240 research outputs found
Classification via score-based generative modelling
In this work, we investigated the application of score-based gradient
learning in discriminative and generative classification settings. Score
function can be used to characterize data distribution as an alternative to
density. It can be efficiently learned via score matching, and used to flexibly
generate credible samples to enhance discriminative classification quality, to
recover density and to build generative classifiers. We analysed the decision
theories involving score-based representations, and performed experiments on
simulated and real-world datasets, demonstrating its effectiveness in achieving
and improving binary classification performance, and robustness to
perturbations, particularly in high dimensions and imbalanced situations
Bayesian inference and neural estimation of acoustic wave propagation
In this work, we introduce a novel framework which combines physics and
machine learning methods to analyse acoustic signals. Three methods are
developed for this task: a Bayesian inference approach for inferring the
spectral acoustics characteristics, a neural-physical model which equips a
neural network with forward and backward physical losses, and the non-linear
least squares approach which serves as benchmark. The inferred propagation
coefficient leads to the room impulse response (RIR) quantity which can be used
for relocalisation with uncertainty. The simplicity and efficiency of this
framework is empirically validated on simulated data
Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World
We address the two fundamental problems of spatial field reconstruction and
sensor selection in heterogeneous sensor networks: (i) how to efficiently
perform spatial field reconstruction based on measurements obtained
simultaneously from networks with both high and low quality sensors; and (ii)
how to perform query based sensor set selection with predictive MSE performance
guarantee. For the first problem, we developed a low complexity algorithm based
on the spatial best linear unbiased estimator (S-BLUE). Next, building on the
S-BLUE, we address the second problem, and develop an efficient algorithm for
query based sensor set selection with performance guarantee. Our algorithm is
based on the Cross Entropy method which solves the combinatorial optimization
problem in an efficient manner.Comment: Presented at NIPS 2017 Workshop on Machine Learning for the
Developing Worl
Sub-daily simulation of mountain flood processes based on the modified soil water assessment tool (SWAT) model
Floods not only provide a large amount of water resources, but they also cause serious disasters. Although there have been numerous hydrological studies on flood processes, most of these investigations were based on rainfall-type floods in plain areas. Few studies have examined high temporal resolution snowmelt floods in high-altitude mountainous areas. The Soil Water Assessment Tool (SWAT) model is a typical semi-distributed, hydrological model widely used in runoff and water quality simulations. The degree-day factor method used in SWAT utilizes only the average daily temperature as the criterion of snow melting and ignores the influence of accumulated temperature. Therefore, the influence of accumulated temperature on snowmelt was added by increasing the discriminating conditions of rain and snow, making that more suitable for the simulation of snowmelt processes in high-altitude mountainous areas. On the basis of the daily scale, the simulation of the flood process was modeled on an hourly scale. This research compared the results before and after the modification and revealed that the peak error decreased by 77% and the time error was reduced from +/- 11 h to +/- 1 h. This study provides an important reference for flood simulation and forecasting in mountainous areas
Accurate simulation of ice and snow runoff for the mountainous terrain of the Kunlun Mountains, China
While mountain runoff provides great potential for the development and life quality of downstream populations, it also frequently causes seasonal disasters. The accurate modeling of hydrological processes in mountainous areas, as well as the amount of meltwater from ice and snow, is of great significance for the local sustainable development, hydropower regulations, and disaster prevention. In this study, an improved model, the Soil Water Assessment Tool with added ice-melt module (SWATAI) was developed based on the Soil Water Assessment Tool (SWAT), a semi-distributed hydrological model, to simulate ice and snow runoff. A temperature condition used to determine precipitation types has been added in the SWATAI model, along with an elevation threshold and an accumulative daily temperature threshold for ice melt, making it more consistent with the runoff process of ice and snow. As a supplementary reference, the comparison between the normalized difference vegetation index (NDVI) and the quantity of meltwater were conducted to verify the simulation results and assess the impact of meltwater on the ecology. Through these modifications, the accuracy of the daily flow simulation results has been considerably improved, and the contribution rate of ice and snow melt to the river discharge calculated by the model increased by 18.73%. The simulation comparison of the flooding process revealed that the accuracy of the simulated peak flood value by the SWATAI was 77.65% higher than that of the SWAT, and the temporal accuracy was 82.93% higher. The correlation between the meltwater calculated by the SWATAI and the NDVI has also improved significantly. This improved model could simulate the flooding processes with high temporal resolution in alpine regions. The simulation results could provide technical support for economic benefits and reasonable reference for flood prevention
Design of the Tsinghua Tabletop Kibble Balance
The Kibble balance is a precision instrument for realizing the mass unit, the
kilogram, in the new international system of units (SI). In recent years, an
important trend for Kibble balance experiments is to go tabletop, in which the
instrument's size is notably reduced while retaining a measurement accuracy of
. In this paper, we report a new design of a tabletop Kibble balance
to be built at Tsinghua University. The Tsinghua Kibble balance aims to deliver
a compact instrument for robust mass calibrations from 10 g to 1 kg with a
targeted measurement accuracy of 50 g or less. Some major features of the
Tsinghua Kibble balance system, including the design of a new magnet, one-mode
measurement scheme, the spring-compensated magnet moving mechanism, and
magnetic shielding considerations, are discussed.Comment: 8 pages, 9 figure
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