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Chang Liu

Publications and source records attributed to Chang Liu.

At least 739 records · Page 41Linked to original sources
arXiv2023

Atomic-scale Mapping Unravel Anisotropic Dissolution Behaviors of Gibbsite Nanosheets

This study examines the anisotropic dissolution of the basal plane gibbsite (γ-Al(OH)3) nanoplates in sodium hydroxide solution using in situ atomic force microscopy (AFM) and density functional theory (DFT) calculations. In the surface-reaction controlled regime, in situ AFM measurements reveal anisotropic dissolution of hillocks and etch pits on the gibbsite basal plane, with preferred dissolution directions alternating between layers. The mirror-symmetric pattern of dissolution preference between adjacent gibbsite aluminum hydroxide sheet, observed along the crystallographic a-c plane, results from the matching symmetry between the structures of the adjacent (001) and (002) crystal planes. Consequently, the overall dissolution rate of gibbsite nanoplates exhibits crystallographic a-c plane symmetry, as the rate of parallel steps is governed by the slower ones. DFT calculations suggest that the anisotropic dissolution is partially due to the orientation and strength of Al-OH-Al linkages pair within gibbsite surface structure. These findings offer a comprehensive understanding of anisotropic dissolution behavior of gibbsite and illuminate the mechanisms behind preferential dissolution.

cond-mat.mtrl-sci↗
arXiv2023

Plunging Breakers - Part 2. Droplet Generation

The positions, diameters ($d\geq 100$ $μ$m), times and velocities of droplets generated by three plunging breaking waves are measured as the droplets move up across a measurement plane located 1.2 cm above the highest point reached by the crests during breaking. The three breakers are created by dispersively focused wave packets that differ primarily only through the overall amplitude of the wave maker motion used to generate them. The breakers are designated qualitatively by their intensities: weak, moderate and strong. The droplets are measured with two in-line cinematic holographic systems operating at 650 holograms per second with measurement volumes that span the width of the tank. In combination with the wave profile measurements described in Part 1 of this two part paper, the droplet measurements are used to explore the mechanisms of droplet generation and the effects of these mechanisms on the measured properties of the droplets and their motion. It is found that there are four major mechanisms for droplet production, closure of the indentation between the top surface of the plunging jet and the splash that it creates (labeled Region I-A), the bursting of large bubbles that were entrapped under the plunging jet at impact, splashing and bubble bursting in the turbulent zone of the front face of the wave (combined with the large bursting bubbles in a region labeled I-B) and the bursting of small bubbles that reach the water surface at the crest of the nonbreaking wave following the breaker (Region II). The droplet diameter distributions for the entire droplet set for each breaker contain separate small- and large-diameter regions of power law behavior that cross at a diameter, $d_i$, which increases monotonically from 820 $μ$m to 1480 $μ$m from the weak to the strong breaker, respectively. Similar power law behavior is found in Regions I-A and I-B.

physics.flu-dyn↗
arXiv2023

Analysing the impact of bottom friction on shallow water waves over idealised bottom topographies

Analysing the impact of bottom friction on shallow water waves over bottom terrains is important in areas including environmental and coastal engineering as well as the oceanic and atmospheric sciences. However, current theoretical developments rely on making certain limiting assumptions about these flows and thus more development is needed to be able to further generalise this behaviour. This work uses Adomian decomposition method (ADM) to not only develop semi-analytical formulations describing this behaviour, for flat terrains, but also as reverse-engineering mechanisms to develop new closed-form solutions describing this type of phenomena. Specifically, we respectively focus on inertial geostrophic oscillations and anticyclonic vortices with finite escape times in which our results directly demonstrate the direct correlation between the constant Coriolis force, the constant bottom friction, and the overall dynamics. Additionally, we illustrate elements of dissipation-induced instability with respect to constant bottom friction in these types of flows where we also demonstrate the connection to the initial dynamics for certain cases.

physics.flu-dyn↗
arXiv2023

Deep Learning-empowered Predictive Precoder Design for OTFS Transmission in URLLC

