Abstract
3D reconstruction from anatomical slices allows anatomists to create three dimensional depictions of real structures by tracing organs from sequences of cryosections. However, conventional user interfaces rely on single-user experiences and mouse-based input to create content for education or training purposes. In this work, we present Anatomy Studio, a collaborative Mixed Reality tool for virtual dissection that combines tablets with styli and see-through head-mounted displays to assist anatomists by easing manual tracing and exploring cryosection images. We contribute novel interaction techniques intended to promote spatial understanding and expedite manual segmentation. By using mid-air interactions and interactive surfaces, anatomists can easily access any cryosection and edit contours, while following other user's contributions. A user study including experienced anatomists and medical professionals, conducted in real working sessions, demonstrates that Anatomy Studio is appropriate and useful for 3D reconstruction. Results indicate that Anatomy Studio encourages closely-coupled collaborations and group discussion, to achieve deeper insights.

Abstract
This paper presents a new method to identify classes of events, by processing phasor measurement units (PMU) frequency data through deep neural networks. Deep tapered Multi-layer Perceptrons of the half-autoencoder type, Deep Belief Networks and Convolutional Neural Networks (CNN) are compared, using real data from Brazil. A sound success is obtained by a transformation of time-domain signals, from dynamic events recorded, into 2D images; these images wee processed with a CNN, taking advantage of the strong dependency existing among neighboring pixels in images. The training, computing and processing was achieved with a GPU (Graphics Processing Unit), allowing speeding-up of the process up to 30 times and rendering the process suitable to increase the online situational awareness of network operators.

Abstract
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is composed of two blocks: the first one provides an automatic segmentation and the second one allows to correct it by analyzing 2 points introduced by the user in the nodule's boundary. For this purpose, a physics inspired weight map that takes the user input into account is proposed, which is used both as a feature map and in the system's loss function. Our approach is extensively evaluated on the public LIDC-IDRI dataset, where we achieve a state-of-the-art performance of 0.55 intersection over union vs the 0.59 inter-observer agreement. Also, we show that iW-Net allows to correct the segmentation of small nodules, essential for proper patient referral decision, as well as improve the segmentation of the challenging non-solid nodules and thus may be an important tool for increasing the early diagnosis of lung cancer.

Abstract
Cutting phases occur in many production processes when a larger object must be cut into multiple smaller pieces. Some examples of relevant industries being clothing, footwear, metalware and furniture. The cutting phase is composed of two stages. The first stage consists of finding a good layout for the set of small pieces that must be cut from the larger object and minimizing some objective such as raw-material waste (The Cutting and Packing Problem). Once this good layout has been established, it is provided as input for the second stage which consists of determining the path to cut the pieces which minimizes another objective, such as the total cutting time or distance (The Cutting Path Determination Problem). This second stage is crucial for efficient production planning. Only one linear mathematical model has previously been proposed for the Cutting Path Determination Problem. In this paper, this problem is addressed using two exact approaches based on the Rural Postman Problem (RPP) and the Traveling Salesman Problem (TSP). The RPP approach, in particular, is able to produce optimal solutions for instances containing more than 2000 edges in under 1 h.

Abstract

Abstract
Interactions within virtual environments often require manipulating 3D virtual objects. To this end, researchers have endeavoured to find efficient solutions using either traditional input devices or focusing on different input modalities, such as touch and mid-air gestures. Different virtual environments and diverse input modalities present specific issues to control object position, orientation and scaling: traditional mouse input, for example, presents non-trivial challenges because of the need to map between 2D input and 3D actions. While interactive surfaces enable more natural approaches, they still require smart mappings. Mid-air gestures can be exploited to offer natural manipulations mimicking interactions with physical objects. However, these approaches often lack precision and control. All these issues and many others have been addressed in a large body of work. In this article, we survey the state-of-the-art in 3D object manipulation, ranging from traditional desktop approaches to touch and mid-air interfaces, to interact in diverse virtual environments. We propose a new taxonomy to better classify manipulation properties. Using our taxonomy, we discuss the techniques presented in the surveyed literature, highlighting trends, guidelines and open challenges, that can be useful both to future research and to developers of 3D user interfaces.