ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories

Diffusion models have recently exhibited remarkable abilities to synthesize striking image samples since the introduction of denoising diffusion probabilistic models (DDPMs). Their key idea is to disrupt images into noise through a fixed forward process and learn its reverse process to generate samples from noise in a denoising way. For conditional DDPMs, most existing practices relate conditions only to the reverse process and fit it to the reversal of unconditional forward process. We find this will limit the condition modeling and generation in a small time window. In this paper, we propose a novel and flexible conditional diffusion model by introducing conditions into the forward process. We utilize extra latent space to allocate an exclusive diffusion trajectory for each condition based on some shifting rules, which will disperse condition modeling to all timesteps and improve the learning capacity of model. We formulate our method, which we call \textbf{ShiftDDPMs}, and provide a unified point of view on existing related methods. Extensive qualitative and quantitative experiments on image synthesis demonstrate the feasibility and effectiveness of ShiftDDPMs.Comment: Accepted by AAAI 2023 Conferenc

Effects of β-blockers on all-cause mortality in patients with diabetes and coronary heart disease: A systematic review and meta-analysis

Beta-blockers have been considered as an effective treatment in secondary prevention of coronary heart disease (CHD). However, there is still disputed whether β-blockers can increase all-cause mortality in patients with coronary heart disease and diabetes mellitus (DM). Here, our systematic review and meta-analysis is aiming to assess the effects of β-blockers on all-cause mortality in patients with coronary heart disease and diabetes mellitus. Four databases (PubMed, Embase, Cochrane Library and Web of Science) and other sources were searched to collect randomized controlled trials (RCTs) and cohort studies related to the treatment of β-blockers for coronary heart disease and diabetes mellitus patients. We further evaluated quality of evidence using the grading of recommendations assessment, development, and evaluation (GRADE) approach. Finally, a total of 16,188 records were identified, and four randomized controlled trials and six cohort studies (206,490 patients) were included. Random effects analysis revealed that β-blockers combined with routine treatment (RT) significantly decreased all-cause mortality in patients with coronary heart disease and diabetes mellitus compared with RT in control group (RR 0.59, 95% CI 0.47 to 0.75; p < 0.000 01; I2 = 72%). Subgroup analysis of all-cause mortality by the subtype of diabetes mellitus and definite MI patients (RR 0.54, 95% CI 0.45 to 0.65, p < 0.000 01, I2 = 29%) and the subtype of randomized controlled trials (RR 0.49, 95% CI 0.32 to 0.76, p = 0.001, I2 = 0%) indicated a relatively small heterogeneity and stable results. β-blockers application significantly reduced cardiovascular death as well (RR 0.56, 95% CI 0.42 to 0.74; p < 0.000 1; I2 = 0%). Our meta-analysis provided critical evidence of β-blockers treatment for patients with coronary heart disease (especially MI type) and diabetes mellitus, and discussed the advantages and potential metabolic risks for the clinical use of β-blockers. This study suggested that β-blockers application may improve all-cause mortality and cardiovascular death in coronary heart disease (especially MI type) and diabetes mellitus patients. However, given a small number of included studies, the aforementioned conclusion should be confirmed in a multi-center, large-scale, and strictly designed trial

PSYCHOSOCIAL FACTORS LEAD TO DELINQUENCY ITENTION ON ONLINE PEER-TO-PEER LENDING PLATFORM: A SURVEY EVIDENCE

In recent years, online P2P lending grows remarkably. Past studies mainly used direct second-hand data from P2P platforms to conclude many factors are related to people\u27s delinquency and default behaviours, lacking further exploration on how people\u27s social and psychological status could impact their behaviour during the borrowing and repayment process. On foundation of general strain theory (GST) and the model of frame selection (MFS), we used survey method and collected data from more than 700 Chinese subjects. A two-stage structural equation model was proposed. In the first stage, we investigated how people\u27s psychosocial factors (e.g. economic capacity, sense of fairness and sociability etc.) could shape their individual feelings and attitudes in social context (e.g. life satisfaction and self-esteem) as well as morality. In the second stage, we tested the relationship between life satisfaction, self-esteem, moral norm and people\u27s delinquency intention on P2P lending platform. The empirical results suggest that higher psychosocial status will be conductive to better individual feelings of life satisfaction and self-esteem. Moreover, better psychosocial factors will mostly lead to a higher moral norm of people. Therefore, these favourable feelings and morality further contribute to less delinquency intention on P2P lending platform. Our research has both academic and practical implications

