A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks

Deep neural networks (DNNs) have achieved significant success in a variety of real world applications, i.e., image classification. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference. Both low-rankness and sparsity are appealing properties for the network approximation. In this paper we propose a unified framework to compress the convolutional neural networks (CNNs) by combining these two properties, while taking the nonlinear activation into consideration. Each layer in the network is approximated by the sum of a structured sparse component and a low-rank component, which is formulated as an optimization problem. Then, an extended version of alternating direction method of multipliers (ADMM) with guaranteed convergence is presented to solve the relaxed optimization problem. Experiments are carried out on VGG-16, AlexNet and GoogLeNet with large image classification datasets. The results outperform previous work in terms of accuracy degradation, compression rate and speedup ratio. The proposed method is able to remarkably compress the model (with up to 4.9x reduction of parameters) at a cost of little loss or without loss on accuracy.Comment: 8 pages, 5 figures, 6 table

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

IntroductionPulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients.MethodsCombining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed.ResultsTo confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital.DiscussionThe experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model’s accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE

IFI16 directly senses viral RNA and enhances RIG-I transcription and activation to restrict influenza virus infection

The retinoic acid-inducible gene I (RIG-I) receptor senses cytoplasmic viral RNA and activates type I interferons (IFN-I) and downstream antiviral immune responses. How RIG-I binds to viral RNA and how its activation is regulated remains unclear. Here, using IFI16 knockout cells and p204-deficient mice, we demonstrate that the DNA sensor IFI16 enhances IFN-I production to inhibit influenza A virus (IAV) replication. IFI16 positively upregulates RIG-I transcription through direct binding to and recruitment of RNA polymerase II to the RIG-I promoter. IFI16 also binds to influenza viral RNA via its HINa domain and to RIG-I protein with its PYRIN domain, thus promoting IAV-induced K63-linked polyubiquitination and RIG-I activation. Our work demonstrates that IFI16 is a positive regulator of RIG-I signalling during influenza virus infection, highlighting its role in the RIG-I-like-receptor-mediated innate immune response to IAV and other RNA viruses, and suggesting its possible exploitation to modulate the antiviral response

Energy saving potential of fragmented green spaces due to their temperature regulating ecosystem services in the summer

Urban green spaces help to moderate the urban heat island (UHI) effects, and can provide important temperature regulating ecosystem services and opportunities for savings in cooling energy. However, because explicit market values for these benefits are still lacking, they are rarely incorporated into urban planning actions. Green spaces can generate a three-dimensional (3D) cool island that may reduce the cooling energy requirements within and around urban areas, but such 3D cooling effect has not been considered in previous studies quantifying energy savings from green spaces. This study presents a new and simple approach to quantify potential energy savings due to the temperature regulating ecosystem services of small-scale fragmented green spaces using the 3D simulation of the summer-day outdoor thermal environment in Nanjing, China. Field survey data and the microclimate model ENVI-met were applied to examine the outdoor 3D thermal environmental patterns at Gulou Campus of Nanjing University under two different scenarios: “with” and “without” green spaces. Modeling results were applied to quantify potential cooling energy savings based on the effect of green spaces on the outdoor urban environment and to calculate the cumulative temperature reduction due to green spaces using a regression model. The results show that, in the horizontal direction, the simulated distribution of wind speed and mean air temperature at 1.5 m height were closely related to the spatial distribution of the underlying surface types. Removal of green spaces increased mean air temperature by 0.5 °C (33.1 °C vs. 33.6 °C). In the vertical direction, removal of green spaces had little effect on the near-surface wind field; however, above the surface, the turbulence perpendicular to the main wind direction significantly increased. Quantification of the cooling benefits of green spaces in relation to the mean height of buildings on Gulou Campus yielded 5.2 W/m2 cooling energy, saving totally 1.3 × 104 kW h during a single daytime hot summer period. This case study corroborates the importance of green space for cooling and informs city planners and decision-makers on how microclimate is impacted by the loss of green spaces. These findings will facilitate preservation, planning, and design of green spaces to increase urban environmental benefits and to improve the microclimate of urban areas at neighborhood, city, and regional scales

Large-Scale Assembly and Mask-Free Fabrication of Graphene Transistors via Optically Induced Electrodeposition

