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Rapid Fabrication of Large-sized Solid Shape using Variable Lamination Manufacturing and Multi-functional Hotwire Cutting System
Rapid prototyping (RP) technologies have been widely used to reduce the lead-time and
development cost of new products. The VLM-ST process has been developed to overcome the
currently developed RP technologies such as a large building time, a high building cost, an
additional post-processing and a large apparatus cost. However, the VLM-ST process has the
limitation of fabricated model size (VLM300: 297×210 mm, VLM400: 420×297 mm) and the
limitation of slope angle when the large-sized model more than 600 × 600 × 600 mm or
axisymmetric shape is fabricated. The objective of this paper is to develop a multi-functional
hotwire cutting system (MHC) using EPS-foam block or sheet as the working material in order to
fabricate a large-sized shape more than 600 × 600 × 600 mm. Because the MHC apparatus
employs a four-axis synchronized hotwire cutter with the structure of two XY movable heads and
a turn-table, it allows the easy fabrication of various 3D shapes, such as (1) an axisymmetric
shape or a sweeping cross-sectioned pillar shape using the hot-strip in the form of sweeping
surface and EPS foam block on the turn-table, (2) a polyhedral complex shape using the hotwire
and EPS foam block on the turn-table, and (3) a ruled surface approximated freeform shape using
the hotwire and EPS foam sheet. In order to examine the applicability of the developed MHC
apparatus, an axisymmetric shape, a polyhedral shape and a large-sized freeform shape were
fabricated by the apparatus.Mechanical Engineerin
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
Deep networks consume a large amount of memory by their nature. A natural
question arises can we reduce that memory requirement whilst maintaining
performance. In particular, in this work we address the problem of memory
efficient learning for multiple tasks. To this end, we propose a novel network
architecture producing multiple networks of different configurations, termed
deep virtual networks (DVNs), for different tasks. Each DVN is specialized for
a single task and structured hierarchically. The hierarchical structure, which
contains multiple levels of hierarchy corresponding to different numbers of
parameters, enables multiple inference for different memory budgets. The
building block of a deep virtual network is based on a disjoint collection of
parameters of a network, which we call a unit. The lowest level of hierarchy in
a deep virtual network is a unit, and higher levels of hierarchy contain lower
levels' units and other additional units. Given a budget on the number of
parameters, a different level of a deep virtual network can be chosen to
perform the task. A unit can be shared by different DVNs, allowing multiple
DVNs in a single network. In addition, shared units provide assistance to the
target task with additional knowledge learned from another tasks. This
cooperative configuration of DVNs makes it possible to handle different tasks
in a memory-aware manner. Our experiments show that the proposed method
outperforms existing approaches for multiple tasks. Notably, ours is more
efficient than others as it allows memory-aware inference for all tasks.Comment: CVPR 201
Geometrically Induced Phase Transitions at Large N
Utilizing the large N dual description of a metastable system of branes and
anti-branes wrapping rigid homologous S^2's in a non-compact Calabi-Yau
threefold, we study phase transitions induced by changing the positions of the
S^2's. At leading order in 1/N the effective potential for this system is
computed by the planar limit of an auxiliary matrix model. Beginning at the two
loop correction, the degenerate vacuum energy density of the discrete confining
vacua split, and a potential is generated for the axion. Changing the relative
positions of the S^2's causes discrete jumps in the energetically preferred
confining vacuum and can also obstruct direct brane/anti-brane annihilation
processes. The branes must hop to nearby S^2's before annihilating, thus
significantly increasing the lifetime of the corresponding non-supersymmetric
vacua. We also speculate that misaligned metastable glueball phases may
generate a repulsive inter-brane force which stabilizes the radial mode present
in compact Calabi-Yau threefolds.Comment: 47 pages, 7 figure
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