id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.19045 | Aggregating Data for Optimal and Private Learning | [
"cs.LG"
] | Multiple Instance Regression (MIR) and Learning from Label Proportions (LLP) are learning frameworks arising in many applications, where the training data is partitioned into disjoint sets or bags, and only an aggregate label i.e., bag-label for each bag is available to the learner. In the case of MIR, the bag-label is... | {
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2411.19050 | I Dream My Painting: Connecting MLLMs and Diffusion Models via Prompt
Generation for Text-Guided Multi-Mask Inpainting | [
"cs.CV"
] | Inpainting focuses on filling missing or corrupted regions of an image to blend seamlessly with its surrounding content and style. While conditional diffusion models have proven effective for text-guided inpainting, we introduce the novel task of multi-mask inpainting, where multiple regions are simultaneously inpainte... | {
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2411.19054 | An isogemetric analysis formulation for the dynamics of geometrically
exact viscoelastic beams and beam systems with arbitrarily curved initial
geometry | [
"cs.CE",
"cs.NA",
"math.NA"
] | We present a novel formulation for the dynamics of geometrically exact Timoshenko beams and beam structures made of viscoelastic material featuring complex, arbitrarily curved initial geometries. An $\textrm{SO}(3)$-consistent and second-order accurate time integration scheme for accelerations, velocities and rate-depe... | {
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2411.19056 | Stochastic models for online optimization | [
"math.OC",
"cs.SY",
"eess.SY"
] | In this paper, we propose control-theoretic methods as tools for the design of online optimization algorithms that are able to address dynamic, noisy, and partially uncertain time-varying quadratic objective functions. Our approach introduces two algorithms specifically tailored for scenarios where the cost function fo... | {
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2411.19058 | Quality Time: Carbon-Aware Quality Adaptation for Energy-Intensive
Services | [
"cs.DC",
"cs.SY",
"eess.SY"
] | The energy demand of modern cloud services, particularly those related to generative AI, is increasing at an unprecedented pace. While hyperscalers collectively fail to meet their self-imposed emission reduction targets, they face increasing pressure from environmental sustainability reporting across many jurisdictions... | {
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2411.19064 | Way to Specialist: Closing Loop Between Specialized LLM and Evolving
Domain Knowledge Graph | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have demonstrated exceptional performance across a wide variety of domains. Nonetheless, generalist LLMs continue to fall short in reasoning tasks necessitating specialized knowledge. Prior investigations into specialized LLMs focused on domain-specific training, which entails substantial e... | {
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2411.19065 | Distributed matrix multiplication with straggler tolerance over very
small field | [
"cs.IT",
"math.IT"
] | The problem of distributed matrix multiplication with straggler tolerance over finite fields is considered, focusing on field sizes for which previous solutions were not applicable (for instance, the field of two elements). We employ Reed-Muller-type codes for explicitly constructing the desired algorithms and study th... | {
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2411.19067 | MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image
Segmentation | [
"cs.CV"
] | Referring Image Segmentation (RIS) is an advanced vision-language task that involves identifying and segmenting objects within an image as described by free-form text descriptions. While previous studies focused on aligning visual and language features, exploring training techniques, such as data augmentation, remains ... | {
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2411.19071 | Dynamic Attention and Bi-directional Fusion for Safety Helmet Wearing
Detection | [
"cs.CV"
] | Ensuring construction site safety requires accurate and real-time detection of workers' safety helmet use, despite challenges posed by cluttered environments, densely populated work areas, and hard-to-detect small or overlapping objects caused by building obstructions. This paper proposes a novel algorithm for safety h... | {
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2411.19075 | LADDER: Multi-objective Backdoor Attack via Evolutionary Algorithm | [
"cs.CR",
"cs.AI",
"cs.LG",
"cs.NE"
] | Current black-box backdoor attacks in convolutional neural networks formulate attack objective(s) as single-objective optimization problems in single domain. Designing triggers in single domain harms semantics and trigger robustness as well as introduces visual and spectral anomaly. This work proposes a multi-objective... | {
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2411.19077 | Improving sub-seasonal wind-speed forecasts in Europe with a non-linear
model | [
"cs.LG",
"physics.ao-ph"
] | Sub-seasonal wind speed forecasts provide valuable guidance for wind power system planning and operations, yet the forecasting skills of surface winds decrease sharply after two weeks. However, large-scale variables exhibit greater predictability on this time scale. This study explores the potential of leveraging non-l... | {
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2411.19083 | ObjectRelator: Enabling Cross-View Object Relation Understanding in
Ego-Centric and Exo-Centric Videos | [
"cs.CV",
"cs.AI"
] | In this paper, we focus on the Ego-Exo Object Correspondence task, an emerging challenge in the field of computer vision that aims to map objects across ego-centric and exo-centric views. We introduce ObjectRelator, a novel method designed to tackle this task, featuring two new modules: Multimodal Condition Fusion (MCF... | {
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2411.19087 | A geometric invariant of linear rank-metric codes | [
"cs.IT",
"math.CO",
"math.IT"
