Learning to Learn Variational Semantic Memory

In this paper, we introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning. The variational semantic memory accrues and stores semantic information for the probabilistic inference of class prototypes in a hierarchical Bayesian framework. The semantic memory is grown from scratch and gradually consolidated by absorbing information from tasks it experiences. By doing so, it is able to accumulate long-term, general knowledge that enables it to learn new concepts of objects. We formulate memory recall as the variational inference of a latent memory variable from addressed contents, which offers a principled way to adapt the knowledge to individual tasks. Our variational semantic memory, as a new long-term memory module, confers principled recall and update mechanisms that enable semantic information to be efficiently accrued and adapted for few-shot learning. Experiments demonstrate that the probabilistic modelling of prototypes achieves a more informative representation of object classes compared to deterministic vectors. The consistent new state-of-the-art performance on four benchmarks shows the benefit of variational semantic memory in boosting few-shot recognition.Comment: accepted to NeurIPS 2020; code is available in https://github.com/YDU-uva/VS

Coherent imaging using laser feedback interferometry with pulsed-mode terahertz quantum cascade lasers

We report a coherent terahertz (THz) imaging system that utilises a quantum cascade laser (QCL) operating in pulsed-mode as both the source and detector. The realisation of a short-pulsed THz QCL feedback interferometer permits both high peak powers and improved thermal efficiency, which enables the cryogen-free operation of the system. In this work, we demonstrated pulsed-mode swept-frequency laser feedback interferometry experimentally. Our interferometric detection scheme not only permits the simultaneous creation of both amplitude and phase images, but inherently suppresses unwanted background radiation. We demonstrate that the proposed system utilising microsecond pulses has the potential to achieve 0.25 mega-pixel per second acquisition rates, paving the pathway to video frame rate THz imaging

Application Communities Detection in Network

The continuous growth of Internet traffic and its applications causes more difficulties for analyzing Internet communications. It has become an increasingly challenging task to discover latent community structure and find abnormal behavior patterns in network communication. In this paper, we propose a new type of network community—the application community—which can help understand large network structure and find anomaly network behavior. To detect such a community, a method is proposed whose first step is aggregating the nodes according to their topological relationships of the communication. It then clusters different application nodes according to the communication behavior modes in the same topological partition. Empirical results show that this method can accurately detect communities of different applications without any prior knowledge. In addition, it can identify the communities more accurately than other methods. Thus, this research greatly benefits the administration of IoT and cyber security

Spatial and temporal population genetic variation and structure of Nothotsuga longibracteata (Pinaceae), a relic conifer species endemic to subtropical China

Nothotsuga longibracteata, a relic and endangered conifer species endemic to subtropical China, was studied for examining the spatial-temporal population genetic variation and structure to understand the historical biogeographical processes underlying the present geographical distribution. Ten populations were sampled over the entire natural range of the species for spatial analysis, while three key populations with large population sizes and varied age structure were selected for temporal analyses using both nuclear microsatellites (nSSR) and chloroplast microsatellites (cpSSR). A recent bottleneck was detected in the natural populations of N. longibracteata. The spatial genetic analysis showed significant population genetic differentiation across its total geographical range. Notwithstanding, the temporal genetic analysis revealed that the level of genetic diversity between different age class subpopulations remained constant over time. Eleven refugia of the Last Glacial Maximum were identified, which deserve particular attention for conservation management

Goose Nephritic Astrovirus Infection of Goslings Induces Lymphocyte Apoptosis, Reticular Fiber Destruction, and CD8 T-Cell Depletion in Spleen Tissue

The emergence of a novel goose nephritic astrovirus (GNAstV) has caused economic losses to the Chinese goose industry. High viral load is found in the spleen of goslings infected with GNAstV, but pathological injuries to the spleen due to GNAstV are largely unknown. In this study, 50 two-day-old goslings were infected orally with GNAstV, and 50 goslings were treated with PBS as control. Spleens were collected at different times following infection to assess damage. GNAstV infection caused visceral gout and urate deposition in joints, and resulted in 16% mortality. GNAstV was found in the lymphocytes and macrophages within the spleen. Lymphocyte loss, especially around the white pulp, and destruction and decline in the number of reticular fibers was observed in GNAstV-infected goslings. Moreover, in GNAstV-infected goslings, ultrahistopathological examination found that splenic lymphocytes exhibited condensed chromatin and apoptotic bodies, and reticular cells displayed damage to plasma membrane integrity and swollen mitochondria. Furthermore, TUNEL staining confirmed apoptosis of lymphocytes, and the mRNA levels of Fas and FasL were significantly increased in the GNAstV-infected goslings. In addition, GNAstV infection reduced the number and protein expression of CD8. In conclusion, GNAstV infection causes lymphocyte depletion, reticular cell necrosis, reticular fiber destruction, lymphocyte apoptosis, and reduction in CD8 levels, which contribute to spleen injury

Learning to Learn with Variational Information Bottleneck for Domain Generalization

© 2020, Springer Nature Switzerland AG. Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift. In this paper, we address both problems. We introduce a probabilistic meta-learning model for domain generalization, in which classifier parameters shared across domains are modeled as distributions. This enables better handling of prediction uncertainty on unseen domains. To deal with domain shift, we learn domain-invariant representations by the proposed principle of meta variational information bottleneck, we call MetaVIB. MetaVIB is derived from novel variational bounds of mutual information, by leveraging the meta-learning setting of domain generalization. Through episodic training, MetaVIB learns to gradually narrow domain gaps to establish domain-invariant representations, while simultaneously maximizing prediction accuracy. We conduct experiments on three benchmarks for cross-domain visual recognition. Comprehensive ablation studies validate the benefits of MetaVIB for domain generalization. The comparison results demonstrate our method outperforms previous approaches consistently

Carbon Nanotube Feedback-Gate Field-Effect Transistor: Suppressing Current Leakage and Increasing On/Off Ratio

Field-effect transistors (FETs) based on moderate or large diameter carbon nanotubes (CNTs) usually suffer from ambipolar behavior, large off-state current and small current on/off ratio, which are highly undesirable for digital electronics. To overcome these problems, a feedback-gate (FBG) FET structure is designed and tested. This FBG FET differs from normal top-gate FET by an extra feedback-gate, which is connected directly to the drain electrode of the FET. It is demonstrated that a FBG FET based on a semiconducting CNT with a diameter of 1.5 nm may exhibit low off-state current of about 1 × 10^–13 A, high current on/off ratio of larger than 1 × 10⁸, negligible drain-induced off-state leakage current, and good subthreshold swing of 75 mV/DEC even at large source-drain bias and room temperature. The FBG structure is promising for CNT FETs to meet the standard for low-static-power logic electronics applications, and could also be utilized for building FETs using other small band gap semiconductors to suppress leakage current