Dynamic motion of polar skyrmions in oxide heterostructures

Polar skyrmions have been widely investigated in oxide heterostructure recently, due to their exotic properties and intriguing physical insights. Meanwhile, so far, the external field-driven motion of the polar skyrmion, akin to the magnetic counterpart, has yet to be discovered. Here, using phase-field simulations, we demonstrate the dynamic motion of the polar skyrmions with integrated external thermal, electrical, and mechanical stimuli. The external heating reduces the spontaneous polarization hence the skyrmion motion barrier, while the skyrmions shrink under the electric field, which could weaken the lattice pinning and interactions between the skyrmions. The mechanical force transforms the skyrmions into c-domain in the vicinity of the indenter center under the electric field, providing the space and driving force needed for the skyrmions to move. This study confirmed that the skyrmions are quasi-particles that can move collectively, while also providing concrete guidance for the further design of polar skyrmion-based electronic devices.Comment: 17 pages, 4 figure

Appling an Improved Method Based on ARIMA Model to Predict the Short-Term Electricity Consumption Transmitted by the Internet of Things (IoT)

The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results

Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network

AbstractMassive energy consumption data of buildings was generated with the development of information technology, and the real-time energy consumption data was transmitted to energy consumption monitoring system by the distributed wireless sensor network (WSN). Accurately predicting the energy consumption is of importance for energy manager to make advisable decision and achieve the energy conservation. In recent years, considerable attention has been gained on predicting energy use of buildings in China. More and more predictive models appeared in recent years, but it is still a hard work to construct an accurate model to predict the energy consumption due to the complexity of the influencing factors. In this paper, 40 weather factors were considered into the research as input variables, and the electricity of supermarket which was acquired by the energy monitoring system was taken as the target variable. With the aim to seek the optimal subset, three feature selection (FS) algorithms were involved in the study, respectively: stepwise, least angle regression (Lars), and Boruta algorithms. In addition, three machine learning methods that include random forest (RF) regression, gradient boosting regression (GBR), and support vector regression (SVR) algorithms were utilized in this paper and combined with three feature selection (FS) algorithms, totally are nine hybrid models aimed to explore an improved model to get a higher prediction performance. The results indicate that the FS algorithm Boruta has relatively better performance because it could work well both on RF and SVR algorithms, the machine learning method SVR could get higher accuracy on small dataset compared with the RF and GBR algorithms, and the hybrid model called SVR-Boruta was chosen to be the proposed model in this paper. What is more, four evaluate indicators were selected to verify the model performance respectively are the mean absolute error (MAE), the mean squared error(MSE), the root mean squared error (RMSE), and the R-squared (R2), and the experiment results further verified the superiority of the recommended methodology

Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks

Adapting large-scale pretrained models to various downstream tasks via fine-tuning is a standard method in machine learning. Recently, parameter-efficient fine-tuning methods show promise in adapting a pretrained model to different tasks while training only a few parameters. Despite their success, most existing methods are proposed in Natural Language Processing tasks with language Transformers, and adaptation to Computer Vision tasks with Vision Transformers remains under-explored, especially for dense vision tasks. Further, in multi-task settings, individually fine-tuning and storing separate models for different tasks is inefficient. In this work, we provide an extensive multi-task parameter-efficient benchmark and examine existing parameter-efficient fine-tuning NLP methods for vision tasks. Our results on four different dense vision tasks showed that existing methods cannot be efficiently integrated due to the hierarchical nature of the Hierarchical Vision Transformers. To overcome this issue, we propose Polyhistor and Polyhistor-Lite, consisting of Decomposed HyperNetworks and Layer-wise Scaling Kernels, to share information across different tasks with a few trainable parameters. This leads to favorable performance improvements against existing parameter-efficient methods while using fewer trainable parameters. Specifically, Polyhistor achieves competitive accuracy compared to the state-of-the-art while only using ~10% of their trainable parameters. Furthermore, our methods show larger performance gains when large networks and more pretraining data are used.Comment: Accepted to NeurIPS 2022; Project Page is at https://ycliu93.github.io/projects/polyhistor.htm

A Crosslinked Polyethyleneglycol Solid Electrolyte Dissolving Lithium Bis(trifluoromethylsulfonyl)imide for Rechargeable Lithium Batteries

Replacing liquid electrolytes with solid ones can provide advantages in safety, and all-solid-state batteries with solid electrolytes are proposed to solve the issue of the formation of lithium dendrites. In this study, a crosslinked polymer composite solid electrolyte was presented, which enabled the construction of lithium batteries with outstanding electrochemical behavior over long-term cycling. The crosslinked polymeric host was synthesized through polymerization of the terminal amines of O,O-bis(2-aminopropyl) polypropylene glycol-blockpolyethylene glycol-block-polypropylene glycol and terminal epoxy groups of bisphenol A diglycidyl ether at 90°C and provided an amorphous matrix for Li⁺ dissolution. This composite solid electrolyte containing Li⁺ salt and garnet filler exhibited high flexibility, which supported the formation of favorable interfaces with the active materials, and possessed enough mechanical strength to suppress the penetration of lithium dendrites. Ionic conductivities higher than 5.0x10⁻⁴ Scm⁻¹ above 45°C were obtained as well as a wide electrochemical stability window (>4.51 V vs. Li/Li⁺) and a high Li⁺ diffusion coefficient (≈16.6x10⁻¹³m² s¯¹). High cycling stability (>500 cycles or 1000 h) was demonstrated

Ouabain Interaction with Cardiac Na+/K+-ATPase Initiates Signal Cascades Independent of Changes in Intracellular Na+ and Ca2+

We have shown previously that partial inhibition of the cardiac myocyte Na+/K+-ATPase activates signal pathways that regulate myocyte growth and growth-related genes and that increases in intracellular Ca2+ concentration ([Ca2+]i) and reactive oxygen species (ROS) are two essential second messengers within these pathways. The aim of this work was to explore the relation between [Ca2+]i and ROS. When myocytes were in a Ca2+-free medium, ouabain caused no change in [Ca2+]i, but it increased ROS as it did when the cells were in a Ca2+-containing medium. Ouabain-induced increase in ROS also occurred under conditions where there was little or no change in [Na+]i. Exposure of myocytes in Ca2+-free medium to monensin did not increase ROS. Increase in protein tyrosine phosphorylation, an early event induced by ouabain, was also independent of changes in [Ca2+]i and [Na+]i. Ouabain-induced generation of ROS in myocytes was antagonized by genistein, a dominant negative Ras, and myxothiazol/diphenyleneiodonium, indicating a mitochondrial origin for the Ras-dependent ROS generation. These findings, along with our previous data, indicate that increases in [Ca2+]i and ROS in cardiac myocytes are induced by two parallel pathways initiated at the plasma membrane: One being the ouabain-altered transient interactions of a fraction of the Na+/K+-ATPase with neighboring proteins (Src, growth factor receptors, adaptor proteins, and Ras) leading to ROS generation, and the other, inhibition of the transport function of another fraction of the Na+/K+-ATPase leading to rise in [Ca2+]i. Evidently, the gene regulatory effects of ouabain in cardiac myocytes require the downstream collaborations of ROS and [Ca2+]i

Study of the Lithium Storage Mechanism of N-Doped Carbon-Modified Cu₂S Electrodes for Lithium-Ion Batteries

Owing to their high specific capacity and abundant reserve, Cu$_{x}$S compounds are promising electrode materials for lithium-ion batteries (LIBs). Carbon compositing could stabilize the Cu$_{x}$S structure and repress capacity fading during the electrochemical cycling, but the corresponding Li$^{+}$ storage mechanism and stabilization effect should be further clarified. In this study, nanoscale Cu$_{2}$S was synthesized by CuS co-precipitation and thermal reduction with polyelectrolytes. High-temperature synchrotron radiation diffraction was used to monitor the thermal reduction process. During the first cycle, the conversion mechanism upon lithium storage in the Cu$_{2}$S/carbon was elucidated by operando synchrotron radiation diffraction and in situ X-ray absorption spectroscopy. The N-doped carbon-composited Cu$_{2}$S (Cu$_{2}$S/C) exhibits an initial discharge capacity of 425 mAh g$^{-1}$ at 0.1 A g$^{-1}$, with a higher, long-term capacity of 523 mAh g$^{-1}$ at 0.1 A g$^{-1}$ after 200 cycles; in contrast, the bare CuS electrode exhibits 123 mAh g$^{-1}$ after 200 cycles. Multiple-scan cyclic voltammetry proves that extra Li+ storage can mainly be ascribed to the contribution of the capacitive storage