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1. Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean

   https://doi.org/10.3390/rs13163241  

   Hassanzadeh, Amirhossein ; Zhang, Fei ; van Aardt, Jan ; Murphy, Sean P. ; Pethybridge, Sarah J. ( August 2021 , Remote Sensing)

   null (Ed.)

   Accurate, precise, and timely estimation of crop yield is key to a grower’s ability to proactively manage crop growth and predict harvest logistics. Such yield predictions typically are based on multi-parametric models and in-situ sampling. Here we investigate the extension of a greenhouse study, to low-altitude unmanned aerial systems (UAS). Our principal objective was to investigate snap bean crop (Phaseolus vulgaris) yield using imaging spectroscopy (hyperspectral imaging) in the visible to near-infrared (VNIR; 400–1000 nm) region via UAS. We aimed to solve the problem of crop yield modelling by identifying spectral features explaining yield and evaluating the best time period for accurate yield prediction, early in time. We introduced a Python library, named Jostar, for spectral feature selection. Embedded in Jostar, we proposed a new ranking method for selected features that reaches an agreement between multiple optimization models. Moreover, we implemented a well-known denoising algorithm for the spectral data used in this study. This study benefited from two years of remotely sensed data, captured at multiple instances over the summers of 2019 and 2020, with 24 plots and 18 plots, respectively. Two harvest stage models, early and late harvest, were assessed at two different locations in upstate New York, USA. Six varieties of snap bean were quantified using two components of yield, pod weight and seed length. We used two different vegetation detection algorithms. the Red-Edge Normalized Difference Vegetation Index (RENDVI) and Spectral Angle Mapper (SAM), to subset the fields into vegetation vs. non-vegetation pixels. Partial least squares regression (PLSR) was used as the regression model. Among nine different optimization models embedded in Jostar, we selected the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) and their resulting joint ranking. The findings show that pod weight can be explained with a high coefficient of determination (R2 = 0.78–0.93) and low root-mean-square error (RMSE = 940–1369 kg/ha) for two years of data. Seed length yield assessment resulted in higher accuracies (R2 = 0.83–0.98) and lower errors (RMSE = 4.245–6.018 mm). Among optimization models used, ACO and SA outperformed others and the SAM vegetation detection approach showed improved results when compared to the RENDVI approach when dense canopies were being examined. Wavelengths at 450, 500, 520, 650, 700, and 760 nm, were identified in almost all data sets and harvest stage models used. The period between 44–55 days after planting (DAP) the optimal time period for yield assessment. Future work should involve transferring the learned concepts to a multispectral system, for eventual operational use; further attention should also be paid to seed length as a ground truth data collection technique, since this yield indicator is far more rapid and straightforward. 

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2. Beyond UVJ: Color Selection of Galaxies at z > 3

   Antwi-Danso, J. Papovich ( January 2021 , Bulletin of the American Astronomical Society)

   We calibrate and validate different methods of rest-frame color-color selection to identify galaxies in active star-forming and quiescent stages of their evolution. Our method is similar to the widely-used UVJ color-color diagram, which is an effective way to distinguish between quiescent and star-forming galaxies using their rest-frame U-V and V-J colors. UVJ colors suffer known systematics, and at z > 4 the method must be extrapolated because the rest-frame J-band moves beyond the coverage of the deepest bandpasses (typically IRAC 4.5 µm). This leads to biases: for example, spectroscopic campaigns have shown that UVJ-quiescent samples include ~10-30% contamination from galaxies with significant amounts of star formation. Alternative selection methods will be important not just to mitigate these biases, but also in the JWST era where NIRCam coverage is also limited to ~5 µm . In this poster, we present calibrations of alternative rest-frame filter combinations that are applicable for galaxies at redshifts z = 4 - 6. We apply our method to a stellar mass-limited sample of galaxies at 4 < z < 6 from the FLAMINGOS-2 Extragalactic Near-Infrared K-Split (FENIKS) survey. FENIKS is a deep (23.1 - 24.5 AB mag) survey employing two novel filters which split the Ks band ( λc = 2.2 µm) K-blue and K-red filters ( λc = 1.9 and 2.3 µm, respectively), allowing for finer sampling of the Balmer/4000 Å break of galaxies with evolved populations. We quantify the improvement in the selection of quiescent and star-forming galaxies using the alternative color-color selection methods. Furthermore, we investigate correlations between galaxy properties and their rest-frame colors, in particular examining purity and completeness of these selection methods. Finally, we explore the above for a wide range of synthetic filter combinations to inform accurate selections of various galaxy populations and rule out unphysical areas of parameter space for these populations. 

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3. Dose kernel decomposition for spot‐based radiotherapy treatment planning

   https://doi.org/10.1002/mp.15415  

   Chen, Mingli ; Yang, Zi ; Wardak, Zabi ; Stojadinovic, Strahinja ; Gu, Xuejun ; Lu, Weiguo ( December 2021 , Medical Physics)

   Pre‐calculation of accurate dose deposition kernels for treatment planning of spot‐based radiotherapies, such as Gamma Knife (GK) and Gamma Pod (GP), can be very time‐consuming and may require large data storage with an enormous number of possible spots. We proposed a novel kernel decomposition (KD) model to address accurate and fast (real‐time) dose calculation with reduced data storage requirements for spot‐based treatment planning. The application of the KD model was demonstrated for clinical GK and GP radiotherapy platforms.

