Traditional conflict-based cognitive assessment tools are highly behaviorally restrictive, which prevents them from capturing the dynamic nature of human cognition, such as the tendency to make error-correcting responses. The cognitive game Tunnel Runner measures interference control, response inhibition, and response-rule switching in a less restrictive manner than traditional cognitive assessment tools by giving players movement control after an initial response and encouraging error-correcting responses. Nevertheless, error-correcting responses remain unused due to a limited understanding of what they measure and how to use them. To facilitate the use of error-correcting responses to measure and understand human cognition, we developed theoretically-grounded measures of error-correcting responses in Tunnel Runner and assessed whether they reflected the same cognitive functions measured via initial responses. Furthermore, we evaluated the measurement potential of error-correcting responses. We found that initial and error-correcting responses similarly reflected players\' response inhibition and interference control, but not their response-rule switching. Furthermore, combining the two response types increased the reliability of interference control and response inhibition measurements. Lastly, error-correcting responses showed the potential to measure response inhibition on their own. Our results pave the way toward understanding and using post-decision change of mind data for cognitive measurement and other research and application contexts.

Long-read sequencing data, particularly those derived from the Oxford Nanopore sequencing platform, tend to exhibit high error rates. Here, we present NextDenovo, an efficient error correction and assembly tool for noisy long reads, which achieves a high level of accuracy in genome assembly. We apply NextDenovo to assemble 35 diverse human genomes from around the world using Nanopore long-read data. These genomes allow us to identify the landscape of segmental duplication and gene copy number variation in modern human populations. The use of NextDenovo should pave the way for population-scale long-read assembly using Nanopore long-read data.

We construct new stabilizer quantum error-correcting codes from generalized monomial-Cartesian codes. Our construction uses an explicitly defined twist vector, and we present formulas for the minimum distance and dimension. Generalized monomial-Cartesian codes arise from polynomials in m variables. When m=1 our codes are MDS, and when m=2 and our lower bound for the minimum distance is 3, the codes are at least Hermitian almost MDS. For an infinite family of parameters, when m=2 we prove that our codes beat the Gilbert-Varshamov bound. We also present many examples of our codes that are better than any known code in the literature.

The use of static-demand systems in empirical analysis assumes that consumers adjust immediately to a new long-run equilibrium path when a shock is encountered. However, adjustment is not always instantaneous. This paper utilizes an Error-Corrected Linear Approximate Almost Ideal Demand System (EC-LAAIDS) model to analyze the import demand for meat and seafood in Indonesia from 1976 to 2020 using annual data. The study found that the theoretical restrictions of homogeneity and symmetry held in the EC-LAAIDS model but did not in the long-run model. The adjustment parameter reveals that imported mutton had the slowest adjustment to long-run equilibrium, while all other meats and seafood had a moderate adjustment speed. The study calculated income elasticities for both the short- and long-run revealing that imported beef was a luxury good in the short-run and most responsive to changes in income. In the short-run, imported poultry was the least responsive to changes in income. In the long-run, all imported meats were found to be luxuries except for imported seafood and beef. Uncompensated price elasticity of demand reveals that in the short-run all imported items had inelastic demand except for imported beef which had elastic demand. In the long-run, however, imported beef and pork had elastic demand, while all other items were inelastic. Compensated cross-price elasticities found that mostly substitution relationships existed among pairs of imported commodities. Finally, a few policy suggestions were discussed, such as production subsidies to producers.

Humans can move objects to target positions out of their reach with certain accuracy by throwing or hitting them with tools. However, the outcome-the final object position-after the same movement varies due to various internal and external factors. Therefore, to improve outcome accuracy, humans correct their movements in the following trial as necessary by estimating the relationship between movement and visual outcome (visuomotor map). In the present study, we compared participants\' error-correction behaviors to visual errors under three conditions, wherein the relationship between joystick movement direction and cursor projection direction on the monitor covertly differed. This allowed us to examine whether the error-correction behavior changed depending on the visuomotor map. Moreover, to determine whether participants maintain the visuomotor map regardless of the visual error size (cursor projection) and proprioceptive errors (joystick movement), we for the first time focused on whether temporary visual errors deviating from the conventional relationship between joystick movement direction and cursor projection direction (i.e., visual perturbation) are ignored. The visual information was occasionally perturbed in two ways to create a situation wherein the visual error was larger or smaller than the proprioceptive error. We found that participants changed their error-correction behaviors according to the conditions and could ignore visual perturbations. This suggests that humans can be implicitly aware of differences in visuomotor maps and adapt accordingly to visual errors.NEW & NOTEWORTHY We found that participants changed their error-correction behaviors according to the conditions and could ignore visual perturbations. This suggests that humans can be implicitly aware of differences in visuomotor maps and adapt accordingly to visual errors. These findings provide suggestions for how to notice and adapt our movements to the environment and our own dynamically changing conditions, to perform accurate movements consistently.

