A robust method for designing multistable systems by embedding bistable subsystems

Although multistability is an important dynamic property of a wide range of complex systems, it is still a challenge to develop mathematical models for realising high order multistability using realistic regulatory mechanisms. To address this issue, we propose a robust method to develop multistable mathematical models by embedding bistable models together. Using the GATA1-GATA2-PU.1 module in hematopoiesis as the test system, we first develop a tristable model based on two bistable models without any high cooperative coefficients, and then modify the tristable model based on experimentally determined mechanisms. The modified model successfully realises four stable steady states and accurately reflects a recent experimental observation showing four transcriptional states. In addition, we develop a stochastic model, and stochastic simulations successfully realise the experimental observations in single cells. These results suggest that the proposed method is a general approach to develop mathematical models for realising multistability and heterogeneity in complex systems

Theoretical Analysis of Impact of Delayed Updates on Decentralized Federated Learning

Decentralized Federated learning is a distributed edge intelligence framework by exchanging parameter updates instead of training data among participators, in order to retrain or fine-tune deep learning models for mobile intelligent applications. Considering the various topologies of edge networks in mobile internet, the impact of transmission delay of updates during model training is non-negligible for data-intensive intelligent applications on mobile devices, e.g., intelligent medical services, automated driving vehicles, etc.. To address this problem, we analyze the impact of delayed updates for decentralized federated learning, and provide a theoretical bound for these updates to achieve model convergence. Within the theoretical bound of updating period, the latest versions for the delayed updates are reused to continue aggregation, in case the model parameters from a specific neighbor are not collected or updated in time

Forecasting dryland vegetation condition months in advance through satellite data assimilation

Dryland ecosystems are characterised by rainfall variability and strong vegetation response to changes in water availability over a range of timescales. Forecasting dryland vegetation condition can be of great value in planning agricultural decisions, drought relief, land management and fire preparedness. At monthly to seasonal time scales, knowledge of water stored in the system contributes more to predictability than knowledge of the climate system state. However, realising forecast skill requires knowledge of the vertical distribution of moisture below the surface and the capacity of the vegetation to access this moisture. Here, we demonstrate that contrasting satellite observations of water presence over different vertical domains can be assimilated into an eco-hydrological model and combined with vegetation observations to infer an apparent vegetation-accessible water storage (hereafter called accessible storage). Provided this variable is considered explicitly, skilful forecasts of vegetation condition are achievable several months in advance for most of the world’s drylands.This research was supported through ARC Discovery grant DP140103679. We thank Professor Michael L. Roderick and Professor Jeffery P. Walker for their kind help and suggestions in data analysis. This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government

Modification Method of Tooth Profile of Locomotive Traction Gear Based on Rodent Arm Variation

Locomotive traction gear is the key component to power transmission and speed control in locomotive transmission system, which plays an important role in locomotive running speed and load-carrying torque. Considering that there is not universal rule for the method of modification of locomotive gear at present, in this paper, the tooth profile modification is considered with the combination of the increased contact ratio and the variation of the moment arm of action. Based on the principle of modification, according to the load direction after modification, the change rule of moment arm of action after modification is determined, and the interval range of tooth profile modification is also determined. Taking a certain locomotive traction gear as an example, the results obtained through the method of modification which based on combining moment arm of action variation with the increase of contact ratio and the method based on the traditional empirical formula are compared through finite element simulation respectively, on this account to verify the superiority of the theory of modification, which has important theoretical significance for profile modification of locomotive traction gear

Continental scale downscaling of AWRA-L analysed soil moisture using random forest regression

The Australian Water Resource Assessment Landscape (AWRA-L) model as used by the Bureau of Meteorology (BoM) provides daily continental scale soil moisture (SM) estimates (among other landscape water variables) at ~5-km resolution. At such a coarse scale these data cannot represent the high spatiotemporal variability of SM across heterogeneous land surfaces. Downscaling of coarse SM products based on machine learning (ML) has become increasingly popular due to its robust predictions and potential for large-scale applications. As a first step towards high-resolution daily Australia-wide SM estimation, a downscaling framework was developed to generate monthly SM with 500-m spatial resolution using analysed SM from AWRA-L and multisource geospatial predictors in random forest (RF) regression. Candidate predictors include digital elevation model (DEM), soil properties from the Australian soil and landscape grids, and several retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS). Ten experiments were conducted to decide the best combination of predictors. In the chosen model, DEM and available water capacity (AWC) were consistently identified as the most important predictors based on the ranking of variable importance. The downscaled SM shows greatly enhanced spatial details at the local scale while maintaining consistent patterns with AWRA-L analysis at the continental scale. Validations against in-situ measurement networks using Pearson correlation coefficient (R) show that there is very little difference in the performance between the downscaled and AWRA-L SM. Average R values for the downscaled SM against CosmOz, OzFlux and OzNet were 0.87, 0.68 and 0.75, respectively, while the original AWRA-L SM average R were 0.86, 0.68 and 0.76, respectively. Furthermore, the time series comparison based on a wetness unit shows that the downscaled SM can well catch up the fluctuations of in-situ SM. In general, this study explores the potential of ML approach for the SM downscaling applications at the continental scale. It could be a promising direction to exploit the modelling capability of integrating multisource geospatial data including satellite retrievals, land surface models (LSM) and interpolated ground observation data. Future directions should concentrate on integrating this approach into an operational framework with a daily frequency. Exploration of the relationships between SM and auxiliaries under difference scales would be essential, in order to better understand the dominant physical controls on spatial variability of SM.This research was undertaken while supported by the Australian National University (ANU) University Research Scholarship and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and ANU Digital Agriculture Supplementary Scholarship through the Centre for Entrepreneurial Agri-Technology (CEAT). This research was supported with funds from the University of Sydney (USYD) and Grains Research and Development Corporation (GRDC) project SoilWaterNow

