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Abstract In this paper, we introduce a new, open-source software developed in Python for analyzing Auditory Brainstem Response (ABR) waveforms. ABRs are a far-field recording of synchronous neural activity generated by the auditory fibers in the ear in response to sound, and used to study acoustic neural information traveling along the ascending auditory pathway. Common ABR data analysis practices are subject to human interpretation and are labor-intensive, requiring manual annotations and visual estimation of hearing thresholds. The proposed new Auditory Brainstem Response Analyzer (ABRA) software is designed to facilitate the analysis of ABRs by supporting batch data import/export, waveform visualization, and statistical analysis. Techniques implemented in this software include algorithmic peak finding, threshold estimation, latency estimation, time warping for curve alignment, and 3D plotting of ABR waveforms over stimulus frequencies and decibels. The excellent performance on a large dataset of ABR collected from three labs in the field of hearing research that use different experimental recording settings illustrates the efficacy, flexibility, and wide utility of ABRA. 

Several recent works have found the emergence of grounded com-positional language in the communication protocols developed bymostly cooperative multi-agent systems when learned end-to-endto maximize performance on a downstream task. However, humanpopulations learn to solve complex tasks involving communicativebehaviors not only in fully cooperative settings but also in scenar-ios where competition acts as an additional external pressure forimprovement. In this work, we investigate whether competitionfor performance from an external, similar agent team could actas a social influence that encourages multi-agent populations todevelop better communication protocols for improved performance,compositionality, and convergence speed. We start fromTask &Talk, a previously proposed referential game between two coopera-tive agents as our testbed and extend it intoTask, Talk & Compete,a game involving two competitive teams each consisting of twoaforementioned cooperative agents. Using this new setting, we pro-vide an empirical study demonstrating the impact of competitiveinfluence on multi-agent teams. Our results show that an externalcompetitive influence leads to improved accuracy and generaliza-tion, as well as faster emergence of communicative languages thatare more informative and compositional. 

Abstract A nanoengineered bioink loaded with therapeutic proteins is designed to direct cell function in a 3D printed construct. The bioink is developed from a hydrolytically degradable polymer and 2D synthetic nanoparticle. The synthesis of poly(ethylene glycol)‐dithiothreitol (PEGDTT) via a Michael‐like step growth polymerization results in acrylate terminated degradable macromer. The addition of 2D nanosilicates to PEGDTT results in formation of shear‐thinning bioinks with high printability and structural fidelity. The mechanical properties, swelling kinetics, and degradation rate of 3D printed constructs can be modulated by changing the ratio of PEG:PEGDTT and nanosilicates concentration. Due to high surface area and charged characteristic of nanosilicates, protein therapeutics can be sequestered in 3D printing structure for prolong duration. Sustained release of pro‐angiogenic therapeutics from 3D printed structure, promoted rapid migration of human endothelial umbilical vein cell. This approach to design biologically active inks to control and direct cell behavior can be used to engineer 3D complex tissue structure for regenerative medicine.