Universally-composable finite-key analysis for efficient four-intensity decoy-state quantum key distribution

We propose an efficient four-intensity decoy-state BB84 protocol and derive concise security bounds for this protocol with the universally composable finite-key analysis method. Comparing with the efficient three-intensity protocol, we find that our efficient four-intensity protocol can increase the secret key rate by at least $30\%$. Particularly, this increasing rate of secret key rate will be raised as the transmission distance increases. At a large transmission distance, our efficient four-intensity protocol can improve the performance of quantum key distribution profoundly.Comment: accepted by Eur. Phys. J.

GazeMoDiff: Gaze-guided Diffusion Model for Stochastic Human Motion Prediction

Human motion prediction is important for virtual reality (VR) applications, e.g., for realistic avatar animation. Existing methods have synthesised body motion only from observed past motion, despite the fact that human gaze is known to correlate strongly with body movements and is readily available in recent VR headsets. We present GazeMoDiff -- a novel gaze-guided denoising diffusion model to generate stochastic human motions. Our method first uses a graph attention network to learn the spatio-temporal correlations between eye gaze and human movements and to fuse them into cross-modal gaze-motion features. These cross-modal features are injected into a noise prediction network via a cross-attention mechanism and progressively denoised to generate realistic human full-body motions. Experimental results on the MoGaze and GIMO datasets demonstrate that our method outperforms the state-of-the-art methods by a large margin in terms of average displacement error (15.03% on MoGaze and 9.20% on GIMO). We further conducted an online user study to compare our method with state-of-the-art methods and the responses from 23 participants validate that the motions generated by our method are more realistic than those from other methods. Taken together, our work makes a first important step towards gaze-guided stochastic human motion prediction and guides future work on this important topic in VR research

When Urban Region Profiling Meets Large Language Models

Urban region profiling from web-sourced data is of utmost importance for urban planning and sustainable development. We are witnessing a rising trend of LLMs for various fields, especially dealing with multi-modal data research such as vision-language learning, where the text modality serves as a supplement information for the image. Since textual modality has never been introduced into modality combinations in urban region profiling, we aim to answer two fundamental questions in this paper: i) Can textual modality enhance urban region profiling? ii) and if so, in what ways and with regard to which aspects? To answer the questions, we leverage the power of Large Language Models (LLMs) and introduce the first-ever LLM-enhanced framework that integrates the knowledge of textual modality into urban imagery profiling, named LLM-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining (UrbanCLIP). Specifically, it first generates a detailed textual description for each satellite image by an open-source Image-to-Text LLM. Then, the model is trained on the image-text pairs, seamlessly unifying natural language supervision for urban visual representation learning, jointly with contrastive loss and language modeling loss. Results on predicting three urban indicators in four major Chinese metropolises demonstrate its superior performance, with an average improvement of 6.1% on R^2 compared to the state-of-the-art methods. Our code and the image-language dataset will be released upon paper notification

Short-term interval prediction of PV power based on quantile regression-stacking model and tree-structured parzen estimator optimization algorithm

In recent years, the photovoltaic (PV) industry has grown rapidly and the scale of grid-connected PV continues to increase. The random and fluctuating nature of PV power output is beginning to threaten the safe and stable operation of the power system. PV power interval forecasting can provide more comprehensive information to power system decision makers and help to achieve risk control and risk decision. PV power interval forecasting is of great importance to power systems. Therefore, in this study, a Quantile Regression-Stacking (QR-Stacking) model is proposed to implement PV power interval prediction. This integrated model uses three models, extreme gradient boosting (Xgboost), light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), as the base learners and Quantile Regression-Long and Short Term Memory (QR-LSTM) model as the meta-learner. It is worth noting that in order to determine the hyperparameters of the three base learners and one meta-learner, the optimal hyperparameters of the model are searched using a Tree-structured Parzen Estimator (TPE) optimization algorithm based on Bayesian ideas. Meanwhile, the correlation coefficient is applied to determine the input characteristics of the model. Finally, the validity of the proposed model is verified using the actual data of a PV plant in China

Comparison of Non-human Primate versus Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes for Treatment of Myocardial Infarction.

