id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.06402 | Fineweb-Edu-Ar: Machine-translated Corpus to Support Arabic Small
Language Models | [
"cs.CL",
"cs.AI"
] | As large language models (LLMs) grow and develop, so do their data demands. This is especially true for multilingual LLMs, where the scarcity of high-quality and readily available data online has led to a multitude of synthetic dataset generation approaches. A key technique in this space is machine translation (MT), wh... | {
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2411.06403 | Mastering NIM and Impartial Games with Weak Neural Networks: An
AlphaZero-inspired Multi-Frame Approach | [
"cs.AI"
] | This paper provides a theoretical framework that validates and explains the results in the work with Bei Zhou experimentally finding that AlphaZero-style reinforcement learning algorithms struggle to learn optimal play in NIM, a canonical impartial game proposed as an AI challenge by Harvey Friedman in 2017. Our analys... | {
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2411.06404 | MA-DV2F: A Multi-Agent Navigation Framework using Dynamic Velocity
Vector Field | [
"cs.MA"
] | In this paper we propose MA-DV2F: Multi-Agent Dynamic Velocity Vector Field. It is a framework for simultaneously controlling a group of vehicles in challenging environments. DV2F is generated for each vehicle independently and provides a map of reference orientation and speed that a vehicle must attain at any point on... | {
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2411.06406 | Locally Adaptive One-Class Classifier Fusion with Dynamic $\ell$p-Norm
Constraints for Robust Anomaly Detection | [
"cs.LG",
"stat.ML"
] | This paper presents a novel approach to one-class classifier fusion through locally adaptive learning with dynamic $\ell$p-norm constraints. We introduce a framework that dynamically adjusts fusion weights based on local data characteristics, addressing fundamental challenges in ensemble-based anomaly detection. Our me... | {
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2411.06408 | Visuotactile-Based Learning for Insertion with Compliant Hands | [
"cs.RO"
] | Compared to rigid hands, underactuated compliant hands offer greater adaptability to object shapes, provide stable grasps, and are often more cost-effective. However, they introduce uncertainties in hand-object interactions due to their inherent compliance and lack of precise finger proprioception as in rigid hands. Th... | {
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2411.06409 | Automated Strategy Invention for Confluence of Term Rewrite Systems | [
"cs.LO",
"cs.AI"
] | Term rewriting plays a crucial role in software verification and compiler optimization. With dozens of highly parameterizable techniques developed to prove various system properties, automatic term rewriting tools work in an extensive parameter space. This complexity exceeds human capacity for parameter selection, moti... | {
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2411.06414 | Psycho Gundam: Electroencephalography based real-time robotic control
system with deep learning | [
"cs.RO",
"q-bio.NC"
] | The Psycho Frame, a sophisticated system primarily used in Universal Century (U.C.) series mobile suits for NEWTYPE pilots, has evolved as an integral component in harnessing the latent potential of mental energy. Its ability to amplify and resonate with the pilot's psyche enables real-time mental control, creating uni... | {
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2411.06420 | Generating Mixcode Popular Songs with Artificial Intelligence: Concepts,
Plans, and Speculations | [
"cs.IR",
"cs.AI"
] | Music is a potent form of expression that can communicate, accentuate or even create the emotions of an individual or a collective. Both historically and in contemporary experiences, musical expression was and is commonly instrumentalized for social, political and/or economic purposes. Generative artificial intelligenc... | {
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2411.06424 | Beyond Toxic Neurons: A Mechanistic Analysis of DPO for Toxicity
Reduction | [
"cs.LG",
"cs.CL"
] | Safety fine-tuning algorithms are widely used to reduce harmful outputs in language models, but how they achieve this remain unclear. Studying the Direct Preference Optimization (DPO) algorithm for toxicity reduction, current explanations claim that DPO achieves this by dampening the activations of toxic MLP neurons. H... | {
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2411.06425 | Results of the 2023 CommonRoad Motion Planning Competition for
Autonomous Vehicles | [
"cs.RO"
] | In recent years, different approaches for motion planning of autonomous vehicles have been proposed that can handle complex traffic situations. However, these approaches are rarely compared on the same set of benchmarks. To address this issue, we present the results of a large-scale motion planning competition for auto... | {
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2411.06426 | SequentialBreak: Large Language Models Can be Fooled by Embedding
Jailbreak Prompts into Sequential Prompt Chains | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.LG"
] | As the integration of the Large Language Models (LLMs) into various applications increases, so does their susceptibility to misuse, raising significant security concerns. Numerous jailbreak attacks have been proposed to assess the security defense of LLMs. Current jailbreak attacks mainly rely on scenario camouflage, p... | {
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2411.06427 | UniGAD: Unifying Multi-level Graph Anomaly Detection | [
"cs.LG"
] | Graph Anomaly Detection (GAD) aims to identify uncommon, deviated, or suspicious objects within graph-structured data. Existing methods generally focus on a single graph object type (node, edge, graph, etc.) and often overlook the inherent connections among different object types of graph anomalies. For instance, a mon... | {
