arXiv subjects

Wenhao Liu

Publications and source records attributed to Wenhao Liu.

At least 55 records · Page 3Linked to original sources

arXiv2025

FreeDOM: Online Dynamic Object Removal Framework for Static Map Construction Based on Conservative Free Space Estimation

Online map construction is essential for autonomous robots to navigate in unknown environments. However, the presence of dynamic objects may introduce artifacts into the map, which can significantly degrade the performance of localization and path planning. To tackle this problem, a novel online dynamic object removal framework for static map construction based on conservative free space estimation (FreeDOM) is proposed, consisting of a scan-removal front-end and a map-refinement back-end. First, we propose a multi-resolution map structure for fast computation and effective map representation. In the scan-removal front-end, we employ raycast enhancement to improve free space estimation and segment the LiDAR scan based on the estimated free space. In the map-refinement back-end, we further eliminate residual dynamic objects in the map by leveraging incremental free space information. As experimentally verified on SemanticKITTI, HeLiMOS, and indoor datasets with various sensors, our proposed framework overcomes the limitations of visibility-based methods and outperforms state-of-the-art methods with an average F1-score improvement of 9.7%.

cs.RO↗

arXiv2025

A bio-inspired sand-rolling robot: effect of body shape on sand rolling performance

The capability of effectively moving on complex terrains such as sand and gravel can empower our robots to robustly operate in outdoor environments, and assist with critical tasks such as environment monitoring, search-and-rescue, and supply delivery. Inspired by the Mount Lyell salamander's ability to curl its body into a loop and effectively roll down {\Revision hill slopes}, in this study we develop a sand-rolling robot and investigate how its locomotion performance is governed by the shape of its body. We experimentally tested three different body shapes: Hexagon, Quadrilateral, and Triangle. We found that Hexagon and Triangle can achieve a faster rolling speed on sand, but exhibited more frequent failures of getting stuck. Analysis of the interaction between robot and sand revealed the failure mechanism: the deformation of the sand produced a local ``sand incline'' underneath robot contact segments, increasing the effective region of supporting polygon (ERSP) and preventing the robot from shifting its center of mass (CoM) outside the ERSP to produce sustainable rolling. Based on this mechanism, a highly-simplified model successfully captured the critical body pitch for each rolling shape to produce sustained rolling on sand, and informed design adaptations that mitigated the locomotion failures and improved robot speed by more than 200$\%$. Our results provide insights into how locomotors can utilize different morphological features to achieve robust rolling motion across deformable substrates.

cs.RO↗

arXiv2025

Emerging orthorhombic two-dimensional van der Waals magnets

Two-dimensional (2D) magnetism realized in van der Waals (vdW) materials has expanded to include a great variety of magnetic phases, over a short decade since its first discovery in 2016-2017. However, most of the investigated vdW magnets so far have highly symmetric crystal fields and isotropic in-plane lattice structures, making their 2D magnetism robust but often classical and expected. In this Perspective, we highlight a family of vdW magnets that have distorted crystal fields with low symmetries and orthorhombic crystal lattices with strong anisotropy, and furthermore, show their prospects for realizing a much richer landscape of magnetic phases and phase transitions. First, we introduce the classification of 2D vdW magnets based on their crystal fields and make a contrast between the orthorhombic vdW magnets of focus here and other popular honeycomb/hexagonal/kagome vdW magnets. We then discuss the material candidates, the emergent properties, and the designed controls for these orthorhombic 2D vdW magnets, using the representative CrSBr as an example and extending beyond it. Finally, we discuss a few existing challenges in orthorhombic 2D vdW magnets, overcome which would pave the way towards magneto-optical, opto-magnetic, and spintronic devices based on orthorhombic 2D vdW magnetism.

cond-mat.mtrl-sci↗

arXiv2025

Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective

The recent surge in jailbreaking attacks has revealed significant vulnerabilities in Large Language Models (LLMs) when exposed to malicious inputs. While various defense strategies have been proposed to mitigate these threats, there has been limited research into the underlying mechanisms that make LLMs vulnerable to such attacks. In this study, we suggest that the self-safeguarding capability of LLMs is linked to specific activity patterns within their representation space. Although these patterns have little impact on the semantic content of the generated text, they play a crucial role in shaping LLM behavior under jailbreaking attacks. Our findings demonstrate that these patterns can be detected with just a few pairs of contrastive queries. Extensive experimentation shows that the robustness of LLMs against jailbreaking can be manipulated by weakening or strengthening these patterns. Further visual analysis provides additional evidence for our conclusions, providing new insights into the jailbreaking phenomenon. These findings highlight the importance of addressing the potential misuse of open-source LLMs within the community.

