arXiv subjects

Bin Zhang

Publications and source records attributed to Bin Zhang.

At least 181 records · Page 10Linked to original sources

arXiv2022

High-Order Large-Eddy Simulations of a Wind Turbine in Ducted and Open-Rotor Configurations

High-order large-eddy simulations are performed to study the performance and flow field of a ducted wind turbine operating at different tip speed ratios. To evaluate the effects of the duct, simulations with the same tip speed ratios are also performed on the corresponding open-rotor turbine. It is found that the ducted turbine consistently obtains higher power outputs than the open-rotor counterpart, and the duct itself enhances flow turbulence and blade trailing-edge vortices but weakens tip and hub vortices. Flow bifurcation is observed at the largest tip speed ratio and is identified to be caused by blade blockage effects. Comparative simulations are also performed on both turbines under different yaw angles. It is noticed that the ducted configuration is insensitive to small yaw angles and maintains higher power outputs than the open-rotor configuration at all yaw angles. Moreover, it is observed that the wakes of both configurations recover more quickly as the yaw angle increases.

physics.flu-dyn↗

arXiv2022

Contrastive Learning of Coarse-Grained Force Fields

Coarse-grained models have proven helpful for simulating complex systems over long timescales to provide molecular insights into various processes. Methodologies for systematic parameterization of the underlying energy function, or force field that describes the interactions among different components of the system are of great interest for ensuring simulation accuracy. We present a new method, potential contrasting, to enable efficient learning of force fields that can accurately reproduce the conformational distribution produced with all-atom simulations. Potential contrasting generalizes the noise contrastive estimation method with umbrella sampling to better learn the complex energy landscape of molecular systems. When applied to the Trp-cage protein, we found that the technique produces force fields that thoroughly capture the thermodynamics of the folding process despite the use of only $α$-Carbons in the coarse-grained model. We further showed that potential contrasting could be applied over large datasets that combine the conformational ensembles of many proteins to ensure the transferability of coarse-grained force fields. We anticipate potential contrasting to be a powerful tool for building general-purpose coarse-grained force fields.

physics.chem-ph↗

arXiv2022

Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution

Recent years have witnessed the great success of multi-agent systems (MAS). Value decomposition, which decomposes joint action values into individual action values, has been an important work in MAS. However, many value decomposition methods ignore the coordination among different agents, leading to the notorious "lazy agents" problem. To enhance the coordination in MAS, this paper proposes HyperGraph CoNvolution MIX (HGCN-MIX), a method that incorporates hypergraph convolution with value decomposition. HGCN-MIX models agents as well as their relationships as a hypergraph, where agents are nodes and hyperedges among nodes indicate that the corresponding agents can coordinate to achieve larger rewards. Then, it trains a hypergraph that can capture the collaborative relationships among agents. Leveraging the learned hypergraph to consider how other agents' observations and actions affect their decisions, the agents in a MAS can better coordinate. We evaluate HGCN-MIX in the StarCraft II multi-agent challenge benchmark. The experimental results demonstrate that HGCN-MIX can train joint policies that outperform or achieve a similar level of performance as the current state-of-the-art techniques. We also observe that HGCN-MIX has an even more significant improvement of performance in the scenarios with a large amount of agents. Besides, we conduct additional analysis to emphasize that when the hypergraph learns more relationships, HGCN-MIX can train stronger joint policies.

cs.AI↗

arXiv2022

Soliton Microcombs in Integrated Chalcogenide Microresonators

Photonic integrated microcombs have enabled advanced applications in optical communication, microwave synthesis, and optical metrology, which in nature unveil an optical dissipative soliton pattern under cavity-enhanced nonlinear processes. The most decisive factor of microcombs lies in the photonic material platforms, where materials with high nonlinearity and in capacity of high-quality chip integration are highly demanded. In this work, we present a home-developed chalcogenide glasses-Ge25Sb10S65 (GeSbS) for the nonlinear photonic integration and for the dissipative soliton microcomb generation. Compared with the current integrated nonlinear platforms, the GeSbS features wider transparency from the visible to 11 um region, stronger nonlinearity, and lower thermo-refractive coefficient, and is CMOS compatible in fabrication. In this platform, we achieve chip-integrated optical microresonators with a quality (Q) factor above 2 x 10^6, and carry out lithographically controlled dispersion engineering. In particular, we demonstrate that both a bright soliton-based microcomb and a dark-pulsed comb are generated in a single microresonator, in its separated fundamental polarized mode families under different dispersion regimes. The overall pumping power is on the ten-milliwatt level, determined by both the high Q-factor and the high material nonlinearity of the microresonator. Our results may contribute to the field of nonlinear photonics with an alternative material platform for highly compact and high-intensity nonlinear interactions, while on the application aspect, contribute to the development of soliton microcombs at low operation power, which is potentially required for monolithically integrated optical frequency combs.

