id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.07261 | MemHunter: Automated and Verifiable Memorization Detection at
Dataset-scale in LLMs | [
"cs.CR",
"cs.LG"
] | Large language models (LLMs) have been shown to memorize and reproduce content from their training data, raising significant privacy concerns, especially with web-scale datasets. Existing methods for detecting memorization are primarily sample-specific, relying on manually crafted or discretely optimized memory-inducin... | {
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2412.07262 | Deep Lidar-guided Image Deblurring | [
"cs.CV",
"eess.IV"
] | The rise of portable Lidar instruments, including their adoption in smartphones, opens the door to novel computational imaging techniques. Being an active sensing instrument, Lidar can provide complementary data to passive optical sensors, particularly in situations like low-light imaging where motion blur can affect p... | {
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2412.07264 | QuantFormer: Learning to Quantize for Neural Activity Forecasting in
Mouse Visual Cortex | [
"q-bio.NC",
"cs.CV",
"eess.IV",
"eess.SP"
] | Understanding complex animal behaviors hinges on deciphering the neural activity patterns within brain circuits, making the ability to forecast neural activity crucial for developing predictive models of brain dynamics. This capability holds immense value for neuroscience, particularly in applications such as real-time... | {
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2412.07265 | Modeling High-Resolution Spatio-Temporal Wind with Deep Echo State
Networks and Stochastic Partial Differential Equations | [
"stat.ML",
"cs.LG"
] | In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi Arabia is gradually shifting its energy portfolio from an exclusive use of oil to a reliance on renewable energy, and, in particular, wind. Modeling wind for assessi... | {
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2412.07268 | PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards
Algorithms and Models | [
"cs.LG",
"cs.MM"
] | With the increased attention to model efficiency, post-training sparsity (PTS) has become more and more prevalent because of its effectiveness and efficiency. However, there remain questions on better practice of PTS algorithms and the sparsification ability of models, which hinders the further development of this area... | {
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2412.07273 | Temporal-Aware Evaluation and Learning for Temporal Graph Neural
Networks | [
"cs.LG",
"cs.AI"
] | Temporal Graph Neural Networks (TGNNs) are a family of graph neural networks designed to model and learn dynamic information from temporal graphs. Given their substantial empirical success, there is an escalating interest in TGNNs within the research community. However, the majority of these efforts have been channelle... | {
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2412.07274 | A Generative Victim Model for Segmentation | [
"cs.CV"
] | We find that the well-trained victim models (VMs), against which the attacks are generated, serve as fundamental prerequisites for adversarial attacks, i.e. a segmentation VM is needed to generate attacks for segmentation. In this context, the victim model is assumed to be robust to achieve effective adversarial pertur... | {
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2412.07277 | Backdoor Attacks against No-Reference Image Quality Assessment Models
via a Scalable Trigger | [
"cs.CV",
"cs.CR"
] | No-Reference Image Quality Assessment (NR-IQA), responsible for assessing the quality of a single input image without using any reference, plays a critical role in evaluating and optimizing computer vision systems, e.g., low-light enhancement. Recent research indicates that NR-IQA models are susceptible to adversarial ... | {
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2412.07278 | Superficial Consciousness Hypothesis for Autoregressive Transformers | [
"cs.AI",
"cs.IT",
"math.IT"
] | The alignment between human objectives and machine learning models built on these objectives is a crucial yet challenging problem for achieving Trustworthy AI, particularly when preparing for superintelligence (SI). First, given that SI does not exist today, empirical analysis for direct evidence is difficult. Second, ... | {
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2412.07282 | HARP: Hesitation-Aware Reframing in Transformer Inference Pass | [
"cs.CL",
"cs.AI",
"cs.LG"
] | This paper aims to improve the performance of large language models by addressing the variable computational demands in inference steps, where some tokens require more computational resources than others. We present HARP, a simple modification to "off-the-shelf" Transformer forward pass. Drawing from hesitation and the... | {
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2412.07288 | Image Classification Using Singular Value Decomposition and Optimization | [
"cs.CV",
"cs.NA",
"math.NA"
] | This study investigates the applicability of Singular Value Decomposition for the image classification of specific breeds of cats and dogs using fur color as the primary identifying feature. Sequential Quadratic Programming (SQP) is employed to construct optimally weighted templates. The proposed method achieves 69% ac... | {
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2412.07289 | Enhancing Relation Extraction via Supervised Rationale Verification and
Feedback | [
"cs.CL",
"cs.AI"
] | Despite the rapid progress that existing automated feedback methods have made in correcting the output of large language models (LLMs), these methods cannot be well applied to the relation extraction (RE) task due to their designated feedback objectives and correction manner. To address this problem, we propose a novel... | {
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2412.07290 | CEEMS: A Resource Manager Agnostic Energy and Emissions Monitoring Stack | [
"eess.SY",
"cs.SY"
] | With the rapid acceleration of ML/AI research in the last couple of years, the energy consumption of the Information and Communication Technology (ICT) domain has rapidly increased. As a major part of this energy consumption is due to users' workloads, it is evident that users need to be aware of the energy footprint o... | {
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2412.07292 | Multimodal Sentiment Analysis Based on Causal Reasoning | [
"cs.MM",
"cs.CL"
] | With the rapid development of multimedia, the shift from unimodal textual sentiment analysis to multimodal image-text sentiment analysis has obtained academic and industrial attention in recent years. However, multimodal sentiment analysis is affected by unimodal data bias, e.g., text sentiment is misleading due to exp... | {
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2412.07293 | EventSplat: 3D Gaussian Splatting from Moving Event Cameras for
Real-time Rendering | [
"cs.CV"
] | We introduce a method for using event camera data in novel view synthesis via Gaussian Splatting. Event cameras offer exceptional temporal resolution and a high dynamic range. Leveraging these capabilities allows us to effectively address the novel view synthesis challenge in the presence of fast camera motion. For ini... | {
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2412.07298 | The Rise and Down of Babel Tower: Investigating the Evolution Process of
Multilingual Code Large Language Model | [
"cs.CL"
] | Large language models (LLMs) have shown significant multilingual capabilities. However, the mechanisms underlying the development of these capabilities during pre-training are not well understood. In this paper, we use code LLMs as an experimental platform to explore the evolution of multilingual capabilities in LLMs d... | {
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2412.07302 | Compression of Large-Scale 3D Point Clouds Based on Joint Optimization
of Point Sampling and Feature Extraction | [
"cs.CV",
"eess.IV"
] | Large-scale 3D point clouds (LS3DPC) obtained by LiDAR scanners require huge storage space and transmission bandwidth due to a large amount of data. The existing methods of LS3DPC compression separately perform rule-based point sampling and learnable feature extraction, and hence achieve limited compression performance... | {
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2412.07303 | Filipino Benchmarks for Measuring Sexist and Homophobic Bias in
Multilingual Language Models from Southeast Asia | [
"cs.CL"
] | Bias studies on multilingual models confirm the presence of gender-related stereotypes in masked models processing languages with high NLP resources. We expand on this line of research by introducing Filipino CrowS-Pairs and Filipino WinoQueer: benchmarks that assess both sexist and anti-queer biases in pretrained lang... | {
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2412.07312 | High-dimensional classification problems with Barron regular boundaries
under margin conditions | [
"cs.LG",
"math.PR",
"stat.ML"
] | We prove that a classifier with a Barron-regular decision boundary can be approximated with a rate of high polynomial degree by ReLU neural networks with three hidden layers when a margin condition is assumed. In particular, for strong margin conditions, high-dimensional discontinuous classifiers can be approximated wi... | {
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2412.07313 | FaceX: Understanding Face Attribute Classifiers through Summary Model
Explanations | [
"cs.CV"
] | EXplainable Artificial Intelligence (XAI) approaches are widely applied for identifying fairness issues in Artificial Intelligence (AI) systems. However, in the context of facial analysis, existing XAI approaches, such as pixel attribution methods, offer explanations for individual images, posing challenges in assessin... | {
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2412.07320 | CoMA: Compositional Human Motion Generation with Multi-modal Agents | [
"cs.CV"
] | 3D human motion generation has seen substantial advancement in recent years. While state-of-the-art approaches have improved performance significantly, they still struggle with complex and detailed motions unseen in training data, largely due to the scarcity of motion datasets and the prohibitive cost of generating new... | {
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2412.07322 | ConceptSearch: Towards Efficient Program Search Using LLMs for
Abstraction and Reasoning Corpus (ARC) | [
"cs.LG"
] | The Abstraction and Reasoning Corpus (ARC) poses a significant challenge to artificial intelligence, demanding broad generalization and few-shot learning capabilities that remain elusive for current deep learning methods, including large language models (LLMs). While LLMs excel in program synthesis, their direct applic... | {
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2412.07324 | Label Distribution Learning using the Squared Neural Family on the
Probability Simplex | [
"cs.LG"
] | Label distribution learning (LDL) provides a framework wherein a distribution over categories rather than a single category is predicted, with the aim of addressing ambiguity in labeled data. Existing research on LDL mainly focuses on the task of point estimation, i.e., pinpointing an optimal distribution in the probab... | {
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2412.07326 | Addressing Key Challenges of Adversarial Attacks and Defenses in the
Tabular Domain: A Methodological Framework for Coherence and Consistency | [
"cs.LG"
] | Machine learning models trained on tabular data are vulnerable to adversarial attacks, even in realistic scenarios where attackers have access only to the model's outputs. Researchers evaluate such attacks by considering metrics like success rate, perturbation magnitude, and query count. However, unlike other data doma... | {
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2412.07331 | NeSyA: Neurosymbolic Automata | [
"cs.AI",
"cs.LG"
] | Neurosymbolic Artificial Intelligence (NeSy) has emerged as a promising direction to integrate low level perception with high level reasoning. Unfortunately, little attention has been given to developing NeSy systems tailored to temporal/sequential problems. This entails reasoning symbolically over sequences of subsymb... | {
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2412.07332 | Model predictive control-based trajectory generation for agile landing