To guarantee excellent reliability performance in ultra-reliable low-latency communications (URLLC), pragmatic precoder design is an effective approach. However, an efficient precoder design highly depends on the accurate instantaneous channel state information at the transmitter (ICSIT), which however, is not always available in practice. To overcome this problem, in this paper, we focus on the orthogonal time frequency space (OTFS)-based URLLC system and adopt a deep learning (DL) approach to directly predict the precoder for the next time frame to minimize the frame error rate (FER) via implicitly exploiting the features from estimated historical channels in the delay-Doppler domain. By doing this, we can guarantee the system reliability even without the knowledge of ICSIT. To this end, a general precoder design problem is formulated where a closed-form theoretical FER expression is specifically derived to characterize the system reliability. Then, a delay-Doppler domain channels-aware convolutional long short-term memory (CLSTM) network (DDCL-Net) is proposed for predictive precoder design. In particular, both the convolutional neural network and LSTM modules are adopted in the proposed neural network to exploit the spatial-temporal features of wireless channels for improving the learning performance. Finally, simulation results demonstrated that the FER performance of the proposed method approaches that of the perfect ICSI-aided scheme.

cs.IT↗
arXiv2023

MAS: A versatile Landau-fluid eigenvalue code for plasma stability analysis in general geometry

We have developed a new global eigenvalue code, Multiscale Analysis for plasma Stabilities (MAS), for studying plasma problems with wave toroidal mode number n and frequency omega in a broad range of interest in general tokamak geometry, based on a five-field Landau-fluid description of thermal plasmas. Beyond keeping the necessary plasma fluid response, we further retain the important kinetic effects including diamagnetic drift, ion finite Larmor radius, finite parallel electric field, ion and electron Landau resonances in a self-consistent and non-perturbative manner without sacrificing the attractive efficiency in computation. The physical capabilities of the code are evaluated and examined in the aspects of both theory and simulation. In theory, the comprehensive Landau-fluid model implemented in MAS can be reduced to the well-known ideal MHD model, electrostatic ion-fluid model, and drift-kinetic model in various limits, which clearly delineates the physics validity regime. In simulation, MAS has been well benchmarked with theory and other gyrokinetic and kinetic-MHD hybrid codes in a manner of adopting the unified physical and numerical framework, which covers the kinetic Alfven wave, ion sound wave, low-n kink, high-n ion temperature gradient mode and kinetic ballooning mode. Moreover, MAS is successfully applied to model the Alfven eigenmode (AE) activities in DIII-D discharge #159243, which faithfully captures the frequency sweeping of RSAE, the tunneling damping of TAE, as well as the polarization characteristics of KBAE and BAAE being consistent with former gyrokinetic theory and simulation. With respect to the key progress contributed to the community, MAS has the advantage of combining rich physics ingredients, realistic global geometry and high computation efficiency together for plasma stability analysis in linear regime.

physics.plasm-ph↗
arXiv2023

Transient interactions between bubbles and a high-speed cylinder in underwater launches: An experimental and numerical study

The underwater launch of high-speed vehicles involves complex bubble-structure interactions, which are not currently well understood. In this study, two small-scale experiments are carried out involving transient bubble-cylinder interactions. We adopt the underwater electric discharge method to generate a high-pressure bubble that drives a cylinder to a maximum velocity of about 25 m/s within 1 ms. A tail bubble forms as the cylinder is ejected from the launch tube. Moreover, we observe a shoulder cavity around the head of the cylinder due to the pressure reduction in the flow. To better understand the complex interaction between bubbles and the high-speed cylinder, we use the boundary element method to establish a bubble-structure interaction model. Our numerical model reproduces the experimental observations quite well, including the cylinder motion and the transient evolution of the bubbles. Thereafter, a systematic study is carried out to reveal the dependence of the bubble-cylinder interactions on the initial pressure of the tail bubble P0. We obtain a scaling law for the maximum velocity of the cylinder with respect to P0. The findings from this study may provide a reference for subsequent research into underwater launches.

physics.flu-dyn↗
arXiv2023

Rapid search for massive black hole binary coalescences using deep learning

The coalescences of massive black hole binaries are one of the main targets of space-based gravitational wave observatories. Such gravitational wave sources are expected to be accompanied by electromagnetic emissions. Low latency detection of the massive black hole mergers provides a start point for a global-fit analysis to explore the large parameter space of signals simultaneously being present in the data but at great computational cost. To alleviate this issue, we present a deep learning method for rapidly searching for signals of massive black hole binaries in gravitational wave data. Our model is capable of processing a year of data, simulated from the LISA data challenge, in only several seconds, while identifying all coalescences of massive black hole binaries with no false alarms. We further demonstrate that the model shows robust resistance to a wide range of generalization cases, including various waveform families and updated instrumental configurations. This method offers an effective approach that combines advances in artificial intelligence to open a new pathway for space-based gravitational wave observations.

