Robust prior-based single image super resolution under multiple Gaussian degradations

Although SISR (Single Image Super Resolution) problem can be effectively solved by deep learning based methods, the training phase often considers single degradation type such as bicubic interpolation or Gaussian blur with fixed variance. These priori hypotheses often fail and lead to reconstruction error in real scenario. In this paper, we propose an end-to-end CNN model RPSRMD to handle SR problem in multiple Gaussian degradations by extracting and using as side information a shared image prior that is consistent in different Gaussian degradations. The shared image prior is generated by an AED network RPGen with a rationally designed loss function that contains two parts: consistency loss and validity loss. These losses supervise the training of AED to guarantee that the image priors of one image with different Gaussian blurs to be very similar. Afterwards we carefully designed a SR network, which is termed as PResNet (Prior based Residual Network) in this paper, to efficiently use the image priors and generate high quality and robust SR images when unknown Gaussian blur is presented. When we applied variant Gaussian blurs to the low resolution images, the experiments prove that our proposed RPSRMD, which includes RPGen and PResNet as two core components, is superior to many state-of-the-art SR methods that were designed and trained to handle multi-degradation

Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models

Emergency management urgently requires comprehensive knowledge while having a high possibility to go beyond individuals' cognitive scope. Therefore, artificial intelligence(AI) supported decision-making under that circumstance is of vital importance. Recent emerging large language models (LLM) provide a new direction for enhancing targeted machine intelligence. However, the utilization of LLM directly would inevitably introduce unreliable output for its inherent issue of hallucination and poor reasoning skills. In this work, we develop a system called Enhancing Emergency decision-making with Knowledge Graph and LLM (E-KELL), which provides evidence-based decision-making in various emergency stages. The study constructs a structured emergency knowledge graph and guides LLMs to reason over it via a prompt chain. In real-world evaluations, E-KELL receives scores of 9.06, 9.09, 9.03, and 9.09 in comprehensibility, accuracy, conciseness, and instructiveness from a group of emergency commanders and firefighters, demonstrating a significant improvement across various situations compared to baseline models. This work introduces a novel approach to providing reliable emergency decision support.Comment: 26 pages, 6 figure

SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series

Time series classification (TSC) has emerged as a critical task in various domains, and deep neural models have shown superior performance in TSC tasks. However, these models are vulnerable to adversarial attacks, where subtle perturbations can significantly impact the prediction results. Existing adversarial methods often suffer from over-parameterization or random logit perturbation, hindering their effectiveness. Additionally, increasing the attack success rate (ASR) typically involves generating more noise, making the attack more easily detectable. To address these limitations, we propose SWAP, a novel attacking method for TSC models. SWAP focuses on enhancing the confidence of the second-ranked logits while minimizing the manipulation of other logits. This is achieved by minimizing the Kullback-Leibler divergence between the target logit distribution and the predictive logit distribution. Experimental results demonstrate that SWAP achieves state-of-the-art performance, with an ASR exceeding 50% and an 18% increase compared to existing methods.Comment: 10 pages, 8 figure

People opinion topic model: opinion based user clustering in social networks

Mining various hot discussed topics and corresponding opinions from different groups of people in social media (e.g., Twitter) is very useful. For example, a decision maker in a company wants to know how different groups of people (customers, staff, competitors, etc.) think about their services, facilities, and things happened around. In this paper, we are focusing on the problem of finding opinion variations based on different groups of people and introducing the concept of opinion based community detection. Further, we also introduce a generative graphic model, namely People Opinion Topic (POT) model, which detects social communities, associated hot discussed topics, and perform sentiment analysis simultaneously by modelling user's social connections, common interests, and opinions in a unified way. This paper is the first attempt to study community and opinion mining together. Compared with traditional social communities detection, the detected communities by POT model are more interpretable and meaningful. In addition, we further analyse how diverse opinions distributed and propagated among various social communities. Experiments on real twitter dataset indicate our model is effective

Efficient traffic congestion estimation using multiple spatio-temporal properties

