Tootfinder

Puck acquires Artelligence, a Substack newsletter about the global art market with 32K free and paid subscribers; Puck now has 17 journalists and 12 newsletters (Sara Fischer/Axios)
https://www.axios.com/2024/04/09/puck-acquires-substack-newsletter-…

Exclusive: Puck acquires Substack newsletter Artelligence
Puck's business model is built to incentivize subject matter experts by giving them a cut of revenue from the business they drive.

Numerical Fuzz: A Type System for Rounding Error Analysis
Ariel E. Kellison, Justin Hsu
https://arxiv.org/abs/2405.04612 https://arxi…

Numerical Fuzz: A Type System for Rounding Error Analysis
Algorithms operating on real numbers are implemented as floating-point computations in practice, but floating-point operations introduce roundoff errors that can degrade the accuracy of the result. We propose $Λ_{num}$, a functional programming language with a type system that can express quantitative bounds on roundoff error. Our type system combines a sensitivity analysis, enforced through a linear typing discipline, with a novel graded monad to track the accumulation of roundoff errors. We …

360{\deg}REA: Towards A Reusable Experience Accumulation with 360{\deg} Assessment for Multi-Agent System
Shen Gao, Hao Li, Zhengliang Shi, Chengrui Huang, Quan Tu, Zhiliang Tian, Minlie Huang, Shuo Shang
https://arxiv.org/abs/2404.05569

360°REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System
Large language model agents have demonstrated remarkable advancements across various complex tasks. Recent works focus on optimizing the agent team or employing self-reflection to iteratively solve complex tasks. Since these agents are all based on the same LLM, only conducting self-evaluation or removing underperforming agents does not substantively enhance the capability of the agents. We argue that a comprehensive evaluation and accumulating experience from evaluation feedback is an effectiv…

Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation
Tong Su, Xin Peng, Sarubi Thillainathan, David Guzm\'an, Surangika Ranathunga, En-Shiun Annie Lee
https://arxiv.org/abs/2404.04212

Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation
Parameter-efficient fine-tuning (PEFT) methods are increasingly vital in adapting large-scale pre-trained language models for diverse tasks, offering a balance between adaptability and computational efficiency. They are important in Low-Resource Language (LRL) Neural Machine Translation (NMT) to enhance translation accuracy with minimal resources. However, their practical effectiveness varies significantly across different languages. We conducted comprehensive empirical experiments with varying…

This https://arxiv.org/abs/2403.13801 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Natural Language as Policies: Reasoning for Coordinate-Level Embodied Control with LLMs
We demonstrate experimental results with LLMs that address robotics task planning problems. Recently, LLMs have been applied in robotics task planning, particularly using a code generation approach that converts complex high-level instructions into mid-level policy codes. In contrast, our approach acquires text descriptions of the task and scene objects, then formulates task planning through natural language reasoning, and outputs coordinate level control commands, thus reducing the necessity f…

AI Knowledge and Reasoning: Emulating Expert Creativity in Scientific Research
Anirban Mukherjee, Hannah Hanwen Chang
https://arxiv.org/abs/2404.04436 http…

AI Knowledge and Reasoning: Emulating Expert Creativity in Scientific Research
We investigate whether modern AI can emulate expert creativity in complex scientific endeavors. We introduce novel methodology that utilizes original research articles published after the AI's training cutoff, ensuring no prior exposure, mitigating concerns of rote memorization and prior training. The AI are tasked with redacting findings, predicting outcomes from redacted research, and assessing prediction accuracy against reported results. Analysis on 589 published studies in four leading psy…

Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study
Myrthe Reuver, Suzan Verberne, Antske Fokkens
https://arxiv.org/abs/2404.03987

Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study
For a viewpoint-diverse news recommender, identifying whether two news articles express the same viewpoint is essential. One way to determine "same or different" viewpoint is stance detection. In this paper, we investigate the robustness of operationalization choices for few-shot stance detection, with special attention to modelling stance across different topics. Our experiments test pre-registered hypotheses on stance detection. Specifically, we compare two stance task definitions (Pro/Con ve…

Uncovering implementable dormant pruning decisions from three different stakeholder perspectives
Deanna Flynn, Abhinav Jain, Heather Knight, Cristina G. Wilson, Cindy Grimm
https://arxiv.org/abs/2405.04030

Uncovering implementable dormant pruning decisions from three different stakeholder perspectives
Dormant pruning, or the removal of unproductive portions of a tree while a tree is not actively growing, is an important orchard task to help maintain yield, requiring years to build expertise. Because of long training periods and an increasing labor shortage in agricultural jobs, pruning could benefit from robotic automation. However, to program robots to prune branches, we first need to understand how pruning decisions are made, and what variables in the environment (e.g., branch size and thi…

Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?
Ilya Ilyankou, Aldo Lipani, Stefano Cavazzi, Xiaowei Gao, James Haworth
https://arxiv.org/abs/2404.04169

Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?
Sentence transformers are language models designed to perform semantic search. This study investigates the capacity of sentence transformers, fine-tuned on general question-answering datasets for asymmetric semantic search, to associate descriptions of human-generated routes across Great Britain with queries often used to describe hiking experiences. We find that sentence transformers have some zero-shot capabilities to understand quasi-geospatial concepts, such as route types and difficulty, s…

This https://arxiv.org/abs/2401.08298 has been replaced.
link: https://scholar.google.com/scholar?q=a

Online Elasticity Estimation and Material Sorting Using Standard Robot Grippers
Standard robot grippers are not designed for material recognition. We experimentally evaluated the accuracy with which material properties can be estimated through object compression by two standard parallel jaw grippers and a force/torque sensor mounted at the robot wrist, with a professional biaxial compression device used as reference. Gripper effort versus position curves were obtained and transformed into stress/strain curves. The modulus of elasticity was estimated at different strain poi…