The Role and Place of Artificial Neural Network Architectures Structural Redundancy in the Input Data Prototypes and Generalization Development

Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.

Generalization of PhaseNet in Shandong and its application to the Changqing M4.1 earthquake sequence

Waveforms of seismic events,extracted from January 2019 to December 2021 were used to construct a test dataset to investigate the generalizability of PhaseNet in the Shandong region.The results show that errors in the picking of seismic phases(P-and Swaves)had a broadly normal distribution,mainly concentrated in the ranges of−0.4–0.3 s and−0.4–0.8 s,respectively.These results were compared with those published in the original PhaseNet article and were found to be approximately 0.2–0.4 s larger.PhaseNet had a strong generalizability for P-and S-wave picking for epicentral distances of less than 120 km and 110 km,respectively.However,the phase recall rate decreased rapidly when these distances were exceeded.Furthermore,the generalizability of PhaseNet was essentially unaffected by magnitude.The M4.1 earthquake sequence in Changqing,Shandong province,China,that occurred on February 18,2020,was adopted as a case study.PhaseNet detected more than twice the number of earthquakes in the manually obtained catalog.This further verified that PhaseNet has strong generalizability in the Shandong region,and a high-precision earthquake catalog was constructed.According to these precise positioning results,two earthquake sequences occurred in the study area,and the southern cluster may have been triggered by the northern cluster.The focal mechanism solution,regional stress field,and the location results of the northern earthquake sequence indicated that the seismic force of the earthquake was consistent with the regional stress field.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

General anesthetic agents induce neurotoxicity through oligodendrocytes in the developing brain

General anesthetic agents can impact brain function through interactions with neurons and their effects on glial cells.Oligodendrocytes perform essential roles in the central nervous system,including myelin sheath formation,axonal metabolism,and neuroplasticity regulation.They are particularly vulnerable to the effects of general anesthetic agents resulting in impaired proliferation,differentiation,and apoptosis.Neurologists are increasingly interested in the effects of general anesthetic agents on oligodendrocytes.These agents not only act on the surface receptors of oligodendrocytes to elicit neuroinflammation through modulation of signaling pathways,but also disrupt metabolic processes and alter the expression of genes involved in oligodendrocyte development and function.In this review,we summarize the effects of general anesthetic agents on oligodendrocytes.We anticipate that future research will continue to explore these effects and develop strategies to decrease the incidence of adverse reactions associated with the use of general anesthetic agents.

General anesthetic agents induce neurotoxicity through astrocytes

Neuroscientists have recognized the importance of astrocytes in regulating neurological function and their influence on the release of glial transmitters.Few studies,however,have focused on the effects of general anesthetic agents on neuroglia or astrocytes.Astrocytes can also be an important target of general anesthetic agents as they exert not only sedative,analgesic,and amnesic effects but also mediate general anesthetic-induced neurotoxicity and postoperative cognitive dysfunction.Here,we analyzed recent advances in understanding the mechanism of general anesthetic agents on astrocytes,and found that exposure to general anesthetic agents will destroy the morphology and proliferation of astrocytes,in addition to acting on the receptors on their surface,which not only affect Ca^(2+)signaling,inhibit the release of brain-derived neurotrophic factor and lactate from astrocytes,but are even involved in the regulation of the pro-and anti-inflammatory processes of astrocytes.These would obviously affect the communication between astrocytes as well as between astrocytes and neighboring neurons,other neuroglia,and vascular cells.In this review,we summarize how general anesthetic agents act on neurons via astrocytes,and explore potential mechanisms of action of general anesthetic agents on the nervous system.We hope that this review will provide a new direction for mitigating the neurotoxicity of general anesthetic agents.