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2411.06428 | Neuro-Symbolic Rule Lists | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Machine learning models deployed in sensitive areas such as healthcare must be interpretable to ensure accountability and fairness. Rule lists (if Age < 35 $\wedge$ Priors > 0 then Recidivism = True, else if Next Condition . . . ) offer full transparency, making them well-suited for high-stakes decisions. However, lear... | {
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2411.06429 | Reinforcement learning for Quantum Tiq-Taq-Toe | [
"cs.AI"
] | Quantum Tiq-Taq-Toe is a well-known benchmark and playground for both quantum computing and machine learning. Despite its popularity, no reinforcement learning (RL) methods have been applied to Quantum Tiq-Taq-Toe. Although there has been some research on Quantum Chess this game is significantly more complex in terms o... | {
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2411.06436 | Predictors of disease outbreaks at continentalscale in the African
region: Insights and predictions with geospatial artificial intelligence
using earth observations and routine disease surveillance data | [
"cs.LG"
] | Objectives: Our research adopts computational techniques to analyze disease outbreaks weekly over a large geographic area while maintaining local-level analysis by incorporating relevant high-spatial resolution cultural and environmental datasets. The abundance of data about disease outbreaks gives scientists an excell... | {
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2411.06437 | CTC-Assisted LLM-Based Contextual ASR | [
"eess.AS",
"cs.AI",
"cs.CL"
] | Contextual ASR or hotword customization holds substantial practical value. Despite the impressive performance of current end-to-end (E2E) automatic speech recognition (ASR) systems, they often face challenges in accurately recognizing rare words. Typical E2E contextual ASR models commonly feature complex architectures ... | {
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2411.06438 | Conditional [MASK] Discrete Diffusion Language Model | [
"cs.CL",
"cs.AI"
] | Although auto-regressive models excel in natural language processing, they often struggle to generate diverse text and provide limited controllability. Non-auto-regressive methods could be an alternative but often produce degenerate outputs and exhibit shortcomings in conditional generation. To address these challenges... | {
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2411.06441 | Detecting AutoEncoder is Enough to Catch LDM Generated Images | [
"cs.CV",
"cs.CR",
"cs.LG"
] | In recent years, diffusion models have become one of the main methods for generating images. However, detecting images generated by these models remains a challenging task. This paper proposes a novel method for detecting images generated by Latent Diffusion Models (LDM) by identifying artifacts introduced by their aut... | {
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2411.06442 | Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution | [
"cs.CV",
"cs.AI"
] | Implicit neural representations have recently demonstrated promising potential in arbitrary-scale Super-Resolution (SR) of images. Most existing methods predict the pixel in the SR image based on the queried coordinate and ensemble nearby features, overlooking the importance of incorporating high-frequency prior inform... | {
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2411.06444 | SamRobNODDI: Q-Space Sampling-Augmented Continuous Representation
Learning for Robust and Generalized NODDI | [
"cs.CV",
"eess.IV"
] | Neurite Orientation Dispersion and Density Imaging (NODDI) microstructure estimation from diffusion magnetic resonance imaging (dMRI) is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods accelerate the speed of NODDI parameter estimation and impr... | {
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2411.06445 | Prompt-Efficient Fine-Tuning for GPT-like Deep Models to Reduce
Hallucination and to Improve Reproducibility in Scientific Text Generation
Using Stochastic Optimisation Techniques | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) are increasingly adopted for complex scientific text generation tasks, yet they often suffer from limitations in accuracy, consistency, and hallucination control. This thesis introduces a Parameter-Efficient Fine-Tuning (PEFT) approach tailored for GPT-like models, aiming to mitigate halluc... | {
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2411.06447 | Multi-Parameter Molecular MRI Quantification using Physics-Informed
Self-Supervised Learning | [
"physics.med-ph",
"cs.LG",
"physics.comp-ph"
] | Biophysical model fitting plays a key role in obtaining quantitative parameters from physiological signals and images. However, the model complexity for molecular magnetic resonance imaging (MRI) often translates into excessive computation time, which makes clinical use impractical. Here, we present a generic computati... | {
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2411.06448 | Over-parameterized Student Model via Tensor Decomposition Boosted
Knowledge Distillation | [
"cs.AI"
] | Increased training parameters have enabled large pre-trained models to excel in various downstream tasks. Nevertheless, the extensive computational requirements associated with these models hinder their widespread adoption within the community. We focus on Knowledge Distillation (KD), where a compact student model is t... | {
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2411.06449 | Improved Video VAE for Latent Video Diffusion Model | [
"cs.CV",
"eess.IV"
] | Variational Autoencoder (VAE) aims to compress pixel data into low-dimensional latent space, playing an important role in OpenAI's Sora and other latent video diffusion generation models. While most of existing video VAEs inflate a pretrained image VAE into the 3D causal structure for temporal-spatial compression, this... | {
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2411.06456 | Dropout the High-rate Downsampling: A Novel Design Paradigm for UHD
Image Restoration | [
"cs.CV"
