A Resource Allocation Algorithm for Ultra-Dense Networks Based on Deep Reinforcement Learning

The resource optimization of ultra-dense networks (UDNs) is critical to meet the huge demand of users for wireless data traffic. But the mainstream optimization algorithms have many problems, such as the poor optimization effect, and high computing load. This paper puts forward a wireless resource allocation algorithm based on deep reinforcement learning (DRL), which aims to maximize the total throughput of the entire network and transform the resource allocation problem into a deep Q-learning process. To effectively allocate resources in UDNs, the DRL algorithm was introduced to improve the allocation efficiency of wireless resources; the authors adopted the resource allocation strategy of the deep Q-network (DQN), and employed empirical repetition and target network to overcome the instability and divergence of the results caused by the previous network state, and to solve the overestimation of the Q value. Simulation results show that the proposed algorithm can maximize the total throughput of the network, while making the network more energy-efficient and stable. Thus, it is very meaningful to introduce the DRL to the research of UDN resource allocation

Generalizable Synthetic Image Detection via Language-guided Contrastive Learning

The heightened realism of AI-generated images can be attributed to the rapid development of synthetic models, including generative adversarial networks (GANs) and diffusion models (DMs). The malevolent use of synthetic images, such as the dissemination of fake news or the creation of fake profiles, however, raises significant concerns regarding the authenticity of images. Though many forensic algorithms have been developed for detecting synthetic images, their performance, especially the generalization capability, is still far from being adequate to cope with the increasing number of synthetic models. In this work, we propose a simple yet very effective synthetic image detection method via a language-guided contrastive learning and a new formulation of the detection problem. We first augment the training images with carefully-designed textual labels, enabling us to use a joint image-text contrastive learning for the forensic feature extraction. In addition, we formulate the synthetic image detection as an identification problem, which is vastly different from the traditional classification-based approaches. It is shown that our proposed LanguAge-guided SynThEsis Detection (LASTED) model achieves much improved generalizability to unseen image generation models and delivers promising performance that far exceeds state-of-the-art competitors by +22.66% accuracy and +15.24% AUC. The code is available at https://github.com/HighwayWu/LASTED

The Influence of L1 Background and Other Meta-linguistic and Background Variables on the Learning of Pinyin and Hanzi by Arabic and English Learners of Chinese as a Second Language

Alphabetic Pinyin and morphosyllabic Hanzi are two different writing systems used in the Chinese language. Though Pinyin and Hanzi utilize different orthographies, the development of literacy skills in both writing systems depends on phonological processing skills. Becoming aware of the phonological structure in Chinese and the orthographic structure in Hanzi are crucial for the growth of literacy skills in Pinyin and Hanzi. The present study investigated the influence of L1 background and other meta-linguistic and background variables on Chinese phonological awareness, phonetic radical awareness, Pinyin spelling, Hanzi reading and Hanzi writing among adult Arabic and English CSL learners. There are five important findings from this study. First, L1 background influenced the performance in Chinese phonological awareness and Pinyin spelling, in which the English participants outperformed the Arabic participants arguably due to the greater similarities in phonology and orthography between English and Pinyin. Second, the Arabic participants’ better achievements in Hanzi writing compared to the English participants might originate from their experience in using the Arabic script and in learning two different scripts. Third, the two CSL groups did not differ in phonetic radical awareness or Hanzi reading, probably due to the unique characteristics of Hanzi orthography and the far distance between Arabic, English and Hanzi. Fourth, L1 background influenced the importance of phonological awareness and phonetic radical awareness in developing Chinese literacy skills, which might relate to the different orthographies used in English and Arabic, as well as the learning contexts. Fifth, Chinese language proficiency, the length of staying in China, the number of languages previously learnt, phonological working memory and phonetic coding ability significantly predicted the Arabic and English CSL learners’ performance in these measures. Theoretical implications for understanding the role of L1 transfer in L2 literacy acquisition, and educational implications for teaching Chinese as a second language were discussed

Towards a QoE Model to Evaluate Holographic Augmented Reality Devices

Augmented reality (AR) technology is developing fast and provides users with new ways to interact with the real-world surrounding environment. Although the performance of holographic AR multimedia devices can be measured with traditional quality-of-service parameters, a quality-of-experience (QoE) model can better evaluate the device from the perspective of users. As there are currently no well-recognized models for measuring the QoE of a holographic AR multimedia device, we present a QoE framework and model it with a fuzzy inference system to quantitatively evaluate the device

Using Jackknife to Assess the Quality of Gene Order Phylogenies

Background In recent years, gene order data has attracted increasing attention from both biologists and computer scientists as a new type of data for phylogenetic analysis. If gene orders are viewed as one character with a large number of states, traditional bootstrap procedures cannot be applied. Researchers began to use a jackknife resampling method to assess the quality of gene order phylogenies. Results In this paper, we design and conduct a set of experiments to validate the performance of this jackknife procedure and provide discussions on how to conduct it properly. Our results show that jackknife is very useful to determine the confidence level of a phylogeny obtained from gene orders and a jackknife rate of 40% should be used. However, although a branch with support value of 85% can be trusted, low support branches require careful investigation before being discarded. Conclusions Our experiments show that jackknife is indeed necessary and useful for gene order data, yet some caution should be taken when the results are interpreted