Spatial manipulation of suspended cells based on their properties is an essential part of numerous microfluidic assays. To further read and analyze the manipulation result, a microscopy system is typically required, which, however, increases the cost and reduces the portability of the entire system. As an alternative, a network of integrated Coulter sensors, distributed over a microfluidic chip, provide rapid and reliable detection of spatially-manipulated cells. Code-multiplexing of distributed Coulter sensors enables simplification of such integration by offloading the hardware complexity into advanced signal processing techniques that are needed to interpret the coded sensor outputs. In this work, we combine code-multiplexed Coulter sensor networks with an error-correction technique, a strategy typically used in telecommunication systems for controlling errors in data over unreliable communication channels. Specifically, we include redundancy in the physical sensor design to alleviate the ambiguity in the signal-decoding process, so that interfering sensor signals due to coincidently-detected cells can be resolved reliably. The presented sensor technology not only tracks the spatiotemporal state of cells under test but also measures their sizes and flow speeds. To demonstrate the sensor concept experimentally, we fabricated a microfluidic device with 10 distributed Coulter sensors designed to produce distinct signal waveforms and performed experiments with suspended human cancer cells to characterize the performance of the sensor platform.

When using aggressive undersampling, it is difficult to recover the high quality image with reliably fine features. In this paper, we propose an enhanced recursive residual network (ERRN) that improves the basic recursive residual network with a high-frequency feature guidance, an error-correction unit and dense connections. The feature guidance is designed to predict the underlying anatomy based on image a priori learned from the label data, playing a complementary role to the residual learning. The ERRN is adapted for two important applications: compressed sensing (CS) MRI and super resolution (SR) MRI, while an application-specific error-correction unit is added into the framework, i.e. data consistency for CS-MRI and back projection for SR-MRI due to their different sampling schemes. Our proposed network was evaluated using a real-valued brain dataset, a complex-valued knee dataset, pathological brain data and in vivo rat brain data with different undersampling masks and rates. Experimental results demonstrated that ERRN presented superior reconstructions at all cases with distinctly restored structural features and highest image quality metrics compared to both the state-of-the-art convolutional neural networks and the conventional optimization-based methods, particularly for the undersampling rate over 5-fold. Thus, an excellent framework design can endow the network with a flexible architecture, fewer parameters, outstanding performances for various undersampling schemes, and reduced overfitting in generalization, which will facilitate real-time reconstruction on MRI scanners.

Teaching with acoustical guidance involves auditory feedback (e.g., a click sound when a desired behavior occurs) as part of a multicomponent intervention known as TAGteach. TAGteach has been found to improve performance in sport, dance, surgical technique, and walking. We compared the efficacy and efficiency of the standard TAGteach error-correction procedure and a modified TAGteach error-correction procedure to teach 4 novice adult yoga practitioners beginner yoga poses. Both error-correction procedures were effective for all participants; however, the relative efficiency of these error-correction procedures was unclear. Results are discussed in terms of limitations and considerations for future research.

We present a free-space measurement technique for non-destructive non-contact electrical and dielectric characterization of nano-carbon composites in the Q-band frequency range of 30 GHz to 50 GHz. The experimental system and error correction model accurately reconstruct the conductivity of composite materials that are either thicker than the wave penetration depth, and therefore exhibit negligible microwave transmission (less than -40 dB), or thinner than the wave penetration depth and, therefore, exhibit significant microwave transmission. This error correction model implements a fixed wave propagation distance between antennas and corrects the complex scattering parameters of the specimen from two references, an air slab having geometrical propagation length equal to that of the specimen under test, and a metallic conductor, such as an aluminum plate. Experimental results were validated by reconstructing the relative dielectric permittivity of known dielectric materials and then used to determine the conductivity of nano-carbon composite laminates. This error correction model can simplify routine characterization of thin conducting laminates to just one measurement of scattering parameters, making the method attractive for research, development, and for quality control in the manufacturing environment.