of unmanned aerial vehicle on a moving boat | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper proposes a novel trajectory generation method based on Model Predictive Control (MPC) for agile landing of an Unmanned Aerial Vehicle (UAV) onto an Unmanned Surface Vehicle (USV)'s deck in harsh conditions. The trajectory generation exploits the state predictions of the USV to create periodically updated tra... | {
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2412.07333 | Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion
Model | [
"cs.CV",
"cs.AI"
] | Pose-Guided Person Image Synthesis (PGPIS) aims to synthesize high-quality person images corresponding to target poses while preserving the appearance of the source image. Recently, PGPIS methods that use diffusion models have achieved competitive performance. Most approaches involve extracting representations of the t... | {
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2412.07334 | Frame Representation Hypothesis: Multi-Token LLM Interpretability and
Concept-Guided Text Generation | [
"cs.CL"
] | Interpretability is a key challenge in fostering trust for Large Language Models (LLMs), which stems from the complexity of extracting reasoning from model's parameters. We present the Frame Representation Hypothesis, a theoretically robust framework grounded in the Linear Representation Hypothesis (LRH) to interpret a... | {
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2412.07338 | Contextualized Counterspeech: Strategies for Adaptation,
Personalization, and Evaluation | [
"cs.HC",
"cs.AI",
"cs.SI"
] | AI-generated counterspeech offers a promising and scalable strategy to curb online toxicity through direct replies that promote civil discourse. However, current counterspeech is one-size-fits-all, lacking adaptation to the moderation context and the users involved. We propose and evaluate multiple strategies for gener... | {
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2412.07344 | Virtual Reflections on a Dynamic 2D Eye Model Improve Spatial Reference
Identification | [
"cs.HC",
"cs.RO"
] | The visible orientation of human eyes creates some transparency about people's spatial attention and other mental states. This leads to a dual role for the eyes as a means of sensing and communication. Accordingly, artificial eye models are being explored as communication media in human-machine interaction scenarios. O... | {
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2412.07347 | Quantitative Comparison of the Total Focusing Method, Reverse Time
Migration, and Full Waveform Inversion for Ultrasonic Imaging | [
"cs.CE",
"cs.NA",
"math.NA"
] | Phased array ultrasound is a widely used technique in non-destructive testing. Using piezoelectric elements as both sources and receivers provides a significant gain in information and enables more accurate defect detection. When all source-receiver combinations are used, the process is called full matrix capture. The ... | {
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2412.07349 | Disturbance Observer-Parameterized Control Barrier Function with
Adaptive Safety Bounds | [
"eess.SY",
"cs.SY"
] | This letter presents a nonlinear disturbance observer-parameterized control barrier function (DOp-CBF) designed for a robust safety control system under external disturbances. This framework emphasizes that the safety bounds are relevant to the disturbances, acknowledging the critical impact of disturbances on system s... | {
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2412.07355 | Towards Predictive Communication with Brain-Computer Interfaces
integrating Large Language Models | [
"cs.HC",
"cs.CL"
] | This perspective article aims at providing an outline of the state of the art and future developments towards the integration of cutting-edge predictive language models with BCI. A synthetic overview of early and more recent linguistic models, from natural language processing (NLP) models to recent LLM, that to a varyi... | {
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2412.07359 | Characterization of Indoor RIS-Assisted Channels at 304 GHz:
Experimental Measurements, Challenges, and Future Directions | [
"cs.IT",
"cs.ET",
"math.IT"
] | Reconfigurable Intelligent Surfaces (RISs) are expected to play a pivotal role in future indoor ultra high data rate wireless communications as well as highly accurate three-dimensional localization and sensing, mainly due to their capability to provide flexible, cost- and power-efficient coverage extension, even under... | {
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2412.07360 | Efficient 3D Recognition with Event-driven Spike Sparse Convolution | [
"cs.CV"
] | Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. Point clouds are sparse 3D spatial data, which suggests that SNNs should be well-suited for processing them. However, when applying SNNs to point clouds, they often exhibit limited performance and fewer application sc... | {
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2412.07367 | My Words Imply Your Opinion: Reader Agent-Based Propagation Enhancement
for Personalized Implicit Emotion Analysis | [
"cs.CL"
] | The subtlety of emotional expressions makes implicit emotion analysis (IEA) particularly sensitive to user-specific characteristics. Current studies personalize emotion analysis by focusing on the author but neglect the impact of the intended reader on implicit emotional feedback. In this paper, we introduce Personaliz... | {
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2412.07369 | ITPNet: Towards Instantaneous Trajectory Prediction for Autonomous
Driving | [
"cs.CV"
] | Trajectory prediction of agents is crucial for the safety of autonomous vehicles, whereas previous approaches usually rely on sufficiently long-observed trajectory to predict the future trajectory of the agents. However, in real-world scenarios, it is not realistic to collect adequate observed locations for moving agen... | {
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2412.07371 | PRM: Photometric Stereo based Large Reconstruction Model | [
"cs.CV",
"cs.GR"
