OpenRaiser/Intern-Atlas huggingface dataset:
Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning
Venue: arXiv cs.CV 2024
Year: 2024

Self-supervised representation learning has been highly promising for histopathology image analysis with numerous approaches leveraging their patient-slide-patch hierarchy to learn better representations. In this paper, we explore how the combination of domain specific natural language information with such hierarchical visual representations can benefit rich representation learning for medical image tasks. Building on automated language description generation for features visible in histopathology images, we present a novel language-tied self-supervised learning framework, Hierarchical Language-tied Self-Supervision (HLSS) for histopathology images. We explore contrastive objectives and granular language description based text alignment at multiple hierarchies to inject language modality information into the visual representations. Our resulting model achieves state-of-the-art performance on two medical imaging benchmarks, OpenSRH and TCGA datasets. Our framework also provides better interpretability with our language aligned representation space. Code is available at https://github.com/Hasindri/HLSS.
AI Sustainability in Practice Part One: Foundations for Sustainable AI Projects
Venue: arXiv cs.CY 2024
Year: 2024

Sustainable AI projects are continuously responsive to the transformative effects as well as short-, medium-, and long-term impacts on individuals and society that the design, development, and deployment of AI technologies may have. Projects, which centre AI Sustainability, ensure that values-led, collaborative, and anticipatory reflection both guides the assessment of potential social and ethical impacts and steers responsible innovation practices. This workbook is the first part of a pair that provides the concepts and tools needed to put AI Sustainability into practice. It introduces the SUM Values, which help AI project teams to assess the potential societal impacts and ethical permissibility of their projects. It then presents a Stakeholder Engagement Process (SEP), which provides tools to facilitate proportionate engagement of and input from stakeholders with an emphasis on equitable and meaningful participation and positionality awareness.
AI Fairness in Practice
Venue: arXiv cs.CY 2024
Year: 2024

Reaching consensus on a commonly accepted definition of AI Fairness has long been a central challenge in AI ethics and governance. There is a broad spectrum of views across society on what the concept of fairness means and how it should best be put to practice. In this workbook, we tackle this challenge by exploring how a context-based and society-centred approach to understanding AI Fairness can help project teams better identify, mitigate, and manage the many ways that unfair bias and discrimination can crop up across the AI project workflow. We begin by exploring how, despite the plurality of understandings about the meaning of fairness, priorities of equality and non-discrimination have come to constitute the broadly accepted core of its application as a practical principle. We focus on how these priorities manifest in the form of equal protection from direct and indirect discrimination and from discriminatory harassment. These elements form ethical and legal criteria based upon which instances of unfair bias and discrimination can be identified and mitigated across the AI project workflow. We then take a deeper dive into how the different contexts of the AI project lifecycle give rise to different fairness concerns. This allows us to identify several types of AI Fairness (Data Fairness, Application Fairness, Model Design and Development Fairness, Metric-Based Fairness, System Implementation Fairness, and Ecosystem Fairness) that form the basis of a multi-lens approach to bias identification, mitigation, and management. Building on this, we discuss how to put the principle of AI Fairness into practice across the AI project workflow through Bias Self-Assessment and Bias Risk Management as well as through the documentation of metric-based fairness criteria in a Fairness Position Statement.
Digital Twin for Wind Energy: Latest updates from the NorthWind project
Venue: arXiv cs.CY 2024
Year: 2024

NorthWind, a collaborative research initiative supported by the Research Council of Norway, industry stakeholders, and research partners, aims to advance cutting-edge research and innovation in wind energy. The core mission is to reduce wind power costs and foster sustainable growth, with a key focus on the development of digital twins. A digital twin is a virtual representation of physical assets or processes that uses data and simulators to enable real-time forecasting, optimization, monitoring, control and informed decision-making. Recently, a hierarchical scale ranging from 0 to 5 (0 - Standalone, 1 - Descriptive, 2 - Diagnostic, 3 - Predictive, 4 - Prescriptive, 5 - Autonomous has been introduced within the NorthWind project to assess the capabilities of digital twins. This paper elaborates on our progress in constructing digital twins for wind farms and their components across various capability levels.
The economic value of scientific software
Venue: arXiv cs.CY 2024
Year: 2024

Academic institutions and their staff use, adapt and create software. We're thinking of business tools used to carry out their mission: teaching management (Moodle) or subject teaching support (such as Maxima for formal calculus), for example. We're talking about software resulting from research work, designed by a researcher or a team as part of a research project (funded by ANR, Europe, etc. or not) or as a research service for a third party. These projects can last for decades (such as the Coq program proof assistant project, or the GPAC multimedia content distribution platform).We discuss why this software is produced, with what resources, the interest that institutions derive from it, what we call the ''valorization'' of software resulting from scientific research. The latter is multifaceted, as are the missions of scientific institutions: social value (contribution to the world heritage of knowledge), financial value (contracts), economic value (business creation), scientific value (publication), image value (visibility of the institution among target audiences: students, researchers, companies, prescribers).
Machina Economicus: A New Paradigm for Prosumers in the Energy Internet of Smart Cities
Venue: arXiv cs.CY 2024
Year: 2024

