DiffTalker: Co-driven audio-image diffusion for talking faces via intermediate landmarks

Generating realistic talking faces is a complex and widely discussed task with numerous applications. In this paper, we present DiffTalker, a novel model designed to generate lifelike talking faces through audio and landmark co-driving. DiffTalker addresses the challenges associated with directly applying diffusion models to audio control, which are traditionally trained on text-image pairs. DiffTalker consists of two agent networks: a transformer-based landmarks completion network for geometric accuracy and a diffusion-based face generation network for texture details. Landmarks play a pivotal role in establishing a seamless connection between the audio and image domains, facilitating the incorporation of knowledge from pre-trained diffusion models. This innovative approach efficiently produces articulate-speaking faces. Experimental results showcase DiffTalker's superior performance in producing clear and geometrically accurate talking faces, all without the need for additional alignment between audio and image features.Comment: submmit to ICASSP 202

Improved Operator Learning by Orthogonal Attention

Neural operators, as an efficient surrogate model for learning the solutions of PDEs, have received extensive attention in the field of scientific machine learning. Among them, attention-based neural operators have become one of the mainstreams in related research. However, existing approaches overfit the limited training data due to the considerable number of parameters in the attention mechanism. To address this, we develop an orthogonal attention based on the eigendecomposition of the kernel integral operator and the neural approximation of eigenfunctions. The orthogonalization naturally poses a proper regularization effect on the resulting neural operator, which aids in resisting overfitting and boosting generalization. Experiments on six standard neural operator benchmark datasets comprising both regular and irregular geometries show that our method can outperform competing baselines with decent margins.Comment: 14 pages, 5 figure

Orthogonal optimization of the ratio of nano-silica sol-EVA-fly ash cement-based composite slurry and the effect on its physical properties

To address the problem that traditional cement-based slurry materials cannot meet the actual demand for grouting and reinforcement of large deformation roadways in coal mines, some high-performance composite slurry materials are obtained by modifying ordinary Portland cement with nano-silica sol, ethylene-vinyl acetate copolymer (EVA) and fly ash. The orthogonal test and extreme difference analysis are used to systematically study the variation of the physical and mechanical properties of the composite slurries, determine the optimal ratio, and further analyze the difference in the physical properties between the optimal ratio of composite slurries and pure cement, construct the hydration reaction mechanism model of the composite slurries, and elucidate the mechanical properties of the composite slurries for reinforcing broken rocks. The results of the study show that the optimum proportion of composite slurry is 0.7 water-cement ratio, 15% fly ash, 2% silica sol and 7.5% EVA. Compared with pure cement, the rheology of composite slurry is slightly decreased, but the stability and mechanical properties of the slurry are significantly improved, with the initial setting time shortened by 38.9%, the final setting time shortened by 53.8%, the precipitation rate reduced by 60%, the stone rate increased by 3.3%, the uniaxial compressive strength increased by 39.1%, the tensile strength increased by 97.2%, and the tensile/compression ratio increased by 41.7%. Silica sol and fly ash undergo volcanic ash reaction with Ca(OH)2 to generate more calcium silicate hydrate (C-S-H) and calcium aluminate hydrate (C-A-H) at different times, which promotes the hydration reaction of the composite slurry and accelerates the film formation of EVA to make the stone body more dense. The injection volume of the composite grout and the uniaxial compressive strength of the bonded body both increase with the increasing grouting pressure. With the increase in the Talbot index, the compressive strength first increases and then decreases. The failure mode is often characterized by bulging, and shear dilation deformation is pronounced. When the grouting pressure exceeds 2 MPa and the Talbot index is 0.5, the bond strength of the grout is higher, and the damage is reduced. This study provides a feasible way for early strength and toughening modification of ce-mentitious composite pastes

Layer-by-Layer Degradation of Methylammonium Lead Tri-iodide Perovskite Microplates

The methylammonium lead iodide (MAPbI3) perovskite has attracted considerable interest for its high-efficiency, low-cost solar cells, but is currently plagued by its poor environmental and thermal stability. To aid the development of robust devices, we investigate here the microscopic degradation pathways of MAPbI3 microplates. Using in situ transmission electron microscopy to follow the thermal degradation process, we find that under moderate heating at 85°C the crystalline structure shows a gradual evolution from tetragonal MAPbI3 to trigonal lead iodide layered crystals with a fixed crystallographic direction. Our solid-state nudged elastic band calculations confirm that the surface-initiated layer-by-layer degradation path exhibits the lowest energy barrier for crystal transition. We further show experimentally and theoretically that encapsulation of the perovskites with boron nitride flakes suppresses the surface degradation, greatly improving its thermal stability. These studies provide mechanistic insight into the thermal stability of perovskites that suggests new designs for improved stability

Layer-by-Layer Degradation of Methylammonium Lead Tri-iodide Perovskite Microplates

The methylammonium lead iodide (MAPbI3) perovskite has attracted considerable interest for its high-efficiency, low-cost solar cells, but is currently plagued by its poor environmental and thermal stability. To aid the development of robust devices, we investigate here the microscopic degradation pathways of MAPbI3 microplates. Using in situ transmission electron microscopy to follow the thermal degradation process, we find that under moderate heating at 85°C the crystalline structure shows a gradual evolution from tetragonal MAPbI3 to trigonal lead iodide layered crystals with a fixed crystallographic direction. Our solid-state nudged elastic band calculations confirm that the surface-initiated layer-by-layer degradation path exhibits the lowest energy barrier for crystal transition. We further show experimentally and theoretically that encapsulation of the perovskites with boron nitride flakes suppresses the surface degradation, greatly improving its thermal stability. These studies provide mechanistic insight into the thermal stability of perovskites that suggests new designs for improved stability