通智测试——基于动态具身物理社会交互环境的通用人工智能测试

The release of the generative pre-trained transformer(GPT)series has brought artificial general intelligence(AGI)to the forefront of the artificial intelligence(AI)field once again.However,the questions of how to define and evaluate AGI remain unclear.This perspective article proposes that the evaluation of AGI should be rooted in dynamic embodied physical and social interactions(DEPSI).More specifically,we propose five critical characteristics to be considered as AGI benchmarks and suggest the Tong test as an AGI evaluation system.The Tong test describes a value-and ability-oriented testing system that delineates five levels of AGI milestones through a virtual environment with DEPSI,allowing for infinite task generation.We contrast the Tong test with classical AI testing systems in terms of various aspects and propose a systematic evaluation system to promote standardized,quantitative,and objective benchmarks and evaluation of AGI.

Analysis of Bright Source Hardness Ratios in the 4 yr Insight-HXMT Galactic Plane Scanning Survey Catalog

We conduct a statistical analysis of the hardness ratio(HR)for bright sources in the 4 yr Galactic Plane Scanning Survey catalog of Insight-HXMT.Depending on the stable(variable)flux F_(s)(F_(v))or spectrum S_(s)(S_(v))of each source,the bright sources are classified into three groups:F_(v)&S_(v),F_(v)&S_(s),and F_(s)&_(s).Our study of the HR characteristics in different types of sources reveals that accretion-powered neutron star(NS)low-mass X-ray binaries(LMXBs)exhibit softer energy spectra than NS high-mass X-ray binaries(HMXBs),but harder energy spectra than black hole binaries in most cases.This difference is probably due to their different magnetic field strengths.Additionally,Fv&Sv LMXBs tend to be harder than Fv&Ss LMXBs below 7 keV,while the opposite is true for HMXBs.Our results suggest that LMXBs may dominate unclassified sources,and NS binaries are likely to be the primary type of X-ray binaries with ambiguous compact stars.By comparing the HR of transient sources in their outburst and low-flux states,it is found that the averaged HR of four sources in the two states are roughly comparable within uncertainties.We also investigate the spatial properties of the three groups and find that Fv&Sv sources are mainly located in the longitude of-20°<l<9°,Fv&Ss sources cross the Galactic Plane,and Fs&Ss sources are predominantly concentrated in 19°<l<42°.In addition,analyzing the HR spatial distributions shows the absorption of soft X-rays(primarily below 2 keV)in the Galactic Plane.

Extended wet sieving method for determination of complete particle size distribution of general soils

The traditional standard wet sieving method uses steel sieves with aperture?0.063 mm and can only determine the particle size distribution(PSD)of gravel and sand in general soil.This paper extends the traditional method and presents an extended wet sieving method.The extended method uses both the steel sieves and the nylon filter cloth sieves.The apertures of the cloth sieves are smaller than 0.063 mm and equal 0.048 mm,0.038 mm,0.014 mm,0.012 mm,0.0063 mm,0.004 mm,0.003 mm,0.002 mm,and 0.001 mm,respectively.The extended method uses five steps to separate the general soil into many material sub-groups of gravel,sand,silt and clay with known particle size ranges.The complete PSD of the general soil is then calculated from the dry masses of the individual material sub-groups.The extended method is demonstrated with a general soil of completely decomposed granite(CDG)in Hong Kong,China.The silt and clay materials with different particle size ranges are further examined,checked and verified using stereomicroscopic observation,physical and chemical property tests.The results further confirm the correctness of the extended wet sieving method.

Free Energy of Anisotropic Strangeon Stars

Can pulsar-like compact objects release further huge free energy besides the kinematic energy of rotation?This is actually relevant to the equation of state of cold supra-nuclear matter,which is still under hot debate.Enormous energy is surely needed to understand various observations,such asγ-ray bursts,fast radio bursts and softγ-ray repeaters.In this paper,the elastic/gravitational free energy of solid strangeon stars is revisited for strangeon stars,with two anisotropic models to calculate in general relativity.It is found that huge free energy(>10^(46)erg)could be released via starquakes,given an extremely small anisotropy((p_(t)-p_(r))/p_(r)~10^(-4),with pt/pr the tangential/radial pressure),implying that pulsar-like stars could have great potential of free energy release without extremely strong magnetic fields in the solid strangeon star model.

