id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.05596 | TB-HSU: Hierarchical 3D Scene Understanding with Contextual Affordances | [
"cs.CV"
] | The concept of function and affordance is a critical aspect of 3D scene understanding and supports task-oriented objectives. In this work, we develop a model that learns to structure and vary functional affordance across a 3D hierarchical scene graph representing the spatial organization of a scene. The varying functio... | {
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2412.05600 | Global and Dense Embeddings of Earth: Major TOM Floating in the Latent
Space | [
"cs.CV",
"cs.DB"
] | With the ever-increasing volumes of the Earth observation data present in the archives of large programmes such as Copernicus, there is a growing need for efficient vector representations of the underlying raw data. The approach of extracting feature representations from pretrained deep neural networks is a powerful ap... | {
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2412.05602 | Multispecies Animal Re-ID Using a Large Community-Curated Dataset | [
"cs.CV"
] | Recent work has established the ecological importance of developing algorithms for identifying animals individually from images. Typically, a separate algorithm is trained for each species, a natural step but one that creates significant barriers to wide-spread use: (1) each effort is expensive, requiring data collecti... | {
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2412.05605 | RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image
Segmentation | [
"cs.CV"
] | The Segment Anything Model (SAM), originally built on a 2D Vision Transformer (ViT), excels at capturing global patterns in 2D natural images but struggles with 3D medical imaging modalities like CT and MRI. These modalities require capturing spatial information in volumetric space for tasks such as organ segmentation ... | {
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2412.05611 | Rethinking Annotation for Object Detection: Is Annotating Small-size
Instances Worth Its Cost? | [
"cs.CV"
] | Detecting objects occupying only small areas in an image is difficult, even for humans. Therefore, annotating small-size object instances is hard and thus costly. This study questions common sense by asking the following: is annotating small-size instances worth its cost? We restate it as the following verifiable quest... | {
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2412.05619 | Do We Need to Design Specific Diffusion Models for Different Tasks? Try
ONE-PIC | [
"cs.CV"
] | Large pretrained diffusion models have demonstrated impressive generation capabilities and have been adapted to various downstream tasks. However, unlike Large Language Models (LLMs) that can learn multiple tasks in a single model based on instructed data, diffusion models always require additional branches, task-speci... | {
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2412.05625 | Can Large Language Models Help Developers with Robotic Finite State
Machine Modification? | [
"cs.RO"
] | Finite state machines (FSMs) are widely used to manage robot behavior logic, particularly in real-world applications that require a high degree of reliability and structure. However, traditional manual FSM design and modification processes can be time-consuming and error-prone. We propose that large language models (LL... | {
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2412.05628 | Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising | [
"cs.CV",
"cs.AI"
] | Transformer-based diffusion models have achieved significant advancements across a variety of generative tasks. However, producing high-quality outputs typically necessitates large transformer models, which result in substantial training and inference overhead. In this work, we investigate an alternative approach invol... | {
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2412.05630 | Dislocation-based crystal plasticity simulation on grain-size dependence
of mechanical properties in dual-phase steels | [
"cs.CE"
] | In this study, the effect of ferrite grain size on the mechanical properties and dislocation behavior of dual-phase (DP) steel is investigated using dislocation-based crystal plasticity finite element analysis. DP steel, composed of a soft ferritic phase and a hard martensitic phase, shows mechanical properties that ar... | {
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2412.05631 | CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in
Text-Based Virtual Worlds | [
"cs.CL",
"cs.AI"
] | Role-playing is a crucial capability of Large Language Models (LLMs), enabling a wide range of practical applications, including intelligent non-player characters, digital twins, and emotional companions. Evaluating this capability in LLMs is challenging due to the complex dynamics involved in role-playing, such as mai... | {
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2412.05632 | Biological Brain Age Estimation using Sex-Aware Adversarial Variational
Autoencoder with Multimodal Neuroimages | [
"cs.CV",
"cs.AI"
] | Brain aging involves structural and functional changes and therefore serves as a key biomarker for brain health. Combining structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) has the potential to improve brain age estimation by leveraging complementary data. However, fMRI data,... | {
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2412.05633 | Efficient Continuous Video Flow Model for Video Prediction | [
"cs.CV"
] | Multi-step prediction models, such as diffusion and rectified flow models, have emerged as state-of-the-art solutions for generation tasks. However, these models exhibit higher latency in sampling new frames compared to single-step methods. This latency issue becomes a significant bottleneck when adapting such methods ... | {
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2412.05641 | Hyperedge Anomaly Detection with Hypergraph Neural Network | [
"cs.LG",
"cs.AI",
"cs.SI"
] | Hypergraph is a data structure that enables us to model higher-order associations among data entities. Conventional graph-structured data can represent pairwise relationships only, whereas hypergraph enables us to associate any number of entities, which is essential in many real-life applications. Hypergraph learning a... | {