] | We propose PRM, a novel photometric stereo based large reconstruction model to reconstruct high-quality meshes with fine-grained local details. Unlike previous large reconstruction models that prepare images under fixed and simple lighting as both input and supervision, PRM renders photometric stereo images by varying ... | {
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2412.07375 | StoryWeaver: A Unified World Model for Knowledge-Enhanced Story
Character Customization | [
"cs.CV"
] | Story visualization has gained increasing attention in artificial intelligence. However, existing methods still struggle with maintaining a balance between character identity preservation and text-semantics alignment, largely due to a lack of detailed semantic modeling of the story scene. To tackle this challenge, we p... | {
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2412.07377 | CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings | [
"cs.CV"
] | We introduce CADSpotting, an efficient method for panoptic symbol spotting in large-scale architectural CAD drawings. Existing approaches struggle with the diversity of symbols, scale variations, and overlapping elements in CAD designs. CADSpotting overcomes these challenges by representing each primitive with dense po... | {
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2412.07378 | A Spectral Framework for Tracking Communities in Evolving Networks | [
"cs.SI",
"cs.LG",
"stat.ML"
] | Discovering and tracking communities in time-varying networks is an important task in network science, motivated by applications in fields ranging from neuroscience to sociology. In this work, we characterize the celebrated family of spectral methods for static clustering in terms of the low-rank approximation of high-... | {
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2412.07380 | SpecFuse: Ensembling Large Language Models via Next-Segment Prediction | [
"cs.CL",
"cs.AI"
] | Ensembles of generative large language models (LLMs) can integrate the strengths of different LLMs to compensate for the limitations of individual models. However, recent work has focused on training an additional fusion model to combine complete responses from multiple LLMs, failing to tap into their collaborative pot... | {
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2412.07382 | Temporal Linear Item-Item Model for Sequential Recommendation | [
"cs.IR",
"cs.LG"
] | In sequential recommendation (SR), neural models have been actively explored due to their remarkable performance, but they suffer from inefficiency inherent to their complexity. On the other hand, linear SR models exhibit high efficiency and achieve competitive or superior accuracy compared to neural models. However, t... | {
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2412.07384 | Label up: Learning Pulmonary Embolism Segmentation from Image Level
Annotation through Model Explainability | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Pulmonary Embolisms (PE) are a leading cause of cardiovascular death. Computed tomographic pulmonary angiography (CTPA) stands as the gold standard for diagnosing pulmonary embolisms (PE) and there has been a lot of interest in developing AI-based models for assisting in PE diagnosis. Performance of these algorithms ha... | {
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2412.07385 | LOGen: Toward Lidar Object Generation by Point Diffusion | [
"cs.CV"
] | A common strategy to improve lidar segmentation results on rare semantic classes consists of pasting objects from one lidar scene into another. While this augments the quantity of instances seen at training time and varies their context, the instances fundamentally remain the same. In this work, we explore how to enhan... | {
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2412.07386 | Algorithmic Phase Transitions in Language Models: A Mechanistic Case
Study of Arithmetic | [
"cs.CL"
] | Zero-shot capabilities of large language models make them powerful tools for solving a range of tasks without explicit training. It remains unclear, however, how these models achieve such performance, or why they can zero-shot some tasks but not others. In this paper, we shed some light on this phenomenon by defining a... | {
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2412.07387 | Enhanced MRI Representation via Cross-series Masking | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Magnetic resonance imaging (MRI) is indispensable for diagnosing and planning treatment in various medical conditions due to its ability to produce multi-series images that reveal different tissue characteristics. However, integrating these diverse series to form a coherent analysis presents significant challenges, suc... | {
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2412.07388 | A Review of Challenges in Speech-based Conversational AI for Elderly
Care | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.ET"
] | Artificially intelligent systems optimized for speech conversation are appearing at a fast pace. Such models are interesting from a healthcare perspective, as these voice-controlled assistants may support the elderly and enable remote health monitoring. The bottleneck for efficacy, however, is how well these devices wo... | {
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2412.07391 | Post-Training Non-Uniform Quantization for Convolutional Neural Networks | [
"cs.CV",
"cs.LG"
] | Despite the success of CNN models on a variety of Image classification and segmentation tasks, their extensive computational and storage demands pose considerable challenges for real-world deployment on resource constrained devices. Quantization is one technique that aims to alleviate these large storage requirements a... | {
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2412.07392 | Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime
Environments | [
"cs.CV",
"cs.RO"
] | Vision-based target tracking is crucial for unmanned surface vehicles (USVs) to perform tasks such as inspection, monitoring, and surveillance. However, real-time tracking in complex maritime environments is challenging due to dynamic camera movement, low visibility, and scale variation. Typically, object detection met... | {
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2412.07393 | CMT: A Memory Compression Method for Continual Knowledge Learning of
