219 research outputs found
Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation
Existing autoregressive models follow the two-stage generation paradigm that
first learns a codebook in the latent space for image reconstruction and then
completes the image generation autoregressively based on the learned codebook.
However, existing codebook learning simply models all local region information
of images without distinguishing their different perceptual importance, which
brings redundancy in the learned codebook that not only limits the next stage's
autoregressive model's ability to model important structure but also results in
high training cost and slow generation speed. In this study, we borrow the idea
of importance perception from classical image coding theory and propose a novel
two-stage framework, which consists of Masked Quantization VAE (MQ-VAE) and
Stackformer, to relieve the model from modeling redundancy. Specifically,
MQ-VAE incorporates an adaptive mask module for masking redundant region
features before quantization and an adaptive de-mask module for recovering the
original grid image feature map to faithfully reconstruct the original images
after quantization. Then, Stackformer learns to predict the combination of the
next code and its position in the feature map. Comprehensive experiments on
various image generation validate our effectiveness and efficiency. Code will
be released at https://github.com/CrossmodalGroup/MaskedVectorQuantization.Comment: accepted by CVPR 202
Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming
To facilitate the development of active distribution networks with high penetration of large‐scale distributed generation (DG) and electric vehicles (EVs), active management strategies should be considered at the planning stage to implement the coordinated optimal allocations of DG and electric vehicle charging stations (EVCSs). In this article, EV charging load curves are obtained by the Monte Carlo simulation method. This article reduces the number of photovoltaic outputs and load scenarios by the K‐means++ clustering algorithm to obtain a typical scenario set. Additionally, we propose a bi‐level programming model for the coordinated DG and EVCSs planning problem. The maximisation of annual overall profit for the power supply company is taken as the objective function for the upper planning level. Then, each scenario is optimised at the lower level by using active management strategies. The improved harmonic particle swarm optimisation algorithm is used to solve the bi‐level model. The validation results for the IEEE‐33 node, PG&E‐69 node test system and an actual regional 30‐node distribution network show that the bi‐level programming model proposed in this article can improve the planning capacity of DG and EVCSs, and effectively increase the annual overall profit of the power supply company, while improving environmental and social welfare, and reducing system power losses and voltage shifts. The study provides a new perspective on the distribution network planning problem.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/1/etep12366.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/2/etep12366_am.pd
Is ProtoPNet Really Explainable? Evaluating and Improving the Interpretability of Prototypes
ProtoPNet and its follow-up variants (ProtoPNets) have attracted broad
research interest for their intrinsic interpretability from prototypes and
comparable accuracy to non-interpretable counterparts. However, it has been
recently found that the interpretability of prototypes can be corrupted due to
the semantic gap between similarity in latent space and that in input space. In
this work, we make the first attempt to quantitatively evaluate the
interpretability of prototype-based explanations, rather than solely
qualitative evaluations by some visualization examples, which can be easily
misled by cherry picks. To this end, we propose two evaluation metrics, termed
consistency score and stability score, to evaluate the explanation consistency
cross images and the explanation robustness against perturbations, both of
which are essential for explanations taken into practice. Furthermore, we
propose a shallow-deep feature alignment (SDFA) module and a score aggregation
(SA) module to improve the interpretability of prototypes. We conduct
systematical evaluation experiments and substantial discussions to uncover the
interpretability of existing ProtoPNets. Experiments demonstrate that our
method achieves significantly superior performance to the state-of-the-arts,
under both the conventional qualitative evaluations and the proposed
quantitative evaluations, in both accuracy and interpretability. Codes are
available at https://github.com/hqhQAQ/EvalProtoPNet
Large-Signal Stability Criteria in DC Power Grids with Distributed-Controlled Converters and Constant Power Loads
The increasing adoption of power electronic devices may lead to large
disturbance and destabilization of future power systems. However, stability
criteria are still an unsolved puzzle, since traditional small-signal stability
analysis is not applicable to power electronics-enabled power systems when a
large disturbance occurs, such as a fault, a pulse power load, or load
switching. To address this issue, this paper presents for the first time the
rigorous derivation of the sufficient criteria for large-signal stability in DC
microgrids with distributed-controlled DC-DC power converters. A novel type of
closed-loop converter controllers is designed and considered. Moreover, this
paper is the first to prove that the well-known and frequently cited
Brayton-Moser mixed potential theory (published in 1964) is incomplete. Case
studies are carried out to illustrate the defects of Brayton-Moser mixed
potential theory and verify the effectiveness of the proposed novel stability
criteria
Data-Driven Power System Optimal Decision Making Strategy under Wildfire Events
Wildfire activities are increasing in the western United States in recent years, causing escalating threats to power systems. This paper developed an optimal and data-driven decision-making framework that improves power system resilience under wildfire risks. An optimal load shedding plan is formulated based on optimal power flow analysis. To avoid power system cascading failure caused by wildfire, we added additional transmission line flow constraints based on the identification of power lines with high ignition risk. Finally, a data-driven method is developed, leveraging multiple machine learning techniques, to model the complex correlations between input wildfire scenarios and the output power management strategy with significantly reduced computational complexities. The proposed data-driven decision-making framework can reduce the safety impacts on the electricity consumers, improve power system resilience under wildfire events
Reaction kinetics of CN + toluene and its implication on the productions of aromatic nitriles in the Taurus molecular cloud and Titan's atmosphere
Reactions between cyano radical and aromatic hydrocarbons are believed to be
important pathways for the formation of aromatic nitriles in the interstellar
medium (ISM) including those identified in the Taurus molecular cloud (TMC-1).
Aromatic nitriles might participate in the formation of polycyclic aromatic
nitrogen containing hydrocarbons (PANHs) in Titan's atmosphere. Here, ab initio
kinetics simulations reveal a high efficiency of and the competition of the different products of
30-1800 K and -100 atm of the CN + toluene reaction. In the
star-forming region of TMC-1 environment, the product yields of benzonitrile
and tolunitriles for CN reacting with toluene may be approximately 17 and
83, respectively. The detection of main products, tolunitriles, can serve
as proxies for the undetected toluene in the ISM due to their much larger
dipole moments. The competition between bimolecular and unimolecular products
is extremely intense under the warmer and denser PANH forming region of Titan's
stratosphere. The computational results show that the fractions of
tolunitriles, adducts, and benzonitrile are 19-68, 15-64 and
17, respectively, at 150-200 K and 0.0001-0.001 atm (Titan's stratosphere).
Then, benzonitrile and tolunitriles may contribute to the formation of PANHs by
consecutive additions. Kinetic information of aromatic nitriles
for the CN + toluene reaction calculated here helps to explain the formation
mechanism of polycyclic aromatic hydrocarbons (PAHs) or PANHs under different
interstellar environments and constrains corresponding astrochemical models
Spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: A case study in Singapore
Singapore Management University; Ministry of Education, Singapore under its Academic Research Funding Tier
Chronic lead exposure reduces doublecortinexpressing immature neurons in young adult guinea pig cerebral cortex
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