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2412.05644 | Mixture of Hidden-Dimensions Transformer | [
"cs.CL"
] | Transformer models encounter challenges in scaling hidden dimensions efficiently, as uniformly increasing them inflates computational and memory costs while failing to emphasize the most relevant features for each token. For further understanding, we study hidden dimension sparsity and observe that trained Transformers... | {
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2412.05647 | Deep Reinforcement Learning-Based Resource Allocation for Hybrid Bit and
Generative Semantic Communications in Space-Air-Ground Integrated Networks | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, we introduce a novel framework consisting of hybrid bit-level and generative semantic communications for efficient downlink image transmission within space-air-ground integrated networks (SAGINs). The proposed model comprises multiple low Earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and... | {
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2412.05651 | Error Feedback Approach for Quantization Noise Reduction of Distributed
Graph Filters | [
"eess.SY",
"cs.SY"
] | This work introduces an error feedback approach for reducing quantization noise of distributed graph filters. It comes from error spectrum shaping techniques from state-space digital filters, and therefore establishes connections between quantized filtering processes over different domains. Quantization noise expressio... | {
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2412.05655 | Shifting NER into High Gear: The Auto-AdvER Approach | [
"cs.CL"
] | This paper presents a case study on the development of Auto-AdvER, a specialised named entity recognition schema and dataset for text in the car advertisement genre. Developed with industry needs in mind, Auto-AdvER is designed to enhance text mining analytics in this domain and contributes a linguistically unique NER ... | {
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2412.05657 | Towards Robust Spatio-Temporal Auto-Regressive Prediction:
Adams-Bashforth Time Integration with Adaptive Multi-Step Rollout | [
"cs.LG",
"physics.flu-dyn"
] | This study addresses the critical challenge of error accumulation in spatio-temporal auto-regressive predictions within scientific machine learning models by introducing innovative temporal integration schemes and adaptive multi-step rollout strategies. We present a comprehensive analysis of time integration methods, h... | {
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2412.05660 | Multimodal Biometric Authentication Using Camera-Based PPG and
Fingerprint Fusion | [
"cs.CV"
] | Camera-based photoplethysmography (PPG) obtained from smartphones has shown great promise for personalized healthcare and secure authentication. This paper presents a multimodal biometric system that integrates PPG signals extracted from videos with fingerprint data to enhance the accuracy of user verification. The sys... | {
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2412.05666 | Early Diagnosis of Alzheimer's Diseases and Dementia from MRI Images
Using an Ensemble Deep Learning | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Alzheimer's Disease (AD) is a progressive neurological disorder that can result in significant cognitive impairment and dementia. Accurate and timely diagnosis is essential for effective treatment and management of this disease. In this study, we proposed two low-parameter Convolutional Neural Networks (CNNs), IR-BRAIN... | {
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2412.05667 | Training neural networks without backpropagation using particles | [
"cs.NE",
"cs.AI"
] | Neural networks are a group of neurons stacked together in multiple layers to mimic the biological neurons in a human brain. Neural networks have been trained using the backpropagation algorithm based on gradient descent strategy for several decades. Several variants have been developed to improve the backpropagation a... | {
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2412.05669 | Detecting outliers by clustering algorithms | [
"cs.LG",
"stat.ML"
] | Clustering and outlier detection are two important tasks in data mining. Outliers frequently interfere with clustering algorithms to determine the similarity between objects, resulting in unreliable clustering results. Currently, only a few clustering algorithms (e.g., DBSCAN) have the ability to detect outliers to eli... | {
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2412.05672 | Graph with Sequence: Broad-Range Semantic Modeling for Fake News
Detection | [
"cs.CL"
] | The rapid proliferation of fake news on social media threatens social stability, creating an urgent demand for more effective detection methods. While many promising approaches have emerged, most rely on content analysis with limited semantic depth, leading to suboptimal comprehension of news content.To address this li... | {
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2412.05674 | No-Free-Lunch Theories for Tensor-Network Machine Learning Models | [
"quant-ph",
"cs.AI",
"cs.DS",
"cs.LG",
"stat.ML"
] | Tensor network machine learning models have shown remarkable versatility in tackling complex data-driven tasks, ranging from quantum many-body problems to classical pattern recognitions. Despite their promising performance, a comprehensive understanding of the underlying assumptions and limitations of these models is s... | {
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2412.05675 | M$^3$PC: Test-time Model Predictive Control for Pretrained Masked
Trajectory Model | [
"cs.LG",
"cs.RO",
"cs.SY",
"eess.SY"
] | Recent work in Offline Reinforcement Learning (RL) has shown that a unified Transformer trained under a masked auto-encoding objective can effectively capture the relationships between different modalities (e.g., states, actions, rewards) within given trajectory datasets. However, this information has not been fully ex... | {
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2412.05676 | Nearly Solved? Robust Deepfake Detection Requires More than Visual