] | Rank-metric codes have been a central topic in coding theory due to their theoretical and practical significance, with applications in network coding, distributed storage, crisscross error correction, and post-quantum cryptography. Recent research has focused on constructing new families of rank-metric codes with disti... | {
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2411.19088 | On the Goppa morphism | [
"math.AG",
"cs.IT",
"math.IT"
] | We investigate the geometric foundations of the space of geometric Goppa codes using the Tsfasman-Vladut H-construction. These codes are constructed from level structures, which extend the classical Goppa framework by incorporating invertible sheaves and their trivializations over rational points. A key contribution is... | {
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2411.19090 | ABROCA Distributions For Algorithmic Bias Assessment: Considerations
Around Interpretation | [
"stat.ML",
"cs.LG"
] | Algorithmic bias continues to be a key concern of learning analytics. We study the statistical properties of the Absolute Between-ROC Area (ABROCA) metric. This fairness measure quantifies group-level differences in classifier performance through the absolute difference in ROC curves. ABROCA is particularly useful for ... | {
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2411.19092 | Neural Window Decoder for SC-LDPC Codes | [
"cs.LG",
"cs.IT",
"math.IT"
] | In this paper, we propose a neural window decoder (NWD) for spatially coupled low-density parity-check (SC-LDPC) codes. The proposed NWD retains the conventional window decoder (WD) process but incorporates trainable neural weights. To train the weights of NWD, we introduce two novel training strategies. First, we rest... | {
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2411.19093 | Tracking Progress Towards Sustainable Development Goal 6 Using Satellite
Imagery | [
"cs.CV",
"cs.CY",
"cs.LG"
] | Clean water and sanitation are essential for health, well-being, and sustainable development, yet significant global disparities remain. Although the United Nations' Sustainable Development Goal 6 has clear targets for universal access to clean water and sanitation, data coverage and openness remain obstacles for track... | {
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2411.19094 | Beautimeter: Harnessing GPT for Assessing Architectural and Urban Beauty
based on the 15 Properties of Living Structure | [
"physics.soc-ph",
"cs.AI"
] | Beautimeter is a new tool powered by generative pre-trained transformer (GPT) technology, designed to evaluate architectural and urban beauty. Rooted in Christopher Alexander's theory of centers, this work builds on the idea that all environments possess, to varying degrees, an innate sense of life. Alexander identifie... | {
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2411.19096 | Pralekha: An Indic Document Alignment Evaluation Benchmark | [
"cs.CL"
] | Mining parallel document pairs poses a significant challenge because existing sentence embedding models often have limited context windows, preventing them from effectively capturing document-level information. Another overlooked issue is the lack of concrete evaluation benchmarks comprising high-quality parallel docum... | {
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2411.19101 | Syndrome-Based Error-Erasure Decoding of Interleaved Linearized
Reed-Solomon Codes | [
"cs.IT",
"math.IT"
] | Linearized Reed--Solomon (LRS) codes are sum-rank-metric codes that generalize both Reed--Solomon and Gabidulin codes. We study vertically and horizontally interleaved LRS (VILRS and HILRS) codes whose codewords consist of a fixed number of stacked or concatenated codewords of a chosen LRS code. Our unified presentatio... | {
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2411.19102 | 360Recon: An Accurate Reconstruction Method Based on Depth Fusion from
360 Images | [
"cs.CV"
] | 360-degree images offer a significantly wider field of view compared to traditional pinhole cameras, enabling sparse sampling and dense 3D reconstruction in low-texture environments. This makes them crucial for applications in VR, AR, and related fields. However, the inherent distortion caused by the wide field of view... | {
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2411.19103 | VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models | [
"cs.CV",
"cs.CL"
] | In this paper, we introduce an open-source Korean-English vision-language model (VLM), VARCO-VISION. We incorporate a step-by-step training strategy that allows a model learn both linguistic and visual information while preserving the backbone model's knowledge. Our model demonstrates outstanding performance in diverse... | {
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2411.19106 | Detailed Object Description with Controllable Dimensions | [
"cs.CV"
] | Object description plays an important role for visually impaired individuals to understand and compare the differences between objects. Recent multimodal large language models(MLLMs) exhibit powerful perceptual abilities and demonstrate impressive potential for generating object-centric descriptions. However, the descr... | {
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2411.19107 | Headache to Overstock? Promoting Long-tail Items through Debiased
Product Bundling | [
"cs.IR"
] | Product bundling aims to organize a set of thematically related items into a combined bundle for shipment facilitation and item promotion. To increase the exposure of fresh or overstocked products, sellers typically bundle these items with popular products for inventory clearance. This specific task can be formulated a... | {
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2411.19108 | Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model | [
"cs.CV"
] | As a fundamental backbone for video generation, diffusion models are challenged by low inference speed due to the sequential nature of denoising. Previous methods speed up the models by caching and reusing model outputs at uniformly selected timesteps. However, such a strategy neglects the fact that differences among m... | {
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2411.19113 | Integration of Contextual Descriptors in Ontology Alignment for
Enrichment of Semantic Correspondence | [