Large Language Models | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) need to adapt to the continuous changes in data, tasks, and user preferences. Due to their massive size and the high costs associated with training, LLMs are not suitable for frequent retraining. However, updates are necessary to keep them in sync with rapidly evolving human knowledge. To a... | {
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2412.07402 | Non-Progressive Influence Maximization in Dynamic Social Networks | [
"cs.SI",
"cs.AI"
] | The influence maximization (IM) problem involves identifying a set of key individuals in a social network who can maximize the spread of influence through their network connections. With the advent of geometric deep learning on graphs, great progress has been made towards better solutions for the IM problem. In this pa... | {
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2412.07403 | RLT4Rec: Reinforcement Learning Transformer for User Cold Start and Item
Recommendation | [
"cs.IR"
] | We introduce a new sequential transformer reinforcement learning architecture RLT4Rec and demonstrate that it achieves excellent performance in a range of item recommendation tasks. RLT4Rec uses a relatively simple transformer architecture that takes as input the user's (item,rating) history and outputs the next item t... | {
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2412.07405 | MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task
Learning | [
"cs.LG",
"cs.AI"
] | The growing demand for larger-scale models in the development of \textbf{L}arge \textbf{L}anguage \textbf{M}odels (LLMs) poses challenges for efficient training within limited computational resources. Traditional fine-tuning methods often exhibit instability in multi-task learning and rely heavily on extensive training... | {
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2412.07406 | Learning Self-Supervised Audio-Visual Representations for Sound
Recommendations | [
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | We propose a novel self-supervised approach for learning audio and visual representations from unlabeled videos, based on their correspondence. The approach uses an attention mechanism to learn the relative importance of convolutional features extracted at different resolutions from the audio and visual streams and use... | {
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2412.07407 | Towards Graph Foundation Models: A Study on the Generalization of
Positional and Structural Encodings | [
"cs.LG"
] | Recent advances in integrating positional and structural encodings (PSEs) into graph neural networks (GNNs) have significantly enhanced their performance across various graph learning tasks. However, the general applicability of these encodings and their potential to serve as foundational representations for graphs rem... | {
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2412.07408 | Explainability of Deep Learning-Based Plant Disease Classifiers Through
Automated Concept Identification | [
"cs.CV",
"cs.AI"
] | While deep learning has significantly advanced automatic plant disease detection through image-based classification, improving model explainability remains crucial for reliable disease detection. In this study, we apply the Automated Concept-based Explanation (ACE) method to plant disease classification using the widel... | {
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2412.07411 | DSFEC: Efficient and Deployable Deep Radar Object Detection | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Deploying radar object detection models on resource-constrained edge devices like the Raspberry Pi poses significant challenges due to the large size of the model and the limited computational power and the memory of the Pi. In this work, we explore the efficiency of Depthwise Separable Convolutions in radar object det... | {
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2412.07412 | Generating Knowledge Graphs from Large Language Models: A Comparative
Study of GPT-4, LLaMA 2, and BERT | [
"cs.CL",
"cs.AI",
"cs.DB"
] | Knowledge Graphs (KGs) are essential for the functionality of GraphRAGs, a form of Retrieval-Augmented Generative Systems (RAGs) that excel in tasks requiring structured reasoning and semantic understanding. However, creating KGs for GraphRAGs remains a significant challenge due to accuracy and scalability limitations ... | {
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2412.07415 | Machine Learning Algorithms for Detecting Mental Stress in College
Students | [
"cs.LG",
"cs.CY"
] | In today's world, stress is a big problem that affects people's health and happiness. More and more people are feeling stressed out, which can lead to lots of health issues like breathing problems, feeling overwhelmed, heart attack, diabetes, etc. This work endeavors to forecast stress and non-stress occurrences among ... | {
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2412.07419 | Composing or Not Composing? Towards Distributional Construction Grammars | [
"cs.CL"
] | The mechanisms of comprehension during language processing remains an open question. Classically, building the meaning of a linguistic utterance is said to be incremental, step-by-step, based on a compositional process. However, many different works have shown for a long time that non-compositional phenomena are also a... | {
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2412.07420 | RAG-based Question Answering over Heterogeneous Data and Text | [
"cs.CL",
"cs.IR"
] | This article presents the QUASAR system for question answering over unstructured text, structured tables, and knowledge graphs, with unified treatment of all sources. The system adopts a RAG-based architecture, with a pipeline of evidence retrieval followed by answer generation, with the latter powered by a moderate-si... | {
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2412.07421 | A Robust Sustainability Assessment Methodology for Aircraft Parts:
Application to a Fuselage Panel | [
"cs.CE",
"cs.NA",
"cs.PF",
"math.NA"
