Urban Freight Management with Stochastic Time-Dependent Travel Times and Application to Large-Scale Transportation Networks

This paper addressed the vehicle routing problem (VRP) in large-scale urban transportation networks with stochastic time-dependent (STD) travel times. The subproblem which is how to find the optimal path connecting any pair of customer nodes in a STD network was solved through a robust approach without requiring the probability distributions of link travel times. Based on that, the proposed STD-VRP model can be converted into solving a normal time-dependent VRP (TD-VRP), and algorithms for such TD-VRPs can also be introduced to obtain the solution. Numerical experiments were conducted to address STD-VRPTW of practical sizes on a real world urban network, demonstrated here on the road network of Shenzhen, China. The stochastic time-dependent link travel times of the network were calibrated by historical floating car data. A route construction algorithm was applied to solve the STD problem in 4 delivery scenarios efficiently. The computational results showed that the proposed STD-VRPTW model can improve the level of customer service by satisfying the time-window constraint under any circumstances. The improvement can be very significant especially for large-scale network delivery tasks with no more increase in cost and environmental impacts

Aligning Language Models with Human Preferences via a Bayesian Approach

In the quest to advance human-centric natural language generation (NLG) systems, ensuring alignment between NLG models and human preferences is crucial. For this alignment, current popular methods leverage a reinforcement learning (RL) approach with a reward model trained on feedback from humans. However, inherent disagreements due to the subjective nature of human preferences pose a significant challenge for training the reward model, resulting in a deterioration of the NLG performance. To tackle this issue, previous approaches typically rely on majority voting or averaging to consolidate multiple inconsistent preferences into a merged one. Although straightforward to understand and execute, such methods suffer from an inability to capture the nuanced degrees of disaggregation among humans and may only represent a specialized subset of individuals, thereby lacking the ability to quantitatively disclose the universality of human preferences. To address this challenge, this paper proposes a novel approach, which employs a Bayesian framework to account for the distribution of disagreements among human preferences as training a preference model, and names it as d-PM. Besides, considering the RL strategy's inefficient and complex training process over the training efficiency, we further propose utilizing the contrastive learning strategy to train the NLG model with the preference scores derived from the d-PM model. Extensive experiments on two human-centric NLG tasks, i.e., emotional support conversation and integrity "Rule-of-Thumb" generation, show that our method consistently exceeds previous SOTA models in both automatic and human evaluations.Comment: NeurIPS 202

Numerical study on springback prediction of aged steel based on quasi-static strain-hardening material model

Material model of aged steel, the material\u27s stress and strain behavior in the region of yielding, plays a critical role in springback prediction of FEA simulation. In most of the FEA simulations, the material is modeled as a linear elastic and then after yielding, a linear strain-hardening model or a power-law strain-hardening model. In the prediction of springback by FEA simulation, there is always a problem that the prediction is far away to the true springback of the products or samples. This paper employs a quasi-static strain-hardening material model which gives a more accurate description of the material properties of aged steel for FEA simulation of the forming. A typical V-bending for aged steel is simulated with finite element software, ABAQUS, to predict the springback. A 2D model which consists of multiple rigid bodies and a deformable body is performed in this study adopting four-node linear plane-strain elements. Meanwhile, a 2D model that applies a power-law strain-hardening material model is implemented as a contrast. By comparing the results from the simulations based on different material models with those from experiments, a better correlation between the simulations using a quasi-static strain-hardening material model and the experiments is achieved. Besides, the bending process for aged steel is also analyzed utilizing quasi-static strain-hardening material model with variable ageing coefficients. It is drawn that the greater the ageing coefficient within certain range, the greater the springback simulation accuracy by comparison between simulation and experiment results. In addition, as the upper yield stress is not easy to be measured directly, a method for reverse calculation of upper yield stress through springback ratio and quasi-static flow stress is proposed

Can glucagon-like peptide-1 receptor agonists cause acute kidney injury? An analytical study based on post-marketing approval pharmacovigilance data

