EDOS: Edge Assisted Offloading System for Mobile Devices

Offloading resource-intensive jobs to the cloud and nearby users is a promising approach to enhance mobile devices. This paper investigates a hybrid offloading system that takes both infrastructure-based networks and Ad-hoc networks into the scope. Specifically, we propose EDOS, an edge assisted offloading system that consists of two major components, an Edge Assistant (EA) and Offload Agent (OA). EA runs on the routers/towers to manage registered remote cloud servers and local service providers and OA operates on the users’ devices to discover the services in proximity. We present the system with a suite of protocols to collect the potential service providers and algorithms to allocate tasks according to user-specified constraints. To evaluate EDOS, we prototype it on commercial mobile devices and evaluate it with both experiments on a small-scale testbed and simulations. The results show that EDOS is effective and efficient for offloading jobs

Dichlorido(dipyrido[3,2-a:2′,3′-c]phenazine)manganese(II)

The complete mol­ecule of the title compound, [MnCl2(C18H10N4)2], is generated by crystallographic twofold symmetry with the Mn atom lying on the rotation axis. The Mn coordination geometry is a distorted cis-MnCl2N4 octa­hedron, arising from two N,N′-bidentate dipyrido[3,2-a:2′,3′-c]phenazine (DPPZ) ligands and two chloride ions. In the crystal structure, neighbouring mononuclear units pack together through π–π contacts between the DPPZ rings [shortest centroid–centroid distance = 3.480 (2) Å], leading to a chain-like structure along [001]. C—H⋯Cl hydrogen bonds complete the structure

Quantitative evaluation of protein–DNA interactions using an optimized knowledge-based potential

Computational evaluation of protein–DNA interaction is important for the identification of DNA-binding sites and genome annotation. It could validate the predicted binding motifs by sequence-based approaches through the calculation of the binding affinity between a protein and DNA. Such an evaluation should take into account structural information to deal with the complicated effects from DNA structural deformation, distance-dependent multi-body interactions and solvation contributions. In this paper, we present a knowledge-based potential built on interactions between protein residues and DNA tri-nucleotides. The potential, which explicitly considers the distance-dependent two-body, three-body and four-body interactions between protein residues and DNA nucleotides, has been optimized in terms of a Z-score. We have applied this knowledge-based potential to evaluate the binding affinities of zinc-finger protein–DNA complexes. The predicted binding affinities are in good agreement with the experimental data (with a correlation coefficient of 0.950). On a larger test set containing 48 protein–DNA complexes with known experimental binding free energies, our potential has achieved a high correlation coefficient of 0.800, when compared with the experimental data. We have also used this potential to identify binding motifs in DNA sequences of transcription factors (TF). The TFs in 79.4% of the known TF–DNA complexes have accurately found their native binding sequences from a large pool of DNA sequences. When tested in a genome-scale search for TF-binding motifs of the cyclic AMP regulatory protein (CRP) of Escherichia coli, this potential ranks all known binding motifs of CRP in the top 15% of all candidate sequences

Cotton plants expressing CYP6AE14 double-stranded RNA show enhanced resistance to bollworms

RNA interference (RNAi) plays an important role in regulating gene expression in eukaryotes. Previously, we generated Arabidopsis and tobacco plants expressing double-stranded RNA (dsRNA) targeting a cotton bollworm (Helicoverpa armigera) P450 gene, CYP6AE14. Bollworms fed on transgenic dsCYP6AE14 plants showed suppressed CYP6AE14 expression and reduced growth on gossypol-containing diet (Mao et al., in Nat Biotechnol 25: 1307–1313, 2007). Here we report generation and analysis of dsRNA-expressing cotton (Gossypium hirsutum) plants. Bollworm larvae reared on T2 plants of the ds6-3 line exhibited drastically retarded growth, and the transgenic plants were less damaged by bollworms than the control. Quantitative reverse-transcription polymerase chain reaction (RT-PCR) showed that the CYP6AE14 expression level was reduced in the larvae as early as 4 h after feeding on the transgenic plants; accordingly, the CYP6AE14 protein level dropped. These results demonstrated that transgenic cotton plants expressing dsCYP6AE14 acquired enhanced resistance to cotton bollworms, and that RNAi technology can be used for engineering insect-proof cotton cultivar

Bis[3,5-difluoro-2-(2-pyrid­yl)phen­yl](picolinato)iridium(III)

The Ir centre in the title complex, [Ir(C11H6F2N)2(C6H4NO2)], is six-coordinated in a slightly distorted octa­hedral IrC2N3O fashion

Spin-resolved imaging of atomic-scale helimagnetism in monolayer NiI2

Identifying intrinsic noncollinear magnetic order in monolayer van der Waals (vdW) crystals is highly desirable for understanding the delicate magnetic interactions at reduced spatial constraints and miniaturized spintronic applications, but remains elusive in experiments. Here, we achieved spin-resolved imaging of helimagnetism at atomic scale in monolayer NiI2 crystals, that were grown on graphene-covered SiC(0001) substrate, using spin-polarized scanning tunneling microscopy. Our experiments identify the existence of a spin spiral state with canted plane in monolayer NiI2. The spin modulation Q vector of the spin spiral is determined as (0.2203, 0, 0), which is different from its bulk value or its in-plane projection, but agrees well with our first principles calculations. The spin spiral surprisingly indicates collective spin switching behavior under magnetic field, whose origin is ascribed to the incommensurability between the spin spiral and the crystal lattice. Our work unambiguously identifies the helimagnetic state in monolayer NiI2, paving the way for illuminating its expected type-II multiferroic order and developing spintronic devices based on vdW magnets.Comment: 22 pages, 4 figure

Geometric bionics: Lotus effect helps polystyrene nanotube films get good blood compatibility

Various biomaterials have been widely used for manufacturing biomedical applications including artificial organs, medical devices and disposable clinical apparatus, such as vascular prostheses, blood pumps, artificial kidney, artificial hearts, dialyzers and plasma separators, which could be used in contact with blood^1^. However, the research tasks of improving hemocompatibility of biomaterials have been carrying out with the development of biomedical requirements^2^. Since the interactions that lead to surface-induced thrombosis occurring at the blood-biomaterial interface become a reason of familiar current complications with grafts therapy, improvement of the blood compatibility of artificial polymer surfaces is, therefore a major issue in biomaterials science^3^. After decades of focused research, various approaches of modifying biomaterial surfaces through chemical or biochemical methods to improve their hemocompatibility were obtained^1^. In this article, we report that polystyrene nanotube films with morphology similar to the papilla on lotus leaf can be used as blood-contacted biomaterials by virtue of Lotus effect^4^. Clearly, this idea, resulting from geometric bionics that mimicking the structure design of lotus leaf, is very novel technique for preparation of hemocompatible biomaterials

QuGAN: A Quantum State Fidelity based Generative Adversarial Network

Tremendous progress has been witnessed in artificial intelligence where neural network backed deep learning systems have been used, with applications in almost every domain. As a representative deep learning framework, Generative Adversarial Network (GAN) has been widely used for generating artificial images, text-to-image or image augmentation across areas of science, arts and video games. However, GANs are computationally expensive, sometimes computationally prohibitive. Furthermore, training GANs may suffer from convergence failure and modal collapse. Aiming at the acceleration of use cases for practical quantum computers, we propose QuGAN, a quantum GAN architecture that provides stable convergence, quantum-state based gradients and significantly reduced parameter sets. The QuGAN architecture runs both the discriminator and the generator purely on quantum state fidelity and utilizes the swap test on qubits to calculate the values of quantum-based loss functions. Built on quantum layers, QuGAN achieves similar performance with a 94.98% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms state-of-the-art quantum based GANs in the literature providing a 48.33% improvement in system performance compared to others attaining less than 0.5% in terms of similarity between generated distributions and original data sets. QuGAN code is released at https://github.com/yingmao/Quantum-Generative-Adversarial-NetworkComment: 2021 IEEE International Conference on Quantum Computing and Engineering (QCE