arXiv subjects

Xin Liu

Publications and source records attributed to Xin Liu.

At least 811 records · Page 45Linked to original sources

arXiv2021

[Ag(NH3)2]2SO4: A coordination strategy on the cationic-moiety to design nonlinear optical materials

Over decades, guided by the anionic group theory, the majority work has been focused on the anionic-moiety of a nonlinear optical material, however, the property guided structure modification and design on the cationic-moiety has long been neglected. Herein, we report for the first time a coordination strategy on the cationic-moiety, as demonstrated by the first exmple, [Ag(NH3)2]2SO4 vs Ag2SO4, the coordination of the plus one Ag+ cation by the neutral ligand forming the [Ag(NH3)2]+ cationic-moiety drives the formation of the noncentrosymmetric tetragonal P-421c structure which exhibits a remarkable property improvement, including a strong SHG intersity (1.4*KDP vs 0 @1064 nm), and a large birefringence (delta(n)cal.: 0.102 vs 0.012 @1064 nm). Furthermore, we discover that owing to the strong hydrogen bonds and spatial confinement of [SO4]2- anions, the cation [Ag(NH3)2]+ bends parallel to the crystallographic c axis with a bond angle of N-Ag-N = 174.35deg, generating a permenant dipole moment z = -0.12 D that is responsible for the large birefringence that enlarges the range of the phase matching SHG laser output. We believe what we discover is only a glimpse of the long been neglected crystal engineering on the cationic-moiety. Encourage by this work, exciting works on other ionic compound systems shall flood in.

cond-mat.mtrl-sci↗

arXiv2021

Simple Quantum Key Distribution using a Stable Transmitter-Receiver Scheme

Quantum Key Distribution (QKD) is a technology that allows secure key exchange between two distant users. A widespread adoption of QKD requires the development of simple, low-cost, and stable systems. However, implementation of the current QKD requires a complex self-alignment process during the initial stage and an additional hardware to compensate the environmental disturbances. In this study, we have presented the implementation of a simple QKD with the help of a stable transmitter-receiver scheme, which simplifies the self-alignment and is robust enough to withstand environmental disturbances. In case of the stability test, the implementation system is able to remain stable for 48 hours and exhibits an average quantum bit error rate of less than 1\% without any feedback control. The scheme is also tested over a fiber spool, obtaining a stable and secure finite key rate of 7.32k bits per second over a fiber spool extending up to 75 km. The demonstrated long-term stability and obtained secure key rate prove that our method of implementation is a promising alternative for practical QKD systems, in particular, for Cubesat platform and satellite applications.

quant-ph↗

arXiv2021

Pure annihilation decays of $B_s^0 \to a_0^+ a_0^-$ and $B_d^0 \to K_0^{+} K_0^{-}$ in the PQCD approach

We study the {\it CP}-averaged branching fractions and the {\it CP}-violating asymmetries in the pure annihilation decays of $B_s^0 \to a_0^+ a_0^-$ and $B_d^0 \to K_0^{*+} K_0^{*-}$, where $a_0\;[K_0^*]$ denotes the scalar $a_0(980)$ and $a_0(1450)$ [$K_0^*(800) ( {\rm or}\; κ)$ and $K_0^*(1430)$], with the perturbative QCD factorization approach under the assumption of two-quark structure for the $a_0$ and $K_0^*$ states. The numerical results show that the branching ratios of the $B_d^0 \to K_0^{*+} K_0^{*-}$ decays are in the order of $10^{-6}$, while the decay rates of the $B_s^0 \to a_0^+ a_0^-$ modes are in the order of $10^{-5}$. In light of the measured modes with the same quark components in the pseudoscalar sector, namely, $B_d^0 \to K^+ K^-$ and $B_s^0 \to π^+ π^-$, the predictions for the considered decay modes in this work are expected to be measured at the Large Hadron Collider beauty and/or Belle-II experiments in the (near) future. Meanwhile, it is of great interest to find that the twist-3 distribution amplitudes $ϕ^{S}$ and $ϕ^{T}$ with inclusion of the Gegenbauer polynomials for the scalar $a_0(1450)$ and $K_0^*(1430)$ states in scenario 2 contribute slightly to the branching ratios while significantly to the {\it CP} violations in the $B_d^0 \to {K_0^*}(1430)^+ {K_0^*}(1430)^-$ and $B_s^0 \to a_0(1450)^+ a_0(1450)^-$ decays, which indicates that, compared to the asymptotic $ϕ^S$ and $ϕ^T$, these Gegenbauer polynomials could change the strong phases evidently in these pure annihilation decay channels. These predictions await for the future confirmation experimentally, which could further provide useful information to help explore the inner structure of the scalars and shed light on the annihilation decay mechanism.

