11,140 research outputs found
Unifying and Merging Well-trained Deep Neural Networks for Inference Stage
We propose a novel method to merge convolutional neural-nets for the
inference stage. Given two well-trained networks that may have different
architectures that handle different tasks, our method aligns the layers of the
original networks and merges them into a unified model by sharing the
representative codes of weights. The shared weights are further re-trained to
fine-tune the performance of the merged model. The proposed method effectively
produces a compact model that may run original tasks simultaneously on
resource-limited devices. As it preserves the general architectures and
leverages the co-used weights of well-trained networks, a substantial training
overhead can be reduced to shorten the system development time. Experimental
results demonstrate a satisfactory performance and validate the effectiveness
of the method.Comment: To appear in the 27th International Joint Conference on Artificial
Intelligence and the 23rd European Conference on Artificial Intelligence,
2018. (IJCAI-ECAI 2018
Compressing nearly hard sphere fluids increases glass fragility
We use molecular dynamics to investigate the glass transition occurring at
large volume fraction, phi, and low temperature, T, in assemblies of soft
repulsive particles. We find that equilibrium dynamics in the (phi, T) plane
obey a form of dynamic scaling in the proximity of a critical point at T=0 and
phi=phi_0, which should correspond to the ideal glass transition of hard
spheres. This glass point, `point G', is distinct from athermal jamming
thresholds. A remarkable consequence of scaling behaviour is that the dynamics
at fixed phi passes smoothly from that of a strong glass to that of a very
fragile glass as phi increases beyond phi_0. Correlations between fragility and
various physical properties are explored.Comment: 5 pages, 3 figures; Version accepted at Europhys. Let
Privacy-Preserving Shortest Path Computation
Navigation is one of the most popular cloud computing services. But in
virtually all cloud-based navigation systems, the client must reveal her
location and destination to the cloud service provider in order to learn the
fastest route. In this work, we present a cryptographic protocol for navigation
on city streets that provides privacy for both the client's location and the
service provider's routing data. Our key ingredient is a novel method for
compressing the next-hop routing matrices in networks such as city street maps.
Applying our compression method to the map of Los Angeles, for example, we
achieve over tenfold reduction in the representation size. In conjunction with
other cryptographic techniques, this compressed representation results in an
efficient protocol suitable for fully-private real-time navigation on city
streets. We demonstrate the practicality of our protocol by benchmarking it on
real street map data for major cities such as San Francisco and Washington,
D.C.Comment: Extended version of NDSS 2016 pape
An Information-Theoretic Test for Dependence with an Application to the Temporal Structure of Stock Returns
Information theory provides ideas for conceptualising information and
measuring relationships between objects. It has found wide application in the
sciences, but economics and finance have made surprisingly little use of it. We
show that time series data can usefully be studied as information -- by noting
the relationship between statistical redundancy and dependence, we are able to
use the results of information theory to construct a test for joint dependence
of random variables. The test is in the same spirit of those developed by
Ryabko and Astola (2005, 2006b,a), but differs from these in that we add extra
randomness to the original stochatic process. It uses data compression to
estimate the entropy rate of a stochastic process, which allows it to measure
dependence among sets of random variables, as opposed to the existing
econometric literature that uses entropy and finds itself restricted to
pairwise tests of dependence. We show how serial dependence may be detected in
S&P500 and PSI20 stock returns over different sample periods and frequencies.
We apply the test to synthetic data to judge its ability to recover known
temporal dependence structures.Comment: 22 pages, 7 figure
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