Enhanced Temporal Correlation for Universal Lesion Detection

Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.

Layer chicken microbiota:a comprehensive analysis of spatial and temporal dynamics across all major gut sections

Background The gut microbiota influences chicken health,welfare,and productivity.A diverse and balanced microbiota has been associated with improved growth,efficient feed utilisation,a well-developed immune system,disease resistance,and stress tolerance in chickens.Previous studies on chicken gut microbiota have predominantly focused on broiler chickens and have usually been limited to one or two sections of the digestive system,under con-trolled research environments,and often sampled at a single time point.To extend these studies,this investigation examined the microbiota of commercially raised layer chickens across all major gut sections of the digestive system and with regular sampling from rearing to the end of production at 80 weeks.The aim was to build a detailed picture of microbiota development across the entire digestive system of layer chickens and study spatial and temporal dynamics.Results The taxonomic composition of gut microbiota differed significantly between birds in the rearing and pro-duction stages,indicating a shift after laying onset.Similar microbiota compositions were observed between proven-triculus and gizzard,as well as between jejunum and ileum,likely due to their anatomical proximity.Lactobacil-lus dominated the upper gut in pullets and the lower gut in older birds.The oesophagus had a high proportion of Proteobacteria,including opportunistic pathogens such as Gallibacterium.Relative abundance of Gallibacterium increased after peak production in multiple gut sections.Aeriscardovia was enriched in the late-lay phase compared to younger birds in multiple gut sections.Age influenced microbial richness and diversity in different organs.The upper gut showed decreased diversity over time,possibly influenced by dietary changes,while the lower gut,specifi-cally cecum and colon,displayed increased richness as birds matured.However,age-related changes were inconsist-ent across all organs,suggesting the influence of organ-specific factors in microbiota maturation.Conclusion Addressing a gap in previous research,this study explored the microbiota across all major gut sections and tracked their dynamics from rearing to the end of the production cycle in commercially raised layer chickens.This study provides a comprehensive understanding of microbiota structure and development which help to develop targeted strategies to optimise gut health and overall productivity in poultry production.

Temporal and spatial differences in human activities performed in Urban Green Spaces of Vilnius(Lithuania)

Urban green spaces(UGS)are relevant to city well-being,as recognized by the United Nations’Sustainable Development Goals(SDGs).However,few studies have studied the temporal use of UGS.This work assessed the seasonal,weekly,and daily use of three urban green spaces(Vingis Park,Bernardino Garden,and Jomantas Park)in Vilnius(Lithuania).The study is based on an on-site observation-based survey,which recorded users’characteristics,activities,and weather conditions during summer and winter.The results showed that UGS’s seasonal,weekly,and daily use differed according to park and users’characteristics.Parks with a higher diversity of facilities had a high seasonal difference in the number of observed activities.User numbers were higher in the summer for activities with children,social activities,sports,and water activities than in the winter.Jomantas Park had the lowest variability in user characteristics.Weather variables were linked to changes in users’activities.Higher precipitation and lower temperature were associated with reducing the number of users and the diversity of registered activities.Most of the stationary activities were observed during summer.The diversity of the observed activities was associated with the available facilities rather than the park size.The distribution of stationary activities was spatially correlated with facility/equipment(benches,playgrounds,sports,and fitness equipment)and proximity to water features.The results of this study are relevant for UGS design,planning,and management.

Spatial correlations in ground motion intensity measuring from the 2023 Türkiye earthquake

When evaluating an area's seismic risk or resilience,it is necessary to use the spatial correlation to analyze the ground motion parameters of multiple sites together in an earthquake.These two large earthquakes in Türkiye provided the possibility for spatial correlation analysis of ground motion intensity measurements in this area.Based on the strong motion records provided by The Disaster and Emergency Management Authority of Türkiye(AFAD),this study uses the local ground motion prediction equation in Türkiye to give spatial correlation analysis of Intensity Measurements.This study gives an exponential model based on a semivariogram and compares it with the correlation model obtained from previous studies.

Temporal-spatial cross-correlation analysis of non-stationary near-surface wind speed time series

Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.

Detecting Encrypted Botnet Traffic Using Spatial-Temporal Correlation

In this paper, we propose a novel method to detect encrypted botnet traffic. During the traffic preprocessing stage, the proposed payload extraction method can identify a large amount of encrypted applications traffic. It can filter out a large amount of non-malicious traffic, greatly improving the detection efficiency. A Sequential Probability Ratio Test (SPRT)-based method can find spatial-temporal correlations in suspicious botnet traffic and make an accurate judgment. Experimental results show that the false positive and false negative rates can be controlled within a certain range.

Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.

The Spatiotemporal Distribution Characteristics of Cloud Types and Phases in the Arctic Based on CloudSat and CALIPSO Cloud Classification Products

The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types(high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases(ice,mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows:(1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents.(2)The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer;altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer;stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn.(3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly.(4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas.(5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated.(6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.

Flight parameter calculation method of multi-projectiles using temporal and spatial information constraint

The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time,making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles.Using six intersecting-screen testing system,in this paper,we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism.We extract the output signal from each detection screen and then use the wavelet transform to process the output signal.We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory.We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles.Based on the premise of linear projectile motion,we establish a temporal and spatial constraint matching model using the projectile’s position coordinates in each detection screen and the projectile’s time constraints within the multiple intersecting-screen geometry.We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration,and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty.The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.

Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated immediately.Color fundus imaging(CFI)is a screening technology that is both effective and economical.According to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic algorithms.The traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes.In addition,they usually only target one or a few different kinds of eye diseases at the same time.In this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs classification.PLML_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification scores.The DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right CFI.After then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel basis.After the attributes have been analyzed,they are integrated to provide a representation at the patient level.Throughout the whole process of ODs categorization,the patient-level representation will be used.The efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

Deepfake Video Detection Based on Improved CapsNet and Temporal–Spatial Features

Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms,presenting risks for numerous countries,societies,and individuals,and posing a serious threat to cyberspace security.To address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection,we propose a detection method based on improved CapsNet and temporal–spatial features(iCapsNet–TSF).First,the dynamic routing algorithm of CapsNet is improved using weight initialization and updating.Then,the optical flow algorithm is used to extract interframe temporal features of the videos to form a dataset of temporal–spatial features.Finally,the iCapsNet model is employed to fully learn the temporal–spatial features of facial videos,and the results are fused.Experimental results show that the detection accuracy of iCapsNet–TSF reaches 94.07%,98.83%,and 98.50%on the Celeb-DF,FaceSwap,and Deepfakes datasets,respectively,displaying a better performance than most existing mainstream algorithms.The iCapsNet–TSF method combines the capsule network and the optical flow algorithm,providing a novel strategy for the deepfake detection,which is of great significance to the prevention of deepfake attacks and the preservation of cyberspace security.

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

The ever-growing available visual data(i.e.,uploaded videos and pictures by internet users)has attracted the research community’s attention in the computer vision field.Therefore,finding efficient solutions to extract knowledge from these sources is imperative.Recently,the BlazePose system has been released for skeleton extraction from images oriented to mobile devices.With this skeleton graph representation in place,a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action.We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest,it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks.Hence,in this study,we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition.Moreover,we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor.Additionally,we propose different skeleton detection thresholds that can improve the accuracy performance even further.We reached a top-1 accuracy performance of 40.1%on the Kinetics dataset.For the NTU-RGB+D dataset,we achieved 87.59%and 92.1%accuracy for Cross-Subject and Cross-View evaluation criteria,respectively.

Temporal and spatial variation of cloud cover in arid regions of Central Asia in the last 40 years

Water resources are one of the key factors restricting the development of arid areas,and cloud water resources is an important part of water resources.The arid region of central Asia is the core region of the current national green silk road construction,and is the largest arid region in the world.Based on cloud cover data of ECMWF,the current study analyzed temporal and spatial characteristics of cloud properties in arid regions of Central Asia between 1980 and 2019.Our findings show that:(1)From the point of view of spatial distribution,total cloudiness in arid regions of Central Asia was low in the south and high in the north.The distribution of high cloud frequency and medium cloud frequency was higher in the south and lower in the north,while low cloud frequency distribution was low in the south and high in the north.(2)In terms of time,the variation of cloud cover and cloud type frequency had obvious seasonal characteristics.From winter to spring,cloud cover increased,and the change of cloud type frequency increased.From spring to summer,cloud cover continued to increase and the change of cloud type frequency increased further.Cloud cover began to decrease from summer to autumn,and the change of cloud type frequency also decreased.(3)Generally,average total cloud cover decreased in most of central Asia,and high and medium cloud cover increased while low cloud cover decreased.This study provides a reference for the rational development of cloud resources in the region.

