Cointegrating Polynomial Regressions with Power Law Trends: Environmental Kuznets Curve or Omitted Time Effects?

The environmental Kuznets curve predicts an inverted U-shaped relationship between environmental pollution and economic growth. Current analyses frequently employ models which restrict nonlinearities in the data to be explained by the economic growth variable only. We propose a Generalized Cointegrating Polynomial Regression (GCPR) to allow for an alternative source of nonlinearity. More specifically, the GCPR is a seemingly unrelated regression with (1) integer powers of deterministic and stochastic trends for the individual units, and (2) a common flexible global trend. We estimate this GCPR by nonlinear least squares and derive its asymptotic distribution. Endogeneity of the regressors will introduce nuisance parameters into the limiting distribution but a simulation-based approach nevertheless enables us to conduct valid inference. A multivariate subsampling KPSS test is proposed to verify the correct specification of the cointegrating relation. Our simulation study shows good performance of the simulated inference approach and subsampling KPSS test. We illustrate the GCPR approach using data for Austria, Belgium, Finland, the Netherlands, Switzerland, and the UK. A single global trend accurately captures all nonlinearities leading to a linear cointegrating relation between GDP and CO2 for all countries. This suggests that the environmental improvement of the last years is due to economic factors different from GDP

Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash

This paper investigates an approach to both speed up business decision-making and lower the cost of learning through experimentation by factorizing business policies and employing fractional factorial experimental designs for their evaluation. We illustrate how this method integrates with advances in the estimation of heterogeneous treatment effects, elaborating on its advantages and foundational assumptions. We empirically demonstrate the implementation and benefits of our approach and assess its validity in evaluating consumer promotion policies at DoorDash, which is one of the largest delivery platforms in the US. Our approach discovers a policy with 5% incremental profit at 67% lower implementation cost.Comment: 14 page

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction

Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services

In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i.e., in different geographical locations and at different times, user click behavior will change significantly. Appropriate spatiotemporal enhancement modeling of user click behavior and large-scale sparse attributes is key to building an LBS model. Although most of existing methods have been proved to be effective, they are difficult to apply to takeaway scenarios due to insufficient modeling of spatiotemporal information. In this paper, we address this challenge by seeking to explicitly model the timing and locations of interactions and proposing a Spatiotemporal-Enhanced Network, namely StEN. In particular, StEN applies a Spatiotemporal Profile Activation module to capture common spatiotemporal preference through attribute features. A Spatiotemporal Preference Activation is further applied to model the personalized spatiotemporal preference embodied by behaviors in detail. Moreover, a Spatiotemporal-aware Target Attention mechanism is adopted to generate different parameters for target attention at different locations and times, thereby improving the personalized spatiotemporal awareness of the model.Comprehensive experiments are conducted on three large-scale industrial datasets, and the results demonstrate the state-of-the-art performance of our methods. In addition, we have also released an industrial dataset for takeaway industry to make up for the lack of public datasets in this community.Comment: accepted by CIKM workshop 202

Impaired Sensorimotor Integration in Restless Legs Syndrome

Objective: Restless legs syndrome (RLS) is a complicated sensorimotor syndrome that may be linked to changes in sensorimotor integration. The mechanism of such changes is unclear. The aim of this study was to investigate sensorimotor integration in patients with RLS through transcranial magnetic stimulation-motor evoked potentials (TMS-MEPs) preceded by peripheral electric stimulation.Methods: Fourteen RLS patients and 12 healthy, age-matched controls were investigated. The clinical severity of RLS was evaluated based on the International Criteria of the International Restless Legs Syndrome Study Group (IRLSSG) severity scores. The tibial and median H-reflexes and the resting motor threshold (RMT) of the abductor pollicis brevis (APB) were tested in all 26 subjects. The RMT of the tibialis anterior (TA) was tested in 8 patients and 7 controls. All 26 subjects underwent measurement of unconditioned MEPs of the APB. Electric pulses were applied to the right median nerve, followed by TMS pulses over the left motor cortex at interstimulus intervals (ISIs) of 20, 25, 30, 50, 100, 150, and 200 ms. Unconditioned MEPs of the TA were measured in 8 patients and 7 controls. Electric pulses were applied to the right peroneal nerve, followed by TMS pulses over the left motor cortex at ISIs of 30, 35, 45, 60, 100, and 200 ms. The degree of modulation of MEPs by electric stimulation was expressed as the ratio of the conditioned MEP amplitude to the unconditioned MEP amplitude. Ratios <1 indicated inhibition, and ratios >1 indicated facilitation.Results: No significant differences in RMT or H-reflex latencies or amplitudes were found between RLS patients and controls. A significant increase in unconditioned MEP amplitudes of the TA was observed in patients compared to controls (p = 0.03). Long-latency afferent inhibition (LAI) of the median nerve in RLS patients was decreased significantly at ISIs of 150 (p = 0.000) and 200 ms (p = 0.004). Upon peroneal nerve stimulation, no significant difference was observed between the two groups at any ISI.Conclusions: Our results suggest increased motor cortical excitability of the legs and disturbed sensorimotor integration in RLS patients; this disturbance might originate at the cortical level

Transcranial Magnetic Stimulation to the Middle Frontal Gyrus During Attention Modes Induced Dynamic Module Reconfiguration in Brain Networks

The interaction between dorsal and ventral attention networks (VANs) is mediated by the middle frontal gyrus (MFG), which is functionally connected to both networks. However, the direct role of the MFG in selective and sustained attention remains controversial. In the current study, we used transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to probe the connectivity dynamic changes of MFG-associated regions during different attention modes. The participants underwent visual, selective, and sustained attention tasks to observe TMS-induced network changes. Twenty healthy participants received single-pulse TMS over the left or right MFG during tasks, while synchronous EEG data was acquired. Behavioral results were recorded and time-varying brain network analyses were performed. We found that the MFG is involved in attention processing and that sustained attention was preferentially controlled by the right MFG. Moreover, compared with the right hemisphere, the left hemisphere was associated with selective attention tasks. Visual and selective attention tasks induced MFG-related changes in network nodes were within the left hemisphere; however, sustained attention induced changes in network nodes were in the bilateral posterior MFG. Our findings indicated that the MFG plays a crucial role in regulating attention networks. In particular, TMS-induced MFG alterations influenced key nodes of the time-varying brain network, leading to the reorganization of brain network modules

Lateralization Value of Low Frequency Band Beamformer Magnetoencephalography Source Imaging in Temporal Lobe Epilepsy

Objective: In presurgical evaluation of temporal lobe epilepsy (TLE), selection of the resection side is challenging when bilateral temporal epileptiform discharges or structural abnormalities are present. We aim to evaluate the lateralization value of beamformer analysis of magnetoencephalography (MEG) in TLE.Methods: MEG data from 14 TLE patients were analyzed through beamformer analysis. We measured the hemispherical power distribution of beamformer sources and calculated the lateralization index (LI). We calculated the LI at multiple frequencies to explore the frequency dependency and at the delta frequency to define laterality. LI values ranging from −1 to −0.05 indicated right hemispheric dominance. LI values ranging from 0.05 to 1 indicated left hemispheric dominance. LI values ranging from −0.05 to 0.05 defined bilaterality. We measured the power of beamformer sources with a 9-s duration to explore time dependency.Results: The beamformer analysis showed that 10/14 patients had power dominance ipsilateral to resection. The delta frequency band had a higher lateralization value than other frequency bands. A time-dependent power fluctuation was found in the delta frequency band.Conclusions: MEG beamformer analysis, especially in the delta band, might efficiently provide additional information regarding lateralization in TLE