id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.07091 | Creative Portraiture: Exploring Creative Adversarial Networks and
Conditional Creative Adversarial Networks | [
"cs.CV",
"cs.LG"
] | Convolutional neural networks (CNNs) have been combined with generative adversarial networks (GANs) to create deep convolutional generative adversarial networks (DCGANs) with great success. DCGANs have been used for generating images and videos from creative domains such as fashion design and painting. A common critiqu... | {
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2412.07093 | Streaming Private Continual Counting via Binning | [
"cs.LG",
"cs.CR",
"cs.DS"
] | In differential privacy, $\textit{continual observation}$ refers to problems in which we wish to continuously release a function of a dataset that is revealed one element at a time. The challenge is to maintain a good approximation while keeping the combined output over all time steps differentially private. In the spe... | {
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2412.07094 | Access Point Deployment for Localizing Accuracy and User Rate in
Cell-Free Systems | [
"cs.NI",
"cs.AI"
] | Evolving next-generation mobile networks is designed to provide ubiquitous coverage and networked sensing. With utility of multi-view sensing and multi-node joint transmission, cell-free is a promising technique to realize this prospect. This paper aims to tackle the problem of access point (AP) deployment in cell-free... | {
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2412.07096 | QAPyramid: Fine-grained Evaluation of Content Selection for Text
Summarization | [
"cs.CL",
"cs.AI"
] | How to properly conduct human evaluations for text summarization is a longstanding challenge. The Pyramid human evaluation protocol, which assesses content selection by breaking the reference summary into sub-units and verifying their presence in the system summary, has been widely adopted. However, it suffers from a l... | {
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2412.07097 | On Evaluating the Durability of Safeguards for Open-Weight LLMs | [
"cs.CR",
"cs.AI"
] | Stakeholders -- from model developers to policymakers -- seek to minimize the dual-use risks of large language models (LLMs). An open challenge to this goal is whether technical safeguards can impede the misuse of LLMs, even when models are customizable via fine-tuning or when model weights are fully open. In response,... | {
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2412.07102 | Primary visual cortex contributes to color constancy by predicting
rather than discounting the illuminant: evidence from a computational study | [
"q-bio.NC",
"cs.CV"
] | Color constancy (CC) is an important ability of the human visual system to stably perceive the colors of objects despite considerable changes in the color of the light illuminating them. While increasing evidence from the field of neuroscience supports that multiple levels of the visual system contribute to the realiza... | {
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2412.07105 | A Powered Prosthetic Hand with Vision System for Enhancing the
Anthropopathic Grasp | [
"cs.RO",
"cs.CV",
"cs.HC",
"cs.SY",
"eess.SY"
] | The anthropomorphism of grasping process significantly benefits the experience and grasping efficiency of prosthetic hand wearers. Currently, prosthetic hands controlled by signals such as brain-computer interfaces (BCI) and electromyography (EMG) face difficulties in precisely recognizing the amputees' grasping gestur... | {
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2412.07106 | Covered Forest: Fine-grained generalization analysis of graph neural
networks | [
"cs.LG",
"cs.DM",
"cs.DS",
"cs.NE",
"stat.ML"
] | The expressive power of message-passing graph neural networks (MPNNs) is reasonably well understood, primarily through combinatorial techniques from graph isomorphism testing. However, MPNNs' generalization abilities -- making meaningful predictions beyond the training set -- remain less explored. Current generalizatio... | {
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2412.07108 | Improving the Natural Language Inference robustness to hard dataset by
data augmentation and preprocessing | [
"cs.CL"
] | Natural Language Inference (NLI) is the task of inferring whether the hypothesis can be justified by the given premise. Basically, we classify the hypothesis into three labels(entailment, neutrality and contradiction) given the premise. NLI was well studied by the previous researchers. A number of models, especially th... | {
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2412.07111 | Predictable Emergent Abilities of LLMs: Proxy Tasks Are All You Need | [
"cs.CL"
] | While scaling laws optimize training configurations for large language models (LLMs) through experiments on smaller or early-stage models, they fail to predict emergent abilities due to the absence of such capabilities in these models. To address this, we propose a method that predicts emergent abilities by leveraging ... | {
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2412.07112 | Maya: An Instruction Finetuned Multilingual Multimodal Model | [
"cs.CV",
"cs.CL"
] | The rapid development of large Vision-Language Models (VLMs) has led to impressive results on academic benchmarks, primarily in widely spoken languages. However, significant gaps remain in the ability of current VLMs to handle low-resource languages and varied cultural contexts, largely due to a lack of high-quality, d... | {
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2412.07113 | Exploring Coding Spot: Understanding Parametric Contributions to LLM
Coding Performance | [
"cs.CL"
] | Large Language Models (LLMs) have demonstrated notable proficiency in both code generation and comprehension across multiple programming languages. However, the mechanisms underlying this proficiency remain underexplored, particularly with respect to whether distinct programming languages are processed independently or... | {
