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1. Differential transpiration between pods and leaves during stress combination in soybean

   https://doi.org/10.1093/plphys/kiad114  

   Sinha, Ranjita; Shostak, Benjamin; Induri, Sai Preethi; Sen, Sidharth; Zandalinas, Sara I; Joshi, Trupti; Fritschi, Felix B; Mittler, Ron (February 2023, Plant Physiology)

   Abstract Climate change is causing an increase in the frequency and intensity of droughts, heat waves, and their combinations, diminishing agricultural productivity and destabilizing societies worldwide. We recently reported that during a combination of water deficit (WD) and heat stress (HS), stomata on leaves of soybean (Glycine max) plants are closed, while stomata on flowers are open. This unique stomatal response was accompanied by differential transpiration (higher in flowers, while lower in leaves) that cooled flowers during a combination of WD + HS. Here, we reveal that developing pods of soybean plants subjected to a combination of WD + HS use a similar acclimation strategy of differential transpiration to reduce internal pod temperature by approximately 4 °C. We further show that enhanced expression of transcripts involved in abscisic acid degradation accompanies this response and that preventing pod transpiration by sealing stomata causes a significant increase in internal pod temperature. Using an RNA-Seq analysis of pods developing on plants subjected to WD + HS, we also show that the response of pods to WD, HS, or WD + HS is distinct from that of leaves or flowers. Interestingly, we report that although the number of flowers, pods, and seeds per plant decreases under conditions of WD + HS, the seed mass of plants subjected to WD + HS increases compared to plants subjected to HS, and the number of seeds with suppressed/aborted development is lower in WD + HS compared to HS. Taken together, our findings reveal that differential transpiration occurs in pods of soybean plants subjected to WD + HS and that this process limits heat-induced damage to seed production. 

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2. How do blind people know that blue is cold? Distributional semantics encode color-adjective associations

   https://doi.org/10.31234/osf.io/vyxpq  

   van Paridon, J.; Liu, Q; Lupyan, G. (July 2021, Proceedings of the Annual Meeting of the Cognitive Science Society)

   Certain colors are strongly associated with certain adjectives (e.g. red is hot, blue is cold). Some of these associations are grounded in visual experiences like seeing hot embers glow red. Surprisingly, many congenitally blind people show similar color associations, despite lacking all visual experience of color. Presumably, they learn these associations via language. Can we detect these associations in the statistics of language? And if so, what form do they take? We apply a projection method to word embeddings trained on corpora of spoken and written text to identify color-adjective associations as they are represented in language. We show that these projections are predictive of color-adjective ratings collected from blind and sighted people, and that the effect size depends on the training corpus. Finally, we examine how color-adjective associations might be represented in language by training word embeddings on corpora from which various sources of color-semantic information are removed. 

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3. Sketching the Krylov subspace: faster computation of the entire ridge regularization path

   https://doi.org/10.1007/s11227-023-05309-w  

   Wang, Yifei; Pilanci, Mert (January 2023, The Journal of Supercomputing)

   We propose a fast algorithm for computing the entire ridge regression regularization path in nearly linear time. Our method constructs a basis on which the solution of ridge regression can be computed instantly for any value of the regularization parameter. Consequently, linear models can be tuned via cross-validation or other risk estimation strategies with substantially better efciency. The algorithm is based on iteratively sketching the Krylov subspace with a binomial decomposition over the regularization path. We provide a convergence analysis with various sketching matrices and show that it improves the state-of-the-art computational complexity. We also provide a technique to adaptively estimate the sketching dimension. This algorithm works for both the over-determined and under-determined problems. We also provide an extension for matrix-valued ridge regression. The numerical results on real medium and large-scale ridge regression tasks illustrate the efectiveness of the proposed method compared to standard baselines which require super-linear computational time. 

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4. Block Coordinate Regularization by Denoising

   Sun, Yu; Liu, Jiaming; Kamilov, Ulugbek S (January 2019, Advances in neural information processing systems)

   We consider the problem of estimating a vector from its noisy measurements using a prior specified only through a denoising function. Recent work on plug- and-play priors (PnP) and regularization-by-denoising (RED) has shown the state- of-the-art performance of estimators under such priors in a range of imaging tasks. In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables. We theoretically analyze the convergence of the algorithm and discuss its relationship to the traditional proximal optimization. Our analysis complements and extends recent theoretical results for RED-based estimation methods. We numerically validate our method using several denoiser priors, including those based on convolutional neural network (CNN) denoisers. 

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5. A Nested Bi-level Optimization Framework for Robust Few Shot Learning

   https://doi.org/10.1609/aaai.v36i7.20678  

   Killamsetty, Krishnateja; Li, Changbin; Zhao, Chen; Chen, Feng; Iyer, Rishabh (June 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal.Hence, it fails to address the domain shift between base and novel classes in few-shot learning. In this work, we propose a novel robust meta-learning algorithm, NESTEDMAML, which learns to assign weights to training tasks or instances. We con-sider weights as hyper-parameters and iteratively optimize them using a small set of validation tasks set in a nested bi-level optimization approach (in contrast to the standard bi-level optimization in MAML). We then applyNESTED-MAMLin the meta-training stage, which involves (1) several tasks sampled from a distribution different from the meta-test task distribution, or (2) some data samples with noisy labels.Extensive experiments on synthetic and real-world datasets demonstrate that NESTEDMAML efficiently mitigates the effects of ”unwanted” tasks or instances, leading to significant improvement over the state-of-the-art robust meta-learning methods. 

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