Arrow Research search

Author name cluster

Xin Tan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

19 papers
1 author row

Possible papers

19

JBHI Journal 2026 Journal Article

A Causal Learning-Based sEMG Disentanglement Framework for Multi-Posture Domain Generalization

  • Tanying Su
  • Xin Tan
  • Xiao Liu
  • Chenyun Dai

Surface electromyography (sEMG) -based human-computer interaction (HCI) systems achieve high accuracy in controlled environments, but their robustness under daily life remains challenging. In real-world scenarios, variations in user posture introduce personalized biases that can significantly degrade model performance. A viable solution is to train a highly generalized network using existing data from various postures, enabling the model to become less sensitive to posture variations. In this work, we treat the original sEMG signals as a coupling of pattern and posture components, where each component can be considered as a causal signal specific to corresponding labels. We use the causal encoders to understand the generative relationships between data and labels, facilitating the disentanglement of components into different latent spaces and promoting clustering within each space. This enables the model to extract posture-invariant pattern components and train a robust pattern recognition model with strong generalization capabilities. We developed a high-density sEMG (HD-sEMG) dataset with 16 subjects performing in four common HCI postures, addressing the lack of posture variation samples in existing sEMG datasets. Our model achieved an average accuracy of 90. 3% across four generalization tasks, outperforming other domain generalization models and demonstrating its superiority.

Details DOI

AAAI Conference 2026 Conference Paper

Diffusion Implicit Policy for Unpaired Scene-aware Motion Synthesis

  • Jingyu Gong
  • Chong Zhang
  • Fengqi Liu
  • Ke Fan
  • Qianyu Zhou
  • Xin Tan
  • Zhizhong Zhang
  • Yuan Xie

Scene-aware motion synthesis has been widely researched recently due to its numerous applications. Prevailing methods rely heavily on paired motion-scene data, while it is difficult to generalize to diverse scenes when trained only on a few specific ones. Thus, we propose a unified framework, termed Diffusion Implicit Policy (DIP), for scene-aware motion synthesis, where paired motion-scene data are no longer necessary. In this paper, we disentangle human-scene interaction from motion synthesis during training, and then introduce an interaction-based implicit policy into motion diffusion during inference. Synthesized motion can be derived through iterative diffusion denoising and implicit policy optimization, thus motion naturalness and interaction plausibility can be maintained simultaneously. For long-term motion synthesis, we introduce motion blending in joint rotation power space. The proposed method is evaluated on synthesized scenes with ShapeNet furniture, and real scenes from PROX and Replica. Results show that our framework presents better motion naturalness and interaction plausibility than cutting-edge methods. This also indicates the feasibility of utilizing the DIP for motion synthesis in more general tasks and versatile scenes.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Human Motion Synthesis in 3D Scenes via Unified Scene Semantic Occupancy

  • Jingyu Gong
  • Kunkun Tong
  • Zhuoran Chen
  • Chuanhan Yuan
  • Mingang Chen
  • Zhizhong Zhang
  • Xin Tan
  • Yuan Xie

Human motion synthesis in 3D scenes relies heavily on scene comprehension, while current methods focus mainly on scene structure but ignore the semantic understanding. In this paper, we propose a human motion synthesis framework that take an unified Scene Semantic Occupancy (SSO) for scene representation, termed SSOMotion. We design a bi-directional tri-plane decomposition to derive a compact version of the SSO, and scene semantics are mapped to an unified feature space via CLIP encoding and shared linear dimensionality reduction. Such strategy can derive the fine-grained scene semantic structures while significantly reduce redundant computations. We further take these scene hints and movement direction derived from instructions for motion control via frame-wise scene query. Extensive experiments and ablation studies conducted on cluttered scenes using ShapeNet furniture, as well as scanned scenes from PROX and Replica datasets, demonstrate its cutting-edge performance while validating its effectiveness and generalization ability.

