SPATIO-TEMPORAL COVARIANCE MODELING WITH SOME ARMA TEMPORAL MARGINS

A valid covariance structure is needed to model spatio-temporal data in various disciplines, such as environmental science, climatology and agriculture. In this work we propose a collection of spatio-temporal functions whose discrete temporal margins are some autoregressive and moving average (ARMA) models, obtain a necessary and sufficient condition for them to be covariance functions. An asymmetric version of this model is also provided to account for space-time irreversibility property in practice. Finally, a spatio-temporal model with AR(2) discrete margin is fitted to wind data from Ireland for estimation and prediction, which are compared with some general existing parametric models in terms of likelihood and mean squared prediction error

Element learning: a systematic approach of accelerating finite element-type methods via machine learning, with applications to radiative transfer

In this paper, we propose a systematic approach for accelerating finite element-type methods by machine learning for the numerical solution of partial differential equations (PDEs). The main idea is to use a neural network to learn the solution map of the PDEs and to do so in an element-wise fashion. This map takes input of the element geometry and the PDEs' parameters on that element, and gives output of two operators -- (1) the in2out operator for inter-element communication, and (2) the in2sol operator (Green's function) for element-wise solution recovery. A significant advantage of this approach is that, once trained, this network can be used for the numerical solution of the PDE for any domain geometry and any parameter distribution without retraining. Also, the training is significantly simpler since it is done on the element level instead on the entire domain. We call this approach element learning. This method is closely related to hybridizbale discontinuous Galerkin (HDG) methods in the sense that the local solvers of HDG are replaced by machine learning approaches. Numerical tests are presented for an example PDE, the radiative transfer equation, in a variety of scenarios with idealized or realistic cloud fields, with smooth or sharp gradient in the cloud boundary transition. Under a fixed accuracy level of $10^{-3}$ in the relative $L^2$ error, and polynomial degree $p=6$ in each element, we observe an approximately 5 to 10 times speed-up by element learning compared to a classical finite element-type method

S.F. DuPont, aboard U.S.S. Wabash, orders Commander T.H. Patterson, aboard the U.S.S. James Adger, to take the Montauk in tow to Ossabaw Sound, Georgia. Port Royal Harbor, S.C., January, 1863.

Samuel Francis DuPont, Rear Admiral Commanding, South Atlantic Blocking Squadron, aboard U.S.S. Wabash, orders Commander T.H. Patterson, aboard the U.S.S. James Adger, to take the Montauk in tow to Ossabaw Sound, Georgia. DuPont also mentions the ships that are already in the sound. Port Royal Harbor, S.C., January, 1863.https://digitalcommons.wofford.edu/littlejohnmss/1246/thumbnail.jp

PFL-LSTR: A privacy-preserving framework for driver intention inference based on in-vehicle and out-vehicle information

Intelligent vehicle anticipation of the movement intentions of other drivers can reduce collisions. Typically, when a human driver of another vehicle (referred to as the target vehicle) engages in specific behaviors such as checking the rearview mirror prior to lane change, a valuable clue is therein provided on the intentions of the target vehicle's driver. Furthermore, the target driver's intentions can be influenced and shaped by their driving environment. For example, if the target vehicle is too close to a leading vehicle, it may renege the lane change decision. On the other hand, a following vehicle in the target lane is too close to the target vehicle could lead to its reversal of the decision to change lanes. Knowledge of such intentions of all vehicles in a traffic stream can help enhance traffic safety. Unfortunately, such information is often captured in the form of images/videos. Utilization of personally identifiable data to train a general model could violate user privacy. Federated Learning (FL) is a promising tool to resolve this conundrum. FL efficiently trains models without exposing the underlying data. This paper introduces a Personalized Federated Learning (PFL) model embedded a long short-term transformer (LSTR) framework. The framework predicts drivers' intentions by leveraging in-vehicle videos (of driver movement, gestures, and expressions) and out-of-vehicle videos (of the vehicle's surroundings - frontal/rear areas). The proposed PFL-LSTR framework is trained and tested through real-world driving data collected from human drivers at Interstate 65 in Indiana. The results suggest that the PFL-LSTR exhibits high adaptability and high precision, and that out-of-vehicle information (particularly, the driver's rear-mirror viewing actions) is important because it helps reduce false positives and thereby enhances the precision of driver intention inference.Comment: Submitted for presentation only at the 2024 Annual Meeting of the Transportation Research Boar

Books Received

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S-Store: Streaming Meets Transaction Processing

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees

BeeMapper Quick Guide

BeeMapper is an interactive web tool that displays land cover and predicted wild bee abundance throughout the Maine wild blueberry production landscape. Information from BeeMapper can be used to: 1. Determine placement of honey bee hives during blueberry pollination. 2. Establish a pollinator conservation plan for particular crop fields. 3. Understand wild bee communities in different types of land. The Users Guide provides instructions on using the tool, interpreting its data, and suggests wild bee conservation and management actions. View the Bee Mapper Website

Noncyclic Pancharatnam phase for mixed state SU(2) evolution in neutron polarimetry

We have measured the Pancharatnam relative phase for spin-1/2 states. In a neutron polarimetry experiment the minima and maxima of intensity modulations, giving the Pancharatnam phase, were determined. We have also considered general SU(2) evolution for mixed states. The results are in good agreement with theory.Comment: 5 pages, 4 figures, to be published in Phys.Lett.

BeeMapper Users Guide

BeeMapper is an interactive web tool that displays land cover and predicted wild bee abundance throughout the Maine wild blueberry production landscape. Information from BeeMapper can be used to: 1. Determine placement of honey bee hives during blueberry pollination. 2. Establish a pollinator conservation plan for particular crop fields. 3. Understand wild bee communities in different types of land. The Users Guide provides instructions on using the tool, interpreting its data, and suggests wild bee conservation and management actions. View the Bee Mapper Website

Quantification and visulization of taurine delivery and penetration into skin

Taurine is used in many personal care products to help deliver skin repair and anti-irritation benefits. Enhancing the deposition and penetration of taurine in skin is likely to boost the performance of these products. In this study, we demonstrated the deposition of taurine onto skin surfaces through a serum formulation, as well as enhanced penetration of taurine into deeper skin layers, aided by permeation enhancers such as glycerin and dimethyl isosorbide. We used a tape stripping method to collect samples from porcine skin and, coupled with HPLC analysis, to quantify the deposition and penetration of taurine. Serum formulations containing different levels of the permeation enhancers were tested. Glycerin and dimethyl isosorbide were found particularly effective and showed a dose-response manner to enhance the taurine penetration. We also employed two spectroscopic techniques, ATR-FTIR and confocal Raman to visualize the taurine distribution in the skin. The hyperspectral images of both IR and Raman clearly demonstrated the increased penetration of taurine into the deeper layers of the skin, beyond stratum corneum and into the epidermis, through the use of these permeation enhancers. These observations are consistent with the results from the tape stripping-HPLC analyses