cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7294/26339563
title: Dangerous Harbor Project - Stafford County - Reel 7A - 1686-1694 - TIFFs 351-400

description: This dataset includes 50 TIFF files showing court orders from Stafford County in Virginia. They were created to settle disputes between residents within Stafford County and ultimately issue punishments, judgments, and fines. These documents were used to identify escaped unfree laborers in the 17th century Chesapeake and the punishments associated with their escape. 
<br>
To learn more about this project, please go to the Dangerous Harbor Website.
DOI: 10.7294/26339563.v1
title: Dangerous Harbor Project - Stafford County - Reel 7A - 1686-1694 - TIFFs 351-400

description: This dataset includes 50 TIFF files showing court orders from Stafford County in Virginia. They were created to settle disputes between residents within Stafford County and ultimately issue punishments, judgments, and fines. These documents were used to identify escaped unfree laborers in the 17th century Chesapeake and the punishments associated with their escape. 
<br>
To learn more about this project, please go to the Dangerous Harbor Website.
DOI: 10.7294/26339581
title: Dangerous Harbor Project - Stafford County - Reel 7A - 1686-1694 - TIFFs 401-450

description: This dataset includes 50 TIFF files showing court orders from Stafford County in Virginia. They were created to settle disputes between residents within Stafford County and ultimately issue punishments, judgments, and fines. These documents were used to identify escaped unfree laborers in the 17th century Chesapeake and the punishments associated with their escape. 
<br>
To learn more about this project, please go to the Dangerous Harbor Website.
DOI: 10.7294/26339581.v1
title: Dangerous Harbor Project - Stafford County - Reel 7A - 1686-1694 - TIFFs 401-450

description: This dataset includes 50 TIFF files showing court orders from Stafford County in Virginia. They were created to settle disputes between residents within Stafford County and ultimately issue punishments, judgments, and fines. These documents were used to identify escaped unfree laborers in the 17th century Chesapeake and the punishments associated with their escape. 
<br>
To learn more about this project, please go to the Dangerous Harbor Website.
DOI: 10.15468/dl.92m4pf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Polyommatus icarus (von Rottemburg, 1775)",
 "Year is 48",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008122-241107131044228/datasets/export for details.
DOI: 10.5281/zenodo.14075401
title: Nucleotide and protein database of Campylobacter virulence-associated genes

description: Nucleotide and protein databases of 334 Campylobacter virulence-associated genes. The .XLSX file contains detailed information on these genes. It includes data on the strain from which each sequence was derived, as well as the corresponding locus tag, gene and protein lengths, and the classification of each gene by virulence factor category. Additionally, genes exhibiting phase-variation are also indicated. For these genes, the functional version (ON) of the protein is included.

The combined use of the two databases for gene searching using BLASTn and tBLASTn for the nucleotide and protein databases, respectively, allows the identification of those genes present that might not be functional, especially those affected by phase-variation.
DOI: 10.5281/zenodo.14075400
title: Nucleotide and protein database of Campylobacter virulence-associated genes

description: Nucleotide and protein databases of 334 Campylobacter virulence-associated genes. The .XLSX file contains detailed information on these genes. It includes data on the strain from which each sequence was derived, as well as the corresponding locus tag, gene and protein lengths, and the classification of each gene by virulence factor category. Additionally, genes exhibiting phase-variation are also indicated. For these genes, the functional version (ON) of the protein is included.

The combined use of the two databases for gene searching using BLASTn and tBLASTn for the nucleotide and protein databases, respectively, allows the identification of those genes present that might not be functional, especially those affected by phase-variation.
DOI: 10.7274/25527742
title: Software-Hardware Co-Design of Neural Network Accelerators Using Emerging Technologies

description: Deep Neural Networks (DNNs) have shown excellent results in various perception tasks, but their deployment on edge devices with limited computation resources and power budgets is challenging. Non-volatile Computing-in-Memory (nvCiM) DNN accelerators, employing emerging non-volatile memory (NVM) devices, offer an efficient solution for edge applications by minimizing data movement, enhancing energy efficiency, and improving memory density. However, these accelerators are not optimized due to energy and latency overhead from analog-digital conversion and reduced DNN performance from device variations. To overcome these issues, we propose to utilize software-hardware (SW/HW) co-design, which enables cross-layer multi-objective optimization to leverage nvCiM accelerators' potential while mitigating their inherent challenges.


