arXiv subjects

Yuxiang Zhao

Publications and source records attributed to Yuxiang Zhao.

At least 55 records · Page 3Linked to original sources

arXiv2024

The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Within the Electronic Design Automation (EDA) domain, AI-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an AI4EDA approach falls short of achieving a holistic design synthesis and understanding, overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This paper argues for a paradigm shift from AI4EDA towards AI-native EDA, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, RTL designs, circuit netlists, and physical layouts. We champion the creation of large circuit models (LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-native philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound shift-left in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area (PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

cs.AR↗

arXiv2024

A global analysis of $π^0$, $K_S^0$ and $η$ fragmentation functions with BESIII data

In this research, we conduct a global QCD analysis of fragmentation functions (FFs) for neutral pions ($π^0$), neutral kaons ($K_S^0$), and eta mesons ($η$), utilizing world data of single inclusive hadron production in $e^+e^-$ annihilation involving the most recent BESIII data with low collision energy, to test the operational region of QCD collinear factorization for single inclusive hadron production. We found that the QCD-based analysis at next-to-next-to leading order in perturbative QCD with parameterized higher-twist effects can explain both existing high-energy world data and the BESIII new measurements, while the latter cannot be explained with existing FFs extracted with high-energy data. To investigate the higher-twist contributions to this discrepancy, a direct functional approach is employed, providing testing framework for characterizing the experimental results over a wide range of energy scales, from low to high, thus extending the classical theoretical models to the BESIII domain.

hep-ph↗

arXiv2024

Extraction of trans-helicity worm-gear distributions and opportunities at the Electron-Ion Collider in China

We present a global analysis of the trans-helicity worm-gear distribution function, $g_{1T}^\perp$, by fitting the longitudinal-transverse double spin asymmetry data of the semi-inclusive deep inelastic scattering. The analysis is performed within the framework of transverse momentum dependent factorization and evolution. It is found that the $u$-quark favors a positive distribution and the $d$-quark favors a negative distribution, which is consistent with previous model calculations and phenomenological extractions. Based on the fit to existing world data, we also study the impact of the proposed electron-ion collider in China and conclude that it can significantly improve the precision of the worm-gear distribution function and hence enhance our understanding of nucleon spin structures.

hep-ph↗

arXiv2024

Towards three-dimensional nucleon structures at the Electron-Ion Collider in China: A study of the Sivers function

Transverse momentum-dependent parton distribution functions (TMDs) provide three-dimensional imaging of the nucleon in the momentum space. With its fundamental importance in understanding the spin structure of the nucleon, the precise measurement of TMDs is considered as one of the main physics topics of the proposed Electron-Ion Collider in China (EicC). In this paper, we investigate the impact of future semi-inclusive deep inelastic scattering (SIDIS) data from EicC on the extraction of TMDs. Taking the Sivers function as an example, we revisited the world SIDIS data, which serves as the input for the simulated EicC data. By performing a combined analysis of the world data and the EicC simulated data, we quantitatively demonstrate that the Sivers functions can be precisely determined for various quark flavors, especially for sea quarks, in the region, $x>0.005$, directly covered by the EicC.

hep-ph↗

arXiv2024

Simultaneous Blind Demixing and Super-resolution via Vectorized Hankel Lift

In this work, we investigate the problem of simultaneous blind demixing and super-resolution. Leveraging the subspace assumption regarding unknown point spread functions, this problem can be reformulated as a low-rank matrix demixing problem. We propose a convex recovery approach that utilizes the low-rank structure of each vectorized Hankel matrix associated with the target matrix. Our analysis reveals that for achieving exact recovery, the number of samples needs to satisfy the condition $n\gtrsim Ksr \log (sn)$. Empirical evaluations demonstrate the recovery capabilities and the computational efficiency of the convex method.

cs.IT↗

arXiv2023

A Direct Approach for Solving Cloud Computing Task Assignment with Soft Deadlines

