arXiv subjects

Xinyu Li

Publications and source records attributed to Xinyu Li.

At least 217 records · Page 12Linked to original sources

arXiv2020

Dual-comb delay spectroscopy with attometer resolution

Spectroscopy has attracted much attention in molecular detection, biomolecular identification, and chemical analysis for providing accurate measurement. However, it is almost unable to distinguish different sources with overlapped resonances in mixed analytes. Here, we present dual-comb delay spectroscopy to overcome this problem. The introduction of group delay spectroscopy provides a new tool to identify sources that would lead to overlapped resonances in intensity or phase spectroscopy. To obtain sufficiently high spectral resolution and signal-to-noise ratio for achieving reliable group delay spectrum, a probe comb with the wavelengths precisely scaned by a microwave source is applied, leading to attometer-level resolution and million-level signal-to-noise ratio. In an experiment, spectroscopy with an optional resolution up to 1 kHz (8 attometer), an average signal-to-noise ratio surpassing 2,000,000, and a span exceeding 33 nm is demonstrated. Two overlapped resonances from two different sources are clearly differentiated. Our work offers a new perspective for exploring the interaction between matter and light.

physics.ins-det↗

arXiv2020

Simulation of a Compact Object with Outflows Moving Through a Gaseous Background

A compact object moving relative to surrounding gas accretes material and perturbs the density of gas in its vicinity. In the classical picture of Bondi-Hoyle-Lyttleton accretion, the perturbation takes the form of an overdense wake behind the object, which exerts a dynamical friction drag. We use hydrodynamic simulations to investigate how the accretion rate and strength of dynamical friction are modified by the presence of outflow from the compact object. We show that the destruction of the wake by an outflow reduces dynamical friction, and reverses its sign when the outflow is strong enough, in good quantitative agreement with analytic calculations. For a strong isotropic outflow, the outcome on scales that we have simulated is a negative dynamical friction, i.e., net acceleration. For jet-like outflows driven by reprocessed accretion, both the rate of accretion and the magnitude of dynamical friction drop for more powerful jets. The accretion rate is strongly intermittent when the jet points to the same direction as the motion of the compact object. The dynamical effects of outflows may be important for the evolution of compact objects during the common envelope phase of binary systems, and for accreting compact objects and massive stars encountering AGN discs.

astro-ph.HE↗

arXiv2019

Consequences of Energetic Magnetar-Like Outbursts of Nearby Neutron Stars: $^{14}$C Events and the Cosmic Electron Spectrum

Four significant events of rapid $^{14}$C increase have taken place within the past several thousand years. The physical origin of these rapid increases is still a mystery but must be associated with extremely energetic cosmic processes. Pulsars are highly magnetized neutron stars that emit a beam of electromagnetic radiations. Any sudden release of the energy stored in the magnetic multipole field will trigger outbursts similar to the giant flares of magnetars. Here we show that the relativistic outflow from the outbursts of a nearby pulsar interacting with the interstellar medium generates a shock, which accelerates electrons to trillions of electron volts. The high-energy photons from synchrotron emission of the shock interact with Earth's atmosphere, producing the cosmogenic nuclide $^{14}$C, which can cause the rapid $^{14}$C increases discovered in tree rings. These same relativistic electrons can account for a significant fraction of the cosmic electron spectrum in the trillion electron volts energy range, as observed by space-borne satellites. Since these outburst events can significantly affect our environment, monitoring nearby pulsars for such outbursts may be important in the future.

astro-ph.HE↗

arXiv2019

Puncturable Signatures and Applications in Proof-of-Stake Blockchain Protocol

Proof-of-stake blockchain protocols are becoming one of the most promising alternatives to the energy-consuming proof-of-work protocols. However, one particularly critical threat in the PoS setting is the well-known long-range attacks caused by secret key leakage (LRSL attack). Specifically, an adversary can attempt to control/compromise accounts possessing substantial stake at some past moment such that double-spend or erase past transactions, violating the fundamental persistence property of blockchain. Puncturable signatures provide a satisfying solution to construct practical proof-of-stake blockchain resilient to LRSL attack, despite of the fact that existent constructions are not efficient enough for practical deployments. In this paper, we provide an in-depth study of puncturable signatures and explore its applications in the proof-of-stake blockchain. We formalize a security model that allows the adversary for adaptive signing and puncturing queries, and show a construction with efficient puncturing operations based on the Bloom filter data structure and strong Diffie-Hellman assumption. The puncturing functionality we desire is for a particular part of message, like prefix, instead of the whole message. Furthermore, we use puncturable signatures to construct practical proof-of-stake blockchain protocols that are resilient to LRSL attack, while previously the forward-secure signature is used to immunize this attack. We implement our scheme and provide experimental results showing that in comparison with the forward-secure signature, our construction performs substantially better on signature size, signing and verification efficiency, significantly on key update efficiency.

cs.CR↗

arXiv2019

Differential Evolution with Better and Nearest Option for Function Optimization

Differential evolution(DE) is a conventional algorithm with fast convergence speed. However, DE may be trapped in local optimal solution easily. Many researchers devote themselves to improving DE. In our previously work, whale swarm algorithm have shown its strong searching performance due to its niching based mutation strategy. Based on this fact, we propose a new DE algorithm called DE with Better and Nearest option (NbDE). In order to evaluate the performance of NbDE, NbDE is compared with several meta-heuristic algorithms on nine classical benchmark test functions with different dimensions. The results show that NbDE outperforms other algorithms in convergence speed and accuracy.

cs.NE↗

arXiv2019

Whale swarm algorithm with the mechanism of identifying and escaping from extreme points for multimodal function optimization

Most real-world optimization problems often come with multiple global optima or local optima. Therefore, increasing niching metaheuristic algorithms, which devote to finding multiple optima in a single run, are developed to solve these multimodal optimization problems. However, there are two difficulties urgently to be solved for most existing niching metaheuristic algorithms: how to set the optimal values of niching parameters for different optimization problems, and how to jump out of the local optima efficiently. These two difficulties limited their practicality largely. Based on Whale Swarm Algorithm (WSA) we proposed previously, this paper presents a new multimodal optimizer named WSA with Iterative Counter (WSA-IC) to address these two difficulties. In the one hand, WSA-IC improves the iteration rule of the original WSA for multimodal optimization, which removes the need of specifying different values of attenuation coefficient for different problems to form multiple subpopulations, without introducing any niching parameter. In the other hand, WSA-IC enables the identification of extreme point during iterations relying on two new parameters (i.e., stability threshold Ts and fitness threshold Tf), to jump out of the located extreme point. Moreover, the convergence of WSA-IC is proved. Finally, the proposed WSA-IC is compared with several niching metaheuristic algorithms on CEC2015 niching benchmark test functions and five additional classical multimodal functions with high dimensions. The experimental results demonstrate that WSA-IC statistically outperforms other niching metaheuristic algorithms on most test functions.

cs.NE↗

arXiv2019

Black Hole Hair from Scalar Dark Matter

We show that a black hole surrounded by scalar dark matter develops scalar hair. This is the generalization of a phenomenon pointed out by Jacobson, that a minimally coupled scalar with a non-trivial time dependence far away from the black hole would endow the black hole with hair. In our case, the time dependence arises from the oscillation of a scalar field with a non-zero mass. We systematically explore the scalar profile around the black hole for different scalar masses. In the small mass limit, the scalar field has a $1/r$ component at large radius $r$, consistent with Jacobson's result. In the large mass limit (with the Compton wavelength of order of the horizon or smaller), the scalar field has a $1/r^{3/4}$ profile yielding a pile-up close to the horizon, while distinctive nodes occur for intermediate masses. Thus, the dark matter profile around a black hole, while challenging to measure, contains information about the dark matter particle mass. As an application, we consider the case of the supermassive black hole at the center of M87, recently imaged by the Event Horizon Telescope. Its horizon size is roughly the Compton wavelength of a scalar particle of mass $10^{-20}$ eV. We consider the implications of the expected scalar pile-up close to the horizon, for fuzzy dark matter at a mass of $10^{-20}$ eV or below.

gr-qc↗

arXiv2019

BERTSel: Answer Selection with Pre-trained Models

Recently, pre-trained models have been the dominant paradigm in natural language processing. They achieved remarkable state-of-the-art performance across a wide range of related tasks, such as textual entailment, natural language inference, question answering, etc. BERT, proposed by Devlin et.al., has achieved a better marked result in GLUE leaderboard with a deep transformer architecture. Despite its soaring popularity, however, BERT has not yet been applied to answer selection. This task is different from others with a few nuances: first, modeling the relevance and correctness of candidates matters compared to semantic relatedness and syntactic structure; second, the length of an answer may be different from other candidates and questions. In this paper. we are the first to explore the performance of fine-tuning BERT for answer selection. We achieved STOA results across five popular datasets, demonstrating the success of pre-trained models in this task.

cs.CL↗

arXiv2019

Everyone is a Cartoonist: Selfie Cartoonization with Attentive Adversarial Networks

