Large kernel convolution YOLO for ship detection in surveillance video

At present, ship detectors have many problems, such as too many hyperparameter, poor recognition accuracy and imprecise regression boundary. In this article, we designed a large kernel convolutional YOLO (Lk-YOLO) detection model based on Anchor free for one-stage ship detection. First, we discuss the introduction of large size convolution kernel in the residual module of the backbone network, so that the backbone network has a stronger feature extraction capability. Second, in order to solve the problem of conflict regression and classification fusion under the coupling of detection heads, we split the detection head into two branches, so that the detection head has better representation ability for different branches of the task and improves the accuracy of the model in regression tasks. Finally, in order to solve the problem of complex and computationally intensive anchor hyperparameter design of ship data sets, we use anchor free algorithm to predict ships. Moreover, the model adopts an improved sampling matching strategy for both positive and negative samples to expand the number of positive samples in GT (Ground Truth) while achieving high-quality sample data and reducing the imbalance between positive and negative samples caused by anchor. We used NVIDIA 1080Ti GPU as the experimental environment, and the results showed that the mAP@50 Reaching 97.7%, [email protected]:.95 achieved 78.4%, achieving the best accuracy among all models. Therefore, the proposed method does not need to design the parameters of the anchor, and achieves better detection efficiency and robustness without hyperparameter input

Wave basin testing of optimal PTO control of 6-float M4 WEC

Power-take-off (PTO) control is applied to the multi-float attenuator-type wave energy converter (WEC) M4, with two PTOs in a 6-float configuration, and tested experimentally in a wave basin. Previous control applications using wave predictions from auto-regression (AR) and linear non-causal optimal control (LNOC) have shown average power to be increased by 30%-100% in numerical simulations. In this experiment two DC servo-motors are used to execute the control algorithm implemented on a microprocessor. Average power is calculated from the recorded torque and hinge velocity measurements. Both causal and non-causal control are applied, with deterministic sea wave prediction (DSWP) rather than AR, and a torque limit of 3 Nm, and also 6 Nm for the non-causal case. Causal control improves average power by up to 49% while non-causal (LNOC) by up to about 116% with a 3 Nm torque limit and 274% with a torque limit of 6 Nm

Ceratophysella species from mushrooms in China (Collembola, Hypogastruridae)

Four species of the genus Ceratophysella living on mushrooms are reported from China, including a new species, Ceratophysella skarzynskii Weiner & Sun, sp. n., which is described from alpine mushrooms. The new species belongs to the Ceratophysella group of species with a dorsal chaetotaxy of type B and differs from the other species in a combination of characters. Ceratophysella skarzynskii sp. n. is distinguished by its small body size (maximum length 1.09 mm), number of peg-like s-chaetae (30–32) in the ventral sensory file, the trilobed apical vesicle of antennal segment IV, five modified chaetae on dens, and serrated dorsal chaetae. A key to the Chinese species of the genus has been provided

Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral–spatial classification of hyperspectral images.

Although extreme learning machine (ELM) has successfully been applied to a number of pattern recognition problems, only with the original ELM it can hardly yield high accuracy for the classification of hyperspectral images (HSIs) due to two main drawbacks. The first is due to the randomly generated initial weights and bias, which cannot guarantee optimal output of ELM. The second is the lack of spatial information in the classifier as the conventional ELM only utilizes spectral information for classification of HSI. To tackle these two problems, a new framework for ELM-based spectral-spatial classification of HSI is proposed, where probabilistic modeling with sparse representation and weighted composite features (WCFs) is employed to derive the optimized output weights and extract spatial features. First, ELM is represented as a concave logarithmic-likelihood function under statistical modeling using the maximum a posteriori estimator. Second, sparse representation is applied to the Laplacian prior to efficiently determine a logarithmic posterior with a unique maximum in order to solve the ill-posed problem of ELM. The variable splitting and the augmented Lagrangian are subsequently used to further reduce the computation complexity of the proposed algorithm. Third, the spatial information is extracted using the WCFs to construct the spectral-spatial classification framework. In addition, the lower bound of the proposed method is derived by a rigorous mathematical proof. Experimental results on three publicly available HSI data sets demonstrate that the proposed methodology outperforms ELM and also a number of state-of-the-art approaches

Measuring Water Transport Efficiency in the Yangtze River Economic Zone, China

Water transport, a component of integrated transport systems, is a key strategic resource for achieving sustainable economic and social development, particularly in the Yangtze River Economic Zone (YREZ). Unfortunately, systematic studies on water transport efficiency are not forthcoming. Using Data Envelopment Analysis (DEA) and the Malmquist index as a model framework, this paper measures water transport efficiency in YREZ, conducts spatial analysis to identify the leading factors influencing efficiency, and provides scientific evidence for a macroscopic grasp of water transport development and the optimization of YREZ. The results indicate that water transport technical efficiency (TE) in YREZ is low and in fluctuating decline. Therefore, it has seriously restricted performance and improvements in the service function. Additionally, the spatial pattern of TE has gradually changed from complexity and dispersion to clarity and contiguity with a larger inter-provincial gap. Water transport efficiency has slightly improved through technological change (TECHch), whereas deteriorating pure technical efficiency change (PEch) is the main cause of a TE decrease. According to our findings, decision-makers should consider strengthening intra-port competition and promoting water transport efficiency

