1,508 research outputs found
Carotid artery resection and reconstruction with superficial femoral artery transplantation: a case report
Freight Operations Modelling for Urban Delivery and Pickup with Flexible Routing: Cluster Transport Modelling Incorporating Discrete-Event Simulation and GIS
Urban pickup and delivery (PUD) activities are important for logistics operations. Real operations for general freight involve a high degree of complexity due to daily variability. Discrete-event simulation (DES) is a method that can mimic real operations and include stochastic parameters. However, realistic vehicle routing is difficult to build in DES models. The objective is to create a DES model for realistic freight routing, which considers the driver’s routing decisions. Realistic models need to predict the delivery route (including time and distance) for variable consignment address and backhaul pickup. Geographic information systems (GIS) and DES were combined to develop freight PUD models. GIS was used to process geographical data. Two DES models were developed and compared. The first was a simple suburb model, and the second an intersection-based model. Real industrial data were applied including one-year consignment data and global positioning system (GPS) data. A case study of one delivery tour is shown, with results validated with actual GPS data. The DES results were also compared with conventional GIS models. The result shows the intersection-based model is adequate to mimic actual PUD routing. This work provides a method for combining GIS and DES to build freight operation models for urban PUD. This has the potential to help industry logistics practitioners better understand their current operations and experiment with different scenarios
Minimum Viable Model (MVM) Methodology for Integration of Agile Methods into Operational Simulation of Logistics
Background: Logistics problems involve a large number of complexities, which makes the development of models challenging. While computer simulation models are developed for addressing complexities, it is essential to ensure that the necessary operational behaviours are captured, and that the architecture of the model is suitable to represent them. The early stage of simulation modelling, known as conceptual modelling (CM), is thus dependent on successfully extracting tacit operational knowledge and avoiding misunderstanding between the client (customer of the model) and simulation analyst. Objective: This paper developed a methodology for managing the knowledge-acquisition process needed to create a sufficient simulation model at the early or the CM stage to ensure the correctness of operation representation. Methods: A minimum viable model (MVM) methodology was proposed with five principles relevant to CM: iterative development, embedded communication, soliciting tacit knowledge, interactive face validity, and a sufficient model. The method was validated by a case study of freight operations, and the results were encouraging. Conclusions: The MVM method improved the architecture of the simulation model through eliciting tacit knowledge and clearing up communication misunderstandings. It also helped shape the architecture of the model towards the features most appreciated by the client, and features not needed in the model. Originality: The novel contribution of this work is the presentation of a method for eliciting tacit information from industrial clients, and building a minimally sufficient simulation model at the early modelling stage. The framework is demonstrated for logistics operations, though the principles may benefit simulation practitioners more generally.</jats:p
Scale without Conformal Invariance at Three Loops
We carry out a three-loop computation that establishes the existence of scale
without conformal invariance in dimensional regularization with the MS scheme
in d=4-epsilon spacetime dimensions. We also comment on the effects of scheme
changes in theories with many couplings, as well as in theories that live on
non-conformal scale-invariant renormalization group trajectories. Stability
properties of such trajectories are analyzed, revealing both attractive and
repulsive directions in a specific example. We explain how our results are in
accord with those of Jack & Osborn on a c-theorem in d=4 (and d=4-epsilon)
dimensions. Finally, we point out that limit cycles with turning points are
unlike limit cycles with continuous scale invariance.Comment: 21 pages, 3 figures, Erratum adde
From QCD lattice calculations to the equation of state of quark matter
We describe two-flavor QCD lattice data for the pressure at finite
temperature and zero chemical potential within a quasiparticle model. Relying
only on thermodynamic selfconsistency, the model is extended to nonzero
chemical potential. The results agree with lattice calculations in the region
of small chemical potential.Comment: 5 eps figure
SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing
While models of 3D clothing learned from real data exist, no method can
predict clothing deformation as a function of garment size. In this paper, we
introduce SizerNet to predict 3D clothing conditioned on human body shape and
garment size parameters, and ParserNet to infer garment meshes and shape under
clothing with personal details in a single pass from an input mesh. SizerNet
allows to estimate and visualize the dressing effect of a garment in various
sizes, and ParserNet allows to edit clothing of an input mesh directly,
removing the need for scan segmentation, which is a challenging problem in
itself. To learn these models, we introduce the SIZER dataset of clothing size
variation which includes different subjects wearing casual clothing items
in various sizes, totaling to approximately 2000 scans. This dataset includes
the scans, registrations to the SMPL model, scans segmented in clothing parts,
garment category and size labels. Our experiments show better parsing accuracy
and size prediction than baseline methods trained on SIZER. The code, model and
dataset will be released for research purposes.Comment: European Conference on Computer Vision 202
Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery
Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc
E7(7) invariant Lagrangian of d=4 N=8 supergravity
We present an E7(7) invariant Lagrangian that leads to the equations of
motion of d=4 N=8 supergravity without using Lagrange multipliers. The
superinvariance of this new action and the closure of the supersymmetry algebra
are proved explicitly for the terms that differ from the Cremmer--Julia
formulation. Since the diffeomorphism symmetry is not realized in the standard
way on the vector fields, we switch to the Hamiltonian formulation in order to
prove the invariance of the E7(7) invariant action under general coordinate
transformations. We also construct the conserved E7(7)-Noether current of
maximal supergravity and we conclude with comments on the implications of this
manifest off-shell E7(7)-symmetry for quantizing d=4 N=8 supergravity, in
particular on the E7(7)-action on phase space.Comment: 45 pages, references adde
Gauge transformations for higher-order lagrangians
Noether's symmetry transformations for higher-order lagrangians are studied.
A characterization of these transformations is presented, which is useful to
find gauge transformations for higher-order singular lagrangians. The case of
second-order lagrangians is studied in detail. Some examples that illustrate
our results are given; in particular, for the lagrangian of a relativistic
particle with curvature, lagrangian gauge transformations are obtained, though
there are no hamiltonian gauge generators for them.Comment: 22 pages, LaTe
Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On
The 2D virtual try-on task has recently attracted a great interest from the
research community, for its direct potential applications in online shopping as
well as for its inherent and non-addressed scientific challenges. This task
requires fitting an in-shop cloth image on the image of a person, which is
highly challenging because it involves cloth warping, image compositing, and
synthesizing. Casting virtual try-on into a supervised task faces a difficulty:
available datasets are composed of pairs of pictures (cloth, person wearing the
cloth). Thus, we have no access to ground-truth when the cloth on the person
changes. State-of-the-art models solve this by masking the cloth information on
the person with both a human parser and a pose estimator. Then, image synthesis
modules are trained to reconstruct the person image from the masked person
image and the cloth image. This procedure has several caveats: firstly, human
parsers are prone to errors; secondly, it is a costly pre-processing step,
which also has to be applied at inference time; finally, it makes the task
harder than it is since the mask covers information that should be kept such as
hands or accessories. In this paper, we propose a novel student-teacher
paradigm where the teacher is trained in the standard way (reconstruction)
before guiding the student to focus on the initial task (changing the cloth).
The student additionally learns from an adversarial loss, which pushes it to
follow the distribution of the real images. Consequently, the student exploits
information that is masked to the teacher. A student trained without the
adversarial loss would not use this information. Also, getting rid of both
human parser and pose estimator at inference time allows obtaining a real-time
virtual try-on.Comment: Accepted at ECCV 2020. arXiv admin note: text overlap with
arXiv:1906.0134
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