id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.17777 | Network Inversion and Its Applications | [
"cs.LG",
"cs.CV",
"cs.LO"
] | Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios. Network inversion tech... | {
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2411.17781 | MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization
via Sensor Fusion | [
"eess.SP",
"cs.LG",
"cs.NI"
] | Accurate indoor localization remains challenging due to variations in wireless signal environments and limited data availability. This paper introduces MetaGraphLoc, a novel system leveraging sensor fusion, graph neural networks (GNNs), and meta-learning to overcome these limitations. MetaGraphLoc integrates received s... | {
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2411.17782 | Joint Resource Optimization, Computation Offloading and Resource Slicing
for Multi-Edge Traffic-Cognitive Networks | [
"cs.DC",
"cs.AI"
] | The evolving landscape of edge computing envisions platforms operating as dynamic intermediaries between application providers and edge servers (ESs), where task offloading is coupled with payments for computational services. Ensuring efficient resource utilization and meeting stringent Quality of Service (QoS) require... | {
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2411.17783 | KACDP: A Highly Interpretable Credit Default Prediction Model | [
"q-fin.RM",
"cs.LG"
] | In the field of finance, the prediction of individual credit default is of vital importance. However, existing methods face problems such as insufficient interpretability and transparency as well as limited performance when dealing with high-dimensional and nonlinear data. To address these issues, this paper introduces... | {
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2411.17784 | Diffusion Autoencoders for Few-shot Image Generation in Hyperbolic Space | [
"cs.CV"
] | Few-shot image generation aims to generate diverse and high-quality images for an unseen class given only a few examples in that class. However, existing methods often suffer from a trade-off between image quality and diversity while offering limited control over the attributes of newly generated images. In this work, ... | {
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2411.17785 | New Test-Time Scenario for Biosignal: Concept and Its Approach | [
"eess.SP",
"cs.LG"
] | Online Test-Time Adaptation (OTTA) enhances model robustness by updating pre-trained models with unlabeled data during testing. In healthcare, OTTA is vital for real-time tasks like predicting blood pressure from biosignals, which demand continuous adaptation. We introduce a new test-time scenario with streams of unlab... | {
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2411.17786 | DreamCache: Finetuning-Free Lightweight Personalized Image Generation
via Feature Caching | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Personalized image generation requires text-to-image generative models that capture the core features of a reference subject to allow for controlled generation across different contexts. Existing methods face challenges due to complex training requirements, high inference costs, limited flexibility, or a combination of... | {
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2411.17787 | Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient | [
"cs.CV"
] | In the rapidly advancing field of image generation, Visual Auto-Regressive (VAR) modeling has garnered considerable attention for its innovative next-scale prediction approach. This paradigm offers substantial improvements in efficiency, scalability, and zero-shot generalization. Yet, the inherently coarse-to-fine natu... | {
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2411.17788 | Geometric Point Attention Transformer for 3D Shape Reassembly | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Shape assembly, which aims to reassemble separate parts into a complete object, has gained significant interest in recent years. Existing methods primarily rely on networks to predict the poses of individual parts, but often fail to effectively capture the geometric interactions between the parts and their poses. In th... | {
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2411.17790 | Self-supervised Monocular Depth and Pose Estimation for Endoscopy with
Generative Latent Priors | [
"cs.CV",
"cs.AI"
] | Accurate 3D mapping in endoscopy enables quantitative, holistic lesion characterization within the gastrointestinal (GI) tract, requiring reliable depth and pose estimation. However, endoscopy systems are monocular, and existing methods relying on synthetic datasets or complex models often lack generalizability in chal... | {
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2411.17792 | $H^3$Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMs | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Alignment of pretrained LLMs using instruction-based datasets is critical for creating fine-tuned models that reflect human preference. A growing number of alignment-based fine-tuning algorithms and benchmarks emerged recently, fueling the efforts on effective alignments of pre-trained LLMs to ensure helpful, harmless,... | {
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2411.17793 | Engineering AI Judge Systems | [
"cs.SE",
"cs.AI"
] | AI judge systems are designed to automatically evaluate Foundation Model-powered software (i.e., FMware). Due to the intrinsic dynamic and stochastic nature of FMware, the development of AI judge systems requires a unique engineering life cycle and presents new challenges. In this paper, we discuss the challenges based... | {
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2411.17794 | NEMO: Can Multimodal LLMs Identify Attribute-Modified Objects? | [
"cs.CV"
] | Multimodal Large Language Models (MLLMs) have made notable advances in visual understanding, yet their abilities to recognize objects modified by specific attributes remain an open question. To address this, we explore MLLMs' reasoning capabilities in object recognition, ranging from commonsense to beyond-commonsense s... | {