hep-ph↗

arXiv2021

$B_{d,s}^0 \to f_1 f_1$ decays with $f_1(1285)-f_1(1420)$ mixing in the perturbative QCD approach

We investigate the $B_{d,s}^0 \to f_1 f_1$ decays in the framework of perturbative QCD(PQCD) approach with a referenced value $ϕ_{f_1} \sim 24^\circ$. Here, $f_1$ denotes the axial-vector mesons $f_1(1285)$ and $f_1(1420)$ with mixing angle $ϕ_{f_1}$ in the quark-flavor basis. The observables such as branching ratios, direct CP violations, and polarization fractions of the $B_s^0 \to f_1 f_1$ decays are predicted for the first time. We find that: (i) the almost pure penguin modes $B_s^0 \to f_1 f_1$ have large branching ratios in the order of $10^{-6} \sim 10^{-5}$ due to the Cabibbo-Kobayashi-Maskawa enhancement and generally constructive interferences between the amplitudes of $B_s^0 \to f_n f_s$ and $B_s^0 \to f_s f_s$ with $f_n$ and $f_s$ being the quark-flavor states of $f_1$ mesons; (ii) The observables receive important contributions from the weak annihilation diagrams in the PQCD approach. In particular, without the annihilation contributions, the $B_s^0 \to f_1(1420) f_1(1420)$ branching ratio will decrease about 81% and its longitudinal polarization fraction will reduce around 43%. And (iii) the dependence of the $B_{d,s}^0 \to f_1 f_1$ decay rates on $ϕ_{f_1}$ exhibits some interesting line shapes, whose confirmations would be helpful to constrain the determination of $ϕ_{f_1}$ inversely. All the PQCD predictions await for the (near) future examinations at Large Hadron Collider beauty and/or Belle-II experiments to further understand the properties of the axial-vector mesons and the perturbative dynamics released from the considered decay modes.

hep-ph↗

arXiv2021

On the AGN Nature of Broad Balmer Emission in Four Low-Redshift Metal-Poor Galaxies

We report on continued, $\sim$15 year-long, broad Balmer emission lines in three metal-poor dwarf emission-line galaxies selected from Sloan Digital Sky Survey spectroscopy. The persistent luminosity of the broad Balmer emission indicates the galaxies are active galactic nuclei (AGNs) with virial black hole masses of $\sim 10^{6.7}{-10^{7.0}\ M_{\odot}}$. The lack of observed hard X-ray emission and the possibility that the Balmer emission could be due to a long-lived stellar transient motivated additional follow-up spectroscopy. We also identify a previously-unreported blueshifted narrow absorption line in the broad H$α$ feature in one of the AGNs, indicating an AGN-driven outflow with hydrogen column densities of order $10^{17}$ cm$^{-2}$. We also extract light curves from the Catalina Real-Time Transient Survey and the Zwicky Transient Facility. We detect probable AGN-like variability in three galaxies, further supporting the AGN scenario. This also suggests the AGNs are not strongly obscured. This sample of galaxies are among the most metal-poor which host an AGN ($Z=0.05 - 0.16\ Z_\odot$). We speculate they may be analogues to seed black holes which formed in unevolved galaxies at high redshift. Given the rarity of metal-poor AGNs and small sample size available, we investigate prospects for their identification in future spectroscopic and photometric surveys.

astro-ph.GA↗

arXiv2021

Learning to Filter: Siamese Relation Network for Robust Tracking

Despite the great success of Siamese-based trackers, their performance under complicated scenarios is still not satisfying, especially when there are distractors. To this end, we propose a novel Siamese relation network, which introduces two efficient modules, i.e. Relation Detector (RD) and Refinement Module (RM). RD performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into the Siamese framework to generate accurate tracking result. Moreover, to further improve the discriminability and robustness of the tracker, we introduce a contrastive training strategy that attempts not only to learn matching the same target but also to learn how to distinguish the different objects. Therefore, our tracker can achieve accurate tracking results when facing background clutters, fast motion, and occlusion. Experimental results on five popular benchmarks, including VOT2018, VOT2019, OTB100, LaSOT, and UAV123, show that the proposed method is effective and can achieve state-of-the-art results. The code will be available at https://github.com/hqucv/siamrn

cs.CV↗

arXiv2021

Quasi-two-body $B_{(s)}\to V ππ$ Decays with Resonance $f_0(980)$ in PQCD Approach

Motivated by the measurements of branching fractions of the quasi-two-body decays $B^0\to K^{*0}(f_0(980)\to )π^+π^-$ and $B^0\to ρ^0(f_0(980)\to )π^+π^-$, we study the charmless $B\to V (f_0(980)\to ) π^+π^-$ decays in the perturbative QCD approach. Supposing that $f_0(980)$ is a two-quark state and mixture of $n\bar n=(u\bar u+d\bar d)/\sqrt 2$ and $s\bar s$ with the mixing angle $θ$, we calculate the branching fractions of these decays with the new introduced $s$-wave $π^+π^-$-pair wave function. When the mixing angle $θ$ lies in the range $[135^{\circ}, 155^{\circ}]$, the calculated branching fractions of the $B^0\to K^{*0}f_0(980)\to K^{*0}π^+π^-$ and $B^0\to ρ^0f_0(980)\to ρ^0π^+π^-$ decays are in agreement with the experimental data. The branching fractions of other decays could be measured in the current LHCb and Belle II experiments. Considering the isospin symmetry, we also estimate the branching fractions of the quasi-two-body decays $B\to Vf_0(980)\to Vπ^0π^0$, which are half of those the corresponding decays $B\to Vf_0(980)\to Vπ^+π^-$. Moreover, the direct $CP$ asymmetries of these decays are also calculated, and some of them can be tested in the current experiments.

hep-ph↗

arXiv2021

Do LIGO/Virgo black hole mergers produce AGN flares? The case of GW190521 and prospects for reaching a confident association

The recent report of an association of the gravitational-wave (GW) binary black hole (BBH) merger GW190521 with a flare in the Active Galactic Nuclei (AGN) J124942.3+344929 has generated tremendous excitement. However, GW190521 has one of the largest localization volumes amongst all of the GW events detected so far. The 90\% localization volume likely contains $7,400$ unobscured AGN brighter than $g \leq 20.5$ AB mag, and it results in a $\gtrsim 70\%$ probability of chance coincidence for an AGN flare consistent with the GW event. We present a Bayesian formalism to estimate the confidence of an AGN association by analyzing a population of BBH events with dedicated follow-up observations. Depending on the fraction of BBH arising from AGNs, counterpart searches of $\mathcal{O}(1)-\mathcal{O}(100)$ GW events are needed to establish a confident association, and more than an order of magnitude more for searches without followup (i.e, using only the locations of AGNs and GW events). Follow-up campaigns of the top $\sim 5\%$ (based on volume localization and binary mass) of BBH events with total rest frame mass $\ge 50~M_\odot$ are expected to establish a confident association during the next LIGO/Virgo/KAGRA observing run (O4), as long as the true value of the fraction of BBH giving rise to AGN flares is $>0.1$. Our formalism allows us to jointly infer cosmological parameters from a sample of BBH events that include chance coincidence flares. Until the confidence of AGN associations is established, the probability of chance coincidence must be taken into account to avoid biasing astrophysical and cosmological constraints.

