Searcharxiv⌕ Search

arXiv subjects

Hongyu Wang

Publications and source records attributed to Hongyu Wang.

At least 91 records · Page 5Linked to original sources
arXiv2024

On non-elliptic symplectic manifolds

Let $M$ be a closed symplectic manifold of dimension $2n$ with non-ellipticity. We can define an almost Kähler structure on $M$ by using the given symplectic form. Hence, we have a $\G=π_1(M)$-invariant almost Kähler structure on the universal covering, $\ti M$, of $M$. Using Darboux coordinate charts, we globally deform the given almost Kähler structure on $\ti M$ off a Lebesgue measure zero subset to obtain a $\G$-invariant Lipschitz Kähler flat structure on $\ti M$ which is $\G$-homotopy equivalent to the given almost Kähler structure. Analogous to Teleman's $L^2$-Hodge decomposition on PL manifolds or Lipschitz Riemannian manifolds, we give a $L^2$-Hodge decomposition theorem on $\ti M$ with respect to the Lipschitz Kähler flat metric. Using an argument of Gromov, we give a vanishing theorem for $L^2$ harmonic $p$-forms, $p\not=n$ (resp. a non-vanishing theorem for $L^2$ harmonic $n$-forms) on $\ti M$, then the signed Euler characteristic satisfies $(-1)^nχ(M)\geq0$ (resp. $(-1)^nχ(M)>0$). Similarly, for any closed even dimensional Riemannian manifold $(M, g)$, we can construct a $\G$-invariant Lipschitz Kähler flat structure on the universal covering, $(\ti M, \ti g)$, of $(M, g)$ which is $\G$-homotopy equivalent to and quasi-isometric to the metric $\ti g$. As an application, using Gromov's method we show that the Chern-Hopf conjecture holds true in closed even dimensional Riemannian manifolds with nonpositive curvature (resp. strictly negative curvature), it gives a positive answer to a Yau's problem due to S. S. Chern and H. Hopf.

math.SG↗
arXiv2024

PCG: Mitigating Conflict-based Cache Side-channel Attacks with Prefetching

To defend against conflict-based cache side-channel attacks, cache partitioning or remapping techniques were proposed to prevent set conflicts between different security domains or obfuscate the locations of such conflicts. But such techniques complicate cache design and may result in significant performance penalties. Therefore, there have been lightweight prefetching-based schemes proposed to introduce noise to confuse attackers' observation. However, we have validated experimentally that relying on prefetching to only introduce noise is insufficient, as attackers can still reliably distinguish the victim's cache accesses. This paper proposes a novel prefetching-based scheme, called PCG. It combines adding victim-irrelevant cache occupancy changes and reducing victim-relevant cache occupancy changes to disrupt attackers by generating noisy and indistinguishable cache access patterns. Additionally, PCG can either work independently or seamlessly be integrated with most of the commonly used prefetchers. We have implemented and evaluated PCG in both gem5 and the open-source RISC-V core BOOMv3. The evaluation results show the PCG's robust security superior to the existing solutions, while without resulting in significant performance degradation. According to the evaluation based on the SPEC CPU 2017 benchmark suite, PCG even shows an average performance improvement of about 1.64%. Moreover, it incurs only 1.26% overhead on hardware resource consumption.

cs.CR↗
arXiv2024

Prompt-Guided Generation of Structured Chest X-Ray Report Using a Pre-trained LLM

Medical report generation automates radiology descriptions from images, easing the burden on physicians and minimizing errors. However, current methods lack structured outputs and physician interactivity for clear, clinically relevant reports. Our method introduces a prompt-guided approach to generate structured chest X-ray reports using a pre-trained large language model (LLM). First, we identify anatomical regions in chest X-rays to generate focused sentences that center on key visual elements, thereby establishing a structured report foundation with anatomy-based sentences. We also convert the detected anatomy into textual prompts conveying anatomical comprehension to the LLM. Additionally, the clinical context prompts guide the LLM to emphasize interactivity and clinical requirements. By integrating anatomy-focused sentences and anatomy/clinical prompts, the pre-trained LLM can generate structured chest X-ray reports tailored to prompted anatomical regions and clinical contexts. We evaluate using language generation and clinical effectiveness metrics, demonstrating strong performance.

