Quantifying numerical data involves addressing two key challenges: first, determining whether the data can be naturally quantified, and second, identifying the numerical intervals or ranges of values that correspond to specific value classes, referred to as "quantums," which represent statistically meaningful states. If such quantification is feasible, continuous streams of numerical data can be transformed into sequences of "symbols" that reflect the states of the system described by the measured parameter. People often perform this task intuitively, relying on common sense or practical experience, while information theory and computer science offer computable metrics for this purpose. In this study, we assess the applicability of metrics based on information compression and the Silhouette coefficient for quantifying numerical data. We also investigate the extent to which these metrics correlate with one another and with what is commonly referred to as "human intuition." Our findings suggest that the ability to classify numeric data values into distinct categories is associated with a Silhouette coefficient above 0.65 and a Dip Test below 0.5; otherwise, the data can be treated as following a unimodal normal distribution. Furthermore, when quantification is possible, the Silhouette coefficient appears to align more closely with human intuition than the "normalized centroid distance" method derived from information compression perspective.

However, click patterns in real-world interactive segmentation scenarios remain largely unexplored. Most methods rely on the assumption that users would click in the center of the largest erroneous area.

We evaluated adversarial robustness with CAA, an ensemble of gradient and search attacks which was recently demonstrated as the most effective attack against a tabular model.

Pansharpening is a significant image fusion technique that merges the spatial content and spectral characteristics of remote sensing images to generate high-resolution multispectral images.

R1: Cut down on some sections (3.2.1, 3.2.2 and 3.2.5) to spare space for the qualitative examples. We will revise our paper according to the suggestion in the final version. We added experiments on MS-COCO and Flicker30k using single-head attention, Table 1. R2: The base attention model performs better than up-down and GCN-LSTM. In addition, our experimental results showed that increasing the number of min.

Designing effective policies for the online 3D bin packing problem (3D-BPP) has been a long-standing challenge, primarily due to the unpredictable nature of incoming box sequences and stringent physical constraints.

In this paper, we propose to factorize the selection of a variable subset into decisions on selection of each variable, under our LNS framework. To represent such action factorization, we employ the bipartite GCN as the destroy operator, as shown in Figure A.1. MLP module that computes probabilities of selecting each variable in parallel.Figure A.1: Illustration of our LNS framework with the bipartite GCN based destroy operator. Our RL algorithm for training LNS policies is depicted by the pseudo code in Algorithm 1. The architecture of the neural network is displayed in the upper half of Figure A.2, which MLPs by a parameter-sharing MLP, as shown in the lower half of Figure A.2. S. / D. V ariable features ( V) Normalized reduced cost. 1 S. Normalized objective coefficient. 1 S. Normalized LP age. 1 S. Equality of solution value and lower bound, 0 or 1. 1 S. Equality of solution value and upper bound, 0 or 1 . 1 S. Fractionality of solution value. 1 S. One-hot encoding of simplex basis status (i.e., lower, basic, upper).