arXiv subjects

Yi Jin

Publications and source records attributed to Yi Jin.

At least 91 records · Page 5Linked to original sources

arXiv2021

Colour and baryon number fluctuation of preconfinement system in production process and T_cc structure

We suggest to study the production mechanism and some details of the production properties to probe the structure of the DDpi resonance T_cc^+ recently observed by the LHCb Collaboration. If the resonance is produced as a four-quark state, one can find the corresponding finger prints via measurements on some production properties that are the same as those of Xi_cc. The reason is that a special colour connection on the interface between the perturbative and non perturbative QCD is required for these doubly-charm hadron production. On the other hand, if T_cc^+ is produced as a hadron molecule, the measurement on the momentum correlation of DD^* can be a smoking gun to make the judgement. The present data favour a compact four-quark state production via the cc diquark fragmentation.

hep-ph↗

arXiv2021

GM-MLIC: Graph Matching based Multi-Label Image Classification

Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, and reformulate the task of MLIC as an instance-label matching selection problem. To model such problem, we propose a novel deep learning framework named Graph Matching based Multi-Label Image Classification (GM-MLIC), where Graph Matching (GM) scheme is introduced owing to its excellent capability of excavating the instance and label relationship. Specifically, we first construct an instance spatial graph and a label semantic graph respectively, and then incorporate them into a constructed assignment graph by connecting each instance to all labels. Subsequently, the graph network block is adopted to aggregate and update all nodes and edges state on the assignment graph to form structured representations for each instance and label. Our network finally derives a prediction score for each instance-label correspondence and optimizes such correspondence with a weighted cross-entropy loss. Extensive experiments conducted on various image datasets demonstrate the superiority of our proposed method.

cs.CV↗

arXiv2021

Same-Sign Tetralepton Signature in Type-II Seesaw at Lepton Colliders

The same-sign tetralepton signature via mixing of neutral Higgs bosons and their cascade decays to charged Higgs bosons is a unique signal in the type-II seesaw model. In this paper, we study this signature at future lepton colliders, such as ILC, CLIC, and MuC. Constrained by direct search, $H^{\pm\pm}\to W^\pm W^\pm$ is the only viable decay mode for $M_{A^0}=400$ GeV at $\sqrt{s}=1$ TeV ILC. With an integrated luminosity of $\mathcal{L}=8~ \mathrm{ab}^{-1}$, the promising region with about 150 signal events corresponds to a narrow band in the range of $10^{-4}~\text{GeV}\lesssim v_Δ\lesssim10^{-2}$ GeV. For heavier triplet scalars $M_{A^0}\gtrsim 900$ GeV, although the $H^{\pm\pm}\to \ell^\pm \ell^\pm$ decay mode is allowed, the cascade decays are suppressed. A maximum event number $\sim 16$ can be obtained around $v_Δ\sim4\times10^{-4}$ GeV and $λ_4\sim0.26$ for $M_{A^0}=1000$ GeV with $\mathcal{L}=5~ \mathrm{ab}^{-1}$ at $\sqrt{s}=3$ TeV CLIC. Meanwhile, we find that this signature is not promising for $M_{A^0}=1500$ GeV at $\sqrt{s}=6$ TeV MuC.

hep-ph↗

arXiv2021

Attention Models for Point Clouds in Deep Learning: A Survey

Recently, the advancement of 3D point clouds in deep learning has attracted intensive research in different application domains such as computer vision and robotic tasks. However, creating feature representation of robust, discriminative from unordered and irregular point clouds is challenging. In this paper, our ultimate goal is to provide a comprehensive overview of the point clouds feature representation which uses attention models. More than 75+ key contributions in the recent three years are summarized in this survey, including the 3D objective detection, 3D semantic segmentation, 3D pose estimation, point clouds completion etc. We provide a detailed characterization (1) the role of attention mechanisms, (2) the usability of attention models into different tasks, (3) the development trend of key technology.

