Tootfinder

This https://arxiv.org/abs/2403.01802 has been replaced.
link: https://scholar.google.com/scholar?q=a

TNF: Tri-branch Neural Fusion for Multimodal Medical Data Classification
This paper presents a Tri-branch Neural Fusion (TNF) approach designed for classifying multimodal medical images and tabular data. It also introduces two solutions to address the challenge of label inconsistency in multimodal classification. Traditional methods in multi-modality medical data classification often rely on single-label approaches, typically merging features from two distinct input modalities. This becomes problematic when features are mutually exclusive or labels differ across mod…

This https://arxiv.org/abs/2401.18024 has been replaced.
link: https://scholar.google.com/scholar?q=a

Benchmarking Private Population Data Release Mechanisms: Synthetic Data vs. TopDown
Differential privacy (DP) is increasingly used to protect the release of hierarchical, tabular population data, such as census data. A common approach for implementing DP in this setting is to release noisy responses to a predefined set of queries. For example, this is the approach of the TopDown algorithm used by the US Census Bureau. Such methods have an important shortcoming: they cannot answer queries for which they were not optimized. An appealing alternative is to generate DP synthetic da…

This https://arxiv.org/abs/2402.00160 has been replaced.
link: https://scholar.google.com/scholar?q=a

Emergency Department Decision Support using Clinical Pseudo-notes
In this work, we introduce the Multiple Embedding Model for EHR (MEME), an approach that serializes multimodal EHR tabular data into text using pseudo-notes, mimicking clinical text generation. This conversion not only preserves better representations of categorical data and learns contexts but also enables the effective employment of pretrained foundation models for rich feature representation. To address potential issues with context length, our framework encodes embeddings for each EHR modal…

This https://arxiv.org/abs/2401.18024 has been replaced.
link: https://scholar.google.com/scholar?q=a

Benchmarking Private Population Data Release Mechanisms: Synthetic Data vs. TopDown
Differential privacy (DP) is increasingly used to protect the release of hierarchical, tabular population data, such as census data. A common approach for implementing DP in this setting is to release noisy responses to a predefined set of queries. For example, this is the approach of the TopDown algorithm used by the US Census Bureau. Such methods have an important shortcoming: they cannot answer queries for which they were not optimized. An appealing alternative is to generate DP synthetic da…

This https://arxiv.org/abs/2311.07550 has been replaced.
link: https://scholar.google.com/scholar?q=a

Tabdoor: Backdoor Vulnerabilities in Transformer-based Neural Networks for Tabular Data
Deep Neural Networks (DNNs) have shown great promise in various domains. Alongside these developments, vulnerabilities associated with DNN training, such as backdoor attacks, are a significant concern. These attacks involve the subtle insertion of triggers during model training, allowing for manipulated predictions. More recently, DNNs for tabular data have gained increasing attention due to the rise of transformer models. Our research presents a comprehensive analysis of backdoor attacks on ta…

This https://arxiv.org/abs/2404.10305 has been replaced.
link: https://scholar.google.com/scholar?q=a

TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content
The automatic recognition of tabular data in document images presents a significant challenge due to the diverse range of table styles and complex structures. Tables offer valuable content representation, enhancing the predictive capabilities of various systems such as search engines and Knowledge Graphs. Addressing the two main problems, namely table detection (TD) and table structure recognition (TSR), has traditionally been approached independently. In this research, we propose an end-to-end…

This https://arxiv.org/abs/2112.14489 has been replaced.
link: https://scholar.google.com/scholar?q=a

Sums of squares of integer-multiple of an integral element on real bi-quadratic fields
For any given positive integer $m$ we construct certain totally positive algebraic integers $α$ of a real bi-quadratic field $K$ and obtain some necessary conditions for which $mα$ can not be represented as sum of integral squares. We show this for integers lie in quadratic subfields of $K$ and for integers which are in $K$ but not in any quadratic subfield of $K$. We provide examples in tabular form for each cases to corroborate the results.