Temporal and spatial variation characteristics of extreme precipitation on the Loess Plateau of China facing the precipitation process

The preceding and succeeding precipitation(PSP)often act together with extreme precipitation(EP),in turn,causing floods,which is an objective component that is often overlooked with regard to summer flood hazards in the arid region of Northwest China.In this study,event-based extreme precipitation(EEP)was defined as continuous precipitation that includes at least one day of EP.We analyzed the spatiotemporal variation characteristics of four EEP types(front EEP,late EEP,balanced EEP,and single day EEP)across the Loess Plateau(LP)based on data acquired from 87 meteorological stations from 1960 to 2019.Precipitation on the LP basically maintained a spatial pattern of"low in the northwest region and high in the southeast region",and EP over the last 10 a increased significantly.The cumulative precipitation percentage of single day EEP was 34%and was dominant for 60 a,while the cumulative precipitation percentage of front,late,and balanced EEP types associated with PSP accounted for 66%,which confirms to the connotation of EEP.The cumulative frequencies of front and late EEP types were 23%and 21%,respectively,while the cumulative frequency of balanced EEP had the lowest value at only 13%.Moreover,global warming could lead to more single day EEP across the LP,and continuous EEP could increase in the northwestern region and decrease in the eastern region in the future.The concept of process-oriented EP could facilitate further exploration of disaster-causing processes associated with different types of EP,and provide a theoretical basis for deriving precipitation disaster chains and construction of disaster cluster characteristics.

Eff ects of environmental factors on the temporal and spatial variations in branch and leaf CO_(2)effl ux of Larix gmelinii var.principis-rupprechtii Mayr

The CO_(2)effl ux of branches and leaves plays an important role in ecosystem carbon balance.Using a carbon fl ux system,the effl ux of Larix gmelinii var.principisrupprechtii(Dahurian larch)was investigated in 27 years(immature),31 years(near-mature),and 47 years(mature)stands at diurnal,seasonal,and spatial scales(direction and height)as well as its connection with environmental factors from May to October 2020.Diurnal variation in effl ux was a single peak,and the maximum occurring between 14:00 and 16:00.Seasonal variation also exhibited a single peak,with the maximum in late July and the minimum in early October.From May to September,effl ux on the south side was the largest among the three stands,and mean values on the south side of 27 year-old,31 year-old,and 47 year-old trees were 0.50,0.97 and 1.05μmol·m^(–2)·s^(–1),respectively.The minimum occurred on the north side.Except for the maximum in July and September in the 27 year-old stand in the middle of the canopy,the maximum effl ux in the upper canopy,and the means in the 27 year-old,31 year-old,and 47 year-old stands were 0.49,0.96 and 1.04μmol·m^(-2)·s^(-1),respectively;the minimum occurred in the lower canopy.Temperatures and relative humidity infl uenced seasonal variations in effl ux.Seasonal variation in temperature sensitivity coeffi cient(Q 10)was opposite that of temperature,increasing with decreasing temperature.At the spatial scale,maximum Q 10 occurred in the mid canopy.With the effl ux and temperature data in diff erent locations,it is possible to better estimate effl ux variations in each stand.

Spatial and Temporal Evolution Characteristics of Extreme Precipitation and Isohyets in Ningxia

The multi-scale spatial and temporal evolution characteristics of extreme precipitation and isohyets in Ningxia were studied using daily,monthly,and annual precipitation data from 20 meteorological stations in Ningxia over the last 60 years.The results revealed that the intensity of rainstorms in Ningxia had decreased slightly over the previous 60 years,with the intensity of rainstorms in southern and central Ningxia being higher than in northern areas.Ningxia's annual and seasonal precipitation varied regionally,declining from the southeast to the northwest.Annual,spring and autumn precipitation exhibited a significantly declining trend from 1960 to the early 21^(st)century;summer precipitation displayed a slightly decreasing trend;and winter precipitation showed a significantly increasing trend.Nevertheless,there was a noticeable increase in annual and seasonal precipitation after 2005.From the 1960s to the 2000s,the 200 mm isohyet moved slowly southward,while the 400 mm isohyet jumped southward twice in the 1970s and 2000s before jumping considerably northward in the 2010s to reach their northernmost region.

