The evolution of rhythmic cognition: New perspectives and technologies in comparative research

Music is a pervasive phenomenon in human culture, and musical rhythm is virtually present in all musical traditions. Research on the evolution and cognitive underpinnings of rhythm can benefit from a number of approaches. We outline key concepts and definitions, allowing fine-grained analysis of rhythmic cognition in experimental studies. We advocate comparative animal research as a useful approach to answer questions about human music cognition and review experimental evidence from different species. Finally, we suggest future directions for research on the cognitive basis of rhythm. Apart from research in semi-natural setups, possibly allowed by “drum set for chimpanzees” prototypes presented here for the first time, mathematical modeling and systematic use of circular statistics may allow promising advances

Bootstrapping navigation and path planning using human positional traces

Navigating and path planning in environments with limited a priori knowledge is a fundamental challenge for mobile robots. Robots operating in human-occupied environments must also respect sociocontextual boundaries such as personal workspaces. There is a need for robots to be able to navigate in such environments without having to explore and build an intricate representation of the world. In this paper, a method for supplementing directly observed environmental information with indirect observations of occupied space is presented. The proposed approach enables the online inclusion of novel human positional traces and environment information into a probabilistic framework for path planning. Encapsulation of sociocontextual information, such as identifying areas that people tend to use to move through the environment, is inherently achieved without supervised learning or labelling. Our method bootstraps navigation with indirectly observed sensor data, and leverages the flexibility of the Gaussian process (GP) for producing a navigational map that sampling based path planers such as Probabilistic Roadmaps (PRM) can effectively utilise. Empirical results on a mobile platform demonstrate that a robot can efficiently and socially-appropriately reach a desired goal by exploiting the navigational map in our Bayesian statistical framework. © 2013 IEEE

Sex Differences in Mechanisms and Outcome of Neonatal Hypoxia-Ischemia in Rodent Models: Implications for Sex-Specific Neuroprotection in Clinical Neonatal Practice

Clinical findings show that male infants with hypoxic-ischemic injury (HI) fare more poorly than matched females on cognitive outcomes. Rodent models of neonatal hypoxia-ischemia support this difference, with data showing that perinatal brain injury leads to long-term behavioral deficits primarily in male rodents and in female rodents treated with early androgens. Results support the idea that sex-specific gonadal hormones may modulate developmental response to injury and dovetail with overwhelming evidence of developmental androgen effects on typical brain morphology and behavior. However, mechanisms underlying sex differences in response to early brain injury may be more complicated. Specifically, activation of cell death pathways in response to HI may also differ by sex. In females, the preferential activation of the caspase-dependent apoptotic pathway may actually afford greater protection, potentially due to the actions of X-linked inhibitor of apoptosis (XIAP) within this pathway. This contrasts the pattern of preferential activation of the caspase-independent pathway in males. While an integrated model of sex-specific hormonal and genetic modulation of response to early injury remains to be fully elucidated, these findings suggest that infants might benefit from sex-specific neuroprotection following HI injury

Communication-aware information gathering with dynamic information flow

© The Author(s) 2014. We are interested in the problem of how to improve estimation in multi-robot information gathering systems by actively controlling the rate of communication between robots. Communication is essential in such systems for decentralized data fusion and decision-making, but wireless networks impose capacity constraints that are frequently overlooked. In order to make efficient use of available capacity, it is necessary to consider a fundamental trade-off between communication cost, computation cost and information value. We introduce a new problem, dynamic information flow, that formalizes this trade-off in terms of decentralized constrained optimization. We propose algorithms that dynamically adjust the data rate of each communication link to maximize an information gain metric subject to constraints on communication and computation resources. The metric is balanced against the communication resources required to transmit data and the computation cost of processing sensor data to form observations. The optimization process selectively routes raw sensor data or processed observation data to zero, one or many robots. Our algorithms therefore allow large systems with many different types of sensors and computational resources to maximize information gain performance while satisfying realistic communication constraints. We also present experimental results with multiple ground robots and multiple sensor types that demonstrate the benefit of dynamic information flow in comparison to simpler bandwidth-limiting methods

Motion states inference through 3D shoulder gait analysis and Hierarchical Hidden Markov Models