cs.CV↗

arXiv2021

Same-Sign Dilepton Signature in the Inert Doublet Model

In this paper, we perform a detailed analysis on the same-sign dilepton signature in the inert doublet model. Focusing on the low dark matter mass region, we randomly scan the corresponding parameter space. Viable samples allowed by various constraints are obtained, among which twenty benchmark points are selected for further collider signature study. At hadron colliders, the same-sign dilepton signature is produced via $pp\to W^{\pm *}W^{\pm *}jj \to H^\pm H^\pm jj$ with the leptonic decay mode $ H^\pm \to HW^\pm (\to l^\pm ν)$, where $H$ is the dark matter candidate. We investigate the testability of this signal at the high-luminosity LHC (HL-LHC) and the proposed 27 TeV high-energy LHC (HE-LHC). According to our simulation, the HL-LHC with $\mathcal{L}=3~ab^{-1}$ can hardly probe this signal. Meanwhile, for the HE-LHC with $\mathcal{L}=15~ab^{-1}$, it is promising to obtain a $5σ$ significance when $250~\text{GeV}\lesssim m_{H^\pm}-m_H\lesssim 300$ GeV with dark matter mass $m_H\sim 60$ or 71 GeV.

hep-ph↗

arXiv2020

Magnetic Dynamic Polymers for Modular Assembling and Reconfigurable Morphing Architectures

Shape morphing magnetic soft materials, composed of magnetic particles in a soft polymer matrix, can transform shapes reversibly, remotely, and rapidly, finding diverse applications in actuators, soft robotics, and biomedical devices. To achieve on-demand and sophisticated shape morphing, the manufacturing of structures with complex geometry and magnetization distribution is highly desired. Here, we report a magnetic dynamic polymer composite composed of hard-magnetic microparticles in a dynamic polymer network with thermal-responsive reversible linkages, which permit functionalities including targeted welding, magnetization reprogramming, and structural reconfiguration. These functions not only provide highly desirable structural and material programmability and reprogrammability but also enable the manufacturing of structures with complex geometry and magnetization distribution. The targeted welding is exploited for modular assembling of fundamental building modules with specific logics for complex actuation. The magnetization reprogramming enables altering the morphing mode of the manufactured structures. The shape reconfiguration under magnetic actuation is coupled with network plasticity to remotely transform two-dimensional tessellations into complex three-dimensional architectures, providing a new strategy of manufacturing functional soft architected materials such as three-dimensional kirigami. We anticipate that the reported magnetic dynamic polymer provides a new paradigm for the design and manufacturing of future multifunctional assemblies and reconfigurable morphing architectures and devices.

cond-mat.soft↗

arXiv2020

EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising

In the past few decades, to reduce the risk of X-ray in computed tomography (CT), low-dose CT image denoising has attracted extensive attention from researchers, which has become an important research issue in the field of medical images. In recent years, with the rapid development of deep learning technology, many algorithms have emerged to apply convolutional neural networks to this task, achieving promising results. However, there are still some problems such as low denoising efficiency, over-smoothed result, etc. In this paper, we propose the Edge enhancement based Densely connected Convolutional Neural Network (EDCNN). In our network, we design an edge enhancement module using the proposed novel trainable Sobel convolution. Based on this module, we construct a model with dense connections to fuse the extracted edge information and realize end-to-end image denoising. Besides, when training the model, we introduce a compound loss that combines MSE loss and multi-scales perceptual loss to solve the over-smoothed problem and attain a marked improvement in image quality after denoising. Compared with the existing low-dose CT image denoising algorithms, our proposed model has a better performance in preserving details and suppressing noise.

eess.IV↗

arXiv2020

High-Temperature Ultra-Broad UV-MIR High-Efficiency Absorber Based on Double Ring-Shaped Titanium Nitride Resonators

An ultrabroad absorber based on double-ring-shaped titanium nitride (TiN) nanoresonators, which can work in high temperatures, is proposed and numerically studied. The absorber with some optimal parameters exhibits an averaged absorption of 94.6% in the range of 200 - 4000 nm (from ultraviolet to mid-infrared) and a band from 200 - 3518 nm having an absorption > 90%. We have demonstrated in detail the physical mechanisms of the ultra-broad absorption, including the dielectric lossy property of TiN material itself in shorter wavelengths and plasmonic resonances caused by the metallic property of TiN nano-resonators in longer wavelengths. In addition, the absorber shows polarization independent and wide-angle acceptance. Another absorber with double TiN nano-rings of different heights has flatter and higher absorption efficiency (more than 95% absorption) at 200-2860 nm waveband. These properties make the proposed absorbers based on TiN has great potentials in many applications, such as light trapping, photovoltaics, thermal emitters.