This dissertation covers optimizations from three levels: (1) Novel SW/HW co-design techniques tailored for nvCiM accelerators. We demonstrate the effectiveness of reinforcement learning and differentiable neural architecture search in SW/HW co-design. (2) Device variation and quantization-aware training methods for more robust DNN models. We introduce methods to identify worst-case scenarios under device variations, and a novel noise-aware training technique to improve worst-case DNN performance. (3) Novel device programming methods to mitigate device variation effects during the programming phase. We propose methods to identify important DNN weights requiring precise RRAM device programming, expand this to other devices, and validate it on fabricated nvCiM platforms.
DOI: 10.7274/25527742.v1
title: Software-Hardware Co-Design of Neural Network Accelerators Using Emerging Technologies

description: Deep Neural Networks (DNNs) have shown excellent results in various perception tasks, but their deployment on edge devices with limited computation resources and power budgets is challenging. Non-volatile Computing-in-Memory (nvCiM) DNN accelerators, employing emerging non-volatile memory (NVM) devices, offer an efficient solution for edge applications by minimizing data movement, enhancing energy efficiency, and improving memory density. However, these accelerators are not optimized due to energy and latency overhead from analog-digital conversion and reduced DNN performance from device variations. To overcome these issues, we propose to utilize software-hardware (SW/HW) co-design, which enables cross-layer multi-objective optimization to leverage nvCiM accelerators' potential while mitigating their inherent challenges.


This dissertation covers optimizations from three levels: (1) Novel SW/HW co-design techniques tailored for nvCiM accelerators. We demonstrate the effectiveness of reinforcement learning and differentiable neural architecture search in SW/HW co-design. (2) Device variation and quantization-aware training methods for more robust DNN models. We introduce methods to identify worst-case scenarios under device variations, and a novel noise-aware training technique to improve worst-case DNN performance. (3) Novel device programming methods to mitigate device variation effects during the programming phase. We propose methods to identify important DNN weights requiring precise RRAM device programming, expand this to other devices, and validate it on fabricated nvCiM platforms.
DOI: 10.7274/25527775
title: Annotation Efficient Deep Learning Methods for Problems in Biomedical Image Analysis

description: Biomedical image analysis plays an essential role in providing both qualitative and quantitative insights crucial for advancing biomedical research and applications. In recent years, deep learning has surpassed traditional image processing methods which rely on handcrafted features, achieving superior performance in biomedical image analysis. However, a significant challenge persists: image annotation effort required for training deep learning models is often both costly and time-consuming. The recent accumulation of large volumes of biomedical image data has heightened the demand for more efficient processing methods. Given the complexities surrounding image annotation and the need for practical solutions, there is a pressing demand for annotation-efficient deep learning approaches. In this dissertation, we propose novel deep learning methodologies aimed at reducing annotation effort and enhancing model applicability and practicality for various biomedical image processing tasks.

Firstly, we present a novel cell nucleus generation approach aimed at expanding the training set for cell segmentation tasks. This method enhances cell segmentation performance for both 2D and 3D cells, even when annotations are limited. Secondly, we introduce a method using weak annotation for 3D instance segmentation. Our approach only requires bounding box annotation for all instances and voxel annotation for a small subset. Despite this reduction in annotation effort, our method achieves comparable performance to state-of-the-art approaches. Lastly, we propose a synthetic image-driven approach to improve the quality of fluorescence images. This method does not rely on human annotations and is capable of transforming noisy fluorescence images into clean, interpretable ones. This enhancement facilitates better observations and quantification in biological studies and life sciences.
DOI: 10.7274/25527775.v1
title: Annotation Efficient Deep Learning Methods for Problems in Biomedical Image Analysis

description: Biomedical image analysis plays an essential role in providing both qualitative and quantitative insights crucial for advancing biomedical research and applications. In recent years, deep learning has surpassed traditional image processing methods which rely on handcrafted features, achieving superior performance in biomedical image analysis. However, a significant challenge persists: image annotation effort required for training deep learning models is often both costly and time-consuming. The recent accumulation of large volumes of biomedical image data has heightened the demand for more efficient processing methods. Given the complexities surrounding image annotation and the need for practical solutions, there is a pressing demand for annotation-efficient deep learning approaches. In this dissertation, we propose novel deep learning methodologies aimed at reducing annotation effort and enhancing model applicability and practicality for various biomedical image processing tasks.

