arXiv subjects

Zhuo Chen

Publications and source records attributed to Zhuo Chen.

At least 217 records · Page 12Linked to original sources

arXiv2024

Detecting entanglement and nonlocality with minimum observable length

Quantum entanglement and nonlocality are foundational to quantum technologies, driving quantum computation, communication, and cryptography innovations. To benchmark the capabilities of these quantum techniques, efficient detection and accurate quantification methods are indispensable. This paper focuses on the concept of "detection length" -- a metric that quantifies the extent of measurement globality required to verify entanglement or nonlocality. We extend the detection length framework to encompass various entanglement categories and nonlocality phenomena, providing a comprehensive analytical model to determine detection lengths for specified forms of entanglement. Furthermore, we exploit semidefinite programming techniques to construct entanglement witnesses and Bell's inequalities tailored to specific minimal detection lengths, offering an upper bound for detection lengths in given states. By assessing the noise robustness of these witnesses, we demonstrate that witnesses with shorter detection lengths can exhibit superior performance under certain conditions.

quant-ph↗

arXiv2024

EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction

Reconstructing real-world 3D objects has numerous applications in computer vision, such as virtual reality, video games, and animations. Ideally, 3D reconstruction methods should generate high-fidelity results with 3D consistency in real-time. Traditional methods match pixels between images using photo-consistency constraints or learned features, while differentiable rendering methods like Neural Radiance Fields (NeRF) use differentiable volume rendering or surface-based representation to generate high-fidelity scenes. However, these methods require excessive runtime for rendering, making them impractical for daily applications. To address these challenges, we present $\textbf{EvaSurf}$, an $\textbf{E}$fficient $\textbf{V}$iew-$\textbf{A}$ware implicit textured $\textbf{Surf}$ace reconstruction method. In our method, we first employ an efficient surface-based model with a multi-view supervision module to ensure accurate mesh reconstruction. To enable high-fidelity rendering, we learn an implicit texture embedded with view-aware encoding to capture view-dependent information. Furthermore, with the explicit geometry and the implicit texture, we can employ a lightweight neural shader to reduce the expense of computation and further support real-time rendering on common mobile devices. Extensive experiments demonstrate that our method can reconstruct high-quality appearance and accurate mesh on both synthetic and real-world datasets. Moreover, our method can be trained in just 1-2 hours using a single GPU and run on mobile devices at over 40 FPS (Frames Per Second), with a final package required for rendering taking up only 40-50 MB.

cs.CV↗

arXiv2024

Multi-times Monte Carlo Rendering for Inter-reflection Reconstruction

Inverse rendering methods have achieved remarkable performance in reconstructing high-fidelity 3D objects with disentangled geometries, materials, and environmental light. However, they still face huge challenges in reflective surface reconstruction. Although recent methods model the light trace to learn specularity, the ignorance of indirect illumination makes it hard to handle inter-reflections among multiple smooth objects. In this work, we propose Ref-MC2 that introduces the multi-time Monte Carlo sampling which comprehensively computes the environmental illumination and meanwhile considers the reflective light from object surfaces. To address the computation challenge as the times of Monte Carlo sampling grow, we propose a specularity-adaptive sampling strategy, significantly reducing the computational complexity. Besides the computational resource, higher geometry accuracy is also required because geometric errors accumulate multiple times. Therefore, we further introduce a reflection-aware surface model to initialize the geometry and refine it during inverse rendering. We construct a challenging dataset containing scenes with multiple objects and inter-reflections. Experiments show that our method outperforms other inverse rendering methods on various object groups. We also show downstream applications, e.g., relighting and material editing, to illustrate the disentanglement ability of our method.