astro-ph.IM↗
arXiv2023

A Three-Photon Rydberg Atom-Based Radio Frequency Sensing Scheme with Narrow Linewidth

We demonstrate Rydberg atom-based radio frequency sensing with a colinear three-photon scheme in a room temperature cesium vapor cell that minimizes residual Doppler broadening of the probe laser absorption feature. A sub-200 kHz spectral linewidth is observed and extends the self-calibrated Autler-Townes sensing regime to weaker fields by a factor of ~18 compared to the theoretical limit of the most commonly used two-photon scheme. The sensitivity of the method to microsecond pulses is shown to be sufficient to detect radio frequency Rabi frequencies of 2$π$ x 0.44 MHz using a 480 kHz bandwidth at 108.9 GHz, demonstrating the ability to sense time-dependent signals suitable for radar and communications.

physics.atom-ph↗
arXiv2023

Inverse design of artificial skins

Mimicking the perceptual functions of human cutaneous mechanoreceptors, artificial skins or flexible pressure sensors can transduce tactile stimuli to quantitative electrical signals. Conventional methods to design such devices follow a forward structure-to-property routine based on trial-and-error experiments/simulations, which take months or longer to determine one solution valid for one specific material. Target-oriented inverse design that shows far higher output efficiency has proven effective in other fields, but is still absent for artificial skins because of the difficulties in acquiring big data. Here, we report a property-to-structure inverse design of artificial skins based on small dataset machine learning, exhibiting a comprehensive efficiency at least four orders of magnitude higher than the conventional routine. The inverse routine can predict hundreds of solutions that overcome the intrinsic signal saturation problem for linear response in hours, and the solutions are valid to a variety of materials. Our results demonstrate that the inverse design allowed by small dataset is an efficient and powerful tool to target multifarious applications of artificial skins, which can potentially advance the fields of intelligent robots, advanced healthcare, and human-machine interfaces.

cond-mat.soft↗
arXiv2023

SN 2020jgb: A Peculiar Type Ia Supernova Triggered by a Helium-Shell Detonation in a Star-Forming Galaxy

The detonation of a thin ($\lesssim$$0.03\,\mathrm{M_\odot}$) helium shell (He-shell) atop a $\sim$$1\,\mathrm{M_\odot}$ white dwarf (WD) is a promising mechanism to explain normal Type Ia supernovae (SNe Ia), while thicker He-shells and less massive WDs may explain some recently observed peculiar SNe Ia. We present observations of SN 2020jgb, a peculiar SN Ia discovered by the Zwicky Transient Facility (ZTF). Near maximum light, SN 2020jgb is slightly subluminous (ZTF $g$-band absolute magnitude $M_g$ between $-18.2$ and $-18.7$ mag depending on the amount of host galaxy extinction) and shows an unusually red color ($g_\mathrm{ZTF}-r_\mathrm{ZTF}$ between 0.4 and 0.2 mag) due to strong line-blanketing blueward of $\sim$5000 $Å$. These properties resemble those of SN 2018byg, a peculiar SN Ia consistent with a thick He-shell double detonation (DDet) SN. Using detailed radiative transfer models, we show that the optical spectroscopic and photometric evolution of SN 2020jgb are broadly consistent with a $\sim$$0.95\,\mathrm{M_\odot}$ (C/O core + He-shell; up to $\sim$$1.00\,\mathrm{M_\odot}$ depending on the total host extinction) progenitor ignited by a thick ($\sim$$0.13\,\mathrm{M_\odot}$) He-shell. We detect a prominent absorption feature at $\sim$1 $μ\mathrm{m}$ in the near-infrared (NIR) spectrum of SN 2020jgb, which could originate from unburnt helium in the outermost ejecta. While the sample size is limited, similar 1 $μ\mathrm{m}$ features have been detected in all the thick He-shell DDet candidates with NIR spectra obtained to date. SN 2020jgb is also the first subluminous, thick He-shell DDet SN discovered in a star-forming galaxy, indisputably showing that He-shell DDet objects occur in both star-forming and passive galaxies, consistent with the normal SN Ia population.