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2412.07114 | TT-MPD: Test Time Model Pruning and Distillation | [
"cs.CV"
] | Pruning can be an effective method of compressing large pre-trained models for inference speed acceleration. Previous pruning approaches rely on access to the original training dataset for both pruning and subsequent fine-tuning. However, access to the training data can be limited due to concerns such as data privacy a... | {
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2412.07116 | A Review of Human Emotion Synthesis Based on Generative Technology | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Human emotion synthesis is a crucial aspect of affective computing. It involves using computational methods to mimic and convey human emotions through various modalities, with the goal of enabling more natural and effective human-computer interactions. Recent advancements in generative models, such as Autoencoders, Gen... | {
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2412.07119 | DiffCLIP: Few-shot Language-driven Multimodal Classifier | [
"cs.CV"
] | Visual language models like Contrastive Language-Image Pretraining (CLIP) have shown impressive performance in analyzing natural images with language information. However, these models often encounter challenges when applied to specialized domains such as remote sensing due to the limited availability of image-text pai... | {
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2412.07120 | Corrupted Learning Dynamics in Games | [
"cs.GT",
"cs.LG",
"stat.ML"
] | Learning in games refers to scenarios where multiple players interact in a shared environment, each aiming to minimize their regret. An equilibrium can be computed at a fast rate of $O(1/T)$ when all players follow the optimistic follow-the-regularized-leader (OFTRL). However, this acceleration is limited to the honest... | {
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2412.07121 | Bridging the Gap for Test-Time Multimodal Sentiment Analysis | [
"cs.LG",
"cs.CL"
] | Multimodal sentiment analysis (MSA) is an emerging research topic that aims to understand and recognize human sentiment or emotions through multiple modalities. However, in real-world dynamic scenarios, the distribution of target data is always changing and different from the source data used to train the model, which ... | {
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2412.07127 | Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient
Solvers in Large-Scale PDE Systems | [
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA"
] | Preconditioning techniques are crucial for enhancing the efficiency of solving large-scale linear equation systems that arise from partial differential equation (PDE) discretization. These techniques, such as Incomplete Cholesky factorization (IC) and data-driven neural network methods, accelerate the convergence of it... | {
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2412.07129 | StyleMark: A Robust Watermarking Method for Art Style Images Against
Black-Box Arbitrary Style Transfer | [
"cs.CV"
] | Arbitrary Style Transfer (AST) achieves the rendering of real natural images into the painting styles of arbitrary art style images, promoting art communication. However, misuse of unauthorized art style images for AST may infringe on artists' copyrights. One countermeasure is robust watermarking, which tracks image pr... | {
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2412.07132 | Revisiting Lesion Tracking in 3D Total Body Photography | [
"cs.CV"
] | Melanoma is the most deadly form of skin cancer. Tracking the evolution of nevi and detecting new lesions across the body is essential for the early detection of melanoma. Despite prior work on longitudinal tracking of skin lesions in 3D total body photography, there are still several challenges, including 1) low accur... | {
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2412.07136 | A multimodal ensemble approach for clear cell renal cell carcinoma
treatment outcome prediction | [
"cs.CV",
"q-bio.QM"
] | Purpose: A reliable cancer prognosis model for clear cell renal cell carcinoma (ccRCC) can enhance personalized treatment. We developed a multi-modal ensemble model (MMEM) that integrates pretreatment clinical data, multi-omics data, and histopathology whole slide image (WSI) data to predict overall survival (OS) and d... | {
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2412.07138 | Unlocking TriLevel Learning with Level-Wise Zeroth Order Constraints:
Distributed Algorithms and Provable Non-Asymptotic Convergence | [
"cs.LG",
"math.OC"
] | Trilevel learning (TLL) found diverse applications in numerous machine learning applications, ranging from robust hyperparameter optimization to domain adaptation. However, existing researches primarily focus on scenarios where TLL can be addressed with first order information available at each level, which is inadequa... | {
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2412.07140 | FIRE: Robust Detection of Diffusion-Generated Images via
Frequency-Guided Reconstruction Error | [
"cs.CV"
] | The rapid advancement of diffusion models has significantly improved high-quality image generation, making generated content increasingly challenging to distinguish from real images and raising concerns about potential misuse. In this paper, we observe that diffusion models struggle to accurately reconstruct mid-band f... | {
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2412.07141 | Integrating MedCLIP and Cross-Modal Fusion for Automatic Radiology
Report Generation | [
"cs.CV"
] | Automating radiology report generation can significantly reduce the workload of radiologists and enhance the accuracy, consistency, and efficiency of clinical documentation.We propose a novel cross-modal framework that uses MedCLIP as both a vision extractor and a retrieval mechanism to improve the process of medical r... | {
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2412.07144 | Political Actor Agent: Simulating Legislative System for Roll Call Votes