] | With the popularization of high-end mobile devices, Ultra-high-definition (UHD) images have become ubiquitous in our lives. The restoration of UHD images is a highly challenging problem due to the exaggerated pixel count, which often leads to memory overflow during processing. Existing methods either downsample UHD ima... | {
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2411.06463 | RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN
Compression and Acceleration | [
"cs.CV",
"cs.AI"
] | Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in recent years. Compressing these models not only reduces storage requirements, making deployment to edge devices feasible, but also accelerates inference, thereby reducing latency and computational costs. Structured pruning, which removes ... | {
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2411.06465 | Accelerating Large Language Model Training with 4D Parallelism and
Memory Consumption Estimator | [
"cs.LG",
"cs.DC"
] | In large language model (LLM) training, several parallelization strategies, including Tensor Parallelism (TP), Pipeline Parallelism (PP), Data Parallelism (DP), as well as Sequence Parallelism (SP) and Context Parallelism (CP), are employed to distribute model parameters, activations, and optimizer states across device... | {
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2411.06469 | ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical
Prediction? | [
"cs.CL"
] | Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams. Meanwhile, traditional ML models such as SVM and XGBoost have still been mainly adopted in clinical prediction tasks. An emerging question... | {
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2411.06477 | VocalTweets: Investigating Social Media Offensive Language Among
Nigerian Musicians | [
"cs.CL",
"cs.LG"
] | Musicians frequently use social media to express their opinions, but they often convey different messages in their music compared to their posts online. Some utilize these platforms to abuse their colleagues, while others use it to show support for political candidates or engage in activism, as seen during the #EndSars... | {
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2411.06478 | Superpixel Segmentation: A Long-Lasting Ill-Posed Problem | [
"cs.CV"
] | For many years, image over-segmentation into superpixels has been essential to computer vision pipelines, by creating homogeneous and identifiable regions of similar sizes. Such constrained segmentation problem would require a clear definition and specific evaluation criteria. However, the validation framework for supe... | {
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2411.06481 | KMM: Key Frame Mask Mamba for Extended Motion Generation | [
"cs.CV"
] | Human motion generation is a cut-edge area of research in generative computer vision, with promising applications in video creation, game development, and robotic manipulation. The recent Mamba architecture shows promising results in efficiently modeling long and complex sequences, yet two significant challenges remain... | {
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2411.06482 | One controller to rule them all | [
"eess.SY",
"cs.SY"
] | Imagine having a system to control and only know that it belongs to a certain class of dynamical systems. Would it not be amazing to simply plug in a controller and have it work as intended? With the rise of in-context learning and powerful architectures like Transformers, this might be possible, and we want to show it... | {
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2411.06486 | DDIM-Driven Coverless Steganography Scheme with Real Key | [
"cs.CR",
"cs.CV"
] | Typical steganography embeds secret information into images by exploiting their redundancy. Since the visual imperceptibility of secret information is a key factor in scheme evaluation, conventional methods aim to balance this requirement with embedding capacity. Consequently, integrating emerging image generation mode... | {
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2411.06490 | Hermes: A Large Language Model Framework on the Journey to Autonomous
Networks | [
"cs.AI",
"cs.NI"
] | The drive toward automating cellular network operations has grown with the increasing complexity of these systems. Despite advancements, full autonomy currently remains out of reach due to reliance on human intervention for modeling network behaviors and defining policies to meet target requirements. Network Digital Tw... | {
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2411.06491 | MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect
Prediction via Automated Machine Learning | [
"cs.NE"
] | Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to proactively identify software defects, especially where project-specific data is scarce. However, developing a robust ML pipeline with optimal hyperparameters that effectively use cross-project information and yield satisfactory perfor... | {
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2411.06493 | LProtector: An LLM-driven Vulnerability Detection System | [
"cs.CR",
"cs.AI"
] | This paper presents LProtector, an automated vulnerability detection system for C/C++ codebases driven by the large language model (LLM) GPT-4o and Retrieval-Augmented Generation (RAG). As software complexity grows, traditional methods face challenges in detecting vulnerabilities effectively. LProtector leverages GPT-4... | {
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2411.06498 | Barriers to Complexity-Theoretic Proofs that Achieving AGI Using Machine
Learning is Intractable | [
"cs.AI",
"cs.CC"
] | A recent paper (van Rooij et al. 2024) claims to have proved that achieving human-like intelligence using learning from data is intractable in a complexity-theoretic sense. We identify that the proof relies on an unjustified assumption about the distribution of (input, output) pairs to the system. We briefly discuss th... | {
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2411.06499 | Mitigating covariate shift in non-colocated data with learned parameter
priors | [
"cs.LG",
"cs.CV"
