A stochastic user-operator assignment game for microtransit service evaluation: A case study of Kussbus in Luxembourg

This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users' travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.Comment: arXiv admin note: substantial text overlap with arXiv:1912.0198

Communication-efficient distributed optimization with adaptability to system heterogeneity

We consider the setting of agents cooperatively minimizing the sum of local objectives plus a regularizer on a graph. This paper proposes a primal-dual method in consideration of three distinctive attributes of real-life multi-agent systems, namely: (i)expensive communication, (ii)lack of synchronization, and (iii)system heterogeneity. In specific, we propose a distributed asynchronous algorithm with minimal communication cost, in which users commit variable amounts of local work on their respective sub-problems. We illustrate this both theoretically and experimentally in the machine learning setting, where the agents hold private data and use a stochastic Newton method as the local solver. Under standard assumptions on Lipschitz continuous gradients and strong convexity, our analysis establishes linear convergence in expectation and characterizes the dependency of the rate on the number of local iterations. We proceed a step further to propose a simple means for tuning agents' hyperparameters locally, so as to adjust to heterogeneity and accelerate the overall convergence. Last, we validate our proposed method on a benchmark machine learning dataset to illustrate the merits in terms of computation, communication, and run-time saving as well as adaptability to heterogeneity.Comment: This paper is accepted by the 62nd IEEE Conference on Decision and Control (CDC 2023

Surface-immobilised micelles via cucurbit[8]uril-rotaxanes for solvent-induced burst release.

The fabrication, characterisation and controlled burst release of naphthol-functionalised micellar (NFM) nanostructures, which were grafted onto gold surfaces through cucurbit[8]uril (CB[8]) mediated host-guest interactions are described. NFMs undergo a facile change in morphology from micelles to diblock copolymers in direct response to exposure to organic solvents, including tetrahydrofuran (THF), toluene and chloroform. This induced transition in conformation lends itself to potential applications including nanocarriers for triggered burst-release of guest molecules. Nile Red was investigated as a NFM encapsulated model hydrophobic cargo inside the surface-attached micelles, which could be fully released upon exposure to THF as measured by both atomic force microscopy and UV/vis spectroscopy.C. Hu thanks BP for supporting this work and Hughes Hall College Cambridge for a student scholarship. Y. Zheng was supported by an ERC starting investigator grant (ASPiRe 240629). Z. Yu is supported by an EPSRC grant (EP/H046593/1).This is the final version. It first appeared at http://pubs.rsc.org/en/Content/ArticleLanding/2015/CC/C5CC00121H#!divAbstract

Clustered Federated Learning based on Nonconvex Pairwise Fusion

This study investigates clustered federated learning (FL), one of the formulations of FL with non-i.i.d. data, where the devices are partitioned into clusters and each cluster optimally fits its data with a localized model. We propose a novel clustered FL framework, which applies a nonconvex penalty to pairwise differences of parameters. This framework can automatically identify clusters without a priori knowledge of the number of clusters and the set of devices in each cluster. To implement the proposed framework, we develop a novel clustered FL method called FPFC. Advancing from the standard ADMM, our method is implemented in parallel, updates only a subset of devices at each communication round, and allows each participating device to perform a variable amount of work. This greatly reduces the communication cost while simultaneously preserving privacy, making it practical for FL. We also propose a new warmup strategy for hyperparameter tuning under FL settings and consider the asynchronous variant of FPFC (asyncFPFC). Theoretically, we provide convergence guarantees of FPFC for general nonconvex losses and establish the statistical convergence rate under a linear model with squared loss. Our extensive experiments demonstrate the advantages of FPFC over existing methods.Comment: 46 pages, 9 figure

Does urbanization have spatial spillover effect on poverty reduction: empirical evidence from rural China

In light of a scarcity of research on the spatial effects of urbanization on poverty reduction, this study uses panel data on 30 provinces in China from 2009 to 2019 to construct a system of indices to assess poverty that spans the four dimensions of the economy, education, health, and living. We use the spatial autocorrelation test and the spatial Durbin model (SDM) to analyze the spatial effects of urbanization on poverty reduction in these different dimensions. The main conclusions are as follows: (a) China’s urbanization has the characteristics of spatial aggregation and a spatial spillover effect. (b) Different dimensions of poverty had the attributes of spatial agglomeration, and Moran’s index of a reduction in economic poverty was the highest. Under the SDM, the different dimensions of poverty also showed a significant positive spatial correlation. (c) Urbanization has a significant effect on poverty reduction along the dimensions of the economy, education, and living, but has little effect on reducing health poverty. It has a spatial spillover effect on poverty reduction in economic and living contexts. (d) There were spatial differences in the effect of urbanization on relieving economic and living-related poverty

Interfacial assembly of dendritic microcapsules with host-guest chemistry.

