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Atlan’s Big Plans for Reimagining the Data Control Plane
16 Aug

Atlan’s Big Plans for Reimagining the Data Control Plane

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(Shutterstock AI Generator/Shutterstock) If you’re in the hunt for an enterprise data catalog, you may want to keep Atlan on your speed dial, as the young company is quickly gathering momentum. In fact, Atlan recently nabbed the number one overall rating from Forrester in the space. But as Atlan’s CEO tells us, there’s a lot more innovation yet to come from data catalogs. In its recent Forrester Wave for Enterprise Data Catalogs, Forrester gave the Atlan Enterprise Data Catalog a score of 4.20 and gave the company’s strategy a score of 4.50, both of which were higher than all 11…
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CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Improving Temporal Knowledge Graph Extrapolation Reasoning
16 Aug

CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Improving Temporal Knowledge Graph Extrapolation Reasoning

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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IIU: Independent Inference Units for Knowledge-based Visual Question Answering
16 Aug

IIU: Independent Inference Units for Knowledge-based Visual Question Answering

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arXiv:2408.07989v1 Announce Type: new Abstract: Knowledge-based visual question answering requires external knowledge beyond visible content to answer the question correctly. One limitation of existing methods is that they focus more on modeling the inter-modal and intra-modal correlations, which entangles complex multimodal clues by implicit embeddings and lacks interpretability and generalization ability. The key challenge to solve the above problem is to separate the information and process it separately at the functional level. By reusing each processing unit, the generalization ability of the model to deal with different data can be increased. In this paper, we propose Independent Inference Units (IIU)…
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AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents
16 Aug

AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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TigerEye Introduces DuckDB.dart to Facilitate Data-Intensive App Development
16 Aug

TigerEye Introduces DuckDB.dart to Facilitate Data-Intensive App Development

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(PhotoJuli86/Shutterstock) TigerEye Labs, an AI-powered planning and revenue management platform, has announced the open-source release of DuckDB.dart, an open-source tool that enables developers to build and run data-intensive applications more efficiently.  DuckDB.dart, a native Dart API for DuckDB, enables developers to build apps across various mobile and desktop environments, including iOS, Android, Linux, and Windows. As DuckDB.dart is specifically designed for analytical queries, it can handle large datasets and complex queries more efficiently within a single machine compared to other technologies like SQLite, which is geared more toward general-purpose database management. With its high performance and the ability to handle…
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KAN versus MLP on Irregular or Noisy Functions
16 Aug

KAN versus MLP on Irregular or Noisy Functions

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arXiv:2408.07906v1 Announce Type: new Abstract: In this paper, we compare the performance of Kolmogorov-Arnold Networks (KAN) and Multi-Layer Perceptron (MLP) networks on irregular or noisy functions. We control the number of parameters and the size of the training samples to ensure a fair comparison. For clarity, we categorize the functions into six types: regular functions, continuous functions with local non-differentiable points, functions with jump discontinuities, functions with singularities, functions with coherent oscillations, and noisy functions. Our experimental results indicate that KAN does not always perform best. For some types of functions, MLP outperforms or performs comparably to KAN. Furthermore, increasing…
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Exploring learning environments for label-efficient cancer diagnosis
16 Aug

Exploring learning environments for label-efficient cancer diagnosis

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arXiv:2408.07988v1 Announce Type: new Abstract: Despite significant research efforts and advancements, cancer remains a leading cause of mortality. Early cancer prediction has become a crucial focus in cancer research to streamline patient care and improve treatment outcomes. Manual tumor detection by histopathologists can be time consuming, prompting the need for computerized methods to expedite treatment planning. Traditional approaches to tumor detection rely on supervised learning, necessitates a large amount of annotated data for model training. However, acquiring such extensive labeled data can be laborious and time-intensive. This research examines the three learning environments: supervised learning (SL), semi-supervised learning (Semi-SL), and…
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Extracting Sentence Embeddings from Pretrained Transformer Models
16 Aug

Extracting Sentence Embeddings from Pretrained Transformer Models

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arXiv:2408.08073v1 Announce Type: new Abstract: Background/introduction: Pre-trained transformer models shine in many natural language processing tasks and therefore are expected to bear the representation of the input sentence or text meaning. These sentence-level embeddings are also important in retrieval-augmented generation. But do commonly used plain averaging or prompt templates surface it enough? Methods: Given 110M parameters BERT's hidden representations from multiple layers and multiple tokens we tried various ways to extract optimal sentence representations. We tested various token aggregation and representation post-processing techniques. We also tested multiple ways of using a general Wikitext dataset to complement BERTs sentence representations. All…
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The Nah Bandit: Modeling User Non-compliance in Recommendation Systems
16 Aug

The Nah Bandit: Modeling User Non-compliance in Recommendation Systems

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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LLaVA-Surg: Towards Multimodal Surgical Assistant via Structured Surgical Video Learning
16 Aug

LLaVA-Surg: Towards Multimodal Surgical Assistant via Structured Surgical Video Learning

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arXiv:2408.07981v1 Announce Type: new Abstract: Multimodal large language models (LLMs) have achieved notable success across various domains, while research in the medical field has largely focused on unimodal images. Meanwhile, current general-domain multimodal models for videos still lack the capabilities to understand and engage in conversations about surgical videos. One major contributing factor is the absence of datasets in the surgical field. In this paper, we create a new dataset, Surg-QA, consisting of 102,000 surgical video-instruction pairs, the largest of its kind so far. To build such a dataset, we propose a novel two-stage question-answer generation pipeline with LLM to…
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