Scale jump-aware pose graph relaxation for monocular SLAM with re-initializations

Pose graph relaxation has become an indispensable addition to SLAM enabling efficient global registration of sensor reference frames under the objective of satisfying pair-wise relative transformation constraints. The latter may be given by incremental motion estimation or global place recognition. While the latter case enables loop closures and drift compensation, care has to be taken in the monocular case in which local estimates of structure and displacements can differ from reality not just in terms of noise, but also in terms of a scale factor. Owing to the accumulation of scale propagation errors, this scale factor is drifting over time, hence scale-drift aware pose graph relaxation has been introduced. We extend this idea to cases in which the relative scale between subsequent sensor frames is unknown, a situation that can easily occur if monocular SLAM enters re-initialization and no reliable overlap between successive local maps can be identified. The approach is realized by a hybrid pose graph formulation that combines the regular similarity consistency terms with novel, scale-blind constraints. We apply the technique to the practically relevant case of small indoor service robots capable of effectuating purely rotational displacements, a condition that can easily cause tracking failures. We demonstrate that globally consistent trajectories can be recovered even if multiple re-initializations occur along the loop, and present an in-depth study of success and failure cases.Comment: 8 pages, 23 figures, International Conference on Intelligent Robots and Systems 202

Terahertz magnetic field induced coherent spin precession in YFeO3

We present the magnetic dipole transition at 0.299 THz excited by magnetic component of terahertz electromagnetic pulse in an antiferromagnetic YFeO3 crystal. The impulsive magnetic field of the terahertz pulse tilts the macroscopic magnetization, causing deviation from the equilibrium position, which is manifested by a sharp absorption at the frequency of the quasiferromagnetic mode of the crystal. The rotating coherent macroscopic magnetization radiates elliptically polarized emission at the frequency of the quasiferromagnetic resonance due to the dichroic absorption in the crystal

LncRNA-p21 alters the antiandrogen enzalutamide-induced prostate cancer neuroendocrine differentiation via modulating the EZH2/STAT3 signaling

While the antiandrogen enzalutamide (Enz) extends the castration resistant prostate cancer (CRPC) patients' survival an extra 4.8 months, it might also result in some adverse effects via inducing the neuroendocrine differentiation (NED). Here we found that lncRNA-p21 is highly expressed in the NEPC patients derived xenograft tissues (NEPC-PDX). Results from cell lines and human clinical sample surveys also revealed that lncRNA-p21 expression is up-regulated in NEPC and Enz treatment could increase the lncRNA-p21 to induce the NED. Mechanism dissection revealed that Enz could promote the lncRNA-p21 transcription via altering the androgen receptor (AR) binding to different androgen-response-elements, which switch the EZH2 function from histone-methyltransferase to non-histone methyltransferase, consequently methylating the STAT3 to promote the NED. Preclinical studies using the PDX mouse model proved that EZH2 inhibitor could block the Enz-induced NED. Together, these results suggest targeting the Enz/AR/lncRNA-p21/EZH2/STAT3 signaling may help urologists to develop a treatment for better suppression of the human CRPC progression

A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication

Generative AI applications are recently catering to a vast user base by creating diverse and high-quality AI-generated content (AIGC). With the proliferation of mobile devices and rapid growth of mobile traffic, providing ubiquitous access to high-quality AIGC services via wireless communication networks is becoming the future direction for AIGC products. However, it is challenging to provide optimal AIGC services in wireless networks with unstable channels, limited bandwidth resources, and unevenly distributed computational resources. To tackle these challenges, we propose a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework, where only semantic information of the content rather than all the binary bits should be extracted and transmitted by using SemCom. Specifically, SemAIGC integrates diffusion-based models within the semantic encoder and decoder for efficient content generation and flexible adjustment of the computing workload of both transmitter and receiver. Meanwhile, we devise a resource-aware workload trade-off (ROOT) scheme into the SemAIGC framework to intelligently decide transmitter/receiver workload, thus adjusting the utilization of computational resource according to service requirements. Simulations verify the superiority of our proposed SemAIGC framework in terms of latency and content quality compared to conventional approaches