Prediction with Large Language Models | [
"cs.AI",
"cs.CL"
] | Predicting roll call votes through modeling political actors has emerged as a focus in quantitative political science and computer science. Widely used embedding-based methods generate vectors for legislators from diverse data sets to predict legislative behaviors. However, these methods often contend with challenges s... | {
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2412.07147 | MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation | [
"cs.CV",
"cs.AI"
] | Image Translation (IT) holds immense potential across diverse domains, enabling the translation of textual content within images into various languages. However, existing datasets often suffer from limitations in scale, diversity, and quality, hindering the development and evaluation of IT models. To address this issue... | {
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2412.07148 | MM-PoE: Multiple Choice Reasoning via. Process of Elimination using
Multi-Modal Models | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | This paper introduces Multiple Choice Reasoning via. Process of Elimination using Multi-Modal models, herein referred to as Multi-Modal Process of Elimination (MM-PoE). This novel methodology is engineered to augment the efficacy of Vision-Language Models (VLMs) in multiple-choice visual reasoning tasks. Diverging from... | {
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2412.07149 | RAP-SR: RestorAtion Prior Enhancement in Diffusion Models for Realistic
Image Super-Resolution | [
"cs.CV"
] | Benefiting from their powerful generative capabilities, pretrained diffusion models have garnered significant attention for real-world image super-resolution (Real-SR). Existing diffusion-based SR approaches typically utilize semantic information from degraded images and restoration prompts to activate prior for produc... | {
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2412.07152 | Hero-SR: One-Step Diffusion for Super-Resolution with Human Perception
Priors | [
"cs.CV"
] | Owing to the robust priors of diffusion models, recent approaches have shown promise in addressing real-world super-resolution (Real-SR). However, achieving semantic consistency and perceptual naturalness to meet human perception demands remains difficult, especially under conditions of heavy degradation and varied inp... | {
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2412.07154 | Unified Vertex Motion Estimation for Integrated Video Stabilization and
Stitching in Tractor-Trailer Wheeled Robots | [
"cs.RO"
] | Tractor-trailer wheeled robots need to perform comprehensive perception tasks to enhance their operations in areas such as logistics parks and long-haul transportation. The perception of these robots face three major challenges: the relative pose change between the tractor and trailer, the asynchronous vibrations betwe... | {
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2412.07155 | Annotation Techniques for Judo Combat Phase Classification from
Tournament Footage | [
"cs.CV",
"cs.MM"
] | This paper presents a semi-supervised approach to extracting and analyzing combat phases in judo tournaments using live-streamed footage. The objective is to automate the annotation and summarization of live streamed judo matches. We train models that extract relevant entities and classify combat phases from fixed-pers... | {
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2412.07156 | QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality
Prediction | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Deep learning has made significant strides in automated brain tumor segmentation from magnetic resonance imaging (MRI) scans in recent years. However, the reliability of these tools is hampered by the presence of poor-quality segmentation outliers, particularly in out-of-distribution samples, making their implementatio... | {
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2412.07157 | Multi-Scale Contrastive Learning for Video Temporal Grounding | [
"cs.CV"
] | Temporal grounding, which localizes video moments related to a natural language query, is a core problem of vision-language learning and video understanding. To encode video moments of varying lengths, recent methods employ a multi-level structure known as a feature pyramid. In this structure, lower levels concentrate ... | {
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2412.07160 | Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph
Generation | [
"cs.CV"
] | To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts visual data into nodes to represent entities and edges to capture temporal relations. Existing methods encode entity masks tracked across temporal dimensions (mask tubes),... | {
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2412.07161 | Compositional Zero-Shot Learning with Contextualized Cues and Adaptive
Contrastive Training | [
"cs.CV"
] | Compositional Zero-Shot Learning (CZSL) aims to recognize unseen combinations of seen attributes and objects. Current CLIP-based methods in CZSL, despite their advancements, often fail to effectively understand and link the attributes and objects due to inherent limitations in CLIP's pretraining mechanisms. To address ... | {
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2412.07163 | Fast Occupancy Network | [
"cs.CV",
"cs.AI"
] | Occupancy Network has recently attracted much attention in autonomous driving. Instead of monocular 3D detection and recent bird's eye view(BEV) models predicting 3D bounding box of obstacles, Occupancy Network predicts the category of voxel in specified 3D space around the ego vehicle via transforming 3D detection tas... | {
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2412.07165 | A Method for Evaluating Hyperparameter Sensitivity in Reinforcement
Learning | [
"cs.LG",
"cs.AI"
