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Bin Liu

Publications and source records attributed to Bin Liu.

At least 253 records · Page 14Linked to original sources
arXiv2023

Coordinated Navigation Control of Cross-Domain Unmanned Systems via Guiding Vector Fields

This paper proposes a distributed guiding-vector-field (DGVF) controller for cross-domain unmanned systems (CDUSs) consisting of heterogeneous unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs), to achieve coordinated navigation whereas maneuvering along their prescribed paths. In particular, the DGVF controller provides a hierarchical architecture of an upper-level heterogeneous guidance velocity controller and a lower-level signal tracking regulator. Therein, the upper-level controller is to govern multiple heterogeneous USVs and UAVs to approach and maneuver along the prescribed paths and coordinate the formation simultaneously, whereas the low-level regulator is to track the corresponding desired guidance signals provided by the upper-level module. Significantly, the heterogeneous coordination among neighboring UAVs and USVs is achieved merely by the lightweight communication of a scalar (i.e., the additional virtual coordinate), which substantially decreases the communication and computational costs. Sufficient conditions assuring asymptotical convergence of the closed-loop system are derived in presence of the exponentially vanishing tracking errors. Finally, real-lake experiments are conducted on a self-established cross-domain heterogeneous platform consisting of three M-100 UAVs, two HUSTER-16 USVs, a HUSTER-12C USV, and a WiFi 5G wireless communication station to verify the effectiveness of the present DGVF controller.

cs.RO↗
arXiv2023

Global martingale weak solutions for the three-dimensional stochastic chemotaxis-Navier-Stokes system with Lévy processes

This paper studies the three-dimensional stochastic chemotaxis-Navier-Stokes (SCNS) system subjected to a Lévy-type random external force in bounded domain. Up to now, the existing results concerning global solvability of SCNS system mainly concentrated on the case of two spacial dimensions, little is known for the SCNS system in dimension three. We prove in present work that the three-dimensional SCNS system possesses at least one global martingale solution under proper assumptions, which is weak both in the analytical sense and in the stochastic sense. A new stochastic analogue of entropy-energy inequality and an uniform boundedness estimate are derived, which enable us to construct global-in-time approximate solutions from a properly regularized SCNS system via the Contraction Mapping Principle. The proof of the existence of martingale solution is based on the stochastic compactness method and an elaborate identification of the limits procedure, where the Jakubowski-Skorokhod Theorem is applied to deal with the phase spaces equipped with weak topology.

math.AP↗
arXiv2023

On the possibility to detect gravitational waves from post-merger super-massive neutron stars with a kilohertz detector

The detection of a secular post-merger gravitational wave (GW) signal in a binary neutron star (BNS) merger serves as strong evidence for the formation of a long-lived post-merger neutron star (NS), which can help constrain the maximum mass of NSs and differentiate NS equation of states. We specifically focus on the detection of GW emissions from rigidly rotating NSs formed through BNS mergers, using several kilohertz GW detectors that have been designed. We simulate the BNS mergers within the detecting limit of LIGO-Virgo-KARGA O4 and attempt to find out on what fraction the simulated sources may have a detectable secular post-merger GW signal. For kilohertz detectors designed in the same configuration of LIGO A+, we find that the design with peak sensitivity at approximately $2{\rm kHz}$ is most appropriate for such signals. The fraction of sources that have a detectable secular post-merger GW signal would be approximately $0.94\% - 11\%$ when the spindowns of the post-merger rigidly rotating NSs are dominated by GW radiation, while be approximately $0.46\% - 1.6\%$ when the contribution of electromagnetic (EM) radiation to the spin-down processes is non-negligible. We also estimate this fraction based on other well-known proposed kilohertz GW detectors and find that, with advanced design, it can reach approximately $12\% - 45\%$ for the GW-dominated spindown case and $4.7\% - 16\%$ when both the GW and EM radiations are considered.

gr-qc↗
arXiv2023

Domain formation and universally critical dynamics through phase separation in two-component Bose-Einstein condensates

We explore the defect formation and universally critical dynamics in two-dimensional (2D) two-component Bose-Einstein condensates(BECs) subjected to two types of potential traps: a homogeneous trap and a harmonic trap.We focus on the non-equilibrium universal dynamics of the miscible-immiscible phase transition with both linear and nonlinear quenching types.Although there exists spatial independence of the critical point, we find that the inhomogeneity of trap doesn't affect the phase transition of system and the critical exponents can still be explained by the homogeneous Kibble-Zurek mechanism. By analyzing the Bogoliubov excitations, we establish a power-law relationship between the domain correlation length, the phase transition delay, and the quench time.Furthermore, through real-time simulations of phase transition dynamics, the formation of domain defects and the delay of phase transition in non-equilibrium dynamics are demonstrated, along with the corresponding universal scaling of correlation length and phase transition delay for various quench time and quench coefficients, which align well with our analytical predictions.Our study confirms that the universality class of two-component BECs remains unaffected by dimensionality, while the larger nonlinear coefficients effectively suppress non-adiabatic excitations, offering a novel perspective for addressing adiabatic evolution.