PDF Details DOI

AAAI Conference 2026 Conference Paper

LidarPainter: One-Step Away from Any Lidar View to Novel Guidance

  • Yuzhou Ji
  • Ke Ma
  • Hong Cai
  • Anchun Zhang
  • Lizhuang Ma
  • Xin Tan

Dynamic driving scene reconstruction is of great importance in fields like digital twin system and autonomous driving simulation. However, unacceptable degradation occurs when the view deviates from the input trajectory, leading to corrupted background and vehicle models. To improve reconstruction quality on novel trajectory, existing methods are subject to various limitations including inconsistency, deformation, and time consumption. This paper proposes LidarPainter, a one-step diffusion model that recovers consistent driving views from sparse LiDAR condition and artifact-corrupted renderings in real-time, enabling high-fidelity lane shifts in driving scene reconstruction. Extensive experiments show that LidarPainter outperforms state-of-the-art methods in speed, quality and resource efficiency, specifically 7 × faster than StreetCrafter with only one fifth of GPU memory required. LidarPainter also supports stylized generation using text prompts such as “foggy” and “night”, allowing for a diverse expansion of the existing asset library.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Multi-Step Deformable Gaussian Splatting for Dynamic Scene Rendering

  • Jiaheng Hu
  • Zhizhong Zhang
  • Jingyu Gong
  • Lizhuang Ma
  • Xin Tan
  • Yuan Xie

Reconstructing dynamic scenes has long been a challenging task in 3D vision. Previous mainstream methods based on 3D Gaussian Splatting typically employ a single deformation field to directly model spatiotemporal changes. However, such one-step deformation struggles to capture diverse and complex motion patterns. To address this limitation, we propose decomposing the one-step deformation into a multi-step process, where each step is represented by a deformation layer. Additionally, we introduce a weight prediction mechanism for each layer to control the extent of deformation at every step. We provide two types of deformation layers based on implicit and explicit approaches. Moreover, while the deformation layer is time-conditioned, the Gaussians' behavior may still be influenced by their time-invariant properties. Therefore, we propose a fully time-agnostic scale modulation block to modulate the scaling changes of Gaussians. Extensive experiments on D-NeRF, Neu3D, and HyperNeRF demonstrate that our method achieves state-of-the-art performance.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Zero-Shot Robotic Manipulation via 3D Gaussian Splatting-Enhanced Multimodal Retrieval-Augmented Generation

  • Zilong Xie
  • Jingyu Gong
  • Xin Tan
  • Zhizhong Zhang
  • Yuan Xie

Existing end-to-end approaches of robotic manipulation often lack generalization to unseen objects or tasks due to limited data and poor interpretability. While recent Multimodal Large Language Models (MLLMs) demonstrate strong commonsense reasoning, they struggle with geometric and spatial understanding required for pose prediction. In this paper, we propose RobMRAG, a 3D Gaussian Splatting-Enhanced Multimodal Retrieval-Augmented Generation (MRAG) framework for zero-shot robotic manipulation. Specifically, We construct a multi-source manipulation knowledge base containing object contact frames, task completion frames, and pose parameters. During inference, a Hierarchical Multimodal Retrieval module first employs hybrid semantic search to find task-relevant object prototypes, then selects the geometrically closest reference example based on pixel-level similarity and Instance Matching Distance (IMD). We further introduce a 3D-Aware Pose Refinement module based on 3D Gaussian Splatting into the MRAG framework, which aligns the pose of the reference object to the target object in 3D space. The aligned results are reprojected onto the image plane and used as input to the MLLM to enhance the generation of the final pose parameters. Extensive experiments show that on a test set containing 30 categories of household objects, our method improves the success rate by 7.76% compared to the best-performing zero-shot baseline under the same setting, and by 6.54% compared to the state-of-the-art supervised baseline. Our results validate that RobMRAG effectively bridges the gap between high-level semantic reasoning and low-level geometric execution, enabling robotic systems that generalize to unseen objects while remaining inherently interpretable.

