Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

Improvements in autonomy offer the potential for positive outcomes in a number of domains, yet guaranteeing their safe deployment is difficult. This work investigates how humans can intelligently supervise agents to achieve some level of safety even when performance guarantees are elusive. The motivating research question is: In safety-critical settings, can we avoid the need to have one human supervise one machine at all times? The paper formalizes this 'scaling supervision' problem, and investigates its application to the safety-critical context of autonomous vehicles (AVs) merging into traffic. It proposes a conservative, reachability-based method to reduce the burden on the AVs' human supervisors, which allows for the establishment of high-confidence upper bounds on the supervision requirements in this setting. Order statistics and traffic simulations with deep reinforcement learning show analytically and numerically that teaming of AVs enables supervision time sublinear in AV adoption. A key takeaway is that, despite present imperfections of AVs, supervision becomes more tractable as AVs are deployed en masse. While this work focuses on AVs, the scalable supervision framework is relevant to a broader array of autonomous control challenges.Comment: 14 pages, 7 figure

Exchange Rate Risk Premium Estimation and an Analysis of Exchange Rate Pass-through into Import Prices

This thesis investigates the workings of the exchange rate as it plays a key role in the financial market and international trading. Moreover, it has essential impacts on the monetary policy effectiveness. Chapter 1 discusses the initial motivations of this work, introduces the content of chapters, and briefly positions each essay. In Chapter 2, an innovative model with high predictive power is developed to estimate the currency risk premium based on the Taylor Rule fundamentals, which builds a bridge between exchange rates risk premium and macroeconomic variables. After that, the focus is switched to the exchange rate pass-through into import prices that measures the response of import prices to fluctuations in exchange rates. Chapter 3 studies the exchange rate pass-through into aggregated import prices for five developed economies while chapter 4 studies it on a disaggregated import price level for the UK. We found exchange rate pass-through differentiate across countries and we provide empirical evidences on the impacts of macroeconomic determinants of exchange rate pass-thorough. Finally, Chapter 5 provides concluding comments and suggestions for the future research. An appendix of all the equations introduced in this thesis is included at the very end

Real-time Model Predictive Control and System Identification Using Differentiable Physics Simulation

Developing robot controllers in a simulated environment is advantageous but transferring the controllers to the target environment presents challenges, often referred to as the "sim-to-real gap". We present a method for continuous improvement of modeling and control after deploying the robot to a dynamically-changing target environment. We develop a differentiable physics simulation framework that performs online system identification and optimal control simultaneously, using the incoming observations from the target environment in real time. To ensure robust system identification against noisy observations, we devise an algorithm to assess the confidence of our estimated parameters, using numerical analysis of the dynamic equations. To ensure real-time optimal control, we adaptively schedule the optimization window in the future so that the optimized actions can be replenished faster than they are consumed, while staying as up-to-date with new sensor information as possible. The constant re-planning based on a constantly improved model allows the robot to swiftly adapt to the changing environment and utilize real-world data in the most sample-efficient way. Thanks to a fast differentiable physics simulator, the optimization for both system identification and control can be solved efficiently for robots operating in real time. We demonstrate our method on a set of examples in simulation and show that our results are favorable compared to baseline methods

Learning to Configure Separators in Branch-and-Cut

Cutting planes are crucial in solving mixed integer linear programs (MILP) as they facilitate bound improvements on the optimal solution. Modern MILP solvers rely on a variety of separators to generate a diverse set of cutting planes by invoking the separators frequently during the solving process. This work identifies that MILP solvers can be drastically accelerated by appropriately selecting separators to activate. As the combinatorial separator selection space imposes challenges for machine learning, we learn to separate by proposing a novel data-driven strategy to restrict the selection space and a learning-guided algorithm on the restricted space. Our method predicts instance-aware separator configurations which can dynamically adapt during the solve, effectively accelerating the open source MILP solver SCIP by improving the relative solve time up to 72% and 37% on synthetic and real-world MILP benchmarks. Our work complements recent work on learning to select cutting planes and highlights the importance of separator management

Effect of Si and C additions on the reaction mechanism and mechanical properties of FeCrNiCu high entropy alloy

FeCrNiCu based high entropy alloy matrix composites were fabricated with addition of Si and C by vacuum electromagnetic induction melting. The primary goal of this research was to analyze the reaction mechanism, microstructure, mechanical properties at room temperature and strengthening mechanism of the composites with addition of Si and C. The reaction mechanism of powders containing (Si, Ni and C) was analyzed, only one reaction occurred (i.e., Si + C → SiC) and its activation energy is 1302.8 kJ/mol. The new composites consist of a face centered cubic (FCC) structured matrix reinforced by submicron sized SiC particles. The addition of Si and C enhances the hardness from 351.4 HV to 626.4 HV and the tensile strength from 565.5 MPa to 846.0 MPa, accompanied by a slight decrease in the plasticity. The main strengthening mechanisms of SiC/FeCrNiCu composites were discussed based on dislocation strengthening, load bearing effect, Orowan mechanism and solid solution hardening, whose contributions to the tensile strength increase are 58.6%, 6.3%, 14.3% and 20.8%, respectively

