Annotating Object Instances with a Polygon-RNN

We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. In particular, our approach takes as input an image crop and sequentially produces vertices of the polygon outlining the object. This allows a human annotator to interfere at any time and correct a vertex if needed, producing as accurate segmentation as desired by the annotator. We show that our approach speeds up the annotation process by a factor of 4.7 across all classes in Cityscapes, while achieving 78.4% agreement in IoU with original ground-truth, matching the typical agreement between human annotators. For cars, our speed-up factor is 7.3 for an agreement of 82.2%. We further show generalization capabilities of our approach to unseen datasets

Correlated disorder in entropic crystals

We report computational evidence of a new type of disordered phase in crystals resulting from entropy driven self-assembly of hard convex polyhedra. The disorder was reflected in the orientations of the anisotropic particles and not in the positions of the centers of geometry. Despite the lack of order, particle orientations were not random and exhibited strong correlations. The correlations were manifested in terms of ``quantized'' rotational motions in a fixed number of absolute orientations, while maintaining equal populations and specific measure of pairwise angular differences among the discrete values. This gave rise to a discretely mobile phase in the low density solid and a quenched disordered state at high pressure. This finding can be interpreted as the simplest example of correlated disorder in crystalline materials.Comment: 15 pages, 4 figures in the main text, 11 figures in the supporting informatio

Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.Comment: Accepted for publication in the proceedings of 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference, North America (ISGT NA

Order review and release in make-to-order flow shops:analysis and design of new methods

Increased customization has strengthened the importance of make-to-order companies. The advent of lean management and the introduction of smart and flexible technologies has enabled many of these companies to create flow shop routings. Order review and release (ORR) research, which originally focused on job shops, has started paying attention to flow shops. However, the results have not provided clarity on the best ORR method for flow shops. This study aims at developing such a method by applying a modular design approach. It identifies the relevant elements of ORR methods for flow shops, combines them into new methods and evaluates them in a simulation study. The simulation results demonstrate that performance in pure flow shops can be strongly improved by applying the right combination of workload measures, load balancing, and order dispatching. Specifically, the results show that (1) classical workload measures are still as effective as novel measures that have been suggested for flow shops, (2) balancing workloads explicitly through optimization at the order release stage strongly improves performance, and (3) shortest processing time dispatching is highly effective in flow shops as it avoids starvation of stations. In-depth analyses have been executed to unravel the reasons of performance improvements. As such, the article provides clarity on the improvement potential that is available for ORR in flow shops, while the new modular methods provide a first step in exploiting this potential

What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions

We propose a novel one-stage Transformer-based semantic and spatial refined transformer (SSRT) to solve the Human-Object Interaction detection task, which requires to localize humans and objects, and predicts their interactions. Differently from previous Transformer-based HOI approaches, which mostly focus at improving the design of the decoder outputs for the final detection, SSRT introduces two new modules to help select the most relevant object-action pairs within an image and refine the queries' representation using rich semantic and spatial features. These enhancements lead to state-of-the-art results on the two most popular HOI benchmarks: V-COCO and HICO-DET.Comment: CVPR 2022 Ora