Energy-Latency Aware Intelligent Reflecting Surface Aided Multi-cell Mobile Edge Computing

The explosive development of the Internet of Things (IoT) has led to increased interest in mobile edge computing (MEC), which provides computational resources at network edges to accommodate computation-intensive and latency-sensitive applications. Intelligent reflecting surfaces (IRSs) have gained attention as a solution to overcome blockage problems during the offloading uplink transmission in MEC systems. This paper explores IRS-aided multi-cell networks that enable servers to serve neighboring cells and cooperate to handle resource exhaustion. We aim to minimize the joint energy and latency cost, by jointly optimizing computation tasks, edge computing resources, user beamforming, and IRS phase shifts. The problem is decomposed into two subproblems--the MEC subproblem and the IRS communication subproblem--using the block coordinate descent (BCD) technique. The MEC subproblem is reformulated as a nonconvex quadratic constrained problem (QCP), while the IRS communication subproblem is transformed into a weight-sum-rate problem with auxiliary variables. We propose an efficient algorithm to iteratively optimize MEC resources and IRS communication until convergence. Numerical results show that our algorithm outperforms benchmarks and that multi-cell MEC systems achieve additional performance gains when supported by IRS.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors

In this work, we study the features extracted by English self-supervised learning (SSL) models in cross-lingual contexts and propose a new metric to predict the quality of feature representations. Using automatic speech recognition (ASR) as a downstream task, we analyze the effect of model size, training objectives, and model architecture on the models' performance as a feature extractor for a set of topologically diverse corpora. We develop a novel metric, the Phonetic-Syntax Ratio (PSR), to measure the phonetic and synthetic information in the extracted representations using deep generalized canonical correlation analysis. Results show the contrastive loss in the wav2vec2.0 objective facilitates more effective cross-lingual feature extraction. There is a positive correlation between PSR scores and ASR performance, suggesting that phonetic information extracted by monolingual SSL models can be used for downstream tasks in cross-lingual settings. The proposed metric is an effective indicator of the quality of the representations and can be useful for model selection.Comment: 12 pages, 5 figures, 4 table

Reward Imputation with Sketching for Contextual Batched Bandits

Contextual batched bandit (CBB) is a setting where a batch of rewards is observed from the environment at the end of each episode, but the rewards of the non-executed actions are unobserved, resulting in partial-information feedback. Existing approaches for CBB often ignore the rewards of the non-executed actions, leading to underutilization of feedback information. In this paper, we propose an efficient approach called Sketched Policy Updating with Imputed Rewards (SPUIR) that completes the unobserved rewards using sketching, which approximates the full-information feedbacks. We formulate reward imputation as an imputation regularized ridge regression problem that captures the feedback mechanisms of both executed and non-executed actions. To reduce time complexity, we solve the regression problem using randomized sketching. We prove that our approach achieves an instantaneous regret with controllable bias and smaller variance than approaches without reward imputation. Furthermore, our approach enjoys a sublinear regret bound against the optimal policy. We also present two extensions, a rate-scheduled version and a version for nonlinear rewards, making our approach more practical. Experimental results show that SPUIR outperforms state-of-the-art baselines on synthetic, public benchmark, and real-world datasets.Comment: Accepted by NeurIPS 202

Projection-Based Adaptive Backstepping Control of a Transport Aircraft for Heavyweight Airdrop

An autopilot inner loop that combines backstepping control with adaptive function approximation is developed for airdrop operations. The complex nonlinear uncertainty of the aircraft-cargo model is factorized into a known matrix and an uncertainty function, and a projection-based adaptive approach is proposed to estimate this function. Using projection in the adaptation law bounds the estimated function and guarantees the robustness of the controller against time-varying external disturbances and uncertainties. The convergence properties and robustness of the control method are proved via Lyapunov theory. Simulations are conducted under the condition that one transport aircraft performs a maximum load airdrop task at a height of 82 ft, using single row single platform mode. The results show good performance and robust operation of the controller, and the airdrop mission performance indexes are satisfied, even in the presence of ±15% uncertainty in the aerodynamic coefficients, ±0.01 rad/s pitch rate disturbance, and 20% actuators faults

HyperStyle3D: Text-Guided 3D Portrait Stylization via Hypernetworks

Portrait stylization is a long-standing task enabling extensive applications. Although 2D-based methods have made great progress in recent years, real-world applications such as metaverse and games often demand 3D content. On the other hand, the requirement of 3D data, which is costly to acquire, significantly impedes the development of 3D portrait stylization methods. In this paper, inspired by the success of 3D-aware GANs that bridge 2D and 3D domains with 3D fields as the intermediate representation for rendering 2D images, we propose a novel method, dubbed HyperStyle3D, based on 3D-aware GANs for 3D portrait stylization. At the core of our method is a hyper-network learned to manipulate the parameters of the generator in a single forward pass. It not only offers a strong capacity to handle multiple styles with a single model, but also enables flexible fine-grained stylization that affects only texture, shape, or local part of the portrait. While the use of 3D-aware GANs bypasses the requirement of 3D data, we further alleviate the necessity of style images with the CLIP model being the stylization guidance. We conduct an extensive set of experiments across the style, attribute, and shape, and meanwhile, measure the 3D consistency. These experiments demonstrate the superior capability of our HyperStyle3D model in rendering 3D-consistent images in diverse styles, deforming the face shape, and editing various attributes