cs.CV↗

arXiv2024

Dg Loday-Pirashvili modules over Lie algebras

A Loday-Pirashvili module over a Lie algebra $\mathfrak{g}$ is a Lie algebra object $\bigl(G\xrightarrow{X} \mathfrak{g} \bigr)$ in the category of linear maps, or equivalently, a $\mathfrak{g}$-module $G$ which admits a $\mathfrak{g}$-equivariant linear map $X:G\to \mathfrak{g}$. We study dg Loday-Pirashvili modules over Lie algebras, which is a generalization of Loday-Pirashvili modules in a natural way, and establish several equivalent characterizations of dg Loday-Pirashvili modules. To provide a concise characterization, a dg Loday-Pirashvili module is a non-negative and bounded dg $\mathfrak{g}$-module $V$ paired with a weak morphism of dg $\mathfrak{g}$-modules $α\colon V\rightsquigarrow \mathfrak{g}$. Such a dg Loday-Pirashvili module resolves an arbitrarily specified classical Loday-Pirashvili module in the sense that it exists and is unique (up to homotopy). Dg Loday-Pirashvili modules can be characterized through dg derivations. This perspective allows the calculation of the corresponding twisted Atiyah classes. By leveraging the Kapranov functor on the dg derivation arising from a dg Loday-Pirashvili module $(V,α)$, a Leibniz$_\infty[1]$ algebra structure can be derived on $\wedge^\bullet \mathfrak{g}^\vee\otimes V[1]$. The binary bracket of this structure corresponds to the twisted Atiyah cocycle. To exemplify these intricate algebraic structures through specific cases, we utilize this machinery to a particular type of dg Loday-Pirashvili modules stemming from Lie algebra pairs.

math.RA↗

arXiv2024

Dissecting Payload-based Transaction Phishing on Ethereum

In recent years, a more advanced form of phishing has arisen on Ethereum, surpassing early-stage, simple transaction phishing. This new form, which we refer to as payload-based transaction phishing (PTXPHISH), manipulates smart contract interactions through the execution of malicious payloads to deceive users. PTXPHISH has rapidly emerged as a significant threat, leading to incidents that caused losses exceeding \$70 million in 2023 reports. Despite its substantial impact, no previous studies have systematically explored PTXPHISH In this paper, we present the first comprehensive study of the PTXPHISH on Ethereum. Firstly, we conduct a long-term data collection and put considerable effort into establishing the first ground-truth PTXPHISH dataset, consisting of 5,000 phishing transactions. Based on the dataset, we dissect PTXPHISH, categorizing phishing tactics into four primary categories and eleven sub-categories. Secondly, we propose a rule-based multi-dimensional detection approach to identify PTXPHISH, achieving over 99% accuracy in the ground-truth dataset. Finally, we conducted a large-scale detection spanning 300 days and discovered a total of 130,637 phishing transactions on Ethereum, resulting in losses exceeding $341.9 million. Our in-depth analysis of these phishing transactions yielded valuable and insightful findings. Furthermore, our work has made significant contributions to mitigating real-world threats. We have reported 1,726 phishing addresses to the community, accounting for 42.7% of total community contributions during the same period. Additionally, we have sent 2,539 on-chain alert messages, assisting 1,980 victims. This research serves as a valuable reference in combating the emerging PTXPHISH and safeguarding users' assets.

cs.CR↗

arXiv2024

DeUEDroid: Detecting Underground Economy Apps Based on UTG Similarity

In recent years, the underground economy is proliferating in the mobile system. These underground economy apps (UEware) make profits from providing non-compliant services, especially in sensitive areas such as gambling, pornography, and loans. Unlike traditional malware, most of them (over 80%) do not have malicious payloads. Due to their unique characteristics, existing detection approaches cannot effectively and efficiently mitigate this emerging threat. To address this problem, we propose a novel approach to effectively and efficiently detect UEware by considering their UI transition graphs (UTGs). Based on the proposed approach, we design and implement a system named DeUEDroid to perform the detection. To evaluate DeUEDroid, we collect 25,717 apps and build the first large-scale ground-truth dataset (1,700 apps) of UEware. The evaluation result based on the ground-truth dataset shows that DeUEDroid can cover new UI features and statically construct precise UTG. It achieves 98.22% detection F1-score and 98.97% classification accuracy, significantly outperforming traditional approaches. The evaluation involving 24,017 apps demonstrates the effectiveness and efficiency of UEware detection in real-world scenarios. Furthermore, the result reveals that UEware are prevalent, with 54% of apps in the wild and 11% of apps in app stores being UEware. Our work sheds light on future work in analyzing and detecting UEware.