"cs.CL",
"cs.IR"
] | This paper proposes a novel approach to semantic ontology alignment using contextual descriptors. A formalization was developed that enables the integration of essential and contextual descriptors to create a comprehensive knowledge model. The hierarchical structure of the semantic approach and the mathematical apparat... | {
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2411.19114 | PREBA: A Hardware/Software Co-Design for Multi-Instance GPU based AI
Inference Servers | [
"cs.DC",
"cs.AI",
"cs.AR",
"cs.LG"
] | NVIDIA's Multi-Instance GPU (MIG) is a feature that enables system designers to reconfigure one large GPU into multiple smaller GPU slices. This work characterizes this emerging GPU and evaluates its effectiveness in designing high-performance AI inference servers. Our study reveals that the data preprocessing stage of... | {
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2411.19117 | Understanding and Improving Training-Free AI-Generated Image Detections
with Vision Foundation Models | [
"cs.CV"
] | The rapid advancement of generative models has introduced serious risks, including deepfake techniques for facial synthesis and editing. Traditional approaches rely on training classifiers and enhancing generalizability through various feature extraction techniques. Meanwhile, training-free detection methods address is... | {
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2411.19119 | Introducing Three New Benchmark Datasets for Hierarchical Text
Classification | [
"cs.IR",
"cs.LG"
] | Hierarchical Text Classification (HTC) is a natural language processing task with the objective to classify text documents into a set of classes from a structured class hierarchy. Many HTC approaches have been proposed which attempt to leverage the class hierarchy information in various ways to improve classification p... | {
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2411.19121 | MSG score: A Comprehensive Evaluation for Multi-Scene Video Generation | [
"cs.CV",
"cs.AI"
] | This paper addresses the metrics required for generating multi-scene videos based on a continuous scenario, as opposed to traditional short video generation. Scenario-based videos require a comprehensive evaluation that considers multiple factors such as character consistency, artistic coherence, aesthetic quality, and... | {
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2411.19124 | Deep Learning for GWP Prediction: A Framework Using PCA, Quantile
Transformation, and Ensemble Modeling | [
"cs.LG",
"cond-mat.mtrl-sci",
"physics.chem-ph"
] | Developing environmentally sustainable refrigerants is critical for mitigating the impact of anthropogenic greenhouse gases on global warming. This study presents a predictive modeling framework to estimate the 100-year global warming potential (GWP 100) of single-component refrigerants using a fully connected neural n... | {
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2411.19125 | Advancing Generalization in PINNs through Latent-Space Representations | [
"cs.LG"
] | Physics-informed neural networks (PINNs) have made significant strides in modeling dynamical systems governed by partial differential equations (PDEs). However, their generalization capabilities across varying scenarios remain limited. To overcome this limitation, we propose PIDO, a novel physics-informed neural PDE so... | {
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2411.19128 | Personalized Federated Fine-Tuning for LLMs via Data-Driven
Heterogeneous Model Architectures | [
"cs.LG"
] | Large-scale instruction data is essential for aligning pretrained Large Language Models (LLMs) with human instructions, but may contain sensitive information that hinders its public sharing. Federated Learning (FL) enables collaborative fine-tuning of LLMs without data sharing. However, existing approaches to federated... | {
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2411.19132 | Conformal Prediction for Distribution-free Optimal Control of Linear
Stochastic Systems | [
"eess.SY",
"cs.SY"
] | We address an optimal control problem for linear stochastic systems with unknown noise distributions and joint chance constraints using conformal prediction. Our approach involves designing a feedback controller to maintain an error system within a prediction region (PR). We define PRs as sublevel sets of a nonconformi... | {
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2411.19133 | TEA: Trajectory Encoding Augmentation for Robust and Transferable
Policies in Offline Reinforcement Learning | [
"cs.LG"
] | In this paper, we investigate offline reinforcement learning (RL) with the goal of training a single robust policy that generalizes effectively across environments with unseen dynamics. We propose a novel approach, Trajectory Encoding Augmentation (TEA), which extends the state space by integrating latent representatio... | {
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2411.19134 | Visual SLAMMOT Considering Multiple Motion Models | [
"cs.RO",
"cs.AI",
"cs.CV"
] | Simultaneous Localization and Mapping (SLAM) and Multi-Object Tracking (MOT) are pivotal tasks in the realm of autonomous driving, attracting considerable research attention. While SLAM endeavors to generate real-time maps and determine the vehicle's pose in unfamiliar settings, MOT focuses on the real-time identificat... | {
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2411.19140 | Examining Multimodal Gender and Content Bias in ChatGPT-4o | [
"cs.CY",
"cs.AI",
"cs.CL",
"cs.HC",
"stat.OT"
] | This study investigates ChatGPT-4o's multimodal content generation, highlighting significant disparities in its treatment of sexual content and nudity versus violent and drug-related themes. Detailed analysis reveals that ChatGPT-4o consistently censors sexual content and nudity, while showing leniency towards violence... | {
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2411.19141 | On Moving Object Segmentation from Monocular Video with Transformers | [
"cs.CV",
"cs.AI"
] | Moving object detection and segmentation from a single moving camera is a challenging task, requiring an understanding of recognition, motion and 3D geometry. Combining both recognition and reconstruction boils down to a fusion problem, where appearance and motion features need to be combined for classification and seg... | {
