Explicit Planning Helps Language Models in Logical Reasoning

Language models have been shown to perform remarkably well on a wide range of natural language processing tasks. In this paper, we propose a novel system that uses language models to perform multi-step logical reasoning. Our system incorporates explicit planning into its inference procedure, thus able to make more informed reasoning decisions at each step by looking ahead into their future effects. In our experiments, our full system significantly outperforms other competing systems. On a multiple-choice question answering task, our system performs competitively compared to GPT-3-davinci despite having only around 1.5B parameters. We conduct several ablation studies to demonstrate that explicit planning plays a crucial role in the system's performance

Non-separable non-stationary random fields

We describe a framework for constructing nonsta- tionary nonseparable random fields that are based on an infinite mixture of convolved stochastic processes. When the mixing process is station- ary but the convolution function is nonstationary we arrive at nonseparable kernels with constant non-separability that are available in closed form. When the mixing is nonstationary and the convolu- tion function is stationary we arrive at nonsepara- ble random fields that have varying nonseparabil- ity and better preserve local structure. These fields have natural interpretations through the spectral representation of stochastic differential equations (SDEs) and are demonstrated on a range of syn- thetic benchmarks and spatio-temporal applica- tions in geostatistics and machine learning. We show how a single Gaussian process (GP) with these random fields can computationally and sta- tistically outperform both separable and existing nonstationary nonseparable approaches such as treed GPs and deep GP constructions

Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning

Large language models have shown astonishing performance on a wide range of reasoning tasks. In this paper, we investigate whether they could reason about real-world events and help improve the prediction performance of event sequence models. We design LAMP, a framework that integrates a large language model in event prediction. Particularly, the language model performs abductive reasoning to assist an event sequence model: the event model proposes predictions on future events given the past; instructed by a few expert-annotated demonstrations, the language model learns to suggest possible causes for each proposal; a search module finds out the previous events that match the causes; a scoring function learns to examine whether the retrieved events could actually cause the proposal. Through extensive experiments on several challenging real-world datasets, we demonstrate that our framework -- thanks to the reasoning capabilities of large language models -- could significantly outperform the state-of-the-art event sequence models.Comment: NeurIPS 2023 camera-read

Ideal cardiovascular health and cardiovascular related events: a systematic review and meta-analysis

Aims The aim of this study was to systematically review and quantitatively summarize the evidence on the association between Life Simple’s 7 (LS7) and multiple cardiovascular diseases (CVDs) and cardiometabolic diseases (CMDs). Methods EMBASE and PubMed were searched from January 2010 to March 2022 for observational studies that investigated the as- and results sociation between ideal cardiovascular health (CVH) with CVD or CMD outcomes in an adult population. Two reviewers independently selected studies according to the eligibility criteria, extracted data, and evaluated risk of bias. Data were analysed with a random-effects meta-analysis. This meta-analysis included 59 studies (1 881 382 participants). Participants with ideal CVH had a considerably lower risk of a variety of CVDs and CMDs as compared with those with poor CVH, varying from 40% lower risk for atrial fibrillation (AF) {hazard ratio [HR] = 0.60 [95% confidence interval (CI) 0.44–0.83]} to 82% lower risk for myocardial infarction [HR = 0.18 (95% CI 0.12–0.28)]. Intermediate CVH was associated with 27–57% lower risk in CVDs and CMDs compared with poor CVH, with the highest hazard for AF [HR = 0.73 (95% CI 0.59–0.91)] and the lowest hazard for peripheral arterial disease [HR = 0.43 (95% CI 0.30–0.60)]. Conclusion Ideal and moderate CVH were associated with a lower incidence of CVDs and CMDs than poor CVH. Life Simple’s 7 holds significant potential for promoting overall CVH and thereby contributing to the prevention of CVDs. Lay summary Healthy lifestyle is very important to prevent cardiovascular diseases (CVDs) and cardiometabolic diseases (CMDs), such as diabetes and kidney diseases. Therefore, in 2010, the American Heart Association introduced Life’s Simple 7 (LS7), a scoring system using seven lifestyle factors to measure cardiovascular health in populations, and these factors are diet, physical activity, smoking, blood pressure, blood lipids, blood sugar, and weight. In this review, we investigated the relationship between LS7 score and CVDs or CMDs

High-Dimensional Bayesian Non-parametric Learning of System Parameters in Different Data Scenarios

