2,642 research outputs found
Differential Harnack Estimates for Parabolic Equations
Let be a solution to the Ricci flow on a closed Riemannian
manifold. In this paper, we prove differential Harnack inequalities for
positive solutions of nonlinear parabolic equations of the type \ppt f=\Delta
f-f \ln f +Rf. We also comment on an earlier result of the first author on
positive solutions of the conjugate heat equation under the Ricci flow.Comment: 10 page
Robust Image Segmentation Quality Assessment
Deep learning based image segmentation methods have achieved great success,
even having human-level accuracy in some applications. However, due to the
black box nature of deep learning, the best method may fail in some situations.
Thus predicting segmentation quality without ground truth would be very crucial
especially in clinical practice. Recently, people proposed to train neural
networks to estimate the quality score by regression. Although it can achieve
promising prediction accuracy, the network suffers robustness problem, e.g. it
is vulnerable to adversarial attacks. In this paper, we propose to alleviate
this problem by utilizing the difference between the input image and the
reconstructed image, which is conditioned on the segmentation to be assessed,
to lower the chance to overfit to the undesired image features from the
original input image, and thus to increase the robustness. Results on ACDC17
dataset demonstrated our method is promising
On Locally Conformally Flat Gradient Shrinking Ricci Solitons
In this paper, we first apply an integral identity on Ricci solitons to prove
that closed locally conformally flat gradient Ricci solitons are of constant
sectional curvature. We then generalize this integral identity to complete
noncompact gradient shrinking Ricci solitons, under the conditions that the
Ricci curvature is bounded from below and the Riemannian curvature tensor has
at most exponential growth. As a consequence of this identity, we classify
complete locally conformally flat gradient shrinking Ricci solitons with Ricci
curvature bounded from below.Comment: 13 pages, revised version, third author adde
Unsupervised anomaly localization using VAE and beta-VAE
Variational Auto-Encoders (VAEs) have shown great potential in the
unsupervised learning of data distributions. An VAE trained on normal images is
expected to only be able to reconstruct normal images, allowing the
localization of anomalous pixels in an image via manipulating information
within the VAE ELBO loss. The ELBO consists of KL divergence loss (image-wise)
and reconstruction loss (pixel-wise). It is natural and straightforward to use
the later as the predictor. However, usually local anomaly added to a normal
image can deteriorate the whole reconstructed image, causing segmentation using
only naive pixel errors not accurate. Energy based projection was proposed to
increase the reconstruction accuracy of normal regions/pixels, which achieved
the state-of-the-art localization accuracy on simple natural images. Another
possible predictors are ELBO and its components gradients with respect to each
pixels. Previous work claimed that KL gradient is a robust predictor. In this
paper, we argue that the energy based projection in medical imaging is not as
useful as on natural images. Moreover, we observe that the robustness of KL
gradient predictor totally depends on the setting of the VAE and dataset. We
also explored the effect of the weight of KL loss within beta-VAE and predictor
ensemble in anomaly localization.Comment: arXiv admin note: substantial text overlap with arXiv:2002.03734 by
other author
Estrogen status alters tissue distribution and metabolism of oral dose of 75Se-selenite
An association between male and female sex hormones and selenium (Se) status has been reported in animals and humans. These relationships may be important relative to the use of selenium in hormone related diseases such as breast cancer. The purpose of this study is to examine the effect of estrogen status on the absorption, tissue distribution and metabolism of Se. 60 µCi of 75Se as selenite was orally administered to bilaterally ovariectomized rats 5 weeks after implantation of either placebo pellet (-E) or pellet with estradiol (+E). Blood and tissues were collected 1, 3, 6, and 24 h after dosing. Although absorption of 75Se was independent of E status, 75Se activity differed (P<0.05) in blood, liver, heart, kidney, spleen, brain, and thymus at certain times. For example, total 75Se activity in liver was greater in -E than in +E rats after 1 hour (13.1% vs. 3.9% of total dose). However, +E group had greater 75Se in liver after 6 h than -E group (18.0% vs. 10.9%). The relative distribution of 75Se between cytosol and membrane fractions in tissues was independent of E status. Nor it affected the relative distribution of 75Se among the selenoproteins in cytosol of the above tissues and plasma. However, larger percent of 75Se was incorporated into selenoprotein P (SeP) and glutathione peroxidase (GPx) in plasma in +E group at 3, 6 and 24 h compared to -E group (P<0.05). These results suggest that the effects of estrogen status on selenium distribution among tissues are tissue specific and time-dependent.Supported by the OARDC Grant OHO0020
Estrogen status alters tissue distribution of oral dose of 75Se-selenite and enhances hepatic levels of SelP mRNA, GPx mRNA, GPx activity, and Se
An association between male and female sex hormones and selenium (Se) status has been reported in animals and humans. These relationships may be important relative to the use of selenium in hormone related diseases such as breast cancer. The purpose of this study was to examine the effect of estrogen status on the tissue distribution of Se and mRNA levels of selenoprotein P (SelP) and glutathione peroxidase (GPx) in liver. 60 µCi of 75Se as selenite was orally administered to each bilaterally ovariectomized rat 5 weeks after implantation with either placebo pellet (OVX) or pellet with estradiol (OVX+E2). Blood and tissues were collected 1, 3, 6, and 24 h after dosing. Differences (P<0.05) in 75Se in blood, liver, heart, kidney, spleen, brain, and thymus were noted at certain times. Plasma SelP in OVX+E2 group contained a greater percentage of 75Se at 3, 6 and 24 h compared to OVX group (P<0.05); 75Se in plasma GPx also was greater in OVX+E2 compared to OVX group at 24 h (P<0.05). Real-time RT-PCR analysis showed that both hepatic SelP mRNA (0.93 vs. 0.50) and GPx mRNA (2.81 vs. 2.24) were significantly greater in OVX+E2 group than in OVX group. These results suggest that estrogen status affects distribution of ingested Se in tissue- and time-dependent manners, as well as the expression of hepatic SelP and GPx at both protein and mRNA level.