] | The paper presents a cradle-to-gate sustainability assessment methodology specifically designed to evaluate aircraft components in a robust and systematic manner. This methodology integrates multi-criteria decision-making (MCDM) analysis across ten criteria, categorized under environmental impact, cost, and performance... | {
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2412.07428 | When UAV Meets Federated Learning: Latency Minimization via Joint
Trajectory Design and Resource Allocation | [
"eess.SP",
"cs.LG"
] | Federated learning (FL) has emerged as a pivotal solution for training machine learning models over wireless networks, particularly for Internet of Things (IoT) devices with limited computation resources. Despite its benefits, the efficiency of FL is often restricted by the communication quality between IoT devices and... | {
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2412.07429 | Optimizing Alignment with Less: Leveraging Data Augmentation for
Personalized Evaluation | [
"cs.CL",
"cs.AI"
] | Automatic evaluation by large language models (LLMs) is a prominent topic today; however, judgment and evaluation tasks are often subjective and influenced by various factors, making adaptation challenging. While many studies demonstrate the capabilities of state-of-the-art proprietary LLMs in comparison to human evalu... | {
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2412.07430 | Knowledge Graph Guided Evaluation of Abstention Techniques | [
"cs.CL",
"cs.AI"
] | To deploy language models safely, it is crucial that they abstain from responding to inappropriate requests. Several prior studies test the safety promises of models based on their effectiveness in blocking malicious requests. In this work, we focus on evaluating the underlying techniques that cause models to abstain. ... | {
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2412.07431 | BENet: A Cross-domain Robust Network for Detecting Face Forgeries via
Bias Expansion and Latent-space Attention | [
"cs.CV",
"cs.AI"
] | In response to the growing threat of deepfake technology, we introduce BENet, a Cross-Domain Robust Bias Expansion Network. BENet enhances the detection of fake faces by addressing limitations in current detectors related to variations across different types of fake face generation techniques, where ``cross-domain" ref... | {
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2412.07435 | Parallel simulation for sampling under isoperimetry and score-based
diffusion models | [
"cs.DS",
"cs.DC",
"cs.LG",
"cs.NA",
"math.NA"
] | In recent years, there has been a surge of interest in proving discretization bounds for sampling under isoperimetry and for diffusion models. As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific co... | {
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2412.07437 | Impact of Sampling Techniques and Data Leakage on XGBoost Performance in
Credit Card Fraud Detection | [
"cs.LG"
] | Credit card fraud detection remains a critical challenge in financial security, with machine learning models like XGBoost(eXtreme gradient boosting) emerging as powerful tools for identifying fraudulent transactions. However, the inherent class imbalance in credit card transaction datasets poses significant challenges ... | {
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2412.07441 | Reconstructing Deep Neural Networks: Unleashing the Optimization
Potential of Natural Gradient Descent | [
"cs.LG",
"cs.AI"
] | Natural gradient descent (NGD) is a powerful optimization technique for machine learning, but the computational complexity of the inverse Fisher information matrix limits its application in training deep neural networks. To overcome this challenge, we propose a novel optimization method for training deep neural network... | {
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2412.07444 | MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms
using IOHprofiler | [
"cs.NE"
] | Benchmarking is one of the key ways in which we can gain insight into the strengths and weaknesses of optimization algorithms. In sampling-based optimization, considering the anytime behavior of an algorithm can provide valuable insights for further developments. In the context of multi-objective optimization, this any... | {
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2412.07446 | Causal World Representation in the GPT Model | [
"cs.AI",
"cs.CL",
"cs.LG",
"stat.ML"
] | Are generative pre-trained transformer (GPT) models only trained to predict the next token, or do they implicitly learn a world model from which a sequence is generated one token at a time? We examine this question by deriving a causal interpretation of the attention mechanism in GPT, and suggesting a causal world mode... | {
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2412.07448 | Dynamic Ensemble Reasoning for LLM Experts | [
"cs.AI"
] | Ensemble reasoning for the strengths of different LLM experts is critical to achieving consistent and satisfactory performance on diverse inputs across a wide range of tasks. However, existing LLM ensemble methods are either computationally intensive or incapable of leveraging complementary knowledge among LLM experts ... | {
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2412.07454 | Tazza: Shuffling Neural Network Parameters for Secure and Private
Federated Learning | [
"cs.LG",
"cs.AI"
] | Federated learning enables decentralized model training without sharing raw data, preserving data privacy. However, its vulnerability towards critical security threats, such as gradient inversion and model poisoning by malicious clients, remain unresolved. Existing solutions often address these issues separately, sacri... | {
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2412.07462 | Bilingual BSARD: Extending Statutory Article Retrieval to Dutch | [
"cs.CL",
"cs.IR"
] | Statutory article retrieval plays a crucial role in making legal information more accessible to both laypeople and legal professionals. Multilingual countries like Belgium present unique challenges for retrieval models due to the need for handling legal issues in multiple languages. Building on the Belgian Statutory Ar... | {
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2412.07464 | Little to lose: the case for a robust European green hydrogen strategy | [
