Superconductivity and metallic behavior in heavily doped bulk single crystal diamond and graphene/diamond heterostructure

Owing to extremely large band gap of 5.5 eV and high thermal conductivity, diamond is recognized as the most important semiconductor. The superconductivity of polycrystalline diamond has always been reported, but there are also many controversies over the existence of superconductivity in bulk single crystal diamond and it remains a question whether a metallic state exists for such a large band gap semiconductor. Herein, we realize a single crystal superconducting diamond with a Hall carrier concentration larger than 3*1020 cm-3 by co-doped of boron and nitrogen. Furthermore, we show that diamond can transform from superconducting to metallic state under similar carrier concentration with tuned carrier mobility degrading from 9.10 cm2 V-1 s-1 or 5.30 cm2 V-1 s-1 to 2.66 cm2 V-1 s-1 or 1.34 cm2 V-1 s-1. Through integrating graphene on a nitrogen and boron heavily co-doped diamond, the monolayer graphene can be superconducting through combining Andreev reflection and exciton mediated superconductivity, which may intrigue more interesting superconducting behavior of diamond heterostructure

PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning

Online class-incremental continual learning is a specific task of continual learning. It aims to continuously learn new classes from data stream and the samples of data stream are seen only once, which suffers from the catastrophic forgetting issue, i.e., forgetting historical knowledge of old classes. Existing replay-based methods effectively alleviate this issue by saving and replaying part of old data in a proxy-based or contrastive-based replay manner. Although these two replay manners are effective, the former would incline to new classes due to class imbalance issues, and the latter is unstable and hard to converge because of the limited number of samples. In this paper, we conduct a comprehensive analysis of these two replay manners and find that they can be complementary. Inspired by this finding, we propose a novel replay-based method called proxy-based contrastive replay (PCR). The key operation is to replace the contrastive samples of anchors with corresponding proxies in the contrastive-based way. It alleviates the phenomenon of catastrophic forgetting by effectively addressing the imbalance issue, as well as keeps a faster convergence of the model. We conduct extensive experiments on three real-world benchmark datasets, and empirical results consistently demonstrate the superiority of PCR over various state-of-the-art methods.Comment: To appear in CVPR 2023. 10 pages, 8 figures and 3 table

UER: A Heuristic Bias Addressing Approach for Online Continual Learning

Online continual learning aims to continuously train neural networks from a continuous data stream with a single pass-through data. As the most effective approach, the rehearsal-based methods replay part of previous data. Commonly used predictors in existing methods tend to generate biased dot-product logits that prefer to the classes of current data, which is known as a bias issue and a phenomenon of forgetting. Many approaches have been proposed to overcome the forgetting problem by correcting the bias; however, they still need to be improved in online fashion. In this paper, we try to address the bias issue by a more straightforward and more efficient method. By decomposing the dot-product logits into an angle factor and a norm factor, we empirically find that the bias problem mainly occurs in the angle factor, which can be used to learn novel knowledge as cosine logits. On the contrary, the norm factor abandoned by existing methods helps remember historical knowledge. Based on this observation, we intuitively propose to leverage the norm factor to balance the new and old knowledge for addressing the bias. To this end, we develop a heuristic approach called unbias experience replay (UER). UER learns current samples only by the angle factor and further replays previous samples by both the norm and angle factors. Extensive experiments on three datasets show that UER achieves superior performance over various state-of-the-art methods. The code is in https://github.com/FelixHuiweiLin/UER.Comment: 9 pages, 12 figures, ACM MM202

HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning

Online continual learning (OCL) aims to continuously learn new data from a single pass over the online data stream. It generally suffers from the catastrophic forgetting issue. Existing replay-based methods effectively alleviate this issue by replaying part of old data in a proxy-based or contrastive-based replay manner. In this paper, we conduct a comprehensive analysis of these two replay manners and find they can be complementary. Inspired by this finding, we propose a novel replay-based method called proxy-based contrastive replay (PCR), which replaces anchor-to-sample pairs with anchor-to-proxy pairs in the contrastive-based loss to alleviate the phenomenon of forgetting. Based on PCR, we further develop a more advanced method named holistic proxy-based contrastive replay (HPCR), which consists of three components. The contrastive component conditionally incorporates anchor-to-sample pairs to PCR, learning more fine-grained semantic information with a large training batch. The second is a temperature component that decouples the temperature coefficient into two parts based on their impacts on the gradient and sets different values for them to learn more novel knowledge. The third is a distillation component that constrains the learning process to keep more historical knowledge. Experiments on four datasets consistently demonstrate the superiority of HPCR over various state-of-the-art methods.Comment: 18 pages, 11 figure

MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning

Equipping a deep model the abaility of few-shot learning, i.e., learning quickly from only few examples, is a core challenge for artificial intelligence. Gradient-based meta-learning approaches effectively address the challenge by learning how to learn novel tasks. Its key idea is learning a deep model in a bi-level optimization manner, where the outer-loop process learns a shared gradient descent algorithm (i.e., its hyperparameters), while the inner-loop process leverage it to optimize a task-specific model by using only few labeled data. Although these existing methods have shown superior performance, the outer-loop process requires calculating second-order derivatives along the inner optimization path, which imposes considerable memory burdens and the risk of vanishing gradients. Drawing inspiration from recent progress of diffusion models, we find that the inner-loop gradient descent process can be actually viewed as a reverse process (i.e., denoising) of diffusion where the target of denoising is model weights but the origin data. Based on this fact, in this paper, we propose to model the gradient descent optimizer as a diffusion model and then present a novel task-conditional diffusion-based meta-learning, called MetaDiff, that effectively models the optimization process of model weights from Gaussion noises to target weights in a denoising manner. Thanks to the training efficiency of diffusion models, our MetaDiff do not need to differentiate through the inner-loop path such that the memory burdens and the risk of vanishing gradients can be effectvely alleviated. Experiment results show that our MetaDiff outperforms the state-of-the-art gradient-based meta-learning family in few-shot learning tasks.Comment: Accepted by AAAI 202

Direct-Current Generator Based on Dynamic Water-Semiconductor Junction with Polarized Water as Moving Dielectric Medium

There is a rising prospective in harvesting energy from water droplets, as microscale energy is required for the distributed sensors in the interconnected human society. However, achieving a sustainable direct-current generating device from water flow is rarely reported, and the quantum polarization principle of the water molecular remains uncovered. Herein, we propose a dynamic water-semiconductor junction with moving water sandwiched between two semiconductors as a moving dielectric medium, which outputs a sustainable direct-current voltage of 0.3 V and current of 0.64 uA with low internal resistance of 390 kilohm. The sustainable direct-current electricity is originating from the dynamic water polarization process in water-semiconductor junction, in which water molecules are continuously polarized and depolarized driven by the mechanical force and Fermi level difference, during the movement of the water on silicon. We further demonstrated an encapsulated portable power-generating device with simple structure and continuous direct-current voltage, which exhibits its promising potential application in the field of wearable electronic generators

Rabies virus pseudotyped with CVS-N2C glycoprotein as a powerful tool for retrograde neuronal network tracing

Abstract Background: Efficient viral vectors for mapping and manipulating long projection neuronal circuits are crucial in brain structural and functional studies. The glycoprotein gene-deleted SAD strain rabies virus pseudotyped with the N2C glycoprotein (SAD-RV(ΔG)-N2C(G)) shows high neuro-tropism in cell culture, but its in vivo retrograde infection efficiency and neuro-tropism have not been systematically characterized. Methods: SAD-RV(ΔG)-N2C(G) and two other broadly used retrograde tracers, SAD-RV(ΔG)-B19(G) and rAAV2-retro were respectively injected into the VTA or DG in C57BL/6 mice. The neuron numbers labeled across the whole brain regions were counted and analyzed by measuring the retrograde infection efficiencies and tropisms of these viral tools. The labeled neural types were analyzed using fluorescence immunohistochemistry or GAD67-GFP mice. Result: We found that SAD-RV (ΔG)-N2C (G) enhanced the infection efficiency of long-projecting neurons by ~ 10 times but with very similar neuro-tropism, compared with SAD-RV (ΔG)-B19(G). On the other hand, SAD-RV(ΔG)-N2C(G) showed comparable infection efficiency with rAAV2-retro, but had a more restricted diffusion range, and broader tropism to different types and regions of long-projecting neuronal populations. Conclusions: These results demonstrate that SAD-RV(ΔG)-N2C(G) can serve as an effective retrograde vector for studying neuronal circuits. Key words：Viral vector, N2C Glycoprotein, Neuronal circuits, Retrograde tracin