BARN:Behavior-Aware Relation Network for multi-label behavior detection in socially housed macaques

Quantification of behaviors in macaques provides crucial support for various scientific disciplines,including pharmacology,neuroscience,and ethology.Despite recent advancements in the analysis of macaque behavior,research on multi-label behavior detection in socially housed macaques,including consideration of interactions among them,remains scarce.Given the lack of relevant approaches and datasets,we developed the Behavior-Aware Relation Network(BARN)for multi-label behavior detection of socially housed macaques.Our approach models the relationship of behavioral similarity between macaques,guided by a behavior-aware module and novel behavior classifier,which is suitable for multi-label classification.We also constructed a behavior dataset of rhesus macaques using ordinary RGB cameras mounted outside their cages.The dataset included 65?913 labels for19 behaviors and 60?367 proposals,including identities and locations of the macaques.Experimental results showed that BARN significantly improved the baseline SlowFast network and outperformed existing relation networks.In conclusion,we successfully achieved multilabel behavior detection of socially housed macaques with both economic efficiency and high accuracy.

New International Partner Network Launched to Further Sino-US Business Relations

"The network will foster newrelationship between US andChinese small and medium-size companies in 14 key busi-ness centers, generating newopportunities for US SMEs inthe China market and prosper-ity for both our great nations,"said Tim Hauser.

Transformer fault diagnosis based on relational teacher-student network

The analysis of dissolved gas in oil can provide an important basis for transformer fault diagnosis.In order to improve the accuracy of transformer fault diagnosis,a method based on the relational teacher-student network(R-TSN)is proposed by analyzing the relationship between the dissolved gas in the oil and the fault type.R-TSN replaces the original hard labels with soft labels,and uses it to measure the similarity between different samples in the space,to a certain extent,it can obtain the hidden feature information in the samples,and clarify the classification boundary.Through the identification experiment,the effect of R-TSN diagnosis model is analyzed,and the influence of the compound fault of discharge and thermal on the diagnosis model is studied.This paper compares R-TSN with support vector machines(SVMs),decision trees and multilayer perceptron models in transformer fault diagnosis.Experimental results show that R-TSN has better performance than the above methods.After adding compound faults in the sample set,the accuracy rate can still reach 86.0%.

Geomorphic indices and relative tectonic uplift in the Guerrero sector of the Mexican forearc

Tectonically active areas,such as forearc regions,commonly show contrasting relief,differential tectonic uplift,variations in erosion rates,in river incision,and in channel gradient produced by ongoing tectonic deformation.Thus,information on the tectonic activity of a defined area could be derived via landscape analysis.This study uses topography and geomorphic indices to extract signals of ongoing tectonic deformation along the Mexican subduction forearc within the Guerrero sector.For this purpose,we use field data,topographical data,knickpoints,the ratio of volume to area（Rva）.the stream-length gradient index（St）,and the normalized channel steepness index（k_（sn））.The results of the applied landscape analysis reveal considerable variations in relief,topography and geomorphic indices values along the Guerrero sector of the Mexican subduction zone.We argue that the reported differences are indicative of tectonic deformation and of variations in relative tectonic uplift along the studied forearc.A significant drop from central and eastern parts of the study area towards the west in values of R_（VA）（from ~500 to^300）,St（from ~500 to ca.400）,maximum St（from ~1500-2500 to ~ 1000） and k_（sn）（from ~150 to ~100） denotes a decrease in relative tectonic uplift in the same direction.We suggest that applied geomorphic indices values and forearc topography are independent of climate and lithology.Actual mechanisms responsible for the observed variations and inferred changes in relative forearc tectonic uplift call for further studies that explain the physical processes that control the forearc along strike uplift variations and that determine the rates of uplift.The proposed methodology and results obtained through this study could prove useful to scientists who study the geomorphology of forearc regions and active subduction zones.

In Silico Investigation of Agonist Activity of a Structurally Diverse Set of Drugs to hPXR Using HM-BSM and HM-PNN

The human pregnane X receptor（hPXR） plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards h PXR. Heuristic method（HM）-Best Subset Modeling（BSM） and HM-Polynomial Neural Networks（PNN） were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain（AD） of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved（for HM-BSM, r^2=0.881, q^2_（LOO）=0.797, q^2_（EXT）=0.674; for HM-PNN, r^2=0.882, q^2_（LOO）=0.856, q^2_（EXT）=0.655）. The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to h PXR.

Understanding Social Relationships with Person-Pair Relations

Social relationship understanding infers existing social relationships among individuals in a given scenario,which has been demonstrated to have a wide range of practical value in reality.However,existing methods infer the social relationship of each person pair in isolation,without considering the context-aware information for person pairs in the same scenario.The context-aware information for person pairs exists extensively in reality,that is,the social relationships of different person pairs in a simple scenario are always related to each other.For instance,if most of the person pairs in a simple scenario have the same social relationship,“friends”,then the other pairs have a high probability of being“friends”or other similar coarse-level relationships,such as“intimate”.This context-aware information should thus be considered in social relationship understanding.Therefore,this paper proposes a novel end-to-end trainable Person-Pair Relation Network(PPRN),which is a GRU-based graph inference network,to first extract the visual and position information as the person-pair feature information,then enable it to transfer on a fully-connected social graph,and finally utilizes different aggregators to collect different kinds of person-pair information.Unlike existing methods,the method—with its message passing mechanism in the graph model—can infer the social relationship of each person-pair in a joint way(i.e.,not in isolation).Extensive experiments on People In Social Context(PISC)-and People In Photo Album(PIPA)-relation datasets show the superiority of our method compared to other methods.