Synthetic Output Research Articles

A new synthetic correlation electron cyclotron emission diagnostic for validating nonlinear gyrokinetic simulations of electron temperature turbulence.

To validate nonlinear gyrokinetic simulations of electron temperature turbulence, the experimental correlation electron cyclotron emission (CECE) measurements are to be compared using a synthetic CECE diagnostic, which generates modeled CECE measurement quantities by implementing realistic measurement parameters (e.g., spatial and wavenumber resolutions, radial location, etc.) to nonlinear gyrokinetic simulations. In this work, we calculate the radial and vertical spatial and wavenumber transfer functions, which are defined by the electron cyclotron emission emissivity radial profile and vertical probing antenna pattern, respectively. These transfer functions are applied to nonlinear gyrokinetic simulations of electron temperature turbulence using the continuum gyrokinetic code. A simultaneous comparison of the experimental electron temperature turbulence power spectrum and root-mean-square (RMS) level, as well as the radial correlation length with the new synthetic CECE diagnostic at a core location ρ ∼ 0.75 in an L-mode DIII-D tokamak plasma, is presented. The preliminary result shows that the synthetic CECE output underestimates the RMS level by ∼42% and overestimates the radial correlation length by ∼40%.

Smart proxy models art and future directions in the oil and gas industry: A review

This paper presents a review of the state of the art of proxy models application in the oil and gas industry from upstream, midstream, and downstream. The focus on proxy models is because they are the pillar of the digital twins. They compensate for the slow response of the numerical simulation models. Proxy models can get updated very quickly with new data compared to numerical models. The proxy models apply to cross-disciplines such as fluid mechanics, thermodynamics, and electronics. Proxy models are tools for the swift response to the process events when the cost of decision time is high. The history of the proxy model is discussed first followed by a review of the literature. First, a literature review and the history of the proxy model are covered. The objectives and uses of proxy models are then thoroughly described while the evolution and development process of proxy models are detailed. A few proxy model examples are provided to emphasize the significance of the proxy model and technical advancement. Regression, artificial neural networks, fuzzy logic, and support vector machines are the major approaches for delivering proxy models. Each method's strengths and drawbacks are addressed. These case studies show the need for data cleansing, data transformation to the appropriate domain, and a precise model verification and validation strategy for creating a trustworthy proxy model. Industry-wide, the gaps, and difficulties are addressed, including data accessibility, workflow, model health check, operability, and model uncertainty. A review of proxy models revealed that both technical solution acceptance and workflow robustness have greatly increased at the maturity level in proxy model applications in the oil and gas industry. However, the typical workflow is not being used, and many case studies show that crucial steps like verification and validation are not being taken. The key issue identified in the review was that proxy models are derived from analytical and numerical models, which is in contrast to proxy models, which by their nature rely on the data at hand to inform predictions and optimizations. The astounding potential of proxy models in prediction and optimization is still untapped, and more research is needed to improve model quality, incorporate data into the core of the proxy models, increase their robustness, and develop robust performance metrics to address and evaluate the performance, dependability, and timeliness of the proxy models. Currently, proxy models are secondary tools for optimization of the processes and decision making, while in the future outlook is to be the main tool not only in daily surveillance and monitoring but for the optimization process. In this paper, a best practice workflow for the proxy model is proposed for the oil and gas industry. The evolution of proxy models and their level of complexity is reviewed. Most published work on proxy models is built based on the synthetic data and simulation model output. In the applied workflow, most model validation and verification are missed or ignored. Except for a few published works on the real-time usage, the models are barely connected to real-time and they are used for static decision-making process other than real-time usage.

Commentary on A Patient with Third-Space Fluid Loss.

