Self-paced Weight Consolidation for Continual Learning

Continual learning algorithms which keep the parameters of new tasks close to that of previous tasks, are popular in preventing catastrophic forgetting in sequential task learning settings. However, 1) the performance for the new continual learner will be degraded without distinguishing the contributions of previously learned tasks; 2) the computational cost will be greatly increased with the number of tasks, since most existing algorithms need to regularize all previous tasks when learning new tasks. To address the above challenges, we propose a self-paced Weight Consolidation (spWC) framework to attain robust continual learning via evaluating the discriminative contributions of previous tasks. To be specific, we develop a self-paced regularization to reflect the priorities of past tasks via measuring difficulty based on key performance indicator (i.e., accuracy). When encountering a new task, all previous tasks are sorted from "difficult" to "easy" based on the priorities. Then the parameters of the new continual learner will be learned via selectively maintaining the knowledge amongst more difficult past tasks, which could well overcome catastrophic forgetting with less computational cost. We adopt an alternative convex search to iteratively update the model parameters and priority weights in the bi-convex formulation. The proposed spWC framework is plug-and-play, which is applicable to most continual learning algorithms (e.g., EWC, MAS and RCIL) in different directions (e.g., classification and segmentation). Experimental results on several public benchmark datasets demonstrate that our proposed framework can effectively improve performance when compared with other popular continual learning algorithms

PYATB: An Efficient Python Package for Electronic Structure Calculations Using Ab Initio Tight-Binding Model

We present PYATB, a Python package designed for computing band structures and related properties of materials using the ab initio tight-binding Hamiltonian. The Hamiltonian is directly obtained after conducting self-consistent calculations with first-principles packages using numerical atomic orbital (NAO) bases, such as ABACUS. The package comprises three modules: Bands, Geometric, and Optical. In the Bands module, one can calculate essential properties of band structures, including the partial density of states (PDOS), fat bands, Fermi surfaces, and Weyl/Dirac points. The band unfolding method is utilized to obtain the energy band spectra of a supercell by projecting the electronic structure of the supercell onto the Brillouin zone of the primitive cell. With the Geometric module, one can compute the Berry phase and Berry curvature-related quantities, such as electric polarization, Wilson loops, Chern numbers, and anomalous Hall conductivities. The Optical module offers a range of optical property calculations, including optical conductivity and nonlinear optical responses, such as shift current and Berry curvature dipole

Block Coordinate Plug-and-Play Methods for Blind Inverse Problems

Plug-and-play (PnP) prior is a well-known class of methods for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and learned image denoisers. While PnP methods have been extensively used for image recovery with known measurement operators, there is little work on PnP for solving blind inverse problems. We address this gap by presenting a new block-coordinate PnP (BC-PnP) method that efficiently solves this joint estimation problem by introducing learned denoisers as priors on both the unknown image and the unknown measurement operator. We present a new convergence theory for BC-PnP compatible with blind inverse problems by considering nonconvex data-fidelity terms and expansive denoisers. Our theory analyzes the convergence of BC-PnP to a stationary point of an implicit function associated with an approximate minimum mean-squared error (MMSE) denoiser. We numerically validate our method on two blind inverse problems: automatic coil sensitivity estimation in magnetic resonance imaging (MRI) and blind image deblurring. Our results show that BC-PnP provides an efficient and principled framework for using denoisers as PnP priors for jointly estimating measurement operators and images

Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees

Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models-implicit neural networks with effectively infinite number of layers-were shown to achieve state-of-the-art image reconstruction without the memory complexity associated with DU. While the performance of DEQ has been widely investigated, the existing work has primarily focused on the settings where groundtruth data is available for training. We present self-supervised deep equilibrium model (SelfDEQ) as the first self-supervised reconstruction framework for training model-based implicit networks from undersampled and noisy MRI measurements. Our theoretical results show that SelfDEQ can compensate for unbalanced sampling across multiple acquisitions and match the performance of fully supervised DEQ. Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data

