arXiv subjects

Hong Zhang

Publications and source records attributed to Hong Zhang.

At least 181 records · Page 10Linked to original sources

arXiv2023

Anisotropic flow and the valence quark skeleton of hadrons

We study transverse momentum anisotropies, in particular, the elliptic flow $v_2$ due to the interference effect sourced by valence quarks in high-energy hadron-hadron collisions. Our main formula is derived as the high-energy (eikonal) limit of the impact-parameter dependent cross section in quantum field theory, which agrees with that in terms of the impact parameter in the classical picture. As a quantitative assessment of the interference effect, we calculate $v_2$ in the azimuthal distribution of gluons at a comprehensive coverage of the impact parameter and the transverse momentum in high-energy pion-pion collisions. In a broad range of the impact parameter, a sizable amount of $v_2$, comparable with that produced due to saturated dense gluons or final-state interactions, is found to develop. In our calculations, the valence sector of the pion wave function is obtained numerically from the Basis Light-Front Quantization, a non-perturbative light-front Hamiltonian approach. And our formalism is generic and can be applied to other small collision systems like proton-proton collisions.

hep-ph↗

arXiv2023

Systematics of planetary ephemeris reference frames inferred from pulsar timing astrometry

This study aims to investigate the systematics in planetary ephemeris reference frames through pulsar timing observations. We used the published data sets from several pulsar timing arrays and performed timing analyses for each pulsar using different planetary ephemerides retrieved from the Jet Propulsion Laboratory's Development Ephemeris (DE), Ephemeris of Planets and the Moon (EPM), and INPOP (Intégration Numérique Planétaire de l'Observatoire de Paris). Then, we compared the timing solutions and modeled the differences in position and proper motion by vector spherical harmonics of the first degree. The timing solutions were also compared with those determined by very long baseline interferometry (VLBI) astrometry. The orientation offsets between the latest editions of the DE, EPM, and INPOP series do not exceed 0.4 milliarcseconds (mas), while the relative spins between these ephemerides are less than 5 microarcseconds per year ($\mathrm{μas\,yr^{-1}}$). We do not detect significant glides in either position or proper motion between these ephemerides. The orientation of the pulsar timing frames deviates from that of the VLBI frame from zero by approximately $\mathrm{0.4\,mas}$ when considering the formal uncertainty and possible systematics. The orientation of current planetary ephemeris frames is as accurate as at least 0.4 mas, and the nonrotating is better than $\mathrm{5\,μas\,yr^{-1}}$.

astro-ph.EP↗

arXiv2023

High-Throughput GPU Implementation of Dilithium Post-Quantum Digital Signature

Digital signatures are fundamental building blocks in various protocols to provide integrity and authenticity. The development of the quantum computing has raised concerns about the security guarantees afforded by classical signature schemes. CRYSTALS-Dilithium is an efficient post-quantum digital signature scheme based on lattice cryptography and has been selected as the primary algorithm for standardization by the National Institute of Standards and Technology. In this work, we present a high-throughput GPU implementation of Dilithium. For individual operations, we employ a range of computational and memory optimizations to overcome sequential constraints, reduce memory usage and IO latency, address bank conflicts, and mitigate pipeline stalls. This results in high and balanced compute throughput and memory throughput for each operation. In terms of concurrent task processing, we leverage task-level batching to fully utilize parallelism and implement a memory pool mechanism for rapid memory access. Considering the impact of varying repetition numbers in Dilithium on the overall execution time and hardware utilization, we propose a dynamic task scheduling mechanism to improve multiprocessor occupancy and significantly reduce execution time. Furthermore, we apply asynchronous computing and launch multiple streams to hide data transfer latencies and maximize the computing capabilities of both CPU and GPU. Across all three security levels, our GPU implementation can concurrently compute ten thousand tasks in less than 32 miliseconds for signing and 15 miliseconds for verification on both commercial and server-grade GPUs. This achieves microsecond-level amortized execution times for each task, offering a high-throughput and quantum-resistant solution suitable for a wide array of applications in real systems.

