From Phrase Structure to Form

2020 ◽  
pp. 140-169
Author(s):  
Megan Kaes Long
Keyword(s):  
Regional Differences ◽  
Multiple Scales ◽  
Phrase Structure ◽  
Small Scale ◽  
Repeat Structure ◽  
English Composers ◽  
Formal Procedures

The balletto unfolds on a uniquely small scale: many balletti can be performed in less than a minute. The genre’s brevity supports a number of perceptual benefits that train listeners to attend to tonal dynamics at multiple scales. The shortest balletti lie at the perceptual limit for entraining hypermeter and within the boundary for remembering tonic. Dynamic attending theory posits that periodic cadences correspond with peaks of attention, facilitating comparison of distant harmonic events. The balletto’s repeat structure fosters a deeper knowledge of tonal and formal procedures, and repetition directs attention to larger groupings. Together, these principles enabled listeners to identify important harmonic events, compare them across broad time spans, and associate them with specific formal units. Furthermore, a comparison of Italian, English, and German balletti reveals important regional differences in tonal and harmonic norms, illustrating how English composers, especially Thomas Morley, maximally leveraged the genre’s profound perceptual benefits.

Geomechanical and Fracture Network Interpretation of a Devonian Outcrop

2021 ◽  
pp. 1-16
Author(s):  
Scott McKean ◽  
Simon Poirier ◽  
Henry Galvis-Portilla ◽  
Marco Venieri ◽  
Jeffrey A. Priest ◽  
...  
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Fracture Network ◽  
Natural Fracture ◽  
Small Scale ◽  
Nugget Effect ◽  
Schmidt Hammer ◽  
Unconventional Reservoir ◽  
Fracture Anisotropy ◽  
Fracture Intensity

Summary The Duvernay Formation is an unconventional reservoir characterized by induced seismicity and fluid migration, with natural fractures likely contributing to both cases. An alpine outcrop of the Perdrix and Flume formations, correlative with the subsurface Duvernay and Waterways formations, was investigated to characterize natural fracture networks. A semiautomated image-segmentation and fracture analysis was applied to orthomosaics generated from a photogrammetric survey to assess small- and large-scale fracture intensity and rock mass heterogeneity. The study also included manual scanlines, fracture windows, and Schmidt hammer measurements. The Perdrix section transitions from brittle fractures to en echelon fractures and shear-damage zones. Multiple scales of fractures were observed, including unconfined, bedbound fractures, and fold-relatedbed-parallel partings (BPPs). Variograms indicate a significant nugget effect along with fracture anisotropy. Schmidt hammer results lack correlation with fracture intensity. The Flume pavements exhibit a regionally extensive perpendicular joint set, tectonically driven fracturing, and multiple fault-damage zones with subvertical fractures dominating. Similar to the Perdrix, variograms show a significant nugget effect, highlighting fracture anisotropy. The results from this study suggest that small-scale fractures are inherently stochastic and that fractures observed at core scale should not be extrapolated to represent large-scale fracture systems; instead, the effects of small-scale fractures are best represented using an effective continuum approach. In contrast, large-scale fractures are more predictable according to structural setting and should be characterized robustly using geological principles. This study is especially applicable for operators and regulators in the Duvernay and similar formations where unconventional reservoir units abut carbonate formations.

scholarly journals RFaNet: Receptive Field-Aware Network with Finger Attention for Fingerspelling Recognition Using a Depth Sensor

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2815
Author(s):  
Shih-Hung Yang ◽  
Yao-Mao Cheng ◽  
Jyun-We Huang ◽  
Yon-Ping Chen
Keyword(s):  
Receptive Field ◽  
American Sign Language ◽  
Multiple Scales ◽  
Receptive Fields ◽  
Depth Image ◽  
Small Scale ◽  
Feature Maps ◽  
Depth Sensor ◽  
Depth Images ◽  
Effective Transfer

