A Comparative Study on Regularization Strategies for Embedding-based Neural Networks

This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP. We chose two widely studied neural models and tasks as our testbed. We tried several frequently applied or newly proposed regularization strategies, including penalizing weights (embeddings excluded), penalizing embeddings, re-embedding words, and dropout. We also emphasized on incremental hyperparameter tuning, and combining different regularizations. The results provide a picture on tuning hyperparameters for neural NLP models.Comment: EMNLP '1

Test methods for measuring pure mode III delamination toughness of composite

This paper focuses on the characterization of pure mode III delamination behavior of composite mate- rials. The development for pure mode III delamination testing methods is reviewed. Two testing methods for mode III experiments were evaluated: a novel test proposed in our previous study, termed Edge Ring Crack Torsion (ERCT) test, and the widely used Edge Crack Torsion (ECT) test. The two methods were compared by experiment and finite element analysis. The results demonstrate the advantage of ERCT. The limitations of the ECT test are discussed

Efficient Geometric Correction Workflow for Airborne Hyperspectral Images through DEM-Driven Correction Techniques

Geometric correction, a pivotal step in the preprocessing of airborne remote sensing imagery, is critical for ensuring the accuracy of subsequent quantitative analyses. Achieving precise and efficient geometric correction for airborne hyperspectral data remains a significant challenge in the field. This study presents a new method for system-level and fine-scale geometric correction of uncontrolled airborne images utilizing DEM data, which integrates forward and inverse transformation algorithms. Furthermore, an optimized workflow is proposed to facilitate the processing of large-scale hyperspectral datasets. The effectiveness of the proposed method is demonstrated through an application analysis using airborne HyMap imagery, with experimental outcomes indicating high application accuracy and enhanced processing efficiency

Performance Analysis of User Ordering Schemes in Cooperative Power-Domain Non-Orthogonal Multiple Access Network

Non-orthogonal multiple access (NOMA) has recently received much attention as a candidate technique for the fifth generation networks. In this paper, considering both the direct and relay-aid paths, we investigate the performance of a downlink NOMA-based cooperative system and further analyze two different user ordering schemes. The outage probability, diversity gain, and ergodic rate are studied as three benchmarks to evaluate the system performance. For different user ordering schemes, the exact outage probabilities of users are first solved in the closed form. Then, the outage behavior in the high signal-to-noise ratio (SNR) region is discussed to obtain the diversity gain. In addition, the closed-form expression of ergodic rate for the strongest user and upper bounds for the rest users at high SNR is provided. Finally, numerical results verify the accuracy of our analysis and demonstrate that sorting users based on the relay-aided path can provide a larger ergodic sum rate in some cases. By contrast, sorting users based on the direct path can provide a better diversity gain, and the corresponding performance is less sensitive to relay's location

Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.</p

Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.</p

Simulation of fluid flow and inclusion removal in five-flow T-type tundish with porous baffle wall

To solve the instability of liquid steel in the continuous casting process and the inconsistent flaw detection of heavy rail steel, steel flow control was studied numerically in a tundish with a po-rous baffle wall by using the fluid dynamics software Fluent. The opening plan of the baffle wall was improved through orthogonal optimization design of holes in porous baffle wall. The test condition was set to a left inclination angle α1 = 22°, a right inclination angle α2 = 48°, an upward elevation angle β = 30°, and an aperture d = 70 mm. The simulation results of optimization scheme showed that the uniformity of the flow and temperature fields had been significantly improved, and the flow in each strand became consistent. The maximum temperature difference was 21 K in tundish, and the maximum temperature difference of three outlets was only 1.7 K. The dead zone volume was reduced by 10.0 % compared to the original tundish, and the plug flow volume was increased by 14.2 %. Comparing the removal efficiency of Al2O3 inclusions with different size, the results showed that the removal efficiency of 10 μm and 30 μm smaller inclusions was above 87 %. The removal rate of ≥ 50 μm larger inclusions also remained about 95 %

STC3141 improves acute lung injury through neutralizing circulating histone in rat with experimentally-induced acute respiratory distress syndrome

Background: Acute respiratory distress syndrome (ARDS) remains a challenge because of its high morbidity and mortality. Circulation histones levels in ARDS patients were correlated to disease severity and mortality. This study examined the impact of histone neutralization in a rat model of acute lung injury (ALI) induced by a lipopolysaccharide (LPS) double-hit.Methods: Sixty-eight male Sprague-Dawley rats were randomized to sham (N = 8, received saline only) or LPS (N = 60). The LPS double-hit consisted of a 0.8 mg/kg intraperitoneal injection followed after 16 h by 5 mg/kg intra-tracheal nebulized LPS. The LPS group was then randomized into five groups: LPS only; LPS +5, 25, or 100 mg/kg intravenous STC3141 every 8 h (LPS + L, LPS + M, LPS + H, respectively); or LPS + intraperitoneal dexamethasone 2.5 mg/kg every 24 h for 56 h (LPS + D). The animals were observed for 72 h.Results: LPS animals developed ALI as suggested by lower oxygenation, lung edema formation, and histological changes compared to the sham animals. Compared to the LPS group, LPS + H and +D groups had significantly lower circulating histone levels and lung wet-to-dry ratio, and the LPS + D group also had lower BALF histone concentrations; the blood neutrophils and platelets counts in LPS + D group did not change, meanwhile, the LPS + L, +M and +H groups had significantly lower neutrophil counts and higher platelet counts in the blood; the total number of BALF WBC, platelet counts, MPO and H3 were significantly lower in the LPS + L, +M, +H and +D groups than in the LPS only group; and the degree of inflammation was significantly less in the LPS + L, +M, +H and +D groups, moreover, inflammation in the LPS + L, +M and +H animals showed a dose-dependent response; finally, the LPS + L, +M, +H and +D groups had improved oxygenation compared to the LPS group, and there were no statistical differences in PCO2 or pH among groups. All animals survived.Conclusion: Neutralization of histone using STC3141, especially at high dose, had similar therapeutic effects to dexamethasone in this LPS double-hit rat ALI model, with significantly decreased circulating histone concentration, improved acute lung injury and oxygenation