Selfie and cartoon are two popular artistic forms that are widely presented in our daily life. Despite the great progress in image translation/stylization, few techniques focus specifically on selfie cartoonization, since cartoon images usually contain artistic abstraction (e.g., large smoothing areas) and exaggeration (e.g., large/delicate eyebrows). In this paper, we address this problem by proposing a selfie cartoonization Generative Adversarial Network (scGAN), which mainly uses an attentive adversarial network (AAN) to emphasize specific facial regions and ignore low-level details. More specifically, we first design a cycle-like architecture to enable training with unpaired data. Then we design three losses from different aspects. A total variation loss is used to highlight important edges and contents in cartoon portraits. An attentive cycle loss is added to lay more emphasis on delicate facial areas such as eyes. In addition, a perceptual loss is included to eliminate artifacts and improve robustness of our method. Experimental results show that our method is capable of generating different cartoon styles and outperforms a number of state-of-the-art methods.

cs.CV↗

arXiv2019

Multi-stream Network With Temporal Attention For Environmental Sound Classification

Environmental sound classification systems often do not perform robustly across different sound classification tasks and audio signals of varying temporal structures. We introduce a multi-stream convolutional neural network with temporal attention that addresses these problems. The network relies on three input streams consisting of raw audio and spectral features and utilizes a temporal attention function computed from energy changes over time. Training and classification utilizes decision fusion and data augmentation techniques that incorporate uncertainty. We evaluate this network on three commonly used data sets for environmental sound and audio scene classification and achieve new state-of-the-art performance without any changes in network architecture or front-end preprocessing, thus demonstrating better generalizability.

cs.SD↗

arXiv2018

Tri-axial Self-Attention for Concurrent Activity Recognition

We present a system for concurrent activity recognition. To extract features associated with different activities, we propose a feature-to-activity attention that maps the extracted global features to sub-features associated with individual activities. To model the temporal associations of individual activities, we propose a transformer-network encoder that models independent temporal associations for each activity. To make the concurrent activity prediction aware of the potential associations between activities, we propose self-attention with an association mask. Our system achieved state-of-the-art or comparable performance on three commonly used concurrent activity detection datasets. Our visualizations demonstrate that our system is able to locate the important spatial-temporal features for final decision making. We also showed that our system can be applied to general multilabel classification problems.

cs.CV↗

arXiv2018

Dissipation of Alfvén Waves in Relativistic Magnetospheres of Magnetars

Magnetar flares excite strong Alfvén waves in the magnetosphere of the neutron star. The wave energy can (1) dissipate in the magnetosphere, (2) convert to "fast modes" and possibly escape, and (3) penetrate the neutron star crust and dissipate there. We examine and compare the three options. Particularly challenging are nonlinear interactions between strong waves, which develop a cascade to small dissipative scales. This process can be studied in the framework of force-free electrodynamics (FFE). We perform three-dimensional FFE simulations to investigate Alfvén wave dissipation, how long it takes, and how it depends on the initial wave amplitude on the driving scale. In the simulations, we launch two large Alfvén wave packets that keep bouncing on closed magnetic field lines and collide repeatedly until the full turbulence spectrum develops. Besides dissipation due to the turbulent cascade, we find that in some simulations spurious energy losses occur immediately in the first collisions. This effect occurs in special cases where the FFE description breaks. It is explained with a simple one-dimensional model, which we examine in both FFE and full magnetohydrodynamic settings. We find that magnetospheric dissipation through nonlinear wave interactions is relatively slow and more energy is drained into the neutron star. The wave energy deposited into the star is promptly dissipated through plastic crustal flows induced at the bottom of the liquid ocean, and a fraction of the generated heat is radiated from the stellar surface.

astro-ph.HE↗

arXiv2018

Numerical and Perturbative Computations of the Fuzzy Dark Matter Model

We investigate nonlinear structure formation in the fuzzy dark matter (FDM) model using both numerical and perturbative techniques. On the numerical side, we examine the virtues and limitations of a Schrodinger-Poisson solver (wave formulation) versus a fluid dynamics solver (Madelung formulation). We also carry out a perturbative computation of the one-loop mass power spectrum. We find that (1) in many cases, the fluid dynamics solver is capable of producing the expected interference patterns, but it fails where destructive interference causes the density to vanish which generally occurs in the nonlinear regime. (2) The Schrodinger-Poisson solver works well in all test cases, but it is demanding in resolution: one must resolve the small de Broglie scale to obtain the correct dynamics on large scales. (3) We compare the mass power spectrum from perturbation theory against that from the Schrodinger-Poisson solver, and find good agreement in the mildly nonlinear regime. Compared with fluid perturbation theory, wave perturbation theory has a more limited range of validity. (4) As an application, we compare the Lyman-alpha forest flux power spectrum obtained from the Schrodinger-Poisson solver versus one from an N-body simulation (which is often used as an approximate method to make predictions for FDM). At redshift 5, the two, starting from the same initial condition, agree to better than 10 % on observationally relevant scales as long as the FDM mass exceeds $2 \times 10^{-23}$ eV.

