Joint content placement and storage allocation based on federated learning in F-RANs

Funding: This work was supported in part by Innovation Project of the Common Key Technology of Chongqing Science and Technology Industry (cstc2018jcyjAX0383), the special fund of Chongqing key laboratory (CSTC), and the Funding of CQUPT (A2016-83, GJJY19-2-23, A2020-270).Peer reviewedPublisher PD

Vitrectomy combine with double membranes peeling for idiopathic epiretinal membrane

AIM: To explore the efficacy and safety of vitrectomy combine with internal limiting membrane peeling and epiretinal membranes peeling in the treatment of idiopathic epiretinal membrane(IERM). METHODS: In this retrospective study, 53 patients(53 eyes)who underwent vitrectomy for IERM from January 2015 to December 2015 were evaluated, with 24-month follow-up. Only removal of the epiretinal membrane(ERM group)was performed in 21 patients, while 32 had removal of the epiretinal membrane associated with internal limiting membrane peeling(ERM+ILM group). The best corrected visual acuity(BCVA), central macular thickness(CMT)and the complications were observed and analyzed before operation, 1, 3, 12, 24mo after operation. RESULTS: Both groups showed significant improvement of BCVA when compared to preoperative BCVA(PP>0.05). After operation, statistically significant CMT reduction occurred in both groups(PP>0.05). There were 3 cases(3 eyes)had retinal punctate hemorrhage after operation, of which 1(5%)was from the ERM group and 2(6%)were from the ERM+ILM group. No significant difference was observed between the groups(P=1.000). At the end of follow-up, 4 cases(19%)presented with recurrence of the epiretinal membrane all from the ERM group, and there no recurrence was found in ERM+ILM group, and the difference was statistically significant(P=0.020).CONCLUSION: Vitrectomy with ILM peeling and epiretinal membranes peeling for the treatment of epiretinal membrane is the efficacy and safety, and it may reduce the recurrence

NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing

We present a novel method, called NeTO, for capturing 3D geometry of solid transparent objects from 2D images via volume rendering. Reconstructing transparent objects is a very challenging task, which is ill-suited for general-purpose reconstruction techniques due to the specular light transport phenomena. Although existing refraction-tracing based methods, designed specially for this task, achieve impressive results, they still suffer from unstable optimization and loss of fine details, since the explicit surface representation they adopted is difficult to be optimized, and the self-occlusion problem is ignored for refraction-tracing. In this paper, we propose to leverage implicit Signed Distance Function (SDF) as surface representation, and optimize the SDF field via volume rendering with a self-occlusion aware refractive ray tracing. The implicit representation enables our method to be capable of reconstructing high-quality reconstruction even with a limited set of images, and the self-occlusion aware strategy makes it possible for our method to accurately reconstruct the self-occluded regions. Experiments show that our method achieves faithful reconstruction results and outperforms prior works by a large margin. Visit our project page at \url{https://www.xxlong.site/NeTO/

Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models).</p

Abomasal mucosal immune responses of cattle with limited or continuous exposure to pasture-borne gastrointestinal nematode parasite infection

It has been well documented that cattle raised on pasture are slow in weight gain when compared to those fed with grain. Inflammation in the digestive system commonly caused by pasture-transmitted gastrointestinal (GI) nematode parasites that could negatively impact feed conversion has never been compared in cattle raised with no pasture exposure (NPE, uninfected), limited pasture exposure (LPE, exposure until weaning), or continuous pasture exposure (CPE, life time exposure). In the present study, the abomasal mucosal immune responses and inflammation of LPE and CPE cattle were investigated. Our results indicate that CPE cattle displayed inflamed abomasa with enlarged draining lymph nodes, the presence of Ostertagia ostertagi larvae and higher levels of Ostertagia-specific antibodies in circulation. The level of B cells was elevated in the abomasal mucosa in the presence (nodular) or absence (non-nodular) of Ostertagia-specific pathology, where B cells were 4-fold higher in the nodular mucosa. Foxp3+ CD4T cells were also noticeably elevated in both the abomasal mucosa and blood, but were only slightly higher in non-nodular mucosa than in the nodular mucosa of CPE animals. In contrast, LPE animals presented no enlargement of abomasal draining lymph nodes and exhibited little to no immune cell infiltration in the abomasal mucosa. Further, CPE animals had higher numbers of mucosal mast cells when compared to LPE animals, though mucosal mast cells were high in all animals. Overall, CPE cattle displayed significantly higher levels of inflammation and pathology in their abomasa and may explain in part slowed weight gain relative to LPE animals. The results of this study emphasize the need for GI nematode parasite control in CPE animals and development and application of vaccines which are compatible with the organic cattle production system

Advertisement calls of Leptobrachella suiyangensis and Leptobrachella bashaensis (Anura, Megophryidae)

In this study, the advertisement calls of Leptobrachella suiyangensis and Leptobrachella bashaensis are described. The advertisement call of L. suiyangensis includes simple and complex calls, with four different call types and a dominant frequency ranging 4.13–4.82 kHz. The advertisement call of L. bashaensis consists of a single note, with a dominant frequency 6.03–6.46 kHz. We compare the advertisement calls with other species in the genus Leptobrachella, and discuss the definitions of primary advertisement calls and secondary advertisement calls. Our results provide basic data for further acoustic, taxonomic and ecological studies in the genus Leptobrachella