Energy Internet (EI) is emerging as new share economy platform for flexible local energy supplies in smart cities. Empowered by the Internet-of-Things (IoT) and Artificial Intelligence (AI), EI aims to unlock peer-to-peer energy trading and sharing among prosumers, who can adeptly switch roles between providers and consumers in localized energy markets with rooftop photovoltaic panels, vehicle-to-everything technologies, packetized energy management, etc. The integration of prosumers in EI, however, will encounter many challenges in modelling, analyzing, and designing an efficient, economic, and social-optimal platform for energy sharing, calling for advanced AI/IoT-based solutions to resource optimization, information exchange, and interaction protocols in the context of the share economy. In this study, we aim to introduce a recently emerged paradigm, Machina Economicus, to investigate the economic rationality in modelling, analysis, and optimization of AI/IoT-based EI prosumer behaviors. The new paradigm, built upon the theory of machine learning and mechanism design, will offer new angles to investigate the selfishness of AI through a game-theoretic perspective, revealing potential competition and collaborations resulting from the self-adaptive learning and decision-making capacity. This study will focus on how the introduction of AI will reshape prosumer behaviors on the EI, and how this paradigm will reveal new research questions and directions when AI meets the share economy. With an extensive case analysis in the literature, we will also shed light on potential solutions for advancements of AI in future smart cities.
Large-Scale Evaluation of Mobility, Technology and Demand Scenarios in the Chicago Region Using POLARIS
Venue: arXiv cs.CY 2024
Year: 2024

Rapid technological progress and innovation in the areas of vehicle connectivity, automation and electrification, new modes of shared and alternative mobility, and advanced transportation system demand and supply management strategies, have motivated numerous questions and studies regarding the potential impact on key performance and equity metrics. Several of these areas of development may or may not have a synergistic outcome on the overall benefits such as reduction in congestion and travel times. In this study, the use of an end-to-end modeling workflow centered around an activity-based agent-based travel demand forecasting tool called POLARIS is explored to provide insights on the effects of several different technology deployments and operational policies in combination for the Chicago region. The objective of the research was to explore the direct impacts and observe any interactions between the various policy and technology scenarios to help better characterize and evaluate their potential future benefits. We analyze system outcome metrics on mobility, energy and emissions, equity and environmental justice and overall efficiency for a scenario design of experiments that looks at combinations of supply interventions (congestion pricing, transit expansion, tnc policy, off-hours freight policy, connected signal optimization) for different potential demand scenarios defined by e-commerce and on-demand delivery engagement, and market penetration of electric vehicles. We found different combinations of strategies that can reduce overall travel times up to 7% and increase system efficiency up to 53% depending on how various metrics are prioritized. The results demonstrate the importance of considering various interventions jointly.
AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps
Venue: arXiv cs.CY 2024
Year: 2024

Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research. This study conducts a comprehensive bibliometric analysis of the AI ethics literature over the past two decades. The analysis reveals a discernible tripartite progression, characterized by an incubation phase, followed by a subsequent phase focused on imbuing AI with human-like attributes, culminating in a third phase emphasizing the development of human-centric AI systems. After that, they present seven key AI ethics issues, encompassing the Collingridge dilemma, the AI status debate, challenges associated with AI transparency and explainability, privacy protection complications, considerations of justice and fairness, concerns about algocracy and human enfeeblement, and the issue of superintelligence. Finally, they identify two notable research gaps in AI ethics regarding the large ethics model (LEM) and AI identification and extend an invitation for further scholarly research.
Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition
Venue: arXiv cs.CV 2024
Year: 2024

In human activity recognition (HAR), the assumption that training and testing data are independent and identically distributed (i.i.d.) often fails, particularly in cross-user scenarios where data distributions vary significantly. This discrepancy highlights the limitations of conventional domain adaptation methods in HAR, which typically overlook the inherent temporal relations in time-series data. To bridge this gap, our study introduces a Conditional Variational Autoencoder with Universal Sequence Mapping (CVAE-USM) approach, which addresses the unique challenges of time-series domain adaptation in HAR by relaxing the i.i.d. assumption and leveraging temporal relations to align data distributions effectively across different users. This method combines the strengths of Variational Autoencoder (VAE) and Universal Sequence Mapping (USM) to capture and utilize common temporal patterns between users for improved activity recognition. Our results, evaluated on two public HAR datasets (OPPT and PAMAP2), demonstrate that CVAE-USM outperforms existing state-of-the-art methods, offering a more accurate and generalizable solution for cross-user activity recognition.
A Moral Imperative: The Need for Continual Superalignment of Large Language Models
Venue: arXiv cs.CY 2024
Year: 2024

