Self-supervised recalibration network for person re-identification

The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

Binary Program Vulnerability Mining Based on Neural Network

Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.

Metformin promotes anti-tumor immunity in STK11 mutant NSCLC through AXIN1-dependent upregulation of multiple nucleotide metabolites

Background:Metformin has pleiotropic effects beyond glucose reduction,including tumor inhibition and immune regulation.It enhanced the anti-tumor effects of programmed cell death protein 1(PD-1)inhibitors in serine/threonine kinase 11(STK11)mutant non-small cell lung cancer(NSCLC)through an axis inhibition protein 1(AXIN1)-dependent manner.However,the alterations of tumor metabolism and metabolites upon metformin administration remain unclear.Methods:We performed untargeted metabolomics using liquid chromatography(LC)-mass spectrometry(MS)/MS system and conducted cell experiments to verify the results of bioinformatics analysis.Results:According to the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway database,most metabolites were annotated into metabolism,including nucleotide metabolism.Next,the differentially expressed metabolites in H460(refers to H460 cells),H460_met(refers to metformin-treated H460 cells),and H460_KO_met(refers to metformin-treated Axin1-/-H460 cells)were distributed into six clusters based on expression patterns.The clusters with a reversed expression pattern upon metformin treatment were selected for further analysis.We screened out metabolic pathways through KEGG pathway enrichment analysis and found that multiple nucleotide metabolites enriched in this pathway were upregulated.Furthermore,these metabolites enhanced the cytotoxicity of activated T cells on H460 cells in vitro and can activate the stimulator of the interferon genes(STING)pathway independently of AXIN1.Conclusion:Relying on AXIN1,metformin upregulated multiple nucleotide metabolites which promoted STING signaling and the killing of activated T cells in STK11 mutant NSCLC,indicating a potential immunotherapeutic strategy for STK11 mutant NSCLC.

Response analysis of NMRG system considering Rb-Xe coupling

The dynamic range of the nuclear magnetic resonance gyroscope can be effectively improved through the closedloop control scheme,which is crucial to its application in inertial measurement.This paper presents the analytical transfer function of Xe closed-loop system in the nuclear magnetic resonance gyroscope considering Rb–Xe coupling effect.It not only considers the dynamic characteristics of the system more comprehensively,but also adds the influence of the practical filters in the gyro signal processing system,which can obtain the accurate response characteristics of signal frequency and amplitude at the same time.The numerical results are compared with an experimentally verified simulation program,which indicate great agreement.The research results of this paper are of great significance to the practical application and development of the nuclear magnetic resonance gyroscope.

Enhanced Evaporation of Ternary Mixtures in Porous Medium with Microcolumn Configuration

The high surface area of porous media enhances its efficacy for evaporative cooling,however,the evaporation of pure substances often encounters issues including local overheating and unstable heat transfer.To address these challenges,a volume of fluid(VOF)model integrated with a species transport model was developed to predict the evaporation processes of ternary mixtures(water,glycerol,and 1,2-propylene glycol)in porous ceramics in this study.It reveals that the synergistic effects of thermal conduction and convective heat transfer significantly influence the mixtures evaporation,causing the fluctuations in evaporation rates.The obtained result shows a significant increase in water evaporation rates with decreasing the microcolumn size.At a pore size of 30μm and a porosity of 30%,an optimal balance between capillary forces and flow resistance yields a peak water release rate of 96.0%.Furthermore,decreasing the glycerol content from 70%to 60%enhances water release by 10.6%.The findings in this work propose the approaches to optimize evaporative cooling technologies by controlling the evaporation of mixtures in porous media.

Targeted doping induces interfacial orientation for constructing surface-functionalized Schottky junctions to coordinate redox reactions in water electrolysis

Tuning the surface properties of catalysts is an effective method for accelerating water electrolysis.Herein,we propose a directional doping and interfacial coupling strategy to design two surface-functionalized Schottky junction catalysts for coordinating the hydrogen evolution reaction(HER)and oxygen evolution reaction(OER).Directional doping with B/S atoms endows amphiphilic g-C_(3)N_(4)with significant n-/p-type semiconductor properties.Further coupling with Fe_(3)C modulates the energy band levels of B-C_(3)N_(4)and S-C_(3)N_(4),thus resulting in functionalized Schottky junction catalysts with specific surface-adsorption properties.The space-charge region generated by the dual modulation induces a local“OH-and Ht-enriched”environment,thus selectively promoting the kinetic behavior of the OER/HER.Impressively,the designed B-C_(3)N_(4)@Fe_(3)C||S-C_(3)N_(4)@Fe_(3)C pair requires only a low voltage of 1.52 V to achieve efficient water electrolysis at 10 mA cm^(-2).This work highlights the potential of functionalized Schottky junction catalysts for coordinating redox reactions in water electrolysis,thereby resolving the trade-off between catalytic activity and stability.