astro-ph.CO↗

arXiv2018

Resistivity of high pressure phosphorus phases

Simple cubic (sc) black phosphorus (denoted BP), stable at P>10GPa, seems an ordinary metal. It has electron-phonon-driven superconductivity with Tc 5-10 K. The A17 phase, stable at atmospheric pressure, has a narrow gap, becomes semimetallic at P=1 GPa, and has a smooth transition to topological metal behavior at P ~ 5 GPa. The A7 phase, stable for 5<P<10 GPa, is metallic, superconducting, and less conventional than the sc phase. Some insights are extracted from analysis of resistivity versus temperature at various pressures. A surprising order-of-magnitude disagreement between theory and experiment is discussed.

cond-mat.supr-con↗

arXiv2018

Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment

Multimodal affective computing, learning to recognize and interpret human affects and subjective information from multiple data sources, is still challenging because: (i) it is hard to extract informative features to represent human affects from heterogeneous inputs; (ii) current fusion strategies only fuse different modalities at abstract level, ignoring time-dependent interactions between modalities. Addressing such issues, we introduce a hierarchical multimodal architecture with attention and word-level fusion to classify utter-ance-level sentiment and emotion from text and audio data. Our introduced model outperforms the state-of-the-art approaches on published datasets and we demonstrated that our model is able to visualize and interpret the synchronized attention over modalities.

cs.CL↗

arXiv2017

Evaluation of Trace Alignment Quality and its Application in Medical Process Mining

Trace alignment algorithms have been used in process mining for discovering the consensus treatment procedures and process deviations. Different alignment algorithms, however, may produce very different results. No widely-adopted method exists for evaluating the results of trace alignment. Existing reference-free evaluation methods cannot adequately and comprehensively assess the alignment quality. We analyzed and compared the existing evaluation methods, identifying their limitations, and introduced improvements in two reference-free evaluation methods. Our approach assesses the alignment result globally instead of locally, and therefore helps the algorithm to optimize overall alignment quality. We also introduced a novel metric to measure the alignment complexity, which can be used as a constraint on alignment algorithm optimization. We tested our evaluation methods on a trauma resuscitation dataset and provided the medical explanation of the activities and patterns identified as deviations using our proposed evaluation methods.

cs.OH↗

arXiv2017

Progress Estimation and Phase Detection for Sequential Processes

Process modeling and understanding are fundamental for advanced human-computer interfaces and automation systems. Most recent research has focused on activity recognition, but little has been done on sensor-based detection of process progress. We introduce a real-time, sensor-based system for modeling, recognizing and estimating the progress of a work process. We implemented a multimodal deep learning structure to extract the relevant spatio-temporal features from multiple sensory inputs and used a novel deep regression structure for overall completeness estimation. Using process completeness estimation with a Gaussian mixture model, our system can predict the phase for sequential processes. The performance speed, calculated using completeness estimation, allows online estimation of the remaining time. To train our system, we introduced a novel rectified hyperbolic tangent (rtanh) activation function and conditional loss. Our system was tested on data obtained from the medical process (trauma resuscitation) and sports events (Olympic swimming competition). Our system outperformed the existing trauma-resuscitation phase detectors with a phase detection accuracy of over 86%, an F1-score of 0.67, a completeness estimation error of under 12.6%, and a remaining-time estimation error of less than 7.5 minutes. For the Olympic swimming dataset, our system achieved an accuracy of 88%, an F1-score of 0.58, a completeness estimation error of 6.3% and a remaining-time estimation error of 2.9 minutes.

cs.LG↗

arXiv2017

Whale swarm algorithm for function optimization

Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization. This paper has proposed a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired by the whales behavior of communicating with each other via ultrasound for hunting. The proposed Whale Swarm Algorithm has been compared with several popular metaheuristic algorithms on comprehensive performance metrics. According to the experimental results, Whale Swarm Algorithm has a quite competitive performance when compared with other algorithms.

cs.NE↗