cs.CR↗

arXiv2023

NPR: Nocturnal Place Recognition in Streets

Visual Place Recognition (VPR) is the task of retrieving database images similar to a query photo by comparing it to a large database of known images. In real-world applications, extreme illumination changes caused by query images taken at night pose a significant obstacle that VPR needs to overcome. However, a training set with day-night correspondence for city-scale, street-level VPR does not exist. To address this challenge, we propose a novel pipeline that divides VPR and conquers Nocturnal Place Recognition (NPR). Specifically, we first established a street-level day-night dataset, NightStreet, and used it to train an unpaired image-to-image translation model. Then we used this model to process existing large-scale VPR datasets to generate the VPR-Night datasets and demonstrated how to combine them with two popular VPR pipelines. Finally, we proposed a divide-and-conquer VPR framework and provided explanations at the theoretical, experimental, and application levels. Under our framework, previous methods can significantly improve performance on two public datasets, including the top-ranked method.

cs.CV↗

arXiv2023

Multirate Partitioned Runge-Kutta Methods for Coupled Navier-Stokes Equations

Earth system models are complex integrated models of atmosphere, ocean, sea ice, and land surface. Coupling the components can be a significant challenge due to the difference in physics, temporal, and spatial scales. This study explores new coupling strategies for the fluid-fluid interaction problem based on multirate partitioned Runge-Kutta methods. We consider compressible Navier-Stokes equations with gravity coupled through a rigid-lid interface. Our large-scale numerical experiments reveal that multirate partitioned Runge-Kutta coupling schemes (1) can conserve total mass; (2) have second-order accuracy in time; and (3) provide favorable strong- and weak-scaling performance on modern computing architectures. We also show that the speedup factors of multirate partitioned Runge-Kutta methods match theoretical expectations over their base (single-rate) method.

math.NA↗

arXiv2023

Meta-lenses for differential imaging based on weak measurement

All-optical information communication, processing and computation have received substantial interest of both fundamental and applied research due to its unrivaled speed and broad bandwidth. Compared to its electronic counterpart, photons seldom interact with each other which makes them obtain a long coherence time on one hand and relieved from heavy energy dissipation on the other. However, one of the hindrances to achieve all-optical circuits is the large volume of all-optical devices to achieve specific functionalities. In this work, we propose and demonstrate experimentally three meta-lenses for differential imaging employing the framework of weak measurement: (1) partial differential lens, (2) total differential lens and (3) second order differential lens compatible with the requirement of miniaturization to achieve all-optical technology. Based on Fresnel-lens-like structures, our meta-lenses incorporated the previous weak-measurement compartment into wavelength scale, which induces a miniature differential operation system as a result. In addition to its potential importance in heavily integrated all-optical neural networks, the differential lens can be easily incorporated in the existing imaging systems like a conventional lens without increasing the complexity of the system of interest.

physics.optics↗

arXiv2023

Subdominant Modes of Scalar Superradiant Instability and Gravitational Wave Beats

Ultralight scalars could extract energy and angular momentum from a Kerr black hole (BH) because of superradiant instability. Multiple modes labelled with $nlm$ grow while rotating around the BH, emitting continuous gravitational wave (GW). In this work, we carefully study the contribution of the subdominant modes with $n\geq 1$ in the evolution of the BH-condensate system. We find the BH still evolves along the Regge trajectory of the $n=0$ modes even with the presence of the subdominant modes. The interference of the dominant and the subdominant modes produces beats in the emitted GW, which could be used to distinguish the BH-condensate systems from other monochromatic GW sources, such as neutron stars.

gr-qc↗

arXiv2023

Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020

People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well. The existence of such connections, which we call connected behavior, gives researchers a unique opportunity to predict how one would behave for a future event given their past behaviors. In this work, we propose a framework to conduct connected behavior analysis. Neural stance detection models are trained on Twitter data collected on three seemingly independent topics, i.e., wearing a mask, racial equality, and Trump, to detect people's stance, which we consider as their online behavior in each topic-related event. Our results reveal a strong connection between the stances toward the three topical events and demonstrate the power of past behaviors in predicting one's future behavior.