PDF Details DOI

AAAI Conference 2025 Conference Paper

DrivingForward: Feed-forward 3D Gaussian Splatting for Driving Scene Reconstruction from Flexible Surround-view Input

  • Qijian Tian
  • Xin Tan
  • Yuan Xie
  • Lizhuang Ma

We propose DrivingForward, a feed-forward Gaussian Splatting model that reconstructs driving scenes from flexible surround-view input. Driving scene images from vehicle-mounted cameras are typically sparse, with limited overlap, and the movement of the vehicle further complicates the acquisition of camera extrinsics. To tackle these challenges and achieve real-time reconstruction, we jointly train a pose network, a depth network, and a Gaussian network to predict the Gaussian primitives that represent the driving scenes. The pose network and depth network determine the position of the Gaussian primitives in a self-supervised manner, without using depth ground truth and camera extrinsics during training. The Gaussian network independently predicts primitive parameters from each input image, including covariance, opacity, and spherical harmonics coefficients. At the inference stage, our model can achieve feed-forward reconstruction from flexible multi-frame surround-view input. Experiments on the nuScenes dataset show that our model outperforms existing state-of-the-art feed-forward and scene-optimized reconstruction methods in terms of reconstruction.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

EyeSeg: An Uncertainty-Aware Eye Segmentation Framework for AR/VR

  • Zhengyuan Peng
  • Jianqing Xu
  • Shen Li
  • Jiazhen Ji
  • Yuge Huang
  • Jingyun Zhang
  • Jinmin Li
  • Shouhong Ding

Human-machine interaction through augmented reality (AR) and virtual reality (VR) is increasingly prevalent, requiring accurate and efficient gaze estimation which hinges on the accuracy of eye segmentation to enable smooth user experiences. We introduce EyeSeg, a novel eye segmentation framework designed to overcome key challenges that existing approaches struggle with: motion blur, eyelid occlusion, and train-test domain gaps. In these situations, existing models struggle to extract robust features, leading to suboptimal performance. Noting that these challenges can be generally quantified by uncertainty, we design EyeSeg as an uncertainty-aware eye segmentation framework for AR/VR wherein we explicitly model the uncertainties by performing Bayesian uncertainty learning of a posterior under the closed set prior. Theoretically, we prove that a statistic of the learned posterior indicates segmentation uncertainty levels and empirically outperforms existing methods in downstream tasks, such as gaze estimation. EyeSeg outputs an uncertainty score and the segmentation result, weighting and fusing multiple gaze estimates for robustness, which proves to be effective especially under motion blur, eyelid occlusion and cross-domain challenges. Moreover, empirical results suggest that EyeSeg achieves segmentation improvements of MIoU, E1, F1, and ACC surpassing previous approaches.

PDF Details DOI

AAAI Conference 2025 Conference Paper

FastLGS: Speeding Up Language Embedded Gaussians with Feature Grid Mapping

  • Yuzhou Ji
  • He Zhu
  • Junshu Tang
  • Wuyi Liu
  • Zhizhong Zhang
  • Xin Tan
  • Yuan Xie

The semantically interactive radiance field has always been an appealing task for its potential to facilitate user-friendly and automated real-world 3D scene understanding applications. However, it is a challenging task to achieve high quality, efficiency and zero-shot ability at the same time with semantics in radiance fields. In this work, we present FastLGS, an approach that supports real-time open-vocabulary query within 3D Gaussian Splatting (3DGS) under high resolution. We propose the semantic feature grid to save multi-view CLIP features which are extracted based on Segment Anything Model (SAM) masks, and map the grids to low dimensional features for semantic field training through 3DGS. Once trained, we can restore pixel-aligned CLIP embeddings through feature grids from rendered features for open-vocabulary queries. Comparisons with other state-of-the-art methods prove that FastLGS can achieve the first place performance concerning both speed and accuracy, where FastLGS is 98 times faster than LERF, 4 times faster than LangSplat and 2.5 times faster than LEGaussians. Meanwhile, experiments show that FastLGS is adaptive and compatible with many downstream tasks, such as 3D segmentation and 3D object inpainting, which can be easily applied to other 3D manipulation systems.

