Estimator: An Effective and Scalable Framework for Transportation Mode Classification over Trajectories

Transportation mode classification, the process of predicting the class labels of moving objects transportation modes, has been widely applied to a variety of real world applications, such as traffic management, urban computing, and behavior study. However, existing studies of transportation mode classification typically extract the explicit features of trajectory data but fail to capture the implicit features that affect the classification performance. In addition, most of the existing studies also prefer to apply RNN-based models to embed trajectories, which is only suitable for classifying small-scale data. To tackle the above challenges, we propose an effective and scalable framework for transportation mode classification over GPS trajectories, abbreviated Estimator. Estimator is established on a developed CNN-TCN architecture, which is capable of leveraging the spatial and temporal hidden features of trajectories to achieve high effectiveness and efficiency. Estimator partitions the entire traffic space into disjointed spatial regions according to traffic conditions, which enhances the scalability significantly and thus enables parallel transportation classification. Extensive experiments using eight public real-life datasets offer evidence that Estimator i) achieves superior model effectiveness (i.e., 99% Accuracy and 0.98 F1-score), which outperforms state-of-the-arts substantially; ii) exhibits prominent model efficiency, and obtains 7-40x speedups up over state-of-the-arts learning-based methods; and iii) shows high model scalability and robustness that enables large-scale classification analytics.Comment: 12 pages, 8 figure

Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression

Nested networks or slimmable networks are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. Recent studies have focused on a "nested dropout" layer, which is able to order the nodes of a layer by importance during training, thus generating a nested set of sub-networks that are optimal for different configurations of resources. However, the dropout rate is fixed as a hyper-parameter over different layers during the whole training process. Therefore, when nodes are removed, the performance decays in a human-specified trajectory rather than in a trajectory learned from data. Another drawback is the generated sub-networks are deterministic networks without well-calibrated uncertainty. To address these two problems, we develop a Bayesian approach to nested neural networks. We propose a variational ordering unit that draws samples for nested dropout at a low cost, from a proposed Downhill distribution, which provides useful gradients to the parameters of nested dropout. Based on this approach, we design a Bayesian nested neural network that learns the order knowledge of the node distributions. In experiments, we show that the proposed approach outperforms the nested network in terms of accuracy, calibration, and out-of-domain detection in classification tasks. It also outperforms the related approach on uncertainty-critical tasks in computer vision.Comment: 16 pages, 10 figure

In situ stress distribution law of fault zone in tunnel site area based on the inversion method with optimized fitting conditions

Tunnel construction in high geo-stress strata faces the risk of extreme natural disasters such as large squeezing deformation and rockburst. Therefore, it is of great significance to adopt a high-precision inversion method to investigate the distribution law of in situ stress in the tunnel site area. In this paper, the in situ stress inversion research was carried out based on a plateau tunnel with a buried depth of more than 1000 m. The idea of improving the inversion accuracy by unifying displacement constraints was proposed by aiming at the defects of the traditional method on the boundary conditions. Furthermore, the impact of the constant term in the regression model on the fitting accuracy was discussed. According to the inversion method with optimized fitting conditions, the in situ stress distribution characteristics in the tunnel site area were obtained, and the variation law of the in situ stress near the fault zone was discussed. The results showed that after unifying displacement constraints, the comprehensive inversion accuracy comprehensive indicator reflecting the inversion accuracy decreased from 15.291 to 12.895, indicating that the inversion error was effectively controlled. Whether the constant term should be retained had a random effect on the inversion accuracy, so it was recommended that this issue be independently verified when fitting the data. When approaching the inner side of the fault from the outer side, the in situ stress first increased slightly and then decreased significantly. Moreover, the wider the fault impact zone and the farther the fault distribution distance, the more significant the amplitude of stress change, e.g., the maximum amplitude of stress change reached 9.0 MPa. In addition, the in situ stress orientation near the fault can be significantly deflected. And the wider the fault impact zone, the more pronounced the deflection

