Tootfinder

RightTyper: Effective and Efficient Type Annotation for Python
Juan Altmayer Pizzorno, Emery D. Berger
https://arxiv.org/abs/2507.16051 https://

RightTyper: Effective and Efficient Type Annotation for Python
Python type annotations bring the benefits of static type checking to the language. However, manually writing annotations can be time-consuming and tedious. The result is that most real-world Python code remains largely untyped. Past approaches to annotating types in Python code fall short in a number of ways. Static approaches struggle with dynamic features and infer overly broad types. AI-based methods are inherently unsound and can miss rare or user-defined types. Dynamic methods can impose …

Antidirected paths in oriented graphs
Andrzej Grzesik, Marek Skrzypczyk
https://arxiv.org/abs/2506.11866 https://arxiv.org/pdf/2506.1…

Antidirected paths in oriented graphs
We show that for any integer $k \ge 4$, every oriented graph with minimum semidegree bigger than $\frac{1}{2}(k-1+\sqrt{k-3})$ contains an antidirected path of length $k$. Consequently, every oriented graph on $n$ vertices with more than $(k-1+\sqrt{k-3})n$ edges contains an antidirected path of length $k$. This asymptotically proves the antidirected path version of a conjecture of Stein and of a conjecture of Addario-Berry, Havet, Linhares Sales, Reed and Thomassé, respectively.

Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks
Linlin Wang, Wei Wang, Dezhao Wang, Shanwen Wang
https://arxiv.org/abs/2506.15125 http…

Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks
With the applicability of optical fiber-based distributed acoustic sensing (DAS) systems, effective signal processing and analysis approaches are needed to promote its popularization in the field of intelligent transportation systems (ITS). This paper presents a signal denoising algorithm using a hybrid deep-learning network (HDLNet). Without annotated data and time-consuming labeling, this self-supervised network runs in parallel, combining an autoencoder for denoising (DAE) and a long short-t…

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning
Hien Ohnaka, Yuma Shirahata, Byeongseon Park, Ryuichi Yamamoto
https://arxiv.org/abs/2506.04527

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning
We propose a model to obtain phonemic and prosodic labels of speech that are coherent with graphemes. Unlike previous methods that simply fine-tune a pre-trained ASR model with the labels, the proposed model conditions the label generation on corresponding graphemes by two methods: 1) Add implicit grapheme conditioning through prompt encoder using pre-trained BERT features. 2) Explicitly prune the label hypotheses inconsistent with the grapheme during inference. These methods enable obtaining p…

The Anatomy of Evidence: An Investigation Into Explainable ICD Coding
Katharina Beckh, Elisa Studeny, Sujan Sai Gannamaneni, Dario Antweiler, Stefan R\"uping
https://arxiv.org/abs/2507.01802

The Anatomy of Evidence: An Investigation Into Explainable ICD Coding
Automatic medical coding has the potential to ease documentation and billing processes. For this task, transparency plays an important role for medical coders and regulatory bodies, which can be achieved using explainability methods. However, the evaluation of these approaches has been mostly limited to short text and binary settings due to a scarcity of annotated data. Recent efforts by Cheng et al. (2023) have introduced the MDACE dataset, which provides a valuable resource containing code ev…

AI-guided digital intervention with physiological monitoring reduces intrusive memories after experimental trauma
Megan T. deBettencourt, Sruthi Sakthivel, Emily A. Holmes, Mark Chevillet
https://arxiv.org/abs/2507.01081

AI-guided digital intervention with physiological monitoring reduces intrusive memories after experimental trauma
Trauma prevalence is vast globally. Evidence-based digital treatments can help, but most require human guidance. Human guides provide tailored instructions and responsiveness to internal cognitive states, but limit scalability. Can generative AI and neurotechnology provide a scalable alternative? Here we test ANTIDOTE, combining AI guidance and pupillometry to automatically deliver and monitor an evidence-based digital treatment, specifically the Imagery Competing Task Intervention (ICTI), to r…