Non-human primates (NHPs) can serve as a human-like model to study cell therapy using induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). However, whether the efficacy of NHP and human iPSC-CMs is mechanistically similar remains unknown. To examine this, RNU rats received intramyocardial injection of 1 × 107 NHP or human iPSC-CMs or the same number of respective fibroblasts or PBS control (n = 9-14/group) at 4 days after 60-min coronary artery occlusion-reperfusion. Cardiac function and left ventricular remodeling were similarly improved in both iPSC-CM-treated groups. To mimic the ischemic environment in the infarcted heart, both cultured NHP and human iPSC-CMs underwent 24-hr hypoxia in vitro. Both cells and media were collected, and similarities in transcriptomic as well as metabolomic profiles were noted between both groups. In conclusion, both NHP and human iPSC-CMs confer similar cardioprotection in a rodent myocardial infarction model through relatively similar mechanisms via promotion of cell survival, angiogenesis, and inhibition of hypertrophy and fibrosis

VPA improves ferroptosis in tubular epithelial cells after cisplatin-induced acute kidney injury

Background: As a novel non-apoptotic cell death, ferroptosis has been reported to play a crucial role in acute kidney injury (AKI), especially cisplatin-induced AKI. Valproic acid (VPA), an inhibitor of histone deacetylase (HDAC) 1 and 2, is used as an antiepileptic drug. Consistent with our data, a few studies have demonstrated that VPA protects against kidney injury in several models, but the detailed mechanism remains unclear.Results: In this study, we found that VPA prevents against cisplatin-induced renal injury via regulating glutathione peroxidase 4 (GPX4) and inhibiting ferroptosis. Our results mainly indicated that ferroptosis presented in tubular epithelial cells of AKI humans and cisplatin-induced AKI mice. VPA or ferrostatin-1 (ferroptosis inhibitor, Fer-1) reduced cisplatin-induced AKI functionally and pathologically, which was characterized by reduced serum creatinine, blood urea nitrogen, and tissue damage in mice. Meanwhile, VPA or Fer-1 treatment in both in vivo and in vitro models, decreased cell death, lipid peroxidation, and expression of acyl-CoA synthetase long-chain family member 4 (ACSL4), reversing downregulation of GPX4. In addition, our study in vitro indicated that GPX4 inhibition by siRNA significantly weakened the protective effect of VPA after cisplatin treatment.Conclusion: Ferroptosis plays an essential role in cisplatin-induced AKI and inhibiting ferroptosis through VPA to protect against renal injury is a viable treatment in cisplatin-induced AKI

Effects of dietary L-Citrulline supplementation on growth performance, meat quality, and fecal microbial composition in finishing pigs

Gut microbiota play an important role in the gut ecology and development of pigs, which is always regulated by nutrients. This study investigated the effect of L-Citrulline on growth performance, carcass characteristics, and its potential regulatory mechanism. The results showed that 1% dietary L-Citrulline supplementation for 52 days significantly increased final weight, liveweight gain, carcass weight, and average backfat and markedly decreased drip loss (p < 0.05) of finishing pigs compared with the control group. Microbial analysis of fecal samples revealed a marked increase in α-diversity and significantly altered composition of gut microbiota in finishing pigs in response to L-Citrulline. In particular, these altered gut microbiota at the phylum and genus level may be mainly involved in the metabolic process of carbohydrate, energy, and amino acid, and exhibited a significant association with final weight, carcass weight, and backfat thickness. Taken together, our data revealed the potential role of L-Citrulline in the modulation of growth performance, carcass characteristics, and the meat quality of finishing pigs, which is most likely associated with gut microbiota

Where Is My Dinner, Traveler?

You are a. You are a traveler that went camping outside the forest. Your friends all had emergency situation, cannot join you for camping, and cannot bring you food to eat. You enter the forest, and find an old man with an abandoned house. "Can you spare me some food, traveler?" A narrative horror game. Twelve endings