Daily Arxiv

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VGR: Visual Grounded Reasoning

Improving Multimodal Learning Balance and Sufficiency through Data Remixing

Autonomous Computer Vision Development with Agentic AI

A Self-Refining Framework for Enhancing ASR Using TTS-Synthesized Data

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation

Task-aligned prompting improves zero-shot detection of AI-generated images by Vision-Language Models

From Reasoning to Code: GRPO Optimization for Underrepresented Languages

Farseer: A Refined Scaling Law in Large Language Models

Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors

VideoDeepResearch: Long Video Understanding With Agentic Tool Using

Data Shifts Hurt CoT: A Theoretical Study

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications

Extended Creativity: A Conceptual Framework for Understanding Human-AI Creative Relations

Disclosure Audits for LLM Agents

A Survey of Generative Categories and Techniques in Multimodal Large Language Models

Resa: Transparent Reasoning Models via SAEs

EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization

Latent Multi-Head Attention for Small Language Models

Intra-Trajectory Consistency for Reward Modeling

Foundation Models in Medical Imaging -- A Review and Outlook

EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model

Segment Concealed Objects with Incomplete Supervision

Inherently Faithful Attention Maps for Vision Transformers

DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression

Agentic Surgical AI: Surgeon Style Fingerprinting and Privacy Risk Quantification via Discrete Diffusion in a Vision-Language-Action Framework

Reparameterized LLM Training via Orthogonal Equivalence Transformation

Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes

SurgBench: A Unified Large-Scale Benchmark for Surgical Video Analysis

Synthesize Privacy-Preserving High-Resolution Images via Private Textual Intermediaries

LeVo: High-Quality Song Generation with Multi-Preference Alignment

FAMSeg: Fetal Femur and Cranial Ultrasound Segmentation Using Feature-Aware Attention and Mamba Enhancement

SALT: A Lightweight Model Adaptation Method for Closed Split Computing Environments

MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystacks

Towards Physics-informed Diffusion for Anomaly Detection in Trajectories

SAFE: Finding Sparse and Flat Minima to Improve Pruning

Tactile MNIST: Benchmarking Active Tactile Perception

SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code

Hierarchical Language Models for Semantic Navigation and Manipulation in an Aerial-Ground Robotic System

MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP

Deep Learning-Based Breast Cancer Detection in Mammography: A Multi-Center Validation Study in Thai Population

KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider

Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models

From Argumentative Text to Argument Knowledge Graph: A New Framework for Structured Argumentation

An Incremental Framework for Topological Dialogue Semantics: Efficient Reasoning in Discrete Spaces

Diffusion Graph Neural Networks for Robustness in Olfaction Sensors and Datasets

COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning

R-KV: Redundancy-aware KV Cache Compression for Reasoning Models

Quantum computing and artificial intelligence: status and perspectives

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks

On the performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models

Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning

SOSBENCH: Benchmarking Safety Alignment on Scientific Knowledge

PARTONOMY: Large Multimodal Models with Part-Level Visual Understanding

Transparency in Healthcare AI: Testing European Regulatory Provisions against Users' Transparency Needs

FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records

Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning

Learning to Rank Chain-of-Thought: An Energy-Based Approach with Outcome Supervision

Fixed Point Explainability

X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real

JAEGER: Dual-Level Humanoid Whole-Body Controller

AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents

AI Recommendations and Non-instrumental Image Concerns

Entropic Time Schedulers for Generative Diffusion Models

JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture

Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design

Scholar Inbox: Personalized Paper Recommendations for Scientists

On Synthesizing Data for Context Attribution in Question Answering

Evaluating how LLM annotations represent diverse views on contentious topics

QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions

ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices

OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents

MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion

Transformers without Normalization

Compute Optimal Scaling of Skills: Knowledge vs Reasoning

Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction

InfiniSST: Simultaneous Translation of Unbounded Speech with Large Language Model

From Euler to AI: Unifying Formulas for Mathematical Constants

C2-DPO: Constrained Controlled Direct Preference Optimization

Less is More: Improving LLM Alignment via Preference Data Selection

Quantifying Memorization and Parametric Response Rates in Retrieval-Augmented Vision-Language Models

ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series

Boosting Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation

HARBOR: Exploring Persona Dynamics in Multi-Agent Competition

SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models

Truth Knows No Language: Evaluating Truthfulness Beyond English

PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation

Multi-Knowledge-oriented Nighttime Haze Imaging Enhancer for Vision-driven Intelligent Systems

Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification

Is attention all you need to solve the correlated electron problem?

Optimizing Temperature for Language Models with Multi-Sample Inference

Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions

Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training

The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective

Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search

Activation-Informed Merging of Large Language Models

Layer by Layer: Uncovering Hidden Representations in Language Models

Search-Based Adversarial Estimates for Improving Sample Efficiency in Off-Policy Reinforcement Learning

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss

Soil analysis with machine-learning-based processing of stepped-frequency GPR field measurements: Preliminary study

Created by

Haebom

저자

Chunlei Xu, Michael Pregesbauer, Naga Sravani Chilukuri, Daniel Windhager, Mahsa Yousefi, Pedro Julian, Lothar Ratschbacher

개요

본 논문은 농업 및 원예에 관련된 토양 매개변수 추출 도구로서 지중 침투 레이더(GPR)의 활용에 대한 연구를 다룬다. 머신러닝(ML) 기법과 결합된 공기 결합 단계 주파수 연속파 GPR(SFCW GPR) 측정은 비용 효율적인 심도별 토양 데이터 획득 방식을 제공할 수 있다. 이 연구의 첫 단계로서 트랙터에 장착된 공기 결합 SFCW GPR 장비를 사용하여 광범위한 현장 조사를 수행하였다. ML 기반 데이터 처리를 활용하여 공동 기록된 전자기 유도(EMI) 장비로 측정된 겉보기 전기 전도도(ECaR)를 예측함으로써 GPR 장비의 성능을 평가하였다. 약 6600 평방미터에 걸쳐 3472개의 공동 등록 및 지리 위치가 지정된 GPR 및 EMI 데이터 샘플을 골프장에서 측정하는 대규모 현장 측정 캠페인을 수행하였다. 이 지형은 높은 표면 균질성을 제공하지만 미묘한 토양 매개변수 변화라는 과제도 제시한다. 결과를 바탕으로 이러한 다중 센서 회귀 설정의 과제를 논의하고 농업 현장 조사 응용 분야에서 ML 모델을 평가하기 위한 성능 지표로 너겟-실 비율의 사용을 제안한다.

시사점, 한계점

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시사점: 공기 결합 SFCW GPR과 머신러닝을 결합하여 비용 효율적으로 심도별 토양 데이터를 획득할 가능성을 제시한다. 너겟-실 비율을 농업 현장 조사 응용 분야에서 ML 모델 성능 평가 지표로 활용하는 것을 제안한다. 대규모 현장 데이터를 기반으로 GPR 데이터의 토양 매개변수 예측 가능성을 실증적으로 보여준다.

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한계점: 골프장이라는 특정 지형에서만 실험이 진행되어 일반화 가능성에 대한 추가 연구가 필요하다. 다양한 토양 유형 및 환경 조건에 대한 추가적인 검증이 필요하다. 미묘한 토양 매개변수 변화를 정확하게 예측하는 데 어려움을 보였으며, 이에 대한 추가적인 연구가 필요하다.

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