Using Non-Additive Measure for Optimization-Based Nonlinear Classification

Over the past few decades, numerous optimization-based methods have been proposed for solving the classification problem in data mining. Classic optimization-based methods do not consider attribute interactions toward classification. Thus, a novel learning machine is needed to provide a better understanding on the nature of classification when the interaction among contributions from various attributes cannot be ignored. The interactions can be described by a non-additive measure while the Choquet integral can serve as the mathematical tool to aggregate the values of attributes and the corresponding values of a non-additive measure. As a main part of this research, a new nonlinear classification method with non-additive measures is proposed. Experimental results show that applying non-additive measures on the classic optimization-based models improves the classification robustness and accuracy compared with some popular classification methods. In addition, motivated by well-known Support Vector Machine approach, we transform the primal optimization-based nonlinear classification model with the signed non-additive measure into its dual form by applying Lagrangian optimization theory and Wolfes dual programming theory. As a result, 2 – 1 parameters of the signed non-additive measure can now be approximated with m (number of records) Lagrangian multipliers by applying necessary conditions of the primal classification problem to be optimal. This method of parameter approximation is a breakthrough for solving a non-additive measure practically when there are a relatively small number of training cases available (). Furthermore, the kernel-based learning method engages the nonlinear classifiers to achieve better classification accuracy. The research produces practically deliverable nonlinear models with the non-additive measure for classification problem in data mining when interactions among attributes are considered

The sources and transport pathways of sediment in the northern Ninety-east Ridge of the India Ocean over the last 35000 years

The Ninety-east Ridge (NER) is located in the southern Bay of Bengal in the northeast Indian Ocean and is composed of pelagic and hemipelagic sediments. In addition to contributions from marine biomass, the ridge also contains terrestrially sourced sedimentary material. However, considerable disagreement remains regarding the origin of these terrestrial materials and transport pathways. This paper discusses the collection of seafloor surface sediments and three sediment cores recovered from the northern region of the NER, as well as the analysis of clay minerals, Sr-Nd isotopes, and sediment grain size. The ages of the three core sediments are constrained by AMS 14C dating to better establish the source and transport pathways of the terrestrial materials within NER sediments over the past 35000 years. The research results show that the Qinghai-Tibet Plateau is the predominate source of terrigenous sedimentary material in the NER. In the plateau, the crustal materials were weathered and stripped and then transported to the Andaman Sea via the Irrawaddy River. From there, the material was transported westward by monsoon-driven circulation to the northernmost part of the NER before being transported to the south for final deposition. This transport mode has changed little over the past 35000 years. However, during the rapidly changing climate of the Younger Dryas (12.9~11.5 ka BP), there were some variations in the input amount, grain size, and Sr-Nd isotope value of the source material. The above conclusions are significant for re-evaluating the source of terrigenous sediments, the temporal and spatial changes in transport modes, and the sensitivity of the NER to climatic shifts

Computation offloading in blockchain-enabled MCS systems : A scalable deep reinforcement learning approach

In Mobile Crowdsensing (MCS) systems, cloud service providers (CSPs) pay for and analyze the sensing data collected by mobile devices (MDs) to enhance the Quality-of-Service (QoS). Therefore, it is necessary to guarantee security when CSPs and users conduct transactions. Blockchain can secure transactions between two parties by using the Proof-of-Work (PoW) to confirm transactions and add new blocks to the chain. Nevertheless, the complex PoW seriously hinders applying Blockchain into MCS since MDs are equipped with limited resources. To address these challenges, we first design a new consortium blockchain framework for MCS, aiming to assure high reliability in complex environments, where a novel Credit-based Proof-of-Work (C-PoW) algorithm is developed to relieve the complexity of PoW while keeping the reliability of blockchain. Next, we propose a new scalable Deep Reinforcement learning based Computation Offloading (DRCO) method to handle the computation-intensive tasks of C-PoW. By combining Proximal Policy Optimization (PPO) and Differentiable Neural Computer (DNC), the DRCO can efficiently make the optimal/near-optimal offloading decisions for C-PoW tasks in blockchain-enabled MCS systems. Extensive experiments demonstrate that the DRCO reaches a lower total cost (weighted sum of latency and power consumption) than state-of-the-art methods under various scenarios

Edge-Computing-Based Channel Allocation for Deadline-Driven IoT Networks

Multichannel communication is an important means to improve the reliability of low-power Internet-of-Things (IoT) networks. Typically, data transmissions in IoT networks are often required to be delivered before a given deadline, making deadline-driven channel allocation an essential task. The existing works on time-division multiple access often fail to establish channel schedules to meet the deadline requirement, as they often assume that transmissions can be successful within one transmission slot. Besides, the allocation and link estimation incur considerable overhead for the IoT nodes. In this article, we propose an edge-based channel allocation (ECA) for unreliable IoT networks. In ECA, we explicitly consider the impact of allocation sequences and employ a recurrent-neural-network-based channel estimation scheme. We utilize link quality and retransmission opportunities to maximize the packet delivery ratio before deadline. The allocation algorithms are executed on edge servers such that: 1) the channel allocation can be updated more frequently to deal with the wireless dynamics; 2) the allocation results can be obtained in real time; and 3) channel estimation can be more accurate. Extensive evaluation results show that ECA can significantly improve the reliability of deadline-driven IoT networks

Tectonic dynamics of the Zhongjiannan Basin in the western South China Sea since the late Miocene