] | The performance of modern reinforcement learning algorithms critically relies on tuning ever-increasing numbers of hyperparameters. Often, small changes in a hyperparameter can lead to drastic changes in performance, and different environments require very different hyperparameter settings to achieve state-of-the-art p... | {
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2412.07166 | equilibrium-c: A Lightweight Modern Equilibrium Chemistry Calculator for
Hypersonic Flow Applications | [
"cs.CE"
] | equilibrium-c (eqc) is a program for computing the composition of gas mixtures in chemical equilibrium. In typical usage, the program is given a known thermodynamic state, such as fixed temperature and pressure, as well as an initial composition of gaseous species, and computes the final composition in the limit of a l... | {
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2412.07167 | Reinforcement Learning Policy as Macro Regulator Rather than Macro
Placer | [
"cs.LG",
"cs.AI"
] | In modern chip design, placement aims at placing millions of circuit modules, which is an essential step that significantly influences power, performance, and area (PPA) metrics. Recently, reinforcement learning (RL) has emerged as a promising technique for improving placement quality, especially macro placement. Howev... | {
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2412.07168 | 3A-YOLO: New Real-Time Object Detectors with Triple Discriminative
Awareness and Coordinated Representations | [
"cs.CV"
] | Recent research on real-time object detectors (e.g., YOLO series) has demonstrated the effectiveness of attention mechanisms for elevating model performance. Nevertheless, existing methods neglect to unifiedly deploy hierarchical attention mechanisms to construct a more discriminative YOLO head which is enriched with m... | {
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2412.07169 | Rate-In: Information-Driven Adaptive Dropout Rates for Improved
Inference-Time Uncertainty Estimation | [
"cs.LG",
"cs.CV",
"stat.ML"
] | Accurate uncertainty estimation is crucial for deploying neural networks in risk-sensitive applications such as medical diagnosis. Monte Carlo Dropout is a widely used technique for approximating predictive uncertainty by performing stochastic forward passes with dropout during inference. However, using static dropout ... | {
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2412.07171 | Breaking the Stage Barrier: A Novel Single-Stage Approach to Long
Context Extension for Large Language Models | [
"cs.CL"
] | Recently, Large language models (LLMs) have revolutionized Natural Language Processing (NLP). Pretrained LLMs, due to limited training context size, struggle with handling long token sequences, limiting their performance on various downstream tasks. Current solutions toward long context modeling often employ multi-stag... | {
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2412.07174 | Post-Training Statistical Calibration for Higher Activation Sparsity | [
"cs.LG",
"cs.AI"
] | We present Statistical Calibrated Activation Pruning (SCAP), a post-training activation pruning framework that (1) generalizes sparsification by input activations of Fully-Connected layers for generic and flexible application across Transformers, and (2) features a simple Mode-Centering technique to pre-calibrate activ... | {
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2412.07175 | Robust Feature Engineering Techniques for Designing Efficient Motor
Imagery-Based BCI-Systems | [
"eess.SP",
"cs.CV",
"cs.LG"
] | A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-Computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative re... | {
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2412.07177 | Effective Reward Specification in Deep Reinforcement Learning | [
"cs.LG"
] | In the last decade, Deep Reinforcement Learning has evolved into a powerful tool for complex sequential decision-making problems. It combines deep learning's proficiency in processing rich input signals with reinforcement learning's adaptability across diverse control tasks. At its core, an RL agent seeks to maximize i... | {
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2412.07180 | Digital Twin Assisted Beamforming Design for Integrated Sensing and
Communication Systems | [
"eess.SP",
"cs.IT",
"math.IT"
] | This paper explores a novel research direction where a digital twin is leveraged to assist the beamforming design for an integrated sensing and communication (ISAC) system. In this setup, a base station designs joint communication and sensing beamforming to serve the communication user and detect the sensing target con... | {
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2412.07182 | An Enhancement of CNN Algorithm for Rice Leaf Disease Image
Classification in Mobile Applications | [
"cs.CV",
"cs.AI"
] | This study focuses on enhancing rice leaf disease image classification algorithms, which have traditionally relied on Convolutional Neural Network (CNN) models. We employed transfer learning with MobileViTV2_050 using ImageNet-1k weights, a lightweight model that integrates CNN's local feature extraction with Vision Tr... | {
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2412.07183 | Exploring What Why and How: A Multifaceted Benchmark for Causation
Understanding of Video Anomaly | [
"cs.CV",
"cs.AI"
] | Recent advancements in video anomaly understanding (VAU) have opened the door to groundbreaking applications in various fields, such as traffic monitoring and industrial automation. While the current benchmarks in VAU predominantly emphasize the detection and localization of anomalies. Here, we endeavor to delve deeper... | {
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2412.07184 | Automatic Doubly Robust Forests | [
"stat.ME",
"cs.LG",
"econ.EM",
"math.ST",
"stat.TH"
] | This paper proposes the automatic Doubly Robust Random Forest (DRRF) algorithm for estimating the conditional expectation of a moment functional in the presence of high-dimensional nuisance functions. DRRF combines the automatic debiasing framework using the Riesz representer (Chernozhukov et al., 2022) with non-parame... | {