] | When training data are distributed across{ time or space,} covariate shift across fragments of training data biases cross-validation, compromising model selection and assessment. We present \textit{Fragmentation-Induced covariate-shift Remediation} ($FIcsR$), which minimizes an $f$-divergence between a fragment's covar... | {
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} |
2411.06500 | Towards Graph Neural Network Surrogates Leveraging Mechanistic Expert
Knowledge for Pandemic Response | [
"cs.LG",
"q-bio.PE"
] | During the COVID-19 crisis, mechanistic models have been proven fundamental to guide evidence-based decision making. However, time-critical decisions in a dynamically changing environment restrict the time available for modelers to gather supporting evidence. As infectious disease dynamics are often heterogeneous on a ... | {
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2411.06501 | Individual Regret in Cooperative Stochastic Multi-Armed Bandits | [
"cs.LG",
"stat.ML"
] | We study the regret in stochastic Multi-Armed Bandits (MAB) with multiple agents that communicate over an arbitrary connected communication graph. We show a near-optimal individual regret bound of $\tilde{O}(\sqrt{AT/m}+A)$, where $A$ is the number of actions, $T$ the time horizon, and $m$ the number of agents. In part... | {
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2411.06503 | Diffusion Sampling Correction via Approximately 10 Parameters | [
"cs.LG",
"cs.CV"
] | Diffusion Probabilistic Models (DPMs) have demonstrated exceptional performance in generative tasks, but this comes at the expense of sampling efficiency. To enhance sampling speed without sacrificing quality, various distillation-based accelerated sampling algorithms have been recently proposed. However, they typicall... | {
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2411.06506 | CULL-MT: Compression Using Language and Layer pruning for Machine
Translation | [
"cs.CL"
] | Multilingual machine translation models often outperform traditional bilingual models by leveraging translation knowledge transfer. Recent advancements have led to these models supporting hundreds of languages and achieving state-of-the-art results across various translation directions. However, as these models grow la... | {
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2411.06508 | Understanding the Role of Equivariance in Self-supervised Learning | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.IT",
"math.IT",
"stat.ML"
] | Contrastive learning has been a leading paradigm for self-supervised learning, but it is widely observed that it comes at the price of sacrificing useful features (\eg colors) by being invariant to data augmentations. Given this limitation, there has been a surge of interest in equivariant self-supervised learning (E-S... | {
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2411.06510 | Offline Handwritten Signature Verification Using a Stream-Based Approach | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Handwritten Signature Verification (HSV) systems distinguish between genuine and forged signatures. Traditional HSV development involves a static batch configuration, constraining the system's ability to model signatures to the limited data available. Signatures exhibit high intra-class variability and are sensitive to... | {
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2411.06511 | Time-delayed Dynamic Mode Decomposition for families of periodic
trajectories in Cislunar Space | [
"eess.SY",
"cs.SY",
"math.DS"
] | In recent years, the development of the Lunar Gateway and Artemis missions has renewed interest in lunar exploration, including both manned and unmanned missions. This interest necessitates accurate initial orbit determination (IOD) and orbit prediction (OP) in this domain, which faces significant challenges such as se... | {
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2411.06513 | PRISM: Privacy-preserving Inter-Site MRI Harmonization via Disentangled
Representation Learning | [
"eess.IV",
"cs.CV"
] | Multi-site MRI studies often suffer from site-specific variations arising from differences in methodology, hardware, and acquisition protocols, thereby compromising accuracy and reliability in clinical AI/ML tasks. We present PRISM (Privacy-preserving Inter-Site MRI Harmonization), a novel Deep Learning framework for h... | {
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2411.06518 | Causal Representation Learning from Multimodal Biological Observations | [
"cs.LG",
"q-bio.QM",
"stat.ME"
] | Prevalent in biological applications (e.g., human phenotype measurements), multimodal datasets can provide valuable insights into the underlying biological mechanisms. However, current machine learning models designed to analyze such datasets still lack interpretability and theoretical guarantees, which are essential t... | {
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2411.06524 | Does This Summary Answer My Question? Modeling Query-Focused Summary
Readers with Rational Speech Acts | [
"cs.AI"
] | Query-focused summarization (QFS) is the task of generating a summary in response to a user-written query. Despite its user-oriented nature, there has been limited work in QFS in explicitly considering a user's understanding of a generated summary, potentially causing QFS systems to underperform at inference time. In t... | {
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2411.06525 | I2VControl-Camera: Precise Video Camera Control with Adjustable Motion
Strength | [
"cs.CV",
"cs.AI"
] | Video generation technologies are developing rapidly and have broad potential applications. Among these technologies, camera control is crucial for generating professional-quality videos that accurately meet user expectations. However, existing camera control methods still suffer from several limitations, including con... | {
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2411.06528 | Epistemic Integrity in Large Language Models | [
"cs.CL",
"cs.AI",
"cs.HC"
] | Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statements with high confidence poses risks for users and society. In this paper, we confront the critical problem of epistemic miscalibration $\unicode{x2013}$ where a model's linguisti... | {