Graphene, known as an alternative for silicon, has significant potential in microelectronic applications. The assembly of graphene on well-defined metal electrodes is a critical step in the fabrication of microelectronic devices. Herein, we present a convenient, rapid, and large-scale assembly method for deposition of Ag electrodes, namely optically induced electrodeposition (OIED). This technique enables us to achieve custom-designed and mask-free fabrication of graphene transistors. The entire assembly process can be completed within a few tens of seconds. Our results show that graphene-based transistors fabricated with Ag electrodes function as a p-type semiconductor. Transfer curves of different samples reveal similar trends of slightly p-type characteristics, which shows that this method is reliable and repeatable

Pathways and Drivers of Gross N Transformation in Different Soil Types under Long-Term Chemical Fertilizer Treatments

Microbial-mediated nitrogen (N) dynamics is not only a key process for crop productivity, but also a driver for N losses. Therefore, a better understanding of N dynamics and controlling factors in different soil types is needed to better manage N fertilization in crop fields. To achieve this, a 15N tracing approach was used to quantify simultaneously occurring N transformation rates in four agricultural trials (>20 years chemical fertilizer application) with contrasting climatic and edaphic types (three upland soils and one paddy soil). The results showed that recalcitrant soil organic carbon (SOC) mineralization was the main source of NH4+ at all the sites, with rates ranging from 0.037 in fluvo-aquic soil to 3.096 mg kg−1 day−1 in paddy red soil. Autotrophic nitrification (ONH4) was the predominant NO3− production mechanism in the black and fluvo-aquic soils, whereas it was negligible in the upland and paddy red soils. Nitrification capacity, as an indicator of nitrate leaching risk, was in the order: upland red soil (1%) 3− leaching in black soil. The partial least squares path modeling (PLS-PM) showed that SOC, temperature and pH were the main factors controlling nitrate immobilization, N mineralization and nitrification. In summary, even under similar chemical fertilization conditions, N transformation dynamics are expected to differ with respect to soil type. Therefore, N management strategies should be adjusted to soil type to control N losses and increase crop yield

Progranulin facilitates conversion and function of regulatory T cells under inflammatory conditions.

The progranulin (PGRN) is known to protect regulatory T cells (Tregs) from a negative regulation by TNF-α, and its levels are elevated in various kinds of autoimmune diseases. Whether PGRN directly regulates the conversion of CD4+CD25-T cells into Foxp3-expressing regulatory T cells (iTreg), and whether PGRN affects the immunosuppressive function of Tregs, however, remain unknown. In this study we provide evidences demonstrating that PGRN is able to stimulate the conversion of CD4+CD25-T cells into iTreg in a dose-dependent manner in vitro. In addition, PGRN showed synergistic effects with TGF-β1 on the induction of iTreg. PGRN was required for the immunosuppressive function of Tregs, since PGRN-deficient Tregs have a significant decreased ability to suppress the proliferation of effector T cells (Teff). In addition, PGRN deficiency caused a marked reduction in Tregs number in the course of inflammatory arthritis, although no significant difference was observed in the numbers of Tregs between wild type and PGRN deficient mice during development. Furthermore, PGRN deficiency led to significant upregulation of the Wnt receptor gene Fzd2. Collectively, this study reveals that PGRN directly regulates the numbers and function of Tregs under inflammatory conditions, and provides new insight into the immune regulatory mechanism of PGRN in the pathogenesis of inflammatory and immune-related diseases

Precision agriculture management based on a surrogate model assisted multiobjective algorithmic framework

Abstract Sustainable intensification needs to optimize irrigation and fertilization strategies while increasing crop yield. To enable more precision and effective agricultural management, a bi-level screening and bi-level optimization framework is proposed. Irrigation and fertilization dates are obtained by upper-level screening and upper-level optimization. Subsequently, due to the complexity of the problem, the lower-level optimization uses a data-driven evolutionary algorithm, which combines the fast non-dominated sorting genetic algorithm (NSGA-II), surrogate-assisted model of radial basis function and Decision Support System for Agrotechnology Transfer to handle the expensive objective problem and produce a set of optimal solutions representing a trade-off between conflicting objectives. Then, the lower-level screening quickly finds better irrigation and fertilization strategies among thousands of solutions. Finally, the experiment produces a better irrigation and fertilization strategy, with water consumption reduced by 44%, nitrogen application reduced by 37%, and economic benefits increased by 7 to 8%