Estimating the Volume of the Solution Space of SMT(LIA) Constraints by a Flat Histogram Method

The satisfiability modulo theories (SMT) problem is to decide the satisfiability of a logical formula with respect to a given background theory. This work studies the counting version of SMT with respect to linear integer arithmetic (LIA), termed SMT(LIA). Specifically, the purpose of this paper is to count the number of solutions (volume) of a SMT(LIA) formula, which has many important applications and is computationally hard. To solve the counting problem, an approximate method that employs a recent Markov Chain Monte Carlo (MCMC) sampling strategy called “flat histogram” is proposed. Furthermore, two refinement strategies are proposed for the sampling process and result in two algorithms, MCMC-Flat1/2 and MCMC-Flat1/t, respectively. In MCMC-Flat1/t, a pseudo sampling strategy is introduced to evaluate the flatness of histograms. Experimental results show that our MCMC-Flat1/t method can achieve good accuracy on both structured and random instances, and our MCMC-Flat1/2 is scalable for instances of convex bodies with up to 7 variables

Dynamic Vision Sensor Tracking Method Based on Event Correlation Index

Dynamic vision sensor is a kind of bioinspired sensor. It has the characteristics of fast response, large dynamic range, and asynchronous output event stream. These characteristics make it have advantages that traditional image sensors do not have in the field of tracking. The output form of the dynamic vision sensor is asynchronous event stream, and the object information needs to be provided by the relevant event cluster. This article proposes a method based on the event correlation index to obtain the object’s position, contour, and other information and is compatible with traditional tracking methods. Experiments show that this method can obtain the position information of the moving object and its continuous motion trajectory and analyze the influence of the parameters on the tracking effect. This method will have broad application prospects in security, transportation, etc

Edge Collaborative Online Task Offloading Method Based on Reinforcement Learning

With the vigorous development of industries such as self-driving, edge intelligence, and the industrial Internet of Things (IoT), the amount and type of data generated are unprecedentedly large, and users’ demand for high-quality services continues to increase. Edge computing has emerged as a new paradigm, providing storage, computing, and networking resources between traditional cloud data centers and end devices with solid timeliness. Therefore, the resource allocation problem in the online task offloading process is the main area of research. It is aimed at the task offloading problem of delay-sensitive customers under capacity constraints in the online task scenario. In this paper, a new edge collaborative online task offloading management algorithm based on the deep reinforcement learning method OTO-DRL is designed. Based on that, a large number of simulations are carried out on synthetic and real data sets, taking obstacle recognition and detection in unmanned driving as a specific task and experiment. Compared with other advanced methods, OTO-DRL can well realize the increase in the number of tasks requested by mobile terminal users in the field of edge collaboration while guaranteeing the service quality of task requests with higher priority

Event-Based Optical Flow Estimation with Spatio-Temporal Backpropagation Trained Spiking Neural Network

The advantages of an event camera, such as low power consumption, large dynamic range, and low data redundancy, enable it to shine in extreme environments where traditional image sensors are not competent, especially in high-speed moving target capture and extreme lighting conditions. Optical flow reflects the target’s movement information, and the target’s detailed movement can be obtained using the event camera’s optical flow information. However, the existing neural network methods for optical flow prediction of event cameras has the problems of extensive computation and high energy consumption in hardware implementation. The spike neural network has spatiotemporal coding characteristics, so it can be compatible with the spatiotemporal data of an event camera. Moreover, the sparse coding characteristic of the spike neural network makes it run with ultra-low power consumption on neuromorphic hardware. However, because of the algorithmic and training complexity, the spike neural network has not been applied in the prediction of the optical flow for the event camera. For this case, this paper proposes an end-to-end spike neural network to predict the optical flow of the discrete spatiotemporal data stream for the event camera. The network is trained with the spatio-temporal backpropagation method in a self-supervised way, which fully combines the spatiotemporal characteristics of the event camera while improving the network performance. Compared with the existing methods on the public dataset, the experimental results show that the method proposed in this paper is equivalent to the best existing methods in terms of optical flow prediction accuracy, and it can save 99% more power consumption than the existing algorithm, which is greatly beneficial to the hardware implementation of the event camera optical flow prediction., laying the groundwork for future low-power hardware implementation of optical flow prediction for event cameras

Event Density Based Denoising Method for Dynamic Vision Sensor

Dynamic vision sensor (DVS) is a new type of image sensor, which has application prospects in the fields of automobiles and robots. Dynamic vision sensors are very different from traditional image sensors in terms of pixel principle and output data. Background activity (BA) in the data will affect image quality, but there is currently no unified indicator to evaluate the image quality of event streams. This paper proposes a method to eliminate background activity, and proposes a method and performance index for evaluating filter performance: noise in real (NIR) and real in noise (RIN). The lower the value, the better the filter. This evaluation method does not require fixed pattern generation equipment, and can also evaluate filter performance using natural images. Through comparative experiments of the three filters, the comprehensive performance of the method in this paper is optimal. This method reduces the bandwidth required for DVS data transmission, reduces the computational cost of target extraction, and provides the possibility for the application of DVS in more fields

Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment

To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model’s understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model

Atomic ruthenium-riveted metal-organic framework with tunable d-band modulates oxygen redox for lithium-oxygen batteries