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2412.07186 | Monte Carlo Tree Search based Space Transfer for Black-box Optimization | [
"cs.LG",
"cs.AI"
] | Bayesian optimization (BO) is a popular method for computationally expensive black-box optimization. However, traditional BO methods need to solve new problems from scratch, leading to slow convergence. Recent studies try to extend BO to a transfer learning setup to speed up the optimization, where search space transfe... | {
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2412.07187 | A New Federated Learning Framework Against Gradient Inversion Attacks | [
"cs.LG",
"cs.CR"
] | Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preservin... | {
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2412.07188 | Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking
from A Spectral Perspective | [
"cs.LG",
"cs.AI"
] | Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based learning tasks. While their performance is often attributed to the powerful neighborhood aggregation mechanism, recent studies suggest that other components such as non-linear layers may also significantly affecting how GNNs process th... | {
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2412.07191 | A Step towards Automated and Generalizable Tactile Map Generation using
Generative Adversarial Networks | [
"cs.CV"
] | Blindness and visual impairments affect many people worldwide. For help with navigation, people with visual impairments often rely on tactile maps that utilize raised surfaces and edges to convey information through touch. Although these maps are helpful, they are often not widely available and current tools to automat... | {
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2412.07192 | PrisonBreak: Jailbreaking Large Language Models with Fewer Than
Twenty-Five Targeted Bit-flips | [
"cs.CR",
"cs.CL",
"cs.LG"
] | We introduce a new class of attacks on commercial-scale (human-aligned) language models that induce jailbreaking through targeted bitwise corruptions in model parameters. Our adversary can jailbreak billion-parameter language models with fewer than 25 bit-flips in all cases$-$and as few as 5 in some$-$using up to 40$\t... | {
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2412.07193 | Epidemiological Model Calibration via Graybox Bayesian Optimization | [
"cs.LG",
"stat.ML"
] | In this study, we focus on developing efficient calibration methods via Bayesian decision-making for the family of compartmental epidemiological models. The existing calibration methods usually assume that the compartmental model is cheap in terms of its output and gradient evaluation, which may not hold in practice wh... | {
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2412.07195 | A Progressive Image Restoration Network for High-order Degradation
Imaging in Remote Sensing | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Recently, deep learning methods have gained remarkable achievements in the field of image restoration for remote sensing (RS). However, most existing RS image restoration methods focus mainly on conventional first-order degradation models, which may not effectively capture the imaging mechanisms of remote sensing image... | {
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2412.07196 | Fine-grained Text to Image Synthesis | [
"cs.CV"
] | Fine-grained text to image synthesis involves generating images from texts that belong to different categories. In contrast to general text to image synthesis, in fine-grained synthesis there is high similarity between images of different subclasses, and there may be linguistic discrepancy among texts describing the sa... | {
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2412.07197 | Hierarchical Split Federated Learning: Convergence Analysis and System
Optimization | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.NI"
] | As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated learning (SFL) has emerged as an FL framework with reduced workload on edge devices via model splitting; it has received extensive attention f... | {
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2412.07199 | A Parametric Approach to Adversarial Augmentation for Cross-Domain Iris
Presentation Attack Detection | [
"cs.CV"
] | Iris-based biometric systems are vulnerable to presentation attacks (PAs), where adversaries present physical artifacts (e.g., printed iris images, textured contact lenses) to defeat the system. This has led to the development of various presentation attack detection (PAD) algorithms, which typically perform well in in... | {
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2412.07200 | Modifying AI, Enhancing Essays: How Active Engagement with Generative AI
Boosts Writing Quality | [
"cs.HC",
"cs.AI",
"cs.CL"
] | Students are increasingly relying on Generative AI (GAI) to support their writing-a key pedagogical practice in education. In GAI-assisted writing, students can delegate core cognitive tasks (e.g., generating ideas and turning them into sentences) to GAI while still producing high-quality essays. This creates new chall... | {
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2412.07201 | A Review on the Applications of Transformer-based language models for
Nucleotide Sequence Analysis | [
"cs.CL",
"cs.AI"
] | In recent times, Transformer-based language models are making quite an impact in the field of natural language processing. As relevant parallels can be drawn between biological sequences and natural languages, the models used in NLP can be easily extended and adapted for various applications in bioinformatics. In this ... | {
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2412.07203 | Learning Spatially Decoupled Color Representations for Facial Image
Colorization | [
"cs.CV"
] | Image colorization methods have shown prominent performance on natural images. However, since humans are more sensitive to faces, existing methods are insufficient to meet the demands when applied to facial images, typically showing unnatural and uneven colorization results. In this paper, we investigate the facial ima... | {