Influence of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys

© 2020 Chinese Materials Research Society The effect of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys (HEAs) was firstly studied by first-principles calculations. The calculated results show that the hardness of the alloys increased with the expense of its plasticity decrease, if the content of Cr in the alloy increased. In order to verify the calculated results, CrxFeNiCu (x = 0.8, 1, 1.5 and 2) high entropy alloys were synthesized by vacuum induction melting in the present study. The results show that as the value of x increased from 0.8 to 2, the crystal structure changed from single phase face centered cubic (FCC) phase to a mixture of FCC and body centered cubic (BCC) phases. For the single phase FCC (x = 0.8) structure, both the tensile strength and hardness values were low, which were 491.6 MPa and 322.2 HV respectively, however, the plasticity was high, reaching 33.2%. With the formation and growth of BCC phase (x = 2) the tensile strength and hardness of the alloy were significantly improved, which were 872.6 MPa and 808 HV, respectively

Effect of Si and C additions on the reaction mechanism and mechanical properties of FeCrNiCu high entropy alloy

FeCrNiCu based high entropy alloy matrix composites were fabricated with addition of Si and C by vacuum electromagnetic induction melting. The primary goal of this research was to analyze the reaction mechanism, microstructure, mechanical properties at room temperature and strengthening mechanism of the composites with addition of Si and C. The reaction mechanism of powders containing (Si, Ni and C) was analyzed, only one reaction occurred (i.e., Si + C → SiC) and its activation energy is 1302.8 kJ/mol. The new composites consist of a face centered cubic (FCC) structured matrix reinforced by submicron sized SiC particles. The addition of Si and C enhances the hardness from 351.4 HV to 626.4 HV and the tensile strength from 565.5 MPa to 846.0 MPa, accompanied by a slight decrease in the plasticity. The main strengthening mechanisms of SiC/FeCrNiCu composites were discussed based on dislocation strengthening, load bearing effect, Orowan mechanism and solid solution hardening, whose contributions to the tensile strength increase are 58.6%, 6.3%, 14.3% and 20.8%, respectively

Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts

Transformer-based pre-trained models have achieved great improvements in semantic matching. However, existing models still suffer from insufficient ability to capture subtle differences. The modification, addition and deletion of words in sentence pairs may make it difficult for the model to predict their relationship. To alleviate this problem, we propose a novel Dual Path Modeling Framework to enhance the model's ability to perceive subtle differences in sentence pairs by separately modeling affinity and difference semantics. Based on dual-path modeling framework we design the Dual Path Modeling Network (DPM-Net) to recognize semantic relations. And we conduct extensive experiments on 10 well-studied semantic matching and robustness test datasets, and the experimental results show that our proposed method achieves consistent improvements over baselines.Comment: ICASSP 2023. arXiv admin note: text overlap with arXiv:2210.0345

Influence of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys

© 2020 Chinese Materials Research Society The effect of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys (HEAs) was firstly studied by first-principles calculations. The calculated results show that the hardness of the alloys increased with the expense of its plasticity decrease, if the content of Cr in the alloy increased. In order to verify the calculated results, CrxFeNiCu (x = 0.8, 1, 1.5 and 2) high entropy alloys were synthesized by vacuum induction melting in the present study. The results show that as the value of x increased from 0.8 to 2, the crystal structure changed from single phase face centered cubic (FCC) phase to a mixture of FCC and body centered cubic (BCC) phases. For the single phase FCC (x = 0.8) structure, both the tensile strength and hardness values were low, which were 491.6 MPa and 322.2 HV respectively, however, the plasticity was high, reaching 33.2%. With the formation and growth of BCC phase (x = 2) the tensile strength and hardness of the alloy were significantly improved, which were 872.6 MPa and 808 HV, respectively

Intelligent Scheduling Method for Bulk Cargo Terminal Loading Process Based on Deep Reinforcement Learning

Funding Information: Funding: This research was funded by the National Natural Science Foundation of China under Grant U1964201 and Grant U21B6001, the Major Scientific and Technological Special Project of Hei-longjiang Province under Grant 2021ZX05A01, the Heilongjiang Natural Science Foundation under Grant LH2019F020, and the Major Scientific and Technological Research Project of Ningbo under Grant 2021Z040. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Sea freight is one of the most important ways for the transportation and distribution of coal and other bulk cargo. This paper proposes a method for optimizing the scheduling efficiency of the bulk cargo loading process based on deep reinforcement learning. The process includes a large number of states and possible choices that need to be taken into account, which are currently performed by skillful scheduling engineers on site. In terms of modeling, we extracted important information based on actual working data of the terminal to form the state space of the model. The yard information and the demand information of the ship are also considered. The scheduling output of each convey path from the yard to the cabin is the action of the agent. To avoid conflicts of occupying one machine at same time, certain restrictions are placed on whether the action can be executed. Based on Double DQN, an improved deep reinforcement learning method is proposed with a fully connected network structure and selected action sets according to the value of the network and the occupancy status of environment. To make the network converge more quickly, an improved new epsilon-greedy exploration strategy is also proposed, which uses different exploration rates for completely random selection and feasible random selection of actions. After training, an improved scheduling result is obtained when the tasks arrive randomly and the yard state is random. An important contribution of this paper is to integrate the useful features of the working time of the bulk cargo terminal into a state set, divide the scheduling process into discrete actions, and then reduce the scheduling problem into simple inputs and outputs. Another major contribution of this article is the design of a reinforcement learning algorithm for the bulk cargo terminal scheduling problem, and the training efficiency of the proposed algorithm is improved, which provides a practical example for solving bulk cargo terminal scheduling problems using reinforcement learning.publishersversionpublishe