Programmable Chip Based High Performance MEC Router for Ultra-Low Latency and High Bandwidth Services in Distributed Computing Environment

Abstract With the spread of smart cities through 5G and the development of IoT devices, the number of services requiring firm assurance of high capacity and ultra-low delay quality in various forms is increasing. However, continuous growth of large data makes it difficult for a centralized cloud to ensure quality of service. For this, a variety of distributed application architecture researches, such as MEC (Mobile|Mutli-access Edge Computing), are in progress. However, vendor-dependent MEC technology based on VNF (Virtual Network Function) has performance and scalability issues when deploying a variety of 5G-based services. This paper proposes PRISM-MECR, an SDN (Software Defined Network) based hardware accelerated MEC router using P4[3] programmable chip, to improve forwarding performance while minimizing load of host CPU cores in charge of forwarding among MEC technologies

Switching Magnetism and Superconductivity with Spin-Polarized Current in Iron-Based Superconductor

We have explored a new mechanism for switching magnetism and superconductivity in a magnetically frustrated iron-based superconductor using spin-polarized scanning tunneling microscopy (SPSTM). Our SPSTM study on single crystal Sr$_2$VO$_3$FeAs shows that a spin-polarized tunneling current can switch the Fe-layer magnetism into a non-trivial $C_4$ (2$\times$2) order, not achievable by thermal excitation with unpolarized current. Our tunneling spectroscopy study shows that the induced $C_4$ (2$\times$2) order has characteristics of plaquette antiferromagnetic order in Fe layer and strongly suppressed superconductivity. Also, thermal agitation beyond the bulk Fe spin ordering temperature erases the $C_4$ state. These results suggest a new possibility of switching local superconductivity by changing the symmetry of magnetic order with spin-polarized and unpolarized tunneling currents in iron-based superconductors.Comment: 33 pages, 16 figure

Interpretable pap smear cell representation for cervical cancer screening

Screening is critical for prevention and early detection of cervical cancer but it is time-consuming and laborious. Supervised deep convolutional neural networks have been developed to automate pap smear screening and the results are promising. However, the interest in using only normal samples to train deep neural networks has increased owing to class imbalance problems and high-labeling costs that are both prevalent in healthcare. In this study, we introduce a method to learn explainable deep cervical cell representations for pap smear cytology images based on one class classification using variational autoencoders. Findings demonstrate that a score can be calculated for cell abnormality without training models with abnormal samples and localize abnormality to interpret our results with a novel metric based on absolute difference in cross entropy in agglomerative clustering. The best model that discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003 area under operating characteristic curve (AUC) and one that discriminates high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other clustering methods, our method enhances the V-measure and yields higher homogeneity scores, which more effectively isolate different abnormality regions, aiding in the interpretation of our results. Evaluation using in-house and additional open dataset show that our model can discriminate abnormality without the need of additional training of deep models.Comment: 20 pages, 6 figure

Correlation of Fe-Based Superconductivity and Electron-Phonon Coupling in an FeAs/Oxide Heterostructure

Interfacial phonons between iron-based superconductors (FeSCs) and perovskite substrates have received considerable attention due to the possibility of enhancing preexisting superconductivity. Using scanning tunneling spectroscopy, we studied the correlation between superconductivity and e−ph interaction with interfacial phonons in an iron-based superconductor Sr2VO3FeAs (Tc≈33  K) made of alternating FeSC and oxide layers. The quasiparticle interference measurement over regions with systematically different average superconducting gaps due to the e−ph coupling locally modulated by O vacancies in the VO2 layer, and supporting self-consistent momentum-dependent Eliashberg calculations provide a unique real-space evidence of the forward-scattering interfacial phonon contribution to the total superconducting pairing. © 2017 American Physical Society6

Clinical Trial of Human Fetal Brain-Derived Neural Stem/Progenitor Cell Transplantation in Patients with Traumatic Cervical Spinal Cord Injury

In a phase I/IIa open-label and nonrandomized controlled clinical trial, we sought to assess the safety and neurological effects of human neural stem/progenitor cells (hNSPCs) transplanted into the injured cord after traumatic cervical spinal cord injury (SCI). Of 19 treated subjects, 17 were sensorimotor complete and 2 were motor complete and sensory incomplete. hNSPCs derived from the fetal telencephalon were grown as neurospheres and transplanted into the cord. In the control group, who did not receive cell implantation but were otherwise closely matched with the transplantation group, 15 patients with traumatic cervical SCI were included. At 1 year after cell transplantation, there was no evidence of cord damage, syrinx or tumor formation, neurological deterioration, and exacerbating neuropathic pain or spasticity. The American Spinal Injury Association Impairment Scale (AIS) grade improved in 5 of 19 transplanted patients, 2 (A → C), 1 (A → B), and 2 (B → D), whereas only one patient in the control group showed improvement (A → B). Improvements included increased motor scores, recovery of motor levels, and responses to electrophysiological studies in the transplantation group. Therefore, the transplantation of hNSPCs into cervical SCI is safe and well-tolerated and is of modest neurological benefit up to 1 year after transplants. This trial is registered with Clinical Research Information Service (CRIS), Registration Number: KCT0000879

Fanout Set Partition Scheme for QoS-Guaranteed Multicast Transmission

Increasing demand for multicast transmission necessitates service-specific and precise quality-of-service (QoS) control. Since existing works provided limited methodologies such as best path selection, their ability is restricted by the given topology and the congestion status of the network. This paper proposes a fanout set partition (FSP) scheme to realize QoS-guaranteed multicast transmission. The FSP scheme adjusts the delay of the multicast flow by dividing its fanout set into smaller subsets. Since it is carried out based on the service requirement, service-specific QoS control is implemented. Mathematical analysis investigates the trade-offs, and the performance evaluation results show significant improvements under various traffic conditions

Prediction of Beck Depression Inventory Score in EEG: Application of Deep-Asymmetry Method

There is ongoing research on using electroencephalography (EEG) to predict depression. In particular, the deep learning method in which brain waves are used as inputs of a convolutional neural network (CNN) is being widely researched and has shown remarkable performance. We built a regression model to predict the severity score (Beck Depression Inventory [BDI]) of depressed patients as an extension of the deep-asymmetry method, which has shown promising performance in depression classification. Predicting the severity of depression is very important because the treatment and coping methods are different for each severity level. We imaged brain waves using the deep-asymmetry method, used them to train a two-dimensional CNN-based deep learning model, and achieved satisfactory performance. The EEG image-based CNN approach will make an important contribution to creating a highly interpretable model for predicting depression in the future