Job scheduling in cloud computing environments is a critical yet complex problem. Cloud computing user job requirements are highly dynamic and uncertain, while cloud computing resources are heterogeneous and constrained. This paper studies the online resource allocation problem for elastic computing jobs with soft deadlines in cloud computing environments. The main contributions include: 1) Integer linear programming modeling is used to design an auction time scheduling framework with three key modules - resource allocation, evaluation, and operation, which can dynamically allocate resources in closed loops. 2) Methods such as time-based single resource utilization evaluation and weighted average evaluation are proposed to evaluate resource usage efficiency. 3) Soft acceptance protocols are introduced to achieve elastic online resource allocation. 4) The time complexity of the proposed algorithms is analyzed and proven to be polynomial time, demonstrating efficiency. 5) Modular design makes the framework extensible. This paper provides a structured cloud computing auction framework as a reference for building practical cloud resource management systems. Future work may explore more complex models of random arrival and multi-dimensional resource constraints, evaluate algorithm performance on real cloud workloads, and further enhance system robustness, efficiency and fairness.

cs.DC↗

arXiv2023

UAS-based Automated Structural Inspection Path Planning via Visual Data Analytics and Optimization

Unmanned Aerial Systems (UAS) have gained significant traction for their application in infrastructure inspections. However, considering the enormous scale and complex nature of infrastructure, automation is essential for improving the efficiency and quality of inspection operations. One of the core problems in this regard is electing an optimal automated flight path that can achieve the mission objectives while minimizing flight time. This paper presents an effective formulation for the path planning problem in the context of structural inspections. Coverage is guaranteed as a constraint to ensure damage detectability and path length is minimized as an objective, thus maximizing efficiency while ensuring inspection quality. A two-stage algorithm is then devised to solve the path planning problem, composed of a genetic algorithm for determining the positions of viewpoints and a greedy algorithm for calculating the poses. A comprehensive sensitivity analysis is conducted to demonstrate the proposed algorithm's effectiveness and range of applicability. Applied examples of the algorithm, including partial space inspection with no-fly zones and focused inspection, are also presented, demonstrating the flexibility of the proposed method to meet real-world structural inspection requirements. In conclusion, the results of this study highlight the feasibility of the proposed approach and establish the groundwork for incorporating automation into UAS-based structural inspection mission planning.

cs.RO↗

arXiv2023

Role of sea quarks in the nucleon transverse spin

We present a phenomenological extraction of transversity distribution functions and Collins fragmentation functions by simultaneously fitting to semi-inclusive deep inelastic scattering and electron-positron annihilation data. The analysis is performed within the transverse momentum dependent factorization formalism, and sea quark transversity distributions are taken into account for the first time. We find the $\bar u$ quark favors a negative transversity distribution while that of the $\bar d$ quark is consistent with zero according to the current accuracy. In addition, based on a combined analysis of world data and simulated data, we quantitatively demonstrate the impact of the proposed Electron-ion Collider in China on precise determinations of the transversity distributions, especially for sea quarks, and the Collins fragmentation functions.

hep-ph↗

arXiv2023

ROMO: Retrieval-enhanced Offline Model-based Optimization

Data-driven black-box model-based optimization (MBO) problems arise in a great number of practical application scenarios, where the goal is to find a design over the whole space maximizing a black-box target function based on a static offline dataset. In this work, we consider a more general but challenging MBO setting, named constrained MBO (CoMBO), where only part of the design space can be optimized while the rest is constrained by the environment. A new challenge arising from CoMBO is that most observed designs that satisfy the constraints are mediocre in evaluation. Therefore, we focus on optimizing these mediocre designs in the offline dataset while maintaining the given constraints rather than further boosting the best observed design in the traditional MBO setting. We propose retrieval-enhanced offline model-based optimization (ROMO), a new derivable forward approach that retrieves the offline dataset and aggregates relevant samples to provide a trusted prediction, and use it for gradient-based optimization. ROMO is simple to implement and outperforms state-of-the-art approaches in the CoMBO setting. Empirically, we conduct experiments on a synthetic Hartmann (3D) function dataset, an industrial CIO dataset, and a suite of modified tasks in the Design-Bench benchmark. Results show that ROMO performs well in a wide range of constrained optimization tasks.

cs.LG↗

arXiv2023

Study of charm hadronization and in-medium modification at the Electron-ion Collider in China