cs.AI↗
arXiv2024

Anyon exclusions statistics on surfaces with gapped boundaries

An anyon exclusion statistics, which generalizes the Bose-Einstein and Fermi-Dirac statistics of bosons and fermions, was proposed by Haldane[1]. The relevant past studies had considered only anyon systems without any physical boundary but boundaries often appear in real-life materials. When fusion of anyons is involved, certain `pseudo-species' anyons appear in the exotic statistical weights of non-Abelian anyon systems; however, the meaning and significance of pseudo-species remains an open problem. In this paper, we propose an extended anyon exclusion statistics on surfaces with gapped boundaries, introducing mutual exclusion statistics between anyons as well as the boundary components. Motivated by Refs. [2, 3], we present a formula for the statistical weight of many-anyon states obeying the proposed statistics. We develop a systematic basis construction for non-Abelian anyons on any Riemann surfaces with gapped boundaries. From the basis construction, we have a standard way to read off a canonical set of statistics parameters and hence write down the extended statistical weight of the anyon system being studied. The basis construction reveals the meaning of pseudo-species. A pseudo-species has different `excitation' modes, each corresponding to an anyon species. The `excitation' modes of pseudo-species corresponds to good quantum numbers of subsystems of a non-Abelian anyon system. This is important because often (e.g., in topological quantum computing) we may be concerned about only the entanglement between such subsystems.

cond-mat.str-el↗
arXiv2024

DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM

SLAM systems based on NeRF have demonstrated superior performance in rendering quality and scene reconstruction for static environments compared to traditional dense SLAM. However, they encounter tracking drift and mapping errors in real-world scenarios with dynamic interferences. To address these issues, we introduce DDN-SLAM, the first real-time dense dynamic neural implicit SLAM system integrating semantic features. To address dynamic tracking interferences, we propose a feature point segmentation method that combines semantic features with a mixed Gaussian distribution model. To avoid incorrect background removal, we propose a mapping strategy based on sparse point cloud sampling and background restoration. We propose a dynamic semantic loss to eliminate dynamic occlusions. Experimental results demonstrate that DDN-SLAM is capable of robustly tracking and producing high-quality reconstructions in dynamic environments, while appropriately preserving potential dynamic objects. Compared to existing neural implicit SLAM systems, the tracking results on dynamic datasets indicate an average 90% improvement in Average Trajectory Error (ATE) accuracy.

cs.CV↗
arXiv2024

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.

cs.CL↗
arXiv2024

PLE-SLAM: A Visual-Inertial SLAM Based on Point-Line Features and Efficient IMU Initialization

Visual-inertial SLAM is crucial in various fields, such as aerial vehicles, industrial robots, and autonomous driving. The fusion of camera and inertial measurement unit (IMU) makes up for the shortcomings of a signal sensor, which significantly improves the accuracy and robustness of localization in challenging environments. This article presents PLE-SLAM, an accurate and real-time visual-inertial SLAM algorithm based on point-line features and efficient IMU initialization. First, we use parallel computing methods to extract features and compute descriptors to ensure real-time performance. Adjacent short line segments are merged into long line segments, and isolated short line segments are directly deleted. Second, a rotation-translation-decoupled initialization method is extended to use both points and lines. Gyroscope bias is optimized by tightly coupling IMU measurements and image observations. Accelerometer bias and gravity direction are solved by an analytical method for efficiency. To improve the system's intelligence in handling complex environments, a scheme of leveraging semantic information and geometric constraints to eliminate dynamic features and A solution for loop detection and closed-loop frame pose estimation using CNN and GNN are integrated into the system. All networks are accelerated to ensure real-time performance. The experiment results on public datasets illustrate that PLE-SLAM is one of the state-of-the-art visual-inertial SLAM systems.

