A Hierarchical Attention-based Contrastive Learning Method for Micro Video Popularity Prediction

Micro videos popularity prediction (MVPP) has recently attracted widespread research interests given the increasing prevalence of video-based social platforms. However, previous studies have overlooked the unique patterns between popular and unpopular videos and the interactions between asynchronous features different data dimensions. To address this, we propose a novel hierarchical attention contrastive learning method named HACL, which extracts explainable representation features, learns their asynchronous interactions from both temporal and spatial levels, and separates the positive and negative embeddings identities. This reveals video popularity in a contrastive and interrelated view, and thus can be responsible for a better MVPP. Dual neural networks account for separate positive and negative patterns via contrastive learning. To obtain the temporal-wise interaction coefficients, we propose a Hadamard-product based attention approach to optimize the trainable attention-map matrices. Results from our experiments on a TikTok micro video dataset show that HACL outperforms benchmarks and provides insightful managerial implications

Design and experiments of an automatic pipe winding machine

To solve the time-consuming and laborious problem of manual winding and unwinding water pipes by field workers during irrigation or pesticide spraying in agricultural production, an automatic pipe winding machine for winding and unwinding water pipes was designed. The guiding mechanism, pipe winding mechanism, and pipe arrangement mechanism of the pipe winding machine are designed, and the automatic deviation correction control method of pipe arrangement based on PID and the constant tension control method of pipe winding and unwinding is put forward, and the control system of the automatic pipe winding machine is developed. By making a prototype of an automatic pipe winding machine, the effects of pipe winding and unwinding and the constant tension control of the automatic winding machine are tested and analyzed. The test results show that under the condition of 4.0 km/h speed, the maximum angle error of the automatic pipe winding machine is 3.32°, the average absolute error is 0.95°, and the water pipes are arranged neatly and tightly. The maximum relative error of the water pipe tension is 9.3%, and the maximum relative error under the 0~4.0 km/h velocity step variable condition is 16.3%. The speed of the pipe winding and unwinding can adapt to the change of the vehicle’s speed automatically, and the tension of the pipe is within a reasonable range. The performance of the pipe arrangement and pipe coiling of the automatic pipe winding machine can meet the operating requirements

The effects of intermittent bolus paravertebral block on analgesia and recovery in open hepatectomy: a randomized, double-blinded, controlled study

Abstract Background We aimed to investigate the effects of intermittent bolus paravertebral block on analgesia and recovery in open hepatectomy. Methods Eighty 18–70 years old, American Society of Anesthesiologists level I-III patients scheduled for hepatectomy with a J-shaped subcostal incision were enrolled and randomized to receive either intermittent bolus paravertebral ropivacaine (0.5% loading, 0.2% infusion) or 0.9% saline infusion at 1:1 ratio (25 ml loading before surgery, 0.125 ml/kg/h bolus for postoperative 48 h). The primary outcome was set as postoperative 48 h cumulative intravenous morphine consumption recorded by a patient-controlled analgesic pump. Results Thirty-eight patients in each group completed the study. The cumulative morphine consumptions were lower in the paravertebral block than control group at postoperative 24 (difference -10.5 mg, 95%CI -16 mg to -6 mg, P < 0.001) and 48 (difference -12 mg, 95%CI -19.5 mg to -5 mg, P = 0.001) hours. The pain numerical rating scales at rest were lower in the paravertebral block than control group at postoperative 4 h (difference -2, 95%CI -3 to -1, P < 0.001). The active pain numerical rating scales were lower in the paravertebral block than control group at postoperative 12 h (difference -1, 95%CI -2 to 0, P = 0.005). Three months postoperatively, the paravertebral block group had lower rates of hypoesthesia (OR 0.28, 95%CI 0.11 to 0.75, P = 0.009) and numbness (OR 0.26, 95%CI 0.07 to 0.88, P = 0.024) than the control group. Conclusions Intermittent bolus paravertebral block provided an opioid-sparing effect and enhanced recovery both in hospital and after discharge in patients undergoing hepatectomy. Trial registration clinicaltrials.gov (NCT04304274), date: 11/03/2020

HeLoDL: Hedgerow Localization Based on Deep Learning

Accurate localization of hedges in 3D space is a key step in automatic pruning. However, due to the irregularity of the hedge shape, the localization accuracy based on traditional algorithms is poor. In this paper, we propose a deep learning approach based on a bird&rsquo;s-eye view to overcoming this problem, which we call HeLoDL. Specifically, we first project the hedge point cloud top-down as a single image and, then, augment the image with morphological operations and rotation. Finally, we trained a convolutional neural network, HeLoDL, based on transfer learning, to regress the center axis and radius of the hedge. In addition, we propose an evaluation metric OIoU that can respond to the radius error, as well as the circle center error in an integrated way. In our test set, HeLoDL achieved an accuracy of 90.44% within the error tolerance, which greatly exceeds the 61.74% of the state-of-the-art algorithm. The average OIoU of HeLoDL is 92.65%; however, the average OIoU of the best conventional algorithm is 83.69%. Extensive experiments demonstrated that HeLoDL shows considerable accuracy in the 3D spatial localization of irregular models

