Methods and approaches are discussed that identify and filter off affecting factors (noise) above primary signals,based on the Adaptive-Nework-Based Fuzzy Inference System. Influences of the zonal winds in equatorial ...Methods and approaches are discussed that identify and filter off affecting factors (noise) above primary signals,based on the Adaptive-Nework-Based Fuzzy Inference System. Influences of the zonal winds in equatorial eastern and middle/western Pacific on the SSTA in the equatorial region and their contribution to the latter are diagnosed and verified with observations of a number of significant El Nio and La Nia episodes. New viewpoints are propsed. The methods of wavelet decomposition and reconstruction are used to build a predictive model based on independent domains of frequency,which shows some advantages in composite prediction and prediction validity.The methods presented above are of non-linearity, error-allowing and auto-adaptive/learning, in addition to rapid and easy access,illustrative and quantitative presentation,and analyzed results that agree generally with facts. They are useful in diagnosing and predicting the El Nio and La Nia problems that are just roughly described in dynamics.展开更多
Electrical resistivity tomography (ERT) has been used to experimentally detect shallow buried faults in urban areas in the past a few years, with some progress and experience obtained. According to the results from Ol...Electrical resistivity tomography (ERT) has been used to experimentally detect shallow buried faults in urban areas in the past a few years, with some progress and experience obtained. According to the results from Olympic Park, Beijing, Shandong Province, Gansu Province and Shanxi Province, we have generalized the method and procedure for inferring the discontinuity of electrical structures (DES) indicating a buried fault in urban areas from resistivity tomograms and its typical electrical features. In general, the layered feature of the electrical structure is first analyzed to preliminarily define whether or not a DES exists in the target area. Resistivity contours in resistivity tomograms are then analyzed from the deep to the shallow. If they extend upward from the deep to the shallow and shape into an integral dislocation, sharp flexure (convergence) or gradient zone, it is inferred that the DES exists, indicating a buried fault. Finally, horizontal tracing is be carried out to define the trend of the DES. The DES can be divided into three types-type AB, ABA and AC. In the present paper, the Zhangdian-Renhe fault system in Zibo city is used as an example to illustrate how to use the method to infer the location and spatial extension of a target fault. Geologic drilling holes are placed based on our research results, and the drilling logs testify that our results are correct. However, the method of this paper is not exclusive and inflexible. It is expected to provide reference and assistance for inferring the shallow buried faults in urban areas from resistivity tomograms in the future.展开更多
The research purpose is invention (construction) of a formal logical inference of the Law of Conservation of Energy within a logically formalized axiomatic epistemology-and-axiology theory Sigma from a precisely defin...The research purpose is invention (construction) of a formal logical inference of the Law of Conservation of Energy within a logically formalized axiomatic epistemology-and-axiology theory Sigma from a precisely defined assumption of a-priori-ness of knowledge. For realizing this aim, the following work has been done: 1) a two-valued algebraic system of formal axiology has been defined precisely and applied to proper-philosophy of physics, namely, to an almost unknown (not-recognized) formal-axiological aspect of the physical law of conservation of energy;2) the formal axiomatic epistemology-and-axiology theory Sigma has been defined precisely and applied to proper-physics for realizing the above-indicated purpose. Thus, a discrete mathematical model of relationship between philosophy of physics and universal epistemology united with formal axiology has been constructed. Results: 1) By accurate computing relevant compositions of evaluation-functions within the discrete mathematical model, it is demonstrated that a formal-axiological analog of the great conservation law of proper physics is a formal-axiological law of two-valued algebra of metaphysics. (A precise algorithmic definition of the unhabitual (not-well-known) notion “formal-axiological law of algebra of metaphysics” is given.) 2) The hitherto never published significantly new nontrivial scientific result of investigation presented in this article is a formal logical inference of the law of conservation of energy within the formal axiomatic theory Sigma from conjunction of the formal-axiological analog of the law of conservation of energy and the assumption of a-priori-ness of knowledge.展开更多
The global Internet is a complex network of interconnected autonomous systems(ASes).Understanding Internet inter-domain path information is crucial for understanding,managing,and improving the Internet.The path inform...The global Internet is a complex network of interconnected autonomous systems(ASes).Understanding Internet inter-domain path information is crucial for understanding,managing,and improving the Internet.The path information can also help protect user privacy and security.However,due to the complicated and heterogeneous structure of the Internet,path information is not publicly available.Obtaining path information is challenging due to the limited measurement probes and collectors.Therefore,inferring Internet inter-domain paths from the limited data is a supplementary approach to measure Internet inter-domain paths.The purpose of this survey is to provide an overview of techniques that have been conducted to infer Internet inter-domain paths from 2005 to 2023 and present the main lessons from these studies.To this end,we summarize the inter-domain path inference techniques based on the granularity of the paths,for each method,we describe the data sources,the key ideas,the advantages,and the limitations.To help readers understand the path inference techniques,we also summarize the background techniques for path inference,such as techniques to measure the Internet,infer AS relationships,resolve aliases,and map IP addresses to ASes.A case study of the existing techniques is also presented to show the real-world applications of inter-domain path inference.Additionally,we discuss the challenges and opportunities in inferring Internet inter-domain paths,the drawbacks of the state-of-the-art techniques,and the future directions.展开更多
Mobile phones are becoming a primary platform for information access. A major aspect of ubiquitous computing is context-aware applications which collect information about the environment that the user is in and use th...Mobile phones are becoming a primary platform for information access. A major aspect of ubiquitous computing is context-aware applications which collect information about the environment that the user is in and use this information to provide better service and improve user experience. Location awareness makes certain applications possible, e.g., recommending nearby businesses and tracking estimated routes. An Android application is able to collect useful Wi-Fi information without registering a location listener with a network-based provider. We passively collected the data of the IDs of Wi-Fi access points and the received signal strengths. We developed and implemented an algorithm to analyse the data;and designed heuristics to infer the location of the device over time—all without ever connecting to the network thus maximally preserving the privacy of the user.展开更多
Human Immunodeficiency Virus (HIV) dynamics in Africa are purely characterised by sparse sampling of DNA sequences for individuals who are infected. There are some sub-groups that are more at risk than the general pop...Human Immunodeficiency Virus (HIV) dynamics in Africa are purely characterised by sparse sampling of DNA sequences for individuals who are infected. There are some sub-groups that are more at risk than the general population. These sub-groups have higher infectivity rates. We came up with a likelihood inference model of multi-type birth-death process that can be used to make inference for HIV epidemic in an African setting. We employ a likelihood inference that incorporates a probability of removal from infectious pool in the model. We have simulated trees and made parameter inference on the simulated trees as well as investigating whether the model distinguishes between heterogeneous and homogeneous dynamics. The model makes fairly good parameter inference. It distinguishes between heterogeneous and homogeneous dynamics well. Parameter estimation was also performed under sparse sampling scenario. We investigated whether trees obtained from a structured population are more balanced than those from a non-structured host population using tree statistics that measure tree balance and imbalance. Trees from non-structured population were more balanced basing on Colless and Sackin indices.展开更多
Deciding the penalty of a law case has always been a complex process, which may involve with much coordination. Despite the judicial study based on the rules and conditions, artificial intelligence and machine learnin...Deciding the penalty of a law case has always been a complex process, which may involve with much coordination. Despite the judicial study based on the rules and conditions, artificial intelligence and machine learning has rarely been used to study the problem of penalty inferring, leaving the large amount of law cases as well as various factors among them untouched. This paper aims to incorporate the state-of-the-art artificial intelligence methods to exploit to what extent this problem can be alleviated. We first analyze 145 000 law cases and observe that there are two sorts of labels, temporal labels and spatial labels, which have unique characteristics. Temporal labels and spatial labels tend to converge towards the final penalty, on condition that the cases are of the same category. In light of this, we propose a latent-class probabilistic generative model, namely Penalty Topic Model （PTM）, to infer the topic of law cases, and the temporal and spatial patterns of topics embedded in the case judgment. Then, the learnt knowledge is utilized to automatically cluster all cases accordingly in a unified way. We conduct extensive experiments to evaluate the performance of the proposed PTM on a real large-scale dataset of law cases. The experimental results show the superiority of our proposed PTM.展开更多
Model checking evaluates whether a statistical model faithfully captures the underlying data-generating process.Classical tests—such as local-smoothing and empirical-process methods—break down in high dimensions.Mor...Model checking evaluates whether a statistical model faithfully captures the underlying data-generating process.Classical tests—such as local-smoothing and empirical-process methods—break down in high dimensions.More recent approaches use predictiveness comparisons with flexible machine-learning model fitting procedures to yield algorithm-agnostic tests,yet they require large labeled samples.The authors introduce a prediction-powered,semi-supervised framework that:1)Imputes responses for unlabeled data via a pretrained model;2)Corrects imputation bias with a rectifier calibrated on labeled data;3)Adaptively balances these components through a data-driven power-tuning parameter.Building on algorithm-agnostic out-of-sample predictiveness comparisons,the proposed method integrates unlabeled information to enhance power.Theoretical analyses and numerical results demonstrate that the proposed test controls Type I error and substantially improves power over fully supervised counterparts,even under imputation-model misspecification.展开更多