cs.CL↗

arXiv2025

Tailoring light holes in $β$-$Ga_{2}O_{3}$ via Anion-Anion Antibonding Coupling

A significant limitation of wide-bandgap materials is their low hole mobility related to localized holes with heavy effective masses ($m_h^*$). We identify in low-symmetric wide-bandgap compounds an anion-anion antibonding coupling (AAAC) effect as the intrinsic factor behind hole localization, which explains the extremely heavy $m_h^*$ and self-trapped hole (STH) formation observed in gallium oxide ($β$-$Ga_{2}O_{3}$). We propose a design principle for achieving light holes by manipulating AAAC, demonstrating that specific strain conditions can reduce $m_h^*$ in $β$-$Ga_{2}O_{3}$ from 4.77 $m_0$ to 0.38 $m_0$, making it comparable to the electron mass (0.28 $m_0$), while also slightly suppresses the formation of self-trapped holes, evidenced by the reduction in the formation energy of hole polarons from -0.57 eV to -0.45 eV under tensile strain. The light holes show significant anisotropy, potentially enabling two-dimensional transport in bulk material. This study provides a fundamental understanding of hole mass enhancement and STH formation in novel wide-bandgap materials and suggest new pathways for engineering hole mobilities.

cond-mat.mtrl-sci↗

arXiv2025

Anomalous Ultrafast Thermalization of Photoexcited Carriers in Two-Dimensional Materials Induced by Orbital Coupling

Understanding the dynamics of photoexcited carriers is essential for advancing photoelectronic device design. Photon absorption generates electron-hole pairs, and subsequent scatterings can induce ultrafast thermalization within a picosecond, forming a quasi-equilibrium distribution with overheated electrons. The high-energy tail of this distribution enables carriers to overcome energy barriers, thereby enhancing quantum efficiency--a phenomenon known as photo-thermionic emission (PTE). Despite its importance, the onset and mechanisms of PTE remain under debate. Using real-time time-dependent density functional theory (rt-TDDFT), we investigate ultrafast carrier thermalization in two-dimensional materials graphene and PtTe2, and the results reveal distinct differences. In graphene, both electrons and holes thermalize into Fermi-Dirac distributions with good agreement to experiment, while PtTe2 exhibits anomalous high-energy tails for both electrons and holes, deviating significantly from Fermi-Dirac behavior. We attribute this anomaly to differences in orbital coupling between the two materials, from which we derive design principles for identifying optimal PTE candidates and, ultimately, improving photodetector performance.

cond-mat.mtrl-sci↗

arXiv2024

Colossal terahertz emission with ultrafast tunability based on van der Waals ferroelectric NbOI$_2$

Terahertz (THz) technology is critical for quantum material physics, biomedical imaging, ultrafast electronics, and next-generation wireless communications. However, standing in the way of widespread applications is the scarcity of efficient ultrafast THz sources with on-demand fast modulation and easy on-chip integration capability. Here we report the discovery of colossal THz emission from a van der Waals (vdW) ferroelectric semiconductor NbOI$_2$. Using THz emission spectroscopy, we observe a THz generation efficiency an order of magnitude higher than that of ZnTe, a standard nonlinear crystal for ultrafast THz generation. We further uncover the underlying generation mechanisms associated with its large ferroelectric polarization by studying the THz emission dependence on excitation wavelength, incident polarization and fluence. Moreover, we demonstrate the ultrafast coherent amplification and annihilation of the THz emission and associated coherent phonon oscillations by employing a double-pump scheme. These findings combined with first-principles calculations, inform new understanding of the THz light-matter interaction in emergent vdW ferroelectrics and pave the way to develop high-performance THz devices on them for quantum materials sensing and ultrafast electronics.

cond-mat.mtrl-sci↗

arXiv2024

Efficient Satellite-Ground Interconnection Design for Low-orbit Mega-Constellation Topology

