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1. Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction

   https://doi.org/10.1177/0361198120917668  

   Yao, Weiran; Qian, Sean (June 2020, Transportation Research Record: Journal of the Transportation Research Board)

   null (Ed.)

   The main question to address in this paper is to recommend optimal signal timing plans in real time under incidents by incorporating domain knowledge developed with the traffic signal timing plans tuned for possible incidents, and learning from historical data of both traffic and implemented signals timing. The effectiveness of traffic incident management is often limited by the late response time and excessive workload of traffic operators. This paper proposes a novel decision-making framework that learns from both data and domain knowledge to real-time recommend contingency signal plans that accommodate non-recurrent traffic, with the outputs from real-time traffic prediction at least 30 min in advance. Specifically, considering the rare occurrences of engagement of contingency signal plans for incidents, it is proposed to decompose the end-to-end recommendation task into two hierarchical models—real-time traffic prediction and plan association. The connections between the two models are learnt through metric learning, which reinforces partial-order preferences observed from historical signal engagement records. The effectiveness of this approach is demonstrated by testing this framework on the traffic network in Cranberry Township, Pennsylvania, U.S., in 2019. Results show that the recommendation system has a precision score of 96.75% and recall of 87.5% on the testing plan, and makes recommendations an average of 22.5 min lead time ahead of Waze alerts. The results suggest that this framework is capable of giving traffic operators a significant time window to access the conditions and respond appropriately. 

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2. Detecting Paleoclimate Transitions With Laplacian Eigenmaps of Recurrence Matrices (LERM)

   https://doi.org/10.1029/2023PA004700  

   James, Alexander; Emile‐Geay, Julien; Malik, Nishant; Khider, Deborah (January 2024, Paleoceanography and Paleoclimatology)

   Abstract Paleoclimate records can be considered low‐dimensional projections of the climate system that generated them. Understanding what these projections tell us about past climates, and changes in their dynamics, is a main goal of time series analysis on such records. Laplacian eigenmaps of recurrence matrices (LERM) is a novel technique using univariate paleoclimate time series data to indicate when notable shifts in dynamics have occurred. LERM leverages time delay embedding to construct a manifold that is mappable to the attractor of the climate system; this manifold can then be analyzed for significant dynamical transitions. Through numerical experiments with observed and synthetic data, LERM is applied to detect both gradual and abrupt regime transitions. Our paragon for gradual transitions is the Mid‐Pleistocene Transition (MPT). We show that LERM can robustly detect gradual MPT‐like transitions for sufficiently high signal‐to‐noise (S/N) ratios, though with a time lag related to the embedding process. Our paragon of abrupt transitions is the “8.2 ka” event; we find that LERM is generally robust at detecting 8.2 ka‐like transitions for sufficiently high S/N ratios, though edge effects become more influential. We conclude that LERM can usefully detect dynamical transitions in paleogeoscientific time series, with the caveat that false positive rates are high when dynamical transitions are not present, suggesting the importance of using multiple records to confirm the robustness of transitions. We share an open‐source Python package to facilitate the use of LERM in paleoclimatology and paleoceanography. 

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3. Efficient, nonparametric removal of noise and recovery of probability distributions from time series using nonlinear-correlation functions: Additive noise

   https://doi.org/10.1063/5.0158199  

   Dhar, Mainak; Dickinson, Joseph A.; Berg, Mark A. (August 2023, The Journal of chemical physics)

   Single-molecule and related experiments yield time series of an observable as it fluctuates due to thermal motion. In such data, it can be difficult to distinguish fluctuating signal from fluctuating noise. We present a method of separating signal from noise using nonlinear-correlation functions. The method is fully nonparametric: No a priori model for the system is required, no knowledge of whether the system is continuous or discrete is needed, the number of states is not fixed, and the system can be Markovian or not. The noise-corrected, nonlinear-correlation functions can be converted to the system’s Green’s function; the noise-corrected moments yield the system’s equilibrium-probability distribution. As a demonstration, we analyze synthetic data from a three-state system. The correlation method is compared to another fully nonparametric approach—time binning to remove noise, and histogramming to obtain the distribution. The correlation method has substantially better resolution in time and in state space. We develop formulas for the limits on data quality needed for signal recovery from time series and test them on datasets of varying size and signal-to-noise ratio. The formulas show that the signal-to-noise ratio needs to be on the order of or greater than one-half before convergence scales at a practical rate. With experimental benchmark data, the positions and populations of the states and their exchange rates are recovered with an accuracy similar to parametric methods. The methods demonstrated here are essential components in building a complete analysis of time series using only high-order correlation functions. 

