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) as  precursor. The samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS) and  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