Charm quark production and its hadronization in ep and eA collisions at the future Electron-Ion Collider in China (EicC) will help us understand the quark/gluon fragmentation processes and the hadronization mechanisms in the nuclear medium, especially within a poorly constrained kinematic region ($x<0.1$). In this paper, we report a study on the production of charmed hadrons, $D^0$ and $Λ_c^+$, reconstructed with a dedicated GEANT4 simulation of vertex$\,\&\,$tracking detectors designed for EicC. The $Λ_c^+$/$D^0$ ratios as functions of multiplicity and $p_T$, as well as the $D^0$ double ratio are presented with projected statistical precision.

nucl-ex↗

arXiv2023

Probing gluon distributions with $D^0$ production at the EicC

The Electron-Ion Collider in China (EicC) has been proposed to study the inner structure of matter and fundamental laws of strong interactions. In this paper, we will present a conceptual design of the tracking system based on the state-of-art silicon detector and Micro-Pattern Gaseous Detector at the EicC and demonstrate that it will enable us to reconstruct charm hadron with good significance, hence study gluonic parton distribution functions in nucleons and nuclei, as well as gluon helicity distributions. The impact study using reweighting techniques shows that the impact of the EicC will be mainly in the large $x$ region. It complements similar physics programs at the Electron-Ion Collider at Brookhaven National Laboratory.

nucl-ex↗

arXiv2023

HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction

Accurate early congestion prediction can prevent unpleasant surprises at the routing stage, playing a crucial character in assisting designers to iterate faster in VLSI design cycles. In this paper, we introduce a novel strategy to fully incorporate topological and geometrical features of circuits by making several key designs in our network architecture. To be more specific, we construct two individual graphs (geometry-graph, topology-graph) with distinct edge construction schemes according to their unique properties. We then propose a dual-branch network with different encoder layers in each pathway and aggregate representations with a sophisticated fusion strategy. Our network, named HybridNet, not only provides a simple yet effective way to capture the geometric interactions of cells, but also preserves the original topological relationships in the netlist. Experimental results on the ISPD2015 benchmarks show that we achieve an improvement of 10.9% compared to previous methods.

cs.LG↗

arXiv2023

Probing gluon TMDs with reconstructed and tagged heavy flavor hadron pairs at EIC

Study of the transverse structure of the proton is one of the major physics goals of the upcoming Electron Ion Collider (EIC). The gluon transverse momentum dependent distributions (TMD) form an essential focus of this effort and are important towards understanding the angular momentum contribution to proton spin as well as QCD factorization. However, very limited experimental constraints on the gluon TMD exist currently. As the heavy quark production in lepton-nucleon DIS gets a dominant contribution from the photon-gluon-fusion process, heavy quark production makes an attractive tool to probe gluon distributions in nucleons. In this paper we present a study of heavy flavor hadron pair reconstruction at a future EIC detector with MAPS based inner tracking and vertexing subsystems to constrain gluon TMD. We utilize the excellent track pointing resolution provided by the detector to exclusively reconstruct heavy flavor hadron pairs via their hadronic decay channels and also to develop a heavy flavor hadron tagging algorithm. Statistical uncertainty projections on azimuthal asymmetries corresponding to gluon TMD at the EIC is evaluated. The heavy flavor tagging is found to substantially enhance the purity of heavy flavor hadron pair selection, and the statistical precision of the measurement compared to that from exclusive reconstruction. The correlation between the azimuthal angle of the transverse momentum of the gluon initiating the process and that of the corresponding heavy flavor hadron pair was also studied and found to be well correlated. This study opens up heavy flavor hadron pair measurements as an attractive channel to access gluon TMD at the EIC.

hep-ph↗

arXiv2022

CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)

The electronic design automation (EDA) community has been actively exploring machine learning (ML) for very large-scale integrated computer-aided design (VLSI CAD). Many studies explored learning-based techniques for cross-stage prediction tasks in the design flow to achieve faster design convergence. Although building ML models usually requires a large amount of data, most studies can only generate small internal datasets for validation because of the lack of large public datasets. In this essay, we present the first open-source dataset called CircuitNet for ML tasks in VLSI CAD.