Forensics | [
"cs.CV",
"cs.CR",
"cs.LG"
] | Deepfakes are on the rise, with increased sophistication and prevalence allowing for high-profile social engineering attacks. Detecting them in the wild is therefore important as ever, giving rise to new approaches breaking benchmark records in this task. In line with previous work, we show that recently developed stat... | {
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2412.05679 | RSUniVLM: A Unified Vision Language Model for Remote Sensing via
Granularity-oriented Mixture of Experts | [
"cs.CV"
] | Remote Sensing Vision-Language Models (RS VLMs) have made much progress in the tasks of remote sensing (RS) image comprehension. While performing well in multi-modal reasoning and multi-turn conversations, the existing models lack pixel-level understanding and struggle with multi-image inputs. In this work, we propose ... | {
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2412.05681 | Leveraging Time-Series Foundation Model for Subsurface Well Logs
Prediction and Anomaly Detection | [
"physics.geo-ph",
"cs.AI",
"cs.LG",
"eess.SP"
] | The rise in energy demand highlights the importance of suitable subsurface storage, requiring detailed and accurate subsurface characterization often reliant on high-quality borehole well log data. However, obtaining complete well-log data is costly and time-consuming, with missing data being common due to borehole con... | {
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2412.05685 | HMGIE: Hierarchical and Multi-Grained Inconsistency Evaluation for
Vision-Language Data Cleansing | [
"cs.CV",
"cs.AI"
] | Visual-textual inconsistency (VTI) evaluation plays a crucial role in cleansing vision-language data. Its main challenges stem from the high variety of image captioning datasets, where differences in content can create a range of inconsistencies (\eg, inconsistencies in scene, entities, entity attributes, entity number... | {
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2412.05686 | Neural network interpretability with layer-wise relevance propagation:
novel techniques for neuron selection and visualization | [
"cs.NE",
"cs.AI",
"cs.CV",
"cs.LG"
] | Interpreting complex neural networks is crucial for understanding their decision-making processes, particularly in applications where transparency and accountability are essential. This proposed method addresses this need by focusing on layer-wise Relevance Propagation (LRP), a technique used in explainable artificial ... | {
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2412.05688 | Flow-based Detection of Botnets through Bio-inspired Optimisation of
Machine Learning | [
"cs.CR",
"cs.AI"
] | Botnets could autonomously infect, propagate, communicate and coordinate with other members in the botnet, enabling cybercriminals to exploit the cumulative computing and bandwidth of its bots to facilitate cybercrime. Traditional detection methods are becoming increasingly unsuitable against various network-based dete... | {
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2412.05689 | Local Linear Convergence of Infeasible Optimization with Orthogonal
Constraints | [
"math.OC",
"cs.LG"
] | Many classical and modern machine learning algorithms require solving optimization tasks under orthogonality constraints. Solving these tasks with feasible methods requires a gradient descent update followed by a retraction operation on the Stiefel manifold, which can be computationally expensive. Recently, an infeasib... | {
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2412.05693 | Batch-Max: Higher LLM Throughput using Larger Batch Sizes and KV Cache
Compression | [
"cs.CL"
] | Several works have developed eviction policies to remove key-value (KV) pairs from the KV cache for more efficient inference. The focus has been on compressing the KV cache after the input prompt has been processed for faster token generation. In settings with limited GPU memory, and when the input context is longer th... | {
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2412.05696 | Jointly RS Image Deblurring and Super-Resolution with Adjustable-Kernel
and Multi-Domain Attention | [
"cs.CV"
] | Remote Sensing (RS) image deblurring and Super-Resolution (SR) are common tasks in computer vision that aim at restoring RS image detail and spatial scale, respectively. However, real-world RS images often suffer from a complex combination of global low-resolution (LR) degeneration and local blurring degeneration. Alth... | {
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2412.05700 | Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes | [
"cs.CV",
"cs.GR"
] | Recent advancements in high-fidelity dynamic scene reconstruction have leveraged dynamic 3D Gaussians and 4D Gaussian Splatting for realistic scene representation. However, to make these methods viable for real-time applications such as AR/VR, gaming, and rendering on low-power devices, substantial reductions in memory... | {
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2412.05707 | Segment-Level Road Obstacle Detection Using Visual Foundation Model
Priors and Likelihood Ratios | [
"cs.CV"
] | Detecting road obstacles is essential for autonomous vehicles to navigate dynamic and complex traffic environments safely. Current road obstacle detection methods typically assign a score to each pixel and apply a threshold to generate final predictions. However, selecting an appropriate threshold is challenging, and t... | {
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2412.05708 | On the effective transfer of knowledge from English to Hindi Wikipedia | [
"cs.CL",
"cs.IR",
"cs.LG"
] | Although Wikipedia is the largest multilingual encyclopedia, it remains inherently incomplete. There is a significant disparity in the quality of content between high-resource languages (HRLs, e.g., English) and low-resource languages (LRLs, e.g., Hindi), with many LRL articles lacking adequate information. To bridge t... | {
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2412.05710 | PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic
Languages with Example Selection from Related Example Banks | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Large Language Models (LLMs) have recently demonstrated impressive few-shot learning capabilities through in-context learning (ICL). However, ICL performance is highly dependent on the choice of few-shot demonstrations, making the selection of the most optimal examples a persistent research challenge. This issue is fur... | {
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2412.05711 | On an Analytical Inversion Formula for the Modulo Radon Transform | [
"math.NA",
"cs.IT",
"cs.NA",
"math.IT"
] | This paper proves a novel analytical inversion formula for the so-called modulo Radon transform (MRT), which models a recently proposed approach to one-shot high dynamic range tomography. It is based on the solution of a Poisson problem linking the Laplacian of the Radon transform (RT) of a function to its MRT in combi... | {
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2412.05712 | Flagellar Swimming at Low Reynolds Numbers: Zoospore-Inspired Robotic
Swimmers with Dual Flagella for High-Speed Locomotion | [
"cs.RO"
] | Traditional locomotion strategies become ineffective at low Reynolds numbers, where viscous forces predominate over inertial forces. To adapt, microorganisms have evolved specialized structures like cilia and flagella for efficient maneuvering in viscous environments. Among these organisms, Phytophthora zoospores demon... | {
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2412.05717 | Learning Soft Driving Constraints from Vectorized Scene Embeddings while
Imitating Expert Trajectories | [
"cs.RO",
"cs.AI",
"cs.LG"
] | The primary goal of motion planning is to generate safe and efficient trajectories for vehicles. Traditionally, motion planning models are trained using imitation learning to mimic the behavior of human experts. However, these models often lack interpretability and fail to provide clear justifications for their decisio... | {
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2412.05718 | RL Zero: Zero-Shot Language to Behaviors without any Supervision | [
"cs.AI",
"cs.GR",
"cs.LG",
"cs.RO"
] | Rewards remain an uninterpretable way to specify tasks for Reinforcement Learning, as humans are often unable to predict the optimal behavior of any given reward function, leading to poor reward design and reward hacking. Language presents an appealing way to communicate intent to agents and bypass reward design, but p... | {
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2412.05719 | Finite Element Neural Network Interpolation. Part I: Interpretable and
Adaptive Discretization for Solving PDEs | [
"math.NA",
"cs.LG",
"cs.NA",
"physics.comp-ph"
] | We present the Finite Element Neural Network Interpolation (FENNI) framework, a sparse neural network architecture extending previous work on Embedded Finite Element Neural Networks (EFENN) introduced with the Hierarchical Deep-learning Neural Networks (HiDeNN). Due to their mesh-based structure, EFENN requires signifi... | {
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2412.05721 | Impact of Sunglasses on One-to-Many Facial Identification Accuracy | [
"cs.CV"
] | One-to-many facial identification is documented to achieve high accuracy in the case where both the probe and the gallery are "mugshot quality" images. However, an increasing number of documented instances of wrongful arrest following one-to-many facial identification have raised questions about its accuracy. Probe ima... | {
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2412.05722 | Evaluating Hallucination in Text-to-Image Diffusion Models with
Scene-Graph based Question-Answering Agent | [
"cs.CV"
] | Contemporary Text-to-Image (T2I) models frequently depend on qualitative human evaluations to assess the consistency between synthesized images and the text prompts. There is a demand for quantitative and automatic evaluation tools, given that human evaluation lacks reproducibility. We believe that an effective T2I eva... | {
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2412.05723 | Training-Free Bayesianization for Low-Rank Adapters of Large Language
Models | [
"stat.ML",
"cs.AI",
"cs.CL",
"cs.LG"
] | Estimating the uncertainty of responses of Large Language Models~(LLMs) remains a critical challenge. While recent Bayesian methods have demonstrated effectiveness in quantifying uncertainty through low-rank weight updates, they typically require complex fine-tuning or post-training procedures. In this paper, we propos... | {
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2412.05724 | A Tiered GAN Approach for Monet-Style Image Generation | [
"cs.CV",
"cs.AI"
] | Generative Adversarial Networks (GANs) have proven to be a powerful tool in generating artistic images, capable of mimicking the styles of renowned painters, such as Claude Monet. This paper introduces a tiered GAN model to progressively refine image quality through a multi-stage process, enhancing the generated images... | {
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2412.05725 | Black Swan: Abductive and Defeasible Video Reasoning in Unpredictable
Events | [
"cs.CV",
"cs.AI"
] | The commonsense reasoning capabilities of vision-language models (VLMs), especially in abductive reasoning and defeasible reasoning, remain poorly understood. Most benchmarks focus on typical visual scenarios, making it difficult to discern whether model performance stems from keen perception and reasoning skills, or r... | {
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2412.05726 | Proximal Iteration for Nonlinear Adaptive Lasso | [
"stat.ML",
"cs.LG"
] | Augmenting a smooth cost function with an $\ell_1$ penalty allows analysts to efficiently conduct estimation and variable selection simultaneously in sophisticated models and can be efficiently implemented using proximal gradient methods. However, one drawback of the $\ell_1$ penalty is bias: nonzero parameters are und... | {
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2412.05728 | Integrating YOLO11 and Convolution Block Attention Module for
Multi-Season Segmentation of Tree Trunks and Branches in Commercial Apple
Orchards | [
"cs.CV"
] | In this study, we developed a customized instance segmentation model by integrating the Convolutional Block Attention Module (CBAM) with the YOLO11 architecture. This model, trained on a mixed dataset of dormant and canopy season apple orchard images, aimed to enhance the segmentation of tree trunks and branches under ... | {
