arXiv subjects

Bin Zhang

Publications and source records attributed to Bin Zhang.

At least 163 records · Page 9Linked to original sources

arXiv2022

Validation and integration tests of the JUNO 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances.

physics.ins-det↗

arXiv2022

Derivative Descendants and Ascendants of Binary Cyclic Codes, and Derivative Decoding

This paper defines cyclic and minimal derivative descendants (DDs) of an extended cyclic code from the derivative of the Mattson-Solomon polynomials, respectively. First, it demonstrates that the cyclic DDs are the same extended cyclic code. It allows us to perform soft-decision decoding for extended cyclic codes based on their cyclic DDs. Then, it proves that the minimal DDs are equivalent codes. It also allows us to perform soft-decision decoding based on the minimal DDs with permutations. Simulation results show that our proposed derivative decoding can be close to the maximum likelihood decoding for certain extended cyclic codes, including some extended BCH codes.

cs.IT↗

arXiv2022

HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism

Recently, some challenging tasks in multi-agent systems have been solved by some hierarchical reinforcement learning methods. Inspired by the intra-level and inter-level coordination in the human nervous system, we propose a novel value decomposition framework HAVEN based on hierarchical reinforcement learning for fully cooperative multi-agent problems. To address the instability arising from the concurrent optimization of policies between various levels and agents, we introduce the dual coordination mechanism of inter-level and inter-agent strategies by designing reward functions in a two-level hierarchy. HAVEN does not require domain knowledge and pre-training, and can be applied to any value decomposition variant. Our method achieves desirable results on different decentralized partially observable Markov decision process domains and outperforms other popular multi-agent hierarchical reinforcement learning algorithms.

cs.MA↗

arXiv2022

Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning

Recently, model-based agents have achieved better performance than model-free ones using the same computational budget and training time in single-agent environments. However, due to the complexity of multi-agent systems, it is tough to learn the model of the environment. The significant compounding error may hinder the learning process when model-based methods are applied to multi-agent tasks. This paper proposes an implicit model-based multi-agent reinforcement learning method based on value decomposition methods. Under this method, agents can interact with the learned virtual environment and evaluate the current state value according to imagined future states in the latent space, making agents have the foresight. Our approach can be applied to any multi-agent value decomposition method. The experimental results show that our method improves the sample efficiency in different partially observable Markov decision process domains.

cs.MA↗

arXiv2022

Consensus Learning for Cooperative Multi-Agent Reinforcement Learning

Almost all multi-agent reinforcement learning algorithms without communication follow the principle of centralized training with decentralized execution. During centralized training, agents can be guided by the same signals, such as the global state. During decentralized execution, however, agents lack the shared signal. Inspired by viewpoint invariance and contrastive learning, we propose consensus learning for cooperative multi-agent reinforcement learning in this paper. Although based on local observations, different agents can infer the same consensus in discrete space. During decentralized execution, we feed the inferred consensus as an explicit input to the network of agents, thereby developing their spirit of cooperation. Our proposed method can be extended to various multi-agent reinforcement learning algorithms with small model changes. Moreover, we carry out them on some fully cooperative tasks and get convincing results.

cs.MA↗

arXiv2022

Mountain muon tomography using a liquid scintillator detector

Muon tomography (MT), based on atmospheric cosmic rays, is a promising technique suitable for nondestructive imaging of the internal structures of mountains. This method uses the measured flux distribution after attenuation, combined with the known muon angular and energy distributions and a 3D satellite map, to perform tomographic imaging of the density distribution inside a probed volume. A muon tomography station (MTS) requires direction-sensitive detectors with a high resolution for optimal tracking of incident cosmic-ray muons. The spherical liquid scintillator detector is one of the best candidates for this application due to its uniform detection efficiency for the whole $4π$ solid angle and its excellent ability to distinguish muon signals from the radioactive background via the difference in the energy deposit. This type of detector, with a 1.3~m diameter, was used in the Jinping Neutrino Experiment~(JNE). Its angular resolution is 4.9~degrees. Following the application of imaging for structures of Jinping Mountain with JNE published results based on the detector, we apply it to geological prospecting. For mountains below 1~km in height and 2.8~${\rm g}/{\rm cm}^3$ in the reference rock, we demonstrate that this kind of detector can image internal regions with densities of $\leq$ 2.1~${\rm g}/{\rm cm}^3$ or $\geq$ 3.5~${\rm g}/{\rm cm}^3$ and hundreds of meters in size.

physics.ins-det↗

arXiv2022

A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process

Kernels on discrete structures evaluate pairwise similarities between objects which capture semantics and inherent topology information. Existing kernels on discrete structures are only developed by topology information(such as adjacency matrix of graphs), without considering original attributes of objects. This paper proposes a two-phase paradigm to aggregate comprehensive information on discrete structures leading to a Discount Markov Diffusion Learnable Kernel (DMDLK). Specifically, based on the underlying projection of DMDLK, we design a Simple Hypergraph Kernel Convolution (SHKC) for hidden representation of vertices. SHKC can adjust diffusion steps rather than stacking convolution layers to aggregate information from long-range neighborhoods which prevents over-smoothing issues of existing hypergraph convolutions. Moreover, we utilize the uniform stability bound theorem in transductive learning to analyze critical factors for the effectiveness and generalization ability of SHKC from a theoretical perspective. The experimental results on several benchmark datasets for node classification tasks verified the superior performance of SHKC over state-of-the-art methods.