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Whole genome sequencing and analysis of selenite-reducing bacteria Bacillus paralicheniformis SR14 in response to different sugar supplements

Abstract The biosynthetic process of selenium nanoparticles (SeNPs) by specific bacterial strain, whose growth directly affects the synthesis efficiency, has attracted great attentions. We previously reported that Bacillus paralicheniformis SR14, a SeNPs-producing bacteria, could improve intestinal antioxidative function in vitro. To further analyze the biological characteristics of SR14, whole genome sequencing was used to reveal the genetic characteristics in selenite reduction and sugar utilization. The results reviewed that the genome size of SR14 was 4,448,062 bp, with a GC content of 45.95%. A total of 4300 genes into 49 biological pathways was annotated to the KEGG database. EC: 1.1.1.49 (glucose-6-phosphate 1-dehydrogenase) and EC: 5.3.1.9 (glucose-6-phosphate isomerase), were found to play a potential role in glucose degradation and EC:2.7.1.4 (fructokinase) might be involved in the fructose metabolism. Growth profile and selenite-reducing ability of SR14 under different sugar supplements were determined and the results reviewed that glucose had a better promoting effect on the reduction of selenite and growth of bacteria than fructose, sucrose, and maltose. Moreover, RT-qPCR experiment proved that glucose supplement remarkably promoted the expressions of thioredoxin, fumarate reductase, and the glutathione peroxidase in SR14. Analysis of mRNA expression showed levels of glucose-6-phosphate dehydrogenase and fructokinase significantly upregulated under the supplement of glucose. Overall, our data demonstrated the genomic characteristics of SR14 and preliminarily determined that glucose supplement was most beneficial for strain growth and SeNPs synthesis

The Rejuvenating Effect in Hot Asphalt Recycling by Mortar Transfer Ratio and Image Analysis

Using a rejuvenator to improve the performance of asphalt pavement is an effective and economic way of hot asphalt recycling. This research analyzes the rejuvenating effect on aged asphalt by means of a Mortar Transfer Ratio (MTR) test, which concerns the ratio of asphalt mortar that moves from recycled aggregates (RAP aggregates) to fresh added aggregates when aged asphalt is treated with a regenerating agent and comes into contact with fresh aggregates. The proposed MTR test analyzes the regeneration in terms of the softening degree on aged asphalt when the rejuvenator is applied. The covered area ratio is studied with an image analyzing tool to understand the possibility of mortar transferring from RAP aggregates to fresh aggregates. Additionally, a micro-crack closure test is conducted and observed through a microscope. The repairing ability and diffusion characteristics of micro-cracks can therefore be analyzed. The test results demonstrate that the proposed mortar transfer ratio is a feasible way to evaluate rejuvenator diffusion during hot recycling. The mortar transfer ratio and uncovered area ratio on fresh aggregates are compatible, and can be used to quantify the contribution of the rejuvenator. Within a certain temperature range, the diffusing effect of the rejuvenator is better when the diffusing temperature is higher. The diffusion time of the rejuvenator is optimum when diffusion occurs for 4–8 h. When the rejuvenator is properly applied, the rough and cracking surface can be repaired, resulting in better covered aggregates. The micro-closure analysis visually indicates that rejuvenators can be used to repair the RAP aggregates during hot recycling

Information gap decision theory-based optimization of joint decision making for power producers participating in carbon and electricity markets

Carbon markets have been established in many countries and regions with the goal of promoting global carbon neutrality. The position of power producers as the dominant carbon emitters necessitates that they engage in both electricity and carbon markets. However, most studies have considered only short-term electricity markets and unrealistically static carbon markets, and the speculative behavior of power producers in the carbon market remains poorly considered. The present study addresses these issues by proposing an optimized joint decision model based on information gap decision theory to facilitate the participation of power producers in annual and monthly electricity markets, and monthly carbon markets, where uncertainties in the prices of electricity and carbon quotas, and the speculative behavior of power producers in the carbon market are explicitly considered to ensure that the revenues of market participants do not fall below a predetermined minimum acceptable value. The results of simulations based on the rules and actual market data obtained for electricity and carbon markets in a specific province of China demonstrate that the proposed model provides power producers with trading solutions to meet different expected revenue targets, and thereby assists them as much as possible in counteracting the risks to profit associated with fluctuations in electricity and carbon market prices

Quantitative Assessment of Road Performance of Recycled Asphalt Mixtures Incorporated with Steel Slag

Circular utilization of reclaimed asphalt pavement (RAP) has received extensive attention for its economic and environmental benefits. The application of recycled asphalt mixtures (RAM) in the upper layer of asphalt pavement faces the issue of inferior anti-slip performance and durability. This study aims to recycle steel slag as virgin aggregates in RAM and quantitatively evaluate the service performance of RAM with steel slag. Steel slag and basalt RAM were firstly fabricated and the five different RAP contents were involved. Then tests of Marshall stability, indirect tensile strength and Cantabro spatter loss were conducted to investigate the moisture susceptibility of RAM. Moreover, their high temperature stability, crack resistance and skid resistance were characterized. Indirect tensile fatigue test combined with Hamburg wheel tracking test were carried out to discuss the durability of RAM. The comprehensive performance of RAM with steel slag were quantitatively assessed based on an improved radar chart evaluation method. The results show that involving steel slag reveals a remarkable enhancement function on water stability, high and low temperature performance, skid resistance and fatigue resistance of RAM. Steel slag RAM with 50% RAP content demonstrates a rutting depth of 7.60 mm and a creep slope of 2.54 × 10−4, indicating its superior durability in high temperature and water environment. Compared with the comprehensive evaluation function of 0.5336 for basalt RAM with 30% RAP dosage, steel slag RAM reaches 0.7801, which represents its preferable road performance