In this paper, we tackle the problem of tiling a domain with a set of deformable templates. A valid solution to this problem completely covers the domain with templates such that the templates do not overlap. We generalize existing specialized solutions and formulate a general layout problem by modeling important constraints and admissible template deformations. Our main idea is to break the layout algorithm into two steps: a discrete step to lay out the approximate template positions and a continuous step to refine the template shapes. Our approach is suitable for a large class of applications, including floorplans, urban layouts, and arts and design.

We develop a general framework to analyze the controllability of multiplex networks using multiple-relation networks and multiple-layer networks with interlayer couplings as two classes of prototypical systems. In the former, networks associated with different physical variables share the same set of nodes and in the latter, diffusion processes take place. We find that, for a multiple-relation network, a layer exists that dominantly determines the controllability of the whole network and, for a multiple-layer network, a small fraction of the interconnections can enhance the controllability remarkably. Our theory is generally applicable to other types of multiplex networks as well, leading to significant insights into the control of complex network systems with diverse structures and interacting patterns.

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.

This paper reports the influence of activator type and concentration on the rheological properties of alkali-activated fly ash suspensions. A thorough investigation of the rheological influences (yield stress and plastic viscosity) of several activator parameters, including: (i) the cation type and concentration of alkali hydroxide and (ii) the alkali-to-binder ratio (n) and silica modulus (M_s), and (iii) the volume of the activation solution, on the suspension rheology is presented. The results indicate a strong dependence on the cation and its concentration in the activation solution. The viscosity of the activation solution and the volumetric solution-to-powder ratio are shown to most strongly influence the plastic viscosity of the suspension. The suspension yield stress is predominantly influenced by the changes in fly ash particle surface charge and the ionic species in the activator. A shift from non-Newtonian to Newtonian flow behavior is noted in the case of silicate-based suspensions for M_s ≤ 1.5. This behavior, which is not observed at higher M_S values, or when the fly ash is dispersed in hydroxide solutions or pure water, is hypothesized to be caused by colloidal siliceous species present in this system, or surface charge effects on the fly ash particles. Comparisons of the rheological response of alkali-activated suspensions to that of portland cement-water suspensions are also reported.

Recent studies about sensorimotor control of the human hand have focused on how dexterous manipulation is learned and generalized. Here we address this question by testing the extent to which learned manipulation can be transferred when the contralateral hand is used and/or object orientation is reversed. We asked subjects to use a precision grip to lift a grip device with an asymmetrical mass distribution while minimizing object roll during lifting by generating a compensatory torque. Subjects were allowed to grasp anywhere on the object’s vertical surfaces, and were therefore able to modulate both digit positions and forces. After every block of eight trials performed in one manipulation context (i.e., using the right hand and at a given object orientation), subjects had to lift the same object in the second context for one trial (transfer trial).

Context changes were made by asking subjects to switch the hand used to lift the object and/or rotate the object 180° about a vertical axis. Therefore, three transfer conditions, hand switch (HS), object rotation (OR), and both hand switch and object rotation (HS+OR), were tested and compared with hand matched control groups who did not experience context changes. We found that subjects in all transfer conditions adapted digit positions across multiple transfer trials similar to the learning of control groups, regardless of different changes of contexts. Moreover, subjects in both HS and HS+OR group also adapted digit forces similar to the control group, suggesting independent learning of the left hand. In contrast, the OR group showed significant negative transfer of the compensatory torque due to an inability to adapt digit forces. Our results indicate that internal representations of dexterous manipulation tasks may be primarily built through the hand used for learning and cannot be transferred across hands.