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2411.17795 | Pan-protein Design Learning Enables Task-adaptive Generalization for
Low-resource Enzyme Design | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | Computational protein design (CPD) offers transformative potential for bioengineering, but current deep CPD models, focused on universal domains, struggle with function-specific designs. This work introduces a novel CPD paradigm tailored for functional design tasks, particularly for enzymes-a key protein class often la... | {
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2411.17796 | Scalable iterative pruning of large language and vision models using
block coordinate descent | [
"cs.LG",
"math.OC",
"quant-ph"
] | Pruning neural networks, which involves removing a fraction of their weights, can often maintain high accuracy while significantly reducing model complexity, at least up to a certain limit. We present a neural network pruning technique that builds upon the Combinatorial Brain Surgeon, but solves an optimization problem... | {
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2411.17798 | DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell
Receptor-antigen Binding Affinity Prediction | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | Identifying T-cell receptors (TCRs) that interact with antigenic peptides provides the technical basis for developing vaccines and immunotherapies. The emergent deep learning methods excel at learning antigen binding patterns from known TCRs but struggle with novel or sparsely represented antigens. However, binding spe... | {
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2411.17799 | Signs as Tokens: An Autoregressive Multilingual Sign Language Generator | [
"cs.CV",
"cs.CL"
] | Sign language is a visual language that encompasses all linguistic features of natural languages and serves as the primary communication method for the deaf and hard-of-hearing communities. While many studies have successfully adapted pretrained language models (LMs) for sign language translation (sign-to-text), drawin... | {
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2411.17800 | STAR: Synthesis of Tailored Architectures | [
"cs.LG",
"cs.AI",
"cs.NE"
] | Iterative improvement of model architectures is fundamental to deep learning: Transformers first enabled scaling, and recent advances in model hybridization have pushed the quality-efficiency frontier. However, optimizing architectures remains challenging and expensive. Current automated or manual approaches fall short... | {
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2411.17807 | A solvable generative model with a linear, one-step denoiser | [
"cs.LG",
"cs.CV"
] | We develop an analytically tractable single-step diffusion model based on a linear denoiser and present explicit formula for the Kullback-Leibler divergence between generated and sampling distribution, taken to be isotropic Gaussian, showing the effect of finite diffusion time and noise scale. Our study further reveals... | {
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2411.17814 | Low-rank Adaptation-based All-Weather Removal for Autonomous Navigation | [
"cs.CV"
] | All-weather image restoration (AWIR) is crucial for reliable autonomous navigation under adverse weather conditions. AWIR models are trained to address a specific set of weather conditions such as fog, rain, and snow. But this causes them to often struggle with out-of-distribution (OoD) samples or unseen degradations w... | {
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2411.17820 | CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos | [
"cs.CV",
"cs.RO"
] | Navigating dynamic urban environments presents significant challenges for embodied agents, requiring advanced spatial reasoning and adherence to common-sense norms. Despite progress, existing visual navigation methods struggle in map-free or off-street settings, limiting the deployment of autonomous agents like last-mi... | {
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2411.17824 | A Cloud-based Real-time Probabilistic Remaining Useful Life (RUL)
Estimation using the Sequential Monte Carlo (SMC) Method | [
"cs.CE",
"cs.DC"
] | The remaining useful life (RUL) estimation is an important metric that helps in condition-based maintenance. Damage data obtained from the diagnostics techniques are often noisy and the RUL estimated from the data is less reliable. Estimating the probabilistic RUL by quantifying the uncertainty in the predictive model ... | {
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2411.17826 | Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling | [
"cs.RO",
"cs.LG",
"stat.ML"
] | Ensuring the safety of autonomous vehicles (AVs) requires both accurate estimation of their performance and efficient discovery of potential failure cases. This paper introduces Bayesian adaptive multifidelity sampling (BAMS), which leverages the power of adaptive Bayesian sampling to achieve efficient discovery while ... | {
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2411.17830 | Securing RIS-Aided Wireless Networks Against Full Duplex Active
Eavesdropping | [
"cs.IT",
"cs.ET",
"cs.SY",
"eess.SY",
"math.IT"
] | This paper investigates the physical layer security of a Reconfigurable Intelligent Surface (RIS)-aided wireless network in the presence of full-duplex active eavesdropping. In this scenario, the RIS cooperates with the Base Station (BS) to transfer information to the intended user while an active attacker attempts to ... | {
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2411.17831 | Rapid Distributed Fine-tuning of a Segmentation Model Onboard Satellites | [
"cs.LG",
"cs.CV",
"cs.DC"
] | Segmentation of Earth observation (EO) satellite data is critical for natural hazard analysis and disaster response. However, processing EO data at ground stations introduces delays due to data transmission bottlenecks and communication windows. Using segmentation models capable of near-real-time data analysis onboard ... | {