The integrated nested Laplace approximation(INLA)algorithm provides a computationally efficient approach for approximate Bayesian inference,overcoming the limitations of traditional Markov chain Monte Carlo(MCMC)metho...The integrated nested Laplace approximation(INLA)algorithm provides a computationally efficient approach for approximate Bayesian inference,overcoming the limitations of traditional Markov chain Monte Carlo(MCMC)methods.This paper reviews INLA algorithm and provides a systematic review of six key books that explore the theoretical foundations,practical implementations,and diverse applications of INLA.These six books cover spatial and spatio-temporal modelling,general Bayesian inference,SPDE-based spatial analysis,geospatial health data,regression modelling,and dynamic time series.In addition,these books highlight the versatility of INLA method in handling complex models while maintaining high computational efficiency.This paper begins with an introduction to the INLA method and algorithm,followed by a systematic review of six key publications in the field.展开更多
Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ...Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ignoring or mishandling such data gaps can introduce systematic bias into the estimation of target variables for natural resource monitoring.This can lead to cascading errors that propagate through forest and ecosystem management decisions,ultimately hindering progress toward sustainable forest management,biodiversity conservation,and climate change mitigation strategies.This study aims to propose and demonstrate a procedure that employs hybrid estimators to address the limitations of missing remotely sensed data in forest inventory,using Landsat 7 ETM+SLC-off data as an archived source for forest resource monitoring as a case in point.We compared forest inventory estimates from the hybrid estimator with those from a conventional model-based(CMB)estimator using Sentinel-2 data without missing values.Monte Carlo simulations revealed three key findings:(1)The hybrid estimator,leveraging missing-data remote sensing represented by Landsat 7 ETM+SLCoff data,achieved a sampling precision of over 90%,meeting China's national standard for the National Forest Inventory(NFI);(2)The hybrid estimator demonstrated comparable efficiency to the CMB estimator;(3)The uncertainty associated with hybrid estimators was primarily dominated by model parameter estimation,which could be effectively mitigated by slightly increasing the training sample size or refining model specification.Overall,in forest inventory,the hybrid estimator can surmount the limitations posed by missing values in remotely sensed auxiliary data,effectively balancing cost-effectiveness and flexibility.展开更多
AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 201...AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 2018 to 2023.A total of 2591 patients were enrolled,including 197 patients with TAO(case group)and 2394 patients with hyperthyroidism without TAO(control group).Serum lipid parameters,including triglycerides,total cholesterol,high-density lipoprotein(HDL),low-density lipoprotein(LDL),and the HDL/total cholesterol ratio,as well as thyroid function markers,were compared between the two groups.Correlation analyses were performed to evaluate the associations between serum lipid levels and key ocular manifestations of TAO,including exophthalmos degree,clinical activity score,and disease severity.Furthermore,Mendelian randomization(MR)analysis was conducted using genome-wide association study(GWAS)datasets,with hyperthyroidism as the exposure variable and serum lipid parameters as the outcome variables,to infer the causal relationship between hyperthyroidism,lipid metabolism,and TAO progression.RESULTS:The TAO group consisted of 101 males and 96 females,while the hyperthyroidism group included 706 males and 1688 females.Compared with the control group,patients with TAO had significantly higher levels of triglycerides(1.83±1.21 vs 1.40±1.08 mmol/L,P<0.01),total cholesterol,LDL,and HDL.Correlation analysis showed that triglyceride levels were positively correlated with exophthalmos degree,whereas HDL levels were inversely correlated with exophthalmos degree.No significant associations were found between serum lipid levels and clinical activity score(P>0.1).MR analysis confirmed that hyperthyroidism exerted a causal effect in reducing serum triglycerides[inverse-variance weighting odds ratio(OR)=0.035,95%confidence interval(CI):0.01-0.12]and total cholesterol(OR=0.085,95%CI:0.02-0.34),with no evidence of horizontal pleiotropy(MR-PRESSO P>0.05).CONCLUSION:Elevated serum triglyceride levels are an independent risk factor for TAO severity,especially exophthalmos,and triglyceride metabolism is inversely regulated by thyroid function.展开更多
Transformer models face significant computational challenges in private inference(PI).Existing optimization methods often rely on isolated techniques,neglecting joint structural and operational improvements.We propose...Transformer models face significant computational challenges in private inference(PI).Existing optimization methods often rely on isolated techniques,neglecting joint structural and operational improvements.We propose IG-3D,a unified framework that integrates structured compression and operator approximation through accurate importance assessment.Our approach first evaluates attention head importance using Integrated Gradients(IG),offering greater stability and theoretical soundness than gradient-based methods.We then apply a threedimensional optimization:(1)structurally pruning redundant attention heads;(2)replacing Softmax with adaptive polynomial approximation to avoid exponential computations;(3)implementing layer-wise GELU substitution to accommodate different layer characteristics.A joint thresholdmechanism coordinates compression across dimensions under accuracy constraints.Experimental results on the GLUE benchmark show that our method achieves an average 2.9×speedup in inference latency and a 50%reduction in communication cost,while controlling the accuracy loss within 2.3%,demonstrating significant synergistic effects and a superior accuracy-efficiency trade-off compared to single-technique optimization strategies.展开更多