cs.SI↗

arXiv2023

Curiosity-based Robot Navigation under Uncertainty in Crowded Environments

Mobile robots have become more and more popular in large-scale and crowded environments, such as airports, shopping malls, etc. However, due to sparse landmarks and crowd noise, localization in this environment is a great challenge. Furthermore, it is unreliable for the robot to navigate safely in crowds while considering human comfort. Thus, how to navigate safely with localization precision in that environment is a critical problem. To solve this problem, we proposed a curiosity-based framework that can find an effective path with the consideration of human comfort and crowds, localization uncertainty, and the cost-to-go to the target. Three parts are involved in the proposed framework: the distance assessment module, the Curiosity for Positive Content (CPC), namely information-rich areas, and the Curiosity for Negative Content (CNC), namely crowded areas. CPC is introduced when the real-time localization uncertainty evaluation is not satisfied. This factor is predicted through the propagation of uncertainty along the candidate trajectory to provoke the robot to approach localization-referenced landmarks. The Human Comfort and Crowd Density Map (HCCDM) based on the Gaussian Mixture Model (GMM) is established to calculate CNC, which drives the robot to bypass the crowd and consider human comfort. The evaluation is conducted in a series of large-scale and crowded environments. The results show that our method can find a feasible path that can consider the localization uncertainty while simultaneously avoiding the crowded area.

cs.RO↗

arXiv2023

DSDP: A Blind Docking Strategy Accelerated by GPUs

Virtual screening, including molecular docking, plays an essential role in drug discovery. Many traditional and machine-learning based methods are available to fulfil the docking task. The traditional docking methods are normally extensively time-consuming, and their performance in blind docking remains to be improved. Although the runtime of docking based on machine learning is significantly decreased, their accuracy is still limited. In this study, we take the advantage of both traditional and machine-learning based methods, and present a method Deep Site and Docking Pose (DSDP) to improve the performance of blind docking. For the traditional blind docking, the entire protein is covered by a cube, and the initial positions of ligands are randomly generated in the cube. In contract, DSDP can predict the binding site of proteins and provide an accurate searching space and initial positions for the further conformational sampling. The docking task of DSDP makes use of the score function and a similar but modified searching strategy of AutoDock Vina, accelerated by implementation in GPUs. We systematically compare its performance with the state-of-the-art methods, including Autodock Vina, GNINA, QuickVina, SMINA, and DiffDock. DSDP reaches a 29.8% top-1 success rate (RMSD < 2 Å) on an unbiased and challenging test dataset with 1.2 s wall-clock computational time per system. Its performances on DUD-E dataset and the time-split PDBBind dataset used in EquiBind, TankBind, and DiffDock are also effective, presenting a 57.2% and 41.8% top-1 success rate with 0.8 s and 1.0 s per system, respectively.

physics.chem-ph↗

arXiv2023

Render unto Numerics: Orthogonal Polynomial Neural Operator for PDEs with Non-periodic Boundary Conditions

By learning the mappings between infinite function spaces using carefully designed neural networks, the operator learning methodology has exhibited significantly more efficiency than traditional methods in solving complex problems such as differential equations, but faces concerns about their accuracy and reliability. To overcomes these limitations, combined with the structures of the spectral numerical method, a general neural architecture named spectral operator learning (SOL) is introduced, and one variant called the orthogonal polynomial neural operator (OPNO), developed for PDEs with Dirichlet, Neumann and Robin boundary conditions (BCs), is proposed later. The strict BC satisfaction properties and the universal approximation capacity of the OPNO are theoretically proven. A variety of numerical experiments with physical backgrounds show that the OPNO outperforms other existing deep learning methodologies, as well as the traditional 2nd-order finite difference method (FDM) with a considerably fine mesh (with the relative errors reaching the order of 1e-6), and is up to almost 5 magnitudes faster than the traditional method.