physics.optics↗

arXiv2020

Creation and Characterization of Matter-Wave Breathers

We report the creation of quasi-1D excited matter-wave solitons, "breathers", by quenching the strength of the interactions in a Bose-Einstein condensate with attractive interactions. We characterize the resulting breathing dynamics and quantify the effects of the aspect ratio of the confining potential, the strength of the quench, and the proximity of the 1D-3D crossover for the 2-soliton breather. We furthermore demonstrate the complex dynamics of a 3-soliton breather created by a stronger interaction quench. Our experimental results, which compare well with numerical simulations, provide a pathway for utilizing matter-wave breathers to explore quantum effects in large many-body systems.

cond-mat.quant-gas↗

arXiv2020

Enhancing single photon emission through quasi-bound states in the continuum of monolithic hexagonal boron nitride metasurface

A patterned structure of monolithic hexagonal boron nitride (hBN) on a glass substrate, which can enhance the emission of the embedded single photon emitters (SPEs), is useful for onchip single-photon sources of high-quality. Here, we design and demonstrate a monolithic hBN metasurface with quasi-bound states in the continuum mode at emission wavelength with ultrahigh Q values to enhance fluorescence emission of SPEs in hBN. Because of ultrahigh electric field enhancement inside the proposed hBN metasurface, an ultrahigh Purcell factor (3.3*10^4) is achieved. In addition, the Purcell factor can also be strongly enhanced in most part of the hBN structure, which makes the hBN metasurface suitable for e.g. monolithic quantum photonics.

physics.optics↗

arXiv2020

Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review

Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering $3$ ethnicities, $3$ modalities, $1,607$ subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face anti-spoofing. We organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted $340$ teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions.

cs.CV↗

arXiv2020

Time-Frequency Analysis based Blind Modulation Classification for Multiple-Antenna Systems

Blind modulation classification is an important step to implement cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in the MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time-frequency analysis method based on the windowed short-time Fourier transform is used to analyse the time-frequency characteristics of time-domain modulated signals. Then the extracted time-frequency characteristics are converted into RGB spectrogram images, and the convolutional neural network based on transfer learning is applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module is used to fuse the classification results of all the receive antennas. Through simulations, we analyse the classification performance at different signal-to-noise ratios (SNRs), the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of -4 dB and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at -4 dB and 10 dB, respectively. This outperforms the existing classification methods based on baseband signals.

cs.LG↗

arXiv2020

Intelligent Reflecting Surface Assisted Secure Wireless Communications with Multiple-Transmit and Multiple-Receive Antennas

In this paper, we propose intelligent reflecting surfaces (IRS) assisted secure wireless communications with multi-input and multi-output antennas (IRS-MIMOME). The considered scenario is an access point (AP) equipped with multiple antennas communicates with a multi-antenna enabled legitimate user in the downlink at the present of an eavesdropper configured with multiple antennas. Particularly, the joint optimization of the transmit covariance matrix at the AP and the reflecting coefficients at the IRS to maximize the secrecy rate for the IRS-MIMOME system is investigated, with two different assumptions on the phase shifting capabilities at the IRS, i.e., the IRS has the continuous reflecting coefficients and the IRS has the discrete reflecting coefficients. For the former case, due to the non-convexity of the formulated problem, an alternating optimization (AO)-based algorithm is proposed, i.e., for given the reflecting coefficients at the IRS, the successive convex approximation (SCA)-based algorithm is used to solve the transmit covariance matrix optimization, while given the transmit covariance matrix at the AP, alternative optimization is used again in individually optimizing of each reflecting coefficient at the IRS with other fixed reflecting coefficients. For the individual reflecting coefficient optimization, the close-form or an interval of the optimal solution is provided. Then, the proposed algorithm is extended to the discrete reflecting coefficient model at the IRS. Finally, some numerical simulations have been done to demonstrate that the proposed algorithm outperforms other benchmark schemes.

