R&B: Region and Boundary Aware Zero-shot Grounded Text-to-image Generation

Recent text-to-image (T2I) diffusion models have achieved remarkable progress in generating high-quality images given text-prompts as input. However, these models fail to convey appropriate spatial composition specified by a layout instruction. In this work, we probe into zero-shot grounded T2I generation with diffusion models, that is, generating images corresponding to the input layout information without training auxiliary modules or finetuning diffusion models. We propose a Region and Boundary (R&B) aware cross-attention guidance approach that gradually modulates the attention maps of diffusion model during generative process, and assists the model to synthesize images (1) with high fidelity, (2) highly compatible with textual input, and (3) interpreting layout instructions accurately. Specifically, we leverage the discrete sampling to bridge the gap between consecutive attention maps and discrete layout constraints, and design a region-aware loss to refine the generative layout during diffusion process. We further propose a boundary-aware loss to strengthen object discriminability within the corresponding regions. Experimental results show that our method outperforms existing state-of-the-art zero-shot grounded T2I generation methods by a large margin both qualitatively and quantitatively on several benchmarks.Comment: Preprint. Under review. Project page: https://sagileo.github.io/Region-and-Boundar

Research on bus elastic departure interval based on Wavelet Neural Network

In recent years, more and more people choose to travel by bus to save time and economic costs, but the problem of inaccurate bus arrival has become increasingly prominent. The reason is the lack of scientific planning of departure time. This paper takes the passenger flow as an important basis for departure interval, proposes a passenger flow prediction method based on wavelet neural network, and uses intelligent optimization algorithm to study the bus elastic departure interval. In this paper, the wavelet neural network prediction model and the elastic departure interval optimization model are established, and then the model is solved by substituting the data, and finally the theoretical optimal departure interval is obtained

A Stacking Machine Learning Method for IL-10-Induced Peptide Sequence Recognition Based on Unified Deep Representation Learning

Interleukin-10 (IL-10) has anti-inflammatory properties and is a crucial cytokine in regulating immunity. The identification of IL-10 through wet laboratory experiments is costly and time-intensive. Therefore, a new IL-10-induced peptide recognition method, IL10-Stack, was introduced in this research, which was based on unified deep representation learning and a stacking algorithm. Two approaches were employed to extract features from peptide sequences: Amino Acid Index (AAindex) and sequence-based unified representation (UniRep). After feature fusion and optimized feature selection, we selected a 1900-dimensional UniRep feature vector and constructed the IL10-Stack model using stacking. IL10-Stack exhibited excellent performance in IL-10-induced peptide recognition (accuracy (ACC) = 0.910, Matthews correlation coefficient (MCC) = 0.820). Relative to the existing methods, IL-10Pred and ILeukin10Pred, the approach increased in ACC by 12.1% and 2.4%, respectively. The IL10-Stack method can identify IL-10-induced peptides, which aids in the development of immunosuppressive drugs

A deep reinforcement learning assisted task offloading and resource allocation approach towards self-driving object detection

Abstract With the development of communication technology and mobile edge computing (MEC), self-driving has received more and more research interests. However, most object detection tasks for self-driving vehicles are still performed at vehicle terminals, which often requires a trade-off between detection accuracy and speed. To achieve efficient object detection without sacrificing accuracy, we propose an end–edge collaboration object detection approach based on Deep Reinforcement Learning (DRL) with a task prioritization mechanism. We use a time utility function to measure the efficiency of object detection task and aim to provide an online approach to maximize the average sum of the time utilities in all slots. Since this is an NP-hard mixed-integer nonlinear programming (MINLP) problem, we propose an online approach for task offloading and resource allocation based on Deep Reinforcement learning and Piecewise Linearization (DRPL). A deep neural network (DNN) is implemented as a flexible solution for learning offloading strategies based on road traffic conditions and wireless network environment, which can significantly reduce computational complexity. In addition, to accelerate DRPL network convergence, DNN outputs are grouped by in-vehicle cameras to form offloading strategies via permutation. Numerical results show that the DRPL scheme is at least 10% more effective and superior in terms of time utility compared to several representative offloading schemes for various vehicle local computing resource scenarios

