Dynamics of Vocalization-Induced Modulation of Auditory Cortical Activity at Mid-utterance

Background: Recent research has addressed the suppression of cortical sensory responses to altered auditory feedback that occurs at utterance onset regarding speech. However, there is reason to assume that the mechanisms underlying sensorimotor processing at mid-utterance are different than those involved in sensorimotor control at utterance onset. The present study attempted to examine the dynamics of event-related potentials (ERPs) to different acoustic versions of auditory feedback at mid-utterance. Methodology/Principal findings: Subjects produced a vowel sound while hearing their pitch-shifted voice (100 cents), a sum of their vocalization and pure tones, or a sum of their vocalization and white noise at mid-utterance via headphones. Subjects also passively listened to playback of what they heard during active vocalization. Cortical ERPs were recorded in response to different acoustic versions of feedback changes during both active vocalization and passive listening. The results showed that, relative to passive listening, active vocalization yielded enhanced P2 responses to the 100 cents pitch shifts, whereas suppression effects of P2 responses were observed when voice auditory feedback was distorted by pure tones or white noise. Conclusion/Significance: The present findings, for the first time, demonstrate a dynamic modulation of cortical activity as a function of the quality of acoustic feedback at mid-utterance, suggesting that auditory cortical responses can be enhanced or suppressed to distinguish self-produced speech from externally-produced sounds

Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation

Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress in narrowing down the chemical research space and generating high-fidelity molecules. However, current generative models only focus on modeling either 2D bonding graphs or 3D geometries, which are two complementary descriptors for molecules. The lack of ability to jointly model both limits the improvement of generation quality and further downstream applications. In this paper, we propose a new joint 2D and 3D diffusion model (JODO) that generates complete molecules with atom types, formal charges, bond information, and 3D coordinates. To capture the correlation between molecular graphs and geometries in the diffusion process, we develop a Diffusion Graph Transformer to parameterize the data prediction model that recovers the original data from noisy data. The Diffusion Graph Transformer interacts node and edge representations based on our relational attention mechanism, while simultaneously propagating and updating scalar features and geometric vectors. Our model can also be extended for inverse molecular design targeting single or multiple quantum properties. In our comprehensive evaluation pipeline for unconditional joint generation, the results of the experiment show that JODO remarkably outperforms the baselines on the QM9 and GEOM-Drugs datasets. Furthermore, our model excels in few-step fast sampling, as well as in inverse molecule design and molecular graph generation. Our code is provided in https://github.com/GRAPH-0/JODO

Training of Working Memory Impacts Neural Processing of Vocal Pitch Regulation

Working memory training can improve the performance of tasks that were not trained. Whether auditory-motor integration for voice control can benefit from working memory training, however, remains unclear. The present event-related potential (ERP) study examined the impact of working memory training on the auditory-motor processing of vocal pitch. Trained participants underwent adaptive working memory training using a digit span backwards paradigm, while control participants did not receive any training. Before and after training, both trained and control participants were exposed to frequency-altered auditory feedback while producing vocalizations. After training, trained participants exhibited significantly decreased N1 amplitudes and increased P2 amplitudes in response to pitch errors in voice auditory feedback. In addition, there was a significant positive correlation between the degree of improvement in working memory capacity and the post-pre difference in P2 amplitudes. Training-related changes in the vocal compensation, however, were not observed. There was no systematic change in either vocal or cortical responses for control participants. These findings provide evidence that working memory training impacts the cortical processing of feedback errors in vocal pitch regulation. This enhanced cortical processing may be the result of increased neural efficiency in the detection of pitch errors between the intended and actual feedback

Automated Synthesis of Planar Mechanisms with Revolute, Prismatic and Pin-In-Slot Joints

The intent of this research is to explore the entire design space of mechanism topologies by using a graph grammar synthesis approach. A new graph representation of planer mechanism has been developed to represent the planar mechanism with revolute (R), prismatic (P), and pin-in-slot (RP) joints. Following Gruebler's equation, the graph grammar rules are designed to increase the complexity of the linkage and avoid changing the default mobility. The "recognize" and "apply" process of graph grammar rules is done through the computer, so that the design space of mechanism topologies can be fully explored automatically. The design space of four to fourteen bar R-joint 1-DOF topologies is obtained through this research. Each of the linkages in this space is valid and does not contain any rigid sub-structure as ensured by the additional graph grammar rules. The higher DOF topologies are also enumerated by degenerating the 1-DOF results. In order to increase the diversity of the topologies design space, P- and RP-joint substitution rules are used to replace the revolute joints in the topologies. With additional functions, the rotatability of the linkage can be preserved after P-joints are introduced. The research results in a total of 159,526 unique mechanism topologies that are each saved as independent computer files. Additionally, a topology design exploration tool is created in this study to provide a convenient approach to generate complex 1-DOF linkage design