In vivo imaging reveals a synchronized correlation among neurotransmitter dynamics during propofol and sevoflurane anesthesia

General anesthesia is widely applied in clinical practice.However,the precise mechanism of loss of consciousness induced by general anesthetics remains unknown.Here,we measured the dynamics of five neurotransmitters,includingγ-aminobutyric acid,glutamate,norepinephrine,acetylcholine,and dopamine,in the medial prefrontal cortex and primary visual cortex of C57BL/6 mice through in vivo fiber photometry and genetically encoded neurotransmitter sensors under anesthesia to reveal the mechanism of general anesthesia from a neurotransmitter perspective.Results revealed that the concentrations of γ-aminobutyric acid,glutamate,norepinephrine,and acetylcholine increased in the cortex during propofol-induced loss of consciousness.Dopamine levels did not change following the hypnotic dose of propofol but increased significantly following surgical doses of propofol anesthesia.Notably,the concentrations of the five neurotransmitters generally decreased during sevoflurane-induced loss of consciousness.Furthermore,the neurotransmitter dynamic networks were not synchronized in the non-anesthesia groups but were highly synchronized in the anesthetic groups.These findings suggest that neurotransmitter dynamic network synchronization may cause anesthetic-induced loss of consciousness.

The GECAM Real-time Burst Alert System

Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor(GECAM),consisting of two microsatellites,is designed to detect gamma-ray bursts associated with gravitational-wave events.Here,we introduce the real-time burst alert system of GECAM,with the adoption of the BeiDou-3 short message communication service.We present the post-trigger operations,the detailed ground-based analysis,and the performance of the system.In the first year of the in-flight operation,GECAM was triggered by 42 gamma-ray bursts.The GECAM real-time burst alert system has the ability to distribute the alert within~1 minute after being triggered,which enables timely follow-up observations.

Mathematical Definitions of Operators for Cartographic Generalization

This paper puts forword 11 cartographic generalization operator models and introduces their mathematical definitions,and thus a precise mathematical form and quantitative description has been given to these formerly limited qualitative concepts.The meaning of mathematical definition of operators for cartographic generalization and the application prospect in computer_aided cartography (CAC) is stated.ract The Jurassic strata in Jingyan of Sichuan containing the Mamenchinsaurus fauna are dealt with and divided in this paper. The Mamenchisaurus fossils contained there are compared in morphological features and stratigraphically with other types of the genus on by one. The comprehensive analysis show that the Mamenchisaurus fauna of Jingyan appeared in the early Late Jurassic and is primitive in morphology. The results of the morphological identification and stratigraphical study agree with each other. Their evolutionary processes in different apoches of the Late Jurassic also made clear. Key words Jingyan, Sichuan, Mamenchisaurus Fauna, stratigraphy, evolution

Short- and long-term outcomes of surgical treatment in patients with intestinal Behcet’s disease