(Supported by the OARDC Grant OHO00201).OARD
Non-smooth model and numerical analysis of a friction driven structure for piezoelectric motors
In this contribution, typical friction driven structures are summarized and
presented considering the mechanical structures and operation principles of
different types of piezoelectric motors. A two degree-of-freedom dynamic model
with one unilateral frictional contact is built for one of the friction driven
structures. Different contact regimes and the transitions between them are
identified and analyzed. Numerical simulations are conducted to find out
different operation modes of the system concerning the sequence of contact
regimes in one steady state period. The influences of parameters on the
operation modes and corresponding steady state characteristics are also
explored. Some advice are then given in terms of the design of friction driven
structures and piezoelectric motors
Concurrency Protocol Aiming at High Performance of Execution and Replay for Smart Contracts
Although the emergence of the programmable smart contract makes blockchain
systems easily embrace a wider range of industrial areas, how to execute smart
contracts efficiently becomes a big challenge nowadays. Due to the existence of
Byzantine nodes, the mechanism of executing smart contracts is quite different
from that in database systems, so that existing successful concurrency control
protocols in database systems cannot be employed directly. Moreover, even
though smart contract execution follows a two-phase style, i.e, the miner node
executes a batch of smart contracts in the first phase and the validators
replay them in the second phase, existing parallel solutions only focus on the
optimization in the first phase, but not including the second phase.
In this paper, we propose a novel efficient concurrency control scheme which
is the first one to do optimization in both phases. Specifically, (i) in the
first phase, we give a variant of OCC (Optimistic Concurrency Control) protocol
based on {\em batching} feature to improve the concurrent execution efficiency
for the miner and produce a schedule log with high parallelism for validators.
Also, a graph partition algorithm is devised to divide the original schedule
log into small pieces and further reduce the communication cost; and (ii) in
the second phase, we give a deterministic OCC protocol to replay all smart
contracts efficiently on multi-core validators where all cores can replay smart
contracts independently. Theoretical analysis and extensive experimental
results illustrate that the proposed scheme outperforms state-of-art solutions
significantly
Nash Equilibrium in the Quantum Battle of Sexes Game
We investigate Nash Equilibrium in the quantum Battle of Sexes Game. We find
the game has infinite Nash Equilibria and all of them leads to the asymmetry
result. We also show that there is no unique but infinite Nash Equilibrium in
it if we use the quantizing scheme proposed by Eisert et al and the two players
are allowed to adopt any unitary operator as his/her strategies
On the Discrimination-Generalization Tradeoff in GANs
Generative adversarial training can be generally understood as minimizing
certain moment matching loss defined by a set of discriminator functions,
typically neural networks. The discriminator set should be large enough to be
able to uniquely identify the true distribution (discriminative), and also be
small enough to go beyond memorizing samples (generalizable). In this paper, we
show that a discriminator set is guaranteed to be discriminative whenever its
linear span is dense in the set of bounded continuous functions. This is a very
mild condition satisfied even by neural networks with a single neuron. Further,
we develop generalization bounds between the learned distribution and true
distribution under different evaluation metrics. When evaluated with neural
distance, our bounds show that generalization is guaranteed as long as the
discriminator set is small enough, regardless of the size of the generator or
hypothesis set. When evaluated with KL divergence, our bound provides an
explanation on the counter-intuitive behaviors of testing likelihood in GAN
training. Our analysis sheds lights on understanding the practical performance
of GANs.Comment: ICLR 201
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