Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference

State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications. We propose a dual dynamic inference (DDI) framework that highlights the following aspects: 1) we integrate both input-dependent and resource-dependent dynamic inference mechanisms under a unified framework in order to fit the varying IoT resource requirements in practice. DDI is able to both constantly suppress unnecessary costs for easy samples, and to halt inference for all samples to meet hard resource constraints enforced; 2) we propose a flexible multi-grained learning to skip (MGL2S) approach for input-dependent inference which allows simultaneous layer-wise and channel-wise skipping; 3) we extend DDI to complex CNN backbones such as DenseNet and show that DDI can be applied towards optimizing any specific resource goals including inference latency or energy cost. Extensive experiments demonstrate the superior inference accuracy-resource trade-off achieved by DDI, as well as the flexibility to control such trade-offs compared to existing peer methods. Specifically, DDI can achieve up to 4 times computational savings with the same or even higher accuracy as compared to existing competitive baselines

Implications of wetland degradation for the potential denitrifying activity and bacterial populations with nirS genes as found in a succession in Qinghai Tibet plateau, China

Alpine wetland in the Zoige Plateau has suffered from serious degradation during"the last 30 years due to global climate change and anthropogenic impact. Denitrification is a key nitrogen removal process which can be performed by different microorganisms, including bacteria harboring ttirS-genes. In this study, a degradation succession was used to study the effect on potential denitrification activity (PDA) and on bacterial communities harboring nirS genes. Based on the determination of the PDA, the abundance, structural diversity, and phylogenetic identity of the soil bacteria with nirS genes were further assessed by qPCR, terminal restriction fragment length polymorphism (T-RFLP), and DNA-sequencing, respectively. The results showed that soil PDA ranged from 8.78 to 52.77 ng N20-N g(-1) dry soil h(-1), being lowest in sandy soil and highest in swamp soil. The abundance of nirS genes (copies g(-1) soil) were also the lowest in the sandy soil while highest in the swamp soil. The average Shannon-Wiener diversity index of the nirS denitrifying bacterial structural ranged from 2.20 in the meadow soil to 3.07 in the swamp soil. Redundancy analysis (RDA) showed that the nirS denitrifying bacterial community correlated with soil water content and available phosphorus, with water content as the major factor in shaping the nirS denitrifying bacterial community. The results of this study suggest that the wetland degradation would decrease soil PDA, and abundance and structural diversity of the denitrifying bacteria with nirS genes. These findings can contribute to support a theoretical foundation for predicting the potential influences of wetland degradation on soil denitrifying bacteria in alpine wetlands. (C) 2017 Elsevier Masson SAS. All rights reserved.Peer reviewe

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Preparation and solidification process of mono-sized Cu–Ni–Sn microspheres by pulsated orifice ejection method

Abstract Cu–Ni–Sn alloys are well known for their excellent properties, including excellent elasticity and high strength, which enable large potentials for applications in microelectronic industry and 3D printing for example. Preparation of the high quality spherical powders should meet the new requirements in these emerging fields. In this work, the mono-sized Cu-13Ni-17Sn (wt%) microspheres with controllable diameters ranging from 84.4 μm to 212.0 μm were prepared by pulsated orifice ejection method (POEM). Solidified Cu–Ni–Sn microspheres exhibit good sphericity and remarkably narrowed size distribution. The percentage of microspheres with sphericity of more than 0.9 is high up to 98.6%, and the average sphericity of microspheres is 0.989. The surface of Cu–Ni–Sn microspheres is smooth, and the interior contains no pores and impurities. Furthermore, the cooling rate of Cu-13Ni-17Sn microspheres was estimated in a Newton’s cooling model. With decreasing particle diameter, the cooling rate of Cu-13Ni-17Sn microspheres increases gradually. When the particle diameter is less than 84.4 μm, the cooling rate of microspheres exceeds 3.64?×?104 K s?1. With increasing particle size, the secondary dendrite arm spacing increases gradually owing to the decrease of the sphere cooling rate

