Utilizing Language Models for Energy Load Forecasting

Energy load forecasting plays a crucial role in optimizing resource allocation and managing energy consumption in buildings and cities. In this paper, we propose a novel approach that leverages language models for energy load forecasting. We employ prompting techniques to convert energy consumption data into descriptive sentences, enabling fine-tuning of language models. By adopting an autoregressive generating approach, our proposed method enables predictions of various horizons of future energy load consumption. Through extensive experiments on real-world datasets, we demonstrate the effectiveness and accuracy of our proposed method. Our results indicate that utilizing language models for energy load forecasting holds promise for enhancing energy efficiency and facilitating intelligent decision-making in energy systems.Comment: BuildSys 2023 Accepte

Human Mobility Question Answering (Vision Paper)

Question answering (QA) systems have attracted much attention from the artificial intelligence community as they can learn to answer questions based on the given knowledge source (e.g., images in visual question answering). However, the research into question answering systems with human mobility data remains unexplored. Mining human mobility data is crucial for various applications such as smart city planning, pandemic management, and personalised recommendation system. In this paper, we aim to tackle this gap and introduce a novel task, that is, human mobility question answering (MobQA). The aim of the task is to let the intelligent system learn from mobility data and answer related questions. This task presents a new paradigm change in mobility prediction research and further facilitates the research of human mobility recommendation systems. To better support this novel research topic, this vision paper also proposes an initial design of the dataset and a potential deep learning model framework for the introduced MobQA task. We hope that this paper will provide novel insights and open new directions in human mobility research and question answering research

MAPLE: Mobile App Prediction Leveraging Large Language model Embeddings

Despite the rapid advancement of mobile applications, predicting app usage remains a formidable challenge due to intricate user behaviours and ever-evolving contexts. To address these issues, this paper introduces the Mobile App Prediction Leveraging Large Language Model Embeddings (MAPLE) model. This innovative approach utilizes Large Language Models (LLMs) to predict app usage accurately. Rigorous testing on two public datasets highlights MAPLE's capability to decipher intricate patterns and comprehend user contexts. These robust results confirm MAPLE's versatility and resilience across various scenarios. While its primary design caters to app prediction, the outcomes also emphasize the broader applicability of LLMs in different domains. Through this research, we emphasize the potential of LLMs in app usage prediction and suggest their transformative capacity in modelling human behaviours across diverse fields

Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training

New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads) are rarely explored. In this paper, we introduce a novel setup called a spatio-temporal (ST) split to evaluate the models' capabilities to generalize to unseen roads. In this setup, the models are trained on data from a sample of roads, but tested on roads not seen in the training data. Moreover, we also present a novel framework called Spatial Contrastive Pre-Training (SCPT) where we introduce a spatial encoder module to extract latent features from unseen roads during inference time. This spatial encoder is pre-trained using contrastive learning. During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training. We also show that the output from the spatial encoder can be used effectively to infer latent node embeddings on unseen roads during inference time. The SCPT framework also incorporates a new layer, named the spatially gated addition (SGA) layer, to effectively combine the latent features from the output of the spatial encoder to existing backbones. Additionally, since there is limited data on the unseen roads, we argue that it is better to decouple traffic signals to trivial-to-capture periodic signals and difficult-to-capture Markovian signals, and for the spatial encoder to only learn the Markovian signals. Finally, we empirically evaluated SCPT using the ST split setup on four real-world datasets. The results showed that adding SCPT to a backbone consistently improves forecasting performance on unseen roads. More importantly, the improvements are greater when forecasting further into the future. The codes are available on GitHub: \burl{https://github.com/cruiseresearchgroup/forecasting-on-new-roads}.Comment: 25 pages including reference, an additional 3 pages of appendix, 8 figure

Protective effects of selenium on oxidative damage and oxidative stress related gene expression in rat liver under chronic poisoning of arsenic

