Analyzing the impact of storage shortage on data availability in decentralized online social networks

Maintaining data availability is one of the biggest challenges in decentralized online social networks (DOSNs). The existing work often assumes that the friends of a user can always contribute to the sufficient storage capacity to store all data. However, this assumption is not always true in today’s online social networks (OSNs) due to the fact that nowadays the users often use the smart mobile devices to access the OSNs. The limitation of the storage capacity in mobile devices may jeopardize the data availability. Therefore, it is desired to know the relation between the storage capacity contributed by the OSN users and the level of data availability that the OSNs can achieve. This paper addresses this issue. In this paper, the data availability model over storage capacity is established. Further, a novel method is proposed to predict the data availability on the fly. Extensive simulation experiments have been conducted to evaluate the effectiveness of the data availability model and the on-the-fly prediction

Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of computation, especially for minor editing operations. In this work, we present Spatially Sparse Inference (SSI), a general-purpose technique that selectively performs computation for edited regions and accelerates various generative models, including both conditional GANs and diffusion models. Our key observation is that users prone to gradually edit the input image. This motivates us to cache and reuse the feature maps of the original image. Given an edited image, we sparsely apply the convolutional filters to the edited regions while reusing the cached features for the unedited areas. Based on our algorithm, we further propose Sparse Incremental Generative Engine (SIGE) to convert the computation reduction to latency reduction on off-the-shelf hardware. With about $1\%$-area edits, SIGE accelerates DDPM by $3.0\times$ on NVIDIA RTX 3090 and $4.6\times$ on Apple M1 Pro GPU, Stable Diffusion by $7.2\times$ on 3090, and GauGAN by $5.6\times$ on 3090 and $5.2\times$ on M1 Pro GPU. Compared to our conference version, we extend SIGE to accommodate attention layers and apply it to Stable Diffusion. Additionally, we offer support for Apple M1 Pro GPU and include more results with large and sequential edits.Comment: NeurIPS 2022 T-PAMI 2023 Website: https://www.cs.cmu.edu/~sige/ Code: https://github.com/lmxyy/sig

Biomass Blending and Densification: Impacts on Feedstock Supply and Biochemical Conversion Performance

The success of lignocellulosic biofuels and biochemical industries depends on an economic and reliable supply of high‐quality biomass. However, research and development efforts have been historically focused on the utilization of agriculturally derived cellulosic feedstocks, without considerations of their low energy density, high variations in compositions and potential supply risks in terms of availability and affordability. This chapter demonstrated a strategy of feedstock blending and densification to address the supply chain challenges. Blending takes advantage of low‐cost feedstock to avoid the prohibitive costs incurred through reliance on a single feedstock resource, while densification produces feedstocks with increased bulk density and desirable feed handling properties, as well as reduced transportation cost. We also review recent research on the blending and densification dealing with various types of feedstocks with a focus on the impacts of these preprocessing steps on biochemical conversion, that is, various thermochemical pretreatment chemistries and enzymatic hydrolysis, into fermentable sugars for biofuel production

Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation

There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies. However, existing transformer-based methods treat body joints as equally important inputs and ignore the prior knowledge of human skeleton topology in the self-attention mechanism. To tackle this issue, in this paper, we propose a Pose-Oriented Transformer (POT) with uncertainty guided refinement for 3D HPE. Specifically, we first develop novel pose-oriented self-attention mechanism and distance-related position embedding for POT to explicitly exploit the human skeleton topology. The pose-oriented self-attention mechanism explicitly models the topological interactions between body joints, whereas the distance-related position embedding encodes the distance of joints to the root joint to distinguish groups of joints with different difficulties in regression. Furthermore, we present an Uncertainty-Guided Refinement Network (UGRN) to refine pose predictions from POT, especially for the difficult joints, by considering the estimated uncertainty of each joint with uncertainty-guided sampling strategy and self-attention mechanism. Extensive experiments demonstrate that our method significantly outperforms the state-of-the-art methods with reduced model parameters on 3D HPE benchmarks such as Human3.6M and MPI-INF-3DHPComment: accepted by AAAI202

Interaction of BES1 and LBD37 transcription factors modulates brassinosteroid-regulated root forging response under low nitrogen in arabidopsis

Brassinosteriod (BR) plays important roles in regulation of plant growth, development and environmental responses. BR signaling regulates multiple biological processes through controlling the activity of BES1/BZR1 regulators. Apart from the roles in the promotion of plant growth, BR is also involved in regulation of the root foraging response under low nitrogen, however how BR signaling regulate this process remains unclear. Here we show that BES1 and LBD37 antagonistically regulate root foraging response under low nitrogen conditions. Both the transcriptional level and dephosphorylated level of BES1, is significant induced by low nitrogen, predominantly in root. Phenotypic analysis showed that BES1 gain-of-function mutant or BES1 overexpression transgenic plants exhibits progressive outgrowth of lateral root in response to low nitrogen and BES1 negatively regulates repressors of nitrate signaling pathway and positively regulates several key genes required for NO3 - uptake and signaling. In contrast, BES1 knock-down mutant BES1-RNAi exhibited a dramatical reduction of lateral root elongation in response to low N. Furthermore, we identified a BES1 interacting protein, LBD37, which is a negative repressor of N availability signals. Our results showed that BES1 can inhibit LBD37 transcriptional repression on N-responsive genes. Our results thus demonstrated that BES1-LBD37 module acts critical nodes to integrate BR signaling and nitrogen signaling to modulate the root forging response at LN condition

Transition of Cellulose Crystalline Structure and Surface Morphology of Biomass as a Function of Ionic Liquid Pretreatment and Its Relation to Enzymatic Hydrolysis

Cellulose is inherently resistant to breakdown, and the native crystalline structure (cellulose I) of cellulose is considered to be one of themajor factors limiting its potential in terms of cost-competitive lignocellulosic biofuel production. Here we report the impact of ionic liquid pretreatment on the cellulose crystalline structure in different feedstocks, including microcrystalline cellulose (Avicel), switchgrass (Panicum virgatum), pine (Pinus radiata), and eucalyptus (Eucalyptus globulus), and its influence on cellulose hydrolysis kinetics of the resultant biomass. These feedstocks were pretreated using 1-ethyl-3-methyl imidazolium acetate ([C2mim][OAc]) at 120 and 160°C for 1, 3, 6, and 12 h. The influence of the pretreatment conditions on the cellulose crystalline structure was analyzed by X-ray diffraction (XRD).On a larger length scale, the impact of ionic liquid pretreatment on the surface roughness of the biomass was determined by small-angle neutron scattering (SANS). Pretreatment resulted in a loss of native cellulose crystalline structure. However, the transformation processes were distinctly different for Avicel and for the biomass samples. For Avicel, a transformation to cellulose II occurred for all processing conditions. For the biomass samples, the data suggest that pretreatment formost conditions resulted in an expanded cellulose I lattice. For switchgrass, first evidence of cellulose II only occurred after 12 h of pretreatment at 120°C. For eucalyptus, first evidence of cellulose II required more intense pretreatment (3 h at 160°C). For pine, no clear evidence of cellulose II contentwas detected for the most intense pretreatment conditions of this study (12 h at 160°C). Interestingly, the rate of enzymatic hydrolysis of Avicel was slightly lower for pretreatment at 160°C compared with pretreatment at 120°C. For the biomass samples, the hydrolysis rate was much greater for pretreatment at 160°C compared with pretreatment at 120°C. The result for Avicel can be explained by more complete conversion to cellulose II upon precipitation after pretreatment at 160°C. By comparison, the result for the biomass samples suggests that another factor, likely lignincarbohydrate complexes, also impacts the rate of cellulose hydrolysis in addition to cellulose crystallinity

Comparison of dilute acid and ionic liquid pretreatment of switchgrass: Biomass recalcitrance, delignification and enzymatic saccharification

The efficiency of two biomass pretreatment technologies, dilute acid hydrolysis and dissolution in an ionic liquid, are compared in terms of delignification, saccharification efficiency and saccharide yields with switchgrass serving as a model bioenergy crop. When subject to ionic liquid pretreatment (dissolution and precipitation of cellulose by anti-solvent) switchgrass exhibited reduced cellulose crystallinity, increased surface area, and decreased lignin content compared to dilute acid pretreatment. Pretreated material was characterized by powder X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy and chemistry methods. Ionic liquid pretreatment enabled a significant enhancement in the rate of enzyme hydrolysis of the cellulose component of switchgrass, with a rate increase of 16.7-fold, and a glucan yield of 96.0% obtained in 24 h. These results indicate that ionic liquid pretreatment may offer unique advantages when compared to the dilute acid pretreatment process for switchgrass. However, the cost of the ionic liquid process must also be taken into consideration

High-speed Gaussian modulated continuous-variable quantum key distribution with a local local oscillator based on pilot-tone-assisted phase compensation

A high-speed Gaussian modulated continuous-variable quantum key distribution (CVQKD) with a local local oscillator (LLO) is experimentally demonstrated based on pilot-tone-assisted phase compensation. In the proposed scheme, the frequency-multiplexing and polarization-multiplexing techniques are used for the separate transmission and heterodyne detection between quantum signal and pilot tone, guaranteeing no crosstalk from strong pilot tone to weak quantum signal and different detection requirements of low-noise for quantum signal and high-saturation limitation for pilot tone. Moreover, compared with the conventional CVQKD based on homodyne detection, the proposed LLO-CVQKD scheme can measure X and P quadrature simultaneously using heterodyne detection without need of extra random basis selection. Besides, the phase noise, which contains the fast-drift phase noise due to the relative phase of two independent lasers and the slow-drift phase noise introduced by quantum channel disturbance, has been compensated experimentally in real time, so that a low level of excess noise with a 25km optical fiber channel is obtained for the achievable secure key rate of 7.04 Mbps in the asymptotic regime and 1.85 Mbps under the finite-size block of 10^7