EQGraphNet:Advancing single-station earthquake magnitude estimation via deep graph networks with residual connections

Magnitude estimation is a critical task in seismology,and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution.In this context,we propose the Earthquake Graph Network(EQGraphNet)to enhance the performance of single-station magnitude estimation.The backbone of the proposed model consists of eleven convolutional neural network layers and ten RCGL modules,where a RCGL combines a Residual Connection and a Graph convolutional Layer capable of mitigating the over-smoothing problem and simultaneously extracting temporal features of seismic signals.Our work uses the STanford EArthquake Dataset for model training and performance testing.Compared with three existing deep learning models,EQGraphNet demonstrates improved accuracy for both local magnitude and duration magnitude scales.To evaluate the robustness,we add natural background noise to the model input and find that EQGraphNet achieves the best results,particularly for signals with lower signal-to-noise ratios.Additionally,by replacing various network components and comparing their estimation performances,we illustrate the contribution of each part of EQGraphNet,validating the rationality of our approach.We also demonstrate the generalization capability of our model across different earthquakes occurring environments,achieving mean errors of±0.1 units.Furthermore,by demonstrating the effectiveness of deeper architectures,this work encourages further exploration of deeper GNN models for both multi-station and single-station magnitude estimation.

A new criterion of coal burst proneness based on the residual elastic energy index

To evaluate the coal burst proneness more precisely,a new energy criterion namely the residual elastic energy index was proposed.This study begins by performing the single-cyclic loading-unloading uniaxial compression tests with five pre-peak unloading stress levels to explore the energy storage characteristics of coal.Five types of coals from different mines were tested,and the instantaneous destruction process of the coal specimens under compression loading was recorded using a high speed camera.The results showed a linear relationship between the elastic strain energy density and input energy density,which confirms the linear energy storage law of coal.Based on this linear energy storage law,the peak elastic strain energy density of each coal specimen was obtained precisely.Subsequently,a new energy criterion of coal burst proneness was established,which was called the residual elastic energy index(defined as the difference between the peak elastic strain energy density and post peak failure energy density).Considering the destruction process and actual failure characteristics of coal specimens,the accuracy of evaluating coal burst proneness based on the residual elastic energy index was examined.The results indicated that the residual elastic energy index enables reliable and precise evaluations of the coal burst proneness.

磷氮阻燃剂PiP-DOPO的制备及对环氧树脂的阻燃

以无水哌嗪、DOPO、四氯化碳为原料制备了磷氮阻燃剂6,6’-(哌嗪-1,4-二基)双(6H-二苯并[c,e][1,2]氧杂磷苯-6-氧化物)(PiP-DOPO),通过红外光谱与核磁共振对其结构进行了表征。将其用于阻燃环氧树脂,通过热重分析(TG)对PiP-DOPO与环氧树脂复合材料的热性能进行了表征,通过氧指数(LOI)、垂直燃烧(UL94)及锥形量热测试对环氧树脂复合材料的阻燃性能进行了表征,通过扫描电镜(SEM)对环氧树脂复合材料残炭形貌进行了表征。结果表明,当体系中磷的质量分数为1.50%时,TG测试表明,600℃时体系残炭量由23.02%(EP-0)增加至25.23%(EP-3)(N2氛围),LOI为27.5,UL94测试为V-0级,最大热释放速率(PHRR)由622.8kW/m2降至325.0kW/m2,总热释放量(THR)由121.8 MJ/m2降至76.8 MJ/m2,SEM表明PiP-DOPO的加入能够明显改变环氧体系残炭的表面形貌,PiPDOPO对环氧树脂有良好的阻燃效果。

Comparative study on breakdown characteristics of trigger gap and overvoltage gap in a gas pressurized closing switch

Gas pressurized closing switches are one of the most important elements in FLTD-based systems. Improving the trigger performance of gas switches is useful for optimizing the output parameters and the reliability of the FLTD. In this paper, the breakdown characteristics of the trigger gap and the overvoltage gap are studied experimentally. The reasons for the different breakdown performance of the two gaps are also investigated. The results show that the breakdown delay of the trigger gap is more influenced by the trigger voltage, while the breakdown delay of the overvoltage gap is more influenced by the working coefficient and always higher than that of the trigger gap. The jitter of the trigger gap is more influenced by the trigger voltage and accounts more than 60% of the total switch jitter, while the jitter of the overvoltage gap is hardly changed with the trigger voltage as well as the working coefficient and maintains less than 1.4 ns. It is proved that the discharging product from the trigger gap can effectively reduce the breakdown delay and jitter of the overvoltage gap. Based on that, the effect and improvement of pre-ionization on the two gaps are also studied. It is concluded that the jitter of the trigger gap reduces obviously when the pre-ionization is added, while the pre-ionization almost has no effect on the jitter of the overvoltage gap. The jitter of the overvoltage gap is about two times higher than the trigger gap in the pre-ionizing switch.