BACKGROUND Behcet’s disease(BD),a chronic vasculitic disorder affecting multiple organs,is characterized by recurrent oral and genital ulcers,arthritis,vasculitis,and intes-tinal ulcers.Although intestinal involvement of BD is common in East Asia,the efficacy and long-term outcomes of surgical treatment of intestinal BD still remain to be established.AIM To evaluate the postoperative clinical course of intestinal BD and determine factors associated with its recurrence.METHODS Data from patients who underwent surgical treatment for intestinal BD between January 2010 and August 2021 were retrospectively reviewed.Patients’demo-graphics,clinical features,postoperative course,complications,and follow-up data were evaluated.RESULTS We analyzed 39 surgeries in 31 patients.The mean patient age was 45.1 years,and the mean interval between the diagnosis of intestinal BD and surgical treatment was 4.9 years(range 1.0-8.0 years).The most common indication for surgery was medical intractability(n=16,41.0%),followed by fistula or abscess(n=11,28.2%).Laparoscopic approaches were used in 19 patients(48.7%),and 5 patients(12.8%)underwent emergency surgeries.The most common surgical procedure was ileocecal resection(n=18,46.2%),followed by right colectomy(n=11,28.2%).A diverting stoma was created in only one patient(2.6%).During a mean follow-up period of 45(range 8-72)months,eight cases(20.5%)of recurrence in five patients required reoperation.The interval between operations was 12.1 months(range 6.3-17.8 mo).Four patients(10.3%)experienced recurrence within 1 year postoperatively,and all eight recurrences occurred within 2 years of the initial surgery.The reoperation rates at 1 and 3 years were 10.3%and 20.5%,respectively.A redo ileocolic anastomosis was performed in all recurrent cases.In multivariate Cox regression analysis,emergency surgery[hazard ratio(HR)9.357,95%confidence interval(CI):1.608-54.453,P=0.013]and elevated C-reactive protein(CRP)levels(HR 1.154,95%CI:1.002–1.328,P=0.047),but not medication use,were predictors of recurrence.CONCLUSION Surgical resection is a feasible treatment option for complicated BD.Reoperation is associated with severe inflam-matory conditions,reflected by increased CRP levels and the requirement for emergency surgery.

Dynamic Vision-Based Machinery Fault Diagnosis With Cross-Modality Feature Alignment

Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely investigated.In many industrial scenarios,contactless sensors are more preferred.The event camera is an emerging bio-inspired technology for vision sensing,which asynchronously records per-pixel brightness change polarity with high temporal resolution and low latency.It offers a promising tool for contactless machine vibration sensing and fault diagnosis.However,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,etc.Furthermore,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault modes.Aiming at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper.It is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance.A crossmodality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge transfer.An event erasing method is further proposed for improving model robustness against variations.The proposed method can effectively identify unseen fault mode with dynamic vision data.Experiments on two rotating machine monitoring datasets are carried out for validations,and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.

Application of Regularization Methods in the Sky Map Reconstruction of the Tianlai Cylinder Pathfinder Array

The Tianlai cylinder pathfinder is a radio interferometer array to test 21 cm intensity mapping techniques in the post-reionization era.It works in passive drift scan mode to survey the sky visible in the northern hemisphere.To deal with the large instantaneous field of view and the spherical sky,we decompose the drift scan data into m-modes,which are linearly related to the sky intensity.The sky map is reconstructed by solving the linear interferometer equations.Due to incomplete uv coverage of the interferometer baselines,this inverse problem is usually ill-posed,and regularization method is needed for its solution.In this paper,we use simulation to investigate two frequently used regularization methods,the Truncated Singular Value Decomposition(TSVD),and the Tikhonov regularization techniques.Choosing the regularization parameter is very important for its application.We employ the generalized cross validation method and the L-curve method to determine the optimal value.We compare the resulting maps obtained with the different regularization methods,and for the different parameters derived using the different criteria.While both methods can yield good maps for a range of regularization parameters,in the Tikhonov method the suppression of noisy modes are more gradually applied,produce more smooth maps which avoids some visual artefacts in the maps generated with the TSVD method.

Evolution of High-energy Electron Distribution in Pulsar Wind Nebulae

In this paper,we analyze the spectral energy distributions of 17 powerful(with a spin-down luminosity greater than10~(35)erg s~(-1))young(with an age less than 15,000 yr)pulsar wind nebulae(PWNe)using a simple timein-dependent one-zone emission model.Our aim is to investigate correlations between model parameters and the ages of the corresponding PWNe,thereby revealing the evolution of high-energy electron distributions within PWNe.Our findings are as follows:(1)The electron distributions in PWNe can be characterized by a double power-law with a super-exponential cutoff.(2)As PWNe evolve,the high-energy end of the electron distribution spectrum becomes harder with the index decreasing from approximately 3.5 to 2.5,while the low-energy end spectrum index remains constant near 1.5.(3)There is no apparent correlation between the break energy or cutoff energy and the age of PWNe.(4)The average magnetic field within PWNe decreases with age,leading to a positive correlation between the energy loss timescale of electrons at the break energy or the high-energy cutoff and the age of the PWN.(5)The total electron energy within PWNe remains constant near 2×10~(48)erg,while the total magnetic energy decreases with age.