Distributed learning is a well-established method for estimation tasks over extensively distributed datasets.However,non-randomly stored data can introduce bias into local parameter estimates,leading to significant pe...Distributed learning is a well-established method for estimation tasks over extensively distributed datasets.However,non-randomly stored data can introduce bias into local parameter estimates,leading to significant performance degradation in classical distributed algorithms.In this paper,the authors propose a novel Distributed Quasi-Newton Pilot(DQNP)method for distributed learning with non-randomly distributed data.The proposed approach accommodates both randomly and non-randomly distributed data settings and imposes no constraints on the uniformity of local sample sizes.Additionally,it avoids the need to transfer the Hessian matrix or compute its inversion,thereby greatly reducing computational and communication complexity.The authors theoretically demonstrate that the resulting estimator achieves statistical efficiency under mild conditions.Extensive numerical experiments on synthetic and real-world data validate the theoretical findings and illustrate the effectiveness of the proposed method.展开更多
Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association betw...Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.展开更多
Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-h...Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.展开更多
The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pil...The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pillars supporting scientific inference and data-driven decisionmaking,have evolved through the collective wisdom of generations of statisticians.This special issue,titled"Recent Developments in Dimension Reduction and Model Checking for regressions",not only aims to showcase cutting-edge advances in the field but also carries a distinct sense of academic homage to honor the groundbreaking and enduring contributions of Professor Lixing Zhu,a leading scholar whose work has profoundly shaped both areas.展开更多
Interferon-related genes are involved in antiviral responses,inflammation,and immunity,which are closely related to sepsis-associated acute respiratory distress syndrome(ARDS).We analyzed 1972 participants with genoty...Interferon-related genes are involved in antiviral responses,inflammation,and immunity,which are closely related to sepsis-associated acute respiratory distress syndrome(ARDS).We analyzed 1972 participants with genotype data and 681 participants with gene expression data from the Molecular Epidemiology of ARDS(MEARDS),the Molecular Epidemiology of Sepsis in the ICU(MESSI),and the Molecular Diagnosis and Risk Stratification of Sepsis(MARS)cohorts in a three-step study focusing on sepsis-associated ARDS and sepsis-only controls.First,we identified and validated interferon-related genes associated with sepsis-associated ARDS risk using genetically regulated gene expression(GReX).Second,we examined the association of the confirmed gene(interferon regulatory factor 1,IRF1)with ARDS risk and survival and conducted a mediation analysis.Through discovery and validation,we found that the GReX of IRF1 was associated with ARDS risk(odds ratio[OR_(MEARDS)]=0.84,P=0.008;OR_(MESSI)=0.83,P=0.034).Furthermore,individual-level measured IRF1 expression was associated with reduced ARDS risk(OR=0.58,P=8.67×10^(-4)),and improved overall survival in ARDS patients(hazard ratio[HR_(28-day)]=0.49,P=0.009)and sepsis patients(HR_(28-day)=0.76,P=0.008).Mediation analysis revealed that IRF1 may enhance immune function by regulating the major histocompatibility complex,including HLA-F,which mediated more than 70%of protective effects of IRF1 on ARDS.The findings were validated by in vitro biological experiments including time-series infection dynamics,overexpression,knockout,and chromatin immunoprecipitation sequencing.Early prophylactic interventions to activate IRF1 in sepsis patients,thereby regulating HLA-F,may reduce the risk of ARDS and mortality,especially in severely ill patients.展开更多
Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional mult...Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional multi-branch convolutional networks suffer from computational redundancy,high memory access overhead,and inefficient branch fusion.Therefore,this paper proposes an adaptive multi-branch convolutional module(AMBC)that integrates software-hardware co-optimization.During training,the learnable fusion coefficients are introduced to enable adaptive fusion of multi-scale features,while in the inference phase,the multiple branches and their normalization parameters are merged with the fusion coefficients into a single 3×3 convolutional kernel through operator fusion.On the SIREA-288 reconfigurable platform,compared with unoptimized multi-branch networks,the proposed AMBC reduces external memory accesses by 47.91%and inference latency by 47.20%,achieving a 1.90×speedup.This approach maximizes the utilization of the reconfigurable logic while minimizing both reconfiguration and data-movement overheads in edge inference.展开更多
文摘Methods and approaches are discussed that identify and filter off affecting factors (noise) above primary signals,based on the Adaptive-Nework-Based Fuzzy Inference System. Influences of the zonal winds in equatorial eastern and middle/western Pacific on the SSTA in the equatorial region and their contribution to the latter are diagnosed and verified with observations of a number of significant El Nio and La Nia episodes. New viewpoints are propsed. The methods of wavelet decomposition and reconstruction are used to build a predictive model based on independent domains of frequency,which shows some advantages in composite prediction and prediction validity.The methods presented above are of non-linearity, error-allowing and auto-adaptive/learning, in addition to rapid and easy access,illustrative and quantitative presentation,and analyzed results that agree generally with facts. They are useful in diagnosing and predicting the El Nio and La Nia problems that are just roughly described in dynamics.