cond-mat.quant-gas↗
arXiv2023

i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems

Input features play a crucial role in DNN-based recommender systems with thousands of categorical and continuous fields from users, items, contexts, and interactions. Noisy features and inappropriate embedding dimension assignments can deteriorate the performance of recommender systems and introduce unnecessary complexity in model training and online serving. Optimizing the input configuration of DNN models, including feature selection and embedding dimension assignment, has become one of the essential topics in feature engineering. However, in existing industrial practices, feature selection and dimension search are optimized sequentially, i.e., feature selection is performed first, followed by dimension search to determine the optimal dimension size for each selected feature. Such a sequential optimization mechanism increases training costs and risks generating suboptimal input configurations. To address this problem, we propose a differentiable neural input razor (i-Razor) that enables joint optimization of feature selection and dimension search. Concretely, we introduce an end-to-end differentiable model to learn the relative importance of different embedding regions of each feature. Furthermore, a flexible pruning algorithm is proposed to achieve feature filtering and dimension derivation simultaneously. Extensive experiments on two large-scale public datasets in the Click-Through-Rate (CTR) prediction task demonstrate the efficacy and superiority of i-Razor in balancing model complexity and performance.

cs.IR↗
arXiv2023

Linear OPF-based Robust Dynamic Operating Envelopes with Uncertainties in Unbalanced Distribution Networks

Dynamic operating envelopes (DOEs), as a key enabler to facilitate DER integration, have attracted increasing attention in the past years. However, uncertainties, which may come from load forecast errors or inaccurate network parameters, have been rarely discussed in DOE calculation, leading to compromised quality of the hosting capacity allocation strategy. This letter studies how to calculate DOEs that are immune to such uncertainties based on a linearised unbalanced three-phase optimal power flow (UTOPF) model. With uncertain parameters constrained by norm balls, formulations for calculating Robust DOEs (RDOEs) are presented along with discussions on their tractability. Two cases, including a 2-bus illustrative network and a representative Australian network, are tested to demonstrate the effectiveness and efficiency of the proposed approach.

math.OC↗
arXiv2023

Active Asteroid 311P/PANSTARRS: Rotational Instability as the Origin of Multi-tails?

The active asteroid 311P is one of the two targets of a planned Chinese asteroid exploration mission Tianwen-2. During 2013, 311P experienced several mass-loss events and exhibited multiple comet-like tails. Here we analyze the morphology and surface brightness of the tails to investigate the dust environment around the nucleus and mechanism of activities. We enhance the features of the tails using image processing techniques to obtain information about the morphology of the tails, and fit processed images to the syndyne-synchrone diagrams. The fitting results give estimations of the upper limits of the durations ($2\sim8$ days) of eruptions and the dust size range ($0.006\sim38.9$ mm) in the tails. The results of surface photometry performed for each dust tail show that the brightness distribution index of each tail ranged from approximately -1.81 to 0 and the dust size distribution indices of 311P's tails ranged from -2.29 to -1.45. The quantity of particles in each tail ranged from 0.5 to 8 $\times10^6$\,kg, which leads to a total dust-loss quantity of $3.0\times10^7$\,kg and a mass loss rate of 1.59 kg s$^{-1}$. Sublimation, continuous impacts or tidal forces of planets are excluded as explanations for 311P's activities, and rotational instability remains a possible activation cause without strong evidence against it.

astro-ph.EP↗
arXiv2023

Filter Pruning For CNN With Enhanced Linear Representation Redundancy

Structured network pruning excels non-structured methods because they can take advantage of the thriving developed parallel computing techniques. In this paper, we propose a new structured pruning method. Firstly, to create more structured redundancy, we present a data-driven loss function term calculated from the correlation coefficient matrix of different feature maps in the same layer, named CCM-loss. This loss term can encourage the neural network to learn stronger linear representation relations between feature maps during the training from the scratch so that more homogenous parts can be removed later in pruning. CCM-loss provides us with another universal transcendental mathematical tool besides L*-norm regularization, which concentrates on generating zeros, to generate more redundancy but for the different genres. Furthermore, we design a matching channel selection strategy based on principal components analysis to exploit the maximum potential ability of CCM-loss. In our new strategy, we mainly focus on the consistency and integrality of the information flow in the network. Instead of empirically hard-code the retain ratio for each layer, our channel selection strategy can dynamically adjust each layer's retain ratio according to the specific circumstance of a per-trained model to push the prune ratio to the limit. Notably, on the Cifar-10 dataset, our method brings 93.64% accuracy for pruned VGG-16 with only 1.40M parameters and 49.60M FLOPs, the pruned ratios for parameters and FLOPs are 90.6% and 84.2%, respectively. For ResNet-50 trained on the ImageNet dataset, our approach achieves 42.8% and 47.3% storage and computation reductions, respectively, with an accuracy of 76.23%. Our code is available at https://github.com/Bojue-Wang/CCM-LRR.

