Discovering Predictable Latent Factors for Time Series Forecasting

Modern time series forecasting methods, such as Transformer and its variants, have shown strong ability in sequential data modeling. To achieve high performance, they usually rely on redundant or unexplainable structures to model complex relations between variables and tune the parameters with large-scale data. Many real-world data mining tasks, however, lack sufficient variables for relation reasoning, and therefore these methods may not properly handle such forecasting problems. With insufficient data, time series appear to be affected by many exogenous variables, and thus, the modeling becomes unstable and unpredictable. To tackle this critical issue, in this paper, we develop a novel algorithmic framework for inferring the intrinsic latent factors implied by the observable time series. The inferred factors are used to form multiple independent and predictable signal components that enable not only sparse relation reasoning for long-term efficiency but also reconstructing the future temporal data for accurate prediction. To achieve this, we introduce three characteristics, i.e., predictability, sufficiency, and identifiability, and model these characteristics via the powerful deep latent dynamics models to infer the predictable signal components. Empirical results on multiple real datasets show the efficiency of our method for different kinds of time series forecasting. The statistical analysis validates the predictability of the learned latent factors

ChatAnything: Facetime Chat with LLM-Enhanced Personas

In this technical report, we target generating anthropomorphized personas for LLM-based characters in an online manner, including visual appearance, personality and tones, with only text descriptions. To achieve this, we first leverage the in-context learning capability of LLMs for personality generation by carefully designing a set of system prompts. We then propose two novel concepts: the mixture of voices (MoV) and the mixture of diffusers (MoD) for diverse voice and appearance generation. For MoV, we utilize the text-to-speech (TTS) algorithms with a variety of pre-defined tones and select the most matching one based on the user-provided text description automatically. For MoD, we combine the recent popular text-to-image generation techniques and talking head algorithms to streamline the process of generating talking objects. We termed the whole framework as ChatAnything. With it, users could be able to animate anything with any personas that are anthropomorphic using just a few text inputs. However, we have observed that the anthropomorphic objects produced by current generative models are often undetectable by pre-trained face landmark detectors, leading to failure of the face motion generation, even if these faces possess human-like appearances because those images are nearly seen during the training (e.g., OOD samples). To address this issue, we incorporate pixel-level guidance to infuse human face landmarks during the image generation phase. To benchmark these metrics, we have built an evaluation dataset. Based on it, we verify that the detection rate of the face landmark is significantly increased from 57.0% to 92.5% thus allowing automatic face animation based on generated speech content. The code and more results can be found at https://chatanything.github.io/

Maternal Glucose during Pregnancy and after Delivery in Women with Gestational Diabetes Mellitus on Overweight Status of Their Children

Objective:. To examine the association of maternal glycemia during pregnancy and after delivery with anthropometry in the offspring of mothers with gestational diabetes mellitus (GDM). Methods:. A total of 1,263 GDM mothers and their children finished the health survey at 1–5 years after delivery. Results:. Offspring of GDM mothers who were diagnosed with diabetes during pregnancy had higher prevalence of overweight, higher mean weight for height Z scores, and higher mean BMI for age Z scores at 1–5 years old than the offspring of GDM mothers who were diagnosed with impaired glucose tolerance (IGT) during pregnancy. Offspring of GDM mothers who developed diabetes 1–5 years after delivery had higher mean values of Z scores for weight for height and BMI for age at 1–5 years old than the offspring of GDM mothers who had normal glucose or prediabetes after delivery. Conclusions:. Offspring of GDM mothers who were diagnosed with diabetes during pregnancy or after delivery had an increased risk of childhood overweight or weight gain at 1–5 years old compared with children of GDM mothers with IGT during pregnancy or with normal glucose or prediabetes after delivery

Joint Associations of Maternal Gestational Diabetes and Hypertensive Disorders of Pregnancy With Overweight in Offspring

Objectives: Either maternal gestational diabetes mellitus (GDM) or hypertensive disorder of pregnancy (HDP) is associated with an increased risk of obesity in the offspring. However, their joint associations with obesity in offspring remain unclear. We investigated the joint associations of maternal GDM and HDP with childhood overweight in offspring.Methods: We performed a large study in 1967 mother-child pairs. Maternal GDM was diagnosed according to the 1999 World Health Organization (WHO) criteria. HDP was defined as self-reported doctor-diagnosed hypertension or treatment of hypertension (including gestational hypertension, preeclampsia, sever preeclampsia or eclampsia) after 20 weeks of gestation on the questionnaire. Body mass index (BMI) for age Z-score and childhood overweight were evaluated according to WHO growth reference. We used the general linear models to compare children's Z score for BMI and logistic regression models to estimate odds ratios of childhood overweight according to maternal different status of GDM and HDP.Results: Offspring of mothers with both GDM and HDP had a higher BMI for age Z-score (0.63 vs. 0.03, P <0.001) than children born to normotensive and normoglycemic pregnancy. After adjustment for maternal and children's major confounding factors, joint GDM and HDP were associated with increased odds ratios of offspring's overweight compared with normotensive and normoglycemic pregnancy (2.97, 95% confidence intervals [CIs] 1.65–5.34) and GDM alone (2.06, 95% CIs 1.20–3.54), respectively. After additional adjustment for maternal pre-pregnancy BMI and gestational weight gain, joint maternal GDM, and HDP was still associated with an increased risk of offspring's overweight compared with the maternal normotensive, and normoglycemic group but became to have a borderline increased risk compared with the maternal GDM alone group.Conclusions: Maternal GDM alone or joint GDM and HDP were associated with increased ratios of offspring's overweight.Peer reviewe

