1,399 research outputs found
A modified Poisson-Boltzmann theory: Effects of co-solvent polarizability
In this paper within a field-theoretical approach taking into account
explicitly a co-solvent with a nonzero dipole and a polarizability tensor, we
derive a modified Poisson-Boltzmann equation. Applying the modified
Poisson-Boltzmann equation, we formulate a generalized Gouy-Chapman theory for
the case when an electrolyte solution is mixed with a polar co-solvent having a
large polarizability. We show that an increase of the co-solvent concentration
as well as the co-solvent polarizability lead to a significant increase of
differential capacitance at sufficiently high surface potentials of the
electrode, whereas the profile of the electrostatic potential becomes
considerably more long-ranged. On the contrary, an increase in the permanent
dipole of the co-solvent only weakly affects the differential capacitance
Boundary States in Graphene Heterojunctions
A new type of states in graphene-based planar heterojunctions has been
studied in the envelope wave function approximation. The condition for the
formation of these states is the intersection between the dispersion curves of
graphene and its gap modification. This type of states can also occur in smooth
graphene-based heterojunctions.Comment: 5 pages, 3 figure
Structure of the Wake of a Magnetic Obstacle
We use a combination of numerical simulations and experiments to elucidate
the structure of the flow of an electrically conducting fluid past a localized
magnetic field, called magnetic obstacle. We demonstrate that the stationary
flow pattern is considerably more complex than in the wake behind an ordinary
body. The steady flow is shown to undergo two bifurcations (rather than one)
and to involve up to six (rather than just two) vortices. We find that the
first bifurcation leads to the formation of a pair of vortices within the
region of magnetic field that we call inner magnetic vortices, whereas a second
bifurcation gives rise to a pair of attached vortices that are linked to the
inner vortices by connecting vortices.Comment: 4 pages, 5 figures, corrected two typos, accepted for PR
Weakly- and Semi-Supervised Panoptic Segmentation
We present a weakly supervised model that jointly performs both semantic- and
instance-segmentation -- a particularly relevant problem given the substantial
cost of obtaining pixel-perfect annotation for these tasks. In contrast to many
popular instance segmentation approaches based on object detectors, our method
does not predict any overlapping instances. Moreover, we are able to segment
both "thing" and "stuff" classes, and thus explain all the pixels in the image.
"Thing" classes are weakly-supervised with bounding boxes, and "stuff" with
image-level tags. We obtain state-of-the-art results on Pascal VOC, for both
full and weak supervision (which achieves about 95% of fully-supervised
performance). Furthermore, we present the first weakly-supervised results on
Cityscapes for both semantic- and instance-segmentation. Finally, we use our
weakly supervised framework to analyse the relationship between annotation
quality and predictive performance, which is of interest to dataset creators.Comment: ECCV 2018. The first two authors contributed equall
Quantum oscillations of nitrogen atoms in uranium nitride
The vibrational excitations of crystalline solids corresponding to acoustic
or optic one phonon modes appear as sharp features in measurements such as
neutron spectroscopy. In contrast, many-phonon excitations generally produce a
complicated, weak, and featureless response. Here we present time-of-flight
neutron scattering measurements for the binary solid uranium nitride (UN),
showing well-defined, equally-spaced, high energy vibrational modes in addition
to the usual phonons. The spectrum is that of a single atom, isotropic quantum
harmonic oscillator and characterizes independent motions of light nitrogen
atoms, each found in an octahedral cage of heavy uranium atoms. This is an
unexpected and beautiful experimental realization of one of the fundamental,
exactly-solvable problems in quantum mechanics. There are also practical
implications, as the oscillator modes must be accounted for in the design of
generation IV nuclear reactors that plan to use UN as a fuel.Comment: 25 pages, 10 figures, submitted to Nature Communications,
supplementary information adde
How to Circumvent the Two-Ciphertext Lower Bound for Linear Garbling Schemes
At EUROCRYPT 2015, Zahur et al.\ argued that all linear, and thus, efficient, garbling schemes need at least two -bit elements to garble an AND gate with security parameter . We show how to circumvent this lower bound, and propose an efficient garbling scheme which requires less than two -bit elements per AND gate for most circuit layouts. Our construction slightly deviates from the linear garbling model, and constitutes no contradiction to any claims in the lower-bound proof. With our proof of concept construction, we hope to spur new ideas for more practical garbling schemes.
Our construction can directly be applied to semi-private function evaluation by garbling XOR, XNOR, NAND, OR, NOR and AND gates in the same way, and keeping the evaluator oblivious of the gate function
FleXOR: Flexible Garbling for XOR Gates That Beats Free-XOR
Most implementations of Yao\u27s garbled circuit approach for 2-party secure computation use the {\em free-XOR} optimization of Kolesnikov \& Schneider (ICALP 2008). We introduce an alternative technique called {\em flexible-XOR} (fleXOR) that generalizes free-XOR and offers several advantages. First, fleXOR can be instantiated under a weaker hardness assumption on the underlying cipher/hash function (related-key security only, compared to related-key and circular security required for free-XOR) while maintaining most of the performance improvements that free-XOR offers. Alternatively, even though XOR gates are not always ``free\u27\u27 in our approach, we show that the other (non-XOR) gates can be optimized more heavily than what is possible when using free-XOR. For many circuits of cryptographic interest, this can yield a significantly (over 30\%) smaller garbled circuit than any other known techniques (including free-XOR) or their combinations
Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data
User-generated data is crucial to predictive modeling in many applications.
With a web/mobile/wearable interface, a data owner can continuously record data
generated by distributed users and build various predictive models from the
data to improve their operations, services, and revenue. Due to the large size
and evolving nature of users data, data owners may rely on public cloud service
providers (Cloud) for storage and computation scalability. Exposing sensitive
user-generated data and advanced analytic models to Cloud raises privacy
concerns. We present a confidential learning framework, SecureBoost, for data
owners that want to learn predictive models from aggregated user-generated data
but offload the storage and computational burden to Cloud without having to
worry about protecting the sensitive data. SecureBoost allows users to submit
encrypted or randomly masked data to designated Cloud directly. Our framework
utilizes random linear classifiers (RLCs) as the base classifiers in the
boosting framework to dramatically simplify the design of the proposed
confidential boosting protocols, yet still preserve the model quality. A
Cryptographic Service Provider (CSP) is used to assist the Cloud's processing,
reducing the complexity of the protocol constructions. We present two
constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of
homomorphic encryption, garbled circuits, and random masking to achieve both
security and efficiency. For a boosted model, Cloud learns only the RLCs and
the CSP learns only the weights of the RLCs. Finally, the data owner collects
the two parts to get the complete model. We conduct extensive experiments to
understand the quality of the RLC-based boosting and the cost distribution of
the constructions. Our results show that SecureBoost can efficiently learn
high-quality boosting models from protected user-generated data
Thermokinetic Diagram of the Nonequilibrium Crystallization of Die Steel 2Kh5MNFSL
© 2014, Springer Science+Business Media New York. A thermokinetic diagram of the hardening of steel 2Kh5MNFSL is plotted on the basis of results of experimental studies of the effect of the rate of cooling of a melt on the temperature field of castings and the rate of displacement of the directional crystallization front. The critical temperature of supercooling of the melt is determined along with the dimensions of the homogenously structured layer that is formed on the surface of the casting; segregation-inducing diffusion of the alloying elements is almost completely suppressed within this layer
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