PDF Details DOI

AAAI Conference 2024 Conference Paper

A Two-Stage Information Extraction Network for Incomplete Multi-View Multi-Label Classification

  • Xin Tan
  • Ce Zhao
  • Chengliang Liu
  • Jie Wen
  • Zhanyan Tang

Recently, multi-view multi-label classification (MvMLC) has received a significant amount of research interest and many methods have been proposed based on the assumptions of view completion and label completion. However, in real-world scenarios, multi-view multi-label data tends to be incomplete due to various uncertainties involved in data collection and manual annotation. As a result, the conventional MvMLC methods fail. In this paper, we propose a new two-stage MvMLC network to solve this incomplete MvMLC issue with partial missing views and missing labels. Different from the existing works, our method attempts to leverage the diverse information from the partially missing data based on the information theory. Specifically, our method aims to minimize task-irrelevant information while maximizing task-relevant information through the principles of information bottleneck theory and mutual information extraction. The first stage of our network involves training view-specific classifiers to concentrate the task-relevant information. Subsequently, in the second stage, the hidden states of these classifiers serve as input for an alignment model, an autoencoder-based mutual information extraction framework, and a weighted fusion classifier to make the final prediction. Extensive experiments performed on five datasets validate that our method outperforms other state-of-the-art methods. Code is available at https://github.com/KevinTan10/TSIEN.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation

  • Yujun Chen
  • Xin Tan
  • Zhizhong Zhang
  • Yanyun Qu
  • Yuan Xie

As the exorbitant expense of labeling autopilot datasets and the growing trend of utilizing unlabeled data, semi-supervised segmentation on point clouds becomes increasingly imperative. Intuitively, finding out more ``unspoken words'' (i.e., latent instance information) beyond the label itself should be helpful to improve performance. In this paper, we discover two types of latent labels behind the displayed label embedded in LiDAR and image data. First, in the LiDAR Branch, we propose a novel augmentation, Cylinder-Mix, which is able to augment more yet reliable samples for training. Second, in the Image Branch, we propose the Instance Position-scale Learning (IPSL) Module to learn and fuse the information of instance position and scale, which is from a 2D pre-trained detector and a type of latent label obtained from 3D to 2D projection. Finally, the two latent labels are embedded into the multi-modal panoptic segmentation network. The ablation of the IPSL module demonstrates its robust adaptability, and the experiments evaluated on SemanticKITTI and nuScenes demonstrate that our model outperforms the state-of-the-art method, LaserMix.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Continuous Piecewise-Affine Based Motion Model for Image Animation

  • Hexiang Wang
  • Fengqi Liu
  • Qianyu Zhou
  • Ran Yi
  • Xin Tan
  • Lizhuang Ma

Image animation aims to bring static images to life according to driving videos and create engaging visual content that can be used for various purposes such as animation, entertainment, and education. Recent unsupervised methods utilize affine and thin-plate spline transformations based on keypoints to transfer the motion in driving frames to the source image. However, limited by the expressive power of the transformations used, these methods always produce poor results when the gap between the motion in the driving frame and the source image is large. To address this issue, we propose to model motion from the source image to the driving frame in highly-expressive diffeomorphism spaces. Firstly, we introduce Continuous Piecewise-Affine based (CPAB) transformation to model the motion and present a well-designed inference algorithm to generate CPAB transformation from control keypoints. Secondly, we propose a SAM-guided keypoint semantic loss to further constrain the keypoint extraction process and improve the semantic consistency between the corresponding keypoints on the source and driving images. Finally, we design a structure alignment loss to align the structure-related features extracted from driving and generated images, thus helping the generator generate results that are more consistent with the driving action. Extensive experiments on four datasets demonstrate the effectiveness of our method against state-of-the-art competitors quantitatively and qualitatively. Code will be publicly available at: https://github.com/DevilPG/AAAI2024-CPABMM.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Domain-Hallucinated Updating for Multi-Domain Face Anti-spoofing

  • Chengyang Hu
  • Ke-Yue Zhang
  • Taiping Yao
  • Shice Liu
  • Shouhong Ding
  • Xin Tan
  • Lizhuang Ma