cs.RO↗
arXiv2024

Temporal Adaptive RGBT Tracking with Modality Prompt

RGBT tracking has been widely used in various fields such as robotics, surveillance processing, and autonomous driving. Existing RGBT trackers fully explore the spatial information between the template and the search region and locate the target based on the appearance matching results. However, these RGBT trackers have very limited exploitation of temporal information, either ignoring temporal information or exploiting it through online sampling and training. The former struggles to cope with the object state changes, while the latter neglects the correlation between spatial and temporal information. To alleviate these limitations, we propose a novel Temporal Adaptive RGBT Tracking framework, named as TATrack. TATrack has a spatio-temporal two-stream structure and captures temporal information by an online updated template, where the two-stream structure refers to the multi-modal feature extraction and cross-modal interaction for the initial template and the online update template respectively. TATrack contributes to comprehensively exploit spatio-temporal information and multi-modal information for target localization. In addition, we design a spatio-temporal interaction (STI) mechanism that bridges two branches and enables cross-modal interaction to span longer time scales. Extensive experiments on three popular RGBT tracking benchmarks show that our method achieves state-of-the-art performance, while running at real-time speed.

cs.CV↗
arXiv2023

End-User Puppeteering of Expressive Movements

The end-user programming of social robot behavior is usually limited by a predefined set of movements. We are proposing a puppeteering robotic interface that provides a more intuitive method of programming robot expressive movements. As the user manipulates the puppet of a robot, the actual robot replicates the movements, providing real-time visual feedback. Through this proposed interface, even with limited training, a novice user can design and program expressive movements efficiently. We present our preliminary user study results in this extended abstract.

cs.RO↗
arXiv2023

The Calabi-Yau Equation on Symplectic Manifolds

By using the global deformation of almost complex structures which are compatible with a symplectic form off a Lebesgue measure zero subset, we construct a (measurable) Lipschitz Kahler metric such that the one-form type Calabi-Yau equation on an open dense submanifold is reduced to the complex Monge-Ampere equation with respect to the measurable Kahler metric. We give an existence theorem for solutions to the one-form type Calabi-Yau equation on closed symplectic manifolds.

math.DG↗
arXiv2023

Classification of Smoking and Calling using Deep Learning

Since 2014, very deep convolutional neural networks have been proposed and become the must-have weapon for champions in all kinds of competition. In this report, a pipeline is introduced to perform the classification of smoking and calling by modifying the pretrained inception V3. Brightness enhancing based on deep learning is implemented to improve the classification of this classification task along with other useful training tricks. Based on the quality and quantity results, it can be concluded that this pipeline with small biased samples is practical and useful with high accuracy.

cs.CV↗
arXiv2023

Feature Extractor Stacking for Cross-domain Few-shot Learning

Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor. This enables efficient inference but necessitates re-computation of the extractor whenever a new source domain is added. Some of these methods are also incompatible with heterogeneous source domain extractor architectures. We propose feature extractor stacking (FES), a new CDFSL method for combining information from a collection of extractors, that can utilise heterogeneous pretrained extractors out of the box and does not maintain a universal model that needs to be re-computed when its extractor collection is updated. We present the basic FES algorithm, which is inspired by the classic stacked generalisation approach, and also introduce two variants: convolutional FES (ConFES) and regularised FES (ReFES). Given a target-domain task, these algorithms fine-tune each extractor independently, use cross-validation to extract training data for stacked generalisation from the support set, and learn a simple linear stacking classifier from this data. We evaluate our FES methods on the well-known Meta-Dataset benchmark, targeting image classification with convolutional neural networks, and show that they can achieve state-of-the-art performance.

cs.CV↗
arXiv2023

Who's Watching Me?: Exploring the Impact of Audience Familiarity on Player Performance, Experience, and Exertion in Virtual Reality Exergames