Genome sequencing and comparative genome analysis of Rhizoctonia solani AG-3

Rhizoctonia solani AG-3 is a plant pathogenic fungus that belongs to the group of multinucleate Rhizoctonia. According to its internal transcribed spacer (ITS) cluster analysis and host range, it is divided into TB, PT, and TM subgroups. AG-3 TB mainly causes tobacco target spots, AG-3 PT mainly causes potato black scurf, and AG-3 TM mainly causes tomato leaf blight. In our previous study, we found that all 36 tobacco target spot strains isolated from Yunnan (Southwest China) were classified into AG-3 TB subgroup, while only two of the six tobacco target spot strains isolated from Liaoning (Northeast China) were classified into AG-3 TB subgroup, and the remaining four strains were classified into AG-3 TM subgroup, which had a unique taxonomic status, and there was no previous report on the whole genome information of AG-3 TM subgroup. In this study, the whole genomes of R. solani AG-3 strains 3T-1 (AG-3 TM isolated from Liaoning) and MJ-102 (AG-3 TB isolated from Yunnan) isolated from tobacco target spot in Liaoning and Yunnan were sequenced by IIumina and PacBio sequencing platforms. Comparative genomic analysis was performed with the previously reported AG-3 PT strain Rhs1AP, revealing their differences in genomes and virulence factors. The results indicated that the genome size of 3T-1 was 42,103,597 bp with 11,290 coding genes and 49.74% GC content, and the genome size of MJ-102 was 41,908,281 bp with 10,592 coding genes and 48.91% GC content. Through comparative genomic analysis with the previously reported strain Rhs1AP (AG-3 PT), it was found that the GC content between the genomes was similar, but the strains 3T-1 and MJ-102 contained more repetitive sequences. Similarly, there are similarities between their virulence factors, but there are also some differences. In addition, the results of collinearity analysis showed that 3T-1 and MJ-102 had lower similarity and longer evolutionary distance with Rhs1AP, but the genetic relationship between 3T-1 and MJ-102 was closer. This study can lay a foundation for studying the molecular pathogenesis and virulence factors of R. solani AG-3, and revealing its genomic composition will also help to develop more effective disease control strategies

Novel end-fly-cutting-servo system for deterministic generation of hierarchical micro–nanostructures

This paper reports on the diamond cutting based generation of hierarchical micro-nanostructures, which are conventionally difficult for both mechanical and non-mechanical methods to achieve. A novel end-fly-cutting-servo (EFCS) system, with four-axis servo motions that combine the concepts of fast/slow tool servo and endface fly-cutting, is proposed and investigated. In the EFCS system, an intricately shaped primary surface is generated by material removal, while the desired secondary nanostructures are simultaneously constructed using residual tool marks by actively controlling tool loci. The potential of the EFCS system is demonstrated firstly by fabricating a nanostructured F-theta freeform surface and a nanostructured micro-aspheric array

Efficacy and safety of molnupiravir in patients with Omicron variant vaccine breakthrough COVID-19 infection: a randomized, controlled trial

Introduction: Randomized, controlled trials of molnupiravir in real-world use during the Omicron wave are scarce. The frequency of hospitalization and death is low, so further research is needed to confirm the virological efficacy of molnupiravir.Methods: A single-center, randomized, controlled clinical trial was conducted, and 111 hospitalized coronavirus disease 2019 (COVID-19) patients were randomly assigned at a ratio of 1:1. Fifty-three patients in the molnupiravir group were administered 800 mg of molnupiravir twice daily for 5 days in addition to the standard therapy, and 58 patients in the control group only received the standard therapy in accordance with local guidelines. The antiviral effect and adverse events were evaluated during the follow-up.Results: The median viral clearance time in the molnupiravir group was significantly shorter than that in the control group (p = 0.003). Furthermore, patients who started molnupiravir therapy within 3 days had significantly shorter viral clearance time than the controls (p = 0.003). In the vaccinated subgroup, molnupiravir therapy was also associated with a shorter viral clearance time (p = 0.003). A total of three adverse events, which were minor, were reported in the molnupiravir group. One of the patients had mild liver function abnormalities, and all of them were resolved without intervention. However, the remission time was similar between the two tested groups.Conclusion: Molnupiravir exhibited good viral replication inhibitor efficacy in patients with Omicron variant vaccine breakthrough COVID-19 infection.Clinical Trial Registration: [https://www.chictr.org.cn/], identifier [ChiCTR2200059796]

Spectromicroscopic measurement of surface and bulk band structure interplay in a disordered topological insulator

Topological insulators are bulk semiconductors that manifest in-gap massless Dirac surface states due to the topological bulk-boundary correspondence principle [1-3]. These surface states have been a subject of tremendous ongoing interest, due both to their intrinsic properties and to higher order emergence phenomena that can be achieved by manipulating the interface environment [4-11]. Here, angle resolved photoemission (ARPES) spectromicroscopy and supplementary scanning tunneling microscopy (STM) are performed on the model topological insulator Bi2Se3 to investigate the interplay of crystallographic inhomogeneity with the topologically ordered bulk and surface band structure. Quantitative analysis methods are developed to obtain key spectroscopic information in spite of a limited dwell time on each measured point. Band energies are found to vary on the scale of 50 meV across the sample surface, enabling single-sample measurements that are analogous to a multi-sample doping series (termed a "binning series"). Focusing separately on the surface and bulk electrons reveals a nontrivial hybridization-like interplay between fluctuations in the surface and bulk state energetics.Comment: 4 figures and 6 supplementary figure