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2411.19143 | Co-Learning: Towards Semi-Supervised Object Detection with Road-side
Cameras | [
"cs.CV"
] | Recently, deep learning has experienced rapid expansion, contributing significantly to the progress of supervised learning methodologies. However, acquiring labeled data in real-world settings can be costly, labor-intensive, and sometimes scarce. This challenge inhibits the extensive use of neural networks for practica... | {
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2411.19144 | Computationally efficient trajectory design from motion primitives for
near time-optimal transitions for systems with oscillating internal dynamics | [
"eess.SY",
"cs.SY"
] | An efficient approach to compute near time-optimal trajectories for linear kinematic systems with oscillatory internal dynamics is presented. Thereby, kinematic constraints with respect to velocity, acceleration and jerk are taken into account. The trajectories are composed of several motion primitives, the most crucia... | {
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2411.19146 | Puzzle: Distillation-Based NAS for Inference-Optimized LLMs | [
"cs.LG"
] | Large language models (LLMs) have demonstrated remarkable capabilities, but their adoption is limited by high computational costs during inference. While increasing parameter counts enhances accuracy, it also widens the gap between state-of-the-art capabilities and practical deployability. We present Puzzle, a framewor... | {
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2411.19148 | Efficient calculation of time-optimal motion primitives for systems
exhibiting oscillatory internal dynamics with multiple applications | [
"eess.SY",
"cs.SY"
] | A fast algorithm for planning near time-optimal trajectories for systems with an oscillatory internal dynamics has been developed in previous work. In this algorithm, trajectories are assembled from special motion primitives called jerk segments, which are connected by segments of constant acceleration and velocity res... | {
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2411.19149 | Counting Stacked Objects from Multi-View Images | [
"cs.CV"
] | Visual object counting is a fundamental computer vision task underpinning numerous real-world applications, from cell counting in biomedicine to traffic and wildlife monitoring. However, existing methods struggle to handle the challenge of stacked 3D objects in which most objects are hidden by those above them. To addr... | {
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2411.19154 | DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual
Learning | [
"cs.LG",
"cs.AI"
] | Continual learning aims to equip models with the ability to retain previously learned knowledge like a human. Recent work incorporating Parameter-Efficient Fine-Tuning has revitalized the field by introducing lightweight extension modules. However, existing methods usually overlook the issue of information leakage caus... | {
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2411.19156 | LoRA of Change: Learning to Generate LoRA for the Editing Instruction
from A Single Before-After Image Pair | [
"cs.CV"
] | In this paper, we propose the LoRA of Change (LoC) framework for image editing with visual instructions, i.e., before-after image pairs. Compared to the ambiguities, insufficient specificity, and diverse interpretations of natural language, visual instructions can accurately reflect users' intent. Building on the succe... | {
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2411.19158 | Bayesian Deconvolution of Astronomical Images with Diffusion Models:
Quantifying Prior-Driven Features in Reconstructions | [
"astro-ph.IM",
"astro-ph.GA",
"cs.CV"
] | Deconvolution of astronomical images is a key aspect of recovering the intrinsic properties of celestial objects, especially when considering ground-based observations. This paper explores the use of diffusion models (DMs) and the Diffusion Posterior Sampling (DPS) algorithm to solve this inverse problem task. We apply... | {
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2411.19161 | Neural Shadow Art | [
"cs.CV"
] | Shadow art is a captivating form of sculptural expression, where the projection of a sculpture in a specific direction reveals a desired shape with high accuracy. In this work, we introduce Neural Shadow Art, which leverages implicit function representations to expand the possibilities of shadow art. Our method provide... | {
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2411.19162 | Lost & Found: Updating Dynamic 3D Scene Graphs from Egocentric
Observations | [
"cs.RO",
"cs.CV"
] | Recent approaches have successfully focused on the segmentation of static reconstructions, thereby equipping downstream applications with semantic 3D understanding. However, the world in which we live is dynamic, characterized by numerous interactions between the environment and humans or robotic agents. Static semanti... | {
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2411.19167 | HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos | [
"cs.CV",
"cs.AI",
"cs.RO"
] | We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (more than 3.7M images) of multi-view RGB/monochrome image streams showing 19 subjects interacting with 33 diverse rigid objects, multi-modal signals such as eye gaze or scene point clouds... | {
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2411.19181 | Large width penalization for neural network-based prediction interval
estimation | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Forecasting accuracy in highly uncertain environments is challenging due to the stochastic nature of systems. Deterministic forecasting provides only point estimates and cannot capture potential outcomes. Therefore, probabilistic forecasting has gained significant attention due to its ability to quantify uncertainty, w... | {
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2411.19182 | SOWing Information: Cultivating Contextual Coherence with MLLMs in Image
Generation | [
"cs.CV",
"cs.AI"