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2412.07205 | Crack-EdgeSAM Self-Prompting Crack Segmentation System for Edge Devices | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Structural health monitoring (SHM) is essential for the early detection of infrastructure defects, such as cracks in concrete bridge pier. but often faces challenges in efficiency and accuracy in complex environments. Although the Segment Anything Model (SAM) achieves excellent segmentation performance, its computation... | {
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2412.07207 | MAPLE: A Framework for Active Preference Learning Guided by Large
Language Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | The advent of large language models (LLMs) has sparked significant interest in using natural language for preference learning. However, existing methods often suffer from high computational burdens, taxing human supervision, and lack of interpretability. To address these issues, we introduce MAPLE, a framework for larg... | {
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2412.07210 | EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large
Language Models | [
"cs.DC",
"cs.AI"
] | Distributed training methods are crucial for large language models (LLMs). However, existing distributed training methods often suffer from communication bottlenecks, stragglers, and limited elasticity, particularly in heterogeneous or large-scale environments. Local SGD methods have been proposed to address these issu... | {
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2412.07212 | A Distributed Deep Koopman Learning Algorithm for Control | [
"eess.SY",
"cs.SY"
] | This paper proposes a distributed data-driven framework to address the challenge of dynamics learning from a large amount of training data for optimal control purposes, named distributed deep Koopman learning for control (DDKC). Suppose a system states-inputs trajectory and a multi-agent system (MAS), the key idea of D... | {
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2412.07213 | IntellectSeeker: A Personalized Literature Management System with the
Probabilistic Model and Large Language Model | [
"cs.IR",
"cs.AI"
] | Faced with the burgeoning volume of academic literature, researchers often need help with uncertain article quality and mismatches in term searches using traditional academic engines. We introduce IntellectSeeker, an innovative and personalized intelligent academic literature management platform to address these challe... | {
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2412.07214 | Towards Automated Cross-domain Exploratory Data Analysis through Large
Language Models | [
"cs.DB",
"cs.AI"
] | Exploratory data analysis (EDA), coupled with SQL, is essential for data analysts involved in data exploration and analysis. However, data analysts often encounter two primary challenges: (1) the need to craft SQL queries skillfully, and (2) the requirement to generate suitable visualization types that enhance the inte... | {
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2412.07215 | RoboMM: All-in-One Multimodal Large Model for Robotic Manipulation | [
"cs.RO",
"cs.MM"
] | In recent years, robotics has advanced significantly through the integration of larger models and large-scale datasets. However, challenges remain in applying these models to 3D spatial interactions and managing data collection costs. To address these issues, we propose the multimodal robotic manipulation model, RoboMM... | {
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2412.07216 | Learnable Sparse Customization in Heterogeneous Edge Computing | [
"cs.DC",
"cs.LG"
] | To effectively manage and utilize massive distributed data at the network edge, Federated Learning (FL) has emerged as a promising edge computing paradigm across data silos. However, FL still faces two challenges: system heterogeneity (i.e., the diversity of hardware resources across edge devices) and statistical heter... | {
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2412.07217 | Incremental Gaussian Mixture Clustering for Data Streams | [
"cs.LG",
"cs.DB"
] | The problem of analyzing data streams of very large volumes is important and is very desirable for many application domains. In this paper we present and demonstrate effective working of an algorithm to find clusters and anomalous data points in a streaming datasets. Entropy minimization is used as a criterion for defi... | {
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2412.07219 | Taylor Outlier Exposure | [
"cs.CV",
"cs.LG"
] | Out-of-distribution (OOD) detection is the task of identifying data sampled from distributions that were not used during training. This task is essential for reliable machine learning and a better understanding of their generalization capabilities. Among OOD detection methods, Outlier Exposure (OE) significantly enhanc... | {
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2412.07220 | Comateformer: Combined Attention Transformer for Semantic Sentence
Matching | [
"cs.CL"
] | The Transformer-based model have made significant strides in semantic matching tasks by capturing connections between phrase pairs. However, to assess the relevance of sentence pairs, it is insufficient to just examine the general similarity between the sentences. It is crucial to also consider the tiny subtleties that... | {
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2412.07222 | MPSI: Mamba enhancement model for pixel-wise sequential interaction
Image Super-Resolution | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Single image super-resolution (SR) has long posed a challenge in the field of computer vision. While the advent of deep learning has led to the emergence of numerous methods aimed at tackling this persistent issue, the current methodologies still encounter challenges in modeling long sequence information, leading to li... | {