Control Research of Dual Chamber Hydro-Pneumatic Suspension

Vehicle riding comfort and handling stability are directly affected by suspension performance.A novel dual chamber hydro-pneumatic(DCHP)suspension system is developed in this paper.Based on the structural analysis of the DCHP suspension,an equivalent suspension model is proposed for the control purpose.A cuckoo search(CS)based fuzzy PID controller is proposed for the control of the DCHP suspension system.The proposed controller combines the advantage of fuzzy logic and PID controller,and CS algorithm is used to regulate the membership functions and PID parameters.Compared with tradition LQR controller and passive suspension system,the CSFPID controller can reduce the sprung mass acceleration,and at the same time with no deterioration of tire deflection.

The Entropy Perspective on Human Illness and Aging

1.Entropy and its implications in human disease and aging Entropy,which is a thermodynamic property or an interpreta-tion of the second law of thermodynamics,was first defined in 1865 by the German physicist Rudolph Clausius[1].It eventually evolved into a general scientific concept that is of universal and paramount importance in three aspects.First,entropy is a measur-able physical property that is commonly associated with a state of chaos,disorderliness,randomness,or uncertainty of any systems[2].Second,entropy is a measure of the amount of energy that is unavailable to do work[2].Third,the universe or an isolated sys-tem always obeys“the principle of entropy increase”that irre-versible or spontaneous processes can occur only in the direction of entropy increase—that is,the direction of increasing chaos,dis-order,randomness,or uncertainty[2].

Researches on preconditioned wire arrayZpinches in Xi’an Jiaotong University

The dynamics of wire array Z pinches are greatly affected by the initial state of the wires,which can be preconditioned by a prepulse current.Recent advances in experimental research on preconditioned wire array Z pinches at Xi’an Jiaotong University are presented in this paper.Single-wire explosion experiments were carried out to check the state of the preconditioning and to obtain the current parameters needed for wire gasification.Double-wire explosion experiments were conducted to investigate the temporal evolution of the density distribution of the two gasified wires.Based on the results of these experiments,a double-pulse Z-pinch facility,Qin-1,in which a 10 kA prepulse current was coupled with the 0.8 MA main current was designed and constructed.Wire arrays of different wire materials,including silver and tungsten,can be preconditioned by the prepulse current to a gaseous state.Implosion of the two preconditioned aluminum wires exhibited no ablation and little trailing mass.

Vibration-Based Terrain Classification for Autonomous Vehicles

A method for terrain classification based on vibration response resulted from wheel-terrain interaction is presented. Four types of terrains including sine,gravel,cement and pebble were tested.The vibration data were collected by two single axis accelerometers and a triaxial seat pad accelerometer,and five data sources were utilized. The feature vectors were obtained by combining features extracted from amplitude domain,frequency domain,and time-frequency domain. The ReliefF algorithm was used to evaluate the importance of attributes; accordingly,the optimal feature subsets were selected. Further,the predicted class was determined by fusion of outputs provided by five data sources. Finally,a voting algorithm,wherein a class with the most frequent occurrence is the predicted class,was employed. In addition,four different classifiers,namely support vector machine,k-nearest neighbors,Nave Bayes,and decision tree,were used to perform the classification and to test the proposed method. The results have shown that performances of all classifiers are improved.Therefore,the proposed method is proved to be effective.

Irregularly sampled seismic data interpolation via wavelet-based convolutional block attention deep learning

Seismic data interpolation,especially irregularly sampled data interpolation,is a critical task for seismic processing and subsequent interpretation.Recently,with the development of machine learning and deep learning,convolutional neural networks(CNNs)are applied for interpolating irregularly sampled seismic data.CNN based approaches can address the apparent defects of traditional interpolation methods,such as the low computational efficiency and the difficulty on parameters selection.However,current CNN based methods only consider the temporal and spatial features of irregularly sampled seismic data,which fail to consider the frequency features of seismic data,i.e.,the multi-scale features.To overcome these drawbacks,we propose a wavelet-based convolutional block attention deep learning(W-CBADL)network for irregularly sampled seismic data reconstruction.We firstly introduce the discrete wavelet transform(DWT)and the inverse wavelet transform(IWT)to the commonly used U-Net by considering the multi-scale features of irregularly sampled seismic data.Moreover,we propose to adopt the convolutional block attention module(CBAM)to precisely restore sampled seismic traces,which could apply the attention to both channel and spatial dimensions.Finally,we adopt the proposed W-CBADL model to synthetic and pre-stack field data to evaluate its validity and effectiveness.The results demonstrate that the proposed W-CBADL model could reconstruct irregularly sampled seismic data more effectively and more efficiently than the state-of-the-art contrastive CNN based models.