] | Originating from the diffusion phenomenon in physics, which describes the random movement and collisions of particles, diffusion generative models simulate a random walk in the data space along the denoising trajectory. This allows information to diffuse across regions, yielding harmonious outcomes. However, the chaoti... | {
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2411.19187 | Beyond Logit Lens: Contextual Embeddings for Robust Hallucination
Detection & Grounding in VLMs | [
"cs.CL"
] | The rapid development of Large Multimodal Models (LMMs) has significantly advanced multimodal understanding by harnessing the language abilities of Large Language Models (LLMs) and integrating modality-specific encoders. However, LMMs are plagued by hallucinations that limit their reliability and adoption. While tradit... | {
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2411.19189 | Video Depth without Video Models | [
"cs.CV"
] | Video depth estimation lifts monocular video clips to 3D by inferring dense depth at every frame. Recent advances in single-image depth estimation, brought about by the rise of large foundation models and the use of synthetic training data, have fueled a renewed interest in video depth. However, naively applying a sing... | {
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2411.19193 | Convex Regularization and Convergence of Policy Gradient Flows under
Safety Constraints | [
"cs.LG",
"cs.AI",
"math.OC",
"math.PR",
"stat.ML"
] | This paper studies reinforcement learning (RL) in infinite-horizon dynamic decision processes with almost-sure safety constraints. Such safety-constrained decision processes are central to applications in autonomous systems, finance, and resource management, where policies must satisfy strict, state-dependent constrain... | {
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2411.19203 | An Extensive Evaluation of Factual Consistency in Large Language Models
for Data-to-Text Generation | [
"cs.CL"
] | Large Language Models (LLMs) have shown exceptional performance across various Data-to-Text Generation (DTG) tasks. However, generating factually consistent text in DTG remains challenging for LLMs. Despite this, in-depth evaluations of LLM factual consistency for DTG remain missing in the current literature. This pape... | {
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2411.19209 | A spiking photonic neural network of 40.000 neurons, trained with
rank-order coding for leveraging sparsity | [
"cs.ET",
"cs.NE"
] | piking neural networks are neuromorphic systems that emulate certain aspects of biological neurons, offering potential advantages in energy efficiency and speed by for example leveraging sparsity. While CMOS-based electronic SNN hardware has shown promise, scalability and parallelism challenges remain. Photonics provid... | {
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2411.19210 | Track Anything Behind Everything: Zero-Shot Amodal Video Object
Segmentation | [
"cs.CV"
] | We present Track Anything Behind Everything (TABE), a novel dataset, pipeline, and evaluation framework for zero-shot amodal completion from visible masks. Unlike existing methods that require pretrained class labels, our approach uses a single query mask from the first frame where the object is visible, enabling flexi... | {
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2411.19211 | On the Ethical Considerations of Generative Agents | [
"cs.CY",
"cs.AI",
"cs.ET",
"cs.MA"
] | The Generative Agents framework recently developed by Park et al. has enabled numerous new technical solutions and problem-solving approaches. Academic and industrial interest in generative agents has been explosive as a result of the effectiveness of generative agents toward emulating human behaviour. However, it is n... | {
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2411.19213 | ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel
Realities | [
"cs.CV"
] | Inspired by the Many-Worlds Interpretation (MWI), this work introduces a novel neural network architecture that splits the same input signal into parallel branches at each layer, utilizing a Hyper Rectified Activation, referred to as ANDHRA. The branched layers do not merge and form separate network paths, leading to m... | {
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2411.19214 | Parallel and Mini-Batch Stable Matching for Large-Scale Reciprocal
Recommender Systems | [
"cs.IR"
] | Reciprocal recommender systems (RRSs) are crucial in online two-sided matching platforms, such as online job or dating markets, as they need to consider the preferences of both sides of the match. The concentration of recommendations to a subset of users on these platforms undermines their match opportunities and reduc... | {
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2411.19215 | Cross-Spectral Attention for Unsupervised RGB-IR Face Verification and
Person Re-identification | [
"cs.CV"
] | Cross-spectral biometrics, such as matching imagery of faces or persons from visible (RGB) and infrared (IR) bands, have rapidly advanced over the last decade due to increasing sensitivity, size, quality, and ubiquity of IR focal plane arrays and enhanced analytics beyond the visible spectrum. Current techniques for mi... | {
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2411.19220 | Automatic Prompt Generation and Grounding Object Detection for Zero-Shot
Image Anomaly Detection | [
"cs.CV",
"cs.MM"
] | Identifying defects and anomalies in industrial products is a critical quality control task. Traditional manual inspection methods are slow, subjective, and error-prone. In this work, we propose a novel zero-shot training-free approach for automated industrial image anomaly detection using a multimodal machine learning... | {
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2411.19223 | On the Unknowable Limits to Prediction | [
"cs.LG",
"cs.AI",
"cs.CY",
"stat.ME"
] | We propose a rigorous decomposition of predictive error, highlighting that not all 'irreducible' error is genuinely immutable. Many domains stand to benefit from iterative enhancements in measurement, construct validity, and modeling. Our approach demonstrates how apparently 'unpredictable' outcomes can become more tra... | {
