Energy Efficient Resource Allocation for Mobile-Edge Computation Networks with NOMA

This paper investigates an uplink non-orthogonal multiple access (NOMA)-based mobile-edge computing (MEC) network. Our objective is to minimize the total energy consumption of all users including transmission energy and local computation energy subject to computation latency and cloud computation capacity constraints. We first prove that the total energy minimization problem is a convex problem, and it is optimal to transmit with maximal time. Then, we accordingly proposed an iterative algorithm with low complexity, where closed-form solutions are obtained in each step. The proposed algorithm is successfully shown to be globally optimal. Numerical results show that the proposed algorithm achieves better performance than the conventional methods.Comment: 7 pages 5 figures. arXiv admin note: text overlap with arXiv:1807.1184

Resource Allocation for UAV Assisted Wireless Networks with QoS Constraints

For crowded and hotspot area, unmanned aerial vehicles (UAVs) are usually deployed to increase the coverage rate. In the considered model, there are three types of services for UAV assisted communication: control message, non-realtime communication, and real-time communication, which can cover most of the actual demands of users in a UAV assisted communication system. A bandwidth allocation problem is considered to minimize the total energy consumption of this system while satisfying the requirements. Two techniques are introduced to enhance the performance of the system. The first method is to categorize the ground users into multiple user groups and offer each group a unique RF channel with different bandwidth. The second method is to deploy more than one UAVs in the system. Bandwidth optimization in each scheme is proved to be a convex problem. Simulation results show the superiority of the proposed schemes in terms of energy consumption.Comment: Submitted to IEEE WCNC 202

TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise

Large Language Models (LLMs) exhibit impressive reasoning and data augmentation capabilities in various NLP tasks. However, what about small models? In this work, we propose TeacherLM-7.1B, capable of annotating relevant fundamentals, chain of thought, and common mistakes for most NLP samples, which makes annotation more than just an answer, thus allowing other models to learn "why" instead of just "what". The TeacherLM-7.1B model achieved a zero-shot score of 52.3 on MMLU, surpassing most models with over 100B parameters. Even more remarkable is its data augmentation ability. Based on TeacherLM-7.1B, we augmented 58 NLP datasets and taught various student models with different parameters from OPT and BLOOM series in a multi-task setting. The experimental results indicate that the data augmentation provided by TeacherLM has brought significant benefits. We will release the TeacherLM series of models and augmented datasets as open-source.Comment: 5 figures, 15 page

Interface-engineered paclitaxel-based hollow mesoporous organosilica nanoplatforms for photothermal-enhanced chemotherapy of tumor

Having benefited from the combination of different therapeutic modalities, functionalized nanoplatforms with synergistic strategies have aroused great interest in anticancer treatment. Herein, an engineered, a biodegradable hollow mesoporous organosilica nanoparticle (HMON)-based nanoplatform was fabricated for photothermal-enhanced chemotherapy of tumor. For the first time, we demonstrated that HMONs could serve as nanocarriers for co-delivering of both the paclitaxel and photothermal agent new indocyanine green (IR820), denoted as Paclitaxel/IR820@ HMONs-PEG. The as-prepared nanosystem exhibited a high paclitaxel-loading capacity of 28.4%, much higher than most paclitaxel-loaded nanoformulations. Furthermore, incorporating thioether bonds (S-S) into the HMONs’ framework endowed them with GSH-responsive biodegradation behavior, leading to the controllable release of drugs under a tumor reducing microenvironment, and hindered the premature release of paclitaxel. Upon being irradiated with an NIR laser, the obtained co-delivery nanosystem exhibited great photothermal properties generated from IR820. The fabricated nanocomposites could significantly suppress tumor growth under NIR laser irradiation, as validated by in vitro and in vivo assessments. Combined with outstanding biocompatibility, the constructed nanosystem holds great potential in combinational antitumor therapy

Genome dynamics and diversity of Shigella species, the etiologic agents of bacillary dysentery

The Shigella bacteria cause bacillary dysentery, which remains a significant threat to public health. The genus status and species classification appear no longer valid, as compelling evidence indicates that Shigella, as well as enteroinvasive Escherichia coli, are derived from multiple origins of E.coli and form a single pathovar. Nevertheless, Shigella dysenteriae serotype 1 causes deadly epidemics but Shigella boydii is restricted to the Indian subcontinent, while Shigella flexneri and Shigella sonnei are prevalent in developing and developed countries respectively. To begin to explain these distinctive epidemiological and pathological features at the genome level, we have carried out comparative genomics on four representative strains. Each of the Shigella genomes includes a virulence plasmid that encodes conserved primary virulence determinants. The Shigella chromosomes share most of their genes with that of E.coli K12 strain MG1655, but each has over 200 pseudogenes, 300∼700 copies of insertion sequence (IS) elements, and numerous deletions, insertions, translocations and inversions. There is extensive diversity of putative virulence genes, mostly acquired via bacteriophage-mediated lateral gene transfer. Hence, via convergent evolution involving gain and loss of functions, through bacteriophage-mediated gene acquisition, IS-mediated DNA rearrangements and formation of pseudogenes, the Shigella spp. became highly specific human pathogens with variable epidemiological and pathological features