cs.CV↗
arXiv2023

Addressing the Dark State Problem in Strongly Coupled Organic Exciton-Polariton Systems

The manipulation of molecular excited state processes through strong coupling has attracted significant interest for its potential to provide precise control of photochemical phenomena. However, the key limiting factor for achieving this control has been the dark state problem, in which photoexcitation populates long-lived reservoir states with similar energies and dynamics to bare excitons. Here, we use a sensitive ultrafast transient reflection method with momentum and spectral resolution to achieve the selective excitation of organic exciton-polaritons in open photonic cavities. We show that the energy dispersions of these systems allow us to avoid the parasitic effect of reservoir states. Under phase-matching conditions, we observe the direct population and decay of polaritons on time scales of less than 100 fs and find that momentum scattering processes occur on even faster timescales. We establish that it is possible to overcome the dark state problem through careful design of strongly coupled systems.

physics.optics↗
arXiv2023

The GBT Diffuse Ionized Gas Survey (GDIGS): Discrete Sources

The Green Bank Telescope (GBT) Diffuse Ionized Gas Survey (GDIGS) traces ionized gas in the Galactic midplane by observing radio recombination line (RRL) emission from 4-8 GHz. The nominal survey zone is $32.3^{\circ}> {\ell} > -5^{\circ}$, $|b|<0.5^{\circ}$. Here, we analyze GDIGS Hn$α$ ionized gas emission toward discrete sources. Using GDIGS data, we identify the velocity of 35 H II regions that have multiple detected RRL velocity components. We identify and characterize RRL emission from 88 H II regions that previously lacked measured ionized gas velocities. We also identify and characterize RRL emission from eight locations that appear to be previously-unidentified H II regions and 30 locations of RRL emission that do not appear to be H II regions based on their lack of mid-infrared emission. This latter group may be a compact component of the Galactic Diffuse Ionized Gas (DIG). There are an additional 10 discrete sources that have anomalously high RRL velocities for their locations in the Galactic plane. We compare these objects' RRL data to 13CO, H I and mid-infrared data, and find that these sources do not have the expected 24 $μ$m emission characteristic of H II regions. Based on this comparison we do not think these objects are H II regions, but we are unable to classify them as a known type of object.

astro-ph.GA↗
arXiv2023

Unveiling the Regulatory Factors for Phase Transitions in Zeolitic Imidazolate Frameworks: A High-Throughout Calculations and Data Mining

Recently, there have been significant advancements in the study of Metal-Organic Frameworks (MOFs), particularly in the discovery of glassy states in zeolitic imidazolate frameworks (ZIFs), a subset of MOFs. However, the correlation between the glass-forming ability (GFA) of MOFs and their structural characteristics remains poorly understood. To address this gap, we analyze over 20,000 MOFs from the QMOF database, comparing geometric and electronic order parameters between ZIFs and other MOFs. Our findings reveal a set of order parameters that effectively capture the thermal stability and GFA of MOFs, acting as structural genes to predict glass formation. Through molecular dynamics simulations on representative ZIF structures, we validate the reliability of these order parameters, particularly the total bond-order density (TBOD) and the largest cavity diameter (LCD), for screening potential melt-quenched MOFs. Furthermore, we predict that external pressure and electric fields can control the TBOD of MOFs, expanding our understanding of their thermal stability. This work identifies structural genes associated with the melt stability of MOFs,and contributes valuable insights into the prediction and understanding of MOF glass-forming ability.

cond-mat.mtrl-sci↗
arXiv2023

Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records

Survival analysis plays a crucial role in many healthcare decisions, where the risk prediction for the events of interest can support an informative outlook for a patient's medical journey. Given the existence of data censoring, an effective way of survival analysis is to enforce the pairwise temporal concordance between censored and observed data, aiming to utilize the time interval before censoring as partially observed time-to-event labels for supervised learning. Although existing studies mostly employed ranking methods to pursue an ordering objective, contrastive methods which learn a discriminative embedding by having data contrast against each other, have not been explored thoroughly for survival analysis. Therefore, in this paper, we propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv) analysis framework that utilizes survival durations from both censored and observed data to define temporal distinctiveness and construct negative sample pairs with adjustable hardness for contrastive learning. Specifically, we first use an ontological encoder and a sequential self-attention encoder to represent the longitudinal EHR data with rich contexts. Second, we design a temporal contrastive loss to capture varying survival durations in a supervised setting through a hardness-aware negative sampling mechanism. Last, we incorporate the contrastive task into the time-to-event predictive task with multiple loss components. We conduct extensive experiments using a large EHR dataset to forecast the risk of hospitalized patients who are in danger of developing acute kidney injury (AKI), a critical and urgent medical condition. The effectiveness and explainability of the proposed model are validated through comprehensive quantitative and qualitative studies.