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4. Exploring Realistic Nanohertz Gravitational-wave Backgrounds

   https://doi.org/10.3847/1538-4357/aca1b2  

   Bécsy, Bence; Cornish, Neil J.; Kelley, Luke Zoltan (December 2022, The Astrophysical Journal)

   Abstract Hundreds of millions of supermassive black hole binaries are expected to contribute to the gravitational-wave signal in the nanohertz frequency band. Their signal is often approximated either as an isotropic Gaussian stochastic background with a power-law spectrum or as an individual source corresponding to the brightest binary. In reality, the signal is best described as a combination of a stochastic background and a few of the brightest binaries modeled individually. We present a method that uses this approach to efficiently create realistic pulsar timing array data sets using synthetic catalogs of binaries based on the Illustris cosmological hydrodynamic simulation. We explore three different properties of such realistic backgrounds that could help distinguish them from those formed in the early universe: (i) their characteristic strain spectrum, (ii) their statistical isotropy, and (iii) the variance of their spatial correlations. We also investigate how the presence of confusion noise from a stochastic background affects detection prospects of individual binaries. We calculate signal-to-noise ratios of the brightest binaries in different realizations for a simulated pulsar timing array based on the NANOGrav 12.5 yr data set extended to a time span of 15 yr. We find that ∼6% of the realizations produce systems with signal-to-noise ratios larger than 5, suggesting that individual systems might soon be detected (the fraction increases to ∼41% at 20 yr). These can be taken as a pessimistic prediction for the upcoming NANOGrav 15 yr data set, since it does not include the effect of potentially improved timing solutions and newly added pulsars. 

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5. Ongoing Increase in Eastern Tropical North Pacific Denitrification as Interpreted Through the Santa Barbara Basin Sedimentary δ ¹⁵ N Record

   https://doi.org/10.1029/2019PA003578  

   Davis, C. V.; Ontiveros‐Cuadras, J. F.; Benitez‐Nelson, C.; Schmittner, A.; Tappa, E. J.; Osborne, E.; Thunell, R. C. (September 2019, Paleoceanography and Paleoclimatology)

   Abstract Decades of observations show that the world's oceans have been losing oxygen, with far‐reaching consequences for ecosystems and biogeochemical cycling. To reconstruct oxygenation beyond the limited scope of instrumental records, proxy records are needed, such as sedimentary δ¹⁵N. We combine two δ¹⁵N records from the Santa Barbara Basin (SBB), a 24‐year‐long, biweekly sediment trap time series, and a 114‐year, high‐resolution sediment core together spanning the years 1892–2017. These records allow for the examination of δ¹⁵N variability on seasonal to centennial timescales. Seasonal variability in SBB δ¹⁵N is consistent in timing with the poleward advection of a high δ¹⁵N signal from the Eastern Tropical North Pacific in the summer and fall. Strong El Niño events result in variable δ¹⁵N signatures, reflective of local rainfall, and neither the Pacific Decadal Oscillation nor North Pacific Gyre Oscillation impose strong controls on bulk sedimentary δ¹⁵N. Seasonal and interannual variability in sediment trap δ¹³C_orgis consistent with local productivity as a driver; however, this signal is not retained in the sediment core. The time series from the sediment trap and core show that bulk sedimentary δ¹⁵N in SBB has now exceeded that measured for the past 2,000 years. We hypothesize that the change in δ¹⁵N reflects the increasing influence of denitrified waters from the Eastern Tropical North Pacific and ongoing deoxygenation of the Eastern Pacific. When juxtaposed with other regional δ¹⁵N records our results further suggest that SBB is uniquely situated to record long‐term change in the Eastern Tropical North Pacific. 

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