This is an interesting clinical case highlighting the value of body fluid testing to assist in diagnosing a complex patient presenting with multiple effusions. The clinical presentation of unilateral pleural effusion is usually more suggestive of exudative causes, although bilateral effusion with right-side predominance is observed in transudative effusions. Bilateral leg swelling is observed more often with transudates, such as those caused by heart or liver failure (1). Severe hypoalbuminemia is frequently caused by renal failure associated nephrotic syndrome and/or end stage liver failure associated with reduced synthetic output. Renal and liver impairment were inconsistent with clinical findings until the globin predominance prompted protein electrophoresis, which returned a diagnosis of myeloma as an explanation for severe hypoalbuminemia. It is possible that severe hypoalbuminemia may have contributed to the transudative pleural effusion as well as ascites. The successful chemotherapeutic treatment for myeloma resolved the ascites and presumably the pleural effusion as serum albumin normalized. Lack of measurement of serum or plasma NT-ProBNP makes heart failure a questionable contributor to the transudative effusion, and follow-up may be warranted posttreatment to definitively rule out cardiac disease.

Robust Adaptive Beamforming Algorithm for Sparse Subarray Antenna Array Based on Hierarchical Weighting

Sparse antenna arrays based on subarrays have more and more broad application prospects for the limitation of array space, real-time algorithm and hardware costs. Aiming at the beamforming technology of sparse antenna arrays based on subarrays, this paper proposes a robust adaptive beamforming algorithm based on hierarchical weighting. The algorithm performs conventional beamforming to calculate the weights of each element in the subarray, then the synthetic signals output by each subarray are used as sparse array metadata. The Interference-plus-Noise Covariance Matrix (INCM) is reconstructed by integration in two-dimensional space, and a convex optimization model of a multi-constraint array containing the signal pointing error was established to estimate the real guide vector. Finally, using the reconstructed INCM and the estimation of the guide vector, we obtain a weighted vector between the subarrays and output signal for the whole array. The simulation results show that the proposed algorithm has better Signal-to-Interference-and-Noise Ratio (SINR) and robustness compared with other algorithms for sparse subarray antenna array beamforming.

Oxygen Tolerance in Surface-Initiated Reversible Deactivation Radical Polymerizations: Are Polymer Brushes Turning into Technology?

Over the past three decades, the development of reversible deactivation radical polymerizations (RDRP), and advancements toward more user-friendly and accessible experimental setups have opened the door for nonexperts to design complex macromolecules with well-defined properties. External mediation, improved tolerance to oxygen, and increased reaction volumes for higher synthetic output are some of the many noteworthy technical improvements. The development of RDRPs in solution was paralleled by their application on solid substrates to synthesize surface-grafted "polymer brushes" via surface-initiated RDRP (SI-RDRP). This Viewpoint paper provides a current perspective on recent developments in SI-RDRP methods that are tolerant to oxygen, especially highlighting those that could potentially enable scaling up of the synthesis of brushes for the functionalization of technologically relevant materials.

From Missing Data Imputation to Data Generation

Real datasets often lack values, compromising the quality of data analyses. Adequate data may be synthetically imputed to replace missing values – a technique known as missing data imputation – avoiding deletion of incomplete observations. Several data imputation methods have been proposed and generative methods based on Artificial Neural Networks (ANN) are successful alternatives to discriminative methods. In this extended version of our work presented at the International Conference on Computational Science Neves et al. (2021), we propose three novel data imputation methods based on Generative Adversarial Networks (GAN): SGAIN, WSGAIN-CP, and WSGAIN-GP.We further studied how data imputation methods can be used to generate fully synthetic datasets. Among other benefits, the generation of synthetic data can help to mitigate legal, ethical, and data privacy issues, as well as to augment original data. In this context, we introduce tabulator, which is a novel meta-method for synthetic data generation that uses the data imputation methods as back-end engines for tabular data generation.We evaluated our data imputation methods using datasets with different amputation rates following the Missing Completely At Random (MCAR) setting. The results show that our methods are en-par or outperform state-of-the-art imputation methods in terms of response time and the quality of imputed data. We further evaluated and compared our data generation methods, which were derived from tabulator, with a state-of-the-art approach, the Conditional Tabular GAN (CTGAN). The evaluation results show that our tabulator methods outperform CTGAN in many cases, for example regarding the accuracy of machine learning tasks (e.g., prediction or classification) performed on the synthetic output data.

