Evaluating Traffic Congestion Using the Traffic Occupancy and Speed Distribution Relationship: An Application of Bayesian Dirichlet Process Mixtures of Generalized Linear Model

Accurate classification and prediction of future traffic conditions are essential for developing effective strategies for congestion mitigation on the highway systems. Speed distribution is one of the traffic stream parameters, which has been used to quantify the traffic conditions. Previous studies have shown that multi-modal probability distribution of speeds gives excellent results when simultaneously evaluating congested and free-flow traffic conditions. However, most of these previous analytical studies do not incorporate the influencing factors in characterizing these conditions. This study evaluates the impact of traffic occupancy on the multi-state speed distribution using the Bayesian Dirichlet Process Mixtures of Generalized Linear Models (DPM-GLM). Further, the study estimates the speed cut-point values of traffic states, which separate them into homogeneous groups using Bayesian change-point detection (BCD) technique. The study used 2015 archived one-year traffic data collected on Florida’s Interstate 295 freeway corridor. Information criteria results revealed three traffic states, which were identified as free-flow, transitional flow condition (congestion onset/offset), and the congested condition. The findings of the DPM-GLM indicated that in all estimated states, the traffic speed decreases when traffic occupancy increases. Comparison of the influence of traffic occupancy between traffic states showed that traffic occupancy has more impact on the free-flow and the congested state than on the transitional flow condition. With respect to estimating the threshold speed value, the results of the BCD model revealed promising findings in characterizing levels of traffic congestion.

A Multi-Task Motion Generation Model that Fuses a Discriminator and a Generator

The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spatial-temporal structures,and the deep learning model can fully describe the potential semantic structure of human motion.To improve the authenticity of the generated human motion sequences,we propose a multi-task motion generation model that consists of a discriminator and a generator.The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees.And target motion sequences are created with these styles by the generator.Unlike traditional related works,our model can handle multiple tasks,such as identifying styles and generating data.In addition,by extracting 17 crucial joints from 29 human joints,our model avoids data redundancy and improves the accuracy of model recognition.The experimental results show that the discriminator of the model can effectively recognize diversified movements,and the generated data can correctly fit the actual data.The combination of discriminator and generator solves the problem of low reuse rate of motion data,and the generated motion sequences are more suitable for actual movement.

GENERALIZED SIMULATION MODEL FOR MILLED SURFACE TOPOGRAPHY-APPLICATION TO PERIPHERAL MILLING

Based on analyzing various factors influencing milled surface topography, firstly, a generalized model for milled surface topography is proposed. Secondly, using the principles of transformation matrix and vector operation, the trajectory equation of cutting edge relative to workpiece is derived. Then, a three dimensional topography simulation algorithm is constructed through dividing the workpiece into regular grids. Finally, taking the peripheral milling process as an example, the generalized model is simplified, and the corresponding simulation examples are given. The results indicate that it is very efficient for the generalized model to be used to analyze and simulate the peripherally milled surface topography.

ARMA–GARCH model with fractional generalized hyperbolic innovations

In this study,a multivariate ARMA–GARCH model with fractional generalized hyperbolic innovations exhibiting fat-tail,volatility clustering,and long-range dependence properties is introduced.To define the fractional generalized hyperbolic process,the non-fractional variant is derived by subordinating time-changed Brownian motion to the generalized inverse Gaussian process,and thereafter,the fractional generalized hyperbolic process is obtained using the Volterra kernel.Based on the ARMA–GARCH model with standard normal innovations,the parameters are estimated for the high-frequency returns of six U.S.stocks.Subsequently,the residuals extracted from the estimated ARMA–GARCH parameters are fitted to the fractional and non-fractional generalized hyperbolic processes.The results show that the fractional generalized hyperbolic process performs better in describing the behavior of the residual process of high-frequency returns than the non-fractional processes considered in this study.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Generalized Predictive Control with Online Least Squares Support Vector Machines