physics.optics↗

arXiv2022

ASFD: Automatic and Scalable Face Detector

Along with current multi-scale based detectors, Feature Aggregation and Enhancement (FAE) modules have shown superior performance gains for cutting-edge object detection. However, these hand-crafted FAE modules show inconsistent improvements on face detection, which is mainly due to the significant distribution difference between its training and applying corpus, COCO vs. WIDER Face. To tackle this problem, we essentially analyse the effect of data distribution, and consequently propose to search an effective FAE architecture, termed AutoFAE by a differentiable architecture search, which outperforms all existing FAE modules in face detection with a considerable margin. Upon the found AutoFAE and existing backbones, a supernet is further built and trained, which automatically obtains a family of detectors under the different complexity constraints. Extensive experiments conducted on popular benchmarks, WIDER Face and FDDB, demonstrate the state-of-the-art performance-efficiency trade-off for the proposed automatic and scalable face detector (ASFD) family. In particular, our strong ASFD-D6 outperforms the best competitor with AP 96.7/96.2/92.1 on WIDER Face test, and the lightweight ASFD-D0 costs about 3.1 ms, more than 320 FPS, on the V100 GPU with VGA-resolution images.

cs.CV↗

arXiv2022

Neural Architecture Searching for Facial Attributes-based Depression Recognition

Recent studies show that depression can be partially reflected from human facial attributes. Since facial attributes have various data structure and carry different information, existing approaches fail to specifically consider the optimal way to extract depression-related features from each of them, as well as investigates the best fusion strategy. In this paper, we propose to extend Neural Architecture Search (NAS) technique for designing an optimal model for multiple facial attributes-based depression recognition, which can be efficiently and robustly implemented in a small dataset. Our approach first conducts a warmer up step to the feature extractor of each facial attribute, aiming to largely reduce the search space and providing customized architecture, where each feature extractor can be either a Convolution Neural Networks (CNN) or Graph Neural Networks (GNN). Then, we conduct an end-to-end architecture search for all feature extractors and the fusion network, allowing the complementary depression cues to be optimally combined with less redundancy. The experimental results on AVEC 2016 dataset show that the model explored by our approach achieves breakthrough performance with 27\% and 30\% RMSE and MAE improvements over the existing state-of-the-art. In light of these findings, this paper provides solid evidences and a strong baseline for applying NAS to time-series data-based mental health analysis.

cs.CV↗

arXiv2022

Particle manipulation behind turbid medium based on intensity transmission matrix

Optical tweezers can manipulate tiny particles. However, the distortion caused by the scattering medium restricts the applications of optical tweezers. Wavefront shaping techniques including the transmission matrix (TM) method are powerful tools to achieve light focusing behind the scattering medium. In this paper, we propose a new kind of TM, named intensity transmission matrix (ITM). Only relying on the intensity distribution, we can calculate the ITM with only about 1/4 measurement time of the widely used four-phase method. Meanwhile, ITM method can avoid the energy loss in diffraction introduced by holographic modulation. Based on the ITM, we have implemented particle manipulation with a high degree of freedom on single and multiple particles. In addition, the manipulation range is enlarged over twenty times (compared with the memory effect) to 200 μm.

physics.optics↗

arXiv2021

SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning

As one of the solutions to the decentralized partially observable Markov decision process (Dec-POMDP) problems, the value decomposition method has achieved significant results recently. However, most value decomposition methods require the fully observable state of the environment during training, but this is not feasible in some scenarios where only incomplete and noisy observations can be obtained. Therefore, we propose a novel value decomposition framework, named State Inference for value DEcomposition (SIDE), which eliminates the need to know the global state by simultaneously seeking solutions to the two problems of optimal control and state inference. SIDE can be extended to any value decomposition method to tackle partially observable problems. By comparing with the performance of different algorithms in StarCraft II micromanagement tasks, we verified that though without accessible states, SIDE can infer the current state that contributes to the reinforcement learning process based on past local observations and even achieve superior results to many baselines in some complex scenarios.

