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Net Fluxes of Broadband Shortwave and Photosynthetically Active Radiation Complement NDVI and Near Infrared Reflectance of Vegetation to Explain Gross Photosynthesis Variability Across Ecosystems and Climate
No full textA significant challenge in global change research is understanding how vegetation interacts with the environment to influence ecosystem gross primary productivity (GPP) through carbon assimilation. One emerging objective is to consistently predict GPP fluctuations worldwide by establishing a robust scaling relationship between GPP measured at flux towers and satellite spectral reflectance data. However, a major hurdle in achieving this goal is the discrepancy in spatial resolution between early satellite measurements and eddy flux measurements. By using a large set of growing season data covering 100 site-years in North and Central America, we explored the potential of transforming incident and reflected shortwave (Rg) and photosynthetically active radiation (PAR) measurements into a broadband normalized difference vegetation index (NDVI) and near-infrared (NIR) reflectance of vegetation (NIRv) which simultaneously explains the GPP variability. We found that the broadband NDVI and NIRv derived from Rg and PAR measurements at the daily time scale were highly correlated with Planet Fusion, Landsat-8/9, and Sentinel-2 narrowband NDVI and NIRv across a wide range of climate and ecological gradients. The differences between satellite and broadband NDVI and NIRv were found to be significantly associated with soil background variations, phenological stages, water stress and signal saturation of broadband NIR reflectance at high biomass. The seasonal variability of broadband NDVI and NIRv remarkably captured the seasonality of vegetation phenology, evaporative fraction, GPP and rainfall in different ecosystems. Although saturation of GPP at high NDVI was evident, a linear relationship between broadband NIRv times incident PAR versus GPP indicated the effectiveness of NIRv-based approach to capture the hidden light use efficiency impacts on GPP. Our study concludes that inexpensive measurement of Rg and PAR components can provide reliable information on NDVI, NIRv, and GPP uninterruptedly. This enhances the sensing capability of flux tower sites without requiring additional spectrometer measurements. The proposed in-situ vegetation indices make a compelling case on using radiation signals for handshaking between ecosystem-scale measurements and remote sensing observables relevant to carbon uptake
Maximizing Generative AI Benefits with Task Creativity and Human Validation
Get PDFMuch of the existing literature on generative AI applications is conflicting, with findings suggesting that investing in AI will lead to better organizational outcomes but also pointing out that incorporating AI may be a wasteful even counterproductive initiative. We develop a conceptual frame-work to characterize generative AI benefits based on the types of tasks that generative AI may be used for in management. Our work suggests that task creativity plays a key role in successful generative AI outcomes, but human validation - the extent to which a human engages in a supervisory role - is required to reap the benefits. Our conceptual framework is focused on white collar jobs and suggests that the management of generative AI is a strategic choice with important managerial implications
Slow Ramping Emerges from Spontaneous Fluctuations in Spiking Neural Networks
Get PDFThe capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ~2 s before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses
Defining Evolution: Exploring Students’ Conceptions of Evolution in Introductory Biology Courses
Get PDFBackground
Understanding evolution is an important part of undergraduate biology education. Despite its importance, however, students often struggle to understand evolution, often holding preconceived notions of what evolution is. Here, we investigate how students in both majors and non-majors introductory biology define and conceive of evolution at the start of the semester for a two-year college and a four-year university near each other. We analyze open-ended responses to an in-class activity on the first day of the semester that asked students to define evolution, generating insight into how students are thinking of evolution prior to any formal instruction on evolution in college. Results
Our analysis of over 300 student responses reveals that students hold diverse conceptions about evolution, with some students perceiving evolution in the context of evolutionary processes while other students define evolution by referring to perceived evolutionary consequences. In addition, we identify multiple non-normative conceptions about evolution, including students viewing evolution and natural selection as synonymous and not recognizing other evolutionary forces, and find that very few students likely have developed mental models linking evolution and genetics. In addition, we find few differences between how students at the two- and four-year institutions perceive evolution, and similarly few differences between students in a majors and non-majors introductory biology, suggesting that these conceptions of evolution are widespread at the beginning of introductory biology, regardless of major or institution. Conclusions
We situate our results in the existing literature examining student conceptions of evolution, with our results extending past work that has primarily relied on more closed-ended questions or focused on specific evolutionary concepts (e.g., natural selection). Our results largely align with past work on student thinking of evolution but provide a broader, more holistic perspective at the ideas and framework that students are drawing upon when introductory biology instructors first introduce the term ‘evolution’. We conclude our paper by discussing implications for the biology education research community as well as instructors
Quantum Field Theory and the Limits of Reductionism
Get PDFI suggest that the current situation in quantum field theory (QFT) provides some reason to question the universal validity of ontological reductionism. I argue that the renormalization group flow is reversible except at fixed points, which makes the relation between large and small distance scales quite symmetric in QFT, opening up at least the technical possibility of a non-reductionist approach to QFT. I suggest that some conceptual problems encountered within QFT may potentially be mitigated by moving to an alternative picture in which it is no longer the case that the large supervenes on the small. Finally, I explore some specific models in which a form of non-reductionism might be implemented, and consider the prospects for future development of these models
Review of Data Bias in Healthcare Applications
Get PDFIn the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus on different methods used for creating synthetic data. This paper explores several mitigating algorithms like SMOTE, AdaSyn, Fair-SMOTE, and BayesBoost. The optimized Bayesboost algorithm has been discussed. This approach showed more accuracy and addressed the error handling mechanism
Optimal Radar Ranging Pulse to Resolve Two Reflectors
Get PDFPrevious work established fundamental bounds on subwavelength resolution for the radar range resolution problem, called superradar [Phys. Rev. Appl. 20, 064046 (2023)]. In this work, we identify the optimal waveforms for distinguishing the range resolution between two reflectors of identical strength, leveraging results in quantum metrology. We discuss both the unnormalized optimal waveform as well as the best square-integrable pulse and their variants. Using orthogonal function theory, we give an explicit algorithm to optimize the wave pulse in finite time to have the best performance. We also explore range resolution estimation with unnormalized waveforms with multiparameter methods to also independently estimate loss and time of arrival. These results are consistent with the earlier single parameter approach of range resolution only and give deeper insight into the ranging estimation problem. Experimental results are presented using radio pulse reflections inside coaxial cables, showing robust range resolution smaller than a tenth of the inverse bandlimit, with uncertainties close to the derived Cramér-Rao bound
Review of \u3cem\u3ePious Peripheries: Runaway Women in post-Taliban Afghanistan\u3c/em\u3e
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Review of \u3cem\u3eHow AI Works: From Sorcery to Science\u3c/em\u3e, by Ronald T. Kneusel
Get PDFA review of How AI Works: From Sorcery to Science, by Ronald T. Kneusel