这份报纸基于能有效地处理非线性的系统的联机最少的广场支持向量机器(LS-SVM ) 建议一个实际概括预兆的控制(GPC ) 算法。在每个采样时期，算法递归地由增加新数据对并且在实时性质上从考虑删除最不重要的修改模型。删除的数据对被 lagrange 的绝对值从最后一个采样时期更多样地决定。当增加新数据对并且删除存在的时，纸给模型参数的递归的算法分别地，一个大矩阵的倒置被避免，存储器能被算法完全控制。非线性的 LS-SVM 模型在每个采样时期在 GPC 算法被使用。抵销过程的 pH 上的概括预兆的控制的实验显示出建议算法的有效性和实物。

Optimization of a precise integration method for seismic modeling based on graphic processing unit

General purpose graphic processing unit （GPU） calculation technology is gradually widely used in various fields. Its mode of single instruction, multiple threads is capable of seismic numerical simulation which has a huge quantity of data and calculation steps. In this study, we introduce a GPU-based parallel calculation method of a precise integration method （PIM） for seismic forward modeling. Compared with CPU single-core calculation, GPU parallel calculating perfectly keeps the features of PIM, which has small bandwidth, high accuracy and capability of modeling complex substructures, and GPU calculation brings high computational efficiency, which means that high-performing GPU parallel calculation can make seismic forward modeling closer to real seismic records.

Grey GRM(1, 1) Model Based on Reciprocal Accumulated Generating and Its Application

Aiming the problem of low accuracy during establishing grey model in which monotonically decreasing sequence data and traditional modeling methods are used, this paper applied the reciprocal accumulated generating and the approach optimizing grey derivative which is based on three points to deduce the calculation formulas for model parameters, established grey GRM(1, 1) model based on reciprocal accumulated generating. It provides a new method for the grey modeling. The example validates the practicability and reliability of the proposed model.

Quantum Generative Model with Variable-Depth Circuit

In recent years,an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm.The hybrid quantum-classical framework,which is constructed by a variational quantum circuit(VQC)and an optimizer,plays a key role in the latest quantum machine learning studies.Nevertheless,in these hybrid-framework-based quantum machine learning models,the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems.There are also few studies focused on comparing the performance of quantum generative models with different loss functions.In this study,we address the inflexibility problem by adopting the variable-depth VQC model to automatically change the structure of the quantum circuit according to the qBAS score.The basic idea behind the variable-depth VQC is to consider the depth of the quantum circuit as a parameter during the training.Meanwhile,we compared the performance of the variable-depth VQC model based on four widely used statistical distances set as the loss functions,including Kullback-Leibler divergence(KL-divergence),Jensen-Shannon divergence(JS-divergence),total variation distance,and maximum mean discrepancy.Our numerical experiment shows a promising result that the variable-depth VQC model works better than the original VQC in the generative learning tasks.

ON THE BASIC REPRODUCTION NUMBER OF GENERAL BRANCHING PROCESSES

Under a very general condition （TNC condition） we show that the spectral radius of the kernel of a general branching process is a threshold parameter and hence plays a role as the basic reproduction number in usual CMJ processes. We discuss also some properties of the extinction probability and the generating operator of general branching processes. As an application in epidemics, in the final section we suggest a generalization of SIR model which can describe infectious diseases transmission in an inhomogeneous population.

Investigation of Probability Generating Function in an Interdependent <i>M/M/</i>1:(∞;GD) Queueing Model with Controllable Arrival Rates Using Rouche’s Theorem

Present paper deals a M/M/1:(∞;GD) queueing model with interdependent controllable arrival and service rates where- in customers arrive in the system according to poisson distribution with two different arrivals rates-slower and faster as per controllable arrival policy. Keeping in view the general trend of interdependent arrival and service processes, it is presumed that random variables of arrival and service processes follow a bivariate poisson distribution and the server provides his services under general discipline of service rule in an infinitely large waiting space. In this paper, our central attention is to explore the probability generating functions using Rouche’s theorem in both cases of slower and faster arrival rates of the queueing model taken into consideration;which may be helpful for mathematicians and researchers for establishing significant performance measures of the model. Moreover, for the purpose of high-lighting the application aspect of our investigated result, very recently Maurya [1] has derived successfully the expected busy periods of the server in both cases of slower and faster arrival rates, which have also been presented by the end of this paper.