This paper examines the challenges associated with achieving life-long superalignment in AI systems, particularly large language models (LLMs). Superalignment is a theoretical framework that aspires to ensure that superintelligent AI systems act in accordance with human values and goals. Despite its promising vision, we argue that achieving superalignment requires substantial changes in the current LLM architectures due to their inherent limitations in comprehending and adapting to the dynamic nature of these human ethics and evolving global scenarios. We dissect the challenges of encoding an ever-changing spectrum of human values into LLMs, highlighting the discrepancies between static AI models and the dynamic nature of human societies. To illustrate these challenges, we analyze two distinct examples: one demonstrates a qualitative shift in human values, while the other presents a quantifiable change. Through these examples, we illustrate how LLMs, constrained by their training data, fail to align with contemporary human values and scenarios. The paper concludes by exploring potential strategies to address and possibly mitigate these alignment discrepancies, suggesting a path forward in the pursuit of more adaptable and responsive AI systems.
Cyclical Log Annealing as a Learning Rate Scheduler
Venue: arXiv cs.LG 2024
Year: 2024

A learning rate scheduler is a predefined set of instructions for varying search stepsizes during model training processes. This paper introduces a new logarithmic method using harsh restarting of step sizes through stochastic gradient descent. Cyclical log annealing implements the restart pattern more aggressively to maybe allow the usage of more greedy algorithms on the online convex optimization framework. The algorithm was tested on the CIFAR-10 image datasets, and seemed to perform analogously with cosine annealing on large transformer-enhanced residual neural networks. Future experiments would involve testing the scheduler in generative adversarial networks and finding the best parameters for the scheduler with more experiments.
Visualizing Progress in Broadening Participation in Computing: The Value of Context
Venue: arXiv cs.CY 2024
Year: 2024

Concerns about representation in computing within the U.S. have driven numerous activities to broaden participation. Assessment of the impact of these efforts and, indeed, a clear assessment of the actual "problem" being addressed are limited by the nature of the most common data analysis which looks at the representation of each population as a percentage of the number of students graduating with a degree in computing. This use of a single metric cannot adequately assess the impact of broadening participation efforts. First, this approach fails to account for changing demographics of the undergraduate population in terms of overall numbers and relative proportion of the Federally designated gender, race, and ethnicity groupings. A second issue is that the majority of literature on broadening participation in computing (BPC) reports data on gender or on race/ethnicity, omitting data on students' intersectional identities. This leads to an incorrect understanding of both the data and the challenges we face as a field. In this paper we present several different approaches to tracking the impact of BPC efforts. We make three recommendations: 1) cohort-based analysis should be used to accurately show student engagement in computing; 2) the field as a whole needs to adopt the norm of always reporting intersectional data; 3) university demographic context matters when looking at how well a CS department is doing to broaden participation in computing, including longitudinal analysis of university demographic shifts that impact the local demographics of computing.
Auditing Fairness under Unobserved Confounding
Venue: arXiv cs.LG 2024
Year: 2024

Many definitions of fairness or inequity involve unobservable causal quantities that cannot be directly estimated without strong assumptions. For instance, it is particularly difficult to estimate notions of fairness that rely on hard-to-measure concepts such as risk (e.g., quantifying whether patients at the same risk level have equal probability of treatment, regardless of group membership). Such measurements of risk can be accurately obtained when no unobserved confounders have jointly influenced past decisions and outcomes. However, in the real world, this assumption rarely holds. In this paper, we show that, surprisingly, one can still compute meaningful bounds on treatment rates for high-risk individuals (i.e., conditional on their true, \textit{unobserved} negative outcome), even when entirely eliminating or relaxing the assumption that we observe all relevant risk factors used by decision makers. We use the fact that in many real-world settings (e.g., the release of a new treatment) we have data from prior to any allocation to derive unbiased estimates of risk. This result enables us to audit unfair outcomes of existing decision-making systems in a principled manner. We demonstrate the effectiveness of our framework with a real-world study of Paxlovid allocation, provably identifying that observed racial inequity cannot be explained by unobserved confounders of the same strength as important observed covariates.
FedSR: A Semi-Decentralized Federated Learning Algorithm for Non-IIDness in IoT System
Venue: arXiv cs.LG 2024
Year: 2024

