5,748 research outputs found
First-principles study of the Young's modulus of Si <001> nanowires
We report the results of first-principles density functional theory
calculations of the Young's modulus and other mechanical properties of
hydrogen-passivated Si nanowires. The nanowires are taken to have
predominantly {100} surfaces, with small {110} facets. The Young's modulus, the
equilibrium length and the residual stress of a series of prismatic wires are
found to have a size dependence that scales like the surface area to volume
ratio for all but the smallest wires. We analyze the physical origin of the
size dependence, and compare the results to two existing models.Comment: 5 pages, 3 figure
First-principles calculation of mechanical properties of Si <001> nanowires and comparison to nanomechanical theory
We report the results of first-principles density functional theory
calculations of the Young's modulus and other mechanical properties of
hydrogen-passivated Si nanowires. The nanowires are taken to have
predominantly {100} surfaces, with small {110} facets according to the Wulff
shape. The Young's modulus, the equilibrium length and the constrained residual
stress of a series of prismatic beams of differing sizes are found to have size
dependences that scale like the surface area to volume ratio for all but the
smallest beam. The results are compared with a continuum model and the results
of classical atomistic calculations based on an empirical potential. We
attribute the size dependence to specific physical structures and interactions.
In particular, the hydrogen interactions on the surface and the charge density
variations within the beam are quantified and used both to parameterize the
continuum model and to account for the discrepancies between the two models and
the first-principles results.Comment: 14 pages, 10 figure
CALIPER: Continuous Authentication Layered with Integrated PKI Encoding Recognition
Architectures relying on continuous authentication require a secure way to
challenge the user's identity without trusting that the Continuous
Authentication Subsystem (CAS) has not been compromised, i.e., that the
response to the layer which manages service/application access is not fake. In
this paper, we introduce the CALIPER protocol, in which a separate Continuous
Access Verification Entity (CAVE) directly challenges the user's identity in a
continuous authentication regime. Instead of simply returning authentication
probabilities or confidence scores, CALIPER's CAS uses live hard and soft
biometric samples from the user to extract a cryptographic private key embedded
in a challenge posed by the CAVE. The CAS then uses this key to sign a response
to the CAVE. CALIPER supports multiple modalities, key lengths, and security
levels and can be applied in two scenarios: One where the CAS must authenticate
its user to a CAVE running on a remote server (device-server) for access to
remote application data, and another where the CAS must authenticate its user
to a locally running trusted computing module (TCM) for access to local
application data (device-TCM). We further demonstrate that CALIPER can leverage
device hardware resources to enable privacy and security even when the device's
kernel is compromised, and we show how this authentication protocol can even be
expanded to obfuscate direct kernel object manipulation (DKOM) malwares.Comment: Accepted to CVPR 2016 Biometrics Worksho
Adversarial Diversity and Hard Positive Generation
State-of-the-art deep neural networks suffer from a fundamental problem -
they misclassify adversarial examples formed by applying small perturbations to
inputs. In this paper, we present a new psychometric perceptual adversarial
similarity score (PASS) measure for quantifying adversarial images, introduce
the notion of hard positive generation, and use a diverse set of adversarial
perturbations - not just the closest ones - for data augmentation. We introduce
a novel hot/cold approach for adversarial example generation, which provides
multiple possible adversarial perturbations for every single image. The
perturbations generated by our novel approach often correspond to semantically
meaningful image structures, and allow greater flexibility to scale
perturbation-amplitudes, which yields an increased diversity of adversarial
images. We present adversarial images on several network topologies and
datasets, including LeNet on the MNIST dataset, and GoogLeNet and ResidualNet
on the ImageNet dataset. Finally, we demonstrate on LeNet and GoogLeNet that
fine-tuning with a diverse set of hard positives improves the robustness of
these networks compared to training with prior methods of generating
adversarial images.Comment: Accepted to CVPR 2016 DeepVision Worksho
Toward Open-Set Face Recognition
Much research has been conducted on both face identification and face
verification, with greater focus on the latter. Research on face identification
has mostly focused on using closed-set protocols, which assume that all probe
images used in evaluation contain identities of subjects that are enrolled in
the gallery. Real systems, however, where only a fraction of probe sample
identities are enrolled in the gallery, cannot make this closed-set assumption.
Instead, they must assume an open set of probe samples and be able to
reject/ignore those that correspond to unknown identities. In this paper, we
address the widespread misconception that thresholding verification-like scores
is a good way to solve the open-set face identification problem, by formulating
an open-set face identification protocol and evaluating different strategies
for assessing similarity. Our open-set identification protocol is based on the
canonical labeled faces in the wild (LFW) dataset. Additionally to the known
identities, we introduce the concepts of known unknowns (known, but
uninteresting persons) and unknown unknowns (people never seen before) to the
biometric community. We compare three algorithms for assessing similarity in a
deep feature space under an open-set protocol: thresholded verification-like
scores, linear discriminant analysis (LDA) scores, and an extreme value machine
(EVM) probabilities. Our findings suggest that thresholding EVM probabilities,
which are open-set by design, outperforms thresholding verification-like
scores.Comment: Accepted for Publication in CVPR 2017 Biometrics Worksho
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