Observations of lightning processes using VHF radio interferometry

A single station, multiple baseline radio interferometer was used to locate the direction of VHF radiation from lightning discharges with microsec time resolution. Radiation source directions and electric field waveforms were analyzed for various types of breakdown events. These include initial breakdown and K type events of in-cloud activity, and the leaders of initial and subsequent strokes to ground and activity during and following return strokes. Radiation during the initial breakdown of a flash and in the early stages of initial leaders to ground is found to be similar. In both instances, the activity consists of localized bursts of radiation that are intense and slow moving. Motion within a given burst is unresolved by the interferometer. Radiation from in-cloud K type events is essentially the same as that from dart leaders; in both cases it is produced at the leading edge of a fast moving streamer that propagates along a well defined, often extensive path. K type events are sometimes terminated by fast field changes that are similar to the return stroke initiated by dart leaders; such K type events are the in-cloud analog of the dart leader return stroke process

Microwave Slow-Wave Structure and Phase-Compensation Technique for Microwave Power Divider

In this paper, T-shaped electromagnetic bandgap is loaded on a coupled transmission line itself and its electric performance is studied. Results show that microwave slow-wave effect can be enhanced and therefore, size reduction of a transmission-line-based circuit is possible. However, the transmission-line-based circuits characterize varied phase responses against frequency, which becomes a disadvantage where constant phase response is required. Consequently, a phase-compensation technique is further presented and studied. For demonstration purpose, an 8-way coupled-line power divider with 22.5 degree phase shifts between adjacent output ports, based on the studied slow-wave structure and phase-compensation technique, is developed. Results show both compact circuit architecture and improved phase imbalance are realized, confirming the investigated circuit structures and analyzing methodologies

Region Based Adversarial Synthesis of Facial Action Units

Facial expression synthesis or editing has recently received increasing attention in the field of affective computing and facial expression modeling. However, most existing facial expression synthesis works are limited in paired training data, low resolution, identity information damaging, and so on. To address those limitations, this paper introduces a novel Action Unit (AU) level facial expression synthesis method called Local Attentive Conditional Generative Adversarial Network (LAC-GAN) based on face action units annotations. Given desired AU labels, LAC-GAN utilizes local AU regional rules to control the status of each AU and attentive mechanism to combine several of them into the whole photo-realistic facial expressions or arbitrary facial expressions. In addition, unpaired training data is utilized in our proposed method to train the manipulation module with the corresponding AU labels, which learns a mapping between a facial expression manifold. Extensive qualitative and quantitative evaluations are conducted on the commonly used BP4D dataset to verify the effectiveness of our proposed AU synthesis method.Comment: Accepted by MMM202

Evaluation of Directive-Based GPU Programming Models on a Block Eigensolver with Consideration of Large Sparse Matrices

Achieving high performance and performance portability for large-scale scientific applications is a major challenge on heterogeneous computing systems such as many-core CPUs and accelerators like GPUs. In this work, we implement a widely used block eigensolver, Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG), using two popular directive based programming models (OpenMP and OpenACC) for GPU-accelerated systems. Our work differs from existing work in that it adopts a holistic approach that optimizes the full solver performance rather than narrowing the problem into small kernels (e.g., SpMM, SpMV). Our LOPBCG GPU implementation achieves a 2.8$${\times }$$–4.3$${\times }$$ speedup over an optimized CPU implementation when tested with four different input matrices. The evaluated configuration compared one Skylake CPU to one Skylake CPU and one NVIDIA V100 GPU. Our OpenMP and OpenACC LOBPCG GPU implementations gave nearly identical performance. We also consider how to create an efficient LOBPCG solver that can solve problems larger than GPU memory capacity. To this end, we create microbenchmarks representing the two dominant kernels (inner product and SpMM kernel) in LOBPCG and then evaluate performance when using two different programming approaches: tiling the kernels, and using Unified Memory with the original kernels. Our tiled SpMM implementation achieves a 2.9$${\times }$$ and 48.2$${\times }$$ speedup over the Unified Memory implementation on supercomputers with PCIe Gen3 and NVLink 2.0 CPU to GPU interconnects, respectively

Reduced expression of CENP-E contributes to the development of hepatocellular carcinoma and is associated with adverse clinical features

© 2019 The Author(s) Human kinesin centromere-associated protein E (CENP-E), one of spindle checkpoint proteins, has been identified as a tumor suppressor in several types of cancer, however, its role in hepatocarcinogenesis remains unknown. Here we investigated the role of CENP-E in human hepatocellular carcinoma (HCC) employing HCC cell lines (Hep3B, SMMC7721, and QGY7701), animal models, and patient's clinical samples and data. We demonstrated that down-regulation of CENP-E by CENP-E-silencing shRNAs significantly promoted HCC proliferation/growth both in vitro and in vivo. Further studies found that CENP-E suppressed the proliferation of HCC cells by halting cell cycle progression at the G1-S phase and accelerating cell apoptosis. Analyses of HCC patient samples and clinical data revealed that CENP-E was significantly down-regulated in HCC tissues and low CENP-E expression was significantly associated with patient's adverse clinicopathological features: poor prognosis, advanced TNM stage, metastasis, and larger tumor size. Multivariate analysis indicated that CENP-E was an independent prognostic factor predicting outcomes of advanced HCC patients. Our data suggest that loss of CENP-E contributes to HCC development and is strongly associated with adverse HCC clinical pathology. Thus, CENP-E could be a novel target for new treatments and a useful prognostic biomarker for HCC patients

Acoustic black holes from supercurrent tunneling

We present a version of acoustic black holes by using the principle of the Josephson effect. We find that in the case two superconductors $A$ and $B$ are separated by an insulating barrier, an acoustic black hole may be created in the middle region between the two superconductors. We discuss in detail how to describe an acoustic black hole in the Josephson junction and write the metric in the langauge of the superconducting electronics. Our final results infer that for big enough tunneling current and thickness of the junction, experimental verification of the Hawking temperature could be possible.Comment: 15pages,1 figure, to appear in IJMP

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal