arXiv subjects

Bin Yang

Publications and source records attributed to Bin Yang.

At least 379 records · Page 21Linked to original sources

arXiv2019

Isospin violating decay $D^*_s \to D_s π^0$ in chiral perturbation theory

We systematically calculate the isospin violating decay, $D_s^*\to D_sπ^0$, with the heavy meson chiral perturbation theory up to $\mathcal{O}(p^3)$ including the loop diagrams. The $\mathcal{O}(p^3)$ tree level amplitudes contain four undetermined LECs. We use two strategies to estimate them. With the nonanalytic dominance approximation, we get $Γ[D_s^\ast\to D_sπ^0]=(3.38\pm0.12)$ eV. With the naturalness assumption, we give a possible range of the isospin violating decay width, $[1.11-6.88]$ eV. We find that the contribution of the $\mathcal{O}(p^3)$ corrections might be significant.

hep-ph↗

arXiv2019

Searching for water ice in the coma of interstellar object 2I/Borisov

Interstellar Objects (ISO) passing through our Solar System offer a rare opportunity to probe the physical and chemical processes involved in solid body and planet formation in extrasolar systems. The main objective of our study is to search for diagnostic absorption features of water ice in the near infrared (NIR) spectrum of the second interstellar object 2I/2019 Q4 (Borisov) and compare its ice features to those of the Solar system icy objects. We observed 2I in the NIR on three separate occasions. The first observation was made on 2019 September 19 UT using the SpeX spectrograph at the 3-m IRTF and again on September 24 UT with the GNIRS spectrograph at the 8-m GEMINI telescope and the last observation was made on October 09 UT with IRTF. The spectra obtained from all three nights appear featureless. No absorption features associated with water ice are detected. Spectral modeling suggests that water grains, if present, comprise no more than 10% of the coma cross-section. The comet consistently exhibits a red D-type like spectrum with a spectral slope of about 6% per 100nm, which is similar to that of 1I/'Oumuamua and is comparable to Solar system comets.

astro-ph.EP↗

arXiv2019

Open-Set Recognition Using Intra-Class Splitting

This paper proposes a method to use deep neural networks as end-to-end open-set classifiers. It is based on intra-class data splitting. In open-set recognition, only samples from a limited number of known classes are available for training. During inference, an open-set classifier must reject samples from unknown classes while correctly classifying samples from known classes. The proposed method splits given data into typical and atypical normal subsets by using a closed-set classifier. This enables to model the abnormal classes by atypical normal samples. Accordingly, the open-set recognition problem is reformulated into a traditional classification problem. In addition, a closed-set regularization is proposed to guarantee a high closed-set classification performance. Intensive experiments on five well-known image datasets showed the effectiveness of the proposed method which outperformed the baselines and achieved a distinct improvement over the state-of-the-art methods.

cs.LG↗

arXiv2019

One-Class Feature Learning Using Intra-Class Splitting

This paper proposes a novel generic one-class feature learning method based on intra-class splitting. In one-class classification, feature learning is challenging, because only samples of one class are available during training. Hence, state-of-the-art methods require reference multi-class datasets to pretrain feature extractors. In contrast, the proposed method realizes feature learning by splitting the given normal class into typical and atypical normal samples. By introducing closeness loss and dispersion loss, an intra-class joint training procedure between the two subsets after splitting enables the extraction of valuable features for one-class classification. Various experiments on three well-known image classification datasets demonstrate the effectiveness of our method which outperformed other baseline models in average.

cs.LG↗

arXiv2019

Unsupervised Adversarial Correction of Rigid MR Motion Artifacts

Motion is one of the main sources for artifacts in magnetic resonance (MR) images. It can have significant consequences on the diagnostic quality of the resultant scans. Previously, supervised adversarial approaches have been suggested for the correction of MR motion artifacts. However, these approaches suffer from the limitation of required paired co-registered datasets for training which are often hard or impossible to acquire. Building upon our previous work, we introduce a new adversarial framework with a new generator architecture and loss function for the unsupervised correction of severe rigid motion artifacts in the brain region. Quantitative and qualitative comparisons with other supervised and unsupervised translation approaches showcase the enhanced performance of the introduced framework.

eess.IV↗

arXiv2019

Detection of CN gas in Interstellar Object 2I/Borisov

The detection of Interstellar Objects passing through the Solar System offers the promise of constraining the physical and chemical processes involved in planetary formation in other extrasolar systems. While the effect of outgassing by 1I/2017 U1 ('Oumuamua) was dynamically observed, no direct detection of the ejected material was made. The discovery of the active interstellar comet 2I/Borisov means spectroscopic investigations of the sublimated ices is possible for this object. We report the first detection of gas emitted by an interstellar comet via the near-UV emission of CN from 2I/Borisov at a heliocentric distance of $r$ = 2.7 au on 2019 September 20. The production rate was found to be Q(CN) = $(3.7\pm0.4)\times10^{24}$ s$^{-1}$, using a simple Haser model with an outflow velocity of 0.5 km s$^{-1}$. No other emission was detected, with an upper limit to the production rate of C$_2$ of $4\times10^{24}$ s$^{-1}$. The spectral reflectance slope of the dust coma over $3900$ Å $< λ< 6000$ Å\ is steeper than at longer wavelengths, as found for other comets. Broad band $R_c$ photometry on 2019 September 19 gave a dust production rate of $Afρ=143\pm10$ cm. Modelling of the observed gas and dust production rates constrains the nuclear radius to $0.7-3.3$ km assuming reasonable nuclear properties. Overall, we find the gas, dust and nuclear properties for the first active Interstellar Object are similar to normal Solar System comets.

astro-ph.EP↗

arXiv2019

Obtaining a History of the Flux of Cosmic Rays using In Situ Cosmogenic $^{14}$C Trapped in Polar Ice

Carbon-14 ($^{14}$C) is produced in the atmosphere when neutrons from cosmic-ray air showers are captured by $^{14}$N nuclei. Atmospheric $^{14}$C becomes trapped in air bubbles in polar ice as compacted snow (firn) transforms into ice. $^{14}$C is also produced in situ in ice grains by penetrating cosmic-ray neutrons and muons. Recent ice core measurements indicate that in the $^{14}$CO phase, the $^{14}$C is dominated by the in situ cosmogenic component at most ice coring sites. Thus, it should be possible to use ice-bound $^{14}$CO to reconstruct the historical flux of cosmic rays at Earth, without the transport and deposition uncertainties associated with $^{10}$Be or the carbon cycle uncertainties affecting atmospheric $^{14}$CO$_2$. The measurements will be sensitive to the cosmic-ray flux above the energy range most affected by solar modulation. We present estimates of the expected sensitivity of $^{14}$CO in ice cores to the historical flux of Galactic cosmic rays, based on recent studies of $^{14}$CO in polar ice.

astro-ph.HE↗

arXiv2019

Deep One-Class Classification Using Intra-Class Splitting

This paper introduces a generic method which enables to use conventional deep neural networks as end-to-end one-class classifiers. The method is based on splitting given data from one class into two subsets. In one-class classification, only samples of one normal class are available for training. During inference, a closed and tight decision boundary around the training samples is sought which conventional binary or multi-class neural networks are not able to provide. By splitting data into typical and atypical normal subsets, the proposed method can use a binary loss and defines an auxiliary subnetwork for distance constraints in the latent space. Various experiments on three well-known image datasets showed the effectiveness of the proposed method which outperformed seven baselines and had a better or comparable performance to the state-of-the-art.

cs.LG↗

arXiv2019

Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images

The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to a coarse distribution of sensors or sensor failures. This limitation could be alleviated by leveraging information contained in images of the event posted on social media platforms, so-called "Volunteered Geographic Information (VGI)". To save the analyst from the need to inspect all images posted online manually, we propose to use content-based image retrieval with the possibility of relevance feedback for retrieving only relevant images of the event to be analyzed. To evaluate this approach, we introduce a new dataset of 3,710 flood images, annotated by domain experts regarding their relevance with respect to three tasks (determining the flooded area, inundation depth, water pollution). We compare several image features and relevance feedback methods on that dataset, mixed with 97,085 distractor images, and are able to improve the precision among the top 100 retrieval results from 55% with the baseline retrieval to 87% after 5 rounds of feedback.

cs.IR↗

arXiv2019

Towards Adversarial Denoising of Radar Micro-Doppler Signatures

Generative Adversarial Networks (GANs) are considered the state-of-the-art in the field of image generation. They learn the joint distribution of the training data and attempt to generate new data samples in high dimensional space following the same distribution as the input. Recent improvements in GANs opened the field to many other computer vision applications based on improving and changing the characteristics of the input image to follow some given training requirements. In this paper, we propose a novel technique for the denoising and reconstruction of the micro-Doppler ($\boldsymbolμ$-D) spectra of walking humans based on GANs. Two sets of experiments were collected on 22 subjects walking on a treadmill at an intermediate velocity using a \unit[25]{GHz} CW radar. In one set, a clean $\boldsymbolμ$-D spectrum is collected for each subject by placing the radar at a close distance to the subject. In the other set, variations are introduced in the experiment setup to introduce different noise and clutter effects on the spectrum by changing the distance and placing reflective objects between the radar and the target. Synthetic paired noisy and noise-free spectra were used for training, while validation was carried out on the real noisy measured data. Finally, qualitative and quantitative comparison with other classical radar denoising approaches in the literature demonstrated the proposed GANs framework is better and more robust to different noise levels.

cs.CV↗

arXiv2019

Radiative transitions and magnetic moments of the charmed and bottom vector mesons in chiral perturbation theory

In this work, we systematically study the radiative decays and magnetic moments of the charmed and bottom vector mesons with chiral perturbation theory up to one-loop level. We present the results in SU(2) and SU(3) cases with the mass splitting in loop diagrams kept and unkept, respectively. The obtained decay rates for $D^\ast$ and $B^\ast$ mesons in SU(3) case with the mass splitting kept are: $Γ_{\bar{D}^{\ast 0}\to \bar{D}^0γ}=16.2^{+6.5}_{-6.0}$ keV, $Γ_{D^{\ast-}\to D^-γ}=0.73^{+0.7}_{-0.3}$ keV, $Γ_{D_s^{\ast-}\to D_s^-γ}= 0.32^{+0.3}_{-0.3}$ keV, and $Γ_{B^{\ast+}\to B^+γ}=0.58^{+0.2}_{-0.2}$ keV, $Γ_{B^{\ast0}\to B^0γ}=0.23^{+0.06}_{-0.06}$ keV, $Γ_{B_s^{\ast0}\to B_s^0γ}=0.04^{+0.03}_{-0.03}$ keV. The decay width for $D^{\ast-}\to D^-γ$ is consistent with the experimental measurement. As a byproduct, the full widths of $\bar{D}^{\ast0}$ and $D_s^{\ast-}$ are $Γ_{\mathrm{tot}}(\bar{D}^{\ast0})\simeq77.7^{+26.7}_{-20.5}~\mathrm{keV}$ and $ Γ_{\mathrm{tot}}(D_s^{\ast-})\simeq0.62^{+0.45}_{-0.50}~\mathrm{keV}$, respectively. We also calculate the magnetic moments of the heavy vector mesons. The analytical chiral expressions derived in our work shall be helpful for the extrapolations of lattice QCD simulations in the coming future.

hep-ph↗

arXiv2019

Eigenfunction Behavior and Adaptive Finite Element Approximations of Nonlinear Eigenvalue Problems in Quantum Physics

In this paper, we investigate a class of nonlinear eigenvalue problems resulting from quantum physics. We first prove that the eigenfunction cannot be a polynomial on any open set, which may be reviewed as a refinement of the classic unique continuation property. Then we apply the non-polynomial behavior of eigenfunction to show that the adaptive finite element approximations are convergent even if the initial mesh is not fine enough. We finally remark that similar arguments can be applied to a class of linear eigenvalue problems that improve the relevant existing result.

math.NA↗

arXiv2019

PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks

Modern navigation services often provide multiple paths connecting the same source and destination for users to select. Hence, ranking such paths becomes increasingly important, which directly affects the service quality. We present PathRank, a data-driven framework for ranking paths based on historical trajectories using multi-task learning. If a trajectory used path P from source s to destination d, PathRank considers this as an evidence that P is preferred over all other paths from s to d. Thus, a path that is similar to P should have a larger ranking score than a path that is dissimilar to P. Based on this intuition, PathRank models path ranking as a regression problem, where each path is associated with a ranking score. To enable PathRank, we first propose an effective method to generate a compact set of training data: for each trajectory, we generate a small set of diversified paths. Next, we propose a multi-task learning framework to solve the regression problem. In particular, a spatial network embedding is proposed to embed each vertex to a feature vector by considering both road network topology and spatial properties, such as distances and travel times. Since a path is represented by a sequence of vertices, which is now a sequence of feature vectors after embedding, recurrent neural network is applied to model the sequence. The objective function is designed to consider errors on both ranking scores and spatial properties, making the framework a multi-task learning framework. Empirical studies on a substantial trajectory data set offer insight into the designed properties of the proposed framework and indicating that it is effective and practical.

cs.LG↗

arXiv2019

An Adversarial Super-Resolution Remedy for Radar Design Trade-offs

Radar is of vital importance in many fields, such as autonomous driving, safety and surveillance applications. However, it suffers from stringent constraints on its design parametrization leading to multiple trade-offs. For example, the bandwidth in FMCW radars is inversely proportional with both the maximum unambiguous range and range resolution. In this work, we introduce a new method for circumventing radar design trade-offs. We propose the use of recent advances in computer vision, more specifically generative adversarial networks (GANs), to enhance low-resolution radar acquisitions into higher resolution counterparts while maintaining the advantages of the low-resolution parametrization. The capability of the proposed method was evaluated on the velocity resolution and range-azimuth trade-offs in micro-Doppler signatures and FMCW uniform linear array (ULA) radars, respectively.

cs.CV↗

arXiv2019

Possible Molecular States Composed of Doubly Charmed Baryons with Couple-channel Effect

We systematically investigate the possible molecular states composed of (1) two spin-$\frac{3}{2}$ doubly charmed baryons, and (2) a pair of spin-$\frac{3}{2}$ and spin-$\frac{1}{2}$ doubly charmed baryons. The one-boson-exchange (OBE) model is used to describe the potential between two baryons. The channel mixing effect is considered for the systems with the same quantum number $(I(J^P))$ but different total spin ($S$) and orbital angular momenta ($L$). We also study the channel mixing effect among the systems composed of various doubly charmed baryons if they have the same quantum number. Many of the systems are good candidates of molecular states.

hep-ph↗

arXiv2019

Localizing Catastrophic Forgetting in Neural Networks

Artificial neural networks (ANNs) suffer from catastrophic forgetting when trained on a sequence of tasks. While this phenomenon was studied in the past, there is only very limited recent research on this phenomenon. We propose a method for determining the contribution of individual parameters in an ANN to catastrophic forgetting. The method is used to analyze an ANNs response to three different continual learning scenarios.

cs.LG↗

arXiv2019

2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval

Image retrieval utilizes image descriptors to retrieve the most similar images to a given query image. Convolutional neural network (CNN) is becoming the dominant approach to extract image descriptors for image retrieval. For low-power hardware implementation of image retrieval, the drawback of CNN-based feature descriptor is that it requires hundreds of megabytes of storage. To address this problem, this paper applies deep model quantization and compression to CNN in ASIC chip for image retrieval. It is demonstrated that the CNN-based features descriptor can be extracted using as few as 2-bit weights quantization to deliver a similar performance as floating-point model for image retrieval. In addition, to implement CNN in ASIC, especially for large scale images, the limited buffer size of chips should be considered. To retrieve large scale images, we propose an improved pooling strategy, region nested invariance pooling (RNIP), which uses cropped sub-images for CNN. Testing results on chip show that integrating RNIP with the proposed 2-bit CNN model compression approach is capable of retrieving large scale images.

cs.AI↗

arXiv2019

Learning to Reweight Examples for Robust Deep Learning

Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. In addition to various regularizers, example reweighting algorithms are popular solutions to these problems, but they require careful tuning of additional hyperparameters, such as example mining schedules and regularization hyperparameters. In contrast to past reweighting methods, which typically consist of functions of the cost value of each example, in this work we propose a novel meta-learning algorithm that learns to assign weights to training examples based on their gradient directions. To determine the example weights, our method performs a meta gradient descent step on the current mini-batch example weights (which are initialized from zero) to minimize the loss on a clean unbiased validation set. Our proposed method can be easily implemented on any type of deep network, does not require any additional hyperparameter tuning, and achieves impressive performance on class imbalance and corrupted label problems where only a small amount of clean validation data is available.

cs.LG↗