math.NA↗

arXiv2023

Optimizing SLAM Evaluation Footprint Through Dynamic Range Coverage Analysis of Datasets

Simultaneous Localization and Mapping (SLAM) is considered an ever-evolving problem due to its usage in many applications. Evaluation of SLAM is done typically using publicly available datasets which are increasing in number and the level of difficulty. Each dataset provides a certain level of dynamic range coverage that is a key aspect of measuring the robustness and resilience of SLAM. In this paper, we provide a systematic analysis of the dynamic range coverage of datasets based on a number of characterization metrics, and our analysis shows a huge level of redundancy within and between datasets. Subsequently, we propose a dynamic programming (DP) algorithm for eliminating the redundancy in the evaluation process of SLAM by selecting a subset of sequences that matches a single or multiple dynamic range coverage objectives. It is shown that, with the help of dataset characterization and DP selection algorithm, a reduction in the evaluation effort can be achieved while maintaining the same level of coverage. We also study how the evaluation process of a real-world SLAM system can be optimized utilizing the method proposed.

cs.RO↗

arXiv2023

Prediction of SLAM ATE Using an Ensemble Learning Regression Model and 1-D Global Pooling of Data Characterization

Robustness and resilience of simultaneous localization and mapping (SLAM) are critical requirements for modern autonomous robotic systems. One of the essential steps to achieve robustness and resilience is the ability of SLAM to have an integrity measure for its localization estimates, and thus, have internal fault tolerance mechanisms to deal with performance degradation. In this work, we introduce a novel method for predicting SLAM localization error based on the characterization of raw sensor inputs. The proposed method relies on using a random forest regression model trained on 1-D global pooled features that are generated from characterized raw sensor data. The model is validated by using it to predict the performance of ORB-SLAM3 on three different datasets running on four different operating modes, resulting in an average prediction accuracy of up to 94.7\%. The paper also studies the impact of 12 different 1-D global pooling functions on regression quality, and the superiority of 1-D global averaging is quantitatively proven. Finally, the paper studies the quality of prediction with limited training data, and proves that we are able to maintain proper prediction quality when only 20 \% of the training examples are used for training, which highlights how the proposed model can optimize the evaluation footprint of SLAM systems.

cs.RO↗

arXiv2023

Task-Oriented Grasp Prediction with Visual-Language Inputs

To perform household tasks, assistive robots receive commands in the form of user language instructions for tool manipulation. The initial stage involves selecting the intended tool (i.e., object grounding) and grasping it in a task-oriented manner (i.e., task grounding). Nevertheless, prior researches on visual-language grasping (VLG) focus on object grounding, while disregarding the fine-grained impact of tasks on object grasping. Task-incompatible grasping of a tool will inevitably limit the success of subsequent manipulation steps. Motivated by this problem, this paper proposes GraspCLIP, which addresses the challenge of task grounding in addition to object grounding to enable task-oriented grasp prediction with visual-language inputs. Evaluation on a custom dataset demonstrates that GraspCLIP achieves superior performance over established baselines with object grounding only. The effectiveness of the proposed method is further validated on an assistive robotic arm platform for grasping previously unseen kitchen tools given the task specification. Our presentation video is available at: https://www.youtube.com/watch?v=e1wfYQPeAXU.

cs.RO↗

arXiv2023

Robot Person Following Under Partial Occlusion

Robot person following (RPF) is a capability that supports many useful human-robot-interaction (HRI) applications. However, existing solutions to person following often assume full observation of the tracked person. As a consequence, they cannot track the person reliably under partial occlusion where the assumption of full observation is not satisfied. In this paper, we focus on the problem of robot person following under partial occlusion caused by a limited field of view of a monocular camera. Based on the key insight that it is possible to locate the target person when one or more of his/her joints are visible, we propose a method in which each visible joint contributes a location estimate of the followed person. Experiments on a public person-following dataset show that, even under partial occlusion, the proposed method can still locate the person more reliably than the existing SOTA methods. As well, the application of our method is demonstrated in real experiments on a mobile robot.