Firstly, we present a novel cell nucleus generation approach aimed at expanding the training set for cell segmentation tasks. This method enhances cell segmentation performance for both 2D and 3D cells, even when annotations are limited. Secondly, we introduce a method using weak annotation for 3D instance segmentation. Our approach only requires bounding box annotation for all instances and voxel annotation for a small subset. Despite this reduction in annotation effort, our method achieves comparable performance to state-of-the-art approaches. Lastly, we propose a synthetic image-driven approach to improve the quality of fluorescence images. This method does not rely on human annotations and is capable of transforming noisy fluorescence images into clean, interpretable ones. This enhancement facilitates better observations and quantification in biological studies and life sciences.
DOI: 10.7274/25562967
title: Explainable AI for High-Stakes Decision-Making

description: Canonical AI algorithms are black boxes. When it comes to high-stakes applications, this is highly undesirable—we need algorithms that we can understand and, in turn, trust. Doctors will not, and should not, trust a machine that cannot reason about its decisions. The push for this explainability has led to the emergence of the explainable AI (XAI) sub-field. While XAI has made progress over the years, there still exists a wealth of issues. In this dissertation, we demonstrate a path toward the glass box ideal: highly performant algorithms that are fully human-comprehensible. We first demonstrate that post hoc explainers, while incredibly popular, should not be trusted. In certain cases in which they must be used, we provide a solution that helps the explainers become more reliable. We characterize the open problems of building an AI system feasible for high-stakes applications through the design of a real-world emergency drone response system. This motivates us to challenge the fundamentals of deep learning architectural design. We bridge XAI and automated machine learning (AutoML) to discover intrinsically debuggable neural networks. Next, we propose novel, intrinsically interpretable approaches to computer vision based on prototypical networks, a type of concept-based neural network. Critically, we address the human-machine semantic similarity gap associated with learned prototypes. First, we enable the automatic learning of prototypical parts with weak supervision using receptive-field constrained networks. Second, we enrich the interpretability of learned concepts by learning prototypical distributions in the invertible latent space of a normalizing flow. We demonstrate a measurable improvement in the comprehensibility of decisions made in both predictive and generative computer vision tasks. The contributions of this dissertation open up the AI black box by substituting correlative explainers with faithful, actionable, intuitive, and debuggable algorithms.
DOI: 10.7274/25562967.v1
title: Explainable AI for High-Stakes Decision-Making

description: Canonical AI algorithms are black boxes. When it comes to high-stakes applications, this is highly undesirable—we need algorithms that we can understand and, in turn, trust. Doctors will not, and should not, trust a machine that cannot reason about its decisions. The push for this explainability has led to the emergence of the explainable AI (XAI) sub-field. While XAI has made progress over the years, there still exists a wealth of issues. In this dissertation, we demonstrate a path toward the glass box ideal: highly performant algorithms that are fully human-comprehensible. We first demonstrate that post hoc explainers, while incredibly popular, should not be trusted. In certain cases in which they must be used, we provide a solution that helps the explainers become more reliable. We characterize the open problems of building an AI system feasible for high-stakes applications through the design of a real-world emergency drone response system. This motivates us to challenge the fundamentals of deep learning architectural design. We bridge XAI and automated machine learning (AutoML) to discover intrinsically debuggable neural networks. Next, we propose novel, intrinsically interpretable approaches to computer vision based on prototypical networks, a type of concept-based neural network. Critically, we address the human-machine semantic similarity gap associated with learned prototypes. First, we enable the automatic learning of prototypical parts with weak supervision using receptive-field constrained networks. Second, we enrich the interpretability of learned concepts by learning prototypical distributions in the invertible latent space of a normalizing flow. We demonstrate a measurable improvement in the comprehensibility of decisions made in both predictive and generative computer vision tasks. The contributions of this dissertation open up the AI black box by substituting correlative explainers with faithful, actionable, intuitive, and debuggable algorithms.
DOI: 10.5281/zenodo.14075341
title: Dataset do artigo "O Que os estudos sobre desinformação na Ciência da Informação brasileira têm a nos dizer?

description: Artigo publicado em http://dx.doi.org/10.1590/1981-5344/51376
DOI: 10.7274/25571511
title: Harmonizing Network Measurements with User Experience: Challenges, Innovations, and Solutions

description: In the modern digital age, understanding and accurately measuring network performance has become essential. Yet, the tools and methods we employ, particularly speed tests, have a double-edged sword. On one end, they provide crucial insights into network speeds and potential bottlenecks. On the other, they inadvertently impact the very user experience they aim to optimize. This dissertation investigates the intricate relationship between network measurements and user Quality of Experience (QoE).