astro-ph.HE↗

arXiv2021

No frame left behind: Full Video Action Recognition

Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundreds of frames. A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action. Instead, here we propose full video action recognition and consider all video frames. To make this computational tractable, we first cluster all frame activations along the temporal dimension based on their similarity with respect to the classification task, and then temporally aggregate the frames in the clusters into a smaller number of representations. Our method is end-to-end trainable and computationally efficient as it relies on temporally localized clustering in combination with fast Hamming distances in feature space. We evaluate on UCF101, HMDB51, Breakfast, and Something-Something V1 and V2, where we compare favorably to existing heuristic frame sampling methods.

cs.CV↗

arXiv2021

A Penalty-free Infeasible Approach for a Class of Nonsmooth Optimization Problems over the Stiefel Manifold

Transforming into an exact penalty function model with convex compact constraints yields efficient infeasible approaches for optimization problems with orthogonality constraints. For smooth and $\ell_{2,1}$-norm regularized cases, these infeasible approaches adopt simple and orthonormalization-free updating scheme and show their high efficiency in the test examples. However, to avoid orthonormalization while enforcing the feasibility of the final solution, these infeasible approaches introduce a quadratic penalty term, where an inappropriate penalty parameter can lead to numerical inefficiency. Inspired by penalty-free approaches for smooth optimization problems, we proposed a proximal first-order algorithm for a class of optimization problems with orthogonality constraints and nonsmooth regularization term. The consequent algorithm, named sequential linearized proximal gradient method (SLPG), alternatively takes tangential steps and normal steps to improve the optimality and feasibility respectively. In SLPG, the orthonormalization process is invoked only once at the last step if high precision in feasibility is needed, showing that main iterations in SLPG are orthonormalization-free. Besides, both the tangential steps and normal steps do not involve the penalty parameter, and thus SLPG is penalty-free and avoids the inefficiency by inappropriate penalty parameter. We analyze the global convergence properties of SLPG where the tangential steps are inexactly computed. By inexactly computing tangential steps, for smooth cases and $\ell_{2,1}$-norm regularized cases, SLPG has a closed-form updating scheme, which leads to its cheap tangential steps. Numerical experiments illustrate the numerical advantages of SLPG when compared with existing first-order methods.

math.OC↗

arXiv2021

On the Difficulty of Membership Inference Attacks

Recent studies propose membership inference (MI) attacks on deep models, where the goal is to infer if a sample has been used in the training process. Despite their apparent success, these studies only report accuracy, precision, and recall of the positive class (member class). Hence, the performance of these attacks have not been clearly reported on negative class (non-member class). In this paper, we show that the way the MI attack performance has been reported is often misleading because they suffer from high false positive rate or false alarm rate (FAR) that has not been reported. FAR shows how often the attack model mislabel non-training samples (non-member) as training (member) ones. The high FAR makes MI attacks fundamentally impractical, which is particularly more significant for tasks such as membership inference where the majority of samples in reality belong to the negative (non-training) class. Moreover, we show that the current MI attack models can only identify the membership of misclassified samples with mediocre accuracy at best, which only constitute a very small portion of training samples. We analyze several new features that have not been comprehensively explored for membership inference before, including distance to the decision boundary and gradient norms, and conclude that deep models' responses are mostly similar among train and non-train samples. We conduct several experiments on image classification tasks, including MNIST, CIFAR-10, CIFAR-100, and ImageNet, using various model architecture, including LeNet, AlexNet, ResNet, etc. We show that the current state-of-the-art MI attacks cannot achieve high accuracy and low FAR at the same time, even when the attacker is given several advantages. The source code is available at https://github.com/shrezaei/MI-Attack.