The pursuit of the correlation structure of a high-dimensional random construct, underlines my doctoral studies. This thesis reports on the development of methodologies that help undertake learning of functional relationships between variables, given high-dimensional discontinuous data that exhibit non-stationary correlation structure, with such methods tying in with methods needed to undertake such difficult correlation learning–and its possible intuitive graphical representations as networks. These developed methods are then presented in an application-ready format, in which the relevant inference is typically undertaken with Markov Chain Monte Carlo methods. I have worked on developing Bayesian methodologies for the supervised learning of the functional relationship between a system vector and another tensor-valued observable that affects the system vector, given real training data that consists of known pairs of values of these variables. The probabilistic learning of the functional relation between these variables is done by modelling this function with a high-dimensional Gaussian Process (GP), and the likelihood is then parametrised by multiple covariance matrices. I have developed on the method of nesting GPs of different dimensionalities, to render covariance kernels nonstationary, by treating each kernel hyper-parameter as a realisation from a scalar-valued GP. The inner layer of this learning strategy is then built of scalar-valued GPs, which are nested within a tensor-valued GP, and inference is done with Metropolis-within-Gibbs. It is natural that such interest includes the learning of the correlation structure of multivariate, rectangularly-shaped data, which is manifest in the sought graphical model of this data, where I determine objective uncertainties in the learning of such a graphical models, where such uncertainty learning allows me to quantify the correlation between a pair of such datasets by computing the distance between the (posterior probability densities of the) learnt graphical models of the respective datasets. Applications include the learning of the very large, human disease-symptom network and computation of the distance between the vino-chemical graphical models of red and white Portuguese wines

An Algorithm for Painting Large Objects Based on a Nine-Axis UR5 Robotic Manipulator

An algorithm for automatically planning trajectories designed for painting large objects is proposed in this paper to eliminate the difficulty of painting large objects and ensure their surface quality. The algorithm was divided into three phases, comprising the target point acquisition phase, the trajectory planning phase, and the UR5 robot inverse solution acquisition phase. In the target point acquisition phase, the standard triangle language (STL) file, algorithm of principal component analyses (PCA), and k-dimensional tree (k-d tree) were employed to obtain the point cloud model of the car roof to be painted. Simultaneously, the point cloud data were compressed as per the requirements of the painting process. In the trajectory planning phase, combined with the maximum operating space of the UR5 robot, the painting trajectory of the target points was converted into multiple traveling salesman problem (TSP) models, and each TSP model was created with a genetic algorithm (GA). In the last phase, in conformity with the singularities of the UR5 robot’s motion space, the painting trajectory was divided into a recommended area trajectory and a non-recommended area trajectory and created by the analytical method and sequential quadratic programming (SQP). Finally, the proposed algorithm for painting large objects was deployed in a simulation experiment. Simulation results showed that the accuracy of the algorithm could meet the requirements of painting technology, and it has promising engineering practicability

An Algorithm for Painting Large Objects Based on a Nine-Axis UR5 Robotic Manipulator

An algorithm for automatically planning trajectories designed for painting large objects is proposed in this paper to eliminate the difficulty of painting large objects and ensure their surface quality. The algorithm was divided into three phases, comprising the target point acquisition phase, the trajectory planning phase, and the UR5 robot inverse solution acquisition phase. In the target point acquisition phase, the standard triangle language (STL) file, algorithm of principal component analyses (PCA), and k-dimensional tree (k-d tree) were employed to obtain the point cloud model of the car roof to be painted. Simultaneously, the point cloud data were compressed as per the requirements of the painting process. In the trajectory planning phase, combined with the maximum operating space of the UR5 robot, the painting trajectory of the target points was converted into multiple traveling salesman problem (TSP) models, and each TSP model was created with a genetic algorithm (GA). In the last phase, in conformity with the singularities of the UR5 robot’s motion space, the painting trajectory was divided into a recommended area trajectory and a non-recommended area trajectory and created by the analytical method and sequential quadratic programming (SQP). Finally, the proposed algorithm for painting large objects was deployed in a simulation experiment. Simulation results showed that the accuracy of the algorithm could meet the requirements of painting technology, and it has promising engineering practicability

Evaluation on the tribological performance of ring/liner system under cylinder deactivation with consideration of cylinder liner deformation and oil supply.

In gasoline engines, CDA (cylinder deactivation) affects greatly the tribological performance of ring/liner system while reducing the emissions and improving the fuel economy. The analyses on the tribological performance of ring/liner system under the CDA mainly focus on the ideal circular cylinder liner and fixed fully flooded lubrication condition. In this study, a numerical investigation on the tribological performance of a compression ring-cylinder liner system is presented under the CDA with consideration of the cylinder liner deformation and the transition between the fully flooded and starved lubrication conditions. A mixed lubrication model coupled with oil transport model and JFO (Jacobson-Floberg-Olsson) conservative cavitation algorithm is proposed to evaluate the frictional properties. Based on the model, the tribological performance is investigated under the standard operation condition and the CDA. Meanwhile, the influence of cylinder liner deformation and oil supply on the tribological performance is also evaluated. Results show that the tribological performance of the compression ring-cylinder liner system is greatly changed when the CDA is adopted. In particular, under the CDA, the overall power loss and FMEP (friction mean effective pressure) value are increased about 27.29% and 53.51%. The study also demonstrates the necessity to consider the cylinder liner deformation and oil supply in the simulation of compression ring-cylinder liner system under the CDA