基金The project entitled "Urban Active Fault Surveying Project"(143623) funded by the National Development and Roform Commission of China"Active Faults Exploration and Seismic Hazard Assessment in Zibo City"(SD1501) funded by the Department of Science & Technology of Shangdong Province,China
文摘Electrical resistivity tomography (ERT) has been used to experimentally detect shallow buried faults in urban areas in the past a few years, with some progress and experience obtained. According to the results from Olympic Park, Beijing, Shandong Province, Gansu Province and Shanxi Province, we have generalized the method and procedure for inferring the discontinuity of electrical structures (DES) indicating a buried fault in urban areas from resistivity tomograms and its typical electrical features. In general, the layered feature of the electrical structure is first analyzed to preliminarily define whether or not a DES exists in the target area. Resistivity contours in resistivity tomograms are then analyzed from the deep to the shallow. If they extend upward from the deep to the shallow and shape into an integral dislocation, sharp flexure (convergence) or gradient zone, it is inferred that the DES exists, indicating a buried fault. Finally, horizontal tracing is be carried out to define the trend of the DES. The DES can be divided into three types-type AB, ABA and AC. In the present paper, the Zhangdian-Renhe fault system in Zibo city is used as an example to illustrate how to use the method to infer the location and spatial extension of a target fault. Geologic drilling holes are placed based on our research results, and the drilling logs testify that our results are correct. However, the method of this paper is not exclusive and inflexible. It is expected to provide reference and assistance for inferring the shallow buried faults in urban areas from resistivity tomograms in the future.
文摘The research purpose is invention (construction) of a formal logical inference of the Law of Conservation of Energy within a logically formalized axiomatic epistemology-and-axiology theory Sigma from a precisely defined assumption of a-priori-ness of knowledge. For realizing this aim, the following work has been done: 1) a two-valued algebraic system of formal axiology has been defined precisely and applied to proper-philosophy of physics, namely, to an almost unknown (not-recognized) formal-axiological aspect of the physical law of conservation of energy;2) the formal axiomatic epistemology-and-axiology theory Sigma has been defined precisely and applied to proper-physics for realizing the above-indicated purpose. Thus, a discrete mathematical model of relationship between philosophy of physics and universal epistemology united with formal axiology has been constructed. Results: 1) By accurate computing relevant compositions of evaluation-functions within the discrete mathematical model, it is demonstrated that a formal-axiological analog of the great conservation law of proper physics is a formal-axiological law of two-valued algebra of metaphysics. (A precise algorithmic definition of the unhabitual (not-well-known) notion “formal-axiological law of algebra of metaphysics” is given.) 2) The hitherto never published significantly new nontrivial scientific result of investigation presented in this article is a formal logical inference of the law of conservation of energy within the formal axiomatic theory Sigma from conjunction of the formal-axiological analog of the law of conservation of energy and the assumption of a-priori-ness of knowledge.
基金supported by the National Natural Science Foundation of China(61471343)the National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2014BAK14B03)
基金the China Postdoctoral Science Foundation(2023TQ0089)the National Natural Science Foundation of China(Nos.62072465,62172155)the Science and Technology Innovation Program of Hunan Province(Nos.2022RC3061,2023RC3027).
文摘The global Internet is a complex network of interconnected autonomous systems(ASes).Understanding Internet inter-domain path information is crucial for understanding,managing,and improving the Internet.The path information can also help protect user privacy and security.However,due to the complicated and heterogeneous structure of the Internet,path information is not publicly available.Obtaining path information is challenging due to the limited measurement probes and collectors.Therefore,inferring Internet inter-domain paths from the limited data is a supplementary approach to measure Internet inter-domain paths.The purpose of this survey is to provide an overview of techniques that have been conducted to infer Internet inter-domain paths from 2005 to 2023 and present the main lessons from these studies.To this end,we summarize the inter-domain path inference techniques based on the granularity of the paths,for each method,we describe the data sources,the key ideas,the advantages,and the limitations.To help readers understand the path inference techniques,we also summarize the background techniques for path inference,such as techniques to measure the Internet,infer AS relationships,resolve aliases,and map IP addresses to ASes.A case study of the existing techniques is also presented to show the real-world applications of inter-domain path inference.Additionally,we discuss the challenges and opportunities in inferring Internet inter-domain paths,the drawbacks of the state-of-the-art techniques,and the future directions.
