Existence of APAV(q,k) with q a prime power ≡5(mod8) and k≡1(mod4)

AbstractStinson introduced authentication perpendicular arrays APAλ(t,k,v), as a special kind of perpendicular arrays, to construct authentication and secrecy codes. Ge and Zhu introduced APAV(q,k) to study APA1(2,k,v) for k=5, 7. Chen and Zhu determined the existence of APAV(q,k) with q a prime power ≡3(mod4) and odd k>1. In this article, we show that for any prime power q≡5(mod8) and any k≡1(mod4) there exists an APAV(q,k) whenever q>((E+E2+4F)/2)2, where E=[(7k−23)m+3]25m−3, F=m(2m+1)(k−3)25m and m=(k−1)/4

Deployment Optimization of Connected and Automated Vehicle Lanes with the Safety Benefits on Roadway Networks

Reasonable deployment of connected and automated vehicle (CAV) lanes which separating the heterogeneous traffic flow consisting of both CAVs and human-driven vehicles (HVs) can not only improve traffic safety but also greatly improve the overall roadway efficiency. This paper simplified CAV lane deployment plan into the problem of traffic network design and proposed a comprehensive decision-making method for CAV lane deployment plan. Based on the traffic equilibrium theory, this method aims to reduce the travel cost of the traffic network and the management cost of CAV lanes using a bilevel primary-secondary programming model. In addition, the upper level is the decision-making scheme of the lane deployment, while the lower level is the traffic assignment model including CAV and HV modes based on the decision-making scheme of the upper level. After that, a genetic algorithm was designed to solve the model. Finally, a medium-scaled traffic network was selected to verify the effectiveness of the proposed model and algorithm. The case study shows that the proposed method obtained a feasible scheme for lane deployment considering from both the system travel cost and management cost of CAV lanes. In addition, a sensitivity analysis of the market penetration rate of CAVs, traffic demand, and the capacity of CAVLs further proves the applicability of this model, which can achieve better allocation of system resources and also improve the traffic efficiency. Document type: Articl

Capacity Matching Based Model for Protected Left Turn Phases Design of Adjacent Signalized Intersections Along Arterial Roads

A protected left turn phase is often used at intersections with heavy left turns. This may induce a capacity gap between adjacent intersections along the arterial road among which only parts of intersection are with protected left turn phase. A model for integrated optimization of protected left turn phases for adjacent intersections along the arterial road is developed to solve this problem. Two objectives are considered: capacity gap minimization and capacity maximization. The problems are formulated as Binary-Integer-Linear-Programs, which are solvable by standard branch-and-bound routine. A set of constraints have been set up to ensure the feasibility of the resulting optimal left turn phase type and signal settings. A field intersections group of the Wei-er Road of Ji’nan city is used to test the proposed model. The results show that the method can decrease the capacity gap between adjacent intersections, reduce the delay as well as increase the capacity in comparison with the field signal plan and signal plan optimized by Synchro. The sensitivity analysis has further demonstrated the potential of the proposed approach to be applied in coordinated design of left turn phases between adjacent intersections along the arterial road under different traffic demandpatterns

WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning

Although deep learning has revolutionized music generation, existing methods for structured melody generation follow an end-to-end left-to-right note-by-note generative paradigm and treat each note equally. Here, we present WuYun, a knowledge-enhanced deep learning architecture for improving the structure of generated melodies, which first generates the most structurally important notes to construct a melodic skeleton and subsequently infills it with dynamically decorative notes into a full-fledged melody. Specifically, we use music domain knowledge to extract melodic skeletons and employ sequence learning to reconstruct them, which serve as additional knowledge to provide auxiliary guidance for the melody generation process. We demonstrate that WuYun can generate melodies with better long-term structure and musicality and outperforms other state-of-the-art methods by 0.51 on average on all subjective evaluation metrics. Our study provides a multidisciplinary lens to design melodic hierarchical structures and bridge the gap between data-driven and knowledge-based approaches for numerous music generation tasks

Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method

The past decade has witnessed great strides in video recovery by specialist technologies, like video inpainting, completion, and error concealment. However, they typically simulate the missing content by manual-designed error masks, thus failing to fill in the realistic video loss in video communication (e.g., telepresence, live streaming, and internet video) and multimedia forensics. To address this, we introduce the bitstream-corrupted video (BSCV) benchmark, the first benchmark dataset with more than 28,000 video clips, which can be used for bitstream-corrupted video recovery in the real world. The BSCV is a collection of 1) a proposed three-parameter corruption model for video bitstream, 2) a large-scale dataset containing rich error patterns, multiple corruption levels, and flexible dataset branches, and 3) a plug-and-play module in video recovery framework that serves as a benchmark. We evaluate state-of-the-art video inpainting methods on the BSCV dataset, demonstrating existing approaches' limitations and our framework's advantages in solving the bitstream-corrupted video recovery problem. The benchmark and dataset are released at https://github.com/LIUTIGHE/BSCV-Dataset.Comment: Accepted by NeurIPS Dataset and Benchmark Track 202

c-Lysozyme promotes proliferation of chicken embryonic fibroblast through bFGF pathway

The egg white (EW) contains the majority of bioactive components which maintain embryo growth and differentiation. The discovery of new growth promoting factor in egg white will provide vital clue to understand the developmental regulation of early chicken embryo. The egg white heated with different temperatures (63.5, 70 and 95°C) underwent testing on its growth-promoting effect on chicken fibroblast in vitro. The purified c-lysozyme and the expression of related genes in basic fibroblast growth factor (bFGF) pathway were analyzed to ascertain its growth-promoting mechanism. 13 h after egg white treatment, more fibroblast synchronized with serum starvation transited into S phrase from G0/G1 in EW group than in the control group (CM) and reached the phase of peak proliferation at 15 h after treatment. It was found that c-lysozyme had the function of promoting cells growth and was decided by gradient heat inactivation of egg white. The addition of more than 0.25 mg/ml c-lysozyme produced significant increase in the cellular proliferation during 48 to 72 h of culture. At 13 h after c-lysozyme treatment, the bFGF, cyclin D, cyclin A and CDK2 were up-regulated significantly and promoted the transition from G0/G1 into S phrase and the accurate completion of S phrase. C-Lysozyme contains a growth-activating domain to promote the cell proliferation besides its anti-microbe domain.Key words: c-Lysozyme, fibroblast, fibroblast growth factor receptor (FGFR), cell cycle

ReLyMe: Improving Lyric-to-Melody Generation by Incorporating Lyric-Melody Relationships

Lyric-to-melody generation, which generates melody according to given lyrics, is one of the most important automatic music composition tasks. With the rapid development of deep learning, previous works address this task with end-to-end neural network models. However, deep learning models cannot well capture the strict but subtle relationships between lyrics and melodies, which compromises the harmony between lyrics and generated melodies. In this paper, we propose ReLyMe, a method that incorporates Relationships between Lyrics and Melodies from music theory to ensure the harmony between lyrics and melodies. Specifically, we first introduce several principles that lyrics and melodies should follow in terms of tone, rhythm, and structure relationships. These principles are then integrated into neural network lyric-to-melody models by adding corresponding constraints during the decoding process to improve the harmony between lyrics and melodies. We use a series of objective and subjective metrics to evaluate the generated melodies. Experiments on both English and Chinese song datasets show the effectiveness of ReLyMe, demonstrating the superiority of incorporating lyric-melody relationships from the music domain into neural lyric-to-melody generation.Comment: Accepted by ACMMM 2022, ora

MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation

Pre-trained language models have achieved impressive results in various music understanding and generation tasks. However, existing pre-training methods for symbolic melody generation struggle to capture multi-scale, multi-dimensional structural information in note sequences, due to the domain knowledge discrepancy between text and music. Moreover, the lack of available large-scale symbolic melody datasets limits the pre-training improvement. In this paper, we propose MelodyGLM, a multi-task pre-training framework for generating melodies with long-term structure. We design the melodic n-gram and long span sampling strategies to create local and global blank infilling tasks for modeling the local and global structures in melodies. Specifically, we incorporate pitch n-grams, rhythm n-grams, and their combined n-grams into the melodic n-gram blank infilling tasks for modeling the multi-dimensional structures in melodies. To this end, we have constructed a large-scale symbolic melody dataset, MelodyNet, containing more than 0.4 million melody pieces. MelodyNet is utilized for large-scale pre-training and domain-specific n-gram lexicon construction. Both subjective and objective evaluations demonstrate that MelodyGLM surpasses the standard and previous pre-training methods. In particular, subjective evaluations show that, on the melody continuation task, MelodyGLM gains average improvements of 0.82, 0.87, 0.78, and 0.94 in consistency, rhythmicity, structure, and overall quality, respectively. Notably, MelodyGLM nearly matches the quality of human-composed melodies on the melody inpainting task