Analyzing Input and Output Representations for Speech-Driven Gesture Generation

This paper presents a novel framework for automatic speech-driven gesture generation, applicable to human-agent interaction including both virtual agents and robots. Specifically, we extend recent deep-learning-based, data-driven methods for speech-driven gesture generation by incorporating representation learning. Our model takes speech as input and produces gestures as output, in the form of a sequence of 3D coordinates. Our approach consists of two steps. First, we learn a lower-dimensional representation of human motion using a denoising autoencoder neural network, consisting of a motion encoder MotionE and a motion decoder MotionD. The learned representation preserves the most important aspects of the human pose variation while removing less relevant variation. Second, we train a novel encoder network SpeechE to map from speech to a corresponding motion representation with reduced dimensionality. At test time, the speech encoder and the motion decoder networks are combined: SpeechE predicts motion representations based on a given speech signal and MotionD then decodes these representations to produce motion sequences. We evaluate different representation sizes in order to find the most effective dimensionality for the representation. We also evaluate the effects of using different speech features as input to the model. We find that mel-frequency cepstral coefficients (MFCCs), alone or combined with prosodic features, perform the best. The results of a subsequent user study confirm the benefits of the representation learning.Comment: Accepted at IVA '19. Shorter version published at AAMAS '19. The code is available at https://github.com/GestureGeneration/Speech_driven_gesture_generation_with_autoencode

Chemical Enhanced Oil Recovery and the Dilemma of More and Cleaner Energy

We develop a method based on concept of exergy-return on exergy-investment (ERoEI) to determine the energy efficiency and CO2 footprint of polymer and surfactant enhanced oil recovery (EOR). This integrated approach considers main surface and subsurface elements of the chemical EOR methods. The main energy investment in oil recovery by water injection is mainly related to circulation of water with respect to exergy of the oil produced. At large water cuts of &gt;90%, more than 70% of the total invested energy is spent on pumping the fluids. Consequently, production of barrels of oil is associated with large amounts of CO2 emission for mature oil fields with large water cuts. Our analysis shows that injection of polymer increases the energy efficiency of the oil recovery system. Because of additional oil (exergy gain) and less water circulation (exergy investment), the project-time averaged energy invested (and consequently CO2 emitted) to produce one barrel of oil from polymer flooding is less than that of the water flooding at large water cuts. We conclude that polymer injection into reservoirs with high water cut can be a solution for two major challenges of the transition period: (1) meet the global energy demand via an increase in oil recovery and (2) reduce the CO2 footprint of oil production (more and cleaner oil). For surfactant-polymer EOR, the extent of improvement in energy efficiency depends on the incremental gain and the simplicity of the formulations.</p

Chemical enhanced oil recovery and the dilemma of more and cleaner energy

A method based on the concept of exergy-return on exergy-investment is developed to determine the energy efficiency and CO2 intensity of polymer and surfactant enhanced oil recovery techniques. Exergy is the useful work obtained from a system at a given thermodynamics state. The main exergy investment in oil recovery by water injection is related to the circulation of water required to produce oil. At water cuts (water fraction in the total liquid produced) greater than 90%, more than 70% of the total invested energy is spent on injection and lift pumps, resulting in large CO2 intensity for the produced oil. It is shown that injection of polymer with or without surfactant can considerably reduce CO2 intensity of the mature waterflood projects by decreasing the volume of produced water and the exergy investment associated with its circulation. In the field examples considered in this paper, a barrel of oil produced by injection of polymer has 2–5 times less CO2 intensity compared to the baseline waterflood oil. Due to large manufacturing exergy of the synthetic polymers and surfactants, in some cases, the unit exergy investment for production of oil could be larger than that of the waterflooding. It is asserted that polymer injection into reservoirs with large water cut can be a solution for two major challenges of the energy transition period: (1) meet the global energy demand via an increase in oil recovery and (2) reduce the CO2 intensity of oil production (more and cleaner energy).</p