Examining the Effect of Pre-training on Time Series Classification

Although the pre-training followed by fine-tuning paradigm is used extensively in many fields, there is still some controversy surrounding the impact of pre-training on the fine-tuning process. Currently, experimental findings based on text and image data lack consensus. To delve deeper into the unsupervised pre-training followed by fine-tuning paradigm, we have extended previous research to a new modality: time series. In this study, we conducted a thorough examination of 150 classification datasets derived from the Univariate Time Series (UTS) and Multivariate Time Series (MTS) benchmarks. Our analysis reveals several key conclusions. (i) Pre-training can only help improve the optimization process for models that fit the data poorly, rather than those that fit the data well. (ii) Pre-training does not exhibit the effect of regularization when given sufficient training time. (iii) Pre-training can only speed up convergence if the model has sufficient ability to fit the data. (iv) Adding more pre-training data does not improve generalization, but it can strengthen the advantage of pre-training on the original data volume, such as faster convergence. (v) While both the pre-training task and the model structure determine the effectiveness of the paradigm on a given dataset, the model structure plays a more significant role

WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery

Virtual reality (VR) over wireless is expected to be one of the killer applications in next-generation communication networks. Nevertheless, the huge data volume along with stringent requirements on latency and reliability under limited bandwidth resources makes untethered wireless VR delivery increasingly challenging. Such bottlenecks, therefore, motivate this work to seek the potential of using semantic communication, a new paradigm that promises to significantly ease the resource pressure, for efficient VR delivery. To this end, we propose a novel framework, namely WIreless SEmantic deliveRy for VR (WiserVR), for delivering consecutive 360{\deg} video frames to VR users. Specifically, deep learning-based multiple modules are well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery. Among them, we dedicatedly develop a concept of semantic location graph and leverage the joint-semantic-channel-coding method with knowledge sharing to not only substantially reduce communication latency, but also to guarantee adequate transmission reliability and resilience under various channel states. Moreover, implementation of WiserVR is presented, followed by corresponding initial simulations for performance evaluation compared with benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of WiserVR.Comment: This article has been submitted to IEEE Wireless Communications Magazine (after major revisions) for possible publicatio

A novel method of weakness imbalance fault identification and application in aero-hydraulic pump

A method of combining auto-correlation and Hilbert envelope analysis is proposed and used to identify weakness imbalance fault of aero-hydraulic pump, the central part of hydraulic system of aircraft. Firstly, the integral and polynomial least square fitting is applied to convert acceleration signal to velocity one; secondly, the Hilbert envelope spectrum of auto-correlation function of velocity signal is obtained and used to identify the weakness imbalance fault of aero-hydraulic pump; finally, the energy ratio of velocity signal is calculated according to Hilbert envelope spectrum for identifying imbalance fault of aero-hydraulic pump by means of easier and more visual method. Meanwhile, the comparing analysis is carried out between traditional research method and proposed new one. The result shows that the weakness imbalance fault of aero-hydraulic pump can be identified and diagnosed effectively and correctly according to the velocity signal whether Hilbert envelope spectrum or calculation energy ratio while direct acceleration signal cannot

BIO-SD: a blockchain-empowered intelligent resource management for symbiotic devices

Symbiotic communication (SC), exploiting the analogy of biological ecosystem to establish communication device ecosystems, can enable cooperative service/resource exchanges across heterogeneous devices thus realizing the complementarity among different communication resources. However, considering unstable wireless links, high network dynamics, and complex electromagnetic interference in such an ecosystem, it is difficult to perform service/resource exchanges without securing a trusted environment. Moreover, multi-dimensional service/resource exchange demanded by massive symbiotic devices (SDs) in the ecosystems exposes additional challenges for exchange decision-making. To deal with the above difficulties, in this paper, we propose a blockchain-empowered intelligent co evolution for symbiotic devices (BIO-SD). Specifically, to guarantee the trustworthiness of service/resource exchange and resist malicious attacks, a direct acyclic graph (DAG)-based blockchain architecture is applied to the BIO-SD scheme. Furthermore, a modified multi-agent deep deterministic policy gradient (MADDPG) approach is adopted to make service/resource exchange decisions under this trusted environment. The simulation results show that the proposed BIO-SD scheme outperforms some conventional solutions in terms of transmission rate and transmission latency under both non-attack and malicious attack scenarios