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Free, publicly-accessible full text available December 1, 2025
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Free, publicly-accessible full text available August 3, 2025
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Recent advances in monocular depth estimation have been made by incorporating natural language as additional guidance. Although yielding impressive results the impact of the language prior particularly in terms of generalization and robustness remains unexplored. In this paper we address this gap by quantifying the impact of this prior and introduce methods to benchmark its effectiveness across various settings. We generate "low-level" sentences that convey object-centric three-dimensional spatial relationships incorporate them as additional language priors and evaluate their downstream impact on depth estimation. Our key finding is that current language-guided depth estimators perform optimally only with scene-level descriptions and counter-intuitively fare worse with low level descriptions. Despite leveraging additional data these methods are not robust to directed adversarial attacks and decline in performance with an increase in distribution shift. Finally to provide a foundation for future research we identify points of failures and offer insights to better understand these shortcomings. With an increasing number of methods using language for depth estimation our findings highlight the opportunities and pitfalls that require careful consideration for effective deployment in real-world settings.more » « lessFree, publicly-accessible full text available June 1, 2025
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Free, publicly-accessible full text available June 1, 2025
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Rusănescu, C ; Ungureanu, N (Ed.)
Excessive land application of poultry litter (PL) may lead to surface runoff of nitrogen (N) and phosphorus (P), which cause eutrophication, fish death, and water pollution that ultimately have negative effects on humans and animals. Increases in poultry production in the Delmarva Peninsula underscore the need for more efficient, cost-effective, and sustainable disposal technologies for processing PL instead of direct land application. The pyrolysis conversion process can potentially produce nutrient-rich poultry litter biochar (PLB), while the pyrolysis process can change the N and P to a more stable component, thus reducing its runoff. Pyrolysis also kills off any microorganisms that would otherwise trigger negative environmental health effects. This study is to apply an integrated method and investigate the effect of pyrolysis temperature (300 °C, 500 °C), poultry litter source (different feedstock composition), and bedding material mixture (10% pine shavings) on PLB qualities and quantities. Proximate and ultimate analysis showed PL sources and bedding material addition influenced the physicochemical properties of feedstock. The SEM and BET surface results indicate that pyrolysis temperature had a significant effect on changing the PLB morphology and structure, as well as the pH value (7.78 at 300 °C vs. 8.78 at 500 °C), extractable phosphorus (P) (18.73 ppm at 300 °C vs. 11.72 ppm at 500 °C), sulfur (S) (363 ppm at 300 °C vs. 344 ppm at 500 °C), and production yield of PLBs (47.65% at 300 °C vs. 60.62% at 500 °C). The results further suggest that adding a bedding material mixture (10% pine shavings) to PLs improved qualities by reducing the content of extractable P and S, as well as pH values of PLBs. This study also found the increment in both the pore volume and the area of Bethel Farm was higher than that of Sun Farm. Characterization and investigation of qualities and quantities of PLB using the integrated framework suggest that PL from Bethel Farm could produce better-quality PLB at a higher pyrolysis temperature and bedding material mixture to control N and P runoff problems.
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Cohen, J ; Solano, G (Ed.)Free, publicly-accessible full text available May 25, 2025
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Privacy is a key principle for developing ethical AI technologies, but how does including AI technologies in products and services change privacy risks? We constructed a taxonomy of AI privacy risks by an- alyzing 321 documented AI privacy incidents. We codifed how the unique capabilities and requirements of AI technologies described in those incidents generated new privacy risks, exacerbated known ones, or otherwise did not meaningfully alter the risk. We present 12 high-level privacy risks that AI technologies either newly created (e.g., exposure risks from deepfake pornography) or exacerbated (e.g., surveillance risks from collecting training data). One upshot of our work is that incorporating AI technologies into a product can alter the privacy risks it entails. Yet, current approaches to privacy-preserving AI/ML (e.g., federated learning, diferential pri- vacy, checklists) only address a subset of the privacy risks arising from the capabilities and data requirements of AI.more » « lessFree, publicly-accessible full text available May 11, 2025
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In this paper, we revisit the bilevel optimization problem, in which the upper-level objective function is generally nonconvex and the lower-level objective function is strongly convex. Although this type of problem has been studied extensively, it still remains an open question how to achieve an $\mathcal{O}(\epsilon^{-1.5})$ sample complexity in Hessian/Jacobian-free stochastic bilevel optimization without any second-order derivative computation. To fill this gap, we propose a novel Hessian/Jacobian-free bilevel optimizer named FdeHBO, which features a simple fully single-loop structure, a projection-aided finite-difference Hessian/Jacobian-vector approximation, and momentum-based updates. Theoretically, we show that FdeHBO requires $\mathcal{O}(\epsilon^{-1.5})$ iterations (each using $\mathcal{O}(1)$ samples and only first-order gradient information) to find an $\epsilon$-accurate stationary point. As far as we know, this is the first Hessian/Jacobian-free method with an $\mathcal{O}(\epsilon^{-1.5})$ sample complexity for nonconvex-strongly-convex stochastic bilevel optimization.more » « lessFree, publicly-accessible full text available February 13, 2025