Automatic fingerspelling recognition tackles the communication barrier between deaf and hearing individuals. However, the accuracy of fingerspelling recognition is reduced by high intra-class variability and low inter-class variability. In the existing methods, regular convolutional kernels, which have limited receptive fields (RFs) and often cannot detect subtle discriminative details, are applied to learn features. In this study, we propose a receptive field-aware network with finger attention (RFaNet) that highlights the finger regions and builds inter-finger relations. To highlight the discriminative details of these fingers, RFaNet reweights the low-level features of the hand depth image with those of the non-forearm image and improves finger localization, even when the wrist is occluded. RFaNet captures neighboring and inter-region dependencies between fingers in high-level features. An atrous convolution procedure enlarges the RFs at multiple scales and a non-local operation computes the interactions between multi-scale feature maps, thereby facilitating the building of inter-finger relations. Thus, the representation of a sign is invariant to viewpoint changes, which are primarily responsible for intra-class variability. On an American Sign Language fingerspelling dataset, RFaNet achieved 1.77% higher classification accuracy than state-of-the-art methods. RFaNet achieved effective transfer learning when the number of labeled depth images was insufficient. The fingerspelling representation of a depth image can be effectively transferred from large- to small-scale datasets via highlighting the finger regions and building inter-finger relations, thereby reducing the requirement for expensive fingerspelling annotations.

scholarly journals WS-AM: Weakly Supervised Attention Map for Scene Recognition

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1072 ◽  
Author(s):  
Shifeng Xia ◽  
Jiexian Zeng ◽  
Lu Leng ◽  
Xiang Fu
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Scene Recognition ◽  
Small Scale ◽  
Great Success ◽  
Global Feature ◽  
Benchmark Datasets ◽  
Local Mean ◽  
Weakly Supervised ◽  
Fully Connected

Recently, convolutional neural networks (CNNs) have achieved great success in scene recognition. Compared with traditional hand-crafted features, CNN can be used to extract more robust and generalized features for scene recognition. However, the existing scene recognition methods based on CNN do not sufficiently take into account the relationship between image regions and categories when choosing local regions, which results in many redundant local regions and degrades recognition accuracy. In this paper, we propose an effective method for exploring discriminative regions of the scene image. Our method utilizes the gradient-weighted class activation mapping (Grad-CAM) technique and weakly supervised information to generate the attention map (AM) of scene images, dubbed WS-AM—weakly supervised attention map. The regions, where the local mean and the local center value are both large in the AM, correspond to the discriminative regions helpful for scene recognition. We sampled discriminative regions on multiple scales and extracted the features of large-scale and small-scale regions with two different pre-trained CNNs, respectively. The features from two different scales were aggregated by the improved vector of locally aggregated descriptor (VLAD) coding and max pooling, respectively. Finally, the pre-trained CNN was used to extract the global feature of the image in the fully- connected (fc) layer, and the local features were combined with the global feature to obtain the image representation. We validated the effectiveness of our method on three benchmark datasets: MIT Indoor 67, Scene 15, and UIUC Sports, and obtained 85.67%, 94.80%, and 95.12% accuracy, respectively. Compared with some state-of-the-art methods, the WS-AM method requires fewer local regions, so it has a better real-time performance.

The Small Scale Effect on Linear Vibration of Beam-Like Nanostructures

2012 ◽  
Vol 446-449 ◽  
pp. 3432-3435
Author(s):  
Cheng Li ◽  
Lin Quan Yao
Keyword(s):  
Axial Load ◽  
Vibration Mode ◽  
Multiple Scales ◽  
Separation Of Variables ◽  
Classical Mechanics ◽  
Small Scale ◽  
Transverse Displacement ◽  
Linear Vibration ◽  
Small Scale Effect ◽  
Multiple Scales Analysis

Transverse free dynamics of a beam-like nanostructure with axial load is investigated. The effects of a small size at nano-scale unavailable in classical mechanics are presented. Explicit solutions for natural frequency, vibration mode and transverse displacement are obtained by separation of variables and multiple scales analysis. Results by two methods are in close agreement.

scholarly journals Predictive Representations in Hippocampal and Prefrontal Hierarchies

2019 ◽  
Author(s):  
Iva K. Brunec ◽  
Ida Momennejad
Keyword(s):  
Multiple Scales ◽  
Cognitive Maps ◽  
Small Scale ◽  
Weighted Sum ◽  
Relational Structures ◽  
Relational Knowledge ◽  
Pattern Similarity ◽  
The World ◽  
Neuroimaging Data ◽  
Posterior Anterior

AbstractAs we navigate the world, we use learned representations of relational structures to explore and reach goals. Studies of how relational knowledge enables inference and planning are typically conducted in controlled small-scale settings. It remains unclear, however, how people use stored knowledge in continuously unfolding navigation, e.g., walking long distances in a city. We hypothesized that predictive representations, organized at multiple scales along posterior-anterior prefrontal and hippocampal hierarchies, guide naturalistic navigation. We conducted model-based representational similarity analyses of neuroimaging data measured during navigation of realistically long paths in virtual reality. We tested the pattern similarity of each point–along each path–to a weighted sum of its successor points within different predictive horizons. We found that anterior PFC showed the largest predictive horizons, posterior hippocampus the smallest, with the anterior hippocampus and orbitofrontal regions in between. Our findings offer novel insights into how cognitive maps support hierarchical planning at multiple scales.

scholarly journals Nonlinear vibration analysis of the nanobeams subjected to magneto-electro-thermo loading based on a novel HSDT

2021 ◽  
Author(s):  
Reza Mohammadi
Keyword(s):  
Nonlinear Vibration ◽  
Frequency Ratio ◽  
Vibration Analysis ◽  
Multiple Scales ◽  
Numerical Technique ◽  
Perturbation Technique ◽  
Equations Of Motion ◽  
Nonlocal Elasticity Theory ◽  
Small Scale ◽  
Nonlinear Frequency

Abstract In this paper, the nonlinear vibration analysis of the nanobeams subjected to magneto-electro-thermo loading based on a novel HSDT is studied. Nonlocal elasticity theory is applied to consider the small scale effect. The nonlinear equations of motion are derived using Hamilton’s principle. First, a Galerkin-based numerical technique is applied to reduce the nonlinear governing equation into a set of Duffing-type time-dependent differential equations. Afterward, the analytical solutions are derived based on the method of multiple scales (MMS) and perturbation technique. All of the mechanical properties of the beam are temperature dependent. The impacts of the several variables are investigated on the nonlinear frequency ratio of the nanobeams. The results illustrate that when maximum deflection is smaller/ greater than 0.2, its impact on the nonlinear frequency ratio will decrease/increase.

Self Adapting Mechanical Step Bearings for Variations in Load

Tribology ◽  
2005 ◽  
Author(s):  
Robert L. Jackson
Keyword(s):  
Friction And Wear ◽  
Multiple Scales ◽  
The Self ◽  
Dynamic Responses ◽  
External Control ◽  
Small Scale ◽  
Precision Control ◽  
Surface Profiles ◽  
Bearing Design ◽  
And Behavior

As the application of small-scale and precision technologies increases, the need will grow for bearings which are able to provide precision control of their location. At the micro and nano-scale there is a need for new bearing technologies to reduce friction and wear, and provide precision control of bearing motion. This control can be provided by electronically controlled actuators and sensors, but then the system is dependant on the reliability of the electronics. This work uses numerical methods to research the design and behavior of self adapting smart step bearings. These step bearings are designed to change their surface profiles to achieve an optimal or controlled behavior, without the use electronics or external control. The bearing changes its profile to control the film height of the bearing to a near constant value for different loads. The result is a self adapting step bearing design that may be applied at multiple scales for use in a wide variety of machine components. The numerical simulation shows that the self adapting step bearing is able to autonomously adapt in real time to dynamic loads and maintain a desired film thickness with a relatively small amount of deviation. The self adapting step bearing also exhibits smaller dynamic responses to transient loads in comparison to a conventional static geometry step bearing.

