Showing 1–45 of 45 results for author: Dou, J

V-FIND: Revealing the Intrinsic Forgery Knowledge Encoded in Video Forgery Detectors

Authors: Shichao Kan, Chengpeng Hong, Jingtong Dou, Chuancheng Shi, Yuhan Liu, Linrui Xu, Yixiong Liang, Yigang Cen, Yanpeng Sun, Fei Shen, Tat-Seng Chua

Abstract: As generated videos become increasingly realistic, reliable video forgery detection is increasingly important. Existing studies typically optimize and use video forgery detectors as black boxes, while the latent forgery-discriminative knowledge inside them remains largely unexplored. Instead of continuing to rely on resource-intensive full-model retraining to steadily improve detection performance… ▽ More As generated videos become increasingly realistic, reliable video forgery detection is increasingly important. Existing studies typically optimize and use video forgery detectors as black boxes, while the latent forgery-discriminative knowledge inside them remains largely unexplored. Instead of continuing to rely on resource-intensive full-model retraining to steadily improve detection performance, we ask whether video forgery detection can also be achieved by uncovering and activating sparse forensic knowledge within the detector. We find that forgery-discriminative knowledge is not uniformly distributed across the full representation space, but is concentrated in a sparse set of functionally specialized neurons. Based on this insight, we propose a video forgery-intrinsic neuron discovery (V-FIND) framework. V-FIND first localizes critical layers that exhibit pronounced discrepancies between real and forged videos, and then identifies latent anchor neurons that consistently carry forgery-discriminative signals, organizing them into a compact forensic subspace. With the original backbone frozen and only a lightweight linear classifier trained, this subspace still delivers strong detection performance across multiple external benchmarks for generated videos. Further neuron intervention experiments provide direct evidence for the functional specificity of the discovered neurons. Overall, these results suggest that video forgery detectors contain sparse, extractable, and reusable forgery-discriminative knowledge, offering a new perspective on understanding and exploiting their intrinsic forensic capability. △ Less

Submitted 3 August, 2026; originally announced August 2026.

Comments: 12 pages, 12 figures. Under review

Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling

Authors: Zhilin He, Bowei Li, Jianlin Dou, Yuner Zhang, Changliu Liu

Abstract: Single-Robot Motion Planning (SRMP) in highly non-convex constrained environments, where robots must satisfy collision-free guarantees, dynamic feasibility, and task-related constraints, is challenging under complex constraints and computational limits. Recent Model-Based Diffusion (MBD) approaches recast the SRMP as trajectory optimization that samples from a posterior over trajectories, using kn… ▽ More Single-Robot Motion Planning (SRMP) in highly non-convex constrained environments, where robots must satisfy collision-free guarantees, dynamic feasibility, and task-related constraints, is challenging under complex constraints and computational limits. Recent Model-Based Diffusion (MBD) approaches recast the SRMP as trajectory optimization that samples from a posterior over trajectories, using known dynamics, and analytically estimates the score function from rollout samples to guide diffusion denoising toward a low-cost, clean trajectory without demonstration learning. While existing works further adapt MBD to constrained environments and showcase promising performance, they are still limited by (1) enforcing safety either via soft feasibility diffusion priors or hard projection operators, but lack a unified framework to integrate both, and (2) fixing safety enforcement to neglect the changing of diffusion scheduling. Therefore, we introduce Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling (MD-COAS) for SRMP that unifies the inexact Augmented Lagrangian Method (iALM) soft diffusion prior with a Convex Feasible Set (CFS)-based hard projection operator, and adaptively schedules and co-optimizes safety enforcement, along with diffusion scheduling. Experiments demonstrate that our method achieves higher safety \& success rates, faster convergence, and lower final costs than baseline planners on randomly generated highly non-convex 2D benchmarks and a 7-DoF robot arm avoidance task. △ Less

Submitted 15 July, 2026; originally announced July 2026.

PaMoSplat: Part-Aware Motion-Guided Gaussian Splatting for Dynamic Scene Reconstruction

Authors: Yinan Deng, Jianyu Dou, Jiahui Wang, Jingyu Zhao, Yi Yang, Yufeng Yue

Abstract: Dynamic scene reconstruction represents a fundamental yet demanding challenge in computer vision and robotics. While recent progress in 3DGS-based methods has advanced dynamic scene modeling, obtaining high-fidelity rendering and accurate tracking in scenarios with substantial, intricate motions remains significantly challenging. To address these challenges, we propose PaMoSplat, a novel dynamic G… ▽ More Dynamic scene reconstruction represents a fundamental yet demanding challenge in computer vision and robotics. While recent progress in 3DGS-based methods has advanced dynamic scene modeling, obtaining high-fidelity rendering and accurate tracking in scenarios with substantial, intricate motions remains significantly challenging. To address these challenges, we propose PaMoSplat, a novel dynamic Gaussian splatting framework incorporating part awareness and motion priors. Our approach is grounded in two key observations: 1) Parts serve as primitives for scene deformation, and 2) Motion cues from optical flow can effectively guide part motion. Specifically, PaMoSplat initializes by lifting multi-view segmentation masks into 3D space via graph clustering, establishing coherent Gaussian parts. For subsequent timestamps, we leverage a differential evolutionary algorithm to estimate the rigid motion of these parts using multi-view optical flow cues, providing a robust warm-start for further optimization. Additionally, PaMoSplat introduces an adaptive iteration count mechanism, internal learnable rigidity, and flow-supervised rendering loss to accelerate and optimize the training process. Comprehensive evaluations across diverse scenes, including real-world environments, demonstrate that PaMoSplat delivers superior rendering quality, improved tracking precision, and faster convergence compared to existing methods. Furthermore, it enables multiple part-level downstream applications, such as 4D scene editing. △ Less

Submitted 11 May, 2026; originally announced May 2026.

Comments: Accepted by TCSVT. Project Url: https://pamosplat.github.io

Beyond Surface Artifacts: Capturing Shared Latent Forgery Knowledge Across Modalities

Authors: Jingtong Dou, Chuancheng Shi, Jian Wang, Fei Shen, Zhiyong Wang, Tat-Seng Chua

Abstract: As generative artificial intelligence evolves, deepfake attacks have escalated from single-modality manipulations to complex, multimodal threats. Existing forensic techniques face a severe generalization bottleneck: by relying excessively on superficial, modality-specific artifacts, they neglect the shared latent forgery knowledge hidden beneath variable physical appearances. Consequently, these m… ▽ More As generative artificial intelligence evolves, deepfake attacks have escalated from single-modality manipulations to complex, multimodal threats. Existing forensic techniques face a severe generalization bottleneck: by relying excessively on superficial, modality-specific artifacts, they neglect the shared latent forgery knowledge hidden beneath variable physical appearances. Consequently, these models suffer catastrophic performance degradation when confronted with unseen "dark modalities." To break this limitation, this paper introduces a paradigm shift that redefines multimodal forensics from conventional "feature fusion" to "modality generalization." We propose the first modality-agnostic forgery (MAF) detection framework. By explicitly decoupling modality-specific styles, MAF precisely extracts the essential, cross-modal latent forgery knowledge. Furthermore, we define two progressive dimensions to quantify model generalization: transferability toward semantically correlated modalities (Weak MAF), and robustness against completely isolated signals of "dark modality" (Strong MAF). To rigorously assess these generalization limits, we introduce the DeepModal-Bench benchmark, which integrates diverse multimodal forgery detection algorithms and adapts state-of-the-art generalized learning methods. This study not only empirically proves the existence of universal forgery traces but also achieves significant performance breakthroughs on unknown modalities via the MAF framework, offering a pioneering technical pathway for universal multimodal defense. △ Less

Submitted 8 April, 2026; originally announced April 2026.

UI-Venus-1.5 Technical Report

Authors: Venus Team, Changlong Gao, Zhangxuan Gu, Yulin Liu, Xinyu Qiu, Shuheng Shen, Yue Wen, Tianyu Xia, Zhenyu Xu, Zhengwen Zeng, Beitong Zhou, Xingran Zhou, Weizhi Chen, Sunhao Dai, Jingya Dou, Yichen Gong, Yuan Guo, Zhenlin Guo, Feng Li, Qian Li, Jinzhen Lin, Yuqi Zhou, Linchao Zhu, Liang Chen, Zhenyu Guo , et al. (2 additional authors not shown)

Abstract: GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance remains challenging. In this report, we present UI-Venus-1.5, a unified, end-to-end GUI Agent designed for robust real-world applications. The proposed model family comprises two dense variants (2B and 8B) and one mixture-o… ▽ More GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance remains challenging. In this report, we present UI-Venus-1.5, a unified, end-to-end GUI Agent designed for robust real-world applications. The proposed model family comprises two dense variants (2B and 8B) and one mixture-of-experts variant (30B-A3B) to meet various downstream application scenarios. Compared to our previous version, UI-Venus-1.5 introduces three key technical advances: (1) a comprehensive Mid-Training stage leveraging 10 billion tokens across 30+ datasets to establish foundational GUI semantics; (2) Online Reinforcement Learning with full-trajectory rollouts, aligning training objectives with long-horizon, dynamic navigation in large-scale environments; and (3) a single unified GUI Agent constructed via Model Merging, which synthesizes domain-specific models (grounding, web, and mobile) into one cohesive checkpoint. Extensive evaluations demonstrate that UI-Venus-1.5 establishes new state-of-the-art performance on benchmarks such as ScreenSpot-Pro (69.6%), VenusBench-GD (75.0%), and AndroidWorld (77.6%), significantly outperforming previous strong baselines. In addition, UI-Venus-1.5 demonstrates robust navigation capabilities across a variety of Chinese mobile apps, effectively executing user instructions in real-world scenarios. Code: https://github.com/inclusionAI/UI-Venus; Model: https://huggingface.co/collections/inclusionAI/ui-venus △ Less

Submitted 24 February, 2026; v1 submitted 9 February, 2026; originally announced February 2026.

NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces

Authors: Rong Fu, Chunlei Meng, Haoyu Zhao, Kun Liu, JiaBao Dou, Youjin Wang, Simon James Fong

Abstract: The pursuit of optimal trade-offs in high-dimensional search spaces under stringent computational constraints poses a fundamental challenge for contemporary multi-objective optimization. We develop NeuroPareto, a cohesive architecture that integrates rank-centric filtering, uncertainty disentanglement, and history-conditioned acquisition strategies to navigate complex objective landscapes. A calib… ▽ More The pursuit of optimal trade-offs in high-dimensional search spaces under stringent computational constraints poses a fundamental challenge for contemporary multi-objective optimization. We develop NeuroPareto, a cohesive architecture that integrates rank-centric filtering, uncertainty disentanglement, and history-conditioned acquisition strategies to navigate complex objective landscapes. A calibrated Bayesian classifier estimates epistemic uncertainty across non-domination tiers, enabling rapid generation of high-quality candidates with minimal evaluation cost. Deep Gaussian Process surrogates further separate predictive uncertainty into reducible and irreducible components, providing refined predictive means and risk-aware signals for downstream selection. A lightweight acquisition network, trained online from historical hypervolume improvements, guides expensive evaluations toward regions balancing convergence and diversity. With hierarchical screening and amortized surrogate updates, the method maintains accuracy while keeping computational overhead low. Experiments on DTLZ and ZDT suites and a subsurface energy extraction task show that NeuroPareto consistently outperforms classifier-enhanced and surrogate-assisted baselines in Pareto proximity and hypervolume. △ Less

Submitted 3 August, 2026; v1 submitted 3 February, 2026; originally announced February 2026.

Comments: 39 pages, 19 figures

DNA: Uncovering Universal Latent Forgery Knowledge

Authors: Jingtong Dou, Chuancheng Shi, Yemin Wang, Shiming Guo, Anqi Yi, Wenhua Wu, Li Zhang, Fei Shen, Tat-Seng Chua

Abstract: As generative AI achieves hyper-realism, superficial artifact detection has become obsolete. While prevailing methods rely on resource-intensive fine-tuning of black-box backbones, we propose that forgery detection capability is already encoded within pre-trained models rather than requiring end-to-end retraining. To elicit this intrinsic capability, we propose the discriminative neural anchors (D… ▽ More As generative AI achieves hyper-realism, superficial artifact detection has become obsolete. While prevailing methods rely on resource-intensive fine-tuning of black-box backbones, we propose that forgery detection capability is already encoded within pre-trained models rather than requiring end-to-end retraining. To elicit this intrinsic capability, we propose the discriminative neural anchors (DNA) framework, which employs a coarse-to-fine excavation mechanism. First, by analyzing feature decoupling and attention distribution shifts, we pinpoint critical intermediate layers where the focus of the model logically transitions from global semantics to local anomalies. Subsequently, we introduce a triadic fusion scoring metric paired with a curvature-truncation strategy to strip away semantic redundancy, precisely isolating the forgery-discriminative units (FDUs) inherently imprinted with sensitivity to forgery traces. Moreover, we introduce HIFI-Gen, a high-fidelity synthetic benchmark built upon the very latest models, to address the lag in existing datasets. Experiments demonstrate that by solely relying on these anchors, DNA achieves superior detection performance even under few-shot conditions. Furthermore, it exhibits remarkable robustness across diverse architectures and against unseen generative models, validating that waking up latent neurons is more effective than extensive fine-tuning. △ Less

Submitted 29 January, 2026; originally announced January 2026.

How Order-Sensitive Are LLMs? OrderProbe for Deterministic Structural Reconstruction

Authors: Yingjie He, Zhaolu Kang, Kehan Jiang, Qianyuan Zhang, Jiachen Qian, Chunlei Meng, Yujie Feng, Yuan Wang, Jiabao Dou, Aming Wu, Leqi Zheng, Pengxiang Zhao, Jiaxin Liu, Zeyu Zhang, Lei Wang, Guansu Wang, Qishi Zhan, Xiaomin He, Meisheng Zhang, Jianyuan Ni

Abstract: Large language models (LLMs) excel at semantic understanding, yet their ability to reconstruct internal structure from scrambled inputs remains underexplored. Sentence-level restoration is ill-posed for automated evaluation because multiple valid word orders often exist. We introduce OrderProbe, a deterministic benchmark for structural reconstruction using fixed four-character expressions in Chine… ▽ More Large language models (LLMs) excel at semantic understanding, yet their ability to reconstruct internal structure from scrambled inputs remains underexplored. Sentence-level restoration is ill-posed for automated evaluation because multiple valid word orders often exist. We introduce OrderProbe, a deterministic benchmark for structural reconstruction using fixed four-character expressions in Chinese, Japanese, and Korean, which have a unique canonical order and thus support exact-match scoring. We further propose a diagnostic framework that evaluates models beyond recovery accuracy, including semantic fidelity, logical validity, consistency, robustness sensitivity, and information density. Experiments on twelve widely used LLMs show that structural reconstruction remains difficult even for frontier systems: zero-shot recovery frequently falls below 35%. We also observe a consistent dissociation between semantic recall and structural planning, suggesting that structural robustness is not an automatic byproduct of semantic competence. △ Less

Submitted 17 January, 2026; v1 submitted 13 January, 2026; originally announced January 2026.

HarmoniAD: Harmonizing Local Structures and Global Semantics for Anomaly Detection

Authors: Naiqi Zhang, Chuancheng Shi, Jingtong Dou, Wenhua Wu, Fei Shen, Jianhua Cao

Abstract: Anomaly detection is crucial in industrial product quality inspection. Failing to detect tiny defects often leads to serious consequences. Existing methods face a structure-semantics trade-off: structure-oriented models (such as frequency-based filters) are noise-sensitive, while semantics-oriented models (such as CLIP-based encoders) often miss fine details. To address this, we propose HarmoniAD,… ▽ More Anomaly detection is crucial in industrial product quality inspection. Failing to detect tiny defects often leads to serious consequences. Existing methods face a structure-semantics trade-off: structure-oriented models (such as frequency-based filters) are noise-sensitive, while semantics-oriented models (such as CLIP-based encoders) often miss fine details. To address this, we propose HarmoniAD, a frequency-guided dual-branch framework. Features are first extracted by the CLIP image encoder, then transformed into the frequency domain, and finally decoupled into high- and low-frequency paths for complementary modeling of structure and semantics. The high-frequency branch is equipped with a fine-grained structural attention module (FSAM) to enhance textures and edges for detecting small anomalies, while the low-frequency branch uses a global structural context module (GSCM) to capture long-range dependencies and preserve semantic consistency. Together, these branches balance fine detail and global semantics. HarmoniAD further adopts a multi-class joint training strategy, and experiments on MVTec-AD, VisA, and BTAD show state-of-the-art performance with both sensitivity and robustness. △ Less

Submitted 1 January, 2026; originally announced January 2026.

From Points to Coalitions: Hierarchical Contrastive Shapley Values for Prioritizing Data Samples

Authors: Canran Xiao, Jiabao Dou, Zhiming Lin, Zong Ke, Liwei Hou

Abstract: How should we quantify the value of each training example when datasets are large, heterogeneous, and geometrically structured? Classical Data-Shapley answers in principle, but its O(n!) complexity and point-wise perspective are ill-suited to modern scales. We propose Hierarchical Contrastive Data Valuation (HCDV), a three-stage framework that (i) learns a contrastive, geometry-preserving represen… ▽ More How should we quantify the value of each training example when datasets are large, heterogeneous, and geometrically structured? Classical Data-Shapley answers in principle, but its O(n!) complexity and point-wise perspective are ill-suited to modern scales. We propose Hierarchical Contrastive Data Valuation (HCDV), a three-stage framework that (i) learns a contrastive, geometry-preserving representation, (ii) organizes the data into a balanced coarse-to-fine hierarchy of clusters, and (iii) assigns Shapley-style payoffs to coalitions via local Monte-Carlo games whose budgets are propagated downward. HCDV collapses the factorial burden to O(T sum_{l} K_{l}) = O(T K_max log n), rewards examples that sharpen decision boundaries, and regularizes outliers through curvature-based smoothness. We prove that HCDV approximately satisfies the four Shapley axioms with surplus loss O(eta log n), enjoys sub-Gaussian coalition deviation tilde O(1/sqrt{T}), and incurs at most k epsilon_infty regret for top-k selection. Experiments on four benchmarks--tabular, vision, streaming, and a 45M-sample CTR task--plus the OpenDataVal suite show that HCDV lifts accuracy by up to +5 pp, slashes valuation time by up to 100x, and directly supports tasks such as augmentation filtering, low-latency streaming updates, and fair marketplace payouts. △ Less

Submitted 22 December, 2025; originally announced December 2025.

