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Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description
14 Aug

Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description

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arXiv:2408.06357v1 Announce Type: new Abstract: Zero sample learning is an effective method for data deficiency. The existing embedded zero sample learning methods only use the known classes to construct the embedded space, so there is an overfitting of the known classes in the testing process. This project uses category semantic similarity measures to classify multiple tags. This enables it to incorporate unknown classes that have the same meaning as currently known classes into the vector space when it is built. At the same time, most of the existing zero sample learning algorithms directly use the depth features of medical images…
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Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models
14 Aug

Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models

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arXiv:2408.06518v1 Announce Type: new Abstract: Despite their wide adoption, the biases and unintended behaviors of language models remain poorly understood. In this paper, we identify and characterize a phenomenon never discussed before, which we call semantic leakage, where models leak irrelevant information from the prompt into the generation in unexpected ways. We propose an evaluation setting to detect semantic leakage both by humans and automatically, curate a diverse test suite for diagnosing this behavior, and measure significant semantic leakage in 13 flagship models. We also show that models exhibit semantic leakage in languages besides English and across different settings and…
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Multi-View Neural Differential Equations for Continuous-Time Stream Data in Long-Term Traffic Forecasting
14 Aug

Multi-View Neural Differential Equations for Continuous-Time Stream Data in Long-Term Traffic Forecasting

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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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Enhancing Ecological Monitoring with Multi-Objective Optimization: A Novel Dataset and Methodology for Segmentation Algorithms
14 Aug

Enhancing Ecological Monitoring with Multi-Objective Optimization: A Novel Dataset and Methodology for Segmentation Algorithms

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arXiv:2408.06356v1 Announce Type: new Abstract: We introduce a unique semantic segmentation dataset of 6,096 high-resolution aerial images capturing indigenous and invasive grass species in Bega Valley, New South Wales, Australia, designed to address the underrepresented domain of ecological data in the computer vision community. This dataset presents a challenging task due to the overlap and distribution of grass species, which is critical for advancing models in ecological and agronomical applications. Our study features a homotopy-based multi-objective fine-tuning approach that balances segmentation accuracy and contextual consistency, applicable to various models. By integrating DiceCELoss for pixel-wise classification and a smoothness loss for…
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Cross-Lingual Conversational Speech Summarization with Large Language Models
14 Aug

Cross-Lingual Conversational Speech Summarization with Large Language Models

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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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Dilated Convolution with Learnable Spacings
14 Aug

Dilated Convolution with Learnable Spacings

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[Submitted on 10 Aug 2024] View a PDF of the paper titled Dilated Convolution with Learnable Spacings, by Ismail Khalfaoui-Hassani View PDF HTML (experimental) Abstract:This thesis presents and evaluates the Dilated Convolution with Learnable Spacings (DCLS) method. Through various supervised learning experiments in the fields of computer vision, audio, and speech processing, the DCLS method proves to outperform both standard and advanced convolution techniques. The research is organized into several steps, starting with an analysis of the literature and existing convolution techniques that preceded the development of the DCLS method. We were particularly interested in the methods that are closely…
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Automated Romberg Test: Leveraging a CNN and Centre of Mass Analysis for Sensory Ataxia Diagnosis
14 Aug

Automated Romberg Test: Leveraging a CNN and Centre of Mass Analysis for Sensory Ataxia Diagnosis

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arXiv:2408.06354v1 Announce Type: new Abstract: This paper proposes a novel method to diagnose sensory ataxia via an automated Romberg Test - the current de facto medical procedure used to diagnose this condition. It utilizes a convolutional neural network to predict joint locations, used for the calculation of various bio-mechanical markers such as the center of mass of the subject and various joint angles. This information is used in combination with data filtering techniques such as Kalman Filters, and center of mass analysis which helped make accurate inferences about the relative weight distribution in the lateral and anterior-posterior axes, and provide…
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TOGGL: Transcribing Overlapping Speech with Staggered Labeling
14 Aug

TOGGL: Transcribing Overlapping Speech with Staggered Labeling

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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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FedRobo: Federated Learning Driven Autonomous Inter Robots Communication For Optimal Chemical Sprays
14 Aug

FedRobo: Federated Learning Driven Autonomous Inter Robots Communication For Optimal Chemical Sprays

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arXiv:2408.06382v1 Announce Type: new Abstract: Federated Learning enables robots to learn from each other's experiences without relying on centralized data collection. Each robot independently maintains a model of crop conditions and chemical spray effectiveness, which is periodically shared with other robots in the fleet. A communication protocol is designed to optimize chemical spray applications by facilitating the exchange of information about crop conditions, weather, and other critical factors. The federated learning algorithm leverages this shared data to continuously refine the chemical spray strategy, reducing waste and improving crop yields. This approach has the potential to revolutionize the agriculture industry by…
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Automated Schizophrenia Detection from Handwriting Samples via Transfer Learning Convolutional Neural Networks
14 Aug

Automated Schizophrenia Detection from Handwriting Samples via Transfer Learning Convolutional Neural Networks

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arXiv:2408.06347v1 Announce Type: new Abstract: Schizophrenia is a globally prevalent psychiatric disorder that severely impairs daily life. Schizophrenia is caused by dopamine imbalances in the fronto-striatal pathways of the brain, which influences fine motor control in the cerebellum. This leads to abnormalities in handwriting. The goal of this study was to develop an accurate, objective, and accessible computational method to be able to distinguish schizophrenic handwriting samples from non-schizophrenic handwriting samples. To achieve this, data from Crespo et al. (2019) was used, which contains images of handwriting samples from schizophrenic and non-schizophrenic patients. The data was preprocessed and augmented to…
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