Multi-Domain Face Anti-Spoofing (MD-FAS) is a practical setting that aims to update models on new domains using only novel data while ensuring that the knowledge acquired from previous domains is not forgotten. Prior methods utilize the responses from models to represent the previous domain knowledge or map the different domains into separated feature spaces to prevent forgetting. However, due to domain gaps, the responses of new data are not as accurate as those of previous data. Also, without the supervision of previous data, separated feature spaces might be destroyed by new domains while updating, leading to catastrophic forgetting. Inspired by the challenges posed by the lack of previous data, we solve this issue from a new standpoint that generates hallucinated previous data for updating FAS model. To this end, we propose a novel Domain-Hallucinated Updating (DHU) framework to facilitate the hallucination of data. Specifically, Domain Information Explorer learns representative domain information of the previous domains. Then, Domain Information Hallucination module transfers the new domain data to pseudo-previous domain ones. Moreover, Hallucinated Features Joint Learning module is proposed to asymmetrically align the new and pseudo-previous data for real samples via dual levels to learn more generalized features, promoting the results on all domains. Our experimental results and visualizations demonstrate that the proposed method outperforms state-of-the-art competitors in terms of effectiveness.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

Harmonizing Visual Text Comprehension and Generation

  • Zhen Zhao
  • Jingqun Tang
  • Binghong Wu
  • Chunhui Lin
  • Shu Wei
  • Hao Liu
  • Xin Tan
  • Zhizhong Zhang

In this work, we present TextHarmony, a unified and versatile multimodal generative model proficient in comprehending and generating visual text. Simultaneously generating images and texts typically results in performance degradation due to the inherent inconsistency between vision and language modalities. To overcome this challenge, existing approaches resort to modality-specific data for supervised fine-tuning, necessitating distinct model instances. We propose Slide-LoRA, which dynamically aggregates modality-specific and modality-agnostic LoRA experts, partially decoupling the multimodal generation space. Slide-LoRA harmonizes the generation of vision and language within a singular model instance, thereby facilitating a more unified generative process. Additionally, we develop a high-quality image caption dataset, DetailedTextCaps-100K, synthesized with a sophisticated closed-source MLLM to enhance visual text generation capabilities further. Comprehensive experiments across various benchmarks demonstrate the effectiveness of the proposed approach. Empowered by Slide-LoRA, TextHarmony achieves comparable performance to modality-specific fine-tuning results with only a 2% increase in parameters and shows an average improvement of 2. 5% in visual text comprehension tasks and 4. 0% in visual text generation tasks. Our work delineates the viability of an integrated approach to multimodal generation within the visual text domain, setting a foundation for subsequent inquiries. Code is available at https: //github. com/bytedance/TextHarmony.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Learning Task-Aware Language-Image Representation for Class-Incremental Object Detection

  • Hongquan Zhang
  • Bin-Bin Gao
  • Yi Zeng
  • Xudong Tian
  • Xin Tan
  • Zhizhong Zhang
  • Yanyun Qu
  • Jun Liu

Class-incremental object detection (CIOD) is a real-world desired capability, requiring an object detector to continuously adapt to new tasks without forgetting learned ones, with the main challenge being catastrophic forgetting. Many methods based on distillation and replay have been proposed to alleviate this problem. However, they typically learn on a pure visual backbone, neglecting the powerful representation capabilities of textual cues, which to some extent limits their performance. In this paper, we propose task-aware language-image representation to mitigate catastrophic forgetting, introducing a new paradigm for language-image-based CIOD. First of all, we demonstrate the significant advantage of language-image detectors in mitigating catastrophic forgetting. Secondly, we propose a learning task-aware language-image representation method that overcomes the existing drawback of directly utilizing the language-image detector for CIOD. More specifically, we learn the language-image representation of different tasks through an insulating approach in the training stage, while using the alignment scores produced by task-specific language-image representation in the inference stage. Through our proposed method, language-image detectors can be more practical for CIOD. We conduct extensive experiments on COCO 2017 and Pascal VOC 2007 and demonstrate that the proposed method achieves state-of-the-art results under the various CIOD settings.