Comments: AAAI'26 Oral

Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation

Authors: Chuancheng Shi, Shangze Li, Shiming Guo, Simiao Xie, Wenhua Wu, Jingtong Dou, Chao Wu, Canran Xiao, Cong Wang, Zifeng Cheng, Fei Shen, Tat-Seng Chua

Abstract: Multilingual text-to-image (T2I) models have advanced rapidly in terms of visual realism and semantic alignment, and are now widely utilized. Yet outputs vary across cultural contexts: because language carries cultural connotations, images synthesized from multilingual prompts should preserve cross-lingual cultural consistency. We conduct a comprehensive analysis showing that current T2I models of… ▽ More Multilingual text-to-image (T2I) models have advanced rapidly in terms of visual realism and semantic alignment, and are now widely utilized. Yet outputs vary across cultural contexts: because language carries cultural connotations, images synthesized from multilingual prompts should preserve cross-lingual cultural consistency. We conduct a comprehensive analysis showing that current T2I models often produce culturally neutral or English-biased results under multilingual prompts. Analyses of two representative models indicate that the issue stems not from missing cultural knowledge but from insufficient activation of culture-related representations. We propose a probing method that localizes culture-sensitive signals to a small set of neurons in a few fixed layers. Guided by this finding, we introduce two complementary alignment strategies: (1) inference-time cultural activation that amplifies the identified neurons without backbone fine-tuned; and (2) layer-targeted cultural enhancement that updates only culturally relevant layers. Experiments on our CultureBench demonstrate consistent improvements over strong baselines in cultural consistency while preserving fidelity and diversity. △ Less

Submitted 21 November, 2025; originally announced November 2025.

SemiETPicker: Fast and Label-Efficient Particle Picking for CryoET Tomography Using Semi-Supervised Learning

Authors: Linhan Wang, Jianwen Dou, Wang Li, Shengkun Wang, Zhiwu Xie, Chang-Tien Lu, Yinlin Chen

Abstract: Cryogenic Electron Tomography (CryoET) combined with sub-volume averaging (SVA) is the only imaging modality capable of resolving protein structures inside cells at molecular resolution. Particle picking, the task of localizing and classifying target proteins in 3D CryoET volumes, remains the main bottleneck. Due to the reliance on time-consuming manual labels, the vast reserve of unlabeled tomogr… ▽ More Cryogenic Electron Tomography (CryoET) combined with sub-volume averaging (SVA) is the only imaging modality capable of resolving protein structures inside cells at molecular resolution. Particle picking, the task of localizing and classifying target proteins in 3D CryoET volumes, remains the main bottleneck. Due to the reliance on time-consuming manual labels, the vast reserve of unlabeled tomograms remains underutilized. In this work, we present a fast, label-efficient semi-supervised framework that exploits this untapped data. Our framework consists of two components: (i) an end-to-end heatmap-supervised detection model inspired by keypoint detection, and (ii) a teacher-student co-training mechanism that enhances performance under sparse labeling conditions. Furthermore, we introduce multi-view pseudo-labeling and a CryoET-specific DropBlock augmentation strategy to further boost performance. Extensive evaluations on the large-scale CZII dataset show that our approach improves F1 by 10% over supervised baselines, underscoring the promise of semi-supervised learning for leveraging unlabeled CryoET data. △ Less

Submitted 13 January, 2026; v1 submitted 25 October, 2025; originally announced October 2025.

Comments: IEEE International Symposium on Biomedical Imaging (ISBI) 2026

GLUE: Global-Local Unified Encoding for Imitation Learning via Key-Patch Tracking

Authors: Ye Chen, Zichen Zhou, Jianyu Dou, Te Cui, Yi Yang, Yufeng Yue

Abstract: In recent years, visual representation learning has gained widespread attention in robotic imitation learning. However, in complex Out-of-Distribution(OOD) settings characterized by clutter and occlusion, the attention of global visual representations can be diluted or interfered, leading to degraded policy performance. The invariance of local representations for task-relevant objects offers a sol… ▽ More In recent years, visual representation learning has gained widespread attention in robotic imitation learning. However, in complex Out-of-Distribution(OOD) settings characterized by clutter and occlusion, the attention of global visual representations can be diluted or interfered, leading to degraded policy performance. The invariance of local representations for task-relevant objects offers a solution. By efficiently utilizing these local representations, training and testing data can be mapped to a more similar feature space, thereby mitigating the covariate shift problem. Accordingly, we propose GLUE, a global-local unified encoding framework for imitation learning based on key-patch tracking. GLUE selects and tracks key-patches as critical local representations by employing a text-guided mechanism. It features a novel fusion framework where global patch features query local patches to distill essential information, yielding fine-grained local features with low heterogeneity relative to the global context. This fused representation steers the robot's visual attention toward task-relevant objects and preserves precise global context, which together align the training and testing distributions into a similar and task-informative feature space, ultimately enhancing the robustness of the imitation learning policy. Experiments demonstrate that GLUE achieves strong performance across diverse tasks in both simulation and real-world settings, outperforming the strongest baseline by 17.6% in simulation, 36.3% in real-world environments, and 58.3% on real-world generalization settings. The project website of GLUE is available at https://GLUE666.github.io/. △ Less

Submitted 27 September, 2025; originally announced September 2025.

Comments: 8 pages, 5 figures

A Lightweight Approach for State Machine Replication

Authors: Christian Cachin, Jinfeng Dou, Christian Scheideler, Philipp Schneider

Abstract: We present a lightweight solution for state machine replication with commitment certificates. Specifically, we adapt and analyze a median rule for the stabilizing consensus problem [Doerr11] to operate in a client-server setting where arbitrary servers may be blocked adaptively based on past system information. We further extend our protocol by compressing information about committed commands, thu… ▽ More We present a lightweight solution for state machine replication with commitment certificates. Specifically, we adapt and analyze a median rule for the stabilizing consensus problem [Doerr11] to operate in a client-server setting where arbitrary servers may be blocked adaptively based on past system information. We further extend our protocol by compressing information about committed commands, thus keeping the protocol lightweight, while still enabling clients to easily prove that their commands have indeed been committed on the shared state. Our approach guarantees liveness as long as at most a constant fraction of servers are blocked, ensures safety under any number of blocked servers, and supports fast recovery even after all servers are blocked. In addition to offering near-optimal asymptotic performance in several respects, our method is fully decentralized, unlike other near-optimal solutions that rely on leaders. In particular, our solution is robust against adversaries that target key servers (which captures insider-based denial-of-service attacks), whereas leader-based approaches fail under such a blocking model. △ Less

Submitted 17 November, 2025; v1 submitted 22 September, 2025; originally announced September 2025.

Interpretable Nanoporous Materials Design with Symmetry-Aware Networks

Authors: Zhenhao Zhou, Salman Bin Kashif, Jin-Hu Dou, Chris Wolverton, Kaihang Shi, Tao Deng, Zhenpeng Yao

Abstract: Reticular frameworks hold promise for diverse sustainable applications, yet their immense chemical space limits efficient and systematic design. While machine learning provides a compelling pathway to accelerate exploration, existing approaches often lack either interpretability or fidelity in linking crystal geometry to emergent properties. Here, we introduce a site-resolved equivariant learning… ▽ More Reticular frameworks hold promise for diverse sustainable applications, yet their immense chemical space limits efficient and systematic design. While machine learning provides a compelling pathway to accelerate exploration, existing approaches often lack either interpretability or fidelity in linking crystal geometry to emergent properties. Here, we introduce a site-resolved equivariant learning framework based on three-dimensional periodic space sampling, which decomposes reticular structures into local geometric environments for simultaneous property prediction and site-wise contribution analysis. Trained on a combination of constructed and retrieved datasets, the model achieves state-of-the-art accuracy and data efficiency across gas storage, gas separation, and electronic-property prediction tasks. Importantly, the framework reveals interpretable local structure-property relationships by identifying transferable high-contribution sites across diverse frameworks. Leveraging these learned motifs, we further demonstrate inverse design of new metal-organic frameworks exhibiting record-high N2 storage, strong CO2/N2 separation performance, and near-zero electronic band gaps, validated by physics-based simulations. △ Less

Submitted 22 July, 2026; v1 submitted 19 September, 2025; originally announced September 2025.