Agglomeration-free VN nanoparticles with controllable crystallite size prepared by a clapboard approach from Zn-V-O based precursor

Well dispersed vanadium nitride (VN) nanoparticles were successfully prepared by nitridation of Zn-3(OH)(2)(V2O7)center dot(H2O)(2) nanosheets followed by alkaline leaching. ZnO derived from Zn-3(OH)(2)(-V2O7)center dot(H2O)(2) during nitridation process acted as a clapboard to prevent VN nanoparticles from growing up and agglomerating. In addition, the effects of NH3 flow rate, nitridation time and temperature on the crystallite size of VN nanoparticles were systemically investigated. The results showed that increasing NH3 flow rate from 100 mL min(-1) to 600 mL min(-1) resulted in an increase of the crystallite size from 17 nm to 22 nm, and prolonging nitridation time from 2 h to 8 h resulted in an increase from 15 nm to 24 nm. Additionally, the crystallite size of VN increased with the increase of the nitridation temperature. Furthermore, when the nitridation temperature was fixed at 550 degrees C, agglomeration-free VN nano-particles with a specific surface area of 62.8 m(2) g(-1) and crystallite size in the range of 12-15 nm were synthesized. Moreover, this facile synthetic strategy can be extended further to synthesize other dispersed inorganic nanomaterials. (C) 2018 Elsevier B.V. All rights reserved.</p

Two-Decade GNSS Observation Processing and Analysis with the New IGS Repro3 Criteria: Implications for the Refinement of Velocity Field and Deformation Field in Continental China

Extensive observation collection, unified and rigorous data processing, and accurate construction of the station motion model are the three essential elements for the accuracy and reliability of the Global Navigation Satellite System (GNSS) velocity field. GNSS data reprocessing not only can weaken the influence of untrue nonlinear site signals caused by imperfect models but also can eliminate the displacement offset caused by frame transformation, solution strategy, and model change. Based on the new repro3 criteria of the International GNSS Service (IGS), we process rigorously GNSS observations of continental China from the period 2000 to 2020 to refine GNSS station secular velocities and analyze the present-day crustal deformation in continental China. The main contributions of this work included the followings. Firstly, the repro3 algorithm and model are used to uniformly and rigorously process the two-decade GNSS historical observations to obtain more reliable GNSS coordinate time series with mm-level precision. Combined with the historical records of major earthquakes in continental China, we build a GNSS time series model considering nonlinear factors (velocity, offset, period, co-seismic/post-seismic deformation) to extract GNSS horizontal velocity field whose root mean square (RMS) mean is 0.1 mm/a. Secondly, the GNSS horizontal grid velocity field in continental China is interpolated using the gpsgridder method (the minimum radius is set to 16, and the Poisson&rsquo;s ratio is set to 0.5). Estimation and analysis of the crustal strain rate solution lead to the conclusion that the strain degree in West China (the high strain region is mainly located in the Qinghai Tibet Plateau and Tianshan Mountains) is much more intense than that in the east (the main strain rate is less than 5 nstrain/year). In addition, most strong earthquakes in the Chinese mainland occurred on active blocks and their boundary faults with large changes in the GNSS velocity field and strain field. Then, an improved K-means++ clustering analysis method is proposed to divide active blocks using GNSS horizontal velocity field. Furthermore, different relative motion models of different blocks are constructed using the block division results. Among them, the Eurasian block has the lowest accuracy (the RMS of residual velocity in the east and north directions are 5.60 and 9.65 mm/a, respectively), and the China block 7 has the highest accuracy (the RMS mean of relative velocity in the east and north directions are 2.60 and 2.65 mm/a, respectively). More observations (2260+ sites), longer time (20 years), and updated criteria (Repro3) are to finely obtain the GNSS velocity field in continental China, and depict crustal deformation and active block with the gpsgridder and improved K-means++ methods

Selective Recovery of Gallium (Indium) from Metal Organic Chemical Vapor Deposition Dust-A Sustainable Process

