the reform and points our several opportunities for extending the future forest tenure reform especially for mitigating climate change, and finally making a conclusion to widen the reach of local community tenure and to deepen the exercise of tenure rights

Monitoring human cytomegalovirus infection with nested PCR: comparison of positive rates in plasma and leukocytes and with quantitative PCR

<p>Abstract</p> <p>Background</p> <p>Human cytomegalovirus (HCMV) infection poses a significant health threat to immunocompromised individuals. Here we performed this study to set up a highly sensitive nested PCR method applicable for detecting HCMV infection in high-risk individuals. In this work, 106 blood specimens from 66 patients with potential HCMV infection were obtained. Total DNA was extracted separately from plasma and peripheral blood leukocytes (PBL) of each sample. HCMV DNA was detected in parallel by nested PCR and quantitative real-time PCR (qRT-PCR), and the results were compared.</p> <p>Results</p> <p>Serial dilution test revealed that the detection limit of nested PCR was 180 copies/ml. The nested PCR showed a higher positive rate than qRT-PCR (34.9% vs. 12.3%, p < 0.001). The positive rate of nested PCR based on PBL DNA was significantly higher than that based on plasma DNA (34.9% vs. 18.9%, p = 0.002). Of the 14 patients with serial samples, 11 were positive for HCMV DNA in PBL while only 7 were positive in plasma. Moreover, for each patient, nested PCR using PBL DNA also detected more positive samples than that using plasma DNA.</p> <p>Conclusion</p> <p>Combined use of nested PCR with PBL DNA is highly sensitive in defining HCMV infection. This assay is particularly useful in the case of quantification not essential.</p

CAF: Cluster Algorithm and A-Star with Fuzzy Approach for Lifetime Enhancement in Wireless Sensor Networks

Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria

Unmasked Teacher: Towards Training-Efficient Video Foundation Models

Video Foundation Models (VFMs) have received limited exploration due to high computational costs and data scarcity. Previous VFMs rely on Image Foundation Models (IFMs), which face challenges in transferring to the video domain. Although VideoMAE has trained a robust ViT from limited data, its low-level reconstruction poses convergence difficulties and conflicts with high-level cross-modal alignment. This paper proposes a training-efficient method for temporal-sensitive VFMs that integrates the benefits of existing methods. To increase data efficiency, we mask out most of the low-semantics video tokens, but selectively align the unmasked tokens with IFM, which serves as the UnMasked Teacher (UMT). By providing semantic guidance, our method enables faster convergence and multimodal friendliness. With a progressive pre-training framework, our model can handle various tasks including scene-related, temporal-related, and complex video-language understanding. Using only public sources for pre-training in 6 days on 32 A100 GPUs, our scratch-built ViT-L/16 achieves state-of-the-art performances on various video tasks. The code and models will be released at https://github.com/OpenGVLab/unmasked_teacher.Comment: 16 pages, 5 figures, 28 table

Low titers of measles antibody in mothers whose infants suffered from measles before eligible age for measles vaccination

<p>Abstract</p> <p>Background</p> <p>Resurgence or outbreak of measles recently occurred in both developed and developing countries despite long-standing widespread use of measles vaccine. Measles incidence in China has increased since 2002, particularly in infants and in persons ≥ 15 years of age. It is speculated that infants may acquire fewer measles IgG from their mothers, resulting in the reduced duration of protection during their early months of life. This study aimed to clarify the reason of increased susceptibility to measles in young infants in China. Measles IgG in 24 measles infants ≤ 9 months of age and their vaccinated mothers was quantitatively measured. The mean measles neutralizing titer in the vaccinated mothers and in 13 age-match women with the histories of clinical measles were compared.</p> <p>Results</p> <p>All the mothers were confirmed to be vaccinated successfully by the presence of measles IgG. Six vaccinated mothers were positive for measles IgM and had high concentrations of measles IgG and the neutralizing antibody, indicating underwent natural boosting. The mean measles neutralizing titer in 18 vaccinated mothers without natural boosting were significantly lower than that in 13 age-match women with the histories of clinical measles (1:37 vs 1:182, <it>P </it>< 0.05).</p> <p>Conclusions</p> <p>Our results suggest that infants born to mothers who acquired immunity to measles by vaccination may get a relatively small amount of measles antibody, resulting in loss of the immunity to measles before the vaccination age. Measures to improve the immunity in young infants not eligible for measles vaccination would be critical to interrupt the measles transmission in China.</p

VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking

Scale is the primary factor for building a powerful foundation model that could well generalize to a variety of downstream tasks. However, it is still challenging to train video foundation models with billions of parameters. This paper shows that video masked autoencoder (VideoMAE) is a scalable and general self-supervised pre-trainer for building video foundation models. We scale the VideoMAE in both model and data with a core design. Specifically, we present a dual masking strategy for efficient pre-training, with an encoder operating on a subset of video tokens and a decoder processing another subset of video tokens. Although VideoMAE is very efficient due to high masking ratio in encoder, masking decoder can still further reduce the overall computational cost. This enables the efficient pre-training of billion-level models in video. We also use a progressive training paradigm that involves an initial pre-training on a diverse multi-sourced unlabeled dataset, followed by a post-pre-training on a mixed labeled dataset. Finally, we successfully train a video ViT model with a billion parameters, which achieves a new state-of-the-art performance on the datasets of Kinetics (90.0% on K400 and 89.9% on K600) and Something-Something (68.7% on V1 and 77.0% on V2). In addition, we extensively verify the pre-trained video ViT models on a variety of downstream tasks, demonstrating its effectiveness as a general video representation learner.Comment: CVPR 2023 camera-ready versio

Harvest Video Foundation Models via Efficient Post-Pretraining

Building video-language foundation models is costly and difficult due to the redundant nature of video data and the lack of high-quality video-language datasets. In this paper, we propose an efficient framework to harvest video foundation models from image ones. Our method is intuitively simple by randomly dropping input video patches and masking out input text during the post-pretraining procedure. The patch dropping boosts the training efficiency significantly and text masking enforces the learning of cross-modal fusion. We conduct extensive experiments to validate the effectiveness of our method on a wide range of video-language downstream tasks including various zero-shot tasks, video question answering, and video-text retrieval. Despite its simplicity, our method achieves state-of-the-art performances, which are comparable to some heavily pretrained video foundation models. Our method is extremely efficient and can be trained in less than one day on 8 GPUs, requiring only WebVid-10M as pretraining data. We hope our method can serve as a simple yet strong counterpart for prevalent video foundation models, provide useful insights when building them, and make large pretrained models more accessible and sustainable. This is part of the InternVideo project \url{https://github.com/OpenGVLab/InternVideo}