cs.CR↗

arXiv2024

QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation

We propose Quantum-informed Tensor Adaptation (QuanTA), a novel, easy-to-implement, fine-tuning method with no inference overhead for large-scale pre-trained language models. By leveraging quantum-inspired methods derived from quantum circuit structures, QuanTA enables efficient high-rank fine-tuning, surpassing the limitations of Low-Rank Adaptation (LoRA)--low-rank approximation may fail for complicated downstream tasks. Our approach is theoretically supported by the universality theorem and the rank representation theorem to achieve efficient high-rank adaptations. Experiments demonstrate that QuanTA significantly enhances commonsense reasoning, arithmetic reasoning, and scalability compared to traditional methods. Furthermore, QuanTA shows superior performance with fewer trainable parameters compared to other approaches and can be designed to integrate with existing fine-tuning algorithms for further improvement, providing a scalable and efficient solution for fine-tuning large language models and advancing state-of-the-art in natural language processing.

cs.LG↗

arXiv2024

CMATH: Cross-Modality Augmented Transformer with Hierarchical Variational Distillation for Multimodal Emotion Recognition in Conversation

Multimodal emotion recognition in conversation (MER) aims to accurately identify emotions in conversational utterances by integrating multimodal information. Previous methods usually treat multimodal information as equal quality and employ symmetric architectures to conduct multimodal fusion. However, in reality, the quality of different modalities usually varies considerably, and utilizing a symmetric architecture is difficult to accurately recognize conversational emotions when dealing with uneven modal information. Furthermore, fusing multi-modality information in a single granularity may fail to adequately integrate modal information, exacerbating the inaccuracy in emotion recognition. In this paper, we propose a novel Cross-Modality Augmented Transformer with Hierarchical Variational Distillation, called CMATH, which consists of two major components, i.e., Multimodal Interaction Fusion and Hierarchical Variational Distillation. The former is comprised of two submodules, including Modality Reconstruction and Cross-Modality Augmented Transformer (CMA-Transformer), where Modality Reconstruction focuses on obtaining high-quality compressed representation of each modality, and CMA-Transformer adopts an asymmetric fusion strategy which treats one modality as the central modality and takes others as auxiliary modalities. The latter first designs a variational fusion network to fuse the fine-grained representations learned by CMA- Transformer into a coarse-grained representations. Then, it introduces a hierarchical distillation framework to maintain the consistency between modality representations with different granularities. Experiments on the IEMOCAP and MELD datasets demonstrate that our proposed model outperforms previous state-of-the-art baselines. Implementation codes can be available at https://github.com/ cjw-MER/CMATH.

cs.MM↗

arXiv2024

Scaling Mesh Generation via Compressive Tokenization

We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75\% compared to the original sequences. This compression milestone unlocks the potential to utilize mesh data with significantly more faces, thereby enhancing detail richness and improving generation robustness. Empowered with the BPT, we have built a foundation mesh generative model training on scaled mesh data to support flexible control for point clouds and images. Our model demonstrates the capability to generate meshes with intricate details and accurate topology, achieving SoTA performance on mesh generation and reaching the level for direct product usage.

cs.GR↗

arXiv2024

Polarization study of the P-wave charmonium radiative decay into a light vector meson at $e^{+}e^{-}$ collider experiment

In this work, a formalism is presented for the helicity amplitude analysis of the decays $ψ(2S) \to γ_1 χ_{cJ},~ χ_{cJ} \to γ_2 V (V=ρ^0,~ϕ,~ω)$~(the subscript 1,2 is used to distinguish the two radiative photons), and the polarization expressions of the P-wave charmonia $χ_{cJ}$ and the vector mesons $ρ^0, ϕ, ω$ for experimental measurements at Electron-Positron Collider. In addition, we derive the formulae of the angular distributions of the $χ_{c1,2} \to γV$ to extract the degree of transverse polarization $P_T$ of $e^+ e^-$ pairs with symmetric beam energy as well as the ratios of two helicity amplitudes $x$ (in $χ_{c1}$ decays) and $x,~y$ (in $χ_{c2}$ decays) representing the relative magnitudes of transverse to longitudinal polarization amplitude, and validate it by performing the Monte Carlo simulation. Finally, the statistical sensitivity of $P_T$, $x$ and $y$ are estimated based on the large $ψ(2S)$ data samples collected at the current and proposed future $e^+e^-$ collider experiment.