Familiarity with audiences plays a significant role in shaping individual performance and experience across various activities in everyday life. This study delves into the impact of familiarity with non-playable character (NPC) audiences on player performance and experience in virtual reality (VR) exergames. By manipulating of NPC appearance (face and body shape) and voice familiarity, we explored their effect on game performance, experience, and exertion. The findings reveal that familiar NPC audiences have a positive impact on performance, creating a more enjoyable gaming experience, and leading players to perceive less exertion. Moreover, individuals with higher levels of self-consciousness exhibit heightened sensitivity to the familiarity with NPC audiences. Our results shed light on the role of familiar NPC audiences in enhancing player experiences and provide insights for designing more engaging and personalized VR exergame environments.

cs.HC↗
arXiv2023

BitNet: Scaling 1-bit Transformers for Large Language Models

The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. In this work, we introduce BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. Specifically, we introduce BitLinear as a drop-in replacement of the nn.Linear layer in order to train 1-bit weights from scratch. Experimental results on language modeling show that BitNet achieves competitive performance while substantially reducing memory footprint and energy consumption, compared to state-of-the-art 8-bit quantization methods and FP16 Transformer baselines. Furthermore, BitNet exhibits a scaling law akin to full-precision Transformers, suggesting its potential for effective scaling to even larger language models while maintaining efficiency and performance benefits.

cs.CL↗
arXiv2023

Electrical Characteristics of the GEC Reference Cell with Impedance Matching: A Two-Dimensional PIC/MCC Modeling Study

In this paper, the electrical characteristics of the Gaseous Electronics Conference (GEC) reference cell with impedance matching are investigated through a two-dimensional electrostatic implicit Particle-in-Cell/Monte Carlo Collision (PIC/MCC) model in an axisymmetric coordinate system. The coupling between the complex reactor geometry and the external circuit is included via an equivalent capacitance calculated from the electric energy density. The results of this model are compared with experimental measurements and other model calculations and show good agreement. This simulation obtains the plasma kinetics of the capacitively coupled discharge process at low pressure and detailed external circuit responses, including power transmission, reflection, and higher-order harmonics in the circuit, which provides important insights for impedance-matching design in semiconductor plasma processing.

physics.plasm-ph↗
arXiv2023

PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine

As an effective tool for eliciting the power of Large Language Models (LLMs), prompting has recently demonstrated unprecedented abilities across a variety of complex tasks. To further improve the performance, prompt ensemble has attracted substantial interest for tackling the hallucination and instability of LLMs. However, existing methods usually adopt a two-stage paradigm, which requires a pre-prepared set of prompts with substantial manual effort, and is unable to perform directed optimization for different weak learners. In this paper, we propose a simple, universal, and automatic method named PREFER (Pompt Ensemble learning via Feedback-Reflect-Refine) to address the stated limitations. Specifically, given the fact that weak learners are supposed to focus on hard examples during boosting, PREFER builds a feedback mechanism for reflecting on the inadequacies of existing weak learners. Based on this, the LLM is required to automatically synthesize new prompts for iterative refinement. Moreover, to enhance stability of the prompt effect evaluation, we propose a novel prompt bagging method involving forward and backward thinking, which is superior to majority voting and is beneficial for both feedback and weight calculation in boosting. Extensive experiments demonstrate that our PREFER achieves state-of-the-art performance in multiple types of tasks by a significant margin. We have made our code publicly available.

cs.CL↗
arXiv2023

Symmetry Fractionalized (Irrationalized) Fusion Rules and Two Domain-Wall Verlinde Formulae

We investigate the composite systems consisting of topological orders separated by gapped domain walls. We derive a pair of domain-wall Verlinde formulae, that elucidate the connection between the braiding of interdomain excitations labeled by pairs of anyons in different domains and quasiparticles in the gapped domain wall with their respective fusion rules. Through explicit non-Abelian examples, we showcase the calculation of such braiding and fusion, revealing that the fusion rules for interdomain excitations are generally fractional or irrational. By investigating the correspondence between composite systems and anyon condensation, we unveil the reason for designating these fusion rules as symmetry fractionalized (irrationalized) fusion rules. Our findings hold promise for applications across various fields, such as topological quantum computation, topological field theory, and conformal field theory.

cond-mat.str-el↗