Relationship between tree species diversity and water holding capacity of litter layer in subtropical region.

Litter layer, serving as the "skin" of forest soil, plays a crucial role in conserving water resources and maintaining soil and water conservation. We analyzed the relationship of tree species richness, community weighted mean traits, and functional diversity with the standing mass, maximum water holding rate, and effective water sto-rage capacity of litters from various tree species including , , , , , , , and their combinations of mixed forests in subtropical region. The results showed that across various tree species combinations, the ranges of maximum water holding rate, standing litter mass and effective water storage capacity of undecomposed layer were 0-419%, 0-0.

An Energy-Based Prior for Generative Saliency.

We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution. The energy-based prior model is defined on the latent space of a saliency generator network that generates the saliency map based on a continuous latent variables and an observed image. Both the parameters of saliency generator and the energy-based prior are jointly trained via Markov chain Monte Carlo-based maximum likelihood estimation, in which the sampling from the intractable posterior and prior distributions of the latent variables are performed by Langevin dynamics.

Insight into the fraction variations of selenium and their effects on humification during composting.

This study investigated the variation of selenium fractions and their effects on humification during composting. Selenite and selenate were added to a mixture of goat manure and wheat straw for composting. The results demonstrated that the bioavailable Se in the selenite added treatment (9.

Theoretical Model for the Impact-Initiated Chemical Reaction of Al/PTFE Reactive Material.

Reactive material (RM) is a special kind of energetic material that can react and release chemical energy under highly dynamic loads. However, its energy release behavior is limited by its own strength, showing unique unsustainable characteristics, which lack a theoretical description. In this paper, an impact-initiated chemical reaction model is proposed to describe the ignition and energy release behavior of Al/PTFE RM.

Role of selenite on the nitrogen conservation and greenhouse gases mitigation during the goat manure composting process.

This study aimed to explore the roles of selenite (Se) on nitrogen conservation and greenhouse gases (GHGs) mitigation during the composting process. Six levels of Se(IV) dosages (i.e.

Energy-Based Continuous Inverse Optimal Control.

The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energy-based model (EBM), where the observed expert trajectories are assumed to be random samples from a probability density function defined as the exponential of the negative cost function up to a normalizing constant. The parameters of the cost function are learned by maximum likelihood via an "analysis by synthesis" scheme, which iterates: 1) synthesis step: sample the synthesized trajectories from the current probability density using the Langevin dynamics via backpropagation through time and 2) analysis step: update the model parameters based on the statistical difference between the synthesized trajectories and the observed trajectories.

Formation Behavior and Reaction Characteristic of a PTFE/Al Reactive Jet.

To reveal the expansion phenomenon and reaction characteristics of an aluminum particle filled polytetrafluoroethylene (PTFE/Al) reactive jet during the forming process, and to control the penetration and explosion coupling damage ability of the reactive jet, the temperature and density distribution of the reactive jet were investigated by combining numerical simulation and experimental study. Based on the platform of AUTODYN-3D code, the Smoothed Particle Hydrodynamics (SPH) algorithm was used to study the evolution behaviors and distribution regularity of the morphology, density, temperature, and velocity field during the formation process of the reactive composite jet. The reaction characteristic in the forming process was revealed by combining the distribution of the high-temperature zone in numerical simulation and the Differential Scanning Calorimeter/Thermo-Gravimetry (DSC/TG) experiment results.

Effect of micronutrient selenium on greenhouse gas emissions and related functional genes during goat manure composting.

To explore the effect of microelement selenium on greenhouse gas emission, nitrogen loss and related functional genes during the composting. Selenite and selenate were respectively mixed with goat manure and wheat straw and then composted the mixture without selenium regarded as control. The results indicated adding selenite prolonged the thermophilic phase and improved the organic matter degradation, while the selenate presented the opposite results.

Monocular 3D Pose Estimation via Pose Grammar and Data Augmentation.

In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation from a monocular RGB image. Our model takes estimated 2D pose as the input and learns a generalized 2D-3D mapping function to leverage into 3D pose. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNNs) on the top to explicitly incorporate a set of knowledge regarding human body configuration (i.

Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning.

This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different domains, e.g., the output is a photo image and the input is a sketch image.

Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis.

3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it becomes increasingly crucial to have a powerful 3D generative model for 3D shape synthesis and analysis. This paper proposes a deep 3D energy-based model to represent volumetric shapes.

Penetration Behavior of High-Density Reactive Material Liner Shaped Charge.

The traditional polytetrafluoroethylene (PTFE)/Al reactive material liner shaped charge generally produces insufficient penetration depth, although it enlarges the penetration hole diameter by chemical energy release inside the penetration crater. As such, a novel high-density reactive material liner based on the PTFE matrix was fabricated, and the corresponding penetration performance was investigated. Firstly, the PTFE/W/Cu/Pb high-density reactive material liner was fabricated via a cold pressing/sintering process.

Learning Energy-Based Spatial-Temporal Generative ConvNets for Dynamic Patterns.

Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that an energy-based spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability distribution on the video sequence, and the log probability is defined by a spatial-temporal ConvNet that consists of multiple layers of spatial-temporal filters to capture spatial-temporal patterns of different scales.

Revisiting Video Saliency Prediction in the Deep Learning Era.

Predicting where people look in static scenes, a.k.a visual saliency, has received significant research interest recently.

Cooperative Training of Descriptor and Generator Networks.

This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks (ConvNets). The first model is a deep energy-based model, whose energy function is defined by a bottom-up ConvNet, which maps the observed image to the energy.

Water metamaterial for ultra-broadband and wide-angle absorption.

A subwavelength water metamaterial is proposed and analyzed for ultra-broadband perfect absorption at microwave frequencies. We experimentally demonstrate that this metamaterial shows over 90% absorption within almost the entire frequency band of 12-29.6 GHz.

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

Background: Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks.

Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD.

In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent dimensions, or "topics," provide a sparse summary of the generative process behind the features for each individual.