Pokemon Silencing Leads to Bim-Mediated Anoikis of Human Hepatoma Cell QGY7703

Pokemon is an important proto-oncogene that plays a critical role in cellular oncogenic transformation and tumorigenesis. Anoikis, which is regulated by Bim-mediated apoptosis, is critical to cancer cell invasion and metastasis. We investigated the role of Pokemon in anoikis, and our results show that Pokemon renders liver cells resistant to anoikis via suppression of Bim transcription. We knocked-down Pokemon in human hepatoma cells QGY7703 with small interfering RNAs (siRNA). Knockdown of Pokemon alone did not significantly affect the growth and survival of QGY7703 cells but notably enhanced their sensitivity to apoptotic stress due to the presence of chemical agents or cell detachment, thereby inducing anoikis, as evidenced by flow cytometry and caspase-3 activity assays. In contrast, ectopic expression of Pokemon in HL7702 cells led to resistance to anoikis. Dual-luciferase reporter and ChIP assays illustrated that Pokemon suppressed Bim transcription via direct binding to its promoter. Our results suggest that Pokemon prevents anoikis through the suppression of Bim expression, which facilitates tumor cell invasion and metastasis. This Pokemon-Bim pathway may be an effective target for therapeutic intervention for cancer

A Sustainable Dual Cross-Linked Cellulose Hydrogel Electrolyte for High-Performance Zinc-Metal Batteries

Highlights A sustainable dual cross-linked cellulose hydrogel with excellent mechanical strength was fabricated from aqueous alkali hydroxide/urea solution using a sequential chemical and physical cross-linking strategy. The hydrogel electrolyte effectively suppresses dendrites growth and side reactions to achieve a stable Zn anode (over 2000 h for Zn||Zn cell), which are proved by a multi-perspective and in-depth mechanism investigation. The hydrogel electrolyte is easily accessible and biodegradable, making the zinc batteries attractive in terms of scalability and sustainability

A Lightweight Sensitive Triboelectric Nanogenerator Sensor for Monitoring Loop Drive Technology in Table Tennis Training

As the Internet of Things becomes more and more mainstream, sensors are widely used in the field of motion monitoring. In this paper, we propose a lightweight and sensitive triboelectric nanogenerator (LS-TENG) consisting of transparent polytetrafluoroethylene (PTFE) and polyamide (PA) films as triboelectric layers, polydimethylsiloxane (PDMS) as support layer, and copper foil as electrode. LS-TENG can be attached to the joints of the human body, and the mechanical energy generated by human motion is converted into electric energy based on the triboelectric effect, thus realizing self-power supply. LS-TENG can monitor the angle changes in elbow and wrist joints when athletes pull the loop and actively generate the output voltage as a sensing signal, which is convenient for coaches to monitor the quality of athletes’ hitting in real time. In addition, LS-TENG can also be used as a power supply for other wireless electronic devices, which facilitates the construction and transmission of large motion data and opens up a new development direction for the field of motion monitoring

A Lightweight Sensitive Triboelectric Nanogenerator Sensor for Monitoring Loop Drive Technology in Table Tennis Training

As the Internet of Things becomes more and more mainstream, sensors are widely used in the field of motion monitoring. In this paper, we propose a lightweight and sensitive triboelectric nanogenerator (LS-TENG) consisting of transparent polytetrafluoroethylene (PTFE) and polyamide (PA) films as triboelectric layers, polydimethylsiloxane (PDMS) as support layer, and copper foil as electrode. LS-TENG can be attached to the joints of the human body, and the mechanical energy generated by human motion is converted into electric energy based on the triboelectric effect, thus realizing self-power supply. LS-TENG can monitor the angle changes in elbow and wrist joints when athletes pull the loop and actively generate the output voltage as a sensing signal, which is convenient for coaches to monitor the quality of athletes’ hitting in real time. In addition, LS-TENG can also be used as a power supply for other wireless electronic devices, which facilitates the construction and transmission of large motion data and opens up a new development direction for the field of motion monitoring