Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks

The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm which incorporates the cloud computing into heterogeneous networks (HetNets), thereby taking full advantage of cloud radio access networks (C-RANs) and HetNets. Characterizing the cooperative beamforming with fronthaul capacity and queue stability constraints is critical for multimedia applications to improving energy efficiency (EE) in H-CRANs. An energy-efficient optimization objective function with individual fronthaul capacity and inter-tier interference constraints is presented in this paper for queue-aware multimedia H-CRANs. To solve this non-convex objective function, a stochastic optimization problem is reformulated by introducing the general Lyapunov optimization framework. Under the Lyapunov framework, this optimization problem is equivalent to an optimal network-wide cooperative beamformer design algorithm with instantaneous power, average power and inter-tier interference constraints, which can be regarded as the weighted sum EE maximization problem and solved by a generalized weighted minimum mean square error approach. The mathematical analysis and simulation results demonstrate that a tradeoff between EE and queuing delay can be achieved, and this tradeoff strictly depends on the fronthaul constraint

The Optimal Portfolio Model Based on Multivariate T Distribution with Fuzzy Mathematics Method

This paper proposed the optimal portfolio model maximizing returns and minimizing the risk expressed as CvaR under the assumption that the portfolio yield subject to multivariate t distribution. With Fuzzy Mathematics, we solve the multi-objectives model, and compare the model results to the case under the assumption of normal distribution yield, based on the portfolio VAR through empirical research. It is showed that our returns and risk are higher than M-V model.Key words: Multivariate t distribution; The optimal portfolio; VAR; CVAR; Multi-objectives programming; Fuzzy mathematic

Disengagement of motor cortex from movement control during long-term learning.

Motor learning involves reorganization of the primary motor cortex (M1). However, it remains unclear how the involvement of M1 in movement control changes during long-term learning. To address this, we trained mice in a forelimb-based motor task over months and performed optogenetic inactivation and two-photon calcium imaging in M1 during the long-term training. We found that M1 inactivation impaired the forelimb movements in the early and middle stages, but not in the late stage, indicating that the movements that initially required M1 became independent of M1. As previously shown, M1 population activity became more consistent across trials from the early to middle stage while task performance rapidly improved. However, from the middle to late stage, M1 population activity became again variable despite consistent expert behaviors. This later decline in activity consistency suggests dissociation between M1 and movements. These findings suggest that long-term motor learning can disengage M1 from movement control

Deep Dimension Reduction for Supervised Representation Learning

The success of deep supervised learning depends on its automatic data representation abilities. Among all the characteristics of an ideal representation for high-dimensional complex data, information preservation, low dimensionality and disentanglement are the most essential ones. In this work, we propose a deep dimension reduction (DDR) approach to achieving a good data representation with these characteristics for supervised learning. At the population level, we formulate the ideal representation learning task as finding a nonlinear dimension reduction map that minimizes the sum of losses characterizing conditional independence and disentanglement. We estimate the target map at the sample level nonparametrically with deep neural networks. We derive a bound on the excess risk of the deep nonparametric estimator. The proposed method is validated via comprehensive numerical experiments and real data analysis in the context of regression and classification

Regeneration of Different Plant Functional Types in a Masson Pine Forest Following Pine Wilt Disease

Pine wilt disease is a severe threat to the native pine forests in East Asia. Understanding the natural regeneration of the forests disturbed by pine wilt disease is thus critical for the conservation of biodiversity in this realm. We studied the dynamics of composition and structure within different plant functional types (PFTs) in Masson pine forests affected by pine wilt disease (PWD). Based on plant traits, all species were assigned to four PFTs: evergreen woody species (PFT1), deciduous woody species (PFT2), herbs (PFT3), and ferns (PFT4). We analyzed the changes in these PFTs during the initial disturbance period and during post-disturbance regeneration. The species richness, abundance and basal area, as well as life-stage structure of the PFTs changed differently after pine wilt disease. The direction of plant community regeneration depended on the differential response of the PFTs. PFT1, which has a higher tolerance to disturbances, became dominant during the post-disturbance regeneration, and a young evergreen-broad-leaved forest developed quickly after PWD. Results also indicated that the impacts of PWD were dampened by the feedbacks between PFTs and the microclimate, in which PFT4 played an important ecological role. In conclusion, we propose management at the functional type level instead of at the population level as a promising approach in ecological restoration and biodiversity conservation

Biologics targeting IL-17 and IL-23 maintain stability in patients with psoriasis during COVID-19 infection: a case-control study

BackgroundPsoriasis is a chronic and refractory skin disease. The emergence of biologics provides more options for the treatment of psoriasis, but the COVID-19 pandemic poses challenges for the management of psoriasis.ObjectivesThe purpose of this study was to investigate the effect of different biologics on the stabilization of psoriasis during COVID-19 infection in China.MethodsThis is a single-center, observational, retrospective, case–control study. Using our database, we conducted a remote dermatologic study by means of questionnaire follow-up or telephone follow-up to collect general information of patients, information related to COVID-19 infection and conditions of psoriasis for comparison and further analysis between groups.ResultsOur study ultimately included 274 patients for analysis. We found that the patients in this collection had mild symptoms of COVID-19 infection, and only 13 of them needed to go to the hospital for medical treatment. Further studies found that in biologics, relative to tumor necrosis factor-α inhibitors (TNF-αi), interleukin-17 inhibitors (IL-17i) and interleukin-23 inhibitors (IL-23i) are both protective factors in flare-up of psoriasis [IL-17i: OR (95% CI) = 0.412 (0.189–0.901); IL-23i: OR (95% CI) = 0.291 (0.097–0.876)]. In addition, we also found that the proportion of people with increased psoriasis developing long COVID-19 increased, and we speculated that increased psoriasis may be a potential risk factor for long COVID-19.ConclusionOur study showed that the use of IL-17i and IL-23i was a protective factor for psoriasis compared with TNF-αi, and could keep the psoriasis stable