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The subversive path: Art toward the neganthropocene
No full textThis study examines Chinese contemporary art and technology projects that evoke imaginative and multi-sensory responses to environmental issues by harnessing the subversive potential of contemporary media. Specifically, it argues that these artistic practices can illuminate alternative approaches to practicing the Neganthropocene—a concept introduced by Bernard Stiegler to encourage a collective shift in perspective—while fostering shared affect and a sense of care in response to the challenges of the Anthropocene. The Neganthropocene embodies an act of will, desire, and revolutionary breakthrough from within the system. This study focuses on how the artists’ subversive uses of contemporary media embody and expand Neganthropocenic thinking in creative practices, emphasizing the interdependence of ecosystems in their technological mediations. These artists promote care, or “cooperative intelligence” in Stiegler’s sense, and vulnerability of our beings, highlighting transindividuation between human, technics, and nature. The essay identifies three approaches manifesting the Symbiocene, materializing the inhuman nature, and addressing the other-than-human—as ways of “doing” the Neganthropocene and reconciling the technological with the ecological. Through this analysis, the study sheds light on a transformative shift in collective perspective and offers insights into navigating the challenges posed by the Anthropocene in contemporary art experiences
2023-2024 Illinois Trapper Report: Harvest, Effort, and Trapper Opinions
No full textFederal Aid in Wildlife Restoration W-112-R-33We sampled 2,000 of 2023-24 resident Illinois trapping license purchasers from the Illinois Department of Natural Resources licensing database. License purchasers were mailed an 8-page questionnaire, and we received 872 (44%) questionnaires. Trapping license sales decreased by 0.2% from 2022-23 (6,868) to 2023-24 (6,850). Trappers set an average of 13.1 traps for an average of 26.8 days or nights during the 2023-24 season and harvested an estimated 153,471 furbearers (up 20% from the 127,756 harvested in 2022-23). An estimated 80,073 raccoons (Procyon lotor) were trapped during the 2023-24 Illinois trapping season, an increase of 18% from the estimated 67,892 trapped during the 2022-23 season. Beaver (Castor canadensis) harvest was next highest at 21,830 animals, followed by 16,959 opossums (Didelphis virginiana)
Women in the Ranks: Strategies for Recruitment and Retention in the Croatian Armed Forces
No full textThis interview with Lieutenant Tajana Bosniak highlights the challenges and strategic initiatives undertaken by the Croatian Armed Forces to increase female participation and retention within military service. Bosniak outlines a comprehensive plan designed to raise the percentage of women in uniform, emphasizing both recruitment and long-term career sustainability. Current representation stands at 9.2%, with aspirations to reach approximately 16% over the next decade. Key measures include targeted outreach in schools, universities, and women’s organizations; improved professional opportunities such as training, education, and international deployment; and institutional reforms addressing pay and working conditions. The discussion also acknowledges systemic challenges, such as dual citizenship enabling Croatian women to seek employment abroad, which complicates recruitment and retention efforts. Overall, the interview underscores the importance of gender integration as both a strategic and cultural priority, linking women’s service to broader issues of professional equity, stability, and national security
News Sources
No full textThis chapter discusses a variety of news source formats and types. This chapter also examines how to evaluate news sources to successfully direct patrons to reliable information that will meet their needs. It also provides opportunities for library professionals to reflect on their own interaction with news sources and consider how that may impact what sources they recommend
Urban Heat Mitigation Effects and Greenery Justice in Washington, D.C.