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2411.17832 | SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG
Generation | [
"cs.CV",
"cs.AI"
] | Recently, text-guided scalable vector graphics (SVG) synthesis has demonstrated significant potential in domains such as iconography and sketching. However, SVGs generated from existing Text-to-SVG methods often lack editability and exhibit deficiencies in visual quality and diversity. In this paper, we propose a novel... | {
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2411.17833 | Adaptive Client Selection with Personalization for Communication
Efficient Federated Learning | [
"cs.LG",
"cs.DC"
] | Federated Learning (FL) is a distributed approach to collaboratively training machine learning models. FL requires a high level of communication between the devices and a central server, thus imposing several challenges, including communication bottlenecks and network scalability. This article introduces ACSP-FL (https... | {
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2411.17835 | Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and
Markdown Extraction | [
"cs.CL",
"cs.AI",
"cs.CV"
] | We present Arabic-Nougat, a suite of OCR models for converting Arabic book pages into structured Markdown text. Based on Meta's Nougat architecture, Arabic-Nougat includes three specialized models: arabic-small-nougat, arabic-base-nougat, and arabic-large-nougat. These models are fine-tuned on a synthetic dataset, arab... | {
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2411.17837 | OracleSage: Towards Unified Visual-Linguistic Understanding of Oracle
Bone Scripts through Cross-Modal Knowledge Fusion | [
"cs.CV"
] | Oracle bone script (OBS), as China's earliest mature writing system, present significant challenges in automatic recognition due to their complex pictographic structures and divergence from modern Chinese characters. We introduce OracleSage, a novel cross-modal framework that integrates hierarchical visual understandin... | {
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2411.17838 | Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering | [
"cs.LG"
] | Time series data has become increasingly prevalent across numerous domains, driving a growing demand for time series machine learning techniques. Among these, time series clustering (TSCL) stands out as one of the most popular machine learning tasks. TSCL serves as a powerful exploratory analysis tool and is also emplo... | {
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2411.17840 | Basic Research, Lethal Effects: Military AI Research Funding as
Enlistment | [
"cs.CY",
"cs.AI"
] | In the context of unprecedented U.S. Department of Defense (DoD) budgets, this paper examines the recent history of DoD funding for academic research in algorithmically based warfighting. We draw from a corpus of DoD grant solicitations from 2007 to 2023, focusing on those addressed to researchers in the field of artif... | {
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2411.17845 | CAMLD: Contrast-Agnostic Medical Landmark Detection with
Consistency-Based Regularization | [
"eess.IV",
"cs.CV"
] | Anatomical landmark detection in medical images is essential for various clinical and research applications, including disease diagnosis and surgical planning. However, manual landmark annotation is time-consuming and requires significant expertise. Existing deep learning (DL) methods often require large amounts of wel... | {
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2411.17847 | SoftmAP: Software-Hardware Co-design for Integer-Only Softmax on
Associative Processors | [
"cs.AR",
"cs.AI"
] | Recent research efforts focus on reducing the computational and memory overheads of Large Language Models (LLMs) to make them feasible on resource-constrained devices. Despite advancements in compression techniques, non-linear operators like Softmax and Layernorm remain bottlenecks due to their sensitivity to quantizat... | {
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2411.17850 | Reliability of deep learning models for anatomical landmark detection:
The role of inter-rater variability | [
"eess.IV",
"cs.CV"
] | Automated detection of anatomical landmarks plays a crucial role in many diagnostic and surgical applications. Progresses in deep learning (DL) methods have resulted in significant performance enhancement in tasks related to anatomical landmark detection. While current research focuses on accurately localizing these la... | {
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2411.17855 | "Give me the code" -- Log Analysis of First-Year CS Students'
Interactions With GPT | [
"cs.CY",
"cs.AI",
"cs.ET",
"cs.HC"
] | The impact of Large Language Models (LLMs) like GPT-3, GPT-4, and Bard in computer science (CS) education is expected to be profound. Students now have the power to generate code solutions for a wide array of programming assignments. For first-year students, this may be particularly problematic since the foundational s... | {
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2411.17856 | Integrating Machine Learning and Quantum Circuits for Proton Affinity
Predictions | [
"cs.LG",
"physics.chem-ph",
"quant-ph"
] | A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated structure. In the gas phase, the site of protonation is determined using proton affinity (PA) measurements. Currently, mass spectrometry and a... | {
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2411.17861 | Accelerating Proximal Policy Optimization Learning Using Task Prediction
for Solving Environments with Delayed Rewards | [
"cs.LG",
"cs.AI"
] | In this paper, we tackle the challenging problem of delayed rewards in reinforcement learning (RL). While Proximal Policy Optimization (PPO) has emerged as a leading Policy Gradient method, its performance can degrade under delayed rewards. We introduce two key enhancements to PPO: a hybrid policy architecture that com... | {
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2411.17863 | LongKey: Keyphrase Extraction for Long Documents | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG"