OmniMap: A General Mapping Framework Integrating Optics, Geometry, and Semantics

Authors: Yinan Deng, Yufeng Yue, Jianyu Dou, Jingyu Zhao, Jiahui Wang, Yujie Tang, Yi Yang, Mengyin Fu

Abstract: Robotic systems demand accurate and comprehensive 3D environment perception, requiring simultaneous capture of photo-realistic appearance (optical), precise layout shape (geometric), and open-vocabulary scene understanding (semantic). Existing methods typically achieve only partial fulfillment of these requirements while exhibiting optical blurring, geometric irregularities, and semantic ambiguiti… ▽ More Robotic systems demand accurate and comprehensive 3D environment perception, requiring simultaneous capture of photo-realistic appearance (optical), precise layout shape (geometric), and open-vocabulary scene understanding (semantic). Existing methods typically achieve only partial fulfillment of these requirements while exhibiting optical blurring, geometric irregularities, and semantic ambiguities. To address these challenges, we propose OmniMap. Overall, OmniMap represents the first online mapping framework that simultaneously captures optical, geometric, and semantic scene attributes while maintaining real-time performance and model compactness. At the architectural level, OmniMap employs a tightly coupled 3DGS-Voxel hybrid representation that combines fine-grained modeling with structural stability. At the implementation level, OmniMap identifies key challenges across different modalities and introduces several innovations: adaptive camera modeling for motion blur and exposure compensation, hybrid incremental representation with normal constraints, and probabilistic fusion for robust instance-level understanding. Extensive experiments show OmniMap's superior performance in rendering fidelity, geometric accuracy, and zero-shot semantic segmentation compared to state-of-the-art methods across diverse scenes. The framework's versatility is further evidenced through a variety of downstream applications, including multi-domain scene Q&A, interactive editing, perception-guided manipulation, and map-assisted navigation. △ Less

Submitted 9 September, 2025; originally announced September 2025.

Comments: Accepted by IEEE Transactions on Robotics (TRO), project website: https://omni-map.github.io/

Abex-rat: Synergizing Abstractive Augmentation and Adversarial Training for Classification of Occupational Accident Reports

Authors: Jian Chen, Jiabao Dou

Abstract: The automatic classification of occupational accident reports is pivotal for workplace safety analysis but is persistently hindered by severe class imbalance and data scarcity. In this paper, we propose ABEX-RAT, a resource-efficient framework that synergizes generative data augmentation with robust adversarial learning. Unlike computationally expensive large language models (LLMs) fine-tuning, ou… ▽ More The automatic classification of occupational accident reports is pivotal for workplace safety analysis but is persistently hindered by severe class imbalance and data scarcity. In this paper, we propose ABEX-RAT, a resource-efficient framework that synergizes generative data augmentation with robust adversarial learning. Unlike computationally expensive large language models (LLMs) fine-tuning, our approach employs a two-stage abstractive-expansive (ABEX) pipeline: it first utilizes a prompt-guided LLM to distill label-critical semantics into concise abstracts, which are then expanded into diverse synthetic samples to balance the data distribution. Subsequently, we train a lightweight classifier using a random adversarial training (RAT) protocol, which stochastically injects perturbations to enhance generalization without significant computational overhead. Experimental results on the OSHA dataset demonstrate that ABEXRAT establishes a new state-of-the-art, achieving a Macro-F1 score of 90.32% and significantly outperforming both traditional baselines and fine-tuned large models. This confirms that targeted augmentation combined with robust training offers a superior, data-efficient alternative for specialized domain classification. The source code will be made publicly available upon acceptance. △ Less

Submitted 27 January, 2026; v1 submitted 2 September, 2025; originally announced September 2025.

OpenMulti: Open-Vocabulary Instance-Level Multi-Agent Distributed Implicit Mapping

Authors: Jianyu Dou, Yinan Deng, Jiahui Wang, Xingsi Tang, Yi Yang, Yufeng Yue

Abstract: Multi-agent distributed collaborative mapping provides comprehensive and efficient representations for robots. However, existing approaches lack instance-level awareness and semantic understanding of environments, limiting their effectiveness for downstream applications. To address this issue, we propose OpenMulti, an open-vocabulary instance-level multi-agent distributed implicit mapping framewor… ▽ More Multi-agent distributed collaborative mapping provides comprehensive and efficient representations for robots. However, existing approaches lack instance-level awareness and semantic understanding of environments, limiting their effectiveness for downstream applications. To address this issue, we propose OpenMulti, an open-vocabulary instance-level multi-agent distributed implicit mapping framework. Specifically, we introduce a Cross-Agent Instance Alignment module, which constructs an Instance Collaborative Graph to ensure consistent instance understanding across agents. To alleviate the degradation of mapping accuracy due to the blind-zone optimization trap, we leverage Cross Rendering Supervision to enhance distributed learning of the scene. Experimental results show that OpenMulti outperforms related algorithms in both fine-grained geometric accuracy and zero-shot semantic accuracy. In addition, OpenMulti supports instance-level retrieval tasks, delivering semantic annotations for downstream applications. The project website of OpenMulti is publicly available at https://openmulti666.github.io/. △ Less

Submitted 1 September, 2025; originally announced September 2025.

Comments: Accepted to IEEE Robotics and Automation Letters. Project website: https://openmulti666.github.io/

UI-Venus Technical Report: Building High-performance UI Agents with RFT

Authors: Zhangxuan Gu, Zhengwen Zeng, Zhenyu Xu, Xingran Zhou, Shuheng Shen, Yunfei Liu, Beitong Zhou, Changhua Meng, Tianyu Xia, Weizhi Chen, Yue Wen, Jingya Dou, Fei Tang, Jinzhen Lin, Yulin Liu, Zhenlin Guo, Yichen Gong, Heng Jia, Changlong Gao, Yuan Guo, Yong Deng, Zhenyu Guo, Liang Chen, Weiqiang Wang

Abstract: We present UI-Venus, a native UI agent that takes only screenshots as input based on a multimodal large language model. UI-Venus achieves SOTA performance on both UI grounding and navigation tasks using only several hundred thousand high-quality training samples through reinforcement finetune (RFT) based on Qwen2.5-VL. Specifically, the 7B and 72B variants of UI-Venus obtain 94.1% / 50.8% and 95.3… ▽ More We present UI-Venus, a native UI agent that takes only screenshots as input based on a multimodal large language model. UI-Venus achieves SOTA performance on both UI grounding and navigation tasks using only several hundred thousand high-quality training samples through reinforcement finetune (RFT) based on Qwen2.5-VL. Specifically, the 7B and 72B variants of UI-Venus obtain 94.1% / 50.8% and 95.3% / 61.9% on the standard grounding benchmarks, i.e., Screenspot-V2 / Pro, surpassing the previous SOTA baselines including open-source GTA1 and closed-source UI-TARS-1.5. To show UI-Venus's summary and planing ability, we also evaluate it on the AndroidWorld, an online UI navigation arena, on which our 7B and 72B variants achieve 49.1% and 65.9% success rate, also beating existing models. To achieve this, we introduce carefully designed reward functions for both UI grounding and navigation tasks and corresponding efficient data cleaning strategies. To further boost navigation performance, we propose Self-Evolving Trajectory History Alignment & Sparse Action Enhancement that refine historical reasoning traces and balances the distribution of sparse but critical actions, leading to more coherent planning and better generalization in complex UI tasks. Our contributions include the publish of SOTA open-source UI agents, comprehensive data cleaning protocols and a novel self-evolving framework for improving navigation performance, which encourage further research and development in the community. Code is available at https://github.com/inclusionAI/UI-Venus. △ Less

Submitted 15 August, 2025; v1 submitted 14 August, 2025; originally announced August 2025.

Arce: Augmented Roberta with Contextualized Elucidations for Ner in Automated Rule Checking

Authors: Jian Chen, Jiabao Dou

Abstract: Accurate information extraction from specialized texts is a critical challenge for automated rule checking (ARC) in the architecture, engineering, and construction (AEC) domain. While large language models (LLMs) possess strong reasoning capabilities, their deployment in resource-constrained AEC environments is often impractical. Conversely, standard efficient models struggle with the significant… ▽ More Accurate information extraction from specialized texts is a critical challenge for automated rule checking (ARC) in the architecture, engineering, and construction (AEC) domain. While large language models (LLMs) possess strong reasoning capabilities, their deployment in resource-constrained AEC environments is often impractical. Conversely, standard efficient models struggle with the significant domain gap. Although this gap can be mitigated by pre-training on large, humancurated corpora, such approaches are labor-intensive and costly. To address this, we propose ARCE (Augmented RoBERTa with Contextualized Elucidations), a novel knowledge distillation framework that leverages LLMs to synthesize a task-oriented corpus, termed Cote, for incrementally pre-training smaller models. ARCE systematically explores the optimal strategy for knowledge transfer. Our extensive experiments demonstrate that ARCE establishes a new state-of-the-art on a benchmark AEC dataset, achieving a Macro-F1 score of 77.20% and outperforming both domain-specific baselines and fine-tuned LLMs. Crucially, our study reveals a less is more principle: simple, direct explanations prove significantly more effective for domain adaptation than complex, role-based rationales in the NER task, which tend to introduce semantic noise. The source code will be made publicly available upon acceptance. △ Less

Submitted 27 January, 2026; v1 submitted 10 August, 2025; originally announced August 2025.

Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks

Authors: Chenru Duan, Aditya Nandy, Sizhan Liu, Yuanqi Du, Liu He, Yi Qu, Haojun Jia, Jin-Hu Dou

Abstract: Metal-organic frameworks (MOFs) marry inorganic nodes, organic edges, and topological nets into programmable porous crystals, yet their astronomical design space defies brute-force synthesis. Generative modeling holds ultimate promise, but existing models either recycle known building blocks or are restricted to small unit cells. We introduce Building-Block-Aware MOF Diffusion (BBA MOF Diffusion),… ▽ More Metal-organic frameworks (MOFs) marry inorganic nodes, organic edges, and topological nets into programmable porous crystals, yet their astronomical design space defies brute-force synthesis. Generative modeling holds ultimate promise, but existing models either recycle known building blocks or are restricted to small unit cells. We introduce Building-Block-Aware MOF Diffusion (BBA MOF Diffusion), an SE(3)-equivariant diffusion model that learns 3D all-atom representations of individual building blocks, encoding crystallographic topological nets explicitly. Trained on the CoRE-MOF database, BBA MOF Diffusion readily samples MOFs with unit cells containing 1000 atoms with great geometric validity, novelty, and diversity mirroring experimental databases. Its native building-block representation produces unprecedented metal nodes and organic edges, expanding accessible chemical space by orders of magnitude. One high-scoring [Zn(1,4-TDC)(EtOH)2] MOF predicted by the model was synthesized, where powder X-ray diffraction, thermogravimetric analysis, and N2 sorption confirm its structural fidelity. BBA-Diff thus furnishes a practical pathway to synthesizable and high-performing MOFs. △ Less

Submitted 13 May, 2025; originally announced May 2025.

Feature Selection Based on Reinforcement Learning and Hazard State Classification for Magnetic Adhesion Wall-Climbing Robots

Authors: Zhen Ma, He Xu, Jielong Dou, Yi Qin, Xueyu Zhang

Abstract: Magnetic adhesion tracked wall-climbing robots face potential risks of overturning during high-altitude operations, making their stability crucial for ensuring safety. This study presents a dynamic feature selection method based on Proximal Policy Optimization (PPO) reinforcement learning, combined with typical machine learning models, aimed at improving the classification accuracy of hazardous st… ▽ More Magnetic adhesion tracked wall-climbing robots face potential risks of overturning during high-altitude operations, making their stability crucial for ensuring safety. This study presents a dynamic feature selection method based on Proximal Policy Optimization (PPO) reinforcement learning, combined with typical machine learning models, aimed at improving the classification accuracy of hazardous states under complex operating conditions. Firstly, this work innovatively employs a fiber rod-based MEMS attitude sensor to collect vibration data from the robot and extract high-dimensional feature vectors in both time and frequency domains. Then, a reinforcement learning model is used to dynamically select the optimal feature subset, reducing feature redundancy and enhancing classification accuracy. Finally, a CNN-LSTM deep learning model is employed for classification and recognition. Experimental results demonstrate that the proposed method significantly improves the robot's ability to assess hazardous states across various operational scenarios, providing reliable technical support for robotic safety monitoring. △ Less

Submitted 21 March, 2025; originally announced March 2025.

Comments: 21 pages, 11 figures, manuscript for Journal of Autonomous Robots

MSC Class: 68T05; 68T07; 68T40 ACM Class: I.2.6; I.2.7; K.6.7

Improved ICNN-LSTM Model Classification Based on Attitude Sensor Data for Hazardous State Assessment of Magnetic Adhesion Climbing Wall Robots

Authors: Zhen Ma, He Xu, Jielong Dou, Yi Qin, Xueyu Zhang

Abstract: Magnetic adhesion tracked climbing robots are widely utilized in high-altitude inspection, welding, and cleaning tasks due to their ability to perform various operations against gravity on vertical or inclined walls. However, during operation, the robot may experience overturning torque caused by its own weight and load, which can lead to the detachment of magnetic plates and subsequently pose saf… ▽ More Magnetic adhesion tracked climbing robots are widely utilized in high-altitude inspection, welding, and cleaning tasks due to their ability to perform various operations against gravity on vertical or inclined walls. However, during operation, the robot may experience overturning torque caused by its own weight and load, which can lead to the detachment of magnetic plates and subsequently pose safety risks. This paper proposes an improved ICNN-LSTM network classification method based on Micro-Electro-Mechanical Systems (MEMS) attitude sensor data for real-time monitoring and assessment of hazardous states in magnetic adhesion tracked climbing robots. Firstly, a data acquisition strategy for attitude sensors capable of capturing minute vibrations is designed. Secondly, a feature extraction and classification model combining an Improved Convolutional Neural Network (ICNN) with a Long Short-Term Memory (LSTM) network is proposed. Experimental validation demonstrates that the proposed minute vibration sensing method achieves significant results, and the proposed classification model consistently exhibits high accuracy compared to other models. The research findings provide effective technical support for the safe operation of climbing robots △ Less

Submitted 29 December, 2024; originally announced December 2024.

Comments: 20 pages, 8 figures, manuscript for Journal of Autonomous Robots

MSC Class: 68T05; 68T07; 68T40 ACM Class: I.2.6; I.2.7; K.6.7

Distributed And Parallel Low-Diameter Decompositions for Arbitrary and Restricted Graphs

Authors: Jinfeng Dou, Thorsten Götte, Henning Hillebrandt, Christian Scheideler, Julian Werthmann

Abstract: We consider the distributed and parallel construction of low-diameter decompositions with strong diameter for (weighted) graphs and (weighted) graphs that can be separated through $k \in \tilde{O}(1)$ shortest paths. This class of graphs includes planar graphs, graphs of bounded treewidth, and graphs that exclude a fixed minor $K_r$. We present algorithms in the PRAM, CONGEST, and the novel HYBRID… ▽ More We consider the distributed and parallel construction of low-diameter decompositions with strong diameter for (weighted) graphs and (weighted) graphs that can be separated through $k \in \tilde{O}(1)$ shortest paths. This class of graphs includes planar graphs, graphs of bounded treewidth, and graphs that exclude a fixed minor $K_r$. We present algorithms in the PRAM, CONGEST, and the novel HYBRID communication model that are competitive in all relevant parameters. Given $\mathcal{D} > 0$, our low-diameter decomposition algorithm divides the graph into connected clusters of strong diameter $\mathcal{D}$. For a arbitrary graph, an edge $e \in E$ of length $\ell_e$ is cut between two clusters with probability $O(\frac{\ell_e\cdot\log(n)}{\mathcal{D} })$. If the graph can be separated by $k \in \tilde{O}(1)$ paths, the probability improves to $O(\frac{\ell_e\cdot\log \log n}{\mathcal{D} })$. In either case, the decompositions can be computed in $\tilde{O}(1)$ depth and $\tilde{O}(kn)$ work in the PRAM and $\tilde{O}(1)$ time in the HYBRID model. In CONGEST, the runtimes are $\tilde{O}(HD + \sqrt{n})$ and $\tilde{O}(HD)$ respectively. All these results hold w.h.p. Broadly speaking, we present distributed and parallel implementations of sequential divide-and-conquer algorithms where we replace exact shortest paths with approximate shortest paths. In contrast to exact paths, these can be efficiently computed in the distributed and parallel setting [STOC '22]. Further, and perhaps more importantly, we show that instead of explicitly computing vertex-separators to enable efficient parallelization of these algorithms, it suffices to sample a few random paths of bounded length and the nodes close to them. Thereby, we do not require complex embeddings whose implementation is unknown in the distributed and parallel setting. △ Less

Submitted 29 November, 2024; originally announced November 2024.

Comments: ITCS 2025

FLY-TTS: Fast, Lightweight and High-Quality End-to-End Text-to-Speech Synthesis

Authors: Yinlin Guo, Yening Lv, Jinqiao Dou, Yan Zhang, Yuehai Wang

Abstract: While recent advances in Text-To-Speech synthesis have yielded remarkable improvements in generating high-quality speech, research on lightweight and fast models is limited. This paper introduces FLY-TTS, a new fast, lightweight and high-quality speech synthesis system based on VITS. Specifically, 1) We replace the decoder with ConvNeXt blocks that generate Fourier spectral coefficients followed b… ▽ More While recent advances in Text-To-Speech synthesis have yielded remarkable improvements in generating high-quality speech, research on lightweight and fast models is limited. This paper introduces FLY-TTS, a new fast, lightweight and high-quality speech synthesis system based on VITS. Specifically, 1) We replace the decoder with ConvNeXt blocks that generate Fourier spectral coefficients followed by the inverse short-time Fourier transform to synthesize waveforms; 2) To compress the model size, we introduce the grouped parameter-sharing mechanism to the text encoder and flow-based model; 3) We further employ the large pre-trained WavLM model for adversarial training to improve synthesis quality. Experimental results show that our model achieves a real-time factor of 0.0139 on an Intel Core i9 CPU, 8.8x faster than the baseline (0.1221), with a 1.6x parameter compression. Objective and subjective evaluations indicate that FLY-TTS exhibits comparable speech quality to the strong baseline. △ Less

Submitted 30 June, 2024; originally announced July 2024.