The low-orbit mega-constellation network (LMCN) is an important part of the space-air-ground integrated network system. An effective satellite-ground interconnection design can result in a stable constellation topology for LMCNs. A naive solution is accessing the satellite with the longest remaining service time (LRST), which is widely used in previous designs. The Coordinated Satellite-Ground Interconnecting (CSGI), the state-of-the-art algorithm, coordinates the establishment of ground-satellite links (GSLs). Compared with existing solutions, it reduces latency by 19% and jitter by 70% on average. However, CSGI only supports the scenario where terminals access only one satellite and cannot fully utilize the multi-access capabilities of terminals. Additionally, CSGI's high computational complexity poses deployment challenges. To overcome these problems, we propose the Classification-based Longest Remaining Service Time (C-LRST) algorithm. C-LRST supports the actual scenario with multi-access capabilities. It adds optional paths during routing with low computational complexity, improving end-to-end communications quality. We conduct our 1000s simulation from Brazil to Lithuania on the open-source platform Hypatia. Experiment results show that compared with CSGI, C-LRST reduces the latency and increases the throughput by approximately 60% and 40%, respectively. In addition, C-LRST's GSL switching number is 14, whereas CSGI is 23. C-LRST has better link stability than CSGI.

cs.NI↗

arXiv2024

Aligning Large Language Models with Human Preferences through Representation Engineering

Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness. Existing methods for achieving this alignment often involves employing reinforcement learning from human feedback (RLHF) to fine-tune LLMs based on human labels assessing the relative quality of model responses. Nevertheless, RLHF is susceptible to instability during fine-tuning and presents challenges in implementation.Drawing inspiration from the emerging field of representation engineering (RepE), this study aims to identify relevant representations for high-level human preferences embedded in patterns of activity within an LLM, and achieve precise control of model behavior by transforming its representations. This novel approach, denoted as Representation Alignment from Human Feedback (RAHF), proves to be effective, computationally efficient, and easy to implement.Extensive experiments demonstrate the efficacy of RAHF in not only capturing but also manipulating representations to align with a broad spectrum of human preferences or values, rather than being confined to a singular concept or function (e.g. honesty or bias). RAHF's versatility in accommodating diverse human preferences shows its potential for advancing LLM performance.

cs.CL↗

arXiv2024

UWBAD: Towards Effective and Imperceptible Jamming Attacks Against UWB Ranging Systems with COTS Chips

UWB ranging systems have been adopted in many critical and security sensitive applications due to its precise positioning and secure ranging capabilities. We present a practical jamming attack, namely UWBAD, against commercial UWB ranging systems, which exploits the vulnerability of the adoption of the normalized cross-correlation process in UWB ranging and can selectively and quickly block ranging sessions without prior knowledge of the configurations of the victim devices, potentially leading to severe consequences such as property loss, unauthorized access, or vehicle theft. UWBAD achieves more effective and less imperceptible jamming due to: (i) it efficiently blocks every ranging session by leveraging the field-level jamming, thereby exerting a tangible impact on commercial UWB ranging systems, and (ii) the compact, reactive, and selective system design based on COTS UWB chips, making it affordable and less imperceptible. We successfully conducted real attacks against commercial UWB ranging systems from the three largest UWB chip vendors on the market, e.g., Apple, NXP, and Qorvo. We reported our findings to Apple, related Original Equipment Manufacturers (OEM), and the Automotive Security Research Group, triggering internal security incident response procedures at Volkswagen, Audi, Bosch, and NXP. As of the writing of this paper, the related OEM has acknowledged this vulnerability in their automotive systems and has offered a $5,000 reward as a bounty.

cs.CR↗

arXiv2024

Towards Biologically Plausible Computing: A Comprehensive Comparison

Backpropagation is a cornerstone algorithm in training neural networks for supervised learning, which uses a gradient descent method to update network weights by minimizing the discrepancy between actual and desired outputs. Despite its pivotal role in propelling deep learning advancements, the biological plausibility of backpropagation is questioned due to its requirements for weight symmetry, global error computation, and dual-phase training. To address this long-standing challenge, many studies have endeavored to devise biologically plausible training algorithms. However, a fully biologically plausible algorithm for training multilayer neural networks remains elusive, and interpretations of biological plausibility vary among researchers. In this study, we establish criteria for biological plausibility that a desirable learning algorithm should meet. Using these criteria, we evaluate a range of existing algorithms considered to be biologically plausible, including Hebbian learning, spike-timing-dependent plasticity, feedback alignment, target propagation, predictive coding, forward-forward algorithm, perturbation learning, local losses, and energy-based learning. Additionally, we empirically evaluate these algorithms across diverse network architectures and datasets. We compare the feature representations learned by these algorithms with brain activity recorded by non-invasive devices under identical stimuli, aiming to identify which algorithm can most accurately replicate brain activity patterns. We are hopeful that this study could inspire the development of new biologically plausible algorithms for training multilayer networks, thereby fostering progress in both the fields of neuroscience and machine learning.

cs.NE↗

arXiv2024

Promoting Data and Model Privacy in Federated Learning through Quantized LoRA

Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for parameter updates during the learning process. However, the development of large language models (LLMs) requires substantial data and computational resources, rendering them valuable intellectual properties for their developers and owners. To establish a mechanism that protects both data and model privacy in a federated learning context, we introduce a method that just needs to distribute a quantized version of the model's parameters during training. This method enables accurate gradient estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. Moreover, we combine this quantization strategy with LoRA, a popular and parameter-efficient fine-tuning method, to significantly reduce communication costs in federated learning. The proposed framework, named \textsc{FedLPP}, successfully ensures both data and model privacy in the federated learning context. Additionally, the learned central model exhibits good generalization and can be trained in a resource-efficient manner.

cs.LG↗

arXiv2024

Hurry: Dynamic Collaborative Framework For Low-orbit Mega-Constellation Data Downloading

Low-orbit mega-constellation network, which utilize thousands of satellites to provide a variety of network services and collect a wide range of space information, is a rapidly growing field. Each satellite collects TB-level data daily, including delay-sensitive data used for crucial tasks, such as military surveillance, natural disaster monitoring, and weather forecasting. According to NASA's statement, these data need to be downloaded to the ground for processing within 3 to 5 hours. To reduce the time required for satellite data downloads, the state-of-the-art solution known as CoDld, which is only available for small constellations, uses an iterative method for cooperative downloads via inter-satellite links. However, in LMCN, the time required to download the same amount of data using CoDld will exponentially increase compared to downloading the same amount of data in a small constellation. We have identified and analyzed the reasons for this degradation phenomenon and propose a new satellite data download framework, named Hurry. By modeling and mapping satellite topology changes and data transmission to Time-Expanded Graphs, we implement our algorithm within the Hurry framework to avoid degradation effects. In the fixed data volume download evaluation, Hurry achieves 100% completion of the download task while the CoDld only reached 44% of download progress. In continuous data generation evaluation, the Hurry flow algorithm improves throughput from 11% to 66% compared to the CoDld in different scenarios.

cs.NI↗

arXiv2024

Advancing Parameter Efficiency in Fine-tuning via Representation Editing

Parameter Efficient Fine-Tuning (PEFT) techniques have drawn significant attention due to their ability to yield competitive results while updating only a small portion of the adjustable parameters. However, existing PEFT methods pose challenges in hyperparameter selection, such as choosing the rank for LoRA or Adapter, or specifying the length of soft prompts. To address these challenges, we propose a novel fine-tuning approach for neural models, named Representation EDiting (RED), which modifies the representations generated at some layers through the application of scaling and biasing operations. While existing PEFT methods still demonstrate over-parameterization that could potentially undermine the generalization ability acquired from pre-training, RED can substantially reduce the number of trainable parameters by a factor of 25, 700 compared to full parameter fine-tuning and by a factor of 32 relative to LoRA. Remarkably, RED achieves results comparable or superior to both full parameter fine-tuning and other PEFT methods. Extensive experiments across various model architectures and scales, including RoBERTa, GPT-2, T5, and LLaMA-2, have demonstrated the effectiveness and efficiency of RED1, thereby positioning it as a promising PEFT strategy for large-scale neural models.

cs.LG↗

arXiv2024

X-ray Cool Core Remnants Heated by Strong Radio AGN Feedback

Strong AGN heating provides an alternative means for the disruption of cluster cool cores (CCs) to cluster mergers. In this work we present a systematic Chandra study of a sample of 108 nearby ($z<0.1$) galaxy clusters, to investigate the effect of AGN heating on CCs. About 40% of clusters with small offsets between the BCG and the X-ray centre ($\le50$ kpc) have small CCs. For comparison, 14 of 17 clusters with large offsets have small CCs, which suggests that mergers or sloshing can be efficient in reducing the CC size. Relaxed, small CC clusters generally have weak radio AGNs ($P_{1.4\rm GHz}<10^{23}$ W Hz$^{-1}$), and they show a lack of systems hosting a radio AGN with intermediate radio power ($2\times10^{23}<P_{1.4\rm GHz}<2\times10^{24}$ W Hz$^{-1}$). We found that the strongest circumnuclear ($<1$ kpc) X-ray emission only exists in clusters with strong radio AGN. The duty cycle of relaxed, small CC clusters is less than half of that for large CC clusters. It suggests that the radio activity of BCGs is affected by the properties of the surrounding gas beyond the central $\sim10$ kpc, and strong radio AGNs in small X-ray CCs fade more rapidly than those embedded in large X-ray CCs. A scenario is also presented for the transition of large CCs and coronae due to radio AGN feedback. We also present a detailed analysis of galaxy cluster 3C 129.1 as an example of a CC remnant possibly disrupted by radio AGN.

astro-ph.GA↗

arXiv2024

Reed-Solomon Codes over Cyclic Polynomial Ring with Lower Encoding/Decoding Complexity

Reed-Solomon (RS) codes are constructed over a finite field that have been widely employed in storage and communication systems. Many fast encoding/decoding algorithms such as fast Fourier transform (FFT) and modular approach are designed for RS codes to reduce the encoding/decoding complexity defined as the number of XORs involved in the encoding/decoding procedure. In this paper, we present the construction of RS codes over the cyclic polynomial ring $ \mathbb{F}_2[x]/(1+x+\ldots+x^{p-1})$ and show that our codes are maximum distance separable (MDS) codes. Moreover, we propose the FFT and modular approach over the ring that can be employed in our codes for encoding/decoding complexity reduction. We show that our codes have 17.9\% encoding complexity reduction and 7.5\% decoding complexity reduction compared with RS codes over finite field, for $(n,k)=(2048,1984)$.

cs.IT↗

arXiv2024

Spin-mechanical coupling in 2D antiferromagnet CrSBr

Spin-mechanical coupling is vital in diverse fields including spintronics, sensing and quantum transduction. Two-dimensional (2D) magnetic materials provide a unique platform for investigating spin-mechanical coupling, attributed to their mechanical flexibility and novel spin orderings. However, studying spin-mechanical coupling in 2D magnets presents challenges in probing mechanical deformation and thermodynamic properties change at nanoscale. Here we use nano opto-electro-mechanical interferometry to mechanically detect the phase transition and magnetostriction effect in multilayer CrSBr, an air-stable antiferromagnets with large magnon-exciton coupling. The transitions among antiferromagnetism, spin-canted ferromagnetism and paramagnetism are visualized by optomechanical frequency anomalies. Nontrivial magnetostriction coefficient 2.3x10^(-5) and magnetoelastic coupling strength on the order of 10^6 J/m^3 have been found. Moreover, we demonstrate the substantial tunability of the magnetoelastic constant by nearly 50% via gate-induced strain. Our findings demonstrate the strong spin-mechanical coupling in CrSBr and paves the way for developing sensitive magnetic sensing and efficient quantum transduction at atomically thin limit.

cond-mat.mes-hall↗

arXiv2024

Plotinus: A Satellite Internet Digital Twin System

The development of an integrated space-air-ground network (SAGIN) requires sophisticated satellite Internet emulation tools that can handle complex, dynamic topologies and offer in-depth analysis. Existing emulation platforms struggle with challenges like the need for detailed implementation across all network layers, real-time response, and scalability. This paper proposes a digital twin system based on microservices for satellite Internet emulation, namely Plotinus, which aims to solve these problems. Plotinus features a modular design, allowing for easy replacement of the physical layer to emulate different aerial vehicles and analyze channel interference. It also enables replacing path computation methods to simplify testing and deploying algorithms. In particular, Plotinus allows for real-time emulation with live network traffic, enhancing practical network models. The evaluation result shows Plotinus's effective emulation of dynamic satellite networks with real-world devices. Its adaptability for various communication models and algorithm testing highlights Plotinus's role as a vital tool for developing and analyzing SAGIN systems, offering a cross-layer, real-time and scalable digital twin system.

cs.NI↗