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Abstract Value-based decisions about alternatives we have never experienced can be guided by associations between current choice options and memories of prior reward. A critical question is how similar memories need to be to the current situation to effectively guide decisions. We address this question in the context of associative learning of faces using a sensory preconditioning paradigm. We find that memories of reward spread along established associations between faces to guide decision making. While memory guidance is specific for associated facial identities, it does not only occur for the specific images that were originally encountered. Instead, memory guidance generalizes across different images of the associated identities. This suggests that memory guidance does not rely on a pictorial format of representation but on a higher, view-invariant level of abstraction. Thus, memory guidance operates on a level of representation that neither over- nor underspecifies associative relationships in the context of obtaining reward.

While the advances in synchrotron light sources, together with the development of focusing optics and detectors, allow nanoscale ptychographic imaging of materials and biological specimens, the corresponding experiments can yield terabyte-scale volumes of data that can impose a heavy burden on the computing platform. Although graphics processing units (GPUs) provide high performance for such large-scale ptychography datasets, a single GPU is typically insufficient for analysis and reconstruction. Several works have considered leveraging multiple GPUs to accelerate the ptychographic reconstruction. However, most of these works utilize only the Message Passing Interface to handle the communications between GPUs. This approach poses inefficiency for a hardware configuration that has multiple GPUs in a single node, especially while reconstructing a single large projection, since it provides no optimizations to handle the heterogeneous GPU interconnections containing both low-speed (e.g., PCIe) and high-speed links (e.g., NVLink). In this paper, we provide an optimized intranode multi-GPU implementation that can efficiently solve large-scale ptychographic reconstruction problems. We focus on the maximum likelihood reconstruction problem using a conjugate gradient (CG) method for the solution and propose a novel hybrid parallelization model to address the performance bottlenecks in the CG solver. Accordingly, we have developed a tool, called PtyGer (Ptychographic GPU(multiple)-based reconstruction), implementing our hybrid parallelization model design. A comprehensive evaluation verifies that PtyGer can fully preserve the original algorithm's accuracy while achieving outstanding intranode GPU scalability., (© 2022. The Author(s).)

Computed tomography is a well-established x-ray imaging technique to reconstruct the three-dimensional structure of objects. It has been used extensively in a variety of fields, from diagnostic imaging to materials and biological sciences. One major challenge in some applications, such as in electron or x-ray tomography systems, is that the projections cannot be gathered over all the angles due to the sample holder setup or shape of the sample. This results in an ill-posed problem called the limited angle reconstruction problem. Typical image reconstruction in this setup leads to distortion and artifacts, thereby hindering a quantitative evaluation of the results. To address this challenge, we use a generative model to effectively constrain the solution of a physics-based approach. Our approach is self-training that can iteratively learn the nonlinear mapping from partial projections to the scanned object. Because our approach combines the data likelihood and image prior terms into a single deep network, it is computationally tractable and improves performance through an end-to-end training. We also complement our approach with total-variation regularization to handle high-frequency noise in reconstructions and implement a solver based on alternating direction method of multipliers. We present numerical results for various degrees of missing angle range and noise levels, which demonstrate the effectiveness of the proposed approach., (© 2021. The Author(s).)

The beta-ketoacyl-ACP synthase II (KASII) is an enzyme in fatty acid biosynthesis, catalyzing the elongation of 16:0-acyl carrier protein (ACP) to 18:0-ACP in plastids. Mutations in KASII genes in higher plants can lead to lethality, which makes it difficult to utilize the gene for lipid metabolic engineering. We demonstrated previously that transient expression of plastid-directed fatty acyl reductases and wax ester synthases could result in different compositions of wax esters. We hypothesized that changing the ratio between C16 (palmitoyl-compounds) and C18 (stearoyl-compounds) in the plastidic acyl-ACP pool by inhibition of KASII expression would change the yield and composition of wax esters via substrate preference of the introduced enzymes. Here, we report that transient inhibition of KASII expression by three different RNAi constructs in leaves of N. benthamiana results in almost complete inhibition of KASII expression. The transient RNAi approach led to a shift of carbon flux from a pool of C18 fatty acids to C16, which significantly increased wax ester production in AtFAR6-containing combinations. The results demonstrate that transient inhibition of KASII in vegetative tissues of higher plants enables metabolic studies towards industrial production of lipids such as wax esters with specific quality and composition.