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2412.05729 | Constant di/dz Scanning Tunneling Microscopy: Atomic Precision Imaging
and Hydrogen Depassivation Lithography on a Si(100) - 2 x 1 : H Surface | [
"eess.SY",
"cs.SY"
] | We introduce a novel control mode for Scanning Tunneling Microscopy (STM) that leverages di/dz feedback. By superimposing a high-frequency sinusoidal modulation on the control signal, we extract the amplitude of the resulting tunneling current to obtain a di/dz measurement as the tip is scanned over the surface. A feed... | {
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2412.05731 | A Scoping Review of ChatGPT Research in Accounting and Finance | [
"q-fin.GN",
"cs.AI",
"cs.CE",
"cs.LG",
"econ.GN",
"q-fin.EC"
] | This paper provides a review of recent publications and working papers on ChatGPT and related Large Language Models (LLMs) in accounting and finance. The aim is to understand the current state of research in these two areas and identify potential research opportunities for future inquiry. We identify three common theme... | {
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2412.05734 | PrivAgent: Agentic-based Red-teaming for LLM Privacy Leakage | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Recent studies have discovered that LLMs have serious privacy leakage concerns, where an LLM may be fooled into outputting private information under carefully crafted adversarial prompts. These risks include leaking system prompts, personally identifiable information, training data, and model parameters. Most existing ... | {
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2412.05735 | REGE: A Method for Incorporating Uncertainty in Graph Embeddings | [
"cs.LG"
] | Machine learning models for graphs in real-world applications are prone to two primary types of uncertainty: (1) those that arise from incomplete and noisy data and (2) those that arise from uncertainty of the model in its output. These sources of uncertainty are not mutually exclusive. Additionally, models are suscept... | {
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2412.05741 | Toxic behavior silences online political conversations | [
"cs.SI",
"cs.CY"
] | Quantifying how individuals react to social influence is crucial for tackling collective political behavior online. While many studies of opinion in public forums focus on social feedback, they often overlook the potential for human interactions to result in self-censorship. Here, we investigate political deliberation ... | {
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2412.05747 | Charting the Shapes of Stories with Game Theory | [
"cs.GT",
"cs.AI"
] | Stories are records of our experiences and their analysis reveals insights into the nature of being human. Successful analyses are often interdisciplinary, leveraging mathematical tools to extract structure from stories and insights from structure. Historically, these tools have been restricted to one dimensional chart... | {
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2412.05748 | Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based
Time Shift Governor | [
"eess.SY",
"cs.AI",
"cs.LG",
"cs.RO",
"cs.SY"
] | This paper develops a Time Shift Governor (TSG)-based control scheme to enforce constraints during rendezvous and docking (RD) missions in the setting of the Two-Body problem. As an add-on scheme to the nominal closed-loop system, the TSG generates a time-shifted Chief spacecraft trajectory as a target reference for th... | {
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2412.05749 | A Comparative Study on Code Generation with Transformers | [
"cs.CL",
"cs.AI"
] | In an era of widespread influence of Natural Language Processing (NLP), there have been multiple research efforts to supplant traditional manual coding techniques with automated systems capable of generating solutions autonomously. With rapid research for code generation and a sole focus on large language models, there... | {
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2412.05753 | Can OpenAI o1 outperform humans in higher-order cognitive thinking? | [
"cs.CY",
"cs.AI"
] | This study evaluates the performance of OpenAI's o1-preview model in higher-order cognitive domains, including critical thinking, systematic thinking, computational thinking, data literacy, creative thinking, logical reasoning, and scientific reasoning. Using established benchmarks, we compared the o1-preview models's ... | {
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2412.05754 | Asymptotically Optimal Sampling-Based Path Planning Using Bidirectional
Guidance Heuristic | [
"cs.RO"
] | This paper introduces Bidirectional Guidance Informed Trees (BIGIT*),~a new asymptotically optimal sampling-based motion planning algorithm. Capitalizing on the strengths of \emph{meet-in-the-middle} property in bidirectional heuristic search with a new lazy strategy, and uniform-cost search, BIGIT* constructs an impli... | {
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2412.05756 | Compositional Image Retrieval via Instruction-Aware Contrastive Learning | [
"cs.CV"
] | Composed Image Retrieval (CIR) involves retrieving a target image based on a composed query of an image paired with text that specifies modifications or changes to the visual reference. CIR is inherently an instruction-following task, as the model needs to interpret and apply modifications to the image. In practice, du... | {
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2412.05758 | Emulating Clinical Quality Muscle B-mode Ultrasound Images from Plane
Wave Images Using a Two-Stage Machine Learning Model | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Research ultrasound scanners such as the Verasonics Vantage often lack the advanced image processing algorithms used by clinical systems. Image quality is even lower in plane wave imaging - often used for shear wave elasticity imaging (SWEI) - which sacrifices spatial resolution for temporal resolution. As a result, de... | {
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2412.05759 | Leveraging Black-box Models to Assess Feature Importance in