<i>HeLoDL</i>: Hedgerow Localization Based on Deep Learning

Accurate localization of hedges in 3D space is a key step in automatic pruning. However, due to the irregularity of the hedge shape, the localization accuracy based on traditional algorithms is poor. In this paper, we propose a deep learning approach based on a bird’s-eye view to overcoming this problem, which we call HeLoDL. Specifically, we first project the hedge point cloud top-down as a single image and, then, augment the image with morphological operations and rotation. Finally, we trained a convolutional neural network, HeLoDL, based on transfer learning, to regress the center axis and radius of the hedge. In addition, we propose an evaluation metric OIoU that can respond to the radius error, as well as the circle center error in an integrated way. In our test set, HeLoDL achieved an accuracy of 90.44% within the error tolerance, which greatly exceeds the 61.74% of the state-of-the-art algorithm. The average OIoU of HeLoDL is 92.65%; however, the average OIoU of the best conventional algorithm is 83.69%. Extensive experiments demonstrated that HeLoDL shows considerable accuracy in the 3D spatial localization of irregular models

Seizure detection using dynamic memristor-based reservoir computing and leaky integrate-and-fire neuron for post-processing

Epilepsy is a prevalent neurological disorder, rendering the development of automated seizure detection systems imperative. While complex machine learning models are powerful, their training and hardware deployment remain challenging. The reservoir computing system offers a low-cost solution in terms of both hardware requirements and training. In this paper, we introduce a compact reservoir computing system for seizure detection, based on the α-In2Se3 dynamic memristors. Leaky integrate-and-fire neurons are used for post-processing the output of the system, and experimental results indicate their effectiveness in suppressing erroneous outputs, where both accuracy and specificity are enhanced by over 2.5%. The optimized compact reservoir system achieves 96.40% accuracy, 86.34% sensitivity, and 96.56% specificity in seizure detection tasks. This work demonstrates the feasibility of using reservoir computing for seizure detection and shows its potential for future application in extreme edge devices

All Roads Lead to Rome? : Genes Causing Dravet Syndrome and Dravet Syndrome-Like Phenotypes

Background: Dravet syndrome (DS) is a severe epileptic encephalopathy mainly caused by haploinsufficiency of the gene SCN1A, which encodes the voltage-gated sodium channel NaV1. 1 in the brain. While SCN1A mutations are known to be the primary cause of DS, other genes that may cause DS are poorly understood. Several genes with pathogenic mutations result in DS or DS-like phenotypes, which may require different drug treatment approaches. Therefore, it is urgent for clinicians, especially epilepsy specialists to fully understand these genes involved in DS in addition to SCN1A. Particularly for healthcare providers, a deep understanding of these pathogenic genes is useful in properly selecting and adjusting drugs in a more effective and timely manner. Objective: The purpose of this study was to identify genes other than SCN1A that may also cause DS or DS-like phenotypes. Methods: A comprehensive search of relevant Dravet syndrome and severe myoclonic epilepsy in infancy was performed in PubMed, until December 1, 2021. Two independent authors performed the screening for potentially eligible studies. Disagreements were decided by a third, more professional researcher or by all three. The results reported by each study were narratively summarized. Results: A PubMed search yielded 5,064 items, and other sources search 12 records. A total of 29 studies published between 2009 and 2021 met the inclusion criteria. Regarding the included articles, seven studies on PCDH19, three on SCN2A, two on SCN8A, five on SCN1B, two on GABRA1, three on GABRB3, three on GABRG2, and three on STXBP1 were included. Only one study was recorded for CHD2, CPLX1, HCN1 and KCNA2, respectively. It is worth noting that a few articles reported on more than one epilepsy gene. Conclusion: DS is not only identified in variants of SCN1A, but other genes such as PCDH19, SCN2A, SCN8A, SCN1B, GABRA1, GABRB3, GABRG2, KCNA2, CHD2, CPLX1, HCN1A, STXBP1 can also be involved in DS or DS-like phenotypes. As genetic testing becomes more widely available, more genes associated with DS and DS-like phenotypes may be identified and gene-based diagnosis of subtypes of phenotypes in this spectrum may improve the management of these diseases in the future