Gallium (indium)-containing dust as a hazardous waste generated from light-emitting diode (LED) epitaxial wafer manufacturing attracts worldwide attention because of both resources and environmental importance. Oxidative roasting combined with acidic leaching is frequently utilized to recover the corresponding metals from such dust, while the recovery rate is usually low because of the rather inert physicochemical properties of gallium compounds. Simultaneously, the selectivity of leaching is low, which results in complex separation or purification is required in order to obtain the required product (e.g., metallic gallium, Ga(OH)(3)). In this research, it is demonstrated that the selectivity of leaching can be achieved via properly controlling the physicochemical properties of the leaching solution and the leaching conditions. The leaching rate of gallium can reach 90.01% through optimizing the effects of different parameters, including leaching reagent concentration, solid-to-liquid ratio, reaction temperature, reaction time, and rotation rate, which is about 16% higher than the conventional method. Moreover, the corresponding leaching mechanisms and kinetics were also evaluated, and the apparent activation energy of the reaction is determined as 24.33 kJ/mol. Without further purification, 99.8% of gallium and 99.1% of indium can be further recovered as Ga(OH)(3) and In(OH)(3) from the leaching solutions, respectively. In the whole process, the effective recycling rates of gallium and indium are 89.83% and 92.42%, respectively. This study provides bases for developing an effective recycling process of such waste with high recovery rate, advanced selectivity, and low environmental impacts

Lithium carbonate recovery from lithium-containing solution by ultrasound assisted precipitation

Lithium carbonate (Li2CO3), one of the most important lithium compounds, is usually prepared from lithium-containing solution. The lithium recovery rate and the purity of Li2CO3 are highly dependent on the lithium concentration. In order to get a high lithium recovery rate, high concentrated lithium-containing solution is required, while the purity of Li2CO3 can be low remaining a significant amount of impurities. Usually, it is not possible to obtain high purity Li2CO3 by single-step precipitation with a relatively high lithium recovery rate especially from a low concentrated lithium-containing solution. In this research, ultrasound is introduced to increase lithium recovery rate and prepare industrial grade Li2CO3. The research found that ultrasound can significantly reduce the polymerization of Li2CO3 crystal particles and promote dissociation of impurity ions. At the same time, ultrasound accelerates the nucleation process of Li2CO3 and boosts lithium recovery rate because of cavitation. The different parameters during the Li2CO3 precipitation process were systematically discussed. Under the optimized conditions, the lithium recovery rate can be increased by 12% with a global lithium recovery rate of 97.4%. Li2CO3 with a purity higher than industrial grade can be obtained by one-step precipitation. It demonstrates a potential pathway for effective lithium recovery from low concentrated lithium-containing solution and preparation of industrial grade Li2CO3.status: publishe

Analysis of Regional Satellite Clock Bias Characteristics Based on BeiDou System

With the continuous development of the Global Navigation Satellite System (GNSS), the calculation theory and strategy of the global Satellite Clock Bias (SCB) tends to be mature. However, in some eventualities with restricted conditions, the calculation and application of the global SCB are limited; hence, the application of regional SCB is derived. This paper focuses on the quality of regional SCB products in different regions, calculates three groups of regional SCB products, and analyzes their properties and application effects. We expand the double-differenced assessment method for SCB and extend satellite clock accuracy assessment to regional satellite clock products. Additionally, the Regional Effect Bias (REB) is introduced to analyze the influence of the relative position of satellite geometry on the SCB products due to the regional effects. The conclusions are as follows: (1) In low-latitude regions, SCB products have a high degree of completeness and a large number of satellite observations, which is conducive to expanding the positioning application range of regional SCB; (2) the low-latitude regions SCB will be affected by ionospheric activity, and the accuracy will be slightly lower than that of satellite clocks deviation in mid-latitudes; (3) in this paper, the REB in this area is in the level of 10−7. The experiment displays the result that the values of REB in low-latitude areas are larger, leading to fluctuated Precise Point Position (PPP) results. However, there are fewer stations in the mid-latitude regions, which will also affect the accuracy of PPP; (4) the accuracy of the positioning results of the regional satellite clock deviation in the Chinese region is higher than that of the global clock