arXiv subjects

Tao Huang

Publications and source records attributed to Tao Huang.

At least 235 records · Page 13Linked to original sources

arXiv2022

Efficient Trajectory Planning and Control for USV with Vessel Dynamics and Differential Flatness

Unmanned surface vessels (USVs) are widely used in ocean exploration and environmental protection fields. To ensure that USV can successfully perform its mission, trajectory planning and motion tracking are the two most critical technologies. In this paper, we propose a novel trajectory generation and tracking method for USV based on optimization theory. Specifically, the USV dynamic model is described with differential flatness, so that the trajectory can be generated by dynamic RRT* in a linear invariant system expression form under the objective of optimal boundary value. To reduce the sample number and improve efficiency, we adjust the trajectory through local optimization. The dynamic constraints are considered in the optimization process so that the generated trajectory conforms to the kinematic characteristics of the under-actuated hull, and makes it easier to be tracked. Finally, motion tracking is added with model predictive control under a sequential quadratic programming problem. Experimental results show the planned trajectory is more in line with the kinematic characteristics of USV, and the tracking accuracy remains a higher level.

cs.RO↗

arXiv2022

Wake asymmetry weakening in viscoelastic fluids: Numerical discovery and mechanism exploration

Viscoelasticity weakens the asymmetry of laminar shedding flow behind a blunt body in a free domain. In the present study, this finding is confirmed by four unsteady viscoelastic flows with asymmetric flow configuration, i.e., flow over an inclined flat plate with various angles of incidence, flow over a rotating circular cylinder, flow over a circular cylinder with asymmetric slip boundary distribution, and flow over an inclined row of eight equally closely spaced circular cylinders (which can be considered as a single large blunt body) through direct numerical simulation combined with the Peterlin approximation of the finitely extensible nonlinear elastic (FENE-P) model. At high Weissenberg number, an arc shape region with high elastic stress, which is similar to shock wave, forms in the frontal area of the blunt body. This region acts as a stationary shield to separate the flow into different regions. Thus, the free stream resembles to pass this shield instead of the original blunt body. As this shield has symmetric feature, the wake flow restores symmetry.

physics.flu-dyn↗

arXiv2022

Connection-oriented and Connectionless Quantum Internet Considering Quantum Repeaters

With the rapid development of quantum information and technology in recent years, the construction of quantum internet for interconnecting all kinds of quantum devices, such as quantum processors and sensors, will be the next trend for practical quantum applications. In this paper, we propose the protocols for construction of connection-oriented and connectionless quantum networks by considering the concrete quantum repeater (QR) nodes. Four classes of QRs networks are considered first and designed with two types of protocols in link layer, i.e. simultaneous and one-by-one link. Based on those two link models, the connection-oriented protocol is presented for all classes of QRs networks and the connectionless protocol is proposed for the first, second and third classes QRs networks by only one-by-one link. Furthermore, we introduce a new hybrid connection model of quantum networks combined with connection-oriented and connectionless for practical uses. Our work is a new attempt to study the model of the network layer for different kinds of QR networks and paves the way for developing the protocol stack of universal large-scale quantum internet.

quant-ph↗

arXiv2022

LightViT: Towards Light-Weight Convolution-Free Vision Transformers

Vision transformers (ViTs) are usually considered to be less light-weight than convolutional neural networks (CNNs) due to the lack of inductive bias. Recent works thus resort to convolutions as a plug-and-play module and embed them in various ViT counterparts. In this paper, we argue that the convolutional kernels perform information aggregation to connect all tokens; however, they would be actually unnecessary for light-weight ViTs if this explicit aggregation could function in a more homogeneous way. Inspired by this, we present LightViT as a new family of light-weight ViTs to achieve better accuracy-efficiency balance upon the pure transformer blocks without convolution. Concretely, we introduce a global yet efficient aggregation scheme into both self-attention and feed-forward network (FFN) of ViTs, where additional learnable tokens are introduced to capture global dependencies; and bi-dimensional channel and spatial attentions are imposed over token embeddings. Experiments show that our model achieves significant improvements on image classification, object detection, and semantic segmentation tasks. For example, our LightViT-T achieves 78.7% accuracy on ImageNet with only 0.7G FLOPs, outperforming PVTv2-B0 by 8.2% while 11% faster on GPU. Code is available at https://github.com/hunto/LightViT.

cs.CV↗

arXiv2022

Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning

To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system, called Walle, as the foundation. Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration. Specifically, the compute container is based on Mobile Neural Network (MNN), a tensor compute engine along with the data processing and model execution libraries, which are exposed through a refined Python thread-level virtual machine (VM) to support diverse ML tasks and concurrent task execution. The core of MNN is the novel mechanisms of operator decomposition and semi-auto search, sharply reducing the workload in manually optimizing hundreds of operators for tens of hardware backends and further quickly identifying the best backend with runtime optimization for a computation graph. The data pipeline introduces an on-device stream processing framework to enable processing user behavior data at source. The deployment platform releases ML tasks with an efficient push-then-pull method and supports multi-granularity deployment policies. We evaluate Walle in practical e-commerce application scenarios to demonstrate its effectiveness, efficiency, and scalability. Extensive micro-benchmarks also highlight the superior performance of MNN and the Python thread-level VM. Walle has been in large-scale production use in Alibaba, while MNN has been open source with a broad impact in the community.

cs.LG↗

arXiv2022

Quantum circuit architecture search for variational quantum algorithms

Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS can not only alleviate the influence of quantum noise and barren plateaus, but also outperforms VQAs with pre-selected ansatze.

quant-ph↗

arXiv2022

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

The Internet of intelligence is conceived as an emerging networking paradigm, which will make intelligence as easy to obtain as information. This paper provides an overview of the Internet of intelligence, focusing on motivations, architecture, enabling technologies, applications, and existing challenges. This can provide a good foundation for those who are interested to gain insights into the concept of the Internet of intelligence and the key enablers of this emerging networking paradigm. Specifically, this paper starts by investigating the evolution of networking paradigms and artificial intelligence (AI), based on which we present the motivations of the Internet of intelligence by demonstrating that networking needs intelligence and intelligence needs networking. We then present the layered architecture to characterize the Internet of intelligence systems and discuss the enabling technologies of each layer. Moreover, we discuss the critical applications and their integration with the Internet of intelligence paradigm. Finally, some technical challenges and open issues are summarized to fully exploit the benefits of the Internet of intelligence.

cs.NI↗

arXiv2022

Symmetry-breaking-induced multifunctionalities of two-dimensional chromium-based materials for nanoelectronics and clean energy conversion

Structural symmetry-breaking that could lead to exotic physical properties plays a crucial role in determining the functions of a system, especially for two-dimensional (2D) materials. Here we demonstrate that multiple functionalities of 2D chromium-based materials could be achieved by breaking inversion symmetry via replacing Y atoms in one face of pristine CrY (Y=P, As, Sb) monolayers with N atoms, i.e., forming Janus Cr2NY monolayers. The functionalities include spin-gapless, very low work function, inducing carrier doping and catalytic activity, which are predominately ascribed to the large intrinsic dipole of Janus Cr2NY monolayers, making them having great potentials in various applications. Specifically, Cr2NSb is found to be a spin-gapless semiconductor, Cr2NP and Cr2NHPF could simultaneously induce n- and p-type carrier doping for two graphene sheets with different concentrations (forming intrinsic p-n vertical junction), and Cr2NY exhibits excellent electrocatalytic hydrogen evolution activity, even superior to benchmark Pt. The results confirm that breaking symmetry is a promising approach for the rational design of multifunctional 2D materials.

physics.comp-ph↗

arXiv2022

Stretchable Cells Help DARTS Search Better

Differentiable neural architecture search (DARTS) has gained much success in discovering flexible and diverse cell types. To reduce the evaluation gap, the supernet is expected to have identical layers with the target network. However, even for this consistent search, the searched cells often suffer from poor performance, especially for the supernet with fewer layers, as current DARTS methods are prone to wide and shallow cells, and this topology collapse induces sub-optimal searched cells. In this paper, we alleviate this issue by endowing the cells with explicit stretchability, so the search can be directly implemented on our stretchable cells for both operation type and topology simultaneously. Concretely, we introduce a set of topological variables and a combinatorial probabilistic distribution to explicitly model the target topology. With more diverse and complex topologies, our method adapts well for various layer numbers. Extensive experiments on CIFAR-10 and ImageNet show that our stretchable cells obtain better performance with fewer layers and parameters. For example, our method can improve DARTS by 0.28\% accuracy on CIFAR-10 dataset with 45\% parameters reduced or 2.9\% with similar FLOPs on ImageNet dataset.

