12,652 research outputs found
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation
In spite of the compelling achievements that deep neural networks (DNNs) have
made in medical image computing, these deep models often suffer from degraded
performance when being applied to new test datasets with domain shift. In this
paper, we present a novel unsupervised domain adaptation approach for
segmentation tasks by designing semantic-aware generative adversarial networks
(GANs). Specifically, we transform the test image into the appearance of source
domain, with the semantic structural information being well preserved, which is
achieved by imposing a nested adversarial learning in semantic label space. In
this way, the segmentation DNN learned from the source domain is able to be
directly generalized to the transformed test image, eliminating the need of
training a new model for every new target dataset. Our domain adaptation
procedure is unsupervised, without using any target domain labels. The
adversarial learning of our network is guided by a GAN loss for mapping data
distributions, a cycle-consistency loss for retaining pixel-level content, and
a semantic-aware loss for enhancing structural information. We validated our
method on two different chest X-ray public datasets for left/right lung
segmentation. Experimental results show that the segmentation performance of
our unsupervised approach is highly competitive with the upper bound of
supervised transfer learning
Cosmological Constraints on Variable Warm Dark Matter
Although CDM model is very successful in many aspects, it has been
seriously challenged. Recently, warm dark matter (WDM) remarkably rose as an
alternative of cold dark matter (CDM). In the literature, many attempts have
been made to determine the equation-of-state parameter (EoS) of WDM. However,
in most of the previous works, it is usually assumed that the EoS of dark
matter (DM) is constant (and usually the EoS of dark energy is also constant).
Obviously, this assumption is fairly restrictive. It is more natural to assume
a variable EoS for WDM (and dark energy). In the present work, we try to
constrain the EoS of variable WDM with the current cosmological observations.
We find that the best fits indicate WDM, while CDM is still consistent with the
current observational data. However, CDM is still better than WDM
models from the viewpoint of goodness-of-fit. So, in order to distinguish WDM
and CDM, the further observations on the small/galactic scale are required. On
the other hand, in this work we also consider WDM whose EoS is constant, while
the role of dark energy is played by various models. We find that the
cosmological constraint on the constant EoS of WDM is fairly robust.Comment: 11 pages, 6 figures, 1 table, revtex4; v2: discussions added, Phys.
Lett. B in press; v3: published versio
Predict Forex Trend via Convolutional Neural Networks
Deep learning is an effective approach to solving image recognition problems.
People draw intuitive conclusions from trading charts; this study uses the
characteristics of deep learning to train computers in imitating this kind of
intuition in the context of trading charts. The three steps involved are as
follows: 1. Before training, we pre-process the input data from quantitative
data to images. 2. We use a convolutional neural network (CNN), a type of deep
learning, to train our trading model. 3. We evaluate the model's performance in
terms of the accuracy of classification. A trading model is obtained with this
approach to help devise trading strategies. The main application is designed to
help clients automatically obtain personalized trading strategies.Comment: 30 pages, 41 figure
non-linear massive gravity and the cosmic acceleration
Inspired by the non-linear massive gravity, we propose a new kind of
modified gravity model, namely non-linear massive gravity, by adding the
dRGT mass term reformulated in the vierbein formalism, to the theory. We
then investigate the cosmological evolution of massive gravity, and
constrain it by using the latest observational data. We find that it slightly
favors a crossing of the phantom divide line from the quintessence-like phase
() to the phantom-like one () as redshift decreases.Comment: 12 pages, 4 figures, revtex4, Commun. Theor. Phys. in press; v2:
published versio
VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization
Quantization has been proven to be an effective method for reducing the
computing and/or storage cost of DNNs. However, the trade-off between the
quantization bitwidth and final accuracy is complex and non-convex, which makes
it difficult to be optimized directly. Minimizing direct quantization loss
(DQL) of the coefficient data is an effective local optimization method, but
previous works often neglect the accurate control of the DQL, resulting in a
higher loss of the final DNN model accuracy. In this paper, we propose a novel
metric called Vector Loss. Based on this new metric, we develop a new
quantization solution called VecQ, which can guarantee minimal direct
quantization loss and better model accuracy. In addition, in order to speed up
the proposed quantization process during model training, we accelerate the
quantization process with a parameterized probability estimation method and
template-based derivation calculation. We evaluate our proposed algorithm on
MNIST, CIFAR, ImageNet, IMDB movie review and THUCNews text data sets with
numerical DNN models. The results demonstrate that our proposed quantization
solution is more accurate and effective than the state-of-the-art approaches
yet with more flexible bitwidth support. Moreover, the evaluation of our
quantized models on Saliency Object Detection (SOD) tasks maintains comparable
feature extraction quality with up to 16 weight size reduction.Comment: 14 pages, 9 figures, Journa
Indistinguishability of Warm Dark Matter, Modified Gravity, and Coupled Cold Dark Matter
The current accelerated expansion of our universe could be due to an unknown
energy component with negative pressure (dark energy) or a modification to
general relativity (modified gravity). On the other hand, recently warm dark
matter (WDM) remarkably rose as an alternative of cold dark matter (CDM).
Obviously, it is of interest to distinguish these different types of models. In
fact, many attempts have been made in the literature. However, in the present
work, we show that WDM, modified gravity and coupled CDM form a trinity,
namely, they are indistinguishable by using the cosmological observations of
both cosmic expansion history and growth history. Therefore, to break this
degeneracy, the other complementary probes beyond the ones of cosmic expansion
history and growth history are required.Comment: 13 pages, 4 figures, revtex4; v2: discussions added, Phys. Rev. D in
press; v3: published versio
Data Augmentation for Deep Candlestick Learner
To successfully build a deep learning model, it will need a large amount of
labeled data. However, labeled data are hard to collect in many use cases. To
tackle this problem, a bunch of data augmentation methods have been introduced
recently and have demonstrated successful results in computer vision, natural
language and so on. For financial trading data, to our best knowledge,
successful data augmentation framework has rarely been studied. Here we propose
a Modified Local Search Attack Sampling method to augment the candlestick data,
which is a very important tool for professional trader. Our results show that
the proposed method can generate high-quality data which are hard to
distinguish by human and will open a new way for finance community to employ
existing machine learning techniques even if the dataset is small.Comment: 12 pages, 9 figures, 2 tables, 1 algorith
Ultrafast All-optical Modulation Exploiting the Vibrational Dynamic of Metallic Meta-atoms
Optical control over elementary molecular vibration establishes fundamental
capabilities for exploiting the broad range of optical linear and nonlinear
phenomena. However, experimental demonstration of the coherently driven
molecular vibration remains a challenge task due to the weak optical force
imposed on natural materials. Here we report the design of "meta-atom" that
exhibits giant artificial optical nonlinearity. These "meta-atoms" support
co-localized magnetic resonance at optical frequency and vibration resonance at
GHz frequency with a deep-sub-diffraction-limit spatial confinement
(). The coherent coupling of those two distinct resonances
manifests a strong optical force, which is fundamentally different from the
commonly studied form of radiation forces, the gradient forces, or
photo-thermal induced deformation. It results in a giant third-order
susceptibility of /, which is more than six
orders of magnitude larger than that found in natural materials. The
all-optical modulation at the frequency well above 1 GHz has thus been
demonstrated experimentally
A Program Logic for Verifying Secure Routing Protocols
The Internet, as it stands today, is highly vulnerable to attacks. However,
little has been done to understand and verify the formal security guarantees of
proposed secure inter-domain routing protocols, such as Secure BGP (S-BGP). In
this paper, we develop a sound program logic for SANDLog-a declarative
specification language for secure routing protocols for verifying properties of
these protocols. We prove invariant properties of SANDLog programs that run in
an adversarial environment. As a step towards automated verification, we
implement a verification condition generator (VCGen) to automatically extract
proof obligations. VCGen is integrated into a compiler for SANDLog that can
generate executable protocol implementations; and thus, both verification and
empirical evaluation of secure routing protocols can be carried out in this
unified framework. To validate our framework, we encoded several proposed
secure routing mechanisms in SANDLog, verified variants of path authenticity
properties by manually discharging the generated verification conditions in
Coq, and generated executable code based on SANDLog specification and ran the
code in simulation
Explainable Deep Convolutional Candlestick Learner
Candlesticks are graphical representations of price movements for a given
period. The traders can discovery the trend of the asset by looking at the
candlestick patterns. Although deep convolutional neural networks have achieved
great success for recognizing the candlestick patterns, their reasoning hides
inside a black box. The traders cannot make sure what the model has learned. In
this contribution, we provide a framework which is to explain the reasoning of
the learned model determining the specific candlestick patterns of time series.
Based on the local search adversarial attacks, we show that the learned model
perceives the pattern of the candlesticks in a way similar to the human trader.Comment: Accepted by The 32nd International Conference on Software Engineering
& Knowledge Engineering (SEKE 2020), KSIR Virtual Conference Cener,
Pittsburgh, USA, July 9--July 19, 202
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