Unconditional Distribution | [
"stat.ML",
"cs.LG",
"stat.CO",
"stat.ME"
] | Understanding how changes in explanatory features affect the unconditional distribution of the outcome is important in many applications. However, existing black-box predictive models are not readily suited for analyzing such questions. In this work, we develop an approximation method to compute the feature importance ... | {
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2412.05766 | Policy-shaped prediction: avoiding distractions in model-based
reinforcement learning | [
"cs.LG",
"cs.AI"
] | Model-based reinforcement learning (MBRL) is a promising route to sample-efficient policy optimization. However, a known vulnerability of reconstruction-based MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the m... | {
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2412.05767 | DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization | [
"cs.LG",
"cs.CR"
] | Adversarial robustness, the ability of a model to withstand manipulated inputs that cause errors, is essential for ensuring the trustworthiness of machine learning models in real-world applications. However, previous studies have shown that enhancing adversarial robustness through adversarial training increases vulnera... | {
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2412.05768 | Uncovering Uncertainty in Transformer Inference | [
"cs.CL",
"cs.AI"
] | We explore the Iterative Inference Hypothesis (IIH) within the context of transformer-based language models, aiming to understand how a model's latent representations are progressively refined and whether observable differences are present between correct and incorrect generations. Our findings provide empirical suppor... | {
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2412.05769 | f-P vs P-f based Grid-forming Control under RoCoF Event Considering
Power and Energy Limits | [
"eess.SY",
"cs.SY"
] | Grid-forming (GFM) converter is deemed as one enabler for high penetration of renewable energy resources in power system. However, as will be pointed out in this letter, the conventional power-to-frequency (P-f) GFM control will face a dilemma in keeping power limit and grid synchronization when the energy resource of ... | {
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2412.05770 | KITE-DDI: A Knowledge graph Integrated Transformer Model for accurately
predicting Drug-Drug Interaction Events from Drug SMILES and Biomedical
Knowledge Graph | [
"cs.LG",
"q-bio.BM"
] | It is a common practice in modern medicine to prescribe multiple medications simultaneously to treat diseases. However, these medications could have adverse reactions between them, known as Drug-Drug Interactions (DDI), which have the potential to cause significant bodily injury and could even be fatal. Hence, it is es... | {
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2412.05771 | Prism: Semi-Supervised Multi-View Stereo with Monocular Structure Priors | [
"cs.CV"
] | The promise of unsupervised multi-view-stereo (MVS) is to leverage large unlabeled datasets, yet current methods underperform when training on difficult data, such as handheld smartphone videos of indoor scenes. Meanwhile, high-quality synthetic datasets are available but MVS networks trained on these datasets fail to ... | {
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2412.05776 | ProtGO: A Transformer based Fusion Model for accurately predicting Gene
Ontology (GO) Terms from full scale Protein Sequences | [
"cs.LG",
"q-bio.GN"
] | Recent developments in next generation sequencing technology have led to the creation of extensive, open-source protein databases consisting of hundreds of millions of sequences. To render these sequences applicable in biomedical applications, they must be meticulously annotated by wet lab testing or extracting them fr... | {
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2412.05777 | Strategizing Equitable Transit Evacuations: A Data-Driven Reinforcement
Learning Approach | [
"cs.LG",
"cs.AI",
"cs.CY",
"cs.SY",
"eess.SY"
] | As natural disasters become increasingly frequent, the need for efficient and equitable evacuation planning has become more critical. This paper proposes a data-driven, reinforcement learning-based framework to optimize bus-based evacuations with an emphasis on improving both efficiency and equity. We model the evacuat... | {
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2412.05780 | BudgetFusion: Perceptually-Guided Adaptive Diffusion Models | [
"cs.CV",
"cs.AI"
] | Diffusion models have shown unprecedented success in the task of text-to-image generation. While these models are capable of generating high-quality and realistic images, the complexity of sequential denoising has raised societal concerns regarding high computational demands and energy consumption. In response, various... | {
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2412.05781 | Open-Source Acceleration of Stable-Diffusion.cpp Deployable on All
Devices | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Stable diffusion plays a crucial role in generating high-quality images. However, image generation is time-consuming and memory-intensive. To address this, stable-diffusion.cpp (Sdcpp) emerges as an efficient inference framework to accelerate the diffusion models. Although it is lightweight, the current implementation ... | {
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2412.05783 | Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement
Learning | [
"cs.LG",
"stat.ML"
] | This paper studies off-policy evaluation (OPE) in the presence of unmeasured confounders. Inspired by the two-way fixed effects regression model widely used in the panel data literature, we propose a two-way unmeasured confounding assumption to model the system dynamics in causal reinforcement learning and develop a tw... | {
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2412.05788 | On Diffusion Posterior Sampling via Sequential Monte Carlo for Zero-Shot
Scaffolding of Protein Motifs | [
"q-bio.BM",
"cs.LG",
"stat.ML"
