This paper presents COT-AD, a comprehensive Dataset designed to enhance cotton crop analysis through computer vision. Comprising over 25,000 images captured throughout the cotton growth cycle, with 5,000 annotated images, COT-AD includes aerial imagery for field-scale detection and segmentation and high-resolution DSLR images documenting key diseases. The annotations cover pest and disease recognition, vegetation, and weed analysis, addressing a critical gap in cotton-specific agricultural datasets. COT-AD supports tasks such as classification, segmentation, image restoration, enhancement, deep generative model-based cotton crop synthesis, and early disease management, advancing data-driven crop management .The COT-AD dataset can be found here: https://aamaanakbar.github.io/COT-AD/.

We consider the problem of black-box function optimization over the Boolean hypercube. Despite the vast literature on black-box function optimization over continuous domains, not much attention has been paid to learning models for optimization over combinatorial domains until recently. However, the computational complexity of the recently devised algorithms are prohibitive even for moderate numbers of variables; drawing one sample using the existing algorithms is more expensive than a function evaluation for many black-box functions of interest. To address this problem, we propose a computationally efficient model learning algorithm based on multilinear polynomials and exponential weight updates. In the proposed algorithm, we alternate between simulated annealing with respect to the current polynomial representation and updating the weights using monomial experts' advice. Numerical experiments on various datasets in both unconstrained and sum-constrained Boolean optimization indicate the competitive performance of the proposed algorithm, while improving the computational time up to several orders of magnitude compared to state-of-the-art algorithms in the literature.

Tasks coupled in an in situ workflow may not process data at the same speed, potentially causing overflows in the communication channel between them. To prevent this problem, software infrastructures for in situ workflows usually impose a strict FIFO policy that has the side-effect of slowing down faster tasks to the speed of the slower ones. This may not be the desired behavior; for example, a scientist may prefer to drop older data in the communication channel in order to visualize the latest snapshot of a simulation. In this paper, we present Manala, a flexible flow control library designed to manage the flow of messages between a producer and a consumer in an in situ workflow. Manala intercepts messages from the producer, stores them, and selects the message to forward to the consumer depending on the flow control policy. The library is designed to ease the creation of new flow control policies and buffering mechanisms. We demonstrate with three examples how changing the flow control policy between tasks can influence the performance and results of scientific workflows. The first example focuses on materials science with LAMMPS and a synthetic diffraction analysis code. The second example is an interactive visualization scenario with Gromacs as the producer and Damaris/Viz as consumer. Our third example studies different strategies to perform an asynchronous checkpoint with Gromacs.