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2411.06529 | Thermodynamically-Informed Iterative Neural Operators for Heterogeneous
Elastic Localization | [
"cs.LG",
"cond-mat.mtrl-sci"
] | Engineering problems frequently require solution of governing equations with spatially-varying discontinuous coefficients. Even for linear elliptic problems, mapping large ensembles of coefficient fields to solutions can become a major computational bottleneck using traditional numerical solvers. Furthermore, machine l... | {
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2411.06530 | Image Segmentation from Shadow-Hints using Minimum Spanning Trees | [
"cs.CV",
"cs.GR"
] | Image segmentation in RGB space is a notoriously difficult task where state-of-the-art methods are trained on thousands or even millions of annotated images. While the performance is impressive, it is still not perfect. We propose a novel image segmentation method, achieving similar segmentation quality but without tra... | {
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2411.06531 | Decentralized Bus Voltage Restoration for DC Microgrids | [
"eess.SY",
"cs.SY"
] | Regulating the voltage of the common DC bus, also referred to as the load bus, in DC microgrids is crucial for ensuring reliability and maintaining the nominal load voltage, which is essential for protecting sensitive loads from voltage variations. Stability and reliability are thereby enhanced, preventing malfunctions... | {
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2411.06535 | Probabilistic Consensus through Ensemble Validation: A Framework for LLM
Reliability | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) have shown significant advances in text generation but often lack the reliability needed for autonomous deployment in high-stakes domains like healthcare, law, and finance. Existing approaches rely on external knowledge or human oversight, limiting scalability. We introduce a novel framewor... | {
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2411.06538 | A Next-Generation Approach to Airline Reservations: Integrating Cloud
Microservices with AI and Blockchain for Enhanced Operational Performance | [
"cs.AI",
"cs.CE"
] | This research proposes the development of a next generation airline reservation system that incorporates the Cloud microservices, distributed artificial intelligence modules and the blockchain technology to improve on the efficiency, safety and customer satisfaction. The traditional reservation systems encounter issues... | {
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2411.06542 | Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing
Contact-Rich Plans? | [
"cs.RO",
"cs.AI",
"cs.SY",
"eess.SY"
] | Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesis tools assume. Contact smoothing approximates a non-smooth system with a smooth one, allowing one to use these synthesis tools more effecti... | {
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2411.06543 | Magnetic Field Aided Vehicle Localization with Acceleration Correction | [
"cs.RO"
] | This paper presents a novel approach for vehicle localization by leveraging the ambient magnetic field within a given environment. Our approach involves introducing a global mathematical function for magnetic field mapping, combined with Euclidean distance-based matching technique for accurately estimating vehicle posi... | {
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2411.06548 | CineXDrama: Relevance Detection and Sentiment Analysis of Bangla YouTube
Comments on Movie-Drama using Transformers: Insights from Interpretability
Tool | [
"cs.CL"
] | In recent years, YouTube has become the leading platform for Bangla movies and dramas, where viewers express their opinions in comments that convey their sentiments about the content. However, not all comments are relevant for sentiment analysis, necessitating a filtering mechanism. We propose a system that first asses... | {
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2411.06549 | In-Context Learning for Preserving Patient Privacy: A Framework for
Synthesizing Realistic Patient Portal Messages | [
"cs.AI",
"cs.CL"
] | Since the COVID-19 pandemic, clinicians have seen a large and sustained influx in patient portal messages, significantly contributing to clinician burnout. To the best of our knowledge, there are no large-scale public patient portal messages corpora researchers can use to build tools to optimize clinician portal workfl... | {
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2411.06550 | A Practical Validation of RIS Detection and Identification | [
"eess.SP",
"cs.IT",
"math.IT"
] | Reconfigurable intelligent surface (RIS)-assisted communication is a key enabling technology for next-generation wireless communication networks, allowing for the reshaping of wireless channels without requiring traditional radio frequency (RF) active components. While their passive nature makes RISs highly attractive,... | {
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2411.06553 | Extended multi-stream temporal-attention module for skeleton-based human
action recognition (HAR) | [
"cs.CV"
] | Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input... | {
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2411.06554 | The KIPARLA Forest treebank of spoken Italian: an overview of initial
design choices | [
"cs.CL"
] | The paper presents an overview of initial design choices discussed towards the creation of a treebank for the Italian KIParla corpus | {
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2411.06556 | EO-GRAPE and EO-DRLPE: Open and Closed Loop Approaches for Energy
Efficient Quantum Optimal Control | [
"quant-ph",
"cs.ET",
"cs.SY",
"eess.SY"
] | This research investigates the possibility of using quantum optimal control techniques to co-optimize the energetic cost and the process fidelity of a quantum unitary gate. The energetic cost is theoretically defined, and thereby, the gradient of the energetic cost for pulse engineering is derived. We empirically demon... | {
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2411.06557 | Real-time Deformation-aware Control for Autonomous Robotic Subretinal