No full textThe deterioration of urban heat exposure risks is highlighting the benefits of urban greenery for creating healthy and resilient cities. Using Washington, D.C. as a case, this study demonstrates the urban temperature mitigation effect and effective ranges of GVI and NDVI at street-scale, examines greenery justice in residential zones and affordable housing surroundings, the differentiated cooling effects of different genera
Life Inside the POC: Field Realities of UN Personnel in South Sudan During the Rainy Season
No full textThis field account documents the daily challenges faced by United Nations personnel operating in a remote and insecure area of South Sudan. The testimony captures logistical, environmental, and security constraints encountered within and around a Protection of Civilians (POC) site during the rainy season. Interviewees describe extreme isolation, unpredictable weather, limited transport reliability, and the constant threat posed by armed combatants breaching perimeter fences. Additional hardships include inadequate infrastructure, food scarcity, and poor living conditions—exacerbated by mud, flooding, and minimal access to sanitation. The narrative offers an unfiltered portrayal of humanitarian fieldwork under duress, highlighting the intersection of operational resilience, safety risk, and human endurance in conflict environments
Certifying robustness in inference and learning problems
Get PDFThere is a rich literature of algorithms for inference, prediction, and decision-making problems when the underlying distributions governing the data are known and well-modeled. The research from the past few decades has provided powerful learning algorithms when such distributions cannot be easily modeled, for instance with high dimensional data. As a result, such data driven methods are becoming ubiquitous in a wide variety of real-life applications, including safety-critical ones such as self-driving and medical diagnosis. In order for the reliable and safe deployment of such algorithms in practice, there exist several imminent questions to be answered. In this dissertation, a few topics in robustness of inference and learning methods are studied. One of the key issues with data-driven methods in practice is unexpected changes that can occur at inference time potentially affecting the performance of these methods, for instance, deviations in the data generating distributions, irrelevant or unrecognizable inputs, and adversarial attacks from unknown sources. The underlying theme connecting the topics studied in this dissertation is the development of learning algorithms robust to such unexpected, potentially harmful, deviations. Broadly, three problems in robust inference and learning are discussed in this dissertation - out-of-distribution detection for machine learning models, detection robust to distribution shifts, and multi-player multi-armed bandits robust to adversarial attacks. Principled approaches for these problems with theoretical guarantees are derived using tools from statistics, information theory and optimization, that are practical, resilient and efficiently implementable.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Akshayaa Magesh, accepted the attached license on 2025-02-04 at 04:25.The student, Akshayaa Magesh, submitted this Dissertation for approval on 2025-02-04 at 04:43.This Dissertation was approved for publication on 2025-02-07 at 12:49.DSpace SAF Submission Ingestion Package generated from Vireo submission #21630 on 2025-10-19 at 18:08:4
Examining large language models for safety and robustness through the lens of social science
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Sullam Jeoung, accepted the attached license on 2025-03-05 at 10:51.The student, Sullam Jeoung, submitted this Dissertation for approval on 2025-03-05 at 10:55.This Dissertation was approved for publication on 2025-03-07 at 10:36.DSpace SAF Submission Ingestion Package generated from Vireo submission #21662 on 2025-10-19 at 18:09:09Large language models have demonstrated remarkable capabilities, often achieving human-like performance levels and significantly impacting our daily lives. However, these models can perpetuate and amplify harmful stereotypes and biases associated with socio-demographic representations, potentially generating discriminatory content that adversely affects individuals and communities. Given these risks and their broader societal implications, ensuring the safety and robustness of these models through the identification and mitigation of harmful stereotypes has become imperative. This dissertation presents comprehensive methodologies to address these challenges by integrating insights from social science, psychology, and cognitive studies with methods from natural language processing. First, we present a framework to assess human-like stereotypical patterns in large language models (LLMs), drawing upon established psychological theories of how individuals develop stereotypes toward various social groups. This theoretically-grounded approach provides construct validity in defining and measuring stereotypes. The framework incorporates three key dimensions: warmth-competence analysis, keyword- reasoning patterns, and emotional-behavioral responses. Through clustering analysis, keyword extraction, and reasoning pattern evaluation of LLM responses, we examine how these models align with or deviate from documented human behavioral patterns. Our findings reveal that LLMs demonstrate nuanced perceptions of social groups, consistent with psychological research highlighting the multifaceted nature of stereotypes. Notably, the models’ reasoning patterns, particularly regarding groups’ economic status, demonstrate a nuanced awareness of societal disparities. Second, we propose methods to examine causal sensitivity of language models on socio-demographic attributes. This is based on a controlled experimental framework that uses name frequency analysis from U.S. Census data and systematic evaluation of model predictions through causal graphs. Our findings show that less frequent first names lead to divergent model predictions, highlighting the need for careful demographic consideration in dataset design to ensure fair and consistent model performance across different name representations. Third, we investigate how LLMs exhibit and inflate political stereotypes through the lens of cognitive biases and representative heuristics. We analyze LLMs’ responses using two key theoretical frameworks: ’kernel of truth’ (whether stereotypes reflect empirical realities) and ’representative heuristics’ (whether models overemphasize representative