Automatically inferring human intention from walking movements is an important research concern in robotics and other fields of study. It is generally derived from temporal motion of limb position relative to the body. These changes can also be reected in the change of stance and gait. Conventional systems relying on gait are usually based on tracking the lower body motion (hip, foot) and are extracted from monocular camera data. However, such data can be inaccessible in crowded environments where occlusions of the lower body are prevalent. This paper proposes a novel approach to utilize upper body 3D-motion and Hierarchical Hidden Markov Models to estimate human ambulatory states, such as quietly standing, starting to walk (gait initiation), walking (gait cycle), or stopping (gait termination). Methods have been tested on real data acquired through a motion capture system where foot measurements (heels and toes) were used as ground truth data for labeling the states to train and test the models. Current results demonstrate the feasibility of using such a system to infer lower-body motion states and sub-states through observations of 3D shoulder motion online. Our results enable applications in situations where only upper body motion is readily observable

An approach to autonomous science by modeling geological knowledge in a Bayesian framework

© 2017 IEEE. Autonomous Science is a field of study which aims to extend the autonomy of exploration robots from low level functionality, such as on-board perception and obstacle avoidance, to science autonomy, which allows scientists to specify missions at task level. This will enable more remote and extreme environments such as deep ocean and other planets to be studied, leading to significant science discoveries. This paper presents an approach to extend the high level autonomy of robots by enabling them to model and reason about scientific knowledge on-board. We achieve this by using Bayesian networks to encode scientific knowledge and adapting Monte Carlo Tree Search techniques to reason about the network and plan informative sensing actions. The resulting knowledge representation and reasoning framework is anytime, handles large state spaces and robust to uncertainty making it highly applicable to field robotics. We apply the approach to a Mars exploration mission in which the robot is required to plan paths and decide when to use its sensing modalities to study a scientific latent variable of interest. Extensive simulation results show that our approach has significant performance benefits over alternative methods. We also demonstrate the practicality of our approach in an analog Martian environment where our experimental rover, Continuum, plans and executes a science mission autonomously

Variable temperature study of the crystal and magnetic structures of the giant magnetoresistant materials LMnAsO (L=La, Nd)

Peer reviewedPublisher PD

Enhanced Heat Transfer from Arrays of Jets Impinging on Plates

Multiple jets of various shapes, orientation and configuration are used regularly in a wide range of engineering applications to provide heating or cooling with impingement on a plate being one of the most common configurations due to the improved heat transfer rates. Design optimization has largely relied on empirical correlations that are limited by the range over which they were originally generated. Computational Fluid Mechanics is now sufficiently advanced to be used as an alternative method for obtaining optimal designs. This project uses the commercial Fluent package to compute heat transfer from a bank of jets impinging on a plate. Results for a single jet are validated against experimental data. The use of advanced turbulence modeling and appropriate boundary layer formulations are key ingredients for obtaining reliable calculations. The heat transfer resulting form the use of multi-jet configurations will be discussed in the paper

Peripheral anomalies in USH2A cause central auditory anomalies in a mouse model of Usher syndrome and CAPD

Central auditory processing disorder (CAPD) is associated with difficulties hearing and processing acoustic information, as well as subsequent impacts on the development of higher-order cognitive processes (i.e., attention and language). Yet CAPD also lacks clear and consistent diagnostic criteria, with widespread clinical disagreement on this matter. As such, identification of biological markers for CAPD would be useful. A recent genome association study identified a potential CAPD risk gene, USH2A. In a homozygous state, this gene is associated with Usher syndrome type 2 (USH2), a recessive disorder resulting in bilateral, high-frequency hearing loss due to atypical cochlear hair cell development. However, children with heterozygous USH2A mutations have also been found to show unexpected low-frequency hearing loss and reduced early vocabulary, contradicting assumptions that the heterozygous (carrier) state is “phenotype free”. Parallel evidence has confirmed that heterozygous Ush2a mutations in a transgenic mouse model also cause low-frequency hearing loss (Perrino et al., 2020). Importantly, these auditory processing anomalies were still evident after covariance for hearing loss, suggesting a CAPD profile. Since usherin anomalies occur in the peripheral cochlea and not central auditory structures, these findings point to upstream developmental feedback effects of peripheral sensory loss on high-level processing characteristic of CAPD. In this study, we aimed to expand upon the mouse behavioral battery used in Perrino et al. (2020) by evaluating central auditory brain structures, including the superior olivary complex (SOC) and medial geniculate nucleus (MGN), in heterozygous and homozygous Ush2a mice. We found that heterozygous Ush2a mice had significantly larger SOC volumes while homozygous Ush2a had significantly smaller SOC volumes. Heterozygous mutations did not affect the MGN; however, homozygous Ush2a mutations resulted in a significant shift towards more smaller neurons. These findings suggest that alterations in cochlear development due to USH2A variation can secondarily impact the development of brain regions important for auditory processing ability