"eess.SY",
"cond-mat.mtrl-sci",
"cs.SY"
] | The EU targets 10 Mt of green hydrogen production by 2030, but has not committed to targets for 2040. Green hydrogen competes with carbon capture and storage, biomass and imports in reaching emissions reductions; earlier studies have demonstrated the great uncertainty in future cost-optimal development of green hydroge... | {
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2412.07468 | AHSG: Adversarial Attacks on High-level Semantics in Graph Neural
Networks | [
"cs.LG"
] | Graph Neural Networks (GNNs) have garnered significant interest among researchers due to their impressive performance in graph learning tasks. However, like other deep neural networks, GNNs are also vulnerable to adversarial attacks. In existing adversarial attack methods for GNNs, the metric between the attacked graph... | {
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2412.07469 | Score-matching-based Structure Learning for Temporal Data on Networks | [
"stat.ML",
"cs.LG"
] | Causal discovery is a crucial initial step in establishing causality from empirical data and background knowledge. Numerous algorithms have been developed for this purpose. Among them, the score-matching method has demonstrated superior performance across various evaluation metrics, particularly for the commonly encoun... | {
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2412.07471 | Event-Triggered Memory Control for Interval Type-2 Fuzzy Heterogeneous
Multi-Agent Systems | [
"eess.SY",
"cs.SY"
] | This study explores the design of a memory-based dynamic event-triggered mechanisms (DETM) scheme for heterogeneous multi-agent systems (MASs) characterized by interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy models. To address the complex nonlinear uncertainties inherent in such systems, discrete IT2 T-S fuzzy models are... | {
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2412.07472 | SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in
Cyber World | [
"cs.AI"
] | Recent advances in embodied agents with multimodal perception and reasoning capabilities based on large vision-language models (LVLMs), excel in autonomously interacting either real or cyber worlds, helping people make intelligent decisions in complex environments. However, the current works are normally optimized by g... | {
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2412.07477 | Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution
Simulations for Time-Efficient Fine-Resolution Policy Learning | [
"cs.RO",
"cs.LG"
] | In earthwork and construction, excavators often encounter large rocks mixed with various soil conditions, requiring skilled operators. This paper presents a framework for achieving autonomous excavation using reinforcement learning (RL) through a rock excavation simulator. In the simulation, resolution can be defined b... | {
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2412.07481 | Manta: Enhancing Mamba for Few-Shot Action Recognition of Long
Sub-Sequence | [
"cs.CV"
] | In few-shot action recognition (FSAR), long sub-sequences of video naturally express entire actions more effectively. However, the high computational complexity of mainstream Transformer-based methods limits their application. Recent Mamba demonstrates efficiency in modeling long sequences, but directly applying Mamba ... | {
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2412.07485 | Performance Evaluation of ROS2-DDS middleware implementations
facilitating Cooperative Driving in Autonomous Vehicle | [
"cs.RO"
] | In the autonomous vehicle and self-driving paradigm, cooperative perception or exchanging sensor information among vehicles over wireless communication has added a new dimension. Generally, an autonomous vehicle is a special type of robot that requires real-time, highly reliable sensor inputs due to functional safety. ... | {
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2412.07486 | Real-time Sign Language Recognition Using MobileNetV2 and Transfer
Learning | [
"cs.LG"
] | The hearing-impaired community in India deserves the access to tools that help them communicate, however, there is limited known technology solutions that make use of Indian Sign Language (ISL) at present. Even though there are many ISL users, ISL cannot access social and education arenas because there is not yet an ef... | {
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2412.07487 | Stereo Hand-Object Reconstruction for Human-to-Robot Handover | [
"cs.RO",
"cs.CV"
] | Jointly estimating hand and object shape ensures the success of the robot grasp in human-to-robot handovers. However, relying on hand-crafted prior knowledge about the geometric structure of the object fails when generalising to unseen objects, and depth sensors fail to detect transparent objects such as drinking glass... | {
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2412.07488 | Dual Random Fields and their Application to Mineral Potential Mapping | [
"stat.ML",
"cs.LG",
"stat.AP"
] | In various geosciences branches, including mineral exploration, geometallurgical characterization on established mining operations, and remote sensing, the regionalized input variables are spatially well-sampled across the domain of interest, limiting the scope of spatial uncertainty quantification procedures. In turn,... | {
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2412.07489 | DFT-s-OFDM-based On-Off Keying for Low-Power Wake-Up Signal | [
"cs.IT",
"eess.SP",
"math.IT"
] | 5G-Advanced and likely 6G will support a new low-power wake-up signal (LP-WUS) enabling low-power devices, equipped with a complementary ultra low-power receiver to monitor wireless traffic, to completely switch off their main radio. This orthogonal frequency-division multiplexed (OFDM) signal will emulate an on-off ke... | {
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2412.07493 | Ontology-driven Prompt Tuning for LLM-based Task and Motion Planning | [
"cs.RO",
"cs.AI"
] | Performing complex manipulation tasks in dynamic environments requires efficient Task and Motion Planning (TAMP) approaches, which combine high-level symbolic plan with low-level motion planning. Advances in Large Language Models (LLMs), such as GPT-4, are transforming task planning by offering natural language as an i... | {
