Tootfinder

To me, the most direct genetic DNA line between Blur and Gorillaz is this song https://music.youtube.com/watch?v=1YXc0Zrcsx0&si=ae863mKYRaTvOp5r

Today in the arguments in Murthy v Missouri non-justice Thomas asked what is the dividing line between unlawful gov't coercion of social media and lawful acts of persuasion.
Well, the answer was there to be answered but apparently no one did: The answer is to pull up the standard that SCOTUS used in McDonnell v. United States where they said that a bribe, to be a bribe, had to be very directly tied to a quid-pro-quo.
Well, that same standard could be used with regard to gov…

First path component power based NLOS mitigation in UWB positioning system
Marcin Kolakowski, Jozef Modelski
https://arxiv.org/abs/2403.19706 https://

First path component power based NLOS mitigation in UWB positioning system
The paper describes an NLOS (Non-Line-of-Sight) mitigation method intended for use in a UWB positioning system. In the proposed method propagation conditions between the localized objects and the anchors forming system infrastructure are classified into one of three categories: LOS (Line-of-Sight), NLOS and severe NLOS. Non-Line-of-Sight detection is conducted based on first path signal component power measurements. For each of the categories, average NLOS inducted time of arrival bias and bias…

Selenskyj will Getreidestreit mit Polen beenden
Angesichts der zunehmend schwierigen Lage seines Landes wegen des russischen Angriffskrieges dringt der ukrainische Präsident Wolodymyr Selenskyj auf die sofortige Beilegung des Gereidestreits mit Polen. Er habe seine Regierung angewiesen, darüber schnellstens mit dem polnischen Regierungschef Donald Tusk zu verhandeln, sagte Selenskyj in einer Video-Botschaft. Die beiden Nachbar…
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Ukraine-Liveblog: ++ Selenskyj will Getreidestreit mit Polen beenden ++
Der ukrainische Präsident Selenskyj will den Gereidestreit mit Polen umgehend beenden. EU-Kommissionspräsidentin von der Leyen will die europäische Rüstungsproduktion ankurbeln. Die Entwicklungen im Liveblog.

Iterative Reasoning Preference Optimization
Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, He He, Sainbayar Sukhbaatar, Jason Weston
https://arxiv.org/abs/2404.19733 https://arxiv.org/pdf/2404.19733
arXiv:2404.19733v1 Announce Type: new
Abstract: Iterative preference optimization methods have recently been shown to perform well for general instruction tuning tasks, but typically make little improvement on reasoning tasks (Yuan et al., 2024, Chen et al., 2024). In this work we develop an iterative approach that optimizes the preference between competing generated Chain-of-Thought (CoT) candidates by optimizing for winning vs. losing reasoning steps that lead to the correct answer. We train using a modified DPO loss (Rafailov et al., 2023) with an additional negative log-likelihood term, which we find to be crucial. We show reasoning improves across repeated iterations of this scheme. While only relying on examples in the training set, our approach results in increasing accuracy for Llama-2-70B-Chat from 55.6% to 81.6% on GSM8K (and 88.7% with majority voting out of 32 samples), from 12.5% to 20.8% on MATH, and from 77.8% to 86.7% on ARC-Challenge, which outperforms other Llama-2-based models not relying on additionally sourced datasets.

This https://arxiv.org/abs/2309.08541 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universally beneficial or only effective in specific settings, such as for particular retrieval models, dataset domains, or query types. To answer this, we conduct the first comprehensive analysis of LM-based expansion. We find that there exists a strong negative correlation between retriever performance and gains from expansi…

This https://arxiv.org/abs/2403.17752 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Can multiple-choice questions really be useful in detecting the abilities of LLMs?
Multiple-choice questions (MCQs) are widely used in the evaluation of large language models (LLMs) due to their simplicity and efficiency. However, there are concerns about whether MCQs can truly measure LLM's capabilities, particularly in knowledge-intensive scenarios where long-form generation (LFG) answers are required. The misalignment between the task and the evaluation method demands a thoughtful analysis of MCQ's efficacy, which we undertake in this paper by evaluating nine LLMs on four …

This https://arxiv.org/abs/2401.01979 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Low level definability above large cardinals
We study some connections between definability in generalized descriptive set theory and large cardinals, particularly measurable cardinals and limits thereof, working in ZFC. We show that if $κ$ is a limit of measurable cardinals then there is no $Σ_1(H_κ\cup\mathrm{OR})$ wellorder of a subset of $P(κ)$ of length $\geqκ^+$; this answers a question of Lücke and Müller. However, in $M_1$, the minimal proper class mouse with a Woodin cardinal, for every uncountable cardinal $κ$ which is n…

Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning
Dieter Coppens, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter
https://arxiv.org/abs/2403.19262

Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning
Indoor positioning using UWB technology has gained interest due to its centimeter-level accuracy potential. However, multipath effects and non-line-of-sight conditions cause ranging errors between anchors and tags. Existing approaches for mitigating these ranging errors rely on collecting large labeled datasets, making them impractical for real-world deployments. This paper proposes a novel self-supervised deep reinforcement learning approach that does not require labeled ground truth data. A r…

This https://arxiv.org/abs/2401.01979 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Low level definability above large cardinals
We study some connections between definability in generalized descriptive set theory and large cardinals, particularly measurable cardinals and limits thereof, working in ZFC. We show that if $κ$ is a limit of measurable cardinals then there is no $Σ_1(H_κ\cup\mathrm{OR})$ wellorder of a subset of $P(κ)$ of length $\geqκ^+$; this answers a question of Lücke and Müller. However, in $M_1$, the minimal proper class mouse with a Woodin cardinal, for every uncountable cardinal $κ$ which is n…