699 research outputs found
XCon: Learning with Experts for Fine-grained Category Discovery
We address the problem of generalized category discovery (GCD) in this paper,
i.e. clustering the unlabeled images leveraging the information from a set of
seen classes, where the unlabeled images could contain both seen classes and
unseen classes. The seen classes can be seen as an implicit criterion of
classes, which makes this setting different from unsupervised clustering where
the cluster criteria may be ambiguous. We mainly concern the problem of
discovering categories within a fine-grained dataset since it is one of the
most direct applications of category discovery, i.e. helping experts discover
novel concepts within an unlabeled dataset using the implicit criterion set
forth by the seen classes. State-of-the-art methods for generalized category
discovery leverage contrastive learning to learn the representations, but the
large inter-class similarity and intra-class variance pose a challenge for the
methods because the negative examples may contain irrelevant cues for
recognizing a category so the algorithms may converge to a local-minima. We
present a novel method called Expert-Contrastive Learning (XCon) to help the
model to mine useful information from the images by first partitioning the
dataset into sub-datasets using k-means clustering and then performing
contrastive learning on each of the sub-datasets to learn fine-grained
discriminative features. Experiments on fine-grained datasets show a clear
improved performance over the previous best methods, indicating the
effectiveness of our method
Revisiting Nyquist-Like Impedance-Based Criteria for Converter-Based AC Systems
Multiple types of Nyquist-like impedance-based criteria are utilized for the
small-signal stability analysis of converter-based AC systems. It is usually
considered that the determinant-based criterion can determine the overall
stability of a system while the eigenvalue-based criterion can give more
insights into the mechanism of the instability. This paper specifies such
understandings starting with the zero-pole calculation of impedance matrices
obtained by state-spaces with the Smith-McMillan form, then clarifying the
absolute reliability of determinant-based criterion with the common assumption
for impedance-based analysis that each subsystem can stably operate before the
interconnection. However, ambiguities do exist for the eigenvalue-based
criterion when an anticlockwise encirclement around the origin is observed in
the Nyquist plot. To this end, a logarithmic derivative-based criterion to
directly identify the system modes using the frequency responses of loop
impedances is proposed, which owns a solid theoretical basis of the Schur
complement of transfer function matrices. The theoretical analysis is validated
using a PSCAD simulation of a grid-connected two-level voltage source
converter.Comment: Accepted by CSEE JPE
Slip-enhanced Rayleigh-Plateau instability of a liquid film on a fibre
Boundary conditions at a liquid-solid interface are crucial to dynamics of a
liquid film coated on a fibre. Here a theoretical framework based on
axisymmetric Stokes equations is developed to explore the influence of
liquid-solid slip on the Rayleigh-Plateau instability of a cylindrical film on
a fibre. The new model not only shows that the slip-enhanced growth rate of
perturbations is overestimated by the classical lubrication model, but also
indicates a slip-dependent dominant wavelength, instead of a constant value
obtained by the lubrication method, which leads to larger drops formed on a
more slippery fibre. The theoretical findings are validated by direct numerical
simulations of Navier-Stokes equations via a volume-of-fluid method.