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2411.19224 | Differentiable Voxel-based X-ray Rendering Improves Sparse-View 3D CBCT
Reconstruction | [
"eess.IV",
"cs.CV"
] | We present DiffVox, a self-supervised framework for Cone-Beam Computed Tomography (CBCT) reconstruction by directly optimizing a voxelgrid representation using physics-based differentiable X-ray rendering. Further, we investigate how the different implementations of the X-ray image formation model in the renderer affec... | {
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2411.19229 | Habit Coach: Customising RAG-based chatbots to support behavior change | [
"cs.HC",
"cs.AI",
"cs.CY"
] | This paper presents the iterative development of Habit Coach, a GPT-based chatbot designed to support users in habit change through personalized interaction. Employing a user-centered design approach, we developed the chatbot using a Retrieval-Augmented Generation (RAG) system, which enables behavior personalization wi... | {
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2411.19230 | Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers
for EEG | [
"cs.LG",
"cs.AI"
] | Effectively utilizing extensive unlabeled high-density EEG data to improve performance in scenarios with limited labeled low-density EEG data presents a significant challenge. In this paper, we address this by framing it as a graph transfer learning and knowledge distillation problem. We propose a Unified Pre-trained G... | {
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2411.19231 | Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style
Distribution | [
"cs.CV"
] | Style transfer presents a significant challenge, primarily centered on identifying an appropriate style representation. Conventional methods employ style loss, derived from second-order statistics or contrastive learning, to constrain style representation in the stylized result. However, these pre-defined style represe... | {
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2411.19233 | Gaussians-to-Life: Text-Driven Animation of 3D Gaussian Splatting Scenes | [
"cs.CV"
] | State-of-the-art novel view synthesis methods achieve impressive results for multi-view captures of static 3D scenes. However, the reconstructed scenes still lack "liveliness," a key component for creating engaging 3D experiences. Recently, novel video diffusion models generate realistic videos with complex motion and ... | {
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2411.19234 | SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability
Detection Framework | [
"cs.CR",
"cs.AI"
] | Smart contracts are essential for managing digital assets in blockchain networks, highlighting the need for effective security measures. This paper introduces SmartLLMSentry, a novel framework that leverages large language models (LLMs), specifically ChatGPT with in-context training, to advance smart contract vulnerabi... | {
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2411.19235 | InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for
3D Instance-Level Perception | [
"cs.CV"
] | 3D scene understanding has become an essential area of research with applications in autonomous driving, robotics, and augmented reality. Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful approach, combining explicit modeling with neural adaptability to provide efficient and detailed scene representation... | {
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2411.19236 | Leveraging Aerial Platforms for Downlink Communications in Sparse
Satellite Networks | [
"cs.IT",
"eess.SP",
"math.IT"
] | Although a significant number satellites are deemed essential for facilitating diverse applications of satellite networks, aerial platforms are emerging as excellent alternatives for enabling reliable communications with fewer satellites. In scenarios with sparse satellite networks, aerial platforms participate in down... | {
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2411.19240 | How far can bias go? -- Tracing bias from pretraining data to alignment | [
"cs.CL"
] | As LLMs are increasingly integrated into user-facing applications, addressing biases that perpetuate societal inequalities is crucial. While much work has gone into measuring or mitigating biases in these models, fewer studies have investigated their origins. Therefore, this study examines the correlation between gende... | {
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2411.19242 | Controlling Participation in Federated Learning with Feedback | [
"cs.LG",
"math.OC"
] | We address the problem of client participation in federated learning, where traditional methods typically rely on a random selection of a small subset of clients for each training round. In contrast, we propose FedBack, a deterministic approach that leverages control-theoretic principles to manage client participation ... | {
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2411.19244 | Consolidating and Developing Benchmarking Datasets for the Nepali
Natural Language Understanding Tasks | [
"cs.CL"
] | The Nepali language has distinct linguistic features, especially its complex script (Devanagari script), morphology, and various dialects, which pose a unique challenge for natural language processing (NLP) evaluation. While the Nepali Language Understanding Evaluation (Nep-gLUE) benchmark provides a foundation for eva... | {
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2411.19245 | Contrastive representations of high-dimensional, structured treatments | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Estimating causal effects is vital for decision making. In standard causal effect estimation, treatments are usually binary- or continuous-valued. However, in many important real-world settings, treatments can be structured, high-dimensional objects, such as text, video, or audio. This provides a challenge to tradition... | {
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2411.19246 | Face2QR: A Unified Framework for Aesthetic, Face-Preserving, and
Scannable QR Code Generation | [
"cs.CV"
] | Existing methods to generate aesthetic QR codes, such as image and style transfer techniques, tend to compromise either the visual appeal or the scannability of QR codes when they incorporate human face identity. Addressing these imperfections, we present Face2QR-a novel pipeline specifically designed for generating pe... | {