Impact of Dual Gauge Railway Tracks on Traffic Load Induced Permanent Deformation of Low Embankments

AbstractThere is a growing interest in recent years of many African countries to revamp their neglected railways in order to promote regional trade and transportation integration. Investors are faced with problems of railway track gauge conversions to promote railway inter operability. The objective of the work documented here was to numerically evaluate the impact of track gauge conversions on traffic load induced permanent deformation (PD) of low embankment on soft sub-grade. A method to predict the traffic load induced settlement of low embankment on soft sub-grade is proposed. Using the user-defined material subroutines (UMAT) in ABAQUS, a 2-D finite element (FE) model was formulated. These models are converted into a numerical formulation for implementation in FE analysis and the traffic load induced dynamic stress in the sub grade are calculated by using the multi-layer elastic theory. Then the plastic vertical strain in the sub-grade is calculated by an empirical equation, whose constants are related to the physical and mechanical properties of the sub-grade soil. The method was applied to analyze a 700m long section of a low embankment on the soft black cotton soil of Nakuru plains in Kenya. Corresponding results showed that the application of traffic loads on alternate rail tracks due to gauge conversions have a significant effect on the permanent deformation of the sub grade soil. The depth significantly influenced by traffic loading was found to be close to 6 m below the base of the embankment. The analysis also shows that increasing the thickness and stiffness of the sub grade is a very effective way of reducing the traffic load induced permanent deformation of soft sub grade soil. The proposed method can be used for settlement analysis on low embankments as well as a useful tool for making decisions on railway track gauge conversions

Design Method and Cost-Benefit Analysis of Hybrid Fiber Used in Asphalt Concrete

Fiber, as an additive, can improve the performance of asphalt concrete and be widely studied, but only a few works have been done for hybrid fiber. This paper presents a new and convenient method to design hybrid fiber and verifies hybrid fiber’s superiority in asphalt pavement engineering. Firstly, this paper expounds the design method used as its applied example with the hybrid fiber composed of lignin, polyester, and polypropylene fibers. In this method, a direct shear device (DSD) is used to measure the shear damage energy density (SDED) of hybrid fiber modified asphalts, and range and variance statistical analysis are applied to determine the composition proportion of hybrid fiber. Then, the engineering property of hybrid fiber reinforced asphalt concrete (AC-13) is investigated. Finally, a cost-benefit model is developed to analyze the advantage of hybrid fiber compared to single fibers. The results show that the design method employed in this paper can offer a beneficial reference. A combination of 1.8% of lignin fiber and 2.4% of polyester fiber plus 3.0% polypropylene fiber presented the best reinforcement of the hybrid fiber. The cost-benefit model verifies that the hybrid fiber can bring about comprehensive pavement performance and good economy

Technical Evaluation of HoloLens for Multimedia: A First Look

A recently released cutting-edge AR device, Microsoft HoloLens, has attracted considerable attention with its advanced capabilities. In this article, we report the design and execution of a series of experiments to quantitatively evaluate HoloLens' performance in head localization, real environment reconstruction, spatial mapping, hologram visualization, and speech recognition

The Role of Phonological Awareness and Phonetic Radical Awareness in Acquiring Chinese Literacy Skills in Learners of Chinese as a Second Language

There is much research into the roles of phonological awareness and phonetic radical awareness in the development of Chinese character reading and writing skills in native-speaking children, but there is comparatively little work on the relationship between such metalinguistic skills and character literacy skills in adult learners of Chinese a second language (CSL). In this study, we explored this issue with 83 Arabic and English CSL learners who had studied Chinese in their home country. Their knowledge of phonological awareness, phonetic radical awareness, and Chinese character reading and writing was measured. There were two main findings. Firstly, the learners’ phonological awareness, but not their phonetic radical awareness, predicted the acquisition of character reading and writing skills directly or indirectly. Secondly, phonetic radical awareness did not mediate the effect of phonological awareness on character reading and writing skills. The results point to the different roles that phonological awareness and phonetic radical awareness play in the development of character literacy skills, and the still unclear relationship between phonological awareness and phonetic radical awareness. These findings are important for understanding the contribution of phonological awareness and phonetic radical awareness to the acquisition of character literacy skills for CSL learners

H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces

Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low dimensional spaces while preserving structural and semantic information, is of vital importance in diverse real-life tasks. Researchers have made great efforts on temporal HIN embedding in Euclidean spaces and got some considerable achievements. However, there is always a fundamental conflict that many real-world networks show hierarchical property and power-law distribution, and are not isometric of Euclidean spaces. Recently, representation learning in hyperbolic spaces has been proved to be valid for data with hierarchical and power-law structure. Inspired by this character, we propose a hyperbolic heterogeneous temporal network embedding (H2TNE) model for temporal HINs. Specifically, we leverage a temporally and heterogeneously double-constrained random walk strategy to capture the structural and semantic information, and then calculate the embedding by exploiting hyperbolic distance in proximity measurement. Experimental results show that our method has superior performance on temporal link prediction and node classification compared with SOTA models.Comment: arXiv admin note: text overlap with arXiv:1705.08039 by other author