Associations of Amylin with Inflammatory Markers and Metabolic Syndrome in Apparently Healthy Chinese

BACKGROUND: Cellular and animal studies implicate multiple roles of amylin in regulating insulin action, glucose and lipid metabolisms. However, the role of amylin in obesity related metabolic disorders has not been thoroughly investigated in humans. Therefore, we aimed to evaluate the distribution of circulating amylin and its association with metabolic syndrome (MetS) and explore if this association is influenced by obesity, inflammatory markers or insulin resistance in apparently healthy Chinese. METHODS: A population-based sample of 1,011 Chinese men and women aged 35-54 years was employed to measure plasma amylin, inflammatory markers (C-reactive protein [CRP] and interleukin-6 [IL-6]), insulin, glucose and lipid profiles. MetS was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian-Americans. RESULTS: Plasma amylin concentrations were higher in overweight/obese participants than normal-weight counterparts (P<0.001) without sex difference. Circulating amylin was positively associated with CRP, IL-6, BMI, waist circumference, blood pressure, fasting glucose, insulin, amylin/insulin ratio, HOMA-IR, LDL cholesterol and triglycerides, while negatively associated with HDL cholesterol (all P<0.001). After multiple adjustments, the risk of MetS was significantly higher (odds ratio 3.71; 95% confidence interval: 2.53 to 5.46) comparing the highest with the lowest amylin quartile. The association remained significant even further controlling for BMI, inflammatory markers, insulin or HOMA-IR. CONCLUSIONS: Our study suggests that amylin is strongly associated with inflammatory markers and MetS. The amylin-MetS association is independent of established risk factors of MetS, including obesity, inflammatory markers and insulin resistance. The causal role of hyperamylinemia in the development of MetS needs to be confirmed prospectively

A Robust and Efficient Remote Authentication Scheme from Elliptic Curve Cryptosystem

Abstract Along with the extensive prevalence of the network and the portable equipments, people can access network resources conveniently. The protection of participants&apos; privacy and data confidentiality is significant. Authentication mechanism is essential to assure the authenticity of all participants and forbid the illegal accessing. In this paper, we propose a robust remote authentication scheme with privacy protection, which achieves the efficiency. Besides, we prove the completeness of the proposed scheme through BAN-logic. The performance comparisons show that our proposal is sufficiently robust and suitable to the practical application environment

IFFMStyle: High-Quality Image Style Transfer Using Invalid Feature Filter Modules

Image style transfer is a challenging problem in computer vision which aims at rendering an image into different styles. A lot of progress has been made to transfer the style of one painting of a representative artist in real time, whereas less attention has been focused on transferring an artist’s style from a collection of his paintings. This task requests capturing the artist’s precise style from his painting collection. Existing methods did not pay more attention on the possible disruption of original content details and image structures by texture elements and noises, which leads to the structure deformation or edge blurring of the generated images. To address this problem, we propose IFFMStyle, a high-quality image style transfer framework. Specifically, we introduce invalid feature filtering modules (IFFM) to the encoder–decoder architecture to filter the content-independent features in the original image and the generated image. Then, the content-consistency constraint is used to enhance the model’s content-preserving capability. We also introduce style perception consistency loss to jointly train a network with content loss and adversarial loss to maintain the distinction of different semantic content in the generated image. Additionally, we have no requirement for paired content image and style image. The experimental results show that the stylized image generated by the proposed method significantly improves the quality of the generated images, and can realize the style transfer based on the semantic information of the content image. Compared with the advanced method, our method is more favored by users

Demand Forecasting Model of Port Critical Spare Parts

Demand forecasting for port critical spare parts (CSP) is notoriously difficult as it is expensive, lumpy and intermittent with high variability. In this paper, some influential factors which have an effect on CSP consumption were proposed according to port CSP characteristics and historical data. And analytic hierarchy process (AHP) is used to sieve out the more influential factors. Combined with the influential factors, a least squares support vector machines (LS-SVM) model optimized by particle swarm optimization (PSO) was developed to forecast the demand. And the effectiveness of the model is demonstrated through a real case study, which shows that the proposed model can forecast the demand of port CSP more accurately, and effectively reduce inventory backlog. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.323