scholarly journals Probing the initial conditions of high-mass star formation

2019 ◽  
Vol 627 ◽  
pp. A85 ◽  
Author(s):  
Chuan-Peng Zhang ◽  
Timea Csengeri ◽  
Friedrich Wyrowski ◽  
Guang-Xing Li ◽  
Thushara Pillai ◽  
...  
Keyword(s):  
Star Formation ◽  
Multiple Scales ◽  
Initial Conditions ◽  
Dust Emission ◽  
Radio Continuum ◽  
High Mass ◽  
Small Scale ◽  
High Angular Resolution ◽  
H Ii Regions ◽  
Mass Star

Context. Fragmentation and feedback are two important processes during the early phases of star formation. Aims. Massive clumps tend to fragment into clusters of cores and condensations, some of which form high-mass stars. In this work, we study the structure of massive clumps at different scales, analyze the fragmentation process, and investigate the possibility that star formation is triggered by nearby H ii regions. Methods. We present a high angular resolution study of a sample of massive proto-cluster clumps G18.17, G18.21, G23.97N, G23.98, G23.44, G23.97S, G25.38, and G25.71. Combining infrared data at 4.5, 8.0, 24, and 70 μm, we use a few arcsecond resolution, radiometer and millimeter inteferometric data taken at 1.3 cm, 3.5 mm, 1.3 mm, and 870 μm to study their fragmentation and evolution. Our sample is unique in the sense that all the clumps have neighboring H ii regions. Taking advantage of that, we tested triggered star formation using a novel method where we study the alignment of the center of mass traced by dust emission at multiple scales. Results. The eight massive clumps, identified based on single-dish observations, have masses ranging from 228 to 2279 M⊙ within an effective radius of Reff ~ 0.5 pc. We detect compact structures towards six out of the eight clumps. The brightest compact structures within infrared bright clumps are typically associated with embedded compact radio continuum sources. The smaller scale structures of Reff ~ 0.02 pc observed within each clump are mostly gravitationally bound and massive enough to form at least a B3-B0 type star. Many condensations have masses larger than 8 M⊙ at a small scale of Reff ~ 0.02 pc. We find that the two infrared quiet clumps with the lowest mass and lowest surface density with <300 M⊙ do not host any compact sources, calling into question their ability to form high-mass stars. Although the clumps are mostly infrared quiet, the dynamical movements are active at clump scale (~1 pc). Conclusions. We studied the spatial distribution of the gas conditions detected at different scales. For some sources we find hints of external triggering, whereas for others we find no significant pattern that indicates triggering is dynamically unimportant. This probably indicates that the different clumps go through different evolutionary paths. In this respect, studies with larger samples are highly desired.

scholarly journals Assemblages and Scale in Archaeology

2017 ◽  
Vol 27 (1) ◽  
pp. 127-139 ◽  
Author(s):  
Oliver J.T. Harris
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Daily Life ◽  
Critical Issue ◽  
Small Scale ◽  
Processes Of Change ◽  
Origins Of Agriculture ◽  
The Real ◽  
Historical Processes

The growing interest in assemblages has already opened up a number of important lines of enquiry in archaeology, from the morphogenetic capacities of matter through to a rethinking of the concept of community. In this paper I want to explore how assemblages allow us to reconceptualize the critical issue of scale. Archaeologists have vacillated between expending energy on the ‘great processes’ of change like the evolution of humanity, the colonization of the globe or the origins of agriculture, and focusing on the momentary, fleeting nature of a small-scale ethnographic present. Where archaeologists have attempted to integrate different scales the result has usually been to turn to Annales-influenced or time perspectivism-driven approaches and their fixed, linear and ontologically incompatible layers of history. In contrast, I will use assemblages to examine how we can rethink both the emergence of multiple scales and their role in history, without reducing the differences of the small-scale to an epiphenomenal outcome of larger events, or treating large-scale historical processes as mere reifications of the ‘real’ on-the-ground stuff of daily life. As we will see, this approach also has consequences for the particular kind of reality we accord to large-scale archaeological categories.

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