cs.LG↗

arXiv2021

Probing gluon helicity with heavy flavor at the EIC

We propose a new measurement of the heavy flavor hadron double spin asymmetry in deep-inelastic scattering at a future Electron-Ion Collider (EIC) to constrain the polarized gluon distribution function inside the proton. Statistical projection on $D^0$ meson double spin asymmetry is calculated with an EIC central detector using an all-silicon tracker and vertexing subsystem. A first impact study was done by interpreting pseudo-data at next-to-leading order in QCD. The sensitivity of the experimental observable in constraining gluon helicity distribution in a wide range of parton momentum fraction $x$ has been investigated considering different beam energy configurations. This measurement complements the inclusive spin-dependent structure function measurement and provides an opportunity to constrain the gluon helicity distribution in the moderate $x$ region.

hep-ex↗

arXiv2021

DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning

Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition. Most of the state-of-the-arts focus on short-range spatio-temporal modeling and then average multiple snippet-level predictions to yield the final video-level prediction. Thus, their video-level prediction does not consider spatio-temporal features of how video evolves along the temporal dimension. In this paper, we introduce a novel Dynamic Segment Aggregation (DSA) module to capture relationship among snippets. To be more specific, we attempt to generate a dynamic kernel for a convolutional operation to aggregate long-range temporal information among adjacent snippets adaptively. The DSA module is an efficient plug-and-play module and can be combined with the off-the-shelf clip-based models (i.e., TSM, I3D) to perform powerful long-range modeling with minimal overhead. The final video architecture, coined as DSANet. We conduct extensive experiments on several video recognition benchmarks (i.e., Mini-Kinetics-200, Kinetics-400, Something-Something V1 and ActivityNet) to show its superiority. Our proposed DSA module is shown to benefit various video recognition models significantly. For example, equipped with DSA modules, the top-1 accuracy of I3D ResNet-50 is improved from 74.9% to 78.2% on Kinetics-400. Codes are available at https://github.com/whwu95/DSANet.

cs.CV↗

arXiv2021

Determining the helicity structure of the nucleon at the Electron Ion Collider in China

Understanding how sea quarks behave inside a nucleon is one of the most important physics goals of the proposed Electron-Ion Collider in China (EicC), which is designed to have 3.5 GeV polarized electron beam (80% polarization) colliding with 20 GeV polarized proton beam (70% polarization) at instantaneous luminosity of $2 \times 10^{33} {\rm cm}^{-2} {\rm s}^{-1}$. A specific topic at EicC is to understand the polarization of individual quarks inside a longitudinally polarized nucleon. The potential of various future EicC data, including the inclusive and semi-inclusive deep inelastic scattering data from both doubly polarized electron-proton and electron-$^3{\rm He}$ collisions, to reduce the uncertainties of parton helicity distributions is explored at the next-to-leading order in QCD, using the Error PDF Updating Method Package ({\sc ePump}) which is based on the Hessian profiling method. We show that the semi-inclusive data are well able to provide good separation between flavour distributions, and to constrain their uncertainties in the $x>0.005$ region, especially when electron-$^3{\rm He}$ collisions, acting as effective electron-neutron collisions, are taken into account. To enable this study, we have generated a Hessian representation of the DSSV14 set of PDF replicas, named DSSV14H PDFs.

hep-ph↗

arXiv2021

Good Practices and A Strong Baseline for Traffic Anomaly Detection

The detection of traffic anomalies is a critical component of the intelligent city transportation management system. Previous works have proposed a variety of notable insights and taken a step forward in this field, however, dealing with the complex traffic environment remains a challenge. Moreover, the lack of high-quality data and the complexity of the traffic scene, motivate us to study this problem from a hand-crafted perspective. In this paper, we propose a straightforward and efficient framework that includes pre-processing, a dynamic track module, and post-processing. With video stabilization, background modeling, and vehicle detection, the pro-processing phase aims to generate candidate anomalies. The dynamic tracking module seeks and locates the start time of anomalies by utilizing vehicle motion patterns and spatiotemporal status. Finally, we use post-processing to fine-tune the temporal boundary of anomalies. Not surprisingly, our proposed framework was ranked $1^{st}$ in the NVIDIA AI CITY 2021 leaderboard for traffic anomaly detection. The code is available at: https://github.com/Endeavour10020/AICity2021-Anomaly-Detection .

cs.CV↗