基于生成对抗网络的图像自增强去雾算法

针对现有去雾模型使用合成有雾图像数据集训练后容易出现过拟合的问题,提出了一种融合生成对抗网络的图像自增强去雾算法。在结合两个生成对抗网络的同时估计图像的深度信息。第一个GAN利用清晰图像学习图像加雾过程,将其生成的有雾图像作为第二个GAN的输入,指导第二个GAN如何正确去雾。为了减少图像处理前后的差异,利用一致性损失函数来优化两个网络。在图像加雾部分添加场景深度估计模块,并对散射因子进行随机采样,实现图像自增强功能,更加真实地模拟现实世界中不同浓度的雾气。该算法无需使用合成有雾图像数据集的成对信息,进一步避免过拟合问题。实验结果表明:所提算法能够取得较好的去雾效果,在主观视觉质量和客观评价指标上均有良好表现,优于同类算法。

JADE-DB:基于靶向变异的大语言模型安全通用基准测试集

提出大语言模型安全通用基准测试集—JADE-DB,该数据集基于靶向变异方法自动化构建,能够将经验丰富的大语言模型安全测试员和多学科专家学者手工撰写的测试问题转化为高危通用问题,保持语言自然性的同时不改变其核心语义,且能够攻破十余款国内外知名大语言模型的安全防护机制.根据语言复杂性差异,JADE-DB包含基础、进阶、高危3个安全测试等级,共计上千条覆盖违法犯罪、侵犯权益、歧视偏见和核心价值观4大类违规主题、30多种违规主题的通用测试问题,其中针对国内开源(中文,8款)、国内商用(中文,6款)和国外商用大语言模型(英文,4款)这3组大语言模型分别构建的3款通用高危测试集,可造成每组模型在高危测试集上的平均违规率均超过70%,测试问题均可同时触发多款模型违规生成.这表明,语言的复杂性导致现有大语言模型难以学习到人类无穷多种表达方式,因此无法识别其中不变的违规本质.

基于内在质量约束的文本生成和评价综述

近年来,以ChatGPT为代表的能够适应复杂场景、并能满足人类的各种应用需求为目标的文本生成算法模型成为学术界与产业界共同关注的焦点.然而,ChatGPT等大规模语言模型(Large Language Model,LLM)高度忠实于用户意图的优势隐含了部分的事实性错误,而且也需要依靠提示内容来控制细致的生成质量和领域适应性,因此,研究以内在质量约束为核心的文本生成方法仍具有重要意义.本文在近年来关键的内容生成模型和技术对比研究的基础上,定义了基于内在质量约束的文本生成的基本形式,以及基于“信、达、雅”的6种质量特征;针对这6种质量特征,分析并总结了生成器模型的设计和相关算法;同时,围绕不同的内在质量特征总结了多种自动评价和人工评价指标与方法.最后,本文对文本内在质量约束技术的未来研究方向进行了展望.

基于深度学习的EEG数据分析技术综述

对近年来的相关工作进行全面分析、横向比较,梳理出基于深度学习的EEG数据分析闭环流程.对EEG数据进行介绍,从深度学习在EEG数据预处理、特征提取以及模型泛化3个关键阶段的应用进行展开,梳理深度学习算法在相应阶段提供的研究思路和解决方案,包括各阶段所存在的难点与问题.全方位总结出不同算法的主要贡献和局限性,讨论深度学习技术在各个阶段处理EEG数据时所面临的挑战及未来的发展方向.

面向复杂工业过程的虚拟样本生成综述

用于复杂工业过程难测运行指标和异常故障建模的样本具有量少稀缺、分布不平衡以及内涵机理知识匮乏等特性.虚拟样本生成(Virtual sample generation,VSG)作为扩充建模样本数量及其涵盖空间的技术,已成为解决上述问题的主要手段之一,但已有研究还存在缺乏理论支撑、分类准则与应用边界模糊等问题.本文在描述复杂工业过程难测运行指标和异常故障建模所存在问题的基础上,梳理虚拟样本定义及其内涵,给出面向工业过程回归与分类问题的VSG实现流程;接着,从样本覆盖区域、实现流程与推广应用等方向进行综述;然后,分析讨论VSG的下一步研究方向;最后,对全文进行总结并给出未来挑战.