Injection under iOCT Guidance | [
"cs.RO"
] | Robotic platforms provide repeatable and precise tool positioning that significantly enhances retinal microsurgery. Integration of such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing to autonomously perform advanced treatment possibilities, such as i... | {
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2411.06558 | Region-Aware Text-to-Image Generation via Hard Binding and Soft
Refinement | [
"cs.CV"
] | Regional prompting, or compositional generation, which enables fine-grained spatial control, has gained increasing attention for its practicality in real-world applications. However, previous methods either introduce additional trainable modules, thus only applicable to specific models, or manipulate on score maps with... | {
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2411.06559 | Is Your LLM Secretly a World Model of the Internet? Model-Based Planning
for Web Agents | [
"cs.AI"
] | Language agents have demonstrated promising capabilities in automating web-based tasks, though their current reactive approaches still underperform largely compared to humans. While incorporating advanced planning algorithms, particularly tree search methods, could enhance these agents' performance, implementing tree s... | {
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2411.06560 | ElectricityEmissions.jl: A Framework for the Comparison of Carbon
Intensity Signals | [
"eess.SY",
"cs.SY"
] | An increasing number of individuals, companies and organizations are interested in computing and minimizing the carbon emissions associated with their real-time electricity consumption. To achieve this, they require a carbon signal, i.e. a metric that defines the real-time carbon intensity of their electricity supply. ... | {
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2411.06565 | Foundation Model for Composite Materials and Microstructural Analysis | [
"cs.CE",
"cs.AI"
] | The rapid advancement of machine learning has unlocked numerous opportunities for materials science, particularly in accelerating the design and analysis of materials. However, a significant challenge lies in the scarcity and high cost of obtaining high-quality materials datasets. While foundation models pre-trained on... | {
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2411.06566 | A Fully Analog Pipeline for Portfolio Optimization | [
"q-fin.PM",
"cond-mat.dis-nn",
"cs.CE",
"physics.optics",
"quant-ph"
] | Portfolio optimization is a ubiquitous problem in financial mathematics that relies on accurate estimates of covariance matrices for asset returns. However, estimates of pairwise covariance could be better and calculating time-sensitive optimal portfolios is energy-intensive for digital computers. We present an energy-... | {
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2411.06567 | DERs-Aided Blackstart and Load Restoration Framework for Distribution
Systems Considering Synchronization and Frequency Security Constraints | [
"eess.SY",
"cs.SY"
] | Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids, sequentially energize non-blackstart units, and restore loads. However, ... | {
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2411.06568 | Meta-Learning Objectives for Preference Optimization | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Evaluating preference optimization (PO) algorithms on LLM alignment is a challenging task that presents prohibitive costs, noise, and several variables like model size and hyper-parameters. In this work, we show that it is possible to gain insights on the efficacy of PO algorithm on much simpler benchmarks. We design a... | {
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2411.06572 | Fitting Multiple Machine Learning Models with Performance Based
Clustering | [
"cs.LG",
"eess.SP"
] | Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism assumption can result in suboptimal performance. We introduce a clustering framework that eliminates this assumption by grouping the data ac... | {
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2411.06573 | An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient
Descent: Enhancing Unconstrained Optimization with VAV method | [
"cs.LG",
"math.OC",
"stat.ML"
] | Optimizing the learning rate remains a critical challenge in machine learning, essential for achieving model stability and efficient convergence. The Vector Auxiliary Variable (VAV) algorithm introduces a novel energy-based self-adjustable learning rate optimization method designed for unconstrained optimization proble... | {
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2411.06575 | Adaptive Kinematic Modeling for Improved Hand Posture Estimates Using a
Haptic Glove | [
"cs.HC",
"cs.RO"
] | Most commercially available haptic gloves compromise the accuracy of hand-posture measurements in favor of a simpler design with fewer sensors. While inaccurate posture data is often sufficient for the task at hand in biomedical settings such as VR-therapy-aided rehabilitation, measurements should be as precise as poss... | {
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2411.06577 | Discovering emergent connections in quantum physics research via dynamic
word embeddings | [
"cs.LG",
"cs.AI",
"quant-ph"
] | As the field of quantum physics evolves, researchers naturally form subgroups focusing on specialized problems. While this encourages in-depth exploration, it can limit the exchange of ideas across structurally similar problems in different subfields. To encourage cross-talk among these different specialized areas, dat... | {
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2411.06578 | Enabling ISAC in Real World: Beam-Based User Identification with Machine
Learning | [
"eess.SP",
"cs.IT",
"math.IT"
] | Leveraging perception from radar data can assist multiple communication tasks, especially in highly-mobile and large-scale MIMO systems. One particular challenge, however, is how to distinguish the communication user (object) from the other mobile objects in the sensing scene. This paper formulates this \textit{user id... | {