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2412.07223 | A Consolidated Volatility Prediction with Back Propagation Neural
Network and Genetic Algorithm | [
"q-fin.CP",
"cs.LG",
"cs.NE"
] | This paper provides a unique approach with AI algorithms to predict emerging stock markets volatility. Traditionally, stock volatility is derived from historical volatility,Monte Carlo simulation and implied volatility as well. In this paper, the writer designs a consolidated model with back-propagation neural network ... | {
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2412.07224 | Parseval Regularization for Continual Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequences of tasks -- all referring to the increased difficulty in training on new tasks. We propose to use Parseval regularization, which maintains orthogonality of weight matrices, to p... | {
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2412.07225 | EchoIR: Advancing Image Restoration with Echo Upsampling and Bi-Level
Optimization | [
"cs.CV",
"eess.IV"
] | Image restoration represents a fundamental challenge in low-level vision, focusing on reconstructing high-quality images from their degraded counterparts. With the rapid advancement of deep learning technologies, transformer-based methods with pyramid structures have advanced the field by capturing long-range cross-sca... | {
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2412.07226 | Attention Head Purification: A New Perspective to Harness CLIP for
Domain Generalization | [
"cs.CV"
] | Domain Generalization (DG) aims to learn a model from multiple source domains to achieve satisfactory performance on unseen target domains. Recent works introduce CLIP to DG tasks due to its superior image-text alignment and zeros-shot performance. Previous methods either utilize full fine-tuning or prompt-learning par... | {
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2412.07228 | T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play
BCIs | [
"cs.HC",
"cs.LG"
] | Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming ... | {
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2412.07229 | Moderating the Generalization of Score-based Generative Model | [
"cs.LG",
"cs.CV"
] | Score-based Generative Models (SGMs) have demonstrated remarkable generalization abilities, e.g. generating unseen, but natural data. However, the greater the generalization power, the more likely the unintended generalization, and the more dangerous the abuse. Research on moderated generalization in SGMs remains limit... | {
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2412.07230 | Deep Non-rigid Structure-from-Motion Revisited: Canonicalization and
Sequence Modeling | [
"cs.CV"
] | Non-Rigid Structure-from-Motion (NRSfM) is a classic 3D vision problem, where a 2D sequence is taken as input to estimate the corresponding 3D sequence. Recently, the deep neural networks have greatly advanced the task of NRSfM. However, existing deep NRSfM methods still have limitations in handling the inherent sequen... | {
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2412.07231 | Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based
Brain-Computer Interfaces | [
"cs.HC",
"cs.LG"
] | A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals, while ignoring their security. Recen... | {
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2412.07232 | Learning k-Inductive Control Barrier Certificates for Unknown Nonlinear
Dynamics Beyond Polynomials | [
"eess.SY",
"cs.SY"
] | This work is concerned with synthesizing safety controllers for discrete-time nonlinear systems beyond polynomials with unknown mathematical models using the notion of k-inductive control barrier certificates (k-CBCs). Conventional CBC conditions (with k=1) for ensuring safety over dynamical systems are often restricti... | {
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2412.07233 | Repetitive Action Counting with Hybrid Temporal Relation Modeling | [
"cs.CV"
] | Repetitive Action Counting (RAC) aims to count the number of repetitive actions occurring in videos. In the real world, repetitive actions have great diversity and bring numerous challenges (e.g., viewpoint changes, non-uniform periods, and action interruptions). Existing methods based on the temporal self-similarity m... | {
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2412.07236 | CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding | [
"eess.SP",
"cs.AI",
"cs.LG",
"q-bio.NC"
] | Electroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding methods rely on supervised learning, limited by specific tasks and datasets, hindering model performance and generalizability. With the suc... | {
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2412.07237 | ArtFormer: Controllable Generation of Diverse 3D Articulated Objects | [
"cs.CV",
"cs.AI",
"cs.RO"
] | This paper presents a novel framework for modeling and conditional generation of 3D articulated objects. Troubled by flexibility-quality tradeoffs, existing methods are often limited to using predefined structures or retrieving shapes from static datasets. To address these challenges, we parameterize an articulated obj... | {
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2412.07238 | Speaker effects in spoken language comprehension | [
"cs.CL",
"q-bio.NC"
] | The identity of a speaker significantly influences spoken language comprehension by affecting both perception and expectation. This review explores speaker effects, focusing on how speaker information impacts language processing. We propose an integrative model featuring the interplay between bottom-up perception-based... | {
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2412.07241 | Human-Computer Interaction and Human-AI Collaboration in Advanced Air
Mobility: A Comprehensive Review | [
"cs.HC",
"cs.AI",
"cs.LG"
] | The increasing rates of global urbanization and vehicle usage are leading to a shift of mobility to the third dimension-through Advanced Air Mobility (AAM)-offering a promising solution for faster, safer, cleaner, and more efficient transportation. As air transportation continues to evolve with more automated and auton... | {