Comments: Accepted to Interspeech 2024. 5 pages, 1 figure

ECGMamba: Towards Efficient ECG Classification with BiSSM

Authors: Yupeng Qiang, Xunde Dong, Xiuling Liu, Yang Yang, Yihai Fang, Jianhong Dou

Abstract: Electrocardiogram (ECG) signal analysis represents a pivotal technique in the diagnosis of cardiovascular diseases. Although transformer-based models have made significant progress in ECG classification, they exhibit inefficiencies in the inference phase. The issue is primarily attributable to the secondary computational complexity of Transformer's self-attention mechanism. particularly when proce… ▽ More Electrocardiogram (ECG) signal analysis represents a pivotal technique in the diagnosis of cardiovascular diseases. Although transformer-based models have made significant progress in ECG classification, they exhibit inefficiencies in the inference phase. The issue is primarily attributable to the secondary computational complexity of Transformer's self-attention mechanism. particularly when processing lengthy sequences. To address this issue, we propose a novel model, ECGMamba, which employs a bidirectional state-space model (BiSSM) to enhance classification efficiency. ECGMamba is based on the innovative Mamba-based block, which incorporates a range of time series modeling techniques to enhance performance while maintaining the efficiency of inference. The experimental results on two publicly available ECG datasets demonstrate that ECGMamba effectively balances the effectiveness and efficiency of classification, achieving competitive performance. This study not only contributes to the body of knowledge in the field of ECG classification but also provides a new research path for efficient and accurate ECG signal analysis. This is of guiding significance for the development of diagnostic models for cardiovascular diseases. △ Less

Submitted 14 June, 2024; originally announced June 2024.

Comments: 6 pages, 2 figures. arXiv admin note: text overlap with arXiv:2404.17858 by other authors

OpenObj: Open-Vocabulary Object-Level Neural Radiance Fields with Fine-Grained Understanding

Authors: Yinan Deng, Jiahui Wang, Jingyu Zhao, Jianyu Dou, Yi Yang, Yufeng Yue

Abstract: In recent years, there has been a surge of interest in open-vocabulary 3D scene reconstruction facilitated by visual language models (VLMs), which showcase remarkable capabilities in open-set retrieval. However, existing methods face some limitations: they either focus on learning point-wise features, resulting in blurry semantic understanding, or solely tackle object-level reconstruction, thereby… ▽ More In recent years, there has been a surge of interest in open-vocabulary 3D scene reconstruction facilitated by visual language models (VLMs), which showcase remarkable capabilities in open-set retrieval. However, existing methods face some limitations: they either focus on learning point-wise features, resulting in blurry semantic understanding, or solely tackle object-level reconstruction, thereby overlooking the intricate details of the object's interior. To address these challenges, we introduce OpenObj, an innovative approach to build open-vocabulary object-level Neural Radiance Fields (NeRF) with fine-grained understanding. In essence, OpenObj establishes a robust framework for efficient and watertight scene modeling and comprehension at the object-level. Moreover, we incorporate part-level features into the neural fields, enabling a nuanced representation of object interiors. This approach captures object-level instances while maintaining a fine-grained understanding. The results on multiple datasets demonstrate that OpenObj achieves superior performance in zero-shot semantic segmentation and retrieval tasks. Additionally, OpenObj supports real-world robotics tasks at multiple scales, including global movement and local manipulation. △ Less

Submitted 12 June, 2024; originally announced June 2024.

Comments: 8 pages, 7figures. Project Url: https://openobj.github.io/

Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning

Authors: Jizhe Dou, Haotian Zhang, Guodong Sun

Abstract: Recently there has been a growing interest in industry and academia, regarding the use of wireless chargers to prolong the operational longevity of unmanned aerial vehicles (commonly knowns as drones). In this paper we consider a charger-assisted drone application: a drone is deployed to observe a set points of interest, while a charger can move to recharge the drone's battery. We focus on the rou… ▽ More Recently there has been a growing interest in industry and academia, regarding the use of wireless chargers to prolong the operational longevity of unmanned aerial vehicles (commonly knowns as drones). In this paper we consider a charger-assisted drone application: a drone is deployed to observe a set points of interest, while a charger can move to recharge the drone's battery. We focus on the route and charging schedule of the drone and the mobile charger, to obtain high observation utility with the shortest possible time, while ensuring the drone remains operational during task execution. Essentially, this proposed drone-charger scheduling problem is a multi-stage decision-making process, in which the drone and the mobile charger act as two agents who cooperate to finish a task. The discrete-continuous hybrid action space of the two agents poses a significant challenge in our problem. To address this issue, we present a hybrid-action deep reinforcement learning framework, called HaDMC, which uses a standard policy learning algorithm to generate latent continuous actions. Motivated by representation learning, we specifically design and train an action decoder. It involves two pipelines to convert the latent continuous actions into original discrete and continuous actions, by which the drone and the charger can directly interact with environment. We embed a mutual learning scheme in model training, emphasizing the collaborative rather than individual actions. We conduct extensive numerical experiments to evaluate HaDMC and compare it with state-of-the-art deep reinforcement learning approaches. The experimental results show the effectiveness and efficiency of our solution. △ Less

Submitted 15 March, 2024; originally announced March 2024.

On the use of deep learning for phase recovery

Authors: Kaiqiang Wang, Li Song, Chutian Wang, Zhenbo Ren, Guangyuan Zhao, Jiazhen Dou, Jianglei Di, George Barbastathis, Renjie Zhou, Jianlin Zhao, Edmund Y. Lam

Abstract: Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often imple… ▽ More Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and outlook on how to better use DL to improve the reliability and efficiency in PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR. △ Less

Submitted 2 August, 2023; originally announced August 2023.

Comments: 82 pages, 32 figures

Journal ref: Light: Science & Applications 13, 4 (2024)

Distributed Construction of Near-Optimal Compact Routing Schemes for Planar Graphs

Authors: Jinfeng Dou, Thorsten Götte, Henning Hillebrandt, Christian Scheideler, Julian Werthmann

Abstract: We consider the problem of computing compact routing tables for a (weighted) planar graph $G:= (V, E,w)$ in the PRAM, CONGEST, and the novel HYBRID communication model. We present algorithms with polylogarithmic work and communication that are almost optimal in all relevant parameters, i.e., computation time, table sizes, and stretch. All algorithms are heavily randomized, and all our bounds hold… ▽ More We consider the problem of computing compact routing tables for a (weighted) planar graph $G:= (V, E,w)$ in the PRAM, CONGEST, and the novel HYBRID communication model. We present algorithms with polylogarithmic work and communication that are almost optimal in all relevant parameters, i.e., computation time, table sizes, and stretch. All algorithms are heavily randomized, and all our bounds hold w.h.p. For a given parameter $ε>0$, our scheme computes labels of size $\widetilde{O}(ε^{-1})$ and is computed in $\widetilde{O}(ε^{-2})$ time and $\widetilde{O}(n)$ work in the PRAM and the HYBRID model and $\widetilde{O}(ε^{-2} \cdot HD)$ (Here, $HD$ denotes the network's hop-diameter) time in CONGEST. The stretch of the resulting routing scheme is $1+ε$. To achieve these results, we extend the divide-and-conquer framework of Li and Parter [STOC '19] and combine it with state-of-the-art distributed distance approximation algorithms [STOC '22]. Furthermore, we provide a distributed decomposition scheme, which may be of independent interest. △ Less

Submitted 12 May, 2023; v1 submitted 4 May, 2023; originally announced May 2023.

How Practical Phase-shift Errors Affect Beamforming of Reconfigurable Intelligent Surface?

Authors: Jun Yang, Yijian Chen, Yijun Cui, Qingqing Wu, Jianwu Dou, Yuxin Wang

Abstract: Reconfigurable intelligent surface (RIS) is a new technique that is able to manipulate the wireless environment smartly and has been exploited for assisting the wireless communications, especially at high frequency band. However, it suffers from hardware impairments (HWIs) in practical designs, which inevitably degrades its performance and thus limits its full potential. To address this practical… ▽ More Reconfigurable intelligent surface (RIS) is a new technique that is able to manipulate the wireless environment smartly and has been exploited for assisting the wireless communications, especially at high frequency band. However, it suffers from hardware impairments (HWIs) in practical designs, which inevitably degrades its performance and thus limits its full potential. To address this practical issue, we first propose a new RIS reflection model involving phase-shift errors, which is then verified by the measurement results from field trials. With this beamforming model, various phase-shift errors caused by different HWIs can be analyzed. The phase-shift errors are classified into three categories: (1) globally independent and identically distributed errors, (2) grouped independent and identically distributed errors and (3) grouped fixed errors. The impact of typical HWIs, including frequency mismatch, PIN diode failures and panel deformation, on RIS beamforming ability are studied with the theoretical model and are compared with numerical results. The impact of frequency mismatch are discussed separately for narrow-band and wide-band beamforming. Finally, useful insights and guidelines on the RIS design and its deployment are highlighted for practical wireless systems. △ Less

Submitted 13 April, 2023; originally announced April 2023.

Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge

Authors: Gongning Luo, Kuanquan Wang, Jun Liu, Shuo Li, Xinjie Liang, Xiangyu Li, Shaowei Gan, Wei Wang, Suyu Dong, Wenyi Wang, Pengxin Yu, Enyou Liu, Hongrong Wei, Na Wang, Jia Guo, Huiqi Li, Zhao Zhang, Ziwei Zhao, Na Gao, Nan An, Ashkan Pakzad, Bojidar Rangelov, Jiaqi Dou, Song Tian, Zeyu Liu , et al. (5 additional authors not shown)

Abstract: Efficient automatic segmentation of multi-level (i.e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However, most existing methods concentrate only on main PA or branch PA segmentation separately and ignore segmentation efficiency. Besides, there is no public large-scale dataset focused on PA segmentation, which makes it highly challengi… ▽ More Efficient automatic segmentation of multi-level (i.e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However, most existing methods concentrate only on main PA or branch PA segmentation separately and ignore segmentation efficiency. Besides, there is no public large-scale dataset focused on PA segmentation, which makes it highly challenging to compare the different methods. To benchmark multi-level PA segmentation algorithms, we organized the first \textbf{P}ulmonary \textbf{AR}tery \textbf{SE}gmentation (PARSE) challenge. On the one hand, we focus on both the main PA and the branch PA segmentation. On the other hand, for better clinical application, we assign the same score weight to segmentation efficiency (mainly running time and GPU memory consumption during inference) while ensuring PA segmentation accuracy. We present a summary of the top algorithms and offer some suggestions for efficient and accurate multi-level PA automatic segmentation. We provide the PARSE challenge as open-access for the community to benchmark future algorithm developments at \url{https://parse2022.grand-challenge.org/Parse2022/}. △ Less

Submitted 9 August, 2024; v1 submitted 7 April, 2023; originally announced April 2023.

Decomposable Sparse Tensor on Tensor Regression

Authors: Haiyi Mao, Jason Xiaotian Dou

Abstract: Most regularized tensor regression research focuses on tensors predictors with scalars responses or vectors predictors to tensors responses. We consider the sparse low rank tensor on tensor regression where predictors $\mathcal{X}$ and responses $\mathcal{Y}$ are both high-dimensional tensors. By demonstrating that the general inner product or the contracted product on a unit rank tensor can be de… ▽ More Most regularized tensor regression research focuses on tensors predictors with scalars responses or vectors predictors to tensors responses. We consider the sparse low rank tensor on tensor regression where predictors $\mathcal{X}$ and responses $\mathcal{Y}$ are both high-dimensional tensors. By demonstrating that the general inner product or the contracted product on a unit rank tensor can be decomposed into standard inner products and outer products, the problem can be simply transformed into a tensor to scalar regression followed by a tensor decomposition. So we propose a fast solution based on stagewise search composed by contraction part and generation part which are optimized alternatively. We successfully demonstrate our method can out perform current methods in terms of accuracy and predictors selection by effectively incorporating the structural information. △ Less

Submitted 14 December, 2022; v1 submitted 9 December, 2022; originally announced December 2022.

Unsupervised Sentence Textual Similarity with Compositional Phrase Semantics

Authors: Zihao Wang, Jiaheng Dou, Yong Zhang

Abstract: Measuring Sentence Textual Similarity (STS) is a classic task that can be applied to many downstream NLP applications such as text generation and retrieval. In this paper, we focus on unsupervised STS that works on various domains but only requires minimal data and computational resources. Theoretically, we propose a light-weighted Expectation-Correction (EC) formulation for STS computation. EC fo… ▽ More Measuring Sentence Textual Similarity (STS) is a classic task that can be applied to many downstream NLP applications such as text generation and retrieval. In this paper, we focus on unsupervised STS that works on various domains but only requires minimal data and computational resources. Theoretically, we propose a light-weighted Expectation-Correction (EC) formulation for STS computation. EC formulation unifies unsupervised STS approaches including the cosine similarity of Additively Composed (AC) sentence embeddings, Optimal Transport (OT), and Tree Kernels (TK). Moreover, we propose the Recursive Optimal Transport Similarity (ROTS) algorithm to capture the compositional phrase semantics by composing multiple recursive EC formulations. ROTS finishes in linear time and is faster than its predecessors. ROTS is empirically more effective and scalable than previous approaches. Extensive experiments on 29 STS tasks under various settings show the clear advantage of ROTS over existing approaches. Detailed ablation studies demonstrate the effectiveness of our approaches. △ Less

Submitted 5 October, 2022; originally announced October 2022.

Comments: COLING 2022; Github repository https://github.com/zihao-wang/rots ; Partially overlapped with arXiv:2002.00745 ; 20 pages, 5 figures, 17 tables

Sampling Through the Lens of Sequential Decision Making

Authors: Jason Xiaotian Dou, Alvin Qingkai Pan, Runxue Bao, Haiyi Harry Mao, Lei Luo, Zhi-Hong Mao

Abstract: Sampling is ubiquitous in machine learning methodologies. Due to the growth of large datasets and model complexity, we want to learn and adapt the sampling process while training a representation. Towards achieving this grand goal, a variety of sampling techniques have been proposed. However, most of them either use a fixed sampling scheme or adjust the sampling scheme based on simple heuristics.… ▽ More Sampling is ubiquitous in machine learning methodologies. Due to the growth of large datasets and model complexity, we want to learn and adapt the sampling process while training a representation. Towards achieving this grand goal, a variety of sampling techniques have been proposed. However, most of them either use a fixed sampling scheme or adjust the sampling scheme based on simple heuristics. They cannot choose the best sample for model training in different stages. Inspired by "Think, Fast and Slow" (System 1 and System 2) in cognitive science, we propose a reward-guided sampling strategy called Adaptive Sample with Reward (ASR) to tackle this challenge. To the best of our knowledge, this is the first work utilizing reinforcement learning (RL) to address the sampling problem in representation learning. Our approach optimally adjusts the sampling process to achieve optimal performance. We explore geographical relationships among samples by distance-based sampling to maximize overall cumulative reward. We apply ASR to the long-standing sampling problems in similarity-based loss functions. Empirical results in information retrieval and clustering demonstrate ASR's superb performance across different datasets. We also discuss an engrossing phenomenon which we name as "ASR gravity well" in experiments. △ Less

Submitted 13 December, 2022; v1 submitted 17 August, 2022; originally announced August 2022.

COEM: Cross-Modal Embedding for MetaCell Identification

Authors: Haiyi Mao, Minxue Jia, Jason Xiaotian Dou, Haotian Zhang, Panayiotis V. Benos

Abstract: Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though single-cell multi-omics datasets profile multiple molecular modalities within the same cell. Here, we present \textbf{C}ross-M\textbf{O}dal \textbf{E}mbedding for \textbf{M}etaCell Id… ▽ More Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though single-cell multi-omics datasets profile multiple molecular modalities within the same cell. Here, we present \textbf{C}ross-M\textbf{O}dal \textbf{E}mbedding for \textbf{M}etaCell Identification (COEM), which utilizes an embedded space leveraging the information of both scATAC-seq and scRNA-seq to perform aggregation, balancing the trade-off between fine resolution and sufficient sequencing coverage. COEM outperforms the state-of-the-art method SEACells by efficiently identifying accurate and well-separated metacells across datasets with continuous and discrete cell types. Furthermore, COEM significantly improves peak-to-gene association analyses, and facilitates complex gene regulatory inference tasks. △ Less

Submitted 24 July, 2022; v1 submitted 15 July, 2022; originally announced July 2022.

Comments: 5 pages, 2 figures, ICML workshop on computational biology

Unitail: Detecting, Reading, and Matching in Retail Scene

Authors: Fangyi Chen, Han Zhang, Zaiwang Li, Jiachen Dou, Shentong Mo, Hao Chen, Yongxin Zhang, Uzair Ahmed, Chenchen Zhu, Marios Savvides

Abstract: To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene. Pursuing this goal, we introduce the United Retail Datasets (Unitail), a large-scale benchmark of basic visual tasks on products that challenges algorithms for detecting, reading, and matching. With 1.8M quadrilateral-shaped instances annotated, th… ▽ More To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene. Pursuing this goal, we introduce the United Retail Datasets (Unitail), a large-scale benchmark of basic visual tasks on products that challenges algorithms for detecting, reading, and matching. With 1.8M quadrilateral-shaped instances annotated, the Unitail offers a detection dataset to align product appearance better. Furthermore, it provides a gallery-style OCR dataset containing 1454 product categories, 30k text regions, and 21k transcriptions to enable robust reading on products and motivate enhanced product matching. Besides benchmarking the datasets using various state-of-the-arts, we customize a new detector for product detection and provide a simple OCR-based matching solution that verifies its effectiveness. △ Less

Submitted 20 July, 2022; v1 submitted 1 April, 2022; originally announced April 2022.

Comments: ECCV 2022

GUX-Analyzer: A Deep Multi-modal Analyzer Via Motivational Flow For Game User Experience

Authors: Zhitao Liu, Ning Xie, Guobiao Yang, Jiale Dou, Lanxiao Huang, Guang Yang, Lin Yuan

Abstract: Quantitative analysis of Game User eXperience (GUX) is important to the game industry. Different from the typical questionnaire analysis, this paper focuses on the computational analysis of GUX. We aim to analyze the relationship between game and players using the multi-modal data including physiological data and game process data. We theoretically extend the Flow model from the classic skill-and-… ▽ More Quantitative analysis of Game User eXperience (GUX) is important to the game industry. Different from the typical questionnaire analysis, this paper focuses on the computational analysis of GUX. We aim to analyze the relationship between game and players using the multi-modal data including physiological data and game process data. We theoretically extend the Flow model from the classic skill-and-challenge plane by expanding new dimension on motivation, which is the result of the multi-modal data analysis on affect, and physiological data. We call this 3D Flow as Motivational Flow, MovFlow. Meanwhile, we implement a quantitative GUX Analysis System (GUXAS), which can predict the player's in-game experience state by only using game process data. It analyzes the correlation among not only in-game state, but the player's psychological-and-physiological reaction in the entire interactive game-play process. The experiments demonstrated our MovFlow model efficiently distinguished the users' in-game experience states from the perspective of GUX. △ Less

Submitted 22 December, 2021; originally announced December 2021.