Neural progenitor cells (NPCs) derived from human pluripotent stem cells (hPSCs) are a multipotent cell population that is capable of nearly indefinite expansion and subsequent differentiation into the various neuronal and supporting cell types that comprise the CNS. However, current protocols for differentiating NPCs toward neuronal lineages result in a mixture of neurons from various regions of the CNS. In this study, we determined that endogenous WNT signaling is a primary contributor to the heterogeneity observed in NPC cultures and neuronal differentiation. Furthermore, exogenous manipulation of WNT signaling during neural differentiation, through either activation or inhibition, reduces this heterogeneity in NPC cultures, thereby promoting the formation of regionally homogeneous NPC and neuronal cultures. The ability to manipulate WNT signaling to generate regionally specific NPCs and neurons will be useful for studying human neural development and will greatly enhance the translational potential of hPSCs for neural-related therapies.

Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs). The simulator uses stored motor intent signals to drive a pool of simulated motoneurons with various spike shapes, recruitment characteristics, and firing frequencies. Each electrode records a weighted sum of a subset of simulated motoneuron activity patterns. As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs. In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes. The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms.

Evolutionary dynamical models for cyclic competitions of three species (e.g., rock, paper, and scissors, or RPS) provide a paradigm, at the microscopic level of individual interactions, to address many issues in coexistence and biodiversity. Real ecosystems often involve competitions among more than three species. By extending the RPS game model to five (rock-paper-scissors-lizard-Spock, or RPSLS) mobile species, we uncover a fundamental type of mesoscopic interactions among subgroups of species. In particular, competitions at the microscopic level lead to the emergence of various local groups in different regions of the space, each involving three species. It is the interactions among the groups that fundamentally determine how many species can coexist. In fact, as the mobility is increased from zero, two transitions can occur: one from a five- to a three-species coexistence state and another from the latter to a uniform, single-species state. We develop a mean-field theory to show that, in order to understand the first transition, group interactions at the mesoscopic scale must be taken into account. Our findings suggest, more broadly, the importance of mesoscopic interactions in coexistence of great many species.

In previous work from this laboratory, it has been found that the urban heat island intensity (UHI) can be scaled with the urban length scale and the wind speed, through the time-dependent energy balance. The heating of the urban surfaces during the daytime sets the initial temperature, and this overheating is dissipated during the night-time through mean convection motion over the urban surface. This may appear to be in contrast to the classical work by Oke (1973). However, in this work, we show that if the population density is used in converting the population data into urbanized area, then a good agreement with the current theory is found. An additional parameter is the “urban flow parameter,” which depends on the urban building characteristics and affects the horizontal convection of heat due to wind. This scaling can be used to estimate the UHI intensity in any cities and therefore predict the required energy consumption during summer months. In addition, all urbanized surfaces are expected to exhibit this scaling, so that increase in the surface temperature in large energy-consumption or energy-producing facilities (e.g., solar electric or thermal power plants) can be estimated.

Owing to the suprasegmental behavior of emotional speech, turn-level features have demonstrated a better success than frame-level features for recognition-related tasks. Conventionally, such features are obtained via a brute-force collection of statistics over frames, thereby losing important local information in the process which affects the performance. To overcome these limitations, a novel feature extraction approach using latent topic models (LTMs) is presented in this study. Speech is assumed to comprise of a mixture of emotion-specific topics, where the latter capture emotionally salient information from the co-occurrences of frame-level acoustic features and yield better descriptors. Specifically, a supervised replicated softmax model (sRSM), based on restricted Boltzmann machines and distributed representations, is proposed to learn naturally discriminative topics. The proposed features are evaluated for the recognition of categorical or continuous emotional attributes via within and cross-corpus experiments conducted over acted and spontaneous expressions. In a within-corpus scenario, sRSM outperforms competing LTMs, while obtaining a significant improvement of 16.75% over popular statistics-based turn-level features for valence-based classification, which is considered to be a difficult task using only speech. Further analyses with respect to the turn duration show that the improvement is even more significant, 35%, on longer turns (>6 s), which is highly desirable for current turn-based practices. In a cross-corpus scenario, two novel adaptation-based approaches, instance selection, and weight regularization are proposed to reduce the inherent bias due to varying annotation procedures and cultural perceptions across databases. Experimental results indicate a natural, yet less severe, deterioration in performance - only 2.6% and 2.7%, thereby highlighting the generalization ability of the proposed features.