cs.LG↗

arXiv2021

Soft-Median Choice: An Automatic Feature Smoothing Method for Sound Event Detection

In Sound Event Detection (SED) systems, the lengths of median filters for post-processing have never been optimized during training due to several problems. No gradient is received by the lengths so they cannot be learned during back-propagation. The median-filtering inserted in the models also causes block in gradient flowing and the smoothing process misleads the model by ignoring errors. To resolve these problems, we provide different channels of features smoothed to different extents along with the original feature, so the model can optimize the weights while cognizing all the errors. We then use a linear layer to integrate the results and produce a linear combination. We further design the soft-median function to dredge the gradient flow. The proposed framework is called Soft-Median Choice (SMC). Experiments show that the SMC block not only automatically smooths the features based on the training set, but also forces the model to extract common features shared by all the frames of a sound event. The performance of the proposed method outperforms the baseline by over 10% of Event-Based F1 Score (EBFS) in both the validation and the evaluation set, and also slightly outperforms the single model of the state-of-the-art SED system.

eess.AS↗

arXiv2021

Deep Manifold Learning for Dynamic MR Imaging

Purpose: To develop a deep learning method on a nonlinear manifold to explore the temporal redundancy of dynamic signals to reconstruct cardiac MRI data from highly undersampled measurements. Methods: Cardiac MR image reconstruction is modeled as general compressed sensing (CS) based optimization on a low-rank tensor manifold. The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals. Iterative procedures can be obtained by solving the optimization model on the manifold, including gradient calculation, projection of the gradient to tangent space, and retraction of the tangent space to the manifold. The iterative procedures on the manifold are unrolled to a neural network, dubbed as Manifold-Net. The Manifold-Net is trained using in vivo data with a retrospective electrocardiogram (ECG)-gated segmented bSSFP sequence. Results: Experimental results at high accelerations demonstrate that the proposed method can obtain improved reconstruction compared with a compressed sensing (CS) method k-t SLR and two state-of-the-art deep learning-based methods, DC-CNN and CRNN. Conclusion: This work represents the first study unrolling the optimization on manifolds into neural networks. Specifically, the designed low-rank manifold provides a new technical route for applying low-rank priors in dynamic MR imaging.

eess.IV↗

arXiv2021

MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement

There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging. Existing machine learning systems struggle to generalize well to unseen subjects or contexts and can often contain problematic biases. Video-based physiological measurement is not an exception. Therefore, learning personalized or customized models from a small number of unlabeled samples is very attractive as it would allow fast calibrations to improve generalization and help correct biases. In this paper, we present a novel meta-learning approach called MetaPhys for personalized video-based cardiac measurement for contactless pulse and heart rate monitoring. Our method uses only 18-seconds of video for customization and works effectively in both supervised and unsupervised manners. We evaluate our proposed approach on two benchmark datasets and demonstrate superior performance in cross-dataset evaluation with substantial reductions (42% to 44%) in errors compared with state-of-the-art approaches. We have also demonstrated our proposed method significantly helps reduce the bias in skin type.

cs.CV↗

arXiv2021

Higher-order topological superconductors based on weak topological insulators

High-order topological phases host robust boundary states at the boundary of the boundary, which can be interpreted from their boundary topology. In this work, considering the interplay between superconductors and magnetic fields to gap the surface states of three-dimensional weak topological insulators, we show that second-order topological superconductors (TSCs) featuring helical or chiral Majorana hinge modes and third-order TSC featuring Majorana corner modes can be realized. Remarkably, the higher-order TSCs in our models can be attributed to their certain boundaries, surfaces, or hinges, which naturally behave as first-order TSC in DIII or D symmetry class. Correspondingly, these higher-order TSCs can be characterized by the boundary first-order topological invariants, such as surface Chern numbers or surface $Z_2$ topological invariants for surface TSCs. Our models can effectively capture the topology of iron-based superconductors with desired inverted band structures and superconducting pairings.

