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A review of microgrid development in the United States – A decade of progress on policies, demonstrations, controls, and software tools
Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
To relieve the pain of manually selecting machine learning algorithms and
tuning hyperparameters, automated machine learning (AutoML) methods have been
developed to automatically search for good models. Due to the huge model search
space, it is impossible to try all models. Users tend to distrust automatic
results and increase the search budget as much as they can, thereby undermining
the efficiency of AutoML. To address these issues, we design and implement
ATMSeer, an interactive visualization tool that supports users in refining the
search space of AutoML and analyzing the results. To guide the design of
ATMSeer, we derive a workflow of using AutoML based on interviews with machine
learning experts. A multi-granularity visualization is proposed to enable users
to monitor the AutoML process, analyze the searched models, and refine the
search space in real time. We demonstrate the utility and usability of ATMSeer
through two case studies, expert interviews, and a user study with 13 end
users.Comment: Published in the ACM Conference on Human Factors in Computing Systems
(CHI), 2019, Glasgow, Scotland U
FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization
One underlying assumption of recent federated learning (FL) paradigms is that
all local models usually share the same network architecture and size, which
becomes impractical for devices with different hardware resources. A scalable
federated learning framework should address the heterogeneity that clients have
different computing capacities and communication capabilities. To this end,
this paper proposes FedHM, a novel heterogeneous federated model compression
framework, distributing the heterogeneous low-rank models to clients and then
aggregating them into a full-rank model. Our solution enables the training of
heterogeneous models with varying computational complexities and aggregates
them into a single global model. Furthermore, FedHM significantly reduces the
communication cost by using low-rank models. Extensive experimental results
demonstrate that FedHM is superior in the performance and robustness of models
of different sizes, compared with state-of-the-art heterogeneous FL methods
under various FL settings. Additionally, the convergence guarantee of FL for
heterogeneous devices is first theoretically analyzed
Gridless Evolutionary Approach for Line Spectral Estimation with Unknown Model Order
Gridless methods show great superiority in line spectral estimation. These
methods need to solve an atomic norm (i.e., the continuous analog of
norm) minimization problem to estimate frequencies and model order. Since
this problem is NP-hard to compute, relaxations of atomic norm, such as
nuclear norm and reweighted atomic norm, have been employed for promoting
sparsity. However, the relaxations give rise to a resolution limit,
subsequently leading to biased model order and convergence error. To overcome
the above shortcomings of relaxation, we propose a novel idea of simultaneously
estimating the frequencies and model order by means of the atomic norm.
To accomplish this idea, we build a multiobjective optimization model. The
measurment error and the atomic norm are taken as the two optimization
objectives. The proposed model directly exploits the model order via the atomic
norm, thus breaking the resolution limit. We further design a
variable-length evolutionary algorithm to solve the proposed model, which
includes two innovations. One is a variable-length coding and search strategy.
It flexibly codes and interactively searches diverse solutions with different
model orders. These solutions act as steppingstones that help fully exploring
the variable and open-ended frequency search space and provide extensive
potentials towards the optima. Another innovation is a model order pruning
mechanism, which heuristically prunes less contributive frequencies within the
solutions, thus significantly enhancing convergence and diversity. Simulation
results confirm the superiority of our approach in both frequency estimation
and model order selection.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Orbit- and Atom-Resolved Spin Textures of Intrinsic, Extrinsic and Hybridized Dirac Cone States
Combining first-principles calculations and spin- and angle-resolved
photoemission spectroscopy measurements, we identify the helical spin textures
for three different Dirac cone states in the interfaced systems of a 2D
topological insulator (TI) of Bi(111) bilayer and a 3D TI Bi2Se3 or Bi2Te3. The
spin texture is found to be the same for the intrinsic Dirac cone of Bi2Se3 or
Bi2Te3 surface state, the extrinsic Dirac cone of Bi bilayer state induced by
Rashba effect, and the hybridized Dirac cone between the former two states.
Further orbit- and atom-resolved analysis shows that s and pz orbits have a
clockwise (counterclockwise) spin rotation tangent to the iso-energy contour of
upper (lower) Dirac cone, while px and py orbits have an additional radial spin
component. The Dirac cone states may reside on different atomic layers, but
have the same spin texture. Our results suggest that the unique spin texture of
Dirac cone states is a signature property of spin-orbit coupling, independent
of topology
Broadband, High-Reflectivity Dielectric Mirrors at Wafer Scale: Combining Photonic Crystal and Metasurface Architectures for Advanced Lightsails
Highly ambitious initiatives aspire to propel a miniature spacecraft to a
neighboring star within a human generation, leveraging the radiation pressure
of lasers for propulsion. One of the main challenges to achieving this enormous
feat is to build a meter-scale, ultra-low mass lightsail with broadband
reflectivity. In this work, we present the design and fabrication of such a
lightsail composed of two distinct dielectric layers and patterned with a
photonic crystal structure covering a 4" wafer. We overcome the crucial
challenge of achieving broad band reflection of >70% spanning over the full
Doppler-shifted laser wavelength range during spacecraft acceleration, in
combination with low total mass in the range of a few grams when scaled to
meter size. Furthermore, we find new paths to reliably fabricate these
sub-wavelength structures over macroscopic areas and then systematically
characterize their optical performance, confirming their suitability for future
lightsail applications. Our innovative device design and precise
nanofabrication approaches represent a significant leap toward interstellar
exploration
The scalars from the topcolor scenario and the spin correlations of the top pair production at the LHC
The topcolor scenario predicts the existences of some new scalars. In this
paper, we consider the contributions of these new particles to the observables,
which are related to the top quark pair () production at the LHC. It
is found that these new particles can generate significant corrections to the
production cross section and the spin correlations.Comment: 23 pages, 4 figures; discussions and references added; agrees with
published versio
Sulforaphane induces adipocyte browning and promotes glucose and lipid utilization
Scope: Obesity is closely related to the imbalance of white adipose tissue storing excess calories, and brown adipose tissue dissipating energy to produce heat in mammals. Recent studies revealed that acquisition of brown characteristics by white adipocytes, termed “browning,” may positively contribute to cellular bioenergetics and metabolism homeostasis. The goal was to investigate the putative effects of natural antioxidant sulforaphane (1-isothiocyanate-4-methyl-sulfonyl butane; SFN) on browning of white adipocytes. Methods and Results: 3T3-L1 mature white adipocytes were treated with SFN for 48 h, and then the mitochondrial content, function, and energy utilization were assessed. SFN was found to induce 3T3-L1 adipocytes browning based on the increased mitochondrial content and activity of respiratory chain enzymes, whereas the mechanism involved the upregulation of nuclear factor E2-related factor 2/ sirtuin1/ peroxisome proliferator-activated receptor gamma coactivator 1 alpha signaling. SFN enhanced uncoupling protein 1 expression, a marker for brown adipocyte, leading to the decrease in cellular ATP. SFN also enhanced glucose uptake and oxidative utilization, lipolysis and fatty acid oxidation in 3T3-L1 adipocytes. Conclusion: SFN-induced browning of white adipocytes enhanced the utilization of cellular fuel, and the application of SFN is a promising strategy to combat obesity and obesity-related metabolic disorder
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