Anti-Windup Robust Backstepping Control for an Underactuated Reusable Launch Vehicle

The attitude control of an underactuated reusable launch vehicle (RLV) in the reentry phase involving nonminimum phase problem and control input constraints is investigated in this article. To address the nonminimum phase problem, an approach combining output redefinition and robust backstepping is proposed, where a synthetic output is constructed using the combination of the original output and the internal states to obtain stable zero dynamics, and then robust backstepping is performed on the new output. Besides, the ideal internal dynamics are obtained by using optimal bounded inversion, which are incorporated into the controller as the reference trajectories for the internal states to improve the output tracking accuracy. To cope with the control input constraints, a simple and useful anti-windup strategy is proposed by using feedback error clipping, which is shown to be very effective in mitigating control input saturation. Numerical simulations are given to validate the effectiveness of the proposed method.

Physics-Informed Hyperspectral Remote Sensing Image Synthesis With Deep Conditional Generative Adversarial Networks

High-resolution hyperspectral remote sensing images are of great significance to agricultural, urban, and military applications. However, collecting and labeling hyperspectral images are time-consuming, expensive, and usually heavily rely on domain knowledge. In this article, we propose a new method for generating high-resolution hyperspectral images and subpixel ground-truth annotations from RGB images. Given a single high-resolution RGB image as its conditional input, unlike previous methods that directly predict spectral reflectance and ignores the physics behind it, we consider both imaging mechanism and spectral mixing, introduce a deep generative network that first recovers the spectral abundance for each pixel, and then generate the final spectral data cube with the standard USGS spectral library. In this way, our method not only synthesizes high-quality spectral data existing in the real world but also generates subpixel-level spectral abundance with well-defined spectral reflectance characteristics. We also introduce a spatial discriminative network and a spectral discriminative network to improve the fidelity of the synthetic output from both spatial and spectral perspectives. The whole framework can be trained end-to-end in an adversarial training paradigm. We refer to our method as “Physics-informed Deep Adversarial Spectral Synthesis (PDASS).” On the IEEE <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grss_dfc_2018</i> dataset, our method achieves an MPSNR of 47.56 on spectral reconstruction accuracy and outperforms other state-of-the-art methods. As latent variables, the generated spectral abundance and the atmospheric absorption coefficients of sunlight also suggest the effectiveness of our method.

Do Prosody and Embodiment Influence the Perceived Naturalness of Conversational Agents’ Speech?

For conversational agents’ speech, either all possible sentences have to be prerecorded by voice actors or the required utterances can be synthesized. While synthesizing speech is more flexible and economic in production, it also potentially reduces the perceived naturalness of the agents among others due to mistakes at various linguistic levels. In our article, we are interested in the impact of adequate and inadequate prosody, here particularly in terms of accent placement, on the perceived naturalness and aliveness of the agents. We compare (1) inadequate prosody, as generated by off-the-shelf text-to-speech (TTS) engines with synthetic output; (2) the same inadequate prosody imitated by trained human speakers; and (3) adequate prosody produced by those speakers. The speech was presented either as audio-only or by embodied, anthropomorphic agents, to investigate the potential masking effect by a simultaneous visual representation of those virtual agents. To this end, we conducted an online study with 40 participants listening to four different dialogues each presented in the three Speech levels and the two Embodiment levels. Results confirmed that adequate prosody in human speech is perceived as more natural (and the agents are perceived as more alive) than inadequate prosody in both human (2) and synthetic speech (1). Thus, it is not sufficient to just use a human voice for an agents’ speech to be perceived as natural—it is decisive whether the prosodic realisation is adequate or not. Furthermore, and surprisingly, we found no masking effect by speaker embodiment, since neither a human voice with inadequate prosody nor a synthetic voice was judged as more natural, when a virtual agent was visible compared to the audio-only condition. On the contrary, the human voice was even judged as less “alive” when accompanied by a virtual agent. In sum, our results emphasize, on the one hand, the importance of adequate prosody for perceived naturalness, especially in terms of accents being placed on important words in the phrase, while showing, on the other hand, that the embodiment of virtual agents plays a minor role in the naturalness ratings of voices.

