Hand coordination can allow humans to have dexterous control with many degrees of freedom to perform various tasks in daily living. An important contributing factor to this important ability is the complex biomechanical architecture of the human hand. However, drawing a clear functional link between biomechanical architecture and hand coordination is challenging. It is not understood which biomechanical characteristics are responsible for hand coordination and what specific effect each biomechanical characteristic has. To explore this link, we first inspected the characteristics of hand coordination during daily tasks through a statistical analysis of the kinematic data, which were collected from thirty right-handed subjects during a multitude of grasping tasks. Then, the functional link between biomechanical architecture and hand coordination was drawn by establishing the clear corresponding causality between the tendinous connective characteristics of the human hand and the coordinated characteristics during daily grasping activities. The explicit functional link indicates that the biomechanical characteristic of tendinous connective architecture between muscles and articulations is the proper design by the Creator to perform a multitude of daily tasks in a comfortable way. The clear link between the structure and the function of the human hand also suggests that the design of a multifunctional robotic hand should be able to better imitate such basic architecture.
Movement interactions and the underlying social structure in groups have relevance across many social-living species. Collective motion of groups could be based on an “egalitarian” decision system, but in practice it is often influenced by underlying social network structures and by individual characteristics. We investigated whether dominance rank and personality traits are linked to leader and follower roles during joint motion of family dogs. We obtained high-resolution spatio-temporal GPS trajectory data (823,148 data points) from six dogs belonging to the same household and their owner during 14 30-40 min unleashed walks. We identified several features of the dogs' paths (e.g., running speed or distance from the owner) which are characteristic of a given dog. A directional correlation analysis quantifies interactions between pairs of dogs that run loops jointly. We found that dogs play the role of the leader about 50-85% of the time, i.e. the leader and follower roles in a given pair are dynamically interchangable. However, on a longer timescale tendencies to lead differ consistently. The network constructed from these loose leader-follower relations is hierarchical, and the dogs' positions in the network correlates with the age, dominance rank, trainability, controllability, and aggression measures derived from personality questionnaires. We demonstrated the possibility of determining dominance rank and personality traits of an individual based only on its logged movement data. The collective motion of dogs is influenced by underlying social network structures and by characteristics such as personality differences. Our findings could pave the way for automated animal personality and human social interaction measurements.
- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 4 years ago
First impressions of social traits, such as trustworthiness or dominance, are reliably perceived in faces, and despite their questionable validity they can have considerable real-world consequences. We sought to uncover the information driving such judgments, using an attribute-based approach. Attributes (physical facial features) were objectively measured from feature positions and colors in a database of highly variable “ambient” face photographs, and then used as input for a neural network to model factor dimensions (approachability, youthful-attractiveness, and dominance) thought to underlie social attributions. A linear model based on this approach was able to account for 58% of the variance in raters' impressions of previously unseen faces, and factor-attribute correlations could be used to rank attributes by their importance to each factor. Reversing this process, neural networks were then used to predict facial attributes and corresponding image properties from specific combinations of factor scores. In this way, the factors driving social trait impressions could be visualized as a series of computer-generated cartoon face-like images, depicting how attributes change along each dimension. This study shows that despite enormous variation in ambient images of faces, a substantial proportion of the variance in first impressions can be accounted for through linear changes in objectively defined features.
Analysis of trial documentation has revealed that some industry-funded trials may be done more for marketing purposes than scientific endeavour. We aimed to define characteristics of drug trials that appear to be influenced by marketing considerations and estimate their prevalence.
Observations of the flight paths of pigeons navigating from familiar locations have shown that these birds are able to learn and subsequently follow habitual routes home. It has been suggested that navigation along these routes is based on the recognition of memorized visual landmarks. Previous research has identified the effect of landmarks on flight path structure, and thus the locations of potentially salient sites. Pigeons have also been observed to be particularly attracted to strong linear features in the landscape, such as roads and rivers. However, a more general understanding of the specific characteristics of the landscape that facilitate route learning has remained out of reach. In this study, we identify landscape complexity as a key predictor of the fidelity to the habitual route, and thus conclude that pigeons form route memories most strongly in regions where the landscape complexity is neither too great nor too low. Our results imply that pigeons process their visual environment on a characteristic spatial scale while navigating and can explain the different degrees of success in reproducing route learning in different geographical locations.
Risk of Large-Scale Evacuation Based on the Effectiveness of Rescue Strategies Under Different Crowd Densities.
- Risk analysis : an official publication of the Society for Risk Analysis
- Published over 5 years ago
Crowd density is a key factor that influences the moving characteristics of a large group of people during a large-scale evacuation. In this article, the macro features of crowd flow and subsequent rescue strategies were considered, and a series of characteristic crowd densities that affect large-scale people movement, as well as the maximum bearing density when the crowd is extremely congested, were analyzed. On the basis of characteristic crowd densities, the queuing theory was applied to simulate crowd movement. Accordingly, the moving characteristics of the crowd and the effects of typical crowd density-which is viewed as the representation of the crowd’s arrival intensity in front of the evacuation passageways-on rescue strategies was studied. Furthermore, a “risk axle of crowd density” is proposed to determine the efficiency of rescue strategies in a large-scale evacuation, i.e., whether the rescue strategies are able to effectively maintain or improve evacuation efficiency. Finally, through some rational hypotheses for the value of evacuation risk, a three-dimensional distribution of the evacuation risk is established to illustrate the risk axle of crowd density. This work aims to make some macro, but original, analysis on the risk of large-scale crowd evacuation from the perspective of the efficiency of rescue strategies.
Everyday experience tells us that it is often possible to identify a familiar speaker solely by his/her voice. Such observations reveal that speakers carry individual features in their voices. The present study examines how suprasegmental temporal features contribute to speaker-individuality. Based on data of a homogeneous group of Zurich German speakers, we conducted an experiment that included speaking style variability (spontaneous vs. read speech) and channel variability (high-quality vs. mobile phone-transmitted speech), both of which are characteristic of forensic casework. Speakers demonstrated high between-speaker variability in both read and spontaneous speech, and low within-speaker variability across the two speaking styles. Results further revealed that distortions of the type introduced by mobile telephony had little effect on suprasegmental temporal characteristics. Given this evidence of speaker-individuality, we discuss suprasegmental temporal features' potential for forensic voice comparison.
Relaxor/ferroelectric ceramic/ceramic composites have shown to be promising in generating large electromechanical strain at moderate electric fields. Nonetheless, the mechanisms of polarization and strain coupling between grains of different nature in the composites remain unclear. To rationalize the coupling mechanisms we performed advanced piezoresponse force microscopy (PFM) studies of 0.92BNT-0.06BT-0.02KNN/0.93BNT-0.07BT (ergodic/non-ergodic relaxor) composites. PFM is able to distinguish grains of different phases by characteristic domain patterns. Polarization switching has been probed locally, on a sub-grain scale. k-Means clustering analysis applied to arrays of local hysteresis loops reveals variations of polarization switching characteristics between the ergodic and non-ergodic relaxor grains. We report a different set of switching parameters for grains in the composites as opposed to the pure phase samples. Our results confirm ceramic/ceramic composites to be a viable approach to tailor the piezoelectric properties and optimize the macroscopic electromechanical characteristics.
- IEEE transactions on pattern analysis and machine intelligence
- Published over 1 year ago
This paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with each attribute on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties. With this design, SSDH has a nice characteristic that classification and retrieval are unified in a single learning model. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a point-wised manner, and thus is scalable to large-scale datasets. SSDH is simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms other hashing approaches on several benchmarks and large datasets. Compared with state-of-the-art approaches, SSDH achieves higher retrieval accuracy, while the classification performance is not sacrificed.
In laboratory studies, praising children’s effort encourages them to adopt incremental motivational frameworks-they believe ability is malleable, attribute success to hard work, enjoy challenges, and generate strategies for improvement. In contrast, praising children’s inherent abilities encourages them to adopt fixed-ability frameworks. Does the praise parents spontaneously give children at home show the same effects? Although parents' early praise of inherent characteristics was not associated with children’s later fixed-ability frameworks, parents' praise of children’s effort at 14-38 months (N = 53) did predict incremental frameworks at 7-8 years, suggesting that causal mechanisms identified in experimental work may be operating in home environments.