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2412.07242 | Optimization Can Learn Johnson Lindenstrauss Embeddings | [
"stat.ML",
"cs.LG"
] | Embeddings play a pivotal role across various disciplines, offering compact representations of complex data structures. Randomized methods like Johnson-Lindenstrauss (JL) provide state-of-the-art and essentially unimprovable theoretical guarantees for achieving such representations. These guarantees are worst-case and ... | {
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2412.07243 | A Dynamical Systems-Inspired Pruning Strategy for Addressing
Oversmoothing in Graph Neural Networks | [
"cs.LG",
"cs.AI"
] | Oversmoothing in Graph Neural Networks (GNNs) poses a significant challenge as network depth increases, leading to homogenized node representations and a loss of expressiveness. In this work, we approach the oversmoothing problem from a dynamical systems perspective, providing a deeper understanding of the stability an... | {
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2412.07244 | Developing a Dataset-Adaptive, Normalized Metric for Machine Learning
Model Assessment: Integrating Size, Complexity, and Class Imbalance | [
"cs.LG",
"cs.LO"
] | Traditional metrics like accuracy, F1-score, and precision are frequently used to evaluate machine learning models, however they may not be sufficient for evaluating performance on tiny, unbalanced, or high-dimensional datasets. A dataset-adaptive, normalized metric that incorporates dataset characteristics like size, ... | {
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2412.07246 | Filling Memory Gaps: Enhancing Continual Semantic Parsing via SQL Syntax
Variance-Guided LLMs without Real Data Replay | [
"cs.CL",
"cs.DB"
] | Continual Semantic Parsing (CSP) aims to train parsers to convert natural language questions into SQL across tasks with limited annotated examples, adapting to the real-world scenario of dynamically updated databases. Previous studies mitigate this challenge by replaying historical data or employing parameter-efficient... | {
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2412.07247 | Driving with InternVL: Oustanding Champion in the Track on Driving with
Language of the Autonomous Grand Challenge at CVPR 2024 | [
"cs.CV"
] | This technical report describes the methods we employed for the Driving with Language track of the CVPR 2024 Autonomous Grand Challenge. We utilized a powerful open-source multimodal model, InternVL-1.5, and conducted a full-parameter fine-tuning on the competition dataset, DriveLM-nuScenes. To effectively handle the m... | {
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} |
2412.07249 | Buster: Implanting Semantic Backdoor into Text Encoder to Mitigate NSFW
Content Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The rise of deep learning models in the digital era has raised substantial concerns regarding the generation of Not-Safe-for-Work (NSFW) content. Existing defense methods primarily involve model fine-tuning and post-hoc content moderation. Nevertheless, these approaches largely lack scalability in eliminating harmful c... | {
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} |
2412.07251 | KULTURE Bench: A Benchmark for Assessing Language Model in Korean
Cultural Context | [
"cs.CL"
] | Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultura... | {
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2412.07253 | CapGen:An Environment-Adaptive Generator of Adversarial Patches | [
"cs.CV"
] | Adversarial patches, often used to provide physical stealth protection for critical assets and assess perception algorithm robustness, usually neglect the need for visual harmony with the background environment, making them easily noticeable. Moreover, existing methods primarily concentrate on improving attack performa... | {
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2412.07255 | Label-Confidence-Aware Uncertainty Estimation in Natural Language
Generation | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) display formidable capabilities in generative tasks but also pose potential risks due to their tendency to generate hallucinatory responses. Uncertainty Quantification (UQ), the evaluation of model output reliability, is crucial for ensuring the safety and robustness of AI systems. Recent s... | {
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} |
2412.07256 | Modeling Dual-Exposure Quad-Bayer Patterns for Joint Denoising and
Deblurring | [
"eess.IV",
"cs.CV"
] | Image degradation caused by noise and blur remains a persistent challenge in imaging systems, stemming from limitations in both hardware and methodology. Single-image solutions face an inherent tradeoff between noise reduction and motion blur. While short exposures can capture clear motion, they suffer from noise ampli... | {
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2412.07259 | Goal-Driven Reasoning in DatalogMTL with Magic Sets | [
"cs.AI"
] | DatalogMTL is a powerful rule-based language for temporal reasoning. Due to its high expressive power and flexible modeling capabilities, it is suitable for a wide range of applications, including tasks from industrial and financial sectors. However, due its high computational complexity, practical reasoning in Datalog... | {
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2412.07260 | DFREC: DeepFake Identity Recovery Based on Identity-aware Masked
Autoencoder | [
"cs.CV"
] | Recent advances in deepfake forensics have primarily focused on improving the classification accuracy and generalization performance. Despite enormous progress in detection accuracy across a wide variety of forgery algorithms, existing algorithms lack intuitive interpretability and identity traceability to help with fo... | {
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} |
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