The Zhongjiannan Basin is located west of the South China Sea (SCS) and was affected by the left-lateral strike-slip of the Red River Fault (RRF), the West Edge Fault of the South China Sea (WEFSCS) and the continental rifting of the South China Sea in the early Cenozoic. The Zhongjiannan Basin formed in a strike-pull basin with an S‒N distribution. During the middle Miocene, the sea spreading of the SCS stopped, but the dynamic mechanism of the Zhongjiannan Basin, which controlled the sedimentary and the structural evolution after the late Miocene, remains unclear. In this paper, through the segment interpretation of the latest seismic section in the Zhongjiannan Basin, we conduct a comparative study of the sedimentary structure in the southern and northern Zhongjiannan Basin since the late Miocene. Combined with the regional tectonic dynamics analysis, we propose that the sedimentary and structural evolution of the Zhongjiannan Basin since the late Miocene was mainly controlled by residual magmatic activity in the Southwest Subbasin (SWSB) after expansion stopped, and the compressional structure stress field weakened gradually from south to north. The compressional tectonic stress field from north to south was formed in the northern basin under the dextral strike-slip movement of the RRF. The sedimentary and structural environment was relatively stable in the middle basin. Therefore, the sedimentary-structure evolution of the Zhongjiannan Basin since the late Miocene was controlled by the two different structural stress fields. The above knowledge not only has guiding significance for oil and gas exploration in the Zhongjiannan Basin but also provides a reference for studying the initiation time of dextral strike-slip along the Red River Fault Zone, as well as the junction position between the RRF and the WEFSCS

Identification and validation of PCSK9 as a prognostic and immune-related influencing factor in tumorigenesis: a pan-cancer analysis

IntroductionProprotein convertase subtilisin/kexin-9 (PCSK9) has been primarily studied in the cardiovascular field however, its role in cancer pathophysiology remains incompletely defined. Recently, a pivotal role for PCSK9 in cancer immunotherapy was proposed based on the finding that PCSK9 inhibition was associated with enhancing the antigen presentation efficacy of target programmed cell death-1 (PD-1). Herein, we provide results of a comprehensive pan-cancer analysis of PCSK9 that assessed its prognostic and immunological functions in cancer.MethodsUsing a variety of available online cancer-related databases including TIMER, cBioPortal, and GEPIA, we identified the abnormal expression of PCSK9 and its potential clinical associations in diverse cancer types including liver, brain and lung. We also validated its role in progression-free survival (PFS) and immune infiltration in neuroblastoma.ResultsOverall, the pan-cancer survival analysis revealed an association between dysregulated PCSK9 and poor clinical outcomes in various cancer types. Specifically, PCSK9 was extensively genetically altered across most cancer types and was consistently found in different tumor types and substages when compared with adjacent normal tissues. Thus, aberrant DNA methylation may be responsible for PCSK9 expression in many cancer types. Focusing on liver hepatocellular carcinoma (LIHC), we found that PCSK9 expression correlated with clinicopathological characteristics following stratified prognostic analyses. PCSK9 expression was significantly associated with immune infiltrate since specific markers of CD8+ T cells, macrophage polarization, and exhausted T cells exhibited different PCSK9-related immune infiltration patterns in LIHC and lung squamous cell carcinoma. In addition, PCSK9 was connected with resistance of drugs such as erlotinib and docetaxel. Finally, we validated PCSK9 expression in clinical neuroblastoma samples and concluded that PCSK9 appeared to correlate with a poor PFS and natural killer cell infiltration in neuroblastoma patients.ConclusionPCSK9 could serve as a robust prognostic pan-cancer biomarker given its correlation with immune infiltrates in different cancer types, thus potentially highlighting a new direction for targeted clinical therapy of cancers

Runtime Analysis of Somatic Contiguous Hypermutation Operators in MOEA/D Framework

Somatic contiguous hypermutation (CHM) operators are important variation operators in artificial immune systems. The few existing theoretical studies are only concerned with understanding the optimization behavior of CHM operators on solving single-objective optimization problems. The MOEA/D framework is one of the most popular strategies for solving multi-objective optimization problems (MOPs). In this paper, we present a runtime analysis of using two CHM operators in MOEA/D framework for solving five benchmark MOPs, including four bi-objective and one many-objective problems. Our analyses show that the expected runtimes of CHM operators on the four bi-objective problems are better than or as good as that of the well-studied standard bit mutation operator. Moreover, using CHM operators in MOEA/D framework can improve the best known upper bound on the many-objective problem by a factor of n. This paper provides insight into understanding the optimization behavior of CHM operators in the well-known MOEA/D framework, and indicates that using the CHM operator in MOEA/D framework is a promising method for handling MOPs

Study of stability against deep sliding of gravity dam based on JC method

In this study, stability against deep sliding of gravity dam is analysed in order to provide guidance for ensuring stability against sliding of gravity dam during the construction and operation periods. Based on checking-point method (JC method), the study proposes an analysis model for stability against deep sliding of gravity dams. In addition, the rigid body limit equilibrium method is utilized to construct the limit state function of deep anti-sliding stability. With sliding plane anti-shear friction coefficient and anti-shear cohesion calculated as random variables, safety degree of stability against deep sliding is analysed. With JC method employed, the calculation program is compiled to calculate the stability against deep sliding of gravity dam and analyse the non-overflow section of gravity dam under normal storage level. According to calculation program, the reliability index β of stability against deep sliding of gravity dam is 4.36, and the failure risk Pf is approximately zero. Additionally, based on the traditional rigid body limit equilibrium method, the safety factor K is 3.23, which meets the requirements of the design specification. According to the above methods, stability against deep sliding of the dam section meets the requirement. In conclusion, JC method provides a calculation and analysis model for gravity dam’s stability against deep sliding, serving as references in the design of gravity dams based on reliability theory