Dynamic modeling and vibration analysis of a cracked 3K-II planetary gear set for fault detection

Abstract. The 3K planetary gear system is a basic planetary transmission structure with many advantages over the 2K-H planetary gear system. However, the vibration characteristics will be more complicated due to the increase of central gears meshing with each planet gear simultaneously. In this paper, a lumped-parameter model for a 3K-II planetary gear set was developed to simulate the dynamic response. The time-varying stiffness of each meshing pair for different gear tooth root crack faults is calculated via the finite element method. By considering the effect of time-varying transmission paths, the transverse synthetic vibrations are obtained. Subsequently, the feasibilities of transverse synthetic vibration signals and output torsional vibration signals as reference for fault diagnosis are analyzed by studying the time-domain and frequency-domain characteristics of these two vibration signals. The results indicate that both the transverse synthetic vibration signals and output torsional vibration signals can be used for fault identification and localization of the 3K-II planetary gear train, and yet they both have their limitations. Some results of this paper are available as references for the fault diagnosis of 3K planetary gear trains.

Channel–spatial attention-based pan-sharpening of very high-resolution satellite images

The pan-sharpening process aims to generate a new synthetic output image preserving the spatial details of panchromatic and spectral details of the multi-spectral image inputs. Recently, deep learning-based methods show substantial success in the remote sensing field mostly with the application of traditional Convolutional Neural Networks (CNNs). Most of the traditional CNN-based approaches treat all the channels equitably and cannot learn the correlation. Attention mechanism which can learn the correlations among the channels has been proven to be effective in super-resolution and object detection tasks. In this research, we introduced a novel deep learning framework, channel–spatial attention-based method for pan-sharpening (CSAPAN), by designing a Densely residual attention module (RAM). Besides, we train our model in the high-frequency domain and up-sample the low-resolution multispectral images by using the pixel shuffle method before stacking with the panchromatic images for further feature extraction. We evaluated our proposed CSAPAN along with traditional methods and CNN-based methods in reduced and full resolution and obtained satisfactory quantitative and qualitative results on Pleiades, Worldview-2, and QuickBird-2 satellite image datasets.

Creating synthetic minority class samples based on autoencoder extreme learning machine

This paper reports a new method (simplified as AE-ELM-SynMin) to create the Synthetic Minority class samples for imbalanced classification based on AutoEncoder Extreme Learning Machine (AE-ELM). AE-ELM-SynMin first trains an AE-ELM which is a special ELM with the same input and output, i.e., the original minority class samples. Second, the crossover, mutation and filtration operations are conducted on the hidden-layer output of AE-ELM and then the synthetic hidden-layer output is obtained. Third, the synthetic minority class samples are created by decoding the synthetic hidden-layer output with output-layer weights of AE-ELM. AE-ELM-SynMin guarantees that the synthetic minority class has the higher information amount than original minority class and meanwhile keeps the consistent probability distribution with the original minority class. The experimental results demonstrate the better imbalanced classification performances of AE-ELM-SynMin in comparison with the regular synthetic minority over-sampling technique (Regular-SMOTE) and its variants, e.g., Borderline-SMOTE, Random-SMOTE, and SMOTE-IPF.