cs.CV↗

arXiv2022

GreedyNASv2: Greedier Search with a Greedy Path Filter

Training a good supernet in one-shot NAS methods is difficult since the search space is usually considerably huge (e.g., $13^{21}$). In order to enhance the supernet's evaluation ability, one greedy strategy is to sample good paths, and let the supernet lean towards the good ones and ease its evaluation burden as a result. However, in practice the search can be still quite inefficient since the identification of good paths is not accurate enough and sampled paths still scatter around the whole search space. In this paper, we leverage an explicit path filter to capture the characteristics of paths and directly filter those weak ones, so that the search can be thus implemented on the shrunk space more greedily and efficiently. Concretely, based on the fact that good paths are much less than the weak ones in the space, we argue that the label of "weak paths" will be more confident and reliable than that of "good paths" in multi-path sampling. In this way, we thus cast the training of path filter in the positive and unlabeled (PU) learning paradigm, and also encourage a \textit{path embedding} as better path/operation representation to enhance the identification capacity of the learned filter. By dint of this embedding, we can further shrink the search space by aggregating similar operations with similar embeddings, and the search can be more efficient and accurate. Extensive experiments validate the effectiveness of the proposed method GreedyNASv2. For example, our obtained GreedyNASv2-L achieves $81.1\%$ Top-1 accuracy on ImageNet dataset, significantly outperforming the ResNet-50 strong baselines.

cs.CV↗

arXiv2022

DyRep: Bootstrapping Training with Dynamic Re-parameterization

Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks. Despite the prevalence, current Rep methods simply re-parameterize all operations into an augmented network, including those that rarely contribute to the model's performance. As such, the price to pay is an expensive computational overhead to manipulate these unnecessary behaviors. To eliminate the above caveats, we aim to bootstrap the training with minimal cost by devising a dynamic re-parameterization (DyRep) method, which encodes Rep technique into the training process that dynamically evolves the network structures. Concretely, our proposal adaptively finds the operations which contribute most to the loss in the network, and applies Rep to enhance their representational capacity. Besides, to suppress the noisy and redundant operations introduced by Rep, we devise a de-parameterization technique for a more compact re-parameterization. With this regard, DyRep is more efficient than Rep since it smoothly evolves the given network instead of constructing an over-parameterized network. Experimental results demonstrate our effectiveness, e.g., DyRep improves the accuracy of ResNet-18 by $2.04\%$ on ImageNet and reduces $22\%$ runtime over the baseline. Code is available at: https://github.com/hunto/DyRep.

cs.CV↗

arXiv2022

Relational Surrogate Loss Learning

Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e.g., average precision and F1 score. This paper aims to address this problem by revisiting the surrogate loss learning, where a deep neural network is employed to approximate the evaluation metrics. Instead of pursuing an exact recovery of the evaluation metric through a deep neural network, we are reminded of the purpose of the existence of these evaluation metrics, which is to distinguish whether one model is better or worse than another. In this paper, we show that directly maintaining the relation of models between surrogate losses and metrics suffices, and propose a rank correlation-based optimization method to maximize this relation and learn surrogate losses. Compared to previous works, our method is much easier to optimize and enjoys significant efficiency and performance gains. Extensive experiments show that our method achieves improvements on various tasks including image classification and neural machine translation, and even outperforms state-of-the-art methods on human pose estimation and machine reading comprehension tasks. Code is available at: https://github.com/hunto/ReLoss.

cs.LG↗

arXiv2022

On singularities of Ericksen-Leslie system in dimension three

In this paper, we consider the initial and boundary value problem of Ericksen-Leslie system modeling nematic liquid crystal flows in dimension three. Two examples of singularity at finite time are constructed. The first example is constructed in a special axisymmetric class with suitable axisymmetric initial and boundary data, while the second example is constructed for an initial data with small energy but nontrivial topology. A counter example of maximum principle to the system is constructed by utilizing the Poiseuille flow in dimension one.

math.AP↗

arXiv2022

Large-scale Deterministic Transmission among IEEE 802.1Qbv Time-Sensitive Networks

IEEE 802.1Qbv (TAS) is the most widely used technique in Time-Sensitive Networking (TSN) which aims to provide bounded transmission delays and ultra-low jitters in industrial local area networks. With the development of emerging technologies (e.g., cloud computing), many wide-range time-sensitive network services emerge, such as factory automation, connected vehicles, and smart grids. Nevertheless, TAS is a Layer 2 technique for local networks, and cannot provide large-scale deterministic transmission. To tackle this problem, this paper proposes a hierarchical network containing access networks and a core network. Access networks perform TAS to aggregate time-sensitive traffic. In the core network, we exploit DIP (a well-known deterministic networking mechanism for backbone networks) to achieve long-distance deterministic transmission. Due to the differences between TAS and DIP, we design cross-domain transmission mechanisms at the edge of access networks and the core network to achieve seamless deterministic transmission. We also formulate the end-to-end scheduling to maximize the amount of accepted time-sensitive traffic. Experimental simulations show that the proposed network can achieve end-to-end deterministic transmission even in high-loaded scenarios.

cs.NI↗

arXiv2022

Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning

Learning informative representations from image-based observations is of fundamental concern in deep Reinforcement Learning (RL). However, data-inefficiency remains a significant barrier to this objective. To overcome this obstacle, we propose to accelerate state representation learning by enforcing view-consistency on the dynamics. Firstly, we introduce a formalism of Multi-view Markov Decision Process (MMDP) that incorporates multiple views of the state. Following the structure of MMDP, our method, View-Consistent Dynamics (VCD), learns state representations by training a view-consistent dynamics model in the latent space, where views are generated by applying data augmentation to states. Empirical evaluation on DeepMind Control Suite and Atari-100k demonstrates VCD to be the SoTA data-efficient algorithm on visual control tasks.

cs.LG↗

arXiv2021

Review of opportunities for new long-lived particle triggers in Run 3 of the Large Hadron Collider

Long-lived particles (LLPs) are highly motivated signals of physics Beyond the Standard Model (BSM) with great discovery potential and unique experimental challenges. The LLP search programme made great advances during Run 2 of the Large Hadron Collider (LHC), but many important regions of signal space remain unexplored. Dedicated triggers are crucial to improve the potential of LLP searches, and their development and expansion is necessary for the full exploitation of the new data. The public discussion of triggers has therefore been a relevant theme in the recent LLP literature, in the meetings of the LLP@LHC Community workshop and in the respective experiments. This paper documents the ideas collected during talks and discussions at these Workshops, benefiting as well from the ideas under development by the trigger community within the experimental collaborations. We summarise the theoretical motivations of various LLP scenarios leading to highly elusive signals, reviewing concrete ideas for triggers that could greatly extend the reach of the LHC experiments. We thus expect this document to encourage further thinking for both the phenomenological and experimental communities, as a stepping stone to further develop the LLP@LHC physics programme.

hep-ex↗

arXiv2021

An improved light-cone harmonic oscillator model for the pionic leading-twist distribution amplitude

In this paper, we study the pion leading-twist distribution amplitude $ϕ_{2;π}(x,μ)$ by improving the traditional light-cone harmonic oscillator model within the reconstruction of the function $φ_{2;π}(x)$. In order to constraining the model parameters, we calculate its moments $\langleξ^n\rangle_{2;π}|_μ$ in the framework of QCD background field theory sum rule (BFTSR) up to $10^{\rm th}$ order. Considering the fact that the sum rule of the $0^{\rm th}$ moment $\langleξ^0\rangle_{2;π}|_μ$ cannot be normalized, we suggest a more reasonable sum rule formula for $\langleξ^n\rangle_{2;π}|_μ$. Then, we obtain the values of $\langleξ^n\rangle_{2;π}|_{μ_0}$ with $n=(2,4,6,8,10)$ at the initial scale $μ_0 = 1~{\rm GeV}$. The first two moments are: $\langle ξ^2\rangle_{2;π}|_{μ_0} = 0.271 \pm 0.013$, $\langleξ^4\rangle_{2;π}|_{μ_0} = 0.138 \pm 0.010$; and the corresponding Gegenbauer moments are $a^{2;π}_2(μ_0) = 0.206 \pm 0.038$, $a^{2;π}_4(μ_0) = 0.047 \pm 0.011$, respectively. After fitting the moments $\langleξ^n\rangle_{2;π}|_μ$, we obtained the appropriate model parameters by using the least square method. The resultant behavior for twist-2 pion DA is more closely to the AdS/QCD and lattice result, but is narrower than that by Dyson-Schwinger equation. Furthermore, we calculate the pion-photon transition form factors (TFF) and $B\toπ$ TFF within light-cone sum rule approach, which are conform with experimental and theoretical results.

hep-ph↗

arXiv2021

Gradient Boosted Binary Histogram Ensemble for Large-scale Regression

In this paper, we propose a gradient boosting algorithm for large-scale regression problems called \textit{Gradient Boosted Binary Histogram Ensemble} (GBBHE) based on binary histogram partition and ensemble learning. From the theoretical perspective, by assuming the Hölder continuity of the target function, we establish the statistical convergence rate of GBBHE in the space $C^{0,α}$ and $C^{1,0}$, where a lower bound of the convergence rate for the base learner demonstrates the advantage of boosting. Moreover, in the space $C^{1,0}$, we prove that the number of iterations to achieve the fast convergence rate can be reduced by using ensemble regressor as the base learner, which improves the computational efficiency. In the experiments, compared with other state-of-the-art algorithms such as gradient boosted regression tree (GBRT), Breiman's forest, and kernel-based methods, our GBBHE algorithm shows promising performance with less running time on large-scale datasets.

stat.ML↗