cs.IT↗

arXiv2020

Leptogenesis and Dark Matter from Low Scale Seesaw

In this paper, we perform a detail analysis on leptogenesis and dark matter form low scale seesaw. In the framework of $ν$2HDM, we further introduce one scalar singlet $ϕ$ and one Dirac fermion singlet $χ$, which are charged under a $Z_2$ symmetry. Assuming the coupling of $χ$ is extremely small, it serves as a FIMP dark matter. The heavy right hand neutrinos $N$ provide a common origin for tiny neutrino mass (via seesaw mechanism), leptogenesis (via $N\to \ell_L Φ_ν^*,\bar{\ell}_L Φ_ν$) and dark matter (via $N\to χϕ$). With hierarchical right hand neutrino masses, the explicit calculation shows that success thermal leptogenesis is viable even for TeV scale $N_1$ with $0.4 \lesssim v_ν\lesssim1$ GeV and lightest neutrino mass $m_1\lesssim 10^{-11}$ eV. In such scenario, light FIMP dark matter in the keV to MeV range is naturally expected. The common parameter space for neutrino mass, natural leptogenesis and FIMP DM is also obtained in this paper.

hep-ph↗

arXiv2019

Exclusive Production Ratio of Neutral over Charged Kaon Pair in $e^+e^-$ Annihilation Continuum via `Straton Model'

A completely relativistic quark model in the Bethe-Salpter framework is employed to calculate the exclusive production ratio of the neutral over charged Kaon pair in $e^+e^-$ annihilation continuum region for center of mass energies smaller than the $J/Ψ$ mass. The valence quark charge plays the key rôle. The cancellation of the diagrams for the same charge case (in $K_S + K_L$) and the non-cancellation of the diagrams for the different charge case (in $K^-+K^+$) lead to the ratio as $(m_s-m_d)^2/M_{Kaon}^2 \sim 1/10$.

hep-ph↗

arXiv2019

HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization

Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with such problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this paper, we propose a novel PLL approach called HERA, which simultaneously incorporates the HeterogEneous Loss and the SpaRse and Low-rAnk procedure to estimate the labeling confidence for each instance while training the model. Specifically, the heterogeneous loss integrates the strengths of both the pairwise ranking loss and the pointwise reconstruction loss to provide informative label ranking and reconstruction information for label identification, while the embedded sparse and low-rank scheme constrains the sparsity of ground-truth label matrix and the low rank of noise label matrix to explore the global label relevance among the whole training data for improving the learning model. Extensive experiments on both artificial and real-world data sets demonstrate that our method can achieve superior or comparable performance against the state-of-the-art methods.

cs.LG↗

arXiv2017

Data-driven Job Search Engine Using Skills and Company Attribute Filters

According to a report online, more than 200 million unique users search for jobs online every month. This incredibly large and fast growing demand has enticed software giants such as Google and Facebook to enter this space, which was previously dominated by companies such as LinkedIn, Indeed and CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine", "Google For Jobs" while Facebook released "Facebook Jobs" within their platform. These current job search engines and platforms allow users to search for jobs based on general narrow filters such as job title, date posted, experience level, company and salary. However, they have severely limited filters relating to skill sets such as C++, Python, and Java and company related attributes such as employee size, revenue, technographics and micro-industries. These specialized filters can help applicants and companies connect at a very personalized, relevant and deeper level. In this paper we present a framework that provides an end-to-end "Data-driven Jobs Search Engine". In addition, users can also receive potential contacts of recruiters and senior positions for connection and networking opportunities. The high level implementation of the framework is described as follows: 1) Collect job postings data in the United States, 2) Extract meaningful tokens from the postings data using ETL pipelines, 3) Normalize the data set to link company names to their specific company websites, 4) Extract and ranking the skill sets, 5) Link the company names and websites to their respective company level attributes with the EVERSTRING Company API, 6) Run user-specific search queries on the database to identify relevant job postings and 7) Rank the job search results. This framework offers a highly customizable and highly targeted search experience for end users.

cs.IR↗

arXiv2017

Exotic Hadron Bound State Production at Hadronic Colliders

The non-relativistic wave function framework is applied to study the production and decay of the exotic hadrons which can be effectively described as bound states of other hadrons. Employing the factorized formulation, we investigate the production of exotic hadrons in the multiproduction processes at high energy hadronic colliders with the help of event generators. This study provides crucial information for the measurements of the relevant exotic hadrons.

hep-ph↗