attributes of target groups), comparing model outputs with actual human responses across various political topics. Our findings show that while LLMs can accurately mimic certain political positions, they tend to exaggerate these positions compared to empirical human responses, suggesting a vulnerability to stereotypical thinking similar to human cognitive biases. This implies the need for careful consideration of cognitive bias frameworks in developing and deploying language models, particularly in politically sensitive contexts, and demonstrates the potential effectiveness of prompt-based mitigation strategies in reducing stereotypical responses. Overall, this dissertation enhances the understanding and safety of LLMs by proposing a framework to assess human-like stereotypes, methods to evaluate causal sensitivity based on socio-demographic attributes, and an analysis of political stereotypes through cognitive bias frameworks. By integrating insights from psychology and social sciences with computational methods this research makes a contribution to the ongoing discourse on ethical AI deployment, highlighting the necessity of understanding and addressing biases in language models to promote fairness and reduce discriminatory outcomes
Crafting safe human-centric agents with risk intelligence
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Chenkai Sun, accepted the attached license on 2025-04-17 at 17:14.The student, Chenkai Sun, submitted this Dissertation for approval on 2025-04-17 at 17:19.This Dissertation was approved for publication on 2025-04-18 at 10:30.DSpace SAF Submission Ingestion Package generated from Vireo submission #21745 on 2025-10-19 at 18:09:18Safety has become a critical concern in digital communication environments, where harmful content can propagate rapidly and compromise societal well-being. As artificial intelligence increasingly serves as an automated content creation and distribution mechanism, the risks of adverse algorithmic impacts are amplified when these systems lack awareness of how their outputs influence audiences. This dissertation addresses this challenge by developing an integrated framework for communication risk management that enables systems to assess, anticipate, and mitigate potential negative consequences. The framework comprises four interconnected research contributions. First, we establish the foundations for risk assessment through a novel task formulation and dataset that captures how identical messages affect diverse user personas differently. This approach transcends traditional content moderation by evaluating information safety and appropriateness for different population groups, creating a measurement capability essential for risk-aware AI systems. Furthermore, we address the challenge of evaluating risk for users with minimal digital footprints, often referred to as "lurkers". By leveraging social graphs constructed through large language models, this work presents a solution for more accurately predicting opinions from such users, expanding the applicability of personalized agents in risk management. Another cornerstone of this dissertation is the development of efficient strategies for language model personalization in risk assessment. Through hierarchical and collaborative data refinement techniques, our Persona-DB approach achieves high-accuracy personalization with substantially reduced data retrieval requirements. This work bridges the gap between risk management and practical deployment by addressing the computational costs of personalization. Lastly, we extend risk assessment beyond immediate impacts to encompass temporal dynamics and cascading effects. By employing language models as social simulators, our framework projects how content might influence populations over time, enabling the anticipation of long-term consequences that traditional safety approaches neglect. This capability not only enhances risk assessment but also provides a mechanism for aligning content-generating AI with broader safety considerations
360° and 2D video analytics in network and energy constrained environments
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Benjamin Civjan, accepted the attached license on 2025-04-14 at 11:14.The student, Benjamin Civjan, submitted this Thesis for approval on 2025-04-14 at 12:19.This Thesis was approved for publication on 2025-04-15 at 06:13.DSpace SAF Submission Ingestion Package generated from Vireo submission #21763 on 2025-10-19 at 18:09:22Real-time video analytics enables rapid, automated content understanding, significantly reducing the time required to search through captured footage. However, many real-world scenarios that could benefit from real-time analytics face constraints that remain underexplored. This thesis focuses on one such use case: firefighter training. Firefighters rely on video for decision-making, post-mission feedback, and developing new training scenarios. However, deploying cameras in outdoor training environments presents two major challenges: limited network connectivity due to the distance from Wi-Fi infrastructure and restricted energy availability as cameras operate on battery power. To investigate network limitations, we conducted field tests at the Illinois Fire Service Institute (IFSI) to analyze connectivity from various locations on the grounds. Additionally, we developed a streaming framework supporting multiple video codecs (MJPEG, WebP, Tiled MJPEG, and H.264) and systematically evaluated their impact on bandwidth usage and 360° video streaming performance under real-world conditions at IFSI. To address the problem of energy-efficient video processing we developed a system, EcoLens, that dynamically optimizes processing configurations to minimize energy consumption of the camera while preserving essential video features for deep learning inference. We first conducted an extensive offline evaluation of various configurations comprising of device CPU frequency, frame filtering features, difference thresholds, and video bitrates, to establish apriori knowledge of their impact on energy consumption and inference accuracy. Leveraging this insight, we introduced an online system that employs multi-objective Bayesian optimization to intelligently explore and adapt configurations in real time. Our approach continuously refines processing settings to meet target inference accuracy with minimal edge device energy expenditure. Experimental results demonstrated the system’s effectiveness in reducing video processing energy use while maintaining high analytical performance, offering a practical solution for smart devices and edge computing applications