cs.RO↗

arXiv2023

Searching for wormholes with gravitational wave scattering

Wormholes bridging distant places of the universe are well-known solutions of general relativity. In particular, traversable wormholes which allow interstellar traveling are also popular in science fiction. However, no hint of their existence has been found yet. In this work, we propose using the gravitational wave (GW) scattering off spherical wormholes to search for their existence. We carefully calculate the reflected and transmitted waveforms with time-independent scattering theory. Our results quantitatively show the echo signatures in the two universes on both sides of the wormhole. In a certain wormhole mass range, the transmitted wave has a unique isolated chirp without an inspiral waveform, and the reflected wave has the anti-chirp behavior, i.e., the missing of the chirping signal. We also calculate the searching range of the current and projected GW telescopes. Our method can be adapted to efficiently calculate the templates to search for wormholes.

gr-qc↗

arXiv2023

Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines

NLP-based computer vision models, particularly vision transformers, have been shown to outperform CNN models in many imaging tasks. However, most digital pathology artificial-intelligence models are based on CNN architectures, probably owing to a lack of data regarding NLP models for pathology images. In this study, we developed digital pathology pipelines to benchmark the five most recently proposed NLP models (vision transformer (ViT), Swin Transformer, MobileViT, CMT, and Sequencer2D) and four popular CNN models (ResNet18, ResNet50, MobileNetV2, and EfficientNet) to predict biomarkers in colorectal cancer (microsatellite instability, CpG island methylator phenotype, and BRAF mutation). Hematoxylin and eosin-stained whole-slide images from Molecular and Cellular Oncology and The Cancer Genome Atlas were used as training and external validation datasets, respectively. Cross-study external validations revealed that the NLP-based models significantly outperformed the CNN-based models in biomarker prediction tasks, improving the overall prediction and precision up to approximately 10% and 26%, respectively. Notably, compared with existing models in the current literature using large training datasets, our NLP models achieved state-of-the-art predictions for all three biomarkers using a relatively small training dataset, suggesting that large training datasets are not a prerequisite for NLP models or transformers, and NLP may be more suitable for clinical studies in which small training datasets are commonly collected. The superior performance of Sequencer2D suggests that further research and innovation on both transformer and bidirectional long short-term memory architectures are warranted in the field of digital pathology. NLP models can replace classic CNN architectures and become the new workhorse backbone in the field of digital pathology.

cs.CL↗

arXiv2023

ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition

For named entity recognition (NER) in zero-resource languages, utilizing knowledge distillation methods to transfer language-independent knowledge from the rich-resource source languages to zero-resource languages is an effective means. Typically, these approaches adopt a teacher-student architecture, where the teacher network is trained in the source language, and the student network seeks to learn knowledge from the teacher network and is expected to perform well in the target language. Despite the impressive performance achieved by these methods, we argue that they have two limitations. Firstly, the teacher network fails to effectively learn language-independent knowledge shared across languages due to the differences in the feature distribution between the source and target languages. Secondly, the student network acquires all of its knowledge from the teacher network and ignores the learning of target language-specific knowledge. Undesirably, these limitations would hinder the model's performance in the target language. This paper proposes an unsupervised prototype knowledge distillation network (ProKD) to address these issues. Specifically, ProKD presents a contrastive learning-based prototype alignment method to achieve class feature alignment by adjusting the distance among prototypes in the source and target languages, boosting the teacher network's capacity to acquire language-independent knowledge. In addition, ProKD introduces a prototypical self-training method to learn the intrinsic structure of the language by retraining the student network on the target data using samples' distance information from prototypes, thereby enhancing the student network's ability to acquire language-specific knowledge. Extensive experiments on three benchmark cross-lingual NER datasets demonstrate the effectiveness of our approach.

cs.CL↗