Controlling for the effects of climate on total factor productivity: A case study of Australian farms

Estimates of agricultural Total Factor Productivity (TFP) can be highly sensitive to both short-run climate variability and long-term climate change. This is particularly true in Australia where drought impacts are responsible for most of the annual volatility in official farm TFP statistics. While climate variability can obscure short-term productivity trends, researchers have typically assumed that long run TFP trends are largely unaffected. However, in the presence of global climate change this assumption becomes problematic. For example, in Australia, shifts to higher temperatures and lower winter season rainfall over the last 20–30 years have had a significant negative impact on agricultural productivity. This study presents a framework to account for the effects of climate variability on TFP estimates. In contrast with previous work, this approach applies a reduced form machine learning based model of farm production to generate synthetic (climate-adjusted) farm-level input and output data sets. It therefore has advantages in terms of flexibility—since the synthetic datasets can be combined with any existing TFP estimation framework. In this study, the approach is applied to estimate climate-adjusted TFP indices (TFP under an assumption of constant long-run average climate conditions) for a range of Australian agricultural sectors.

A new synthetic output tracking scheme for non-minimum phase affine nonlinear systems

This paper presents a nonlinear output tracking controller for non-minimum phase affine nonlinear systems. A new analytical method is given to find a proper synthetic output which makes the system minimum phase under a condition of static equivalence. This method is fully derived based on the closed form solution of the Hamilton-Jacobi-Bellman (HJB) equation associated with the cheap control problem. The new methodology employs an algebraic equation for any system with scalar internal dynamics and a differential equation for those with an internal dynamics of higher order. Furthermore, a numerical simulation is carried out to show how the results of the experiment are consistent with those of the theoretical analysis.

Transitioning from a low- to high-tech Augmentative and Alternative Communication (AAC) system: effects on augmented and vocal requesting

A considerable number of studies have demonstrated that augmentative and alternative communication (AAC) is effective in increasing speech production in some children with autism spectrum disorder (ASD). Thus, this study aimed to (a) investigate the effects of a Picture Exchange Communication System (PECS) Phase IV protocol on the acquisition of spontaneous augmented requests, (b) evaluate the impact of progressive time delay and synthetic speech output on the development of vocal requests, and (c) determine the participants’ preferences for each modality after reaching mastery. A multiple-baseline design across four children with ASD was used to measure the acquisition of augmented and vocal requests during the transition from low-tech to high-tech AAC systems. During a natural condition (i.e., playtime), a modified PECS Phase IV protocol was applied to teach the participants to request by producing multisymbol messages (e.g., I WANT + names of a preferred item) using an iPad as well as vocalizations. After mastery, the participants’ preference for using the modified PECS Phase IV app or the communication book was assessed by comparing the response allocations. The preliminary results suggest that the modified PECS protocol can be used to transition from a low- to high-tech communication modality.

Improved lithology prediction in channelized reservoirs by integrating stratigraphic forward modelling: Towards improved model calibration in a case study of the Holocene Rhine-Meuse fluvio-deltaic system

Stratigraphic forward modelling (SFM) provides the means to produce geologically coherent and realistic models. In this paper, we demonstrate the possibility of matching lithological variability simulated with a basin-scale advection-diffusion SFM to a data-rich real-world setting, i.e. the Holocene Rhine-Meuse fluvio-deltaic system in the Netherlands. SFM model calibration to real-world data in general has proven non-trivial. This study focuses on a novel inversion process constrained by the top surface and the sand proportion observed at specific pseudo-wells in the study area. Goodness-of-fit expressed by a new fitness function gives the error calculated as the average of two calibration constraints. Computational efficiency was increased significantly by implementing a new optimization process in two hierarchical steps: a) optimization in terms of sediment load and discharge, which are the most influential parameters having the largest uncertainty and b) optimization with respect to the remaining uncertain parameters, these being sediment transport parameters. The calibration process described allows for the most optimal combination of achieving acceptable levels of goodness-of-fit, feasible runtimes and multiple (non-unique) solutions to obtain synthetic stratigraphic output best matching real-world datasets.By removing model realizations which are geologically unrealistic, calibrated SFM models provide a multiscale stratigraphic framework for reconstructing static models of reservoirs which are consistent with the palaeogeographic layout, basin-fill history and external drivers (e.g. sea level, sediment supply). The static reservoir models that are matched with highest certainty therefore contain the highest geological realism and may be used to improve deep subsurface reservoir or aquifer property prediction.The new methodology was applied to the well-established Holocene Rhine-Meuse dataset, which allows a rigorous testing of the optimization; the calibrated SFM allows investigation of controls of the Holocene development on the sedimentary system.