cs.LG↗
arXiv2023

Unraveling Medium-Range Order and Melting Mechanism of ZIF-4 under High Temperature

Glass formation in Zeolitic Imidazolate Frameworks (ZIFs) has garnered significant attention in the field of Metal-Organic Frameworks (MOFs) in recent years. Numerous works have been conducted to investigate the microscopic mechanisms involved in the melting-quenching process of ZIFs. Understanding the density variations that occur during the melting process of ZIFs is crucial for comprehending the origins of glass formation. However, conducting large-scale simulations has been challenging due to limitations in computational resources. In this work, we utilized deep learning methods to accurately construct a potential function that describes the atomic-scale melting behavior of Zeolitic Imidazolate Framework-4 (ZIF-4). The results revealed the spatial heterogeneity associated with the formation of low-density phases during the melting process of ZIF-4. This work discusses the advantages and limitations of applying deep learning simulation methods to complex structures like ZIFs, providing valuable insights for the development of machine learning approaches in designing Metal-Organic Framework glasses.

cond-mat.mtrl-sci↗
arXiv2023

Emotion-Aware Prosodic Phrasing for Expressive Text-to-Speech

Prosodic phrasing is crucial to the naturalness and intelligibility of end-to-end Text-to-Speech (TTS). There exist both linguistic and emotional prosody in natural speech. As the study of prosodic phrasing has been linguistically motivated, prosodic phrasing for expressive emotion rendering has not been well studied. In this paper, we propose an emotion-aware prosodic phrasing model, termed \textit{EmoPP}, to mine the emotional cues of utterance accurately and predict appropriate phrase breaks. We first conduct objective observations on the ESD dataset to validate the strong correlation between emotion and prosodic phrasing. Then the objective and subjective evaluations show that the EmoPP outperforms all baselines and achieves remarkable performance in terms of emotion expressiveness. The audio samples and the code are available at \url{https://github.com/AI-S2-Lab/EmoPP}.

cs.AI↗
arXiv2023

Ring-shaped quantum droplets with hidden vorticity in a radially-periodic potential

We study the stability and characteristics of two-dimensional (2D) circular quantum droplets (QDs) with embedded hidden vorticity (HV), i.e., opposite angular momenta in two components, formed by binary Bose-Einstein condensates (BECs) trapped in a radially-periodic potential. The system is modeled by the Gross-Pitaevskii (GP) equations with the Lee-Huang-Yang (LHY) terms, which represent the higher-order self-repulsion induced by quantum fluctuations around the meanfield state, and a potential which is a periodic function of the radial coordinate. Ring-shaped QDs with high winding numbers (WNs) of the HV type, which are trapped in particular circular troughs of the radial potential, are produced by means of the imaginary-time-integration method. Effects of the depth and period of the potential on these QD states are studied. The trapping capacity of individual circular troughs is identified. Stable compound states in the form of nested multiring patterns are constructed too, including ones with WNs of opposite signs. The stably coexisting ringshaped QDs with different WNs can be used for the design of BEC-based data-storage schemes.

nlin.PS↗
arXiv2023

MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning

The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia. The challenge focuses on system robustness and consists of three distinct tracks: (1) MER-MULTI, where participants are required to recognize both discrete and dimensional emotions; (2) MER-NOISE, in which noise is added to test videos for modality robustness evaluation; (3) MER-SEMI, which provides a large amount of unlabeled samples for semi-supervised learning. In this paper, we introduce the motivation behind this challenge, describe the benchmark dataset, and provide some statistics about participants. To continue using this dataset after MER 2023, please sign a new End User License Agreement and send it to our official email address merchallenge.contact@gmail.com. We believe this high-quality dataset can become a new benchmark in multimodal emotion recognition, especially for the Chinese research community.

cs.CL↗
arXiv2023

SoccerNet 2023 Challenges Results

The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet.

cs.CV↗
arXiv2023

Calculation of Dispatchable Region for Renewables with Advanced Computational Techniques

Dispatchable region for renewables (DRR) depicts a space for renewables that a power system operator can manage by dispatching controllable resources. The DRR can be used to evaluate the distance from an operating point to a secure boundary and identify ramping events with the highest risk. However, existing approaches based on MILP reformulation or iteration-based LP algorithms may be computationally challenging. This paper investigates if advanced computation techniques, including high-performance computing and parallel computing techniques, can improve the computational performance.

math.OC↗