面向大语言模型的越狱攻击综述

近年来,大语言模型(large language model,LLM)在一系列下游任务中得到了广泛应用,并在多个领域表现出了卓越的文本理解、生成与推理能力.然而,越狱攻击正成为大语言模型的新兴威胁.越狱攻击能够绕过大语言模型的安全机制,削弱价值观对齐的影响,诱使经过对齐的大语言模型产生有害输出.越狱攻击带来的滥用、劫持、泄露等问题已对基于大语言模型的对话系统与应用程序造成了严重威胁.对近年的越狱攻击研究进行了系统梳理,并基于攻击原理将其分为基于人工设计的攻击、基于模型生成的攻击与基于对抗性优化的攻击3类.详细总结了相关研究的基本原理、实施方法与研究结论,全面回顾了大语言模型越狱攻击的发展历程,为后续的研究提供了有效参考.对现有的安全措施进行了简略回顾,从内部防御与外部防御2个角度介绍了能够缓解越狱攻击并提高大语言模型生成内容安全性的相关技术,并对不同方法的利弊进行了罗列与比较.在上述工作的基础上,对大语言模型越狱攻击领域的现存问题与前沿方向进行探讨,并结合多模态、模型编辑、多智能体等方向进行研究展望.

Six-Writings multimodal processing with pictophonetic coding to enhance Chinese language models

While large language models(LLMs)have made significant strides in natural language processing(NLP),they continue to face challenges in adequately addressing the intricacies of the Chinese language in certain scenarios.We propose a framework called Six-Writings multimodal processing(SWMP)to enable direct integration of Chinese NLP(CNLP)with morphological and semantic elements.The first part of SWMP,known as Six-Writings pictophonetic coding(SWPC),is introduced with a suitable level of granularity for radicals and components,enabling effective representation of Chinese characters and words.We conduct several experimental scenarios,including the following:(1)We establish an experimental database consisting of images and SWPC for Chinese characters,enabling dual-mode processing and matrix generation for CNLP.(2)We characterize various generative modes of Chinese words,such as thousands of Chinese idioms,used as question-and-answer(Q&A)prompt functions,facilitating analogies by SWPC.The experiments achieve 100%accuracy in answering all questions in the Chinese morphological data set(CA8-Mor-10177).(3)A fine-tuning mechanism is proposed to refine word embedding results using SWPC,resulting in an average relative error of≤25%for 39.37%of the questions in the Chinese wOrd Similarity data set(COS960).The results demonstrate that SWMP/SWPC methods effectively capture the distinctive features of Chinese and offer a promising mechanism to enhance CNLP with better efficiency.

污水生化处理过程的Model-on-Demand预测控制研究

Model-on -Demand(MOD)是一种很好的针对非线性系统的局部建模方法 ,首先介绍了MOD方法的具体建模过程 ,并研究了其与广义预测控制的结合 ,最后对污水生化处理过程的典型控制回路进行仿真。

非高斯风荷载极值估计:基于HPM转换过程的经验公式

风荷载极值概率信息对于结构抗风可靠性设计十分重要,基于Hermite多项式模型(Hermite polynomial model,HPM)转换过程方法由于在估计强非高斯性风荷载极值方面表现优异,因而受到研究人员的青睐从而被广泛应用。另一方面,该方法的使用过程烦琐且有较强的理论性,不利于工程应用。为此,该文基于该方法提出非高斯风荷载极值估计的经验公式,首先系统地阐述基于HPM转换过程方法,接着对风荷载的极值分布函数模型展开讨论,之后通过回归分析提出风荷载极值分布函数的经验公式,并对其适用性和精确性加以验证。研究成果表明,仅需知晓风荷载若干统计特征的情况下,经验公式可快速估计风荷载极值,精度上与HPM转换过程方法基本相同,而在效率和便捷性方面显著提升,便于在工程中推广使用。