Wind turbine power output very short-term forecast: A comparative study of data clustering techniques in a PSO-ANFIS model

The emergence of new sites for wind energy exploration in South Africa requires an accurate prediction of the potential power output of a typical utility-scale wind turbine in such areas. However, careful selection of data clustering technique is very essential as it has a significant impact on the accuracy of the prediction. Adaptive neurofuzzy inference system (ANFIS), both in its standalone and hybrid form has been applied in offline and online forecast in wind energy studies, however, the effect of clustering techniques has not been reported despite its significance. Therefore, this study investigates the effect of the choice of clustering algorithm on the performance of a standalone ANFIS and ANFIS optimized with particle swarm optimization (PSO) technique using a synthetic wind turbine power output data of a potential site in the Eastern Cape, South Africa. In this study a wind resource map for the Eastern Cape province was developed. Also, autoregressive ANFIS models and their hybrids with PSO were developed. Each model was evaluated based on three clustering techniques (grid partitioning (GP), subtractive clustering (SC), and fuzzy-c-means (FCM)). The gross wind power of the model wind turbine was estimated from the wind speed data collected from the potential site at 10 min data resolution using Windographer software. The standalone and hybrid models were trained and tested with 70% and 30% of the dataset respectively. The performance of each clustering technique was compared for both standalone and PSO-ANFIS models using known statistical metrics. From our findings, ANFIS standalone model clustered with SC performed best among the standalone models with a root mean square error (RMSE) of 0.132, mean absolute percentage error (MAPE) of 30.94, a mean absolute deviation (MAD) of 0.077, relative mean bias error (rMBE) of 0.190 and variance accounted for (VAF) of 94.307. Also, PSO-ANFIS model clustered with SC technique performed the best among the three hybrid models with RMSE of 0.127, MAPE of 28.11, MAD of 0.078, rMBE of 0.190 and VAF of 94.311. The ANFIS-SC model recorded the lowest computational time of 30.23secs among the standalone models. However, the PSO-ANFIS-SC model recorded a computational time of 47.21secs. Based on our findings, a hybrid ANFIS model gives better forecast accuracy compared to the standalone model, though with a trade-off in the computational time. Since, the choice of clustering technique was observed to play a vital role in the forecast accuracy of standalone and hybrid models, this study recommends SC technique for ANFIS modeling at both standalone and hybrid models.