Improved Fine-Tuning by Better Leveraging Pre-Training Data

As a dominant paradigm, fine-tuning a pre-trained model on the target data is widely used in many deep learning applications, especially for small data sets. However, recent studies have empirically shown that training from scratch has the final performance that is no worse than this pre-training strategy once the number of training samples is increased in some vision tasks. In this work, we revisit this phenomenon from the perspective of generalization analysis by using excess risk bound which is popular in learning theory. The result reveals that the excess risk bound may have a weak dependency on the pre-trained model. The observation inspires us to leverage pre-training data for fine-tuning, since this data is also available for fine-tuning. The generalization result of using pre-training data shows that the excess risk bound on a target task can be improved when the appropriate pre-training data is included in fine-tuning. With the theoretical motivation, we propose a novel selection strategy to select a subset from pre-training data to help improve the generalization on the target task. Extensive experimental results for image classification tasks on 8 benchmark data sets verify the effectiveness of the proposed data selection based fine-tuning pipeline

Preoperative Strength Training for Clinical Outcomes Before and After Total Knee Arthroplasty: A Systematic Review and Meta-Analysis

BackgroundThere is an increasing interest in preoperative strength training for promoting post-operative rehabilitation, but the effectiveness of preoperative strength training for clinical outcomes after total knee arthroplasty (TKA) remains controversial.ObjectiveThis study aims to systematically evaluate the effect of preoperative strength training on clinical outcomes before and after TKA.MethodsWe systematically searched PubMed, Cochrane Library, Web of Science, and EMBASE databases from the inception to November 17, 2021. The meta-analysis was performed to evaluate the effects of preoperative strength training on clinical outcomes before and after TKA.ResultsSeven randomized controlled trials (RCTs) were included (n = 306). Immediately before TKA, the pooled results showed significant improvements in pain, knee function, functional ability, stiffness, and physical function in the strength training group compared with the control group, but not in strength (quadriceps), ROM, and WOMAC (total). Compared with the control group, the results indicated strength training had a statistically significant improvement in post-operative knee function, ROM, and functional ability at less than 1 month and 3 months, and had a statistically significant improvement in post-operative strength (quadriceps), stiffness, and WOMAC (total) at 3 months, and had a statistically significant improvement in post-operative pain at 6 months. However, the results indicated strength training had no statistically significant improvement in post-operative strength (quadriceps) at less than 1 month, 6, and 12 months, had no statistically significant improvement in post-operative pain at less than 1 month, 3, and 12 months, had no statistically significant improvement in post-operative knee function at 6 and 12 months, and had no statistically significant improvement in post-operative physical function at 3 months.ConclusionsPreoperative strength training may be beneficial to early rehabilitation after TKA, but the long-term efficacy needs to be further determined. At the same time, more caution should be exercised when interpreting the clinical efficacy of preoperative strength training for TKA

Strong coordination interaction in amorphous Sn-Ti-ethylene glycol compound for stable Li-ion storage

Sn has been considered one of the most promising metallic anode materials for lithium-ion batteries (LIBs) because of its high specific capacity. Herein, we report a novel amorphous tin-titanium-ethylene glycol (Sn-Ti-EG) bimetal organic compound as an anode for LIBs. The Sn-Ti-EG electrode exhibits exceptional cyclic stability with high Li-ion storage capacity. Even after 700 cycles at a current density of 1.0 A g−1, the anode maintains a capacity of 345 mAh g−1. The unique bimetal organic structure of the Sn-Ti-EG anode and the strong coordination interaction between Sn/Ti and O within the framework effectively suppress the aggregation of Sn atoms, eliminating the usual pulverization of bulk Sn through volume expansion. Furthermore, the Sn M-edge of the X-ray absorption near-edge structure spectra obtained using soft X-ray absorption spectroscopy signifies the conversion of Sn2+ ions into Sn0 during the initial lithiation process, which is reversible upon delithiation. These findings reveal that Sn is one of the most active components that account for the excellent electrochemical performance of the Sn-Ti-EG electrode, whereas Ti has no practical contribution to the capacity of the electrode. The reversible formation of organic functional groups on the solid electrolyte interphase is also partly responsible for its cyclic stability

The effects of ECMO on neurological function recovery of critical patients: A double-edged sword