cs.MA↗

arXiv2021

a novel attention-based network for fast salient object detection

In the current salient object detection network, the most popular method is using U-shape structure. However, the massive number of parameters leads to more consumption of computing and storage resources which are not feasible to deploy on the limited memory device. Some others shallow layer network will not maintain the same accuracy compared with U-shape structure and the deep network structure with more parameters will not converge to a global minimum loss with great speed. To overcome all of these disadvantages, we proposed a new deep convolution network architecture with three contributions: (1) using smaller convolution neural networks (CNNs) to compress the model in our improved salient object features compression and reinforcement extraction module (ISFCREM) to reduce parameters of the model. (2) introducing channel attention mechanism in ISFCREM to weigh different channels for improving the ability of feature representation. (3) applying a new optimizer to accumulate the long-term gradient information during training to adaptively tune the learning rate. The results demonstrate that the proposed method can compress the model to 1/3 of the original size nearly without losing the accuracy and converging faster and more smoothly on six widely used datasets of salient object detection compared with the others models. Our code is published in https://gitee.com/binzhangbinzhangbin/code-a-novel-attention-based-network-for-fast-salient-object-detection.git

cs.CV↗

arXiv2021

Phonon-assisted electronic states modulation of few-layer PdSe2 at terahertz frequencies

Information technology demands high-speed optoelectronic devices, but going beyond the one terahertz (THz) barrier is challenging due to the difficulties associated with generating, detecting, and processing high-frequency signals. Here, we show that femtosecond-laser-driven phonons can be utilized to coherently manipulate the excitonic properties of semiconductors at THz frequencies. The precise control of the pump and subsequent time-delayed broadband probe pulses enables the simultaneous generation and detection processes of both periodic lattice vibrations and their couplings with electronic states. Combining ultralow frequency Raman spectroscopy with first-principles calculations, we identify the unique phonon mode-selective and probe-energy dependent features of electron-phonon interactions in layered PdSe2. Two distinctive types of coherent phonon excitations could couple preferentially to different types of electronic excitations: the intralayer (4.3 THz) mode to carriers and the interlayer (0.35 THz) mode to excitons. This work provides new insights to understand the excited-state phonon interactions of 2D materials, and to achieve future applications of optoelectronic devices operating at THz frequencies.

cond-mat.mes-hall↗

arXiv2021

A GPU based single-pulse search pipeline (GSP) with database and its application to the commensal radio astronomy FAST survey (CRAFTS)

We developed a GPU based single-pulse search pipeline (GSP) with candidate-archiving database. Largely based upon the infrastructure of Open source pulsar search and analysis toolkit (PRESTO), GSP implements GPU acceleration of the de-dispersion and integrates a candidate-archiving database. We applied GSP to the data streams from the commensal radio astronomy FAST survey (CRAFTS), which resulted in a quasi-real-time processing. The integrated candidate database facilitates synergistic usage of multiple machine-learning tools and thus improves efficient identification of radio pulsars such as rotating radio transients (RRATs) and Fast Radio Bursts (FRBs). We first tested GSP on pilot CRAFTS observations with the FAST Ultra-Wide Band (UWB) receiver. GSP detected all pulsars known from the the Parkes multibeam pulsar survey in the respective sky area covered by the FAST-UWB. GSP also discovered 13 new pulsars. We measured the computational efficiency of GSP to be ~120 times faster than the original PRESTO and ~60 times faster than a MPI-parallelized version of PRESTO.

astro-ph.IM↗

arXiv2021

On mixing enhancement by secondary baroclinic vorticity in shock-bubble interaction

To investigate the intrinsic mechanism for mixing enhancement by variable density behaviour, a canonical variable density (VD) mixing extracted from a supersonic streamwise vortex protocol, shock bubble interaction (SBI), is numerically studied and compared with a counterpart of passive scalar (PS) mixing. It is meaningful to observe that the maximum concentration decays much faster in VD SBI than in PS SBI regardless of the shock Mach number (Ma = 1.22 ~ 4). The quasi-Lamb-Oseen type velocity distribution in the PS SBI is found by analyzing the azimuthal velocity that stretches the bubble. Meanwhile, for the VD SBI, an additional stretching enhanced by the secondary baroclinic vorticity (SBV) production contributes to the faster-mixing decay. The underlying mechanism of the SBV-enhanced stretching is further revealed through the density and velocity difference between the shocked light bubble and the heavy ambient air. By combining the SBV-accelerated stretching model and the initial shock compression, a novel mixing time estimation for VD SBI is theoretically proposed by solving the advection-diffusion equation under a deformation field of an axisymmetric vortex with the additional SBV induced azimuthal velocity. Based on the mixing time model, a mixing enhancement number defined by the ratio of VD and PS mixing time further reveals the contribution from the variable-density effect, which implies a better control of density distribution for mixing enhancement in a supersonic streamwise vortex.

physics.flu-dyn↗

arXiv2021

A Simple Self-calibration Method for The Internal Time Synchronization of MEMS LiDAR

