A Faster, Lighter and Stronger Deep Learning-Based Approach for Place Recognition

Visual Place Recognition is an essential component of systems for camera localization and loop closure detection, and it has attracted widespread interest in multiple domains such as computer vision, robotics and AR/VR. In this work, we propose a faster, lighter and stronger approach that can generate models with fewer parameters and can spend less time in the inference stage. We designed RepVGG-lite as the backbone network in our architecture, it is more discriminative than other general networks in the Place Recognition task. RepVGG-lite has more speed advantages while achieving higher performance. We extract only one scale patch-level descriptors from global descriptors in the feature extraction stage. Then we design a trainable feature matcher to exploit both spatial relationships of the features and their visual appearance, which is based on the attention mechanism. Comprehensive experiments on challenging benchmark datasets demonstrate the proposed method outperforming recent other state-of-the-art learned approaches, and achieving even higher inference speed. Our system has 14 times less params than Patch-NetVLAD, 6.8 times lower theoretical FLOPs, and run faster 21 and 33 times in feature extraction and feature matching. Moreover, the performance of our approach is 0.5\% better than Patch-NetVLAD in Recall@1. We used subsets of Mapillary Street Level Sequences dataset to conduct experiments for all other challenging conditions.Comment: CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW

DNMT3a in the hippocampal CA1 is crucial in the acquisition of morphine self‐administration in rats

Drug‐reinforced excessive operant responding is one fundamental feature of long-lasting addiction‐like behaviors and relapse in animals. However, the transcriptional regulatory mechanisms responsible for the persistent drug‐specific (not natural rewards) operant behavior are not entirely clear. In this study, we demonstrate a key role for one of the de novo DNA methyltransferase, DNMT3a, in the acquisition of morphine self‐administration (SA) in rats. The expression of DNMT3a in the hippocampal CA1 region but not in the nucleus accumbens shell was significantly up‐regulated after 1‐ and 7‐day morphine SA (0.3 mg/kg/infusion) but not after the yoked morphine injection. On the other hand, saccharin SA did not affect the expression of DNMT3a or DNMT3b. DNMT inhibitor 5‐aza‐2‐deoxycytidine (5‐aza) microinjected into the hippocampal CA1 significantly attenuated the acquisition of morphine SA. Knockdown of DNMT3a also impaired the ability to acquire the morphine SA. Overall, these findings suggest that DNMT3a in the hippocampus plays an important role in the acquisition of morphine SA and may be a valid target to prevent the development of morphine addiction. Includes Supplemental informatio

Unconventional many-body phase transitions in a non-Hermitian Ising chain

We study many-body phase transitions in a one-dimensional ferromagnetic transversed field Ising model with an imaginary field and show that the system exhibits three phase transitions: one second-order phase transition and two $\mathcal{PT}$ phase transitions. The second-order phase transition occurring in the ground state is investigated via biorthogonal and self-normal entanglement entropy, for which we develop an approach to perform finite-size scaling theory to extract the central charge for small systems. Compared with the second-order phase transition, the first $\mathcal{PT}$ transition is characterized by the appearance of an exceptional point in the full energy spectrum, while the second $\mathcal{PT}$ transition only occurs in specific excited states. Furthermore, we interestingly show that both of exceptional points are second-order in terms of scalings of imaginary parts of the energy. This work provides an exact solution for unconventional many-body phase transitions in non-Hermitian systems.Comment: 7 pages, 5 figure

Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients

Deep learning based approaches like Physics-informed neural networks (PINNs) and DeepONets have shown promise on solving PDE constrained optimization (PDECO) problems. However, existing methods are insufficient to handle those PDE constraints that have a complicated or nonlinear dependency on optimization targets. In this paper, we present a novel bi-level optimization framework to resolve the challenge by decoupling the optimization of the targets and constraints. For the inner loop optimization, we adopt PINNs to solve the PDE constraints only. For the outer loop, we design a novel method by using Broyden's method based on the Implicit Function Theorem (IFT), which is efficient and accurate for approximating hypergradients. We further present theoretical explanations and error analysis of the hypergradients computation. Extensive experiments on multiple large-scale and nonlinear PDE constrained optimization problems demonstrate that our method achieves state-of-the-art results compared with strong baselines

A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs

We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary conditions (BCs) are considered. Specifically, we first introduce the "extra fields" from the mixed finite element method to reformulate the PDEs so as to equivalently transform the three types of BCs into linear forms. Based on the reformulation, we derive the general solutions of the BCs analytically, which are employed to construct an ansatz that automatically satisfies the BCs. With such a framework, we can train the neural networks without adding extra loss terms and thus efficiently handle geometrically complex PDEs, alleviating the unbalanced competition between the loss terms corresponding to the BCs and PDEs. We theoretically demonstrate that the "extra fields" can stabilize the training process. Experimental results on real-world geometrically complex PDEs showcase the effectiveness of our method compared with state-of-the-art baselines.Comment: 10 pages, 6 figures, NeurIPS 202

A comparative study on the dependence potential of thienorphine and buprenorphine

Background: As part of research to discover partial opioid agonists for new treatments of opioid abuse and dependency, thienorphine, a buprenorphine analogue, was synthesised and reported to be a potent, long-acting oripavine in multiple mammalian models. Thienorphine binds non-selectively to μ-, δ-, and κ-opioid receptors, and partially stimulates μ- and/or κ-opioid receptors in vitro. Compared with buprenorphine, thienorphine exhibits better analgesic effects and has higher oral bioavailability. Poor oral absorption and dependence have hindered the use of buprenorphine for detoxification therapy and relapse prevention in the clinic. The addiction potential of thienorphine is unknown, and is worthy of in-depth investigation. Methods: In the present study, we conducted a comparison of thienorphine and buprenorphine with respect to their physical and psychological dependence liabilities, using a naloxone-induced withdrawal test, a conditioned place preference test, and a self-administration experiment in rats. Results: In contrast to chronic buprenorphine administration, we failed to observe any severe abstinence syndromes in mice or rats treated with thienorphine after naloxone challenge in a physical dependence model. Compared with the dependence potentials of buprenorphine, rats treated with chronic thienorphine did not show a place conditioning response, self-administration, or psychological dependence. Conclusions: We demonstrated that thienorphine has a lower potential than buprenorphine for physical and psychological dependence. Our results indicate that thienorphine might be a good candidate to treat opioid addiction