Digital Twin-Driven Network Architecture for Video Streaming

Digital twin (DT) is revolutionizing the emerging video streaming services through tailored network management. By integrating diverse advanced communication technologies, DTs are promised to construct a holistic virtualized network for better network management performance. To this end, we develop a DT-driven network architecture for video streaming (DTN4VS) to enable network virtualization and tailored network management. With the architecture, various types of DTs can characterize physical entities' status, separate the network management functions from the network controller, and empower the functions with emulated data and tailored strategies. To further enhance network management performance, three potential approaches are proposed, i.e., domain data exploitation, performance evaluation, and adaptive DT model update. We present a case study pertaining to DT-assisted network slicing for short video streaming, followed by some open research issues for DTN4VS.Comment: 8 pages, 5 figures, submitted to IEEE Network Magazin

Sim2real and Digital Twins in Autonomous Driving: A Survey

Safety and cost are two important concerns for the development of autonomous driving technologies. From the academic research to commercial applications of autonomous driving vehicles, sufficient simulation and real world testing are required. In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue. However, the virtual simulation world differs from the real world in many aspects such as lighting, textures, vehicle dynamics, and agents' behaviors, etc., which makes it difficult to bridge the gap between the virtual and real worlds. This gap is commonly referred to as the reality gap (RG). In recent years, researchers have explored various approaches to address the reality gap issue, which can be broadly classified into two categories: transferring knowledge from simulation to reality (sim2real) and learning in digital twins (DTs). In this paper, we consider the solutions through the sim2real and DTs technologies, and review important applications and innovations in the field of autonomous driving. Meanwhile, we show the state-of-the-arts from the views of algorithms, models, and simulators, and elaborate the development process from sim2real to DTs. The presentation also illustrates the far-reaching effects of the development of sim2real and DTs in autonomous driving

Digital Twin Based User-Centric Resource Management for Multicast Short Video Streaming

Multicast short video streaming (MSVS) can effectively reduce network traffic load by delivering identical video sequences to multiple users simultaneously. The existing MSVS schemes mainly rely on the aggregated video requests to reserve bandwidth and computing resources, which cannot satisfy users' diverse and dynamic service requirements, particularly when users' swipe behaviors exhibit spatiotemporal fluctuation. In this paper, we propose a user-centric resource management scheme based on the digital twin (DT) technique, which aims to enhance user satisfaction as well as reduce resource consumption. Firstly, we design a user DT (UDT)-assisted resource reservation framework. Specifically, UDTs are constructed for individual users, which store users' historical data for updating multicast groups and abstracting useful information. The swipe probability distributions and recommended video lists are abstracted from UDTs to predict bandwidth and computing resource demands. Parameterized sigmoid functions are leveraged to characterize multicast groups' user satisfaction. Secondly, we formulate a joint non-convex bandwidth and computing resource reservation problem which is transformed into a convex piecewise problem by utilizing a tangent function to approximately substitute the concave part. A low-complexity scheduling algorithm is then developed to find the optimal resource reservation decisions. Simulation results based on the real-world dataset demonstrate that the proposed scheme outperforms benchmark schemes in terms of user satisfaction and resource consumption.Comment: 13 pages, 11 figure

FusionPlanner: A Multi-task Motion Planner for Mining Trucks using Multi-sensor Fusion Method

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research, including a simulation platform, a testing benchmark, and a trustworthy and robust motion planner. \textcolor{red}{Firstly, we propose a multi-task motion planning algorithm, called FusionPlanner, for autonomous mining trucks by the Multi-sensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches to evaluate the trustworthiness and robustness of well-trained algorithms in transportation roads of open-pit mines. Finally, we introduce the Parallel Mining Simulator (PMS), a new high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables the users to manage and control open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives.} \textcolor{red}{The performance of FusionPlanner is tested by MiningNav in PMS, and the empirical results demonstrate a significant reduction in the number of collisions and takeovers of our planner. We anticipate our unmanned transportation paradigm will bring mining trucks one step closer to trustworthiness and robustness in continuous round-the-clock unmanned transportation.Comment: 2Pages, 10 figure

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

Due to the extensive social influence, public health emergency has attracted great attention in today’s society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event’s social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency

Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

Thanks to the augmented convenience, safety advantages, and potential commercial value, Intelligent vehicles (IVs) have attracted wide attention throughout the world. Although a few autonomous driving unicorns assert that IVs will be commercially deployable by 2025, their implementation is still restricted to small-scale validation due to various issues, among which precise computation of control commands or trajectories by planning methods remains a prerequisite for IVs. This paper aims to review state-of-the-art planning methods, including pipeline planning and end-to-end planning methods. In terms of pipeline methods, a survey of selecting algorithms is provided along with a discussion of the expansion and optimization mechanisms, whereas in end-to-end methods, the training approaches and verification scenarios of driving tasks are points of concern. Experimental platforms are reviewed to facilitate readers in selecting suitable training and validation methods. Finally, the current challenges and future directions are discussed. The side-by-side comparison presented in this survey not only helps to gain insights into the strengths and limitations of the reviewed methods but also assists with system-level design choices.Comment: 20 pages, 14 figures and 5 table