Non-aqueous Li-O2 batteries have aroused considerable attention because of their ultrahigh theoretical energy density, but they are severely hindered by slow cathode reaction kinetics and large overvoltages, which are closely associated with the discharge product of Li2O2. Herein, hexagonal conductive metal-organic framework nanowire arrays of nickel-hexaiminotriphenylene (Ni-HTP) with quadrilateral Ni-N4 units are synthesized to incorporate Ru atoms into its skeleton for NiRu-HTP. The atomically dispersed Ru-N4 sites manifest strong adsorption for the LiO2 intermediate owing to its tunable d-band center, leading to its high local concentration around NiRu-HTP. This favors the formation of film-like Li2O2 on NiRu-HTP with promoted electron transfer and ion diffusion across the cathode-electrolyte interface, facilitating its reversible decomposition during charge. These allow the Li-O2 battery with NiRu-HTP to deliver a remarkably reduced charge/discharge polarization of 0.76 V and excellent cyclability. This work will enrich the design philosophy of electrocatalysts for regulation of kinetic behaviors of oxygen redox.This work was supported by National Natural Science Foundation of China (52171215), the Tianjin Natural Science Foundation (19JCJQJC62400), and Haihe Laboratory of Sustainable Chemical Transformations

Evaluating efficacy and safety of sub-anesthetic dose esketamine as an adjuvant to propofol/remifentanil analgosedation and spontaneous respiration for children flexible fibreoptic bronchoscopy: a prospective, double-blinded, randomized, and placebo-controlled clinical trial

Background: Flexible fiberoptic bronchoscopy (FFB) for children is widely performed under sedation. Currently, the optimal sedation regimen remains unclear. Esketamine is an N-methyl-D-aspartic acid (NMDA) receptor antagonist, which has stronger sedative and analgesic effects and exerts less cardiorespiratory depression than other sedatives. The purpose of this study was to evaluate whether a subanesthetic dose of esketamine as an adjuvant to propofol/remifentanil and spontaneous ventilation compared with control reduces the procedural and anesthesia-related complications of FFB in children.Materials and methods: Seventy-two children ≤ 12 years of age who were scheduled for FFB were randomly assigned, in a 1:1 ratio, to the esketamine-propofol/remifentanil (Group S, n = 36) or to the propofol/remifentanil group (Group C, n = 36). All children were retained spontaneous ventilation. The primary outcome was the incidence of oxygen desaturation (respiratory depression). Perioperative hemodynamic variables, blood oxygen saturation (SPO2), end-tidal partial pressure of carbon dioxide (PetCO2), respiratory rate (R), and the Bispectral index (BIS), induction time, procedural time, recovery time, the time to the ward from the recovery room, consumption of propofol and remifentanil during the procedure and the appearance of adverse events, including paradoxical agitation following midazolam administration, injection pain, laryngospasm, bronchospasm, PONV, vertigo, and hallucination were also compared.Results: The incidence of oxygen desaturation was significantly lower in Group S (8.3%) compared to Group C (36.1%, p = 0.005). The perioperative hemodynamic profile including SBP, DBP, and HR were more stable in Group S than that in Group C (p < 0.05). Consumption of propofol and remifentanil was lower in Group S than in Group C (p < 0.05). Furthermore, PAED scores, cough scores and injection pain were lower in the Group S than in Group C (p < 0.05). The recovery time of Group S was slightly longer than that of Group C (p < 0.05). Nobody happened paradoxical agitation following midazolam administration, PONV, vertigo, and hallucinations in both groups (p > 0.05).Conclusion: Our findings indicate that a subanesthetic dose of esketamine as an adjuvant to propofol/remifentanil and spontaneous respiration is an effective regimen for children undergoing FFB. Our findings will provide a reference for clinical sedation practice during these procedures in children.Clinical Trail Registration: Chinese clinicaltrials.gov registry (identifier: ChiCTR2100053302)

An HDR imaging method with DTDI technology for push-broom cameras

Abstract Conventionally, high dynamic-range (HDR) imaging is based on taking two or more pictures of the same scene with different exposure. However, due to a high-speed relative motion between the camera and the scene, it is hard for this technique to be applied to push-broom remote sensing cameras. For the sake of HDR imaging in push-broom remote sensing applications, the present paper proposes an innovative method which can generate HDR images without redundant image sensors or optical components. Specifically, this paper adopts an area array CMOS (complementary metal oxide semiconductor) with the digital domain time-delay-integration (DTDI) technology for imaging, instead of adopting more than one row of image sensors, thereby taking more than one picture with different exposure. And then a new HDR image by fusing two original images with a simple algorithm can be achieved. By conducting the experiment, the dynamic range (DR) of the image increases by 26.02 dB. The proposed method is proved to be effective and has potential in other imaging applications where there is a relative motion between the cameras and scenes

The neural mechanisms of immediate and follow-up of the treatment effect of hypnosis on smoking craving

Hypnosis has a therapeutic effect on substance dependence. However, its neural basis remains unclear, which impedes its further clinical applications. This study investigated the mechanisms of smoking treatment based on hypnosis from two perspectives: immediate and follow-up effects. Twenty-four smokers screened from 132 volunteers underwent hypnosis suggestion and performed a smoking-related cue task twice during functional magnetic resonance imaging (fMRI) scanning (in normal and hypnotic states). The number of cigarettes smoked per day was recorded at follow-up visits.The smokers reported decreased craving after hypnosis. The activations in the right dorsal lateral prefrontal cortex (rDLPFC), the left insula and the right middle frontal gyrus (rMFG), and the functional connectivity between the rDLPFC and the left insula were increased in the hypnotic state. The reduced craving was related to the DLPFC-insula network, which reflected the immediate mechanism of hypnosis on smoking. The number of cigarette use at the 1-week and 1 month follow-up was correlated with the rMFG activation which reflecting hypnotic depth, suggesting the follow-up effect of hypnosis on smoking depended on the trait of smokers. We identified two different mechanisms of hypnosis effect on smoking, which have important implications for design and optimization of hypnotic treatments on mental disorders.</p