State-of-the-Art of High-Power Gyro-Devices and Free Electron Masers

This paper presents a review of the experimental achievements related to the development of high-power gyrotron oscillators for long-pulse or CW operation and pulsed gyrotrons for many applications. In addition, this work gives a short overview on the present development status of frequency step-tunable and multi-frequency gyrotrons, coaxial-cavity multi-megawatt gyrotrons, gyrotrons for technological and spectroscopy applications, relativistic gyrotrons, large orbit gyrotrons (LOGs), quasi-optical gyrotrons, fast- and slow-wave cyclotron autoresonance masers (CARMs), gyroklystrons, gyro-TWT amplifiers, gyrotwystron amplifiers, gyro-BWOs, gyro-harmonic converters, gyro-peniotrons, magnicons, free electron masers (FEMs), and dielectric vacuum windows for such high-power mm-wave sources. Gyrotron oscillators (gyromonotrons) are mainly used as high-power millimeter wave sources for electron cyclotron resonance heating (ECRH), electron cyclotron current drive (ECCD), stability control, and diagnostics of magnetically confined plasmas for clean generation of energy by controlled thermonuclear fusion. The maximum pulse length of commercially available 140 GHz, megawatt-class gyrotrons employing synthetic diamond output windows is 30 min (CPI and European KIT-SPC-THALES collaboration). The world record parameters of the European tube are as follows: 0.92 MW output power at 30-min pulse duration, 97.5% Gaussian mode purity, and 44% efficiency, employing a single-stage depressed collector (SDC) for energy recovery. A maximum output power of 1.5 MW in 4.0-s pulses at 45% efficiency was generated with the QST-TOSHIBA (now CANON) 110-GHz gyrotron. The Japan 170-GHz ITER gyrotron achieved 1 MW, 800 s at 55% efficiency and holds the energy world record of 2.88 GJ (0.8 MW, 60 min) and the efficiency record of 57% for tubes with an output power of more than 0.5 MW. The Russian 170-GHz ITER gyrotron obtained 0.99 (1.2) MW with a pulse duration of 1000 (100) s and 53% efficiency. The prototype tube of the European 2-MW, 170-GHz coaxial-cavity gyrotron achieved in short pulses the record power of 2.2 MW at 48% efficiency and 96% Gaussian mode purity. Gyrotrons with pulsed magnet for various short-pulse applications deliver Pout = 210 kW with τ = 20 μs at frequencies up to 670 GHz (η ≅ 20%), Pout = 5.3 kW at 1 THz (η = 6.1%), and Pout = 0.5 kW at 1.3 THz (η = 0.6%). Gyrotron oscillators have also been successfully used in materials processing. Such technological applications require tubes with the following parameters: f > 24 GHz, Pout = 4–50 kW, CW, η > 30%. The CW powers produced by gyroklystrons and FEMs are 10 kW (94 GHz) and 36 W (15 GHz), respectively. The IR FEL at the Thomas Jefferson National Accelerator Facility in the USA obtained a record average power of 14.2 kW at a wavelength of 1.6 μm. The THz FEL (NOVEL) at the Budker Institute of Nuclear Physics in Russia achieved a maximum average power of 0.5 kW at wavelengths 50–240 μm (6.00–1.25 THz).

Rewiring bacterial two-component systems by modular DNA-binding domain swapping

Two-component systems (TCSs) are the largest family of multi-step signal transduction pathways and valuable sensors for synthetic biology. However, most TCSs remain uncharacterized or difficult to harness for applications. Major challenges are that many TCS output promoters are unknown, subject to cross-regulation, or silent in heterologous hosts. Here, we demonstrate that the two largest families of response regulator DNA-binding domains can be interchanged with remarkable flexibility, enabling the corresponding TCSs to be rewired to synthetic output promoters. We exploit this plasticity to eliminate cross-regulation, un-silence a gram-negative TCS in a gram-positive host, and engineer a system with over 1,300-fold activation. Finally, we apply DNA-binding domain swapping to screen uncharacterized Shewanella oneidensis TCSs in Escherichia coli, leading to the discovery of a previously uncharacterized pH sensor. This work should accelerate fundamental TCS studies and enable the engineering of a large family of genetically encoded sensors with diverse applications.

VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control

In this letter, we deal with the reality gap from a novel perspective, targeting transferring deep reinforcement learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, we seek to tackle the problem by translating the real-world image streams back to the synthetic domain during the deployment phase, to make the robot feel at home . We propose this as a lightweight, flexible, and efficient solution for visual control, as first, no extra transfer steps are required during the expensive training of DRL agents in simulation; second, the trained DRL agents will not be constrained to being deployable in only one specific real-world environment; and third, the policy training and the transfer operations are decoupled, and can be conducted in parallel. Besides this, we propose a simple yet effective shift loss that is agnostic to the downstream task, to constrain the consistency between subsequent frames which is important for consistent policy outputs. We validate the shift loss for artistic style transfer for videos and domain adaptation , and validate our visual control approach in indoor and outdoor robotics experiments.