] | In an era of information overload, manually annotating the vast and growing corpus of documents and scholarly papers is increasingly impractical. Automated keyphrase extraction addresses this challenge by identifying representative terms within texts. However, most existing methods focus on short documents (up to 512 t... | {
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2411.17864 | Generative Image Layer Decomposition with Visual Effects | [
"cs.CV"
] | Recent advancements in large generative models, particularly diffusion-based methods, have significantly enhanced the capabilities of image editing. However, achieving precise control over image composition tasks remains a challenge. Layered representations, which allow for independent editing of image components, are ... | {
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2411.17866 | Distributed Sign Momentum with Local Steps for Training Transformers | [
"cs.LG"
] | Pre-training Transformer models is resource-intensive, and recent studies have shown that sign momentum is an efficient technique for training large-scale deep learning models, particularly Transformers. However, its application in distributed training or federated learning remains underexplored. This paper investigate... | {
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2411.17867 | RankMap: Priority-Aware Multi-DNN Manager for Heterogeneous Embedded
Devices | [
"cs.LG",
"cs.DC",
"cs.ET"
] | Modern edge data centers simultaneously handle multiple Deep Neural Networks (DNNs), leading to significant challenges in workload management. Thus, current management systems must leverage the architectural heterogeneity of new embedded systems to efficiently handle multi-DNN workloads. This paper introduces RankMap, ... | {
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2411.17869 | ReC-TTT: Contrastive Feature Reconstruction for Test-Time Training | [
"cs.CV",
"cs.LG"
] | The remarkable progress in deep learning (DL) showcases outstanding results in various computer vision tasks. However, adaptation to real-time variations in data distributions remains an important challenge. Test-Time Training (TTT) was proposed as an effective solution to this issue, which increases the generalization... | {
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2411.17870 | Breast Tumor Classification Using EfficientNet Deep Learning Model | [
"eess.IV",
"cs.CV"
] | Precise breast cancer classification on histopathological images has the potential to greatly improve the diagnosis and patient outcome in oncology. The data imbalance problem largely stems from the inherent imbalance within medical image datasets, where certain tumor subtypes may appear much less frequently. This cons... | {
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2411.17876 | Leveraging Large Language Models and Topic Modeling for Toxicity
Classification | [
"cs.CL",
"cs.LG"
] | Content moderation and toxicity classification represent critical tasks with significant social implications. However, studies have shown that major classification models exhibit tendencies to magnify or reduce biases and potentially overlook or disadvantage certain marginalized groups within their classification proce... | {
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2411.17886 | The Context of Crash Occurrence: A Complexity-Infused Approach
Integrating Semantic, Contextual, and Kinematic Features | [
"cs.CV",
"cs.LG"
] | Understanding the context of crash occurrence in complex driving environments is essential for improving traffic safety and advancing automated driving. Previous studies have used statistical models and deep learning to predict crashes based on semantic, contextual, or vehicle kinematic features, but none have examined... | {
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2411.17891 | HOPPR Medical-Grade Platform for Medical Imaging AI | [
"cs.CL",
"cs.AI",
"cs.CV"
] | Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on millions of paired image and text samples. Subsequent research efforts have demonstrated great potential of LVLMs to achieve high performance in medical imaging use cases (e.g.... | {
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2411.17897 | Automating grapevine LAI features estimation with UAV imagery and
machine learning | [
"cs.CV",
"cs.AI",
"cs.ET",
"cs.LG"
] | The leaf area index determines crop health and growth. Traditional methods for calculating it are time-consuming, destructive, costly, and limited to a scale. In this study, we automate the index estimation method using drone image data of grapevine plants and a machine learning model. Traditional feature extraction an... | {
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2411.17898 | On the ERM Principle in Meta-Learning | [
"stat.ML",
"cs.LG"
] | Classic supervised learning involves algorithms trained on $n$ labeled examples to produce a hypothesis $h \in \mathcal{H}$ aimed at performing well on unseen examples. Meta-learning extends this by training across $n$ tasks, with $m$ examples per task, producing a hypothesis class $\mathcal{H}$ within some meta-class ... | {
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2411.17902 | Nearest-Neighbourless Asymptotically Optimal Motion Planning with Fully
Connected Informed Trees (FCIT*) | [
"cs.RO"
] | Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. Traditionally, and especially for asymptotically optimal sampling-based motion planners, the most expensive operations are local motion valid... | {
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2411.17911 | Passive Deepfake Detection Across Multi-modalities: A Comprehensive
Survey | [
"cs.CV",
"cs.CR"
] | In recent years, deepfakes (DFs) have been utilized for malicious purposes, such as individual impersonation, misinformation spreading, and artists' style imitation, raising questions about ethical and security concerns. However, existing surveys have focused on accuracy performance of passive DF detection approaches f... | {
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2411.17912 | Can LLMs plan paths in the real world? | [
"cs.AI",
"cs.RO"
] | As large language models (LLMs) increasingly integrate into vehicle navigation systems, understanding their path-planning capability is crucial. We tested three LLMs through six real-world path-planning scenarios in various settings and with various difficulties. Our experiments showed that all LLMs made numerous error... | {
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2411.17913 | CrypQ: A Database Benchmark Based on Dynamic, Ever-Evolving Ethereum
Data | [
"cs.DB"
] | Modern database systems are expected to handle dynamic data whose characteristics may evolve over time. Many popular database benchmarks are limited in their ability to evaluate this dynamic aspect of the database systems. Those that use synthetic data generators often fail to capture the complexity and unpredictable n... | {
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2411.17914 | Enhancing Project Performance Forecasting using Machine Learning
Techniques | [
"cs.LG",
"cs.AI",
"cs.CY",
"stat.AP"
] | Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-... | {
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2411.17915 | Stochastic SketchRefine: Scaling In-Database Decision-Making under
Uncertainty to Millions of Tuples | [
"cs.DB"
] | Decision making under uncertainty often requires choosing packages, or bags of tuples, that collectively optimize expected outcomes while limiting risks. Processing Stochastic Package Queries (SPQs) involves solving very large optimization problems on uncertain data. Monte Carlo methods create numerous scenarios, or sa... | {
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2411.17917 | DECODE: Domain-aware Continual Domain Expansion for Motion Prediction | [
"cs.CV",
"cs.RO"
] | Motion prediction is critical for autonomous vehicles to effectively navigate complex environments and accurately anticipate the behaviors of other traffic participants. As autonomous driving continues to evolve, the need to assimilate new and varied driving scenarios necessitates frequent model updates through retrain... | {
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2411.17922 | Exploring Superpixel Segmentation Methods in the Context of Citizen
Science and Deforestation Detection | [
"cs.CV"
] | Tropical forests play an essential role in the planet's ecosystem, making the conservation of these biomes a worldwide priority. However, ongoing deforestation and degradation pose a significant threat to their existence, necessitating effective monitoring and the proposal of actions to mitigate the damage caused by th... | {
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2411.17924 | AI2T: Building Trustable AI Tutors by Interactively Teaching a
Self-Aware Learning Agent | [
"cs.HC",
"cs.AI",
"cs.LG"
] | AI2T is an interactively teachable AI for authoring intelligent tutoring systems (ITSs). Authors tutor AI2T by providing a few step-by-step solutions and then grading AI2T's own problem-solving attempts. From just 20-30 minutes of interactive training, AI2T can induce robust rules for step-by-step solution tracking (i.... | {
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2411.17925 | Stability and Synchronization of Kuramoto Oscillators | [
"math.DS",
"cs.SY",
"eess.SY"
] | Imagine a group of oscillators, each endowed with their own rhythm or frequency, be it the ticking of a biological clock, the swing of a pendulum, or the glowing of fireflies. While these individual oscillators may seem independent of one another at first glance, the true magic lies in their ability to influence and sy... | {
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2411.17928 | MapEval: Towards Unified, Robust and Efficient SLAM Map Evaluation
Framework | [
"cs.RO"
] | Evaluating massive-scale point cloud maps in Simultaneous Localization and Mapping (SLAM) remains challenging, primarily due to the absence of unified, robust and efficient evaluation frameworks. We present MapEval, an open-source framework for comprehensive quality assessment of point cloud maps, specifically addressi... | {
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2411.17931 | Combining Threat Intelligence with IoT Scanning to Predict Cyber Attack | [
"cs.CR",
"cs.AI",
"cs.CY",
"cs.NI"
] | While the Web has become a worldwide platform for communication, hackers and hacktivists share their ideology and communicate with members on the "Dark Web"-the reverse of the Web. Currently, the problems of information overload and difficulty to obtain a comprehensive picture of hackers and cyber-attackers hinder the ... | {
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2411.17932 | Neural Networks Use Distance Metrics | [
"cs.LG",
"cs.AI",
"stat.ML"
] | We present empirical evidence that neural networks with ReLU and Absolute Value activations learn distance-based representations. We independently manipulate both distance and intensity properties of internal activations in trained models, finding that both architectures are highly sensitive to small distance-based per... | {
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2411.17936 | Stealthy Multi-Task Adversarial Attacks | [
"cs.CR",
"cs.CV"
] | Deep Neural Networks exhibit inherent vulnerabilities to adversarial attacks, which can significantly compromise their outputs and reliability. While existing research primarily focuses on attacking single-task scenarios or indiscriminately targeting all tasks in multi-task environments, we investigate selectively targ... | {
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2411.17937 | Spatio-temporal Causal Learning for Streamflow Forecasting | [
"cs.LG",
"cs.AI"
] | Streamflow plays an essential role in the sustainable planning and management of national water resources. Traditional hydrologic modeling approaches simulate streamflow by establishing connections across multiple physical processes, such as rainfall and runoff. These data, inherently connected both spatially and tempo... | {
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2411.17941 | Multi-Label Bayesian Active Learning with Inter-Label Relationships | [
"cs.LG"
] | The primary challenge of multi-label active learning, differing it from multi-class active learning, lies in assessing the informativeness of an indefinite number of labels while also accounting for the inherited label correlation. Existing studies either require substantial computational resources to leverage correlat... | {
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2411.17943 | Evaluating Generative AI-Enhanced Content: A Conceptual Framework Using
Qualitative, Quantitative, and Mixed-Methods Approaches | [
"cs.CL",
"cs.AI"
] | Generative AI (GenAI) has revolutionized content generation, offering transformative capabilities for improving language coherence, readability, and overall quality. This manuscript explores the application of qualitative, quantitative, and mixed-methods research approaches to evaluate the performance of GenAI models i... | {
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2411.17945 | MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D
Content Creation | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and annotation depth of the existing datasets. To address this, we introduce MARVEL-40M+, an extensive dataset with 40 million text annotations for over 8.9 million 3D assets aggr... | {
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2411.17949 | ROICtrl: Boosting Instance Control for Visual Generation | [
"cs.CV"
] | Natural language often struggles to accurately associate positional and attribute information with multiple instances, which limits current text-based visual generation models to simpler compositions featuring only a few dominant instances. To address this limitation, this work enhances diffusion models by introducing ... | {
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2411.17957 | Optimization-Free Image Immunization Against Diffusion-Based Editing | [
"cs.CV"
] | Current image immunization defense techniques against diffusion-based editing embed imperceptible noise in target images to disrupt editing models. However, these methods face scalability challenges, as they require time-consuming re-optimization for each image-taking hours for small batches. To address these challenge... | {
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2411.17959 | Adversarial Training in Low-Label Regimes with Margin-Based
Interpolation | [
"cs.LG",
"cs.CR",
"cs.CV"
] | Adversarial training has emerged as an effective approach to train robust neural network models that are resistant to adversarial attacks, even in low-label regimes where labeled data is scarce. In this paper, we introduce a novel semi-supervised adversarial training approach that enhances both robustness and natural a... | {
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2411.17961 | ESS-ReduNet: Enhancing Subspace Separability of ReduNet via Dynamic
Expansion with Bayesian Inference | [
"cs.LG"
] | ReduNet is a deep neural network model that leverages the principle of maximal coding rate \textbf{redu}ction to transform original data samples into a low-dimensional, linear discriminative feature representation. Unlike traditional deep learning frameworks, ReduNet constructs its parameters explicitly layer by layer,... | {
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2411.17965 | Optimized Tradeoffs for Private Prediction with Majority Ensembling | [
"cs.LG",
"cs.CR"
] | We study a classical problem in private prediction, the problem of computing an $(m\epsilon, \delta)$-differentially private majority of $K$ $(\epsilon, \Delta)$-differentially private algorithms for $1 \leq m \leq K$ and $1 > \delta \geq \Delta \geq 0$. Standard methods such as subsampling or randomized response are w... | {
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2411.17967 | QuaLLM-Health: An Adaptation of an LLM-Based Framework for Quantitative
Data Extraction from Online Health Discussions | [
"cs.CL"
] | Health-related discussions on social media like Reddit offer valuable insights, but extracting quantitative data from unstructured text is challenging. In this work, we present an adapted framework from QuaLLM into QuaLLM-Health for extracting clinically relevant quantitative data from Reddit discussions about glucagon... | {
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2411.17971 | Graph Neural Network for Cerebral Blood Flow Prediction With Clinical
Datasets | [
"eess.IV",
"cs.AI",
"cs.CE",
"cs.LG"
] | Accurate prediction of cerebral blood flow is essential for the diagnosis and treatment of cerebrovascular diseases. Traditional computational methods, however, often incur significant computational costs, limiting their practicality in real-time clinical applications. This paper proposes a graph neural network (GNN) t... | {
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2411.17973 | Improved implicit diffusion model with knowledge distillation to
estimate the spatial distribution density of carbon stock in remote sensing
imagery | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The forest serves as the most significant terrestrial carbon stock mechanism, effectively reducing atmospheric CO$_2$ concentrations and mitigating climate change. Remote sensing provides high data accuracy and enables large-scale observations. Optical images facilitate long-term monitoring, which is crucial for future... | {
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2411.17976 | The importance of visual modelling languages in generative software
engineering | [
"cs.SE",
"cs.AI"
] | Multimodal GPTs represent a watershed in the interplay between Software Engineering and Generative Artificial Intelligence. GPT-4 accepts image and text inputs, rather than simply natural language. We investigate relevant use cases stemming from these enhanced capabilities of GPT-4. To the best of our knowledge, no oth... | {
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} |
2411.17980 | Vision Mamba Distillation for Low-resolution Fine-grained Image
Classification | [
"cs.CV"
] | Low-resolution fine-grained image classification has recently made significant progress, largely thanks to the super-resolution techniques and knowledge distillation methods. However, these approaches lead to an exponential increase in the number of parameters and computational complexity of models. In order to solve t... | {
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2411.17982 | HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene
Reconstruction | [
"cs.RO",
"cs.CV"
] | We present HI-SLAM2, a geometry-aware Gaussian SLAM system that achieves fast and accurate monocular scene reconstruction using only RGB input. Existing Neural SLAM or 3DGS-based SLAM methods often trade off between rendering quality and geometry accuracy, our research demonstrates that both can be achieved simultaneou... | {
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2411.17983 | Optimized Conformal Selection: Powerful Selective Inference After
Conformity Score Optimization | [
"stat.ME",
"cs.AI",
"cs.LG",
"stat.ML"
] | Model selection/optimization in conformal inference is challenging, since it may break the exchangeability between labeled and unlabeled data. We study this problem in the context of conformal selection, which uses conformal p-values to select ``interesting'' instances with large unobserved labels from a pool of unlabe... | {
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2411.17984 | RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation
Model | [
"cs.CV"
] | Remote sensing foundation models largely break away from the traditional paradigm of designing task-specific models, offering greater scalability across multiple tasks. However, they face challenges such as low computational efficiency and limited interpretability, especially when dealing with high-resolution remote se... | {
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2411.17986 | Hybrid Beamforming Design for Covert mmWave MIMO with Finite-Resolution
DACs | [
"cs.IT",
"eess.SP",
"math.IT"
] | We investigate hybrid beamforming design for covert millimeter wave multiple-input multiple-output systems with finite-resolution digital-to-analog converters (DACs), which impose practical hardware constraints not yet considered by the existing works and have negative impact on the covertness. Based on the additive qu... | {
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2411.17989 | Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal
Discovery | [
"cs.LG",
"cs.AI",
"stat.ME"
] | As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from observational data has become a preferred and efficient approach over conducting randomized control trials. However, purely observational data co... | {
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2411.17990 | Beam Switching Based Beam Design for High-Speed Train mmWave
Communications | [
"cs.IT",
"eess.SP",
"math.IT"
] | For high-speed train (HST) millimeter wave (mmWave) communications, the use of narrow beams with small beam coverage needs frequent beam switching, while wider beams with small beam gain leads to weaker mmWave signal strength. In this paper, we consider beam switching based beam design, which is formulated as an optimi... | {
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2411.17991 | VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video
Comprehension with Video-Text Duet Interaction Format | [
"cs.CV",
"cs.CL"
] | Recent researches on video large language models (VideoLLM) predominantly focus on model architectures and training datasets, leaving the interaction format between the user and the model under-explored. In existing works, users often interact with VideoLLMs by using the entire video and a query as input, after which t... | {
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2411.17992 | New Faithfulness-Centric Interpretability Paradigms for Natural Language
Processing | [
"cs.CL",
"cs.LG"
] | As machine learning becomes more widespread and is used in more critical applications, it's important to provide explanations for these models, to prevent unintended behavior. Unfortunately, many current interpretability methods struggle with faithfulness. Therefore, this Ph.D. thesis investigates the question "How to ... | {
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2411.17993 | DRS: Deep Question Reformulation With Structured Output | [
"cs.CL"
] | Question answering represents a core capability of large language models (LLMs). However, when individuals encounter unfamiliar knowledge in texts, they often formulate questions that the text itself cannot answer due to insufficient understanding of the underlying information. Recent studies reveal that while LLMs can... | {
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2411.17994 | Differentiable Inverse Rendering with Interpretable Basis BRDFs | [
"cs.CV",
"cs.GR"
] | Inverse rendering seeks to reconstruct both geometry and spatially varying BRDFs (SVBRDFs) from captured images. To address the inherent ill-posedness of inverse rendering, basis BRDF representations are commonly used, modeling SVBRDFs as spatially varying blends of a set of basis BRDFs. However, existing methods often... | {
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2411.17995 | Revisiting Misalignment in Multispectral Pedestrian Detection: A
Language-Driven Approach for Cross-modal Alignment Fusion | [
"cs.CV"
] | Multispectral pedestrian detection is a crucial component in various critical applications. However, a significant challenge arises due to the misalignment between these modalities, particularly under real-world conditions where data often appear heavily misaligned. Conventional methods developed on well-aligned or min... | {
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2411.17999 | A Novel Pareto-optimal Ranking Method for Comparing Multi-objective
Optimization Algorithms | [
"cs.AI",
"cs.NE"
] | As the interest in multi- and many-objective optimization algorithms grows, the performance comparison of these algorithms becomes increasingly important. A large number of performance indicators for multi-objective optimization algorithms have been introduced, each of which evaluates these algorithms based on a certai... | {
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2411.18000 | Exploring Visual Vulnerabilities via Multi-Loss Adversarial Search for
Jailbreaking Vision-Language Models | [
"cs.CV"
] | Despite inheriting security measures from underlying language models, Vision-Language Models (VLMs) may still be vulnerable to safety alignment issues. Through empirical analysis, we uncover two critical findings: scenario-matched images can significantly amplify harmful outputs, and contrary to common assumptions in g... | {
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2411.18001 | Power-Efficient Actuation for Insect-Scale Autonomous Underwater
Vehicles | [
"cs.RO"
] | We present a new evolution of the Very Little Eel-Inspired roBot, the VLEIBot++, a 900-mg swimmer driven by two 10-mg bare high-work density (HWD) actuators, whose functionality is based on the use of shape-memory alloy (SMA) wires. An actuator of this type consumes an average power of about 40 mW during in-air operati... | {
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} |
2411.18002 | An End-to-End Two-Stream Network Based on RGB Flow and Representation
Flow for Human Action Recognition | [
"cs.CV",
"cs.AI"
] | With the rapid advancements in deep learning, computer vision tasks have seen significant improvements, making two-stream neural networks a popular focus for video based action recognition. Traditional models using RGB and optical flow streams achieve strong performance but at a high computational cost. To address this... | {
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2411.18003 | HAAT: Hybrid Attention Aggregation Transformer for Image
Super-Resolution | [
"eess.IV",
"cs.AI",
"cs.CV"
] | In the research area of image super-resolution, Swin-transformer-based models are favored for their global spatial modeling and shifting window attention mechanism. However, existing methods often limit self-attention to non overlapping windows to cut costs and ignore the useful information that exists across channels.... | {
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2411.18005 | Generative Semantic Communication for Joint Image Transmission and
Segmentation | [
"cs.IT",
"cs.LG",
"math.IT"
] | Semantic communication has emerged as a promising technology for enhancing communication efficiency. However, most existing research emphasizes single-task reconstruction, neglecting model adaptability and generalization across multi-task systems. In this paper, we propose a novel generative semantic communication syst... | {
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} |
2411.18007 | AI-Driven Smartphone Solution for Digitizing Rapid Diagnostic Test Kits
and Enhancing Accessibility for the Visually Impaired | [
"cs.CV"
] | Rapid diagnostic tests are crucial for timely disease detection and management, yet accurate interpretation of test results remains challenging. In this study, we propose a novel approach to enhance the accuracy and reliability of rapid diagnostic test result interpretation by integrating artificial intelligence (AI) a... | {
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} |
2411.18008 | Causal and Local Correlations Based Network for Multivariate Time Series
Classification | [
"cs.LG",
"cs.AI",
"stat.ME",
"stat.ML"
] | Recently, time series classification has attracted the attention of a large number of researchers, and hundreds of methods have been proposed. However, these methods often ignore the spatial correlations among dimensions and the local correlations among features. To address this issue, the causal and local correlations... | {
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} |
2411.18009 | Monocular Obstacle Avoidance Based on Inverse PPO for Fixed-wing UAVs | [
"cs.RO",
"cs.CV"
] | Fixed-wing Unmanned Aerial Vehicles (UAVs) are one of the most commonly used platforms for the burgeoning Low-altitude Economy (LAE) and Urban Air Mobility (UAM), due to their long endurance and high-speed capabilities. Classical obstacle avoidance systems, which rely on prior maps or sophisticated sensors, face limita... | {
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} |
2411.18010 | JPPO: Joint Power and Prompt Optimization for Accelerated Large Language
Model Services | [
"eess.AS",
"cs.CL",
"cs.SD"
] | Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, leading to their increasing deployment in wireless networks for a wide variety of user services. However, the growing longer prompt setting highlights the crucial issue of computational resource demands and huge communication load.... | {
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} |
2411.18011 | Manual-PA: Learning 3D Part Assembly from Instruction Diagrams | [
"cs.CV"
] | Assembling furniture amounts to solving the discrete-continuous optimization task of selecting the furniture parts to assemble and estimating their connecting poses in a physically realistic manner. The problem is hampered by its combinatorially large yet sparse solution space thus making learning to assemble a challen... | {
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} |
2411.18013 | FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like
Autonomous Driving with Adaptive Feedback | [
"cs.RO",
"cs.CV"
] | Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving systems. While end-to-end models trained on large-scale datasets excel in common driving scenarios, they often struggle with rare, long-tail events. Recent progress in large language models (LLMs) has introduced enhanced reas... | {
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} |
2411.18014 | Diffeomorphic Latent Neural Operators for Data-Efficient Learning of
Solutions to Partial Differential Equations | [
"cs.LG"
] | A computed approximation of the solution operator to a system of partial differential equations (PDEs) is needed in various areas of science and engineering. Neural operators have been shown to be quite effective at predicting these solution generators after training on high-fidelity ground truth data (e.g. numerical s... | {
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} |
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