   The dose deposition kernel at each spot (shot position) is modeled as the product of a shift‐invariant kernel based on a reference kernel and spatially variant scale factor. The reference kernel, one for each collimator, is defined at the center of the commissioning phantom for GK and at the center of the treatment target for GP and calculated using the Monte Carlo (MC) method. The spatially variant scale factor is defined as the ratio of the mean tissue maximum ratio (TMR) at the candidate shot position to that at the reference kernel position, and the mean TMR map is calculated within the entire volume through parallel beam ray tracing on the density image followed by averaging over all source directions. The proposed KD dose calculations were compared with the MC method and with the GK and GP treatment planning system (TPS) computations for various shot positions and collimator sizes utilizing a phantom and 14 and 12 clinical plans for GK and GP, respectively.

   For the phantom study, the KD Gamma index (3%/1 mm) passing rates were greater than 99% (median 100%) relative to the MC doses, except for the shots close to the boundary. The passing rates dropped below 90% for 8 mm (16 mm) shots positioned within ∼1 cm (∼2 cm) of the boundary. For the clinical GK plans, the KD Gamma passing rates were greater than 99% (median 100%) compared to the MC and greater than 92% (median 99%) compared to the TPS. For the clinical GP plans, the KD Gamma passing rates were greater than 95% (median 98%) compared to the MC and greater than 91% (median 97%) compared to the TPS. The scale factors were calculated in sub‐seconds with GPU implementation and only need to be calculated once before treatment plan optimization. The calculation of the dose kernel was also within sub‐seconds without requiring beam‐by‐beam calculation commonly done in the TPS.

   The proposed model can provide an accurate dose and enables real‐time dose and derivative calculations by kernel shifting and scaling without pre‐calculating or requiring large data storage for GK and GP dose deposition kernels during treatment planning. This model could be useful for spot‐based radiotherapy treatment planning by allowing an efficient global fine search for optimal spots.

    

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4. Lifted Hinge-Loss Markov Random Fields

   https://doi.org/10.1609/aaai.v33i01.33017975  

   Srinivasan, Sriram ; Babaki, Behrouz ; Farnadi, Golnoosh ; Getoor, Lise ( July 2019 , Proceedings of the AAAI Conference on Artificial Intelligence)

   Statistical relational learning models are powerful tools that combine ideas from first-order logic with probabilistic graphical models to represent complex dependencies. Despite their success in encoding large problems with a compact set of weighted rules, performing inference over these models is often challenging. In this paper, we show how to effectively combine two powerful ideas for scaling inference for large graphical models. The first idea, lifted inference, is a wellstudied approach to speeding up inference in graphical models by exploiting symmetries in the underlying problem. The second idea is to frame Maximum a posteriori (MAP) inference as a convex optimization problem and use alternating direction method of multipliers (ADMM) to solve the problem in parallel. A well-studied relaxation to the combinatorial optimization problem defined for logical Markov random fields gives rise to a hinge-loss Markov random field (HLMRF) for which MAP inference is a convex optimization problem. We show how the formalism introduced for coloring weighted bipartite graphs using a color refinement algorithm can be integrated with the ADMM optimization technique to take advantage of the sparse dependency structures of HLMRFs. Our proposed approach, lifted hinge-loss Markov random fields (LHL-MRFs), preserves the structure of the original problem after lifting and solves lifted inference as distributed convex optimization with ADMM. In our empirical evaluation on real-world problems, we observe up to a three times speed up in inference over HL-MRFs. 

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5. Language systematizes attention: How relational language enhances relational representation by guiding attention

   https://doi.org/10.1016/j.cognition.2023.105671  

   Yuan, Lei ; Novack, Miriam ; Uttal, David ; Franconeri, Steven ( February 2024 , Cognition)

   Language can affect cognition, but through what mechanism? Substantial past research has focused on how labeling can elicit categorical representation during online processing. We focus here on a particularly powerful type of language—relational language—and show that relational language can enhance relational representation in children through an embodied attention mechanism. Four-year-old children were given a color-location conjunction task, in which they were asked to encode a two-color square, split either vertically or horizontally (e.g., red on the left, blue on the right), and later recall the same configuration from its mirror reflection. During the encoding phase, children in the experimental condition heard relational language (e.g., “Red is on the left of blue”), while those in the control condition heard generic non-relational language (e.g., “Look at this one, look at it closely”). At recall, children in the experimental condition were more successful at choosing the correct relational representation between the two colors compared to the control group. Moreover, they exhibited different attention patterns as predicted by the attention shift account of relational representation (Franconeri et al., 2012). To test the sustained effect of language and the role of attention, during the second half of the study, the experimental condition was given generic non-relational language. There was a sustained advantage in the experimental condition for both behavioral accuracies and signature attention patterns. Overall, our findings suggest that relational language enhances relational representation by guiding learners’ attention, and this facilitative effect persists over time even in the absence of language. Implications for the mechanism of how relational language can enhance the learning of relational systems (e.g., mathematics, spatial cognition) by guiding attention will be discussed. 

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