Finite element simulation of the micromachining of nanosized-silicon-carbide-particle reinforced composite materials based on the cohesive zone model

A finite element method based on the cohesive zone model was used to study the micromachining process of nanosized silicon-carbide-particle(SiCp) reinforced aluminum matrix composites. As a hierarchical multiscale simulation method, the parameters for the cohesive zone model were obtained from the stress-displacement curves of the molecular dynamics simulation. The model considers the random properties of the siliconcarbide-particle distribution and the interface of bonding between the silicon carbide particles and the matrix.The machining mechanics was analyzed according to the chip morphology, stress distribution, cutting temperature, and cutting force. The simulation results revealed that the random distribution of nanosized SiCp causes non-uniform interaction between the tool and the reinforcement particles. This deformation mechanics leads to inhomogeneous stress distribution and irregular cutting force variation.

The case-dependent lignin role in lignocellulose nanofibers preparation and functional application-A review

Lignocellulose nanofibers(LCNFs) as a new material is attracting extensive attention. The pretreatment and mechanical fibrillation are the two main stages involved in the preparation of LCNFs, and lignin plays the important role of these two stages. This review discussed the interaction between lignin and chemicals in the pretreatment stage, and discovered the general law of the effect of lignin in the mechanical fibrillation stage.Lignin exhibits both promotion and inhibition effects on mechanical fibrillation, and the mutual competition between the two effects ultimately affects the energy consumption, morphology and yield of LCNFs. Furthermore, the recent research progress related to the contributions of lignin on the functional application of LCNFs was summarized, aiming to provide profound guidance for the preparation and application of LCNFs.

Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence

In this paper,an improved high-order model-free adaptive iterative control(IHOMFAILC)method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method.This method adds the differential of tracking error in the criteria function to compensate for the effect of the random disturbance.Meanwhile,a high-order estimation algorithmis used to estimate the value of pseudo partial derivative(PPD),that is,the current value of PPD is updated by that of previous iterations.Thus the rapid convergence of the maximumtracking error is not limited by the initial value of PPD.The convergence of the maximumtracking error is deduced in detail.This method can track the desired output with enhanced convergence and improved tracking performance.Two examples are used to verify the convergence and effectiveness of the proposed method.

Seismic swarm intelligence inversion with sparse probability distribution of reflectivity

Seismic inversion,such as velocity and impedance,is an ill-posed problem.To solve this problem,swarm intelligence(SI)algorithms have been increasingly applied as the global optimization approach,such as differential evolution(DE)and particle swarm optimization(PSO).Based on the well logs,the sparse probability distribution(PD)of the reflectivity distribution is spatial stationarity.Therefore,we proposed a general SI scheme with constrained by a priori sparse distribution of the reflectivity,which helps to provide more accurate potential solutions for the seismic inversion.In the proposed scheme,as two key operations,the creating of probability density function library and probability transformation are inserted into standard SI algorithms.In particular,two targeted DE-PD and PSO-PD algorithms are implemented.Numerical example of Marmousi2 model and field example of gas hydrates show that the DE-PD and PSO-PD estimate better inversion solutions than the results of the original DE and PSO.In particular,the DE-PD is the best performer both in terms of mean error and fitness value of velocity and impendence inversion.Overall,the proposed SI with sparse distribution scheme is feasible and effective for seismic inversion.

ResGraphNet: GraphSAGE with embedded residual module for prediction of global monthly mean temperature

Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast.For global monthly mean temperature series,considering the strong potential of deep neural network for extracting data features,this paper proposes a data-driven model,ResGraphNet,which improves the prediction accuracy of time series by an embedded residual module in GraphSAGE layers.The experimental results of a global mean temperature dataset,HadCRUT5,show that compared with 11 traditional prediction technologies,the proposed ResGraphNet obtains the best accuracy.The error indicator predicted by the proposed ResGraphNet is smaller than that of the other 11 prediction models.Furthermore,the performance on seven temperature datasets shows the excellent generalization of the ResGraphNet.Finally,based on our proposed ResGraphNet,the predicted 2022 annual anomaly of global temperature is 0.74722℃,which provides confidence for limiting warming to 1.5℃ above pre-industrial levels.