文摘Mobile phones are becoming a primary platform for information access. A major aspect of ubiquitous computing is context-aware applications which collect information about the environment that the user is in and use this information to provide better service and improve user experience. Location awareness makes certain applications possible, e.g., recommending nearby businesses and tracking estimated routes. An Android application is able to collect useful Wi-Fi information without registering a location listener with a network-based provider. We passively collected the data of the IDs of Wi-Fi access points and the received signal strengths. We developed and implemented an algorithm to analyse the data;and designed heuristics to infer the location of the device over time—all without ever connecting to the network thus maximally preserving the privacy of the user.
文摘Human Immunodeficiency Virus (HIV) dynamics in Africa are purely characterised by sparse sampling of DNA sequences for individuals who are infected. There are some sub-groups that are more at risk than the general population. These sub-groups have higher infectivity rates. We came up with a likelihood inference model of multi-type birth-death process that can be used to make inference for HIV epidemic in an African setting. We employ a likelihood inference that incorporates a probability of removal from infectious pool in the model. We have simulated trees and made parameter inference on the simulated trees as well as investigating whether the model distinguishes between heterogeneous and homogeneous dynamics. The model makes fairly good parameter inference. It distinguishes between heterogeneous and homogeneous dynamics well. Parameter estimation was also performed under sparse sampling scenario. We investigated whether trees obtained from a structured population are more balanced than those from a non-structured host population using tree statistics that measure tree balance and imbalance. Trees from non-structured population were more balanced basing on Colless and Sackin indices.
基金This work is supported in part by the National Key Research and Development Program of China under Grant No. 2016YFC0800805 and the National Natural Science Foundation of China under Grant No. 61690201.
文摘Deciding the penalty of a law case has always been a complex process, which may involve with much coordination. Despite the judicial study based on the rules and conditions, artificial intelligence and machine learning has rarely been used to study the problem of penalty inferring, leaving the large amount of law cases as well as various factors among them untouched. This paper aims to incorporate the state-of-the-art artificial intelligence methods to exploit to what extent this problem can be alleviated. We first analyze 145 000 law cases and observe that there are two sorts of labels, temporal labels and spatial labels, which have unique characteristics. Temporal labels and spatial labels tend to converge towards the final penalty, on condition that the cases are of the same category. In light of this, we propose a latent-class probabilistic generative model, namely Penalty Topic Model （PTM）, to infer the topic of law cases, and the temporal and spatial patterns of topics embedded in the case judgment. Then, the learnt knowledge is utilized to automatically cluster all cases accordingly in a unified way. We conduct extensive experiments to evaluate the performance of the proposed PTM on a real large-scale dataset of law cases. The experimental results show the superiority of our proposed PTM.
基金supported by the National Key R&D Program of China under Grant Nos.2022YFA1003800 and 2022YFA1003703the National Natural Science Foundation of China under Grant Nos.12531011,12231011 and 12471255+3 种基金the Natural Science Foundation of Shanghai under Grant No.23ZR1419400the Fundamental Research Funds for the Central Universities under Grant No.63253110supported by China Postdoctoral Science Foundation General Funding Program under Grant No.2025M7730792025 Annual Planning Project of the Commerce Statistical Society of China under Grant No.2025STY115。
文摘Model checking evaluates whether a statistical model faithfully captures the underlying data-generating process.Classical tests—such as local-smoothing and empirical-process methods—break down in high dimensions.More recent approaches use predictiveness comparisons with flexible machine-learning model fitting procedures to yield algorithm-agnostic tests,yet they require large labeled samples.The authors introduce a prediction-powered,semi-supervised framework that:1)Imputes responses for unlabeled data via a pretrained model;2)Corrects imputation bias with a rectifier calibrated on labeled data;3)Adaptively balances these components through a data-driven power-tuning parameter.Building on algorithm-agnostic out-of-sample predictiveness comparisons,the proposed method integrates unlabeled information to enhance power.Theoretical analyses and numerical results demonstrate that the proposed test controls Type I error and substantially improves power over fully supervised counterparts,even under imputation-model misspecification.
基金supported by the National Natural Science Foundation of China[grant number 12001266]the Humanities and Social Science Projects ofMinistry of Education of China[grant number 19YJCZH166]supported by the National Natural Science Foundation of China[grant numbers 12271168 and 12531013].
文摘The integrated nested Laplace approximation(INLA)algorithm provides a computationally efficient approach for approximate Bayesian inference,overcoming the limitations of traditional Markov chain Monte Carlo(MCMC)methods.This paper reviews INLA algorithm and provides a systematic review of six key books that explore the theoretical foundations,practical implementations,and diverse applications of INLA.These six books cover spatial and spatio-temporal modelling,general Bayesian inference,SPDE-based spatial analysis,geospatial health data,regression modelling,and dynamic time series.In addition,these books highlight the versatility of INLA method in handling complex models while maintaining high computational efficiency.This paper begins with an introduction to the INLA method and algorithm,followed by a systematic review of six key publications in the field.