Additionally, the slip-dependent dominant wavelengths predicted by our model
agree with the experimental results provided by Haefner. et al.[Nat. Commun.,
Vol. 6(1), 2015, 18 pp. 1-6]
Toward high-performance nanostructured thermoelectric materials: The progress of bottom-up solution chemistry approaches
Significant research effort has recently gone into the synthesis of thermoelectric nanomaterials through different chemical approaches since nanomaterials chemistry became a promising strategy for improving thermoelectric performance. Different thermoelectric nanocrystals, especially PbTe, Bi2Te3 and CoSb3, with various compositions and morphologies have been successfully prepared by solvo/hydrothermal, electrochemical, and ligand-based synthesis methods. Such nanoscale materials show not only substantial reduction in thermal conductivity due to increased phonon scattering at nanoscale grain boundaries and lower densities of phonon states but possibly also an enhancement in thermopower due to electronic quantum size effects. More recently, the notoriously low power factors of thermoelectric nanomaterials prepared by wet chemistry have been significantly improved by using an increasingly cross-disciplinary approach towards the bottom-up synthesis that combines expertise from chemistry, physics, and materials engineering. In this review, we discuss the recent progress and current challenges of preparing thermoelectric nanomaterials with solution-based chemistry approaches
Photocatalytic Activity of MOF-derived Cu2O/Cu/C/Ag Porous Composites
Cu2O/Cu/C/Ag porous composite was synthesized by heat-treatment and wet-chemical method using a typical metal-organic framework (Cu-BTC) asĀ precursor. The samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS) andĀ ultraviolet-visible spectroscopy (UV-vis). The results showed that the originalstructure of Cu-BTC was retained by high temperature calcination in nitrogen atmosphere. Uniform doping of Cu, C and Ag provided a triple trapping of photogenerated electron hole pairs and the Cu2O/Cu/C/Ag exhibited an enhanced photocatalytic activity for degradation of Congo Red under visible light irradiation. Heat-treatment of the MOFs with high temperature is afacile and effective way for preparation of photocatalytic composite with desirable properties.Keywords: Photocatalyst, cuprous oxide, dye degradation, Cu-BTC
Digital Business Strategy as an Initiator of E-business Capability Generation
Nowadays, many firms formulate and execute digital business strategy to leverage the opportunities of e-business value-creation. In this study, we present a business-level strategic perspective of e-business value-creation and suggest that e-business capability is enabled from strategically deploying IT resources in the inter-organizational context. We propose a research model to capture multiple relationships among digital business strategy, IT resources, and e-business capabilities. The research model was tested using a national survey data from 131 Chinese manufacturing firms. Empirical findings showed that steered by digital business strategy, firms focused on leveraging digital linking, IT human resources, and channel partner relationship in e-business, whereas the exploitation of these resources generated inter-organizational e-business capabilities. This study extends our understanding of the initiation mechanism and the evolving process of e-business values captured through digital business strategy
Coupled hydro-mechanical evolution of fracture permeability in sand injectite intrusions
Acknowledgments The authors would like to thank the support in using FracPaQ from Roberto Rizzo in the University of Aberdeen. We also appreciate the financial support from the Laboratory of Coal Resources and Safe Mining (China University of Mining and Technology, Beijing) (Grant No. SKLCRSM16KFC01).Peer reviewedPublisher PD
Non-intrusive Load Monitoring based on Self-supervised Learning
Deep learning models for non-intrusive load monitoring (NILM) tend to require
a large amount of labeled data for training. However, it is difficult to
generalize the trained models to unseen sites due to different load
characteristics and operating patterns of appliances between data sets. For
addressing such problems, self-supervised learning (SSL) is proposed in this
paper, where labeled appliance-level data from the target data set or house is
not required. Initially, only the aggregate power readings from target data set
are required to pre-train a general network via a self-supervised pretext task
to map aggregate power sequences to derived representatives. Then, supervised
downstream tasks are carried out for each appliance category to fine-tune the
pre-trained network, where the features learned in the pretext task are
transferred. Utilizing labeled source data sets enables the downstream tasks to
learn how each load is disaggregated, by mapping the aggregate to labels.
Finally, the fine-tuned network is applied to load disaggregation for the
target sites. For validation, multiple experimental cases are designed based on
three publicly accessible REDD, UK-DALE, and REFIT data sets. Besides,
state-of-the-art neural networks are employed to perform NILM task in the
experiments. Based on the NILM results in various cases, SSL generally
outperforms zero-shot learning in improving load disaggregation performance
without any sub-metering data from the target data sets.Comment: 12 pages,10 figure
GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis
With the ever-growing popularity of Graph Neural Networks (GNNs), efficient
GNN inference is gaining tremendous attention. Field-Programming Gate Arrays
(FPGAs) are a promising execution platform due to their fine-grained
parallelism, low-power consumption, reconfigurability, and concurrent
execution. Even better, High-Level Synthesis (HLS) tools bridge the gap between
the non-trivial FPGA development efforts and rapid emergence of new GNN models.
In this paper, we propose GNNHLS, an open-source framework to comprehensively
evaluate GNN inference acceleration on FPGAs via HLS, containing a software
stack for data generation and baseline deployment, and FPGA implementations of
6 well-tuned GNN HLS kernels. We evaluate GNNHLS on 4 graph datasets with
distinct topologies and scales. The results show that GNNHLS achieves up to
50.8x speedup and 423x energy reduction relative to the CPU baselines. Compared
with the GPU baselines, GNNHLS achieves up to 5.16x speedup and 74.5x energy
reduction
- ā¦