34,401 research outputs found
Scaling and width distributions of parity-conserving interfaces
We present an alternative finite-size approach to a set of parity-conserving interfaces involving attachment, dissociation, and detachment of extended objects in 1+1 dimensions. With the aid of a nonlocal construct introduced by Barma and Dhar in related systems [Phys. Rev. Lett. 73, 2135 (1994)], we circumvent the subdiffusive dynamics and examine close-to-equilibrium aspects of these interfaces by assembling states of much smaller, numerically accessible scales. As a result, roughening exponents, height correlations, and width distributions exhibiting universal scaling functions are evaluated for interfaces virtually grown out of dimers and trimers on large-scale substrates. Dynamic exponents are also studied by finite-size scaling of the spectrum gaps of evolution operators.Fil: Arlego, Marcelo José Fabián. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - La Plata. Instituto de FÃsica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de FÃsica La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de FÃsica; ArgentinaFil: Grynberg, Marcelo Daniel. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - La Plata. Instituto de FÃsica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de FÃsica La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de FÃsica; Argentin
Joint Multi-Person Pose Estimation and Semantic Part Segmentation
Human pose estimation and semantic part segmentation are two complementary
tasks in computer vision. In this paper, we propose to solve the two tasks
jointly for natural multi-person images, in which the estimated pose provides
object-level shape prior to regularize part segments while the part-level
segments constrain the variation of pose locations. Specifically, we first
train two fully convolutional neural networks (FCNs), namely Pose FCN and Part
FCN, to provide initial estimation of pose joint potential and semantic part
potential. Then, to refine pose joint location, the two types of potentials are
fused with a fully-connected conditional random field (FCRF), where a novel
segment-joint smoothness term is used to encourage semantic and spatial
consistency between parts and joints. To refine part segments, the refined pose
and the original part potential are integrated through a Part FCN, where the
skeleton feature from pose serves as additional regularization cues for part
segments. Finally, to reduce the complexity of the FCRF, we induce human
detection boxes and infer the graph inside each box, making the inference forty
times faster.
Since there's no dataset that contains both part segments and pose labels, we
extend the PASCAL VOC part dataset with human pose joints and perform extensive
experiments to compare our method against several most recent strategies. We
show that on this dataset our algorithm surpasses competing methods by a large
margin in both tasks.Comment: This paper has been accepted by CVPR 201
Can virtual reality predict body part discomfort and performance of people in realistic world for assembling tasks?
This paper presents our work on relationship of evaluation results between
virtual environment (VE) and realistic environment (RE) for assembling tasks.
Evaluation results consist of subjective results (BPD and RPE) and objective
results (posture and physical performance). Same tasks were performed with same
experimental configurations and evaluation results were measured in RE and VE
respectively. Then these evaluation results were compared. Slight difference of
posture between VE and RE was found but not great difference of effect on
people according to conventional ergonomics posture assessment method.
Correlation of BPD and performance results between VE and RE are found by
linear regression method. Moreover, results of BPD, physical performance, and
RPE in VE are higher than that in RE with significant difference. Furthermore,
these results indicates that subjects feel more discomfort and fatigue in VE
than RE because of additional effort required in VE
Solving constraints within a graph based dependency model by digitising a new process of incrementally casting concrete structures
The mechanisation of incrementally casting concrete structures can reduce the economic and environmental cost of the formwork which produces them. Low-tech versions of these forms have been designed to produce structures with cross-sectional continuity, but the design and implementation of complex adaptable formworks remains untenable for smaller projects. Addressing these feasibility issues by digitally modelling these systems is problematic because constraint solvers are the obvious method of modelling the adaptable formwork, but cannot acknowledge the hierarchical relationships created by assembling multiple instances of the system. This thesis hypothesises that these opposing relationships may not be completely disparate and that simple dependency relationships can be used to solve constraints if the real procedure of constructing the system is replicated digitally. The behaviour of the digital model was correlated with the behaviour of physical prototypes of the system which were refined based on digital explorations of its possibilities. The generated output is assessed physically on the basis of its efficiency and ease of assembly and digitally on the basis that permutations can be simply described and potentially built in reality. One of the columns generated by the thesis will be cast by the redesigned system in Lyon at the first F2F (file to factory) continuum workshop
Identifying the task variables that predict object assembly difficulty.
We investigated the physical attributes of an object that influence the difficulty of its assembly. Identifying attributes that contribute to assembly difficulty will provide a method for predicting assembly complexity
The Nature of the Chemical Process. 1. Symmetry Evolution - Revised Information Theory, Similarity Principle and Ugly Symmetry
Three laws of information theory have been proposed. Labeling by introducing
nonsymmetry and formatting by introducing symmetry are defined. The function L
(L=lnw, w is the number of microstates, or the sum of entropy and information,
L=S+I) of the universe is a constant (the first law of information theory). The
entropy S of the universe tends toward a maximum (the second law law of
information theory). For a perfect symmetric static structure, the information
is zero and the static entropy is the maximum (the third law law of information
theory). Based on the Gibbs inequality and the second law of the revised
information theory we have proved the similarity principle (a continuous higher
similarity-higher entropy relation after the rejection of the Gibbs paradox)
and proved the Curie-Rosen symmetry principle (a higher symmetry-higher
stability relation) as a special case of the similarity principle. Some
examples in chemical physics have been given. Spontaneous processes of all
kinds of molecular interaction, phase separation and phase transition,
including symmetry breaking and the densest molecular packing and
crystallization, are all driven by information minimization or symmetry
maximization. The evolution of the universe in general and evolution of life in
particular can be quantitatively considered as a series of symmetry breaking
processes. The two empirical rules - similarity rule and complementarity rule -
have been given a theoretical foundation. All kinds of periodicity in space and
time are symmetries and contribute to the stability. Symmetry is beautiful
because it renders stability. However, symmetry is in principle ugly because it
is associated with information loss.Comment: 29 pages, 14 figure
- …