MIMR-DGSA: unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm

Band selection plays an important role in hyperspectral data analysis as it can improve the performance of data analysis without losing information about the constitution of the underlying data. We propose a MIMR-DGSA algorithm for band selection by following the Maximum-Information-Minimum-Redundancy (MIMR) criterion that maximises the information carried by individual features of a subset and minimises redundant information between them. Subsets are generated with a modified Discrete Gravitational Search Algorithm (DGSA) where we definine a neighbourhood concept for feature subsets. A fast algorithm for pairwise mutual information calculation that incorporates variable bandwidths of hyperspectral bands called VarBWFastMI is also developed. Classification results on three hyperspectral remote sensing datasets show that the proposed MIMR-DGSA performs similar to the original MIMR with Clonal Selection Algorithm (CSA) but is computationally more efficient and easier to handle as it has fewer parameters for tuning

Effects of water temperature on survival, growth, digestive enzyme activities, and body composition of the leopard coral grouper Plectropomus leopardus

The effects of water temperature (15, 20, 25,30, and 35 °C) on survival, growth performance, digestive enzyme activities, and body composition of Plectropomus leopardus were studied for a period of 6 weeks. One hundred eighty fish with initial body weights of 26.5 ± 1.5 g were randomly arranged into 15 glass aquaria in equal numbers in five recirculating systems to form five groups in triplicate. The results showed that survival of P. leopardus at 35 °C was significantly greater (P < 0.05) than survival at 15 °C. No death was recorded at 20, 25, and 30 °C. Among all treatment groups, the significantly highest average individual harvesting weight, weight gain, feed ingestion rate and protease enzyme activity of P. leopardus were observed in 30 °C group. Similar results were also observed in protein and fat content in this species. Based on the present findings, a culture temperature of 30 °C can be considered to be the optimum temperature for the aquaculture of juvenile P. leopardus. However, more research is still needed to optimize the nutrition and photoperiod of P. leopardus culture

Joint bilateral filtering and spectral similarity-based sparse representation : a generic framework for effective feature extraction and data classification in hyperspectral imaging

Classification of hyperspectral images (HSI) has been a challenging problem under active investigation for years especially due to the extremely high data dimensionality and limited number of samples available for training. It is found that hyperspectral image classification can be generally improved only if the feature extraction technique and the classifier are both addressed. In this paper, a novel classification framework for hyperspectral images based on the joint bilateral filter and sparse representation classification (SRC) is proposed. By employing the first principal component as the guidance image for the joint bilateral filter, spatial features can be extracted with minimum edge blurring thus improving the quality of the band-to-band images. For this reason, the performance of the joint bilateral filter has shown better than that of the conventional bilateral filter in this work. In addition, the spectral similarity-based joint SRC (SS-JSRC) is proposed to overcome the weakness of the traditional JSRC method. By combining the joint bilateral filtering and SS-JSRC together, the superiority of the proposed classification framework is demonstrated with respect to several state-of-the-art spectral-spatial classification approaches commonly employed in the HSI community, with better classification accuracy and Kappa coefficient achieved

State-of-the-art generalisation research in NLP: a taxonomy and review

The ability to generalise well is one of the primary desiderata of natural language processing (NLP). Yet, what `good generalisation' entails and how it should be evaluated is not well understood, nor are there any common standards to evaluate it. In this paper, we aim to lay the ground-work to improve both of these issues. We present a taxonomy for characterising and understanding generalisation research in NLP, we use that taxonomy to present a comprehensive map of published generalisation studies, and we make recommendations for which areas might deserve attention in the future. Our taxonomy is based on an extensive literature review of generalisation research, and contains five axes along which studies can differ: their main motivation, the type of generalisation they aim to solve, the type of data shift they consider, the source by which this data shift is obtained, and the locus of the shift within the modelling pipeline. We use our taxonomy to classify over 400 previous papers that test generalisation, for a total of more than 600 individual experiments. Considering the results of this review, we present an in-depth analysis of the current state of generalisation research in NLP, and make recommendations for the future. Along with this paper, we release a webpage where the results of our review can be dynamically explored, and which we intend to up-date as new NLP generalisation studies are published. With this work, we aim to make steps towards making state-of-the-art generalisation testing the new status quo in NLP.Comment: 35 pages of content + 53 pages of reference

Association between body fat and sarcopenia in older adults with type 2 diabetes mellitus: A cross-sectional study

ObjectivesTo investigate the association between body fat (BF%) and sarcopenia in older adults with type 2 diabetes mellitus (T2DM) and potential link with increased levels of inflammatory indicators and insulin resistance.MethodsA total of 543 older adults with T2DM were included in this cross-sectional study. Appendicular skeletal muscle (ASM), handgrip strength and gait speed were measured to diagnose sarcopenia according to the updated Asian Working Group for Sarcopenia (AWGS) 2019 criteria. Body composition data were tested using dual-energy X-ray absorptiometry (DEXA). Levels of serum high-sensitive C-reactive protein (hs-CRP), interleukin-6, fasting blood insulin (FINS), hemoglobin A1c (HbA1c), 25-hydroxyvitamin D3 [25(OH) D3] were also determined.ResultsThe prevalence of sarcopenia in all participants was 8.84%, of which 11.90% were male and 5.84% females. The Pearson’s correlation analysis revealed that BF% was negatively correlated with gait speed in men and women (R =-0.195, P=0.001; R = -0.136, P =0.025, respectively). After adjusting for all potential confounders, sarcopenia was positive associated with BF% (male, OR: 1.38, 95% CI: 1.15–1.65, P&lt; 0.001; female, OR: 1.30, 95% CI: 1.07–1.56, P=0.007), and negatively associated with body mass index (BMI) (male, OR: 0.57, 95% CI: 0.44–0.73, P&lt;0.001; female, OR: 0.48, 95% CI: 0.33–0.70, P&lt;0.001). No significant differences were found in hs-CRP, interleukin-6, and insulin resistance between older T2DM adults with and without sarcopenia.ConclusionHigher BF% was linked to an increased risk of sarcopenia in older adults with T2DM, suggesting the importance of assessing BF% rather than BMI alone to manage sarcopenia