SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities, states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation. In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency, but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only,we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraint

Transaction Fairness in Blockchains, Revisited

With the growing number of decentralized finance applications, transaction fairness in blockchains has gained intensive research interest. As a broad concept in the distributed systems and blockchain literature, fairness has been used in different contexts, varying from ones related to the system\u27s liveness to ones that focus on the received order of transactions. In this work, we revisit the fairness definitions known so far and provide a more generic fairness definition called verifiable fairness. Our definition relaxes the ordering rules that are inherently embedded in prior definitions to a predicate defined by concrete applications. Our notion thus gains flexibility and generality, capturing all existing fairness definitions. We provide a solution that achieves our new fairness definition, leveraging trusted hardware. Unlike prior works that usually design a dedicated consensus protocol to achieve fairness goals, our solution is modular and can be integrated with any blockchain system. We implement a prototype using Go Ethereum (Geth) as the blockchain and OpenSGX as the trusted hardware. Evaluation results reveal that our construction imposes only minimal overhead on existing blockchain systems

A novel data-driven rollover risk assessment for articulated steering vehicles using RNN

Articulated steering vehicles have outstanding capability operating but suffer from frequent rollover accidents due to their complicated structure. It is necessary to accurately detect their rollover risk for drivers to take action in time. Their variable structure and the variable center of mass exhibit nonlinear time-variant behavior and increase the difficulty of dynamic modelling and lateral stability description. This paper proposes a novel data-driven modelling methodology for lateral stability description of articulated steering vehicles. The running data is first collected based on the typical operations that prone to rollover and then classified into two types: Safety and danger. The data quality is further improved by wavelet transformation. Finally, an RNN model is built on the data. The experimental results show that the output of the RNN model can accurately quantify lateral stability of the vehicle, i.e., the risk of rollover, when it is turning and crossing uneven surfaces or obstacles

Using AKF-PSR to Compensate Random Drift Errors of Low-Cost MEMS Gyroscopes

The random drift of a micro-electromechanical system (MEMS) gyroscope seriously affects its measurement accuracy. To model and compensate its random drift, the time series analysis method has widely been deployed, which, however, requires a large amount of data for pre-processing analysis and is unsuitable for real-time applications. This paper proposes a new random drift compensation method based on the adaptive Kalman filter (AKF) and phase space reconstruction (PSR). AKF is first designed to compensate the random drift of the low-cost MEMS gyroscope. The phase variables are then used as phase vectors via PSR. Experiments show that the proposed AKF-PSR method can effectively compensate the random drift of the gyroscope, and the standard deviation is reduced by half

Complex firing activities and bifurcations in memristor-coupled Hindmarsh–Rose neuron

Due to the unique synaptic plasticity and memory effect, a memristor can not only mimic biological synapses but also characterize the influence of external electromagnetic radiation. In this paper, a ReLU-type non-ideal memristor with a simple structure is first coupled to a classical three-dimensional Hindmarsh–Rose neuron to describe the electromagnetic induction effect, which can show period-doubling, period-adding, and saddle-node bifurcations by varying the coupling strength of the memristor. Furthermore, complex discharge behaviors of the system, including bursting discharge and spiking discharge, are exhibited, and some coexisting discharge modes associated with initial values are also presented. Finally, an analog circuit scheme consuming fewer circuit components is designed, and it was found that the experimental results are consistent with the numerical results

SLOTH: Structured Learning and Task-Based Optimization for Time Series Forecasting on Hierarchies

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities, states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation. In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency, but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only, we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraints