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 $\Lambda$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, $\Lambda$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

f(T)f(T) non-linear massive gravity and the cosmic acceleration

Inspired by the $f(R)$ non-linear massive gravity, we propose a new kind of modified gravity model, namely $f(T)$ non-linear massive gravity, by adding the dRGT mass term reformulated in the vierbein formalism, to the $f(T)$ theory. We then investigate the cosmological evolution of $f(T)$ 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 ($w_{de} > -1$) to the phantom-like one ($w_{de} < -1$) 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$\times$ 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 (${\lambda}^2/100$). 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 $\chi^{(3)}$ of $10^{-13}$ $m^2$/$V^2$, 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