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ARPA-H Taps Duality’s Homomorphic Encryption for Rare Disease Research

ARPA-H Taps Duality’s Homomorphic Encryption for Rare Disease Research

(Kateryna-Kon/Shutterstock) The Advanced Research Projects Agency for Health (ARPA-H) in September awarded Duality Technologies a contract worth up to $6 million to develop a framework for enabling healthcare organizations to share highly sensitive patient data. If successful, the project will enable smaller healthcare organizations to securely access sensitive health data to conduct research into rare diseases, including those that have a disparate impact on racial minorities. The phrase “rare disease” is a bit of a misnomer. While some diseases statistically are very rare, the fact is that roughly 20% of the country’s population is affected by a rare disease at…
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Season’s Speedings: Databricks SQL Delivers 4x Performance Boost Over Two Years

Season’s Speedings: Databricks SQL Delivers 4x Performance Boost Over Two Years

As the season of giving approaches, we at Databricks have been making our list and checking it twice--but instead of toys and treats, we've been wrapping up powerful performance improvements for our users. Through analyzing billions of production queries and listening closely to our community's wishes, we're excited to deliver a package of enhancements that make your data workloads run faster and more efficiently than ever.  Crafting performance magic for every workloadJust as Santa's workshop crafts everything from traditional wooden toys to the latest electronic gadgets, Databricks SQL has become the ultimate data workshop, expertly handling diverse workloads for users of…
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A Comparative Analysis of U-Net-based models for Segmentation of Cardiac MRI

A Comparative Analysis of U-Net-based models for Segmentation of Cardiac MRI

[Submitted on 18 Jan 2024 (v1), last revised 7 Nov 2024 (this version, v2)] View a PDF of the paper titled A Comparative Analysis of U-Net-based models for Segmentation of Cardiac MRI, by Ketan Suhaas Saichandran View PDF Abstract:Medical imaging refers to the technologies and methods utilized to view the human body and its inside, in order to diagnose, monitor, or even treat medical disorders. This paper aims to explore the application of deep learning techniques in the semantic segmentation of Cardiac short-axis MRI (Magnetic Resonance Imaging) images, aiming to enhance the diagnosis, monitoring, and treatment of medical disorders related…
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Properties of BV-G structures + textures decomposition models. Application to road detection in satellite images

Properties of BV-G structures + textures decomposition models. Application to road detection in satellite images

arXiv:2411.04456v1 Announce Type: cross Abstract: In this paper we present some theoretical results about a structures-textures image decomposition model which was proposed by the second author. We prove a theorem which gives the behavior of this model in different cases. Finally, as a consequence of the theorem we derive an algorithm for the detection of long and thin objects applied to a road networks detection application in aerial or satellite images. Source link lol
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GRSQA — Graph Reasoning-Structured Question Answering Dataset

GRSQA — Graph Reasoning-Structured Question Answering Dataset

[Submitted on 1 Nov 2024 (v1), last revised 6 Nov 2024 (this version, v2)] View a PDF of the paper titled GRSQA -- Graph Reasoning-Structured Question Answering Dataset, by Anish Pahilajani and 9 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have excelled in multi-hop question-answering (M-QA) due to their advanced reasoning abilities. However, the impact of the inherent reasoning structures on LLM M-QA performance remains unclear, largely due to the absence of QA datasets that provide fine-grained reasoning structures. To address this gap, we introduce the Graph Reasoning-Structured Question Answering Dataset (GRS-QA), which includes both semantic contexts…
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AtScale Launches Public Leaderboard for Evaluating Text-to-SQL Solutions

AtScale Launches Public Leaderboard for Evaluating Text-to-SQL Solutions

As the demand for natural language data queries continues to grow, so does the need for a standardized way to evaluate Text-to-SQL (T2SQL) solutions. Despite rapid advancements in T2SQL technologies, the industry has struggled with inconsistent benchmarks. This lack of uniform standards has made it challenging for stakeholders to accurately assess and compare solution performance. AtScale, a semantic layer platform, has announced an open, public leaderboard for TS2QL solutions, meeting the critical need for a standardized and transparent evaluation of natural language query (NLQ) capabilities. The launch of AtScale’s Text-to-SQL leaderboard comes at a time when the industry is experiencing…
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What’s new with Databricks SQL, October 2024

What’s new with Databricks SQL, October 2024

We are excited to share the latest features and performance improvements that make Databricks SQL simpler, faster, and more affordable than ever. Databricks SQL is an intelligent data warehouse within the Databricks Data Intelligence Platform and is built on the lakehouse architecture. In fact, Databricks SQL has over 8,000 customers today!In this blog, we will share details for AI/BI, intelligent experiences, and predictive optimizations. We also have powerful new price/performance capabilities. We hope you like our innovative features from the last three months.   AI/BISince launching AI/BI at Data and Analytics Summit 2024 (DAIS), we’ve added many exciting new enhancements. If you’ve…
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Abstracted Shapes as Tokens — A Generalizable and Interpretable Model for Time-series Classification

Abstracted Shapes as Tokens — A Generalizable and Interpretable Model for Time-series Classification

[Submitted on 1 Nov 2024 (v1), last revised 7 Nov 2024 (this version, v2)] View a PDF of the paper titled Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification, by Yunshi Wen and 4 other authors View PDF HTML (experimental) Abstract:In time-series analysis, many recent works seek to provide a unified view and representation for time-series across multiple domains, leading to the development of foundation models for time-series data. Despite diverse modeling techniques, existing models are black boxes and fail to provide insights and explanations about their representations. In this paper, we present VQShape, a…
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CapS-Adapter: Caption-based MultiModal Adapter in Zero-Shot Classification

CapS-Adapter: Caption-based MultiModal Adapter in Zero-Shot Classification

[Submitted on 26 May 2024 (v1), last revised 7 Nov 2024 (this version, v2)] View a PDF of the paper titled CapS-Adapter: Caption-based MultiModal Adapter in Zero-Shot Classification, by Qijie Wang and 2 other authors View PDF HTML (experimental) Abstract:Recent advances in vision-language foundational models, such as CLIP, have demonstrated significant strides in zero-shot classification. However, the extensive parameterization of models like CLIP necessitates a resource-intensive fine-tuning process. In response, TIP-Adapter and SuS-X have introduced training-free methods aimed at bolstering the efficacy of downstream tasks. While these approaches incorporate support sets to maintain data distribution consistency between knowledge cache and…
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M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding

M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding

arXiv:2411.04952v1 Announce Type: cross Abstract: Document visual question answering (DocVQA) pipelines that answer questions from documents have broad applications. Existing methods focus on handling single-page documents with multi-modal language models (MLMs), or rely on text-based retrieval-augmented generation (RAG) that uses text extraction tools such as optical character recognition (OCR). However, there are difficulties in applying these methods in real-world scenarios: (a) questions often require information across different pages or documents, where MLMs cannot handle many long documents; (b) documents often have important information in visual elements such as figures, but text extraction tools ignore them. We introduce M3DocRAG, a novel…
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