In the Industrial Internet of Things (IoT), a large amount of data will be generated every day. Due to privacy and security issues, it is difficult to collect all these data together to train deep learning models, thus the federated learning, a distributed machine learning paradigm that protects data privacy, has been widely used in IoT. However, in practical federated learning, the data distributions usually have large differences across devices, and the heterogeneity of data will deteriorate the performance of the model. Moreover, federated learning in IoT usually has a large number of devices involved in training, and the limited communication resource of cloud servers become a bottleneck for training. To address the above issues, in this paper, we combine centralized federated learning with decentralized federated learning to design a semi-decentralized cloud-edge-device hierarchical federated learning framework, which can mitigate the impact of data heterogeneity, and can be deployed at lage scale in IoT. To address the effect of data heterogeneity, we use an incremental subgradient optimization algorithm in each ring cluster to improve the generalization ability of the ring cluster models. Our extensive experiments show that our approach can effectively mitigate the impact of data heterogeneity and alleviate the communication bottleneck in cloud servers.
Bypassing LLM Watermarks with Color-Aware Substitutions
Venue: arXiv cs.CR 2024
Year: 2024

Watermarking approaches are proposed to identify if text being circulated is human or large language model (LLM) generated. The state-of-the-art watermarking strategy of Kirchenbauer et al. (2023a) biases the LLM to generate specific (``green'') tokens. However, determining the robustness of this watermarking method is an open problem. Existing attack methods fail to evade detection for longer text segments. We overcome this limitation, and propose {\em Self Color Testing-based Substitution (SCTS)}, the first ``color-aware'' attack. SCTS obtains color information by strategically prompting the watermarked LLM and comparing output tokens frequencies. It uses this information to determine token colors, and substitutes green tokens with non-green ones. In our experiments, SCTS successfully evades watermark detection using fewer number of edits than related work. Additionally, we show both theoretically and empirically that SCTS can remove the watermark for arbitrarily long watermarked text.
Automated Extraction and Maturity Analysis of Open Source Clinical Informatics Repositories from Scientific Literature
Venue: arXiv cs.DL 2024
Year: 2024

In the evolving landscape of clinical informatics, the integration and utilization of software tools developed through governmental funding represent a pivotal advancement in research and application. However, the dispersion of these tools across various repositories, with no centralized knowledge base, poses significant challenges to leveraging their full potential. This study introduces an automated methodology to bridge this gap by systematically extracting GitHub repository URLs from academic papers indexed in arXiv, focusing on the field of clinical informatics. Our approach encompasses querying the arXiv API for relevant papers, cleaning extracted GitHub URLs, fetching comprehensive repository information via the GitHub API, and analyzing repository maturity based on defined metrics such as stars, forks, open issues, and contributors. The process is designed to be robust, incorporating error handling and rate limiting to ensure compliance with API constraints. Preliminary findings demonstrate the efficacy of this methodology in compiling a centralized knowledge base of NIH-funded software tools, laying the groundwork for an enriched understanding and utilization of these resources within the clinical informatics community. We propose the future integration of Large Language Models (LLMs) to generate concise summaries and evaluations of the tools. This approach facilitates the discovery and assessment of clinical informatics tools and also enables ongoing monitoring of new and actively updated repositories, revolutionizing how researchers access and leverage federally funded software. The implications of this study extend beyond simplification of access to valuable resources; it proposes a scalable model for the dynamic aggregation and evaluation of scientific software, encouraging more collaborative, transparent, and efficient research practices in clinical informatics and beyond.
Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits
Venue: arXiv cs.CY 2024
Year: 2024

General purpose AI, such as ChatGPT, seems to have lowered the barriers for the public to use AI and harness its power. However, the governance and development of AI still remain in the hands of a few, and the pace of development is accelerating without a comprehensive assessment of risks. As a first step towards democratic risk assessment and design of general purpose AI, we introduce PARTICIP-AI, a carefully designed framework for laypeople to speculate and assess AI use cases and their impacts. Our framework allows us to study more nuanced and detailed public opinions on AI through collecting use cases, surfacing diverse harms through risk assessment under alternate scenarios (i.e., developing and not developing a use case), and illuminating tensions over AI development through making a concluding choice on its development. To showcase the promise of our framework towards informing democratic AI development, we run a medium-scale study with inputs from 295 demographically diverse participants. Our analyses show that participants' responses emphasize applications for personal life and society, contrasting with most current AI development's business focus. We also surface diverse set of envisioned harms such as distrust in AI and institutions, complementary to those defined by experts. Furthermore, we found that perceived impact of not developing use cases significantly predicted participants' judgements of whether AI use cases should be developed, and highlighted lay users' concerns of techno-solutionism. We conclude with a discussion on how frameworks like PARTICIP-AI can further guide democratic AI development and governance.