Extracorporeal membrane oxygenation (ECMO) played an important role in the treatment of patients with critical care such as cardiac arrest (CA) and acute respiratory distress syndrome. ECMO is gradually showing its advantages in terms of speed and effectiveness of circulatory support, as it provides adequate cerebral blood flow (CBF) to the patient and ensures the perfusion of organs. ECMO enhances patient survival and improves their neurological prognosis. However, ECMO-related brain complications are also important because of the high risk of death and the associated poor outcomes. We summarized the reported complications related to ECMO for patients with CA, such as north–south syndrome, hypoxic–ischemic brain injury, cerebral ischemia–reperfusion injury, impaired intracranial vascular autoregulation, embolic stroke, intracranial hemorrhage, and brain death. The exact mechanism of ECMO on the role of brain function is unclear. Here we review the pathophysiological mechanisms associated with ECMO in the protection of neurologic function in recent years, as well as the ECMO-related complications in brain and the means to improve it, to provide ideas for the treatment of brain function protection in CA patients

In Vitro Uptake of 140 kDa Bacillus thuringiensis Nematicidal Crystal Proteins by the Second Stage Juvenile of Meloidogyne hapla

Plant-parasitic nematodes (PPNs) are piercing/sucking pests, which cause severe damage to crops worldwide, and are difficult to control. The cyst and root-knot nematodes (RKN) are sedentary endoparasites that develop specialized multinucleate feeding structures from the plant cells called syncytia or giant cells respectively. Within these structures the nematodes produce feeding tubes, which act as molecular sieves with exclusion limits. For example, Heterodera schachtii is reportedly unable to ingest proteins larger than 28 kDa. However, it is unknown yet what is the molecular exclusion limit of the Meloidogyne hapla. Several types of Bacillus thuringiensis crystal proteins showed toxicity to M. hapla. To monitor the entry pathway of crystal proteins into M. hapla, second-stage juveniles (J2) were treated with NHS-rhodamine labeled nematicidal crystal proteins (Cry55Aa, Cry6Aa, and Cry5Ba). Confocal microscopic observation showed that these crystal proteins were initially detected in the stylet and esophageal lumen, and subsequently in the gut. Western blot analysis revealed that these crystal proteins were modified to different molecular sizes after being ingested. The uptake efficiency of the crystal proteins by the M. hapla J2 decreased with increasing of protein molecular mass, based on enzyme-linked immunosorbent assay analysis. Our discovery revealed 140 kDa nematicidal crystal proteins entered M. hapla J2 via the stylet, and it has important implications in designing a transgenic resistance approach to control RKN

Integrated aquaculture contributes to the transfer of mcr-1 between animals and humans via the aquaculture supply chain

Background Since its discovery in 2015, the mobile colistin resistance gene mcr-1 has been reported in bacteria from > 50 countries. Although aquaculture-associated bacteria may act as a significant reservoir for colistin resistance, systematic investigations of mcr-1 in the aquaculture supply chain are scarce. Objectives We investigated the presence of colistin resistance determinants in the aquaculture supply chain in south China and determined their characteristics and relationships. Methods A total of 250 samples were collected from a duck-fish integrated fishery, slaughter house, and market in Guangdong Province, China, in July 2017. Colistin-resistant bacteria were isolated on colistin-supplemented CHROMagar Orientation plates, and the species were identified by matrix-assisted laser desorption/ionization time-of-flight assay. The presence of mcr genes was confirmed by polymerase chain reaction analysis. We examined the minimum inhibitory concentrations (MICs) of 16 antimicrobial agents against the isolates using agar diffusion and broth microdilution methods. Whole-genome sequencing (WGS) was used to explore the molecular characteristics and relationships of mcr-1-positive Escherichia coli (MCRPEC). Results Overall, 143 (57.2%) colistin-resistant bacteria were isolated, of which, 56 (22.4%, including 54 Escherichia coli and two Klebsiella pneumoniae) and four Aeromonas species were positive for mcr-1 and mcr-3, respectively. The animal-derived MCRPEC were significantly more prevalent in integrated fishery samples (40.0%) than those in market (4.8%, P 90%) but were susceptible to carbapenems and tigecycline. WGS analysis suggested that mcr-1 was mainly contained on plasmids, including IncHI2 (29.6%), IncI2 (27.8%), IncX4 (14.8%), and IncP (11.1%). Genomic analysis suggested mcr-1 transmission via the aquatic food chain. Conclusions MCRPEC were highly prevalent in the aquaculture supply chain, with the isolates showing resistance to most antibiotics. The data suggested mcr-1 could be transferred to humans via the aquatic food chain. Taking the “One Health” perspective, aquaculture should be incorporated into systematic surveillance programs with animal, human, and environmental monitoring