Temporal and Spatial Variation of Summer Extreme High Temperature in Guizhou Province from 1970 to 2020

Based on the daily maximum temperature data of 31 meteorological observation stations and some statistical methods, the temporal and spatial characteristics of summer extreme high temperature in Guizhou province from 1970 to 2020 are analyzed. The results indicate that: 1) The threshold of extreme high temperature (EHT) in summer in Guizhou province had a large spatial difference, with decreasing characteristics from the northeast to the southwest, it was negatively correlated with the altitude. 2) In most parts of Guizhou province, the extreme high temperature days (EHTD) in summer can reach about 4.2 d, the lowest EHTD occurred in the southernmost part. From June to August, the EHTD gradually increased, especially in Central and eastern parts of Guizhou province. However, the extreme high temperature intensity (EHTI) displayed similar distribution characteristics in summer, June, July and August, with larger value in the northeast part and lower value in the southwest part of Guizhou. 3) EHTD had a rising trend in almost stations, except for the PZ station, the increased range and intensity gradually increased from June to August. But the EHTI had a larger spatial difference, especially in June, it declined in most parts of Guizhou, the declined scope and intensity gradually decreased in July, and completely increased in August, this made EHTI show an increasing trend in summer in most parts the Guizhou province. 4) The averaged EHTD increased by 0.62 d/10a (p 0.1), the significant increase also occurred in August, but it increased insignificantly in June and July. The averaged EHTI had insignificant increase in summer and the three months. In general, the EHTD and EHTI increased in most parts of Guizhou province during the period of 1970-2020, this may be related to the changes of them in August.

Temporal and Spatial Distribution of Lightning Activity in Ulanqab City

From January 2020 to December 2021,Ulanqab Meteorological Bureau of Inner Mongolia used VLF/LF lightning locator to carry out three-dimensional lightning monitoring in Ulanqab City,and compared with ADTD lightning location data in the same period.The results show that both VLF/LF lightning locator and ADTD lightning locator had excellent monitoring ability for lightning during flood season in Ulanqab.VLF/LF lightning locator was slightly superior to ADTD lightning locator in observation accuracy,the observation ability of low-current cloud-to-ground lightning,intracloud lightning observation and so on.There were obvious temporal and spatial characteristics of cloud-to-ground lightning during flood season in Ulanqab,and there was a certain correlation between the areas where lightning appeared frequently and surface water.Intracloud lightning was mainly concentrated at a height of 1-7 km.Negative cloud-to-ground lightning accounted for about 75%of total cloud-to-ground lightning,and negative intracloud lightning accounted for 39%of total intracloud lightning.

The Grammatical Categorization of Mandarin在/zài:Spatiality,Temporality and Semantic Construal

Mandarin在(pinyin:zài)is the most frequently used character in representing spatial and temporal relationship.Current studies mostly focus on its lexical meaning and syntactic structure while cognitive features of its grammatical categories have been neglected.This paper investigates into the categorization of zài by conducting a morphosyntactic test among College English majors in China.The results show that:prototypes are organizing the grammatical categories of zài at all levels in terms of intra-categorial gradience;the semantic construal of zài construction could significantly influence the accuracy of the grammatical categorization of zài;the syntactic structure can provide viable cue for the identification of grammatical categories of zài;spatiality,temporality and the status of existing are three essential semantic features encoded by zài,the concurrence of which leads to various degree of inter-categorial vagueness,indicating a conflict between the rigid grammatical classification and the indeterminate nature of the grammatical functions of zai,suggesting the necessity to reconsider the efficacy of applying indiscriminately the Anglo-Saxon grammar into the study of Chinese spatial-temporal constructions.

Spatial and temporal heterogeneity of the impact of per capita income on household indirect carbon emissions in western China

With China entering the stage of high-quality development,the issue of carbon emission has become a hot research topic.This paper analyzes the different temporal and spatial effects of per capita income on household indirect carbon emissions in western China.Based on the data of Chinese Family Panel Studies(CFPS)in 2016 and 2018 in the western China,this paper uses Regression analysis and Bayesian correlation analysis to study the relationship between per capita income and household indirect carbon emissions.The results showed that the indirect carbon emissions generated by the expenditure on food,housing and household equipment in the household consumption structure in the western China were relatively high.In 2016-2018,the per capita income and per capita household consumption indirect carbon emissions in the western China showed an increasing trend.There was a positive correlation between per capita income and indirect carbon emissions of per capita household consumption,and its correlation was gradually enhanced in time dimension.In the spatial dimension,the household indirect carbon emissions in Yunnan,Qinghai,Guangxi Zhuang and Ningxia in the western China were greatly affected by per capita income,while the household indirect carbon emissions in Guizhou was least affected by per capita income.Finally,the paper puts forward some problems that we should consider in the process of facing the per capita income growth and climate change:the collection of carbon tax,the optimization of household consumption structure,the research and development of low-carbon products,and the differentiated carbon reduction.