Traffic estimation is an important issue to analyze the traffic congestion in large-scale urban traffic situations. Recently, many researchers have used GPS data to estimate traffic congestion. However, how to fuse the multiple data reasonably and guarantee the accuracy and efficiency of these methods are still challenging problems. In this paper, we propose a novel method Multiple Data Estimation (MDE) to estimate the congestion status in urban environment with GPS trajectory data efficiently, where we estimate the congestion status of the area through utilizing multiple properties, including density, velocity, inflow and previous status. Among them, traffic inflow and previous status (combination of time and space factors) are not both used in other existing methods. In order to ensure the accuracy and efficiency, we apply dynamic weights of data and parameters in MDE method. To evaluate our methods, we apply it on large-scale taxi GPS data of Beijing and Shanghai. Extensive experiments on these two real-world datasets demonstrate the significant improvements of our method over several state-of-the-art methods

Lattice strain enhanced phase transformation of NaYbF4: 2% Er3+ upconverting nanoparticles by tuning the molar ratio of Na+/Yb3+

NaYbF4 upconverting nanoparticles (UCNPs) have enhanced optical properties compared to the NaYF4 UCNPs. However, synthesis of monodisperse NaYbF4 with controllable size and optical properties poses challenges, and the mechanism of phase transformation remains to be understood. Here, they report on the effect of Na+/Yb3+ molar ratio on the morphological and optical properties of upconverting NaYbF4: 2% Er3+ nanoparticles. Controllable transformation of cubic phase nanoparticles produced with [Na+]/[Yb3+]= 1 to hexagonal phase is achieved by increasing Na+ content. The hexagonal UCNPs produced with [Na+]/[Yb3+]= 4 have significantly enhanced intensity of optical emission of ≈600 times compared with the pure cubic phase crystal. The work reveals that the increasing dislocation of sodium and ytterbium distribution cause the accumulation of the lattice strain with increasing Na+ content, and triggers the lattice strain-mediated phase transformation in cubic cell, as confirmed by the Density Function Theory simulations. These results provide new insights into the growth of UCNPs and pave the way for developing controlled synthesis of UCNPs for applications as bio-probes and for energy harvesting

Current situation and factors influencing elderly care in community day care centers: a cross-sectional study

BackgroundThe latest census data show that people over 60 years of age account for about 18.7% of the total population in China, and the aging of the population has become an irreversible trend in the 21st century. This study aimed to investigate the current status and factors influencing the care of the elderly in community day care centers in order to lay the foundation for the development of better services in community day care centers.MethodsThis study was a cross-sectional survey using convenience sampling in Nanjing, China. The survey instrument was the Day care and Elderly Care Service Needs Questionnaire, which included the Ability of Daily Living Assessment (ADL), the Xiao Shuiyuan Social Support Rating Scale (SSRS) and the Day care Elderly Care Service Needs Survey Form, and a general information survey.ResultsA total of 450 elderly people in day care centers were surveyed. The elderly had different levels of demand for day care services, especially regarding daily care. Correlation analyses indicated that age (r = 0.619), education level (r = 0.616), source of income (r = 0.582), caregiver (r = 0.557), satisfaction with care service (r = 0.603), and degree of ADL (r = 0.629) were correlated with the need for elderly day care services (all p < 0.05). The factors influencing the demand for day care services encompassed age, education level, income source, caregiver, satisfaction with service, and ADL (all p < 0.05).ConclusionElderly care services in community day care centers are mainly based on daily and spiritual comfort, and the needs of the elderly are influenced by many factors. Timely nursing care policies and measures that target these factors are needed to improve elderly care

Creating two-dimensional solid helium via diamond lattice confinement

The universe abounds with solid helium in polymorphic forms. Therefore, exploring the allotropes of helium remains vital to our understanding of nature. However, it is challenging to produce, observe and utilize solid helium on the earth because high-pressure techniques are required to solidify helium. Here we report the discovery of room-temperature two-dimensional solid helium through the diamond lattice confinement effect. Controllable ion implantation enables the self-assembly of monolayer helium atoms between {100} diamond lattice planes. Using state-of-the-art integrated differential phase contrast microscopy, we decipher the buckled tetragonal arrangement of solid helium monolayers with an anisotropic nature compressed by the robust diamond lattice. These distinctive helium monolayers, in turn, produce substantial compressive strains to the surrounded diamond lattice, resulting in a large-scale bandgap narrowing up to ~2.2 electron volts. This approach opens up new avenues for steerable manipulation of solid helium for achieving intrinsic strain doping with profound applications

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks