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Bo Yang

Publications and source records attributed to Bo Yang.

At least 325 records · Page 18Linked to original sources
arXiv2023

A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading

Computation offloading has become a popular solution to support computationally intensive and latency-sensitive applications by transferring computing tasks to mobile edge servers (MESs) for execution, which is known as mobile/multi-access edge computing (MEC). To improve the MEC performance, it is required to design an optimal offloading strategy that includes offloading decision (i.e., whether offloading or not) and computational resource allocation of MEC. The design can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which is generally NP-hard and its effective solution can be obtained by performing online inference through a well-trained deep neural network (DNN) model. However, when the system environments change dynamically, the DNN model may lose efficacy due to the drift of input parameters, thereby decreasing the generalization ability of the DNN model. To address this unique challenge, in this paper, we propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs). Specifically, the shared backbone will be invariant during the PHs training and the inferred results will be ensembled, thereby significantly reducing the required training overhead and improving the inference performance. As a result, the joint optimization problem for offloading decision and resource allocation can be efficiently solved even in a time-varying wireless environment. Experimental results show that the proposed MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.

eess.SP↗
arXiv2023

Discrete time crystal made of topological edge magnons

We report the emergence of time-crystalline behavior in the π-Berry phase protected edge states of a Heisenberg ferromagnet in the presence of an external driving field. The magnon amplification due to the external field spontaneously breaks the discrete time-translational symmetry, resulting in a discrete time crystal with a period that is twice that of the applied EM field. We discuss the nature and symmetry protection of the time crystalline edge states and their stability against various perturbations that are expected in real quantum magnets. We propose an experimental signature to unambiguously detect the time crystalline behavior and identify two recently discovered quasi-2D magnets as potential hosts. We present a first-of-its-kind realization of time crystals at topological edge states, which can be generalized and extrapolated to other bosonic quasi-particle systems that exhibit parametric pumping and topological edge states.

cond-mat.str-el↗
arXiv2023

Fermionic fractional quantum Hall states: A modern approach to systems with bulk-edge correspondence

In contemporary physics, especially in condensed matter physics, fermionic topological order and its protected edge modes are one of the most important objects. In this work, we propose a systematic construction of the cylinder partition corresponding to the fermionic fractional quantum Hall effect (FQHE) and a general mechanism for obtaining the candidates of the protected edge modes. In our construction, when the underlying conformal field theory has the $Z_{2}$ duality defects corresponding to the fermionic $Z_{2}$ electric particle, we show that the FQH partition function has a fermionic T duality. This duality is analogous to (hopefully the same as) the dualities in the dual resonance models, typically known as supersymmetry, and gives a renormalization group (RG) theoretic understanding of the topological phases. We also introduce a modern understanding of bulk topological degeneracies and topological entanglement entropy. This understanding is based on the traditional tunnel problem and the recent conjecture of correspondence between the bulk renormalization group flow and the boundary conformal field theory. Our formalism gives an intuitive and general understanding of the modern physics of the topologically ordered systems in the traditional language of RG and fermionization.

cond-mat.str-el↗
arXiv2023

STS-GAN: Can We Synthesize Solid Texture with High Fidelity from Arbitrary 2D Exemplar?

Solid texture synthesis (STS), an effective way to extend a 2D exemplar to a 3D solid volume, exhibits advantages in computational photography. However, existing methods generally fail to accurately learn arbitrary textures, which may result in the failure to synthesize solid textures with high fidelity. In this paper, we propose a novel generative adversarial nets-based framework (STS-GAN) to extend the given 2D exemplar to arbitrary 3D solid textures. In STS-GAN, multi-scale 2D texture discriminators evaluate the similarity between the given 2D exemplar and slices from the generated 3D texture, promoting the 3D texture generator synthesizing realistic solid textures. Finally, experiments demonstrate that the proposed method can generate high-fidelity solid textures with similar visual characteristics to the 2D exemplar.

cs.CV↗
arXiv2023

Hydrogen Supply Infrastructure Network Planning Approach towards Chicken-egg Conundrum

In the early commercialization stage of hydrogen fuel cell vehicles (HFCVs), reasonable hydrogen supply infrastructure (HSI) planning decisions is a premise for promoting the popularization of HFCVs. However, there is a strong causality between HFCVs and hydrogen refueling stations (HRSs): the planning decisions of HRSs could affect the hydrogen refueling demand of HFCVs, and the growth of demand would in turn stimulate the further investment in HRSs, which is also known as the ``chicken and egg'' conundrum. Meanwhile, the hydrogen demand is uncertain with insufficient prior knowledge, and thus there is a decision-dependent uncertainty (DDU) in the planning issue. This poses great challenges to solving the optimization problem. To this end, this work establishes a multi-network HSI planning model coordinating hydrogen, power, and transportation networks. Then, to reflect the causal relationship between HFCVs and HRSs effectively without sufficient historical data, a distributionally robust optimization framework with decision-dependent uncertainty is developed. The uncertainty of hydrogen demand is modeled as a Wasserstein ambiguity set with a decision-dependent empirical probability distribution. Subsequently, to reduce the computational complexity caused by the introduction of a large number of scenarios and high-dimensional nonlinear constraints, we developed an improved distribution shaping method and techniques of scenario and variable reduction to derive the solvable form with less computing burden. Finally, the simulation results demonstrate that this method can reduce costs by at least 10.4% compared with traditional methods and will be more effective in large-scale HSI planning issues. Further, we put forward effective suggestions for the policymakers and investors to formulate relevant policies and decisions.

eess.SY↗
arXiv2023

Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition

Graph convolutional networks have been widely applied in skeleton-based gait recognition. A key challenge in this task is to distinguish the individual walking styles of different subjects across various views. Existing state-of-the-art methods employ uniform convolutions to extract features from diverse sequences and ignore the effects of viewpoint changes. To overcome these limitations, we propose a condition-adaptive graph (CAG) convolution network that can dynamically adapt to the specific attributes of each skeleton sequence and the corresponding view angle. In contrast to using fixed weights for all joints and sequences, we introduce a joint-specific filter learning (JSFL) module in the CAG method, which produces sequence-adaptive filters at the joint level. The adaptive filters capture fine-grained patterns that are unique to each joint, enabling the extraction of diverse spatial-temporal information about body parts. Additionally, we design a view-adaptive topology learning (VATL) module that generates adaptive graph topologies. These graph topologies are used to correlate the joints adaptively according to the specific view conditions. Thus, CAG can simultaneously adjust to various walking styles and viewpoints. Experiments on the two most widely used datasets (i.e., CASIA-B and OU-MVLP) show that CAG surpasses all previous skeleton-based methods. Moreover, the recognition performance can be enhanced by simply combining CAG with appearance-based methods, demonstrating the ability of CAG to provide useful complementary information.The source code will be available at https://github.com/OliverHxh/CAG.

cs.CV↗
arXiv2023

Frequency shift of light in Kerr spacetime

The frequency shift of light in the gravitational field generated by a rotating body is investigated. We consider the scenario in which both the light source and the observer are in motion. The frequency shift is calculated up to the second-order post-Minkowskian approximation via two different methods and the same result is achieved. The higher-order effects of the gravitational source's rotation on the frequency shift is obtained. Especially, when both the light source and the observer are located in the asymptotically flat region, an elegant formula is obtained, which can be easily used in the astronomical observations to determine the rotating gravitational source's mass and angular momentum.

gr-qc↗
arXiv2023

iEDA: An Open-Source Intelligent Physical Implementation Toolkit and Library

Open-source EDA shows promising potential in unleashing EDA innovation and lowering the cost of chip design. This paper presents an open-source EDA project, iEDA, aiming for building a basic infrastructure for EDA technology evolution and closing the industrial-academic gap in the EDA area. iEDA now covers the whole flow of physical design (including Floorplan, Placement, CTS, Routing, Timing Optimization etc.), and part of the analysis tools (Static Timing Analysis and Power Analysis). To demonstrate the effectiveness of iEDA, we implement and tape out three chips of different scales (from 700k to 1.5M gates) on different process nodes (110nm and 28nm) with iEDA. iEDA is publicly available from the project home page http://ieda.oscc.cc.

cs.AR↗
arXiv2023

Ad-hoc polymorphic delimited continuations

We designed and implemented a framework for creating extensible domain-specific languages that consists of library-defined keywords. First-class language features in other programming languages can be implemented as libraries with the help of our framework. The core concept in our framework is the type class Dsl, which can be considered as both the ad-hoc polymorphic version of a delimited continuation and a more generic version of Monad. Thus it can be also used as a statically typed extensible effect system that is more efficient and more concise than existing Monad-based effect systems.