cs.LG↗

arXiv2022

Quantum wavefunction reconstruction by free-electron spectral shearing interferometry

We propose a novel spectral method for reconstructing quantum wavefunction of an electron pulse, free-electron spectral shearing interferometry (FESSI). We employ a Wien filter to generate two time-delayed replicas of the electron wavepacket and then shift one replica in energy using a light-electron modulator driven by a mid-infrared laser. As a direct demonstration, we numerically reconstruct an ultrashort electron pulse with a kinetic energy of 10 keV. FESSI is experimentally feasible and enables us to fully determine distinct orders of spectral phases and their physical implications, providing a universal approach to characterize ultrashort electron pulses.

quant-ph↗

arXiv2022

Engineered Octave Frequency Comb in Integrated Chalcogenide Dual-ring Microresonators

Broadband Kerr combs with a flat comb spectral profile are expected in a number of applications, such as high-capacity optical communication. Here, we propose novel concentric dual-ring microresonators (DRMs) for advanced dispersion engineering to tailor the comb spectral profile. The dispersion can be flexibly engineered not only by the cross-section of the DRMs, but also by the gap between concentric dual-ring microresonators, which provides a new path to geometrically control the spectral profile of the soliton Kerr combs. An octave-spanning dissipative Kerr soliton comb with superior spectral flatness has been achieved numerically, covering from the telecommunication band to the mid-infrared (MIR) band region with a -40 dB bandwidth of 1265 nm (99.82 THz). Our results are promising to fully understand the nonlinear dynamics in hybrid modes in DRMs, which helps control broadband comb formation.

physics.optics↗

arXiv2022

Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network

The application of deep reinforcement learning in multi-agent systems introduces extra challenges. In a scenario with numerous agents, one of the most important concerns currently being addressed is how to develop sufficient collaboration between diverse agents. To address this problem, we consider the form of agent interaction based on neighborhood and propose a multi-agent reinforcement learning (MARL) algorithm based on the actor-critic method, which can adaptively construct the hypergraph structure representing the agent interaction and further implement effective information extraction and representation learning through hypergraph convolution networks, leading to effective cooperation. Based on different hypergraph generation methods, we present two variants: Actor Hypergraph Convolutional Critic Network (HGAC) and Actor Attention Hypergraph Critic Network (ATT-HGAC). Experiments with different settings demonstrate the advantages of our approach over other existing methods.

cs.AI↗

arXiv2022

MAFormer: A Transformer Network with Multi-scale Attention Fusion for Visual Recognition

Vision Transformer and its variants have demonstrated great potential in various computer vision tasks. But conventional vision transformers often focus on global dependency at a coarse level, which suffer from a learning challenge on global relationships and fine-grained representation at a token level. In this paper, we introduce Multi-scale Attention Fusion into transformer (MAFormer), which explores local aggregation and global feature extraction in a dual-stream framework for visual recognition. We develop a simple but effective module to explore the full potential of transformers for visual representation by learning fine-grained and coarse-grained features at a token level and dynamically fusing them. Our Multi-scale Attention Fusion (MAF) block consists of: i) a local window attention branch that learns short-range interactions within windows, aggregating fine-grained local features; ii) global feature extraction through a novel Global Learning with Down-sampling (GLD) operation to efficiently capture long-range context information within the whole image; iii) a fusion module that self-explores the integration of both features via attention. Our MAFormer achieves state-of-the-art performance on common vision tasks. In particular, MAFormer-L achieves 85.9$\%$ Top-1 accuracy on ImageNet, surpassing CSWin-B and LV-ViT-L by 1.7$\%$ and 0.6$\%$ respectively. On MSCOCO, MAFormer outperforms the prior art CSWin by 1.7$\%$ mAPs on object detection and 1.4$\%$ on instance segmentation with similar-sized parameters, demonstrating the potential to be a general backbone network.

cs.CV↗

arXiv2022

SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems

Zero/few-shot transfer to unseen services is a critical challenge in task-oriented dialogue research. The Schema-Guided Dialogue (SGD) dataset introduced a paradigm for enabling models to support any service in zero-shot through schemas, which describe service APIs to models in natural language. We explore the robustness of dialogue systems to linguistic variations in schemas by designing SGD-X - a benchmark extending SGD with semantically similar yet stylistically diverse variants for every schema. We observe that two top state tracking models fail to generalize well across schema variants, measured by joint goal accuracy and a novel metric for measuring schema sensitivity. Additionally, we present a simple model-agnostic data augmentation method to improve schema robustness.

cs.CL↗

arXiv2022

Action Planning for Packing Long Linear Elastic Objects into Compact Boxes with Bimanual Robotic Manipulation

In this paper, we propose a new action planning approach to automatically pack long linear elastic objects into common-size boxes with a bimanual robotic system. For that, we developed a hybrid geometric model to handle large-scale occlusions combining an online vision-based method and an offline reference template. Then, a reference point generator is introduced to automatically plan the reference poses for the predesigned action primitives. Finally, an action planner integrates these components enabling the execution of high-level behaviors and the accomplishment of packing manipulation tasks. To validate the proposed approach, we conducted a detailed experimental study with multiple types and lengths of objects and packing boxes.

cs.RO↗

arXiv2022

Synthetic dimensions using ultrafast free electrons

We propose a synthetic dimension for ultrafast free electrons based on the discrete energy sidebands in photon-induced near-field electron microscopy (PINEM). The PINEM synthetic dimension can offer a powerful way to coherently shape or modulate free-electron wavefunctions in systems such as ultrafast transmission electron microscopes, dielectric laser accelerators, and quantum free-electron lasers. As examples of this paradigm, we demonstrate Bloch oscillations, diffraction management, and linear optics effects such as Talbot self-imaging in the PINEM lattice. These effects show the potential of PINEM synthetic dimensions as a novel quantum simulation platform.

quant-ph↗

arXiv2022

Impact forces of water drops falling on superhydrophobic surfaces

A falling liquid drop, after impact on a rigid substrate, deforms and spreads, owing to the normal reaction force. Subsequently, if the substrate is non-wetting, the drop retracts and then jumps off. As we show here, not only is the impact itself associated with a distinct peak in the temporal evolution of the normal force, but also the jump-off, which was hitherto unknown. We characterize both peaks and elucidate how they relate to the different stages of the drop impact process. The time at which the second peak appears coincides with the formation of a Worthington jet, emerging through flow-focusing, and it is independent of the impact velocity. However, the magnitude of this peak is dictated by the drop's inertia and surface tension. We show that even low-velocity impacts can lead to a surprisingly high peak in the normal force, namely when a more pronounced singular Worthington jet occurs due to the collapse of an air cavity in the drop.

physics.flu-dyn↗

arXiv2022

Measurement of Muon-induced Neutron Production at the China Jinping Underground Laboratory

Solar, terrestrial, and supernova neutrino experiments are subject to muon-induced radioactive backgrounds. The China Jinping Underground Laboratory (CJPL), with its unique advantage of a 2400 m rock coverage and long distance from nuclear power plants, is ideal for MeV-scale neutrino experiments. Using a 1-ton prototype detector of the Jinping Neutrino Experiment (JNE), we detected 343 high-energy cosmic-ray muons and (7.86$ \pm $3.97) muon-induced neutrons from an 820.28-day dataset at the first phase of CJPL (CJPL-I). Based on the muon-induced neutrons, we measured the corresponding muon-induced neutron yield in a liquid scintillator to be $ (3.44 \pm 1.86_{\rm stat.}\pm 0.76_{\rm syst.})\times 10^{-4}μ^{-1}\rm g^{-1}cm^{2} $ at an average muon energy of \SI{340}{GeV}. We provided the first study for such neutron background at CJPL. A global fit including this measurement shows a power-law coefficient of (0.75$ \pm $0.02) for the dependence of the neutron yield at the liquid scintillator on muon energy.

hep-ex↗

arXiv2022

Coherent excitation of bound electron quantum state with quantum electron wavepackets

We present a fully quantum model for the excitation of a bound electron based on the free-electron bound-electron resonant interaction (FEBERI) scheme. The bound electron is modeled as a quantum two-level system (TLS) at any initial quantum (qubit) state, and the free electron is presented as a pre-shaped quantum electron wavepacket (QEW). In the case that the QEW is short or modulated at optical frequency, the TLS quantum state may be coherently controlled with multiple modulation-correlated QEWs. For this case, we derive the transition probability of the TLS due to interaction with a multi-particle beam based on an analytical approximate solution of the Schrodinger equation that amounts to using Born's probabilistic interpretation of the quantum electron wavefunction. We verify the credibility of the analytical model at its validity ranges using a fully quantum density matrix computation procedure. It is shown that the transition probability can grow quadratically with the number of correlated QEWs, and theoretically - exhibit full Rabi oscillation. The study indicates a possibility of engineering the quantum state of a TLS by utilizing a beam of shaped QEWs.

quant-ph↗

arXiv2022

Quantum states interrogation using a pre-shaped free electron wavefunction

We present a comprehensive theory for interrogation of the quantum state of a two-level system (TLS) based on a free-electron - bound-electron resonant interaction scheme. The scheme is based on free electrons, whose quantum electron wavefunction is pre-shaped or optically modulated by lasers in an electron microscope setup and then inelastically scattered by a quantum TLS target (e.g., atom, quantum dot, crystal defect center, etc.) upon traversing in proximity to the target. Measurement of the post-interaction energy spectrum of the electrons, probes and quantifies the full Bloch sphere parameters of a pre-excited TLS and enables coherent control of the qubit states. The exceptional advantage of this scheme over laser-based ones is the atomic-scale spatial resolution of addressing individual TLS targets. Thus, this scheme opens new horizons for electron microscopy in material interrogation and quantum information technology.

quant-ph↗