基金supported by the National Key R&D Program of China(No.2023YFF1304002-05)the National Social Science Fund of China(No.22BTJ005)the National Natural Science Foundation of China(No.32572049)。
文摘Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ignoring or mishandling such data gaps can introduce systematic bias into the estimation of target variables for natural resource monitoring.This can lead to cascading errors that propagate through forest and ecosystem management decisions,ultimately hindering progress toward sustainable forest management,biodiversity conservation,and climate change mitigation strategies.This study aims to propose and demonstrate a procedure that employs hybrid estimators to address the limitations of missing remotely sensed data in forest inventory,using Landsat 7 ETM+SLC-off data as an archived source for forest resource monitoring as a case in point.We compared forest inventory estimates from the hybrid estimator with those from a conventional model-based(CMB)estimator using Sentinel-2 data without missing values.Monte Carlo simulations revealed three key findings:(1)The hybrid estimator,leveraging missing-data remote sensing represented by Landsat 7 ETM+SLCoff data,achieved a sampling precision of over 90%,meeting China's national standard for the National Forest Inventory(NFI);(2)The hybrid estimator demonstrated comparable efficiency to the CMB estimator;(3)The uncertainty associated with hybrid estimators was primarily dominated by model parameter estimation,which could be effectively mitigated by slightly increasing the training sample size or refining model specification.Overall,in forest inventory,the hybrid estimator can surmount the limitations posed by missing values in remotely sensed auxiliary data,effectively balancing cost-effectiveness and flexibility.
基金Supported by the National Natural Science Foundation of China(No.82371104)the Natural Science Foundation of Hunan Province(No.2023JJ30851).
文摘AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 2018 to 2023.A total of 2591 patients were enrolled,including 197 patients with TAO(case group)and 2394 patients with hyperthyroidism without TAO(control group).Serum lipid parameters,including triglycerides,total cholesterol,high-density lipoprotein(HDL),low-density lipoprotein(LDL),and the HDL/total cholesterol ratio,as well as thyroid function markers,were compared between the two groups.Correlation analyses were performed to evaluate the associations between serum lipid levels and key ocular manifestations of TAO,including exophthalmos degree,clinical activity score,and disease severity.Furthermore,Mendelian randomization(MR)analysis was conducted using genome-wide association study(GWAS)datasets,with hyperthyroidism as the exposure variable and serum lipid parameters as the outcome variables,to infer the causal relationship between hyperthyroidism,lipid metabolism,and TAO progression.RESULTS:The TAO group consisted of 101 males and 96 females,while the hyperthyroidism group included 706 males and 1688 females.Compared with the control group,patients with TAO had significantly higher levels of triglycerides(1.83±1.21 vs 1.40±1.08 mmol/L,P<0.01),total cholesterol,LDL,and HDL.Correlation analysis showed that triglyceride levels were positively correlated with exophthalmos degree,whereas HDL levels were inversely correlated with exophthalmos degree.No significant associations were found between serum lipid levels and clinical activity score(P>0.1).MR analysis confirmed that hyperthyroidism exerted a causal effect in reducing serum triglycerides[inverse-variance weighting odds ratio(OR)=0.035,95%confidence interval(CI):0.01-0.12]and total cholesterol(OR=0.085,95%CI:0.02-0.34),with no evidence of horizontal pleiotropy(MR-PRESSO P>0.05).CONCLUSION:Elevated serum triglyceride levels are an independent risk factor for TAO severity,especially exophthalmos,and triglyceride metabolism is inversely regulated by thyroid function.
文摘Transformer models face significant computational challenges in private inference(PI).Existing optimization methods often rely on isolated techniques,neglecting joint structural and operational improvements.We propose IG-3D,a unified framework that integrates structured compression and operator approximation through accurate importance assessment.Our approach first evaluates attention head importance using Integrated Gradients(IG),offering greater stability and theoretical soundness than gradient-based methods.We then apply a threedimensional optimization:(1)structurally pruning redundant attention heads;(2)replacing Softmax with adaptive polynomial approximation to avoid exponential computations;(3)implementing layer-wise GELU substitution to accommodate different layer characteristics.A joint thresholdmechanism coordinates compression across dimensions under accuracy constraints.Experimental results on the GLUE benchmark show that our method achieves an average 2.9×speedup in inference latency and a 50%reduction in communication cost,while controlling the accuracy loss within 2.3%,demonstrating significant synergistic effects and a superior accuracy-efficiency trade-off compared to single-technique optimization strategies.