cond-mat.supr-con↗

arXiv2021

DST: Data Selection and joint Training for Learning with Noisy Labels

Training a deep neural network heavily relies on a large amount of training data with accurate annotations. To alleviate this problem, various methods have been proposed to annotate the data automatically. However, automatically generating annotations will inevitably yields noisy labels. In this paper, we propose a Data Selection and joint Training (DST) method to automatically select training samples with accurate annotations. Specifically, DST fits a mixture model according to the original annotation as well as the predicted label for each training sample, and the mixture model is utilized to dynamically divide the training dataset into a correctly labeled dataset, a correctly predicted set and a wrong dataset. Then, DST is trained with these datasets in a supervised manner. Due to confirmation bias problem, we train the two networks alternately, and each network is tasked to establish the data division to teach another network. For each iteration, the correctly labeled and predicted labels are reweighted respectively by the probabilities from the mixture model, and a uniform distribution is used to generate the probabilities of the wrong samples. Experiments on CIFAR-10, CIFAR-100 and Clothing1M demonstrate that DST is the comparable or superior to the state-of-the-art methods.

cs.CV↗

arXiv2021

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement

Telehealth and remote health monitoring have become increasingly important during the SARS-CoV-2 pandemic and it is widely expected that this will have a lasting impact on healthcare practices. These tools can help reduce the risk of exposing patients and medical staff to infection, make healthcare services more accessible, and allow providers to see more patients. However, objective measurement of vital signs is challenging without direct contact with a patient. We present a video-based and on-device optical cardiopulmonary vital sign measurement approach. It leverages a novel multi-task temporal shift convolutional attention network (MTTS-CAN) and enables real-time cardiovascular and respiratory measurements on mobile platforms. We evaluate our system on an Advanced RISC Machine (ARM) CPU and achieve state-of-the-art accuracy while running at over 150 frames per second which enables real-time applications. Systematic experimentation on large benchmark datasets reveals that our approach leads to substantial (20%-50%) reductions in error and generalizes well across datasets.

eess.SP↗

arXiv2021

Online Mobile App Usage as an Indicator of Sleep Behavior and Job Performance

Sleep is critical to human function, mediating factors like memory, mood, energy, and alertness; therefore, it is commonly conjectured that a good night's sleep is important for job performance. However, both real-world sleep behavior and job performance are hard to measure at scale. In this work, we show that people's everyday interactions with online mobile apps can reveal insights into their job performance in real-world contexts. We present an observational study in which we objectively tracked the sleep behavior and job performance of salespeople (N = 15) and athletes (N = 19) for 18 months, using a mattress sensor and online mobile app. We first demonstrate that cumulative sleep measures are correlated with job performance metrics, showing that an hour of daily sleep loss for a week was associated with a 9.0% and 9.5% reduction in performance of salespeople and athletes, respectively. We then examine the utility of online app interaction time as a passively collectible and scalable performance indicator. We show that app interaction time is correlated with the performance of the athletes, but not the salespeople. To support that our app-based performance indicator captures meaningful variation in psychomotor function and is robust against potential confounds, we conducted a second study to evaluate the relationship between sleep behavior and app interaction time in a cohort of 274 participants. Using a generalized additive model to control for per-participant random effects, we demonstrate that participants who lost one hour of daily sleep for a week exhibited 5.0% slower app interaction times. We also find that app interaction time exhibits meaningful chronobiologically consistent correlations with sleep history, time awake, and circadian rhythms. Our findings reveal an opportunity for online app developers to generate new insights regarding cognition and productivity.

cs.HC↗