Quasiparticle band alignment and stacking-independent exciton in MA2_2Z4_4 (M = Mo, W, Ti; A= Si, Ge; Z = N, P, As)

Motivated by the recently synthesized two-dimensional semiconducting MoSi$_2$N$_4$, we systematically investigate the quasiparticle band alignment and exciton in monolayer MA$_2$Z$_4$ (M = Mo, W, Ti; A= Si, Ge; Z = N, P, As) using ab initio GW and Bethe-Salpeter equation calculations. Compared with the results from density functional theory (DFT), our GW calculations reveal substantially more significant band gaps and different absolute quasiparticle energy but predict the same types of band alignments

An Efficient Top-k Query Scheme Based on Multilayer Grouping

The top-k query is to find the k data that has the highest scores from a candidate dataset. Sorting is a common method to find out top-k results. However, most of existing methods are not efficient enough. To remove this issue, we propose an efficient top-k query scheme based on multilayer grouping. First, we find the reference item by computing the average score of the candidate dataset. Second, we group the candidate dataset into three datasets: winner set, middle set and loser set based on the reference item. Third, we further group the winner set to the second-layer three datasets according to k value. And so on, until the data number of winner set is close to k value. Meanwhile, if k value is larger than the data number of winner set, we directly return the winner set to the user as a part of top-k results almost without sorting. In this case, we also return the top results with the highest scores from the middle set almost without sorting. Based on above innovations, we almost minimize the sorting. Experimental results show that our scheme significantly outperforms the current classical method on the performance of memory consumption and top-k query

An Efficient Feature Extraction Scheme for Mobile Anti-Shake in Augmented Reality

In recent years, augmented reality on mobile devices has become popular. Mobile shakes are the most typical type of interference in mobile augmented reality. To negate such interference, anti-shake is an urgent requirement. To enhance anti-shake efficiency, we propose an efficient feature extraction scheme for mobile anti-shake in augmented reality. The scheme directly detects corners to avoid the non-extreme constraint such that the efficiency of feature extraction is improved. Meanwhile, the scheme only updates the added corners during mobile shakes, which improves the accuracy of feature extraction. In the experiments, the memory consumption of existing methods is almost double compared to that in our scheme. Further, the runtime of our scheme is only half of the runtime of the existing methods. The experimental results demonstrate that our scheme performs better than the existing classic methods on mobile anti-shake in terms of memory consumption, efficiency, and accuracy

Reversible Watermarking Using Prediction-Error Expansion and Extreme Learning Machine

Currently, the research for reversible watermarking focuses on the decreasing of image distortion. Aiming at this issue, this paper presents an improvement method to lower the embedding distortion based on the prediction-error expansion (PE) technique. Firstly, the extreme learning machine (ELM) with good generalization ability is utilized to enhance the prediction accuracy for image pixel value during the watermarking embedding, and the lower prediction error results in the reduction of image distortion. Moreover, an optimization operation for strengthening the performance of ELM is taken to further lessen the embedding distortion. With two popular predictors, that is, median edge detector (MED) predictor and gradient-adjusted predictor (GAP), the experimental results for the classical images and Kodak image set indicate that the proposed scheme achieves improvement for the lowering of image distortion compared with the classical PE scheme proposed by Thodi et al. and outperforms the improvement method presented by Coltuc and other existing approaches