Clinical studies after marketing have shown that the use of glucagon-like peptide-1 receptor agonist(GLP-1RA) may lead to acute kidney injury(AKI). However, few epidemiological studies have investigated the risk, clinical features, and outcomes of AKI caused by different GLP-1RA. In this study, Adverse Event Reporting System (FAERS) data were used to compare the association between different GLP-1RA and AKI in the real world.MethodsFAERS data from January 2004 to December 2021 were mined using disproportionality analysis and Bayesian analysis to determine the correlation between different GLP-1RA and AKI, and the onset time, mortality, and hospitalization rate of different GLP-1RA were analyzed.ResultsWe identified 2670 cases of AKI events associated with GLP-1RA, of which liraglutide was the most commonly reported (34.98%). The patients with AKI were mainly males (47.94%), and the age group was mainly 45-84 years old (73.15%). obese patients with weight more than 99kg (24.42%) were more likely to have AKI. According to different signal mining methods, reporting odds ratio (ROR) (1.50, 95% confidence interval =1.41-1.60) and Bayesian confidence Propagation neural network (0.57, 95% confidence interval =0.54), liraglutide was more strongly associated with AKI than other GLP-1RA. The median time to onset of AKI was 63 days [quartile range (IQR): 15-458.5 days]. In addition, the hospitalization rate and fatality rate of patients with GLP-1RA-related AKI were 45.28% and 4.23% respectively.ConclusionsBased on the data in the FAERS database, we analyzed the risk, onset time, and adverse reaction outcomes of GLP-1RA-induced AKI in detail. The results showed that liraglutide had the highest risk of AKI. From the early stage of treatment, we need to monitor patients’ renal function regularly, especially for patients with high kidney risks such as obesity and age

Comparison of multi-field coupling numerical simulation in hot dry rock thermal exploitation of enhanced geothermal systems

 In order to alleviate the environmental crisis and improve energy structure, countries from all over the world have focused on the hot dry rock geothermal resources with great potential and with little pollution. The geothermal heat production from Enhanced Geothermal System (EGS) comes with complex multi-field coupling process, and it is of great significance to study the temporal and spatial evolution of geothermal reservoir. In this work, a practical numerical model is established to simulate the heat production process in EGS, and the comparison of thermal-hydraulic (TH), thermal-hydraulic-mechanical (THM) and thermal-hydraulic-mechanical-chemical (THMC) coupling in geothermal reservoir is analyzed. The results show that the stable production stage of the three cases is approximately 5 years; however, compared with TH and THMC coupling, the service-life for THM coupling decreased by 1140 days and 332 days, respectively. The mechanical enhanced effects are offset by the chemical precipitation, and the precipitation from SiO2 is much larger than the dissolution of calcite.Cited as: Chen, S., Ding, B., Gong, L., Huang, Z., Yu, B., Sun, S. Comparison of multi-field coupling numerical simulation in hot dry rock thermal exploitation of enhanced geothermal systems. Advances in Geo-Energy Research, 2019, 3(4): 396-409, doi: 10.26804/ager.2019.04.0

Generative Judge for Evaluating Alignment

The rapid development of Large Language Models (LLMs) has substantially expanded the range of tasks they can address. In the field of Natural Language Processing (NLP), researchers have shifted their focus from conventional NLP tasks (e.g., sequence tagging and parsing) towards tasks that revolve around aligning with human needs (e.g., brainstorming and email writing). This shift in task distribution imposes new requirements on evaluating these aligned models regarding generality (i.e., assessing performance across diverse scenarios), flexibility (i.e., examining under different protocols), and interpretability (i.e., scrutinizing models with explanations). In this paper, we propose a generative judge with 13B parameters, Auto-J, designed to address these challenges. Our model is trained on user queries and LLM-generated responses under massive real-world scenarios and accommodates diverse evaluation protocols (e.g., pairwise response comparison and single-response evaluation) with well-structured natural language critiques. To demonstrate the efficacy of our approach, we construct a new testbed covering 58 different scenarios. Experimentally, Auto-J outperforms a series of strong competitors, including both open-source and closed-source models, by a large margin. We also provide detailed analysis and case studies to further reveal the potential of our method and make a variety of resources public at https://github.com/GAIR-NLP/auto-j.Comment: Fix typos in Table

Two-Component Noncollinear Time-Dependent Spin Density Functional Theory for Excited State Calculations

We present a linear response formalism for the description of the electronic excitations of a noncollinear reference defined via Kohn-Sham spin density functional methods. A set of auxiliary variables, defined using the density and noncollinear magnetization density vector, allows the generalization of spin density functional kernels commonly used in collinear DFT to noncollinear cases, including local density, GGA, meta-GGA and hybrid functionals. Working equations and derivations of functional second derivatives with respect to the noncollinear density, required in the linear response noncollinear TDDFT formalism, are presented in this work. This formalism takes all components of the spin magnetization into account independent of the type of reference state (open or closed shell). As a result, the method introduced here is able to afford a nonzero local xc torque on the spin magnetization while still satisfying the zero-Torque theorem globally. The formalism is applied to a few test cases using the variational exact-Two-component reference including spin-orbit coupling to illustrate the capabilities of the method