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2412.07494 | ResGS: Residual Densification of 3D Gaussian for Efficient Detail
Recovery | [
"cs.CV"
] | Recently, 3D Gaussian Splatting (3D-GS) has prevailed in novel view synthesis, achieving high fidelity and efficiency. However, it often struggles to capture rich details and complete geometry. Our analysis highlights a key limitation of 3D-GS caused by the fixed threshold in densification, which balances geometry cove... | {
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2412.07499 | EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap
Expansion | [
"cs.CV"
] | Multi-label Out-Of-Distribution (OOD) detection aims to discriminate the OOD samples from the multi-label In-Distribution (ID) ones. Compared with its multiclass counterpart, it is crucial to model the joint information among classes. To this end, JointEnergy, which is a representative multi-label OOD inference criteri... | {
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2412.07502 | Systematically Examining Reproducibility: A Case Study for High
Throughput Sequencing using the PRIMAD Model and BioCompute Object | [
"cs.CE"
] | The reproducibility of computational pipelines is an expectation in biomedical science, particularly in critical domains like human health. In this context, reporting next generation genome sequencing methods used in precision medicine spurred the development of the IEEE 2791-2020 standard for Bioinformatics Analyses G... | {
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} |
2412.07507 | ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask
Reinforcement Learning | [
"cs.LG",
"cs.NE"
] | Recent advances in Meta-learning for Black-Box Optimization (MetaBBO) have shown the potential of using neural networks to dynamically configure evolutionary algorithms (EAs), enhancing their performance and adaptability across various BBO instances. However, they are often tailored to a specific EA, which limits their... | {
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} |
2412.07508 | Distributed Uplink Rate Splitting Multiple Access (DU-RSMA): Principles
and Performance Analysis | [
"cs.IT",
"math.IT"
] | One of the main goals of the upcoming sixth-generation (6G) wireless networks is the ability to support higher network density, while ensuring a high quality of service for each user. In this paper, we introduce distributed uplink rate-splitting multiple access (DU-RSMA), define its basic principles, and provide insigh... | {
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} |
2412.07509 | Enhancing 3D Object Detection in Autonomous Vehicles Based on Synthetic
Virtual Environment Analysis | [
"cs.CV"
] | Autonomous Vehicles (AVs) use natural images and videos as input to understand the real world by overlaying and inferring digital elements, facilitating proactive detection in an effort to assure safety. A crucial aspect of this process is real-time, accurate object recognition through automatic scene analysis. While t... | {
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} |
2412.07511 | Stealthy and Robust Backdoor Attack against 3D Point Clouds through
Additional Point Features | [
"cs.CV"
] | Recently, 3D backdoor attacks have posed a substantial threat to 3D Deep Neural Networks (3D DNNs) designed for 3D point clouds, which are extensively deployed in various security-critical applications. Although the existing 3D backdoor attacks achieved high attack performance, they remain vulnerable to preprocessing-b... | {
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} |
2412.07512 | Modeling Speculative Trading Patterns in Token Markets: An Agent-Based
Analysis with TokenLab | [
"cs.MA"
] | This paper presents the application of Tokenlab, an agent-based modeling framework designed to analyze price dynamics and speculative behavior within token-based economies. By decomposing complex token systems into discrete agent interactions governed by fundamental behavioral rules, Tokenlab simplifies the simulation ... | {
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} |
2412.07513 | A Real-time Degeneracy Sensing and Compensation Method for Enhanced
LiDAR SLAM | [
"cs.RO"
] | LiDAR is widely used in Simultaneous Localization and Mapping (SLAM) and autonomous driving. The LiDAR odometry is of great importance in multi-sensor fusion. However, in some unstructured environments, the point cloud registration cannot constrain the poses of the LiDAR due to its sparse geometric features, which lead... | {
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} |
2412.07514 | Physics-Based Dynamic Models Hybridisation Using Physics-Informed Neural
Networks | [
"physics.bio-ph",
"cs.LG",
"cs.NA",
"math.NA"
] | Physics-based dynamic models (PBDMs) are simplified representations of complex dynamical systems. PBDMs take specific processes within a complex system and assign a fragment of variables and an accompanying set of parameters to depict the processes. As this often leads to suboptimal parameterisation of the system, a ke... | {
"Other": 1,
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} |
2412.07515 | CoPrUS: Consistency Preserving Utterance Synthesis towards more
realistic benchmark dialogues | [
"cs.CL"
] | Large-scale Wizard-Of-Oz dialogue datasets have enabled the training of deep learning-based dialogue systems. While they are successful as benchmark datasets, they lack certain types of utterances, which would make them more realistic. In this work, we investigate the creation of synthetic communication errors in an au... | {
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} |
2412.07517 | FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing | [
"cs.CV"
] | Though Rectified Flows (ReFlows) with distillation offers a promising way for fast sampling, its fast inversion transforms images back to structured noise for recovery and following editing remains unsolved. This paper introduces FireFlow, a simple yet effective zero-shot approach that inherits the startling capacity o... | {
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} |
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