A random map implementation of implicit filters

Implicit particle filters for data assimilation generate high-probability samples by representing each particle location as a separate function of a common reference variable. This representation requires that a certain underdetermined equation be solved for each particle and at each time an observation becomes available. We present a new implementation of implicit filters in which we find the solution of the equation via a random map. As examples, we assimilate data for a stochastically driven Lorenz system with sparse observations and for a stochastic Kuramoto-Sivashinski equation with observations that are sparse in both space and time

Childhood trauma and suicide risk in hospitalized patients with schizophrenia: the sequential mediating roles of pandemic related post-traumatic stress symptoms, sleep quality, and psychological distress

IntroductionStressful global situation due to the COVID-19 pandemic caused a tremendous impact on mental health in hospitalized patients with schizophrenia. The mediating roles of psychological impact related to COVID-19, sleep quality, and psychological distress were investigated in the association between childhood trauma and suicidal risk in hospitalized patients with schizophrenia.MethodsWe analyzed cross-sectional data of 147 patients with schizophrenia and 189 healthy controls (HCs).ResultsHistories of childhood trauma and schizophrenia were good predictors of COVID-19-related psychological impact, global sleep quality, and psychological distress. Moreover, the series mediation model showed that the effect of childhood trauma on suicidal risk in hospitalized patients with schizophrenia was totally sequential mediated by the psychological impact of COVID-19, sleep quality, and psychological distress.ConclusionClinicians need to recognize the increased suicidal risk associated with COVID-19-related psychological distress in schizophrenia patients with a history of childhood trauma

KIAA1199 promotes migration and invasion by Wnt/β-catenin pathway and MMPs mediated EMT progression and serves as a poor prognosis marker in gastric cancer

Background KIAA1199 was upregulated in diverse cancers, but the association of KIAA1199 with gastric cancer (GC), the biological role of KIAA1199 in GC cells and the related molecular mechanisms remain to be elucidated. Methods KIAA1199 expression was analysed by reverse transcription-polymerase chain reaction assay (RT-PCR) and immunohistochemistry (IHC) in GC patient tissue. The small hairpin RNA (shRNA) was applied for the knockdown of endogenous KIAA1199 in NCI-N87 and AGS cells. MTT, colony formation, scratch wounding migration, transwell chamber migration and invasion assays were employed respectively to investigate the role of KIAA1199 in GC cells. The potential signaling pathway of KIAA1199 induced migration and invasion was detected. Results KIAA1199 was upregulated in GC tissue and was an essential independent marker for poor prognosis. Knockdown KIAA1199 suppressed the proliferation, migration and invasion in GC cells. KIAA1199 stimulated the Wnt/β-catenin signaling pathway and the enzymatic activity of matrix metalloproteinase (MMP) family members and thus accelerated the epithelial-to-mesenchymal transition (EMT) progression in GC cells. Conclusion These findings demonstrated that KIAA1199 was upregulated in GC tissue and associated with worse clinical outcomes in GC, and KIAA1199 acted as an oncogene by promoting migration and invasion through the enhancement of Wnt/β-catenin signaling pathway and MMPs mediated EMT progression in GC cell

Evaluation of the Safety and Effectiveness of Intense Pulsed Light in the Treatment of Meibomian Gland Dysfunction

Purpose. This study aims to explore the safety and efficacy of a novel treatment-intense pulsed light (IPL) in MGD eyes. Methods. This study is a prospective and open label study. Forty eyes of 40 MGD patients were recruited in the study and received 4 consecutive IPL treatments on day 1, day 15, day 45, and day 75. Ten ocular surface symptoms were evaluated with a subjective face score at every visit. Best spectacle corrected visual acuity, intraocular pressure (IOP), conjunctival injection, upper and lower tear meniscus height (TMH), tear break-up time (TBUT), corneal staining, lid margin and meibomian gland assessments, and meibography were also recorded at every visit, as well as the adverse effects on the eye and ocular surface. Results. Significant improvements were observed in single and total ocular surface symptom scores, TBUT, and conjunctival injection at all the visits after the initial IPL treatment (P < 0.05). Compared to baseline, the signs of eyelid margin, meibomian gland secretion quality, and expressibility were significantly improved at every visit after treatments. There was no regional and systemic threat observed in any patient. Conclusion. Intense pulsed light (IPL) therapy is a safe and efficient treatment in relieving symptoms and signs of MGD eyes.SCI(E)[email protected]