cs.PL↗
arXiv2023

Monadic Deep Learning

The Java and Scala community has built a very successful big data ecosystem. However, most of neural networks running on it are modeled in dynamically typed programming languages. These dynamically typed deep learning frameworks treat neural networks as differentiable expressions that contain many trainable variable, and perform automatic differentiation on those expressions when training them. Until 2019, none of the learning frameworks in statically typed languages provided the expressive power of traditional frameworks. Their users are not able to use custom algorithms unless creating plenty of boilerplate code for hard-coded back-propagation. We solved this problem in DeepLearning.scala 2. Our contributions are: 1. We discovered a novel approach to perform automatic differentiation in reverse mode for statically typed functions that contain multiple trainable variable, and can interoperate freely with the metalanguage. 2. We designed a set of monads and monad transformers, which allow users to create monadic expressions that represent dynamic neural networks. 3. Along with these monads, we provide some applicative functors, to perform multiple calculations in parallel. With these features, users of DeepLearning.scala were able to create complex neural networks in an intuitive and concise way, and still maintain type safety.

cs.PL↗
arXiv2023

A Survey on Neural-symbolic Learning Systems

In recent years, neural systems have demonstrated highly effective learning ability and superior perception intelligence. However, they have been found to lack effective reasoning and cognitive ability. On the other hand, symbolic systems exhibit exceptional cognitive intelligence but suffer from poor learning capabilities when compared to neural systems. Recognizing the advantages and disadvantages of both methodologies, an ideal solution emerges: combining neural systems and symbolic systems to create neural-symbolic learning systems that possess powerful perception and cognition. The purpose of this paper is to survey the advancements in neural-symbolic learning systems from four distinct perspectives: challenges, methods, applications, and future directions. By doing so, this research aims to propel this emerging field forward, offering researchers a comprehensive and holistic overview. This overview will not only highlight the current state-of-the-art but also identify promising avenues for future research.

cs.LG↗
arXiv2023

Tuning bulk topological magnon properties with light-induced magnons

Although theoretical modelling and inelastic neutron scattering measurements have indicated the presence of topological magnon bands in multiple quantum magnets, experiments remain unable to detect signal of magnon thermal Hall effect in the quantum magnets, which is a consequence of magnons condensation at the bottom of the bands following Bose Einstein statistics as well as the concentration of Berry curvature at the higher energies. In a recent work, Malz et al.[Nature Communications 10, 3937 (2019)] have shown that topological magnons in edge states in a finite sample can be amplified using tailored electromagnetic fields. We extend their approach by showing that a uniform electromagnetic field can selectively amplify magnons with finite Berry curvature by breaking inversion symmetry of a lattice. Using this approach, we demonstrate the generation of bulk topological magnons in a Heisenberg ferromagnet on the breathing kagome lattice and the consequent amplification of thermal Hall effect.

cond-mat.str-el↗
arXiv2023

Consecutive Inertia Drift of Autonomous RC Car via Primitive-based Planning and Data-driven Control

Inertia drift is an aggressive transitional driving maneuver, which is challenging due to the high nonlinearity of the system and the stringent requirement on control and planning performance. This paper presents a solution for the consecutive inertia drift of an autonomous RC car based on primitive-based planning and data-driven control. The planner generates complex paths via the concatenation of path segments called primitives, and the controller eases the burden on feedback by interpolating between multiple real trajectories with different initial conditions into one near-feasible reference trajectory. The proposed strategy is capable of drifting through various paths containing consecutive turns, which is validated in both simulation and reality.

cs.RO↗
arXiv2023

Imaging magnetism evolution of magnetite to megabar pressure range with quantum sensors in diamond anvil cell

High-pressure diamond anvil cells have been widely used to create novel states of matter. Nevertheless, the lack of universal in-situ magnetic measurement techniques at megabar pressures makes it difficult to understand the underlying physics of materials' behavior at extreme conditions, such as high-temperature superconductivity of hydrides and the formation or destruction of the local magnetic moments in magnetic systems, etc. Here we break through the limitations of pressure on quantum sensors and develop the in-situ magnetic detection technique at megabar pressures with high sensitivity (~1μT/Hz^(1\2)) and sub-microscale spatial resolution. By directly imaging the magnetic field and the evolution of magnetic domains, we observe the macroscopic magnetic transition of Fe3O4 in the megabar pressure range from strong ferromagnetism (α-Fe3O4) to weak ferromagnetism (β-Fe3O4) and finally to non-magnetism (γ-Fe3O4). The scenarios for magnetic changes in Fe3O4 characterized here shed light on the direct magnetic microstructure observation in bulk materials at high pressure and contribute to understanding the mechanism of magnetic moment suppression related to spin crossover. The presented method can potentially investigate the spin-orbital coupling and magnetism-superconductivity competition in magnetic systems.

physics.app-ph↗
arXiv2023

EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection

In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks. They usually contain limited visual clues and make small faces less distinguishable from the other small objects, which poses great challenge to accurate face detection. Although deep convolutional neural network has significantly promoted the research on face detection recently, current deep face detectors rarely take into account low-resolution faces and are still vulnerable to the real-world scenarios where massive amount of low-resolution faces exist. Consequently, they usually achieve degraded performance for low-resolution face detection. In order to alleviate this problem, we develop an efficient detector termed EfficientSRFace by introducing a feature-level super-resolution reconstruction network for enhancing the feature representation capability of the model. This module plays an auxiliary role in the training process, and can be removed during the inference without increasing the inference time. Extensive experiments on public benchmarking datasets, such as FDDB and WIDER Face, show that the embedded image super-resolution module can significantly improve the detection accuracy at the cost of a small amount of additional parameters and computational overhead, while helping our model achieve competitive performance compared with the state-of-the-arts methods.

cs.CV↗
arXiv2023

Self-Replicating Hierarchical Structures Emerge in a Binary Cellular Automaton

We have discovered a novel transition rule for binary cellular automata (CA) that yields self-replicating structures across two spatial and temporal scales from sparsely populated random initial conditions. Lower-level, shapeshifting clusters frequently follow a transient attractor trajectory, generating new clusters, some of which periodically self-duplicate. When the initial distribution of live cells is sufficiently sparse, these clusters coalesce into larger formations that also self-replicate. These formations may further form the boundaries of an expanding complex on an even larger scale. This rule, dubbed ``Outlier,'' is rotationally symmetric and applies to 2D Moore neighborhoods. It was evolved through Genetic Programming during an extensive automated search for rules that foster open-ended evolution in CA. While self-replicating structures, both crafted and emergent, have been created in CA with state sets intentionally designed for this purpose, the Outlier may be the first known rule to facilitate emergent self-replication across two spatial scales in simple binary CA.

nlin.CG↗
arXiv2023

Massive white dwarfs in Rastall-Rainbow gravity

We investigate the hydrostatic equilibrium of white dwarfs within the framework of Rastall-Rainbow gravity, aiming to explore the effects of this modified gravitational theory on their properties. By employing the Chandrasekhar equation of state in conjunction with the modified Tolman-Oppenheimer-Volkoff equation, we derive the mass-radius relations for white dwarfs. Our results show that the maximum mass of white dwarfs deviates significantly from the predictions of general relativity, potentially exceeding the Chandrasekhar limit. Furthermore, we discuss other properties of white dwarfs, such as the gravitational redshift, compactness and dynamical stability, shedding light on their behavior within the context of this modified gravitational framework.

gr-qc↗
arXiv2023

Spin-statistics relation and the Abelian braiding phase for anyons in fractional quantum Hall effect

Quasihole excitations in fractional quantum Hall (FQH) systems exhibit fractional statistics and fractional spin, but how the spin-statistics relation emerges from many-body physics remains poorly understood. Here we prove a spin-statistics relation using only FQH wave functions, on both the sphere and disk geometry. In particular, the proof on the disk generalizes to all quasiholes in realistic systems, which have a finite size and could be deformed into arbitrary shapes. Different components of the quasihole spins are linked to different conformal Hilbert spaces (CHS), which are nullspaces of model Hamiltonians that host the respective FQH ground states and quasihole states. Understanding how the intrinsic spin of the quasiholes is linked to different CHS is crucial for the generalized spin-statistics relation that takes into account the effect of metric deformation. In terms of the experimental relevance, this enables us to study the effect of deformation and disorder that introduces an additional source of Berry curvature, an aspect of anyon braiding that has been largely neglected in previous literature.

cond-mat.mes-hall↗