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2411.06581 | Federated LLMs Fine-tuned with Adaptive Importance-Aware LoRA | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Federated fine-tuning of pre-trained Large Language Models (LLMs) enables task-specific adaptation across diverse datasets while preserving data privacy. However, the large model size and heterogeneity in client resources pose significant computational and communication challenges. To address these issues, in this pape... | {
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2411.06583 | Enhancing frozen histological section images using
permanent-section-guided deep learning with nuclei attention | [
"eess.IV",
"cs.AI",
"cs.CV",
"q-bio.QM"
] | In histological pathology, frozen sections are often used for rapid diagnosis during surgeries, as they can be produced within minutes. However, they suffer from artifacts and often lack crucial diagnostic details, particularly within the cell nuclei region. Permanent sections, on the other hand, contain more diagnosti... | {
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2411.06589 | Skipped Adjacency Pulse Width Modulation: Zero Voltage Switching over
Full Duty Cycle Range for Hybrid Flying Capacitor Multi-Level Converters
without Dynamic Level Changing | [
"eess.SY",
"cs.SY"
] | This paper proposes a method to achieve zero voltage switching (ZVS) across the full duty cycle range in hybrid flying capacitor multilevel (FCML) converters, eliminating the need for dynamic level changing and active re-balancing. Utilizing skipped adjacency pulse width modulation (SAPWM), this approach avoids the nea... | {
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2411.06590 | CriticAL: Critic Automation with Language Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Understanding the world through models is a fundamental goal of scientific research. While large language model (LLM) based approaches show promise in automating scientific discovery, they often overlook the importance of criticizing scientific models. Criticizing models deepens scientific understanding and drives the ... | {
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2411.06596 | Graph Neural Networks for modelling breast biomechanical compression | [
"cs.CV"
] | Breast compression simulation is essential for accurate image registration from 3D modalities to X-ray procedures like mammography. It accounts for tissue shape and position changes due to compression, ensuring precise alignment and improved analysis. Although Finite Element Analysis (FEA) is reliable for approximating... | {
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2411.06600 | Few measurement shots challenge generalization in learning to classify
entanglement | [
"quant-ph",
"cs.LG",
"math-ph",
"math.MP",
"stat.ML"
] | The ability to extract general laws from a few known examples depends on the complexity of the problem and on the amount of training data. In the quantum setting, the learner's generalization performance is further challenged by the destructive nature of quantum measurements that, together with the no-cloning theorem, ... | {
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2411.06601 | OffLight: An Offline Multi-Agent Reinforcement Learning Framework for
Traffic Signal Control | [
"cs.AI",
"cs.LG",
"cs.MA"
] | Efficient traffic control (TSC) is essential for urban mobility, but traditional systems struggle to handle the complexity of real-world traffic. Multi-agent Reinforcement Learning (MARL) offers adaptive solutions, but online MARL requires extensive interactions with the environment, making it costly and impractical. O... | {
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2411.06602 | Adaptive and Temporally Consistent Gaussian Surfels for Multi-view
Dynamic Reconstruction | [
"cs.CV"
] | 3D Gaussian Splatting has recently achieved notable success in novel view synthesis for dynamic scenes and geometry reconstruction in static scenes. Building on these advancements, early methods have been developed for dynamic surface reconstruction by globally optimizing entire sequences. However, reconstructing dynam... | {
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2411.06606 | Gen-AI for User Safety: A Survey | [
"cs.AI",
"cs.CR"
] | Machine Learning and data mining techniques (i.e. supervised and unsupervised techniques) are used across domains to detect user safety violations. Examples include classifiers used to detect whether an email is spam or a web-page is requesting bank login information. However, existing ML/DM classifiers are limited in ... | {
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2411.06608 | MolMiner: Transformer architecture for fragment-based autoregressive
generation of molecular stories | [
"cs.LG",
"cond-mat.mtrl-sci"
] | Deep generative models for molecular discovery have become a very popular choice in new high-throughput screening paradigms. These models have been developed inheriting from the advances in natural language processing and computer vision, achieving ever greater results. However, generative molecular modelling has uniqu... | {
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2411.06611 | vTune: Verifiable Fine-Tuning for LLMs Through Backdooring | [
"cs.LG",
"cs.AI",
"cs.CY"
] | As fine-tuning large language models (LLMs) becomes increasingly prevalent, users often rely on third-party services with limited visibility into their fine-tuning processes. This lack of transparency raises the question: how do consumers verify that fine-tuning services are performed correctly? For instance, a service... | {
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2411.06612 | An exact active sensing strategy for a class of bio-inspired systems | [
"eess.SY",
"cs.SY",
"math.DS"
] | We consider a general class of translation-invariant systems with a specific category of output nonlinearities motivated by biological sensing. We show that no dynamic output feedback can stabilize this class of systems to an isolated equilibrium point. To overcome this fundamental limitation, we propose a simple contr... | {