CenterAtt: Fast 2-stage Center Attention Network

Authors: Jianyun Xu, Xin Tang, Jian Dou, Xu Shu, Yushi Zhu

Abstract: In this technical report, we introduce the methods of HIKVISION_LiDAR_Det in the challenge of waymo open dataset real-time 3D detection. Our solution for the competition are built upon Centerpoint 3D detection framework. Several variants of CenterPoint are explored, including center attention head and feature pyramid network neck. In order to achieve real time detection, methods like batchnorm mer… ▽ More In this technical report, we introduce the methods of HIKVISION_LiDAR_Det in the challenge of waymo open dataset real-time 3D detection. Our solution for the competition are built upon Centerpoint 3D detection framework. Several variants of CenterPoint are explored, including center attention head and feature pyramid network neck. In order to achieve real time detection, methods like batchnorm merge, half-precision floating point network and GPU-accelerated voxelization process are adopted. By using these methods, our team ranks 6th among all the methods on real-time 3D detection challenge in the waymo open dataset. △ Less

Submitted 19 June, 2021; originally announced June 2021.

RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation

Authors: Jianyun Xu, Ruixiang Zhang, Jian Dou, Yushi Zhu, Jie Sun, Shiliang Pu

Abstract: Point clouds can be represented in many forms (views), typically, point-based sets, voxel-based cells or range-based images(i.e., panoramic view). The point-based view is geometrically accurate, but it is disordered, which makes it difficult to find local neighbors efficiently. The voxel-based view is regular, but sparse, and computation grows cubically when voxel resolution increases. The range-b… ▽ More Point clouds can be represented in many forms (views), typically, point-based sets, voxel-based cells or range-based images(i.e., panoramic view). The point-based view is geometrically accurate, but it is disordered, which makes it difficult to find local neighbors efficiently. The voxel-based view is regular, but sparse, and computation grows cubically when voxel resolution increases. The range-based view is regular and generally dense, however spherical projection makes physical dimensions distorted. Both voxel- and range-based views suffer from quantization loss, especially for voxels when facing large-scale scenes. In order to utilize different view's advantages and alleviate their own shortcomings in fine-grained segmentation task, we propose a novel range-point-voxel fusion network, namely RPVNet. In this network, we devise a deep fusion framework with multiple and mutual information interactions among these three views and propose a gated fusion module (termed as GFM), which can adaptively merge the three features based on concurrent inputs. Moreover, the proposed RPV interaction mechanism is highly efficient, and we summarize it into a more general formulation. By leveraging this efficient interaction and relatively lower voxel resolution, our method is also proved to be more efficient. Finally, we evaluated the proposed model on two large-scale datasets, i.e., SemanticKITTI and nuScenes, and it shows state-of-the-art performance on both of them. Note that, our method currently ranks 1st on SemanticKITTI leaderboard without any extra tricks. △ Less

Submitted 24 March, 2021; originally announced March 2021.

BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving

Authors: Jianru Xue, Jianwu Fang, Tao Li, Bohua Zhang, Pu Zhang, Zhen Ye, Jian Dou

Abstract: In autonomous driving community, numerous benchmarks have been established to assist the tasks of 3D/2D object detection, stereo vision, semantic/instance segmentation. However, the more meaningful dynamic evolution of the surrounding objects of ego-vehicle is rarely exploited, and lacks a large-scale dataset platform. To address this, we introduce BLVD, a large-scale 5D semantics benchmark which… ▽ More In autonomous driving community, numerous benchmarks have been established to assist the tasks of 3D/2D object detection, stereo vision, semantic/instance segmentation. However, the more meaningful dynamic evolution of the surrounding objects of ego-vehicle is rarely exploited, and lacks a large-scale dataset platform. To address this, we introduce BLVD, a large-scale 5D semantics benchmark which does not concentrate on the static detection or semantic/instance segmentation tasks tackled adequately before. Instead, BLVD aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking, 5D (4D+interactive) interactive event recognition and intention prediction. This benchmark will boost the deeper understanding of traffic scenes than ever before. We totally yield 249,129 3D annotations, 4,902 independent individuals for tracking with the length of overall 214,922 points, 6,004 valid fragments for 5D interactive event recognition, and 4,900 individuals for 5D intention prediction. These tasks are contained in four kinds of scenarios depending on the object density (low and high) and light conditions (daytime and nighttime). The benchmark can be downloaded from our project site https://github.com/VCCIV/BLVD/. △ Less

Submitted 15 March, 2019; originally announced March 2019.

Comments: To appear in ICRA2019

Impartial redistricting: a Markov chain approach to the "Gerrymandering problem"

Authors: Jason Dou

Abstract: After every U.S. national census, a state legislature is required to redraw the boundaries of congressional districts in order to account for changes in population. At the moment this is done in a highly partisan way, with districting done in order to maximize the benefits to the party in power. This is a threat to U.S's democracy. There have been proposals to take the re-districting out of the ha… ▽ More After every U.S. national census, a state legislature is required to redraw the boundaries of congressional districts in order to account for changes in population. At the moment this is done in a highly partisan way, with districting done in order to maximize the benefits to the party in power. This is a threat to U.S's democracy. There have been proposals to take the re-districting out of the hands of political parties and give to an "independent" commission. Independence is hard to come by and in this thesis we want to explore the possibility of computer generated districts that as far as possible to avoid partisan "gerrymandering". The idea we have is to treat every possible redistricting as a state in a Markov Chain: every state is obtained by its former state in random way. With some technical conditions, we will get a near uniform member of the states after running sufficiently long time (the mixing time). Then we can say the uniform member is an impartial distribution. Based on the geographical and statistical data of Pennsylvania, I have achieved the Markov Chain algorithm with several constraints, done optimization experiments and a web interface is going to be made to show the results. △ Less

Submitted 30 October, 2017; originally announced November 2017.

Comments: Bachelor's thesis, Beijing Univ (2014)

What Words Do We Use to Lie?: Word Choice in Deceptive Messages

Authors: Jason Xiaotian Dou, Michelle Liu, Haaris Muneer, Adam Schlussel

Abstract: Text messaging is the most widely used form of computer-mediated communication (CMC). Previous findings have shown that linguistic factors can reliably indicate messages as deceptive. For example, users take longer and use more words to craft deceptive messages than they do truthful messages. Existing research has also examined how factors, such as student status and gender, affect rates of decept… ▽ More Text messaging is the most widely used form of computer-mediated communication (CMC). Previous findings have shown that linguistic factors can reliably indicate messages as deceptive. For example, users take longer and use more words to craft deceptive messages than they do truthful messages. Existing research has also examined how factors, such as student status and gender, affect rates of deception and word choice in deceptive messages. However, this research has been limited by small sample sizes and has returned contradicting findings. This paper aims to address these issues by using a dataset of text messages collected from a large and varied set of participants using an Android messaging application. The results of this paper show significant differences in word choice and frequency of deceptive messages between male and female participants, as well as between students and non-students. △ Less

Submitted 1 August, 2022; v1 submitted 30 September, 2017; originally announced October 2017.

"Draw My Topics": Find Desired Topics fast from large scale of Corpus

Authors: Jason Dou, Ni Sun, Xiaojun Zou

Abstract: We develop the "Draw My Topics" toolkit, which provides a fast way to incorporate social scientists' interest into standard topic modelling. Instead of using raw corpus with primitive processing as input, an algorithm based on Vector Space Model and Conditional Entropy are used to connect social scientists' willingness and unsupervised topic models' output. Space for users' adjustment on specific… ▽ More We develop the "Draw My Topics" toolkit, which provides a fast way to incorporate social scientists' interest into standard topic modelling. Instead of using raw corpus with primitive processing as input, an algorithm based on Vector Space Model and Conditional Entropy are used to connect social scientists' willingness and unsupervised topic models' output. Space for users' adjustment on specific corpus of their interest is also accommodated. We demonstrate the toolkit's use on the Diachronic People's Daily Corpus in Chinese. △ Less

Submitted 3 February, 2016; originally announced February 2016.

Impartial Redistricting: A Markov Chain Approach

Authors: Lucy Chenyun Wu, Jason Xiaotian Dou, Danny Sleator, Alan Frieze, David Miller

Abstract: The gerrymandering problem is a worldwide problem which sets great threat to democracy and justice in district based elections. Thanks to partisan redistricting commissions, district boundaries are often manipulated to benefit incumbents. Since an independent commission is hard to come by, the possibility of impartially generating districts with a computer is explored in this thesis. We have devel… ▽ More The gerrymandering problem is a worldwide problem which sets great threat to democracy and justice in district based elections. Thanks to partisan redistricting commissions, district boundaries are often manipulated to benefit incumbents. Since an independent commission is hard to come by, the possibility of impartially generating districts with a computer is explored in this thesis. We have developed an algorithm to randomly produce legal redistricting schemes for Pennsylvania. △ Less

Submitted 13 October, 2015; v1 submitted 12 October, 2015; originally announced October 2015.

Comments: about authorship naming problem, will fix soon