Single-photon-driven high-order sideband transitions in an ultrastrongly coupled circuit quantum electrodynamics system

We report the experimental observation of high-order sideband transitions at the single-photon level in a quantum circuit system of a flux qubit ultrastrongly coupled to a coplanar waveguide resonator. With the coupling strength reaching 10% of the resonator's fundamental frequency, we obtain clear signatures of higher-order red and first-order blue-sideband transitions, which are mainly due to the ultrastrong Rabi coupling. Our observation advances the understanding of ultrastrongly-coupled systems and paves the way to study high-order processes in the quantum Rabi model at the single-photon level.Comment: Accepted in Physical Review A. 12 pages, 6 figure

Editing Language Model-based Knowledge Graph Embeddings

Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deployed as static artifacts, which are challenging to modify without re-training after deployment. To address this issue, we propose a new task of editing language model-based KG embeddings in this paper. The proposed task aims to enable data-efficient and fast updates to KG embeddings without damaging the performance of the rest. We build four new datasets: E-FB15k237, A-FB15k237, E-WN18RR, and A-WN18RR, and evaluate several knowledge editing baselines demonstrating the limited ability of previous models to handle the proposed challenging task. We further propose a simple yet strong baseline dubbed KGEditor, which utilizes additional parametric layers of the hyper network to edit/add facts. Comprehensive experimental results demonstrate that KGEditor can perform better when updating specific facts while not affecting the rest with low training resources. Code and datasets will be available in https://github.com/zjunlp/PromptKG/tree/main/deltaKG.Comment: Work in progress and the project website is https://zjunlp.github.io/project/KGE_Editing

Coupled superconducting qudit-resonator system: Energy spectrum, state population, and state transition under microwave drive

Superconducting quantum multilevel systems coupled to resonators have recently been considered in some applications such as microwave lasing and high-fidelity quantum logical gates. In this work, using an rf-SQUID type phase qudit coupled to a microwave coplanar waveguide resonator, we study both theoretically and experimentally the energy spectrum of the system when the qudit level spacings are varied around the resonator frequency by changing the magnetic flux applied to the qudit loop. We show that the experimental result can be well described by a theoretical model that extends from the usual two-level Jaynes-Cummings system to the present four-level system. It is also shown that due to the small anharmonicity of the phase device a simplified model capturing the leading state interactions fits the experimental spectra very well. Furthermore we use the Lindblad master equation containing various relaxation and dephasing processes to calculate the level populations in the simpler qutrit-resonator system, which allows a clear understanding of the dynamics of the system under the microwave drive. Our results help to better understand and perform the experiments of coupled multilevel and resonator systems and can be applied in the case of transmon or Xmon qudits having similar anharmonicity to the present phase device.This work was supported by the Ministry of Science and Technology of China (Grants No. 2014CB921202, No. 2015CB921104, and No. 2016YFA0300601),the National Natural Science Foundation of China (Grants No. 91321208 and No. 11674380)the Key Research Program of the Chinese Academy of Sciences (Grant No. XDPB08-3)S.H. acknowledges support by the US NSF (PHY-1314861)

Quantum Phase Diffusion in a Small Underdamped Josephson Junction

Quantum phase diffusion in a small underdamped Nb/AlO$_x$/Nb junction ($\sim$ 0.4 $\mu$m$^2$) is demonstrated in a wide temperature range of 25-140 mK where macroscopic quantum tunneling (MQT) is the dominant escape mechanism. We propose a two-step transition model to describe the switching process in which the escape rate out of the potential well and the transition rate from phase diffusion to the running state are considered. The transition rate extracted from the experimental switching current distribution follows the predicted Arrhenius law in the thermal regime but is greatly enhanced when MQT becomes dominant.Comment: 4 pages, 4 figures, 1 tabl