Improving Channel Estimation in a NOMA Modulation Environment Based on Ensemble Learning

This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.

A Galaxy Image Augmentation Method Based on Few-shot Learning and Generative Adversarial Networks

Galaxy morphology classifications based on machine learning are a typical technique to handle enormous amounts of astronomical observation data,but the key challenge is how to provide enough training data for the machine learning models.Therefore this article proposes an image data augmentation method that combines few-shot learning and generative adversarial networks.The Galaxy10 DECaLs data set is selected for the experiments with consistency,variance,and augmentation effects being evaluated.Three popular networks,including AlexNet,VGG,and ResNet,are used as examples to study the effectiveness of different augmentation methods on galaxy morphology classifications.Experiment results show that the proposed method can generate galaxy images and can be used for expanding the classification model’s training set.According to comparative studies,the best enhancement effect on model performance is obtained by generating a data set that is 0.5–1 time larger than the original data set.Meanwhile,different augmentation strategies have considerably varied effects on different types of galaxies.FSL-GAN achieved the best classification performance on the ResNet network for In-between Round Smooth Galaxies and Unbarred Loose Spiral Galaxies,with F1 Scores of 89.54%and 63.18%,respectively.Experimental comparison reveals that various data augmentation techniques have varied effects on different categories of galaxy morphology and machine learning models.Finally,the best augmentation strategies for each galaxy category are suggested.

Study of 26 Galactic Open Clusters with Extended Main-sequence Turnoffs

Recent studies indicate that some Galactic open clusters(OCs)exhibit extended main-sequence turnoff(eMSTO)in their color–magnitude diagrams(CMDs).However,the number of Galactic OCs with eMSTO structures detected so far is limited,and the reasons for their formation are still unclear.This work identifies 26 Galactic OCs with undiscovered eMSTOs and investigates the causes of these features.Stellar population types and fundamental parameters of cluster samples are acquired using CMD fitting methods.Among them,the results of 11 OCs are reliable as the observed CMDs are well-reproduced.We propose the crucial role of stellar binarity and confirm the importance of stellar rotation in reproducing eMSTO morphologies.The results also show that the impact of age spread is important,as it can adequately explain the structure of young OCs and fit the observed CMDs of intermediate-age OCs better.

Rotating Massive Strangeon Stars and X-Ray Plateau of Short GRBs

Strangeon stars,which are proposed to describe the nature of pulsar-like compact stars,have passed various observational tests.The maximum mass of a non-rotating strangeon star could be high,which implies that the remnants of binary strangeon star mergers could even be long-lived massive strangeon stars.We study rigidly rotating strangeon stars in the slowly rotating approximation,using the Lennard-Jones model for the equation of state.Rotation can significantly increase the maximum mass of strangeon stars with unchanged baryon numbers,enlarging the mass-range of long-lived strangeon stars.During spin-down after merger,the decrease of radius of the remnant will lead to the release of gravitational energy.Taking into account the efficiency of converting the gravitational energy luminosity to the observed X-ray luminosity,we find that the gravitational energy could provide an alternative energy source for the plateau emission of X-ray afterglow.The fitting results of X-ray plateau emission of some short gamma-ray bursts suggest that the magnetic dipole field strength of the remnants can be much smaller than that of expected when the plateau emission is powered only by spin-down luminosity of magnetars.