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2411.06613 | Are Neuromorphic Architectures Inherently Privacy-preserving? An
Exploratory Study | [
"cs.LG",
"cs.CR",
"cs.NE"
] | While machine learning (ML) models are becoming mainstream, especially in sensitive application areas, the risk of data leakage has become a growing concern. Attacks like membership inference (MIA) have shown that trained models can reveal sensitive data, jeopardizing confidentiality. While traditional Artificial Neura... | {
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2411.06615 | Field Insights for Portable Vine Robots in Urban Search and Rescue | [
"cs.RO"
] | Soft, growing vine robots are well-suited for exploring cluttered, unknown environments, and are theorized to be performant during structural collapse incidents caused by earthquakes, fires, explosions, and material flaws. These vine robots grow from the tip, enabling them to navigate rubble-filled passageways easily. ... | {
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2411.06616 | MEANT: Multimodal Encoder for Antecedent Information | [
"cs.AI"
] | The stock market provides a rich well of information that can be split across modalities, making it an ideal candidate for multimodal evaluation. Multimodal data plays an increasingly important role in the development of machine learning and has shown to positively impact performance. But information can do more than e... | {
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} |
2411.06618 | Using Diffusion Models as Generative Replay in Continual Federated
Learning -- What will Happen? | [
"cs.LG",
"cs.DC"
] | Federated learning (FL) has become a cornerstone in decentralized learning, where, in many scenarios, the incoming data distribution will change dynamically over time, introducing continuous learning (CL) problems. This continual federated learning (CFL) task presents unique challenges, particularly regarding catastrop... | {
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} |
2411.06624 | A Review of Fairness and A Practical Guide to Selecting
Context-Appropriate Fairness Metrics in Machine Learning | [
"cs.AI"
] | Recent regulatory proposals for artificial intelligence emphasize fairness requirements for machine learning models. However, precisely defining the appropriate measure of fairness is challenging due to philosophical, cultural and political contexts. Biases can infiltrate machine learning models in complex ways dependi... | {
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} |
2411.06626 | Exploring social bots: A feature-based approach to improve bot detection
in social networks | [
"cs.SI",
"cs.AI",
"cs.LG"
] | The importance of social media in our daily lives has unfortunately led to an increase in the spread of misinformation, political messages and malicious links. One of the most popular ways of carrying out those activities is using automated accounts, also known as bots, which makes the detection of such accounts a nece... | {
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} |
2411.06627 | Optimal Virtual Model Control for Robotics: Design and Tuning of
Passivity-Based Controllers | [
"cs.RO"
] | Passivity-based control is a cornerstone of control theory and an established design approach in robotics. Its strength is based on the passivity theorem, which provides a powerful interconnection framework for robotics. However, the design of passivity-based controllers and their optimal tuning remain challenging. We ... | {
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} |
2411.06632 | Few-shot Semantic Learning for Robust Multi-Biome 3D Semantic Mapping in
Off-Road Environments | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Off-road environments pose significant perception challenges for high-speed autonomous navigation due to unstructured terrain, degraded sensing conditions, and domain-shifts among biomes. Learning semantic information across these conditions and biomes can be challenging when a large amount of ground truth data is requ... | {
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} |
2411.06634 | Inductive Graph Few-shot Class Incremental Learning | [
"cs.LG"
] | Node classification with Graph Neural Networks (GNN) under a fixed set of labels is well known in contrast to Graph Few-Shot Class Incremental Learning (GFSCIL), which involves learning a GNN classifier as graph nodes and classes growing over time sporadically. We introduce inductive GFSCIL that continually learns nove... | {
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} |
2411.06635 | Mixed Effects Deep Learning for the interpretable analysis of single
cell RNA sequencing data by quantifying and visualizing batch effects | [
"cs.LG",
"q-bio.GN"
] | Single-cell RNA sequencing (scRNA-seq) data are often confounded by technical or biological batch effects. Existing deep learning models mitigate these effects but often discard batch-specific information, potentially losing valuable biological insights. We propose a Mixed Effects Deep Learning (MEDL) autoencoder frame... | {
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} |
2411.06638 | Model Editing for LLMs4Code: How Far are We? | [
"cs.SE",
"cs.CL"
] | Large Language Models for Code (LLMs4Code) have been found to exhibit outstanding performance in the software engineering domain, especially the remarkable performance in coding tasks. However, even the most advanced LLMs4Code can inevitably contain incorrect or outdated code knowledge. Due to the high cost of training... | {
"Other": 1,
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"cs.SY": 0
} |
2411.06639 | Predicting Country Instability Using Bayesian Deep Learning and Random
Forest | [
"cs.AI",
"cs.SI"
] | Country instability is a global issue, with unpredictably high levels of instability thwarting socio-economic growth and possibly causing a slew of negative consequences. As a result, uncertainty prediction models for a country are becoming increasingly important in the real world, and they are expanding to provide mor... | {
"Other": 0,
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} |
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