Chemical enhanced oil recovery and the dilemma of more and cleaner energy

Abstract A method based on the concept of exergy-return on exergy-investment is developed to determine the energy efficiency and CO2 intensity of polymer and surfactant enhanced oil recovery techniques. Exergy is the useful work obtained from a system at a given thermodynamics state. The main exergy investment in oil recovery by water injection is related to the circulation of water required to produce oil. At water cuts (water fraction in the total liquid produced) greater than 90%, more than 70% of the total invested energy is spent on injection and lift pumps, resulting in large CO2 intensity for the produced oil. It is shown that injection of polymer with or without surfactant can considerably reduce CO2 intensity of the mature waterflood projects by decreasing the volume of produced water and the exergy investment associated with its circulation. In the field examples considered in this paper, a barrel of oil produced by injection of polymer has 2–5 times less CO2 intensity compared to the baseline waterflood oil. Due to large manufacturing exergy of the synthetic polymers and surfactants, in some cases, the unit exergy investment for production of oil could be larger than that of the waterflooding. It is asserted that polymer injection into reservoirs with large water cut can be a solution for two major challenges of the energy transition period: (1) meet the global energy demand via an increase in oil recovery and (2) reduce the CO2 intensity of oil production (more and cleaner energy)

Chemical enhanced oil recovery and the dilemma of more and cleaner energy

A method based on the concept of exergy-return on exergy-investment is developed to determine the energy efficiency and CO2 intensity of polymer and surfactant enhanced oil recovery techniques. Exergy is the useful work obtained from a system at a given thermodynamics state. The main exergy investment in oil recovery by water injection is related to the circulation of water required to produce oil. At water cuts (water fraction in the total liquid produced) greater than 90%, more than 70% of the total invested energy is spent on injection and lift pumps, resulting in large CO2 intensity for the produced oil. It is shown that injection of polymer with or without surfactant can considerably reduce CO2 intensity of the mature waterflood projects by decreasing the volume of produced water and the exergy investment associated with its circulation. In the field examples considered in this paper, a barrel of oil produced by injection of polymer has 2–5 times less CO2 intensity compared to the baseline waterflood oil. Due to large manufacturing exergy of the synthetic polymers and surfactants, in some cases, the unit exergy investment for production of oil could be larger than that of the waterflooding. It is asserted that polymer injection into reservoirs with large water cut can be a solution for two major challenges of the energy transition period: (1) meet the global energy demand via an increase in oil recovery and (2) reduce the CO2 intensity of oil production (more and cleaner energy).Petroleum Engineerin

Systematic review and Meta-analysis comparing low-flow duration of extracorporeal and conventional cardiopulmonary resuscitation

OBJECTIVES: After cardiac arrest, a key factor determining survival outcomes is low-flow duration. Our aims were to determine the relation of survival and low-flow duration of extracorporeal cardiopulmonary resuscitation (ECPR) and conventional cardiopulmonary resuscitation (CCPR) and if these 2 therapies have different short-term survival curves in relation to low-flow duration. METHODS: We searched Embase, Medline, Web of Science and Google Scholar from inception up to April 2021. A linear mixed-effect model was used to describe the course of survival over time, based on study-specific and time-specific aggregated survival data. RESULTS: We included 42 observational studies reporting on 1689 ECPR and 375 751 CCPR procedures. Of the included studies, 25 included adults, 13 included children and 4 included both. In adults, survival curves decline rapidly over time (ECPR 37.2%, 29.8%, 23.8% and 19.1% versus CCPR-shockable 36.8%, 7.2%, 1.4% and 0.3% for 15, 30, 45 and 60 min low-flow, respectively). ECPR was associated with a statistically significant slower decline in survival than CCPR with initial shockable rhythms (CCPR-shockable). In children, survival curves decline rapidly over time (ECPR 43.6%, 41.7%, 39.8% and 38.0% versus CCPR-shockable 48.6%, 20.5%, 8.6% and 3.6% for 15, 30, 45 and 60 min low-flow, respectively). ECPR was associated with a statistically significant slower decline in survival than CCPR-shockable. CONCLUSIONS: The short-term survival of ECPR and CCPR-shockable patients both decline rapidly over time, in adults as well as in children. This decline of short-term survival in relation to low-flow duration in ECPR was slower than in conventional cardiopulmonary resuscitation. TRIAL REGISTRATION: Prospero: CRD42020212480, 2 October 2020