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2411.19248 | Reflecting Intelligent Surfaces-Assisted Multiple-Antenna Coded Caching | [
"cs.IT",
"math.IT"
] | Reconfigurable intelligent surface (RIS) has been treated as a core technique in improving wireless propagation environments for the next generation wireless communication systems. This paper proposes a new coded caching problem, referred to as Reconfigurable Intelligent Surface (RIS)-assisted multiple-antenna coded ca... | {
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2411.19250 | Parametric Lattices Are Better Quantizers in Dimensions 13 and 14 | [
"cs.IT",
"math-ph",
"math.IT",
"math.MG",
"math.MP"
] | New lattice quantizers with lower normalized second moments than previously reported are constructed in 13 and 14 dimensions and conjectured to be optimal. Our construction combines an initial numerical optimization with a subsequent analytical optimization of families of lattices, whose Voronoi regions are constructed... | {
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2411.19251 | Skeleton Detection Using Dual Radars with Integration of Dual-View CNN
Models and mmPose | [
"eess.IV",
"cs.CV"
] | Skeleton detection is a technique that can beapplied to a variety of situations. It is especially critical identifying and tracking the movements of the elderly, especially in real-time fall detection. While conventional image processing methods exist, there's a growing preference for utilizing pointclouds data collect... | {
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2411.19253 | Quantum feedback control with a transformer neural network architecture | [
"quant-ph",
"cond-mat.mes-hall",
"cs.LG"
] | Attention-based neural networks such as transformers have revolutionized various fields such as natural language processing, genomics, and vision. Here, we demonstrate the use of transformers for quantum feedback control through a supervised learning approach. In particular, due to the transformer's ability to capture ... | {
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2411.19258 | L4acados: Learning-based models for acados, applied to Gaussian
process-based predictive control | [
"eess.SY",
"cs.SY",
"math.OC"
] | Incorporating learning-based models, such as Gaussian processes (GPs), into model predictive control (MPC) strategies can significantly improve control performance and online adaptation capabilities for real-world applications. Still, despite recent advances in numerical optimization and real-time GP inference, its wid... | {
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2411.19261 | Improving Multi-Subject Consistency in Open-Domain Image Generation with
Isolation and Reposition Attention | [
"cs.CV"
] | Training-free diffusion models have achieved remarkable progress in generating multi-subject consistent images within open-domain scenarios. The key idea of these methods is to incorporate reference subject information within the attention layer. However, existing methods still obtain suboptimal performance when handli... | {
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2411.19271 | AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors
for Indoor Room Reconstruction Using Smartphones | [
"cs.CV"
] | Geometric priors are often used to enhance 3D reconstruction. With many smartphones featuring low-resolution depth sensors and the prevalence of off-the-shelf monocular geometry estimators, incorporating geometric priors as regularization signals has become common in 3D vision tasks. However, the accuracy of depth esti... | {
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2411.19274 | On-chip Hyperspectral Image Segmentation with Fully Convolutional
Networks for Scene Understanding in Autonomous Driving | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | Most of current computer vision-based advanced driver assistance systems (ADAS) perform detection and tracking of objects quite successfully under regular conditions. However, under adverse weather and changing lighting conditions, and in complex situations with many overlapping objects, these systems are not completel... | {
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2411.19278 | OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth
Integration | [
"cs.CV"
] | Depth completion (DC) aims to predict a dense depth map from an RGB image and sparse depth observations. Existing methods for DC generalize poorly on new datasets or unseen sparse depth patterns, limiting their practical applications. We propose OMNI-DC, a highly robust DC model that generalizes well across various sce... | {
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2411.19279 | Economic Dispatch and Power Flow Analysis for Microgrids | [
"eess.SY",
"cs.SY"
] | This study investigates the economic dispatch and optimal power flow (OPF) for microgrids, focusing on two configurations: a single-bus islanded microgrid and a three-bus grid-tied microgrid. The methodologies integrate renewable energy sources (solar PV and wind turbines), battery energy storage systems (BESS), and co... | {
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2411.19281 | The role of data-induced randomness in quantum machine learning
classification tasks | [
"quant-ph",
"cs.LG"
] | Quantum machine learning (QML) has surged as a prominent area of research with the objective to go beyond the capabilities of classical machine learning models. A critical aspect of any learning task is the process of data embedding, which directly impacts model performance. Poorly designed data-embedding strategies ca... | {
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2411.19284 | Fractal Conditional Correlation Dimension Infers Complex Causal Networks | [
"cs.IT",
"math.DS",
"math.IT"
] | Determining causal inference has become popular in physical and engineering applications. While the problem has immense challenges, it provides a way to model the complex networks by observing the time series. In this paper, we present the optimal conditional correlation dimensional geometric information flow principle... | {