This paper proposes a simple self-calibration method for the internal time synchronization of MEMS(Micro-electromechanical systems) LiDAR during research and development. Firstly, we introduced the problem of internal time misalignment in MEMS lidar. Then, a robust Minimum Vertical Gradient(MVG) prior is proposed to calibrate the time difference between the laser and MEMS mirror, which can be calculated automatically without any artificial participation or specially designed cooperation target. Finally, actual experiments on MEMS LiDARs are implemented to demonstrate the effectiveness of the proposed method. It should be noted that the calibration can be implemented in a simple laboratory environment without any ranging equipment and artificial participation, which greatly accelerate the progress of research and development in practical applications.

cs.CV↗

arXiv2021

The third- and fourth-order orbital angular momentum multiplexed amplification with ultra-low differential mode gain

In this Letter, a ring-core erbium-doped fiber (RC-EDF), with two-layer erbium-doped structure, supporting up to the fourth-order orbital angular momentum (OAM) mode is designed and fabricated for OAM mode multiplexed amplification. Using the RC-EDF, the third- and fourth-order OAM modes amplification with ultra-low differential mode gain (DMG) is demonstrated by observing both the modal intensity and phase distribution and measuring the modal gain under the fundamental mode core-pumping. The measured average gain of four modes (l=+3, -3, +4, -4) multiplexed amplification is higher than 19dB cover the C-band and the DMG is less than 1dB. Additionally, the gain of two conjugate OAM modes are almost the same under different pump power no matter they are amplified simultaneously or separately.

physics.optics↗

arXiv2021

Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.

cs.CV↗

arXiv2021

Dimensions of Commonsense Knowledge

Commonsense knowledge is essential for many AI applications, including those in natural language processing, visual processing, and planning. Consequently, many sources that include commonsense knowledge have been designed and constructed over the past decades. Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application to textual tasks, typically at the expense of the semantics of the sources and their harmonization. Efforts to consolidate commonsense knowledge have yielded partial success, with no clear path towards a comprehensive solution. We aim to organize these sources around a common set of dimensions of commonsense knowledge. We survey a wide range of popular commonsense sources with a special focus on their relations. We consolidate these relations into 13 knowledge dimensions. This consolidation allows us to unify the separate sources and to compute indications of their coverage, overlap, and gaps with respect to the knowledge dimensions. Moreover, we analyze the impact of each dimension on downstream reasoning tasks that require commonsense knowledge, observing that the temporal and desire/goal dimensions are very beneficial for reasoning on current downstream tasks, while distinctness and lexical knowledge have little impact. These results reveal preferences for some dimensions in current evaluation, and potential neglect of others.

cs.AI↗

arXiv2021

Engineered Raman Lasing in Photonic Integrated Chalcogenide Microresonators

Chalcogenide glass (ChG) is an attractive material for integrated nonlinear photonics due to its wide transparency and high nonlinearity, and its capability of being directly deposited and patterned on Silicon wafer substrates. It has a singular Raman effect among amorphous materials. Yet, the Raman lasing performance in high quality and chip integrated ChG microresonators remains unexplored. Here, we demonstrate an engineered Raman lasing dynamic based on home developed photonic integrated high-Q ChG microresonators. With a quality factor above 10^6, we achieve the record-low lasing threshold 3.25 mW among integrated planar photonic platforms. Both the single-mode Raman lasers and a broadband Raman-Kerr comb are observed and characterized, which is dependent on the dispersion of our flexible photonic platform and engineered via tuning the waveguide geometric size. The tunability of such a chipscale Raman laser is also demonstrated through tuning the pump wavelength and tuning the operating temperature on the chip. This allows for the access of single-mode lasing at arbitrary wavelengths in the range 1615-1755 nm. Our results may contribute to the understanding of rich Raman and Kerr nonlinear interactions in dissipative and nonlinear microresonators, and on application aspect, may pave a way to chip-scale efficient Raman lasers that is highly desired in spectroscopic applications in the infrared.

physics.optics↗

arXiv2021

A 60-GHz Radar Sensor for Micron-Scale Motion Detection

A compact, continuous-wave, mmWave radar sensor is developed for non-contact detection of micron-scale motions. This board-integrated radar system consists of a pair of mmWave transmitter and receiver, two series-fed microstrip patch arrays, an IF subsystem, and a microcontroller. Working at 60-GHz frequency, this super-heterodyne, digital-IF radar sensor exhibits an agile sensitivity and a robust anti-noise performance. Assisted by a gradient-descent DC offset estimation and a phase demodulation algorithm based on extended differential and cross-multiply, a 45-μm pendulum swing can be experimentally detected with a 20.6-dB SNR, verifying its strong ability in Doppler micro-motion detections.

eess.SY↗