KidneyRegNet: A Deep Learning Method for 3DCT-2DUS Kidney Registration during Breathing

This work proposed a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which consists of a feature network, and a 3D-2D CNN-based registration network. The feature network has handcraft texture feature layers to reduce the semantic gap. The registration network is encoder-decoder structure with loss of feature-image-motion (FIM), which enables hierarchical regression at decoder layers and avoids multiple network concatenation. It was first pretrained with retrospective datasets cum training data generation strategy, then adapted to specific patient data under unsupervised one-cycle transfer learning in onsite application. The experiment was on 132 U/S sequences, 39 multiple phase CT and 210 public single phase CT images, and 25 pairs of CT and U/S sequences. It resulted in mean contour distance (MCD) of 0.94 mm between kidneys on CT and U/S images and MCD of 1.15 mm on CT and reference CT images. For datasets with small transformations, it resulted in MCD of 0.82 and 1.02 mm respectively. For large transformations, it resulted in MCD of 1.10 and 1.28 mm respectively. This work addressed difficulties in 3DCT-2DUS kidney registration during free breathing via novel network structures and training strategy.Comment: 15 pages, 8 figures, 9 table

Broadband surface-wave transformation cloak

Guiding surface electromagnetic waves around disorder without disturbing the wave amplitude or phase is in great demand for modern photonic and plasmonic devices, but is fundamentally difficult to realize because light momentum must be conserved in a scattering event. A partial realization has been achieved by exploiting topological electromagnetic surface states, but this approach is limited to narrow-band light transmission and subject to phase disturbances in the presence of disorder. Recent advances in transformation optics apply principles of general relativity to curve the space for light, allowing one to match the momentum and phase of light around any disorder as if that disorder were not there. This feature has been exploited in the development of invisibility cloaks. An ideal invisibility cloak, however, would require the phase velocity of light being guided around the cloaked object to exceed the vacuum speed of light—a feat potentially achievable only over an extremely narrow band. In this work, we theoretically and experimentally show that the bottlenecks encountered in previous studies can be overcome. We introduce a class of cloaks capable of remarkable broadband surface electromagnetic waves guidance around ultrasharp corners and bumps with no perceptible changes in amplitude and phase. These cloaks consist of specifically designed nonmagnetic metamaterials and achieve nearly ideal transmission efficiency over a broadband frequency range from 0+ to 6 GHz. This work provides strong support for the application of transformation optics to plasmonic circuits and could pave the way toward high-performance, large-scale integrated photonic circuits.National Natural Science Foundation (China) (Grant 61322501)National Natural Science Foundation (China) (Grant 61275183)National Top-Notch Young Professionals Program (Grant FANEDDC-200950)Program for New Century Excellent Talents (NCET-12-0489)Fundamental Research Funds for the Central Universities (Grant FRFCU-2014XZZX003-24)Nanyang Assistant Professorship Start-Up GrantSingapore. Ministry of Education (Grant Tier 1 RG27/12)Singapore. Ministry of Education (Grant MOE2011-T3-1-005)Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies (Contract W911NF-13-D-0001)United States. Dept. of Energy (Solid-State Solar-Thermal Energy Conversion Center Grant de-sc0001299

A Supervised Topic Model Approach to Learning Effective Styles within Human-Agent Negotiation

We present a method that analyzes a person's negotiation behavior to automatically detect co-occurrence of tactics and combination of tactics (i.e., negotiation styles). We first identify action features consistent with use of the common negotiation tactics based on prior research in negotiation. Next, we apply regularized linear regression over a negotiation dataset to assess how effective particular tactics are in predicting the negotiation outcome. Finally, we use a supervised variant of a topic model to derive effective negotiation styles. Results from the clusters produced by the topic models provide insights regarding the effectiveness of negotiation styles that people utilize

Porcine Reproductive and Respiratory Syndrome in Hybrid Wild Boars, China

We conducted a serologic investigation of porcine reproductive and respiratory syndrome virus (PRRSV) in hybrid wild boar herds in China during 2008–2009. PRRSV isolates with novel genetic markers were recovered. Experimental infection of pigs indicated that hybrid wild boars are involved in the epidemiology of PRRSV

A global product of fine-scale urban building height based on spaceborne lidar

Characterizing urban environments with broad coverages and high precision is more important than ever for achieving the UN's Sustainable Development Goals (SDGs) as half of the world's populations are living in cities. Urban building height as a fundamental 3D urban structural feature has far-reaching applications. However, so far, producing readily available datasets of recent urban building heights with fine spatial resolutions and global coverages remains a challenging task. Here, we provide an up-to-date global product of urban building heights based on a fine grid size of 150 m around 2020 by combining the spaceborne lidar instrument of GEDI and multi-sourced data including remotely sensed images (i.e., Landsat-8, Sentinel-2, and Sentinel-1) and topographic data. Our results revealed that the estimated method of building height samples based on the GEDI data was effective with 0.78 of Pearson's r and 3.67 m of RMSE in comparison to the reference data. The mapping product also demonstrated good performance as indicated by its strong correlation with the reference data (i.e., Pearson's r = 0.71, RMSE = 4.60 m). Compared with the currently existing products, our global urban building height map holds the ability to provide a higher spatial resolution (i.e., 150 m) with a great level of inherent details about the spatial heterogeneity and flexibility of updating using the GEDI samples as inputs. This work will boost future urban studies across many fields including climate, environmental, ecological, and social sciences