MCM: Multi-condition Motion Synthesis Framework for Multi-scenario

The objective of the multi-condition human motion synthesis task is to incorporate diverse conditional inputs, encompassing various forms like text, music, speech, and more. This endows the task with the capability to adapt across multiple scenarios, ranging from text-to-motion and music-to-dance, among others. While existing research has primarily focused on single conditions, the multi-condition human motion generation remains underexplored. In this paper, we address these challenges by introducing MCM, a novel paradigm for motion synthesis that spans multiple scenarios under diverse conditions. The MCM framework is able to integrate with any DDPM-like diffusion model to accommodate multi-conditional information input while preserving its generative capabilities. Specifically, MCM employs two-branch architecture consisting of a main branch and a control branch. The control branch shares the same structure as the main branch and is initialized with the parameters of the main branch, effectively maintaining the generation ability of the main branch and supporting multi-condition input. We also introduce a Transformer-based diffusion model MWNet (DDPM-like) as our main branch that can capture the spatial complexity and inter-joint correlations in motion sequences through a channel-dimension self-attention module. Quantitative comparisons demonstrate that our approach achieves SoTA results in both text-to-motion and competitive results in music-to-dance tasks, comparable to task-specific methods. Furthermore, the qualitative evaluation shows that MCM not only streamlines the adaptation of methodologies originally designed for text-to-motion tasks to domains like music-to-dance and speech-to-gesture, eliminating the need for extensive network re-configurations but also enables effective multi-condition modal control, realizing "once trained is motion need"

Self-supervised Learning of Detailed 3D Face Reconstruction

In this paper, we present an end-to-end learning framework for detailed 3D face reconstruction from a single image. Our approach uses a 3DMM-based coarse model and a displacement map in UV-space to represent a 3D face. Unlike previous work addressing the problem, our learning framework does not require supervision of surrogate ground-truth 3D models computed with traditional approaches. Instead, we utilize the input image itself as supervision during learning. In the first stage, we combine a photometric loss and a facial perceptual loss between the input face and the rendered face, to regress a 3DMM-based coarse model. In the second stage, both the input image and the regressed texture of the coarse model are unwrapped into UV-space, and then sent through an image-toimage translation network to predict a displacement map in UVspace. The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage. The advantage of learning displacement map in UV-space is that face alignment can be explicitly done during the unwrapping, thus facial details are easier to learn from large amount of data. Extensive experiments demonstrate the superiority of the proposed method over previous work.Comment: Accepted by IEEE Transactions on Image Processing (TIP

Prevalence and adverse outcomes of pre-operative frailty in patients undergoing carotid artery revascularization: a meta-analysis

IntroductionFrailty can lead to a decrease in the patient's resistance to interference such as injury and disease, and cause a series of complications. An increasing number of studies have found that pre-operative frailty exacerbates the occurrence of adverse events after carotid artery revascularization, but an integrated quantitative analysis is currently lacking. Therefore, we conducted a meta-analysis to evaluate the impact of pre-operative frailty on patients undergoing carotid artery revascularization.MethodAccording to the PRISMA guidelines, we systematically searched for relevant studies on Medline, Embase, Ovid, CINAHL, Web Of Science, and Cochrane Library from establishment until June 2023. Summarize the risk of adverse outcome events through OR and 95% CI.ResultsA total of 16 cohort studies were included, including 1692338 patients. Among patients who underwent carotid artery revascularization surgery, the prevalence of pre-operative frailty was 36% (95% CI = 0.18–0.53, P < 0.001). Compared with non frail individuals, frail individuals have an increased risk of mortality (OR = 2.35, 95% CI = 1.40–3.92, P = 0.001, I2 = 94%), stroke (OR = 1.33, 95% CI = 1.10–1.61, P = 0.003, I2 = 71%), myocardial infarction (OR = 1.86, 95% CI = 1.51–2.30, P < 0.001, I2 = 61%), and non-home discharge (OR = 2.39, 95% CI = 1.85–3.09, P < 0.001, I2 = 63%).ConclusionThe results of this article show that patients undergoing carotid artery revascularization have a higher prevalence of pre-operative frailty, which can lead to an increased risk of postoperative death, stroke, myocardial infarction, and non-home discharge. Strengthening the assessment and management of frailty is of great significance for patient prognosis.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=416234, identifier: CRD42023416234

Reconfigurable thermo-optic polymer switch based true-time-delay network utilizing imprinting and inkjet printing

Abstract: Reconfigurable true-time-delay lines, comprising of 2x2 thermo-optic polymer switches and rib waveguides are fabricated utilizing a combination of roll-to-roll (R2R) compatible UV imprinting and ink-jet printing, which promises high throughput and low cost photonic devices

Effects of temperature frequency trends on projected japonica rice (Oryza sativa L.) yield and dry matter distribution with elevated carbon dioxide