Arsenic (As) is a toxic metalloid existing widely in the environment, and chronic exposure to it through contaminated drinking water has become a global problem of public health. The present study focused on the protective effects of selenium on oxidative damage of chronic arsenic poisoning in rat liver. Rats were divided into four groups at random and given designed treatments for 20 weeks. The oxidative damage of liver tissue was evaluated by lipid peroxidation and antioxidant enzymes. Oxidative stress related genes were detected to reflect the liver stress state at the molecular level. Compared to the control and Na2SeO3 groups, the MDA content in liver tissue was decreased and the activities of antioxidant enzymes were increased in the Na2SeO3 intervention group. The mRNA levels of SOD1, CAT, GPx and Txnrd1 were increased significantly (P < 0.05) in the combined Na2SeO3 + NaAsO2 treatment group. The expressions of HSP70 and HO-1 were significantly (P < 0.05) increased in the NaAsO2 group and reduced in the combined treatment group. The results indicate that long-term intake of NaAsO2 causes oxidative damage in the rat liver, and Na2SeO3 protects liver cells by adjusting the expression of oxidative stress related genes to improve the activities of antioxidant enzymes. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved

Identification of additional risk loci for stroke and small vessel disease: a meta-analysis of genome-wide association studies

BACKGROUND: Genetic determinants of stroke, the leading neurological cause of death and disability, are poorly understood and have seldom been explored in the general population. Our aim was to identify additional loci for stroke by doing a meta-analysis of genome-wide association studies. METHODS: For the discovery sample, we did a genome-wide analysis of common genetic variants associated with incident stroke risk in 18 population-based cohorts comprising 84 961 participants, of whom 4348 had stroke. Stroke diagnosis was ascertained and validated by the study investigators. Mean age at stroke ranged from 45·8 years to 76·4 years, and data collection in the studies took place between 1948 and 2013. We did validation analyses for variants yielding a significant association (at p<5 × 10(-6)) with all-stroke, ischaemic stroke, cardioembolic ischaemic stroke, or non-cardioembolic ischaemic stroke in the largest available cross-sectional studies (70 804 participants, of whom 19 816 had stroke). Summary-level results of discovery and follow-up stages were combined using inverse-variance weighted fixed-effects meta-analysis, and in-silico lookups were done in stroke subtypes. For genome-wide significant findings (at p<5 × 10(-8)), we explored associations with additional cerebrovascular phenotypes and did functional experiments using conditional (inducible) deletion of the probable causal gene in mice. We also studied the expression of orthologs of this probable causal gene and its effects on cerebral vasculature in zebrafish mutants. FINDINGS: We replicated seven of eight known loci associated with risk for ischaemic stroke, and identified a novel locus at chromosome 6p25 (rs12204590, near FOXF2) associated with risk of all-stroke (odds ratio [OR] 1·08, 95% CI 1·05-1·12, p=1·48 × 10(-8); minor allele frequency 21%). The rs12204590 stroke risk allele was also associated with increased MRI-defined burden of white matter hyperintensity-a marker of cerebral small vessel disease-in stroke-free adults (n=21 079; p=0·0025). Consistently, young patients (aged 2-32 years) with segmental deletions of FOXF2 showed an extensive burden of white matter hyperintensity. Deletion of Foxf2 in adult mice resulted in cerebral infarction, reactive gliosis, and microhaemorrhage. The orthologs of FOXF2 in zebrafish (foxf2b and foxf2a) are expressed in brain pericytes and mutant foxf2b(-/-) cerebral vessels show decreased smooth muscle cell and pericyte coverage. INTERPRETATION: We identified common variants near FOXF2 that are associated with increased stroke susceptibility. Epidemiological and experimental data suggest that FOXF2 mediates this association, potentially via differentiation defects of cerebral vascular mural cells. Further expression studies in appropriate human tissues, and further functional experiments with long follow-up periods are needed to fully understand the underlying mechanisms