Our initial exploration reveals that standard speed tests, while diagnostic, can substantially degrade user QoE, extending download times and affecting video streaming quality. Such interruptions not only disrupt user activities but also highlight an inefficiency in our measurement methods. The data consumed in these tests, though essential for diagnostics, is discarded post-measurement, rendering it momentarily useful yet ultimately wasteful.

In response to this challenge, we introduce rePurpose, a novel approach that reimagines speed tests. Instead of discarding the data post-measurement, rePurpose pre-stages video ads, integrating them within the existing advertising ecosystem. This not only offsets the data costs of speed tests but also enhances user QoE by reducing ad and video start-up delays.

As the dissertation progresses, we further explore the concept of orchestrated network measurements, aiming to offer a more coordinated and holistic view of network performance. Through research questions and experimental designs, we highlight the need for scheduling and orchestration in network measurements, improving accuracy without compromising user experience.

This dissertation highlights the critical balance between network performance measurements and user experience, offering solutions to bridge the existing gaps. Through research and analysis, we pave the way for a more harmonious coexistence between network diagnostics and digital user experiences.
DOI: 10.7274/25571511.v1
title: Harmonizing Network Measurements with User Experience: Challenges, Innovations, and Solutions

description: In the modern digital age, understanding and accurately measuring network performance has become essential. Yet, the tools and methods we employ, particularly speed tests, have a double-edged sword. On one end, they provide crucial insights into network speeds and potential bottlenecks. On the other, they inadvertently impact the very user experience they aim to optimize. This dissertation investigates the intricate relationship between network measurements and user Quality of Experience (QoE).

Our initial exploration reveals that standard speed tests, while diagnostic, can substantially degrade user QoE, extending download times and affecting video streaming quality. Such interruptions not only disrupt user activities but also highlight an inefficiency in our measurement methods. The data consumed in these tests, though essential for diagnostics, is discarded post-measurement, rendering it momentarily useful yet ultimately wasteful.

In response to this challenge, we introduce rePurpose, a novel approach that reimagines speed tests. Instead of discarding the data post-measurement, rePurpose pre-stages video ads, integrating them within the existing advertising ecosystem. This not only offsets the data costs of speed tests but also enhances user QoE by reducing ad and video start-up delays.

As the dissertation progresses, we further explore the concept of orchestrated network measurements, aiming to offer a more coordinated and holistic view of network performance. Through research questions and experimental designs, we highlight the need for scheduling and orchestration in network measurements, improving accuracy without compromising user experience.

This dissertation highlights the critical balance between network performance measurements and user experience, offering solutions to bridge the existing gaps. Through research and analysis, we pave the way for a more harmonious coexistence between network diagnostics and digital user experiences.
DOI: 10.7274/25582434
title: Motif Mining: Towards Multimodal Retrieval for Discovering Disinformation Trends in Social Media

description: As the world has become more and more connected online, so too has the amount of data that has been produced. Alongside this increase in data production the amount of disinformation has drastically increased. Coming to the fore in recent years and playing outsized roles in the politics of a myriad of countries, disinformation has become a hot topic. The scale of the data, in particular images and videos, has grown beyond the human capacity for processing in any detail. To help aid reviewers of online content we propose extending the idea of image provenance to include multimodal motif mining. This formalization and implementation of the problem of visual motif mining is designed to help humans process the vast amounts of visual data more quickly and to aid in the discovery of emerging disinformation trends in the online space. Early efforts in this space focused on monomodal capabilities, considering images and texts separately. Efforts to combine the two via fusion techniques left something to be desired. With the release of contrastive language-image pre-training techniques a new door has been opened for multimodal shared embedding spaces. In this work I share some of the earlier monomodal motif mining strategies, focusing on images as their subject. This is followed by studies in using CLIP-based strategies for projecting images and texts from social media into a shared latent space, allowing for crossmodal retrieval and understanding, and thus allowing for earlier motif mining pipelines to be expanded beyond one modality. Finally, an extension of contrastive losses to more than two dimensions is formalized and studied, with trimodal and quadmodal models developed. These allow the pipelines to progress even further beyond bimodal models and given proper training data could allow them to encompass every type of artifact that could appear on social media.