基金supported by the National Natural Science Foundation of China under Grant No.12271034the Open Fund Project of Key Laboratory of Market Regulation under Grant No.2023SYSKF02003。
文摘Distributed learning is a well-established method for estimation tasks over extensively distributed datasets.However,non-randomly stored data can introduce bias into local parameter estimates,leading to significant performance degradation in classical distributed algorithms.In this paper,the authors propose a novel Distributed Quasi-Newton Pilot(DQNP)method for distributed learning with non-randomly distributed data.The proposed approach accommodates both randomly and non-randomly distributed data settings and imposes no constraints on the uniformity of local sample sizes.Additionally,it avoids the need to transfer the Hessian matrix or compute its inversion,thereby greatly reducing computational and communication complexity.The authors theoretically demonstrate that the resulting estimator achieves statistical efficiency under mild conditions.Extensive numerical experiments on synthetic and real-world data validate the theoretical findings and illustrate the effectiveness of the proposed method.
基金supported by the Yanzhao Gold Talent Project of Hebei Province(NO.HJZD202506)。
文摘Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00518960)in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00563192).
文摘Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.
文摘The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pillars supporting scientific inference and data-driven decisionmaking,have evolved through the collective wisdom of generations of statisticians.This special issue,titled"Recent Developments in Dimension Reduction and Model Checking for regressions",not only aims to showcase cutting-edge advances in the field but also carries a distinct sense of academic homage to honor the groundbreaking and enduring contributions of Professor Lixing Zhu,a leading scholar whose work has profoundly shaped both areas.
基金supported by the National Natural Science Foundation of China(Grant No.82220108002 to F.C.and Grant No.82273737 to R.Z.)the U.S.National Institutes of Health(Grant Nos.CA209414,HL060710,and ES000002 to D.C.C.,Grant Nos.CA209414 and CA249096 to Y.L.)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)supported by the Qing Lan Project of the Higher Education Institutions of Jiangsu Province and the Outstanding Young Level Academic Leadership Training Program of Nanjing Medical University.
文摘Interferon-related genes are involved in antiviral responses,inflammation,and immunity,which are closely related to sepsis-associated acute respiratory distress syndrome(ARDS).We analyzed 1972 participants with genotype data and 681 participants with gene expression data from the Molecular Epidemiology of ARDS(MEARDS),the Molecular Epidemiology of Sepsis in the ICU(MESSI),and the Molecular Diagnosis and Risk Stratification of Sepsis(MARS)cohorts in a three-step study focusing on sepsis-associated ARDS and sepsis-only controls.First,we identified and validated interferon-related genes associated with sepsis-associated ARDS risk using genetically regulated gene expression(GReX).Second,we examined the association of the confirmed gene(interferon regulatory factor 1,IRF1)with ARDS risk and survival and conducted a mediation analysis.Through discovery and validation,we found that the GReX of IRF1 was associated with ARDS risk(odds ratio[OR_(MEARDS)]=0.84,P=0.008;OR_(MESSI)=0.83,P=0.034).Furthermore,individual-level measured IRF1 expression was associated with reduced ARDS risk(OR=0.58,P=8.67×10^(-4)),and improved overall survival in ARDS patients(hazard ratio[HR_(28-day)]=0.49,P=0.009)and sepsis patients(HR_(28-day)=0.76,P=0.008).Mediation analysis revealed that IRF1 may enhance immune function by regulating the major histocompatibility complex,including HLA-F,which mediated more than 70%of protective effects of IRF1 on ARDS.The findings were validated by in vitro biological experiments including time-series infection dynamics,overexpression,knockout,and chromatin immunoprecipitation sequencing.Early prophylactic interventions to activate IRF1 in sepsis patients,thereby regulating HLA-F,may reduce the risk of ARDS and mortality,especially in severely ill patients.
基金Supported by the National Science and Technology Major Project of China(2022ZD0119005)the Natural Science Project of Shaanxi Province(2025JC-YBMS-754,2024JC-YBMS-539)。
文摘Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional multi-branch convolutional networks suffer from computational redundancy,high memory access overhead,and inefficient branch fusion.Therefore,this paper proposes an adaptive multi-branch convolutional module(AMBC)that integrates software-hardware co-optimization.During training,the learnable fusion coefficients are introduced to enable adaptive fusion of multi-scale features,while in the inference phase,the multiple branches and their normalization parameters are merged with the fusion coefficients into a single 3×3 convolutional kernel through operator fusion.On the SIREA-288 reconfigurable platform,compared with unoptimized multi-branch networks,the proposed AMBC reduces external memory accesses by 47.91%and inference latency by 47.20%,achieving a 1.90×speedup.This approach maximizes the utilization of the reconfigurable logic while minimizing both reconfiguration and data-movement overheads in edge inference.