A Survey on Generative Diffusion Model

Deep learning shows excellent potential in generation tasks thanks to deep latent representation. Generative models are classes of models that can generate observations randomly concerning certain implied parameters. Recently, the diffusion Model has become a rising class of generative models by its power-generating ability. Nowadays, great achievements have been reached. More applications except for computer vision, speech generation, bioinformatics, and natural language processing are to be explored in this field. However, the diffusion model has its genuine drawback of a slow generation process, single data types, low likelihood, and the inability for dimension reduction. They are leading to many enhanced works. This survey makes a summary of the field of the diffusion model. We first state the main problem with two landmark works -- DDPM and DSM, and a unified landmark work -- Score SDE. Then, we present improved techniques for existing problems in the diffusion-based model field, including speed-up improvement For model speed-up improvement, data structure diversification, likelihood optimization, and dimension reduction. Regarding existing models, we also provide a benchmark of FID score, IS, and NLL according to specific NFE. Moreover, applications with diffusion models are introduced including computer vision, sequence modeling, audio, and AI for science. Finally, there is a summarization of this field together with limitations \& further directions. The summation of existing well-classified methods is in our Github:https://github.com/chq1155/A-Survey-on-Generative-Diffusion-Model

Development of Mechanostimulated Patch-Clamp System for Cellular Physiological Study

Mechanosensitive ion channels play important roles for sensing and responding to the mechanical stimuli signals in living life. Here we report the development of a mechanostimulated patch-clamp system for simultaneous recording of external stimuli and acquisition of cellular physiological responses. This system integrates a custom-designed planar patch-clamp system with a robot-assisted atomic force microscope (AFM) system. The former, with a microfluidic channel, can realize not only recording electrical signals but also exchanging intracellular solution; while the latter, enhanced by robotic techniques (local scan force feedback, augmented reality vision feedback), can generate force stimuli with controllable patterns and magnitudes under the operator’s real-time monitoring. To verify the performance of the developed system, we first measured the whole-cell current of the voltagegated potassium ion channel Kv1.1 expressed on Human Embryonic Kidney (HEK293) cells and then recorded the mechanosensitive ion channel current in amouse neuroblastoma cell line (Neuro2 A) in the whole-cell configuration during the AFM indenting on the membrane surface; finally, confirmed the ability to exchange intracellular solution by delivering propidium iodide into the captured cell through intracellular solution. The results prove the effectiveness of the system

DISCO: Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes

Large-scale well-annotated datasets are of great importance for training an effective object detector. However, obtaining accurate bounding box annotations is laborious and demanding. Unfortunately, the resultant noisy bounding boxes could cause corrupt supervision signals and thus diminish detection performance. Motivated by the observation that the real ground-truth is usually situated in the aggregation region of the proposals assigned to a noisy ground-truth, we propose DIStribution-aware CalibratiOn (DISCO) to model the spatial distribution of proposals for calibrating supervision signals. In DISCO, spatial distribution modeling is performed to statistically extract the potential locations of objects. Based on the modeled distribution, three distribution-aware techniques, i.e., distribution-aware proposal augmentation (DA-Aug), distribution-aware box refinement (DA-Ref), and distribution-aware confidence estimation (DA-Est), are developed to improve classification, localization, and interpretability, respectively. Extensive experiments on large-scale noisy image datasets (i.e., Pascal VOC and MS-COCO) demonstrate that DISCO can achieve state-of-the-art detection performance, especially at high noise levels.Comment: 12 pages, 9 figure

Disorder-induced excitation continuum in a spin-1/2 cobaltate on a triangular lattice

A spin-1/2 triangular-lattice antiferromagnet is a prototypical frustrated quantum magnet, which exhibits remarkable quantum many-body effects that arise from the synergy between geometric spin frustration and quantum fluctuations. It can host quantum frustrated magnetic topological phenomena like quantum spin liquid (QSL) states, highlighted by the presence of fractionalized quasiparticles within a continuum of magnetic excitations. In this work, we use neutron scattering to study CoZnMo$_3$O$_8$, which has a triangular lattice of Jeff = 1/2 Co2+ ions with octahedral coordination. We found a wave-vector-dependent excitation continuum at low energy that disappears with increasing temperature. Although these excitations are reminiscent of a spin excitation continuum in a QSL state, their presence in CoZnMo$_3$O$_8$ originates from magnetic intersite disorder-induced dynamic spin states with peculiar excitations. Our results, therefore, give direct experimental evidence for the presence of a disorder-induced spin excitation continuum