86 research outputs found
Well-posedness and regularity of the Darcy-Boussinesq system in layered porous media
We investigate the Darcy-Boussinesq model for convection in layered porous
media. In particular, we establish the well-posedness of the model in two and
three spatial dimension, and derive the regularity of the solutions in a novel
piecewise H2 space
The Cognition Trend of Chinese Traditional Media on Feminism and the Underlying Reason for Existing Negative Reports
With the positive development of the media, various mainstream cultures and ideologies have also changed. It is difficult for the two to accommodate. “Feminism”, as a progressive thought, spreads in the media space and has a great impact on the cognition of social equality between men and women. This paper studies and analyses about 200 articles from two traditional Chinese print media, exploring the cognition trend of Chinese traditional media on feminism and the deep reasons for existing negative reports. Conclusions can be drawn that the general attitude of traditional Chinese media to feminism is neutral and positive, while both the traditional dualism of patriarchal society and the reform of social economic structure have influenced the cognition and inclusiveness of traditional media to feminism
Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt
In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search
(L2S) solver for routing problems. It learns to perform flexible k-opt
exchanges based on a tailored action factorization method and a customized
recurrent dual-stream decoder. As a pioneering work to circumvent the pure
feasibility masking scheme and enable the autonomous exploration of both
feasible and infeasible regions, we then propose the Guided Infeasible Region
Exploration (GIRE) scheme, which supplements the NeuOpt policy network with
feasibility-related features and leverages reward shaping to steer
reinforcement learning more effectively. Additionally, we equip NeuOpt with
Dynamic Data Augmentation (D2A) for more diverse searches during inference.
Extensive experiments on the Traveling Salesman Problem (TSP) and Capacitated
Vehicle Routing Problem (CVRP) demonstrate that our NeuOpt not only
significantly outstrips existing (masking-based) L2S solvers, but also
showcases superiority over the learning-to-construct (L2C) and
learning-to-predict (L2P) solvers. Notably, we offer fresh perspectives on how
neural solvers can handle VRP constraints. Our code is available:
https://github.com/yining043/NeuOpt.Comment: Accepted at NeurIPS 202
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Most of existing neural methods for multi-objective combinatorial
optimization (MOCO) problems solely rely on decomposition, which often leads to
repetitive solutions for the respective subproblems, thus a limited Pareto set.
Beyond decomposition, we propose a novel neural heuristic with diversity
enhancement (NHDE) to produce more Pareto solutions from two perspectives. On
the one hand, to hinder duplicated solutions for different subproblems, we
propose an indicator-enhanced deep reinforcement learning method to guide the
model, and design a heterogeneous graph attention mechanism to capture the
relations between the instance graph and the Pareto front graph. On the other
hand, to excavate more solutions in the neighborhood of each subproblem, we
present a multiple Pareto optima strategy to sample and preserve desirable
solutions. Experimental results on classic MOCO problems show that our NHDE is
able to generate a Pareto front with higher diversity, thereby achieving
superior overall performance. Moreover, our NHDE is generic and can be applied
to different neural methods for MOCO.Comment: Accepted at NeurIPS 202
Remediation of mercury contaminated soil, water, and air : a review of emerging materials and innovative technologies
Mercury contamination in soil, water and air is associated with potential toxicity to humans and ecosystems. Industrial activities such as coal combustion have led to increased mercury (Hg) concentrations in different environmental media. This review critically evaluates recent developments in technological approaches for the remediation of Hg contaminated soil, water and air, with a focus on emerging materials and innovative technologies. Extensive research on various nanomaterials, such as carbon nanotubes (CNTs), nanosheets and magnetic nanocomposites, for mercury removal are investigated. This paper also examines other emerging materials and their characteristics, including graphene, biochar, metal organic frameworks (MOFs), covalent organic frameworks (COFs), layered double hydroxides (LDHs) as well as other materials such as clay minerals and manganese oxides. Based on approaches including adsorption/desorption, oxidation/reduction and stabilization/containment, the performances of innovative technologies with the aid of these materials were examined. In addition, technologies involving organisms, such as phytoremediation, algae-based mercury removal, microbial reduction and constructed wetlands, were also reviewed, and the role of organisms, especially microorganisms, in these techniques are illustrated
Efficient Neural Neighborhood Search for Pickup and Delivery Problems
We present an efficient Neural Neighborhood Search (N2S) approach for pickup
and delivery problems (PDPs). In specific, we design a powerful Synthesis
Attention that allows the vanilla self-attention to synthesize various types of
features regarding a route solution. We also exploit two customized decoders
that automatically learn to perform removal and reinsertion of a
pickup-delivery node pair to tackle the precedence constraint. Additionally, a
diversity enhancement scheme is leveraged to further ameliorate the
performance. Our N2S is generic, and extensive experiments on two canonical PDP
variants show that it can produce state-of-the-art results among existing
neural methods. Moreover, it even outstrips the well-known LKH3 solver on the
more constrained PDP variant. Our implementation for N2S is available online.Comment: Accepted at IJCAI 2022 (short oral
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
Recently, Meta-Black-Box Optimization with Reinforcement Learning
(MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to
mitigate manual fine-tuning of low-level black-box optimizers. However, this
field is hindered by the lack of a unified benchmark. To fill this gap, we
introduce MetaBox, the first benchmark platform expressly tailored for
developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible
algorithmic template that allows users to effortlessly implement their unique
designs within the platform. Moreover, it provides a broad spectrum of over 300
problem instances, collected from synthetic to realistic scenarios, and an
extensive library of 19 baseline methods, including both traditional black-box
optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three
standardized performance metrics, enabling a more thorough assessment of the
methods. In a bid to illustrate the utility of MetaBox for facilitating
rigorous evaluation and in-depth analysis, we carry out a wide-ranging
benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source
and accessible at: https://github.com/GMC-DRL/MetaBox.Comment: Accepted at NuerIPS 202
Towards standardized metrics for measuring takeover performance in conditionally automated driving: A systematic review
A particular concern with SAE Level 3 automated vehicles is the takeover transition from the automated vehicle to the driver. Prior research has employed a wide range of metrics for measuring takeover performance. However, the lack of a set of standard metrics for measuring takeover performance makes it difficult to consolidate findings and summarize the influence of different factors. This article presents a review of the metrics employed in empirical literature examining takeover transitions in Level 3 automated driving and proposes a framework for standardizing the objective takeover performance metrics.University of Michigan McityNational Science FoundationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/168148/1/Cao et al. 2021 (DeepBlue).pdfDescription of Cao et al. 2021 (DeepBlue).pdf : Main FileSEL
- …