hep-ph↗

arXiv2024

MKGL: Mastery of a Three-Word Language

Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of triplets and allow minimal hallucinations, remains an underexplored frontier. In this paper, we investigate the integration of LLMs with KGs by introducing a specialized KG Language (KGL), where a sentence precisely consists of an entity noun, a relation verb, and ends with another entity noun. Despite KGL's unfamiliar vocabulary to the LLM, we facilitate its learning through a tailored dictionary and illustrative sentences, and enhance context understanding via real-time KG context retrieval and KGL token embedding augmentation. Our results reveal that LLMs can achieve fluency in KGL, drastically reducing errors compared to conventional KG embedding methods on KG completion. Furthermore, our enhanced LLM shows exceptional competence in generating accurate three-word sentences from an initial entity and interpreting new unseen terms out of KGs.

cs.CL↗

arXiv2024

MSW-type compactifications of 6d $(1,0)$ SCFTs on 4-manifolds

In this work, we study compactifications of 6d $(1,0)$ SCFTs, in particular those of conformal matter type, on Kähler 4-manifolds. We show how this can be realized via wrapping M5 branes on 4-cycles of non-compact Calabi-Yau fourfolds with ADE singularity in the fiber. Such compactifications lead to domain walls in 3d $\mathcal{N}=2$ theories which flow to 2d $\mathcal{N}=(0,2)$ SCFTs. We compute the central charges of such 2d CFTs via 6d anomaly polynomials by employing a particular topological twist along the 4-manifold. Moreover, we study compactifications on non-compact 4-manifolds leading to coupled 3d-2d systems. We show how these can be glued together consistently to reproduce the central charge and anomaly polynomial obtained in the compact case. Lastly, we study concrete CFT proposals for some special cases.

hep-th↗

arXiv2024

Toward Scalable Image Feature Compression: A Content-Adaptive and Diffusion-Based Approach

Traditional image codecs emphasize signal fidelity and human perception, often at the expense of machine vision tasks. Deep learning methods have demonstrated promising coding performance by utilizing rich semantic embeddings optimized for both human and machine vision. However, these compact embeddings struggle to capture fine details such as contours and textures, resulting in imperfect reconstructions. Furthermore, existing learning-based codecs lack scalability. To address these limitations, this paper introduces a content-adaptive diffusion model for scalable image compression. The proposed method encodes fine textures through a diffusion process, enhancing perceptual quality while preserving essential features for machine vision tasks. The approach employs a Markov palette diffusion model combined with widely used feature extractors and image generators, enabling efficient data compression. By leveraging collaborative texture-semantic feature extraction and pseudo-label generation, the method accurately captures texture information. A content-adaptive Markov palette diffusion model is then applied to represent both low-level textures and high-level semantic content in a scalable manner. This framework offers flexible control over compression ratios by selecting intermediate diffusion states, eliminating the need for retraining deep learning models at different operating points. Extensive experiments demonstrate the effectiveness of the proposed framework in both image reconstruction and downstream machine vision tasks such as object detection, segmentation, and facial landmark detection, achieving superior perceptual quality compared to state-of-the-art methods.

cs.CV↗

arXiv2024

Revealing Directions for Text-guided 3D Face Editing

3D face editing is a significant task in multimedia, aimed at the manipulation of 3D face models across various control signals. The success of 3D-aware GAN provides expressive 3D models learned from 2D single-view images only, encouraging researchers to discover semantic editing directions in its latent space. However, previous methods face challenges in balancing quality, efficiency, and generalization. To solve the problem, we explore the possibility of introducing the strength of diffusion model into 3D-aware GANs. In this paper, we present Face Clan, a fast and text-general approach for generating and manipulating 3D faces based on arbitrary attribute descriptions. To achieve disentangled editing, we propose to diffuse on the latent space under a pair of opposite prompts to estimate the mask indicating the region of interest on latent codes. Based on the mask, we then apply denoising to the masked latent codes to reveal the editing direction. Our method offers a precisely controllable manipulation method, allowing users to intuitively customize regions of interest with the text description. Experiments demonstrate the effectiveness and generalization of our Face Clan for various pre-trained GANs. It offers an intuitive and wide application for text-guided face editing that contributes to the landscape of multimedia content creation.