] | With the advent of diffusion models, new proteins can be generated at an unprecedented rate. The \textit{motif scaffolding problem} requires steering this generative process to yield proteins with a desirable functional substructure -- a motif. While models have been trained to take the motif as conditional input, rece... | {
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2412.05789 | InfiniteWorld: A Unified Scalable Simulation Framework for General
Visual-Language Robot Interaction | [
"cs.RO"
] | Realizing scaling laws in embodied AI has become a focus. However, previous work has been scattered across diverse simulation platforms, with assets and models lacking unified interfaces, which has led to inefficiencies in research. To address this, we introduce InfiniteWorld, a unified and scalable simulator for gener... | {
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2412.05796 | Language-Guided Image Tokenization for Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Image tokenization, the process of transforming raw image pixels into a compact low-dimensional latent representation, has proven crucial for scalable and efficient image generation. However, mainstream image tokenization methods generally have limited compression rates, making high-resolution image generation computat... | {
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2412.05797 | Speech Is Not Enough: Interpreting Nonverbal Indicators of Common
Knowledge and Engagement | [
"cs.CL",
"cs.AI"
] | Our goal is to develop an AI Partner that can provide support for group problem solving and social dynamics. In multi-party working group environments, multimodal analytics is crucial for identifying non-verbal interactions of group members. In conjunction with their verbal participation, this creates an holistic under... | {
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2412.05808 | SizeGS: Size-aware Compression of 3D Gaussians with Hierarchical Mixed
Precision Quantization | [
"cs.CV",
"cs.MM"
] | Effective compression technology is crucial for 3DGS to adapt to varying storage and transmission conditions. However, existing methods fail to address size constraints while maintaining optimal quality. In this paper, we introduce SizeGS, a framework that compresses 3DGS within a specified size budget while optimizing... | {
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2412.05813 | Risk factor identification and classification of malnutrition among
under-five children in Bangladesh: Machine learning and statistical approach | [
"cs.LG"
] | This study aims to understand the factors that resulted in under-five children's malnutrition from the Multiple Indicator Cluster (MICS-2019) nationwide surveys and classify different malnutrition stages based on the four well-established machine learning algorithms, namely - Decision Tree (DT), Random Forest (RF), Sup... | {
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2412.05818 | SILMM: Self-Improving Large Multimodal Models for Compositional
Text-to-Image Generation | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.MM"
] | Large Multimodal Models (LMMs) have demonstrated impressive capabilities in multimodal understanding and generation, pushing forward advancements in text-to-image generation. However, achieving accurate text-image alignment for LMMs, particularly in compositional scenarios, remains challenging. Existing approaches, suc... | {
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2412.05819 | [CLS] Token Tells Everything Needed for Training-free Efficient MLLMs | [
"cs.CV"
] | Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance across a wide range of vision-language tasks, garnering significant attention in the computer vision. However, their efficient deployment remains a substantial challenge due to high computational costs and memory requirements. Recogn... | {
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2412.05820 | Geomagnetic and Inertial Combined Navigation Approach Based on Flexible
Correction-Model Predictive Control Algorithm | [
"eess.SY",
"cs.SY"
] | This paper proposes a geomagnetic and inertial combined navigation approach based on the flexible correction-model predictive control algorithm (Fc-MPC). This approach aims to overcome the limitations of existing combined navigation methods that require prior geomagnetic maps and the inertial navigation drift of long-r... | {
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2412.05821 | An Entailment Tree Generation Approach for Multimodal Multi-Hop Question
Answering with Mixture-of-Experts and Iterative Feedback Mechanism | [
"cs.CL",
"cs.AI"
] | With the rise of large-scale language models (LLMs), it is currently popular and effective to convert multimodal information into text descriptions for multimodal multi-hop question answering. However, we argue that the current methods of multi-modal multi-hop question answering still mainly face two challenges: 1) The... | {
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2412.05823 | DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning
for Edge Devices | [
"cs.LG",
"cs.AI"
] | Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in... | {
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2412.05825 | Self-Supervised Learning with Probabilistic Density Labeling for
Rainfall Probability Estimation | [
"cs.LG",
"cs.CV"
] | Numerical weather prediction (NWP) models are fundamental in meteorology for simulating and forecasting the behavior of various atmospheric variables. The accuracy of precipitation forecasts and the acquisition of sufficient lead time are crucial for preventing hazardous weather events. However, the performance of NWP ... | {
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2412.05826 | Doppelgangers++: Improved Visual Disambiguation with Geometric 3D
Features | [
"cs.CV"
] | Accurate 3D reconstruction is frequently hindered by visual aliasing, where visually similar but distinct surfaces (aka, doppelgangers), are incorrectly matched. These spurious matches distort the structure-from-motion (SfM) process, leading to misplaced model elements and reduced accuracy. Prior efforts addressed this... | {
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2412.05827 | Self-Guidance: Boosting Flow and Diffusion Generation on Their Own | [
"cs.CV"
] | Proper guidance strategies are essential to get optimal generation results without re-training diffusion and flow-based text-to-image models. However, existing guidances either require specific training or strong inductive biases of neural network architectures, potentially limiting their applications. To address these... | {
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2412.05830 | Large Language Models Merging for Enhancing the Link Stealing Attack on
Graph Neural Networks | [
"cs.CR",
"cs.AI"
] | Graph Neural Networks (GNNs), specifically designed to process the graph data, have achieved remarkable success in various applications. Link stealing attacks on graph data pose a significant privacy threat, as attackers aim to extract sensitive relationships between nodes (entities), potentially leading to academic mi... | {
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2412.05833 | CSG: A Context-Semantic Guided Diffusion Approach in De Novo
Musculoskeletal Ultrasound Image Generation | [
"cs.CV",
"eess.IV"
] | The use of synthetic images in medical imaging Artificial Intelligence (AI) solutions has been shown to be beneficial in addressing the limited availability of diverse, unbiased, and representative data. Despite the extensive use of synthetic image generation methods, controlling the semantics variability and context d... | {
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2412.05835 | Design of Piezoelectric Metastructures with Multi-Patch Isogeometric
Analysis for Enhanced Energy Harvesting and Vibration Suppression | [
"cs.CE"
] | Metastructures are engineered systems composed of periodic arrays of identical components, called resonators, designed to achieve specific dynamic effects, such as creating a band gap-a frequency range where waves cannot propagate through the structure. When equipped with patches of piezoelectric material, these metast... | {
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} |
2412.05837 | Tiny Object Detection with Single Point Supervision | [
"cs.CV"
] | Tiny objects, with their limited spatial resolution, often resemble point-like distributions. As a result, bounding box prediction using point-level supervision emerges as a natural and cost-effective alternative to traditional box-level supervision. However, the small scale and lack of distinctive features of tiny obj... | {
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2412.05838 | A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation
Across Diverse Data | [
"cs.AI"
] | Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external, domain-specific data into the generative process. While LLMs are highly capable, they often rely on static, pre-trained datasets, limiting their ability to integrate dynamic or private data. Traditional RAG systems typ... | {
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} |
2412.05839 | DiTer++: Diverse Terrain and Multi-modal Dataset for Multi-Robot SLAM in
Multi-session Environments | [
"cs.RO"
] | We encounter large-scale environments where both structured and unstructured spaces coexist, such as on campuses. In this environment, lighting conditions and dynamic objects change constantly. To tackle the challenges of large-scale mapping under such conditions, we introduce DiTer++, a diverse terrain and multi-modal... | {
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} |
2412.05840 | LVP-CLIP:Revisiting CLIP for Continual Learning with Label Vector Pool | [
"cs.CV"
] | Continual learning aims to update a model so that it can sequentially learn new tasks without forgetting previously acquired knowledge. Recent continual learning approaches often leverage the vision-language model CLIP for its high-dimensional feature space and cross-modality feature matching. Traditional CLIP-based cl... | {
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} |
2412.05842 | DREAM: Domain-agnostic Reverse Engineering Attributes of Black-box Model | [
"cs.LG",
"cs.AI"
] | Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (e.g., the number of convolutional layers) of a target black-box model can be exposed through a sequence of queries. There is a crucial limitation: these works assume the training dataset... | {
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} |
2412.05843 | A Self-Learning Multimodal Approach for Fake News Detection | [
"cs.CL",
"cs.LG"
] | The rapid growth of social media has resulted in an explosion of online news content, leading to a significant increase in the spread of misleading or false information. While machine learning techniques have been widely applied to detect fake news, the scarcity of labeled datasets remains a critical challenge. Misinfo... | {
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} |
2412.05845 | Are Clinical T5 Models Better for Clinical Text? | [
"cs.CL"
] | Large language models with a transformer-based encoder/decoder architecture, such as T5, have become standard platforms for supervised tasks. To bring these technologies to the clinical domain, recent work has trained new or adapted existing models to clinical data. However, the evaluation of these clinical T5 models a... | {
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2412.05846 | Kernel Stochastic Configuration Networks for Nonlinear Regression | [
"cs.LG",
"cs.AI"
] | Stochastic configuration networks (SCNs), as a class of randomized learner models, are featured by its way of random parameters assignment in the light of a supervisory mechanism, resulting in the universal approximation property at algorithmic level. This paper presents a kernel version of SCNs, termed KSCNs, aiming t... | {
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} |
2412.05848 | MotionStone: Decoupled Motion Intensity Modulation with Diffusion
Transformer for Image-to-Video Generation | [
"cs.CV"
] | The image-to-video (I2V) generation is conditioned on the static image, which has been enhanced recently by the motion intensity as an additional control signal. These motion-aware models are appealing to generate diverse motion patterns, yet there lacks a reliable motion estimator for training such models on large-sca... | {
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} |
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