astro-ph.HE↗
arXiv2023

DeAR: A Deep-learning-based Audio Re-recording Resilient Watermarking

Audio watermarking is widely used for leaking source tracing. The robustness of the watermark determines the traceability of the algorithm. With the development of digital technology, audio re-recording (AR) has become an efficient and covert means to steal secrets. AR process could drastically destroy the watermark signal while preserving the original information. This puts forward a new requirement for audio watermarking at this stage, that is, to be robust to AR distortions. Unfortunately, none of the existing algorithms can effectively resist AR attacks due to the complexity of the AR process. To address this limitation, this paper proposes DeAR, a deep-learning-based audio re-recording resistant watermarking. Inspired by DNN-based image watermarking, we pioneer a deep learning framework for audio carriers, based on which the watermark signal can be effectively embedded and extracted. Meanwhile, in order to resist the AR attack, we delicately analyze the distortions that occurred in the AR process and design the corresponding distortion layer to cooperate with the proposed watermarking framework. Extensive experiments show that the proposed algorithm can resist not only common electronic channel distortions but also AR distortions. Under the premise of high-quality embedding (SNR=25.86dB), in the case of a common re-recording distance (20cm), the algorithm can effectively achieve an average bit recovery accuracy of 98.55%.

cs.SD↗
arXiv2023

ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation

Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e.g., unsupervised semantic segmentation (USS). The extracted relationship among pixel-level representations typically contains rich class-aware information that semantically identical pixel embeddings in the representation space gather together to form sophisticated concepts. However, leveraging the learned models to ascertain semantically consistent pixel groups or regions in the image is non-trivial since over/ under-clustering overwhelms the conceptualization procedure under various semantic distributions of different images. In this work, we investigate the pixel-level semantic aggregation in self-supervised ViT pre-trained models as image Segmentation and propose the Adaptive Conceptualization approach for USS, termed ACSeg. Concretely, we explicitly encode concepts into learnable prototypes and design the Adaptive Concept Generator (ACG), which adaptively maps these prototypes to informative concepts for each image. Meanwhile, considering the scene complexity of different images, we propose the modularity loss to optimize ACG independent of the concept number based on estimating the intensity of pixel pairs belonging to the same concept. Finally, we turn the USS task into classifying the discovered concepts in an unsupervised manner. Extensive experiments with state-of-the-art results demonstrate the effectiveness of the proposed ACSeg.

cs.CV↗
arXiv2023

Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition

Face recognition is a prevailing authentication solution in numerous biometric applications. Physical adversarial attacks, as an important surrogate, can identify the weaknesses of face recognition systems and evaluate their robustness before deployed. However, most existing physical attacks are either detectable readily or ineffective against commercial recognition systems. The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems. It requires that this technique can simultaneously deceive black-box recognition models and evade defensive mechanisms. To fulfill this, we design adversarial textured 3D meshes (AT3D) with an elaborate topology on a human face, which can be 3D-printed and pasted on the attacker's face to evade the defenses. However, the mesh-based optimization regime calculates gradients in high-dimensional mesh space, and can be trapped into local optima with unsatisfactory transferability. To deviate from the mesh-based space, we propose to perturb the low-dimensional coefficient space based on 3D Morphable Model, which significantly improves black-box transferability meanwhile enjoying faster search efficiency and better visual quality. Extensive experiments in digital and physical scenarios show that our method effectively explores the security vulnerabilities of multiple popular commercial services, including three recognition APIs, four anti-spoofing APIs, two prevailing mobile phones and two automated access control systems.

cs.CV↗
arXiv2023

Context-Aware Transformer for 3D Point Cloud Automatic Annotation

3D automatic annotation has received increased attention since manually annotating 3D point clouds is laborious. However, existing methods are usually complicated, e.g., pipelined training for 3D foreground/background segmentation, cylindrical object proposals, and point completion. Furthermore, they often overlook the inter-object feature relation that is particularly informative to hard samples for 3D annotation. To this end, we propose a simple yet effective end-to-end Context-Aware Transformer (CAT) as an automated 3D-box labeler to generate precise 3D box annotations from 2D boxes, trained with a small number of human annotations. We adopt the general encoder-decoder architecture, where the CAT encoder consists of an intra-object encoder (local) and an inter-object encoder (global), performing self-attention along the sequence and batch dimensions, respectively. The former models intra-object interactions among points, and the latter extracts feature relations among different objects, thus boosting scene-level understanding. Via local and global encoders, CAT can generate high-quality 3D box annotations with a streamlined workflow, allowing it to outperform existing state-of-the-art by up to 1.79% 3D AP on the hard task of the KITTI test set.