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2411.19285 | BPQP: A Differentiable Convex Optimization Framework for Efficient
End-to-End Learning | [
"cs.LG",
"cs.AI",
"q-fin.PM"
] | Data-driven decision-making processes increasingly utilize end-to-end learnable deep neural networks to render final decisions. Sometimes, the output of the forward functions in certain layers is determined by the solutions to mathematical optimization problems, leading to the emergence of differentiable optimization l... | {
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2411.19289 | GMS-VINS:Multi-category Dynamic Objects Semantic Segmentation for
Enhanced Visual-Inertial Odometry Using a Promptable Foundation Model | [
"cs.CV"
] | Visual-inertial odometry (VIO) is widely used in various fields, such as robots, drones, and autonomous vehicles, due to its low cost and complementary sensors. Most VIO methods presuppose that observed objects are static and time-invariant. However, real-world scenes often feature dynamic objects, compromising the acc... | {
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2411.19290 | SADG: Segment Any Dynamic Gaussian Without Object Trackers | [
"cs.CV"
] | Understanding dynamic 3D scenes is fundamental for various applications, including extended reality (XR) and autonomous driving. Effectively integrating semantic information into 3D reconstruction enables holistic representation that opens opportunities for immersive and interactive applications. We introduce SADG, Seg... | {
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} |
2411.19292 | UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for
Urban Scene Simulation | [
"cs.CV"
] | Photorealistic 3D vehicle models with high controllability are essential for autonomous driving simulation and data augmentation. While handcrafted CAD models provide flexible controllability, free CAD libraries often lack the high-quality materials necessary for photorealistic rendering. Conversely, reconstructed 3D m... | {
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} |
2411.19295 | Extracting Information in a Low-resource Setting: Case Study on
Bioinformatics Workflows | [
"cs.CL"
] | Bioinformatics workflows are essential for complex biological data analyses and are often described in scientific articles with source code in public repositories. Extracting detailed workflow information from articles can improve accessibility and reusability but is hindered by limited annotated corpora. To address th... | {
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} |
2411.19297 | Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through
Frequency-Based Adaptation | [
"cs.CV",
"cs.LG"
] | Adapting vision transformer foundation models through parameter-efficient fine-tuning (PEFT) methods has become increasingly popular. These methods optimize a limited subset of parameters, enabling efficient adaptation without the need to fine-tune the entire model while still achieving competitive performance. However... | {
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} |
2411.19300 | Fast Switching in Mixed-Integer Model Predictive Control | [
"eess.SY",
"cs.SY",
"math.OC"
] | We derive stability results for finite control set and mixed-integer model predictive control and propose a unified theoretical framework. The presentation rests upon the inherent robustness properties of common model predictive control with stabilizing terminal conditions and techniques for solving mixed-integer optim... | {
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} |
2411.19301 | Structured Object Language Modeling (SoLM): Native Structured Objects
Generation Conforming to Complex Schemas with Self-Supervised Denoising | [
"cs.SE",
"cs.AI"
] | In this paper, we study the problem of generating structured objects that conform to a complex schema, with intricate dependencies between the different components (facets) of the object. The facets of the object (attributes, fields, columns, properties) can be a mix of short, structured, type-constrained facts, or lon... | {
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} |
2411.19304 | Perspective of Software Engineering Researchers on Machine Learning
Practices Regarding Research, Review, and Education | [
"cs.SE",
"cs.LG"
] | Context: Machine Learning (ML) significantly impacts Software Engineering (SE), but studies mainly focus on practitioners, neglecting researchers. This overlooks practices and challenges in teaching, researching, or reviewing ML applications in SE. Objective: This study aims to contribute to the knowledge, about the ... | {
"Other": 1,
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} |
2411.19305 | LD-EnSF: Synergizing Latent Dynamics with Ensemble Score Filters for
Fast Data Assimilation with Sparse Observations | [
"stat.ML",
"cs.LG",
"math.DS"
] | Data assimilation techniques are crucial for correcting the trajectory when modeling complex physical systems. A recently developed data assimilation method, Latent Ensemble Score Filter (Latent-EnSF), has shown great promise in addressing the key limitation of EnSF for highly sparse observations in high-dimensional an... | {
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} |
2411.19309 | GRAPE: Generalizing Robot Policy via Preference Alignment | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Despite the recent advancements of vision-language-action (VLA) models on a variety of robotics tasks, they suffer from critical issues such as poor generalizability to unseen tasks, due to their reliance on behavior cloning exclusively from successful rollouts. Furthermore, they are typically fine-tuned to replicate d... | {
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} |
2411.19320 | Generalized Gaussian Model for Learned Image Compression | [
"eess.IV",
"cs.CV"
] | In learned image compression, probabilistic models play an essential role in characterizing the distribution of latent variables. The Gaussian model with mean and scale parameters has been widely used for its simplicity and effectiveness. Probabilistic models with more parameters, such as the Gaussian mixture models, c... | {
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} |
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