83 research outputs found
The Contribution Of Occupancy Behavior To Energy Consumption In Low Income Residential Buildings
Energy consumption in residential buildings consumes 22% of the total US energy each year and is highly impacted by the occupant behavior. In order to model domestic demand profiles more accurately, it is important to understand occupancy behavior profile. Four low income houses in Texas are used as the test beds. The occupancy sensors are installed in every room. The real-life occupancy data from the occupancy sensors were compared with the American Time Use Survey (ATUS) data. The study period is from July 1 to August 31. The preliminary result shows that there is a similarity between ATUS data and actual occupancy profile. In addition, simulations in EnergyPlus were conducted to test how much energy consumption can be saved based on the thermostat control of real-life occupancy behavior patterns. The results show that such control can save cooling energy by 7%
Designing non-segregating granular mixtures
In bidisperse particle mixtures varying in size or density alone, large
particles rise (driven by percolation) and heavy particles sink (driven by
buoyancy). When the two particle species differ from each other in both size
and density, the two segregation mechanisms either enhance (large/light and
small/heavy) or oppose (large/heavy and small/light) each other. In the latter
case, an equilibrium condition exists in which the two segregation mechanisms
balance and the particles no longer segregate. This leads to a methodology to
design non-segregating particle mixtures by specifying particle size ratio,
density ratio, and mixture concentration to achieve the equilibrium condition.
Using DEM simulations of quasi-2D bounded heap flow, we show that segregation
is significantly reduced for particle mixtures near the equilibrium condition.
In addition, the rise-sink transition for a range of particle size and density
ratios matches the combined size and density segregation model predictions
Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
The study of universal approximation properties (UAP) for neural networks
(NN) has a long history. When the network width is unlimited, only a single
hidden layer is sufficient for UAP. In contrast, when the depth is unlimited,
the width for UAP needs to be not less than the critical width
, where and are the dimensions of the
input and output, respectively. Recently, \cite{cai2022achieve} shows that a
leaky-ReLU NN with this critical width can achieve UAP for functions on a
compact domain , \emph{i.e.,} the UAP for . This
paper examines a uniform UAP for the function class and
gives the exact minimum width of the leaky-ReLU NN as
, which involves the effects of the
output dimensions. To obtain this result, we propose a novel
lift-flow-discretization approach that shows that the uniform UAP has a deep
connection with topological theory.Comment: ICML2023 camera read
C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning
The James-Stein (JS) shrinkage estimator is a biased estimator that captures
the mean of Gaussian random vectors.While it has a desirable statistical
property of dominance over the maximum likelihood estimator (MLE) in terms of
mean squared error (MSE), not much progress has been made on extending the
estimator onto manifold-valued data.
We propose C-SURE, a novel Stein's unbiased risk estimate (SURE) of the JS
estimator on the manifold of complex-valued data with a theoretically proven
optimum over MLE. Adapting the architecture of the complex-valued SurReal
classifier, we further incorporate C-SURE into a prototype convolutional neural
network (CNN) classifier. We compare C-SURE with SurReal and a real-valued
baseline on complex-valued MSTAR and RadioML datasets.
C-SURE is more accurate and robust than SurReal, and the shrinkage estimator
is always better than MLE for the same prototype classifier. Like SurReal,
C-SURE is much smaller, outperforming the real-valued baseline on MSTAR
(RadioML) with less than 1 percent (3 percent) of the baseline sizeComment: Submitted to CVPR PBVS worksho
Quantum NETwork: from theory to practice
The quantum internet is envisioned as the ultimate stage of the quantum
revolution, which surpasses its classical counterpart in various aspects, such
as the efficiency of data transmission, the security of network services, and
the capability of information processing. Given its disruptive impact on the
national security and the digital economy, a global race to build scalable
quantum networks has already begun. With the joint effort of national
governments, industrial participants and research institutes, the development
of quantum networks has advanced rapidly in recent years, bringing the first
primitive quantum networks within reach. In this work, we aim to provide an
up-to-date review of the field of quantum networks from both theoretical and
experimental perspectives, contributing to a better understanding of the
building blocks required for the establishment of a global quantum internet. We
also introduce a newly developed quantum network toolkit to facilitate the
exploration and evaluation of innovative ideas. Particularly, it provides dual
quantum computing engines, supporting simulations in both the quantum circuit
and measurement-based models. It also includes a compilation scheme for mapping
quantum network protocols onto quantum circuits, enabling their emulations on
real-world quantum hardware devices. We showcase the power of this toolkit with
several featured demonstrations, including a simulation of the Micius quantum
satellite experiment, a testing of a four-layer quantum network architecture
with resource management, and a quantum emulation of the CHSH game. We hope
this work can give a better understanding of the state-of-the-art development
of quantum networks and provide the necessary tools to make further
contributions along the way.Comment: 36 pages, 33 figures; comments are welcom
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