In this study, we investigated the effects of temperature frequency trends on the projected yield and dry matter distribution of japonica rice (Oryza sativa L.) with elevated carbon dioxide (CO2) under future climate change scenarios in northwestern China. The Crop Environment Resource Synthesis (CERES)-Rice model was forced with the outputs from three general circulation models (GCMs) to project the rice growth and yield. Future temperature trends had the most significant impact on rice growth, and the frequency of higher than optimal temperatures (∼24–28 oC) for rice growth showed a marked increase in the future, which greatly restricted photosynthesis. The frequency of extreme temperatures (>35 oC) also increased, exerting a strong impact on rice fertilization and producing a significantly reduced yield. Although the increased temperature suppressed photosynthetic production, the elevated CO2 stimulated this production; therefore, the net result was determined by the dominant process. The aboveground biomass at harvest trended downward when temperature became the major factor in photosynthetic production and trended upward when CO2-fertilization dominated the process. The trends for the leaf and stem dry matter at harvest were affected not only by changes in photosynthesis but also by the dry matter distribution to the panicles. The trends for the rice panicle dry matter at harvest were closely related to the effects of temperature and CO2 on photosynthetic production, and extreme temperatures also remarkably affected these trends by reducing the number of fertilized spikelets. The trends of rice yield were very similar to those of panicle dry matter because the panicle dry matter is mostly composed of grain weight (yield). This study provides a better understanding of the japonica rice processes, particularly under extreme climate scenarios, which will likely become more frequent in the future

Assimilation of Remotely Sensed Leaf Area Index for Improving Land Surface Simulation Performance at a Global Scale

The Community Land Model version 4 with carbon and nitrogen components is coupled with data assimilation research testbed to assimilate remotely sensed leaf area index (LAI), to analyze the improvement in model performance for simulating land surface variables and land–atmospheric exchange fluxes. The results demonstrate that assimilation effectively addresses the issue of significant overestimation of LAI values, particularly noticeable in regions characterized by low latitudes and dense vegetation coverage. At a global scale, the disparities between simulated and assimilated LAI relative to observational data, are measured at 0.90 and −0.07, representing 54.1% and 3.9% of the observed values, respectively. The root mean square difference (RMSD) for assimilated LAI is 1.61 comparing with the simulated LAI of 1.85. Assimilating LAI globally leads to a noteworthy 1% reduction in the mean relative difference of the global average 2-m air temperature (T2m) and a concurrent decrease of 0.15 °C in RMSD. However, at the global level, the assimilation of LAI does not yield a significant enhancement in the modeling capability of heat fluxes, although modeling capability of sensible heat (HS) slightly outperforms latent heat. Improvements in land surface variables after assimilation show significant variations at regional scales due to factors such as vegetation coverage and climatic conditions. Overall, in regions characterized by periodic changes in vegetation, such as forested areas in Western Eurasian Continent (region 5), the enhancements in T2m and HS after assimilating LAI are particularly notable, with mean relative difference reduced by 7% and 20%, respectively

Salmonella typhimurium may support cancer treatment: a review

Antitumour treatments are evolving, including bacteria-mediated cancer therapy which is concurrently an ancient and cutting-edge approach. Salmonella typhimurium is a widely studied bacterial species that colonizes tumor tissues, showing oncolytic and immune system-regulating properties. It can be used as a delivery vector for genes and drugs, supporting conventional treatments that lack tumor-targeting abilities. This article summarizes recent evidence on the anticancer mechanisms of S. typhimurium alone and in combination with other anticancer treatments, suggesting that it may be a suitable approach to disease management

Highly Active and Stable Alumina Supported Nickel Nanoparticle Catalysts for Dry Reforming of Methane

A highly stable and extremely active nickel (Ni) nanoparticle catalyst, supported on porous γ-Al2O3 particles, was prepared by atomic layer deposition (ALD). The catalyst was employed to catalyze the reaction of dry reforming of methane (DRM). The catalyst initially gave a low conversion at 850°C, but the conversion increased with an increase in reaction time, and stabilized at 93% (1730 L h-1 g Ni-1 at 850°C). After regeneration, the catalyst showed a very high methane reforming rate (1840 h-1 g Ni-1 at 850°C). The activated catalyst showed exceptionally high catalytic activity and excellent stability of DRM reaction in over 300 h at temperatures that ranged from 700°C to 850°C. The excellent stability of the catalyst resulted from the formation of NiAl2O4 spinel. The high catalytic activity was due to the high dispersion of Ni nanoparticles deposited by ALD and the reduction of NiAl2O4 spinel to Ni during the DRM reaction at 850°C. It was verified that NiAl2O4 can be reduced to Ni in a reductive gas mixture (i.e., carbon monoxide and hydrogen) during the reaction at 850°C, but not by H2 alone