cs.CV↗
arXiv2023

Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection

With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that the root lies in two kinds of ambiguities: (1) Selection ambiguity that selected pseudo labels are less accurate, since classification scores cannot properly represent the localization quality. (2) Assignment ambiguity that samples are matched with improper labels in pseudo-label assignment, as the strategy is misguided by missed objects and inaccurate pseudo boxes. To tackle these problems, we propose a Ambiguity-Resistant Semi-supervised Learning (ARSL) for one-stage detectors. Specifically, to alleviate the selection ambiguity, Joint-Confidence Estimation (JCE) is proposed to jointly quantifies the classification and localization quality of pseudo labels. As for the assignment ambiguity, Task-Separation Assignment (TSA) is introduced to assign labels based on pixel-level predictions rather than unreliable pseudo boxes. It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity. Comprehensive experiments demonstrate that ARSL effectively mitigates the ambiguities and achieves state-of-the-art SSOD performance on MS COCO and PASCAL VOC. Codes can be found at https://github.com/PaddlePaddle/PaddleDetection.

cs.CV↗
arXiv2023

Single crystal synthesis and low-lying electronic structure of V$_3$S$_4$

We report successful growth of millimeter-sized high quality single crystals of V$_3$S$_4$, a candidate topological semimetal belonging to a low-symmetry space group and consisting of only low atomic number elements. Using density functional theory calculations and angle-resolved photoemission spectroscopy, we show that the nonmagnetic phase of monoclinic V$_3$S$_4$ hosts type-II Dirac-like quasiparticles which opens a sizable gap due to spin orbit coupling, as well as theoretical multiple nodal lines that are eliminated also by spin orbit coupling. These results suggest that relativistic effects give rise to profound modifications of the topological properties even in compounds with low-weight elements.

cond-mat.mtrl-sci↗
arXiv2023

Frame Flexible Network

Existing video recognition algorithms always conduct different training pipelines for inputs with different frame numbers, which requires repetitive training operations and multiplying storage costs. If we evaluate the model using other frames which are not used in training, we observe the performance will drop significantly (see Fig.1), which is summarized as Temporal Frequency Deviation phenomenon. To fix this issue, we propose a general framework, named Frame Flexible Network (FFN), which not only enables the model to be evaluated at different frames to adjust its computation, but also reduces the memory costs of storing multiple models significantly. Concretely, FFN integrates several sets of training sequences, involves Multi-Frequency Alignment (MFAL) to learn temporal frequency invariant representations, and leverages Multi-Frequency Adaptation (MFAD) to further strengthen the representation abilities. Comprehensive empirical validations using various architectures and popular benchmarks solidly demonstrate the effectiveness and generalization of FFN (e.g., 7.08/5.15/2.17% performance gain at Frame 4/8/16 on Something-Something V1 dataset over Uniformer). Code is available at https://github.com/BeSpontaneous/FFN.

cs.CV↗
arXiv2023

Spectroscopic signature of obstructed surface states in SrIn$_2$P$_2$

The century-long development of surface sciences has witnessed the discoveries of a variety of quantum states. In the recently proposed "obstructed atomic insulators", insulators with symmetric charges pinned at virtual sites where no real atoms reside, the cleavage through these sites could lead to a set of obstructed surface states with partial occupation. Here, utilizing scanning tunneling microscopy, angle-resolved photoemission spectroscopy and first-principles calculations, we observe spectroscopic signature of obstructed surface states in SrIn$_2$P$_2$. We find a pair of surface states originated from the pristine obstructed surface states split by a unique surface reconstruction. The upper branch is marked with a striking differential conductance peak followed by negative differential conductance, signaling its localized nature, while the lower branch is found to be highly dispersive. This pair of surface states is in consistency with our calculational results. Our finding not only demonstrates a surface quantum state induced by a new type of bulk-boundary correspondence, but also provides a platform for exploring efficient catalysts and related surface engineering.

cond-mat.mtrl-sci↗