The two-dimensional bin packing problem with variable bin sizes and costs

AbstractThe two-dimensional variable sized bin packing problem (2DVSBPP) is the problem of packing a set of rectangular items into a set of rectangular bins. The bins have different sizes and different costs, and the objective is to minimize the overall cost of bins used for packing the rectangles. We present an integer-linear formulation of the 2DVSBPP and introduce several lower bounds for the problem. By using Dantzig–Wolfe decomposition we are able to obtain lower bounds of very good quality. The LP-relaxation of the decomposed problem is solved through delayed column generation, and an exact algorithm based on branch-and-price is developed. The paper is concluded with a computational study, comparing the tightness of the various lower bounds, as well as the performance of the exact algorithm for instances with up to 100 items

Hybrid Neural Networks with Attention-based Multiple Instance Learning for Improved Grain Identification and Grain Yield Predictions

Agriculture is a critical part of the world's food production, being a vital aspect of all societies. Procedures need to be adjusted to their specific environment because of their climate and field condition disparity. Existing research has demonstrated the potential of grain yield predictions on Norwegian farms. However, this research is limited to regional analytics, which is unable to acquire sufficient plant growth factors influenced by field conditions and farmers' decisions. One factor critical for yield prediction is the crop type planted on a per-field basis. This research effort proposes a novel approach for improving crop yield predictions using a hybrid deep neural network utilizing temporal satellite imagery from a remote sensing system. Additionally, We apply a variety of data, including grain production, meteorological data, and geographical data. The crop yield prediction system is supported by a field-based crop type classification model, which supplies features related to crop type and field area. Our crop classification system takes advantage of both raw satellite images as well as carefully chosen vegetation indices. Further, we propose a multi-class attention-based deep multiple instance learning model to utilize semi-labeled datasets, fully benefiting Norwegian data acquisition. Our best crop classification model, which consists of a time distributed network and a gated recurrent unit, classifies crop types with an accuracy of 70\% and is currently state-of-the-art for country-wide crop type mapping in Norway. Lastly, our yield prediction system enables realistic in-season early predictions that could benefit actors in real-life scenarios

Enhancer and Transcription Factor Dynamics during Myeloid Differentiation Reveal an Early Differentiation Block in <i>Cebpa null</i> Progenitors

Transcription factors PU.1 and CEBPA are required for the proper coordination of enhancer activity during granulocytic-monocytic (GM) lineage differentiation to form myeloid cells. However, precisely how these factors control the chronology of enhancer establishment during differentiation is not known. Through integrated analyses of enhancer dynamics, transcription factor binding, and proximal gene expression during successive stages of murine GM-lineage differentiation, we unravel the distinct kinetics by which PU.1 and CEBPA coordinate GM enhancer activity. We find no evidence of a pioneering function of PU.1 during late GM-lineage differentiation. Instead, we delineate a set of enhancers that gain accessibility in a CEBPA-dependent manner, suggesting a pioneering function of CEBPA. Analyses of Cebpa null bone marrow demonstrate that CEBPA controls PU.1 levels and, unexpectedly, that the loss of CEBPA results in an early differentiation block. Taken together, our data provide insights into how PU.1 and CEBPA functionally interact to drive GM-lineage differentiation