Multiple satellite and ground clock sources‐based high‐precision time synchronization and lossless switching for distribution power system

Abstract Precise energy management in distribution power system requires high‐precision time synchronization among large‐scale deployed devices. Multiple clock sources‐based time synchronization possesses advantages of reliability, high precision, and robustness, but still faces several challenges such as coupling between time synchronization error and delay, as well as different timescales between clock source and clock weight optimization. In this paper, a multi‐clock source time synchronization model is constructed and a problem is formulated to minimize the synchronization error and delay through jointly optimizing large‐timescale clock source selection and small‐timescale weight selection. A reinforcement learning‐based multi‐timescale multi‐clock source time synchronization algorithm named RL‐M2 is proposed to solve the formulated problem from a learning perspective. Besides, a lossless switching method is proposed to address the switching problem for multiple clock sources. Simulation results demonstrate the superior performance of RL‐M2 and the lossless switching method in time synchronization delay and error

Effects of Fly Maggot Protein Replacement of Fish Meal on Growth Performance, Immune Level, Antioxidant Level, and Fecal Flora of Blue Foxes at Weaning Stage

Dietary protein is a key nutritional parameter and warrants special attention in animal husbandry. This study aimed to evaluate the effect of replacing fish meal (F) with fly maggot protein (M) on the growth performance, antioxidant levels, immune indexes, and fecal microflora in weaned blue foxes (Alopex lagopus). Twenty weaned blue foxes were randomly assigned to the control (F diet; 6% of F) or experimental (M diet; F substituted by M) group (10 blue foxes per group). The duration of the trial was 28 days. The results showed that there was no significant difference in average daily gain between group M and group F during the experiment (p = 0.473). Moreover, the diarrhea index was similar between group M and group F during the entire experimental period (p = 0.112). At the end of the experiment, the levels of IL-6 and IgG in group M at 28 d were significantly higher than that in group F (p = 0.004, p = 0.025, respectively), but not IL-1β, IL-2, SIgA, IgM, and TNF-α. The levels of SOD in group M at 28 d were significantly higher than those in group F (p = 0.001), and no difference of MDA and T-AOC was found between group F and M (p = 0.073, p = 0.196, respectively). In both groups, the diversity of fecal microbes first increased and then decreased with the progress of the experimental period. Initially, there were differences in the composition of microbial communities between the two groups. However, this difference was attenuated at later stages of the experimental period. In conclusion, fly maggot protein can replace fish meal as a source of animal protein in feed material for blue foxes during the weaning period

Neo-5,22E-Cholestadienol Derivatives from Buthus martensii Karsch and Targeted Bactericidal Action Mechanisms

The discovery and search for new antimicrobial molecules from insects and animals that live in polluted environments is a very important step in the scientific search for solutions to the current problem of antibiotic resistance. Previously, we have reported that the secondary metabolite with the antibacterial action discovered in scorpion. The current study further isolated three new compounds from Buthus martensii karsch, while compounds 1 and 2 possessed 5,22E-cholestadienol derivatives whose structure demonstrated broad spectrum bactericide activities. To explore the antibacterial properties of these new compounds, the result shows that compound 2 inhibited bacterial growth of both S. aureus and P. aeruginosa in a bactericidal rather than a bacteriostatic manner (MBC/MIC ratio ≤ 2). Similarly, with compound 1, a ratio of MBC/MIC ≤ 2 indicates bactericidal activity inhibited bacterial growth of P. aeruginosa. Remarkably, this suggests that two compounds can be classified as bactericidal agents against broad spectrum bactericide activities for 5,22E-cholestadienol derivatives from Buthus martensii karsch. The structures of compounds 1–3 were established by comprehensive spectra analysis including two-dimensional nuclear magnetic resonance (2D-NMR) and high-resolution electrospray ionization-mass spectrometry (HRESI-MS) spectra. The antibacterial mechanism is the specific binding (various of bonding forces between molecules) using compound 1 or 2 as a ligand based on the different receptor proteins’—2XRL or 1Q23—active sites from bacterial ribosome unit A, and thus prevent the synthesis of bacterial proteins. This unique mechanism avoids the cross-resistance issues of other antibacterial drugs