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1. ICRA 2019 Digest Final

   https://doi.org/10.1109/ICRA.2019.8793894  

   Mu, Xiaoqian; Xue, Yuechuan; Jia, Yan-Bin (May 2019, International Conference on Robotics and Automation)

   Effectiveness of cutting is measured by the ability to achieve material fracture with smooth knife movements. The work performed by a knife overcomes the material toughness, acts against the blade-material friction, and generates shape deformation. This paper studies how to control a 2-DOF robotic arm equipped with a force/torque sensor to cut through an object in a sequence of three moves: press, push, and slice. For each move, a separate control strategy in the Cartesian space is designed to incorporate contact and/or force constraints while following some prescribed trajectory. Experiments conducted over several types of natural foods have demonstrated smooth motions like would be commanded by a human hand. 

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2. A hybrid adjacency and time-based data structure for analysis of temporal networks

   https://doi.org/10.1007/s41109-022-00489-5  

   Hilsabeck, Tanner; Arastuie, Makan; Xu, Kevin_S (July 2022, Applied Network Science)

   Abstract Dynamic or temporal networks enable representation of time-varying edges between nodes. Conventional adjacency-based data structures used for storing networks such as adjacency lists were designed without incorporating time and can thus quickly retrieve all edges between two sets of nodes (anode-based slice) but cannot quickly retrieve all edges that occur within a given time interval (atime-based slice). We propose a hybrid data structure for storing temporal networks that stores edges in both an adjacency dictionary, enabling rapid node-based slices, and an interval tree, enabling rapid time-based slices. Our hybrid structure also enablescompound slices, where one needs to slice both over nodes and time, either by slicing first over nodes or slicing first over time. We further propose an approach for predictive compound slicing, which attempts to predict whether a node-based or time-based compound slice is more efficient. We evaluate our hybrid data structure on many real temporal network data sets and find that they achieve much faster slice times than existing data structures with only a modest increase in creation time and memory usage. 

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3. On Log Analysis and Stack Trace Use to Improve Program Slicing

   https://doi.org/10.1007/978-981-33-6385-4_25  

   Bushong, Vincent; Sanders, Russell; Curtis, Jacob; Du, Mark; Cerny, Tomas; Frajtak, Karel; Tisnovsky, Pavel; Shin, Dongwan (April 2021, Information Science and Applications. Lecture Notes in Electrical Engineering)

   null; null; null (Ed.)

   Program slicing is a common technique to help reconstruct the path of execution a program has taken. It is beneficial for assisting developers in debugging their programs, but its usefulness depends on the slice accuracy that can be achieved, which is limited by the sources of information used in building the slice. In this paper, we demonstrate that two sources of information, namely program logs, and stack traces, previously used in isolation to build program slices, can be combined to build a program slicer capable of handling more scenarios than either method individually. We also demonstrate a sample application of our proposed slicing approach by showing how our slicer can deduce integer inputs that will recreate the detected error’s execution path. 

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4. A Reinforcement Learning-Based Routing Strategy for Elastic Network Slices

   Wu, Zhouxiang; Jue, Jason P. (May 2022, ICC 2022 - IEEE International Conference on Communications)

   This paper addresses a routing selection strategy for elastic network slices that dynamically adjust required resources over time. When admitting elastic initial slice requests, sufficient spare resources on the same path should be reserved to allow existing elastic slices to increase their bandwidth dynamically. We demonstrate a deep Reinforcement Learning (RL) model to intelligently make routing choice decisions for elastic slice requests and inelastic slice requests. This model achieves higher revenue and higher acceptance rates compared to traditional heuristic methods. Due to the lightness of this model, it can be deployed in an embedded system. We can also use a relatively small amount of data to train the model and achieve stable performance. Also, we introduce a Recurrent Neural Network to auto-encode the variable-size environment and train the encoder together with the RL model. 

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5. Impact of Proppant Pumping Schedule on Well Production for Slickwater Fracturing

   https://doi.org/10.2118/204235-PA  

   Mao, Shaowen; Siddhamshetty, Prashanth; Zhang, Zhuo; Yu, Wei; Chun, Troy; Kwon, Joseph Sang-Il; Wu, Kan (December 2020, SPE Journal)

   null (Ed.)

   Summary Slickwater fracturing has become one of the most leveraging completion technologies in unlocking hydrocarbon in unconventional reservoirs. In slickwater treatments, proppant transport becomes a big concern because of the inefficiency of low-viscosity fluids to suspend the particles. Many studies have been devoted to proppant transport experimentally and numerically. However, only a few focused on the proppant pumping schedules in slickwater fracturing. The impact of proppant schedules on well production remains unclear. The goal of our work is to simulate the proppant transport under real pumping schedules (multisize proppants and varying concentration) at the field scale and quantitatively evaluate the effects of proppant schedules on well production for slickwater fracturing. The workflow consists of three steps. First, a validated 3D multiphase particle-in-cell (MP-PIC) model has been used to simulate the proppant transport at real pumping schedules in a field-scale fracture (180-m length, 30-m height). Second, we applied a propped fracture conductivity model to calculate the distribution of propped fracture width, permeability, and fracture conductivity. In the last step, we incorporated the fracture geometry, propped fracture conductivity, and the estimated unpropped fracture conductivity into a reservoir simulation model to predict gas production. Based on the field designs of pumping schedules in slickwater treatments, we have generated four proppant schedules, in which 100-mesh and 40/70-mesh proppants were loaded successively with stair-stepped and incremental stages. The first three were used to study the effects of the mass percentages of the multisize proppants. From Schedules 1 through 3, the mass percentage of 100-mesh proppants is 30, 50, and 70%, respectively. Schedule 4 has the same proppant percentage as Schedule 2 but has a flush stage after slurry injection. The comparison between Schedules 2 and 4 enables us to evaluate the effect of the flush stage on well production. The results indicate that the proppant schedule has a significant influence on treatment performance. The schedule with a higher percentage of 100-mesh proppants has a longer proppant transport distance, a larger propped fracture area, but a lower propped fracture conductivity. Then, the reservoir simulation results show that both the small and large percentages of 100-mesh proppants cannot maximize well production because of the corresponding small propped area and low propped fracture conductivity. Schedule 2, with a median percentage (50%) of 100-mesh proppants, has the highest 1,000-day cumulative gas production. For Schedule 4, the flush stage significantly benefits the gas production by 8.2% because of a longer and more uniform proppant bed along the fracture. In this paper, for the first time, we provide both the qualitative explanation and quantitative evaluation for the impact of proppant pumping schedules on the performance of slickwater treatments at the field scale by using an integrated numerical simulation workflow, providing crucial insights for the design of proppant schedules in the field slickwater treatments. 

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