PDF Details DOI

IJCAI Conference 2022 Conference Paper

Towards Discourse-Aware Document-Level Neural Machine Translation

  • Xin Tan
  • Longyin Zhang
  • Fang Kong
  • Guodong Zhou

Current document-level neural machine translation (NMT) systems have achieved remarkable progress with document context. Nevertheless, discourse information that has been proven effective in many NLP tasks is ignored in most previous work. In this work, we aim at incorporating the coherence information hidden within the RST-style discourse structure into machine translation. To achieve it, we propose a document-level NMT system enhanced with the discourse-aware document context, which is named Disco2NMT. Specifically, Disco2NMT models document context based on the discourse dependency structures through a hierarchical architecture. We first convert the RST tree of an article into a dependency structure and then build the graph convolutional network (GCN) upon the segmented EDUs under the guidance of RST dependencies to capture the discourse-aware context for NMT incorporation. We conduct experiments on the document-level English-German and English-Chinese translation tasks with three domains (TED, News, and Europarl). Experimental results show that our Disco2NMT model significantly surpasses both context-agnostic and context-aware baseline systems on multiple evaluation indicators.

PDF Details DOI

AAAI Conference 2021 Conference Paper

Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds

  • Jingyu Gong
  • Jiachen Xu
  • Xin Tan
  • Jie Zhou
  • Yanyun Qu
  • Yuan Xie
  • Lizhuang Ma

Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation where ambiguous features might be generated in feature extraction, leading to misclassification in the transition area between two objects. In this paper, firstly, we propose a Boundary Prediction Module (BPM) to predict boundary points. Based on the predicted boundary, a boundaryaware Geometric Encoding Module (GEM) is designed to encode geometric information and aggregate features with discrimination in a neighborhood, so that the local features belonging to different categories will not be polluted by each other. To provide extra geometric information for boundaryaware GEM, we also propose a light-weight Geometric Convolution Operation (GCO), making the extracted features more distinguishing. Built upon the boundary-aware GEM, we build our network and test it on benchmarks like ScanNet v2, S3DIS. Results show our methods can significantly improve the baseline and achieve state-of-the-art performance.

PDF Details

IJCAI Conference 2021 Conference Paper

Novelty Detection via Contrastive Learning with Negative Data Augmentation

  • Chengwei Chen
  • Yuan Xie
  • Shaohui Lin
  • Ruizhi Qiao
  • Jian Zhou
  • Xin Tan
  • Yi Zhang
  • Lizhuang Ma

Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous generative adversarial networks based methods and self-supervised approaches suffer from instability training, mode dropping, and low discriminative ability. We overcome such problems by introducing a novel decoder-encoder framework. Firstly, a generative network (decoder) learns the representation by mapping the initialized latent vector to an image. In particular, this vector is initialized by considering the entire distribution of training data to avoid the problem of mode-dropping. Secondly, a contrastive network (encoder) aims to ``learn to compare'' through mutual information estimation, which directly helps the generative network to obtain a more discriminative representation by using a negative data augmentation strategy. Extensive experiments show that our model has significant superiority over cutting-edge novelty detectors and achieves new state-of-the-art results on various novelty detection benchmarks, e. g. CIFAR10 and DCASE. Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.

PDF Details DOI

IJCAI Conference 2020 Conference Paper

SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptor

  • Jiachen Xu
  • Jingyu Gong
  • Jie Zhou
  • Xin Tan
  • Yuan Xie
  • Lizhuang Ma

Besides local features, global information plays an essential role in semantic segmentation, while recent works usually fail to explicitly extract the meaningful global information and make full use of it. In this paper, we propose a SceneEncoder module to impose a scene-aware guidance to enhance the effect of global information. The module predicts a scene descriptor, which learns to represent the categories of objects existing in the scene and directly guides the point-level semantic segmentation through filtering out categories not belonging to this scene. Additionally, to alleviate segmentation noise in local region, we design a region similarity loss to propagate distinguishing features to their own neighboring points with the same label, leading to the enhancement of the distinguishing ability of point-wise features. We integrate our methods into several prevailing networks and conduct extensive experiments on benchmark datasets ScanNet and ShapeNet. Results show that our methods greatly improve the performance of baselines and achieve state-of-the-art performance.

PDF Details DOI
v2026.09.13