cs.CV↗

arXiv2024

Seed-Music: A Unified Framework for High Quality and Controlled Music Generation

We introduce Seed-Music, a suite of music generation systems capable of producing high-quality music with fine-grained style control. Our unified framework leverages both auto-regressive language modeling and diffusion approaches to support two key music creation workflows: controlled music generation and post-production editing. For controlled music generation, our system enables vocal music generation with performance controls from multi-modal inputs, including style descriptions, audio references, musical scores, and voice prompts. For post-production editing, it offers interactive tools for editing lyrics and vocal melodies directly in the generated audio. We encourage readers to listen to demo audio examples at https://team.doubao.com/seed-music "https://team.doubao.com/seed-music".

cs.SD↗

arXiv2024

OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step

Despite significant advancements in text generation and reasoning, Large Language Models (LLMs) still face challenges in accurately performing complex arithmetic operations. Language model systems often enable LLMs to generate code for arithmetic operations to achieve accurate calculations. However, this approach compromises speed and security, and fine-tuning risks the language model losing prior capabilities. We propose a framework that enables exact arithmetic in a single autoregressive step, providing faster, more secure, and more interpretable LLM systems with arithmetic capabilities. We use the hidden states of a LLM to control a symbolic architecture that performs arithmetic. Our implementation using Llama 3 with OccamNet as a symbolic model (OccamLlama) achieves 100\% accuracy on single arithmetic operations ($+,-,\times,÷,\sin{},\cos{},\log{},\exp{},\sqrt{}$), outperforming GPT 4o with and without a code interpreter. Furthermore, OccamLlama outperforms GPT 4o with and without a code interpreter on average across a range of mathematical problem solving benchmarks, demonstrating that OccamLLMs can excel in arithmetic tasks, even surpassing much larger models. We will make our code public shortly.

cs.CL↗

arXiv2024

Optimization model for electric aircraft tow tractors considering operator coalition

Horizontal collaboration between operators can save traffic operation costs, a concept that has been particularly validated in the logistics field. Due to the increasing transportation demand and the introduction of charging times for electric vehicles, there is growing pressure on airport ground support services. This study introduces the concept of coalition to airport ground support services. Unlike most studies on horizontal collaboration, the scheduling for airport ground support services needs to particularly consider punctuality issues. In this study, we separately establish electric vehicle scheduling models for operator-separate and operator-cooperated modes and design an algorithm based on the concept of the Adaptive Large Neighborhood Search algorithm, aiming to obtain the electric vehicle scheduling plan with minimum cost and delay time. Furthermore, the study proposes a cost allocation method that considers the degree of sharing among operators to ensure the feasibility of coalition. Finally, we conducted numerical experiments based on actual airport operation data. The experiments verified the effectiveness of the algorithm and the cost allocation method. Compared to solvers, our algorithm can obtain feasible solutions in a shorter time while ensuring that the objective function gap is within 2%. Additionally, the improved cost allocation method is fairer compared to the traditional Shapley method. Numerical experiments also show that coalition can save 15-25% of airport operating costs and 26-39% of delay time, with savings varying based on the sharing parameters. Through quantitative analysis such as sensitivity analysis, the study provides insights into the variation patterns of overall and individual shared utilities and offers suggestions and decision-making mechanisms for the configuration and operation of airport ground operators.

math.OC↗

arXiv2024

Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks

Optical tactile sensors play a pivotal role in robot perception and manipulation tasks. The membrane of these sensors can be painted with markers or remain markerless, enabling them to function in either marker or markerless mode. However, this uni-modal selection means the sensor is only suitable for either manipulation or perception tasks. While markers are vital for manipulation, they can also obstruct the camera, thereby impeding perception. The dilemma of selecting between marker and markerless modes presents a significant obstacle. To address this issue, we propose a novel mode-switchable optical tactile sensing approach that facilitates transitions between the two modes. The marker-to-markerless transition is achieved through a generative model, whereas its inverse transition is realized using a sparsely supervised regressive model. Our approach allows a single-mode optical sensor to operate effectively in both marker and markerless modes without the need for additional hardware, making it well-suited for both perception and manipulation tasks. Extensive experiments validate the effectiveness of our method. For perception tasks, our approach decreases the number of categories that include misclassified samples by 2 and improves contact area segmentation IoU by 3.53%. For manipulation tasks, our method attains a high success rate of 92.59% in slip detection. Code, dataset and demo videos are available at the project website: https://gitouni.github.io/Marker-Markerless-Transition/

cs.RO↗