The self-assembly of nanoscale materials to form hierarchically ordered structures promises new opportunities in drug delivery, as well as magnetic materials and devices. Herein, we report a simple means to promote the self-assembly of two polymers with functional groups at a water-chloroform interface using microfluidic technology. Two polymeric layers can be assembled and disassembled at the droplet interface using the efficiency of cucurbit[8]uril (CB[8]) host-guest supramolecular chemistry. The microcapsules produced are extremely monodisperse in size and can encapsulate target molecules in a robust, well-defined manner. In addition, we exploit a dendritic copolymer architecture to trap a small hydrophilic molecule in the microcapsule skin as cargo. This demonstrates not only the ability to encapsulate small molecules but also the ability to orthogonally store both hydrophilic and hydrophobic cargos within a single microcapsule. The interfacially assembled supramolecular microcapsules can benefit from the diversity of polymeric materials, allowing for fine control over the microcapsule properties.This work was supported by the Engineering Physical Sciences Research Council, Institutional Sponsorship 2012-University of Cambridge EP/K503496/1 and the Translational Grant EP/H046593/1; Dr. Yu Zheng and Dr. Richard M. Parker were also funded from the Starting Investigator grant ASPiRe (No. 240629) from the European Research Council and the Isaac Newton Trust research grant No. 13.7(c).This is the accepted manuscript of a paper published in Nature Communications (Zheng Y, Yu Z, Parker RM, Wu Y, Abell C, Scherman OA, Nature Communications 2014, 5, 5772, doi:10.1038/ncomms6772)

A user-operator assignment game with heterogeneous user groups for empirical evaluation of a microtransit service in Luxembourg

We tackle the problem of evaluating the impact of different operation policies on the performance of a microtransit service. This study is the first empirical application using the stable matching modeling framework to evaluate different operation cost allocation and pricing mechanisms on microtransit service. We extend the deterministic stable matching model to a stochastic reliability-based one to consider user's heterogeneous perceptions of utility on the service routes. The proposed model is applied to the evaluation of Kussbus microtransit service in Luxembourg. We found that the current Kussbus operation is not a stable outcome. By reducing their route operating costs of 50%, it is expected to increase the ridership of 10%. If Kussbus can reduce in-vehicle travel time on their own by 20%, they can significantly increase profit several folds from the baseline

Metabolic reprogramming in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a malignancy with high incidence in China. Due to the lack of effective molecular targets, the prognosis of ESCC patients is poor. It is urgent to explore the pathogenesis of ESCC to identify promising therapeutic targets. Metabolic reprogramming is an emerging hallmark of ESCC, providing a novel perspective for revealing the biological features of ESCC. In the hypoxic and nutrient-limited tumor microenvironment, ESCC cells have to reprogram their metabolic phenotypes to fulfill the demands of bioenergetics, biosynthesis and redox homostasis of ESCC cells. In this review, we summarized the metabolic reprogramming of ESCC cells that involves glucose metabolism, lipid metabolism, and amino acid metabolism and explore how reprogrammed metabolism provokes novel opportunities for biomarkers and potential therapeutic targets of ESCC

Headwater streams contain amounts of heavy metal in an alpine forest in the upper reaches of the Yangtze River

Headwater streams are an essential link in the source and sink dynamics of heavy metals between terrestrial and aquatic ecosystems and are also critically important for downstream ecosystem processes and water quality. However, there is little available information about headwater streams. Therefore, the stream storage and distribution patterns of Cd, Pb, Ni, Cr, Cu, Mn and Zn were investigated in ten headwater streams of an Alpine forest located in the upper Yangtze River during the rainy season. The results indicated that the heavy metal storage per unit area of the investigated streams was as follows: 0.95 mg·m-2 for Cd, 8.36 mg m-2 for Pb, 1.98 mg m-2 for Ni, 136.98 mg m-2 for Cr, 9.29 mg m-2 for Cu, 433.39 mg m-2 for Mn and 29.07 mg m-2 for Zn; while the heavy metal storage per unit area of the catchment was as follows: 1.19 mg hm-2 for Cd, 10.47 mg hm-2 for Pb, 2.48 mg hm-2 for Ni, 171.62 mg hm-2 for Cr, 11.64 mg hm-2 for Cu, 542.99 mg hm-2 for Mn and 36.42 mg hm-2 for Zn. Headwater streams present remarkable potential for contamination, and plant debris from riparian forests may be the most important source of heavy metals, while the stream sediment acts as a significant sink for heavy metals. These results provide new perspectives and data for understanding the ecological links between alpine forests and watersheds

Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Recently, due to their capacity and representation ability, pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without experimental data. However, their predictions are limited in accuracy as well as interpretability. Furthermore, such deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity. By combining the techniques of meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. The experiments across 87 deep mutational scanning datasets underscore its superiority over both unsupervised and supervised approaches, revealing its potential in facilitating AI-guided protein design